In the above article <xref ref-type="bibr" rid="ref1">1 , the authors mistakenly marked <xref rid="deqn1" ref-type="disp-formula">equation (4) of the article as a sensitivity formula but it is a ...specificity formula. The typing mistake is found therein <xref rid="deqn1" ref-type="disp-formula">(4) on page 82767. The corrected equation is given below:
A systematic, user‐friendly assessment tool that delivers a clear overview of the sensitivity of reactions to key parameters is highly desirable. Herein, the development of such a method is ...described. The intuitive, standardized presentation of the results in a radar diagram enables the sensitivity of a protocol to be rapidly assessed. This method was applied to five different visible‐light‐mediated photochemical reactions, and the results were correlated to the underlying mechanism. Ultimately, we believe that this assessment will help to increase the uptake of new synthetic methods and their reproducibility.
Goldilocks and the sensitivity screen: This work introduces a standardized, systematic, and user‐friendly tool to gain valuable information on the sensitivity of a reaction, with the aim of enhancing reproducibility and supporting troubleshooting.
Global Sensitivity Analysis (GSA) is key to assisting appraisal of the behavior of hydrological systems through model diagnosis considering multiple sources of uncertainty. Uncertainty sources ...typically comprise incomplete knowledge in (a) conceptual and mathematical formulation of models and (b) parameters embedded in the models. In this context, there is the need for detailed investigations aimed at a robust quantification of the importance of model and parameter uncertainties in a rigorous multi‐model context. This study aims at evaluating and comparing two modern multi‐model GSA methodologies. These are the first GSA approaches embedding both model and parameter uncertainty sources and encompass the variance‐based framework based on Sobol indices (as derived by Dai & Ye, 2015, https://doi.org/10.1016/j.jhydrol.2015.06.034) and the moment‐based approach upon which the formulation of the multi‐model AMA indices (as derived by Dell'Oca et al., 2020, https://doi.org/10.1029/2019wr025754) is based. We provide an assessment of various aspects of sensitivity upon considering a joint analysis of these two approaches in a multi‐model context. Our work relies on well‐established scenarios that comprise (a) a synthetic setting related to reactive transport across a groundwater system and (b) an experimentally‐based study considering heavy metal sorption onto a soil. Our study documents that the joint use of these GSA approaches can provide different while complementary information to assess mutual consistency of approaches and to enrich the information content provided by GSA under model and parameter uncertainty. While being related to groundwater settings, our results can be considered as reference for future GSA studies coping with model and parameter uncertainty.
Key Points
Two modern multi‐model Global Sensitivity Analysis (GSA) approaches are evaluated and compared upon considering two groundwater‐related scenarios
The results of the two multi‐model GSA methods can be markedly different due to their differing theoretical bases
Joint use of the two GSA methods enhances one's ability for model diagnosis and assessment of system behaviors
This paper investigates the problem of global sensitivity analysis (GSA) of Dynamical Earth System Models and proposes a basis for how such analyses should be performed. We argue that (a) performance ...metric‐based approaches to parameter GSA are actually identifiability analyses, (b) the use of a performance metric to assess sensitivity unavoidably distorts the information provided by the model about relative parameter importance, and (c) it is a serious conceptual flaw to interpret the results of such an analysis as being consistent and accurate indications of the sensitivity of the model response to parameter perturbations. Further, because such approaches depend on availability of system state/output observational data, the analysis they provide is necessarily incomplete. Here we frame the GSA problem from first principles, using trajectories of the partial derivatives of model outputs with respect to controlling factors as the theoretical basis for sensitivity, and construct a global sensitivity matrix from which statistical indices of total period time‐aggregate parameter importance, and time series of time‐varying parameter importance, can be inferred. We demonstrate this framework using the HBV‐SASK conceptual hydrologic model applied to the Oldman basin in Canada and show that it disagrees with performance metric‐based methods regarding which parameters exert the strongest controls on model behavior. Further, it is highly efficient, requiring less than 1,000 base samples to obtain stable and robust parameter importance assessments for our 10‐parameter example.
Plain Language Summary
When developing and using computer‐based models to (a) understand Earth and environmental systems, (b) make predictions, and/or (c) make management or policy decisions, it is very important to know which factors most strongly control the behaviors of the model. Tools to determine this are called sensitivity analysis (SA) methods. This paper shows that the use of model performance metrics to assess sensitivity is based in faulty reasoning. By framing the problem from first principles, a logical approach is developed that provides accurate and cost‐effective assessments of both time‐aggregate and time‐varying parameter importance. Because the approach does not require availability of system output data, it enables a comprehensive assessment and can be applied to historical and predictive conditions, as well as to future scenarios.
Key Points
Questions the use of the performance‐metric‐based sensitivity analysis of Dynamical Earth Systems Models and shows that the analysis it provides is both inaccurate and incomplete
Theoretically frames the global sensitivity analysis problem from first principles and develops a performance metric‐free approach to assessing parameter importance
Demonstrates that the new approach is efficient, stable, and robust and disagrees with metric‐based methods regarding which parameters exert the strongest controls on model behavior
In the last few years, an increasing number of individuals have adopted a gluten free diet (GFD). A significant proportion of that includes patients affected by celiac disease (CD), who have to ...follow a strict GFD for medical purposes. However, a high number of individuals are currently following a GFD without medical counseling and without a specific diagnosis needing a gluten withdrawal from the diet. This is due to the frequently incorrect information diffused on the Internet and mass media on the topic of GFD. For these reasons, research on the GFD and its clinical use and biological effects is urgently needed.
Sensitivity analysis and reliability assessment are two important aspects of structural and system safety. Epistemic uncertainty with respect to probabilistic model of input parameters due to lack of ...knowledge is present in many scarce-data applications and complicates the characterization of uncertainty in model response. In this article, we present two importance measures to evaluate the impact of distribution parameters on the probability distribution function (PDF) of the output and the failure probability. The epistemic uncertainty associated with the distribution parameters is modeled as random variables. A modified extended polynomial chaos expansion (MEPCE) approach is introduced in which aleatory and epistemic random variables are modeled and propagated simultaneously while allowing the separate assessment for any single epistemic variable. A MEPCE-based kernel density estimation (KDE) construction provides a composite map from each epistemic variable to the response PDF. The functional global sensitivity index of the PDF with respect to the distribution parameters is thus derived, as a function of output, which is both more informative and more efficient than standard scalar sensitivity measures. Reliability sensitivity indices can be readily evaluated by integrating the global sensitivity index function over the failure zone. Three illustrative examples are used to demonstrate the proposed methodology.
This paper proposes an innovative tuning approach for fuzzy control systems (CSs) with a reduced parametric sensitivity using the Grey Wolf Optimizer (GWO) algorithm. The CSs consist of servo system ...processes controlled by Takagi-Sugeno-Kang proportional-integral fuzzy controllers (TSK PI-FCs). The process models have second-order dynamics with an integral component, variable parameters, a saturation, and dead-zone static nonlinearity. The sensitivity analysis employs output sensitivity functions of the sensitivity models defined with respect to the parametric variations of the processes. The GWO algorithm is used in solving the optimization problems, where the objective functions include the output sensitivity functions. GWO's motivation is based on its low-computational cost. The tuning approach is validated in an experimental case study of a position control for a laboratory nonlinear servo system, and TSK PI-FCs with a reduced process small time constant sensitivity are offered.
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Variance-based approaches are widely used for Global Sensitivity Analysis (GSA) of environmental models. However, methods that consider the entire Probability Density Function (PDF) of the model ...output, rather than its variance only, are preferable in cases where variance is not an adequate proxy of uncertainty, e.g. when the output distribution is highly-skewed or when it is multi-modal. Still, the adoption of density-based methods has been limited so far, possibly because they are relatively more difficult to implement. Here we present a novel GSA method, called PAWN, to efficiently compute density-based sensitivity indices. The key idea is to characterise output distributions by their Cumulative Distribution Functions (CDF), which are easier to derive than PDFs. We discuss and demonstrate the advantages of PAWN through applications to numerical and environmental modelling examples. We expect PAWN to increase the application of density-based approaches and to be a complementary approach to variance-based GSA.
•We present a new density-based GSA method called PAWN to complement variance-based GSA.•Differently from variance-based methods, PAWN can be applied to highly-skewed or multi-modal output distributions.•Differently from other density-based methods, PAWN uses output CDFs, which simplifies numerical implementation.•PAWN can be easily tailored to focus on output sub-ranges, for instance extreme values.•Intermediate results generated in the application of PAWN can be visualized to gather insights about the model behaviour.
Sensitivity and resolution are important parameters that pose a significant challenge in using microwave microfluidic sensors to monitor low-concentration binary liquid mixtures. This work presents a ...microwave split-ring resonator (SRR) sensor equipped with a substrate-embedded fluidic channel and an active feedback loop to enhance sensitivity and resolution, respectively. The substrate-embedded channel eliminated the impact of the liquid-carrying tubes' wall and maintained a significant interaction between the liquid in the channel and the electromagnetic (EM) field confined between the SRR and ground plane. Therefore, embedding the channel inside the substrate of the SRR sensor, operating at 2.63 GHz, enhanced the sensitivity by 60%. The constructive energy from the active feedback loop boosted the quality factor of the passive SRR by over <inline-formula> <tex-math notation="LaTeX">100\times </tex-math></inline-formula> (from 116 to 13 000), which significantly enhanced the resolution of the sensor. As a proof of concept, two prototypes of the sensor having different substrate-embedded channels with cross-sectional diameters of 1 and 0.762 mm were fabricated and used to detect low concentrations of ethanol (0-0.08 vol%) and salt (0-200 mM) in water. Embedding the channel inside the substrate of the active SRR sensor achieved a linear correlation (<inline-formula> <tex-math notation="LaTeX">R^{2} = 0.99 </tex-math></inline-formula>) between the resonant frequency and the concentration of ethanol and salt in water with high sensitivity (19.95 MHz/vol% for ethanol and 4.35 kHz/mM for salt) and resolution (0.001 vol% for ethanol and 2.298 mM for salt). The achieved results demonstrate the feasibility of using the active SRR microwave sensor with a substrate-embedded channel for monitoring ethanol in fermentation broths and electrolytes in human sweat.
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