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In most variance-based sensitivity analysis (SA) approaches applied to biomechanical models, statistical independence of the model input is assumed. However, often the model inputs are correlated. This might alter the interpretation of the SA results, which may severely impact the guidance provided during model development and personalization. Potential reasons for the infrequent usage of SA techniques that account for input correlation are the associated high computational costs, especially for models with many parameters, and the fact that the input correlation structure is often unknown. The aim of this study was to propose an efficient correlated global sensitivity analysis method by applying a surrogate model-based approach. Furthermore, this article demonstrates how correlated SA should be interpreted and how the applied method can guide the modeler during model development and personalization, even when the correlation structure is not entirely known beforehand. The proposed methodology was applied to a typical example of a pulse wave propagation model and resulted in accurate SA results that could be obtained at a theoretically 27,000× lower computational cost compared to the correlated SA approach without employing a surrogate model. Furthermore, our results demonstrate that input correlations can significantly affect SA results, which emphasizes the need to thoroughly investigate the effect of input correlations during model development. We conclude that our proposed surrogate-based SA approach allows modelers to efficiently perform correlated SA to complex biomechanical models and allows modelers to focus on input prioritization, input fixing and model reduction, or assessing the dependency structure between parameters., (© 2023 The Authors. International Journal for Numerical Methods in Biomedical Engineering published by John Wiley & Sons Ltd.)

Background: Increasing attention is being given to assessing treatment effect heterogeneity among individuals belonging to qualitatively different latent subgroups. Inference routinely proceeds by first partitioning the individuals into subgroups, then estimating the subgroup-specific average treatment effects. However, because the subgroups are only latently associated with the observed variables, the actual individual subgroup memberships are rarely known with certainty in practice and thus have to be imputed. Ignoring the uncertainty in the imputed memberships precludes misclassification errors, potentially leading to biased results and incorrect conclusions., Methods: We propose a strategy for assessing the sensitivity of inference to classification uncertainty when using such classify-analyze approaches for subgroup effect analyses. We exploit each individual's typically nonzero predictive or posterior subgroup membership probabilities to gauge the stability of the resultant subgroup-specific average causal effects estimates over different, carefully selected subsets of the individuals. Because the membership probabilities are subject to sampling variability, we propose Monte Carlo confidence intervals that explicitly acknowledge the imprecision in the estimated subgroup memberships via perturbations using a parametric bootstrap. The proposal is widely applicable and avoids stringent causal or structural assumptions that existing bias-adjustment or bias-correction methods rely on., Results: Using two different publicly available real-world datasets, we illustrate how the proposed strategy supplements existing latent subgroup effect analyses to shed light on the potential impact of classification uncertainty on inference. First, individuals are partitioned into latent subgroups based on their medical and health history. Then within each fixed latent subgroup, the average treatment effect is assessed using an augmented inverse propensity score weighted estimator. Finally, utilizing the proposed sensitivity analysis reveals different subgroup-specific effects that are mostly insensitive to potential misclassification., Conclusions: Our proposed sensitivity analysis is straightforward to implement, provides both graphical and numerical summaries, and readily permits assessing the sensitivity of any machine learning-based causal effect estimator to classification uncertainty. We recommend making such sensitivity analyses more routine in latent subgroup effect analyses., (© 2022. The Author(s).)

Patient-specific computer simulations can be a powerful tool in clinical applications, helping in diagnostics and the development of new treatments. However, its practical use depends on the reliability of the models. The construction of cardiac simulations involves several steps with inherent uncertainties, including model parameters, the generation of personalized geometry and fibre orientation assignment, which are semi-manual processes subject to errors. Thus, it is important to quantify how these uncertainties impact model predictions. The present work performs uncertainty quantification and sensitivity analyses to assess the variability in important quantities of interest (QoI). Clinical quantities are analysed in terms of overall variability and to identify which parameters are the major contributors. The analyses are performed for simulations of the left ventricle function during the entire cardiac cycle. Uncertainties are incorporated in several model parameters, including regional wall thickness, fibre orientation, passive material parameters, active stress and the circulatory model. The results show that the QoI are very sensitive to active stress, wall thickness and fibre direction, where ejection fraction and ventricular torsion are the most impacted outputs. Thus, to improve the precision of models of cardiac mechanics, new methods should be considered to decrease uncertainties associated with geometrical reconstruction, estimation of active stress and of fibre orientation. This article is part of the theme issue 'Uncertainty quantification in cardiac and cardiovascular modelling and simulation'.

Purpose: Calculations of deoxyribonucleic acid (DNA) damages involve many parameters in the computation process. As these parameters are often subject to uncertainties, it is of central importance to comprehensively quantify their impacts on DNA single-strand break (SSB) and double-strand break (DSB) yields. This has been a challenging task due to the required large number of simulations and the relatively low computational efficiency using CPU-based MC packages. In this study, we present comprehensive evaluations on sensitivities and uncertainties of DNA SSB and DSB yields on 12 parameters using our GPU-based MC tool, gMicroMC., Methods: We sampled one electron at a time in a water sphere containing a human lymphocyte nucleus and transport the electrons and generated radicals until 2 Gy dose was accumulated in the nucleus. We computed DNA damages caused by electron energy deposition events in the physical stage and the hydroxyl radicals at the end of the chemical stage. We repeated the computations by varying 12 parameters: (a) physics cross section, (b) cutoff energy for electron transport, (c)-(e) three branching ratios of hydroxyl radicals in the de-excitation of excited water molecules, (f) temporal length of the chemical stage, (g)-(h) reaction radii for direct and indirect damages, (i) threshold energy defining the threshold damage model to generate a physics damage, (j)-(k) minimum and maximum energy values defining the linear-probability damage model to generate a physics damage, and (l) probability to generate a damage by a radical. We quantified sensitivity of SSB and DSB yields with respect to these parameters for cases with 1.0 and 4.5 keV electrons. We further estimated uncertainty of SSB and DSB yields caused by uncertainties of these parameters., Results: Using a threshold of 10% uncertainty as a criterion, threshold energy in the threshold damage model, maximum energy in the linear-probability damage model, and probability for a radical to generate a damage were found to cause large uncertainties in both SSB and DSB yields. The scaling factor of the cross section, cutoff energy, physics reaction radius, and minimum energy in the linear-probability damage model were found to generate large uncertainties in DSB yields., Conclusions: We identified parameters that can generate large uncertainties in the calculations of SSB and DSB yields. Our study could serve as a guidance to reduce uncertainties of parameters and hence uncertainties of the simulation results., (© 2020 American Association of Physicists in Medicine.)

Computational models of the heart have reached a level of maturity that enables sophisticated patient-specific simulations and hold potential for important applications in diagnosis and therapy planning. However, such clinical use puts strict demands on the reliability and accuracy of the models and requires the sensitivity of the model predictions due to errors and uncertainty in the model inputs to be quantified. The models typically contain a large number of parameters, which are difficult to measure and therefore associated with considerable uncertainty. Additionally, patient-specific geometries are usually constructed by semi-manual processing of medical images and must be assumed to be a potential source of model uncertainty. In this paper, we assess the model accuracy by considering the impact of geometrical uncertainties, which typically occur in image-based computational geometries. An approach based on 17 AHA segments diagram is used to consider uncertainties in wall thickness and also in the material properties and fiber orientation, and we perform a comprehensive uncertainty quantification and sensitivity analysis based on polynomial chaos expansions. The quantities considered include stress, strain and global deformation parameters of the left ventricle. The results indicate that important quantities of interest may be more affected by wall thickness, and highlight the need for accurate geometry reconstructions in patient-specific cardiac mechanics models.

The use of the last observation carried forward (LOCF) method for imputing missing outcome data in randomized clinical trials has been much criticized and its shortcomings are well understood. However, only recently have published studies widely started using more appropriate imputation methods. Consequently, meta-analyses often include several studies reporting their results according to LOCF. The results from such meta-analyses are potentially biased and overprecise. We develop methods for estimating summary treatment effects for continuous outcomes in the presence of both missing and LOCF-imputed outcome data. Our target is the treatment effect if complete follow-up was obtained even if some participants drop out from the protocol treatment. We extend a previously developed meta-analysis model, which accounts for the uncertainty due to missing outcome data via an informative missingness parameter. The extended model includes an extra parameter that reflects the level of prior confidence in the appropriateness of the LOCF imputation scheme. Neither parameter can be informed by the data and we resort to expert opinion and sensitivity analysis. We illustrate the methodology using two meta-analyses of pharmacological interventions for depression., (© 2018 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.)

Globalization has led to a rapid increase of container movements in seaports. Risks in seaports need to be appropriately addressed to ensure economic wealth, operational efficiency, and personnel safety. As a result, the safety performance of a Container Terminal Operational System (CTOS) plays a growing role in improving the efficiency of international trade. This paper proposes a novel method to facilitate the application of Failure Mode and Effects Analysis (FMEA) in assessing the safety performance of CTOS. The new approach is developed through incorporating a Fuzzy Rule-Based Bayesian Network (FRBN) with Evidential Reasoning (ER) in a complementary manner. The former provides a realistic and flexible method to describe input failure information for risk estimates of individual hazardous events (HEs) at the bottom level of a risk analysis hierarchy. The latter is used to aggregate HEs safety estimates collectively, allowing dynamic risk-based decision support in CTOS from a systematic perspective. The novel feature of the proposed method, compared to those in traditional port risk analysis lies in a dynamic model capable of dealing with continually changing operational conditions in ports. More importantly, a new sensitivity analysis method is developed and carried out to rank the HEs by taking into account their specific risk estimations (locally) and their Risk Influence (RI) to a port's safety system (globally). Due to its generality, the new approach can be tailored for a wide range of applications in different safety and reliability engineering and management systems, particularly when real time risk ranking is required to measure, predict, and improve the associated system safety performance., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

Today, evaluation of thermohygrometric indoor conditions is one of the most useful tools for building design and re-design and can be used to determine energy consumption in conditioned buildings. Since the beginning of the Predicted Mean Vote index (PMV), researchers have thoroughly investigated its issues in order to reach more accurate results; however, several shortcomings have yet to be solved. Among them is the uncertainty of environmental and subjective parameters linked to the standard PMV approach of ISO 7730 that classifies the thermal environment. To this end, this paper discusses the known thermal comfort models and the measurement approaches, paying particular attention to measurement uncertainties and their influence on PMV determination. Monte Carlo analysis has been applied on a data series in a "black-box" environment, and each involved parameter has been analysed in the PMV range from -0.9 to 0.9 under different Relative Humidity conditions. Furthermore, a sensitivity analysis has been performed in order to define the role of each variable. The results showed that an uncertainty propagation method could improve PMV model application, especially where it should be very accurate (-0.2 < PMV<0.2 range; winter season with Relative Humidity of 30%)., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Quantification of the uncertainty in constitutive model predictions describing arterial wall mechanics is vital towards non-invasive assessment of vascular drug therapies. Therefore, we perform uncertainty quantification to determine uncertainty in mechanical characteristics describing the vessel wall response upon loading. Furthermore, a global variance-based sensitivity analysis is performed to pinpoint measurements that are most rewarding to be measured more precisely. We used previously published carotid diameter-pressure and intima-media thickness (IMT) data (measured in triplicate), and Holzapfel-Gasser-Ogden models. A virtual data set containing 5000 diastolic and systolic diameter-pressure points, and IMT values was generated by adding measurement error to the average of the measured data. The model was fitted to single-exponential curves calculated from the data, obtaining distributions of constitutive parameters and constituent load bearing parameters. Additionally, we (1) simulated vascular drug treatment to assess the relevance of model uncertainty and (2) evaluated how increasing the number of measurement repetitions influences model uncertainty. We found substantial uncertainty in constitutive parameters. Simulating vascular drug treatment predicted a 6% point reduction in collagen load bearing ([Formula: see text]), approximately 50% of its uncertainty. Sensitivity analysis indicated that the uncertainty in [Formula: see text] was primarily caused by noise in distension and IMT measurements. Spread in [Formula: see text] could be decreased by 50% when increasing the number of measurement repetitions from 3 to 10. Model uncertainty, notably that in [Formula: see text], could conceal effects of vascular drug therapy. However, this uncertainty could be reduced by increasing the number of measurement repetitions of distension and wall thickness measurements used for model parameterisation.

Job exposure matrices (JEMs) are used to measure exposures based on information about particular jobs and tasks. JEMs are especially useful when individual exposure data cannot be obtained. Nonetheless, there may be other workplace exposures associated with the study disease that are not measured in available JEMs. When these exposures are also associated with the exposures measured in the JEM, biases due to uncontrolled confounding will be introduced. Furthermore, individual exposures differ from JEM measurements due to differences in job conditions and worker practices. Uncertainty may also be present at the assessor level since exposure information for each job may be imprecise or incomplete. Assigning individuals a fixed exposure determined by the JEM ignores these uncertainty sources. We examine the uncertainty displayed by bias analyses in a study of occupational electric shocks, occupational magnetic fields, and amyotrophic lateral sclerosis., (© 2015 Society for Risk Analysis.)

Mathematical models of chemotherapy planning problems contain various biomedical parameters, whose values are difficult to quantify and thus subject to some uncertainty. This uncertainty propagates into the therapy plans computed on these models, which poses the question of robustness to the expected therapy quality. This work introduces a combined approach for analyzing the quality robustness of plans in terms of dosing levels with respect to model uncertainties in chemotherapy planning. It uses concepts from multi-criteria decision making for studying parameters related to the balancing between the different therapy goals, and concepts from sensitivity analysis for the examination of parameters describing the underlying biomedical processes and their interplay. This approach allows for a profound assessment of a therapy plan, how stable its quality is with respect to parametric changes in the used mathematical model., (Copyright © 2014 Elsevier Inc. All rights reserved.)

Purpose: Biological models to estimate the relative biological effectiveness (RBE) or the equivalent dose in 2 Gy fractions (EQD2) are needed for treatment planning and plan evaluation in carbon ion therapy. We present a model-independent, Monte Carlo based sensitivity analysis (SA) approach to quantify the impact of different uncertainties on the biological models., Methods and Materials: The Monte Carlo based SA is used for the evaluation of variations in biological parameters. The key property of this SA is the high number of simulation runs, each with randomized input parameters, allowing for a statistical variance-based ranking of the input variations. The potential of this SA is shown in a simplified one-dimensional treatment plan optimization. Physical properties of carbon ion beams (e.g. fragmentation) are simulated using the Monte Carlo code FLUKA. To estimate biological effects of ion beams compared to X-rays, we use the Local Effect Model (LEM) in the framework of the linear-quadratic (LQ) model. Currently, only uncertainties in the output of the biological models are taken into account., Results/conclusions: The presented SA is suitable for evaluation of the impact of variations in biological parameters. Major advantages are the possibility to access and display the sensitivity of the evaluated quantity on several parameter variations at the same time. Main challenges for later use in three-dimensional treatment plan evaluation are computational time and memory usage. The presented SA can be performed with any analytical or numerical function and hence be applied to any biological model used in carbon ion therapy., (Copyright © 2014 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.)

This study presented a decision-making methodology to evaluate leadership management strategies in healthcare systems. This study used the multi-criteria decision-making (MCDM) methodology to deal with conflicting criteria in the decision-making process. The MCDM methodology is integrated with neutrosophic sets to deal with uncertainty and vague information in decision-making. The neutrosophic set has three memberships: truth, indeterminacy, and falsity. The type-2 neutrosophic set is a subset of the neutrosophic set with nine membership degrees. The neutrosophic set integrated with the COPRAS method to rank the alternatives. fourteen criteria and eleven alternatives are used to evaluate leadership management in healthcare systems. The sensitivity analysis was conducted to show the stability of the rank. The sensitivity analysis was performed with 15 cases to change the criteria weights and rank the alternatives under different weights. The results show that the rank of other options is stable in various instances. [ABSTRACT FROM AUTHOR]

Natural disasters occur frequently worldwide, causing serious harm to people, resources, and the environment. The disaster mitigation capacity is very important in reducing disaster-caused losses and in informing natural disaster prevention and mitigation plans. However, the disaster mitigation capacity frameworks in recent studies are inconsistent and lack the careful consideration of natural disaster occurrence process, and it remains unclear to what extent the model parameters may affect the robustness of assessment results. To solve this problem, with typhoons as an example, an assessment framework of typhoon disaster mitigation capacity (TDMC) based on the entire process of disaster prevention–resistance–rescue was developed in this study. The TDMC framework was applied in Hainan, China, as a case study, and the associated uncertainty was assessed by analyzing indicator selection and weighting and synthesis methods. The TDMC was very high in Haikou and Sanya but very low in Lingao, Ledong, and Dongfang. Iterative sensitivity analysis of the indicators revealed that the removal of different indicators had little effect on the percentage change in average TDMC of Hainan. The sensitive indicators were warning systems, emergency shelters, and insurance, suggesting that specific strategies for those indicators could improve regional TDMC. The assessment results with different indicator weighting and synthesis methods were generally robust, especially in counties with very low or high TDMC. However, the counties with medium TDMC showed higher sensitivity, which led to changes in TDMC indices. Assessments were more sensitive to changes in weighting method than to changes in synthesis method. This study demonstrated the importance of quantifying uncertainties in assessment model parameters to provide credible TDMC information and to further the effective design of disaster prevention and mitigation strategies. [ABSTRACT FROM AUTHOR]

With rapid urbanization growth, considerable amounts of construction waste are generated on an annual basis, posing significant economic and environmental challenges worldwide. Re-cycling construction waste is a sustainable way for waste disposal, leading to the necessity of meticulous planning of recycling centers. A well-designed plan for constructing recycling centers can effectively improve the recycling rate of construction waste while minimizing investment. This paper formulates a two-stage stochastic model for planning recycling centers with the objective of maximizing the recycling rate under different scenarios. This study comprehensively considers various uncertain factors, such as the amount of construction waste generated and the demand for recycled materials. A case study of Guangzhou is used for validation, which demonstrates the effectiveness of the developed model in planning recycling center construction. The comparison between the proposed model and a conventional mean value model shows the importance of accounting for uncertainties. Specifically, the derived results indicate that 7% more construction waste is recycled with the same investment in constructing recycling centers. Additionally, via a sensitivity analysis, valuable managerial insights on investing resources in recycling center construction are provided to decision makers. Ultimately, the research findings are expected to enhance the recycling rate of construction waste, thereby contributing to sustainable industry development. [ABSTRACT FROM AUTHOR]

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There is uncertainty in the results of any mathematical model due to different reasons. It is important to estimate this uncertainty. Sensitivity analysis is commonly used to estimate how the changes in the input parameters affect the solutions of the model. In this paper, we discuss different ways of performing local and global sensitivity analyses and apply them to two models: an epidemic model and a new myocardial infarction model, both based on ordinary differential equations. The first model is a simple model used to explain the ideas, while the second one shows how to apply them to a model with more state variables and parameters. We find that if the parameters are not accurately known, local sensitivity analysis can be misleading and that global sensitivity methods that sample the whole parameter space, varying all the values of the parameters at the same time, are the most reliable. We also show how the sensitivity analysis results can be used to determine the uncertainty in the results of the model. We present numerical simulations. [ABSTRACT FROM AUTHOR]

Real-life applications in project planning often involve grappling with inaccurate data or unexpected events, which can impact the project duration and cost. The delay in the project execution can be overcome by investing in additional resources to avoid compromising the project duration. The goal of the resource leveling problems (RLP) is to determine the optimal amount of resources to invest in, aiming to minimize the associated complementary costs and adhere to the fixed deadline. To tackle data uncertainty in the RLP, the literature has predominantly focused on developing robust and stochastic approaches. In contrast, sensitivity analysis and reactive approaches have received comparatively less attention, especially concerning the generalized RLP with flexible job durations. In this problem, the duration of each job depends on the amount of resources available for its execution. Therefore, utilizing more resources may help reduce the project duration but at an additional cost. This paper introduces a novel approach that addresses the generalized RLP with uncertain job and resource parameters, incorporating reactive and sensitivity-based methodologies. The proposed approach extends the concept of evaluation metrics from machine scheduling to the domain of the RLP with flexible job durations. It is based on a metric-based function that estimates the impact of changes in input data on the solution quality, considering both optimality and feasibility for the new problem instance. The approach is tested through numerical experiments conducted on benchmark instance sets to investigate the impact of variations in different problem parameters. The obtained results demonstrated a meaningful accuracy in estimating the impact on the value of the objective function. Additionally, they underscored the importance of utilizing optimality/feasibility preservation conditions, as for a significant portion of the tested instances, the use of these conditions gave a satisfactory outcome. [ABSTRACT FROM AUTHOR]

This study analytically develops a new recovery planning optimisation model for managing the impacts of the recent COVID-19 outbreak for online business operations. Firstly, a mathematical model for the ideal plan is designed and then extended to generate a recovery plan in a finite planning horizon that maximises total profit. Recovery plans are generated considering two scenarios, namely the dynamic and uncertain situations. For the dynamic situation, a realistic system with timedependent and dynamic demand, supply, and warehouse capacity for investigating the impacts of the COVID-19 outbreak is developed using several measures, such as collaborating with emergency suppliers, increasing warehouse capacity, and considering back-orders and lost sales to form recovery strategies. For the uncertain situation, demand, supply, and warehouse capacities are considered as uncertain variables. Further, an innovative solution approach using an adapted differential evolution technique, which is capable of (i) generating long-term recovery plans and (ii) solving both small- and large-scale problems, is developed. The results are illustrated using numerical analyses and simulation experiments. A sensitivity analysis is also conducted. In practice, the proposed optimisation model will assist the decision-makers of online business operations facing the COVID-19 outbreak to decide the optimal recovery plans. [ABSTRACT FROM AUTHOR]

Computational models are powerful tools to quantify physical phenomena to gain valuable insights into a manufacturing process. Their accuracy is hindered, however, by uncertainty in the input data. Furthermore, when calibrating models with plant measurements, it helps to understand which variables have greatest effect on the critical model outputs. This work applies uncertainty quantification and sensitivity analysis to determine the most influential input parameters in the CON1D model of heat transfer and solidification in steel continuous casting with slag. Results show that the slag rim greatly affects heat flux near the meniscus, so control of its size is important. Heat flux and temperature down the mold depend greatly on velocity of the solid slag layer, and slag solidification temperature, which control the slag layer thickness, which in turn affects the interfacial resistance that controls heat transfer in the process. Scale formation on the mold coldface greatly increases mold temperatures. Based on the results presented here, models of heat transfer in continuous casting such as CON1D would benefit from plant measurements such as slag rim size and solid slag velocity, and lab measurements such as slag viscosity at lower temperatures, to better characterize this important slag property. [ABSTRACT FROM AUTHOR]

This article discusses a robust network interdiction problem considering uncertainties in arc capacities and resource consumption. The problem involves two players: an adversary seeking to maximize the flow of a commodity through the network and an interdictor whose objective is to minimize this flow. The interdictor plays first and selects network arcs to interdict, subject to a resource constraint. The problem is formulated as a bilevel problem, and an upper bound single level mix-integer linear formulation is derived. The upper bound formulation is solved using three heuristics tailored for this problem and the network structure, based on Lagrangian relaxation and Benders' decomposition. On average, each heuristic provides a reduction in run time of at least 85% compared to a state-of-the-art solver. Enhanced Benders' decomposition achieves a solution with an optimality gap of less than 5% for all tested instances. Sensitivity analyses are conducted for the level of uncertainty in network parameters and the uncertainty budget. Robust decisions are also compared to decisions not accounting for uncertainty to evaluate the value of robustness, showing a reduction in simulation maximum flows by as much as 89.5%. [ABSTRACT FROM AUTHOR]

The strategic development of reverse logistics networks is crucial for addressing the common challenge of low recovery rates for end-of-life vehicles (ELVs) in China. To minimize the total cost of the reverse logistics network for ELVs, this paper proposes a mixed-integer linear programming (MILP) model. The model considers the recycling volume of different vehicle types, facility processing capacity, and the proportions of parts and materials. Building on this foundation, a fuzzy mixed-integer nonlinear programming (FMINLP) model is developed to account for the inherent uncertainty associated with recycling volumes and facility processing capacities. The model was solved using Lingo, and its effectiveness was validated using Jiangsu Province of China as a case study, followed by a sensitivity analysis. The results indicate that dismantling and machining centers incur the highest processing costs. Variations in recycling volume and facility handling capacity significantly impact total costs and site selection, with the former having a more pronounced effect. Increasing facility processing capacity effectively increases the recovery rate. Moreover, a higher confidence level corresponds to higher total costs and a greater demand for facilities. [ABSTRACT FROM AUTHOR]

The uncertainties of distribution generations (DGs) and loads lead to severe voltage fluctuations in active distribution networks (ADNs). Meanwhile, energy storage systems (ESSs) and static var compensators (SVCs) can mitigate the uncertainties of power injections by regulating the active and reactive power. Considering the variations of multiple uncertain factors, this paper proposes a complex affine arithmetic (CAA) based uncertain sensitivity analysis method of voltage fluctuations in ADNs. First, affine models of active and reactive power injections are established. The correlations of noisy symbols are used to reflect the mitigation effects of ESSs and SVCs on the uncertainties introduced by DGs and loads. Next, sensitivity indicators of voltage fluctuations are defined based on the transitivity of noisy symbols. Then, a calculation method for sensitivity indicators based on the micro-increments of coefficients is proposed. Combined with the obtained indicators, a fast sensitivity method for calculating interval values of voltages is further proposed. The modified IEEE 33-bus system is tested to validate the accuracy and efficiency of the proposed method by comparison with the continuous utilization of power flow method. Moreover, the 292-bus system is tested to validate its applicability in a large distribution system. Facts have proved that this method improves the efficiency and reliability of calculations, and in different scenarios, it can achieve fast calculation of nodes and online analysis of the voltage fluctuation range in uncertain environments, provides an effective tool for voltage quality management in active distribution networks. [ABSTRACT FROM AUTHOR]

Dynamic crop models are powerful tools for evaluating crop responses to alternative management options. These models consist of various input parameters that need calibration before being implemented in a new environment. Sensitivity analysis (SA) has proven to be an efficient method for identifying dominant model parameters. This study aimed to determine influential parameters of the AquaCrop model for basil and also quantify output uncertainty under different nitrogen fertilizer rates. The Sobol' SA method was employed to compute both first-order and total-order sensitivity indices. Results indicated that normalized water productivity (WP*) and crop coefficient (KcTr,x) were the most influential parameters across all treatments. Furthermore, as fertility stress increased, the interaction effects among parameters decreased. The temporal analysis of model output (biomass) revealed that parameter sensitivities were highly time-dependent. In addition, the uncertainty of predicted biomass was determined using combined violin and box plots. Results demonstrated that the optimal nitrogen fertilizer application rate extended the distribution of model output. In summary, these findings offer valuable insights for model simplification and effective decision-making. [ABSTRACT FROM AUTHOR]

Stacking is the process of overlaying inferred species potential distributions for multiple species based on outputs of bioclimatic envelope models (BEMs). The approach can be used to investigate patterns and processes of species richness. If data limitations on individual species distributions are inevitable, but how do they affect inferences of patterns and processes of species richness? We investigate the influence of different data sources on estimated species richness gradients in China. We fitted BEMs using species distributions data for 334 bird species obtained from (1) global range maps, (2) regional checklists, (3) museum records and surveys, and (4) citizen science data using presence‐only (Mahalanobis distance), presence‐background (MAXENT), and presence–absence (GAM and BRT) BEMs. Individual species predictions were stacked to generate species richness gradients. Here, we show that different data sources and BEMs can generate spatially varying gradients of species richness. The environmental predictors that best explained species distributions also differed between data sources. Models using citizen‐based data had the highest accuracy, whereas those using range data had the lowest accuracy. Potential richness patterns estimated by GAM and BRT models were robust to data uncertainty. When multiple data sets exist for the same region and taxa, we advise that explicit treatments of uncertainty, such as sensitivity analyses of the input data, should be conducted during the process of modeling. [ABSTRACT FROM AUTHOR]

Random variables introduced in modelling of seismic engineering are often the result of cognitive limitations and the unpredictability of structures, leading to uncertainties in the field. A practical method for dealing with them is to develop sensitivity analysis in the framework of data and probability statistics. Of existing non-structural components, cable tray systems are characterized by a number of uncertainties which may influence their bearing capacity drastically. In this research, the main characteristics of material, geometry, member layout along with the connection stiffness in cable tray are considered as random variables using global sensitivity analysis, with their results relative importance of these potential uncertainties on the seismic performance of cable tray. The sensitivity analysis method developed especially for cable tray under seismic excitation is constructed based on modal analysis and equivalent inertia force method combined with the Latin hypercube sampling method. The final results demonstrate the need to consider the effects of random variables in modeling assumption in seismic performance analyses of cable tray and can be further used in optimization design. [ABSTRACT FROM AUTHOR]

This paper investigates the availability of a system incorporating uncertainty in the inspection period, including multiple failure modes (FMs). We decrease or increase the inspection period depending on the occurrence or nonoccurrence of system failure during any particular inspection, i.e. if, during an inspection, a defect is detected, we reduce the inspection period for the next inspection, and if no defect is found, we increase the inspection period. Also, the proposed model is examined under a pre-defined critical repair time incorporating the Gumbel-Hougaard copula. This paper considers a repairable system having α number of FMs. Random failure times are associated with every single FM, and whenever the system collapses from the ath FM, a subsequent corrective repair is conducted, which requires a random amount of time ψ a (a = 1, 2,..., α). Then the point and long-run availability of the proposed model are derived, and a sensitivity analysis is done to examine how inspection time affects the system's availability. Additionally, the system's average long-run cost rate is analyzed, and the model is employed to determine the optimal inspection period that optimizes the system's ALRCR. Lastly, the results are verified via a numerical illustration of a power transformer. [ABSTRACT FROM AUTHOR]

Our ability to deal with external changes is determined by our collective willingness to transform and adopt new technologies. These factors are driven by people's opinion on the change itself and the proposed policies. Humans constantly update their opinion by integrating new information they hear with their values, which helps them make a judgement about that new information. Here, we create an agent-based model that explicitly incorporates the concept of values to explore possible drivers of opinion dynamics. In the model, we explore several factors and perform local and global sensitivity analysis to test their individual and interaction effects. We find that consensus formation in the model is mainly determined by factors related to (1) the amount of stochasticity in the opinion updating procedure and (2) the relative ease with which old links are removed and new links are created. Our results demonstrate how opinions and values may co-evolve. Furthermore, they may help in understanding human responses to new policies such as covid-related restrictions or calls to shift to a more plant-based diet. [ABSTRACT FROM AUTHOR]

This paper presents the outcome from a case study analysis for a Canadian farm that does site-specific fertilisation (SSF), a precision farming approach which takes into consideration the spatial variability of soils. The economic results for three years of wheat and canola production are compared to a neighbouring farm, which is practicing conventional broadcast application of fertilisers. Since no additional investments in machinery are needed, the annual variable cost is 6 CAD/acre. In the standard case, the average profit is 30 CAD/acre. The rather pronounced difference in the effects from SSF application in wheat vs. canola leads one to question whether this is a crop-related systematic outcome or instead represents something more random. Sensitivity analyses generated two main insights. First, the economics of SSF are sensitive to a modification in commodity prices -- a 50 % cut would reduce the average profit to about 9 CAD/acre. Second, another scenario calculation in which no-till is assumed to generate a 5% increase in yields suggests that the net profit would be just 7 CAD/acre. Given the existence of so many uncertainties, this paper calls for more farm-based economic analysis of SSF, one which should also include a comparison of different service providers for application maps. [ABSTRACT FROM AUTHOR]

Nitrate pollution is of considerable global concern as a threat to human health and aquatic ecosystems. Nowadays, δ15N/δ18O-NO3- combined with a Bayesian-based SIAR model are widely used to identify riverine nitrate sources. However, little is known regarding the effect of variations in pollution source isotopic composition on nitrate source contributions. Herein, we used δ15N/δ18O-NO3-, SIAR modeling, probability statistical analysis and a perturbing method to quantify the contributions and uncertainties of riverine nitrate sources in the Wen-Rui Tang River of China and to further investigate the model sensitivity of each nitrate source. The SIAR model confirmed municipal sewage (MS) as the major nitrate source (58.5-75.7%). Nitrogen fertilizer (NF, 8.6-20.9%) and soil nitrogen (SN, 7.8-20.1%) were also identified as secondary nitrate sources, while atmospheric deposition (AD,

There is uncertainty in the results of any mathematical model due to different reasons. It is important to estimate this uncertainty. Sensitivity analysis is commonly used to estimate how the changes in the input parameters affect the solutions of the model. In this paper, we discuss different ways of performing local and global sensitivity analyses and apply them to two models: an epidemic model and a new myocardial infarction model, both based on ordinary differential equations. The first model is a simple model used to explain the ideas, while the second one shows how to apply them to a model with more state variables and parameters. We find that if the parameters are not accurately known, local sensitivity analysis can be misleading and that global sensitivity methods that sample the whole parameter space, varying all the values of the parameters at the same time, are the most reliable. We also show how the sensitivity analysis results can be used to determine the uncertainty in the results of the model. We present numerical simulations.

This paper introduces innovative sensitivity indices based on Cliff’s Delta for the global sensitivity analysis of structural reliability. These indices build on the Sobol’ method, using binary outcomes (success or failure), but avoid the need to calculate failure probability Pf and the associated distributional assumptions of resistance R and load F. Cliff’s Delta, originally used for ordinal data, evaluates the dominance of resistance over load without specific assumptions. The mathematical formulations for computing Cliff’s Delta between R and F quantify structural reliability by assessing the random realizations of R > F using a double-nested-loop approach. The derived sensitivity indices, based on the squared value of Cliff’s Delta δC2, exhibit properties analogous to those in the Sobol’ sensitivity analysis, including first-order, second-order, and higher-order indices. This provides a framework for evaluating the contributions of input variables on structural reliability. The results demonstrate that the Cliff’s Delta method provides a more accurate estimate of Pf. In one case study, the Cliff’s Delta approach reduces the standard deviation of Pf estimates across various Monte Carlo run counts. This method is particularly significant for FEM applications, where repeated simulations of R or F are computationally intensive. The double-nested-loop algorithm of Cliff’s Delta maximizes the extraction of information about structural reliability from these simulations. However, the high computational demand of Cliff’s Delta is a disadvantage. Future research should optimize computational demands, especially for small values of Pf.

Product configuration involves selecting common components/modules of individualised products. This is done according to customer requirements under mass customisation. However, the strategies adopted by most existing product configurators are often extreme, with either total diversity (mass personalisation) or standardised products (a limited set of products). In order to investigate the intermediate case in this situation, a new uncertain decision model is proposed in this paper. The aim is to find the optimal product configuration using a redundancy and standardisation strategy that minimises the total costs. In this model, customer requirements are defined and assembly sequences are initially constructed. Then, a manufacturing approach of modular multi-platform assembly is employed to increase uniqueness in mass customisation. Next, the uncertain decision model for product configuration is linearised and solved by uncertainty theory using CPLEX 12.8. Finally, a sensitivity analysis is conducted to suggest optimal platform number and producing strategies and determine the final customised product specification. From a case study of the mobile phone, we found that standardisation performs better than the redundancy strategy and a flexible platform strategy effectively reduces production costs. [ABSTRACT FROM AUTHOR]

A precise evaluation of the actual situation is a significant aspect of making a correct and informed decision. Due to the bounded accuracy and elements of uncertainty in the data itself, a point estimate may be less adjusted and rough than an estimate based on fuzzy set theory. The stability of the Fuzzy AHP Arithmetic mean, Geometric mean, Extent analysis, and Lambda Max methods, widely used in practice, is verified. Three stages of verification are considered, investigating the impact of the following: (a) the scale applied; (b) methods of aggregation of the AHP matrices into the FAHP matrix; and (c) methods of combining several FAHP judgments. Slight changes in experts' estimates are programmatically simulated tens of thousands of times to track changes in ranking and deviations of results from the initial estimate. This continues the study of FAHP's stability due to the ambiguous results of such verification by the method of extent analysis. As a result of a comparative analysis of the listed evaluation methods, their specific features and advantages are identified. [ABSTRACT FROM AUTHOR]

Mathematical models help understand a system's behaviour, evaluate hypotheses, control it and use it as a virtual environment for training on the possible outcomes that arise in real systems. Nevertheless, it is not enough to have a model; it should be analysed by considering its components. The research objectives were to perform global sensitivity and uncertainty analyses on a dynamic mathematical model for the output variables: air temperature, thermal mass temperature, relative humidity, and accumulated lettuce biomass inside a greenhouse. The model was based on mass and energy balances at a non-steady state. The global sensitivity analysis of the model's parameters was realised using Standard Regression Coefficient (SRC), Extended Fourier Amplitude Sensitivity Test (EFAST), and Sobol. The first-order indices were found with the three methods, and the total effects indices were found with the EFAST and Sobol methods. The model parameters were analysed to determine their influence over the output variables. The uncertainty analysis considered the variability in the parameters, assuming a uniform distribution for each with a 20% variation from its nominal value. The Monte Carlo and Latin hypercube samplings were used with 5000 samples. The more influential parameters for air temperature were related to the physical characteristics of the greenhouse; for the thermal mass, it was found to be the soil temperature; for relative humidity and biomass, the more significant parameters were those related to the leaf area index. None of the output variables showed a normal distribution. The highest uncertainty was linked with the biomass, followed by the thermal mass temperature, air temperature, and relative humidity. • Global sensitivity analysis helps to determine the influence of the model parameters. • The measurements and focus should be placed on the more influential parameters. • The uncertainty analysis helps address the output uncertainty of the model's inputs. • There is no difference between analysing the output variables' mean or the integral. • Most parameters of a greenhouse climate model are not normally distributed. [ABSTRACT FROM AUTHOR]

An augmented methodology is developed to estimate the reliability of deep excavations along spatially variable massive rock masses using the cohesion weakening friction strengthening (CWFS) model. Sensitive parameters of the CWFS model were initially identified using Sobol's global sensitivity analysis based on their influence on the displacements and excavation damage zone around excavations. The probability of failure was estimated by performing Mont-Carlo Simulations on random finite difference models of excavations generated via MATLAB-FLAC2D coupling, considering the spatial variation of these sensitive parameters. Spatial variation was modeled by generating anisotropic random fields of sensitive CWFS parameters via the recently developed Fourier series method and updated correlations suggested by Walton (2019). The proposed methodology was demonstrated for a proposed deep nuclear waste repository to be located in Canada. Results from the developed methodology were systematically compared with those of traditional reliability (ignoring spatial variation) and deterministic methods (ignoring uncertainty). Although the developed methodology was computationally complex, it was judged to be the most realistic due to the realistic consideration of heterogeneous distributions of rock properties. Traditional methodologies underestimate/overestimate the excavation performance due to negligence of uncertainty and spatial variability. Finally, a parametric analysis was performed using developed methodology by varying the initial friction angle, scale of fluctuations (SOFs) and dilation angle. The effect of initial friction angle was observed to be more pronounced on the probability of failures as compared to SOFs and dilation angle. Similar observations were made related to the excavation damage zone (EDZ) development quantified using yield area ratio. [ABSTRACT FROM AUTHOR]

Adding the skirts around shallow foundations is a method for increasing the bearing capacity of these foundations, and skirted foundations can be considered a suitable alternative to other types, such as shallow, semi-deep, and deep foundations. In this study, the vertical bearing performance of skirted circular foundations located on clay is analyzed by the stress characteristics method, and the Martin method is employed to determine the surface roughness of the foundation with soil interface. For obtaining the skirted circular foundation's bearing capacity, superposition principle is utilized, and the depth factor is evaluated with a three-point technique. It is necessary to pay attention to the effective parameters on skirted circular foundation performance for determination of bearing capacity. Sensitivity analysis on the skirted circular foundation model is performed with various methods as input/output correlation, standard regression coefficients, perturbation method, Cotter's sensitivity indices, Morris elementary effects, Sobol's sensitivity indices, and Borgonovo sensitivity indices. The obtained results show that the order of importance of parameters is the same in all sensitivity analysis methods except the Morris method, and skirt depth is a highly effective parameter for the range of parameters considered in this study. [ABSTRACT FROM AUTHOR]

Maintaining and restoring ecological connectivity is considered a global imperative to help reverse the decline of biodiversity. To be successful, practitioners need to be guided by connectivity modeling research that is rigorous and reliable for the task at hand. However, the methods and workflows within this rapidly growing field are diverse and few have been carefully scrutinized. We propose four steps that should be consistently undertaken in connectivity modeling studies in order to improve rigour and utility: 1) describe the type of connectivity being modeled, 2) assess the uncertainty and sensitivity of model parameters, 3) validate the model outputs, ideally with independent data and 4) make non‐sensitive raw data and code openly available to enhance computational reproducibility. We reviewed the literature to determine the extent to which studies included these four steps. We focused on studies that generated novel landscape connectivity outputs using circuit theory and restricted our assessment to studies concerning terrestrial mammals. Among 181 studies meeting our search criteria, 39% communicated the type of connectivity being modeled, 18% conducted some form of sensitivity or uncertainty analysis (or both), 18% of studies attempted to validate their connectivity model outputs and only 7% used fully independent data to do so. Lastly, 13% of the studies made all raw data available, 2% provided all required code and only two studies provided both. Our findings highlight a clear need and opportunity to improve the reliability, reproducibility and utility of connectivity modeling research. We provide a checklist that researchers can consult and include with outputs. This will help practitioners make more informed decisions and ensure limited resources for connectivity conservation and restoration are allocated appropriately. [ABSTRACT FROM AUTHOR]

Uncertainty associated with the identification of geochemical anomalies linked to mineralization has been a major concern in processing geochemical survey data. In this study, a Monte Carlo-based workflow, consisting of simulation-based local singularity analysis (LSA) and distance-based generalized sensitivity analysis (DGSA), was presented to identify geochemical anomalies associated with mineralization under uncertainty and simultaneously perform sensitivity analysis of model parameters. A case study of processing 1:200,000 stream sediment geochemical samples collected from the southwestern Fujian Province of China further illustrates and validates the procedure. The obtained results suggest that (i) the procedure is useful and flexible for identifying geochemical anomalies associated with mineralization under uncertainty and studying the sensitivities of model parameters, because different types of uncertain parameters and responses can be incorporated into the procedure, (ii) the window parameters are critical for applying LSA to identify geochemical anomalies related to mineralization, and spatial uncertainty associated with the gridding procedure is also important and can not be ignored, and (iii) the averaged singularity intensity map of the study area can serve as a valid indicator of Cu mineralization, while the uncertainty map of local singularity exponents can provide crucial information for decision-making in later stages of mineral exploration. [ABSTRACT FROM AUTHOR]

Many factors contribute to the inherent uncertainty of energy consumption modeling in buildings. It is essential to perform a calibration and sensitivity analysis in order to manage these uncertainties. Despite the availability of several calibration methods, they are often deterministic and lack quantified uncertainties. Moreover, the selection of parameters in building energy modeling for calibration depends on the user's experience. Therefore, a more rigorous selection process is required. This study developed a new automated Bayesian Inference calibration platform running as an R package. A sensitivity analysis module and a Bayesian inference module determine the calibration parameters and uncertainties, respectively. The Meta-model module is developed to replace the building energy model for the Markov Chain Monte Carlo process to save computing time. The proposed platform is successfully demonstrated on a synthetic high-rise office building and a real high-rise residential building in a hot and arid climate. The relationship between the number of calibration parameters, calibration performance, and the accuracy of the Meta-model is further discussed. The developed calibration platform in this study proved to have clear advantages over the existing platforms, with the ability to reasonably estimate building energy performance in a short computing time. [ABSTRACT FROM AUTHOR]

The uncertainties of distribution generations (DGs) and loads lead to severe voltage fluctuations in active distribution networks (ADNs). Meanwhile, energy storage systems (ESSs) and static var compensators (SVCs) can mitigate the uncertainties of power injections by regulating the active and reactive power. Considering the variations of multiple uncertain factors, this paper proposes a complex affine arithmetic (CAA) based uncertain sensitivity analysis method of voltage fluctuations in ADNs. First, affine models of active and reactive power injections are established. The correlations of noisy symbols are used to reflect the mitigation effects of ESSs and SVCs on the uncertainties introduced by DGs and loads. Next, sensitivity indicators of voltage fluctuations are defined based on the transitivity of noisy symbols. Then, a calculation method for sensitivity indicators based on the micro-increments of coefficients is proposed. Combined with the obtained indicators, a fast sensitivity method for calculating interval values of voltages is further proposed. The modified IEEE 33-bus system is tested to validate the accuracy and efficiency of the proposed method by comparison with the continuous utilization of power flow method. Moreover, the 292-bus system is tested to validate its applicability in a large distribution system. Facts have proved that this method improves the efficiency and reliability of calculations, and in different scenarios, it can achieve fast calculation of nodes and online analysis of the voltage fluctuation range in uncertain environments, provides an effective tool for voltage quality management in active distribution networks.

DRAINMOD is a process-based hydrologic model used to analyze the effectiveness of various drainage systems and management strategies. In this study, a sensitivity analysis of DRAINMOD hydrologic parameters for two different field settings located at Champaign, Illinois, was performed to determine the most sensitive parameters that affect the subsurface flow and relative productivity of corn. Latin-Hypercube One-Factor-at-a-Time (LH-OAT) was used to determine the sensitivity index of 17 parameters for six objective functions for daily flow, water balance, and relative yield for the productivity of corn. The results indicated that flow and yield were highly sensitive to drainage design parameters such as drainage depth and spacing. Winter flow and the water balance were sensitive to soil thermal conductivity parameters; however, they had no impact on the relative corn yield. The significant difference in sensitivity of the two fields was observed in the hydraulic conductivity of soil layers due to varying thicknesses for different soil types. This study highlights the need for more careful calibration of these sensitive parameters to reduce equifinality and model output uncertainty and appropriate drainage design for optimizing crop productivity and drainage outflow. [ABSTRACT FROM AUTHOR]

This paper presents a new inventory model for deteriorating items, allowable shortages, and stochastic inationary conditions, considering a belief-rule-base inventory control (BRB-IC) method. This is a new insight in comparison with the previous research, which considers a belief-rule-based inventory control under nonstationary demand. The Genetic Algorithm and exact methods have been considered for minimizing the objective function. The numerical example and a sensitivity analysis provided to illustrate the theoretical results. [ABSTRACT FROM AUTHOR]

An increasing number of studies is investigating insect population trends based on time series data. However, the available data is often subject to temporal pseudoreplication. Inter‐annual variability of environmental conditions and strong fluctuations in insect abundances can impede reliable trend estimation. Temporal random effect structures in regression models have been proposed as solution for this issue, but remain controversial.We investigated trends in ground beetle abundance across 24 years using generalised linear mixed models. We fitted four models: A base model, a model featuring a random year intercept, a model featuring basic weather parameters, and a model featuring both random year intercept and weather parameters. We then performed a simple sensitivity analysis to assess the robustness of the four models with respect to influential years, also testing for possible spurious baseline and snapshot effects.The model structure had a significant impact on the overall magnitude of the estimated trends. However, we found almost no difference among the models in how the removal of single years (sensitivity analysis) relatively affected trend coefficients. The two models with a random year intercept yielded significantly larger confidence intervals and their p‐values were more sensitive during sensitivity analysis. Significant differences of the model with random year intercept and weather parameters to all other models suggest that the random year effects and specific weather effects are rather additive than interchangeable.We conclude that random year intercepts help to produce more reliable and cautious uncertainty measures for insect population trends. Moreover, they might help to identify influential years in sensitivity analyses more easily. We recommend random year intercepts in addition to any variables representing temporally variable environmental conditions, such as weather variables. [ABSTRACT FROM AUTHOR]

In sensitivity analysis, ensemble sensitivity is defined as the regression coefficients resulting from a simple linear regression of changes of a response function on initial perturbations. One of the interpretations for ensemble sensitivity considers this a simplified version of regression-based adjoint sensitivity called univariate ensemble sensitivity whose derivation involves the so-called diagonal approximation. This approximation, which replaces the analysis error covariance matrix by a diagonal matrix with the same diagonal, helps to avoid inversion of the analysis error covariance, but, at the same time causes confusion in understanding and practical application of ensemble sensitivity. However, some authors have challenged such a controversial interpretation by showing that univariate ensemble sensitivity is multivariate in nature, which raises the necessity for the foundation of ensemble sensitivity. In this study, we have tried to resolve the confusion by establishing a robust foundation for ensemble sensitivity without relying on the controversial diagonality assumption. As employed in some studies, we adopt an impact-based definition for ensemble sensitivity by taking into account probability distributions of analysis perturbations. The mathematical results show that standardized ensemble sensitivity carries in itself three important quantities at the same time: 1) standardized changes of the forecast response with one standard deviation changes of individual state variables, 2) correlations between the forecast response and individual state variables, and 3) the most sensitive analysis perturbation. The theory guarantees validity of ensemble sensitivity, demonstrates its multivariate nature, and explains why ensemble sensitivity is effective in practice. [ABSTRACT FROM AUTHOR]

Voltage sags are power quality disturbances mainly caused by faults with a highly random nature. For this reason, the severity and frequency of occurrence of voltage sags is typically analyzed by means of probabilistic methods, such as Monte Carlo simulations, where different probability distributions are selected to model the uncertain inputs (e.g., fault impedance, fault location, fault rate, etc.). The selection and parameterization of the appropriate probability functions involves many difficulties to ensure that they represent the distribution of input variables in a realistic way and so, many different approaches can be found in literature. This paper studies the sensitivity of voltage sags severity to the uncertainty in the input models in order to focus modeling effort on the input variables that are more critical and effectively have an influence on voltage sags levels, whereas non-influential parameters can be neglected or modeled by means of simplified hypothesis. The method proposed is based on Design of Experiments and Analysis of Variance (ANOVA) and is able to discriminate with a statistic confidence level whether the uncertainty in the input significantly affects voltage sags severity indices or not. The effect of uncertainty on voltage sags indices SARFI90 and SARFI70 has been evaluated in realistic scenarios for IEEE test networks of 24 and 118 buses and in the power system of Ecuador with 357 nodes. General trends have been also established that help to understand the effect of the modeling of input parameters on the estimated number of sags.

This paper presents a new receptance-based method for the robust assignment of natural frequencies. This method aims to address the deviation of the actually achieved natural frequencies for target natural frequencies when assigning natural frequencies, which usually arises due to uncertainties in passive structural modifications (i.e. mass and stiffness modifications). The mathematical expression is derived for the sensitivity of the target natural frequency to the uncertainties in modification parameters, which are subsequently employed to characterize the potential deviations of the target natural frequencies in the total differential form. The obtained deviation expression is integrated into the optimization calculation of the natural frequency assignment as an extra penalty term to boost the acquisition of highly robust modification parameters. The proposed robust assignment method, as a non-probabilistic technique, can be employed without any knowledge of the probabilistic distribution of uncertainty in modification parameters. Moreover, it uses only the measured frequency response functions (FRFs), preserving the advantage of the receptance method not needing a numerical model. Two numerical examples and a comparison with a typical eigenstructure assignment method (reported in the literature) demonstrate that the proposed method can provide highly robust modification parameter solutions. The application of the proposed method to the natural frequency assignment of a laboratory water-filled pipeline system gives experimental evidence of its effectiveness. [ABSTRACT FROM AUTHOR]

Modeling is crucial to understand the behavior of environmental systems. A deeper comprehension of a model can be aided by global sensitivity analysis (GSA). Variability ascribed to model variables could have a stochastic (i.e., lack of knowledge) or an operational (i.e., possible design values) origin. Despite the possible different nature in the variability, current GSA strategies do not distinguish the latter in their formal derivations/developments. We propose to disentangle the variability in the operational and stochastic variables while assessing the model output sensitivity with respect to the former. Two operational sensitivity indices are introduced that serve to characterize the sensitivity of a model output of interest with respect to an operational variable in terms of (a) its average (with respect to the stochastic variables) intensity and (b) its degree of fluctuation (across the set of possible realizations of the stochastic variables), respectively. We exemplify our developments considering two scenarios. Results highlight the relevance of employing an operational GSA when the focus is on the influence of operational variables on model output. Key Points: Distinct natures of model variables are recognized within a global sensitivity analysis (GSA) strategyTwo global sensitivity indices for operational variables are proposed under uncertainty in the stochastic model variablesThe novel GSA strategy could help management and risk analysis for operational variables [ABSTRACT FROM AUTHOR]