Sensitivity analysis of wall coating thickness to paint characteristics in the spray painting process phase 1 : paint characterization

2014 ◽  
Author(s):  
Barry Moore ◽  
Farhad Nabhani ◽  
Vahid Askari ◽  
Desmond McMenamim
Keyword(s):  
Sensitivity Analysis ◽  
Coating Thickness ◽  
Phase 1 ◽  
Spray Painting ◽  
Painting Process

A sensitivity analysis of the WIPP disposal room model: Phase 1

1995 ◽  
Author(s):  
D. A. Labreche ◽  
M. A. Beikmann ◽  
J. D. Osnes ◽  
B. M. Butcher
Keyword(s):  
Sensitivity Analysis ◽  
Phase 1 ◽  
Room Model ◽  
Model Phase

scholarly journals Adjusting iron and vitamin A status in settings of inflammation: a sensitivity analysis of the Biomarkers Reflecting Inflammation and Nutritional Determinants of Anemia (BRINDA) approach

2020 ◽  
Vol 112 (Supplement_1) ◽  
pp. 458S-467S
Author(s):  
Sorrel M L Namaste ◽  
Jiangda Ou ◽  
Anne M Williams ◽  
Melissa F Young ◽  
Emma X Yu ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Iron Deficiency ◽  
Vitamin A ◽  
Vitamin A Deficiency ◽  
Phase 1 ◽  
Regression Coefficients ◽  
Phase 2 ◽  
Vitamin A Status ◽  
Iron Deficient ◽  
Regression Approach

ABSTRACT Background Accurate assessment of iron and vitamin A status is needed to inform public health decisions, but most population-level iron and vitamin A biomarkers are independently influenced by inflammation. Objectives We aimed to assess the reproducibility of the Biomarkers Reflecting Inflammation and Nutritional Determinants of Anemia (BRINDA) regression approach to adjust iron [ferritin, soluble transferrin receptor (sTfR)] and vitamin A [retinol-binding protein (RBP), retinol] biomarkers for inflammation (α-1-acid glycoprotein and C-reactive protein). Methods We conducted a sensitivity analysis comparing unadjusted and adjusted estimates of iron and vitamin A deficiency using the internal-survey regression approach from BRINDA phase 1 (16 surveys in children, 10 surveys in women) and 13 additional surveys for children and women (BRINDA phase 2). Results The relations between inflammation and iron or vitamin A biomarkers were statistically significant except for vitamin A biomarkers in women. Heterogeneity of the regression coefficients across surveys was high. Among children, internal-survey adjustments increased the estimated prevalence of depleted iron stores (ferritin <12 µg/L) by a median of 11 percentage points (pp) (24 pp and 9 pp in BRINDA phase 1 and phase 2, respectively), whereas estimates of iron-deficient erythropoiesis (sTfR >8.3 mg/L) decreased by a median of 15 pp (15 pp and 20 pp in BRINDA phase 1 and phase 2, respectively). Vitamin A deficiency (RBP <0.7 µmol/L or retinol <0.7 µmol/L) decreased by a median of 14 pp (18 pp and 8 pp in BRINDA phase 1 and phase 2, respectively) in children. Adjustment for inflammation in women resulted in smaller differences in estimated iron deficiency than in children. Conclusions Our findings are consistent with previous BRINDA conclusions that not accounting for inflammation may result in an underestimation of iron deficiency and overestimation of vitamin A deficiency. Research is needed to understand the etiology of the heterogeneity in the regression coefficients before a meta-analyzed regression correction can be considered.

scholarly journals Sensitivity Analysis of Biome-BGCMuSo for Gross and Net Primary Productivity of Typical Forests in China

2021 ◽  
Author(s):  
Hongge Ren ◽  
Li Zhang ◽  
Min Yan ◽  
Xin Tian ◽  
Xingbo Zheng
Keyword(s):  
Sensitivity Analysis ◽  
Primary Productivity ◽  
Model Calibration ◽  
Field Experiments ◽  
Net Primary Productivity ◽  
Phase 1 ◽  
Light Extinction ◽  
Sensitivity Index ◽  
Sensitivity Testing ◽  
Climate Gradient

Abstract Background: Process-based models are widely used to simulate forest productivity, but complex parameterization and calibration challenge the application and development of these models. Sensitivity analysis of numerous parameters is an essential step in model calibration and carbon flux simulation. However, parameters are not dependent on each other, and the results of sensitivity analysis usually vary due to different forest types and regions. Hence, global and representative sensitivity analysis would provide reliable information for simple calibration. Methods: To determine the contributions of input parameters to gross primary productivity (GPP) and net primary productivity (NPP), regression analysis and extended Fourier amplitude sensitivity testing (EFAST) were conducted for Biome-BGCMuSo to calculate the sensitivity index of the parameters at four observation sites under climate gradient from ChinaFLUX. Results: Generally, GPP and NPP were highly sensitive to C:N leaf (C:N of leaves), W int (canopy water interception coefficient), k (canopy light extinction coefficient), FLNR (fraction of leaf N in Rubisco), MR pern (maintenance respiration in kg C/day per kg of tissue N), VPD f (vapor pressure deficit complete conductance reduction), and SLA1 (canopy average specific leaf area in phenological phase 1) at all observation sites. Various sensitive parameters occurred at four observation sites within different climate zones. GPP and NPP were particularly sensitive to FLNR, SLA1 and W int , and C:N leaf in temperate, alpine and subtropical zones, respectively. Conclusions: The results indicated that sensitivity parameters of China's forest ecosystems change with climate gradient. We found that parameter calibration should be performed according to plant functional type (PFT), and more attention needs to be paid to the differences in climate and environment. These findings contribute to determining the target parameters in field experiments and model calibration.

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