Sensitive Analytical Procedure Research Articles

Integration of a power thermal plant under operation to a biomass gasification system: An approach through sensitivity analysis and optimization procedure

Integration of a power thermal plant under operation to a biomass gasification system: An approach through sensitivity analysis and optimization procedure

Optimization of microwave components using machine learning and rapid sensitivity analysis

Recent years have witnessed a tremendous popularity growth of optimization methods in high-frequency electronics, including microwave design. With the increasing complexity of passive microwave components, meticulous tuning of their geometry parameters has become imperative to fulfill demands imposed by the diverse application areas. More and more often, achieving the best possible performance requires global optimization. Unfortunately, global search is an intricate undertaking. To begin with, reliable assessment of microwave components involves electromagnetic (EM) analysis entailing significant CPU expenses. On the other hand, the most widely used nature-inspired algorithms require large numbers of system simulations to yield a satisfactory design. The associated costs are impractically high if not prohibitive. The use of available mitigation methods, primarily surrogate-based approaches, is impeded by dimensionality-related problems and the complexity in microwave circuit characteristics. This research introduces a procedure for expedited globalized parameter adjustment of microwave passives. The search process is embedded in a surrogate-assisted machine learning framework that operates in a dimensionality-restricted domain, spanned by the parameter space directions being of importance in terms of their effects on the circuit characteristic variability. These directions are established using a fast global sensitivity analysis procedure developed for this purpose. Domain confinement reduces the cost of surrogate model establishment and improves its predictive power. The global optimization phase is complemented by local tuning. Verification experiments demonstrate the remarkable efficacy of the presented approach and its advantages over the benchmark methods that include machine learning in full-dimensionality space and population-based metaheuristics.

Urban Building Energy Models: how can we improve the treatment of uncertainty for energy policy decision-making?

Abstract Urban Building Energy Models (UBEMs) are emerging as a powerful tool for cities and regions seeking to make decisions on the best pathways for increasing the energy efficiency of their buildings. As model results are used to inform critical policy decisions, it is essential to understand and communicate the limits of inference of model results and how sensitive they are to changes in inputs. In the absence of standard datasets and protocols for model validation, Uncertainty Analysis and Sensitivity Analysis (UASA) procedures offer vital insights. However, there is no consensus on how UASA should be applied to bottom-up building physics-based UBEMs, nor on how different use cases might influence the choice of UASA approach. This study uses a systematic review of the literature (2009-2023) to explore the procedures which are applied and assess their appropriateness. We find a need for a more holistic view of uncertainty to be taken, and present a decion framework for selecting the most appropriate form of quantiative sensitivity analysis, based on model form, data provenance and use case. We also propose a number of approaches to improve the application of sensitivity analysis in UBEM studies, including the importance of undertaking a complementary assessment of information quality.

In situ growth of hierarchical porous covalent organic framework coating for enhanced solid-phase microextraction of phenolic compounds

Covalent organic frameworks (COFs), when utilized as solid-phase microextraction (SPME) coating materials, exhibit remarkable abilities to concentrate phenols by thousands-fold owing to their vast surface area and exceptional stability. However, the prevalent micropores inherent in COFs can impede rapid mass transfer of target molecules, prolonging SPME extraction times. To addresses this limitation, this work introduces a novel approach by integrating hierarchical porous structures into COFs, leveraging polystyrene microspheres as hard templates during the in situ growth process of the SPME coating. Due to the presence of a hierarchical porous structure derived from the template, the resulting hierarchical porous TpBD coating, termed HP-TpBD, demonstrated enhanced extraction efficiency, accelerated extraction kinetics, and notably shortened extraction times for phenolic compounds. Coupled with gas chromatography-mass spectrometry, a highly sensitive method featuring a low limit of detection (0.20–0.28 ng L-1), a broad linear range (1.0∼1.0 × 104 ng L-1), and excellent precision (RSD < 8.5 %) was established. This methodology enables accurate quantitative analysis of phenols in water and soil samples. This work provides valuable insights into developing COF-based SPME coatings for the efficient extraction of volatile contaminants, paving the way for more efficient and sensitive analytical procedures.

Quantitative determination of naftifine and its degradation products in medicines by HPLC with small-volume columns

INTRODUCTION. Naftifine medicinal products are widely used in medical practice to treat fungal infections. A key quality attribute of a naftifine medicinal product is the content of the active substance. It is essential to develop quantitative analytical procedures for the routine control of medicines with a particular focus on reducing time and costs.AIM. This study aimed to update the analytical procedures for the quantitative determination of naftifine and its impurities in medicines by high-performance liquid chromatography (HPLC) with small-volume columns providing a reduction in assay time and reagent consumption.MATERIALS AND METHODS. This study focused on the active substance naftifine and naftifine-based medicinal products, including a 1% naftifine alcohol solution and a 1% naftifine cream for cutaneous use. The solutions were analysed on Agilent 1200 Infinity and Agilent Infinity II 1290 liquid chromatography systems equipped with diode-array detectors and several chromatographic columns: XBridge Phenyl, 20×4.6 mm, 2.5 μm; XBridge Phenyl, 20×4.6 mm, 3.5 μm; and Acquity BEH Phenyl, 75×2.1 mm, 1.7 μm. The specificity of the analytical procedure was evaluated using spiking solutions of N-methyl-1-naphthalenemethylamine and cinnamaldehyde as well as naftifine solutions after chemical, thermal, and photolytic decomposition.RESULTS. The authors identified the optimal non-toxic solvent (0.1% orthophosphoric acid solution) and demonstrated the applicability of different solvents to different dosage forms. Additionally, the selected analytical conditions included the following: 10 μg/mL naftifine solutions were chromatographed on an XBridge Phenyl column (20×4.6 mm; 2.5 μm) using a gradient of 0.1% perchloric acid and acetonitrile at an elution rate of 1 mL/min. The study showed that the selected detection wavelength of 254 nm provided the best signal-to-noise ratio for the naftifine peak. The reproducibility of the developed quantitative determination procedure was confirmed by validation in accordance with the current requirements of the State Pharmacopoeia of the Russian Federation. The specificity of the analytical procedure was shown by chromatographic analysis of the solvent, mobile phase, and solutions containing the main naftifine impurities. The validation study confirmed the linearity of the analytical procedure in the range of 80–120% (with a correlation coefficient of 0.995). During the accuracy validation, the recovery rate was 100.2%. The validation study demonstrated the robustness of the analytical procedure to minor changes in the chromatographic parameters. The naftifine retention time amounted to approximately 2 minutes.CONCLUSIONS. The authors developed a selective and sensitive HPLC-based analytical procedure for the quantitative determination of naftifine in medicines. This analytical procedure provides for a reduction in assay time and reagent consumption, and its compliance with the validation acceptance criteria indicates its suitability and reproducibility.

Latently Mediating: A Bayesian Take on Causal Mediation Analysis with Structured Survey Data

In this paper, we propose a Bayesian causal mediation approach to the analysis of experimental data when both the outcome and the mediator are measured through structured questionnaires based on Likert-scaled inquiries. Our estimation strategy builds upon the error-in-variables literature and, specifically, it leverages Item Response Theory to explicitly model the observed surrogate mediator and outcome measures. We employ their elicited latent counterparts in a simple g-computation algorithm, where we exploit the fundamental identifying assumptions of causal mediation analysis to impute all the relevant counterfactuals and estimate the causal parameters of interest. We finally devise a sensitivity analysis procedure to test the robustness of the proposed methods to the restrictive requirement of mediator’s conditional ignorability. We demonstrate the functioning of our proposed methodology through an empirical application using survey data from an online experiment on food purchasing intentions and the effect of different labeling regimes.

Development and Validation of an Analytical Procedure for Determining Convallatoxin in Medicinal Products Containing Cardiac Glycosides of Lily of the Valley

INTRODUCTION. The safe use of lily-of-the-valley medicinal products requires adequately controlling the content of cardiac glycosides. The State Pharmacopoeia of the Russian Federation stipulates that the content of cardiac glycosides in lily-of-the-valley medicinal products should be quantified using bioassays and spectrophotometry. However, the determination of cardiac glycosides needs more accurate and selective physicochemical methods, such as high-performance liquid chromatography (HPLC).AIM. This study aimed to develop and validate an HPLC analytical procedure for the quantitative determination of cardiac glycosides (convallatoxin) in lily-of-the-valley herbal medicinal products.MATERIALS AND METHODS. The study examined the Lily-of-the-Valley Tincture, Zelenin Drops, Valocormid, Carniland®, and Lily-of-the-Valley Extract Reference Standard samples. The content of cardiac glycosides was determined by comparison with a reference standard for convallatoxin by HPLC and spectrophotometry. The samples were prepared as outlined in Monograph 3.4.0003.18 Lily-of-the-Valley Herb Tincture of the State Pharmacopoeia of the Russian Federation. Mixed cardiac glycosides were separated on a Luna 5 µm C18(2) column by gradient elution with 0.1% orthophosphoric acid in water and acetonitrile. The analysis was performed using an autoinjector with sample cooling to 5 °C.RESULTS. The developed analytical procedure met the acceptance criteria for specificity, intermediate precision, linearity (correlation coefficient of 0.99985), and repeatability (relative standard deviation of convallatoxin measurements of 1.61%). The analytical procedure is suitable for the quantitative determination of convallatoxin in lily-of-the-valley herbal medicinal products because it produces reliable and repeatable results.CONCLUSIONS. The authors developed a highly sensitive and selective HPLC analytical procedure for the quantitative determination of convallatoxin in lily-of-the-valley herbal medicinal products. The content of convallatoxin ranged from 0.012 to 0.018 mg/mL in the liquid active substance Lily-of-the-Valley Tincture, from 0.004 to 0.013 mg/mL in the medicinal product Zelenin Drops, and from 0.005 to 0.007 mg/mL in the medicinal products Carniland® and Valocormid. For the Lily-of-the-Valley Extract Reference Standard, the content of convallatoxin amounted to 0.029 mg/mL.

Improved efficacy behavioral modeling of microwave circuits through dimensionality reduction and fast global sensitivity analysis

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

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 this 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 R2 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.

Amino‐Functionalized Metal Organic Framework‐Based Dispersive Micro‐Solid‐Phase Extraction Coupled With HPLC for Sensitive Quantification of Bisphenol A and Alkyl Phenols in Aqueous Samples

ABSTRACTA sensitive analytical procedure for simultaneous extraction and determination of bisphenol A and alkyl phenols, namely, 4‐sec‐butylphenol, 4‐tert‐butylphenol, and 4‐tert‐amylphenol from aqueous samples with metal–organic framework (MOF)‐based dispersive micro‐solid‐phase extraction (D‐µSPE) and high‐performance liquid chromatography (HPLC) technique is developed. Sample contaminants were preconcentrated with amino‐functionalized aluminum MOF and later eluted with acetonitrile. The reconstituted sample was subjected to HPLC analysis. Various procedural parameters related to D‐µSPE were optimized. Under optimized preconcentration and chromatographic conditions, calibration curves with excellent linearity (R2 &gt; 0.9986) in the working range of 0.25–100 ng mL−1 were prepared. Limit of detection (S/N = 3) for every analyte within the range of 0.020–0.022 ng mL−1 was obtained. Analyte's recovery 91.16%–97.01% (RSD = 1.02–3.01) for intraday and 89.86%–95.83% (RSD = 1.15–3.13) for inter‐day analysis was obtained from real water samples.

Ultrasound-Assisted Extraction- and Liquid Chromatography-Based Method Development and Validation for Obtaining and Qualitative Determination of Apple Pomace Three Triterpene Acids using Analytical Quality by Design

Background: Apple pomace has garnered significant attention within the life sciences domain due to its underutilized status as a waste material from apple processing. It represents a cost-effective and abundant source of triterpene acids due to its multifunctional clinical, nutritional, and pharmaceutical benefits. Purpose: The present study aimed to develop and validate a new, selective, effective, robust and reproducible laboratory methodology based on extraction, purification and analytical procedures to obtain and determining three major triterpene acids – Ursolic acid (UA), Oleanolic acid (OA) and Betulinic acid (BA) into the dry extracted product from apple pomace. Method: A new, cost-efficient, rapid, selective and high-yield two-stage ultrasound-assisted extraction procedure was developed and the effect of critical parameters: ultrasonic power, extraction time, solvent volume, temperature, and the amount of raw material on the extraction process were investigated. The dry column vacuum chromatography technique was used for purification to remove unwanted non-polar and polar impurities from the target bioactive compounds; A new, effective, specific, sensitive, and rapid HPLC analytical procedure was developed using analytical quality by design (AQbD) approach and validated according to ICH guidelines. Conclusion: The method has a good accuracy (the mean recovery &gt;95 %) and linearity (R2&gt;0.999). The limit of quantitation (LOQ) is 0.0001 mg/mL for UA, 0.00005 mg/mL for OA and 0.000025 mg/mL for BA. The validation results confirm that the method is specific, precise and robust. The purity of the extracted and purified target product from apple pomace is not less than 93 %. The developed laboratory methodology is capable of being considered for industrial purposes and through the appropriate technology transfer process can be successfully transferred to the industrial scale.

Handling Measurement Error and Omitted Confounders Considering Informativeness of the Confounding Effect under Mediation Modeling

In the article, we focused on the issues of measurement error and omitted confounders while conducting mediation analysis under experimental studies. Depending on informativeness of the confounders between the mediator (M) and outcome (Y), we described two approaches. When researchers are confident that primary confounders are included (e.g., based on theory, literature), mediation effects can be estimated using Bayesian estimation with informative priors for the correlation of residuals between latent M and Y (ρ). Simulation study results showed that mediation analysis without accounting for secondary confounders yielded inaccurate inferences about mediation effects. Without prior knowledge about the primary confounders between M and Y, we suggest using sensitivity analysis to probe robustness of mediation analysis results. ρ was used as the sensitivity parameter and an R shiny app was developed to conduct sensitivity analysis: https://qwang17.shinyapps.io/sensitivity/. Both unidimensional and multidimensional models are allowed under our developed R shiny app to conduct sensitivity analysis. Two real data examples were used for illustrating the procedure of sensitivity analysis. We ended the article by making recommendations and entertaining future directions in mediation analysis.

Sensitivity analysis of multiple time-scale building energy using Bayesian adaptive spline surfaces

Sensitivity analysis has been widely used in building energy analysis to identify key variables influencing building energy and carbon emissions. However, most previous studies only focus on the single time scale energy use of buildings when conducting sensitivity analysis. The multiple time-scale sensitivity analysis would lead to increased complexity and challenges in building energy analysis. This paper proposes a new systematical procedure for global variance-based sensitivity analysis of multiple time-scale energy use of buildings. A case study of an office building is used to demonstrate the application of this approach using the Bayesian adaptive spline surface (BASS) models at three different time scales (annual, monthly, and daily). The results indicate that the procedure proposed here can provide fast and reliable sensitivity results for the multiple time-scale building energy assessment. The BASS learning models have the advantages of high predictive performance and the fast computation of sensitivity indicators in a closed form without Monte Carlo integrals. The application of principal component analysis can deal with the highly correlated multi-output energy use at a smaller time scale, which can further reduce computational costs. Moreover, the sensitivity analysis at the different time scales can provide new insights into energy characteristics due to the intrinsic variations of weather conditions. This systematical procedure can provide useful guidance for the multiple time-scale model calibration and multi-step ahead predictions of buildings. Moreover, the method proposed here can be extended to the sensitivity analysis of energy use in different time scales (sub-hourly, hourly, or daily) for other energy systems (such as PV, solar thermal, and wind).

A Small-Signal Stability Analysis Method Based on Minimum Characteristic Locus and Its Application in Controller Parameter Tuning

A small-signal stability analysis method based on minimum characteristic locus (MCL) was recently proposed by the authors. The method is found to be particularly useful in analyzing various oscillation modes in a unified way. The present work continues this line of research. After providing a brief review of the basic principle of MCL method, the paper proceeds to show the mathematical relationship between the stability margin obtained using an MCL method, and that obtained by traditional modal analysis method. Next, a sensitivity analysis procedure is proposed to study quantitatively the effects of control parameters on the stability of the system. Third, the properties of the so-called exciter mode are explained in detail, in particular, explicit formulas of exciter mode frequency and power system stabilizer (PSS) critical gain are derived. Finally, taking PSS4B as an example, a controller parameter tuning method is proposed. The effectiveness and practicability of the proposed method are verified by case studies of Kundur's four-machine-two-area system and China's Yunnan power grid.

An explainable machine learning-based probabilistic framework for the design of scaffolds in bone tissue engineering.

Recently, 3D-printed biodegradable scaffolds have shown great potential for bone repair in critical-size fractures. The differentiation of the cells on a scaffold is impacted among other factors by the surface deformation of the scaffold due to mechanical loading and the wall shear stresses imposed by the interstitial fluid flow. These factors are in turn significantly affected by the material properties, the geometry of the scaffold, as well as the loading and flow conditions. In this work, a numerical framework is proposed to study the influence of these factors on the expected osteochondral cell differentiation. The considered scaffold is rectangular with a 0/90 lay-down pattern and a four-layered strut made of polylactic acid with a 5% steel particle content. The distribution of the different types of cells on the scaffold surface is estimated through a scalar stimulus, calculated by using a mechanobioregulatory model. To reduce the simulation time for the computation of the stimulus, a probabilistic machine learning (ML)-based reduced-order model (ROM) is proposed. Then, a sensitivity analysis is performed using the Shapley additive explanations to examine the contribution of the various parameters to the framework stimulus predictions. In a final step, a multiobjective optimization procedure is implemented using genetic algorithms and the ROM, aiming to identify the material parameters and loading conditions that maximize the percentage of surface area populated by bone cells while minimizing the area corresponding to the other types of cells and the resorption condition. The results of the performed analysis highlight the potential of using ROMs for the scaffold design, by dramatically reducing the simulation time while enabling the efficient implementation of sensitivity analysis and optimization procedures.

An Efficient Code for the Multi-Material Topology Optimization of 2D/3D Continuum Structures Written in Matlab

This paper presents an efficient and compact Matlab code for 2D and 3D topology optimization of multi-materials. We extend a classical 88-line-based educational code to the multi-material problem using the mapping-based interpolation function, with which each material is modeled equally and can obtain a clear 0, 1 result for each material of the optimized structures that can be manufactured easily. A solution of topology optimization of multi-materials with minimum compliance under volume constraints is documented, including the details of the optimization model, filtering, projection, and sensitivity analysis procedures. Several numerical examples are also conducted to demonstrate the effectiveness of the code, and the influence of the parameter on the optimized results is also analyzed. Complete 2D and 3D Matlab codes are provided.

Analytical Validation Study with Uncertainty Evaluation of the Method for Quantitative Determination of Hesperidin in the Extracted Product and Mandarin Processing Waste

The aim of this study was to develop and validate an alternative, selective, reproducible, and high-yield extraction-based method by ultrasound-assisted technique combined with an effective, specific, sensitive, and rapid analytical HPLC procedure for obtaining hesperidin from mandarin (Citrus unshiu) peel as waste material in citrus processing industries and its quantitative determination in the obtained extracted product and citrus waste materials as well. The evaluation of the uncertainty of the developed method is also described in the present work. The proposed method is focused not only on obtaining hesperidin but also provides the possibility for obtaining other valuable bioactive compounds within the single stepwise extraction process. The proposed method was validated concerning the following validation parameters: robustness, system suitability test, specificity, linearity range, precision, accuracy, sensitivity, limits of quantification (LOQ), and detection (LOD). A design of experiments using the Placket-Burman approach was used for the robustness study of the combined method. The measurement uncertainty of the proposed method was evaluated based on the four-step process using a combination of two appropriate - bottom-up and top-down approaches using the method validation data. The LOD and LOQ of the analytical procedure were 0.00001 mg/mL and 0.000025 mg/mL, respectively. The calibration curve (0.000025-0.5 mg/mL) is linear and the square of the correlation coefficient is equal to 0.99992. The determined average amount of hesperidin in the dry citrus peel samples is equal to 35.36 ± 3.14 mg/g (k=2, 95 % level of confidence). The mean recovery of the combined method is 91.48 %. The purity of the dry-extracted product of hesperidin is not less than 90 %.

A sensitivity analysis for temporal bias in cross-sectional mediation.

For over three decades, methodologists have cautioned against the use of cross-sectional mediation analyses because they yield biased parameter estimates. Yet, cross-sectional mediation models persist in practice and sometimes represent the only analytic option. We propose a sensitivity analysis procedure to encourage a more principled use of cross-sectional mediation analysis, drawing inspiration from Gollob and Reichardt (1987, 1991). The procedure is based on the two-wave longitudinal mediation model and uses phantom variables for the baseline data. After a researcher provides ranges of possible values for cross-lagged, autoregressive, and baseline Y and M correlations among the phantom and observed variables, they can use the sensitivity analysis to identify longitudinal conditions in which conclusions from a cross-sectional model would differ most from a longitudinal model. To support the procedure, we first show that differences in sign and effect size of the b-path occur most often when the cross-sectional effect size of the b-path is small and the cross-lagged and the autoregressive correlations are equal or similar in magnitude. We then apply the procedure to cross-sectional analyses from real studies and compare the sensitivity analysis results to actual results from a longitudinal mediation analysis. While no statistical procedure can replace longitudinal data, these examples demonstrate that the sensitivity analysis can recover the effect that was actually observed in the longitudinal data if provided with the correct input information. Implications of the routine application of sensitivity analysis to temporal bias are discussed. R code for the procedure is provided in the online supplementary materials. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Causal effects of gut microbiota on appendicitis: a two-sample Mendelian randomization study.

Previous research has posited a potential correlation between the gut microbiota and the onset of appendicitis; however, the precise causal connection between appendicitis and the gut microbiota remains an unresolved and contentious issue. In this investigation, we performed a Mendelian randomization (MR) analysis employing publicly accessible summary data extracted from genome-wide association studies (GWAS) to elucidate the potential causal nexus between the gut microbiota and the development of appendicitis. We initially identified instrumental variables (IVs) through a comprehensive array of screening methodologies, subsequently executing MR analyses using the Inverse Variance Weighted (IVW) technique as our primary approach, supplemented by several alternative methods such as MR Egger, weighted median, simple mode, and weighted mode. Additionally, we implemented a series of sensitivity analysis procedures, encompassing Cochran's Q test, MR-Egger intercept test, Mendelian Randomized Polymorphism Residual and Outlier (MR-PRESSO) test, and a leave-one-out test, to affirm the robustness and validity of our findings. Our investigation indicates that an elevated prevalence of Deltaproteobacteria, Christensenellaceae, Desulfovibrionaceae, Eubacterium ruminantium group, Lachnospiraceae NK4A136 group, Methanobrevibacter, Desulfovibrionales, and Euryarchaeota is inversely associated with the risk of appendicitis. Conversely, we observed a positive correlation between an increased abundance of Family XIII, Howardella, and Veillonella and the susceptibility to appendicitis. Sensitivity analyses have corroborated the robustness of these findings, and Mendelian randomization analyses provided no indications of reverse causality. Our Mendelian randomization (MR) analysis has unveiled potential advantageous or detrimental causal associations between the gut microbiota and the occurrence of appendicitis. This study offers novel theoretical and empirical insights into the understanding of appendicitis pathogenesis, along with its implications for preventive and therapeutic strategies.

On the selection of Morris trajectories for parametric sensitivity analysis in spectroscopic ellipsometry modeling

Spectroscopic ellipsometry and some other optical metrology techniques, such as reflectometry and scatterometry, are model-based optical measurements and, therefore, require appropriate modeling to determine the geometric and material properties of substrates, thin films, and multilayer structures. Parametric sensitivity analysis (SA) provides essential assistance in the model-building process to quantify the relative importance of model parameters for model output and to identify those with high/little influence. SA can be performed in a variety of ways, and this article discusses an application of the Morris or elementary effect (EE) method, a screening type SA procedure, to spectroscopic ellipsometry modeling. The method is a global SA technique and uses a stepping of m parameters along certain so-called “trajectories” or sequences of points in parameter space, randomly constructed in order to maximally fill the volume of the m-dimensional parameter space. However, it was thought that the EE method relies greatly on a sampling strategy or a way of selecting “optimized trajectories” in the parameter space, i.e., a necessary number of trajectories chosen to be well spread over the space to properly cover the entire realistic ranges of all input factors. Here, we use two sampling methods for selecting trajectories with possibly different distributions and investigate their effects on the estimation of various sensitivity measures in spectroscopic ellipsometry data modeling. The SA results indicate that the performance of the sampling strategy should not be judged only by maximizing the trajectory spread but also include some additional convergence criteria for the sensitivity measures.