Monte Carlo Simulation Method Research Articles

Non-stationary response analysis for sandwich panels with corrugated cores under moving random loads

In this work, a recursion reverberation-ray matrix method (RRRM) is presented to formulate an exact and unified solution for the non-stationary responses analysis of the sandwich panels with corrugated cores (SPCC) subjected to the moving random loads. The governing equations for the basic units are obtained by employing the simple first-order shear deformation theory (S-FSDT) and Hamilton's principle. Then, based on the traditional reverberation ray matrix method (MRRM), the RRRM is established by incorporating a bidirectional recursion technique to effectively calculate the exact solutions of the whole model. Thereinto, the kinematic relation matrix between the cell units can be constructed to facilitate the coupling of various desired numbers of elements by employing the virtual coupling spring. In addition, a unified loading mechanism for multi-scenario loads is proposed by integrating the pseudo-excitation method (PEM) with the load stage division technique. The applicability and accuracy of the current method to non-stationary response behaviors of the SPCC are clarified by carrying out sufficient comparative studies between the calculated results with reference solutions from FEM and Monte Carlo simulation method. Furthermore, several meaningful conclusions are drawn regarding the effects of structural and load parameters on the vibration characteristic, non-stationary displacement response, power spectrum density (PSD), and time-varying root mean square (RMS).

Safety Evaluation and Influential Factor Analysis of Urban Expressway Diversion Areas Based on Non-Stationary Conflict Extremes

To evaluate the safety conditions of expressway exit areas with different exit forms, a safety analysis method based on the conflict extreme model is proposed. Based on video data from four typical urban expressway diversion areas, longitudinal conflicts and lateral conflicts were identified using time to collision and cross time to collision methods, respectively, and traffic parameters such as traffic flow were extracted. A Bayesian hierarchical conflict extreme model for joint conflict types was constructed by integrating the peak over threshold and Bayesian hierarchical approach. The threshold for conflicts was determined using the mean residual life plot and the threshold stability plot. Subsequently, a logarithmic link function for the scale parameter was established by combining different covariates. Then, the model parameters were estimated using the Markov chain Monte Carlo simulation method. Finally, based on the fitted distribution, a comprehensive safety evaluation and influential factor analysis were conducted for each diversion area. The results show that an increase in traffic flow and the ratio of large vehicles leads to a higher probability of traffic accidents. However, an increase in the average speed reduces the probability of traffic accidents. The construction form of the diversion area and the number of off-ramp lanes do not demonstrate an explicit relationship with the probability of traffic accidents. The research conclusion indicates that the Bayesian hierarchical extreme model can effectively utilize short-term observed conflicts for long-term crash estimation, and it has the potential to be applied in the field of safety evaluation.

Prediksi Jumlah Mahasiswa Baru FMIPA UNIMED Dengan Menggunakan Teknik Simulasi Monte Carlo

Universities' development hinges significantly on student admissions, necessitating accurate predictions for effective planning. This study applies the Monte Carlo simulation method to forecast new student arrivals at the Faculty of Mathematics and Natural Sciences (FMIPA) at Universitas Negeri Medan (UNIMED). Utilizing data from 2021 to 2023 sourced from the PDDikti website, the research employs PHP programming for implementation. The Monte Carlo algorithm's numerical prowess ensures precise statistical data simulation, comprising data collection, probabilistic distribution computations, cumulative distribution determinations, random number generation, and simulation analyses. Simulation results for 2022, 2023, and 2024 exhibit consistent trends, projecting an average of 860 to 930 new students per program. This methodology surpasses manual estimations, offering robust insights for university resource allocation and strategic management. Despite its effectiveness, study limitations, such as model assumptions, warrant continuous validation with actual data. This research advances predictive modeling in higher education, providing a foundation for future enhancements and comprehensive prediction integrations.

Weak Approximation for a Black-Scholes Type Regime Switching Model

ABSTRACT We provide a Monte Carlo simulation method for a European type option under a regime switching type Black-Scholes model where the volatility is a discontinuous step function which takes n + 1 different values depending on the value of the underlying within n + 1 disjoint partition intervals. Our simulation method is based on a combination of the Euler scheme which is used in the case that the process is away from the boundary points of the partition intervals and the skew Brownian motion in the case that it the process in near the boundary points of the partition intervals. We show that the weak error of approximation is exponentially small for the proposed method and show some simulation results.

Pendekatan Metode Monte Carlo untuk Simulasi Pergerakan Harga Saham

This research applies the Monte Carlo simulation method to predict the movement of Apple Inc.'s stock price over a long period of time. Using historical data of Apple's stock price from 12 December 1980 to 24 March 2022, this study aims to generate a probability distribution of the future stock price. The method involves several steps, including data collection, log return calculation, parameter estimation, and simulation of the stock price path through random iterations based on the log return distribution. The simulation results show that the closing price of Apple stock can be predicted by following the historical trend, although there are differences with the real data due to the stochastic nature of the Monte Carlo technique. This research also applies a variance reduction method to improve simulation efficiency. The findings provide a valuable perspective for investors and financial analysts in identifying investment risks and opportunities through an in-depth understanding of the dynamics of stock price movements using Monte Carlo simulation. Suggestions for future research include the use of VaR methods with historical variance and covariance approaches, as well as considering longer data periods and more stock indices for more comprehensive results.

A novel formulation of low voltage distribution network equivalents for reliability analysis

Reliability analysis of large power networks requires accurate aggregate models of low voltage (LV) networks to allow for reasonable calculation complexity and to prevent long computational times. However, commonly used lumped load models neglect the differences in spatial distribution of demand, type of phase-connection of served customers and implemented protection system components (e.g., single-pole vs three-pole). This paper proposes a novel use of state enumeration (SE) and Monte Carlo simulation (MCS) techniques to formulate more accurate LV network reliability equivalents. The combined SE and MCS method is illustrated using a generic suburban LV test network, which is realistically represented by a reduced number of system states. This approach allows for a much faster and more accurate reliability assessments, where further reduction of system states results in a single-component equivalent reliability model with the same unavailability as the original LV network. Both mean values and probability distributions of standard reliability indices are calculated, where errors associated with the use of single-line models, as opposed to more detailed three-phase models, are quantified.

Assessing the Air Pollution Tolerance Index of Urban Plantation: A Case Study Conducted along High-Traffic Roadways

Road transport and traffic congestion significantly contribute to dust pollution, which negatively impacts the growth of roadside plants in urban areas. This study aims to quantify the air pollution tolerance index (APTI) and analyze the impacts of dust deposition on different plant species and trees planted along a busy urban roadside in Lahore, Pakistan by considering seasonal variations. The APTI of each species is determined based on inputs of various biochemical parameters (leaf extract pH, ascorbic acid content, relative water content, and total chlorophyll levels), including dust deposition. In this study, laboratory analysis techniques are employed to assess these factors in selected plant species such as Mangifera indica, Saraca asoca, Cassia fistula, and Syzygium cumini. A statistical analysis is conducted to understand the pairwise correlation between various parameters and the APTI at significant and non-significant levels. Additionally, uncertainties in the inputs and APTI are addressed through a probabilistic analysis using the Monte Carlo simulation method. This study unveils seasonal variations in key parameters among selected plant species. Almost all biochemical parameters exhibit higher averages during the rainy season, followed by the summer and winter. Conversely, dust deposition on plants follows an inverse trend, with values ranging from 0.19 to 4.8 g/cm2, peaking during winter, notably in Mangifera indica. APTI values, ranging from 9.39 to 14.75, indicate varying sensitivity levels across species, from sensitive (Syzygium cumini) to intermediate tolerance (Mangifera indica). Interestingly, plants display increased tolerance during regular traffic hours, reflecting a 0.9 to 5% difference between the APTI at peak and regular traffic hours. Moreover, a significant negative correlation (−0.86 at p < 0.05 level) between APTI values and dust deposition suggests a heightened sensitivity to pollutants during the winter. These insights into the relationship between dust pollution and plant susceptibility will help decision makers in the selection of resilient plants for urban areas and improve air quality.

Assessing the Environmental, Economic, and Route Optimization Impacts of Implementing a Green Bus System in Urban Areas

The proliferation of e-bus has swept the globe. Many countries have begun the transformation of electric buses. Electric buses are embraced worldwide for their environmental and low-cost advantages. This work will help local governments establish a complete e-bus system.The first question mainly addresses the ecological impact that would be brought by the impact. In this work, the Ecological Impact Quantification Model (EIQM) is developed to address gaseous pollution including Greenhouse gas carbon dioxide, pollutants nitric oxide and PM2.5. The ecological effect enhancement with e-bus system is presented, and especially, the gaseous pollution caused by electricity production is approximated by the Monte Carlo simulation method. As a result, this team figures out that with the development of e-bus system, the average CO2 emission per person decreases, having a reduction of 11391g at first, and 14039g after five years.The second question mainly addresses the financial problem that the government needs to face. In this work, the Quantification of Financial Impact Model (QFIM) is developed. This team utilizes the differential equation to obtain the exact varying rate of diesel fuel and electricity fee, and finally determined the possible range of the government deficit. By synthesizing these data, this team discovers that the government could start reducing its deficit after 8 years of the transformation from diesel bus to e-bus, having deficit from $-88,412,180 to $-64,182,722.The third question addresses the 10-year plan for the selected city and 2 additional cities. This team develops Optimized Deployment Model based on Greedy Algorithm (ODGA) to optimize the transformation plan over 10 years of existing bus routes and the number of e-bus to be constructed per year. Monetary and time utility is considered and Greedy Algorithm is introduced to find out the best travelling route for e-buses. This team successfully figures out the best transformation plan of travelling routes and number of e-bus to be constructed per year to maximize the efficiency, including 5-min interval departure time and 867 buses are required to run the proposed route in Chicago. Finally, the team writes a piece of letter to Chicago Transit Authority and proposes the 10-year plan of e-bus system construction to guarantee the steady implementation of e-bus.

SIMULASI PENGENDALIAN PERSEDIAAN ALAT TULIS KANTOR PADA DINAS PERKEBUNAN DAN PETERNAKAN PROVINSI SUMATERA UTARA DENGAN METODE MONTE CARLO

In a Government Agency, office stationery supplies are an absolute necessity. The provision of adequate office stationery will facilitate performance. This study aims to predict the demand for office stationery using Monte Carlo Simulation. Monte Carlo is a numerical analysis method that uses random number samples. The data used in this study are primary data in the form of the number of stock items and the number of requests for goods from January to December 2023. The accuracy result using the Monte Carlo method for Year 2024 is 91.78%. This shows that the Monte Carlo method simulation can be used to predict the demand for stationery for the following year.

Molecular Dynamics Simulation of Methane Adsorption and Diffusion

To study the adsorption and diffusion mechanism of low-rank coal at the microscopic level, samples of Fukang low-rank coal were collected, and the elemental composition, carbon type distribution and functional group type of the Fukang low-rank coal structure were determined by elemental analysis (Ea), Fourier-transform interferometric radiometer (FTIR), X-ray photoelectron spectroscopy (XPS) and 13C nuclear magnetic resonance (13C NMR) experiments to construct a 2D molecular structure of the coal and a 3D macromolecular structure model. The adsorption and diffusion characteristics of methane were researched by giant regular Monte Carlo (GCMC) and molecular dynamics (MD) simulation methods. The results showed that the excess adsorption amount of methane increased and then decreased with the increase in pressure. The diffusion of methane showed two stages with increasing pressure: a sharp decrease in the diffusion coefficient from 0.5 to 5.0 MPa and a slow decrease in the diffusion coefficient from 5.0 to 15.0 MPa. The lower the pressure, the larger the effective radius of the CH4 and C atoms, and the higher the temperature, the more pronounced the diffusion and the larger the effective radius.

New adaptive EWMA CV control chart with application to the sintering process

This research presents a new adaptive exponentially weighted moving average control chart, known as the coefficient of variation (CV) EWMA statistic to study the relative process variability. The production process CV monitoring is a long-term process observation with an unstable mean. Therefore, a new modified adaptive exponentially weighted moving average (AAEWMA) CV monitoring chart using a novel function hereafter referred to as the "AAEWMA CV" monitoring control chart. the novelty of the suggested AAEWMA CV chart statistic is to identify the infrequent process CV changes. A continuous function is suggested to be used to adapt the plotting statistic smoothing constant value as per the process estimated shift size that arises in the CV parametric values. The Monte Carlo simulation method is used to compute the run-length values, which are used to analyze efficiency. The existing AEWMA CV chart is less effective than the proposed AAEWMA CV chart. An industrial data example is used to examine the strength of the proposed AAEWMA CV chart and to clarify the implementation specifics which is provided in the example section. The results strongly recommend the implementation of the proposed AAEWMA CV control chart.

Stability Evaluation of Slope Based on Global Sensitivity Analysis

The uncertainty of parameters will have a significant impact on slope stability, where sensitivity analysis is a commonly used method in uncertainty research. However, traditional sensitivity analysis method costs much computation time. When calculating the sensitivity index of one parameter, all other parameters are taken as fixed values, and the uncertainty of all parameters cannot be considered simultaneously. Therefore, the variance-based and the moment-independent global sensitivity analysis (GSA) methods are both introduced to determine the influence of geotechnical parameters on slope stability in this study. To solve the importance index of GSA, the least angle regression algorithm, the kernel density estimation, and orthogonal polynomial estimation methods are developed to obtain variance-based importance index and the moment-independent importance index, respectively. The proposed methods allow all variables to change simultaneously within their variation range and have high computational efficiency. The results are in good at with those obtained by the variance-based Monte Carlo simulation method, which is considered as the exact solution forobtaining the importance index. The influence of the correlation between the shear strength parameters (c and φ) on the importance index is also studied, which indicates that the negative correlation will have a great impact on the importance index, which in turn affects the safety assessment of slope. Three engineering cases have been studied for engineering application, and the compared results indicate that the impact of the geotechnical parameters uncertainty on the safety factor (Fs) and failure probability (pf) are different. Therefore, the approaches based on GSA which can integrate the Fs with pf will be a promising approach for slope stability evaluation.

Machine learning-assisted high-throughput screening of MOFs for efficient adsorption and separation of CF4/N2

There is an imperative need for top-performing materials with extraordinary adsorption selectivity and working capacity, in order to achieve productive adsorption of CF4 in a CF4/N2 mixture. In this work, the High-Throughput Grand Canonical Monte Carlo (HT-GCMC) simulation method and the Machine Learning (ML) method were employed to predict and screen the adsorption performance of 10 143 computation-ready experimental metal-organic frameworks (CoRE-MOFs) for separating CF4/N2 mixed gas. Through computational simulation and ML prediction, 15 and 73 highly promising adsorbents were selected out of the 690 randomly sampled MOFs and the CoRE-MOFs database. The selection process was based on criteria that balanced favorable CF4 selectivity, working capacity, and regenerability: selectivity >60, working capacity >70 mg g−1 (0.8 mmol g−1) and regenerability >70%. The maximum observed capacity of the 15 top evaluated metal-organic frameworks (MOFs) was: 52.85 mg g−1 (0.6 mmol g−1) at 1 bar; and 204.90 mg g−1 (2.3 mmol g−1) at 10 bar. The maximum working capacity was 152.06 mg g−1 (1.7 mmol g−1) and the highest selectivity reached was 118.12 (YEGCUJ) and 101.80 (VEHLIE) at 1 bar and 10 bar, respectively. Notably, the most promising MOFs exhibited elevated Zn content relative to the overall MOF population and also possessed a significant nitrogen content. This result should serve as a compelling motivation to further investigate the utilisation of MOFs with a high Zn content (e. g. zeolitic imidazolate frameworks), for enhanced adsorption applications.

Determining the range of negotiable prices for public–private partnership infrastructure projects: a simulation approach

PurposeThis research aims to measure the public sector comparator (PSC) to reach public–private partnership (PPP) projects' negotiable price range for water and sewage companies in Iran. PSC measurement drives the public sector to make valid decisions about costs.Design/methodology/approachAround 170 risks were primarily determined through studying numerous articles. Then, risk effects were specified by distributing questionnaires in two steps. The questionnaires are distributed among experts on PPP-related projects and the Monte Carlo simulation method is used for confidence factors of 70, 80 and 90%. PSC is measured based on these results to study cases of Sirjan’s sewerage and sewage purification systems.Findings11 risks were identified as the main risks that are effective on PSC, and project implementation costs were specified based on the modeling. The corruption of the private and public sectors was identified as the most effective risk in this research. It can affect a project’s cost up to 158% in the construction period and up to 134% in the operation period. Based on the obtained results, 63% of this risk’s cost goes to the public sector.Originality/valueThe originality of this research is the PSC measurement method and appointing the risk share of each private and public sector. The results of this research can be applied to all the infrastructure and PPP projects in Iran and other developing countries as a way for employers to estimate accurate negotiable price ranges.

Integration of Financial and ESG Performance Indicators to Measure and Evaluate Egyptian Insurance Companies Performance

This study aims to evaluate the performance of Egyptian insurance companies through the integration of financial and non-financial performance indicators through the dimensions of sustainable development as a mediator variable. The study relied on Allianz Egypt property and liability insurance company data from 2012 to 2021, and the Monte Carlo simulation method was used based on the Mathcad program. This study used structural Equations Modeling via AMOS program to extract the direct and indirect effect through path analysis. The results showed that each environmental dimension, social dimension, and governance dimension plays a mediator role in the relationship between solvency, liquidity, credit, activity, underwriting profitability, and both market share and investment profitability. Our research contributes to the literature of achieving sustainable development in the insurance sector, to keep pace with global developments. This paper presents evidence of the relationship between integrating ESG indicators into insurance companies’ performance and achieving an additional competitive advantage.

Concentration of Potentially Toxic Elements (PTEs) in Rapid Coffee Products in Bandar Abbas, Iran: Probabilistic Non-Carcinogenic and Carcinogenic Risk Assessment.

Coffee is one of the most widely consumed beverages in the world. However, coffee plants are often exposed to potentially toxic elements (PTEs) pollution. The main aims of current study were to detect the PTEs in instant coffee and health risk assessment of consumers in Bandar Abbas city. To achieve this, 40 samples of instant coffee were randomly collected from various points in the city in 2023 and PTEs concentrations were measured using flame atomic absorption spectrometry (FAAS). The non-carcinogenic and carcinogenic risks were calculated using Monte Carlo simulation (MCS) method. The concentrations of Fe and Cu were higher than other PTEs, equaling 404.41 mg/kg and 0.0046 mg/kg, respectively. The non-carcinogenic risk assessment revealed that THQ (Fe > Pb > As > Cd > Ni > Cu) and TTHQ levels were less than 1 based on the 95% percentile in adults and children, indicating there is no possibility of a non-carcinogenic risk associated with instant coffee. The carcinogenic risk due to inorganic As in instant coffee was acceptable (2.63E-5 and 1.27E-5 based on the 95% percentile for adults and children, respectively), therefore PTEs in instant coffee do not endanger the health of consumers.

Robust Ferromagnetism in Hexagonal Honeycomb Transition Metal Nitride Monolayer.

Two-dimensional intrinsic magnetic materials with high Curie temperature are promising candidates for next-generation spintronic devices. In this work, we design two kinds of two-dimensional transition metal nitrides, VN2 and FeN2, both with a hexagonal honeycomb lattice. Based on the formation energy, and phonon spectra calculations as well as the molecular dynamics simulations, their structural stability is demonstrated. Then, we determine the ferromagnetic ground states of VN2 and FeN2 monolayers through the energy calculations, and the Curie temperatures of 222 K and 238 K are estimated by solving the Heisenberg model using the Monte Carlo simulation method. Hence, the VN2 and FeN2 monolayers are demonstrated to be new two-dimensional ferromagnetic materials with high temperature ferromagnetism or large-gap half-metallicity.

A simulation-based DEA approach for multiple criterion decision-making problems with uncertain mixed-criteria values

In ex-ante decision scenarios, predicting criterion values accurately is difficult for decision makers (DMs). Inconsiderable work is normally required for measuring criteria by uncertain random values or ordinal values. However, in the classical data envelopment analysis (DEA) model, criterion values are the constants that limit the application of the classical DEA model in ex-ante decision scenarios. This paper presents a simulation-based DEA approach, which captures random and ordinal criterion values by a simple and direct simulation-based approach. The approach includes three steps. In the first step, Monte Carlo simulation methods are used to convert uncertain random values or ordinal values into cardinal data. In the second step, we use traditional DEA methods to compute the efficiency score of decision-making units (DMUs). In the third step, we ranked all DMUs by calculating the DEA-efficient acceptability of each DMU in multiple simulations and then selected the optimal DMU. The proposed approach is illustrated by experimental examples and a case study of a municipal wastewater treatment system.

Manufacturing sensitivity study of tensegrity structures using Monte Carlo simulations

The successful construction of a tensegrity structure requires not only design techniques but also a means to account for any manufacturing or assembly errors. As a tensegrity is prestressed and is often stabilised by its prestress, it requires a form-finding technique to determine the balanced configuration in the design phase; this property also makes it challenging for engineers to evaluate the effect of manufacturing imperfections on the self-equilibrated configuration. In this paper we investigate the sensitivity of tensegrity structures to manufacturing imperfections, using a stiffness-matrix-based form-finding technique in combination with the Monte Carlo simulation method to introduce manufacturing length errors. The effects at the structural level are captured, and two general relationships between the variance of the input manufacturing error and the output distribution of the member tensions, between the variance and the mean across all groups, are observed. By identifying the most sensitive member group, this work provides a design and analysis strategy when the natural length errors of members are considered for tensegrity structures.

Data-driven uncertainty quantification and sensitivity studies in free vibration behavior of bio-inspired helicoidal laminated composite cylindrical shells

The uncertainty in the ply orientation of bio-inspired helicoidal laminated composite cylindrical shells is quantified. The frequencies obtained from the higher-order shear deformation theory are trained to formulate a surrogate model in the multi-output Gaussian Process Regression algorithm framework, and the stochastic analysis is then carried out using the Monte Carlo simulation method. For simply supported and clamped shells, uncertainties in the top and bottom-most layers widely affect the free vibration behavior of the shells compared to the inner-most or central layers, whereas for cantilever shells, opposite trend is observed. The inclusion of noise widely affects the free vibration behavior.