Combining Monte Carlo Simulations Research Articles

A Systematic and Objective Framework for Evaluating Subcontractor Performance Using Monte Carlo Simulation Coupled with the Analytic Hierarchy Process and a Linear Additive Utility Model

The general contractor (GC)–subcontractor (SC) relationship is a crucial aspect of construction supply chain management, heavily influencing project outcomes. This study investigates a method for assessing SC performance and underscores its essential role in construction projects. Traditionally, SC assessments are based on subjective evaluations, which can lead to biased decision-making. To counter this, this study introduces a comprehensive framework that employs objective indices and a systematic evaluation method. The study begins with a comprehensive literature review and expert consultations to identify key indices for SC evaluation: time, cost, quality, safety, resources, satisfaction, and leadership. A hybrid method combining Monte Carlo simulation and the Analytic Hierarchy Process (AHP) is employed to assign weights to these indices through the development of probability distributions, thereby reducing judgment uncertainty. The developed evaluation model incorporates normalization and a linear additive utility model (LAUM) to calculate a performance index (PI) that quantifies SC performance across various levels, from outstanding to poor. The normalization process is applied with three tolerance levels (high, medium, and low). A real case study with a three-scenario sensitivity analysis demonstrates the model’s effectiveness. This approach provides general contractors with a more objective and transparent assessment process, minimizing bias in evaluations.

Potential Corrosion Inhibition Properties of Flavone Derivatives on the Cu(111) Surface: A Combined DFT and Monte Carlo Simulation Study.

Since corrosion causes significant harm to the environment and economy, sustainable corrosion inhibitors are essential. This study set out to examine Anti-corrosion ability of a number of closely related polycyclic compounds of flavone derivatives, namely 5,7-dimethoxyflavone (1), 4',5,7-trimethoxyflavone (2), 3',4',5'-trimethoxyflavone (3), 5-hydroxy-3,3',4',7-tetramethoxyflavone (4), tangeretin (5), 3,3',4',5,6,7,8-heptamethoxyflavone (6), 3',5,7-trihydroxy-4',5',6-trimethoxyflavone (7) and 3',4',5,7-tetrahydroxy-3,6,8-trimethoxyflavone (8), using the DFT/B3LYP/6-311 + + G(d, p) basis set. Monte Carlo simulations were used to reveal the adsorption of the investigated compounds on the Cu(111) surface. The results showed that all compounds exhibited excellent or good molecule-to-metal electron charge transfer, with compounds 6 and 8 performing the best, followed by compounds 2, 4, 5, and 7. According to the simulations, compound 6 and compound 7 exhibited the lowest adsorption energy, which can likely be attributed to the significant role of the methoxy and hydroxy substituents on both benzene rings. This analysis indicates that compounds 6 and 7 are the most promising candidates for preventing corrosion on copper.

Towards eco-friendly alternatives to prevent CaSO4·2H2O scale formation in water industrial systems: A combined experimental study and Monte Carlo simulations

Towards eco-friendly alternatives to prevent CaSO4·2H2O scale formation in water industrial systems: A combined experimental study and Monte Carlo simulations

Cross-shore hydrodynamics and morphodynamics modeling of an erosive event in the inner surf zone

The phase-averaged and depth-integrated coastal morphodynamic model, XBeach-Surfbeat, was investigated for its capability of predicting the cross-shore hydrodynamics and morphodynamics in the inner surf zone by simulating the storm-induced berm erosion, sediment transport, and subsequent sand bar formation. By utilizing a comprehensive hydrodynamic and morphodynamic dataset measured in a large wave flume and high-fidelity 3D large-eddy simulation (LES) data, a rigorous model validation was conducted to assess its capability in predicting inner-surf zone hydrodynamics and to explore how the improved hydrodynamic performance impacts the predicted morphodynamics. Using the default model parameters of the model, the undertow was overestimated with the peak magnitude being 30%–35% larger in the inner surf zone. Combining Monte Carlo simulation, the optimum hydrodynamic calibration for the simulated undertow was achieved when the roller energy dissipation parameter (β) was maintained below 0.1, and the threshold water depth (hmin) exceeded 0.25 m. The calibrated undertow improved the morphodynamic predictions by reducing the excessive berm erosion (Event I) and sand bar growth in the inner surf zone (Event II). Further improved morphodynamic predictions were achieved by calibrating sediment transport parameters, including the onshore sediment transport coefficient (γua) and the bore interval coefficient (Tbfac) associated with turbulence-bed interaction. A consistent set of optimized model coefficients for the model is shown to be effective in simulating the entire erosive event (combined Events I and II). This study reveals that further improvement of the model's capability may require incorporating new parameterizations and physics, such as wave-breaking-induced turbulence and wave nonlinearity associated with sediment transport in the inner surf and swash zones.

Understanding the mechano-fluorochromic enhancement in a cyanoethylene-functionalized pyrene-based luminogen: An experimental and theoretical study

Pyrene-based luminogens have attracted wide attention due to their promising applications in organic electronics, chemical sensing, and bioimaging. In this study, a cyanoethylene-functionalized pyrene-based luminogen CNPyCH3 was designed and synthesized. Experimental results show that CNPyCH3 exhibits distinct photophysical properties in dilute solution and various solid states. The mechano-fluorochromic (MFC) property is more attractive and interesting, as a red shift with enhanced emission was observed upon mechanical stimulation. The intriguing MFC enhancement behavior was further investigated by in-depth theoretical calculations combining Monte Carlo simulations, quantum mechanics/molecular mechanics calculations, and time-dependent density functional theory. It was found that the calculated emission wavelengths for both pristine and ground CNPyCH3 are in good agreement with the experimental values, and that the ground CNPyCH3 has a greater oscillator strength. From the perspective of intermolecular interactions, the weakened π-π interactions induced by the half-switched “face-to-edge” packing modes are beneficial for achieving brighter emission upon grinding. In terms of intramolecular structures, the ground CNPyCH3 has a more planar conformation around the cyanoethylene moiety in the excited-state equilibrium geometry than the pristine CNPyCH3, resulting in the grinding-induced red-shifted emission. Our study provides a unique insight into the MFC enhancement behavior of the pyrene-based luminogens.

Establishment of an absolute measurement of the reference air kerma rate for HDR 192Ir brachytherapy sources

Before this study, no institution in China had undertaken the calculation and official establishment of reference values for the reference air kerma rate associated with high-dose rate 192Ir sources used in brachytherapy. This research, carried out at the National Institute of Metrology (NIM) in China, has successfully established an 192Ir reference radiation facility. Consequently, it has achieved the absolute measurement of the reference air kerma rate for high-dose rate brachytherapy using 192Ir sources. For this study, a medical afterloader machine was acquired. The radiation source employed was a high-dose rate 192Ir source for brachytherapy, produced by HTA Co., Ltd. A reference graphite cavity ionization chamber with a volume of 100 cm3 was designed to serve as the ionization chamber for absolute measurements. The determination of various correction factors and physical constants was achieved through a method that combines Monte Carlo simulations with experimental techniques. The study successfully accomplished the absolute measurement of the reference air kerma rate for the high-dose rate brachytherapy 192Ir radiation source. The expanded uncertainty of the measurement results was 0.72% (k = 2). The relative standard deviation for the RAKR of the same 192Ir radiation source was 0.073%. This study marks a significant advancement in the field of radiation therapy in China, particularly in the accurate dosimetry for brachytherapy using high-dose rate 192Ir sources. The study will contribute to the BIPM.RI(I)-K8 international comparison, organized by the Bureau International des Poids et Mesures (BIPM), thus facilitating the international recognition of the measurement values.

Research on Deep Bomb Delivery Strategy Based on Monte Carlo Simulation and Adaptive Optimization Algorithm

Aiming at the problem of insufficient precision of deep bomb delivery in traditional anti-submarine warfare, this paper proposes a novel algorithm combining Monte Carlo simulation and adaptive optimization [1]. The algorithm optimises the deep bomb delivery strategy by defining the probability distribution of the submarine position. Specifically, Monte Carlo simulation is used to generate samples of possible submarine positions, analyse the effect of the submarine's three-dimensional positional error underwater on the probability of hitting a single depth charge, and explore the role of the dual-fuse detonation mechanism [2]. On this basis, the submarine's orientation error in 3D space is further considered, and an optimisation model is used to determine the optimal fixed-depth fuse detonation depth to maximise the hit probability of a single deep bomb [3]. In addition, this paper extends to the case of multiple depth charges, and optimises the overall hit probability by designing the array of charges and plane spacing to ensure that at least one depth charge can hit the submarine [4]. The experimental results show that the algorithm significantly improves the hit probability of the deep bomb, effectively reduces the impact of positioning error on the combat effect and demonstrates its potential application in practical anti-submarine warfare.

Rheological modelling of apple puree based on machine learning combined Monte Carlo simulation: Insight into the fundamental light- particle structure interaction processes

In this work, apple purees from different particle concentration and verifying in size were reconstituted to investigate their impacts on rheological behaviors, optical properties and light interaction at 900–1650 nm. The optical scattering of different puree particle concentration varied more intensively than particle size. Based on Monte Carol simulation (MCX), the highest proportion of diffuse reflection energy and absorption were observed in 1050 nm and 1480 nm, respectively. The averaged light propagation of all the photons in purees varied from 259.29 mm to 199.17 mm with the particle concentration from 25 % to 45 %. Finally, support vector machine regression based on optical scattering properties fused with MCX parameters at 1050 nm effectively evaluated puree apparent viscosity parameters (RPD > 2.39), viscous and elastic modulus (RPD > 2.47). These extended our knowledge of the fundamental light - particle structure interaction processes and provided a new solution for rheological modelling in pureed food.

Assessment of care provision integration in a community-based mental health system: balanced care model implementation in Andalusia (Spain)

BackgroundAndalusia is the second largest region in Spain, and it has developed a comprehensive mental health (MH) plan that encourages the consolidation of the balanced care model. However, its geographical and socioeconomic disparity is a great challenge for a community-based MH system. Both the assessment of the implementation of the MH plan and the development of new tools to support decision-making can be considered critical.ObjectivesThe present study aims (i) to assess how the integration of different types of MH care may influence system performance and (ii) to check the performance evolution of the integration process geographically regarding the small MH areas of Andalusia.MethodsThe performance of the Andalusian MH system was assessed by combining Monte Carlo simulation, fuzzy inference and data envelopment analysis. The relative technical efficiency was the main performance indicator.ResultsA correct integration of appropriate types of MH care, according to population needs, increases the performance of the Andalusian MH system both from global and regional perspectives. The spatial representation (based on small MH areas) of the results highlights how the performance depends on specific geographical characteristics. By analyzing the identified spatial clusters, defined by different management patterns depending on user and socioeconomic characteristics, benchmark areas and areas for improvement can be studied to design evidence-informed policies and interventions.ConclusionsA global analysis of MH system performance was carried out, including both the successive integration of different types of care and its spatial evolution. Although an appropriate integration of different types of MH care has a positive effect on the Andalusian MH system, this process has different profiles depending on specific geographically based user and socioeconomic characteristics. The balanced care model can be considered the paradigm for assessing the performance of a large and populated territory such as Andalusia, which has a community-based MH system. This methodological approach (performance assessment and spatial analysis) may be used as a guide for developing future evidence-informed policies and managerial interventions.

A new formulation for predicting the ultimate capacities of FRP-confined concrete using advanced machine learning framework: Developed structural reliability analysis

Maintaining the safety levels of concrete structures is critical, where boosting the strength and deformability of such components utilizing Fibre Reinforced Polymer (FRP) composites is well adopted. However, the outside environment and applied loads can influence the FRP confined concrete. Therefore, accurate assessment of the ultimate capacities of FRP-confined concrete is highly essential. This paper proposes an advanced machine-learning framework using a novel approach called Group Method Data Handling Neural Network (GMDH-NN) for developing novel formulations that properly predict the ultimate capacities of FRP-confined concrete. The GMDH-NN ability in terms of tendency, agreement, and accuracy is compared to the existing empirical correlations and well-known machine-learning methodologies, notably Artificial Neural Network (ANN) and Response Surface Method (RSM). Furthermore, to address the failure modes of strength and ductility, the reliability analysis of FRP-confined concrete using combined Monte Carlo simulation (MCS) with the proposed GMDH-NN is examined for three types of composite sheets: Aramid, Carbon, and Glass. Results prove the GMDH-NN outperformance for modeling the ultimate strength (R2 =0871) and strain (R2 =0.79) capacities for FRP-confined concrete. The structural reliability analysis indicated that the choice of FRP sheets had a substantial influence on the increase of the safety levels of confined concrete cores, as well as the requirement for early prediction of these parameters to avoid failures.

Adsorption of cytochrome c on different self-assembled monolayers: The role of surface chemistry and charge density.

In this work, the adsorption behavior of cytochrome c (Cyt-c) on five different self-assembled monolayers (SAMs) (i.e., CH3-SAM, OH-SAM, NH2-SAM, COOH-SAM, and OSO3--SAM) was studied by combined parallel tempering Monte Carlo and molecular dynamics simulations. The results show that Cyt-c binds to the CH3-SAM through a hydrophobic patch (especially Ile81) and undergoes a slight reorientation, while the adsorption on the OH-SAM is relatively weak. Cyt-c cannot stably bind to the lower surface charge density (SCD, 7% protonation) NH2-SAM even under a relatively high ionic strength condition, while a higher SCD of 25% protonation promotes Cyt-c adsorption on the NH2-SAM. The preferred adsorption orientations of Cyt-c on the negatively-charged surfaces are very similar, regardless of the surface chemistry and the SCD. As the SCD increases, more counterions are attracted to the charged surfaces, forming distinct counterion layers. The secondary structure of Cyt-c is well kept when adsorbed on these SAMs except the OSO3--SAM surface. The deactivation of redox properties for Cyt-c adsorbed on the highly negatively-charged surface is due to the confinement of heme reorientation and the farther position of the central iron to the surfaces, as well as the relatively larger conformation change of Cyt-c adsorbed on the OSO3--SAM surface. This work may provide insightful guidance for the design of Cyt-c-based bioelectronic devices and controlled enzyme immobilization.

Reliability analysis of a grid-connected hybrid renewable energy system using hybrid Monte-Carlo and Newton Raphson methods

The integration of renewable energy sources into the power grid is essential for sustainable development, yet it presents significant dependability challenges, particularly in terms of reliability, stability, and robustness due to the inherent variability of these sources. This research introduces a novel hybrid methodology that combines Monte Carlo simulation with Newton-Raphson power flow analysis to enhance the reliability assessment of grid-connected hybrid renewable energy systems. This innovative approach uniquely addresses the limitations of existing methodologies by merging the probabilistic handling of uncertainties with precise deterministic power flow analysis. Our hybrid method significantly reduces the Loss of Load Expectation (LOLE) to 5 h per year and the Loss of Load Energy Expectation (LOEE) to 200 MWh per year, outperforming traditional methods which typically report LOLEs of 2020 h/year and LOEEs of 10001000 MWh/year. Additionally, the hybrid method achieves a reduction in power losses to 1.2%, showcasing its superior efficiency compared to the 2.5% losses seen with standalone Monte Carlo methods. Real-time validation using the IEEE-30 bus model further confirms the practical applicability and robustness of our approach, making it a pivotal tool for enhancing grid stability and optimizing renewable energy integration. This research not only advances the methodology for reliability assessment but also sets a new standard for balancing accuracy and computational efficiency in energy system management. The implications of this work are far-reaching, offering significant contributions to both grid reliability and the sustainable management of renewable energy resources.

Combined DFT and Monte Carlo simulation studies of potential corrosion inhibition properties of coumarin derivatives.

Corrosion, the degradation of materials due to chemical reactions with their environment presents significant challenges both economically and environmentally. It affects various industries, including construction, transportation, and manufacturing, leading to equipment failures, safety hazards, and increased maintenance costs. Coumarin derivatives have shown promise due to their inherent chemical properties and potential for biodegradability. In this study, a series of the coumarin derivatives were examined in silico to reveal their potential corrosion inhibition properties toward the Fe(110) and Cu(111) surfaces. The compounds investigated include coumarin (2H-chromen-2-one, 1), furanocoumarin (7H-furo[3,2-g]chromen-7-one, 2), dihydrofurano coumarin (2,3-dihydro-7H-furo[3,2-g]chromen-7-one, 3), pyrano coumarin-linear type (8,8-dimethyl-2H,8H-pyrano[3,2-g]chromen-2-one, 4), pyrano coumarin-angular type (8,8-dimethyl-2H,8H-pyrano[2,3-f]chromen-2-one, 5), bicoumarin (3,3'-methylenebis(2H-chromen-2-one), 6), and phenyl coumarin (4-phenyl-2H-chromen-2-one, 7). The findings suggest that the bicoumarin derivative 6 exhibits the lowest adsorption energy with the Fe(110) surface, while the same energy absolute value is about two times lower for the Cu(111) surface. This is due to the formation of a planar configuration of a molecule of 6 on the metal surfaces with the participation of both coumarin fragments upon interacting with the Fe(110) surface, while one coumarin fragment interacts with the Cu(111) surface. Density functional theory (DFT) calculations were employed to study the electronic properties of the coumarin derivatives. The specific computational method used was B3LYP, a hybrid functional that combines with the 6-311 + + G(d,p) basis set. Each coumarin derivative was first subjected to a geometry optimization to find the most stable molecular structure. Electronic properties, dipole moments, and molecular electrostatic potential surfaces were calculated. The Monte Carlo simulations were used to model the adsorption behavior of the coumarin derivatives on metal surfaces, namely, Fe(110) and Cu(111). These simulations allowed to visualize interaction of the studied molecules with the metal surfaces, which is crucial for their function as corrosion inhibitors. The present study provides a comprehensive understanding of the corrosion inhibition potential of the applied coumarin derivatives. The insights gained from these methods can inform the development of effective, sustainable corrosion inhibitors that are both environmentally friendly and highly efficient.

Research on Probabilistic Load Modeling and Simulation Model of Electric Vehicle Charging Station

Abstract The stochastic nature of electric vehicle charging station loads poses certain challenges in establishing relevant load models. This paper introduces a probabilistic load modeling approach that considers factors such as the time electric vehicles begin charging, daily travel distance, charging power, and charging duration. By comprehensively studying the influencing factors of charging station load modeling and combining Monte Carlo simulation with probabilistic statistical analysis, a comprehensive probability load model for typical charging stations is developed. Building upon this, a charging load simulation model is constructed using Simulink. This model can generate daily load curves consistent with the probability load model, making a positive contribution to the study of the impact of electric vehicles on the power grid.

Percolation thresholds of disks with random nonoverlapping patches on four regular two-dimensional lattices.

In percolation of patchy disks on lattices, each site is occupied by a disk, and neighboring disks are regarded as connected when their patches contact. Clusters of connected disks become larger as the patchy coverage of each disk χ increases. At the percolation threshold χ_{c}, an incipient cluster begins to span the whole lattice. For systems of disks with n symmetric patches on Archimedean lattices, a recent work [Wang et al., Phys. Rev. E 105, 034118 (2022)2470-004510.1103/PhysRevE.105.034118] found symmetric properties of χ_{c}(n), which are due to the coupling of the patches' symmetry and the lattice geometry. How does χ_{c} behave with increasing n if the patches are randomly distributed on the disks? We consider two typical random distributions of the patches, i.e., the equilibrium distribution and a distribution from random sequential adsorption. Combining Monte Carlo simulations and the critical polynomial method, we numerically determine χ_{c} for 106 models of different n on the square, honeycomb, triangular, and kagome lattices. The rules governing χ_{c}(n) are investigated in detail. They are quite different from those for disks with symmetric patches and could be useful for understanding similar systems.

Numerical Optimization of Variable Blank Holder Force Trajectories in Stamping Process for Multi-Defect Reduction.

An intelligent optimization technology was proposed to mitigate prevalent multi-defects, particularly failure, wrinkling, and springback in sheet metal forming. This method combined deep neural networks (DNNs), genetic algorithms (GAs), and Monte Carlo simulation (MCS), collectively as DNN-GA-MCS. Our primary aim was to determine intricate process parameters while elucidating the intricate relationship between processing methodologies and material properties. To achieve this goal, variable blank holder force (VBHF) trajectories were implemented into five sub-stroke steps, facilitating adjustments to the blank holder force via numerical simulations with an oil pan model. The Forming Limit Diagram (FLD) predicted by machine learning algorithms based on the Generalized Incremental Stress State Dependent Damage (GISSMO) model provided a robust framework for evaluating sheet failure dynamics during the stamping process. Numerical results confirmed significant improvements in formed quality: compared with the average value of training sets, the improvements of 18.89%, 13.59%, and 14.26% are achieved in failure, wrinkling, and springback; in the purposed two-segmented mode VBHF case application, the average value of three defects is improved by 12.62%, and the total summation of VBHF is reduced by 14.07%. Statistical methodologies grounded in material flow analysis were applied, accompanied by the proposal of distinctive optimization strategies for the die structure aimed at enhancing material flow efficiency. In conclusion, our advanced methodology exhibits considerable potential to improve sheet metal forming processes, highlighting its significant effect on defect reduction.

A state-selected continuous wave laser excitation method for determining CO2's rotational state distribution in a supersonic molecular beam.

State-resolved experiments can provide fundamental insight into the mechanisms behind chemical reactions. Here, we describe our methods for characterizing state-resolved experiments probing the outcome of the collision between CO2 molecules and surfaces. We create a molecular beam from a supersonic expansion that passes through an ultra-high vacuum system. The CO2 is vibrationally excited by a continuous wave infrared (IR) laser using rapid adiabatic passage. We attenuate the fractional excitation using a CO2 absorption cell in the IR beam path. We combine Monte Carlo simulations and molecular beam energy measurements to find the initial rotational state distribution of the molecular beam. We find that our pure CO2 beam from a 300K source has a rotational temperature of ∼26K.

Epidemic criticality in temporal networks

Analytical studies of network epidemiology almost exclusively focus on the extreme situations where the timescales of network dynamics are well separated (longer or shorter) from that of epidemic propagation. In realistic scenarios, however, these timescales could be similar, which has profound implications for epidemic modeling (e.g., one can no longer reduce the dimensionality of epidemic models). Combining Monte Carlo simulations and mean-field theory, we analyze the critical behavior of susceptible-infected-susceptible epidemics in the vicinity of the critical threshold on the activity-driven model of temporal networks. We find that the persistence of links in the network causes the threshold to decrease as the recovery rate increases. Dynamic correlations (coming from being close to infected nodes increases the likelihood of infection) drive the threshold in the opposite direction. These two counteracting effects make epidemic criticality in temporal networks a remarkably complex phenomenon. Published by the American Physical Society 2024

Reliability and sensitivity assessment of laminated composite plates with high-dimensional uncertainty variables using active learning-based ensemble metamodels

Abstract Laminated composite materials play a crucial role in various engineering applications due to their exceptional mechanical properties. This article explores the analysis of reliability and the local sensitivity of failure probability associated with laminated composite materials. It delves into the impact of uncertainties on the performance of these materials and presents an active learning-based reliability methodology. This methodology combines Monte Carlo simulation with a metamodel derived from the combination of three metamodels: artificial neural networks, support vector regression, and Kriging. The article illustrates this methodology through two practical applications on laminated composite plates and compares its performance with other reliability methods. This approach offers valuable insights to enhance the analysis of reliability, strengthen the design process, and facilitate decision-making while fully considering uncertainties related to material properties.

Introducing an Artificial Neural Network for Virtually Increasing the Sample Size of Bioequivalence Studies

Sample size is a key factor in bioequivalence and clinical trials. An appropriately large sample is necessary to gain valuable insights into a designated population. However, large sample sizes lead to increased human exposure, costs, and a longer time for completion. In a previous study, we introduced the idea of using variational autoencoders (VAEs), a type of artificial neural network, to synthetically create in clinical studies. In this work, we further elaborate on this idea and expand it in the field of bioequivalence (BE) studies. A computational methodology was developed, combining Monte Carlo simulations of 2 × 2 crossover BE trials with deep learning algorithms, specifically VAEs. Various scenarios, including variability levels, the actual sample size, the VAE-generated sample size, and the difference in performance between the two pharmaceutical products under comparison, were explored. All simulations showed that incorporating AI generative algorithms for creating virtual populations in BE trials has many advantages, as less actual human data can be used to achieve similar, and even better, results. Overall, this work shows how the application of generative AI algorithms, like VAEs, in clinical/bioequivalence studies can be a modern tool to significantly reduce human exposure, costs, and trial completion time.