Sensitivity Analysis Techniques Research Articles

COVID-19 is associated with the risk of cardiovascular disease death: A two-sample Mendelian randomization study.

ObjectiveThis study aimed to estimate the causal effects of Coronavirus disease 2019 susceptibility and hospitalization on cardiovascular disease death using two-sample Mendelian randomization analysis.MethodsWe used statistics from a genome-wide association study. A total of 2,568,698 participants were assessed in this study, including 1,299,010 in Coronavirus disease 2019 susceptibility databases, 908,494 in Coronavirus disease 2019 hospitalization database, and 361,194 in a cardiovascular disease death database. We performed two-sample Mendelian randomization analysis using the inverse variance weighted method. As sensitivity analysis techniques, Mendelian randomization-Egger regression, heterogeneity analyses, and Leave-one-out analysis were employed. Reverse Mendelian randomization analysis was used to detect reverse causality. Statistical significance was defined as P < 0.05.ResultsCoronavirus disease 2019 susceptibility may be a causal factor for cardiovascular disease death (β = 2.188 × 10–3, P = 0.002), which involves five common single nucleotide polymorphisms. Similarly, Coronavirus disease 2019 hospitalization may also be a causal factor for cardiovascular disease death (β = 8.626 × 10–4, P = 0.010), which involves nine common single nucleotide polymorphisms. Furthermore, sensitivity and reverse Mendelian randomization analysis suggested that no heterogeneity, horizontal pleiotropy or reverse causality was found between Coronavirus disease 2019 and cardiovascular disease death.ConclusionOur bidirectional Mendelian randomization analysis showed a causal relationship between Coronavirus disease 2019 susceptibility and hospitalization associated with an increased risk of cardiovascular disease death.

Uncertainty and sensitivity analysis applied on commercial tariff with off-peak tariff rider: a case study

Commercial Tariff with Off-Peak Tariff Rider (C1 OPTR) is one type of time-based electricity tariff. The C1 OPTR charges electricity consumers with different electricity rates instead of a flat rate tariff. This paper investigates the C1 OPTR tariff adopted recently by Universiti Teknologi MARA Cawangan Pulau Pinang (UiTMCPP) from its previous flat rate tariff. The investigation involves applying the uncertainty and sensitivity analysis to the average load factor (ALF) model of the UiTMCPP. The ALF model consists of two major factors, namely kilowatt-hour (kWh) and maximum demand (kW). The analysis aims to identify the most contributing factor between the kWh and kW to the uncertainty of the ALF in a systematic way using Monte Carlo simulation. The factor identified is important for improvement by UiTMCPP to ensure that the suitable target ALF can be easily achieved. Based on Sobol uncertainty and sensitivity analysis technique, 60,000 samples for the respective kWh and kW have been generated and executed to produce the output of the ALF model. The result of the uncertainty analysis shows that the ALF output is uncertain between 0.195 and 0.343. Furthermore, the applied sensitivity analysis discovers that the kW is the most contributing factor to the ALF output uncertainty, with the sensitivity index indicating 0.8853.

SINDy-SA framework: enhancing nonlinear system identification with sensitivity analysis.

Machine learning methods have revolutionized studies in several areas of knowledge, helping to understand and extract information from experimental data. Recently, these data-driven methods have also been used to discover structures of mathematical models. The sparse identification of nonlinear dynamics (SINDy) method has been proposed with the aim of identifying nonlinear dynamical systems, assuming that the equations have only a few important terms that govern the dynamics. By defining a library of possible terms, the SINDy approach solves a sparse regression problem by eliminating terms whose coefficients are smaller than a threshold. However, the choice of this threshold is decisive for the correct identification of the model structure. In this work, we build on the SINDy method by integrating it with a global sensitivity analysis (SA) technique that allows to hierarchize terms according to their importance in relation to the desired quantity of interest, thus circumventing the need to define the SINDy threshold. The proposed SINDy-SA framework also includes the formulation of different experimental settings, recalibration of each identified model, and the use of model selection techniques to select the best and most parsimonious model. We investigate the use of the proposed SINDy-SA framework in a variety of applications. We also compare the results against the original SINDy method. The results demonstrate that the SINDy-SA framework is a promising methodology to accurately identify interpretable data-driven models.Supplementary InformationThe online version contains supplementary material available at 10.1007/s11071-022-07755-2.

Synthesis, characterization and biological evaluation of some metals complexes with new Azo-Schiff ligand

In this research the ligand 5-((3-(1-((5-bromopyridin-2yl)imino)ethyl)-2-hydroxyphenyl)diazenyl)-2-phenyl-2,4-dihydro-3H-pyrazol-3-one (BHAP)was used to prepare new complexes with elements Cu(II) ,Ni(II), Pt(II),Co(II) ,Cr(III). The new ligand was analyzed by partial analysis of the elements (C.H.N). UV- visible and infrared rays(FTIR), spectroscopy H1NMR, C13NMR and mass spectrometry. The complexes were prepared after determining the optimum pH and concentration values ​​by examining the spectra (UV - Vis) of the solutions of these complexes to find out the pH (3-10), concentration (1*10 -4-5.5*10-4) and molar ratios that agree with Beer-Lambert's law. The chemical measurement of the complexes was known with respect to the ligand ratio, which was verified by spectroscopy technique, after choosing the maximum absorption of the solutions of those complexes, where the ligand ratio (1:1) was achieved for all complexes. The properties of the prepared complexes were identified by (FTIR), ultraviolet and visible rays, molar conduction, atomic absorption, magnetic sensitivity and elemental analysis techniques (C.H.N. Through the results obtained from physical, chemical and spectroscopic techniques, the geometrical shape of these complexes was suggested as octahedral. All these complexes were evaluated against two types of pathogenic bacteria (positive and negative).

Value Creation and Investment Projects: An Application of Fuzzy Sensitivity Analysis to Project Financing Transactions

This paper presents a methodology which blends sensitivity analysis (SA) and fuzzy arithmetic for managing uncertainty in project financing transactions. Specifically, we adopt the perspective of the equityholders and use the average return on equity (ROE) to measure shareholder value creation and, in particular, the financial efficiency of the equity investment. We cope with uncertainty via global and local SA and fuzzy arithmetic; we use the fuzzy version of the well-known (global) Gamma indicator and we introduce the fuzzy versions of two (local) importance measures, the differential importance measures (DIM). We then apply them to the pro forma financial statements drawn up by the analyst for measuring and ranking the impact of the key accounting parameters on the resulting values and we show how the uncertain accounting and financial magnitudes of the project company affect the financial efficiency. Among the advantages of this analysis, aimed to enhance the managerial insights generated by the financial model and to lead to appropriate managerial actions, we focus on the attractiveness of fuzzy calculus and possibility theory to represent and compute all relevant financial data that appear in project financing and budgeting, where available information is characterized by incompleteness or nonstatistical uncertainty. In this context, fuzzy computing and appropriate SA techniques, based on application of the extension principle, allow complete investigation of the project characteristics.

An XAI method for convolutional neural networks in self-driving cars.

eXplainable Artificial Intelligence (XAI) is a new trend of machine learning. Machine learning models are used to predict or decide something, and they derive output based on a large volume of data set. Here, the problem is that it is hard to know why such prediction was derived, especially when using deep learning models. It makes the models unreliable in the case of reliability-critical applications. So, it is required to explain how they derived such output. It is a reliability-critical application for self-driving cars because the mistakes made by the computers inside them can lead to critical accidents. So, it is necessary to adopt XAI models in this field. In this paper, we propose an XAI method based on computing and explaining the difference of the output values of the neurons in the last hidden layer of convolutional neural networks. First, we input the original image and some modified images of it. Then we derive output values for each image and compare these values. Then, we introduce the Sensitivity Analysis technique to explain which parts of the original image are needed to distinguish the category. In detail, we divide the image into several parts and fill these parts with shades. First, we compute the influence value on the vector indicating the last hidden layer of the model for each of these parts. Then we draw shades whose darkness is in proportion to the influence values. The experimental results show that our approach for XAI in self-driving cars finds the parts needed to distinguish the category of these images accurately.

Monte Carlo Sensitivities and Performance of Woven Deployable Entry Systems

A material response model is developed for the determination of erosion rates and thermal response of woven materials to enable accurate thermal protection material sizing and future missions to Venus and giant planets. The stagnation-point thermal response is evaluated using the model developed in Icarus, a material response finite volume solver. A Monte Carlo global sensitivity analysis is further performed using a stand-alone Python wrapper script developed to determine the interactions between key parameters in the material surface energy balance of the one-dimensional simulation, such as the convective blowing correction parameter and weave material properties. The time-accurate material response for the dual-weave erosion and surface temperatures show good agreement with stagnation flat face arcjet tests involving time-varying aeroheating–cooling cycles. The high-fidelity Monte Carlo sensitivity analysis technique was used to address challenges in thermal protection sizing for the Adaptive Deployable Entry and Placement Technology (ADEPT) system, a low ballistic coefficient hypersonic decelerator. The results provide new correlations for such weaves involving carbon–air equilibrium oxidation and wall enthalpy imposition through a B-prime formalism. Through the multivariate regression analyses and uncertainty rankings performed, only two properties were found to largely contribute to the transient thermal response of the gore, with emissivity contributing up to of the total output uncertainty at the front face and at the back face.

Mediation analysis using Bayesian tree ensembles.

We present a general framework for causal mediation analysis using nonparametric Bayesian methods in the potential outcomes framework. Our model, which we refer to as the Bayesian causal mediation forests model, combines recent advances in Bayesian machine learning using decision tree ensembles, Bayesian nonparametric causal inference, and a Bayesian implementation of the g-formula for computing causal effects. Because of its strong performance on simulated data and because it greatly reduces researcher degrees of freedom, we argue that Bayesian causal mediation forests are highly attractive as a default approach. Of independent interest, we also introduce a new sensitivity analysis technique for mediation analysis with continuous outcomes that is widely applicable. We demonstrate our approach on both simulated and real data sets, and show that our approach obtains low mean squared error and close to nominal coverage of 95% interval estimates, even in highly nonlinear problems on which other methods fail. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Application of Isothermal Signal Amplification Technique in the Etiological Diagnosis of Gonorrhea and Drug Resistance Gene Detection.

Background: Isothermal signal amplification technique is developed based on the rolling ring amplification mechanism of cyclic DNA molecules in nature. This technique plays an extremely beneficial role in gonorrhea pathogen identification and drug resistance gene detection. Aims: This study analyzes the isothermal signal amplification techniques in the etiological diagnosis of gonorrhea and drug resistance gene detection. Materials and Methods: Urethral, cervical secretion, or prostatic fluid samples from 322 cases of gonorrhea collected from January 2018 to December 2021 at the STD clinic of our hospital dermatology department were selected for direct smear examination and gonococcal culture examination; DNA was extracted from urethral, cervical secretion, or prostatic fluid samples and then used for pathogen identification by SAT assay and rolling loop nucleic acid amplification technique, smear examination and pathogen culture examination methods, SAT assay, and isothermal signal amplification technique for comparative sensitivity and specificity analysis. Results: The highest rate of gonorrhea positivity was for the urine rolling loop nucleic acid amplification technique, followed by the swab rolling loop nucleic acid amplification technique, and the lowest rate of gonorrhea positivity was for the urine SAT test. The difference in the positivity rate between the two urine testing methods was statistically significant (P < 0.05). The highest sensitivity of the urine rolling loop nucleic acid amplification technique method for the detection of gonorrhea pathogens and the lowest sensitivity of the urine SAT method were statistically significant (P < 0.01). The differences in sensitivity and specificity between the swab rolling loop nucleic acid amplification technique and the swab SAT method were not statistically significant (P > 0.05). ROC curves were plotted based on sensitivity and specificity, with swab SAT assay (AUC = 0.998) > rolling loop nucleic acid amplification technique (AUC = 0.981). Comparing the negative rates of urine and swab rolling loop nucleic acid amplification technique and urine SAT assay, the differences were not statistically significant (P > 0.05). Conclusion: The isothermal signal amplification technique improves the shortcomings of gonorrhea pathogen identification means and drug resistance gene detection methods, with good detection sensitivity and specificity, simple operation, low price, and easy promotion, which has obvious advantages in clinical applications and epidemiological studies.

Neumann Series Based Voltage Sensitivity Analysis for Three Phase Distribution System

In this letter, a simplified voltage sensitivity analysis technique that can provide accurate estimates of voltage change across the network for a given change in bus power injections in a three-phase unbalanced distribution network is proposed. This technique is derived from the first-order approximation of the Neumann series, which allows maintaining the accuracy of the solution while the computational effort is reduced. The proposed technique is tested on a 559-bus unbalanced distribution system with multiple distributed generation resources. The results show that the average error in the voltage estimates with the proposed method is not more than 0.3% with the execution time of similar order relative to the state-of-the-art sensitivity analysis methods.

Input parameter and thermal load representation uncertainties effects on the buckling and dynamic characteristics of cylindrical tubes

This research considers different methods in sensitivity analysis and uncertainty quantification applied to cylindrical tubes subject to three types of thermal load representation, namely, uniform, linear, and nonlinear in the radial direction. Sensitivity analysis techniques are used to quantify and rank the parameters that are most influential in altering the critical buckling temperatures, the post-buckled configuration amplitudes, and the natural frequencies in the pre- and post-buckling regimes. The uncertainty quantification and sensitivity analysis strategies show a great potential and usefulness in terms of determining the most influential input parameters for cylindrical tubes subject to thermal loads. Based on the set of nominal parameters in this study, it is shown that the length is the most sensitive parameter in altering the critical thermal buckling load. Additionally, it is demonstrated how uncertainty in the thermal load representation can lead to over or underestimation in both sensitivity analysis and uncertainty quantification findings. The outcome of this analysis can be utilized by other researchers in the design and optimization of cylindrical tubes in thermal environment.

Thermomechanical analysis of induction assisted friction stir welding of Inconel 718 alloy: A finite element approach

Friction stir welding (FSW) of hard and high melting point materials such as Nickel and Titanium based alloys is challenging due to excessive tool wear and low weld quality. Various auxiliary energy sources have been proposed in the past decade as an assistance to conventional FSW process. In this work, a modified 3D Finite element (FE) model was developed to simulate the induction preheated FSW of Inconel 718 alloy using ABAQUS/Explicit software package. Investigation of temperature distribution and residual stress generation was carried out for induction assisted FSW (IAFSW) process and compared with those of FSW process. Features such as Arbitrary Lagrangian–Eulerian (ALE) formulation, adaptive meshing technique, mesh sensitivity analysis and mass scaling techniques have been utilised in order to develop a reliable and computationally efficient FE model. Experimental study was carried out with Induction assistance as a preheating method to FSW process. The thermal history of the numerical model was fairly validated with experimental results. Results showed that temperature increased by 138 °C in IAFSW process and was more uniformly distributed as compared to FSW. Residual stresses decreased by 15% on an average in the IAFSW joints. In addition to that, plastic strain and tool reaction forces were also predicted using the FE model proposed in this work. Plastic strain values were higher in IAFSW process with a maximum difference of 55% over the average plastic strain value of FSW at the beginning of the traverse period when temperature was highest. Reaction forces on probe tip reduced by 23% in IAFSW process during the plunge stage.

Sensitivity analysis for direct and indirect effects in the presence of exposure-induced mediator-outcome confounders.

Questions of mediation are often of interest in reasoning about mechanisms, and methods have been developed to address these questions. However, these methods make strong assumptions about the absence of confounding. Even if exposure is randomized, there may be mediator-outcome confounding variables. Inference about direct and indirect effects is particularly challenging if these mediator-outcome confounders are affected by the exposure because in this case these effects are not identified irrespective of whether data is available on these exposure-induced mediator-outcome confounders. In this paper, we provide a sensitivity analysis technique for natural direct and indirect effects that is applicable even if there are mediator-outcome confounders affected by the exposure. We give techniques for both the difference and risk ratio scales and compare the technique to other possible approaches.

Screening: From tornado diagrams to effective dimensions

Popular sensitivity analysis techniques such as Tornado Diagrams or the Morris method are based on one-at-a-time input variations (local main effects, henceforth). We evidence a link between local main effects of screening methods and global sensitivity measures. The link allows analysts to obtain insights on interactions on the local and global scales simultaneously. We evidence a mirroring effect according to which a local main effect calculated from the base case to the sensitivity case is equal in magnitude to the local total effect in the opposite direction, and their difference yields an overall local interaction index. We then obtain the exact relationship between Morris sensitivity measures and Sobol’ total indices and discuss its implications on the choice of the sampling scheme. We show that the non-diagonal elements of the variance-covariance matrix of main effects computed at randomized locations yield Liu and Owen’s global interaction indices. Asymptotic results are derived with particular reference to confidence intervals for the mean dimension. We illustrate findings and insights through numerical experiments on two well known simulators.

Robust Design Optimization of Mechatronics Systems: Parallel Electric Drivetrain Application

AbstractThis paper addresses the problem of finding a robust optimal design when uncertain parameters in the form of crisp or interval sets are present in the optimization. Furthermore, in order to make the approach as general as possible, direct search methods with the help of sensitivity analysis techniques are employed to optimize the design. Consequently, the presented approach is suitable for black box models for which no, or very little, information of the equations governing the model is available. The design of an electric drivetrain is used to illustrate the benefits of the proposed method.

Application of Epistemic Uncertainty Analysis and Sensitivity Analysis in Green Construction Design

Building performance is often expressed according to primary energy use; however studies should also include environmental load. To mitigate the effects of increased CO2 emissions, green building designs are now increasingly popular options for stakeholders. In contrast, impacts of green design parameters, building performance assessment, and design optimization objectives are not addressed sufficiently for American infrastructure. Designing low-energy architecture to minimize carbon emissions requires thoughtful articulation of green building design alternatives. A common barrier for green building design in the U.S. is the time needed to identify and evaluate alternatives. Modeling energy consumption in buildings is essential for different applications, including building energy management and establishing baselines for sustainable building performance codes. This paper proposes a new optimization objective, which considers annual carbon emissions for multi-family construction in Florida. A multi-step sensitivity and epistemic uncertainty approach are proposed to identify the critical design parameters during the early stages of sustainable building design. Building simulation software was utilized to model green building configuration for carbon emission values, further analyzed using Monte Carlo and Morris Method sensitivity analysis techniques. The present study considered green building design alternatives as variables since carbon emission reduction potential is sensitive to these variables. Results indicate that PV panel efficiency, PV payback limit, lighting efficiency, plug load efficiency, and solar heat gain coefficient are highly influential parameters. The methodology is presented, and an example is applied to a new construction multi-family apartment design in Tallahassee, FL. This uncertainty and sensitivity analysis improved the design efficiency, while emphasizing usefulness in the green building optimization process.

Sensitivity Analysis and Quantification of the Role of Governing Transport Mechanisms and Parameters in a Gas Flow Model for Low-Permeability Porous Media

Recent models represent gas (methane) migration in low-permeability media as a weighted sum of various contributions, each associated with a given flow regime. These models typically embed numerous chemical/physical parameters that cannot be easily and unambiguously evaluated via experimental investigations. In this context, modern sensitivity analysis techniques enable us to diagnose the behavior of a given model through the quantification of the importance and role of model input uncertainties with respect to a target model output. Here, we rely on two global sensitivity analysis approaches and metrics (i.e., variance-based Sobol’ indices and moment-based AMA indices) to assess the behavior of a recent interpretive model that conceptualizes gas migration as the sum of a surface diffusion mechanism and two weighted bulk flow components. We quantitatively investigate the impact of (i) each uncertain model parameter and (ii) the type of their associated probability distribution on the evaluation of methane flow. We then derive the structure of an effective diffusion coefficient embedding all complex mechanisms of the model considered and allowing quantification of the relative contribution of each flow mechanism to the overall gas flow.

A trait-based model ensemble approach to design rice plant types for future climate.

Crop models are powerful tools to support breeding because of their capability to explore genotype×environment×management interactions that can help design promising plant types under climate change. However, relationships between plant traits and model parameters are often model specific and not necessarily direct, depending on how models formulate plant morphological and physiological features. This hinders model application in plant breeding. We developed a novel trait-based multi-model ensemble approach to improve the design of rice plant types for future climate projections. We conducted multi-model simulations targeting enhanced productivity, and aggregated results into model-ensemble sets of phenotypic traits as defined by breeders rather than by model parameters. This allowed to overcome the limitations due to ambiguities in trait-parameter mapping from single modelling approaches. Breeders' knowledge and perspective were integrated to provide clear mapping from designed plant types to breeding traits. Nine crop models from the AgMIP-Rice Project and sensitivity analysis techniques were used to explore trait responses under different climate and management scenarios at four sites. The method demonstrated the potential of yield improvement that ranged from 15.8% to 41.5% compared to the current cultivars under mid-century climate projections. These results highlight the primary role of phenological traits to improve crop adaptation to climate change, as well as traits involved with canopy development and structure. The variability of plant types derived with different models supported model ensembles to handle related uncertainty. Nevertheless, the models agreed in capturing the effect of the heterogeneity in climate conditions across sites on key traits, highlighting the need for context-specific breeding programmes to improve crop adaptation to climate change. Although further improvement is needed for crop models to fully support breeding programmes, a trait-based ensemble approach represents a major step towards the integration of crop modelling and breeding to address climate change challenges and develop adaptation options.

Parametric optimization of the effective thermal conductivity for a three-phase particle-filled composite

Functionalized materials, consisting of a particle-filled polymer matrix, enable a considerable increase in thermal conductivity compared to standard plastics. This is particularly important in electromobility applications, e.g. in encapsulated circuit boards, to ensure sufficient thermal management. In this paper, a method for the optimization of the effective thermal conductivity (ETC) by considering electrically insulating composites is presented. To show the diversity of microstructural design options by using different filler/matrix compositions, the method is introduced by taking a particle-filled plastic with three phases and high contrast in the conductivities of the individual phases as example. Particles with different sizes and shapes in the micrometer range are considered. The task of setting a target ETC is formulated as a parameter optimization problem. The ETC is determined by microstructure simulation and numerical homogenization where virtually created representative volume elements (RVEs) are used. To study the influence of the different design variables, metamodeling and sensitivity analysis techniques are introduced. It turns out, that only the filler volume fraction, the aspect ratio and the proportions of the individual fillers are sensitive parameters. After neglecting non-sensitive parameters, global optimization in the reduced design space using an evolutionary algorithm is performed. The novelty of the paper is that several fillers with different microstructural parameters, such as diameter, aspect ratio and shape, are included as design variables in the structural optimization. Since the evaluation of the objective function of the structural optimization problem is very expensive, a cheaper surrogate model is introduced in the paper. This approach allows an efficient computation of microstructural realizations with a target ETC and can be extended to other microstructures and effective properties as well.

Decomposing complex traits through crop modelling to support cultivar recommendation. A proof of concept with a focus on phenology and field pea

Cultivar recommendation is crucial for achieving high and stable yields, and crop models can successfully support it because of their capability of exploring genotype environment management interactions. Different modelling approaches have been developed to this end, primarily relying on dedicated field trials to characterize the germplasm of interest. Here, we show how even data routinely collected in operational contexts can be used for model-based cultivar recommendation, with a case study on phenological traits and field pea (Pisum sativum L.). Eight hundred and four datasets, including days from sowing to plant emergence, first flower, and maturity, were collected in Northern Italy from 2017 to 2020, and they were used to optimise six parameters (base, optimum, and maximum temperature for development, growing degree days to reach emergence, flowering, and maturity) of the crop model WOFOST-GT2 for 13 cultivars. This allowed obtaining the phenotypic profiles for these cultivars at the level of the functional trait, without the need of carrying out dedicated phenotypisations. Sensitivity analysis (SA) techniques (E-FAST) and the statistical distributions of the optimised parameters were used to design pea ideotypes able to maximise yields and yield stability in 24 agro-climatic contexts (three soil conditions two sowing times four agro-climatic classes). For each context, the 13 cultivars were ranked according to their similarity to the ideotype based on the weighted Euclidean distance. Results of SA identified growing degree days to reach flowering as the trait mainly affecting crop productivity, although cardinal temperatures also played a role, especially in the case of early sowings. This is reflected in the ideotypes and, therefore, in cultivar ranking, leading to recommend a panel of cultivars characterised by low base temperature and high thermal requirements to reach flowering. Despite the limits of the study, which is focused only on phenological traits, it represents an extension of available approaches for model-aided cultivar recommendation, given that the methodology we propose can take full advantage of the potentialities of crop models without requiring dedicated experiments aimed at profiling the germplasm of interest at the level of functional traits. Highlights- Crop models are powerful tools to support cultivar choice by exploring genotype x environment x management interactions.- Crop models require cultivar-specific phenotyping data at the level of functional traits.- We propose a methodology that uses data routinely collected in operational contexts to derive functional trait values.- This study is a proof of concept of how to increase the applicability of model-based approaches for cultivar choice.