Monte Carlo Simulation Approach Research Articles

How much can farmers pay for weeding robots? A Monte Carlo simulation study

This paper investigates the Maximum Acquisition Values (MAVs) of weeding robots and their determinants in both organic and conventional sugar beet farming in Germany. The MAV is defined in this paper as the price of the weeding robot that renders the same net profit as the current weeding methods. For our analysis, a Monte Carlo simulation approach is used, combined with empirical data and data collected from weeding robot companies. The results show that the MAVs of mechanical weeding robots for organic farming are substantially higher than that of spot spraying robots for conventional farming. Technology attributes are more influential than labour cost in determining the MAVs of weeding robots: in organic farming, technology attributes such as area capacity and weeding efficiency impact the MAVs of mechanical weeding robots the most; in conventional farming, supervision intensity and the robot’s ability to save herbicides are the most influential factors. The wage rate of unskilled labour, relevant for manual weeding, plays a more important role in determining the MAVs than that of skilled labour, relevant for supervision of the robot. This implies that a shortage of seasonal workers and hence increases in the wage of low-skilled labour could be important drivers of the adoption of mechanical weeding robots. Plot characteristics such as plot size and mechanisation level only have limited impacts on the MAVs.

Estimating the health impacts of sugar-sweetened beverage tax for informing policy decisions about the obesity burden in Vietnam.

Taxation on sugar-sweetened beverages (SSBs) has been adopted in more than 40 countries but remained under discussion in Vietnam. This study aimed to estimate the health impacts of different SSBs tax plans currently under discussion to provide an evidence base to inform decision-making about a SSBs tax policy in Vietnam. Five tax scenarios were modelled, representing three levels of price increase: 5%, 11% and 19-20%. Scenarios of the highest price increase were assessed across three different tax designs: ad valorem, volume-based specific tax & sugar-based specific tax. We modelled SSBs consumption in each tax scenario; how this reduction in consumption translates to a reduction in total energy intake and how this relationship in turn translates to an average change in body weight and obesity status among adults by applying the calorie-to weight conversion factor. Changes in type 2 diabetes burden were then calculated based on the change in average BMI of the modelled cohort. A Monte Carlo simulation approach was applied on the conversion factor of weight change and diabetes risk reduction for the sensitivity analysis. We found that the taxation that involved a 5% price increase gave relatively small impacts while increasing SSBs' price up to 20% appeared to impact substantially on overweight and obesity rates (reduction of 12.7% and 12.4% respectively) saving 27 million USD for direct medical cost. The greatest reduction was observed for overweight and obesity class I. The decline in overweight and obesity rates was slightly higher for women than men. This study supports the SSB tax policy in pursuit of public health benefits, especially where the tax increase involves around a 20% price increase. The health benefit and revenue gains were evident across all three tax designs with the specific tax based on sugar density achieving greatest effects.

Reliability Evaluation for Integrated Electricity-Gas Systems Considering Hydrogen

Regarded as the cleanest and a versatile energy carrier, green hydrogen generated with renewable energy is receiving increased attention in the transition to a carbon-neutral society. On the other hand, the integration of hydrogen tightens the coupling between power systems and natural gas systems, which highlights the importance of reliability evaluation of the integrated electricity-gas systems (IEGSs) with hydrogen. This paper proposes a reliability evaluation model for the IEGSs considering the effects of hydrogen. The power to hydrogen and methane process is proposed to convert the surplus renewable energy to hydrogen and methane, which are then mixed into natural gas systems. A novel optimal energy shedding model is proposed to explicitly account for the impact of hydrogen on the energy flow. To consider the temporal feature of renewable energy, a sequential Monte Carlo simulation approach is applied to evaluate the reliability of the IEGSs. The numerical simulations are performed on the integrated IEEE 24-bus and 20-node energy systems and the integrated IEEE 72-bus and 40-node energy systems to verify the effectiveness of the proposed model.

Estimate the incubation period of coronavirus 2019 (COVID-19)

COVID-19 is an infectious disease that presents unprecedented challenges to society. Accurately estimating the incubation period of the coronavirus is critical for effective prevention and control. However, the exact incubation period remains unclear, as COVID-19 symptoms can appear in as little as 2 days or as long as 14 days or more after exposure. Accurate estimation requires original chain-of-infection data, which may not be fully available from the original outbreak in Wuhan, China. In this study, we estimated the incubation period of COVID-19 by leveraging well-documented and epidemiologically informative chain-of-infection data collected from 10 regions outside the original Wuhan areas prior to February 10, 2020. We employed a proposed Monte Carlo simulation approach and nonparametric methods to estimate the incubation period of COVID-19. We also utilized manifold learning and related statistical analysis to uncover incubation relationships between different age and gender groups. Our findings revealed that the incubation period of COVID-19 did not follow general distributions such as lognormal, Weibull, or Gamma. Using proposed Monte Carlo simulations and nonparametric bootstrap methods, we estimated the mean and median incubation periods as 5.84 (95% CI, 5.42–6.25 days) and 5.01 days (95% CI 4.00–6.00 days), respectively. We also found that the incubation periods of groups with ages greater than or equal to 40 years and less than 40 years demonstrated a statistically significant difference. The former group had a longer incubation period and a larger variance than the latter, suggesting the need for different quarantine times or medical intervention strategies. Our machine-learning results further demonstrated that the two age groups were linearly separable, consistent with previous statistical analyses. Additionally, our results indicated that the incubation period difference between males and females was not statistically significant.

A Monte Carlo simulation approach for the assessment of health risk from NO$$_{3}^{-}$$-N perturbation in groundwater

A Monte Carlo simulation approach for the assessment of health risk from NO$$_{3}^{-}$$-N perturbation in groundwater

The novel Kumaraswamy power Frechet distribution with data analysis related to diverse scientific areas

The study’s major goal is to design a superior creative distribution by using the Kumaraswamy-G family of distributions to power Frechet distribution. The Kumaraswamy power Frechet distribution (KPFD) with four parameters is the full name of the revolutionary model. The distribution’s probability density function may take numerous forms and graphs and can be used to describe complicated data sets efficiently. Several features of the new distribution are obtained, including dependability, hazard rate, quantile, and moments. The estimation of the unknown parameters of KPFD are provided using the KPFD maximum likelihood estimation technique. Furthermore, a study was performed using the Monte Carlo simulation approach to test estimator accuracy regarding average bias (AB) and mean square error (MSE). Last but not least, two genuine data sets are supplied to compare the proposed model to existing models.

Back-analysis of a rainfall-induced landslide case history using deterministic and random limit equilibrium methods

Because recurrent rainfall-induced landslide episodes mimic prior failures, this study builds a back-analysis approach to identify those prior failure events and assess the prospective slope performance going forward. Engineers use the results of this approach to project the slope performance based on the quantity of rainfall accumulation during the anticipated rainfall event. A landslide case that occurred in 2004, as a result of a typhoon event, was selected for the back-analysis procedure. Four typhon events were chosen to simulate the pore water pressure conditions, which may have caused damage of varying severity (including landslide failure) to the slope. The numerical model was initially analyzed via the deterministic limit equilibrium slope stability approach to evaluate the most critical failure surface. By adopting the Monte Carlo simulation approach, the following step assessed the random limit equilibrium slope stability on the most critical failure surfaces. The probabilistic distributions of the safety factor were then generated for each typhoon event. Subsequently, the simulation outcomes provide safety indicator values incorporating the mean safety factor, failure probability, and reliability index. From the failure probability and reliability index, a zonation approach with various site-specific performance levels were established by using the corresponding accumulated rainfall to define the exceedance threshold values for each zone. In order to confirm the site-specific performance, we compared the normalized error values acquired from the analysis technique to those measured under two recent typhoon occurrences. An acceptable consistency was attained, validating the procedure's appropriateness for future rainfall events.

Development of a simulation-based optimization approach to integrate the decisions of maintenance planning and safety stock determination in deteriorating manufacturing systems

In this article, a stochastic model is presented to schedule the maintenance tasks and control the inventory simultaneously in an unreliable deteriorating production system. The optimal planning of these aspects improves the productivity of the production system and reduces the costs. The shift time of the states follows a general continuous distribution. The time of maintenance is also considered as a random variable, and a buffer stock is maintained to avoid possible shortage and satisfy the demand during the performance of maintenance. The purpose of this study is to integrate the decisions of safety stock determination and maintenance scheduling to minimize the expected total cost of the system. Due to the complexity of the equations of the model, at first, a genetic algorithm (GA) is employed for optimization. To validate the GA solutions, they are compared with those obtained by a grid search algorithm. It is found that the differences between the optimal values of the objective function (ECT) in GA and grid search are almost small and, in some cases, only 5.58% and 2.03%. This result indicates the validity of the solutions obtained by the GA. In the next step, to speed up the run time of the proposed GA, a Monte Carlo simulation approach is developed, and the GA is combined with the simulation approach. In particular, the fitness function of each chromosome is obtained using the proposed simulation approach. In order to examine the performance of the proposed simulation approach, the results of simulation are compared with the findings on optimization by the GA. This comparison indicates that the difference between the values of ECT calculated through the GA and simulation is averagely 4.55%, which is negligible. The results denote the appropriate performance of the model and the efficiency of the proposed hybrid approach. Finally, comprehensive sensitivity analyses are performed regarding the parameters of the model.

Optimal allocation of renewable DGs using artificial hummingbird algorithm under uncertainty conditions

Renewable distributed generators (RDGs) have been widely used in distribution networks for technological, economic, and environmental reasons. The main concern with renewable-based distributed generators, particularly photovoltaic and wind systems, is their intermittent nature, which causes output power to fluctuate, increasing power system uncertainty. As a result, it's critical to think about the resource's uncertainty when deciding where it should go in the grid. The main innovation of this paper is proposing an efficient and the most recent technique for optimal sizing and placement of the RDGs in radial distribution systems considering the uncertainties of the loading and RDGs output powers. Monte-Carlo simulation approach and backward reduction algorithm are used to generate 12 scenarios to model the uncertainties of loading and RDG output power. The artificial hummingbird algorithm (AHA), which is considered the most recent and efficient technique, is used to determine the RDG ratings and placements for a multi-objective function that includes minimizing expected total cost, the expected total emissions, and the expected total voltage deviation, as well as improving expected total voltage stability with considering the uncertainties of loading and RDGs output powers. The proposed technique is tested using an IEEE 33-bus network and an actual distribution system in Portugal (94-bus network). Simulations show that the suggested method effectively solves the problem of optimal DG allocation. In addition of that the expected costs, the emissions, the voltage deviation, are reduced considerably and the voltage stability is also enhanced with inclusion of RDGs in the tested systems.

Plausible explanation for the third COVID-19 wave in India and its implications

Recently some of us used a random-walk Monte Carlo simulation approach to study the spread of COVID-19. The calculations were reasonably successful in describing secondary and tertiary waves of infection, in countries such as the USA, India, South Africa and Serbia. However, they failed to predict the observed third wave for India. In this work we present a more complete set of simulations for India, that take into consideration two aspects that were not incorporated previously. These include the stochastic movement of an erstwhile protected fraction of the population, and the reinfection of some recovered individuals because of their exposure to a new variant of the SARS-CoV-2 virus. The extended simulations now show the third COVID-19 wave for India that was missing in the earlier calculations. They also suggest an additional fourth wave, which was indeed observed during approximately the same time period as the model prediction.

Modelling the Operation Process of Light Utility Vehicles in Transport Systems Using Monte Carlo Simulation and Semi-Markov Approach

This research paper presents studies on the operation process of the Honker 2000 light utility vehicles that are part of the Polish Armed Forces transport system. The phase space of the process was identified based on the assumption that at any given moment the vehicle remains in one of four states, namely, task execution, awaiting a transport task, periodic maintenance, or repair. Vehicle functional readiness and technical suitability indices were adopted as performance measures for the technical system. A simulation model based on Monte Carlo methods was developed to determine the changes in the operational states. The occurrence of the periodic maintenance state is strictly determined by a planned and preventive strategy of operation applied within the analysed system. Other states are implementations of stochastic processes. The original source code was developed in the MATLAB environment to implement the model. Based on estimated probabilistic characteristics, the authors validated 16 simulation models resulting from all possible cumulative distribution functions (CDFs) that satisfied the condition of a proper match to empirical data. Based on the simulated operation process for a sample of 19 vehicles over the assumed 20-year forecast horizon, it was possible to determine the functional readiness and technical suitability indices. The relative differences between the results of all simulation models and the results obtained through the semi-Markov model did not exceed 6%. The best-fit model was subjected to sensitivity analysis in terms of the dependence between functional readiness and technical suitability indices on vehicle operation intensity. As a result, the proposed simulation system based on Monte Carlo methods turned out to be a useful tool in analysing the current operation process of means of transport in terms of forecasts related to a current environment, as well as when attempting its extrapolation.

Radiotoxicity and health risk assessment of 222Rn in groundwater using statistical and Monte Carlo simulation approaches

Water is an important natural resource to humans, and itsquality has been a big concern from time immemorial. Water is polluted by the increasing activities of man. Pollutants of major concern include naturally occurring radioactive materials (NORM), heavy and potentially toxic metals, which are ubiquitous in the environment. Naturally occurring radionuclides such as 222Rn are the major precursors of lung and other types of cancer in humans. Exposure to radon in drinking water in excess of 300 pCi/L is treated as a radiological risk, while toxic metals in excess of the World Health Organization (WHO) maximum permissible levels in drinking water are considered both environmental and health risks to humans. A total of 34 groundwater samples were collected in the Akoko area of Ondo State, Nigeria for 222Rn activity concentration determination. The water samples were collected using standard techniques. RAD7 solid-state-detector was utilized for the 222Rn measurements. The results show the activity concentration of 222Rn ranging from 76 pCi/L to 3593 pCi/L with a mean value of 705 ± 76 pCi/L. Inhalation and ingestion doses from 222Rn dissolved in groundwater were evaluated for the infants, children, and adult populations in the study area. Radiological risks were evaluated through statistical and Monte Carlos simulation methods. It can be concluded from this study that there exists a likelihood of radiation-induced health risks due to inhalation of 222Rn from the sampled water.

A Simulink model for modified fountain codes

This paper introduces a Simulink model design for a modified fountain code. The code is a new version of the traditional Luby transform (LT) codes. The design constructs the blocks required for generation of the generator matrix of a limited-degree-hopping-segment Luby transform (LDHS-LT) codes. This code is especially designed for short length data files which have assigned a great interest for wireless sensor networks. It generates the degrees in a predetermined sequence but random generation and partitioned the data file in segments. The data packets selection has been made serialy according to the integer generated from both degree and segment generators. The code is tested using Monte Carlo simulation approach with the conventional code generation using robust soliton distribution (RSD) for degree generation, and the simulation results approve better performance with all testing parameter.

Exposure to air pollution and gains in body weight and waist circumference among middle-aged and older adults

IntroductionEmerging research suggested a nexus between air pollution exposure and risks of overweight and obesity, while existing longitudinal evidence was extensively sparse, particularly in densely populated regions. This study aimed to quantify concentration-response associations of changes in weight and waist circumference (WC) related to air pollution in Chinese adults. MethodsWe conceived a nationally representative longitudinal study from 2011 to 2015, by collecting 34,854 observations from 13,757 middle-aged and older adults in 28 provincial regions of China. Participants' height, weight and WC were measured by interviewers using standardized devices. Concentrations of major air pollutants including fine particulate matter (PM2.5), nitrogen dioxide (NO2) and ozone (O3) predicted by well-validated spatiotemporal models were assigned to participants according to their residential cities. Possible exposure biases were checked through 1000 random simulated exposure at individual level, using a Monte Carlo simulation approach. Linear mixed-effects models were applied to estimate the relationships of air pollution with weight and WC changes, and restricted cubic spline functions were adopted to smooth concentration-response (C-R) curves. ResultsEach 10-μg/m3 rise in PM2.5, NO2 and O3 was associated with an increase of 0.825 (95% confidence interval: 0.740, 0.910), 0.921 (0.811, 1.032) and 1.379 (1.141, 1.616) kg in weight, respectively, corresponding to WC gains of 0.688 (0.592, 0.784), 1.189 (1.040, 1.337) and 0.740 (0.478, 1.002) cm. Non-significant violation for linear C-R relationships was observed with exception of NO2-weight and PM2.5/NO2-WC associations. Sex-stratified analyses revealed elevated vulnerability in women to gain of weight in exposure to PM2.5 and NO2. Sensitive analyses largely supported our primary findings via assessing exposure estimates from 1000 random simulations, and performing reanalysis based on non-imputed covariates and non-obese participants, as well as alternative indicators (i.e., body mass index and waist-to-height ratio). ConclusionsWe found positively robust associations of later-life exposure to air pollutants with gains in weight and WC based on a national sample of Chinese adult men and women. Our findings suggested that mitigation of air pollution may be an efficient intervention to relieve obesity burden.

Reliability based topology optimization of thermoelastic structures using bi-directional evolutionary structural optimization method

The aim of this paper is to propose a novel computational technique of applying reliability-based design to thermoelastic structural topology optimization. Therefore, the optimization of thermoelastic structures' topology based on reliability-based design is considered by utilizing geometrical nonlinearity analysis. For purposes of introducing reliability-based optimization, the volume fraction parameter is viewed as a random variable with a normal distribution having a mean value and standard deviation. The Monte Carlo simulation approach for probabilistic designs is used to calculate the reliability index, which is used as a constraint related to the volume fraction constraint of the deterministic problem. A new bi-directional evolutionary structural optimization scheme is developed, in which a geometrically nonlinear thermoelastic model is applied in the sensitivity analysis. The impact of changing the constraint of a defined volume of the required design in deterministic problems is examined. Additionally, the impact of altering the reliability index in probabilistic problems is investigated. The effectiveness of the suggested approach is shown using a benchmark problem. Additionally, this research takes into account probabilistic thermoelastic topology optimization for a 2D L-shaped beam problem.

Mapping the strength of agro-ecological lightweight concrete containing oil palm by-product using artificial intelligence techniques

The critical challenge for the cement production industry is the high emission of greenhouse gases. For the sustainability in a cycling economy context civil and environmental engineers should reconsider future generations and should redesign and manufacturing in a safe and eco-friendly alternative. Previous experimental studies have shown, that using oil palm by-product in the production of sustainable lightweight structural concrete, (SLSC) presents good life cycle, land-use efficiency and economic benefits. In this work a formula-based model is proposed to evaluate the strength of SLSC, knowledge-aided modeling based on machine learning methods (e. g., for further development, Multivariate Adaptive Regression Splines (MARS), Gene Expression Programming (GEP), M5P Model Tree (M5P) and Extreme Learning Machine (ELM)) were selected. A comprehensive and reliable experimental record comprising of 449 data of SLSC containing oil palm by-product was utilized to formulate strength predictive models. The proposed models were investigated, in terms of assessment criteria, error measurements, and performance evaluation. The efficiency of the MARS model was shown by comparing its performance with GEP, M5P and ELM. As well as Monte-Carlo simulation approach was conducted to verify the proposed models. Evaluation of the relative importance of effective variables presented that the gravel (11.84%), oil palm waste aggregate (11.38%) and water to binder ratio (11.15%) were the most influential features in the strength prediction of the SLSC. Also, a parametric study is developed to provide robustness of the proposed predictive model. Results of the modeling provide a new understanding of the provision of an auto-estimated strength evaluation of SLSC incorporating oil palm by-product with superiority promotion of knowledge-aided modeling mechanism.

Ground Motion Analysis Toolbox and Seismic Scenarios Toolbox: software tools for seismic damage scenarios assessment in the Emilia-Romagna Region

After an earthquake, it is essential to have estimates of the potential destructiveness of ground motions and predictions of damage scenarios. These rely on the knowledge of specific features of the potentially affected area, both in terms of intrinsic characteristics (e.g. morphology, near surface geology, etc.) and elements at risk (e.g. building typologies, population distribution, etc.). Within this context, this paper describes a series of software tools developed that allow to analyze recorded accelerograms, create shake maps in terms of different ground motion intensity measures, and estimate damage scenarios. In particular, ground-motion recordings from the Italian National Accelerometric Network are processed to compute a number of ground motion Intensity Measures (IMs), while acceleration spectra are used to compare displacement demands for a series of equivalent single degree of freedom systems, derived from pushover analyses on a series of representative buildings, by using simplified methods (e.g. N2, CSM, IN2, etc.). The IMs calculated from the recordings at the various stations are used, together with attenuation and spatial correlation models, to obtain shake maps, using different criteria for quantifying site effects. These maps are used together with regional census data and various fragility models to estimate damage scenarios, using a Monte Carlo simulation approach.

Health risk assessments of exposure carbon dioxide among communities and children around Tonasa cement plant, Pangkep Regency, Indonesia. Monte Carlo Simulation (MCS) application.

Chronic exposure to carbon dioxide (CO2) can have a negative influence on one's health and be hazardous to the environment. It could be both directly and indirectly to those communities who are living near the CO2 point sources. This study aimed to investigate the magnitude of CO2 level in ambient air and its spatial distribution which then continued to assess the potential health risks posed by communities living surround the site as well as applied the Monte Carlo Simulation (MCS) approach to predict the risks magnitude among adult and children due to CO2 air pollution from the cement industry activities in Pangkep. This observational analytic study applied health risk assessment due to the CO2 exposure both to adult and children population. To estimate the non carcinogenic risk, study used the Monte Carlo Simulation model with 10,000 iterations to estimate the risk through the inhalation pathway suffered by communities, as well as analyzing the sensitivity level every single health risk parameter. The highest risks for the adults was in station 7 with 7,641 whereas the lowest risks was in station 3 with 1,194, respectively. Furthermore, the highest risks for child was in station 4 with 498 whereas the lowest one was in station 15 with 32, respectively. Those non carcinogenic HQ were exceed the standard for adult but not at risks for children. The results of the Monte Carlo Simulation that assessed the non risks cancer probability with the 5th and 95th percentiles demonstrated that adult population were at value of 0.83 and 1.53 0.83 and 1.53 respectively, that still indicated at low risk for developing adverse health effects among those communities temporarily. However, at the same percentiles children indicated at value of 199 and 388 that indicated at risk for developing adverse health effects among those children. In addition, level of sensitivity analysis result indicated that exposure frequency with (20,9%) for adult and the exposure duration with (25,6%) for children were the most contributing factors to health risks among, respectively. Simulation determines the critical factors with major effects in reducing health risks. The CO2 magnitude not poses risks to adults, by contrast, children are at risk. Thus, limiting exposure frequency and inhalation of CO2 levels in the school for children area are highly demanded.

Neutrosophic Monte Carlo Simulation Approach for Decision Making In Medical Diagnostic Process Under Uncertain Environment

This work emphasis on the basic notions regarding the Neutrosophic Fuzzy Sets (NFSs) with operations and their applicability in medical diagnostic process. In this manuscript, we developed neutrosophic fuzzy set-based Monte Carlo simulation technique for the decision making in medical diagnostic processin fuzzy environment. In this work, we managed the waiting time and idle time of the doctor during the treatment process of the patients. The various parameters are stated as linguistic variable in the form of NFSs. The developed neutrosophic Monte Carlo simulation technique (NMCST) is extended in the planning strategy of a doctor to treat the patient in a neutrosophic fuzzy environment. For the validation and authentication of the efficiency of the proposed NMCST, numerical computations are carried out with the examples of medical problems.

A Clutter Loss Model for Satellite Communication Systems

Buildings and vegetation in the proximity of a terrestrial base station induce a significant additional loss, typically referred to as “clutter loss”, which sums up to free space loss and atmospheric attenuation. Clutter loss is essentially due to the radiowave reflection and diffraction caused by buildings and vegetation, and tends to reduce the interference between terrestrial systems, such as upper 6 GHz (U6G), and satellite systems operating in the same frequency bands. In fact, for example, at low elevation angles, the clutter loss could reach some tens of dB in the U6G band. A novel clutter loss model in urban and suburban environments for frequencies up to 10 GHz is proposed. The model relies on the Monte Carlo simulation approach presented in Report ITU-R P.2402-0, but some limitations have been removed to extend its applicability to more complex scenarios and possibly increase its accuracy for U6G systems. An analytical approach is also proposed to model the clutter loss statistics obtained by properly fitting the obtained statistics for the cities of London and Melbourne. Finally, the proposed model is validated by comparing its results to those obtained by a commercial ray tracer.