Sensitivity Analysis Approach Research Articles

Neural network driven sensitivity analysis of diffraction-based overlay metrology performance to target defect features

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.

Transient forward harmonic adjoint sensitivity analysis

AbstractThis paper presents a transient forward harmonic adjoint sensitivity analysis (TFHA), which is a combination of a transient forward circuit analysis with a harmonic balance-based adjoint sensitivity analysis. TFHA provides sensitivities of quantities of interest from time-periodic problems with many design parameters, as used in the design process of power-electronics devices. The TFHA shows advantages in applications where the harmonic balance-based adjoint sensitivity analysis or finite difference approaches for sensitivity analysis perform poorly. In contrast to existing methods, the TFHA can be used in combination with arbitrary forward solvers, i.e., general transient solvers.

Unified framework for geometric error compensation and shape-adaptive scanning in five-axis metrology systems

In response to the escalating demand for precise shape metrology of complex optical surfaces, this study unveils a unified geometric error compensation and trajectory planning framework tailored for high-accuracy five-axis scanning metrology systems, which remains a notably underexplored field compared to error compensation in machine tools. Founded on a unified geometric model, the proposed framework seamlessly integrates a versatile shape-adaptive trajectory planning strategy, a thorough global error sensitivity analysis approach, and an exhaustive geometric error compensation scheme. Leveraging inverse kinematics, an innovative shape-adaptive scanning trajectory generation strategy is mathematically formulated, thereby facilitating adaptable measurement trajectory generation for diverse surface geometries. Employing forward kinematics, an exhaustive geometric error model is established to extensively address the 53 distinct geometric errors in the metrology system. This proposed error model fundamentally augments conventional geometric error models in machine tool by managing not only the geometric errors from the motion system, but also those from the probe and workpiece. To streamline the error compensation procedure, a novel global error sensitivity analysis approach is introduced, identifying both system-oriented and process-oriented sensitive geometric errors for targeted compensation. Experimental validation using a standard ball, which achieved an exceptional 89.35% reduction in the root mean square of the measurement errors, further confirms the feasibility and effectiveness of the proposed framework. By offering an universal trajectory planning, sensitivity analysis and error compensation trinity for five-axis scanning metrology systems, this study sets the stage for precision advancements and design optimization across diverse configurations of metrology systems.

High throughput screening of promoted bimetallic catalysts supported on alumina for production of light olefins via Fischer-Tropsch synthesis with artificial neural network and response surface methodology

The production of light olefins via Fischer-Tropsch Synthesis (FTS) was investigated by both experimental and modeling studies. Experimental studies were conducted by employing promoted FeCo/α-Al2O3 (FeCo) bimetallic catalysts. The Response Surface Methodology (RSM) and Artificial Neural Network (ANN) methods were used in modeling. Specifically, effects of the types and concentration of promoters on the catalytic performances of α-Al2O3 supported iron-cobalt bimetallic catalysts are investigated by using Ce, Ni, La, Ho, Ga Cu, Mn, and Zn used as promoters. A total of 49 catalysts were prepared by co-impregnation method and tested at 310 °C and 1 bar in a high-speed catalyst performance test system (HT-CPA). The FTS performances of catalysts showed that both the types and amounts of the promoters can affect light olefin production. The light olefin (C2=–C3=) production of unpromoted FeCo catalyst was about 3.87 × 10−3 (mol C/g active metal. h). The best performances were obtained from FeCo catalysts promoted with Ho, Cu, and Zn. Presence of these promoters increased olefin production up to ∼ 84 %, 76 % and 70 %, respectively.Performance parameters (MSE and R2) of RSM and ANN models revealed that both methods can be used to support experimental studies and provide a procedure for optimizing catalyst formulations yielding the desired product selectivity in FTS processes. The RSM outcomes aligned with experimental findings, suggest that that Ho, Cu, and Zn can serve as suitable promoters for the light olefin production in FTS. ANN model attained higher performance parameters (R2 = 0.95 and MSE = 2E−7) than RSM model (R2 = 0.85 and MSE = 3.4E−7). This indicates that ANN model, which designed as one hidden layer including 7 neurons, can provide better predictions and a fitting capacity. Therefore, it may be used in further optimization and sensitivity analysis approaches. Alignment degree between the experimental and modeling results demonstrates that the proposed methodology can provide a reliable and efficient way to guide experiments to optimize catalyst formulations through identification of potential promoters and their amounts to be used.

Assessing the cascading impact of industry 4.0 disruption on supply chain analytics through the lens of dependency concepts

Nowadays, Industry 4.0 (I4.0) is regarded as one of the most sophisticated technologies that businesses need to have to stay competitive. I4.0 infrastructure system may be seen to function as a network of interdependent elements connected with multiple networks through various means. One such network is the supply chain which has an interrelation with Industry 4.0. Any disruption to Industry 4.0 will ripple through its entire supply chain and will lead to cascading effects. Thus, there is a need to examine how to minimize the disruption of I4.0 and make its dependent supply chain more functional and cost-saving. To that end, the aim of this research is threefold: (i) modeling the dependence between fourth industrial revolution and supply chain performance using the Bayesian network, (ii) capturing how a disruption in one network may propagate through the entire network and impact its overall robustness, (iii) conducting a series of advance analyses including belief propagation, sensitivity analysis, and information theory approach to validate the result and draw further insights. As a result of these analyses, it was found that technological factors and supplier quality are vital to the I4.0 disruption and performance of supply chain performance.

A highly sensitive first derivative synchronous spectrofluorimetric approach for the simultaneous analysis of the anti-breast cancer co-administered drugs, letrozole and tramadol in dosage forms and human plasma at nanogram levels

Letrozole is an anticancer medication prescribed for the management of estrogen receptor-positive breast cancer in postmenopausal women. Chronic pain is prevalent in patients receiving chemotherapy, leading to the use of adjuvant analgesics such as tramadol. This work introduces the first analytical approach for the concurrent quantification of letrozole and tramadol, two co-administered drugs, employing a rapid, highly sensitive, eco-friendly, and cost-effective first derivative synchronous spectrofluorimetric technique. The fluorescence of tramadol and letrozole was measured at wavelengths of 235.9 nm and 241.9 nm, respectively using a wavelength difference (Δλ) of 60.0 nm. The developed approach demonstrated exceptional linearity (r ˃ 0.999) within the specified concentration ranges for tramadol (10.0–1200.0 ng/mL) and letrozole (1.0–140.0 ng/mL). The results demonstrated that the proposed technique exhibits a high level of sensitivity, with detection limits of 0.569 and 0.143 ng/mL for tramadol and letrozole, respectively, indicating the good bioanalytical applicability. The within-run precisions, both intra-day and inter-day, for both analytes, were less than 0.71 % RSD. The developed approach was effectively applied to simultaneously estimate the mentioned drugs in their tablets and human plasma samples, achieving high percentage recoveries and low % RSD values. In order to assess the environmental sustainability of the developed approach, Analytical GREEnnessNNESS (AGREE) and the Green Analytical Procedure Index (GAPI) metric tools were employed. Both tools revealed that the developed approach is excellent green, suggesting its usage as an environmentally-friendly alternative for the routine assayof the investigated pharmaceuticals. The developed approach was validated according to the ICHQ2 (R1) requirements.

Optimizing membrane reactor structures for enhanced hydrogen yield in CH4 tri-reforming: Insights from sensitivity analysis and machine learning approaches

This study explores the optimization of membrane reactor configurations to enhance hydrogen production through CH4 tri-reforming. The investigation employs ceramic membranes for oxygen, vapor, and carbon dioxide distribution within the reactor bed. A differential evolution algorithm is utilized alongside cuckoo search algorithm (CSA) and support vector regression (SVR) to determine optimal values for O2/CH4, H2O/CH4, and CO2/CH4 ratios, membrane thickness, and shell pressure, with hydrogen yield as the objective function. Results demonstrate that the oxygen membrane reactor achieves the highest hydrogen yield, reaching 2.02 and 1.75 for direct methanol synthesis and Fischer–Tropsch processes, respectively, representing a 7.98 % and 10.03 % increase compared to the conventional tri-reforming reactor. Furthermore, CSA and SVR emerge as invaluable tools, facilitating robust optimization and predictive modeling. The CSA efficiently navigates complex solution spaces to identify optimal parameters, while SVR accurately models relationships between input variables and hydrogen yield. Incorporating these methodologies enhances the effectiveness of membrane reactor design and synthesis gas production. This study contributes to advancements in clean energy technologies by providing insights into efficient hydrogen production methods using membrane reactors.

Determine the pharmacokinetics (half-life, volume of distribution and clearance) of AMB-FUBINACA in rats plasma using GC–MS / MS

BackgroundSeveral novel synthetic cannabinoids, including methyl 2-(1-(4-fluorobenzyl)-1Hindazole-3-carboxamido)-3-methylbutanoate (AMB-FUBINACA), have recently surfaced on the illicit drug market. To determine the pharmacokinetic properties (half-life, volume of distribution, and clearance) of AMB-FUBINACA in rats plasma, a straightforward, quick, and highly sensitive analytical approach was developed. MethodsEighteen Wistar rats were divided into two groups: one control (saline vehicle) and one treatment group (AMB-FUBINACA at 50 mg/kg). Blood samples (400 μL) were withdrawn via catheters immediately before (t = 0) and at 30, 60, 90, 120, and 240 min following injection. Samples were collected into 1 mL tuberculin syringes, then transferred to 1.5 mL plastic tubes containing 5 μL of 1000 IU/mL K3-EDTA (Thomas Scientific). Place the EDTA tubes containing samples in a centrifuge and spin at 1000 g for 10 min at 4 °C. The top layer is the plasma fraction, which is decanted into cryovials and stored at −20 °C until analysis. The gas chromatography tandem mass spectrometry (GC–MS/MS) method was optimized and validated, combined with liquid-liquid extraction, to analyze AMB-FUBINACA in rat plasma. ResultsThe research method successfully met the validation requirements set by the FDA, demonstrating selectivity and linear calibration curves within a concentration range of 0.5–1000 ng/ml. The correlation coefficient (r2) was determined to be 0.99, indicating a strong linear relationship. The analyte's limit of quantitation (LOQ) was determined to be 1–5 ng/mL. Subsequently, the method was successfully applied to investigate the pharmacokinetics of AMB-FUBINACA in rats' blood samples. Following oral administration, AMB-FUBINACA was rapidly absorbed, with a plasma half-life (t1/2) of 5.91 h, a volume of distribution (Vd) of 203.13 l, and a plasma clearance of 23.81122 L/h. ConclusionThese findings contribute to the understanding of AMB-FUBINACA's pharmacokinetics and pharmacodynamics.

Hydrologic Sensitivity of a Critical Turkish Watershed to Inform Water Resource Management in an Altered Climate

This study introduces a novel sensitivity analysis approach to assess the resilience and susceptibility of hydrologic systems to the stresses of climate change, moving away from conventional top-down methodologies. By exploring the hydrological sensitivity of the upper Kızılırmak River basin using the Variable Infiltration Capacity (VIC) hydrologic model, we employed a sensitivity-based approach as an alternative to the traditional Global Climate Model (GCM)-based methods, providing more insightful information for water managers. Considering the consistent projections of increasing temperature over this region in GCMs, the hydrologic system was perturbed to examine gradients of a more challenging climate characterized by warming and drying conditions. The sensitivity of streamflow, snow water equivalent, and evapotranspiration to temperature (T) and precipitation (P) variations under each perturbation or “reference” climate was quantified. Results indicate that streamflow responds to T negatively under all warming scenarios. As the reference climates become drier, streamflow sensitivity to P increases, indicating that meteorological drought impacts on water availability could be exacerbated. These results suggest that there will be heightened difficulty in managing water resources in the region if it undergoes both warming and drying due to the following setbacks: (1) water availability will shift away from the summer season of peak water demand due to the warming effects on the snowpack, (2) annual water availability will likely decrease due to a combination of warming and lower precipitation, and (3) streamflow sensitivity to hydroclimatic variability will increase, meaning that there will be more extreme impacts to water availability. Water managers will need to plan for a larger set of extreme conditions.

A sensitive analytical strategy of oligonucleotide functionalized fluorescent probes for detection of nusinersen sodium in human serum

Spinal muscular atrophy (SMA) is a rare autosomal recessive neuromuscular disease. Nusinersen sodium (NS) is the world's first antisense oligonucleotide (ASO) drug for SMA precise targeted therapy. However, the limited half-life of oligonucleotides and their tendency to accumulate in hepatic and renal tissues presented significant challenges for clinical investigation and therapeutic drug monitoring. In this study, we proposed an analytical strategy based on the specific capture of oligonucleotide functionalized fluorescent probes by single stranded binding proteins (SSB) for ultra-sensitive and high-throughput detection of nusinersen sodium in human serum. The magnetic nanoparticles modified with single-strand binding protein (MNPs-SSB) selectively bonded to the red fluorescent quantum dots functionalized with oligonucleotides (RQDs-ssDNA) that were complementary to nusinersen sodium. Upon interaction with nusinersen sodium, RQDs-ssDNA formed a double-stranded complex (RQDs-ssDNA-NS), resulting in enhanced red fluorescence after magnetic separation as it was no longer captured by MNPs-SSB but remained in the supernatant. A quantitative analysis of nusinersen sodium in biological samples was successfully achieved by establishing a relationship between fluorescence intensity and its concentration. The detection signal F/F0 exhibited a linear correlation (R2 = 0.9871) over a wide range from 0.1 nM to 200 nM, with a limit of detection (LOD) of 0.03 nM, demonstrating the high specificity and rapid analysis time (only 30 min). This method provided a novel approach for sensitive, high-throughput, and specific analysis of nusinersen sodium and similar ASO drugs.

The Modelling of the Evapotranspiration Portion of the Water Footprint: A Global Sensitivity Analysis in the Brazilian Serra Gaúcha

Water footprints have been widely used to illustrate the consumption of water in many situations, for instance, in products, processes, or regions of interest. In this work, we analyzed—using a sensitivity analysis approach—the effect of some variables in the calculation of the water footprint in the viticulture in the Brazilian Serra Gaúcha (the major producing region of Brazilian wine). The classical Penman–Monteith model for evapotransporation was considered, with uncertainties in some parameters (dead mulch covering a fraction of the vineyard, maximum temperatures for some months, the altitudes and latitudes of the site). A sensitivity analysis was conducted using the SAFE toolbox under Octave framework. The results indicated that the the portion of the water footprint corresponding to evapotranspiration is more sensitive to the values of the mulch-covered fraction and the altitude of the site in comparison with the latitude and the maximum temperatures.

Sensitivity analysis for matched observational studies with continuous exposures and binary outcomes

Summary Matching is one of the most widely used study designs for adjusting for measured confounders in observational studies. However, unmeasured confounding may exist and cannot be removed by matching. Therefore, a sensitivity analysis is typically needed to assess a causal conclusion’s sensitivity to unmeasured confounding. Sensitivity analysis frameworks for binary exposures have been well-established for various matching designs and are commonly used in various studies. However, unlike the binary exposure case, there still lacks valid and general sensitivity analysis methods for continuous exposures, except in some special cases such as pair matching. To fill this gap in the binary outcome case, we develop a sensitivity analysis framework for general matching designs with continuous exposures and binary outcomes. First, we use probabilistic lattice theory to show our sensitivity analysis approach is finite-population- exact under Fisher’s sharp null. Second, we prove a novel design sensitivity formula as a powerful tool for asymptotically evaluating the performance of our sensitivity analysis approach. Third, to allow effect heterogeneity with binary outcomes, we introduce a framework for conducting asymptotically exact inference and sensitivity analysis on generalized attributable effects with binary outcomes via mixed- integer programming. Fourth, for the continuous outcomes case, we show that conducting an asymptotically exact sensitivity analysis in matched observational studies when both the exposures and outcomes are continuous is generally NP-hard, except in some special cases such as pair matching. As a real data application, we apply our new methods to study the effect of early-life lead exposure on juvenile delinquency. An implementation of the methods in this work is available in the R package doseSens.

A Systematic Approach of Global Sensitivity Analysis and Its Application to a Model for the Quantification of Resilience of Interconnected Critical Infrastructures

We consider a model for the resilience analysis of interconnected critical infrastructures (ICIs) that describes the dependencies among the subsystems within the ICIs and their time-varying behavior. The model response is a function of uncertain inputs comprising ICIs design parameters and failure magnitudes of vulnerable elements in the system, etc. In this methodological paper, we present a systematic approach based on an innovative blend of methods to perform a sensitivity analysis for identifying the most relevant variables affecting the system resilience at different stages, during a disruptive event. The methods considered include the following: the use of the graphical representation of Cusunoro curves for a visualization of the impact of an input on the resilience metric and an understanding of whether the associated dependence is monotonic, increasing, or decreasing; the introduction of an ensemble of indicators related to different properties of the resilience metric to allow the prioritization of variable importance and avoid false negatives, meaning to regard a variable as non-influential when, instead, it plays a relevant role in the determination of the model response; the calculation of first-order variance-based sensitivity indices to have an appreciation on the relevance of interactions when inputs are independent; and a data approach to visually identify relevant second-order interactions. All the sensitivity methods considered are performed on a provided sample, and do not require additional model evaluations. They allow the analyst to post-process the data to extract, simultaneously, several desirable insights. The systematic approach proposed to apply these methods allows us to identify the model input variables and parameters that are not very relevant, while it enables the identification of the relevant ones which allows prioritizing interventions on the vulnerable elements of the system for its resilience at different stages during a disruptive event. Given the methodological nature of the work, a simplified infrastructure model describing an interconnected gas network and electric power grid is taken as case study: this allows us to show that the approach is straightforward to understand and implement, and the results obtained show the usefulness of the approach in providing meaningful insights that can be used by stakeholders and decision makers to inform strategies for the improvement of system resilience. By the application of the simplified ICIs model to the case study, it is shown that the approach can be straightforwardly implemented to identify the most relevant variables on system resilience and obtain the most important subsystems. The key factors which affect system resilience in multiple initial failures scenarios are found; this allows us to identify the key resilience improvement measurements, and their priorities.

A highly sensitive micelle-sensitized spectrofluorimetric approach for analysis of montelukast in dosage forms and human plasma: Greenness assessment and application to content uniformity testing

In this work, a highly sensitive, rapid, affordable, and green micellar-enhanced spectrofluorimetric method was developed for the estimation of montelukast in its tablets and human plasma for the first time. After excitation at 224 nm, a micellar solution of cetrimide containing montelukast produced high native fluorescence emission at 404 nm. A 3-fold enhancement of the drug's fluorescence intensity and quantum yield, as well as the method's sensitivity, was produced using cetrimide as a micellar system. The experimental parameters influencing the spectrofluorimetric behavior of the studied drug were carefully investigated. The quantum yield of montelukast was also computed and found to be as high as 20 %. A straight-lined calibration plot was obtained over the concentration range of 50.0–1000.0 ng/mL. The proposed method showed high sensitivity down to the nanogram level with quantitation and detection limits of 34.68 and 11.45 ng/mL, respectively. The method was successfully applied for the analysis of the studied drug in its tablets with high % recoveries and low %RSD values. As the proposed fluorimetric method is highly sensitive and selective towards montelukast, it could successfully test the content uniformity of its tablets following the guidelines of the British Pharmacopoeia. Montelukast was also successfully determined in spiked human plasma samples due to the increased sensitivity of the method. The greenness profile and ecofriendliness of the proposed method were verified using two different green metrics, including AGREE and GAPI, which demonstrated the excellent greenness of the method. The developed method was validated according to ICH Q2 (R1) guidelines.

Analysis of vitamin D and its metabolites in biological samples – Part I: Optimization and comparison of UHPSFC-MS/MS and UHPLC-MS/MS methods

Fat-soluble vitamin D is an essential bioactive compound important for human health. Insufficient vitamin D levels can result not only in bone disease but also in other disorders, such as cancer, metabolic disorders, and diseases related to poor immune function. The current methods commonly used for vitamin D analysis are often applied to determine the levels of the most abundant metabolite in plasma, i.e., 25-OH-D2/D3. These methods do not consider the presence of other hydroxylated and esterified metabolites, including isomers and epimers, which are typically found in low concentrations.In this study, we developed a fast and selective ultra-high performance supercritical fluid chromatography (UHPSFC) method using a 150 mm long 1-amino anthracene (1-AA) column and a mobile phase consisting of carbon dioxide and methanol/isopropanol (1/1, v/v) mixed with 8 % water. After thorough optimization of column temperature and back pressure, the separation of four vitamin D3 esters, vitamin D3 and D2, and eight mono- and di-hydroxylated metabolites, including three groups of isomers, was achieved in 10 min. Two ion sources, atmospheric pressure chemical ionization (APCI) and atmospheric pressure photoionization optimized within this study, were compared in tandem mass spectrometry (MS/MS) detection. No significant sensitivity differences were observed. Subsequently, the same 1-AA column chemistry was examined in ultra-high performance liquid chromatography (UHPLC) as the stationary phase that could hypothetically bring different selectivity in the separation of vitamin D and its metabolites. However, this hypothesis was rejected, and C18 was used as a stationary phase in the final optimized UHPLC-MS/MS method. Despite detailed optimization, the final 15 min UHPLC method was not able to separate di-hydroxylated isomers of vitamin D3, while it enabled better resolution of esterified forms compared to UHPSFC.Optimized methods provided similar repeatability of retention times and peak areas, with RSD < 2 % and 10 %, respectively. The lowest limits of quantification were in the range of 1.2 – 4.9 ng/mL for UHPSFC-APCI-MS/MS, while for UHPLC-APCI-MS/MS, they were typically in the range of 2.6 – 9.6 ng/mL. Based on the obtained results, the UHPSFC-APCI-MS/MS method was the most promising approach for fast, selective, and sensitive analysis that could be applied in the analysis of biological samples with emphasis on the separation of both hydroxylated and esterified metabolites, including isomeric forms.

A Monte Carlo Sensitivity Analysis for a Dimensionally Reduced-Order Model of the Aortic Dissection.

Aortic dissection is associated with a high mortality rate. Although computational approaches have shed light on many aspects of the disease, a sensitivity analysis is required to determine the significance of different factors. Because of its complex geometry and high computational expense, the three-dimensional (3D) fluid-structure interaction (FSI) simulation is not a suitable approach for sensitivity analysis. We performed a Monte Carlo simulation (MCS) to investigate the sensitivity of hemodynamic quantities to the lumped parameters of our zero-dimensional (0D) model with numerically calculated lumped parameters. We performed local and global analyses on the effect of the model parameters on important hemodynamic quantities. The MCS showed that a larger lumped resistance value for the false lumen and the tears result in a higher retrograde flow rate in the false lumen (the coefficient of variation, [Formula: see text], the sensitivity [Formula: see text], Spearman's coefficient,[Formula: see text]). For the intraluminal pressure, our results show a significant role in the resistance and inertance of the true lumen (the coefficient of variation, [Formula: see text], the sensitivity [Formula: see text], and Spearman's coefficient,[Formula: see text] for the inertance of the true lumen). This study highlights the necessity of comparing the results of the local and global sensitivity analyses to understand the significance of multiple lumped parameters. Because of the efficiency of the method, our approach is potentially useful to investigate and analyze medical planning.

Investigating the sensitivity of losses to time-dependent components of seismic risk modeling

Conventional earthquake risk modeling involves several notable simplifications, which neglect: (1) the effects on seismicity of interactions between adjacent faults and the long-term elastic rebound behavior of faults; (2) short-term hazard increases associated with aftershocks; and (3) the accumulation of damage in assets due to the occurrence of multiple earthquakes in a short time window, without repairs. Several recent earthquake events (e.g. 2010–2011 Canterbury earthquakes, New Zealand; 2019 Ridgecrest earthquakes, USA; and 2023 Turkey–Syria earthquakes) have emphasized the need for risk models to account for the aforementioned short- and long-term time-dependent characteristics of earthquake risk. This study specifically investigates the sensitivity of monetary loss (i.e. a possible earthquake-risk-model output) to these time dependencies, for a case-study portfolio in Central Italy. The investigation is intended to provide important insights for the catastrophe risk insurance and reinsurance industry. In addition to salient catastrophe risk insurance features, the end-to-end approach for time-dependent earthquake risk modeling used in this study incorporates recent updates in long-term time-dependent fault modeling, aftershock forecasting, and vulnerability modeling that accounts for damage accumulation. The sensitivity analysis approach presented may provide valuable guidance on the importance and appropriate treatment of time dependencies in regional (i.e. portfolio) earthquake risk models. We find that the long-term fault and aftershock occurrence models are the most crucial features of a time-dependent seismic risk model to constrain, at least for the monetary loss metrics examined in this study. Accounting for damage accumulation is also found to be important, if there is a high insurance deductible associated with portfolio assets.

Design sensitivity analysis of modal frequencies of elastic structures submerged in an infinite fluid domain

SummaryThis paper presents a numerical approach for the design sensitivity analysis of modal frequencies of elastic structures submerged in an unbounded heavy fluid domain. Because the feedback of sound pressure onto the fluid‐loaded structures has to be taken into account in this case, a fully coupled scheme which combines the structural finite element method (FEM) and the acoustic boundary element method (BEM) is employed in the numerical simulation. The resulting nonlinear eigenvalue problem created by the coupled finite element and boundary element (FE‐BE) method is converted into a generalized eigenvalue problem (GEP) of reduced dimension by using a contour integral method. The design sensitivity formulation for non‐repeated eigenvalues is derived based on an adjoint method which uses both the left and right eigenvectors, and a small GEP is formed to calculate the gradients of repeated eigenvalues. The Burton‐Miller formulation is employed to shift the fictitious eigenfrequencies and their derivatives, and the parameter setting which is beneficial to the filtering of fictitious eigenfrequencies is investigated. Numerical examples are given to verify the accuracy and effectiveness of the developed method.

Discrepancy Measures for Global Sensitivity Analysis

While sensitivity analysis improves the transparency and reliability of mathematical models, its uptake by modelers is still scarce. This is partially explained by its technical requirements, which may be hard to understand and implement by the nonspecialist. Here we propose a sensitivity analysis approach based on the concept of discrepancy that is as easy to understand as the visual inspection of input-output scatterplots. First, we show that some discrepancy measures are able to rank the most influential parameters of a model almost as accurately as the variance-based total sensitivity index. We then introduce an ersatz-discrepancy whose performance as a sensitivity measure is similar that of the best-performing discrepancy algorithms, is simple to implement, easier to interpret and orders of magnitude faster.

An Alternative Sensitivity Analysis for the Evaluation of MCDA Applications: The Significance of Brand Value in the Comparative Financial Performance Analysis of BIST High-End Companies

Multi-criteria decision analysis (MCDA) applications consist of techniques that enable the decision maker to make clearer decisions in scenarios where there is more than one alternative and criterion. The general approach for sensitivity analysis in MCDA applications implies sensitivity to the weight coefficient. In this study, as an alternative approach, we reinterpret sensitivity by using the statistical relationship between the final ranking produced by an MCDA method and a constant external factor. Thus, we both verify through an anchor and reveal to what extent the change in the weight coefficient changes the external relations of MCDA. The motivation for this study is to propose an alternative sensitivity methodology. On the other hand, brand value is a parameter that contains critical information about the future of the company, which has not integrated into financial performance studies made with MCDAs before. To that end, the financial performance of 31 companies with the highest brand value in Turkey and trading on Borsa Istanbul between 2013 and 2022 was analyzed with seven different MCDA applications via integrating brand value into the criteria for the first time. The study’s findings revealed that the proposed innovative sensitivity tests produced similarly robust results as traditional tests. In addition, brand value has been proved to be an advantageous criterion to be implemented into MCDAs for financial performance problems through the sensitivity analysis made.