Explore the vast range of titles available.

In the current study, the concentration of heavy metals including lead (Pb), chromium (Cr), cadmium (Cd), and aluminum (Al) in commonly instant noodles consumed in Iran (either imported from other countries or produced in Iran) was investigated by acid digestion method followed by an inductively coupled plasma optical emission spectrometry system (ICP-OES). Also, the associated non-carcinogenic risk due to ingestion of heavy metals for adults and children was estimated by calculating percentile 95% target hazard quotient (THQ) in the Monte Carlo simulation (MCS) method. The average concentrations of Pb, Cr, Cd, and Al in Iranian instant noodle samples were measured as 1.21 ± 0.81, 0.08 ± 0.10, 0.03 ± 0.06, and 9.15 ± 4.82 (mg/kg) and in imported instant noodle samples were 1.00 ± 0.61, 0.07 ± 0.07, 0.04 ± 0.03, and 15.90 ± 0.93 (mg/kg), respectively. A significant difference ( p value < 0.05) in the mean concentration of Pb, Cr, Cd, and Al of Iranian instant noodle and imported instant noodle samples was observed. Also, the concentration of Pb, Cr, Cd, and Al in all brands of instant noodle (0.025 mg/kg, 0.050 mg/kg, 0.003 mg/kg, and 0.237 mg/kg, respectively) surpassed the WHO-permitted limits for Pb, Cr, Cd, and Al. Percentile 95% of THQ of Pb, Cr, Cd, and Al for the adult consumers was calculated as 0.012, 0.000007, 0.010, and 1.789; while in the case of children, percentile 95% of THQ of Pb, Cr, Cd, and Al was defined as 0.044, 0.00023, 0.035, and 6.167, respectively. Health risk assessment indicated that both adults and children are at considerable non-carcinogenic health risk for Al (THQ > 1). Therefore, approaching the required strategies in order to reduce the concentration of heavy metals particularly Al in the instant noodle is recommended.

A comprehensive overview of Monte Carlo simulation that explores the latest topics, techniques, and real-world applicationsMore and more of today’s numerical problems found in engineering and finance are solved through Monte Carlo methods. The heightened popularity of these methods and their continuing development makes it important for researchers to have a comprehensive understanding of the Monte Carlo approach. Handbook of Monte Carlo Methodsprovides the theory, algorithms, and applications that helps provide a thorough understanding of the emerging dynamics of this rapidly-growing field.The authors begin with a discussion of fundamentals such as how to generate random numbers on a computer. Subsequent chapters discuss key Monte Carlo topics and methods, including:Random variable and stochastic process generation Markov chain Monte Carlo, featuring key algorithms such as the Metropolis-Hastings method, the Gibbs sampler, and hit-and-run Discrete-event simulation Techniques for the statistical analysis of simulation data including the delta method, steady-state estimation, and kernel density estimation Variance reduction, including importance sampling, latin hypercube sampling, and conditional Monte Carlo Estimation of derivatives and sensitivity analysis Advanced topics including cross-entropy, rare events, kernel density estimation, quasi Monte Carlo, particle systems, and randomized optimization The presented theoretical concepts are illustrated with worked examples that use MATLAB®, a related Web site houses the MATLAB®code, allowing readers to work hands-on with the material and also features the author's own lecture notes on Monte Carlo methods. Detailed appendices provide background material on probability theory, stochastic processes, and mathematical statistics as well as the key optimization concepts and techniques that are relevant to Monte Carlo simulation.Handbook of Monte Carlo Methodsis an excellent reference for applied statisticians and practitioners working in the fields of engineering and finance who use or would like to learn how to use Monte Carlo in their research. It is also a suitable supplement for courses on Monte Carlo methods and computational statistics at the upper-undergraduate and graduate levels.

A sediment source fingerprinting method, including a Monte Carlo simulation framework, was used to quantify the contributions of terrestrial sources of fine- (< 63 μm) and coarse-grained (63–500 μm) sediments sampled from three categories of coastal sediment deposits in the Jagin catchment, south-east of Jask, Hormozgan province, southern Iran: coastal dunes (CD), terrestrial sand dunes or onshore sediments (TSD), and marine or offshore sediments (MD). Forty-nine geochemical properties were measured in the two size fractions and a three-stage statistical process consisting of a conservation test, the Kruskal–Wallis H test, and stepwise discriminant function analysis (DFA) was applied to select final composite fingerprints for terrestrial source discrimination. Based on the statistical tests, four final fingerprints comprising Be, Ni, K and Cu and seven final fingerprints consisting Cu, Th, Be, Al, La, Mg and Fe were selected for discriminating terrestrial sources of the coastal fine- and coarse-grained sediments, respectively. Two geological spatial sources, including Quaternary (clay flat, high and low level fans and valley terraces) and Palaeocene age deposits, were identified as the main terrestrial sources of the fine-grained sediment sampled from the coastal deposits. A geological spatial source consisting of sandstone with siltstone, mudstone and minor conglomerate (Palaeocene age deposits) was identified as the main terrestrial source for coarse-grained sediment sampled from the coastal deposits.

Since their popularization in the 1990s, Markov chain Monte Carlo (MCMC) methods have revolutionized statistical computing and have had an especially profound impact on the practice of Bayesian statistics. Furthermore, MCMC methods have enabled the development and use of intricate models in an astonishing array of disciplines as diverse as fisherie

Uncertainties of renewable energy sources (RESs) such as wind turbine (WT) and photovoltaic (PV) units are one of the considerable challenges of prosumer microgrids (PMGs) for the optimal day‐ahead operation. In this study, a new probabilistic scenario‐based method of optimal scheduling and operation of PMGs is developed. In this regard, different scenarios are generated using Monte Carlo Simulations (MCS). Furthermore, k‐means, k‐medoids, and differential evolution algorithms (DEA) are deployed to cluster the scenarios in the proposed method. A realistic commercial PMG in Iran is selected to apply the introduced method. The validity of the developed probabilistic optimization method for PMG operation is examined by comparing the results under various scenario reduction algorithms and MCS ones. The comparison of the obtained results and those of other existing deterministic methods highlights the advantages of the presented method. Furthermore, the sensitivity analyses are carried out to investigate the robustness of the developed method against the increase in the system uncertainty level. According to the test results, it is concluded that the k‐medoids algorithm has the best performance in comparison with the k‐means and the DEA‐based clustering under various conditions. Proposing a novel scenario‐based O.F to optimize the operation costs of prosumers. Comparison of the proposed method and other available deterministic ones. Comparison of different scenario reduction methods. Validation of the scenario reduction‐based method by using MCS. Investigation of the proposed method robustness against the uncertainty increment.

Increasingly frequent natural disasters and man-made malicious attacks threaten the power systems. Improving the resilience has become an inevitable requirement for the development of power systems. The importance assessment of components is of significance for resilience improvement, since it plays a crucial role in strengthening grid structure, designing restoration strategy, and improving resource allocation efficiency for disaster prevention and mitigation. This paper proposes a component importance assessment approach of power systems for improving resilience under wind storms. Firstly, the component failure rate model under wind storms is established. According to the model, system states under wind storms can be sampled by the non-sequential Monte Carlo simulation method. For each system state, an optimal restoration model is then figured out by solving a component repair sequence optimization model considering crew dispatching. The distribution functions of component repair moment can be obtained after a sufficient system state sampling. And Copeland ranking method is adopted to rank the component importance. Finally, the feasibility of the proposed approach is validated by extensive case studies.

It is widely agreed in empirical studies that allowing for potential structural change in economic processes is an important issue. In existing literature, tests for cointegration between time series data allow for one regime shift. This paper extends three residual-based test statistics for cointegration to the cases that take into account two possible regime shifts. The timing of each shift is unknown a priori and it is determined endogenously. The distributions of the tests are non-standard. We generate new critical values via simulation methods. The size and power properties of these test statistics are evaluated through Monte Carlo simulations, which show the tests have small size distortions and very good power properties. The test methods introduced in this paper are applied to determine whether the financial markets in the US and the UK are integrated.

Reliability prediction of spinning machines can result in a time-saving and cost-saving development process with high reliability. Based on an analysis of failure times among systems and subsystems, a simulation method for reliability prediction of spinning machines is proposed by using the Monte Carlo simulation model. Firstly, factor weights are determined according to the fuzzy scoring and analytic hierarchy process. According to the status of reliability growth, growth coefficients are proposed based on reliability influencing factor weights and fuzzy scoring. To achieve the prediction of reliability distribution law, reliability index, and fault frequency, the reliability prediction model is constituted by combining the reliability growth coefficient and the Monte Carlo simulation model. Simulation results for spinning machines are obtained via the model thus built, which are confirmed with a practical example.

The rationale of this paper was to investigate the response of agriculture production to the simultaneous shock of foreign direct investment and public agricultural spending in South Africa during 1991-2019. Data were collected from secondary sources and analyzed using Monte Carlo simulation. Results revealed that agriculture production is maximized at 1,73% if Foreign direct investment inflows, agricultural credit, and the number of employees increase by 10% while public spending is decreased by 10%. Hence, it is recommended that policymakers should combine FDI inflows in agriculture, and agricultural credit in a complementary manner, with emphasis to attract more extensive farm workers to ensure the sustainability of production in the agriculture sector in South Africa. This paper contributes to enhance agriculture sector by using the best combination of input in agriculture in order to maximize production.

Purpose A novel approach was used for quantifying uncertainty propagation in life cycle assessment (LCA). The approach was designed to be efficient and applicable in practice. The model was applied to a specific case study concerning alternative strategies for managing bio-waste: incineration versus anaerobic digestion followed by composting. Methods The uncertainty of each impact category was calculated starting from the variance (σ 2 ) and geometric mean (μ) of the lognormal distribution of each input data. A procedure consisting of three mandatory steps and one facultative step was developed. Mandatory steps were calculation of the associated normal distribution for each input, calculation of the percentile curve for each input, and calculation of the percentile curve of the impact categories. The facultative step consisted in calculating the lognormal distribution of the impact categories if all the values of the percentile curve were >0. Results and discussion The uncertainty associated with the results of the anaerobic digestion and composting scenario was significantly higher than those associated with the incineration scenario. These results were confirmed by those obtained by Monte Carlo simulations. Environmental gains calculated for the scenario with incineration concerning acidification, global warming, terrestrial eutrophication, and photochemical ozone creation had a high level of probability (i.e., >90 %). On the contrary, the impact categories of the scenario with anaerobic digestion and composting had higher uncertainties. Conclusions The source of uncertainty in LCA analysis can be due to multiple factors. Among these, the variability of the values of the LCI can have a significant influence on the results of the study. LCA analysis based on the exploitation of geometric means and/or average values of inputs reported in LCI can lead to results affected by a low level of reliability. In particular, this aspect can play a relevant role for LCA-based decisions when different scenarios and options are compared. As in the case study reported in this work, neglecting the propagation of uncertainty can result in a relevant bias for obtaining a full informative impression of the problem analyzed.