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This paper investigates the acoustic performance of a double-wall system with a porous layer and presents a global sensitivity analysis of sound attenuation. The transfer matrix method is first applied to predict sound transmission through the structure. This method offers a relatively simple and cost-effective way to model the complex acoustic interactions in these systems and provides rich high-frequency information. The method represents sound wave propagation for each layer using a transfer matrix that depends on the thickness and physical characteristics of each material, and interface matrices are introduced to consider boundary conditions between adjacent layers. The poroelastic layer is modeled using the Biot-Allard approach with nine parameters. Then a global sensitivity analysis is performed using Morris and Sobol methods to identify the parameters that have a significant impact on sound transmission loss. The Morris method is used first to eliminate the parameters that have the least impact, and the Sobol method is then employed to analyze the remaining parameters and their interactions in more detail. The study focuses on eleven parameters, including all the physical parameters of the foam, and thicknesses of plates, cavity, and foam. The results of the sensitivity analysis indicate that geometrical parameters such as the thickness of different layers have the most significant impact on the sound transmission loss response in the lower frequency range. In contrast, foam properties such as flow resistivity have more influence in the higher frequency range. Overall, this study is the first to apply sensitivity analysis methods to the problem of sound transmission through double wall systems with porous layers, providing valuable insights into the system's behavior and its design optimization. [ABSTRACT FROM AUTHOR]

Model reduction aims to simplify complex models by decreasing the number of equations, variables, or parameters while preserving key characteristics. This approach enhances accessibility, comprehensibility, and computational efficiency, enabling a more focused analysis of relevant variables. In this study, we describe the reduction process of a population model that incorporates cancer cell differentiation and its interaction with the immune system, maintaining the fundamental dynamics and evolution of the original model. This led to a substantial reduction in variables and parameters, creating a more efficient model suitable for computational simulations, mathematical analysis, and quantitative understanding of population dynamics. Additionally, we performed a global sensitivity analysis of model parameters using the Sobol and eFast methods, revealing insights into differences and similarities in results from a biological perspective. Our findings emphasize the critical importance of understanding and controlling parameters related to the reproduction and death rates of differentiated cancer cells, as small variations in these parameters can have significant effects on model outcomes. This underscores the importance of thoroughly understanding these essential biological variables and processes in cancer treatment, as they have a significant impact on model outcomes and, consequently, on the development of more effective therapies. [ABSTRACT FROM AUTHOR]

The Double Asteroid Redirection Test (DART) mission demonstrated the feasibility of altering an asteroid's orbit through kinetic impact. However, uncertainties associated with the collision and the complex dynamics of the binary asteroid system often result in rough and inefficient predictions of the system's post-impact evolution. This paper proposes the use of arbitrary polynomial chaos expansion (aPCE) to efficiently evaluate the state uncertainty of a post-impact binary asteroid system without requiring complete information on the uncertainty sources. First, a perturbed full two-body problem model is developed to assess the momentum transfer during the collision and the system's subsequent evolution. The irregular shapes of the components and the momentum enhancement from the ejecta are considered to achieve reasonable evaluations. Next, aPCE is employed to construct a surrogate model capable of efficiently quantifying uncertainties. Global sensitivity analysis is then conducted to identify the main sources of uncertainty affecting the system's evolution. Benchmarking tests show that the aPCE model produces results comparable to Monte Carlo simulations, offering a good balance between accuracy and efficiency. The data-driven nature of aPCE is further demonstrated by comparing its performance to generalized polynomial chaos expansion. Under the framework of the DART mission, the aPCE solution yields results consistent with observed data. Additionally, global sensitivity analysis reveals that the shape and density of the primary, as well as the collision target's strength and porosity, contribute most to the system uncertainty. [ABSTRACT FROM AUTHOR]

A substantial amount of wastewater is generated during the extraction of oil and gas. Wastewater that is generated as a result of oil and gas extraction activities is known as produced water (PW), and managing PW is one of the greatest challenges faced by the oil and gas industry. Effective management of PW requires an understanding of the complex impact it may have on both oil production and water treatment operations. Here, a numerical model has been applied to a real-world case study, that of Kuwait Oil Company (KOC), to examine the relationships among various input and output factors in PW supply chain management. Specifically, the numerical model is a multi-objective optimisation model for the management of PW supply chains that takes into account economic as well as environmental objectives. A global sensitivity analysis has also been performed to assess the economic and operational factors that can influence the management of PW in KOC. The findings of this study show that the operational costs, treatment costs and environmental impacts can be assessed and used to guide the management of the PW supply chain in KOC. [ABSTRACT FROM AUTHOR]

AbstractAlthough there is a plethora of methods to estimate sensitivity indices associated with individual inputs, there is much less work on interaction effects of every order, especially when it comes to make inferences about the true underlying values of the indices. To fill this gap, a method that allows one to make such inferences simultaneously from a Monte Carlo sample is given. One advantage of this method is its simplicity: it leverages the fact that Shapley effects and Sobol indices are only linear transformations of total indices, so that standard asymptotic theory suffices to get confidence intervals and to carry out statistical tests. To perform the numerical computations efficiently, Möbius inversion formulas are used, and linked to the fast Möbius transform algorithm. The method is illustrated on two dynamical systems, both with an application in life sciences: a Boolean network modeling a cellular decision-making process involving 12 inputs, and a system of ordinary differential equations modeling some population dynamics involving 10 inputs. [ABSTRACT FROM AUTHOR]

This study is threefold objective. First, a nonlinear one-dimensional steady-state simulation model for hydrogen production from proton exchange membrane water electrolyzer (PEMWE) is established. The model comprehensively addresses activation, diffusion overpotentials and ohmic potential drop, and particularly incorporating Nafion membrane properties in the presence of liquid water. The model is enhanced by analyzing the hydrogen crossover, introducing corresponding diffusion equations to evaluate Faraday efficiency. The model accurately quantifies voltage efficiency, hydrogen production rate and specific energy consumption, and it is validated with published experimental data found in the scientific literature. Second, a variance-based global sensitivity analysis (GSA) on the model, is employed as a metric to assess the impact of operating conditions and material characteristics on three objective functions: the hydrogen production rate; the voltage efficiency and the specific energy consumption. Third, to support the GSA analysis, genetic algorithm (GA) technique is performed to maximize hydrogen production rate while minimizing specific energy consumption. In the course of this novel investigation, it was discerned that the number of cells, the cross-sectional area of the electrolyzer, the current density and temperature all exerted a significant impact on the three objective functions. This combined approach GSA-GA provides a thorough exploration of parameter influences and an efficient optimization strategy for enhancing the model's performance. These findings provide valuable insights that can guide analysis of the performance and the optimization of the PEMWE system. • Detailed modeling of PEM water electrolyzer operations. • PEM water electrolyzer performance defined as H 2 rate, voltage efficiency and energy consumption. • Employed Kendall, Spearman, and Pearson correlation coefficients for the assessment of factors' influence. • Global sensitivity analysis of the performance of PEM water electrolyzer. [ABSTRACT FROM AUTHOR]

Generalized Additive Models (GAMs) are widely used in statistics. In this work, we aim to tackle the challenge of identifying the most influential variables in GAMs. To accomplish this, we introduce a variance allocation approach based on the Shapley value. We derive a closed-form expression for this importance index, which allows for its computation on high-dimensional datasets and with any dependence structure. We discuss the practical implication that when a variable's importance is negligible, it can be safely eliminated from the GAM, simplifying the model. Through our case studies, we demonstrate that Shapley values offer more informative insights than p-values in terms of ranking the importance of variables. All the code is available online in the . [ABSTRACT FROM AUTHOR]

In this paper we apply a methodology introduced in Navarro Jimenez et al. (J Chem Phys 145(24):244106, 2016) in the framework of chemical reaction networks to perform a global sensitivity analysis on simulations of a continuous-time Markov chain model motivated by epidemiology. Our goal is to quantify not only the effects of uncertain parameters such as epidemic parameters (transmission rate, mean sojourn duration in compartments), but also those of intrinsic randomness and interactions between epidemic parameters and intrinsic randomness. For that purpose, following what was proposed in Navarro Jimenez et al. (2016), we leverage three exact simulation algorithms for continuous-time Markov chains from the state of the art which we combine with common tools from variance-based sensitivity analysis as introduced in Sobol' (Math Model Comput Exp 1:407-414, 1993). Also, we discuss the impact of the choice of the simulation algorithm used for the simulations on the results of sensitivity analysis. Such a discussion is new, at least to our knowledge. In a numerical section, we implement and compare three sensitivity analyses based on simulations obtained from different exact simulation algorithms of a SARS-CoV-2 epidemic model. [ABSTRACT FROM AUTHOR]

In this paper, the Hu 2003 plasticity criterion performance is assessed against an experimental database, and a comparison is made with two variants of Hill 48 criterion for 10 different materials, mainly through the examination of the yield stress and anisotropy coefficients evolutions and the calculation of precision indices. The results show that the Hill 48-R and Hu criteria demonstrate superior performance with the latter also showing a good compromise in predicting both σ θ and R θ behavior reasonably well. Furthermore, the occasional oscillatory nature of Hu's criterion for certain materials is confirmed. Subsequently, a global sensitivity analysis using the variational approach proposed by Sobol is conducted on the formulation σ θ of the Hu criterion. The aim is to understand its occasional oscillatory behavior and identify the significant inputs in relation to this phenomenon. Through this analysis, the preponderant effects of certain parameters, particularly σ b and R 45 , on the criterion's oscillation are elucidated. This study also provides insights into the applicability range of the Hu 2003 criterion. [ABSTRACT FROM AUTHOR]

ABSTRACTA substantial amount of wastewater is generated during the extraction of oil and gas. Wastewater that is generated as a result of oil and gas extraction activities is known as produced water (PW), and managing PW is one of the greatest challenges faced by the oil and gas industry. Effective management of PW requires an understanding of the complex impact it may have on both oil production and water treatment operations. Here, a numerical model has been applied to a real-world case study, that of Kuwait Oil Company (KOC), to examine the relationships among various input and output factors in PW supply chain management. Specifically, the numerical model is a multi-objective optimisation model for the management of PW supply chains that takes into account economic as well as environmental objectives. A global sensitivity analysis has also been performed to assess the economic and operational factors that can influence the management of PW in KOC. The findings of this study show that the operational costs, treatment costs and environmental impacts can be assessed and used to guide the management of the PW supply chain in KOC.

This study focuses on formulating and analysing a COVID-19 transmission dynamics model using integer and fractional-order derivatives in the Caputo sense. The model considers two doses of vaccination, confinement, and treatment with limited resources. The control reproduction number is computed and the asymptotic stability analysis of the disease-free equilibrium is proved. We also prove the existence of at least one endemic equilibrium whenever $ \mathcal{R}_c>1 $. Using real data from Germany, we calibrate our models by performing parameter estimations. We find that the control reproduction number is approximately equal to 1.90, which implies that the disease remains endemic in Germany. We also perform global sensitivity analysis by computing partial rank correlation coefficients (PRCC) between $ \mathcal{R}_c $ (respectively compartments of infected individuals) and each model parameter. By fixing vaccine coverage at 70%, we observe that it might be more effective to increase the vaccine efficacy than increasing the numbers of vaccinated people. After that, we formulate the corresponding fractional model in the Caputo sense, proving positivity, boundedness, existence, and uniqueness of solutions. We calculate the control reproduction number of the fractional model, and prove the asymptotic stability of the DFE, and existence of at least one endemic equilibrium point. We find from numerical simulations, that for a long-term forecasting, it seems better to use a fractional derivative in the range $ \varphi\in (0,0.87] $ than using just ordinary derivatives. Indeed, for this range of the fractional-order parameter, daily detected cases are closer to those the classical model predicts. [ABSTRACT FROM AUTHOR]

Objective Sensitivity analysis is an crucial tool in groundwater modelling for measuring the importance of various model inputs, enabling better allocation of limited funds and resources to reduce predictive uncertainty. Methods In this paper, we propose an enhanced hierarchical global sensitivity analysis method to quantify contribution of different types of input uncertainty to model outputs, and to assess the impact of each uncertain process on groundwater model predictions. To test and demonstrate the new method, a hypothetical case study of groundwater flow and contaminant transport is used to validate. Results The results indicate that model uncertainty is the main source of prediction uncertainty in this case, and uncertainty from the geological model is more important than that of other models. Conclusion The proposed method offers a more comprehensive sensitivity analysis for groundwater models. Compared with traditional parameter sensitivity analysis, the new method can consider more uncertain input factors, significantly improve computational efficiency, and provide more useful sensitivity information for model users and managers.

Makalah ini menyajikan analisis kepekaan global (global sensitivity analysis, GSA) dengan skrining faktor untuk pengujian secara efisien terhadap model algoritma relai jarak konvensional ketika digunakan sebagai perangkat proteksi saluran transmisi dengan kompensator seri. Sejumlah parameter ketidakpastian (faktor) sistem dapat memengaruhi kinerja fungsional model algoritma pengukuran impedansi gangguan dari perangkat elektronik cerdas (intelligent electronic devices), yaitu relai jarak tipe SEL-421. Pengujian kepekaan global dimaksudkan untuk menentukan tingkat kekuatan pengaruh dari individu maupun interaksi antarfaktor terhadap keluaran algoritma pengukuran impedansi gangguan. GSA melalui analisis varians menggunakan kuasi-Monte Carlo dimaksudkan untuk melakukan komputasi terhadap kesalahan hasil pengukuran impedansi gangguan. Metode Morris sebagai langkah awal digunakan sebagai penyaringan terhadap faktor yang tidak terlalu dominan memengaruhi kinerja relai, sehingga mengurangi beban komputasi pada GSA. Sejumlah simulasi gangguan pada saluran transmisi dengan kompensator seri dan sejumlah faktor dimodelkan dengan DIgSILENT PowerFactory. Simulasi gangguan secara otomatis sebelum dan sesudah kompensator dikembangkan menggunakan DIgSILENT. Kepekaan keluaran algoritma relai diuji untuk setiap simulasi berdasarkan sinyal tegangan dan arus gangguan yang terbaca dan juga nilai dari sampel faktor pada ruang faktor melalui kedua metode, yaitu Morris dan Sobol. Untuk sejumlah vektor sampel, varians dari model keluaran algoritma yang dipengaruhi oleh sejumlah faktor dihitung melalui perangkat lunak SIMLAB. Resistansi kegagalan adalah faktor yang dominan memengaruhi kinerja algoritma untuk gangguan sebelum dan sesudah kompensator. Indeks kepekaan relai terhadap resistansi kegagalan sangat dominan, yaitu lebih dari 0,9 dan 0,7, masing-masing untuk gangguan sebelum dan sesudah kompensator. Teknik ini berhasil digunakan dalam pengujian algoritma relai jarak SEL-421.

Abstract Behavioral models have garnered significant interest in the realm of high-frequency electronics. Their primary function is to substitute costly computational tools, notably electromagnetic (EM) analysis, for repetitive evaluations of the structure under consideration. These evaluations are often necessary for tasks like parameter tuning, statistical analysis, or multi-criterial design. However, constructing reliable surrogate models faces several challenges, including the nonlinearity of circuit characteristics and the vast size of the parameter space, encompassing both dimensionality and design variable ranges. Additionally, ensuring the validity of the model across broad geometry/material parameter and frequency ranges is crucial for its utility in design. The purpose of this paper is to introduce an innovative approach to cost-effective and dependable behavioral modeling of microwave passives. Central to our method is a fast global sensitivity analysis (FGSA) procedure, which is devised to identify correlations between design parameters and quantify their impacts on circuit characteristics. The most significant directions identified through FGSA are utilized to establish a reduced-dimensionality domain. Within this domain, the model may be constructed using a limited amount of data samples while capturing a significant portion of the circuit response variability, rendering it suitable for design purposes. The outstanding predictive capability of the proposed model, its superiority over traditional techniques, and its readiness for design applications are demonstrated through the analysis of three microstrip circuits of diverse characteristics.

Abstract Background Many European countries experienced outbreaks of mpox in 2022, and there was an mpox outbreak in 2023 in the Democratic Republic of Congo. There were many apparent differences between these outbreaks and previous outbreaks of mpox; the recent outbreaks were observed in men who have sex with men after sexual encounters at common events, whereas earlier outbreaks were observed in a wider population with no identifiable link to sexual contacts. These apparent differences meant that data from previous outbreaks could not reliably be used to parametrise infectious disease models during the 2022 and 2023 mpox outbreaks, and modelling efforts were hampered by uncertainty around key transmission and immunity parameters. Methods We developed a stochastic, discrete-time metapopulation model for mpox that allowed for sexual and non-sexual transmission and the implementation of non-pharmaceutical interventions, specifically contact tracing and pre- and post-exposure vaccinations. We calibrated the model to case data from Berlin and used Sobol sensitivity analysis to identify parameters that mpox transmission is especially sensitive to. We also briefly analysed the sensitivity of the effectiveness of non-pharmaceutical interventions to various efficacy parameters. Results We found that variance in the transmission probabilities due to both sexual and non-sexual transmission had a large effect on mpox transmission in the model, as did the level of immunity to mpox conferred by a previous smallpox vaccination. Furthermore, variance in the number of pre-exposure vaccinations offered was the dominant contributor to variance in mpox dynamics in men who have sex with men. If pre-exposure vaccinations were not available, both the accuracy and timeliness of contact tracing had a large impact on mpox transmission in the model. Conclusions Our results are valuable for guiding epidemiological studies for parameter ascertainment and identifying key factors for success of non-pharmaceutical interventions.

Abstract We propose a compartmental model for investigating smoking dynamics in an Italian region (Tuscany). Calibrating the model on local data from 1993 to 2019, we estimate the probabilities of starting and quitting smoking and the probability of smoking relapse. Then, we forecast the evolution of smoking prevalence until 2043 and assess the impact on mortality in terms of attributable deaths. We introduce elements of novelty with respect to previous studies in this field, including a formal definition of the equations governing the model dynamics and a flexible modelling of smoking probabilities based on cubic regression splines. We estimate model parameters by defining a two-step procedure and quantify the sampling variability via a parametric bootstrap. We propose the implementation of cross-validation on a rolling basis and variance-based Global Sensitivity Analysis to check the robustness of the results and support our findings. Our results suggest a decrease in smoking prevalence among males and stability among females, over the next two decades. We estimate that, in 2023, 18% of deaths among males and 8% among females are due to smoking. We test the use of the model in assessing the impact on smoking prevalence and mortality of different tobacco control policies, including the tobacco-free generation ban recently introduced in New Zealand.

In this paper, we propose a new tritrophic food chain model. We address the conditions for the coexistence of two different zooplankton species and one phytoplankton species. The results of the numerical and analytical studies of the model show that the most relevant parameters influencing the plankton ecosystem, to maintain a stable coexistence equilibrium, are the: carrying capacity and the constant intrinsic growth rate of the phytoplankton population, and the conversion rate with a mortality rate of carnivorous zooplankton. We prove the existence and direction of Hopf bifurcation in non delay system. To account for the time delay in the conversion of herbivorous consumption to carnivorous zooplankton, we incorporate a discrete delay into the consume response function. Furthermore, we establish some adequate conditions to prove the occurrence of Hopf bifurcation induced by delay in the delayed model. We also plot two-parameter bifurcation diagrams. The numerical results support the analytical findings. [ABSTRACT FROM AUTHOR]

The assessment of casualty risks associated with re-entry necessitates a comprehensive analysis of trajectories and the examination of pertinent safety-related quantities such as the ground impact area and ground reaching velocity. In practical scenarios, the presence of uncertainty in input conditions introduces variability in safety-related quantities. Consequently, employing stochastic re-entry trajectory analysis becomes crucial in overcoming the limitations of conventional deterministic analyses. Conducting sensitivity assessments during the break-up phase is imperative to gain more insights into how to manage the variability of safety-related measures. Therefore, this paper conducted a surrogate-based global sensitivity analysis and employed explainability machine learning techniques to unveil the complexities of the relationship between input uncertainty conditions and three key measures: ground-reaching velocity, falling range, and falling time, with the object of interest being the Apollo-type capsule. A three-step polynomial chaos expansion-based strategy was devised to efficiently approximate the discontinuous relationships. The results show that the relationship is characterized by severe discontinuity that separates two modes: low- and high-ground reaching velocity caused by the presence of two distinct trim points, with precautionary measures that should be taken to prevent the occurrence of the latter. From this set of procedures, three key input conditions that significantly affect the safety-related measures were identified, namely, the altitude, pitch rate, and path angle of the capsule during the breakup. Subsequently, explainability techniques were utilized to give suggestions on how to control the input variability and avoid the high ground reaching velocity mode, aiming to achieve more dependable predictions for the three safety parameters mentioned earlier. • Performed surrogate-based sensitivity analysis for stochastic re-entry trajectory. • Utilized explainability machine learning techniques to decipher relationships. • Identified key input conditions influencing safety measures for Apollo-type capsules. • Contributed insights to minimize hazardous re-entry modes and mitigate risks. • Uncovered complex discontinuity in response surface with explainable surrogate models. [ABSTRACT FROM AUTHOR]

Designing underwater vehicles considering uncertainties in mechanical properties of composites is computationally expensive. In this paper, inexpensive-to-evaluate sparse polynomial chaos expansion (PCE) based on small data is employed to alleviate computational burden arising in uncertainty analysis. Experiments and finite element analysis for buckling are performed. Relative contribution of mechanical properties to critical buckling pressure is quantified. Distribution function histogram and risk of structral failure are obtained by performing Monte Carlo simulation (MCS) on inexpensive-to-evaluate sparse PCE. Mean of critical buckling pressure is 8.27 MPa, with coefficient of variation 8.59%. 95% confidence interval is 6.86 MPa–9.65 MPa. [ABSTRACT FROM AUTHOR]

In engineering design, global sensitivity analysis (GSA) is used for analyzing the effects of inputs on the system response and is commonly studied with analytical or surrogate models. However, such models fail to capture nonlinear behaviors in complex systems and involve several modeling assumptions. Besides model-focused methods, a data-driven GSA approach, rooted in interpretable machine learning, would also identify the relationships between system components. Moreover, a special need in engineering design extends beyond performing GSA for input variables individually, but instead evaluating the contributions of variable groups on the system response. In this article, we introduce a flexible, interpretable artificial neural network model to uncover individual as well as grouped global sensitivity indices for understanding complex physical interactions in engineering design problems. The proposed model allows the investigation of the main effects and second-order effects in GSA according to functional analysis of variance (FANOVA) decomposition. To draw a higher-level understanding, we further use the subset decomposition method to analyze the significance of the groups of input variables. Using the design of a programmable material system (PMS) as an example, we demonstrate the use of our approach for examining the impact of material, architecture, and stimulus variables as well as their interactions. This information lays the foundation for managing design space complexity, summarizing the relationships between system components, and deriving design guidelines for PMS development. [ABSTRACT FROM AUTHOR]

Overlay (OVL) is one significant performance indicator for the lithography process control in semiconductor manufacturing. The accuracy of the OVL metrology is extremely critical for guarantee the lithography quality. Currently, diffraction-based overlay (DBO) is one of the mainstream OVL metrology techniques. Unfortunately, the accuracy of the DBO metrology is largely affected by the defect features of the OVL target. Therefore, there is a strong need to investigate the impacts of these target defects on the DBO metrology performance. However, efficiently investigating the statistical and interactive impacts of various DBO target defects remains challenging. This study aims to address this issue through proposing an intelligent sensitivity analysis approach. A cumulative distribution based global sensitivity analysis (GSA) method is utilized to assess the nonlinear influences of multiple defects in the OVL target on the DBO inaccuracy. The scenarios with both known and unknown distributions of the OVL target defects are considered. For the former, a neural network driven forward model is constructed for fast calculating the optical diffraction responses to accelerate the GSA process. For the latter, another neural network based inverse model are built for efficiently estimating the distribution of the target defects. Finally, a series of simulation experiments are conduct for typical DBO targets with multiple common defect features. The results demonstrate the effectiveness and robustness of the proposed approach as well as give valuable insights into the DBO defect analysis. Our study provides a strong tool to assist the practitioners in achieving intelligent and efficient DBO analysis and thus in enhancing OVL metrology performance. [ABSTRACT FROM AUTHOR]

Symmetrical global sensitivity analysis (SGSA) can aid practitioners in reducing the model complexity by identifying symmetries within the model. In this paper, we propose a nested symmetrical Latin hypercube design (NSLHD) for implementing SGSA in a sequential manner. By combining the strengths of the nested Latin hypercube design and symmetrical design, the proposed design allows for the implementation of SGSA without the need to pre-determine the sample size of the experiment. We develop a random sampling procedure and an efficient sequential optimization algorithm to construct flexible NSLHDs in terms of runs and factors. Sampling properties of the constructed designs are studied. Numerical examples are given to demonstrate the effectiveness of the NSLHD for designing sequential sensitivity analysis. [ABSTRACT FROM AUTHOR]

Understanding the dynamics and evolution of climate change and associated uncertainties is key for designing robust policy actions. Computer models are key tools in this scientific effort, which have now reached a high level of sophistication and complexity. Model auditing is needed in order to better understand their results, and to deal with the fact that such models are increasingly opaque with respect to their inner workings. Current techniques such as Global Sensitivity Analysis (GSA) are limited to dealing either with multivariate outputs, stochastic ones, or finite‐change inputs. This limits their applicability to time‐varying variables such as future pathways of greenhouse gases. To provide additional semantics in the analysis of a model ensemble, we provide an extension of GSA methodologies tackling the case of stochastic functional outputs with finite change inputs. To deal with finite change inputs and functional outputs, we propose an extension of currently available GSA methodologies while we deal with the stochastic part by introducing a novel, domain‐selective inferential technique for sensitivity indices. Our method is explored via a simulation study that shows its robustness and efficacy in detecting sensitivity patterns. We apply it to real‐world data, where its capabilities can provide to practitioners and policymakers additional information about the time dynamics of sensitivity patterns, as well as information about robustness. [ABSTRACT FROM AUTHOR]

Groundwater flow and transport models are essential tools for assessing and quantifying the migration of organic contaminants at polluted sites. Uncertainties in the hydrodynamic and transport parameters of the aquifer have a significant effect on model predictions. Uncertainties can be quantified with advanced sensitivity methods such as Sobol's High Dimensional Model Reduction (HDMR) and Variogram Analysis of Response Surfaces (VARS). Here we present the application of VARS and HDMR to assess the global sensitivities of the outputs of a transient groundwater flow model of the Gállego alluvial aquifer which is located downstream of the Sardas landfill in Huesca (Spain). The aquifer is subject to the tidal effects caused by the daily oscillations of the water level in the Sabiñánigo reservoir. Global sensitivities are analyzed for hydraulic heads, aquifer/reservoir fluxes, groundwater Darcy velocity, and hydraulic head calibration metrics. Input parameters include aquifer hydraulic conductivities and specific storage, aquitard vertical hydraulic conductivities, and boundary inflows and conductances. VARS, HDMR, and graphical methods agree to identify the most influential parameters, which for most of the outputs are the hydraulic conductivities of the zones closest to the landfill, the vertical hydraulic conductivity of the most permeable zones of the aquitard, and the boundary inflow coming from the landfill. The sensitivity of heads and aquifer/reservoir fluxes with respect to specific storage change with time. The aquifer/reservoir flux when the reservoir level is high shows interactions between specific storage and aquitard conductivity. VARS and HDMR parameter rankings are similar for the most influential parameters. However, there are discrepancies for the less relevant parameters. The efficiency of VARS was demonstrated by achieving stable results with a relatively small number of simulations. [ABSTRACT FROM AUTHOR]

Dynamic models generating time‐dependent model predictions are typically associated with high‐dimensional input spaces and high‐dimensional output spaces, in particular if time is discretized. It is computationally prohibitive to apply traditional global sensitivity analysis (SA) separately on each time output, as is common in the literature on multivariate SA. As an alternative, we propose a novel method for efficient global SA of dynamic models with high‐dimensional inputs by combining a new polynomial chaos expansion (PCE)‐driven partial least squares (PLS) algorithm with the analysis of variance. PLS is used to simultaneously reduce the dimensionality of the input and output variables spaces, by identifying the input and output latent variables that account for most of their joint variability. PCE is incorporated into the PLS algorithm to capture the non‐linear behavior of the physical system. We derive the sensitivity indices associated with each output latent variable, based on which we propose generalized sensitivity indices that synthesize the influence of each input on the variance of entire output time series. All sensitivities can be computed analytically by post‐processing the coefficients of the PLS‐PCE representation. Hence, the computational cost of global SA for dynamic models essentially reduces to the cost for estimating these coefficients. We numerically compare the proposed method with existing methods by several dynamic models with high‐dimensional inputs. The results show that the PLS‐PCE method can obtain accurate sensitivity indices at low computational cost, even for models with strong interaction among the inputs. [ABSTRACT FROM AUTHOR]

Inversion of relevant parameters of cutting analysis model through experimental data is an effective method to improve the prediction accuracy of the model. In view of the present situation that the prediction accuracy of the unequal division shear zone model needs to be improved and there are a large number of uncertain parameters that need to be inversed, a method of sensitivity analysis and classification of the uncertain parameters of the model is proposed to reduce the number of parameters that need to be inversed, and the model is improved. Firstly, the Sobol method is used to analyze the global sensitivity of 10 uncertain parameters such as the shear zone thickness h, Taylor–Quinney coefficient μ and JC parameters of the unequal division shear zone model. According to the analysis results, they are divided into sensitive parameters and insensitive parameters. Then, the sensitive parameters are inversed by genetic algorithm using experimental data. Finally, the sensitive parameters are substituted into the unequal division shear zone model to improve the prediction accuracy of the model. When the workpiece material is Al 6061-T6 and 42CrMo4 steel, the uncertain parameters that need to be inversed determined by this method are reduced from 10 to 4 and 3, respectively. The verification results show that the prediction accuracy of the main cutting force and feed force of the improved model for orthogonal cutting Al 6061-T6 workpiece and 42CrMo4 steel workpiece is much higher than that of the original model. [ABSTRACT FROM AUTHOR]

This study aims to thoroughly investigate the dynamics of a predator–prey model with a Beddington-De Angelis functional response. We assume that the prey refuge is proportional to both species. We establish the standard properties of boundedness, permanence, and local stability. We show that under certain parameter conditions, transcritical bifurcation and Hopf bifurcation occur. To understand the nature of the limit cycle, we determine the direction of the Hopf bifurcation. We focus on the significant ranges of the predators' prey capturing rate and examine how the level of prey fear and the predator's mutual interference affect the system's stability. Through numerical analysis, we study the behavior of the Lyapunov exponent and observe multiple self-repeating shrimp-shaped patterns that indicate periodic attractors in discrete-time predator–prey system. These structures appear across a broad region associated with chaotic dynamics. Additionally, if the intensity of white noise is kept below a specific threshold, the deterministic control approach is equally effective in environmental fluctuation. Numerical simulations support these findings. [ABSTRACT FROM AUTHOR]

Hepatitis B virus (HBV) infection is a deadly liver disease. The main aim of this work is to explore the role of cytoplasmic recycling of rcDNA-containing capsids in the hepatitis B virus (HBV) infection. To this purpose, considering the recycling of capsids, a novel mathematical model is proposed in order to understand the dynamics of this viral infection in a better way. Through a rigorous comparison with the experimental data obtained from two chimpanzees, the proposed model exhibits a robust alignment with the dynamics of infection. The effects of three parameters (recycling rate, virus production rate, and volume fraction of newly produced capsids) are examined, revealing an interesting observation: the inclusion of recycling reverses the influence of both virus production rate and the volume fraction of newly produced capsids in infection. A comprehensive global sensitivity analysis is conducted to identify the most positively as well as negatively sensitive parameters for each model compartment. In conclusion, this study underscores that the accumulation of rcDNA-containing capsids within the infected hepatocyte is a key factor contributing to the exacerbation of the disease. In addition, another major finding of our study is that due to recycling of capsids, the number of released viruses increases in spite of low virus production rate. In other words, the recycling of capsids acts as a positive feedback loop in the viral infection. [ABSTRACT FROM AUTHOR]

Reusable glass bottles are experiencing a resurgence, driven notably by societal concerns and regulations. While single-use glass bottles generally have higher environmental footprint compared to plastic bottles, reusable systems could reduce both impacts related to single-use (e.g., climate change, energy consumption) and plastics (e.g., microplastic pollution). The environmental benefits of reusable bottles can vary across systems and this can be overlooked by stakeholders who rely on generic results for communication and a limited number of parameters to design their systems. This study addresses this gap by developing a systematic analysis of the variability of life cycle assessment results, within the specific case study of a new beverage. As a result, a list of key parameters to consider for the specific case study is set, enabling to propose targeted mitigation strategies. The commonly used generic key parameters are complemented with context-specific key parameters, empowering stakeholders to develop efficient systems and communicate their environmental performance accurately. Different configurations are likely to be influenced by other key parameters, and require specific mitigation strategies. In this perspective, stakeholders need assistance in: (1) designing context-specific strategies, and (2) translating – complex and plural – life cycle assessment results into actionable decisions.

Variance-based analysis has emerged as method of choice for quantifying the sensitivity of the output, y, of a scalar-valued square-integrable function, f ∈ L2((Formula presented.)), to its d ≥ 1 input variables, x = {x1, …, xd}, with support (Formula presented.). The prototype of this approach, Sobol's method is a generalization of the analysis of variance (ANOVA) to d > 2 independent input variables and decomposes y, as sum of elementary functions of zeroth-, first-, second-, up to dth-order. This independence assumption is mathematically convenient but may not be borne out of the causal or correlational relationships between the x's. This paper is concerned with variance-based sensitivity analysis (SA) for correlated input variables, for example, multivariate dependencies in a posterior parameter distribution. We use high-dimensional model representation (HDMR) of Li et al. (2010, https://doi.org/10.1021/jp9096919), Li and Rabitz (2012, https://doi.org/10.1007/s10910-011-9898-0) and replace Sobol's elementary functions with so-called component functions with unknown expansion coefficients to disentangle the structural, correlative and total contribution of input factors. We contrast the default HDMR methodology with cubic B-splines and sequential coefficient estimation against its successor, HDMRext of Li and Rabitz (2012, https://doi.org/10.1007/s10910-011-9898-0), which uses polynomial component functions with an extended orthonormalized basis. Benchmark experiments confirm that HDMR and HDMRext parse out the structural and correlative contributions of input factors to the model output and infer an optimal experimental design with parameter correlation. Our last study applies HDMRext to probabilistic SA of a watershed model. The multivariate posterior parameter distribution supports model emulation and yields sensitivity indices that pertain to measured discharge data.

Abstract Ionic liquids (ILs) are highly effective for capturing carbon dioxide (CO2). The prediction of CO2 solubility in ILs is crucial for optimizing CO2 capture processes. This study investigates the use of deep learning models for CO2 solubility prediction in ILs with a comprehensive dataset of 10,116 CO2 solubility data in 164 kinds of ILs under different temperature and pressure conditions. Deep neural network models, including Artificial Neural Network (ANN) and Long Short-Term Memory (LSTM), were developed to predict CO2 solubility in ILs. The ANN and LSTM models demonstrated robust test accuracy in predicting CO2 solubility, with coefficient of determination (R2) values of 0.986 and 0.985, respectively. Both model's computational efficiency and cost were investigated, and the ANN model achieved reliable accuracy with a significantly lower computational time (approximately 30 times faster) than the LSTM model. A global sensitivity analysis (GSA) was performed to assess the influence of process parameters and associated functional groups on CO2 solubility. The sensitivity analysis results provided insights into the relative importance of input attributes on output variables (CO2 solubility) in ILs. The findings highlight the significant potential of deep learning models for streamlining the screening process of ILs for CO2 capture applications.

Abstract This research introduces a novel global sensitivity analysis (GSA) framework for agent-based models (ABMs) that explicitly handles their distinctive features, such as multi-level structure and temporal dynamics. The framework uses Grassmannian diffusion maps to reduce output data dimensionality and sparse polynomial chaos expansion (PCE) to compute sensitivity indices for stochastic input parameters. To demonstrate the versatility of the proposed GSA method, we applied it to a non-linear system dynamics model and epidemiological and economic ABMs, depicting different dynamics. Unlike traditional GSA approaches, the proposed method enables a more general estimation of parametric sensitivities spanning from the micro level (individual agents) to the macro level (entire population). The new framework encourages the use of manifold-based techniques in uncertainty quantification, enhances understanding of complex spatio-temporal processes, and equips ABM practitioners with robust tools for detailed model analysis. This empowers them to make more informed decisions when developing, fine-tuning, and verifying models, thereby advancing the field and improving routine practice for GSA in ABMs.

In response to the COVID-19 pandemic, many higher educational institutions moved their courses on-line in hopes of slowing disease spread. The advent of multiple highly-effective vaccines offers the promise of a return to "normal" in-person operations, but it is not clear if-or for how long-campuses should employ non-pharmaceutical interventions such as requiring masks or capping the size of in-person courses. In this study, we develop and fine-tune a model of COVID-19 spread to UC Merced's student and faculty population. We perform a global sensitivity analysis to consider how both pharmaceutical and non-pharmaceutical interventions impact disease spread. Our work reveals that vaccines alone may not be sufficient to eradicate disease dynamics and that significant contact with an infectious surrounding community will maintain infections on-campus. Our work provides a foundation for higher-education planning allowing campuses to balance the benefits of in-person instruction with the ability to quarantine/isolate infectious individuals.

This research introduces a novel global sensitivity analysis (GSA) framework for agent-based models (ABMs) that explicitly handles their distinctive features, such as multi-level structure and temporal dynamics. The framework uses Grassmannian diffusion maps to reduce output data dimensionality and sparse polynomial chaos expansion (PCE) to compute sensitivity indices for stochastic input parameters. To demonstrate the versatility of the proposed GSA method, we applied it to a non-linear system dynamics model and epidemiological and economic ABMs, depicting different dynamics. Unlike traditional GSA approaches, the proposed method enables a more general estimation of parametric sensitivities spanning from the micro level (individual agents) to the macro level (entire population). The new framework encourages the use of manifold-based techniques in uncertainty quantification, enhances understanding of complex spatio-temporal processes, and equips ABM practitioners with robust tools for detailed model analysis. This empowers them to make more informed decisions when developing, fine-tuning, and verifying models, thereby advancing the field and improving routine practice for GSA in ABMs. [ABSTRACT FROM AUTHOR]

Aero‐disk is a key component of aero‐engine. Due to its complex working conditions, aero‐disk is prone to structural failures. Therefore, it is essential to analyze the reliability and importance input variables of aero‐disk. In this paper, a framework of aero‐disk reliability analysis and global sensitivity analysis (GSA) was established. First, the hazardous regions of aero‐disk were determined by finite element analysis, and the limit state function of aero‐disk was defined by the life interference model. Then D‐Vine model was utilized to establish the correlation model for aero‐disk hazardous regions to evaluate the reliability of aero‐disk. In addition, two GSA methods based on Copula and space partition were proposed to identify important input random variables considering underlying correlation, and the accuracy of the proposed method was verified by two examples. Finally, the proposed method was applied to the GSA of aero‐disk. The results show that the established framework fills in the gap of uncertainty analysis of aero‐disk, which can be extended to other engineering fields. The proposed GSA methods have both high efficiency and accuracy and can realize multi‐dimensional GSA when the correlation of input variables is considered. [ABSTRACT FROM AUTHOR]

This study compares adjacent and bivariate maps in communicating variance-based global sensitivity analysis (GSA) results for a geodiversity assessment spatial multi-criteria model and examines the influence of prior exposure to geodiversity and map reading skills on interpretation. It analyzes the quality of map interpretation, confidence levels, and map communication effectiveness. The findings indicate that there is no significant difference in the quality of map interpretation or confidence levels between the two map types. However, there are nuanced differences in interpretive patterns, suggesting the need for further investigation into factors affecting map interpretation. Adjacent maps are more effective in identifying factors linked to uncertainty in high geodiversity values, while bivariate maps excel in understanding spatial variability. Prior exposure to geodiversity and map reading skills do not significantly impact interpretation quality or confidence levels. Future research could explore other factors influencing map effectiveness and explore the cognitive processes underlying map interpretation. Understanding these processes could lead to more effective strategies for communicating the results of a GSA for spatial models through maps. [ABSTRACT FROM AUTHOR]

The design of stud connectors is aided by determining the relationship between shear strength and the input variables (number, diameter, height, tensile strength and elastic modulus of the studs, and compressive strength and elastic modulus of the concrete) that influence strength. Since strength is nonlinearly related to the influencing variables, which makes the predictions of the relevant empirical equations unreliable, the use of machine learning (ML) models is preferred. The prediction results of eight machine learning models were evaluated, including linear regression (LR1), ridge regression (RR), lasso regression (LR2), back-propagation artificial neural network (BP ANN), genetic algorithm optimized BP ANN (GA-BP ANN), extreme learning machines (ELM), random forests (RF), and support vector machines (SVM). The results show that the GA-BP ANN model is the most accurate model for prediction with a mean absolute percentage error (MAPE) of 6.17% and an R² of 0.9599. Based on the GA-BP ANN model and the global sensitivity analysis (GSA) method, a new parameter importance analysis method was developed to compare the magnitude of the effect of different input variables on strength. It was found that stud diameter had the greatest effect on shear strength. [ABSTRACT FROM AUTHOR]

Typhoid fever has become a public health concern, especially in developing countries where sanitation and personal hygiene are not taken seriously, coupled with the non-availability of safe drinking water. Despite the antibiotic treatment, about 2–5% of recovered humans still harbour the typhoid bacteria in their bodies and shed it via their faeces in the population, making it difficult to eradicate the disease. Thus, the effect of limited clinical efficacy of the antibiotics with corresponding relapse response to treatment on infected humans is examined in this paper by formulating a deterministic mathematical model for direct and indirect transmission mode of Typhoid infection. The basic reproduction number is analytically derived and used to implement the global sensitivity analysis of the model's parameters that employed Latin hypercube Sampling (LHS) with Partial Rank Correlation Coefficient (PRCC). Regarding the sensitivity analysis result, the optimal control and cost-effectiveness analysis are analysed and simulated numerically with four controls, the water, sanitation and hygiene (WASH) practice and awareness campaign control, the sterilisation and disinfection control, the potency of antibiotics control and the screening control. The optimal control analysis applied Pontrygin's maximum principle to the optimal control problem. The limited efficacy of antibiotics with corresponding relapse response to treatment is shown to influence the spread of typhoid infection in the population. Furthermore, the cost-effectiveness analysis employed Infected Averted Ratio (IAR), Average Cost-effectiveness Ratio (ACER) and Increment Cost-effectiveness Ratio (ICER) techniques to four cases (I–IV) that compared fifteen strategies. The results indicate that the WASH practice and awareness campaign (Strategy 1) is good to implement for single control implementation, while for double control implementation, the WASH practice and awareness campaign and the potency of antibiotics administered to typhoid patients (Strategy 6) is the best to consider. Combining Strategy 6 and screening control is the most cost-effective for triple controls. Furthermore, the overall computation of cost-effectiveness among all the most cost-effective with all the controls combined suggests that Strategy 1 is the most cost-effective strategy to implement for eradicating typhoid infection in the population. [ABSTRACT FROM AUTHOR]

Seismic consequences estimation for individual buildings is valuable for various stakeholders, including government entities, building owners, and insurers. The robustness of estimation results in the presence of incomplete input information can typically be investigated through sensitivity analysis. However, the estimation process's complexity and the sensitivity analysis's computational burden hinder its practical application, which requires a more efficient procedure to facilitate broader use. This paper proposes a novel framework for sensitivity analysis of seismic consequences estimation to improve the efficiency and reliability of such analysis. The proposed approach encompasses three key components: (1) stochastic ground motion modeling (SGMM)‐based seismic consequences estimation to evaluate the economic, environmental, and social consequences given specific buildings by considering different hazard levels, (2) the training of surrogate model (Gaussian process model) for structural analysis to reduce the computational cost of the evaluation process, and (3) variance‐based global sensitivity analysis to investigate the importance of parameters of concern in the estimation process. The entire procedure is implemented in Python, adhering to object‐oriented programming, and does not rely on external software. Then, the proposed methodology is applied to two distinct three‐story steel moment‐resistant frames (SMRFs) subjected to four different hazard levels to demonstrate its effectiveness. The SGMM method can generate specific ground motions for each hazard level, mitigating the potential for result bias from using ground motions with unrealistic characteristics. Furthermore, the SGMM method is particularly suitable for automated analysis processes reducing the laborious task of screening ground motions from the database. Comparative analysis with surrogate‐free estimation reveals that the surrogate‐based analysis delivers reliable results with significantly reduced computational cost. The results of analyzing different structures under varying hazard levels reflect the variability in sensitivity analysis of consequences estimation, highlighting the necessity of the proposed flexible and efficient framework. Furthermore, the proposed framework's advantages, limitations, and future research needs are discussed. [ABSTRACT FROM AUTHOR]

The increasing demand for sustainable air mobility has led to the development of innovative aircraft designs, necessitating a balance between environmental responsibility and profitability. However, despite technological advancements, there is still limited understanding of the maintenance implications for hydrogen systems in aviation. The aim of this study is to estimate the maintenance costs of replacing the hydrogen storage system in an aircraft as part of its life cycle costs. To achieve this, we compared conventional and hydrogen-powered aircraft. As there is insufficient data for new aircraft concepts, typical probabilistic methods are not applicable. However, by combining global sensitivity analysis with Dempster–Shafer Theory of Evidence and discrete event simulation, it is possible to identify key uncertainties that impact maintenance costs and economic efficiency. This innovative framework offers an early estimate of maintenance costs under uncertainty, enhancing understanding and assisting in decision-making when integrating hydrogen storage systems and new aviation technologies. • Examining hydrogen storage exchange necessity in an aircraft's life cycle. • Pioneering use of evidence theory and global sensitivity analysis amid data scarcity. • Identifying maintenance impact on hydrogen aircraft life cycle costs. • Uncertainties in maintenance events have a high economic impact. [ABSTRACT FROM AUTHOR]

An integrated model that considers multiphysics is necessary to accurately analyze the time-dependent response of hydraulic structures on soft foundations. This study develops an integrated superstructure–foundation–backfills model and investigates the time-dependent displacement and stress of a lock head project on a soft foundation during the construction period. Finite element analyses are conducted, incorporating a transient thermal creep model for concrete and an elasto-plastic consolidation model for the soil. The modified Cam-clay model is employed to describe the elasto-plastic behavior of the soil. Subsequently, global sensitivity analyses are conducted to determine the relative importance of the model parameters on the system's response, using Garson's and partial derivative algorithms based on the backpropagation (BP) neural network. The results indicate that the integrated system exhibits pronounced time-dependent displacement and stress, with dangerous values appearing during specific periods. These values are easily neglected, highlighting the importance of integrated time-dependent analysis. Construction activities, particularly the backfilling process, could cause a sudden change in stress and significantly impact the stress redistribution of the superstructure. Additionally, the mechanical properties of concrete have a significant impact on the stress on the superstructure, while the mechanical properties of the soil control the settlement of the integrated system. [ABSTRACT FROM AUTHOR]

Most previous studies in the field of sensor placement have focused on only one aim. In this study, pressure sensor placement is done for calibration and leakage detection simultaneously to use pressure data optimally to save money and time. The sensor placement method implemented in this paper consists of two main parts: clustering the nodes of a water distribution network (WDN) and determining the representative node of each cluster as the sensors' location. A new multiview clustering approach is implemented to cluster nodes of a WDN based on two pressure sensitivity matrices. In fact, two different aspects of nodes' characteristics are used for clustering. The representative node from each cluster is also chosen based on global sensitivity and the number of detection criteria. The Sobol method is used for global sensitivity analysis, and the number of detections is calculated with the local sensitivity matrices. The performance of sensor placement is evaluated individually and collectively for different goals in the Anytown network. The accuracy of network calibration with the sample design proposed in this study is equal to 0.0707 m, which is the lowest value between previous studies. Leakage detection also has a significant performance ratio than random pressure sampling. Furthermore, this new method of sensor placement is evaluated in the more extensive networks of the C-town and the Modena, which have high complexity. The performance of the presented pressure sampling is significantly different from the random pressure sampling, and the pressure data collected at the selected nodes can be used for the calibration procedure efficiently. [ABSTRACT FROM AUTHOR]