Key Factors for Improving the Carcinogenic Risk Assessment of PAH Inhalation Exposure by Monte Carlo Simulation

Monte Carlo simulation (MCS) is a computational technique widely used in exposure and risk assessment. However, the result of traditional health risk assessment based on the MCS method has always been questioned due to the uncertainty introduced in parameter estimation and the difficulty in result validation. Herein, data from a large-scale investigation of individual polycyclic aromatic hydrocarbon (PAH) exposure was used to explore the key factors for improving the MCS method. Research participants were selected using a statistical sampling method in a typical PAH polluted city. Atmospheric PAH concentrations from 25 sampling sites in the area were detected by GC-MS and exposure parameters of participants were collected by field measurement. The incremental lifetime cancer risk (ILCR) of participants was calculated based on the measured data and considered to be the actual carcinogenic risk of the population. Predicted risks were evaluated by traditional assessment method based on MCS and three improved models including concentration-adjusted, age-stratified, and correlated-parameter-adjusted Monte Carlo methods. The goodness of fit of the models was evaluated quantitatively by comparing with the actual risk. The results showed that the average risk derived by traditional and age-stratified Monte Carlo simulation was 2.6 times higher, and the standard deviation was 3.7 times higher than the actual values. In contrast, the predicted risks of concentration- and correlated-parameter-adjusted models were in good agreement with the actual ILCR. The results of the comparison suggested that accurate simulation of exposure concentration and adjustment of correlated parameters could greatly improve the MCS. The research also reveals that the social factors related to exposure and potential relationship between variables are important issues affecting risk assessment, which require full consideration in assessment and further study in future research.

Gravity Balancing Reliability and Sensitivity Analysis of Robotic Manipulators With Uncertainties

This paper presents the gravity balancing reliability and sensitivity analysis of robotic manipulators with uncertainties. The gravity balancing reliability of the robot is defined as the probability that the reduction torque ratio of the robot reduces below a specified threshold. This index is of great importance for assessing and guaranteeing the balancing performance of the robot in the presence of uncertainties in input parameters. In this work, the balancing design for an industrial robot using the gear-spring modules (GSMs) is proposed with the adoption of a simulation-based analysis of the gravity effect of the robot. The Monte Carlo Simulation (MCS) method with normally distributed variables (i.e., link dimensions, masses, and spring stiffness coefficients) is employed to analyze and simulate the reliability. A case study with an industrial robot is then given to illustrate the reliability performance and the sensibility of the uncertain parameters. It is found that the gravity balancing behavior is achieved even when the uncertainties are applied. The uncertainties could deteriorate the balancing performance when increasing the standard deviations by more than seven percent of their means. The dimensional parameters enjoy the most critical influence on the balancing performance.

An Improved Monte Carlo Method Based on Neural Network and Fuzziness Analysis: A Case Study of the Nanpo Dump of the Chengmenshan Copper Mine

The landslide of dump is a man-made geological disaster which will bring great harm to the surrounding people and environment, and probabilistic reliability analysis is commonly used to analyze the probability of slope landslide or whether protective measures should be taken. Monte Carlo simulation is the most commonly used method, but there are some problems, such as low efficiency, statistical ambiguity of small samples, and the fuzzy transition interval of the stability criterion. This paper proposes an improved Monte Carlo method that uses an improved bootstrap method to process small samples of geotechnical data, employs ELM (extreme learning machine) based on PSO (particle swarm optimization) to fit the limit equilibrium method function, and constructs the safety factor membership function of the dump site considering the fuzzy transition interval. This method was applied to an example slope of the dump site in Chengmenshan, Jiangxi. Comparing the analysis result with the result of the traditional MCS (Monte Carlo Search) method, it was found that after adding the safety factor membership function, the result was closer to the actual situation of the dump site, and the probability of failure and reliability index values were closer to those of the dangerous state; after the original function was replaced by the PSO-ELM model, the efficiency of the MCS method was greatly improved while the results maintained high consistency with the original results; the MCS method combined with the bootstrap method not only simulated the fuzzy uncertainty of the original sample statistics and distribution type but also expressed the reliability index and probability of failure as a two-sided confidence interval with a certain confidence level. The above conclusion proves the effectiveness and superiority of this method compared with the original MCS method.

An Efficient Framework for Adequacy Evaluation through Extraction of Rare Load Curtailment Events in Composite Power Systems

With the growing robustness of modern power systems, the occurrence of load curtailment events is becoming lower. Hence, the simulation of these events constitutes a challenge in adequacy indices assessment. Due to the rarity of the load curtailment events, the standard Monte Carlo simulation (MCS) estimator of adequacy indices is not practical. Therefore, a framework based on the enhanced cross-entropy-based importance sampling (ECE-IS) method is introduced in this paper for computing the adequacy indices. The framework comprises two stages. Using the proposed ECE-IS method, the first stage’s purpose is to identify the samples or states of the nodal generation and load that are greatly significant to the adequacy indices estimators. In the second stage, the density of the input variables’ conditional on the load curtailment domain obtained by the first stage are used to compute the nodal and system adequacy indices. The performance of the ECE-IS method is verified through a comparison with the standard MCS method and the recent techniques of rare events simulation in literature. The results confirm that the proposed method develops an accurate estimation for the nodal and system adequacy indices (loss of load probability (LOLP), expected power not supplied (EPNS)) with appropriate convergence value and low computation time.

Dynamic Force Loading Strategy for Effective Force Testing Considering Natural Velocity Feedback Compensation and Nonlinearity

The effective force testing is a promising seismic testing method for evaluating the structural dynamic response to earthquakes for conciseness and efficiency. However, two challenging loading issues are associated with this method, i.e. the natural velocity feedback (NVF) and nonlinearities related to the interaction between the loading system and specimen, thereby hindering its development and extensive applications. To address these issues, this study proposes a dynamic force loading strategy using a hybrid algorithm with linear compensation for NVF and model reference adaptive control via the minimal control synthesis (MCS) method. Online identification of linear compensation gain in preliminary tests is conceived based on the gradient descent method. A series of numerical simulations on a nonlinear loading system model with linear/trilinear single/two degree(s)-of-freedom specimens are conducted using five loading strategies, including linear and nonlinear compensations and MCS method. Comparative studies show that the proposed method and nonlinear compensation strategy outperform the other three methods, and sometimes the proposed method performs best. In summary, the proposed method is promising because of its accuracy and robustness as well as its ease of implementation and cost-effectiveness.

Uncertainty Analysis of Greenhouse Gas (GHG) Emissions Simulated by the Parametric Monte Carlo Simulation and Nonparametric Bootstrap Method

Uncertainty of greenhouse gas (GHG) emissions was analyzed using the parametric Monte Carlo simulation (MCS) method and the non-parametric bootstrap method. There was a certain number of observations required of a dataset before GHG emissions reached an asymptotic value. Treating a coefficient (i.e., GHG emission factor) as a random variable did not alter the mean; however, it yielded higher uncertainty of GHG emissions compared to the case when treating a coefficient constant. The non-parametric bootstrap method reduces the variance of GHG. A mathematical model for estimating GHG emissions should treat the GHG emission factor as a random variable. When the estimated probability density function (PDF) of the original dataset is incorrect, the nonparametric bootstrap method, not the parametric MCS method, should be the method of choice for the uncertainty analysis of GHG emissions.