Variance-based Sensitivity Analysis Research Articles

Sensitivity analysis of the INRA 2018 feeding system for ruminants by hybrid local and global approaches: Comparing the contribution of dietary input variables to multiple response prediction in dairy cattle

We conducted sensitivity analysis (SA) of the INRA 2018 feeding system for ruminants applied to dairy cows. We evaluated which dietary input variables contribute most to changes in each output variable, considering the potential interactions presence among input variables. A quantitative analysis (one-at-a-time analysis, OAT; i.e., local SA) and a relative comparative analysis (global SA, GSA) through variance-based SA considering potential interactions and non-monotonicity were applied. The 5 likely influential dietary input variables were selected: CP, Gross energy (GE), OM apparent digestibility (OMd), effective degradability of nitrogen assuming a passage rate of 6%/h (ED6_N) and true intestinal digestibility of nitrogen. The sensitivity of 5 selected animal responses (output variables) to input variables was analyzed: DMI, milk protein yield (MPY), energy in methane (ECH4), nitrogen utilization efficiency (NUE), and ratio between urine and total N excretion (UN/TN). Six diets for dairy cattle, reflecting the diversity of diets commonly used in practice, were formulated to meet 95% of the potential milk production (37.5 kg/d) of a multiparous dairy cow at wk 14 of lactation. For each diet, the 5 input variables were randomly sampled around the INRA 2018 feed table values (reference point), and the animal responses around this reference situation were calculated using the rationing software INRAtion®V5. In OAT, the sensitivity of animal responses was quantified by calculating the normalized tangent value at the reference point, and in GSA, the Sobol indices were calculated for relative influence of each input and their interaction. The influence of the 5 key input variables on the 5 main animal responses predicted from the INRA feeding system was consistent across both SA approaches. With the 6 diets, GE and OMd appeared as the main contributors to changes in DMI, MPY, ECH4, and NUE. Crude protein was the main contributor to changes in UN/TN and another major contributor to changes in NUE. When considering OAT, the sensitivity of outputs showed differences depending on diet, more particularly for DMI and MPY. With grass hay-based diets (GH diets), DMI was less sensitive and MPY was more sensitive to variations in input variables than other diets. When considering GSA, interactions between input variables were also noticeable for DMI and MPY; the interactions were high with the GH diets for DMI, and with fresh ryegrass and grass silage diets for MPY. On the other hand, for MPY, the non-GH diets were less sensitive to variations in input variables and the interaction between inputs was higher than with GH diets. In both cases, the interactions were mainly related to energy-related inputs (i.e., OMd and GE). Our results support the hypothesis that MPY, unlike DMI, is more responsive to energy-related factors at high PDI/UFL ratio (i.e., between metabolizable protein and NEL, e.g., GH diets >117 g PDI/UFL), than at lower PDI/UFL ratio. Hence, hybridizing the SA methods can help to interpret the system and facilitate a more precise evaluation thereof, especially GSA, which is amenable to non-monotonic models such as those characterizing complex feeding systems integrating multiple nutritional and animal factors.

Autoinjector optimization through cavitation response and severity minimization

Abrupt acceleration of the syringe of an autoinjector (AI) upon rod-plunger impact may induce undesired severe cavitation events and impose extraneous stresses upon the device, leading to device failure. Cavitation results from a rapid and significant pressure drop in a liquid, leading to the formation and growth of small vapor-filled cavities. Upon collapse, these cavities generate an intense shock wave that may lead to protein aggregation and device container damage and shatter. Since the maximum acceleration of the syringe depends upon the operating conditions of the AI, the severity of cavitation will likewise depend on the operating conditions of the AI. Likewise, injection time and ensuring proper needle displacement before drug release also depend on operating conditions, making optimization of the autoinjector a multiobjective optimization problem.Therefore, in this study, optimization of an autoinjector to limit cavitation severity is pursued via an experimentally validated computational model for cavitation in spring-driven autoinjectors. Our goal is to locate AI design configurations that balance maximizing device performance and patient comfort and minimizing the risks of device damage and severe cavitation upon actuation.Relevant parameters of interest are the drive spring force, air gap height, solution viscosity, friction between the rod and spring, frictional force on the plunger, rates of change of frictional force on the plunger, elasticity of plunger, viscosity of the plunger, and initial displacement between the plunger and the driving rod. The kinematics of the syringe barrel, needle displacement (travel distance) at the start of drug delivery, and injection time are gathered using an experimentally validated autoinjector kinematics model. At the same time, cavitation bubble dynamics are resolved using an experimentally validated cavitation model that takes the temporal displacement of the syringe and temporal air gap pressure as inputs.We use our experimentally validated models to explore the parameter space and understand the driving factors of our desired outcomes. Subsequently, we pose the design problem as a multi-objective optimization problem and develop a deep neural network surrogate model supplemented with iterative learning to speed up optimization. A variance-based sensitivity analysis was performed to determine the sensitivity and influence of design parameters on the outcomes, and the main contributors to the outcomes of interest were isolated. Using a multi-objective optimization framework, we located 300 + successful candidates and evaluated them through uncertainty analysis to identify three promising candidates that meet all criteria for drug viscosities of interest. Finally, we show that this methodology can be used to conduct hypothesis testing, leading to novel design configurations.

An Experimental and Numerical Study on the Lateral Stiffness Limits of Straddle-Type Monorail Tour-Transit Systems

The development of the straddle-type monorail tour-transit system (MTTS) has received keen attention. Regarding the unspecified regulations on the lateral stiffness limit of steel substructures, which is essential for the design of MTTSs, this work presents a comprehensive assessment of the issue. Firstly, a wind–vehicle–bridge coupling model was established, integrating multibody dynamics and finite element methods. This model was then validated against field test results, considering measured track irregularities and simulated wind loadings as the excitations. Afterwards, a trend analysis and a variance-based sensitivity analysis was performed to investigate the effect of various factors on the dynamic response of the MTTS. Results indicate that the pier height significantly impacts the lateral displacement of the pier top, accounting for 87% of the first-order sensitivity index and 75% of the total sensitivity index. In comparison, the lateral stiffness of track beams contributes over 70% to the maximum responses at the mid-span. Based on this, two responses, the lateral displacement of the pier top and the lateral deflection–span ratio of the track beam, were utilized as evaluation indicators. With the analysis of indicators in terms of lateral acceleration, Sperling index, and lateral wheel force, the limited values for the two indicators were determined as 8.04 mm and L/4200, respectively. These findings can serve as valuable references for future research and designs in this field.

Accounting for the mechanical response of the cell membrane during the uptake of random nanoparticles

In order to improve the efficiency of the delivery of cancer treatments to cancer cells, the cellular uptake of nanoparticles (NPs), used as drug delivery systems, is numerically investigated through a mechanical approach. The objective is to optimize the NP's mechanical and geometrical properties to enhance their entry into cancer cells while avoiding benign ones. In previous studies, these properties are modeled as constant during the process of cellular uptake. However, recent observations of the displacement of the membrane's constituents towards the region in the cell membrane where the uptake of the NPs takes place show that the mechanical properties of the membrane vary during this process. Reason for writing The important contribution of adhesion to the wrapping process is already well documented in literature. It is therefore crucial to model this parameter properly as the conclusions made with a constant adhesion model may not be accurate compared to reality. Methodology Based on the existing knowledge on the reaction of membrane constituents to interaction with NPs, a 3-parameter sigmoidal function, accounting for the delay, amplitude, and speed of the reaction, has been used to model the evolution of adhesion. A variance-based sensitivity analysis has then been performed in order to quantify the influence of these parameters on the outputs of the model. Results It was found that the introduction of a variable adhesion tends to alter the predictions of endocytosis of NPs. The contribution of the amplitude and delay is respectively 0.32 and 0.43 times as important as that of the NP's aspect ratio, which is the prominent parameter. The influence of the slope of the transition is the least important parameter and does not appear to contribute to endocytosis. Implications Hence, models of the cellular uptake of NPs should use a variable, instead of constant, adhesion in order a representative as possible of the behavior of the cell membrane. The predictions are different from those obtained using a model with constant adhesion.

An artificial neural network-based predictive model for tensile behavior estimation under uncertainty for fused deposition modeling

PurposeThis paper aims to present a predictive model approach to estimate the tensile behavior of polylactic acid (PLA) under uncertainty using the fused deposition modeling (FDM) and American Society for Testing and Materials (ASTM) D638’s Types I and II test standards.Design/methodology/approachThe prediction approach combines artificial neural network (ANN) and finite element analysis (FEA), Monte Carlo simulation (MCS) and experimental testing for estimating tensile behavior for FDM considering uncertainties of input parameters. FEA with variance-based sensitivity analysis is used to quantify the impacts of uncertain variables, resulting in determining the significant variables for use in the ANN model. ANN surrogates FEA models of ASTM D638’s Types I and II standards to assess their prediction capabilities using MCS. The developed model is applied for testing the tensile behavior of PLA given probabilistic variables of geometry and material properties.FindingsThe results demonstrate that Type I is more appropriate than Type II for predicting tensile behavior under uncertainty. With a training accuracy of 98% and proven presence of overfitting, the tensile behavior can be successfully modeled using predictive methods that consider the probabilistic nature of input parameters. The proposed approach is generic and can be used for other testing standards, input parameters, materials and response variables.Originality/valueUsing the proposed predictive approach, to the best of the authors’ knowledge, the tensile behavior of PLA is predicted for the first time considering uncertainties of input parameters. Also, incorporating global sensitivity analysis for determining the most contributing parameters influencing the tensile behavior has not yet been studied for FDM. The use of only significant variables for FEA, ANN and MCS minimizes the computational effort, allowing to simulate more runs with reduced number of variables within acceptable time.

A computational study on the influence of antegrade accessory pathway location on the 12-lead electrocardiogram in Wolff-Parkinson-White syndrome.

Wolff-Parkinson-White syndrome is a cardiovascular disease characterized by abnormal atrio-ventricular conduction facilitated by accessory pathways (APs). Invasive catheter ablation of the AP represents the primary treatment modality. Accurate localization of APs is crucial for successful ablation outcomes, but current diagnostic algorithms based on the 12 lead electrocardiogram (ECG) often struggle with precise determination of AP locations. In order to gain insight into the mechanisms underlying localization failures observed in current diagnostic algorithms, we employ a virtual cardiac model to elucidate the relationship between AP location and ECG morphology. We first introduce a cardiac model of electrophysiology that was specifically tailored to represent antegrade APs in the form of a short atrio-ventricular bypass tract. Locations of antegrade APs were then automatically swept across both ventricles in the virtual model to generate a synthetic ECG database consisting of 9271 signals. Regional grouping of antegrade APs revealed overarching morphological patterns originating from diverse cardiac regions. We then applied variance-based sensitivity analysis relying on polynomial chaos expansion on the ECG database to mathematically quantify how variation in AP location and timing relates to morphological variation in the 12 lead ECG. We utilized our mechanistic virtual model to showcase limitations of AP localization using standard ECG-based algorithms and provide mechanistic explanations through exemplary simulations. Our findings highlight the potential of virtual models of cardiac electrophysiology not only to deepen our understanding of the underlying mechanisms of Wolff-Parkinson-White syndrome but also to potentially enhance the diagnostic accuracy of ECG-based algorithms and facilitate personalized treatment planning.

Sensitivity and Optimization Analysis of Torsional Behavior in Multicellular Thin-Walled Tubes

Multicellular thin-walled tubes are widely used due to their lightweight, economical design, and superior shear and torsional performance. Their design is sometimes governed by the available materials and the required dimensions. The current study uses advanced sensitivity analysis with meta-modeling tools to understand how different geometric and mechanical factors affect the torsional performance of multicellular thin-walled tubes. The geometric factors include the length, thickness, and width of the beams, while the mechanical properties involve the shear modulus. Variance-based sensitivity analysis is used to assess how variations in these factors impact the rate of twist, torsional stiffness, and shear stress. The interconnected relations between input parameters are exploited for optimal design and superior performance. The results revealed that for a three-celled tube, thick horizontal interior elements with thin deep vertical elements and thin exterior elements provide an optimal design when the cross-sectional area is constrained. This finding, combined with varying the geometrical and material properties, results in an optimal design using CFRP composites when constrained by minimizing the total weight and superior torsional performance. The analysis can be extended to include other constraint(s), but changing the design constraints might change the optimal design. Doi: 10.28991/CEJ-2024-010-09-09 Full Text: PDF

Variance-based global sensitivity analysis of the performance of a proton exchange membrane water electrolyzer

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.

Numerical analysis of tapered wick configurations in cylindrical heat pipes

Heat pipes are essential devices in thermal engineering due to their ability to transport heat with remarkable efficiency. They are crucial for heat distribution and control in a variety of applications, such as aircraft systems and electronics cooling. This paper presents a numerical analysis on the performance characteristics of a cylindrical heat pipe featuring a novel tapered wick geometry. A two-dimensional numerical model incorporating non-Darcian liquid transport is formulated and solved at steady state. To investigate the effect of the tapered wick geometry, major characteristics such as axial distribution of vapor pressure, liquid pressure and wall temperature are evaluated and analyzed. Based on the direction of taper, three configurations viz., THPE (Tapering wick heat pipe from evaporator end), THPC (Tapering wick heat pipe from condenser end) and THPA (Tapering wick heat pipe at adiabatic center) are studied. A novel non-dimensional parameter termed as Temperature Pressure Index (TPI) is proposed to signify the extent to which an HP can reduce its evaporator temperature relative to increase in pressure drop. Initially a parametric study is performed. Results showed a superior performance of THPE and THPC designs over THPA in terms of output parameters. The numerical findings demonstrate that when compared to a uniform wick heat pipe, both the THPE and THPC configurations exhibit a notable enhancement of 58 % in effective thermal conductivity under a maximum load of 455 W. Further, by conducting a variance-based (Sobol) sensitivity analysis, the THPC design is found to be marginally better as compared to THPE. Finally, the BOBYQA optimization algorithm is employed to find optimum taper angle for THPC design that maximizes the output parameters. The optimum taper angle of the wick is found to be 0.0372° for minimum average evaporator temperature in the present case. For this optimized THPC design, a reduction in average evaporator temperature of 6.51 K is obtained compared to uniform wick heat pipe. Additionally, the obtained TPI value is about 1.86 times than that of a uniform wick heat pipe.

Building retrofit optimization considering future climate and decision-making under various mindsets

Building retrofit is effective in reducing building energy use and improving comfort levels for existing buildings. However, conducting multi-objective optimization for individual buildings can be challenging due to the laborious computational cost of using white box models and the difficulty in visualizing and understanding the decision-making process. Additionally, the impact of climate change has not been fully considered for the post-retrofit lifecycle. This research proposes a pragmatic automated scheme that integrates a feature selection method based on marginal abatement cost analysis and variance-based sensitivity analysis, multi-objective optimization supported by non-dominated sorting differential evolution (NSDE) algorithm, tailor-made decision-making support under different mindsets, and tree-based retrospection scheme of decision-making pathways. The simulation engine used in this study is a low-order white box modeling tool developed by the research team. The proposed scheme was applied to two educational buildings with different thermal characteristics, and the results showed that a certain number of sampling sizes were needed to achieve reliable feature selection results. The hierarchical clustering based decision-making support scheme has demonstrated robustness in visualizing and supporting decision-making for Pareto front. Two retrofit mindsets - aggressive and balanced - were assumed in the decision-making process, and the proposed method produced distinct final solutions accoridng to the two mindsets. This framework can support informed decision-making, helping stakeholders implement sustainable practices and transition to a low-carbon built environment.

Variance-based sensitivity analysis for weighting estimators results in more informative bounds

Abstract Weighting methods are popular tools for estimating causal effects, and assessing their robustness under unobserved confounding is important in practice. Current approaches to sensitivity analyses rely on bounding a worst-case error from omitting a confounder. In the following paper, we introduce a new sensitivity model called the variance-based sensitivity model, which instead bounds the distributional differences that arise in the weights from omitting a confounder. The variance-based sensitivity model can be parameterized by an R 2 parameter that is both standardized and bounded. We demonstrate, both empirically and theoretically, that the variance-based sensitivity model provides improvements on the stability of the sensitivity analysis procedure over existing methods. We show that by moving away from worst-case bounds, we are able to obtain more interpretable and informative bounds. We illustrate our proposed approach on a study examining blood mercury levels using the National Health and Nutrition Examination Survey (NHANES).

Modeling green recycled aggregate concrete using machine learning and variance-based sensitivity analysis

Recycled aggregate concrete (RAC) is commonly used to lessen the environmental effect of concrete building and demolition waste. The compressive strength of the RAC is one of the most critical factors influencing concrete quality. The compressive strength is assessed by a compression test, which takes a large number of materials and is expensive and time-consuming. With the development of novel concrete mixes and applications, academics are obliged to seek accurate models for forecasting mechanical strength. A significant source of difficulty in compressive strength modeling is that there are many mixture components and testing conditions whose variation significantly influences the predicted values. To this end, this study explores the mixture design of sustainable concrete in order to generate eco-friendly concrete mixes. Tests are conducted on 18 different mixtures comprising different proportions of waste tires, plastic, cement, and red brick to experiment with new green RAC mixtures. For the modeling part, the deep residual neural networks (DRNNs) method is first presented to the problem, aided by a database from the literature for a pretraining task. The proposed DRNNs structure uses shortcuts (i.e., residual connections) that bypass some layers in the deep network structure to alleviate the problem of training with high accuracy. The performance of the proposed DRNNs is evaluated using different goodness of fit measures and compared with well-known machine learning tools. The findings showed that the suggested model could provide credible predictions about the desired mechanical parameter, saving the required lab efforts by 40 %. Finally, a variance-based global sensitivity analysis is performed with the Latin hypercube simulation method to help rank/prioritize each mixture component's impact on determining the compressive strength in practice while mitigating the potential misrepresentation of results due to the correlations between the input parameters. The analysis showed that cement and waste contents are the most significant ones in their first and total order effects.

A compartmental model for smoking dynamics in Italy: a pipeline for inference, validation, and forecasting under hypothetical scenarios

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.

Deflection and stress characteristics of single lap joint (adhesively bonded) theoretical analysis and experimental verification

The adhesively bonded single-lap joint strength is computed numerically and verified with the experiment. An ABAQUS model is prepared to analyze by adding the primary information, i.e., geometry, material properties, element, and solution type, including the boundary conditions. The model accuracy has been verified through two-step comparisons with published numerical deflection data and in-house experiments. The validated model is used to compute the energy-absorbing capacity better to understand lap joint strength. Further, the statistical analysis (variance-based sensitivity analysis) is conducted to measure the model output variability with the model input parameter. Additionally, the influences of geometry and property-dependent design parameters (layup schemes, loading position, adherend thickness ratio (L/t), and adhesive thickness ratio: (a/h), including the overlapping length (25, 30, 35, and 40 mm) are examined through several examples. The conclusions on the overlap length in bonded joints are that an increase in intact/lap length improves the joint stiffness and decreases the deflection. Similarly, a few insights on layer sequence (angle-ply) and shear stress thickness ratio are discussed in detail.

Parametric Sensitivity Analysis of Stability Margins of Holos-Quad Microreactor

An analysis of the stability margins of the innovative Holos-Quad microreactor design is presented. This high-temeprature gas-cooled reactor (HTGR) system is designed to operate fully autonomously with passive safety mechanisms. Therefore, the inherent stability of the reactor is of great importance. Using a point-reactor model, which couples point kinetics to thermal-hydraulic heat balance equations and includes reactivity feedback effects of the fuel and moderator temperatures, the closed-loop transfer function of the reactor is derived. Applying the approach of linear systems and control theory, both the gain and phase margins of the Holos-Quad design are obtained. The analysis demonstrates that the design is stable, with an infinite gain margin and a finite phase margin. A parametric uncertainty quantification study is also performed using a total Monte Carlo approach. The stability of the reactor for different power levels, such as during reactor startup or load-following transients, is also explored. Finally, two sensitivity analysis methods are applied, namely, multiple regression (deriving standardized regression coefficients) and variance-based sensitivity analysis (known as the Sobol method), to study the contribution of each of the parameters to the stability margins’ uncertainty. This analysis improves our understanding of the role of each of the parameters in the stability of the reactor.

Emergence of pathological beta oscillation and its uncertainty quantification in a time-delayed feedback Parkinsonian model

It has been experimentally found that transmission delays play an important role in the emergence of excessively synchronized beta oscillations (13–30 Hz) related to the onset of Parkinson’s symptoms. In order to clarify the dynamical mechanism underlying beta oscillations, we generalize a conventional resonance model based on the subthalamic nucleus (STN)-external segment of globus pallidus (GPe)-cortex circuit to a feedback Parkinsonian model by incorporating the transmission delay within the basal ganglia (STN-GPe circuit) and the transmission delay from the STN to the cortex. By combining the theory of center manifolds and normal forms with numerical simulations, it is revealed how the pathological beta oscillations occur as the transmission delays increase beyond critical values. Furthermore, by regarding the transmission delays and the connection weights as random variables, variance-based sensitivity analysis is performed based on polynomial chaos expansion. It is identified that the transmission delays and connection strength in the long STN-cortex circuit are more significant than those in the STN-GPe circuit for beta oscillations. Our results also suggest that the severity of the Parkinsonian symptoms could be alleviated if a clinical therapy, such as deep brain stimulation, can enlarge the transmission delays in the STN-GPe and STN-cortex circuits simultaneously.

Effectiveness of Adjacent and Bivariate Maps in Communicating Global Sensitivity Analysis for Geodiversity Assessment

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.

Investigating the effects of spatial scales on social vulnerability index: A hybrid uncertainty and sensitivity analysis approach combined with remote sensing land cover data.

Investigating the effects of spatial scales on the uncertainty and sensitivity analysis of the social vulnerability index (SoVI) model output is critical, especially for spatial scales finer than the census block group or census block. This study applied the intelligent dasymetric mapping approach to spatially disaggregate the census tract scale SoVI model into a 300-m grids resolution SoVI map in Davidson County, Nashville. Then, uncertainty analysis and variance-based global sensitivity analysis were conducted on two scales of SoVI models: (a) census tract scale; (b) 300-m grids scale. Uncertainty analysis results indicate that the SoVI model has better confidence in identifying places with a higher socially vulnerable status, no matter the spatial scales in which the SoVI is constructed. However, the spatial scale of SoVI does affect the sensitivity analysis results. The sensitivity analysis suggests that for census tract scale SoVI, the indicator transformation and weighting scheme are the two major uncertainty contributors in the SoVI index modeling stages. While for finer spatial scales like the 300-m grid's resolution, the weighting scheme becomes the uttermost dominant uncertainty contributor, absorbing uncertainty contributions from indicator transformation.

Global sensitivity analysis with limited data via sparsity-promoting D-MORPH regression: Application to char combustion

In uncertainty quantification, variance-based global sensitivity analysis quantitatively determines the effect of each input random variable on the output by partitioning the total output variance into contributions from each input. However, computing conditional expectations can be prohibitively costly when working with expensive-to-evaluate models. Surrogate models can accelerate this, yet their accuracy depends on the quality and quantity of training data, which is expensive to generate (experimentally or computationally) for complex engineering systems. Thus, methods that work with limited data are desirable. We propose a diffeomorphic modulation under observable response preserving homotopy (D-MORPH) regression to train a polynomial dimensional decomposition surrogate of the output that minimizes the number of training data. The new method first computes a sparse Lasso solution and uses it to define the cost function. A subsequent D-MORPH regression minimizes the difference between the D-MORPH and Lasso solution. The resulting D-MORPH based surrogate is more robust to input variations and more accurate with limited training data. We illustrate the accuracy and computational efficiency of the new surrogate for global sensitivity analysis using mathematical functions and an expensive-to-simulate model of char combustion. The new method is highly efficient, requiring only 15% of the training data compared to conventional regression.

Evaluating spray drift from Uncrewed Aerial Spray Systems: A machine learning and variance-based sensitivity analysis of environmental and spray system parameters

Uncrewed Aerial Spray Systems (UASS), commonly called drones, have become an important application technique for plant protection products in Asia and worldwide. As such, environmental variables and spray system parameters influencing spray drift deserve detailed investigations. This study presents the data analysis of 114 UASS drift trials conducted between December 2021 and December 2022 in China. Study design was based on the ISO 22866:2005 protocol for spray drift trials and considered different UASS platforms, nozzles, and release heights, and specifically continuously measured weather conditions. The relative importance of the environmental variables and spray system parameters was evaluated by a random forest (RF) feature importance analysis, a Sobol sensitivity analysis and partial dependence plots. This approach was preferred to linear ranking techniques such as ANOVA (analysis of variance) due to the non-linearity of the system. In addition, partial dependence plots are proposed to visualize the relationship between specific input parameters within the system.Drift deposition curves calculated from the 114 trials show good agreement with previous UASS trials reported in the literature. As reported in previous studies, spray drift following UASS applications is lower than for manned aerial vehicles, greater than for ground spray applications, and similar to drift observed from orchard air blast applications. In addition, 9 trials were conducted on corn fields in order to evaluate the potential effect of crop cover on spray drift. Spray drift was observed to be reduced over the cropped soil, suggesting that plant cover might possibly reduce spray drift. These findings could help supporting drift mitigation policies, stewardship advice and product labelling around the world.