Monte Carlo Simulations Research Articles

Refractory carbon depletion by photolysis through dust collisions and vertical mixing

The inner Solar System is depleted in refractory carbon in comparison to the interstellar medium and the depletion likely took place in the protoplanetary disk phase of the Solar System. We study the effect of photolysis of refractory carbon in the upper layers of the protosolar disk and its interplay with dust collisional growth and vertical mixing. We make use of a 1D Monte Carlo model to simulate dust coagulation and vertical mixing. To model the far ultraviolet (FUV) flux of the disk, we used a simple analytical prescription and benchmarked it with data from a radiative transfer simulation. We study the effects of fragmentation and bouncing on dust distribution and the propagation of carbon depletion. We find that when bouncing is included, the size distribution is truncated at smaller sizes than fragmentation-limited size distributions but there is a loss of small grains as well. The population of small grains is reduced due to fewer fragmentation events and this reduces the effectiveness of photolysis. We find that dust collisional growth and vertical mixing increase the effectiveness of carbon depletion by efficiently replenishing carbon to the upper regions of the disk with higher FUV flux. It takes around 100-300 kyr to reach the measured carbon abundances at 1 au, depending on the strength of the turbulence in the disk. These timescales are faster than reported by previous studies. Collisional redistribution and turbulent mixing are important aspects of dust evolution that should be included when modeling dust chemistry as they can influence the efficiency of chemical processes. Photolysis, along with another process such as sublimation, most likely played a key role in refractory carbon depletion that we see around us in the inner Solar System.

How to escape atypical regions in the symmetric binary perceptron: A journey through connected-solutions states

We study the binary symmetric perceptron model, and in particular its atypical solutions. While the solution-space of this problem is dominated by isolated configurations, it is also solvable for a certain range of constraint density \alphaα and threshold \kappaκ. We provide in this paper a statistical measure probing sequences of solutions, where two consecutive elements shares a strong overlap. After simplifications, we test its predictions by comparing it to Monte-Carlo simulations. We obtain good agreement and show that connected states with a Markovian correlation profile can fully decorrelate from their initialization only for \kappa>\kappa_{no-mem. state}κ>κno−mem.state (\kappa_{no-mem. state}\sim \sqrt{0.91\log(N)}κno−mem.state∼0.91log(N) for \alpha=0.5α=0.5 and NN being the dimension of the problem). For \kappa<\kappa_{no-mem. state}κ<κno−mem.state, we show that decorrelated sequences still exist but have a non-trivial correlations profile. To study this regime we introduce an Ansatz for the correlations that we label as the nested Markov chain.

Reliability Function of the Special (3+1) Cascade Model

In this paper, a reliability function is found for the special case of cascade model where the model consists of (3 + 1) units and trace random variables that represent the strength and stress Rayleigh distribution, respectively. The mathematical formula of the reliability function was estimated by estimating the parameters of Rayleigh distribution in the five different estimation methods, Maximum likelihood method, Percentile method, Least Squares method, Weighted Least Squares method and Regression method. Using the MATLAB software, A Monte Carlo simulation is performed to compare the results of different estimation methods using the mean square error criterion. The simulation results showed that the best estimation methods for estimating the reliability model are ML estimator and Pr estimator.

Energy‐Efficient Styrene Production by Incorporating Ionic Liquid‐Based Separation Technology

AbstractThe separation of ethylbenzene/styrene represents a pivotal step in the styrene production process, which is associated with significant energy consumption, substantial costs, and considerable environmental impact. In this study, ionic liquids (ILs) were incorporated into the separation process, thereby enabling energy‐efficient styrene production through the use of extractive distillation. By solving a formulated mixed‐integer nonlinear programming (MINLP) problem based on computer‐aided ionic liquid design (CAILD), 1‐methylpyridinium trifluoromethanesulfonate ([mPy][CF3SO3]) and 1‐ethylpyridinium tetrafluoroborate ([C2Py][BF4]) were identified as optimal IL candidates for this application. Comprehensive process simulations and optimizations were conducted, focusing on energy consumption, environmental impact, and economic performance. In comparison to the conventional process, the [mPy][CF3SO3]‐based and [C2Py][BF4]‐based processes achieved reductions in energy consumption by 44.2 % and 59.0 %, respectively. Furthermore, there was a notable reduction in carbon emissions, amounting to 28.9 % and 25.4 %. However, processes utilizing [mPy][CF3SO3] showed an increase in total annual cost (TAC) by 11.8 %. Meanwhile, processes based on [C2Py][BF4] demonstrated a diminution in TAC by 3.7 %. Nonetheless, Monte Carlo simulations indicate that IL‐based processes exhibit slightly better resilience to economic uncertainties compared to conventional processes. Overall, the significant energy and environmental benefits of IL‐based processes highlight their potential in styrene production, especially with the implementation of active policies related to energy use and carbon emissions (e.g., carbon taxes).

The Kumaraswamy Log Composite Distribution Based on the Median: Inference and Applications

In this paper a new univariate distribution with bounded support on the Kumaraswamy distribution is defined. Some of its main properties are studied, and a Monte Carlo simulation is implemented to evaluate the behavior of the maximum likelihood estimators. Two applications to real data sets are presented. In particular goodness of fit and quantile regression are show in order to illustrate the potential and flexibility  of this new distribution.

Inverse Chen Bayesian analysis for Parallel Redundant Stress –Strength Reliability

In this paper, a Bayesian analysis made to estimate the reliability of a parallel redundant system with independent stress and strength of Inverse Chen distribution(ICD). The analysis is focused on gamma-primed left-censored samples under four distinct loss functions. (squared error, Quadratic, weighted and Linear exponential loss function). A Monte Carlo simulation was conducted to compare the estimates using the mean square error to obtain the best estimate. It was found that the best estimators can be under squared error loss function.

ВДОСКОНАЛЕННЯ СИСТЕМИ УПРАВЛІННЯ РИЗИКАМИ ЯК СПОСІБ ПІДВИЩЕННЯ ФІНАНСОВОЇ СТІЙКОСТІ ПІДПРИЄМСТВ

The article explores the main ways to improve the risk management system within an enterprise, with a particular emphasis on the necessity of integrating a risk-oriented approach into the company's management processes. In the context of global political and economic changes, especially due to the impact of war and increasing market instability, the need for companies to adapt to new conditions has become critically important. The risks faced by enterprises are all-encompassing and diverse, requiring timely identification, assessment, and appropriate responses to minimize potential losses and ensure the stable functioning of the organization. The purpose of the article is to explore ways to improve risk management within enterprises and implement risk-oriented management. The research employs methods of systems analysis, induction, deduction, and data visualization. The article focuses on the theoretical foundations and practical aspects of risk management. In particular, it highlights the importance of integrating risk-oriented approaches at all levels of the enterprise's management activities, ensuring effective responses to both external and internal threats. The article discusses the main stages in building a risk management system, with particular emphasis placed on risk identification and quantitative risk assessment. Risk identification involves detecting all potential threats that may affect the enterprise's operations. The risk assessment methodology, along with their aggregation using the Monte Carlo method, allows for a quantitative assessment of overall risk, which is essential for making informed strategic decisions. It also emphasizes the necessity of cultivating an appropriate risk culture within the enterprise, which helps engage every employee in risk management. The article underscores the importance of integrating risk management at all levels of the enterprise as a foundation for enhancing its financial resilience. Identifying risks, quantitatively assessing them, and implementing effective management models not only help prevent potential losses but also significantly increase the chances of successful development in a constantly changing external environment.

A correction for modeling radial, spiral, and PROPELLER dynamic contrast-enhanced data: Time-averaged extended Tofts.

Dynamic contrast-enhanced sequences (e.g. spiral, radial, PROPELLER MRI) often rely on oversampling the center of k-space. Instead of the discrete snapshots obtained by Cartesian sampling, oversampling the k-space center results in time-averaging of the signal. We hypothesize that these time-averaged signals decrease the accuracy of pharmacokinetic modeling and propose a model that accounts for this effect. To test our hypothesis, a modified extended Tofts model tailored to accommodate time-averaged signals is proposed. Simulated Monte Carlo experiments were conducted to compare the performance of the modified model with the conventional model. Additionally, to validate the findings in vivo, models were fitted to pseudo-spiral variable-density dynamic contrast-enhanced MRI scans of pancreatic cancer patients reconstructed at 4, 8, 10, and 15 s/frame. The simulations demonstrated that for time-averaged acquisitions, our modified extended Tofts model provided more accurate and precise results than conventional models. Additionally, by integrating signals, some information on high temporal behavior was recovered. Particularly, at long acquisitions (15 s/frame), variable-density sampling with the modified model outperformed conventional discrete sampling. In vivo experiments confirmed these findings, as the corrected model showed more consistent estimates of parameters and over the tested sampling frequencies, highlighting its potential to improve accuracy in clinical settings. Our study demonstrates that time-averaged signals lead to decreased accuracy and precision in pharmacokinetic modeling when ignored. We suggest using our corrected pharmacokinetic model when performing dynamic contrast-enhanced with variable-density acquisitions, especially for dynamic scan times that are 8 s and longer.

Marginal Effects for Probit and Tobit with Endogeneity

Abstract When evaluating partial effects, it is important to distinguish between structural endogeneity and measurement errors. In contrast to linear models, these two sources of endogeneity affect partial effects differently in nonlinear models. We study this issue focusing on the Instrumental Variable (IV) Probit and Tobit models. We show that even when a valid IV is available, failing to differentiate between the two types of endogeneity can lead to either under- or over-estimation of the partial effects. We develop simple estimators of the bounds on the partial effects and provide easy to implement confidence intervals that correctly account for both types of endogeneity. We illustrate the methods in a Monte Carlo simulation and an empirical application.

Energy Hub Operation Under Uncertainty: Monte Carlo Risk Assessment Using Gaussian and KDE-Based Data

Energy hubs integrating onsite renewable generation and battery storage provide cost-efficient solutions for meeting building electricity requirements. This study presents methods for modeling uncertainties in load demand and solar generation, ranging from normal distribution assumptions to distributions sourced from CityLearn 2.3.0. We also implement kernel density estimation (KDE) to represent the non-parametric distribution characteristics of actual data. Through Monte Carlo simulation, we emphasize the value of robust, data-driven methodologies in optimizing energy hub operations under realistic uncertainty conditions and effectively conducting risk assessment. The CityLearn real-world data confirms that the non-Gaussian nature of building-level energy demand and solar PV electricity output is most accurately represented through KDE, leading to more precise cost projections for the considered energy hub.

Matrix Diffusion Controls Mountain Hillslope Groundwater Ages and Inferred Storage Dynamics.

Groundwater age distributions provide fundamental insights on coupled water and biogeochemical processes in mountain watersheds. Field-based studies have found mixtures of young and old-aged groundwater in mountain catchments underlain by bedrock; yet, the processes that dictate these groundwater age distributions are poorly understood. In this work, we use the coupled ParFlow-CLM integrated hydrologic and EcoSLIM particle tracking models to simulate groundwater age distributions on a lower montane hillslope in the East River Watershed, Colorado (USA). We develop a convolution-based approach to propagate fracture-matrix diffusion processes to the EcoSLIM advection-dominated age distributions. We compare observed 3H and 4He concentrations from two groundwater wells against model predictions that have varying advective transport times and matrix diffusion magnitudes. Based on a Monte Carlo analysis that considers uncertain matrix and fracture parameters, we find that matrix diffusion is needed to jointly predict 3H and 4He observations at both wells. The advection-dominated age distributions lack adequate mixing of young and old-aged water to capture the observed co-occurrence of 3H and 4He. The model scenario that best matches the 3H, 4He, and water level observations when considering both advective flowpath and matrix diffusion mixing processes has a dynamic bedrock groundwater reservoir that is susceptible to considerable storage losses during low-snow periods. This dynamic groundwater system amplifies the need to assimilate deeper bedrock groundwater into watershed hydro-biogeochemical predictions. This work further highlights the importance of considering matrix diffusion when interpreting environmental tracers in bedrock groundwater systems.

Proton dose calculation with transformer: Transforming spot map to dose.

Conventional proton dose calculation methods are either time- and resource-intensive, like Monte Carlo (MC) simulations, or they sacrifice accuracy, as seen with analytical methods. This trade-off between computational efficiency and accuracy highlights the need for improved dose calculation approaches in clinical settings. This study aims to develop a deep-learning-based model that calculates dose-to-water (DW) and dose-to-medium (DM) using patient anatomy and proton spot map (PSM), achieving approaching MC-level accuracy with significantly reduced computation time. Additionally, the study seeks to generalize the model to different treatment sites using transfer learning. A SwinUNetr model was developed using 259 four-field prostate proton stereotactic body radiation therapy (SBRT) plans to calculate patient-specific DW and DM distributions from CT and projected PSM (PPSM). The PPSM was created by projecting PSM into the CT scans using spot coordinates, stopping power ratio, beam divergence, and water-equivalent thickness. Fine-tuning was then performed for the central nervous system (CNS) site using 84 CNS plans. The model's accuracy was evaluated against MC simulation benchmarks using mean absolute error (MAE), gamma analysis (2% local dose difference, 2-mm distance-to-agreement, 10% low dose threshold), and relevant clinical indices on the test dataset. The trained model achieved a single-field dose calculation time of 0.07s on a Nvidia-A100 GPU, over 100 times faster than MC simulators. For the prostate site, the best-performing model showed an average MAE of 0.26±0.17Gy and a gamma index of 92.2%±3.1% in dose regions above 10% of the maximum dose for DW calculations, and an MAE of 0.30±0.19Gy with a gamma index of 89.7%±3.9% for DM calculations. After transfer learning for CNS plans, the model achieved an MAE of 0.49±0.24Gy and a gamma index of 90.1%±2.7% for DW computations, and an MAE of 0.47±0.25Gy with a gamma index of 85.4%±7.1% for DM computations. The SwinUNetr model provides an efficient and accurate method for computing dose distributions in proton therapy. It also opens the possibility of reverse-engineering PSM from DW, potentially speeding up treatment planning while maintaining accuracy.

Solvation structure of LiCl aqueous solution from interaction energy computation based on structural features: Using the HRMC simulation

Building upon our previous work that used radial and orientational distribution functions obtained from Hybrid Reverse Monte Carlo (HRMC) modeling combined with neutron scattering, this study reexamines the structures and overlaps of solvation shells around LiCl ion pairs. We calculate the energy based on the thermodynamic theory of aqueous electrolyte solutions, using pair radial distribution functions [Formula: see text] and Environment-Dependent Interatomic Potential (EDIP). The results reaffirm our earlier findings regarding the stability of lithium’s hydration shells compared to chlorine and offer an energetic explanation for the phenomenon of overlapping hydration layers.

Lower Limit of Percolation Threshold on Square Lattice with Complex Neighborhoods

In this paper, the 60-year-old concept of long-range interaction in percolation problems introduced by Dalton, Domb and Sykes is reconsidered. With Monte Carlo simulation—based on the Newman–Ziff algorithm and the finite-size scaling hypothesis—we estimate 64 percolation thresholds for a random site percolation problem on a square lattice with neighborhoods that contain sites from the seventh coordination zone. The percolation thresholds obtained range from 0.27013 (for the neighborhood that contains only sites from the seventh coordination zone) to 0.11535 (for the neighborhood that contains all sites from the first to the seventh coordination zone). Similarly to neighborhoods with smaller ranges, the power-law dependence of the percolation threshold on the effective coordination number with an exponent close to −1/2 is observed. Finally, we empirically determine the limit of the percolation threshold on square lattices with complex neighborhoods. This limit scales with the inverse square of the mean radius of the neighborhood. The boundary of this limit is touched for threshold values associated with extended (compact) neighborhoods.

A Non-Parametric Test for a Two-Way Analysis of Variance

The methodology carried out in this work is based on non-parametric inference. The problem is framed as a regression analysis, and the solution is derived using the permutation approach. The proposed test does not rely on the assumption that the distribution of the response follows a specific family of probability laws, unlike other parametric approaches. This makes the test powerful, particularly when the typical assumptions of parametric approaches, such as the normality of data, are not satisfied and parametric tests are not reliable. Furthermore, this method is more flexible and robust with respect to parametric tests. A permutation test on the goodness-of-fit of a multiple regression model is applied. Hence, proposed solution consists of the application of permutation tests on the significance of the single coefficients and then a combined permutation test (CPT) to solve the overall goodness-of-fit testing problem. Furthermore, a Monte Carlo simulation study was performed to evaluate the power of the previously mentioned permutation approach, comparing it with the conventional parametric F-test for ANOVA and the bootstrap combined test, both commonly discussed in the literature on this statistical problem. Finally, the proposed non-parametric test was applied to real-world data to investigate the impact of age and smoking habits on medical insurance costs in the USA. The findings suggest that smoking and being at least 50 years old significantly contribute to increased medical insurance costs.

Structural and Practical Identifiability of Phenomenological Growth Models for Epidemic Forecasting

Phenomenological models are highly effective tools for forecasting disease dynamics using real-world data, particularly in scenarios where detailed knowledge of disease mechanisms is limited. However, their reliability depends on the model parameters’ structural and practical identifiability. In this study, we systematically analyze the identifiability of six commonly used growth models in epidemiology: the generalized growth model (GGM), the generalized logistic model (GLM), the Richards model, the generalized Richards model (GRM), the Gompertz model, and a modified SEIR model with inhomogeneous mixing. To address challenges posed by non-integer power exponents in these models, we reformulate them by introducing additional state variables. This enables rigorous structural identifiability analysis using the StructuralIdentifiability.jl package in JULIA. We validated the structural identifiability results by performing parameter estimation and forecasting using the GrowthPredict MATLAB Toolbox. This toolbox is designed to fit and forecast time series trajectories based on phenomenological growth models. We applied it to three epidemiological datasets: weekly incidence data for monkeypox, COVID-19, and Ebola. Additionally, we assessed practical identifiability through Monte Carlo simulations to evaluate parameter estimation robustness under varying levels of observational noise. Our results confirm that all six models are structurally identifiable under the proposed reformulation. Furthermore, practical identifiability analyses demonstrate that parameter estimates remain robust across different noise levels, though sensitivity varies by model and dataset. These findings provide critical insights into the strengths and limitations of phenomenological models to characterize epidemic trajectories, emphasizing their adaptability to real-world challenges and their role in informing public health interventions.

Dual-channel pulse-dye densitometry can enable correction of fluorescent targeted and control agent plasma input function differences for quantitative paired-agent molecular imaging: a simulation study.

Paired-agent fluorescent molecular imaging approaches involve co-administration of a control (untargeted) imaging agent with a molecularly targeted agent to account for non-specific effects and quantify binding potential (BP)-a parameter proportional to the concentration of the targeted biomolecule. Accurate BP estimation often requires correction for differences in targeted and control agent plasma input functions (PIFs). We provide a simulation-based evaluation of whether dual-channel pulse dye densitometry (PDD) can be used to measure the PIFs of co-administered targeted and control imaging agents, to enable accurate BP estimation. Monte-Carlo simulations of light propagation were carried out using the anatomy and optical properties of a finger, as well as experimentally measured PIFs of co-administered anti-epidermal growth factor receptor fluorescent affibody, ABY-029, and IRDye 680LT, a control imaging agent from past mouse experiments. The accuracy of PIF shape estimation from PDD and PIF difference correction was evaluated by assessing BP estimation accuracy in a simulated "tumor" tissue. "Tumor" BP measurements using deconvolution correction with noise-free PIFs versus PDD-measured PIFs were compared. The relative error in PDD PIF deconvolution BP estimation was . No statistical difference was found between the estimated BP via deconvolution correction with true PIFs and the estimated BP via the reconstructed PIFs using the proposed PAF-PDD methodology. These results highlight the potential for developing a PDD instrument that can directly measure targeted and control agent PIFs and be used to correct for any PIF differences between agents for more quantitative estimates of BP in paired-agent imaging studies.

Population pharmacokinetics of aciclovir and its major metabolite 9-carboxymethoxymethylguanine and safety profile of valaciclovir in early liver transplant recipients.

Valaciclovir is frequently prescribed for cytomegalovirus infection prophylaxis. Its major metabolite 9-carboxymethoxymethylguanine (9-CMMG), when accumulated in renally impaired patients, is neurotoxic. Its synthesis involves enzymes that could be impacted in liver transplant recipients. This retrospective study aimed to describe the pharmacokinetic (PK) and safety profile of aciclovir and 9-CMMG early after liver transplantation in patients receiving valaciclovir prophylaxis. Consecutive (ideally five) blood samples were drawn. Plasma concentrations of aciclovir/9-CMMG were quantified by UPLC-MS/MS. Medical data were collected from digital records. A joint population PK model for aciclovir/9-CMMG was developed (Monolix 2023R1). Monte Carlo simulations were used to estimate Cmin and AUC0-24. Fifty patients (21 women) in the postoperative phase of liver transplantation were enrolled, with median age of 56.0 years and median weight of 69.5 kg; 255 samples were collected 19.0 days after transplantation. No drug-drug interaction was reported. A one-compartment model with first-order absorption best described the pharmacokinetics (PK). Covariate analysis showed that aciclovir and 9-CMMG clearances correlated with estimated glomerular filtration rate (eGFR). In normorenal patients, receiving valaciclovir 2000 mg q8h, estimated AUC0-24 values were 44.8 and 13.3 mg·h/L for aciclovir and 9-CMMG, respectively. The median estimated metabolic ratio of AUC0-24 (9-CMMG/aciclovir) was 30.4% and 129.9% for patients with >90 and <30 mL/min/1.73 m2 eGFR, respectively. There were no valaciclovir-related adverse events during hospitalization. This model allowed the PK and basal metabolic ratio of aciclovir and 9-CMMG in early liver transplantation to be defined. The correlation with renal function suggests important implications for therapeutic drug monitoring of these compounds, which will need confirmation in different cohorts.

Seismic Reliability Evaluation of Interdependent Water and Power Supply Networks

ABSTRACTSeismic reliability assessments of water and power supply networks are typically conducted under the assumption of system independence. This article introduces a novel framework for evaluating the seismic reliability of interdependent water and power supply networks, grounded in a comprehensive analysis of their interdependence mechanisms. The proposed framework integrates network flow theory and the Monte Carlo simulation method, leveraging network flow theory to determine the functional status of various system nodes and employing Monte Carlo simulations to account for random factors such as earthquake intensity. The practicality of the framework is validated through a case study in Shangcheng District. The numerical results reveal that: (1) the proposed method effectively evaluates the seismic reliability of interdependent water and power supply networks, and (2) the interdependent mechanisms and network topology significantly impact the seismic reliability of these networks.

99mTc-radiolabeled sunitinib encapsulated chitosan nanoparticles: an innovative radiopharmaceutical-based approach in cancer treatment

Sunitinib is a tyrosine kinase inhibitor drug that is used due to its anti-tumor activities. It has various side effects when administered orally. Thus, the development of nanoparticles bearing sunitinib can be advantageous for practical use in anti-tumor treatment. In the present work, sunitinib-encapsulated chitosan nanoparticles were manufactured. Sunitinib-loaded chitosan nanoparticles were also labeled using Technetium-99 m (99mTc). The particle sizes of optimum formulations were found to be below 150 nm, and their zeta potential values were approximately 30 mV. The radiolabeling efficiency and in vitro stability of 99mTc-radiolabeled nanoparticles were also evaluated. Their radiolabeling efficiency was found to be >90% for 6 h. Monte Carlo Simulation is a prediction-based technique that is utilized in drug development, especially to predict the radiation behaviors of radiopharmaceuticals. Its most important advantage in this field is that it is a simulation-based method that can reduce the need for animal experiments and clinical studies. In the current work, the results obtained from the Monte Carlo simulation showed that the radiolabeled sunitinib nanoparticles could effectively interact with tumor tissue. ABBREVIATIONS DLS: dynamic light scattering; NP: nanoparticle; PDI: polydispersity index; PS: particle size; RTLC: radio thin layer chromatography; TKI: tyrosine kinase inhibitor; TPP: tripolyphosphate; ZP: zeta potential