Skip to content
Free planNo credit card requiredStart writing
AI for Research home
AI for Research home
Dashboard
Sign inGet started

Search 250 million research papers

Read the abstract, see who wrote it and who cites it, and get the citation in APA, MLA, Chicago or Harvard.

About 10,000 papers for “Sensitivity analysis”

Sensitivity Analysis

Way Kuo, Xiaoyan Zhu · 2012

Sensitivity analysis is very useful in determining the impact of a particular input on an output under a set of assumptions, such as the effect that changes in interest rates have on a bond’s price. This chapter gives an introductory and illuminative discussion on general optimization problems, focusing on importance measures in local sensitivity and perturbation analysis, and global sensitivity and uncertainty analysis. Both of them help in understanding the insights of a problem. It specially introduces the variance based uncertainty importance measures that are used in the sensitivity analysis of uncertainty in systems reliability, including software reliability. The chapter describes the applications of sensitivity analysis, especially global sensitivity measures, in various areas. Controlled Vocabulary Terms consecutive system reliability

Cited by 0

Sensitivity analysis.

Moath Alatefi (20058654), Abdulrahman M. Al-Ahmari (20390288), Abdullah Yahia AlFaify (20390291) et al. · Figshare · 2024

<div><p>The rapid advancement of additive manufacturing (AM) requires researchers to keep up with these advancements by continually improving the AM processes. Improving manufacturing processes involves evaluating the process outputs and their conformity to the required specifications. Process capability indices, calculated using critical quality characteristics (QCs), have long been used in the evaluation process due to their proven effectiveness. AM processes typically involve multi-correlated critical QCs, indicating the need to develop a multivariate process capability index (MPCI) rather than a univariate capability index, which may lead to misleading results. In this regard, this study proposes a general methodological framework for evaluating AM processes using MPCI. The proposed framework starts by identifying the AM process and product design. Fused Deposition Modeling (FDM) is chosen for this investigation. Then, the specification limits associated with critical QCs are established. To ensure that the MPCI assumptions are met, the critical QCs data are examined for normality, stability, and correlation. Additionally, the MPCI is estimated by simulating a large sample using the properties of the collected QCs data and determining the percent of nonconforming (PNC). Furthermore, the FDM process and its capable tolerance limits are then assessed using the proposed MPCI. Finally, the study presents a sensitivity analysis of the FDM process and suggestions for improvement based on the analysis of assignable causes of variation. The results revealed that the considered process mean is shifted for all QCs, and the most variation is associated with part diameter data. Moreover, the process data are not normally distributed, and the proposed transformation algorithm performs well in reducing data skewness. Also, the performance of the FDM process according to different designations of specification limits was estimated. The results showed that the FDM process is incapable of different designs except with very coarse specifications.</p></div>

Cited by 0Open access

Sensitivity analysis.

David J. Haw (6251006), Derek A. T. Cummings (116201), Justin Lessler (116180) et al. · Figshare · 2019

<p>Distribution of local attack rates with respect to <b>(A)</b> offset <i>a</i> using S-mobility. <b>(B)</b> offset <i>a</i> using I-mobility, <b>(C)</b> distance power <i>p</i> using S-mobility, <b>(D)</b> distance power <i>p</i> using I-mobility, <b>(E)</b> population power <i>α</i> using S-mobility, and <b>(F)</b> population power <i>α</i> using I-mobility. Box plots show standard percentiles and outliers, solid lines show global attack rate, and dashed lines show parameter values used in the main result. When fixed, all parameters are as in main result, i.e. <i>a</i> = 0.58, <i>p</i> = 2.72, <i>α</i> = 0.52. Dual mobility are omitted as they are flat with variance <i>σ</i><sup>2</sup> = 0. Empty pixels yield attack rate zero and are omitted from calculations.</p>

Cited by 0Open access

Sensitivity Analysis

Pramod Belkhode, Prashant Maheshwary, Kanchan Borkar et al. · Mathematical Modelling · 2023

Cited by 0

Sensitivity analysis.

Aihua Li (464034) · Figshare · 2024

<div><p>Human error plays a significant role in crane safety. To increase the accuracy and rationality of human error risk prioritization for crane operations, this study proposes a risk prioritization model for human errors in crane operations based on the cumulative prospect theory (CPT) and the improved combination weighting model of game theory (ICWGT). The ICWGT integrates the risk-factor weights obtained via subjective and objective methods. Trapezoidal fuzzy numbers are used to describe experts’ uncertainty information. Then, the CPT is applied to handle the assessment of experts’ risk attitudes in the decision process. The human error risk ranking of crane operations is obtained according to the overall prospect values calculated using the CPT. A case study of human error in overhead crane operations was conducted, and sensitivity and comparison analyses confirmed the feasibility of the proposed model. The proposed ranking mechanism for human error risk priority in crane operations is helpful for crane risk management.</p></div>

Cited by 0Open access

Sensitivity analysis.

Alexandre Poulain (1595023), Jørgen Riseth (14755668), Vegard Vinje (6814343) · Figshare · 2023

<div><p>The glymphatic system is the subject of numerous pieces of research in biology. Mathematical modelling plays a considerable role in this field since it can indicate the possible physical effects of this system and validate the biologists’ hypotheses. The available mathematical models that describe the system at the scale of the brain (<i>i.e.</i> the macroscopic scale) are often solely based on the diffusion equation and do not consider the fine structures formed by the perivascular spaces. We therefore propose a mathematical model representing the time and space evolution of a mixture flowing through multiple compartments of the brain. We adopt a macroscopic point of view in which the compartments are all present at any point in space. The equations system is composed of two coupled equations for each compartment: One equation for the pressure of a fluid and one for the mass concentration of a solute. The fluid and solute can move from one compartment to another according to certain membrane conditions modelled by transfer functions. We propose to apply this new modelling framework to the clearance of <sup>14</sup>C-inulin from the rat brain.</p></div>

Cited by 0Open access

Sensitivity analysis.

Maxwell Ayindenaba Dalaba (624553), Patricia Akweongo (488556), Raymond Akawire Aborigo (739161) et al. · Figshare · 2015

Cited by 0Open access

Sensitivity analysis.

Kenneth F. Kellner (651754), Robert K. Swihart (173623) · Figshare · 2017

Cited by 0Open access

Sensitivity analysis.

Xiaodong Chen (410427), Jiaqi Li (220961), Ningda Xu (8190480) et al. · Figshare · 2025

<div> Background This study aims to investigate the connection between serum lipids, per- and polyfluoroalkyl substances (PFAS), and age-related macular degeneration (AMD) risk and assess whether serum lipids mediate the association between PFAS and AMD. Methods 1605 participants were enrolled from NHANES 2005–2008. Four serum PFAS levels with high detective rates, including perfluorononanoic acid (PFNA), perfluorooctanoic acid (PFOA), perfluorooctane sulfonic acid (PFOS), and perfluorohexane sulfonic acid (PFHxS) were examined. Restricted cubic spline analysis (RCS) and weighted quantile sum (WQS) analysis were employed to detect nonlinear and mixed exposure effects. Results PFOS level was associated with any AMD (OR, 1.54; 95% CI, 1.12 to 2.11; P = 0.011), early AMD (OR, 1.43; 95% CI, 1.06 to 1.95; P = 0.024), and late AMD (OR, 3.35; 95% CI, 1.55 to 7.23; P = 0.004) risk. PFHxS (OR, 1.72; 95% CI, 1.01 to 2.93; P = 0.045) and PFOA (OR, 2.10; 95% CI, 1.21 to 3.63; P = 0.011) levels were associated with late AMD risk. The RCS showed a nonlinear connection between PFOS exposure and AMD risk (P nonlinear = 0.006). WQS analysis indicated a positive relationship between mixed PFAS exposure and AMD risk (OR, 1.34; 95% CI, 1.03 to 1.75; P = 0.030). Serum total cholesterol (TC) and high-density lipoprotein (HDL) cholesterol were associated with AMD risk (OR<sub>TC</sub>, 1.005; 95% CI, 1.001 to 1.009; P = 0.026. OR<sub>HDL</sub>, 1.028; 95% CI, 1.014 to 1.042; P<0.001), and mediated the relationship of PFOS exposure and AMD risk, with mediation proportions of 5.73% (P = 0.020) and 7.27% (P = 0.032), respectively. Conclusions PFOS exposure was connected with AMD risk and serum TC and HDL mediated this relationship. </div>

Cited by 0Open access

Sensitivity analysis.

Shiu-Dong Chung (398440), Kuo-Hsien Wang (523899), Ming-Chieh Tsai (341337) et al. · Figshare · 2016

Cited by 0Open access

Sensitivity analysis.

Erlend B. Nilsen (487861), Olav Strand (3272031) · Figshare · 2018

&lt;p&gt;Sensitivity analysis examining the effects of unequal detection probability among age- and sex classes on estimated model parameters. Roman numbers (I–IV) refers to I) FY detection rate bias relative to calf detection rate in pre-harvest survey, II) female detection rate in relation to calf–and male detection rate in post-harvest surveys, III) FY (pre-harvest) and female (post-harvest) detection rate relative to other classes in the post-harvest samples, and IV) male detection rate relative to calves and females in post-harvest samples. Demographic rates were drawn at random from the distributions in &lt;a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0194566#pone.0194566.t001" target="_blank"&gt;Table 1&lt;/a&gt;.&lt;/p&gt;

Cited by 0Open access

Sensitivity analysis.

Svea Nolte (9470809), Marco van Londen (9470812), Jan Willem J. Elting (6933875) et al. · Figshare · 2020

&lt;p&gt;Patients who underwent a third measurement were excluded from the study population and the statistical analyses were carried out as described in the Methodology section.&lt;/p&gt; &lt;p&gt;(DOCX)&lt;/p&gt;

Cited by 0Open access

Sensitivity analysis.

Prasad Liyanage (10953862), Joacim Rocklöv (296647), Hasitha Aravinda Tissera (10953865) · Figshare · 2021

Cited by 0Open access