Relative Sensitivity Analysis Research Articles

Reactive contaminant infiltration under dynamic preferential flow

Biodegradation is an important mechanism of contaminant removal from soils. We use numerical simulations to study the contaminant transport in heterogeneous soils subject to transient flow conditions and anaerobic multicomponent biodegradation. These processes and their interactions affect contaminant travel times, the extent of reactant mixing, solute and microbial biomass distributions in the soil, and biodegradation outcomes. Especially when flux variations are large, the combination of soil heterogeneity and transient flow gives rise to dynamic preferential flow zones, which affects reactant mixing and biodegradation outcomes. Results show that soil heterogeneity may reduce contaminant leaching due to enhanced reactant mixing, especially when biodegradation is more limited by reactant mixing. Furthermore, unlike under steady-state flow, under transient flow soil heterogeneity does not substantially reduce contaminant residence times. As preferential flow zones change dynamically, the spatio-temporally averaged transport, mixing, and biodegradation experienced by various parts of contaminant plumes become homogenized. Therefore, knowing the initial biodegradation rate of a contaminant upon infiltration, and its mean residence time in the soil, enables a relative sensitivity analysis. This allows biodegradation outcomes of various scenarios to be approximately ranked, even under soil heterogeneity and transient flow, using information that is straightforward to measure or estimate in the field.

Intervertebral Disk Degeneration and Bone Mineral Density: A Bidirectional Mendelian Randomization Study.

This study aimed to investigate the causal relationship between bone mineral density (BMD) and intervertebral disk degeneration (IVDD) using a two-sample bidirectional Mendelian randomization analysis. Summary-level data from the Genome-Wide Association Study (GWAS) were used. Instrumental variables (IVs) for IVDD were selected from the large-scale Genome-Wide Association Study (GWAS) (20,001 cases and 164,682 controls). Bone mineral density (BMD) at five different sites (heel (n = 426,824), total body (TB) (n = 56,284), forearm (FA) (n = 8143), femoral neck (FN) (n = 32,735), and lumbar spine (LS) (n = 28,498)) was used as a phenotype for OP. Bidirectional causality between IVDD and BMD was assessed using inverse variance weighting (IVW) and other methods. Related sensitivity analyses were performed. Myopia was also analyzed as a negative control result to ensure the validity of IVs. Heel bone mineral density (heel BMD), total body bone mineral density (TB-BMD), femoral neck bone mineral density (FN-BMD), and lumbar spine bone mineral density (LS-BMD) have a direct causal relationship on intervertebral disk degeneration (IVDD) [heel BMD-related analysis: beta = 0.06, p = 0.03; TB-BMD-related analysis: beta = 0.18, p = 8.72E-08; FN-BMD-related analysis: beta = 0.15, p = 4.89E-03; LS-BMD-related analysis: beta = 0.16, p = 1.43E-04]. There was no evidence of a significant causal effect of IVDD on BMD. In conclusion, our study found a significant positive causal effect of lower BMD on IVDD, and we identified significant causal effects of heel, TB-, FN-, and LS-BMD on IVDD, but there was no evidence of a significant causal effect of IVDD on BMD.

Optimization as generator of structural instability: Koiter’s worst imperfection

This paper is focused on the interaction of local modes in pin-jointed structures, with the aim to present an algorithm for the appraisal of the lowest critical load characterizing the structure under the effect of small imperfections. This interaction phenomenon is framed within Koiter’s theory of elastic instability as a quartic system. First of all, the paper discusses the theoretical results of Koiter’s theorem about the determination of the worst imperfection with respect to the critical load. In doing this, the characterization of the fold lines in the modal space is recalled and used for the purposes of the reformulation of the theorem. Then, a FEM code aimed at the determination of the most dangerous shape of the imperfection, and at performing the related sensitivity analysis, is presented. The basis of the code is a nonlinear model of a 3D rod equipped with a planar flexural deformation, within the context of Eulerian instability. Some numerical results having a practical interest are discussed.

A robust ordinal regression feedback consensus model with dynamic trust propagation in social network group decision-making

This paper proposes a robust ordinal regression feedback consensus model based on trust relationships for social network group decision-making problems in intuitionistic fuzzy environment. The model consists of two main parts: (a) the construction of the complete trust matrix among decision makers (DMs) and (b) the design of a feedback mechanism formulated on robust ordinal regression. In Part (a), to complete the initial trust matrix between DMs, a dynamic trust propagation mode founded on maximum credibility is proposed to investigate indirect trust relationships between unfamiliar DMs from the perspective of overall optimization. In Part (b), when the group consensus does not reach the consensus threshold, the utility function compatible with the preference information of DMs is constructed through robust ordinal regression, and the most representative utility function is selected by maximizing the difference of the necessary weak preference relationship. In addition, the performance of DMs under four evaluation dimensions, individual consensus degree, individual comprehensive influence, individual opinion retention, and individual proximity centrality, is aggregated into a total utility value. In each iteration, the DM with the lowest total utility value is identified for opinion adjustments. Further, based on trust-similarity analysis, recommendations are designed for opinion adjustments to improve the group consensus. An illustrative example along with the related sensitivity analysis and comparative study is used to examine the effectiveness and advantages of the proposed model.

A Mathematical Programming Approach for Multi-Objective Optimization in Designing Flexible Manufacturing Cells

The study aims to design flexible manufacturing cells with routing flexibility. A weighted mixed-integer linear mathematical programming model that aims to find optimal routing of parts in flexible manufacturing cells under constraints such as minimum machine utilization rate, maximum machine utilization rate, tool capacities, the utilization rate of workers, and labor-system unbalance is developed. The mathematical programming model aims to minimize the weighted sum of five objective functions: (1) the total number of intracellular movements; (2) the total number of intercellular movements; (3) the total workload unbalance of the machine system; (4) the total number of tools in all machines in the cells; and (5) the total labor-system workload unbalance. The main contribution of this study is to obtain these five objectives simultaneously, which have not been encountered to handle together before. By integrating these factors, the study presents a comprehensive approach to optimizing the design of flexible manufacturing cells. This study also has the potential to enhance system performance by addressing these factors. An illustrative problem tests the developed model, and the LINGO 17.0 optimization program is used to solve the generated mathematical programming model. Moreover, the related sensitivity analysis is performed with some parameters to examine the obtained results.

Application of genetic programming in presenting novel equations for longitudinal dispersion coefficient in natural streams considering rivers geometry - Implementation in assimilation capacity simulation

Precise estimation of the longitudinal dispersion coefficient (LDC) is crucial for the accurate simulation of water quality management tools such as assimilation capacity. Previous research analyzed the LDC of natural streams in two general categories: ignoring or considering the river sinuosity (σ). Genetic programming (GP) is used in this study to investigate both mentioned categories by applying two experimental datasets from 56 to 24 different rivers worldwide. The first proposed LDC equation of this research (without σ) improves the amounts of statistical measures R2 (Determination Coefficient), OI (Overall Index), NSE (Nash-Sutcliffe Efficiency), WI (Willmott's Index of Agreement), RMSE (Root Mean Square Error), and MAE (Mean Absolute Error) by 3.75%, 4.71%, 7.81%, 0.85%, 13.72%, and 0.68%, respectively, compared to the best values of these indicators in the previous investigations. Regarding the second category, relative and absolute sensitivity analyses are conducted, which reveal that σ is the most influential parameter in the accurate prediction of the LDC among all hydraulics and geometric parameters of the river. This part of the investigation presents four unique LDC equations that closely match the experimental results. Significant improvement of the most accurate presented LDC for statistical indices R2, OI, NSE, WI, RMSE, MAE, and accuracy percentage are obtained equal to 3.27%, 2.41%, 3.16%, 0.81%, 35.1%, 24.47%, 3.8%, respectively, in comparison with the best previous relations. Also, a new indicator for measuring the efficiency of mathematical equations called Mean Normalized Statistical Index (MNSI) is introduced and applied in different parts of this research. Finally, the assimilation capacity of the Kashafrud River is determined based on the analytical method of pollution propagation for three types of water demands utilizing the accurately presented LDC in 1993–2020. The average amount of river assimilation capacity using accurate LDC is simulated at 91.93 tons/day, much lower than the currently reported pollution entrance, which equals 540 tons/day.

Combination of Sequential Sampling Technique with GLR Control Charts for Monitoring Linear Profiles Based on the Random Explanatory Variables

Control charts play a beneficial role in the manufacturing process by reduction of non-compatible products and improving the final quality. In line with these aims, several adaptive methods in which samples can be taken with variable sampling rates and intervals have been proposed in the area of statistical process control (SPC). In some SPC applications, it is important to monitor a relationship between the response and independent variables—this is called profile monitoring. This article proposes adaptive generalized likelihood ratio (GLR) control charts based on variable sampling interval (VSI) and sequential sampling (SS) techniques for monitoring simple linear profiles. Because in some real-life problems, it may be possible that the user cannot control the values of explanatory variables; thus, in this paper, we focus on such a scenario. The performance of the proposed method is compared under three different situations, i.e., the fixed sampling rate (FSR), VSI, and SS, based on average time to signal (ATS) criteria for phase II analysis. Since the SS approach uses a novel sampling procedure based on the statistic magnitude, it has a superior performance over other competing charts. Several simulation studies indicate the superiority as the SS approach yields lower ATS values when there are single-step changes in the intercept, slope, standard deviation of the error term, and explanatory variables. In addition, some other related sensitivity analysis indicates that other aspects of the proposed methods, such as computational time, comparison with other control charts, and consideration of fixed explanatory variables. Furthermore, the results are supported by a real-life illustrative example from the adhesive manufacturing industry.

Investigation of the Relationship Between the Tourism Revenue and the Economic Growth for Türkiye: A Relative Sensitivity Analysis Approach

Tourism revenue is an important income component of the Turkish economy since the tourism revenue of Türkiye is ranked among the first fifteen countries in the World. Therefore, the analysis of the effects of tourism revenue of Türkiye on its economic stance is an active research area. This paper investigates the impact of the tourism revenue of Türkiye on the economic growth for the period of 2003-2022 using quarterly data. The causality relationship between the tourism revenue and the economic growth is ssanalysed as the first step employing the Granger causality tests. Then, the relative sensitivity of the economic growth dependent on the tourism income is computed in Python programming language. The result of the relative sensitivity analysis shows that the 1% increment in the tourism revenue leads to the 0.12% increment of the economic growth. The presented method can be applied for the analysis of the effect of the tourism revenue on the economic growth also for other countries.

Psychiatric traits and intracerebral hemorrhage: A Mendelian randomization study.

Psychiatric traits have been associated with intracerebral hemorrhage (ICH) in observational studies, although their causal relationships remain uncertain. We used Mendelian randomization analyses to infer causality between psychiatric traits and ICH. We collected data from genome-wide association studies of ICH (n = 361,194) and eight psychiatric traits among Europeans, including mood swings (n = 451,619), major depressive disorder (n = 480,359), attention-deficit/hyperactivity disorder (n = 53,293), anxiety (n = 459,560), insomnia (n = 462,341), schizophrenia (n = 77,096), neuroticism (n = 374,323), and bipolar disorder (n = 51,710). We performed a series of bidirectional two-sample Mendelian randomization and related sensitivity analyses. A Bonferroni corrected threshold of p < 0.00625 (0.05/8) was considered to be significant, and p < 0.05 was considered suggestive of evidence for a potential association. Mendelian randomization analyses revealed suggestive positive causality of mood swings on ICH (odds ratio = 1.006, 95% confidence interval = 1.001-1.012, p = 0.046), and the result was consistent after sensitivity analysis. However, major depressive disorder (p = 0.415), attention-deficit/hyperactivity disorder (p = 0.456), anxiety (p = 0.664), insomnia (p = 0.699), schizophrenia (p = 0.799), neuroticism (p = 0.140), and bipolar disorder (p = 0.443) are not significantly associated with the incidence of ICH. In the reverse Mendelian randomization analyses, no causal effects of ICH on mood swings (p = 0.565), major depressive disorder (p = 0.630), attention-deficit/hyperactivity disorder (p = 0.346), anxiety (p = 0.266), insomnia (p = 0.102), schizophrenia (p = 0.463), neuroticism (p = 0.261), or bipolar disorder (p = 0.985) were found. Our study revealed that mood swings are suggestively causal of ICH and increase the risk of ICH. These results suggest the clinical significance of controlling mood swings for ICH prevention.

Robust Extreme Learning Machine for estimation of triangular, rectangular, and parabolic weirs

In this paper, for the first time, the DC of triangular, rectangular, and parabolic weirs is simulated by a new learning machine called “Robust Extreme Learning Machine” (RELM). The used laboratory data are divided into two categories: training (70% data) and testing (30% data). In the next step, the number of neurons inside the hidden layer is examined. For the structure of the proposed RELM algorithm, 10 hidden layer neurons are embedded. For the learning machine used in the present study, six different activation functions are evaluated that the Sigmoid activation function has better performance and is used for the RELM structure. Next, the calibration parameter of the RELM algorithm is discussed. The optimal regularization parameter is selected for the present study equal to 0.0001. Then, using parameters affecting the DC, four RELM models are developed. By performing various analyzes, the superior RELM model and the most effective input parameters are identified. Also, comparing the performance of the RELM model with ELM shows the superiority of the RELM algorithm. For the superior model, a relative derivative sensitivity analysis is performed to investigate the behaviour of the input parameters on the DC. Finally, an equation for estimating the DCs of triangular, rectangular, and parabolic weirs is proposed.

Potential Strong Inhibition on Ozone Production Sensitivity by Particle Uptake

The accurate identification of ozone (O3) production sensitivity is central to developing O3 pollution control policies. It is determined by the relative ratio of the radical loss to the total primary radical production. However, such radical losses in the traditional sensitivity analysis typically rely on nitrogen oxide (NOx) sinks while ignoring particle uptake (collisions between compounds in the gas phase and condensed phases that result in irreversible uptake due to chemical reactions). Therefore, we combine NOx and particle uptakes to optimize peroxy radical loss estimates and thus analyze the relative sensitivity. We also assess the absolute responses of precursor reduction to O3 production. Such relative and absolute sensitivity analysis is applied to measurements in Chun’an, a county in China, where volatile organic compounds (VOCs) and NOx are both rich. Consequently, the relative sensitivity analysis presents that the dominant precursor for O3 production sensitivity shifts from volatile organic compounds (VOCs) in the morning and evening to NOx in the afternoon, the main driver of which is related to NO depletion. In contrast, the absolute sensitivity analysis confirms that VOCs persistently determine the diurnal ozone production sensitivity. Moreover, they both show that particle uptake does not change the regime classification of O3 production sensitivity (i.e., VOC- or NOx-sensitive regime) but potentially has a strong inhibition on the sensitivity magnitude (within 16% and 38% for VOC- or NOx-sensitive regimes, respectively). Our results partly explain more insensitive O3 production measurements than those suggested by traditional sensitivity analyses, which has important implications for synergistic controls on O3 and fine particulate matter pollution.

Transient analysis and reliability modeling of fault-tolerant system operating under admission control policy with double retrial features and working vacation

Maintaining a high-level reliability and efficiency without interruption are the key concerns for many real-time machining systems. Using the redundancy and repair facility features, we develop a double retrial orbit queueing model for the fault-tolerant machining system (FTMS) operating under the restriction of admission of repair jobs based on threshold policy and working vacation. The provision of primary and secondary orbits is made so that the failed units can wait there based on the facility available in case the repairman is occupied. From the orbits, the failed units retry to get the repairman free so that the repair job can be accomplished. Chapman–Kolmogorov equations for the system states of FTMS have been constructed to evaluate the transient reliability and queueing indices using the spectral technique. The sensitivity along with the relative sensitivity analysis of crucial system parameters, have facilitated. The impacts of parameter variability on the system metrics and total expected cost are examined for illustrative examples.

Power correlations as complementary tools for generalised analysis and preliminary design of Stirling engines

The use of dimensionless variables facilitates the generalised analysis of Stirling engines and provides tools for preliminary design, validation of simulation codes and feasibility studies of energy conversion systems. This article provides correlations to estimate the dimensionless values of maximum indicated and brake power, as well as their corresponding speeds. The concept of effective pressure allows power correlations to be related to gas circuit models and mechanical efficiency calculations. Previous correlations based on data from eight very different prototypes are revised using the criterion of minimising the relative root mean square errors (RRMSE) and sensitivity analysis to select influential variables. The application of previously established performance models provided additional data from the M102C and ST05G engines for validation purposes. The procedure also allows detecting inconsistent measurements. Data deviations seem acceptable for most operating points of the 10 analysed engines. The errors inherent to the procedure, as well as the number and quality of experimental data, determine the following average values of accuracy indicators: RRMSE = 3.20% for the dimensionless maximum indicated power, RRMSE = 6.35% for the dimensionless speed corresponding to the maximum indicated power, RRMSE = 6.39% for the dimensionless maximum brake power, and RRMSE = 10.93% for the dimensionless speed corresponding to the maximum brake power.

Reliability estimation of a Tibetan heritage timber structure with inclination in its Que-Ti joints

The reliability of a Tibetan timber frame in a heritage building in Lasha, China is studied. The frame is featured with stochastic Que-Ti joints with inclination. A new method integrating the relative entropy-based probabilistic sensitivity analysis and the Probabilistic Density Evolution Method with absorbing boundary condition is developed for this study. The contributions of stochastic parameters to the probabilistic bearing capacity of the joint can be clearly defined from the differences between the probability density functions of the bearing capacity with different parameter perturbation. Results are obtained via sensitivity analysis with engineering considerations of the heritage frame structure with a large number of uncertain parameters. The Que-Ti inclination angle, column inclination angle, the parallel-to-grain shear modulus and perpendicular-to-grain elastic modulus, which have greater influence on the performances of the heritage frame structure are identified with acceptable computation requirements. A reduced-dimensioned model of the structure with selected variables is then verified and used for the reliability estimation. The structural reliability is noted efficiently implemented with the random variables of the frame system reduced from 13 to 4. The frames in this heritage building with stochastic inclined Que-Ti joints are relatively safe comparing to the recommended reliability limit in the design code.

Human-Induced Response and Reliability Analysis of Stochastic Dynamic System

This paper proposed a method analyzing crowd-induced stochastic vibration response by the dynamic reliability method. The efficiency of the probability density evolution method (PDEM) is improved by embedding the Kullback-Leibler (K-L) relative sensitivity analysis in response analysis of stochastic dynamic system. Then the complicated point selection technique of high dimension uncertain variables is avoided. The proposed method is illustrated by the response analysis of random crowd-structure interaction system where the load parameter randomness is considered. The acceleration induced by the crossing of crowd is predicted with the proposed method. Results obtained highlight the computation improvement in PDEM probabilistic response analysis in high dimension dynamic system. And the method could be used as an efficient way to predict and evaluate vibration serviceability of structures with human-induced vibration.

Energy balance analysis of a DI diesel engine with multiple pilot injections strategy and optimization of brake thermal efficiency

• Timing and quantity changes of two pilot injections were applied to diesel engine. • The effects of each change on total fuel energy distribution were investigated. • 2nd PiI quantity change has the strongest effect on components of total fuel energy. • 2nd PiI timing change has the least effect on components of total fuel energy. • NSGA-III optimization maximized brake thermal efficiency with an increase of 0.29%. • An increase of 0.74% was obtained in the total exhaust energy percentage which is recoverable. In this study, multiple pilot injections (MPI) strategy having two pilot injections was applied to direct injection (DI) diesel engine running at partial load which is usually within legal emission cycle. Optimization of timing and quantity of the pilot injections is a key factor in achieving performance and emission targets. Due to the advantages of pilot injections in terms of smooth and gradual release of energy from the fuel, the energy distribution in the engine has been put under scope and investigated thoroughly. The effects of varying the timing and quantity of both pilot injections within a reasonable range on brake thermal efficiency, total exhaust energy, in-cylinder heat transfer and friction were investigated and results were presented using a well calibrated and validated thermodynamic model. Relative sensitivity analysis and Spearman correlation coefficients analysis were performed to show the effects of each MPI parameter on the total fuel energy components. The variation of 2nd pilot injection quantity determined the most influential parameter on brake thermal efficiency with a relative sensitivity value of 0.4612, on in-cylinder heat transfer with a relative sensitivity value of 0.4708, on total exhaust energy with a relative sensitivity value of 0.4757 and on friction power with a relative sensitivity value of 0.4677 respectively. Similar to the variation in the 2nd pilot injection quantity, the variation in the 1st pilot injection quantity affected these responses in the same way but with less effect. Timing variations in both pilot injections were found out to be the third and the fourth parameters affecting responses. Especially the variation in 2nd pilot injection timing has the least effect on components of total fuel energy. Retarding both pilot injections increased the brake thermal efficiency and the variation of both pilot injection timing has similar effect on brake thermal efficiency. Retarding the 1st pilot injection timing augmented the total exhaust energy while reducing the in-cylinder heat transfer to coolant and friction power. Non-dominated Sorting Genetic Algorithm-III (NSGA-III) optimization tool was used for finding the optimal timing and quantity of both pilot injections to maximize brake thermal efficiency. A growth of 0.29% at a rate of %1 in the brake thermal efficiency was achieved. Additionally, an increase of 0.74% at a rate of 2.4% in the total exhaust energy has the potential to be used and recovered in the turbine.

Investigation of average asphalt film thickness of dense graded asphalt mixtures with compaction effects

The asphalt mixtures with inadequate durability may become too lean, resulting in low rutting and fatigue resistance. The asphalt film thickness (AFT) has been used as a crucial property for the durability of asphalt mixtures. However, the relationship between AFT and volumetric properties is ambiguous with the absence of compaction effects in the standard film thickness equation. This study aims to investigate the relationship between AFT and volumetric properties of dense-graded asphalt mixtures with consideration of the compaction degree. The compaction-property-dependent (CPD) AFT model considering compaction effects was proposed and compared with the standard AFT equation. Then, asphalt mixtures with 112 different gradations were used to calculate the AFT with and without compaction effects. The relationship between new AFT and volumetric parameters (Pb, VMA, VFA, Vc, Vfine and Vfiller) was investigated by linear correlation analysis. After that, two regression models and a back-propagation (BP) neural network were used to predict the new AFT with volumetric parameters. By comparing the prediction accuracy of these models, a reasonable model with higher prediction accuracy was selected. Results show that the new AFT is 2% to 30% less than the AFT in the standard model, and the surface area of air void boundary has a negative effect on the new AFT. The correlation analysis indicates that the new AFT of dense-graded asphalt mixtures has a rough correlation with changes in VMA, binder content and VFA. The prediction accuracy of the BP neural network has the highest R-squared. The relative importance and sensitivity analysis of the BP neural network verified a relatively good relationship between VMA and new AFT. These findings can give a considerable interest for the tendency of using the new AFT to supplement the current specified VMA criteria.

Reliability Analysis of a Two Nonidentical Unit Parallel System with Optional Vacations under Poisson Shocks

In this article, we study the impact of some system parameters on an industrial system consisting of two nonidentical parallel units with one repairer. The active unit may fail due to essential factors such as aging or deterioration, or due to external phenomena such as Poisson shocks occurring at different time periods. Whenever the value of a shock is greater than the specified threshold of the active unit, the active unit fails. The article assumes that the repairman can make one of two decisions at the beginning of system operation: either he takes a vacation when the two units are operating normally, or he remains in the system to monitor it until the first failure of the system. If a failure occurs in either unit during the repairman’s absence, the failed unit must wait until the repairman is called back to work. We assume that the value of each shock is i.i.d. with a known distribution. The length of the repairman’s vacation, the repair time, and the recall time are arbitrary distributions. Various reliability measures were calculated using the supplementary variable technique and the theory of Markov vector processes. Sensitivity and relative sensitivity analyses were also performed for the system parameters. Finally, numerical calculations and graphical analyses were performed to validate the derived indices.

Multifractal features of activity concentration and stochastic risk assessment of naturally occurring and technogenic radionuclides in the soil of Yerevan, Armenia

Spatial patterns and background ranges of naturally occurring radionuclides (NORs) (i.e. U-238, Th-232, K-40) and Cs-137 were studied in the urban soils of Yerevan, the capital city of Armenia. Multifractal Inverse Distance Weighting (MIDW) was used to generate and analyze distribution patterns of radionuclide activities. Based on Fourier transformation of radioactivity data, a spectral analysis was also applied to separate, where possible, background/baseline patterns from local anomalies: two ranges of background values were found to characterise the Yerevan territory.Specifically, in the south and south-east of Yerevan, the lower background ranges of U-238, Th-232 and K-40 comprised in the intervals 2.60–36.42 Bq/kg, 4.04–30.63 Bq/kg and 147.7–396.7 Bq/kg, respectively, were observed in association with the presence of sedimentary formations. In contrast, the higher ones were found, instead, in the central and northern parts of the city where andesite-basalt lavas and ignimbrite tuffs occur. Here, the background values rise to 142.4 Bq/kg, 138.76 Bq/kg and 1502 Bq/kg, respectively.As for the distribution of artificial Cs-137, its baseline levels in Yerevan seem to depend mostly on the global radioactive fallout and some local technogenic sources. Its distribution patterns partially differ from those of NORs.In the framework of this paper, Radium equivalent activity (RaEq), outdoor absorbed dose rate in air (ODRA) and annual effective dose equivalent (AEDEs) were also determined and mapped. They show a good coincidence of their spatial variations with those of NORs.The Monte Carlo simulation was used to assess excess lifetime cancer risk from a stochastic perspective. The related sensitivity analysis revealed that, among NORs, U-238 and Th-232 give the greatest contribution to the total variance (45.7% 42.8%, respectively). In comparison, K-40 has the lowest share (11.3%). Regarding Cs-137, a highly negligible contribution to the onset of health risks (accounting for 0.02%) was observed.

Socioeconomic analysis of infectious diseases based on different scenarios using uncertain SEIAR system dynamics with effective subsystems and ANFIS

Purpose This paper aims to analyze the socioeconomic impacts of infectious diseases based on uncertain behaviors of social and effective subsystems in the countries. The economic impacts of infectious diseases in comparison with predicted gross domestic product (GDP) in future years could be beneficial for this aim along with predicted social impacts of infectious diseases in countries. Design/methodology/approach The proposed uncertain SEIAR (susceptible, exposed, infectious, asymptomatic and removed) model evaluates the impacts of variables on different trends using scenario base analysis. This model considers different subsystems including healthcare systems, transportation, contacts and capacities of food and pharmaceutical networks for sensitivity analysis. Besides, an adaptive neuro-fuzzy inference system (ANFIS) is designed to predict the GDP of countries and determine the economic impacts of infectious diseases. These proposed models can predict the future socioeconomic trends of infectious diseases in each country based on the available information to guide the decisions of government planners and policymakers. Findings The proposed uncertain SEIAR model predicts social impacts according to uncertain parameters and different coefficients appropriate to the scenarios. It analyzes the sensitivity and the effects of various parameters. A case study is designed in this paper about COVID-19 in a country. Its results show that the effect of transportation on COVID-19 is most sensitive and the contacts have a significant effect on infection. Besides, the future annual costs of COVID-19 are evaluated in different situations. Private transportation, contact behaviors and public transportation have significant impacts on infection, especially in the determined case study, due to its circumstance. Therefore, it is necessary to consider changes in society using flexible behaviors and laws based on the latest status in facing the COVID-19 epidemic. Practical implications The proposed methods can be applied to conduct infectious diseases impacts analysis. Originality/value In this paper, a proposed uncertain SEIAR system dynamics model, related sensitivity analysis and ANFIS model are utilized to support different programs regarding policymaking and economic issues to face infectious diseases. The results could support the analysis of sensitivities, policies and economic activities. Highlights: A new system dynamics model is proposed in this paper based on an uncertain SEIAR model (Susceptible, Exposed, Infectious, Asymptomatic, and Removed) to model population behaviors; Different subsystems including healthcare systems, transportation, contacts, and capacities of food and pharmaceutical networks are defined in the proposed system dynamics model to find related sensitivities; Different scenarios are analyzed using the proposed system dynamics model to predict the effects of policies and related costs. The results guide lawmakers and governments' actions for future years; An adaptive neuro-fuzzy inference system (ANFIS) is designed to estimate the gross domestic product (GDP) in future years and analyze effects of COVID-19 based on them; A real case study is considered to evaluate the performances of the proposed models.