Explore the vast range of titles available.

PurposeUncertainty and variability need to be taken into account in life cycle assessment (LCA) studies to make robust decisions. We introduce a novel approach in LCA that allows to decide if either uncertainty or variability is dominating in the results: two-dimensional Monte Carlo simulations (2DMC). We aim to do so in a pedagogical and transparent way, allowing interested readers to fully grasp all technical details for their own potential use in future studies.MethodsIn 2DMC, an approach from quantitative risk assessment, the model parameters are divided into four categories: deterministic, variable, uncertain, and uncertain ánd variable; and appropriate distributions are selected. These distributions are sampled separately, so they can be assessed separately in the output as well. Firstly, the approach was translated to the LCA context with an illustrative proof of concept model, freely available on our website. Further, two variants of the post-harvest apple chain in Belgium (bulk versus pre-packed) are worked out as a real life comparative LCA case study. This real-life case study is also analyzed in a classical, deterministic way and by performing a more often used one-dimensional Monte Carlo simulation (1DMC), allowing a comparison with the 2DMC results and associated interpretations.Results and discussionDeterministic results do not reflect the complexity of reality. 1DMC results provide an indication on the robustness and conclusiveness of the result of a comparative LCA, but do not provide a way to guide further decisions. 2DMC results do provide this as results typically belong to one out of three possibilities. Firstly, the 2DMC results may confirm the result of the deterministic results. Secondly, the 2DMC curves may show proof that the two products are equivalent when it comes to environmental impact. One may then decide to analyze the variability causes further or that other reflections, like cost, should be considered as well. Thirdly, the 2DMC curves may indicate that more detailed and accurate information is needed to come to conclusive results.Conclusions1DMC results give a first indication on the need for a 2DMC analysis. If that is the case, 2DMC can be used in a comparative LCA to take uncertainty and variability separately into account. 2DMC results can guide decisions to obtain more conclusive results. We recommend to consider a 2DMC analysis when comparing two products or processes if needed, hereto, our proof of concept model fully documented available online may be a starting point.

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.

Understanding plasma distribution, characteristics and phenomena is important for the development and optimization of semiconductor device manufacturing plasma equipment, such as etching and deposition tools. For this reason, plasma simulation is currently being utilized at every stage of equipment design, development and improvement. The cross section sets obtained by applying Denpoh-Nanbu theory to Ar+ on CF4 collisions were found to be in general qualitative and in part quantitative agreement with data from the literature. The Monte Carlo technique was applied to perform calculations of transport parameters. Calculated cross sections can be used to obtain transport coefficients, specially drift velocity, characteristic energy, reduced mobility, longitudinal diffusion and rate coefficients for low and moderate reduced electric fields E/N (E-electric field strength; N-gas density) and accounting for the non-conservative collisions.

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.

To determine the number of independent variables that can be included in a linear regression model. We used a series of Monte Carlo simulations to examine the impact of the number of subjects per variable (SPV) on the accuracy of estimated regression coefficients and standard errors, on the empirical coverage of estimated confidence intervals, and on the accuracy of the estimated R2 of the fitted model. A minimum of approximately two SPV tended to result in estimation of regression coefficients with relative bias of less than 10%. Furthermore, with this minimum number of SPV, the standard errors of the regression coefficients were accurately estimated and estimated confidence intervals had approximately the advertised coverage rates. A much higher number of SPV were necessary to minimize bias in estimating the model R2, although adjusted R2 estimates behaved well. The bias in estimating the model R2 statistic was inversely proportional to the magnitude of the proportion of variation explained by the population regression model. Linear regression models require only two SPV for adequate estimation of regression coefficients, standard errors, and confidence intervals.

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.

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.

PurposeDistribution. The purpose of this study is to obtain the modified maximum likelihood estimator of stress–strength model using the ranked set sampling, to obtain the asymptotic and bootstrap confidence interval of P[Y < X], to compare the performance of author’s estimates with the estimates under simple random sampling and to apply author’s estimates on head and neck cancer.Design/methodology/approachThe maximum likelihood estimator of R = P[Y < X], where X and Y are two independent inverse Weibull random variables common shape parameter that affect the shape of the distribution, and different scale parameters that have an effect on the distribution dispersion are given under ranked set sampling. Together with the asymptotic and bootstrap confidence interval, Monte Carlo simulation shows that this estimator performs better than the estimator under simple random sampling. Also, the asymptotic and bootstrap confidence interval under ranked set sampling is better than these interval estimators under simple random sampling. The application to head and neck cancer disease data shows that the estimator of R = P[Y < X] that shows the treatment with radiotherapy is more efficient than the treatment with a combined radiotherapy and chemotherapy under ranked set sampling that is better than these estimators under simple random sampling.FindingsThe ranked set sampling is more effective than the simple random sampling for the inference of stress-strength model based on inverse Weibull distribution.Originality/valueThis study sheds light on the author’s estimates on head and neck cancer.

hy2Foam is a newly-coded open-source two-temperature computational fluid dynamics (CFD) solver that has previously been validated for zero-dimensional test cases. It aims at (1) giving open-source access to a state-of-the-art hypersonic CFD solver to students and researchers; and (2) providing a foundation for a future hybrid CFD-DSMC (direct simulation Monte Carlo) code within the OpenFOAM framework. This paper focuses on the multi-dimensional verification of hy2Foam and firstly describes the different models implemented. In conjunction with employing the coupled vibration-dissociation-vibration (CVDV) chemistry–vibration model, novel use is made of the quantum-kinetic (QK) rates in a CFD solver. hy2Foam has been shown to produce results in good agreement with previously published data for a Mach 11 nitrogen flow over a blunted cone and with the dsmcFoam code for a Mach 20 cylinder flow for a binary reacting mixture. This latter case scenario provides a useful basis for other codes to compare against.

\"The purpose of this handbook is to provide an accessible and comprehensive compendium of Monte Carlo techniques and related topics. It contains a mix of theory (summarized), algorithms (pseudo and actual), and applications. Since the audience is broad, the theory is kept to a minimum, this without sacrificing rigor. The book is intended to be used as an essential guide to Monte Carlo methods to quickly look up ideas, procedures, formulas, pictures, etc., rather than purely a monograph for researchers or a textbook for students. As the popularity of these methods continues to grow, and new methods are developed in rapid succession, the staggering number of related techniques, ideas, concepts and algorithms makes it difficult to maintain an overall picture of the Monte Carlo approach. This book attempts to encapsulate the emerging dynamics of this field of study\"-