Monte Carlo Sensitivity Analysis Research Articles

Non-carcinogenic risk assessment and sources apportionment of heavy metals in pomegranate in Anhui, China

This study examined the concentrations, sources, and health risks associated with 7 heavy metals in Chinese pomegranates. The results showed that the heavy metal in pomegranates was within standard limits, and the accumulation of heavy metals was low. PCA showed that Cd and Hg in pomegranates originate from irrigation water, Zn, As, and Cu from soil, and Cr and Pb from fertilizers. MLR and Monte Carlo sensitivity analysis showed that soil and irrigation water were the primary sources of heavy metals in pomegranates, with Zn, As, Cu, and Pb primarily originating from soil, and Cd, Cr, and Pb predominantly coming from irrigation water. The presence of organic matter and Ca in soil influenced heavy metal accumulation in pomegranates, other properties (Mg, Fe, and pH value in soil) have minor effects. The TTHQ for both adults and children remained below 1, while the TTRisk exceeded 1×10−4, mainly due to the hazardous impact of As in water. The upper 95% uncertainty limit indicated that residents were exposed to both non-carcinogenic and carcinogenic risks, primarily due to the toxicity of Cr in soil and As in water. Strengthening heavy metal screening in soil and water and enhancing pollution management policies are imperative.

A Monte Carlo Sensitivity Analysis for a Dimensionally Reduced-Order Model of the Aortic Dissection.

Aortic dissection is associated with a high mortality rate. Although computational approaches have shed light on many aspects of the disease, a sensitivity analysis is required to determine the significance of different factors. Because of its complex geometry and high computational expense, the three-dimensional (3D) fluid-structure interaction (FSI) simulation is not a suitable approach for sensitivity analysis. We performed a Monte Carlo simulation (MCS) to investigate the sensitivity of hemodynamic quantities to the lumped parameters of our zero-dimensional (0D) model with numerically calculated lumped parameters. We performed local and global analyses on the effect of the model parameters on important hemodynamic quantities. The MCS showed that a larger lumped resistance value for the false lumen and the tears result in a higher retrograde flow rate in the false lumen (the coefficient of variation, [Formula: see text], the sensitivity [Formula: see text], Spearman's coefficient,[Formula: see text]). For the intraluminal pressure, our results show a significant role in the resistance and inertance of the true lumen (the coefficient of variation, [Formula: see text], the sensitivity [Formula: see text], and Spearman's coefficient,[Formula: see text] for the inertance of the true lumen). This study highlights the necessity of comparing the results of the local and global sensitivity analyses to understand the significance of multiple lumped parameters. Because of the efficiency of the method, our approach is potentially useful to investigate and analyze medical planning.

Distribution, Sources, and Health Risk of Polycyclic Aromatic Hydrocarbons in Farmland Soil of Helan, China

With the development of industry and agriculture, polycyclic aromatic hydrocarbons (PAHs) in the agricultural sector have gradually increased to different degrees, leading to an escalation in environmental pollution. In turn, this escalation has presented a significant possibility of endangering agricultural practices on farmland and has had a serious impact on regional sustainable development. Therefore, a total of 117 samples of soil were gathered to research the pollution level, distribution, sources, and health risk of PAHs in Helan farmland soils. A reference was used for the identification and quantification of PAH content using high-performance liquid chromatography (HPLC) with an ultraviolet detector, and their spatial distribution was analyzed utilizing the Arc Geographic Information System (ArcGIS). The source of PAHs was analyzed by absolute principal component scores/multiple linear regression (APCS-MLR). The lifetime cancer risk increment model and Monte Carlo sensitivity analysis were used to assess the potential health hazards to humans associated with PAHs in soil. Within the current study area, PAHs were higher in the northwest. The results showed that the total content of PAHs in Helan farmland soil ranged from 17.82 to 1544.73 ng·g−1 with a mean of 408.18 ng·g−1, which indicated the middle degree of pollution in farmland soil. The verification results of the APCS-MLR model showed that the correlation coefficient between the measured values and the predicted values ranged from 0.661 to 0.984, which suggested that the APCS-MLR model demonstrated favorable suitability for conducting source analysis of PAHs in the soil within the study region. Based on the contribution of PAHs from each source, the main sources of PAHs in Helan farmland soil were the combustion source (biomass, diesel, and natural gas combustion) and the transportation source (gasoline for vehicles and traffic exhaust emissions). The health risks’ estimation showed that PAHs in farmland soil did not have potential health risks for adults but represented a carcinogenic risk for children via the main exposure pathway of ingestion with the mean intake of 1.28 × 10−5. Meanwhile, the carcinogenic risks (CRs) of dermal contact for the mean value of adults (9.32 × 10−7) was found to be higher than that for children (3.18 × 10−8). From the Monte Carlo simulation, the soil particle uptake rate was the most sensitive to the health risks of children and adults with risk probabilities of 26% and 52%, and the risk probabilities from body weight were −11% and −1%, whose negative value indicated that the increase in body weight could reduce the health risks to human. These findings could provide reference for the study of soil organic pollution in Helan farmland soil and contribute significantly to the preservation of the ecological environment, maintaining human health and safety, and promoting the sustainable development of regional farmland.

Human health risk assessment of PM10-bound heavy metals and PAHs around the Latin America’s Largest opencast coal mine

Air pollution in opencast coal mine areas is a critical issue, resulting in harmful severe effects on human health. Therefore, it is essential to understand the air pollution factors and to assess the risks to humans. This study evaluated the potential risks (carcinogen and non-carcinogen) of inhalation exposure to PM10-bound heavy metals and Polycyclic Aromatic Hydrocarbons (PAHs) in an open pit mine in northern Colombia. During February-May 2022, PM10 samples were collected at eight sites. Heavy metals (i.e., Al, Cr(VI), Mn, Cu, Zn, As, Pb) and PAHs (thirteen priority PAHs, and one non-priority PAH) levels linked to PM10 were analyzed by X-ray fluorescence and gas chromatography–mass spectrometry, respectively. PM10 concentrations were found to range between 4.70 and 59.90 µg m−3. Out of the three different zones of the study area (i.e., North Zone, South Zone, and Populated Zone), the North Zone recorded the highest daily average concentrations of Cr(VI) (104.16 ng m−3), Mn (28.39 ng m−3), Cu (33.75 ng m−3), Zn (57.99 ng m−3), As (44.92 ng m−3), and Pb (27.13 ng m−3). The fraction of the analyzed heavy metals at all monitoring sites was 82%–89% for Al, followed by Cr(VI) with 3%–6%. Cr(VI) was the major contributor to the carcinogenic risk values, while Cu, Cr(VI), and As were the main drivers for the non-carcinogenic risk. The average cancer risk range for heavy metals was 3.30 × 10–04 –5.47 × 10–04. On the other hand, the cancer risk for PAHs exposure was acceptable. The average incremental lifetime cancer risk (ILCR) values varied between 2.87 × 10–07 and 4.21 × 10–07. Benzo[a]pyrene contributed to 54%–56% of the total risk from inhalation of PM10-bound PAHs, while Indeno[1,2,3-cd]pyrene contributed to 16%–19%. Based on the Monte Carlo sensitivity analysis, exposure to Cr(VI) was the main factor affecting cancer risk in the North, South, and Populated Zones. A suitable risk assessment and management plan requires understanding PM10-bound heavy metals and PAHs concentration levels as well as their potential health risks, mainly in open-cast coal mine zones. Our study found that people living near open-pit mines face potential health risks, so it is crucial to establish policies and regulations to control emission sources.

Impact of Alcohol Consumption on Multiple Sclerosis Using Model-based Standardization and Misclassification Adjustment Via Probabilistic Bias Analysis.

The etiology of multiple sclerosis (MS) is still not well-demonstrated, and assessment of some risk factors like alcohol consumption has problems like confounding and measurement bias. To determine the causal effect of alcohol consumption on MS after adjusting for alcohol consumption misclassification bias and confounders. In a population-based incident case-control study, 547 patients with MS and 1057 healthy people were recruited. A minimally sufficient adjustment set of confounders was derived using the causal directed acyclic graph. The probabilistic bias analysis method (PBAM) using beta, logit-logistic, and triangular probability distributions for sensitivity/specificity to adjust for misclassification bias in self-reporting alcohol consumption and model-based standardization (MBS) to estimate the causal effect of alcohol consumption were used. Population attributable fraction (PAF) estimates with 95% Monte Carlo sensitivity analysis (MCSA) intervals were calculated using PBAM and MBS analysis. Bootstrap was used to deal with random errors. The adjusted risk ratio (95% MCSA interval) from the probabilistic bias analysis and MBS between alcohol consumption and MS using the three distribution was in the range of 1.93 (1.07 to 4.07) to 2.02 (1.15 to 4.69). The risk difference (RD) in all three scenarios was 0.0001 (0.0000 to 0.0005) and PAF was in the range of 0.15 (0.010 to 0.50) to 0.17 (0.001 to 0.47). After adjusting for measurement bias, confounding, and random error alcohol consumption had a positive causal effect on the incidence of MS.

Arsenic and fluoride exposure in drinking water caused human health risk in coastal groundwater aquifers

Groundwater (GW) is a precious resource for human beings as we depend on it as a source of fresh drinking water, agricultural practices, industrial and domestic uses, etc. Extreme exposure of arsenic (As) and fluoride (F-) concentrations along the coastal GW aquifers of “South 24 Parganas and East Medinipur” diluted the quality of GW and created serious health issues. Various chronic health disorders such as – black foot disease, fluorosis skin cancer, cardiac problems, and other water borne diseases have been noticed in these two coastal districts. The comprehensive entropy-weighted water quality index (EWQI) and health risk assessment (HRA) were applied to evaluate the quality of GW and probable health risks in the coastal districts. Monte Carlo simulation and sensitivity analysis methods were simultaneously adopted to identify the non-carcinogenic health risk assessment due to regular ingestion of contaminated GW. As the study region is densely populated and part of the Sundarbans Ramsar site, it has greater importance at the international level along with regional importance to address the GWQ of this region. The major findings of the present study highlight that almost 55% of the study area is confronting serious GW quality issues and associated probable health risk (HR) due to the intense accumulation of As and F- in the GW aquifers of the study area. Children's health is more vulnerable due to the consumption of As containing GW, and adults are highly affected due to the intake of F- bearing GW in the coastal districts. The findings of the current study will draw the attention of hydrologists, groundwater management authorities, government bodies, and NGOs to regulate and monitor the GW aquifers routinely, enhance GW quality, minimizing the health hazards and sustainable water management in a more scientific and sustainable way which must be advantageous for coastal people.

Cost-Effectiveness Analysis of Adjuvant Endocrine Therapy With Ovarian Suppression in Premenopausal Patients With Hormone Receptor-Positive Early Breast Cancer in China

PurposeEndocrine therapy combined with ovarian function suppression (OFS) is recommended in intermediate- or high-risk patients among premenopausal women with hormone receptor-positive early breast cancer. However, in China, the cost-effectiveness of this strategy compared with endocrine therapy alone is unclear. This study aimed to evaluate the long-term cost-effectiveness of tamoxifen (TAM), TAM+OFS, and exemestane plus OFS (EXE+OFS). MethodsOn the basis of prognostic data from the Suppression of Ovarian Function Trial (SOFT), cost data from the Hospital Information System of the West China Hospital of Sichuan University, and health utility values from the published literature, a Markov model was established. The incremental cost-effectiveness ratio (ICER) was used to compare the treatment strategies. ResultsIn a 25-year simulation of adjuvant therapy in Chinese women with early breast cancer, the total costs of TAM, TAM+OFS, and EXE+OFS were $7821, $9318, and $9445, respectively. The quality-adjusted life-years (QALYs) were 11.615, 11.896, and 11.734 years, respectively. Compared with TAM, the ICERs of TAM+OFS and EXE+OFS were $5,327.4021/QALY and $13,647.0588/QALY, respectively. The ICERs of TAM+OFS and EXE+OFS were below the threshold of $32,517/QALY. The reliability and stability of the simulation results were verified using Monte Carlo simulation and sensitivity analysis. ConclusionIn the context of limited resources in China, TAM+OFS and EXE+OFS are cost-effective options compared with TAM.

Chatter reliability of high speed cylindrical grinding with uncertain parameters

Cylindrical grinding is commonly used as the final process of machining, but its quality and efficiency are often limited by chatter phenomenon. Many research show that the result from conventional chatter analysis by using the deterministic parameters is not consistent with the practical working condition and the chatter reliability prediction in high speed cylindrical grinding with uncertain parameters is studied in this paper. The dynamic model of the cylindrical grinding system is established, and uncertain parameters such as mass, stiffness and damping coefficient are introduced, and the stability lobe diagrams with uncertain parameters were obtained. The reliability model of the grinding system was established, and the reliability was solved by using the Monte Carlo simulation method, the improved first-order second-moment method and the second-order second-moment method, respectively. Then, the chatter-free reliability of the system was predicted by using the reliability lobe diagram. The accuracy of the grinding reliability prediction results is verified by the experiment results. Furthermore, the Monte Carlo sensitivity analysis method is used to study the influence of random parameters on the grinding system, and the results show that the variation of modal mass has the greatest influence on the reliability of the grinding system.

Chemical and radiological human health risk assessment from uranium and fluoride concentrations in tap water samples collected from Shiraz, Iran; Monte-Carlo simulation and sensitivity analysis

ABSTRACT Uranium is one of the most critical components in the nuclear field due to its chemical and radiological properties. Since few studies were conducted on uranium concentration in drinking water in Iranian cities, this study aimed to determine the concentration of uranium and flouridein the samples of drinking water collected from tap water in Shiraz, Fars province. Fifty samples were examined for uranium and fluoride presence using the ICP-MS and spectrophotometry methods. Hazard Quotient (HQ) and sensitivity analysis (SA) using the Monte Carlo Simulation technique with 10,000 iterations were applied to determine the human carcinogenic and non-carcinogenic effects of Uranium and Fluoride in three different groups (children, teenagers, and adults). The results showed that uranium and fluoride concentrations varied between 0.18–1.12 μg/L and 0.06–0.56 mg/L, respectively, which is less than the EPA recommendation for both elements. pH, EC, and TDS values were in accordance with the standard, and a positive correlation was observed between fluoride and total dissolved solids (TDS). The highest risk of carcinogenicity was found in the adult group with a value of 3.24E-9, lower than the allowable limit (1E-6). The highest hazard quotient (HQ) for uranium and fluoride was obtained in children with 0.13 and 0.66. Monte Carlo simulation results showed that the hazard quotient (HQ) for both elements in all groups was less than one and consistent with the values of the deterministic method. Also, water swallowing rate and contaminant concentration were the most influential parameters in the sensitivity analysis.

A Review of Conventional versus Additive Manufacturing for Metals: Life-Cycle Environmental and Economic Analysis

Additive manufacturing (AM) is a recent emerging technology that is being adopted in various industry sectors and supply chains. Life cycle assessment (LCA) and life cycle costing (LCC) are powerful methods that can be used for assessing the environmental and economic performance of emerging manufacturing technologies. This study aims to evaluate the life cycle environmental impacts and cost of computerized numerical control-based (CNC) manufacturing and direct metal laser sintering technology (DMLS) through a cradle-to-gate life cycle analysis. This research has four main novel elements: (i) conducting a recent comprehensive review of metal AM and conventional manufacturing (CM) processes using a systematic method and meta-analysis (ii) comparing the conventional process “CNC machining” and the additive technology “direct metal laser sintering” from the environmental (LCA) and economic (LCC) perspectives, (iii) investigating the influence of geometry complexity and shape size factors on the environmental and cost performance of both manufacturing methods, and (iv) conducting a Monte Carlo simulation-based sensitivity analysis to tackle uncertainty in LCC input parameters. The midpoints and endpoints impact for CNC and AM processes were estimated using the Ecoinvent v3.8 database and ReCiPe (E) impact assessment method. The review revealed that global warming potential is one of the most widely studied environmental indicators; however, only 6% of the studies have investigated the life cycle economic impacts of AM technologies using sensitivity and uncertainty analysis. The results have shown that in terms of ReCiPe endpoints, DMLS has the highest environmental impact on human health while CM has more impact on the eco-system quality. Electricity consumption is the main contributor to environmental impact categories in both manufacturing technologies. This is due to the high electricity utilization for casting and milling conventionally manufactured parts and printing the AM parts. LCC net present values revealed that manufacturing all parts with AM costs 91% more compared to CNC. The LCC analysis has shown that AM is more suitable and cost-effective for parts with highly complex geometries. Whereas CNC machining was found to be economically feasible for large-sized and low-complexity parts. The Monte Carlo sensitivity analysis demonstrated that for the case of AM, the most significant parameter is the processing cost followed by material cost, which highlighted the importance of energy-efficient AM and dematerialization through design for circularity.

Monte Carlo simulation and sensitivity analysis of the Michaëlis-Menten kinetic equation for the CO2 inhibition response to O2 consumption during storage of fresh produce

Monte Carlo simulation and sensitivity analysis of the Michaëlis-Menten kinetic equation for the CO2 inhibition response to O2 consumption during storage of fresh produce

Comprehensive assessment on groundwater quality, pollution characteristics, and ecological health risks under seasonal thaws: Spatial insights with Monte Carlo simulations

Groundwater pollution is a major environmental concern and may persist under diverse ecological conditions. This study describes the seasonal impacts on pollution characteristics coupled with the degree of human health risks in groundwater. For this purpose, a highly populated city of Pakistan, Lahore was planned and about 50 groundwater samples were collected during the summer and winter seasons in 2021 and 2022. Various physiochemical parameters (pH, EC, TDS, and hardness), cations, anions, and toxic metals (As, Cd, Co, Cr, Mn, Ni, and Pb) were analyzed to evaluate the seasonal impacts on the groundwater quality. Besides, a Monte Carlo simulation model and sensitivity analysis were performed to obtain a high-level accuracy in cancer risk estimation. Results revealed that physiochemical parameters, cations, and anions in most of the samples were within the WHO limit. However, the heavy metals concentration was higher in winter than in summer season. Most metals showed a significant spatial variability were found in older settlements and densely populated areas of the city for both seasons. Factor analysis (FA) and cluster analysis (CA) revealed a significant anthropogenic contribution to Ni, Co, Cd, Cu, Cr, and Pb contaminations in groundwater. Drinking water quality indices (DWQI) and heavy metal pollution indices (HMEI) points in both seasons revealed a poor to moderated pollution pattern. Pollution load index (PLI) and potential ecological risk index (PERI) indicated the medium pollution with low risks. Through Monte Carlo simulation at 95% confidence level revealed that carcinogenic risks associated with As, Cr, Cd, and Ni has exceeded from safer limits. Consequently, winter season was perceived more sensitive owing to the substantial increase of pollutants proclamation in groundwater and posturing a serious threat to humans. Hence, instant remedial actions are suggested to ensure safe drinking water.

The Economic Impact Associated with the Direct Connection Strength of Micropiles in Foundation Retrofit Projects

Building foundations are usually retrofitted with directly connected micropiles; however, at the present time, there are different approaches for predicting shear capacity in the micropile–foundation connection. At first, the concrete shear strength was considered. Nowadays, in the EU countries, it is prescribed to use the shear strength of the interface between successive concrete casts at different times. This implies a reduction of the connection capacity by half, and these values are not in consonance with the lab results. This work analyses the economic impact of the previous considerations on retrofit projects with micropiles. To this aim, firstly, seven different formulations were applied to 29 building projects, and the results were compared. Secondly, a Monte Carlo sensitivity analysis was performed using bond stress distribution data obtained from lab tests. Thus, numerical results acquired by comparing European and American regulations show an average difference in cost of around 40%, which may reach up to 50%. Moreover, the Monte Carlo simulation confirms that the connection strength may become a limitation in retrofit projects, also indicating that the application of European codes usually leads to the most expensive designs. Finally, the results show that it is not worth improving the connection to exceed a bond stress of 0.60 MPa, since no relevant savings are produced by achieving higher values.

Monte Carlo sensitivity analysis for unmeasured confounding in dynamic treatment regimes.

Data-driven methods for personalizing treatment assignment have garnered much attention from clinicians and researchers. Dynamic treatment regimes formalize this through a sequence of decision rules that map individual patient characteristics to a recommended treatment. Observational studies are commonly used for estimating dynamic treatment regimes due to the potentially prohibitive costs of conducting sequential multiple assignment randomized trials. However, estimating a dynamic treatment regime from observational data can lead to bias in the estimated regime due to unmeasured confounding. Sensitivity analyses are useful for assessing how robust the conclusions of the study are to a potential unmeasured confounder. A Monte Carlo sensitivity analysis is a probabilistic approach that involves positing and sampling from distributions for the parameters governing the bias. We propose a method for performing a Monte Carlo sensitivity analysis of the bias due to unmeasured confounding in the estimation of dynamic treatment regimes. We demonstrate the performance of the proposed procedure with a simulation study and apply it to an observational study examining tailoring the use of antidepressant medication for reducing symptoms of depression using data from Kaiser PermanenteWashington.

A Monte Carlo simulation and sensitivity analysis framework demonstrating the advantages of probabilistic forecasting over deterministic forecasting in terms of flood warning reliability

Despite the significant progress in probabilistic forecasting science in the last two decades, particularly in the quantification of predictive uncertainty (PU), most operational flood early warning systems (FEWSs) continue to be based on deterministic forecasts. Thereupon, additional work is needed to demonstrate the advantages of using PU over deterministic forecasting to enhance the uptake of probabilistic forecasts in flood warning decision-making. In this paper, a Monte-Carlo (MC)-based sensitivity analysis is done to explore how the outcomes of flood peak water level-based deterministic and -probabilistic warning strategies behave when factors controlling the forecast quality are perturbed. The flood warning reliability is evaluated through the probability of detection (POD) and false alarm ratio (FAR) based on two criteria: a flooding threshold-based criterion (FTC), and a new floodplain property-based criterion (FPC) based on inundation level forecasting. The results of this work show that the advantage of a probabilistic strategy over a deterministic one is greater when PU is relatively high. The probabilistic strategy is robust to biases in the mean and variance of forecasts by maintaining POD and FAR stability, while this is not the case for a deterministic warning strategy. Likewise, it was concluded that if inundation level forecasting is undertaken (FPC), improved forecasts would be needed to achieve the same reliability level as for FTC, reflecting the more demanding FPC criterion. Also, the levels of correlation needed to achieve acceptable operational values of POD and FAR are shown. These results provide new insight into the advantages of a probabilistic forecasting strategy under several forecast quality scenarios and guidance for the design of operational FEWSs.

Pollution and health risk assessment of mine tailings contaminated soils in India from toxic elements with statistical approaches

The rapid mining activities of mica mines in Giridih district, India, have led to toxic metal pollution of agricultural soil. This is a key concern for environmental risk and human health. 63 top soil samples were collected at a distance of 10 m (Zone 1), 50 m (Zone 2), and 100 m (Zone 3) from near 21 mica mines with agriculture fields. The mean concentration of total and bio-available toxic elements (TEs - Cr, Ni, Pb, Cu, Zn, and Cd) was higher in zone 1 across three zones. The Positive matrix factorization model (PMF) and Pearson Correlation analysis were used to identify waste mica soils with TEs. Based on PMF results, Ni, Cr, Cd, and Pb were the most promising pollutants and carried higher environmental risks than the other TEs. Using the self-organizing map (SOM), zone 1 was identified as a high-potential source of TEs. Soil quality indexes for TEs risk zone 1 were found to be higher across three zones. Based on the health risk index (HI), children are more adversely affected than adults. Monte Carlo simulations (MCS) model and sensitivity analysis of total carcinogenic risk (TCR), children were more affected by Cr and Ni than adults through ingestion exposure pathways. Finally, a geostatistical tool was developed to predict the spatial distribution patterns of TEs contributed by mica mines. In a probabilistic assessment of all populations, non-carcinogenic risks appeared to be negligible. The fact that there is a TCR can't be ignored, and children are more likely to develop it than adults. Mica mines with TEs contamination were found to be the most significant anthropogenic contributor to health risks based on source-oriented risk assessment.

A Vulnerability Assessment Approach for Transportation Networks Subjected to Cyber–Physical Attacks

Transportation networks are fundamental to the efficient and safe functioning of modern societies. In the past, physical and cyber space were treated as isolated environments, resulting in transportation network being considered vulnerable only to threats from the physical space (e.g., natural hazards). The integration of Internet of Things-based wireless sensor networks into the sensing layer of critical transportation infrastructure has resulted in transportation networks becoming susceptible to cyber–physical attacks due to the inherent vulnerabilities of IoT devices. However, current vulnerability assessment methods lack details related to the integration of the cyber and physical space in transportation networks. In this paper, we propose a new vulnerability assessment approach for transportation networks subjected to cyber–physical attacks at the sensing layer. The novelty of the approach used relies on the combination of the physical and cyber space, using a Bayesian network attack graph that enables the probabilistic modelling of vulnerability states in both spaces. A new probability indicator is proposed to enable the assignment of probability scores to vulnerability states, considering different attacker profile characteristics and control barriers. A probability-based ranking table is developed that details the most vulnerable nodes of the graph. The vulnerability of the transportation network is measured as a drop in network efficiency after the removal of the highest probability-based ranked nodes. We demonstrate the application of the approach by studying the vulnerability of a transportation network case study to a cyber–physical attack at the sensing layer. Monte Carlo simulations and sensitivity analysis are performed as methods to evaluate the results. The results indicate that the vulnerability of the transportation network depends to a large extent on the successful exploitation of vulnerabilities, both in the cyber and physical space. Additionally, we demonstrate the usefulness of the proposed approach by comparing the results with other currently available methods. The approach is of interest to stakeholders who are attempting to incorporate the cyber domain into the vulnerability assessment procedures of their system.

A multi-objective optimization method based on NSGA-II algorithm and entropy weighted TOPSIS for fuzzy active seat suspension of articulated truck semi-trailer

This paper aims to optimize a fuzzy logic controller (FLC) active seat suspension applied to an articulated truck semi-trailer seat to improve ride comfort considering the energy consumption of the controller. The proposed truck model is a linear truck with 13 degree-of-freedom (DOF). Two objective functions are defined seat root mean square (RMS) acceleration related to ride comfort and controller RMS force pertaining to the energy consumption of the controller. The Pareto Front is obtained for these two objective functions using the multi-objective optimization method in MATLAB. The optimization is based on the Non-Dominated Sorting Genetic Algorithm (NSGA-II), which has been proposed as a powerful decision space exploration engine based on a genetic algorithm (GA) for solving a multi-objective function problem. Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), which is simple and effective, selects a set of optimal controller parameters. In addition, considering the changes of effective parameters in the truck, including trailer load and location, tyres’ stiffness and the driver’s mass, has been investigated to provide Monte Carlo sensitivity analysis of these parameters on objective functions. Finally, using ISO 2631-1, the ride comfort and controller required force levels before and after optimization are compared. The results of this optimization indicate a significant improvement in ride comfort and controller force which has been different in various conditions of truck speed and road classes. The results have shown that the maximum amount of improvement in ride comfort is about 25% which happens on Class-C road (at the truck speed of 60 km/h), and the control force reduction peaks at around 60%, which occurs on Class-A road (at the truck speed of 60 km/h). The simulation is validated by MSC-ADAMS software.

Monitoring and Health Risk Assessment of Fluoride in Drinking Water Based on Monte Carlo Simulation and Sensitivity Analysis: a Study in Rural Areas of East Azerbaijan Province, Iran

Monitoring and Health Risk Assessment of Fluoride in Drinking Water Based on Monte Carlo Simulation and Sensitivity Analysis: a Study in Rural Areas of East Azerbaijan Province, Iran

First principles reaction discovery: from the Schrodinger equation to experimental prediction for methane pyrolysis.

Our recent success in exploiting graphical processing units (GPUs) to accelerate quantum chemistry computations led to the development of the ab initio nanoreactor, a computational framework for automatic reaction discovery and kinetic model construction. In this work, we apply the ab initio nanoreactor to methane pyrolysis, from automatic reaction discovery to path refinement and kinetic modeling. Elementary reactions occurring during methane pyrolysis are revealed using GPU-accelerated ab initio molecular dynamics simulations. Subsequently, these reaction paths are refined at a higher level of theory with optimized reactant, product, and transition state geometries. Reaction rate coefficients are calculated by transition state theory based on the optimized reaction paths. The discovered reactions lead to a kinetic model with 53 species and 134 reactions, which is validated against experimental data and simulations using literature kinetic models. We highlight the advantage of leveraging local brute force and Monte Carlo sensitivity analysis approaches for efficient identification of important reactions. Both sensitivity approaches can further improve the accuracy of the methane pyrolysis kinetic model. The results in this work demonstrate the power of the ab initio nanoreactor framework for computationally affordable systematic reaction discovery and accurate kinetic modeling.