Handbook of univariate and multivariate data analysis with IBM SPSS
Ho, Robert
2013-01-01
Using the same accessible, hands-on approach as its best-selling predecessor, the Handbook of Univariate and Multivariate Data Analysis with IBM SPSS, Second Edition explains how to apply statistical tests to experimental findings, identify the assumptions underlying the tests, and interpret the findings. This second edition now covers more topics and has been updated with the SPSS statistical package for Windows.New to the Second EditionThree new chapters on multiple discriminant analysis, logistic regression, and canonical correlationNew section on how to deal with missing dataCoverage of te
[A SAS marco program for batch processing of univariate Cox regression analysis for great database].
Yang, Rendong; Xiong, Jie; Peng, Yangqin; Peng, Xiaoning; Zeng, Xiaomin
2015-02-01
To realize batch processing of univariate Cox regression analysis for great database by SAS marco program. We wrote a SAS macro program, which can filter, integrate, and export P values to Excel by SAS9.2. The program was used for screening survival correlated RNA molecules of ovarian cancer. A SAS marco program could finish the batch processing of univariate Cox regression analysis, the selection and export of the results. The SAS macro program has potential applications in reducing the workload of statistical analysis and providing a basis for batch processing of univariate Cox regression analysis.
Davis, Tyler; LaRocque, Karen F; Mumford, Jeanette A; Norman, Kenneth A; Wagner, Anthony D; Poldrack, Russell A
2014-08-15
Multi-voxel pattern analysis (MVPA) has led to major changes in how fMRI data are analyzed and interpreted. Many studies now report both MVPA results and results from standard univariate voxel-wise analysis, often with the goal of drawing different conclusions from each. Because MVPA results can be sensitive to latent multidimensional representations and processes whereas univariate voxel-wise analysis cannot, one conclusion that is often drawn when MVPA and univariate results differ is that the activation patterns underlying MVPA results contain a multidimensional code. In the current study, we conducted simulations to formally test this assumption. Our findings reveal that MVPA tests are sensitive to the magnitude of voxel-level variability in the effect of a condition within subjects, even when the same linear relationship is coded in all voxels. We also find that MVPA is insensitive to subject-level variability in mean activation across an ROI, which is the primary variance component of interest in many standard univariate tests. Together, these results illustrate that differences between MVPA and univariate tests do not afford conclusions about the nature or dimensionality of the neural code. Instead, targeted tests of the informational content and/or dimensionality of activation patterns are critical for drawing strong conclusions about the representational codes that are indicated by significant MVPA results. Copyright © 2014 Elsevier Inc. All rights reserved.
A Guideline to Univariate Statistical Analysis for LC/MS-Based Untargeted Metabolomics-Derived Data
Maria Vinaixa
2012-10-01
Full Text Available Several metabolomic software programs provide methods for peak picking, retention time alignment and quantification of metabolite features in LC/MS-based metabolomics. Statistical analysis, however, is needed in order to discover those features significantly altered between samples. By comparing the retention time and MS/MS data of a model compound to that from the altered feature of interest in the research sample, metabolites can be then unequivocally identified. This paper reports on a comprehensive overview of a workflow for statistical analysis to rank relevant metabolite features that will be selected for further MS/MS experiments. We focus on univariate data analysis applied in parallel on all detected features. Characteristics and challenges of this analysis are discussed and illustrated using four different real LC/MS untargeted metabolomic datasets. We demonstrate the influence of considering or violating mathematical assumptions on which univariate statistical test rely, using high-dimensional LC/MS datasets. Issues in data analysis such as determination of sample size, analytical variation, assumption of normality and homocedasticity, or correction for multiple testing are discussed and illustrated in the context of our four untargeted LC/MS working examples.
Univariate and multivariate analysis on processing tomato quality under different mulches
Carmen Moreno
2014-04-01
Full Text Available The use of eco-friendly mulch materials as alternatives to the standard polyethylene (PE has become increasingly prevalent worldwide. Consequently, a comparison of mulch materials from different origins is necessary to evaluate their feasibility. Several researchers have compared the effects of mulch materials on each crop variable through univariate analysis (ANOVA. However, it is important to focus on the effect of these materials on fruit quality, because this factor decisively influences the acceptance of the final product by consumers and the industrial sector. This study aimed to analyze the information supplied by a randomized complete block experiment combined over two seasons, a principal component analysis (PCA and a cluster analysis (CA when studying the effects of mulch materials on the quality of processing tomato (Lycopersicon esculentum Mill.. The study focused on the variability in the quality measurements and on the determination of mulch materials with a similar response to them. A comparison of the results from both types of analysis yielded complementary information. ANOVA showed the similarity of certain materials. However, considering the totality of the variables analyzed, the final interpretation was slightly complicated. PCA indicated that the juice color, the fruit firmness and the soluble solid content were the most influential factors in the total variability of a set of 12 juice and fruit variables, and CA allowed us to establish four categories of treatment: plastics (polyethylene - PE, oxo- and biodegradable materials, papers, manual weeding and barley (Hordeum vulgare L. straw. Oxobiodegradable and PE were most closely related based on CA.
... page: //medlineplus.gov/ency/article/003741.htm Sensitivity analysis To use the sharing features on this page, please enable JavaScript. Sensitivity analysis determines the effectiveness of antibiotics against microorganisms (germs) ...
Who uses nursing theory? A univariate descriptive analysis of five years' research articles.
Bond, A Elaine; Eshah, Nidal Farid; Bani-Khaled, Mohammed; Hamad, Atef Omar; Habashneh, Samira; Kataua', Hussein; al-Jarrah, Imad; Abu Kamal, Andaleeb; Hamdan, Falastine Rafic; Maabreh, Roqia
2011-06-01
Since the early 1950s, nursing leaders have worked diligently to build the Scientific Discipline of Nursing, integrating Theory, Research and Practice. Recently, the role of theory has again come into question, with some scientists claiming nurses are not using theory to guide their research, with which to improve practice. The purposes of this descriptive study were to determine: (i) Were nursing scientists' research articles in leading nursing journals based on theory? (ii) If so, were the theories nursing theories or borrowed theories? (iii) Were the theories integrated into the studies, or were they used as organizing frameworks? Research articles from seven top ISI journals were analysed, excluding regularly featured columns, meta-analyses, secondary analysis, case studies and literature reviews. The authors used King's dynamic Interacting system and Goal Attainment Theory as an organizing framework. They developed consensus on how to identify the integration of theory, searching the Title, Abstract, Aims, Methods, Discussion and Conclusion sections of each research article, whether quantitative or qualitative. Of 2857 articles published in the seven journals from 2002 to, and including, 2006, 2184 (76%) were research articles. Of the 837 (38%) authors who used theories, 460 (55%) used nursing theories, 377 (45%) used other theories: 776 (93%) of those who used theory integrated it into their studies, including qualitative studies, while 51 (7%) reported they used theory as an organizing framework for their studies. Closer analysis revealed theory principles were implicitly implied, even in research reports that did not explicitly report theory usage. Increasing numbers of nursing research articles (though not percentagewise) continue to be guided by theory, and not always by nursing theory. Newer nursing research methods may not explicitly state the use of nursing theory, though it is implicitly implied. © 2010 The Authors. Scandinavian Journal of Caring
Oliveira, M M; Sousa, L B; Reis, M C; Silva Junior, E G; Cardoso, D B O; Hamawaki, O T; Nogueira, A P O
2017-05-31
The genetic diversity study has paramount importance in breeding programs; hence, it allows selection and choice of the parental genetic divergence, which have the agronomic traits desired by the breeder. This study aimed to characterize the genetic divergence between 24 soybean genotypes through their agronomic traits, using multivariate clustering methods to select the potential genitors for the promising hybrid combinations. Six agronomic traits evaluated were number of days to flowering and maturity, plant height at flowering and maturity, insertion height of the first pod, and yield. The genetic divergence evaluated by multivariate analysis that esteemed first the Mahalanobis' generalized distance (D 2 ), then the clustering using Tocher's optimization methods, and then the unweighted pair group method with arithmetic average (UPGMA). Tocher's optimization method and the UPGMA agreed with the groups' constitution between each other, the formation of eight distinct groups according Tocher's method and seven distinct groups using UPGMA. The trait number of days for flowering (45.66%) was the most efficient to explain dissimilarity between genotypes, and must be one of the main traits considered by the breeder in the moment of genitors choice in soybean-breeding programs. The genetic variability allowed the identification of dissimilar genotypes and with superior performances. The hybridizations UFU 18 x UFUS CARAJÁS, UFU 15 x UFU 13, and UFU 13 x UFUS CARAJÁS are promising to obtain superior segregating populations, which enable the development of more productive genotypes.
Takayama, Motoharu; Terui, Keita; Oiwa, Yoshitsugu
2012-10-01
Chronic subdural hematoma is common in elderly individuals and surgical procedures are simple. The recurrence rate of chronic subdural hematoma, however, varies from 9.2 to 26.5% after surgery. The authors studied factors of the recurrence using univariate and multivariate analyses in patients with chronic subdural hematoma We retrospectively reviewed 239 consecutive cases of chronic subdural hematoma who received burr-hole surgery with irrigation and closed-system drainage. We analyzed the relationships between recurrence of chronic subdural hematoma and factors such as sex, age, laterality, bleeding tendency, other complicated diseases, density on CT, volume of the hematoma, residual air in the hematoma cavity, use of artificial cerebrospinal fluid. Twenty-one patients (8.8%) experienced a recurrence of chronic subdural hematoma. Multiple logistic regression found that the recurrence rate was higher in patients with a large volume of the residual air, and was lower in patients using artificial cerebrospinal fluid. No statistical differences were found in bleeding tendency. Techniques to reduce the air in the hematoma cavity are important for good outcome in surgery of chronic subdural hematoma. Also, the use of artificial cerebrospinal fluid reduces recurrence of chronic subdural hematoma. The surgical procedures can be the same for patients with bleeding tendencies.
de Brito, Aila Riany; Santos Reis, Nadabe Dos; Silva, Tatielle Pereira; Ferreira Bonomo, Renata Cristina; Trovatti Uetanabaro, Ana Paula; de Assis, Sandra Aparecida; da Silva, Erik Galvão Paranhos; Aguiar-Oliveira, Elizama; Oliveira, Julieta Rangel; Franco, Marcelo
2017-11-26
Endoglucanase production by Aspergillus oryzae ATCC 10124 cultivated in rice husks or peanut shells was optimized by experimental design as a function of humidity, time, and temperature. The optimum temperature for the endoglucanase activity was estimated by a univariate analysis (one factor at the time) as 50°C (rice husks) and 60°C (peanut shells), however, by a multivariate analysis (synergism of factors), it was determined a different temperature (56°C) for endoglucanase from peanut shells. For the optimum pH, values determined by univariate and multivariate analysis were 5 and 5.2 (rice husk) and 5 and 7.6 (peanut shells). In addition, the best half-lives were observed at 50°C as 22.8 hr (rice husks) and 7.3 hr (peanut shells), also, 80% of residual activities was obtained between 30 and 50°C for both substrates, and the pH stability was improved at 5-7 (rice hulls) and 6-9 (peanut shells). Both endoglucanases obtained presented different characteristics as a result of the versatility of fungi in different substrates.
Sensitivity and uncertainty analysis
Cacuci, Dan G; Navon, Ionel Michael
2005-01-01
As computer-assisted modeling and analysis of physical processes have continued to grow and diversify, sensitivity and uncertainty analyses have become indispensable scientific tools. Sensitivity and Uncertainty Analysis. Volume I: Theory focused on the mathematical underpinnings of two important methods for such analyses: the Adjoint Sensitivity Analysis Procedure and the Global Adjoint Sensitivity Analysis Procedure. This volume concentrates on the practical aspects of performing these analyses for large-scale systems. The applications addressed include two-phase flow problems, a radiative c
Rossi, M.; Apuani, T.; Felletti, F.
2009-04-01
The aim of this paper is to compare the results of two statistical methods for landslide susceptibility analysis: 1) univariate probabilistic method based on landslide susceptibility index, 2) multivariate method (logistic regression). The study area is the Febbraro valley, located in the central Italian Alps, where different types of metamorphic rocks croup out. On the eastern part of the studied basin a quaternary cover represented by colluvial and secondarily, by glacial deposits, is dominant. In this study 110 earth flows, mainly located toward NE portion of the catchment, were analyzed. They involve only the colluvial deposits and their extension mainly ranges from 36 to 3173 m2. Both statistical methods require to establish a spatial database, in which each landslide is described by several parameters that can be assigned using a main scarp central point of landslide. The spatial database is constructed using a Geographical Information System (GIS). Each landslide is described by several parameters corresponding to the value of main scarp central point of the landslide. Based on bibliographic review a total of 15 predisposing factors were utilized. The width of the intervals, in which the maps of the predisposing factors have to be reclassified, has been defined assuming constant intervals to: elevation (100 m), slope (5 °), solar radiation (0.1 MJ/cm2/year), profile curvature (1.2 1/m), tangential curvature (2.2 1/m), drainage density (0.5), lineament density (0.00126). For the other parameters have been used the results of the probability-probability plots analysis and the statistical indexes of landslides site. In particular slope length (0 ÷ 2, 2 ÷ 5, 5 ÷ 10, 10 ÷ 20, 20 ÷ 35, 35 ÷ 260), accumulation flow (0 ÷ 1, 1 ÷ 2, 2 ÷ 5, 5 ÷ 12, 12 ÷ 60, 60 ÷27265), Topographic Wetness Index 0 ÷ 0.74, 0.74 ÷ 1.94, 1.94 ÷ 2.62, 2.62 ÷ 3.48, 3.48 ÷ 6,00, 6.00 ÷ 9.44), Stream Power Index (0 ÷ 0.64, 0.64 ÷ 1.28, 1.28 ÷ 1.81, 1.81 ÷ 4.20, 4.20 ÷ 9
Iulian N. BUJOREANU
2011-01-01
Full Text Available Sensitivity analysis represents such a well known and deeply analyzed subject that anyone to enter the field feels like not being able to add anything new. Still, there are so many facets to be taken into consideration.The paper introduces the reader to the various ways sensitivity analysis is implemented and the reasons for which it has to be implemented in most analyses in the decision making processes. Risk analysis is of outmost importance in dealing with resource allocation and is presented at the beginning of the paper as the initial cause to implement sensitivity analysis. Different views and approaches are added during the discussion about sensitivity analysis so that the reader develops an as thoroughly as possible opinion on the use and UTILITY of the sensitivity analysis. Finally, a round-up conclusion brings us to the question of the possibility of generating the future and analyzing it before it unfolds so that, when it happens it brings less uncertainty.
Sensitivity Analysis Without Assumptions.
Ding, Peng; VanderWeele, Tyler J
2016-05-01
Unmeasured confounding may undermine the validity of causal inference with observational studies. Sensitivity analysis provides an attractive way to partially circumvent this issue by assessing the potential influence of unmeasured confounding on causal conclusions. However, previous sensitivity analysis approaches often make strong and untestable assumptions such as having an unmeasured confounder that is binary, or having no interaction between the effects of the exposure and the confounder on the outcome, or having only one unmeasured confounder. Without imposing any assumptions on the unmeasured confounder or confounders, we derive a bounding factor and a sharp inequality such that the sensitivity analysis parameters must satisfy the inequality if an unmeasured confounder is to explain away the observed effect estimate or reduce it to a particular level. Our approach is easy to implement and involves only two sensitivity parameters. Surprisingly, our bounding factor, which makes no simplifying assumptions, is no more conservative than a number of previous sensitivity analysis techniques that do make assumptions. Our new bounding factor implies not only the traditional Cornfield conditions that both the relative risk of the exposure on the confounder and that of the confounder on the outcome must satisfy but also a high threshold that the maximum of these relative risks must satisfy. Furthermore, this new bounding factor can be viewed as a measure of the strength of confounding between the exposure and the outcome induced by a confounder.
Zhang, Jing; Liang, Lichen; Anderson, Jon R; Gatewood, Lael; Rottenberg, David A; Strother, Stephen C
2008-01-01
As functional magnetic resonance imaging (fMRI) becomes widely used, the demands for evaluation of fMRI processing pipelines and validation of fMRI analysis results is increasing rapidly. The current NPAIRS package, an IDL-based fMRI processing pipeline evaluation framework, lacks system interoperability and the ability to evaluate general linear model (GLM)-based pipelines using prediction metrics. Thus, it can not fully evaluate fMRI analytical software modules such as FSL.FEAT and NPAIRS.GLM. In order to overcome these limitations, a Java-based fMRI processing pipeline evaluation system was developed. It integrated YALE (a machine learning environment) into Fiswidgets (a fMRI software environment) to obtain system interoperability and applied an algorithm to measure GLM prediction accuracy. The results demonstrated that the system can evaluate fMRI processing pipelines with univariate GLM and multivariate canonical variates analysis (CVA)-based models on real fMRI data based on prediction accuracy (classification accuracy) and statistical parametric image (SPI) reproducibility. In addition, a preliminary study was performed where four fMRI processing pipelines with GLM and CVA modules such as FSL.FEAT and NPAIRS.CVA were evaluated with the system. The results indicated that (1) the system can compare different fMRI processing pipelines with heterogeneous models (NPAIRS.GLM, NPAIRS.CVA and FSL.FEAT) and rank their performance by automatic performance scoring, and (2) the rank of pipeline performance is highly dependent on the preprocessing operations. These results suggest that the system will be of value for the comparison, validation, standardization and optimization of functional neuroimaging software packages and fMRI processing pipelines.
Interference and Sensitivity Analysis.
VanderWeele, Tyler J; Tchetgen Tchetgen, Eric J; Halloran, M Elizabeth
2014-11-01
Causal inference with interference is a rapidly growing area. The literature has begun to relax the "no-interference" assumption that the treatment received by one individual does not affect the outcomes of other individuals. In this paper we briefly review the literature on causal inference in the presence of interference when treatments have been randomized. We then consider settings in which causal effects in the presence of interference are not identified, either because randomization alone does not suffice for identification, or because treatment is not randomized and there may be unmeasured confounders of the treatment-outcome relationship. We develop sensitivity analysis techniques for these settings. We describe several sensitivity analysis techniques for the infectiousness effect which, in a vaccine trial, captures the effect of the vaccine of one person on protecting a second person from infection even if the first is infected. We also develop two sensitivity analysis techniques for causal effects in the presence of unmeasured confounding which generalize analogous techniques when interference is absent. These two techniques for unmeasured confounding are compared and contrasted.
Lund, Henrik; Sorknæs, Peter; Mathiesen, Brian Vad
2018-01-01
of electricity, which have been introduced in recent decades. These uncertainties pose a challenge to the design and assessment of future energy strategies and investments, especially in the economic assessment of renewable energy versus business-as-usual scenarios based on fossil fuels. From a methodological...... point of view, the typical way of handling this challenge has been to predict future prices as accurately as possible and then conduct a sensitivity analysis. This paper includes a historical analysis of such predictions, leading to the conclusion that they are almost always wrong. Not only...... are they wrong in their prediction of price levels, but also in the sense that they always seem to predict a smooth growth or decrease. This paper introduces a new method and reports the results of applying it on the case of energy scenarios for Denmark. The method implies the expectation of fluctuating fuel...
Chemical kinetic functional sensitivity analysis: Elementary sensitivities
Demiralp, M.; Rabitz, H.
1981-01-01
Sensitivity analysis is considered for kinetics problems defined in the space--time domain. This extends an earlier temporal Green's function method to handle calculations of elementary functional sensitivities deltau/sub i//deltaα/sub j/ where u/sub i/ is the ith species concentration and α/sub j/ is the jth system parameter. The system parameters include rate constants, diffusion coefficients, initial conditions, boundary conditions, or any other well-defined variables in the kinetic equations. These parameters are generally considered to be functions of position and/or time. Derivation of the governing equations for the sensitivities and the Green's funciton are presented. The physical interpretation of the Green's function and sensitivities is given along with a discussion of the relation of this work to earlier research
MOVES regional level sensitivity analysis
2012-01-01
The MOVES Regional Level Sensitivity Analysis was conducted to increase understanding of the operations of the MOVES Model in regional emissions analysis and to highlight the following: : the relative sensitivity of selected MOVES Model input paramet...
Ostafew, C. [Azure Dynamics Corp., Toronto, ON (Canada)
2010-07-01
This presentation included a sensitivity analysis of electric vehicle components on overall efficiency. The presentation provided an overview of drive cycles and discussed the major contributors to range in terms of rolling resistance; aerodynamic drag; motor efficiency; and vehicle mass. Drive cycles that were presented included: New York City Cycle (NYCC); urban dynamometer drive cycle; and US06. A summary of the findings were presented for each of the major contributors. Rolling resistance was found to have a balanced effect on each drive cycle and proportional to range. In terms of aerodynamic drive, there was a large effect on US06 range. A large effect was also found on NYCC range in terms of motor efficiency and vehicle mass. figs.
Sensitivity Analysis in Sequential Decision Models.
Chen, Qiushi; Ayer, Turgay; Chhatwal, Jagpreet
2017-02-01
Sequential decision problems are frequently encountered in medical decision making, which are commonly solved using Markov decision processes (MDPs). Modeling guidelines recommend conducting sensitivity analyses in decision-analytic models to assess the robustness of the model results against the uncertainty in model parameters. However, standard methods of conducting sensitivity analyses cannot be directly applied to sequential decision problems because this would require evaluating all possible decision sequences, typically in the order of trillions, which is not practically feasible. As a result, most MDP-based modeling studies do not examine confidence in their recommended policies. In this study, we provide an approach to estimate uncertainty and confidence in the results of sequential decision models. First, we provide a probabilistic univariate method to identify the most sensitive parameters in MDPs. Second, we present a probabilistic multivariate approach to estimate the overall confidence in the recommended optimal policy considering joint uncertainty in the model parameters. We provide a graphical representation, which we call a policy acceptability curve, to summarize the confidence in the optimal policy by incorporating stakeholders' willingness to accept the base case policy. For a cost-effectiveness analysis, we provide an approach to construct a cost-effectiveness acceptability frontier, which shows the most cost-effective policy as well as the confidence in that for a given willingness to pay threshold. We demonstrate our approach using a simple MDP case study. We developed a method to conduct sensitivity analysis in sequential decision models, which could increase the credibility of these models among stakeholders.
Maternal sensitivity: a concept analysis.
Shin, Hyunjeong; Park, Young-Joo; Ryu, Hosihn; Seomun, Gyeong-Ae
2008-11-01
The aim of this paper is to report a concept analysis of maternal sensitivity. Maternal sensitivity is a broad concept encompassing a variety of interrelated affective and behavioural caregiving attributes. It is used interchangeably with the terms maternal responsiveness or maternal competency, with no consistency of use. There is a need to clarify the concept of maternal sensitivity for research and practice. A search was performed on the CINAHL and Ovid MEDLINE databases using 'maternal sensitivity', 'maternal responsiveness' and 'sensitive mothering' as key words. The searches yielded 54 records for the years 1981-2007. Rodgers' method of evolutionary concept analysis was used to analyse the material. Four critical attributes of maternal sensitivity were identified: (a) dynamic process involving maternal abilities; (b) reciprocal give-and-take with the infant; (c) contingency on the infant's behaviour and (d) quality of maternal behaviours. Maternal identity and infant's needs and cues are antecedents for these attributes. The consequences are infant's comfort, mother-infant attachment and infant development. In addition, three positive affecting factors (social support, maternal-foetal attachment and high self-esteem) and three negative affecting factors (maternal depression, maternal stress and maternal anxiety) were identified. A clear understanding of the concept of maternal sensitivity could be useful for developing ways to enhance maternal sensitivity and to maximize the developmental potential of infants. Knowledge of the attributes of maternal sensitivity identified in this concept analysis may be helpful for constructing measuring items or dimensions.
Global optimization and sensitivity analysis
Cacuci, D.G.
1990-01-01
A new direction for the analysis of nonlinear models of nuclear systems is suggested to overcome fundamental limitations of sensitivity analysis and optimization methods currently prevalent in nuclear engineering usage. This direction is toward a global analysis of the behavior of the respective system as its design parameters are allowed to vary over their respective design ranges. Presented is a methodology for global analysis that unifies and extends the current scopes of sensitivity analysis and optimization by identifying all the critical points (maxima, minima) and solution bifurcation points together with corresponding sensitivities at any design point of interest. The potential applicability of this methodology is illustrated with test problems involving multiple critical points and bifurcations and comprising both equality and inequality constraints
Comparison of spectrum normalization techniques for univariate ...
Laser-induced breakdown spectroscopy; univariate study; normalization models; stainless steel; standard error of prediction. Abstract. Analytical performance of six different spectrum normalization techniques, namelyinternal normalization, normalization with total light, normalization with background along with their ...
Ivona Strug
2014-01-01
Full Text Available Biological samples present a range of complexities from homogeneous purified protein to multicomponent mixtures. Accurate qualification of such samples is paramount to downstream applications. We describe the development of an MIR spectroscopy-based analytical method offering simultaneous protein quantitation (0.25–5 mg/mL and analysis of total lipid or detergent species, as well as the identification of other biomolecules present in biological samples. The method utilizes a hydrophilic PTFE membrane engineered for presentation of aqueous samples in a dried format compatible with fast infrared analysis. Unlike classical quantification techniques, the reported method is amino acid sequence independent and thus applicable to complex samples of unknown composition. By comparison to existing platforms, this MIR-based method enables direct quantification using minimal sample volume (2 µL; it is well-suited where repeat access and limited sample size are critical parameters. Further, accurate results can be derived without specialized training or knowledge of IR spectroscopy. Overall, the simplified application and analysis system provides a more cost-effective alternative to high-throughput IR systems for research laboratories with minimal throughput demands. In summary, the MIR-based system provides a viable alternative to current protein quantitation methods; it also uniquely offers simultaneous qualification of other components, notably lipids and detergents.
Horwedel, J.E.; Wright, R.Q.; Maerker, R.E.
1990-01-01
A sensitivity analysis of EQ3, a computer code which has been proposed to be used as one link in the overall performance assessment of a national high-level waste repository, has been performed. EQ3 is a geochemical modeling code used to calculate the speciation of a water and its saturation state with respect to mineral phases. The model chosen for the sensitivity analysis is one which is used as a test problem in the documentation of the EQ3 code. Sensitivities are calculated using both the CHAIN and ADGEN options of the GRESS code compiled under G-float FORTRAN on the VAX/VMS and verified by perturbation runs. The analyses were performed with a preliminary Version 1.0 of GRESS which contains several new algorithms that significantly improve the application of ADGEN. Use of ADGEN automates the implementation of the well-known adjoint technique for the efficient calculation of sensitivities of a given response to all the input data. Application of ADGEN to EQ3 results in the calculation of sensitivities of a particular response to 31,000 input parameters in a run time of only 27 times that of the original model. Moreover, calculation of the sensitivities for each additional response increases this factor by only 2.5 percent. This compares very favorably with a running-time factor of 31,000 if direct perturbation runs were used instead. 6 refs., 8 tabs
High order depletion sensitivity analysis
Naguib, K.; Adib, M.; Morcos, H.N.
2002-01-01
A high order depletion sensitivity method was applied to calculate the sensitivities of build-up of actinides in the irradiated fuel due to cross-section uncertainties. An iteration method based on Taylor series expansion was applied to construct stationary principle, from which all orders of perturbations were calculated. The irradiated EK-10 and MTR-20 fuels at their maximum burn-up of 25% and 65% respectively were considered for sensitivity analysis. The results of calculation show that, in case of EK-10 fuel (low burn-up), the first order sensitivity was found to be enough to perform an accuracy of 1%. While in case of MTR-20 (high burn-up) the fifth order was found to provide 3% accuracy. A computer code SENS was developed to provide the required calculations
VC-dimension of univariate decision trees.
Yildiz, Olcay Taner
2015-02-01
In this paper, we give and prove the lower bounds of the Vapnik-Chervonenkis (VC)-dimension of the univariate decision tree hypothesis class. The VC-dimension of the univariate decision tree depends on the VC-dimension values of its subtrees and the number of inputs. Via a search algorithm that calculates the VC-dimension of univariate decision trees exhaustively, we show that our VC-dimension bounds are tight for simple trees. To verify that the VC-dimension bounds are useful, we also use them to get VC-generalization bounds for complexity control using structural risk minimization in decision trees, i.e., pruning. Our simulation results show that structural risk minimization pruning using the VC-dimension bounds finds trees that are more accurate as those pruned using cross validation.
Sensitivity Analysis of Simulation Models
Kleijnen, J.P.C.
2009-01-01
This contribution presents an overview of sensitivity analysis of simulation models, including the estimation of gradients. It covers classic designs and their corresponding (meta)models; namely, resolution-III designs including fractional-factorial two-level designs for first-order polynomial
Sensitivity analysis using probability bounding
Ferson, Scott; Troy Tucker, W.
2006-01-01
Probability bounds analysis (PBA) provides analysts a convenient means to characterize the neighborhood of possible results that would be obtained from plausible alternative inputs in probabilistic calculations. We show the relationship between PBA and the methods of interval analysis and probabilistic uncertainty analysis from which it is jointly derived, and indicate how the method can be used to assess the quality of probabilistic models such as those developed in Monte Carlo simulations for risk analyses. We also illustrate how a sensitivity analysis can be conducted within a PBA by pinching inputs to precise distributions or real values
Univariate characterization of the German business cycle 1955-1994
Weihs, Claus; Garczarek, Ursula
2002-01-01
We present a descriptive analysis of stylized facts for the German business cycle. We demonstrate that simple ad-hoc instructions for identifying univariate rules characterizing the German business cycle 1955-1994 lead to an error rate comparable to standard multivariate methods.
Sensitivity analysis in remote sensing
Ustinov, Eugene A
2015-01-01
This book contains a detailed presentation of general principles of sensitivity analysis as well as their applications to sample cases of remote sensing experiments. An emphasis is made on applications of adjoint problems, because they are more efficient in many practical cases, although their formulation may seem counterintuitive to a beginner. Special attention is paid to forward problems based on higher-order partial differential equations, where a novel matrix operator approach to formulation of corresponding adjoint problems is presented. Sensitivity analysis (SA) serves for quantitative models of physical objects the same purpose, as differential calculus does for functions. SA provides derivatives of model output parameters (observables) with respect to input parameters. In remote sensing SA provides computer-efficient means to compute the jacobians, matrices of partial derivatives of observables with respect to the geophysical parameters of interest. The jacobians are used to solve corresponding inver...
Sensitivity Analysis of Viscoelastic Structures
A.M.G. de Lima
2006-01-01
Full Text Available In the context of control of sound and vibration of mechanical systems, the use of viscoelastic materials has been regarded as a convenient strategy in many types of industrial applications. Numerical models based on finite element discretization have been frequently used in the analysis and design of complex structural systems incorporating viscoelastic materials. Such models must account for the typical dependence of the viscoelastic characteristics on operational and environmental parameters, such as frequency and temperature. In many applications, including optimal design and model updating, sensitivity analysis based on numerical models is a very usefull tool. In this paper, the formulation of first-order sensitivity analysis of complex frequency response functions is developed for plates treated with passive constraining damping layers, considering geometrical characteristics, such as the thicknesses of the multi-layer components, as design variables. Also, the sensitivity of the frequency response functions with respect to temperature is introduced. As an example, response derivatives are calculated for a three-layer sandwich plate and the results obtained are compared with first-order finite-difference approximations.
Barimani, Shirin; Kleinebudde, Peter
2017-10-01
A multivariate analysis method, Science-Based Calibration (SBC), was used for the first time for endpoint determination of a tablet coating process using Raman data. Two types of tablet cores, placebo and caffeine cores, received a coating suspension comprising a polyvinyl alcohol-polyethylene glycol graft-copolymer and titanium dioxide to a maximum coating thickness of 80µm. Raman spectroscopy was used as in-line PAT tool. The spectra were acquired every minute and correlated to the amount of applied aqueous coating suspension. SBC was compared to another well-known multivariate analysis method, Partial Least Squares-regression (PLS) and a simpler approach, Univariate Data Analysis (UVDA). All developed calibration models had coefficient of determination values (R 2 ) higher than 0.99. The coating endpoints could be predicted with root mean square errors (RMSEP) less than 3.1% of the applied coating suspensions. Compared to PLS and UVDA, SBC proved to be an alternative multivariate calibration method with high predictive power. Copyright © 2017 Elsevier B.V. All rights reserved.
UMTS Common Channel Sensitivity Analysis
Pratas, Nuno; Rodrigues, António; Santos, Frederico
2006-01-01
and as such it is necessary that both channels be available across the cell radius. This requirement makes the choice of the transmission parameters a fundamental one. This paper presents a sensitivity analysis regarding the transmission parameters of two UMTS common channels: RACH and FACH. Optimization of these channels...... is performed and values for the key transmission parameters in both common channels are obtained. On RACH these parameters are the message to preamble offset, the initial SIR target and the preamble power step while on FACH it is the transmission power offset....
TEMAC, Top Event Sensitivity Analysis
Iman, R.L.; Shortencarier, M.J.
1988-01-01
1 - Description of program or function: TEMAC is designed to permit the user to easily estimate risk and to perform sensitivity and uncertainty analyses with a Boolean expression such as produced by the SETS computer program. SETS produces a mathematical representation of a fault tree used to model system unavailability. In the terminology of the TEMAC program, such a mathematical representation is referred to as a top event. The analysis of risk involves the estimation of the magnitude of risk, the sensitivity of risk estimates to base event probabilities and initiating event frequencies, and the quantification of the uncertainty in the risk estimates. 2 - Method of solution: Sensitivity and uncertainty analyses associated with top events involve mathematical operations on the corresponding Boolean expression for the top event, as well as repeated evaluations of the top event in a Monte Carlo fashion. TEMAC employs a general matrix approach which provides a convenient general form for Boolean expressions, is computationally efficient, and allows large problems to be analyzed. 3 - Restrictions on the complexity of the problem - Maxima of: 4000 cut sets, 500 events, 500 values in a Monte Carlo sample, 16 characters in an event name. These restrictions are implemented through the FORTRAN 77 PARAMATER statement
Evaluation of droplet size distributions using univariate and multivariate approaches
Gauno, M.H.; Larsen, C.C.; Vilhelmsen, T.
2013-01-01
of the distribution. The current study was aiming to compare univariate and multivariate approach in evaluating droplet size distributions. As a model system, the atomization of a coating solution from a two-fluid nozzle was investigated. The effect of three process parameters (concentration of ethyl cellulose...... in ethanol, atomizing air pressure, and flow rate of coating solution) on the droplet size and droplet size distribution using a full mixed factorial design was used. The droplet size produced by a two-fluid nozzle was measured by laser diffraction and reported as volume based size distribution....... Investigation of loading and score plots from principal component analysis (PCA) revealed additional information on the droplet size distributions and it was possible to identify univariate statistics (volume median droplet size), which were similar, however, originating from varying droplet size distributions...
Evaluation of droplet size distributions using univariate and multivariate approaches.
Gaunø, Mette Høg; Larsen, Crilles Casper; Vilhelmsen, Thomas; Møller-Sonnergaard, Jørn; Wittendorff, Jørgen; Rantanen, Jukka
2013-01-01
Pharmaceutically relevant material characteristics are often analyzed based on univariate descriptors instead of utilizing the whole information available in the full distribution. One example is droplet size distribution, which is often described by the median droplet size and the width of the distribution. The current study was aiming to compare univariate and multivariate approach in evaluating droplet size distributions. As a model system, the atomization of a coating solution from a two-fluid nozzle was investigated. The effect of three process parameters (concentration of ethyl cellulose in ethanol, atomizing air pressure, and flow rate of coating solution) on the droplet size and droplet size distribution using a full mixed factorial design was used. The droplet size produced by a two-fluid nozzle was measured by laser diffraction and reported as volume based size distribution. Investigation of loading and score plots from principal component analysis (PCA) revealed additional information on the droplet size distributions and it was possible to identify univariate statistics (volume median droplet size), which were similar, however, originating from varying droplet size distributions. The multivariate data analysis was proven to be an efficient tool for evaluating the full information contained in a distribution.
Data fusion qualitative sensitivity analysis
Clayton, E.A.; Lewis, R.E.
1995-09-01
Pacific Northwest Laboratory was tasked with testing, debugging, and refining the Hanford Site data fusion workstation (DFW), with the assistance of Coleman Research Corporation (CRC), before delivering the DFW to the environmental restoration client at the Hanford Site. Data fusion is the mathematical combination (or fusion) of disparate data sets into a single interpretation. The data fusion software used in this study was developed by CRC. The data fusion software developed by CRC was initially demonstrated on a data set collected at the Hanford Site where three types of data were combined. These data were (1) seismic reflection, (2) seismic refraction, and (3) depth to geologic horizons. The fused results included a contour map of the top of a low-permeability horizon. This report discusses the results of a sensitivity analysis of data fusion software to variations in its input parameters. The data fusion software developed by CRC has a large number of input parameters that can be varied by the user and that influence the results of data fusion. Many of these parameters are defined as part of the earth model. The earth model is a series of 3-dimensional polynomials with horizontal spatial coordinates as the independent variables and either subsurface layer depth or values of various properties within these layers (e.g., compression wave velocity, resistivity) as the dependent variables
Probabilistic sensitivity analysis of biochemical reaction systems.
Zhang, Hong-Xuan; Dempsey, William P; Goutsias, John
2009-09-07
Sensitivity analysis is an indispensable tool for studying the robustness and fragility properties of biochemical reaction systems as well as for designing optimal approaches for selective perturbation and intervention. Deterministic sensitivity analysis techniques, using derivatives of the system response, have been extensively used in the literature. However, these techniques suffer from several drawbacks, which must be carefully considered before using them in problems of systems biology. We develop here a probabilistic approach to sensitivity analysis of biochemical reaction systems. The proposed technique employs a biophysically derived model for parameter fluctuations and, by using a recently suggested variance-based approach to sensitivity analysis [Saltelli et al., Chem. Rev. (Washington, D.C.) 105, 2811 (2005)], it leads to a powerful sensitivity analysis methodology for biochemical reaction systems. The approach presented in this paper addresses many problems associated with derivative-based sensitivity analysis techniques. Most importantly, it produces thermodynamically consistent sensitivity analysis results, can easily accommodate appreciable parameter variations, and allows for systematic investigation of high-order interaction effects. By employing a computational model of the mitogen-activated protein kinase signaling cascade, we demonstrate that our approach is well suited for sensitivity analysis of biochemical reaction systems and can produce a wealth of information about the sensitivity properties of such systems. The price to be paid, however, is a substantial increase in computational complexity over derivative-based techniques, which must be effectively addressed in order to make the proposed approach to sensitivity analysis more practical.
Sensitivity Analysis of a Physiochemical Interaction Model ...
In this analysis, we will study the sensitivity analysis due to a variation of the initial condition and experimental time. These results which we have not seen elsewhere are analysed and discussed quantitatively. Keywords: Passivation Rate, Sensitivity Analysis, ODE23, ODE45 J. Appl. Sci. Environ. Manage. June, 2012, Vol.
Sensitivity Analysis of Multidisciplinary Rotorcraft Simulations
Wang, Li; Diskin, Boris; Biedron, Robert T.; Nielsen, Eric J.; Bauchau, Olivier A.
2017-01-01
A multidisciplinary sensitivity analysis of rotorcraft simulations involving tightly coupled high-fidelity computational fluid dynamics and comprehensive analysis solvers is presented and evaluated. An unstructured sensitivity-enabled Navier-Stokes solver, FUN3D, and a nonlinear flexible multibody dynamics solver, DYMORE, are coupled to predict the aerodynamic loads and structural responses of helicopter rotor blades. A discretely-consistent adjoint-based sensitivity analysis available in FUN3D provides sensitivities arising from unsteady turbulent flows and unstructured dynamic overset meshes, while a complex-variable approach is used to compute DYMORE structural sensitivities with respect to aerodynamic loads. The multidisciplinary sensitivity analysis is conducted through integrating the sensitivity components from each discipline of the coupled system. Numerical results verify accuracy of the FUN3D/DYMORE system by conducting simulations for a benchmark rotorcraft test model and comparing solutions with established analyses and experimental data. Complex-variable implementation of sensitivity analysis of DYMORE and the coupled FUN3D/DYMORE system is verified by comparing with real-valued analysis and sensitivities. Correctness of adjoint formulations for FUN3D/DYMORE interfaces is verified by comparing adjoint-based and complex-variable sensitivities. Finally, sensitivities of the lift and drag functions obtained by complex-variable FUN3D/DYMORE simulations are compared with sensitivities computed by the multidisciplinary sensitivity analysis, which couples adjoint-based flow and grid sensitivities of FUN3D and FUN3D/DYMORE interfaces with complex-variable sensitivities of DYMORE structural responses.
Chang, M; Raimondi, S C; Ravindranath, Y; Carroll, A J; Camitta, B; Gresik, M V; Steuber, C P; Weinstein, H
2000-07-01
The purpose of the paper was to define clinical or biological features associated with the risk for treatment failure for children with acute myeloid leukemia. Data from 560 children and adolescents with newly diagnosed acute myeloid leukemia who entered the Pediatric Oncology Group Study 8821 from June 1988 to March 1993 were analyzed by univariate and recursive partitioning methods. Children with Down syndrome or acute promyelocytic leukemia were excluded from the study. Factors examined included age, number of leukocytes, sex, FAB morphologic subtype, cytogenetic findings, and extramedullary disease at the time of diagnosis. The overall event-free survival (EFS) rate at 4 years was 32.7% (s.e. = 2.2%). Age > or =2 years, fewer than 50 x 10(9)/I leukocytes, and t(8;21) or inv(16), and normal chromosomes were associated with higher rates of EFS (P value = 0.003, 0.049, 0.0003, 0.031, respectively), whereas the M5 subtype of AML (P value = 0.0003) and chromosome abnormalities other than t(8;21) and inv(16) were associated with lower rates of EFS (P value = 0.0001). Recursive partitioning analysis defined three groups of patients with widely varied prognoses: female patients with t(8;21), inv(16), or a normal karyotype (n = 89) had the best prognosis (4-year EFS = 55.1%, s.e. = 5.7%); male patients with t(8;21), inv(16) or normal chromosomes (n = 106) had an intermediate prognosis (4-year EFS = 38.1%, s.e. = 5.3%); patients with chromosome abnormalities other than t(8;21) and inv(16) (n = 233) had the worst prognosis (4-year EFS = 27.0%, s.e. = 3.2%). One hundred and thirty-two patients (24%) could not be grouped because of missing cytogenetic data, mainly due to inadequate marrow samples. The results suggest that pediatric patients with acute myeloid leukemia can be categorized into three potential risk groups for prognosis and that differences in sex and chromosomal abnormalities are associated with differences in estimates of EFS. These results are tentative and
Univaried models in the series of temperature of the air
Leon Aristizabal Gloria esperanza
2000-01-01
The theoretical framework for the study of the air's temperature time series is the theory of stochastic processes, particularly those known as ARIMA, that make it possible to carry out a univaried analysis. ARIMA models are built in order to explain the structure of the monthly temperatures corresponding to the mean, the absolute maximum, absolute minimum, maximum mean and minimum mean temperatures, for four stations in Colombia. By means of those models, the possible evolution of the latter variables is estimated with predictive aims in mind. The application and utility of the models is discussed
Risk Characterization uncertainties associated description, sensitivity analysis
Carrillo, M.; Tovar, M.; Alvarez, J.; Arraez, M.; Hordziejewicz, I.; Loreto, I.
2013-01-01
The power point presentation is about risks to the estimated levels of exposure, uncertainty and variability in the analysis, sensitivity analysis, risks from exposure to multiple substances, formulation of guidelines for carcinogenic and genotoxic compounds and risk subpopulations
Object-sensitive Type Analysis of PHP
Van der Hoek, Henk Erik; Hage, J
2015-01-01
In this paper we develop an object-sensitive type analysis for PHP, based on an extension of the notion of monotone frameworks to deal with the dynamic aspects of PHP, and following the framework of Smaragdakis et al. for object-sensitive analysis. We consider a number of instantiations of the
The pathways for intelligible speech: multivariate and univariate perspectives.
Evans, S; Kyong, J S; Rosen, S; Golestani, N; Warren, J E; McGettigan, C; Mourão-Miranda, J; Wise, R J S; Scott, S K
2014-09-01
An anterior pathway, concerned with extracting meaning from sound, has been identified in nonhuman primates. An analogous pathway has been suggested in humans, but controversy exists concerning the degree of lateralization and the precise location where responses to intelligible speech emerge. We have demonstrated that the left anterior superior temporal sulcus (STS) responds preferentially to intelligible speech (Scott SK, Blank CC, Rosen S, Wise RJS. 2000. Identification of a pathway for intelligible speech in the left temporal lobe. Brain. 123:2400-2406.). A functional magnetic resonance imaging study in Cerebral Cortex used equivalent stimuli and univariate and multivariate analyses to argue for the greater importance of bilateral posterior when compared with the left anterior STS in responding to intelligible speech (Okada K, Rong F, Venezia J, Matchin W, Hsieh IH, Saberi K, Serences JT,Hickok G. 2010. Hierarchical organization of human auditory cortex: evidence from acoustic invariance in the response to intelligible speech. 20: 2486-2495.). Here, we also replicate our original study, demonstrating that the left anterior STS exhibits the strongest univariate response and, in decoding using the bilateral temporal cortex, contains the most informative voxels showing an increased response to intelligible speech. In contrast, in classifications using local "searchlights" and a whole brain analysis, we find greater classification accuracy in posterior rather than anterior temporal regions. Thus, we show that the precise nature of the multivariate analysis used will emphasize different response profiles associated with complex sound to speech processing. © The Author 2013. Published by Oxford University Press.
A hybrid approach for global sensitivity analysis
Chakraborty, Souvik; Chowdhury, Rajib
2017-01-01
Distribution based sensitivity analysis (DSA) computes sensitivity of the input random variables with respect to the change in distribution of output response. Although DSA is widely appreciated as the best tool for sensitivity analysis, the computational issue associated with this method prohibits its use for complex structures involving costly finite element analysis. For addressing this issue, this paper presents a method that couples polynomial correlated function expansion (PCFE) with DSA. PCFE is a fully equivalent operational model which integrates the concepts of analysis of variance decomposition, extended bases and homotopy algorithm. By integrating PCFE into DSA, it is possible to considerably alleviate the computational burden. Three examples are presented to demonstrate the performance of the proposed approach for sensitivity analysis. For all the problems, proposed approach yields excellent results with significantly reduced computational effort. The results obtained, to some extent, indicate that proposed approach can be utilized for sensitivity analysis of large scale structures. - Highlights: • A hybrid approach for global sensitivity analysis is proposed. • Proposed approach integrates PCFE within distribution based sensitivity analysis. • Proposed approach is highly efficient.
Sensitivity analysis of a PWR pressurizer
Bruel, Renata Nunes
1997-01-01
A sensitivity analysis relative to the parameters and modelling of the physical process in a PWR pressurizer has been performed. The sensitivity analysis was developed by implementing the key parameters and theoretical model lings which generated a comprehensive matrix of influences of each changes analysed. The major influences that have been observed were the flashing phenomenon and the steam condensation on the spray drops. The present analysis is also applicable to the several theoretical and experimental areas. (author)
Sensitivity analysis for large-scale problems
Noor, Ahmed K.; Whitworth, Sandra L.
1987-01-01
The development of efficient techniques for calculating sensitivity derivatives is studied. The objective is to present a computational procedure for calculating sensitivity derivatives as part of performing structural reanalysis for large-scale problems. The scope is limited to framed type structures. Both linear static analysis and free-vibration eigenvalue problems are considered.
Sensitivity analysis in life cycle assessment
Groen, E.A.; Heijungs, R.; Bokkers, E.A.M.; Boer, de I.J.M.
2014-01-01
Life cycle assessments require many input parameters and many of these parameters are uncertain; therefore, a sensitivity analysis is an essential part of the final interpretation. The aim of this study is to compare seven sensitivity methods applied to three types of case stud-ies. Two
Ethical sensitivity in professional practice: concept analysis.
Weaver, Kathryn; Morse, Janice; Mitcham, Carl
2008-06-01
This paper is a report of a concept analysis of ethical sensitivity. Ethical sensitivity enables nurses and other professionals to respond morally to the suffering and vulnerability of those receiving professional care and services. Because of its significance to nursing and other professional practices, ethical sensitivity deserves more focused analysis. A criteria-based method oriented toward pragmatic utility guided the analysis of 200 papers and books from the fields of nursing, medicine, psychology, dentistry, clinical ethics, theology, education, law, accounting or business, journalism, philosophy, political and social sciences and women's studies. This literature spanned 1970 to 2006 and was sorted by discipline and concept dimensions and examined for concept structure and use across various contexts. The analysis was completed in September 2007. Ethical sensitivity in professional practice develops in contexts of uncertainty, client suffering and vulnerability, and through relationships characterized by receptivity, responsiveness and courage on the part of professionals. Essential attributes of ethical sensitivity are identified as moral perception, affectivity and dividing loyalties. Outcomes include integrity preserving decision-making, comfort and well-being, learning and professional transcendence. Our findings promote ethical sensitivity as a type of practical wisdom that pursues client comfort and professional satisfaction with care delivery. The analysis and resulting model offers an inclusive view of ethical sensitivity that addresses some of the limitations with prior conceptualizations.
Comparison of spectrum normalization techniques for univariate ...
2016-02-29
Feb 29, 2016 ... 1Fuel Chemistry Division, Bhabha Atomic Research Centre, Mumbai 400 085, India. 2Department of ... their three-point smoothing methods were studied using LIBS for quantification of Cr, Mn and Ni ... nique for the qualitative and quantitative analysis of the samples. .... SEP is a type of mean square error.
LBLOCA sensitivity analysis using meta models
Villamizar, M.; Sanchez-Saez, F.; Villanueva, J.F.; Carlos, S.; Sanchez, A.I.; Martorell, S.
2014-01-01
This paper presents an approach to perform the sensitivity analysis of the results of simulation of thermal hydraulic codes within a BEPU approach. Sensitivity analysis is based on the computation of Sobol' indices that makes use of a meta model, It presents also an application to a Large-Break Loss of Coolant Accident, LBLOCA, in the cold leg of a pressurized water reactor, PWR, addressing the results of the BEMUSE program and using the thermal-hydraulic code TRACE. (authors)
Sensitivity analysis in optimization and reliability problems
Castillo, Enrique; Minguez, Roberto; Castillo, Carmen
2008-01-01
The paper starts giving the main results that allow a sensitivity analysis to be performed in a general optimization problem, including sensitivities of the objective function, the primal and the dual variables with respect to data. In particular, general results are given for non-linear programming, and closed formulas for linear programming problems are supplied. Next, the methods are applied to a collection of civil engineering reliability problems, which includes a bridge crane, a retaining wall and a composite breakwater. Finally, the sensitivity analysis formulas are extended to calculus of variations problems and a slope stability problem is used to illustrate the methods
Sensitivity analysis in optimization and reliability problems
Castillo, Enrique [Department of Applied Mathematics and Computational Sciences, University of Cantabria, Avda. Castros s/n., 39005 Santander (Spain)], E-mail: castie@unican.es; Minguez, Roberto [Department of Applied Mathematics, University of Castilla-La Mancha, 13071 Ciudad Real (Spain)], E-mail: roberto.minguez@uclm.es; Castillo, Carmen [Department of Civil Engineering, University of Castilla-La Mancha, 13071 Ciudad Real (Spain)], E-mail: mariacarmen.castillo@uclm.es
2008-12-15
The paper starts giving the main results that allow a sensitivity analysis to be performed in a general optimization problem, including sensitivities of the objective function, the primal and the dual variables with respect to data. In particular, general results are given for non-linear programming, and closed formulas for linear programming problems are supplied. Next, the methods are applied to a collection of civil engineering reliability problems, which includes a bridge crane, a retaining wall and a composite breakwater. Finally, the sensitivity analysis formulas are extended to calculus of variations problems and a slope stability problem is used to illustrate the methods.
Techniques for sensitivity analysis of SYVAC results
Prust, J.O.
1985-05-01
Sensitivity analysis techniques may be required to examine the sensitivity of SYVAC model predictions to the input parameter values, the subjective probability distributions assigned to the input parameters and to the relationship between dose and the probability of fatal cancers plus serious hereditary disease in the first two generations of offspring of a member of the critical group. This report mainly considers techniques for determining the sensitivity of dose and risk to the variable input parameters. The performance of a sensitivity analysis technique may be improved by decomposing the model and data into subsets for analysis, making use of existing information on sensitivity and concentrating sampling in regions the parameter space that generates high doses or risks. A number of sensitivity analysis techniques are reviewed for their application to the SYVAC model including four techniques tested in an earlier study by CAP Scientific for the SYVAC project. This report recommends the development now of a method for evaluating the derivative of dose and parameter value and extending the Kruskal-Wallis technique to test for interactions between parameters. It is also recommended that the sensitivity of the output of each sub-model of SYVAC to input parameter values should be examined. (author)
Multiple predictor smoothing methods for sensitivity analysis
Helton, Jon Craig; Storlie, Curtis B.
2006-01-01
The use of multiple predictor smoothing methods in sampling-based sensitivity analyses of complex models is investigated. Specifically, sensitivity analysis procedures based on smoothing methods employing the stepwise application of the following nonparametric regression techniques are described: (1) locally weighted regression (LOESS), (2) additive models, (3) projection pursuit regression, and (4) recursive partitioning regression. The indicated procedures are illustrated with both simple test problems and results from a performance assessment for a radioactive waste disposal facility (i.e., the Waste Isolation Pilot Plant). As shown by the example illustrations, the use of smoothing procedures based on nonparametric regression techniques can yield more informative sensitivity analysis results than can be obtained with more traditional sensitivity analysis procedures based on linear regression, rank regression or quadratic regression when nonlinear relationships between model inputs and model predictions are present
Multiple predictor smoothing methods for sensitivity analysis.
Helton, Jon Craig; Storlie, Curtis B.
2006-08-01
The use of multiple predictor smoothing methods in sampling-based sensitivity analyses of complex models is investigated. Specifically, sensitivity analysis procedures based on smoothing methods employing the stepwise application of the following nonparametric regression techniques are described: (1) locally weighted regression (LOESS), (2) additive models, (3) projection pursuit regression, and (4) recursive partitioning regression. The indicated procedures are illustrated with both simple test problems and results from a performance assessment for a radioactive waste disposal facility (i.e., the Waste Isolation Pilot Plant). As shown by the example illustrations, the use of smoothing procedures based on nonparametric regression techniques can yield more informative sensitivity analysis results than can be obtained with more traditional sensitivity analysis procedures based on linear regression, rank regression or quadratic regression when nonlinear relationships between model inputs and model predictions are present.
Dynamic Resonance Sensitivity Analysis in Wind Farms
Ebrahimzadeh, Esmaeil; Blaabjerg, Frede; Wang, Xiongfei
2017-01-01
(PFs) are calculated by critical eigenvalue sensitivity analysis versus the entries of the MIMO matrix. The PF analysis locates the most exciting bus of the resonances, where can be the best location to install the passive or active filters to reduce the harmonic resonance problems. Time...
Greenspan, E.
1982-01-01
This chapter presents the mathematical basis for sensitivity functions, discusses their physical meaning and information they contain, and clarifies a number of issues concerning their application, including the definition of group sensitivities, the selection of sensitivity functions to be included in the analysis, and limitations of sensitivity theory. Examines the theoretical foundation; criticality reset sensitivities; group sensitivities and uncertainties; selection of sensitivities included in the analysis; and other uses and limitations of sensitivity functions. Gives the theoretical formulation of sensitivity functions pertaining to ''as-built'' designs for performance parameters of the form of ratios of linear flux functionals (such as reaction-rate ratios), linear adjoint functionals, bilinear functions (such as reactivity worth ratios), and for reactor reactivity. Offers a consistent procedure for reducing energy-dependent or fine-group sensitivities and uncertainties to broad group sensitivities and uncertainties. Provides illustrations of sensitivity functions as well as references to available compilations of such functions and of total sensitivities. Indicates limitations of sensitivity theory originating from the fact that this theory is based on a first-order perturbation theory
Probabilistic sensitivity analysis in health economics.
Baio, Gianluca; Dawid, A Philip
2015-12-01
Health economic evaluations have recently become an important part of the clinical and medical research process and have built upon more advanced statistical decision-theoretic foundations. In some contexts, it is officially required that uncertainty about both parameters and observable variables be properly taken into account, increasingly often by means of Bayesian methods. Among these, probabilistic sensitivity analysis has assumed a predominant role. The objective of this article is to review the problem of health economic assessment from the standpoint of Bayesian statistical decision theory with particular attention to the philosophy underlying the procedures for sensitivity analysis. © The Author(s) 2011.
TOLERANCE SENSITIVITY ANALYSIS: THIRTY YEARS LATER
Richard E. Wendell
2010-12-01
Full Text Available Tolerance sensitivity analysis was conceived in 1980 as a pragmatic approach to effectively characterize a parametric region over which objective function coefficients and right-hand-side terms in linear programming could vary simultaneously and independently while maintaining the same optimal basis. As originally proposed, the tolerance region corresponds to the maximum percentage by which coefficients or terms could vary from their estimated values. Over the last thirty years the original results have been extended in a number of ways and applied in a variety of applications. This paper is a critical review of tolerance sensitivity analysis, including extensions and applications.
Sensitivity Analysis of Centralized Dynamic Cell Selection
Lopez, Victor Fernandez; Alvarez, Beatriz Soret; Pedersen, Klaus I.
2016-01-01
and a suboptimal optimization algorithm that nearly achieves the performance of the optimal Hungarian assignment. Moreover, an exhaustive sensitivity analysis with different network and traffic configurations is carried out in order to understand what conditions are more appropriate for the use of the proposed...
Sensitivity analysis in a structural reliability context
Lemaitre, Paul
2014-01-01
This thesis' subject is sensitivity analysis in a structural reliability context. The general framework is the study of a deterministic numerical model that allows to reproduce a complex physical phenomenon. The aim of a reliability study is to estimate the failure probability of the system from the numerical model and the uncertainties of the inputs. In this context, the quantification of the impact of the uncertainty of each input parameter on the output might be of interest. This step is called sensitivity analysis. Many scientific works deal with this topic but not in the reliability scope. This thesis' aim is to test existing sensitivity analysis methods, and to propose more efficient original methods. A bibliographical step on sensitivity analysis on one hand and on the estimation of small failure probabilities on the other hand is first proposed. This step raises the need to develop appropriate techniques. Two variables ranking methods are then explored. The first one proposes to make use of binary classifiers (random forests). The second one measures the departure, at each step of a subset method, between each input original density and the density given the subset reached. A more general and original methodology reflecting the impact of the input density modification on the failure probability is then explored. The proposed methods are then applied on the CWNR case, which motivates this thesis. (author)
Applications of advances in nonlinear sensitivity analysis
Werbos, P J
1982-01-01
The following paper summarizes the major properties and applications of a collection of algorithms involving differentiation and optimization at minimum cost. The areas of application include the sensitivity analysis of models, new work in statistical or econometric estimation, optimization, artificial intelligence and neuron modelling.
*Corresponding Author Sensitivity Analysis of a Physiochemical ...
Michael Horsfall
The numerical method of sensitivity or the principle of parsimony ... analysis is a widely applied numerical method often being used in the .... Chemical Engineering Journal 128(2-3), 85-93. Amod S ... coupled 3-PG and soil organic matter.
Sensitivity Analysis in Two-Stage DEA
Athena Forghani
2015-07-01
Full Text Available Data envelopment analysis (DEA is a method for measuring the efficiency of peer decision making units (DMUs which uses a set of inputs to produce a set of outputs. In some cases, DMUs have a two-stage structure, in which the first stage utilizes inputs to produce outputs used as the inputs of the second stage to produce final outputs. One important issue in two-stage DEA is the sensitivity of the results of an analysis to perturbations in the data. The current paper looks into combined model for two-stage DEA and applies the sensitivity analysis to DMUs on the entire frontier. In fact, necessary and sufficient conditions for preserving a DMU's efficiency classiffication are developed when various data changes are applied to all DMUs.
Sensitivity Analysis in Two-Stage DEA
Athena Forghani
2015-12-01
Full Text Available Data envelopment analysis (DEA is a method for measuring the efficiency of peer decision making units (DMUs which uses a set of inputs to produce a set of outputs. In some cases, DMUs have a two-stage structure, in which the first stage utilizes inputs to produce outputs used as the inputs of the second stage to produce final outputs. One important issue in two-stage DEA is the sensitivity of the results of an analysis to perturbations in the data. The current paper looks into combined model for two-stage DEA and applies the sensitivity analysis to DMUs on the entire frontier. In fact, necessary and sufficient conditions for preserving a DMU's efficiency classiffication are developed when various data changes are applied to all DMUs.
Sensitivity analysis and related analysis : A survey of statistical techniques
Kleijnen, J.P.C.
1995-01-01
This paper reviews the state of the art in five related types of analysis, namely (i) sensitivity or what-if analysis, (ii) uncertainty or risk analysis, (iii) screening, (iv) validation, and (v) optimization. The main question is: when should which type of analysis be applied; which statistical
Global sensitivity analysis by polynomial dimensional decomposition
Rahman, Sharif, E-mail: rahman@engineering.uiowa.ed [College of Engineering, The University of Iowa, Iowa City, IA 52242 (United States)
2011-07-15
This paper presents a polynomial dimensional decomposition (PDD) method for global sensitivity analysis of stochastic systems subject to independent random input following arbitrary probability distributions. The method involves Fourier-polynomial expansions of lower-variate component functions of a stochastic response by measure-consistent orthonormal polynomial bases, analytical formulae for calculating the global sensitivity indices in terms of the expansion coefficients, and dimension-reduction integration for estimating the expansion coefficients. Due to identical dimensional structures of PDD and analysis-of-variance decomposition, the proposed method facilitates simple and direct calculation of the global sensitivity indices. Numerical results of the global sensitivity indices computed for smooth systems reveal significantly higher convergence rates of the PDD approximation than those from existing methods, including polynomial chaos expansion, random balance design, state-dependent parameter, improved Sobol's method, and sampling-based methods. However, for non-smooth functions, the convergence properties of the PDD solution deteriorate to a great extent, warranting further improvements. The computational complexity of the PDD method is polynomial, as opposed to exponential, thereby alleviating the curse of dimensionality to some extent.
Siamphukdee, Kanjana; Collins, Frank; Zou, Roger
2013-06-01
Chloride-induced reinforcement corrosion is one of the major causes of premature deterioration in reinforced concrete (RC) structures. Given the high maintenance and replacement costs, accurate modeling of RC deterioration is indispensable for ensuring the optimal allocation of limited economic resources. Since corrosion rate is one of the major factors influencing the rate of deterioration, many predictive models exist. However, because the existing models use very different sets of input parameters, the choice of model for RC deterioration is made difficult. Although the factors affecting corrosion rate are frequently reported in the literature, there is no published quantitative study on the sensitivity of predicted corrosion rate to the various input parameters. This paper presents the results of the sensitivity analysis of the input parameters for nine selected corrosion rate prediction models. Three different methods of analysis are used to determine and compare the sensitivity of corrosion rate to various input parameters: (i) univariate regression analysis, (ii) multivariate regression analysis, and (iii) sensitivity index. The results from the analysis have quantitatively verified that the corrosion rate of steel reinforcement bars in RC structures is highly sensitive to corrosion duration time, concrete resistivity, and concrete chloride content. These important findings establish that future empirical models for predicting corrosion rate of RC should carefully consider and incorporate these input parameters.
Cain, Meghan K; Zhang, Zhiyong; Yuan, Ke-Hai
2017-10-01
Nonnormality of univariate data has been extensively examined previously (Blanca et al., Methodology: European Journal of Research Methods for the Behavioral and Social Sciences, 9(2), 78-84, 2013; Miceeri, Psychological Bulletin, 105(1), 156, 1989). However, less is known of the potential nonnormality of multivariate data although multivariate analysis is commonly used in psychological and educational research. Using univariate and multivariate skewness and kurtosis as measures of nonnormality, this study examined 1,567 univariate distriubtions and 254 multivariate distributions collected from authors of articles published in Psychological Science and the American Education Research Journal. We found that 74 % of univariate distributions and 68 % multivariate distributions deviated from normal distributions. In a simulation study using typical values of skewness and kurtosis that we collected, we found that the resulting type I error rates were 17 % in a t-test and 30 % in a factor analysis under some conditions. Hence, we argue that it is time to routinely report skewness and kurtosis along with other summary statistics such as means and variances. To facilitate future report of skewness and kurtosis, we provide a tutorial on how to compute univariate and multivariate skewness and kurtosis by SAS, SPSS, R and a newly developed Web application.
Demonstration sensitivity analysis for RADTRAN III
Neuhauser, K.S.; Reardon, P.C.
1986-10-01
A demonstration sensitivity analysis was performed to: quantify the relative importance of 37 variables to the total incident free dose; assess the elasticity of seven dose subgroups to those same variables; develop density distributions for accident dose to combinations of accident data under wide-ranging variations; show the relationship between accident consequences and probabilities of occurrence; and develop limits for the variability of probability consequence curves
Systemization of burnup sensitivity analysis code. 2
Tatsumi, Masahiro; Hyoudou, Hideaki
2005-02-01
Towards the practical use of fast reactors, it is a very important subject to improve prediction accuracy for neutronic properties in LMFBR cores from the viewpoint of improvements on plant efficiency with rationally high performance cores and that on reliability and safety margins. A distinct improvement on accuracy in nuclear core design has been accomplished by the development of adjusted nuclear library using the cross-section adjustment method, in which the results of criticality experiments of JUPITER and so on are reflected. In the design of large LMFBR cores, however, it is important to accurately estimate not only neutronic characteristics, for example, reaction rate distribution and control rod worth but also burnup characteristics, for example, burnup reactivity loss, breeding ratio and so on. For this purpose, it is desired to improve prediction accuracy of burnup characteristics using the data widely obtained in actual core such as the experimental fast reactor 'JOYO'. The analysis of burnup characteristics is needed to effectively use burnup characteristics data in the actual cores based on the cross-section adjustment method. So far, a burnup sensitivity analysis code, SAGEP-BURN, has been developed and confirmed its effectiveness. However, there is a problem that analysis sequence become inefficient because of a big burden to users due to complexity of the theory of burnup sensitivity and limitation of the system. It is also desired to rearrange the system for future revision since it is becoming difficult to implement new functions in the existing large system. It is not sufficient to unify each computational component for the following reasons; the computational sequence may be changed for each item being analyzed or for purpose such as interpretation of physical meaning. Therefore, it is needed to systemize the current code for burnup sensitivity analysis with component blocks of functionality that can be divided or constructed on occasion. For
Systemization of burnup sensitivity analysis code
Tatsumi, Masahiro; Hyoudou, Hideaki
2004-02-01
To practical use of fact reactors, it is a very important subject to improve prediction accuracy for neutronic properties in LMFBR cores from the viewpoints of improvements on plant efficiency with rationally high performance cores and that on reliability and safety margins. A distinct improvement on accuracy in nuclear core design has been accomplished by development of adjusted nuclear library using the cross-section adjustment method, in which the results of critical experiments of JUPITER and so on are reflected. In the design of large LMFBR cores, however, it is important to accurately estimate not only neutronic characteristics, for example, reaction rate distribution and control rod worth but also burnup characteristics, for example, burnup reactivity loss, breeding ratio and so on. For this purpose, it is desired to improve prediction accuracy of burnup characteristics using the data widely obtained in actual core such as the experimental fast reactor core 'JOYO'. The analysis of burnup characteristics is needed to effectively use burnup characteristics data in the actual cores based on the cross-section adjustment method. So far, development of a analysis code for burnup sensitivity, SAGEP-BURN, has been done and confirmed its effectiveness. However, there is a problem that analysis sequence become inefficient because of a big burden to user due to complexity of the theory of burnup sensitivity and limitation of the system. It is also desired to rearrange the system for future revision since it is becoming difficult to implement new functionalities in the existing large system. It is not sufficient to unify each computational component for some reasons; computational sequence may be changed for each item being analyzed or for purpose such as interpretation of physical meaning. Therefore it is needed to systemize the current code for burnup sensitivity analysis with component blocks of functionality that can be divided or constructed on occasion. For this
Barber, A. D.; Busch, R.
2009-01-01
The goal of this work is to obtain sensitivities from direct uncertainty analysis calculation and correlate those calculated values with the sensitivities produced from TSUNAMI-3D (Tools for Sensitivity and Uncertainty Analysis Methodology Implementation in Three Dimensions). A full sensitivity analysis is performed on a critical experiment to determine the overall uncertainty of the experiment. Small perturbation calculations are performed for all known uncertainties to obtain the total uncertainty of the experiment. The results from a critical experiment are only known as well as the geometric and material properties. The goal of this relationship is to simplify the uncertainty quantification process in assessing a critical experiment, while still considering all of the important parameters. (authors)
Sensitivity analysis of the Two Geometry Method
Wichers, V.A.
1993-09-01
The Two Geometry Method (TGM) was designed specifically for the verification of the uranium enrichment of low enriched UF 6 gas in the presence of uranium deposits on the pipe walls. Complications can arise if the TGM is applied under extreme conditions, such as deposits larger than several times the gas activity, small pipe diameters less than 40 mm and low pressures less than 150 Pa. This report presents a comprehensive sensitivity analysis of the TGM. The impact of the various sources of uncertainty on the performance of the method is discussed. The application to a practical case is based on worst case conditions with regards to the measurement conditions, and on realistic conditions with respect to the false alarm probability and the non detection probability. Monte Carlo calculations were used to evaluate the sensitivity for sources of uncertainty which are experimentally inaccessible. (orig.)
Comparison of multivariate and univariate statistical process control and monitoring methods
Leger, R.P.; Garland, WM.J.; Macgregor, J.F.
1996-01-01
Work in recent years has lead to the development of multivariate process monitoring schemes which use Principal Component Analysis (PCA). This research compares the performance of a univariate scheme and a multivariate PCA scheme used for monitoring a simple process with 11 measured variables. The multivariate PCA scheme was able to adequately represent the process using two principal components. This resulted in a PCA monitoring scheme which used two charts as opposed to 11 charts for the univariate scheme and therefore had distinct advantages in terms of both data representation, presentation, and fault diagnosis capabilities. (author)
Sensitivity analysis of reactive ecological dynamics.
Verdy, Ariane; Caswell, Hal
2008-08-01
Ecological systems with asymptotically stable equilibria may exhibit significant transient dynamics following perturbations. In some cases, these transient dynamics include the possibility of excursions away from the equilibrium before the eventual return; systems that exhibit such amplification of perturbations are called reactive. Reactivity is a common property of ecological systems, and the amplification can be large and long-lasting. The transient response of a reactive ecosystem depends on the parameters of the underlying model. To investigate this dependence, we develop sensitivity analyses for indices of transient dynamics (reactivity, the amplification envelope, and the optimal perturbation) in both continuous- and discrete-time models written in matrix form. The sensitivity calculations require expressions, some of them new, for the derivatives of equilibria, eigenvalues, singular values, and singular vectors, obtained using matrix calculus. Sensitivity analysis provides a quantitative framework for investigating the mechanisms leading to transient growth. We apply the methodology to a predator-prey model and a size-structured food web model. The results suggest predator-driven and prey-driven mechanisms for transient amplification resulting from multispecies interactions.
Global sensitivity analysis using polynomial chaos expansions
Sudret, Bruno
2008-01-01
Global sensitivity analysis (SA) aims at quantifying the respective effects of input random variables (or combinations thereof) onto the variance of the response of a physical or mathematical model. Among the abundant literature on sensitivity measures, the Sobol' indices have received much attention since they provide accurate information for most models. The paper introduces generalized polynomial chaos expansions (PCE) to build surrogate models that allow one to compute the Sobol' indices analytically as a post-processing of the PCE coefficients. Thus the computational cost of the sensitivity indices practically reduces to that of estimating the PCE coefficients. An original non intrusive regression-based approach is proposed, together with an experimental design of minimal size. Various application examples illustrate the approach, both from the field of global SA (i.e. well-known benchmark problems) and from the field of stochastic mechanics. The proposed method gives accurate results for various examples that involve up to eight input random variables, at a computational cost which is 2-3 orders of magnitude smaller than the traditional Monte Carlo-based evaluation of the Sobol' indices
Global sensitivity analysis using polynomial chaos expansions
Sudret, Bruno [Electricite de France, R and D Division, Site des Renardieres, F 77818 Moret-sur-Loing Cedex (France)], E-mail: bruno.sudret@edf.fr
2008-07-15
Global sensitivity analysis (SA) aims at quantifying the respective effects of input random variables (or combinations thereof) onto the variance of the response of a physical or mathematical model. Among the abundant literature on sensitivity measures, the Sobol' indices have received much attention since they provide accurate information for most models. The paper introduces generalized polynomial chaos expansions (PCE) to build surrogate models that allow one to compute the Sobol' indices analytically as a post-processing of the PCE coefficients. Thus the computational cost of the sensitivity indices practically reduces to that of estimating the PCE coefficients. An original non intrusive regression-based approach is proposed, together with an experimental design of minimal size. Various application examples illustrate the approach, both from the field of global SA (i.e. well-known benchmark problems) and from the field of stochastic mechanics. The proposed method gives accurate results for various examples that involve up to eight input random variables, at a computational cost which is 2-3 orders of magnitude smaller than the traditional Monte Carlo-based evaluation of the Sobol' indices.
Kryklywy, James H; Macpherson, Ewan A; Mitchell, Derek G V
2018-04-01
Emotion can have diverse effects on behaviour and perception, modulating function in some circumstances, and sometimes having little effect. Recently, it was identified that part of the heterogeneity of emotional effects could be due to a dissociable representation of emotion in dual pathway models of sensory processing. Our previous fMRI experiment using traditional univariate analyses showed that emotion modulated processing in the auditory 'what' but not 'where' processing pathway. The current study aims to further investigate this dissociation using a more recently emerging multi-voxel pattern analysis searchlight approach. While undergoing fMRI, participants localized sounds of varying emotional content. A searchlight multi-voxel pattern analysis was conducted to identify activity patterns predictive of sound location and/or emotion. Relative to the prior univariate analysis, MVPA indicated larger overlapping spatial and emotional representations of sound within early secondary regions associated with auditory localization. However, consistent with the univariate analysis, these two dimensions were increasingly segregated in late secondary and tertiary regions of the auditory processing streams. These results, while complimentary to our original univariate analyses, highlight the utility of multiple analytic approaches for neuroimaging, particularly for neural processes with known representations dependent on population coding.
Regression Is a Univariate General Linear Model Subsuming Other Parametric Methods as Special Cases.
Vidal, Sherry
Although the concept of the general linear model (GLM) has existed since the 1960s, other univariate analyses such as the t-test and the analysis of variance models have remained popular. The GLM produces an equation that minimizes the mean differences of independent variables as they are related to a dependent variable. From a computer printout…
New Riemannian Priors on the Univariate Normal Model
Salem Said
2014-07-01
Full Text Available The current paper introduces new prior distributions on the univariate normal model, with the aim of applying them to the classification of univariate normal populations. These new prior distributions are entirely based on the Riemannian geometry of the univariate normal model, so that they can be thought of as “Riemannian priors”. Precisely, if {pθ ; θ ∈ Θ} is any parametrization of the univariate normal model, the paper considers prior distributions G( θ - , γ with hyperparameters θ - ∈ Θ and γ > 0, whose density with respect to Riemannian volume is proportional to exp(−d2(θ, θ - /2γ2, where d2(θ, θ - is the square of Rao’s Riemannian distance. The distributions G( θ - , γ are termed Gaussian distributions on the univariate normal model. The motivation for considering a distribution G( θ - , γ is that this distribution gives a geometric representation of a class or cluster of univariate normal populations. Indeed, G( θ - , γ has a unique mode θ - (precisely, θ - is the unique Riemannian center of mass of G( θ - , γ, as shown in the paper, and its dispersion away from θ - is given by γ. Therefore, one thinks of members of the class represented by G( θ - , γ as being centered around θ - and lying within a typical distance determined by γ. The paper defines rigorously the Gaussian distributions G( θ - , γ and describes an algorithm for computing maximum likelihood estimates of their hyperparameters. Based on this algorithm and on the Laplace approximation, it describes how the distributions G( θ - , γ can be used as prior distributions for Bayesian classification of large univariate normal populations. In a concrete application to texture image classification, it is shown that this leads to an improvement in performance over the use of conjugate priors.
Contributions to sensitivity analysis and generalized discriminant analysis
Jacques, J.
2005-12-01
Two topics are studied in this thesis: sensitivity analysis and generalized discriminant analysis. Global sensitivity analysis of a mathematical model studies how the output variables of this last react to variations of its inputs. The methods based on the study of the variance quantify the part of variance of the response of the model due to each input variable and each subset of input variables. The first subject of this thesis is the impact of a model uncertainty on results of a sensitivity analysis. Two particular forms of uncertainty are studied: that due to a change of the model of reference, and that due to the use of a simplified model with the place of the model of reference. A second problem was studied during this thesis, that of models with correlated inputs. Indeed, classical sensitivity indices not having significance (from an interpretation point of view) in the presence of correlation of the inputs, we propose a multidimensional approach consisting in expressing the sensitivity of the output of the model to groups of correlated variables. Applications in the field of nuclear engineering illustrate this work. Generalized discriminant analysis consists in classifying the individuals of a test sample in groups, by using information contained in a training sample, when these two samples do not come from the same population. This work extends existing methods in a Gaussian context to the case of binary data. An application in public health illustrates the utility of generalized discrimination models thus defined. (author)
Univariate normalization of bispectrum using Hölder's inequality.
Shahbazi, Forooz; Ewald, Arne; Nolte, Guido
2014-08-15
Considering that many biological systems including the brain are complex non-linear systems, suitable methods capable of detecting these non-linearities are required to study the dynamical properties of these systems. One of these tools is the third order cummulant or cross-bispectrum, which is a measure of interfrequency interactions between three signals. For convenient interpretation, interaction measures are most commonly normalized to be independent of constant scales of the signals such that its absolute values are bounded by one, with this limit reflecting perfect coupling. Although many different normalization factors for cross-bispectra were suggested in the literature these either do not lead to bounded measures or are themselves dependent on the coupling and not only on the scale of the signals. In this paper we suggest a normalization factor which is univariate, i.e., dependent only on the amplitude of each signal and not on the interactions between signals. Using a generalization of Hölder's inequality it is proven that the absolute value of this univariate bicoherence is bounded by zero and one. We compared three widely used normalizations to the univariate normalization concerning the significance of bicoherence values gained from resampling tests. Bicoherence values are calculated from real EEG data recorded in an eyes closed experiment from 10 subjects. The results show slightly more significant values for the univariate normalization but in general, the differences are very small or even vanishing in some subjects. Therefore, we conclude that the normalization factor does not play an important role in the bicoherence values with regard to statistical power, although a univariate normalization is the only normalization factor which fulfills all the required conditions of a proper normalization. Copyright © 2014 Elsevier B.V. All rights reserved.
Simple Sensitivity Analysis for Orion GNC
Pressburger, Tom; Hoelscher, Brian; Martin, Rodney; Sricharan, Kumar
2013-01-01
The performance of Orion flight software, especially its GNC software, is being analyzed by running Monte Carlo simulations of Orion spacecraft flights. The simulated performance is analyzed for conformance with flight requirements, expressed as performance constraints. Flight requirements include guidance (e.g. touchdown distance from target) and control (e.g., control saturation) as well as performance (e.g., heat load constraints). The Monte Carlo simulations disperse hundreds of simulation input variables, for everything from mass properties to date of launch.We describe in this paper a sensitivity analysis tool (Critical Factors Tool or CFT) developed to find the input variables or pairs of variables which by themselves significantly influence satisfaction of requirements or significantly affect key performance metrics (e.g., touchdown distance from target). Knowing these factors can inform robustness analysis, can inform where engineering resources are most needed, and could even affect operations. The contributions of this paper include the introduction of novel sensitivity measures, such as estimating success probability, and a technique for determining whether pairs of factors are interacting dependently or independently. The tool found that input variables such as moments, mass, thrust dispersions, and date of launch were found to be significant factors for success of various requirements. Examples are shown in this paper as well as a summary and physics discussion of EFT-1 driving factors that the tool found.
Sensitivity analysis of floating offshore wind farms
Castro-Santos, Laura; Diaz-Casas, Vicente
2015-01-01
Highlights: • Develop a sensitivity analysis of a floating offshore wind farm. • Influence on the life-cycle costs involved in a floating offshore wind farm. • Influence on IRR, NPV, pay-back period, LCOE and cost of power. • Important variables: distance, wind resource, electric tariff, etc. • It helps to investors to take decisions in the future. - Abstract: The future of offshore wind energy will be in deep waters. In this context, the main objective of the present paper is to develop a sensitivity analysis of a floating offshore wind farm. It will show how much the output variables can vary when the input variables are changing. For this purpose two different scenarios will be taken into account: the life-cycle costs involved in a floating offshore wind farm (cost of conception and definition, cost of design and development, cost of manufacturing, cost of installation, cost of exploitation and cost of dismantling) and the most important economic indexes in terms of economic feasibility of a floating offshore wind farm (internal rate of return, net present value, discounted pay-back period, levelized cost of energy and cost of power). Results indicate that the most important variables in economic terms are the number of wind turbines and the distance from farm to shore in the costs’ scenario, and the wind scale parameter and the electric tariff for the economic indexes. This study will help investors to take into account these variables in the development of floating offshore wind farms in the future
Weathers, J.B.; Luck, R.; Weathers, J.W.
2009-01-01
The complexity of mathematical models used by practicing engineers is increasing due to the growing availability of sophisticated mathematical modeling tools and ever-improving computational power. For this reason, the need to define a well-structured process for validating these models against experimental results has become a pressing issue in the engineering community. This validation process is partially characterized by the uncertainties associated with the modeling effort as well as the experimental results. The net impact of the uncertainties on the validation effort is assessed through the 'noise level of the validation procedure', which can be defined as an estimate of the 95% confidence uncertainty bounds for the comparison error between actual experimental results and model-based predictions of the same quantities of interest. Although general descriptions associated with the construction of the noise level using multivariate statistics exists in the literature, a detailed procedure outlining how to account for the systematic and random uncertainties is not available. In this paper, the methodology used to derive the covariance matrix associated with the multivariate normal pdf based on random and systematic uncertainties is examined, and a procedure used to estimate this covariance matrix using Monte Carlo analysis is presented. The covariance matrices are then used to construct approximate 95% confidence constant probability contours associated with comparison error results for a practical example. In addition, the example is used to show the drawbacks of using a first-order sensitivity analysis when nonlinear local sensitivity coefficients exist. Finally, the example is used to show the connection between the noise level of the validation exercise calculated using multivariate and univariate statistics.
Weathers, J.B. [Shock, Noise, and Vibration Group, Northrop Grumman Shipbuilding, P.O. Box 149, Pascagoula, MS 39568 (United States)], E-mail: James.Weathers@ngc.com; Luck, R. [Department of Mechanical Engineering, Mississippi State University, 210 Carpenter Engineering Building, P.O. Box ME, Mississippi State, MS 39762-5925 (United States)], E-mail: Luck@me.msstate.edu; Weathers, J.W. [Structural Analysis Group, Northrop Grumman Shipbuilding, P.O. Box 149, Pascagoula, MS 39568 (United States)], E-mail: Jeffrey.Weathers@ngc.com
2009-11-15
The complexity of mathematical models used by practicing engineers is increasing due to the growing availability of sophisticated mathematical modeling tools and ever-improving computational power. For this reason, the need to define a well-structured process for validating these models against experimental results has become a pressing issue in the engineering community. This validation process is partially characterized by the uncertainties associated with the modeling effort as well as the experimental results. The net impact of the uncertainties on the validation effort is assessed through the 'noise level of the validation procedure', which can be defined as an estimate of the 95% confidence uncertainty bounds for the comparison error between actual experimental results and model-based predictions of the same quantities of interest. Although general descriptions associated with the construction of the noise level using multivariate statistics exists in the literature, a detailed procedure outlining how to account for the systematic and random uncertainties is not available. In this paper, the methodology used to derive the covariance matrix associated with the multivariate normal pdf based on random and systematic uncertainties is examined, and a procedure used to estimate this covariance matrix using Monte Carlo analysis is presented. The covariance matrices are then used to construct approximate 95% confidence constant probability contours associated with comparison error results for a practical example. In addition, the example is used to show the drawbacks of using a first-order sensitivity analysis when nonlinear local sensitivity coefficients exist. Finally, the example is used to show the connection between the noise level of the validation exercise calculated using multivariate and univariate statistics.
Univariate and Bivariate Empirical Mode Decomposition for Postural Stability Analysis
Jacques Duchêne
2008-05-01
Full Text Available The aim of this paper was to compare empirical mode decomposition (EMD and two new extended methods of Ã¢Â€Â‰EMD named complex empirical mode decomposition (complex-EMD and bivariate empirical mode decomposition (bivariate-EMD. All methods were used to analyze stabilogram center of pressure (COP time series. The two new methods are suitable to be applied to complex time series to extract complex intrinsic mode functions (IMFs before the Hilbert transform is subsequently applied on the IMFs. The trace of the analytic IMF in the complex plane has a circular form, with each IMF having its own rotation frequency. The area of the circle and the average rotation frequency of IMFs represent efficient indicators of the postural stability status of subjects. Experimental results show the effectiveness of these indicators to identify differences in standing posture between groups.
Sensitivity analysis of a modified energy model
Suganthi, L.; Jagadeesan, T.R.
1997-01-01
Sensitivity analysis is carried out to validate model formulation. A modified model has been developed to predict the future energy requirement of coal, oil and electricity, considering price, income, technological and environmental factors. The impact and sensitivity of the independent variables on the dependent variable are analysed. The error distribution pattern in the modified model as compared to a conventional time series model indicated the absence of clusters. The residual plot of the modified model showed no distinct pattern of variation. The percentage variation of error in the conventional time series model for coal and oil ranges from -20% to +20%, while for electricity it ranges from -80% to +20%. However, in the case of the modified model the percentage variation in error is greatly reduced - for coal it ranges from -0.25% to +0.15%, for oil -0.6% to +0.6% and for electricity it ranges from -10% to +10%. The upper and lower limit consumption levels at 95% confidence is determined. The consumption at varying percentage changes in price and population are analysed. The gap between the modified model predictions at varying percentage changes in price and population over the years from 1990 to 2001 is found to be increasing. This is because of the increasing rate of energy consumption over the years and also the confidence level decreases as the projection is made far into the future. (author)
Sensitivity Analysis for Design Optimization Integrated Software Tools, Phase I
National Aeronautics and Space Administration — The objective of this proposed project is to provide a new set of sensitivity analysis theory and codes, the Sensitivity Analysis for Design Optimization Integrated...
Sensitivity analysis approaches applied to systems biology models.
Zi, Z
2011-11-01
With the rising application of systems biology, sensitivity analysis methods have been widely applied to study the biological systems, including metabolic networks, signalling pathways and genetic circuits. Sensitivity analysis can provide valuable insights about how robust the biological responses are with respect to the changes of biological parameters and which model inputs are the key factors that affect the model outputs. In addition, sensitivity analysis is valuable for guiding experimental analysis, model reduction and parameter estimation. Local and global sensitivity analysis approaches are the two types of sensitivity analysis that are commonly applied in systems biology. Local sensitivity analysis is a classic method that studies the impact of small perturbations on the model outputs. On the other hand, global sensitivity analysis approaches have been applied to understand how the model outputs are affected by large variations of the model input parameters. In this review, the author introduces the basic concepts of sensitivity analysis approaches applied to systems biology models. Moreover, the author discusses the advantages and disadvantages of different sensitivity analysis methods, how to choose a proper sensitivity analysis approach, the available sensitivity analysis tools for systems biology models and the caveats in the interpretation of sensitivity analysis results.
A new importance measure for sensitivity analysis
Liu, Qiao; Homma, Toshimitsu
2010-01-01
Uncertainty is an integral part of risk assessment of complex engineering systems, such as nuclear power plants and space crafts. The aim of sensitivity analysis is to identify the contribution of the uncertainty in model inputs to the uncertainty in the model output. In this study, a new importance measure that characterizes the influence of the entire input distribution on the entire output distribution was proposed. It represents the expected deviation of the cumulative distribution function (CDF) of the model output that would be obtained when one input parameter of interest were known. The applicability of this importance measure was tested with two models, a nonlinear nonmonotonic mathematical model and a risk model. In addition, a comparison of this new importance measure with several other importance measures was carried out and the differences between these measures were explained. (author)
DEA Sensitivity Analysis for Parallel Production Systems
J. Gerami
2011-06-01
Full Text Available In this paper, we introduce systems consisting of several production units, each of which include several subunits working in parallel. Meanwhile, each subunit is working independently. The input and output of each production unit are the sums of the inputs and outputs of its subunits, respectively. We consider each of these subunits as an independent decision making unit(DMU and create the production possibility set(PPS produced by these DMUs, in which the frontier points are considered as efficient DMUs. Then we introduce models for obtaining the efficiency of the production subunits. Using super-efficiency models, we categorize all efficient subunits into different efficiency classes. Then we follow by presenting the sensitivity analysis and stability problem for efficient subunits, including extreme efficient and non-extreme efficient subunits, assuming simultaneous perturbations in all inputs and outputs of subunits such that the efficiency of the subunit under evaluation declines while the efficiencies of other subunits improve.
Sensitivity of SBLOCA analysis to model nodalization
Lee, C.; Ito, T.; Abramson, P.B.
1983-01-01
The recent Semiscale test S-UT-8 indicates the possibility for primary liquid to hang up in the steam generators during a SBLOCA, permitting core uncovery prior to loop-seal clearance. In analysis of Small Break Loss of Coolant Accidents with RELAP5, it is found that resultant transient behavior is quite sensitive to the selection of nodalization for the steam generators. Although global parameters such as integrated mass loss, primary inventory and primary pressure are relatively insensitive to the nodalization, it is found that the predicted distribution of inventory around the primary is significantly affected by nodalization. More detailed nodalization predicts that more of the inventory tends to remain in the steam generators, resulting in less inventory in the reactor vessel and therefore causing earlier and more severe core uncovery
Subset simulation for structural reliability sensitivity analysis
Song Shufang; Lu Zhenzhou; Qiao Hongwei
2009-01-01
Based on two procedures for efficiently generating conditional samples, i.e. Markov chain Monte Carlo (MCMC) simulation and importance sampling (IS), two reliability sensitivity (RS) algorithms are presented. On the basis of reliability analysis of Subset simulation (Subsim), the RS of the failure probability with respect to the distribution parameter of the basic variable is transformed as a set of RS of conditional failure probabilities with respect to the distribution parameter of the basic variable. By use of the conditional samples generated by MCMC simulation and IS, procedures are established to estimate the RS of the conditional failure probabilities. The formulae of the RS estimator, its variance and its coefficient of variation are derived in detail. The results of the illustrations show high efficiency and high precision of the presented algorithms, and it is suitable for highly nonlinear limit state equation and structural system with single and multiple failure modes
Which DTW Method Applied to Marine Univariate Time Series Imputation
Phan , Thi-Thu-Hong; Caillault , Émilie; Lefebvre , Alain; Bigand , André
2017-01-01
International audience; Missing data are ubiquitous in any domains of applied sciences. Processing datasets containing missing values can lead to a loss of efficiency and unreliable results, especially for large missing sub-sequence(s). Therefore, the aim of this paper is to build a framework for filling missing values in univariate time series and to perform a comparison of different similarity metrics used for the imputation task. This allows to suggest the most suitable methods for the imp...
Univariate decision tree induction using maximum margin classification
Yıldız, Olcay Taner
2012-01-01
In many pattern recognition applications, first decision trees are used due to their simplicity and easily interpretable nature. In this paper, we propose a new decision tree learning algorithm called univariate margin tree where, for each continuous attribute, the best split is found using convex optimization. Our simulation results on 47 data sets show that the novel margin tree classifier performs at least as good as C4.5 and linear discriminant tree (LDT) with a similar time complexity. F...
Acceleration techniques in the univariate Lipschitz global optimization
Sergeyev, Yaroslav D.; Kvasov, Dmitri E.; Mukhametzhanov, Marat S.; De Franco, Angela
2016-10-01
Univariate box-constrained Lipschitz global optimization problems are considered in this contribution. Geometric and information statistical approaches are presented. The novel powerful local tuning and local improvement techniques are described in the contribution as well as the traditional ways to estimate the Lipschitz constant. The advantages of the presented local tuning and local improvement techniques are demonstrated using the operational characteristics approach for comparing deterministic global optimization algorithms on the class of 100 widely used test functions.
Calibration, validation, and sensitivity analysis: What's what
Trucano, T.G.; Swiler, L.P.; Igusa, T.; Oberkampf, W.L.; Pilch, M.
2006-01-01
One very simple interpretation of calibration is to adjust a set of parameters associated with a computational science and engineering code so that the model agreement is maximized with respect to a set of experimental data. One very simple interpretation of validation is to quantify our belief in the predictive capability of a computational code through comparison with a set of experimental data. Uncertainty in both the data and the code are important and must be mathematically understood to correctly perform both calibration and validation. Sensitivity analysis, being an important methodology in uncertainty analysis, is thus important to both calibration and validation. In this paper, we intend to clarify the language just used and express some opinions on the associated issues. We will endeavor to identify some technical challenges that must be resolved for successful validation of a predictive modeling capability. One of these challenges is a formal description of a 'model discrepancy' term. Another challenge revolves around the general adaptation of abstract learning theory as a formalism that potentially encompasses both calibration and validation in the face of model uncertainty
Global sensitivity analysis in wind energy assessment
Tsvetkova, O.; Ouarda, T. B.
2012-12-01
Wind energy is one of the most promising renewable energy sources. Nevertheless, it is not yet a common source of energy, although there is enough wind potential to supply world's energy demand. One of the most prominent obstacles on the way of employing wind energy is the uncertainty associated with wind energy assessment. Global sensitivity analysis (SA) studies how the variation of input parameters in an abstract model effects the variation of the variable of interest or the output variable. It also provides ways to calculate explicit measures of importance of input variables (first order and total effect sensitivity indices) in regard to influence on the variation of the output variable. Two methods of determining the above mentioned indices were applied and compared: the brute force method and the best practice estimation procedure In this study a methodology for conducting global SA of wind energy assessment at a planning stage is proposed. Three sampling strategies which are a part of SA procedure were compared: sampling based on Sobol' sequences (SBSS), Latin hypercube sampling (LHS) and pseudo-random sampling (PRS). A case study of Masdar City, a showcase of sustainable living in the UAE, is used to exemplify application of the proposed methodology. Sources of uncertainty in wind energy assessment are very diverse. In the case study the following were identified as uncertain input parameters: the Weibull shape parameter, the Weibull scale parameter, availability of a wind turbine, lifetime of a turbine, air density, electrical losses, blade losses, ineffective time losses. Ineffective time losses are defined as losses during the time when the actual wind speed is lower than the cut-in speed or higher than the cut-out speed. The output variable in the case study is the lifetime energy production. Most influential factors for lifetime energy production are identified with the ranking of the total effect sensitivity indices. The results of the present
Frontier Assignment for Sensitivity Analysis of Data Envelopment Analysis
Naito, Akio; Aoki, Shingo; Tsuji, Hiroshi
To extend the sensitivity analysis capability for DEA (Data Envelopment Analysis), this paper proposes frontier assignment based DEA (FA-DEA). The basic idea of FA-DEA is to allow a decision maker to decide frontier intentionally while the traditional DEA and Super-DEA decide frontier computationally. The features of FA-DEA are as follows: (1) provides chances to exclude extra-influential DMU (Decision Making Unit) and finds extra-ordinal DMU, and (2) includes the function of the traditional DEA and Super-DEA so that it is able to deal with sensitivity analysis more flexibly. Simple numerical study has shown the effectiveness of the proposed FA-DEA and the difference from the traditional DEA.
Sensitivity analysis of Smith's AMRV model
Ho, Chih-Hsiang
1995-01-01
Multiple-expert hazard/risk assessments have considerable precedent, particularly in the Yucca Mountain site characterization studies. In this paper, we present a Bayesian approach to statistical modeling in volcanic hazard assessment for the Yucca Mountain site. Specifically, we show that the expert opinion on the site disruption parameter p is elicited on the prior distribution, π (p), based on geological information that is available. Moreover, π (p) can combine all available geological information motivated by conflicting but realistic arguments (e.g., simulation, cluster analysis, structural control, etc.). The incorporated uncertainties about the probability of repository disruption p, win eventually be averaged out by taking the expectation over π (p). We use the following priors in the analysis: priors chosen for mathematical convenience: Beta (r, s) for (r, s) = (2, 2), (3, 3), (5, 5), (2, 1), (2, 8), (8, 2), and (1, 1); and three priors motivated by expert knowledge. Sensitivity analysis is performed for each prior distribution. Estimated values of hazard based on the priors chosen for mathematical simplicity are uniformly higher than those obtained based on the priors motivated by expert knowledge. And, the model using the prior, Beta (8,2), yields the highest hazard (= 2.97 X 10 -2 ). The minimum hazard is produced by the open-quotes three-expert priorclose quotes (i.e., values of p are equally likely at 10 -3 10 -2 , and 10 -1 ). The estimate of the hazard is 1.39 x which is only about one order of magnitude smaller than the maximum value. The term, open-quotes hazardclose quotes, is defined as the probability of at least one disruption of a repository at the Yucca Mountain site by basaltic volcanism for the next 10,000 years
Wear-Out Sensitivity Analysis Project Abstract
Harris, Adam
2015-01-01
During the course of the Summer 2015 internship session, I worked in the Reliability and Maintainability group of the ISS Safety and Mission Assurance department. My project was a statistical analysis of how sensitive ORU's (Orbital Replacement Units) are to a reliability parameter called the wear-out characteristic. The intended goal of this was to determine a worst case scenario of how many spares would be needed if multiple systems started exhibiting wear-out characteristics simultaneously. The goal was also to determine which parts would be most likely to do so. In order to do this, my duties were to take historical data of operational times and failure times of these ORU's and use them to build predictive models of failure using probability distribution functions, mainly the Weibull distribution. Then, I ran Monte Carlo Simulations to see how an entire population of these components would perform. From here, my final duty was to vary the wear-out characteristic from the intrinsic value, to extremely high wear-out values and determine how much the probability of sufficiency of the population would shift. This was done for around 30 different ORU populations on board the ISS.
Supercritical extraction of oleaginous: parametric sensitivity analysis
Santos M.M.
2000-01-01
Full Text Available The economy has become universal and competitive, thus the industries of vegetable oil extraction must advance in the sense of minimising production costs and, at the same time, generating products that obey more rigorous patterns of quality, including solutions that do not damage the environment. The conventional oilseed processing uses hexane as solvent. However, this solvent is toxic and highly flammable. Thus the search of substitutes for hexane in oleaginous extraction process has increased in the last years. The supercritical carbon dioxide is a potential substitute for hexane, but it is necessary more detailed studies to understand the phenomena taking place in such process. Thus, in this work a diffusive model for semi-continuous (batch for the solids and continuous for the solvent isothermal and isobaric extraction process using supercritical carbon dioxide is presented and submitted to a parametric sensitivity analysis by means of a factorial design in two levels. The model parameters were disturbed and their main effects analysed, so that it is possible to propose strategies for high performance operation.
Effect Sizes for Research Univariate and Multivariate Applications
Grissom, Robert J
2011-01-01
Noted for its comprehensive coverage, this greatly expanded new edition now covers the use of univariate and multivariate effect sizes. Many measures and estimators are reviewed along with their application, interpretation, and limitations. Noted for its practical approach, the book features numerous examples using real data for a variety of variables and designs, to help readers apply the material to their own data. Tips on the use of SPSS, SAS, R, and S-Plus are provided. The book's broad disciplinary appeal results from its inclusion of a variety of examples from psychology, medicine, educa
Sensitivity analysis of ranked data: from order statistics to quantiles
Heidergott, B.F.; Volk-Makarewicz, W.
2015-01-01
In this paper we provide the mathematical theory for sensitivity analysis of order statistics of continuous random variables, where the sensitivity is with respect to a distributional parameter. Sensitivity analysis of order statistics over a finite number of observations is discussed before
Riad, Safaa M.; Salem, Hesham; Elbalkiny, Heba T.; Khattab, Fatma I.
2015-04-01
Five, accurate, precise, and sensitive univariate and multivariate spectrophotometric methods were developed for the simultaneous determination of a ternary mixture containing Trimethoprim (TMP), Sulphamethoxazole (SMZ) and Oxytetracycline (OTC) in waste water samples collected from different cites either production wastewater or livestock wastewater after their solid phase extraction using OASIS HLB cartridges. In univariate methods OTC was determined at its λmax 355.7 nm (0D), while (TMP) and (SMZ) were determined by three different univariate methods. Method (A) is based on successive spectrophotometric resolution technique (SSRT). The technique starts with the ratio subtraction method followed by ratio difference method for determination of TMP and SMZ. Method (B) is successive derivative ratio technique (SDR). Method (C) is mean centering of the ratio spectra (MCR). The developed multivariate methods are principle component regression (PCR) and partial least squares (PLS). The specificity of the developed methods is investigated by analyzing laboratory prepared mixtures containing different ratios of the three drugs. The obtained results are statistically compared with those obtained by the official methods, showing no significant difference with respect to accuracy and precision at p = 0.05.
SENSIT: a cross-section and design sensitivity and uncertainty analysis code
Gerstl, S.A.W.
1980-01-01
SENSIT computes the sensitivity and uncertainty of a calculated integral response (such as a dose rate) due to input cross sections and their uncertainties. Sensitivity profiles are computed for neutron and gamma-ray reaction cross sections of standard multigroup cross section sets and for secondary energy distributions (SEDs) of multigroup scattering matrices. In the design sensitivity mode, SENSIT computes changes in an integral response due to design changes and gives the appropriate sensitivity coefficients. Cross section uncertainty analyses are performed for three types of input data uncertainties: cross-section covariance matrices for pairs of multigroup reaction cross sections, spectral shape uncertainty parameters for secondary energy distributions (integral SED uncertainties), and covariance matrices for energy-dependent response functions. For all three types of data uncertainties SENSIT computes the resulting variance and estimated standard deviation in an integral response of interest, on the basis of generalized perturbation theory. SENSIT attempts to be more comprehensive than earlier sensitivity analysis codes, such as SWANLAKE
Multitarget global sensitivity analysis of n-butanol combustion.
Zhou, Dingyu D Y; Davis, Michael J; Skodje, Rex T
2013-05-02
A model for the combustion of butanol is studied using a recently developed theoretical method for the systematic improvement of the kinetic mechanism. The butanol mechanism includes 1446 reactions, and we demonstrate that it is straightforward and computationally feasible to implement a full global sensitivity analysis incorporating all the reactions. In addition, we extend our previous analysis of ignition-delay targets to include species targets. The combination of species and ignition targets leads to multitarget global sensitivity analysis, which allows for a more complete mechanism validation procedure than we previously implemented. The inclusion of species sensitivity analysis allows for a direct comparison between reaction pathway analysis and global sensitivity analysis.
Sensitivity analysis in multi-parameter probabilistic systems
Walker, J.R.
1987-01-01
Probabilistic methods involving the use of multi-parameter Monte Carlo analysis can be applied to a wide range of engineering systems. The output from the Monte Carlo analysis is a probabilistic estimate of the system consequence, which can vary spatially and temporally. Sensitivity analysis aims to examine how the output consequence is influenced by the input parameter values. Sensitivity analysis provides the necessary information so that the engineering properties of the system can be optimized. This report details a package of sensitivity analysis techniques that together form an integrated methodology for the sensitivity analysis of probabilistic systems. The techniques have known confidence limits and can be applied to a wide range of engineering problems. The sensitivity analysis methodology is illustrated by performing the sensitivity analysis of the MCROC rock microcracking model
An ESDIRK Method with Sensitivity Analysis Capabilities
Kristensen, Morten Rode; Jørgensen, John Bagterp; Thomsen, Per Grove
2004-01-01
of the sensitivity equations. A key feature is the reuse of information already computed for the state integration, hereby minimizing the extra effort required for sensitivity integration. Through case studies the new algorithm is compared to an extrapolation method and to the more established BDF based approaches...
Sensitivity Analysis of Fire Dynamics Simulation
Brohus, Henrik; Nielsen, Peter V.; Petersen, Arnkell J.
2007-01-01
(Morris method). The parameters considered are selected among physical parameters and program specific parameters. The influence on the calculation result as well as the CPU time is considered. It is found that the result is highly sensitive to many parameters even though the sensitivity varies...
Superconducting Accelerating Cavity Pressure Sensitivity Analysis
Rodnizki, J.; Horvits, Z.; Ben Aliz, Y.; Grin, A.; Weissman, L.
2014-01-01
The measured sensitivity of the cavity was evaluated and it is full consistent with the measured values. It was explored that the tuning system (the fog structure) has a significant contribution to the cavity sensitivity. By using ribs or by modifying the rigidity of the fog we may reduce the HWR sensitivity. During cool down and warming up we have to analyze the stresses on the HWR to avoid plastic deformation to the HWR since the Niobium yield is an order of magnitude lower in room temperature
Derivative based sensitivity analysis of gamma index
Biplab Sarkar
2015-01-01
Full Text Available Originally developed as a tool for patient-specific quality assurance in advanced treatment delivery methods to compare between measured and calculated dose distributions, the gamma index (γ concept was later extended to compare between any two dose distributions. It takes into effect both the dose difference (DD and distance-to-agreement (DTA measurements in the comparison. Its strength lies in its capability to give a quantitative value for the analysis, unlike other methods. For every point on the reference curve, if there is at least one point in the evaluated curve that satisfies the pass criteria (e.g., δDD = 1%, δDTA = 1 mm, the point is included in the quantitative score as "pass." Gamma analysis does not account for the gradient of the evaluated curve - it looks at only the minimum gamma value, and if it is <1, then the point passes, no matter what the gradient of evaluated curve is. In this work, an attempt has been made to present a derivative-based method for the identification of dose gradient. A mathematically derived reference profile (RP representing the penumbral region of 6 MV 10 cm × 10 cm field was generated from an error function. A general test profile (GTP was created from this RP by introducing 1 mm distance error and 1% dose error at each point. This was considered as the first of the two evaluated curves. By its nature, this curve is a smooth curve and would satisfy the pass criteria for all points in it. The second evaluated profile was generated as a sawtooth test profile (STTP which again would satisfy the pass criteria for every point on the RP. However, being a sawtooth curve, it is not a smooth one and would be obviously poor when compared with the smooth profile. Considering the smooth GTP as an acceptable profile when it passed the gamma pass criteria (1% DD and 1 mm DTA against the RP, the first and second order derivatives of the DDs (δD', δD" between these two curves were derived and used as the
MOVES2010a regional level sensitivity analysis
2012-12-10
This document discusses the sensitivity of various input parameter effects on emission rates using the US Environmental Protection Agencys (EPAs) MOVES2010a model at the regional level. Pollutants included in the study are carbon monoxide (CO),...
Esfahlani, Farnaz Zamani; Sayama, Hiroki; Visser, Katherine Frost; Strauss, Gregory P
2017-12-01
Objective: The Positive and Negative Syndrome Scale is a primary outcome measure in clinical trials examining the efficacy of antipsychotic medications. Although the Positive and Negative Syndrome Scale has demonstrated sensitivity as a measure of treatment change in studies using traditional univariate statistical approaches, its sensitivity to detecting network-level changes in dynamic relationships among symptoms has yet to be demonstrated using more sophisticated multivariate analyses. In the current study, we examined the sensitivity of the Positive and Negative Syndrome Scale to detecting antipsychotic treatment effects as revealed through network analysis. Design: Participants included 1,049 individuals diagnosed with psychotic disorders from the Phase I portion of the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) study. Of these participants, 733 were clinically determined to be treatment-responsive and 316 were found to be treatment-resistant. Item level data from the Positive and Negative Syndrome Scale were submitted to network analysis, and macroscopic, mesoscopic, and microscopic network properties were evaluated for the treatment-responsive and treatment-resistant groups at baseline and post-phase I antipsychotic treatment. Results: Network analysis indicated that treatment-responsive patients had more densely connected symptom networks after antipsychotic treatment than did treatment-responsive patients at baseline, and that symptom centralities increased following treatment. In contrast, symptom networks of treatment-resistant patients behaved more randomly before and after treatment. Conclusions: These results suggest that the Positive and Negative Syndrome Scale is sensitive to detecting treatment effects as revealed through network analysis. Its findings also provide compelling new evidence that strongly interconnected symptom networks confer an overall greater probability of treatment responsiveness in patients with
NPV Sensitivity Analysis: A Dynamic Excel Approach
Mangiero, George A.; Kraten, Michael
2017-01-01
Financial analysts generally create static formulas for the computation of NPV. When they do so, however, it is not readily apparent how sensitive the value of NPV is to changes in multiple interdependent and interrelated variables. It is the aim of this paper to analyze this variability by employing a dynamic, visually graphic presentation using…
Sensitivity Analysis for Multidisciplinary Systems (SAMS)
2016-12-01
release. Distribution is unlimited. 14 Server and Client Code Server from geometry import Point, Geometry import math import zmq class Server...public release; Distribution is unlimited. DISTRIBUTION STATEMENT A: Approved for public release. Distribution is unlimited. 19 Example Application Boeing...Materials Conference, 2011. Cross, D. M., Local continuum sensitivity method for shape design derivatives using spatial gradient reconstruction. Diss
Extended forward sensitivity analysis of one-dimensional isothermal flow
Johnson, M.; Zhao, H.
2013-01-01
Sensitivity analysis and uncertainty quantification is an important part of nuclear safety analysis. In this work, forward sensitivity analysis is used to compute solution sensitivities on 1-D fluid flow equations typical of those found in system level codes. Time step sensitivity analysis is included as a method for determining the accumulated error from time discretization. The ability to quantify numerical error arising from the time discretization is a unique and important feature of this method. By knowing the relative sensitivity of time step with other physical parameters, the simulation is allowed to run at optimized time steps without affecting the confidence of the physical parameter sensitivity results. The time step forward sensitivity analysis method can also replace the traditional time step convergence studies that are a key part of code verification with much less computational cost. One well-defined benchmark problem with manufactured solutions is utilized to verify the method; another test isothermal flow problem is used to demonstrate the extended forward sensitivity analysis process. Through these sample problems, the paper shows the feasibility and potential of using the forward sensitivity analysis method to quantify uncertainty in input parameters and time step size for a 1-D system-level thermal-hydraulic safety code. (authors)
The role of sensitivity analysis in probabilistic safety assessment
Hirschberg, S.; Knochenhauer, M.
1987-01-01
The paper describes several items suitable for close examination by means of application of sensitivity analysis, when performing a level 1 PSA. Sensitivity analyses are performed with respect to; (1) boundary conditions, (2) operator actions, and (3) treatment of common cause failures (CCFs). The items of main interest are identified continuously in the course of performing a PSA, as well as by scrutinising the final results. The practical aspects of sensitivity analysis are illustrated by several applications from a recent PSA study (ASEA-ATOM BWR 75). It is concluded that sensitivity analysis leads to insights important for analysts, reviewers and decision makers. (orig./HP)
Automated sensitivity analysis using the GRESS language
Pin, F.G.; Oblow, E.M.; Wright, R.Q.
1986-04-01
An automated procedure for performing large-scale sensitivity studies based on the use of computer calculus is presented. The procedure is embodied in a FORTRAN precompiler called GRESS, which automatically processes computer models and adds derivative-taking capabilities to the normal calculated results. In this report, the GRESS code is described, tested against analytic and numerical test problems, and then applied to a major geohydrological modeling problem. The SWENT nuclear waste repository modeling code is used as the basis for these studies. Results for all problems are discussed in detail. Conclusions are drawn as to the applicability of GRESS in the problems at hand and for more general large-scale modeling sensitivity studies
Sensitivity Analysis of a Simplified Fire Dynamic Model
Sørensen, Lars Schiøtt; Nielsen, Anker
2015-01-01
This paper discusses a method for performing a sensitivity analysis of parameters used in a simplified fire model for temperature estimates in the upper smoke layer during a fire. The results from the sensitivity analysis can be used when individual parameters affecting fire safety are assessed...
Automatic Image Segmentation Using Active Contours with Univariate Marginal Distribution
I. Cruz-Aceves
2013-01-01
Full Text Available This paper presents a novel automatic image segmentation method based on the theory of active contour models and estimation of distribution algorithms. The proposed method uses the univariate marginal distribution model to infer statistical dependencies between the control points on different active contours. These contours have been generated through an alignment process of reference shape priors, in order to increase the exploration and exploitation capabilities regarding different interactive segmentation techniques. This proposed method is applied in the segmentation of the hollow core in microscopic images of photonic crystal fibers and it is also used to segment the human heart and ventricular areas from datasets of computed tomography and magnetic resonance images, respectively. Moreover, to evaluate the performance of the medical image segmentations compared to regions outlined by experts, a set of similarity measures has been adopted. The experimental results suggest that the proposed image segmentation method outperforms the traditional active contour model and the interactive Tseng method in terms of segmentation accuracy and stability.
Global Sensitivity Analysis of Environmental Models: Convergence, Robustness and Validation
Sarrazin, Fanny; Pianosi, Francesca; Khorashadi Zadeh, Farkhondeh; Van Griensven, Ann; Wagener, Thorsten
2015-04-01
Global Sensitivity Analysis aims to characterize the impact that variations in model input factors (e.g. the parameters) have on the model output (e.g. simulated streamflow). In sampling-based Global Sensitivity Analysis, the sample size has to be chosen carefully in order to obtain reliable sensitivity estimates while spending computational resources efficiently. Furthermore, insensitive parameters are typically identified through the definition of a screening threshold: the theoretical value of their sensitivity index is zero but in a sampling-base framework they regularly take non-zero values. There is little guidance available for these two steps in environmental modelling though. The objective of the present study is to support modellers in making appropriate choices, regarding both sample size and screening threshold, so that a robust sensitivity analysis can be implemented. We performed sensitivity analysis for the parameters of three hydrological models with increasing level of complexity (Hymod, HBV and SWAT), and tested three widely used sensitivity analysis methods (Elementary Effect Test or method of Morris, Regional Sensitivity Analysis, and Variance-Based Sensitivity Analysis). We defined criteria based on a bootstrap approach to assess three different types of convergence: the convergence of the value of the sensitivity indices, of the ranking (the ordering among the parameters) and of the screening (the identification of the insensitive parameters). We investigated the screening threshold through the definition of a validation procedure. The results showed that full convergence of the value of the sensitivity indices is not necessarily needed to rank or to screen the model input factors. Furthermore, typical values of the sample sizes that are reported in the literature can be well below the sample sizes that actually ensure convergence of ranking and screening.
Automating sensitivity analysis of computer models using computer calculus
Oblow, E.M.; Pin, F.G.
1986-01-01
An automated procedure for performing sensitivity analysis has been developed. The procedure uses a new FORTRAN compiler with computer calculus capabilities to generate the derivatives needed to set up sensitivity equations. The new compiler is called GRESS - Gradient Enhanced Software System. Application of the automated procedure with direct and adjoint sensitivity theory for the analysis of non-linear, iterative systems of equations is discussed. Calculational efficiency consideration and techniques for adjoint sensitivity analysis are emphasized. The new approach is found to preserve the traditional advantages of adjoint theory while removing the tedious human effort previously needed to apply this theoretical methodology. Conclusions are drawn about the applicability of the automated procedure in numerical analysis and large-scale modelling sensitivity studies
Accelerated Sensitivity Analysis in High-Dimensional Stochastic Reaction Networks.
Arampatzis, Georgios; Katsoulakis, Markos A; Pantazis, Yannis
2015-01-01
Existing sensitivity analysis approaches are not able to handle efficiently stochastic reaction networks with a large number of parameters and species, which are typical in the modeling and simulation of complex biochemical phenomena. In this paper, a two-step strategy for parametric sensitivity analysis for such systems is proposed, exploiting advantages and synergies between two recently proposed sensitivity analysis methodologies for stochastic dynamics. The first method performs sensitivity analysis of the stochastic dynamics by means of the Fisher Information Matrix on the underlying distribution of the trajectories; the second method is a reduced-variance, finite-difference, gradient-type sensitivity approach relying on stochastic coupling techniques for variance reduction. Here we demonstrate that these two methods can be combined and deployed together by means of a new sensitivity bound which incorporates the variance of the quantity of interest as well as the Fisher Information Matrix estimated from the first method. The first step of the proposed strategy labels sensitivities using the bound and screens out the insensitive parameters in a controlled manner. In the second step of the proposed strategy, a finite-difference method is applied only for the sensitivity estimation of the (potentially) sensitive parameters that have not been screened out in the first step. Results on an epidermal growth factor network with fifty parameters and on a protein homeostasis with eighty parameters demonstrate that the proposed strategy is able to quickly discover and discard the insensitive parameters and in the remaining potentially sensitive parameters it accurately estimates the sensitivities. The new sensitivity strategy can be several times faster than current state-of-the-art approaches that test all parameters, especially in "sloppy" systems. In particular, the computational acceleration is quantified by the ratio between the total number of parameters over the
Accelerated Sensitivity Analysis in High-Dimensional Stochastic Reaction Networks.
Georgios Arampatzis
Full Text Available Existing sensitivity analysis approaches are not able to handle efficiently stochastic reaction networks with a large number of parameters and species, which are typical in the modeling and simulation of complex biochemical phenomena. In this paper, a two-step strategy for parametric sensitivity analysis for such systems is proposed, exploiting advantages and synergies between two recently proposed sensitivity analysis methodologies for stochastic dynamics. The first method performs sensitivity analysis of the stochastic dynamics by means of the Fisher Information Matrix on the underlying distribution of the trajectories; the second method is a reduced-variance, finite-difference, gradient-type sensitivity approach relying on stochastic coupling techniques for variance reduction. Here we demonstrate that these two methods can be combined and deployed together by means of a new sensitivity bound which incorporates the variance of the quantity of interest as well as the Fisher Information Matrix estimated from the first method. The first step of the proposed strategy labels sensitivities using the bound and screens out the insensitive parameters in a controlled manner. In the second step of the proposed strategy, a finite-difference method is applied only for the sensitivity estimation of the (potentially sensitive parameters that have not been screened out in the first step. Results on an epidermal growth factor network with fifty parameters and on a protein homeostasis with eighty parameters demonstrate that the proposed strategy is able to quickly discover and discard the insensitive parameters and in the remaining potentially sensitive parameters it accurately estimates the sensitivities. The new sensitivity strategy can be several times faster than current state-of-the-art approaches that test all parameters, especially in "sloppy" systems. In particular, the computational acceleration is quantified by the ratio between the total number of
Lauss, Martin; Frigyesi, Attila; Ryden, Tobias; Höglund, Mattias
2010-01-01
Genome wide gene expression data is a rich source for the identification of gene signatures suitable for clinical purposes and a number of statistical algorithms have been described for both identification and evaluation of such signatures. Some employed algorithms are fairly complex and hence sensitive to over-fitting whereas others are more simple and straight forward. Here we present a new type of simple algorithm based on ROC analysis and the use of metagenes that we believe will be a good complement to existing algorithms. The basis for the proposed approach is the use of metagenes, instead of collections of individual genes, and a feature selection using AUC values obtained by ROC analysis. Each gene in a data set is assigned an AUC value relative to the tumor class under investigation and the genes are ranked according to these values. Metagenes are then formed by calculating the mean expression level for an increasing number of ranked genes, and the metagene expression value that optimally discriminates tumor classes in the training set is used for classification of new samples. The performance of the metagene is then evaluated using LOOCV and balanced accuracies. We show that the simple uni-variate gene expression average algorithm performs as well as several alternative algorithms such as discriminant analysis and the more complex approaches such as SVM and neural networks. The R package rocc is freely available at http://cran.r-project.org/web/packages/rocc/index.html
Shi, Weiwei; Bugrim, Andrej; Nikolsky, Yuri; Nikolskya, Tatiana; Brennan, Richard J
2008-01-01
ABSTRACT The ideal toxicity biomarker is composed of the properties of prediction (is detected prior to traditional pathological signs of injury), accuracy (high sensitivity and specificity), and mechanistic relationships to the endpoint measured (biological relevance). Gene expression-based toxicity biomarkers ("signatures") have shown good predictive power and accuracy, but are difficult to interpret biologically. We have compared different statistical methods of feature selection with knowledge-based approaches, using GeneGo's database of canonical pathway maps, to generate gene sets for the classification of renal tubule toxicity. The gene set selection algorithms include four univariate analyses: t-statistics, fold-change, B-statistics, and RankProd, and their combination and overlap for the identification of differentially expressed probes. Enrichment analysis following the results of the four univariate analyses, Hotelling T-square test, and, finally out-of-bag selection, a variant of cross-validation, were used to identify canonical pathway maps-sets of genes coordinately involved in key biological processes-with classification power. Differentially expressed genes identified by the different statistical univariate analyses all generated reasonably performing classifiers of tubule toxicity. Maps identified by enrichment analysis or Hotelling T-square had lower classification power, but highlighted perturbed lipid homeostasis as a common discriminator of nephrotoxic treatments. The out-of-bag method yielded the best functionally integrated classifier. The map "ephrins signaling" performed comparably to a classifier derived using sparse linear programming, a machine learning algorithm, and represents a signaling network specifically involved in renal tubule development and integrity. Such functional descriptors of toxicity promise to better integrate predictive toxicogenomics with mechanistic analysis, facilitating the interpretation and risk assessment of
QRS complex detection based on continuous density hidden Markov models using univariate observations
Sotelo, S.; Arenas, W.; Altuve, M.
2018-04-01
In the electrocardiogram (ECG), the detection of QRS complexes is a fundamental step in the ECG signal processing chain since it allows the determination of other characteristics waves of the ECG and provides information about heart rate variability. In this work, an automatic QRS complex detector based on continuous density hidden Markov models (HMM) is proposed. HMM were trained using univariate observation sequences taken either from QRS complexes or their derivatives. The detection approach is based on the log-likelihood comparison of the observation sequence with a fixed threshold. A sliding window was used to obtain the observation sequence to be evaluated by the model. The threshold was optimized by receiver operating characteristic curves. Sensitivity (Sen), specificity (Spc) and F1 score were used to evaluate the detection performance. The approach was validated using ECG recordings from the MIT-BIH Arrhythmia database. A 6-fold cross-validation shows that the best detection performance was achieved with 2 states HMM trained with QRS complexes sequences (Sen = 0.668, Spc = 0.360 and F1 = 0.309). We concluded that these univariate sequences provide enough information to characterize the QRS complex dynamics from HMM. Future works are directed to the use of multivariate observations to increase the detection performance.
Sensitivity Analysis Based on Markovian Integration by Parts Formula
Yongsheng Hang
2017-10-01
Full Text Available Sensitivity analysis is widely applied in financial risk management and engineering; it describes the variations brought by the changes of parameters. Since the integration by parts technique for Markov chains is well developed in recent years, in this paper we apply it for computation of sensitivity and show the closed-form expressions for two commonly-used time-continuous Markovian models. By comparison, we conclude that our approach outperforms the existing technique of computing sensitivity on Markovian models.
Advanced Fuel Cycle Economic Sensitivity Analysis
David Shropshire; Kent Williams; J.D. Smith; Brent Boore
2006-12-01
A fuel cycle economic analysis was performed on four fuel cycles to provide a baseline for initial cost comparison using the Gen IV Economic Modeling Work Group G4 ECON spreadsheet model, Decision Programming Language software, the 2006 Advanced Fuel Cycle Cost Basis report, industry cost data, international papers, the nuclear power related cost study from MIT, Harvard, and the University of Chicago. The analysis developed and compared the fuel cycle cost component of the total cost of energy for a wide range of fuel cycles including: once through, thermal with fast recycle, continuous fast recycle, and thermal recycle.
Lei Zhang
2009-08-01
Full Text Available As genome-wide association studies (GWAS are becoming more popular, two approaches, among others, could be considered in order to improve statistical power for identifying genes contributing subtle to moderate effects to human diseases. The first approach is to increase sample size, which could be achieved by combining both unrelated and familial subjects together. The second approach is to jointly analyze multiple correlated traits. In this study, by extending generalized estimating equations (GEEs, we propose a simple approach for performing univariate or multivariate association tests for the combined data of unrelated subjects and nuclear families. In particular, we correct for population stratification by integrating principal component analysis and transmission disequilibrium test strategies. The proposed method allows for multiple siblings as well as missing parental information. Simulation studies show that the proposed test has improved power compared to two popular methods, EIGENSTRAT and FBAT, by analyzing the combined data, while correcting for population stratification. In addition, joint analysis of bivariate traits has improved power over univariate analysis when pleiotropic effects are present. Application to the Genetic Analysis Workshop 16 (GAW16 data sets attests to the feasibility and applicability of the proposed method.
The role of sensitivity analysis in assessing uncertainty
Crick, M.J.; Hill, M.D.
1987-01-01
Outside the specialist world of those carrying out performance assessments considerable confusion has arisen about the meanings of sensitivity analysis and uncertainty analysis. In this paper we attempt to reduce this confusion. We then go on to review approaches to sensitivity analysis within the context of assessing uncertainty, and to outline the types of test available to identify sensitive parameters, together with their advantages and disadvantages. The views expressed in this paper are those of the authors; they have not been formally endorsed by the National Radiological Protection Board and should not be interpreted as Board advice
Analysis of Sensitivity Experiments - An Expanded Primer
2017-03-08
conducted with this purpose in mind. Due diligence must be paid to the structure of the dosage levels and to the number of trials. The chosen data...analysis. System reliability is of paramount importance for protecting both the investment of funding and human life . Failing to accurately estimate
Sensitivity analysis of hybrid thermoelastic techniques
W.A. Samad; J.M. Considine
2017-01-01
Stress functions have been used as a complementary tool to support experimental techniques, such as thermoelastic stress analysis (TSA) and digital image correlation (DIC), in an effort to evaluate the complete and separate full-field stresses of loaded structures. The need for such coupling between experimental data and stress functions is due to the fact that...
Automating sensitivity analysis of computer models using computer calculus
Oblow, E.M.; Pin, F.G.
1985-01-01
An automated procedure for performing sensitivity analyses has been developed. The procedure uses a new FORTRAN compiler with computer calculus capabilities to generate the derivatives needed to set up sensitivity equations. The new compiler is called GRESS - Gradient Enhanced Software System. Application of the automated procedure with ''direct'' and ''adjoint'' sensitivity theory for the analysis of non-linear, iterative systems of equations is discussed. Calculational efficiency consideration and techniques for adjoint sensitivity analysis are emphasized. The new approach is found to preserve the traditional advantages of adjoint theory while removing the tedious human effort previously needed to apply this theoretical methodology. Conclusions are drawn about the applicability of the automated procedure in numerical analysis and large-scale modelling sensitivity studies. 24 refs., 2 figs
Sensitivity Analysis of the Critical Speed in Railway Vehicle Dynamics
Bigoni, Daniele; True, Hans; Engsig-Karup, Allan Peter
2014-01-01
We present an approach to global sensitivity analysis aiming at the reduction of its computational cost without compromising the results. The method is based on sampling methods, cubature rules, High-Dimensional Model Representation and Total Sensitivity Indices. The approach has a general applic...
Global and Local Sensitivity Analysis Methods for a Physical System
Morio, Jerome
2011-01-01
Sensitivity analysis is the study of how the different input variations of a mathematical model influence the variability of its output. In this paper, we review the principle of global and local sensitivity analyses of a complex black-box system. A simulated case of application is given at the end of this paper to compare both approaches.…
Adjoint sensitivity analysis of high frequency structures with Matlab
Bakr, Mohamed; Demir, Veysel
2017-01-01
This book covers the theory of adjoint sensitivity analysis and uses the popular FDTD (finite-difference time-domain) method to show how wideband sensitivities can be efficiently estimated for different types of materials and structures. It includes a variety of MATLAB® examples to help readers absorb the content more easily.
Dispersion sensitivity analysis & consistency improvement of APFSDS
Sangeeta Sharma Panda
2017-08-01
In Bore Balloting Motion simulation shows that reduction in residual spin by about 5% results in drastic 56% reduction in first maximum yaw. A correlation between first maximum yaw and residual spin is observed. Results of data analysis are used in design modification for existing ammunition. Number of designs are evaluated numerically before freezing five designs for further soundings. These designs are critically assessed in terms of their comparative performance during In-bore travel & external ballistics phase. Results are validated by free flight trials for the finalised design.
Adjoint sensitivity analysis of plasmonic structures using the FDTD method.
Zhang, Yu; Ahmed, Osman S; Bakr, Mohamed H
2014-05-15
We present an adjoint variable method for estimating the sensitivities of arbitrary responses with respect to the parameters of dispersive discontinuities in nanoplasmonic devices. Our theory is formulated in terms of the electric field components at the vicinity of perturbed discontinuities. The adjoint sensitivities are computed using at most one extra finite-difference time-domain (FDTD) simulation regardless of the number of parameters. Our approach is illustrated through the sensitivity analysis of an add-drop coupler consisting of a square ring resonator between two parallel waveguides. The computed adjoint sensitivities of the scattering parameters are compared with those obtained using the accurate but computationally expensive central finite difference approach.
Sensitivity analysis of the RESRAD, a dose assessment code
Yu, C.; Cheng, J.J.; Zielen, A.J.
1991-01-01
The RESRAD code is a pathway analysis code that is designed to calculate radiation doses and derive soil cleanup criteria for the US Department of Energy's environmental restoration and waste management program. the RESRAD code uses various pathway and consumption-rate parameters such as soil properties and food ingestion rates in performing such calculations and derivations. As with any predictive model, the accuracy of the predictions depends on the accuracy of the input parameters. This paper summarizes the results of a sensitivity analysis of RESRAD input parameters. Three methods were used to perform the sensitivity analysis: (1) Gradient Enhanced Software System (GRESS) sensitivity analysis software package developed at oak Ridge National Laboratory; (2) direct perturbation of input parameters; and (3) built-in graphic package that shows parameter sensitivities while the RESRAD code is operational
A sensitivity analysis approach to optical parameters of scintillation detectors
Ghal-Eh, N.; Koohi-Fayegh, R.
2008-01-01
In this study, an extended version of the Monte Carlo light transport code, PHOTRACK, has been used for a sensitivity analysis to estimate the importance of different wavelength-dependent parameters in the modelling of light collection process in scintillators
sensitivity analysis on flexible road pavement life cycle cost model
user
of sensitivity analysis on a developed flexible pavement life cycle cost model using varying discount rate. The study .... organizations and specific projects needs based. Life-cycle ... developed and completed urban road infrastructure corridor ...
Sobol’ sensitivity analysis for stressor impacts on honeybee colonies
We employ Monte Carlo simulation and nonlinear sensitivity analysis techniques to describe the dynamics of a bee exposure model, VarroaPop. Daily simulations are performed of hive population trajectories, taking into account queen strength, foraging success, mite impacts, weather...
Experimental Design for Sensitivity Analysis of Simulation Models
Kleijnen, J.P.C.
2001-01-01
This introductory tutorial gives a survey on the use of statistical designs for what if-or sensitivity analysis in simulation.This analysis uses regression analysis to approximate the input/output transformation that is implied by the simulation model; the resulting regression model is also known as
Sensitivity analysis of a greedy heuristic for knapsack problems
Ghosh, D; Chakravarti, N; Sierksma, G
2006-01-01
In this paper, we carry out parametric analysis as well as a tolerance limit based sensitivity analysis of a greedy heuristic for two knapsack problems-the 0-1 knapsack problem and the subset sum problem. We carry out the parametric analysis based on all problem parameters. In the tolerance limit
Sensitivity analysis of numerical solutions for environmental fluid problems
Tanaka, Nobuatsu; Motoyama, Yasunori
2003-01-01
In this study, we present a new numerical method to quantitatively analyze the error of numerical solutions by using the sensitivity analysis. If a reference case of typical parameters is one calculated with the method, no additional calculation is required to estimate the results of the other numerical parameters such as more detailed solutions. Furthermore, we can estimate the strict solution from the sensitivity analysis results and can quantitatively evaluate the reliability of the numerical solution by calculating the numerical error. (author)
Adkins, Daniel E.; McClay, Joseph L.; Vunck, Sarah A.; Batman, Angela M.; Vann, Robert E.; Clark, Shaunna L.; Souza, Renan P.; Crowley, James J.; Sullivan, Patrick F.; van den Oord, Edwin J.C.G.; Beardsley, Patrick M.
2014-01-01
Behavioral sensitization has been widely studied in animal models and is theorized to reflect neural modifications associated with human psychostimulant addiction. While the mesolimbic dopaminergic pathway is known to play a role, the neurochemical mechanisms underlying behavioral sensitization remain incompletely understood. In the present study, we conducted the first metabolomics analysis to globally characterize neurochemical differences associated with behavioral sensitization. Methamphetamine-induced sensitization measures were generated by statistically modeling longitudinal activity data for eight inbred strains of mice. Subsequent to behavioral testing, nontargeted liquid and gas chromatography-mass spectrometry profiling was performed on 48 brain samples, yielding 301 metabolite levels per sample after quality control. Association testing between metabolite levels and three primary dimensions of behavioral sensitization (total distance, stereotypy and margin time) showed four robust, significant associations at a stringent metabolome-wide significance threshold (false discovery rate < 0.05). Results implicated homocarnosine, a dipeptide of GABA and histidine, in total distance sensitization, GABA metabolite 4-guanidinobutanoate and pantothenate in stereotypy sensitization, and myo-inositol in margin time sensitization. Secondary analyses indicated that these associations were independent of concurrent methamphetamine levels and, with the exception of the myo-inositol association, suggest a mechanism whereby strain-based genetic variation produces specific baseline neurochemical differences that substantially influence the magnitude of MA-induced sensitization. These findings demonstrate the utility of mouse metabolomics for identifying novel biomarkers, and developing more comprehensive neurochemical models, of psychostimulant sensitization. PMID:24034544
Risk and sensitivity analysis in relation to external events
Alzbutas, R.; Urbonas, R.; Augutis, J.
2001-01-01
This paper presents risk and sensitivity analysis of external events impacts on the safe operation in general and in particular the Ignalina Nuclear Power Plant safety systems. Analysis is based on the deterministic and probabilistic assumptions and assessment of the external hazards. The real statistic data are used as well as initial external event simulation. The preliminary screening criteria are applied. The analysis of external event impact on the NPP safe operation, assessment of the event occurrence, sensitivity analysis, and recommendations for safety improvements are performed for investigated external hazards. Such events as aircraft crash, extreme rains and winds, forest fire and flying parts of the turbine are analysed. The models are developed and probabilities are calculated. As an example for sensitivity analysis the model of aircraft impact is presented. The sensitivity analysis takes into account the uncertainty features raised by external event and its model. Even in case when the external events analysis show rather limited danger, the sensitivity analysis can determine the highest influence causes. These possible variations in future can be significant for safety level and risk based decisions. Calculations show that external events cannot significantly influence the safety level of the Ignalina NPP operation, however the events occurrence and propagation can be sufficiently uncertain.(author)
High sensitivity analysis of atmospheric gas elements
Miwa, Shiro; Nomachi, Ichiro; Kitajima, Hideo
2006-01-01
We have investigated the detection limit of H, C and O in Si, GaAs and InP using a Cameca IMS-4f instrument equipped with a modified vacuum system to improve the detection limit with a lower sputtering rate We found that the detection limits for H, O and C are improved by employing a primary ion bombardment before the analysis. Background levels of 1 x 10 17 atoms/cm 3 for H, of 3 x 10 16 atoms/cm 3 for C and of 2 x 10 16 atoms/cm 3 for O could be achieved in silicon with a sputtering rate of 2 nm/s after a primary ion bombardment for 160 h. We also found that the use of a 20 K He cryo-panel near the sample holder was effective for obtaining better detection limits in a shorter time, although the final detection limits using the panel are identical to those achieved without it
High sensitivity analysis of atmospheric gas elements
Miwa, Shiro [Materials Analysis Lab., Sony Corporation, 4-16-1 Okata, Atsugi 243-0021 (Japan)]. E-mail: Shiro.Miwa@jp.sony.com; Nomachi, Ichiro [Materials Analysis Lab., Sony Corporation, 4-16-1 Okata, Atsugi 243-0021 (Japan); Kitajima, Hideo [Nanotechnos Corp., 5-4-30 Nishihashimoto, Sagamihara 229-1131 (Japan)
2006-07-30
We have investigated the detection limit of H, C and O in Si, GaAs and InP using a Cameca IMS-4f instrument equipped with a modified vacuum system to improve the detection limit with a lower sputtering rate We found that the detection limits for H, O and C are improved by employing a primary ion bombardment before the analysis. Background levels of 1 x 10{sup 17} atoms/cm{sup 3} for H, of 3 x 10{sup 16} atoms/cm{sup 3} for C and of 2 x 10{sup 16} atoms/cm{sup 3} for O could be achieved in silicon with a sputtering rate of 2 nm/s after a primary ion bombardment for 160 h. We also found that the use of a 20 K He cryo-panel near the sample holder was effective for obtaining better detection limits in a shorter time, although the final detection limits using the panel are identical to those achieved without it.
Sensitivity Analysis of BLISK Airfoil Wear †
Andreas Kellersmann
2018-05-01
Full Text Available The decreasing performance of jet engines during operation is a major concern for airlines and maintenance companies. Among other effects, the erosion of high-pressure compressor (HPC blades is a critical one and leads to a changed aerodynamic behavior, and therefore to a change in performance. The maintenance of BLISKs (blade-integrated-disks is especially challenging because the blade arrangement cannot be changed and individual blades cannot be replaced. Thus, coupled deteriorated blades have a complex aerodynamic behavior which can have a stronger influence on compressor performance than a conventional HPC. To ensure effective maintenance for BLISKs, the impact of coupled misshaped blades are the key factor. The present study addresses these effects on the aerodynamic performance of a first-stage BLISK of a high-pressure compressor. Therefore, a design of experiments (DoE is done to identify the geometric properties which lead to a reduction in performance. It is shown that the effect of coupled variances is dependent on the operating point. Based on the DoE analysis, the thickness-related parameters, the stagger angle, and the max. profile camber as coupled parameters are identified as the most important parameters for all operating points.
Hasegawa, Raiden; Small, Dylan
2017-12-01
In matched observational studies where treatment assignment is not randomized, sensitivity analysis helps investigators determine how sensitive their estimated treatment effect is to some unmeasured confounder. The standard approach calibrates the sensitivity analysis according to the worst case bias in a pair. This approach will result in a conservative sensitivity analysis if the worst case bias does not hold in every pair. In this paper, we show that for binary data, the standard approach can be calibrated in terms of the average bias in a pair rather than worst case bias. When the worst case bias and average bias differ, the average bias interpretation results in a less conservative sensitivity analysis and more power. In many studies, the average case calibration may also carry a more natural interpretation than the worst case calibration and may also allow researchers to incorporate additional data to establish an empirical basis with which to calibrate a sensitivity analysis. We illustrate this with a study of the effects of cellphone use on the incidence of automobile accidents. Finally, we extend the average case calibration to the sensitivity analysis of confidence intervals for attributable effects. © 2017, The International Biometric Society.
A comparison of bivariate and univariate QTL mapping in livestock populations
Sorensen Daniel
2003-11-01
Full Text Available Abstract This study presents a multivariate, variance component-based QTL mapping model implemented via restricted maximum likelihood (REML. The method was applied to investigate bivariate and univariate QTL mapping analyses, using simulated data. Specifically, we report results on the statistical power to detect a QTL and on the precision of parameter estimates using univariate and bivariate approaches. The model and methodology were also applied to study the effectiveness of partitioning the overall genetic correlation between two traits into a component due to many genes of small effect, and one due to the QTL. It is shown that when the QTL has a pleiotropic effect on two traits, a bivariate analysis leads to a higher statistical power of detecting the QTL and to a more precise estimate of the QTL's map position, in particular in the case when the QTL has a small effect on the trait. The increase in power is most marked in cases where the contributions of the QTL and of the polygenic components to the genetic correlation have opposite signs. The bivariate REML analysis can successfully partition the two components contributing to the genetic correlation between traits.
Application of Stochastic Sensitivity Analysis to Integrated Force Method
X. F. Wei
2012-01-01
Full Text Available As a new formulation in structural analysis, Integrated Force Method has been successfully applied to many structures for civil, mechanical, and aerospace engineering due to the accurate estimate of forces in computation. Right now, it is being further extended to the probabilistic domain. For the assessment of uncertainty effect in system optimization and identification, the probabilistic sensitivity analysis of IFM was further investigated in this study. A set of stochastic sensitivity analysis formulation of Integrated Force Method was developed using the perturbation method. Numerical examples are presented to illustrate its application. Its efficiency and accuracy were also substantiated with direct Monte Carlo simulations and the reliability-based sensitivity method. The numerical algorithm was shown to be readily adaptable to the existing program since the models of stochastic finite element and stochastic design sensitivity are almost identical.
The EVEREST project: sensitivity analysis of geological disposal systems
Marivoet, Jan; Wemaere, Isabelle; Escalier des Orres, Pierre; Baudoin, Patrick; Certes, Catherine; Levassor, Andre; Prij, Jan; Martens, Karl-Heinz; Roehlig, Klaus
1997-01-01
The main objective of the EVEREST project is the evaluation of the sensitivity of the radiological consequences associated with the geological disposal of radioactive waste to the different elements in the performance assessment. Three types of geological host formations are considered: clay, granite and salt. The sensitivity studies that have been carried out can be partitioned into three categories according to the type of uncertainty taken into account: uncertainty in the model parameters, uncertainty in the conceptual models and uncertainty in the considered scenarios. Deterministic as well as stochastic calculational approaches have been applied for the sensitivity analyses. For the analysis of the sensitivity to parameter values, the reference technique, which has been applied in many evaluations, is stochastic and consists of a Monte Carlo simulation followed by a linear regression. For the analysis of conceptual model uncertainty, deterministic and stochastic approaches have been used. For the analysis of uncertainty in the considered scenarios, mainly deterministic approaches have been applied
Multiple predictor smoothing methods for sensitivity analysis: Description of techniques
Storlie, Curtis B.; Helton, Jon C.
2008-01-01
The use of multiple predictor smoothing methods in sampling-based sensitivity analyses of complex models is investigated. Specifically, sensitivity analysis procedures based on smoothing methods employing the stepwise application of the following nonparametric regression techniques are described: (i) locally weighted regression (LOESS), (ii) additive models, (iii) projection pursuit regression, and (iv) recursive partitioning regression. Then, in the second and concluding part of this presentation, the indicated procedures are illustrated with both simple test problems and results from a performance assessment for a radioactive waste disposal facility (i.e., the Waste Isolation Pilot Plant). As shown by the example illustrations, the use of smoothing procedures based on nonparametric regression techniques can yield more informative sensitivity analysis results than can be obtained with more traditional sensitivity analysis procedures based on linear regression, rank regression or quadratic regression when nonlinear relationships between model inputs and model predictions are present
Multiple predictor smoothing methods for sensitivity analysis: Example results
Storlie, Curtis B.; Helton, Jon C.
2008-01-01
The use of multiple predictor smoothing methods in sampling-based sensitivity analyses of complex models is investigated. Specifically, sensitivity analysis procedures based on smoothing methods employing the stepwise application of the following nonparametric regression techniques are described in the first part of this presentation: (i) locally weighted regression (LOESS), (ii) additive models, (iii) projection pursuit regression, and (iv) recursive partitioning regression. In this, the second and concluding part of the presentation, the indicated procedures are illustrated with both simple test problems and results from a performance assessment for a radioactive waste disposal facility (i.e., the Waste Isolation Pilot Plant). As shown by the example illustrations, the use of smoothing procedures based on nonparametric regression techniques can yield more informative sensitivity analysis results than can be obtained with more traditional sensitivity analysis procedures based on linear regression, rank regression or quadratic regression when nonlinear relationships between model inputs and model predictions are present
Carbon dioxide capture processes: Simulation, design and sensitivity analysis
Zaman, Muhammad; Lee, Jay Hyung; Gani, Rafiqul
2012-01-01
equilibrium and associated property models are used. Simulations are performed to investigate the sensitivity of the process variables to change in the design variables including process inputs and disturbances in the property model parameters. Results of the sensitivity analysis on the steady state...... performance of the process to the L/G ratio to the absorber, CO2 lean solvent loadings, and striper pressure are presented in this paper. Based on the sensitivity analysis process optimization problems have been defined and solved and, a preliminary control structure selection has been made.......Carbon dioxide is the main greenhouse gas and its major source is combustion of fossil fuels for power generation. The objective of this study is to carry out the steady-state sensitivity analysis for chemical absorption of carbon dioxide capture from flue gas using monoethanolamine solvent. First...
Global sensitivity analysis in stochastic simulators of uncertain reaction networks.
Navarro Jimenez, M; Le Maître, O P; Knio, O M
2016-12-28
Stochastic models of chemical systems are often subjected to uncertainties in kinetic parameters in addition to the inherent random nature of their dynamics. Uncertainty quantification in such systems is generally achieved by means of sensitivity analyses in which one characterizes the variability with the uncertain kinetic parameters of the first statistical moments of model predictions. In this work, we propose an original global sensitivity analysis method where the parametric and inherent variability sources are both treated through Sobol's decomposition of the variance into contributions from arbitrary subset of uncertain parameters and stochastic reaction channels. The conceptual development only assumes that the inherent and parametric sources are independent, and considers the Poisson processes in the random-time-change representation of the state dynamics as the fundamental objects governing the inherent stochasticity. A sampling algorithm is proposed to perform the global sensitivity analysis, and to estimate the partial variances and sensitivity indices characterizing the importance of the various sources of variability and their interactions. The birth-death and Schlögl models are used to illustrate both the implementation of the algorithm and the richness of the proposed analysis method. The output of the proposed sensitivity analysis is also contrasted with a local derivative-based sensitivity analysis method classically used for this type of systems.
Sensitivity Analysis for Urban Drainage Modeling Using Mutual Information
Chuanqi Li
2014-11-01
Full Text Available The intention of this paper is to evaluate the sensitivity of the Storm Water Management Model (SWMM output to its input parameters. A global parameter sensitivity analysis is conducted in order to determine which parameters mostly affect the model simulation results. Two different methods of sensitivity analysis are applied in this study. The first one is the partial rank correlation coefficient (PRCC which measures nonlinear but monotonic relationships between model inputs and outputs. The second one is based on the mutual information which provides a general measure of the strength of the non-monotonic association between two variables. Both methods are based on the Latin Hypercube Sampling (LHS of the parameter space, and thus the same datasets can be used to obtain both measures of sensitivity. The utility of the PRCC and the mutual information analysis methods are illustrated by analyzing a complex SWMM model. The sensitivity analysis revealed that only a few key input variables are contributing significantly to the model outputs; PRCCs and mutual information are calculated and used to determine and rank the importance of these key parameters. This study shows that the partial rank correlation coefficient and mutual information analysis can be considered effective methods for assessing the sensitivity of the SWMM model to the uncertainty in its input parameters.
Global sensitivity analysis in stochastic simulators of uncertain reaction networks
Navarro, María
2016-12-26
Stochastic models of chemical systems are often subjected to uncertainties in kinetic parameters in addition to the inherent random nature of their dynamics. Uncertainty quantification in such systems is generally achieved by means of sensitivity analyses in which one characterizes the variability with the uncertain kinetic parameters of the first statistical moments of model predictions. In this work, we propose an original global sensitivity analysis method where the parametric and inherent variability sources are both treated through Sobol’s decomposition of the variance into contributions from arbitrary subset of uncertain parameters and stochastic reaction channels. The conceptual development only assumes that the inherent and parametric sources are independent, and considers the Poisson processes in the random-time-change representation of the state dynamics as the fundamental objects governing the inherent stochasticity. A sampling algorithm is proposed to perform the global sensitivity analysis, and to estimate the partial variances and sensitivity indices characterizing the importance of the various sources of variability and their interactions. The birth-death and Schlögl models are used to illustrate both the implementation of the algorithm and the richness of the proposed analysis method. The output of the proposed sensitivity analysis is also contrasted with a local derivative-based sensitivity analysis method classically used for this type of systems.
Allergen Sensitization Pattern by Sex: A Cluster Analysis in Korea.
Ohn, Jungyoon; Paik, Seung Hwan; Doh, Eun Jin; Park, Hyun-Sun; Yoon, Hyun-Sun; Cho, Soyun
2017-12-01
Allergens tend to sensitize simultaneously. Etiology of this phenomenon has been suggested to be allergen cross-reactivity or concurrent exposure. However, little is known about specific allergen sensitization patterns. To investigate the allergen sensitization characteristics according to gender. Multiple allergen simultaneous test (MAST) is widely used as a screening tool for detecting allergen sensitization in dermatologic clinics. We retrospectively reviewed the medical records of patients with MAST results between 2008 and 2014 in our Department of Dermatology. A cluster analysis was performed to elucidate the allergen-specific immunoglobulin (Ig)E cluster pattern. The results of MAST (39 allergen-specific IgEs) from 4,360 cases were analyzed. By cluster analysis, 39items were grouped into 8 clusters. Each cluster had characteristic features. When compared with female, the male group tended to be sensitized more frequently to all tested allergens, except for fungus allergens cluster. The cluster and comparative analysis results demonstrate that the allergen sensitization is clustered, manifesting allergen similarity or co-exposure. Only the fungus cluster allergens tend to sensitize female group more frequently than male group.
A general first-order global sensitivity analysis method
Xu Chonggang; Gertner, George Zdzislaw
2008-01-01
Fourier amplitude sensitivity test (FAST) is one of the most popular global sensitivity analysis techniques. The main mechanism of FAST is to assign each parameter with a characteristic frequency through a search function. Then, for a specific parameter, the variance contribution can be singled out of the model output by the characteristic frequency. Although FAST has been widely applied, there are two limitations: (1) the aliasing effect among parameters by using integer characteristic frequencies and (2) the suitability for only models with independent parameters. In this paper, we synthesize the improvement to overcome the aliasing effect limitation [Tarantola S, Gatelli D, Mara TA. Random balance designs for the estimation of first order global sensitivity indices. Reliab Eng Syst Safety 2006; 91(6):717-27] and the improvement to overcome the independence limitation [Xu C, Gertner G. Extending a global sensitivity analysis technique to models with correlated parameters. Comput Stat Data Anal 2007, accepted for publication]. In this way, FAST can be a general first-order global sensitivity analysis method for linear/nonlinear models with as many correlated/uncorrelated parameters as the user specifies. We apply the general FAST to four test cases with correlated parameters. The results show that the sensitivity indices derived by the general FAST are in good agreement with the sensitivity indices derived by the correlation ratio method, which is a non-parametric method for models with correlated parameters
Sensitivity Analysis of Criticality for Different Nuclear Fuel Shapes
Kang, Hyun Sik; Jang, Misuk; Kim, Seoung Rae
2016-01-01
Rod-type nuclear fuel was mainly developed in the past, but recent study has been extended to plate-type nuclear fuel. Therefore, this paper reviews the sensitivity of criticality according to different shapes of nuclear fuel types. Criticality analysis was performed using MCNP5. MCNP5 is well-known Monte Carlo codes for criticality analysis and a general-purpose Monte Carlo N-Particle code that can be used for neutron, photon, electron or coupled neutron / photon / electron transport, including the capability to calculate eigenvalues for critical systems. We performed the sensitivity analysis of criticality for different fuel shapes. In sensitivity analysis for simple fuel shapes, the criticality is proportional to the surface area. But for fuel Assembly types, it is not proportional to the surface area. In sensitivity analysis for intervals between plates, the criticality is greater as the interval increases, but if the interval is greater than 8mm, it showed an opposite trend that the criticality decrease by a larger interval. As a result, it has failed to obtain the logical content to be described in common for all cases. The sensitivity analysis of Criticality would be always required whenever subject to be analyzed is changed
Sensitivity Analysis of Criticality for Different Nuclear Fuel Shapes
Kang, Hyun Sik; Jang, Misuk; Kim, Seoung Rae [NESS, Daejeon (Korea, Republic of)
2016-10-15
Rod-type nuclear fuel was mainly developed in the past, but recent study has been extended to plate-type nuclear fuel. Therefore, this paper reviews the sensitivity of criticality according to different shapes of nuclear fuel types. Criticality analysis was performed using MCNP5. MCNP5 is well-known Monte Carlo codes for criticality analysis and a general-purpose Monte Carlo N-Particle code that can be used for neutron, photon, electron or coupled neutron / photon / electron transport, including the capability to calculate eigenvalues for critical systems. We performed the sensitivity analysis of criticality for different fuel shapes. In sensitivity analysis for simple fuel shapes, the criticality is proportional to the surface area. But for fuel Assembly types, it is not proportional to the surface area. In sensitivity analysis for intervals between plates, the criticality is greater as the interval increases, but if the interval is greater than 8mm, it showed an opposite trend that the criticality decrease by a larger interval. As a result, it has failed to obtain the logical content to be described in common for all cases. The sensitivity analysis of Criticality would be always required whenever subject to be analyzed is changed.
Global sensitivity analysis of computer models with functional inputs
Iooss, Bertrand; Ribatet, Mathieu
2009-01-01
Global sensitivity analysis is used to quantify the influence of uncertain model inputs on the response variability of a numerical model. The common quantitative methods are appropriate with computer codes having scalar model inputs. This paper aims at illustrating different variance-based sensitivity analysis techniques, based on the so-called Sobol's indices, when some model inputs are functional, such as stochastic processes or random spatial fields. In this work, we focus on large cpu time computer codes which need a preliminary metamodeling step before performing the sensitivity analysis. We propose the use of the joint modeling approach, i.e., modeling simultaneously the mean and the dispersion of the code outputs using two interlinked generalized linear models (GLMs) or generalized additive models (GAMs). The 'mean model' allows to estimate the sensitivity indices of each scalar model inputs, while the 'dispersion model' allows to derive the total sensitivity index of the functional model inputs. The proposed approach is compared to some classical sensitivity analysis methodologies on an analytical function. Lastly, the new methodology is applied to an industrial computer code that simulates the nuclear fuel irradiation.
A tool model for predicting atmospheric kinetics with sensitivity analysis
无
2001-01-01
A package( a tool model) for program of predicting atmospheric chemical kinetics with sensitivity analysis is presented. The new direct method of calculating the first order sensitivity coefficients using sparse matrix technology to chemical kinetics is included in the tool model, it is only necessary to triangularize the matrix related to the Jacobian matrix of the model equation. The Gear type procedure is used to integrate amodel equation and its coupled auxiliary sensitivity coefficient equations. The FORTRAN subroutines of the model equation, the sensitivity coefficient equations, and their Jacobian analytical expressions are generated automatically from a chemical mechanism. The kinetic representation for the model equation and its sensitivity coefficient equations, and their Jacobian matrix is presented. Various FORTRAN subroutines in packages, such as SLODE, modified MA28, Gear package, with which the program runs in conjunction are recommended.The photo-oxidation of dimethyl disulfide is used for illustration.
Sensitivity analysis of the nuclear data for MYRRHA reactor modelling
Stankovskiy, Alexey; Van den Eynde, Gert; Cabellos, Oscar; Diez, Carlos J.; Schillebeeckx, Peter; Heyse, Jan
2014-01-01
A global sensitivity analysis of effective neutron multiplication factor k eff to the change of nuclear data library revealed that JEFF-3.2T2 neutron-induced evaluated data library produces closer results to ENDF/B-VII.1 than does JEFF-3.1.2. The analysis of contributions of individual evaluations into k eff sensitivity allowed establishing the priority list of nuclides for which uncertainties on nuclear data must be improved. Detailed sensitivity analysis has been performed for two nuclides from this list, 56 Fe and 238 Pu. The analysis was based on a detailed survey of the evaluations and experimental data. To track the origin of the differences in the evaluations and their impact on k eff , the reaction cross-sections and multiplicities in one evaluation have been substituted by the corresponding data from other evaluations. (authors)
Deterministic Local Sensitivity Analysis of Augmented Systems - I: Theory
Cacuci, Dan G.; Ionescu-Bujor, Mihaela
2005-01-01
This work provides the theoretical foundation for the modular implementation of the Adjoint Sensitivity Analysis Procedure (ASAP) for large-scale simulation systems. The implementation of the ASAP commences with a selected code module and then proceeds by augmenting the size of the adjoint sensitivity system, module by module, until the entire system is completed. Notably, the adjoint sensitivity system for the augmented system can often be solved by using the same numerical methods used for solving the original, nonaugmented adjoint system, particularly when the matrix representation of the adjoint operator for the augmented system can be inverted by partitioning
The identification of model effective dimensions using global sensitivity analysis
Kucherenko, Sergei; Feil, Balazs; Shah, Nilay; Mauntz, Wolfgang
2011-01-01
It is shown that the effective dimensions can be estimated at reasonable computational costs using variance based global sensitivity analysis. Namely, the effective dimension in the truncation sense can be found by using the Sobol' sensitivity indices for subsets of variables. The effective dimension in the superposition sense can be estimated by using the first order effects and the total Sobol' sensitivity indices. The classification of some important classes of integrable functions based on their effective dimension is proposed. It is shown that it can be used for the prediction of the QMC efficiency. Results of numerical tests verify the prediction of the developed techniques.
The identification of model effective dimensions using global sensitivity analysis
Kucherenko, Sergei, E-mail: s.kucherenko@ic.ac.u [CPSE, Imperial College London, South Kensington Campus, London SW7 2AZ (United Kingdom); Feil, Balazs [Department of Process Engineering, University of Pannonia, Veszprem (Hungary); Shah, Nilay [CPSE, Imperial College London, South Kensington Campus, London SW7 2AZ (United Kingdom); Mauntz, Wolfgang [Lehrstuhl fuer Anlagensteuerungstechnik, Fachbereich Chemietechnik, Universitaet Dortmund (Germany)
2011-04-15
It is shown that the effective dimensions can be estimated at reasonable computational costs using variance based global sensitivity analysis. Namely, the effective dimension in the truncation sense can be found by using the Sobol' sensitivity indices for subsets of variables. The effective dimension in the superposition sense can be estimated by using the first order effects and the total Sobol' sensitivity indices. The classification of some important classes of integrable functions based on their effective dimension is proposed. It is shown that it can be used for the prediction of the QMC efficiency. Results of numerical tests verify the prediction of the developed techniques.
Application of Sensitivity Analysis in Design of Sustainable Buildings
Heiselberg, Per; Brohus, Henrik; Rasmussen, Henrik
2009-01-01
satisfies the design objectives and criteria. In the design of sustainable buildings, it is beneficial to identify the most important design parameters in order to more efficiently develop alternative design solutions or reach optimized design solutions. Sensitivity analyses make it possible to identify...... possible to influence the most important design parameters. A methodology of sensitivity analysis is presented and an application example is given for design of an office building in Denmark....
Sensitivity Analysis of the Integrated Medical Model for ISS Programs
Goodenow, D. A.; Myers, J. G.; Arellano, J.; Boley, L.; Garcia, Y.; Saile, L.; Walton, M.; Kerstman, E.; Reyes, D.; Young, M.
2016-01-01
Sensitivity analysis estimates the relative contribution of the uncertainty in input values to the uncertainty of model outputs. Partial Rank Correlation Coefficient (PRCC) and Standardized Rank Regression Coefficient (SRRC) are methods of conducting sensitivity analysis on nonlinear simulation models like the Integrated Medical Model (IMM). The PRCC method estimates the sensitivity using partial correlation of the ranks of the generated input values to each generated output value. The partial part is so named because adjustments are made for the linear effects of all the other input values in the calculation of correlation between a particular input and each output. In SRRC, standardized regression-based coefficients measure the sensitivity of each input, adjusted for all the other inputs, on each output. Because the relative ranking of each of the inputs and outputs is used, as opposed to the values themselves, both methods accommodate the nonlinear relationship of the underlying model. As part of the IMM v4.0 validation study, simulations are available that predict 33 person-missions on ISS and 111 person-missions on STS. These simulated data predictions feed the sensitivity analysis procedures. The inputs to the sensitivity procedures include the number occurrences of each of the one hundred IMM medical conditions generated over the simulations and the associated IMM outputs: total quality time lost (QTL), number of evacuations (EVAC), and number of loss of crew lives (LOCL). The IMM team will report the results of using PRCC and SRRC on IMM v4.0 predictions of the ISS and STS missions created as part of the external validation study. Tornado plots will assist in the visualization of the condition-related input sensitivities to each of the main outcomes. The outcomes of this sensitivity analysis will drive review focus by identifying conditions where changes in uncertainty could drive changes in overall model output uncertainty. These efforts are an integral
Sensitivity analysis of network DEA illustrated in branch banking
N. Avkiran
2010-01-01
Users of data envelopment analysis (DEA) often presume efficiency estimates to be robust. While traditional DEA has been exposed to various sensitivity studies, network DEA (NDEA) has so far escaped similar scrutiny. Thus, there is a need to investigate the sensitivity of NDEA, further compounded by the recent attention it has been receiving in literature. NDEA captures the underlying performance information found in a firm?s interacting divisions or sub-processes that would otherwise remain ...
Sensitivity analysis of periodic errors in heterodyne interferometry
Ganguly, Vasishta; Kim, Nam Ho; Kim, Hyo Soo; Schmitz, Tony
2011-01-01
Periodic errors in heterodyne displacement measuring interferometry occur due to frequency mixing in the interferometer. These nonlinearities are typically characterized as first- and second-order periodic errors which cause a cyclical (non-cumulative) variation in the reported displacement about the true value. This study implements an existing analytical periodic error model in order to identify sensitivities of the first- and second-order periodic errors to the input parameters, including rotational misalignments of the polarizing beam splitter and mixing polarizer, non-orthogonality of the two laser frequencies, ellipticity in the polarizations of the two laser beams, and different transmission coefficients in the polarizing beam splitter. A local sensitivity analysis is first conducted to examine the sensitivities of the periodic errors with respect to each input parameter about the nominal input values. Next, a variance-based approach is used to study the global sensitivities of the periodic errors by calculating the Sobol' sensitivity indices using Monte Carlo simulation. The effect of variation in the input uncertainty on the computed sensitivity indices is examined. It is seen that the first-order periodic error is highly sensitive to non-orthogonality of the two linearly polarized laser frequencies, while the second-order error is most sensitive to the rotational misalignment between the laser beams and the polarizing beam splitter. A particle swarm optimization technique is finally used to predict the possible setup imperfections based on experimentally generated values for periodic errors
Sensitivity analysis of periodic errors in heterodyne interferometry
Ganguly, Vasishta; Kim, Nam Ho; Kim, Hyo Soo; Schmitz, Tony
2011-03-01
Periodic errors in heterodyne displacement measuring interferometry occur due to frequency mixing in the interferometer. These nonlinearities are typically characterized as first- and second-order periodic errors which cause a cyclical (non-cumulative) variation in the reported displacement about the true value. This study implements an existing analytical periodic error model in order to identify sensitivities of the first- and second-order periodic errors to the input parameters, including rotational misalignments of the polarizing beam splitter and mixing polarizer, non-orthogonality of the two laser frequencies, ellipticity in the polarizations of the two laser beams, and different transmission coefficients in the polarizing beam splitter. A local sensitivity analysis is first conducted to examine the sensitivities of the periodic errors with respect to each input parameter about the nominal input values. Next, a variance-based approach is used to study the global sensitivities of the periodic errors by calculating the Sobol' sensitivity indices using Monte Carlo simulation. The effect of variation in the input uncertainty on the computed sensitivity indices is examined. It is seen that the first-order periodic error is highly sensitive to non-orthogonality of the two linearly polarized laser frequencies, while the second-order error is most sensitive to the rotational misalignment between the laser beams and the polarizing beam splitter. A particle swarm optimization technique is finally used to predict the possible setup imperfections based on experimentally generated values for periodic errors.
2012-01-01
OVERVIEW OF PRESENTATION : Evaluation Parameters : EPAs Sensitivity Analysis : Comparison to Baseline Case : MOVES Sensitivity Run Specification : MOVES Sensitivity Input Parameters : Results : Uses of Study
Sensitivity analysis of the reactor safety study. Final report
Parkinson, W.J.; Rasmussen, N.C.; Hinkle, W.D.
1979-01-01
The Reactor Safety Study (RSS) or Wash 1400 developed a methodology estimating the public risk from light water nuclear reactors. In order to give further insights into this study, a sensitivity analysis has been performed to determine the significant contributors to risk for both the PWR and BWR. The sensitivity to variation of the point values of the failure probabilities reported in the RSS was determined for the safety systems identified therein, as well as for many of the generic classes from which individual failures contributed to system failures. Increasing as well as decreasing point values were considered. An analysis of the sensitivity to increasing uncertainty in system failure probabilities was also performed. The sensitivity parameters chosen were release category probabilities, core melt probability, and the risk parameters of early fatalities, latent cancers and total property damage. The latter three are adequate for describing all public risks identified in the RSS. The results indicate reductions of public risk by less than a factor of two for factor reductions in system or generic failure probabilities as high as one hundred. There also appears to be more benefit in monitoring the most sensitive systems to verify adherence to RSS failure rates than to backfitting present reactors. The sensitivity analysis results do indicate, however, possible benefits in reducing human error rates
Sensitivity analysis technique for application to deterministic models
Ishigami, T.; Cazzoli, E.; Khatib-Rahbar, M.; Unwin, S.D.
1987-01-01
The characterization of sever accident source terms for light water reactors should include consideration of uncertainties. An important element of any uncertainty analysis is an evaluation of the sensitivity of the output probability distributions reflecting source term uncertainties to assumptions regarding the input probability distributions. Historically, response surface methods (RSMs) were developed to replace physical models using, for example, regression techniques, with simplified models for example, regression techniques, with simplified models for extensive calculations. The purpose of this paper is to present a new method for sensitivity analysis that does not utilize RSM, but instead relies directly on the results obtained from the original computer code calculations. The merits of this approach are demonstrated by application of the proposed method to the suppression pool aerosol removal code (SPARC), and the results are compared with those obtained by sensitivity analysis with (a) the code itself, (b) a regression model, and (c) Iman's method
Probabilistic Sensitivities for Fatigue Analysis of Turbine Engine Disks
Harry R. Millwater
2006-01-01
Full Text Available A methodology is developed and applied that determines the sensitivities of the probability-of-fracture of a gas turbine disk fatigue analysis with respect to the parameters of the probability distributions describing the random variables. The disk material is subject to initial anomalies, in either low- or high-frequency quantities, such that commonly used materials (titanium, nickel, powder nickel and common damage mechanisms (inherent defects or surface damage can be considered. The derivation is developed for Monte Carlo sampling such that the existing failure samples are used and the sensitivities are obtained with minimal additional computational time. Variance estimates and confidence bounds of the sensitivity estimates are developed. The methodology is demonstrated and verified using a multizone probabilistic fatigue analysis of a gas turbine compressor disk analysis considering stress scatter, crack growth propagation scatter, and initial crack size as random variables.
Application of sensitivity analysis for optimized piping support design
Tai, K.; Nakatogawa, T.; Hisada, T.; Noguchi, H.; Ichihashi, I.; Ogo, H.
1993-01-01
The objective of this study was to see if recent developments in non-linear sensitivity analysis could be applied to the design of nuclear piping systems which use non-linear supports and to develop a practical method of designing such piping systems. In the study presented in this paper, the seismic response of a typical piping system was analyzed using a dynamic non-linear FEM and a sensitivity analysis was carried out. Then optimization for the design of the piping system supports was investigated, selecting the support location and yield load of the non-linear supports (bi-linear model) as main design parameters. It was concluded that the optimized design was a matter of combining overall system reliability with the achievement of an efficient damping effect from the non-linear supports. The analysis also demonstrated sensitivity factors are useful in the planning stage of support design. (author)
Sensitivity and uncertainty analysis of the PATHWAY radionuclide transport model
Otis, M.D.
1983-01-01
Procedures were developed for the uncertainty and sensitivity analysis of a dynamic model of radionuclide transport through human food chains. Uncertainty in model predictions was estimated by propagation of parameter uncertainties using a Monte Carlo simulation technique. Sensitivity of model predictions to individual parameters was investigated using the partial correlation coefficient of each parameter with model output. Random values produced for the uncertainty analysis were used in the correlation analysis for sensitivity. These procedures were applied to the PATHWAY model which predicts concentrations of radionuclides in foods grown in Nevada and Utah and exposed to fallout during the period of atmospheric nuclear weapons testing in Nevada. Concentrations and time-integrated concentrations of iodine-131, cesium-136, and cesium-137 in milk and other foods were investigated. 9 figs., 13 tabs
Discrete non-parametric kernel estimation for global sensitivity analysis
Senga Kiessé, Tristan; Ventura, Anne
2016-01-01
This work investigates the discrete kernel approach for evaluating the contribution of the variance of discrete input variables to the variance of model output, via analysis of variance (ANOVA) decomposition. Until recently only the continuous kernel approach has been applied as a metamodeling approach within sensitivity analysis framework, for both discrete and continuous input variables. Now the discrete kernel estimation is known to be suitable for smoothing discrete functions. We present a discrete non-parametric kernel estimator of ANOVA decomposition of a given model. An estimator of sensitivity indices is also presented with its asymtotic convergence rate. Some simulations on a test function analysis and a real case study from agricultural have shown that the discrete kernel approach outperforms the continuous kernel one for evaluating the contribution of moderate or most influential discrete parameters to the model output. - Highlights: • We study a discrete kernel estimation for sensitivity analysis of a model. • A discrete kernel estimator of ANOVA decomposition of the model is presented. • Sensitivity indices are calculated for discrete input parameters. • An estimator of sensitivity indices is also presented with its convergence rate. • An application is realized for improving the reliability of environmental models.
Sensitivity analysis for missing data in regulatory submissions.
Permutt, Thomas
2016-07-30
The National Research Council Panel on Handling Missing Data in Clinical Trials recommended that sensitivity analyses have to be part of the primary reporting of findings from clinical trials. Their specific recommendations, however, seem not to have been taken up rapidly by sponsors of regulatory submissions. The NRC report's detailed suggestions are along rather different lines than what has been called sensitivity analysis in the regulatory setting up to now. Furthermore, the role of sensitivity analysis in regulatory decision-making, although discussed briefly in the NRC report, remains unclear. This paper will examine previous ideas of sensitivity analysis with a view to explaining how the NRC panel's recommendations are different and possibly better suited to coping with present problems of missing data in the regulatory setting. It will also discuss, in more detail than the NRC report, the relevance of sensitivity analysis to decision-making, both for applicants and for regulators. Published 2015. This article is a U.S. Government work and is in the public domain in the USA. Published 2015. This article is a U.S. Government work and is in the public domain in the USA.
Sobol' sensitivity analysis for stressor impacts on honeybee ...
We employ Monte Carlo simulation and nonlinear sensitivity analysis techniques to describe the dynamics of a bee exposure model, VarroaPop. Daily simulations are performed of hive population trajectories, taking into account queen strength, foraging success, mite impacts, weather, colony resources, population structure, and other important variables. This allows us to test the effects of defined pesticide exposure scenarios versus controlled simulations that lack pesticide exposure. The daily resolution of the model also allows us to conditionally identify sensitivity metrics. We use the variancebased global decomposition sensitivity analysis method, Sobol’, to assess firstand secondorder parameter sensitivities within VarroaPop, allowing us to determine how variance in the output is attributed to each of the input variables across different exposure scenarios. Simulations with VarroaPop indicate queen strength, forager life span and pesticide toxicity parameters are consistent, critical inputs for colony dynamics. Further analysis also reveals that the relative importance of these parameters fluctuates throughout the simulation period according to the status of other inputs. Our preliminary results show that model variability is conditional and can be attributed to different parameters depending on different timescales. By using sensitivity analysis to assess model output and variability, calibrations of simulation models can be better informed to yield more
Lee, Yeonok; Wu, Hulin
2012-01-01
Differential equation models are widely used for the study of natural phenomena in many fields. The study usually involves unknown factors such as initial conditions and/or parameters. It is important to investigate the impact of unknown factors (parameters and initial conditions) on model outputs in order to better understand the system the model represents. Apportioning the uncertainty (variation) of output variables of a model according to the input factors is referred to as sensitivity analysis. In this paper, we focus on the global sensitivity analysis of ordinary differential equation (ODE) models over a time period using the multivariate adaptive regression spline (MARS) as a meta model based on the concept of the variance of conditional expectation (VCE). We suggest to evaluate the VCE analytically using the MARS model structure of univariate tensor-product functions which is more computationally efficient. Our simulation studies show that the MARS model approach performs very well and helps to significantly reduce the computational cost. We present an application example of sensitivity analysis of ODE models for influenza infection to further illustrate the usefulness of the proposed method.
Variance estimation for sensitivity analysis of poverty and inequality measures
Christian Dudel
2017-04-01
Full Text Available Estimates of poverty and inequality are often based on application of a single equivalence scale, despite the fact that a large number of different equivalence scales can be found in the literature. This paper describes a framework for sensitivity analysis which can be used to account for the variability of equivalence scales and allows to derive variance estimates of results of sensitivity analysis. Simulations show that this method yields reliable estimates. An empirical application reveals that accounting for both variability of equivalence scales and sampling variance leads to confidence intervals which are wide.
Sensitivity analysis of water consumption in an office building
Suchacek, Tomas; Tuhovcak, Ladislav; Rucka, Jan
2018-02-01
This article deals with sensitivity analysis of real water consumption in an office building. During a long-term real study, reducing of pressure in its water connection was simulated. A sensitivity analysis of uneven water demand was conducted during working time at various provided pressures and at various time step duration. Correlations between maximal coefficients of water demand variation during working time and provided pressure were suggested. The influence of provided pressure in the water connection on mean coefficients of water demand variation was pointed out, altogether for working hours of all days and separately for days with identical working hours.
Probabilistic and sensitivity analysis of Botlek Bridge structures
Králik Juraj
2017-01-01
Full Text Available This paper deals with the probabilistic and sensitivity analysis of the largest movable lift bridge of the world. The bridge system consists of six reinforced concrete pylons and two steel decks 4000 tons weight each connected through ropes with counterweights. The paper focuses the probabilistic and sensitivity analysis as the base of dynamic study in design process of the bridge. The results had a high importance for practical application and design of the bridge. The model and resistance uncertainties were taken into account in LHS simulation method.
Applying DEA sensitivity analysis to efficiency measurement of Vietnamese universities
Thi Thanh Huyen Nguyen
2015-11-01
Full Text Available The primary purpose of this study is to measure the technical efficiency of 30 doctorate-granting universities, the universities or the higher education institutes with PhD training programs, in Vietnam, applying the sensitivity analysis of data envelopment analysis (DEA. The study uses eight sets of input-output specifications using the replacement as well as aggregation/disaggregation of variables. The measurement results allow us to examine the sensitivity of the efficiency of these universities with the sets of variables. The findings also show the impact of variables on their efficiency and its “sustainability”.
Seismic analysis of steam generator and parameter sensitivity studies
Qian Hao; Xu Dinggen; Yang Ren'an; Liang Xingyun
2013-01-01
Background: The steam generator (SG) serves as the primary means for removing the heat generated within the reactor core and is part of the reactor coolant system (RCS) pressure boundary. Purpose: Seismic analysis in required for SG, whose seismic category is Cat. I. Methods: The analysis model of SG is created with moisture separator assembly and tube bundle assembly herein. The seismic analysis is performed with RCS pipe and Reactor Pressure Vessel (RPV). Results: The seismic stress results of SG are obtained. In addition, parameter sensitivities of seismic analysis results are studied, such as the effect of another SG, support, anti-vibration bars (AVBs), and so on. Our results show that seismic results are sensitive to support and AVBs setting. Conclusions: The guidance and comments on these parameters are summarized for equipment design and analysis, which should be focused on in future new type NPP SG's research and design. (authors)
Automated differentiation of computer models for sensitivity analysis
Worley, B.A.
1990-01-01
Sensitivity analysis of reactor physics computer models is an established discipline after more than twenty years of active development of generalized perturbations theory based on direct and adjoint methods. Many reactor physics models have been enhanced to solve for sensitivities of model results to model data. The calculated sensitivities are usually normalized first derivatives although some codes are capable of solving for higher-order sensitivities. The purpose of this paper is to report on the development and application of the GRESS system for automating the implementation of the direct and adjoint techniques into existing FORTRAN computer codes. The GRESS system was developed at ORNL to eliminate the costly man-power intensive effort required to implement the direct and adjoint techniques into already-existing FORTRAN codes. GRESS has been successfully tested for a number of codes over a wide range of applications and presently operates on VAX machines under both VMS and UNIX operating systems
Automated differentiation of computer models for sensitivity analysis
Worley, B.A.
1991-01-01
Sensitivity analysis of reactor physics computer models is an established discipline after more than twenty years of active development of generalized perturbations theory based on direct and adjoint methods. Many reactor physics models have been enhanced to solve for sensitivities of model results to model data. The calculated sensitivities are usually normalized first derivatives, although some codes are capable of solving for higher-order sensitivities. The purpose of this paper is to report on the development and application of the GRESS system for automating the implementation of the direct and adjoint techniques into existing FORTRAN computer codes. The GRESS system was developed at ORNL to eliminate the costly man-power intensive effort required to implement the direct and adjoint techniques into already-existing FORTRAN codes. GRESS has been successfully tested for a number of codes over a wide range of applications and presently operates on VAX machines under both VMS and UNIX operating systems. (author). 9 refs, 1 tab
A Global Sensitivity Analysis Methodology for Multi-physics Applications
Tong, C H; Graziani, F R
2007-02-02
Experiments are conducted to draw inferences about an entire ensemble based on a selected number of observations. This applies to both physical experiments as well as computer experiments, the latter of which are performed by running the simulation models at different input configurations and analyzing the output responses. Computer experiments are instrumental in enabling model analyses such as uncertainty quantification and sensitivity analysis. This report focuses on a global sensitivity analysis methodology that relies on a divide-and-conquer strategy and uses intelligent computer experiments. The objective is to assess qualitatively and/or quantitatively how the variabilities of simulation output responses can be accounted for by input variabilities. We address global sensitivity analysis in three aspects: methodology, sampling/analysis strategies, and an implementation framework. The methodology consists of three major steps: (1) construct credible input ranges; (2) perform a parameter screening study; and (3) perform a quantitative sensitivity analysis on a reduced set of parameters. Once identified, research effort should be directed to the most sensitive parameters to reduce their uncertainty bounds. This process is repeated with tightened uncertainty bounds for the sensitive parameters until the output uncertainties become acceptable. To accommodate the needs of multi-physics application, this methodology should be recursively applied to individual physics modules. The methodology is also distinguished by an efficient technique for computing parameter interactions. Details for each step will be given using simple examples. Numerical results on large scale multi-physics applications will be available in another report. Computational techniques targeted for this methodology have been implemented in a software package called PSUADE.
Automated sensitivity analysis: New tools for modeling complex dynamic systems
Pin, F.G.
1987-01-01
Sensitivity analysis is an established methodology used by researchers in almost every field to gain essential insight in design and modeling studies and in performance assessments of complex systems. Conventional sensitivity analysis methodologies, however, have not enjoyed the widespread use they deserve considering the wealth of information they can provide, partly because of their prohibitive cost or the large initial analytical investment they require. Automated systems have recently been developed at ORNL to eliminate these drawbacks. Compilers such as GRESS and EXAP now allow automatic and cost effective calculation of sensitivities in FORTRAN computer codes. In this paper, these and other related tools are described and their impact and applicability in the general areas of modeling, performance assessment and decision making for radioactive waste isolation problems are discussed
The Volatility of Data Space: Topology Oriented Sensitivity Analysis
Du, Jing; Ligmann-Zielinska, Arika
2015-01-01
Despite the difference among specific methods, existing Sensitivity Analysis (SA) technologies are all value-based, that is, the uncertainties in the model input and output are quantified as changes of values. This paradigm provides only limited insight into the nature of models and the modeled systems. In addition to the value of data, a potentially richer information about the model lies in the topological difference between pre-model data space and post-model data space. This paper introduces an innovative SA method called Topology Oriented Sensitivity Analysis, which defines sensitivity as the volatility of data space. It extends SA into a deeper level that lies in the topology of data. PMID:26368929
Interactive Building Design Space Exploration Using Regionalized Sensitivity Analysis
Østergård, Torben; Jensen, Rasmus Lund; Maagaard, Steffen
2017-01-01
simulation inputs are most important and which have negligible influence on the model output. Popular sensitivity methods include the Morris method, variance-based methods (e.g. Sobol’s), and regression methods (e.g. SRC). However, all these methods only address one output at a time, which makes it difficult...... in combination with the interactive parallel coordinate plot (PCP). The latter is an effective tool to explore stochastic simulations and to find high-performing building designs. The proposed methods help decision makers to focus their attention to the most important design parameters when exploring......Monte Carlo simulations combined with regionalized sensitivity analysis provide the means to explore a vast, multivariate design space in building design. Typically, sensitivity analysis shows how the variability of model output relates to the uncertainties in models inputs. This reveals which...
Sensitization trajectories in childhood revealed by using a cluster analysis
Schoos, Ann-Marie M.; Chawes, Bo L.; Melen, Erik
2017-01-01
Prospective Studies on Asthma in Childhood 2000 (COPSAC2000) birth cohort with specific IgE against 13 common food and inhalant allergens at the ages of ½, 1½, 4, and 6 years. An unsupervised cluster analysis for 3-dimensional data (nonnegative sparse parallel factor analysis) was used to extract latent......BACKGROUND: Assessment of sensitization at a single time point during childhood provides limited clinical information. We hypothesized that sensitization develops as specific patterns with respect to age at debut, development over time, and involved allergens and that such patterns might be more...... biologically and clinically relevant. OBJECTIVE: We sought to explore latent patterns of sensitization during the first 6 years of life and investigate whether such patterns associate with the development of asthma, rhinitis, and eczema. METHODS: We investigated 398 children from the at-risk Copenhagen...
Zhang, Yong; Zhong, Miner; Geng, Nana; Jiang, Yunjian
2017-01-01
The market demand for electric vehicles (EVs) has increased in recent years. Suitable models are necessary to understand and forecast EV sales. This study presents a singular spectrum analysis (SSA) as a univariate time-series model and vector autoregressive model (VAR) as a multivariate model. Empirical results suggest that SSA satisfactorily indicates the evolving trend and provides reasonable results. The VAR model, which comprised exogenous parameters related to the market on a monthly basis, can significantly improve the prediction accuracy. The EV sales in China, which are categorized into battery and plug-in EVs, are predicted in both short term (up to December 2017) and long term (up to 2020), as statistical proofs of the growth of the Chinese EV industry.
Lower bounds on the run time of the univariate marginal distribution algorithm on OneMax
Krejca, Martin S.; Witt, Carsten
2017-01-01
The Univariate Marginal Distribution Algorithm (UMDA), a popular estimation of distribution algorithm, is studied from a run time perspective. On the classical OneMax benchmark function, a lower bound of Ω(μ√n + n log n), where μ is the population size, on its expected run time is proved...... values maintained by the algorithm, including carefully designed potential functions. These techniques may prove useful in advancing the field of run time analysis for estimation of distribution algorithms in general........ This is the first direct lower bound on the run time of the UMDA. It is stronger than the bounds that follow from general black-box complexity theory and is matched by the run time of many evolutionary algorithms. The results are obtained through advanced analyses of the stochastic change of the frequencies of bit...
Time-dependent reliability sensitivity analysis of motion mechanisms
Wei, Pengfei; Song, Jingwen; Lu, Zhenzhou; Yue, Zhufeng
2016-01-01
Reliability sensitivity analysis aims at identifying the source of structure/mechanism failure, and quantifying the effects of each random source or their distribution parameters on failure probability or reliability. In this paper, the time-dependent parametric reliability sensitivity (PRS) analysis as well as the global reliability sensitivity (GRS) analysis is introduced for the motion mechanisms. The PRS indices are defined as the partial derivatives of the time-dependent reliability w.r.t. the distribution parameters of each random input variable, and they quantify the effect of the small change of each distribution parameter on the time-dependent reliability. The GRS indices are defined for quantifying the individual, interaction and total contributions of the uncertainty in each random input variable to the time-dependent reliability. The envelope function method combined with the first order approximation of the motion error function is introduced for efficiently estimating the time-dependent PRS and GRS indices. Both the time-dependent PRS and GRS analysis techniques can be especially useful for reliability-based design. This significance of the proposed methods as well as the effectiveness of the envelope function method for estimating the time-dependent PRS and GRS indices are demonstrated with a four-bar mechanism and a car rack-and-pinion steering linkage. - Highlights: • Time-dependent parametric reliability sensitivity analysis is presented. • Time-dependent global reliability sensitivity analysis is presented for mechanisms. • The proposed method is especially useful for enhancing the kinematic reliability. • An envelope method is introduced for efficiently implementing the proposed methods. • The proposed method is demonstrated by two real planar mechanisms.
Probabilistic sensitivity analysis of system availability using Gaussian processes
Daneshkhah, Alireza; Bedford, Tim
2013-01-01
The availability of a system under a given failure/repair process is a function of time which can be determined through a set of integral equations and usually calculated numerically. We focus here on the issue of carrying out sensitivity analysis of availability to determine the influence of the input parameters. The main purpose is to study the sensitivity of the system availability with respect to the changes in the main parameters. In the simplest case that the failure repair process is (continuous time/discrete state) Markovian, explicit formulae are well known. Unfortunately, in more general cases availability is often a complicated function of the parameters without closed form solution. Thus, the computation of sensitivity measures would be time-consuming or even infeasible. In this paper, we show how Sobol and other related sensitivity measures can be cheaply computed to measure how changes in the model inputs (failure/repair times) influence the outputs (availability measure). We use a Bayesian framework, called the Bayesian analysis of computer code output (BACCO) which is based on using the Gaussian process as an emulator (i.e., an approximation) of complex models/functions. This approach allows effective sensitivity analysis to be achieved by using far smaller numbers of model runs than other methods. The emulator-based sensitivity measure is used to examine the influence of the failure and repair densities' parameters on the system availability. We discuss how to apply the methods practically in the reliability context, considering in particular the selection of parameters and prior distributions and how we can ensure these may be considered independent—one of the key assumptions of the Sobol approach. The method is illustrated on several examples, and we discuss the further implications of the technique for reliability and maintenance analysis
Analytic uncertainty and sensitivity analysis of models with input correlations
Zhu, Yueying; Wang, Qiuping A.; Li, Wei; Cai, Xu
2018-03-01
Probabilistic uncertainty analysis is a common means of evaluating mathematical models. In mathematical modeling, the uncertainty in input variables is specified through distribution laws. Its contribution to the uncertainty in model response is usually analyzed by assuming that input variables are independent of each other. However, correlated parameters are often happened in practical applications. In the present paper, an analytic method is built for the uncertainty and sensitivity analysis of models in the presence of input correlations. With the method, it is straightforward to identify the importance of the independence and correlations of input variables in determining the model response. This allows one to decide whether or not the input correlations should be considered in practice. Numerical examples suggest the effectiveness and validation of our analytic method in the analysis of general models. A practical application of the method is also proposed to the uncertainty and sensitivity analysis of a deterministic HIV model.
Sensitivity Analysis Applied in Design of Low Energy Office Building
Heiselberg, Per; Brohus, Henrik
2008-01-01
satisfies the design requirements and objectives. In the design of sustainable Buildings it is beneficial to identify the most important design parameters in order to develop more efficiently alternative design solutions or reach optimized design solutions. A sensitivity analysis makes it possible...
Application of Sensitivity Analysis in Design of Sustainable Buildings
Heiselberg, Per; Brohus, Henrik; Hesselholt, Allan Tind
2007-01-01
satisfies the design requirements and objectives. In the design of sustainable Buildings it is beneficial to identify the most important design parameters in order to develop more efficiently alternative design solutions or reach optimized design solutions. A sensitivity analysis makes it possible...
Sensitivity analysis of physiochemical interaction model: which pair ...
... of two model parameters at a time on the solution trajectory of physiochemical interaction over a time interval. Our aim is to use this powerful mathematical technique to select the important pair of parameters of this physical process which is cost-effective. Keywords: Passivation Rate, Sensitivity Analysis, ODE23, ODE45 ...
Bayesian Sensitivity Analysis of Statistical Models with Missing Data.
Zhu, Hongtu; Ibrahim, Joseph G; Tang, Niansheng
2014-04-01
Methods for handling missing data depend strongly on the mechanism that generated the missing values, such as missing completely at random (MCAR) or missing at random (MAR), as well as other distributional and modeling assumptions at various stages. It is well known that the resulting estimates and tests may be sensitive to these assumptions as well as to outlying observations. In this paper, we introduce various perturbations to modeling assumptions and individual observations, and then develop a formal sensitivity analysis to assess these perturbations in the Bayesian analysis of statistical models with missing data. We develop a geometric framework, called the Bayesian perturbation manifold, to characterize the intrinsic structure of these perturbations. We propose several intrinsic influence measures to perform sensitivity analysis and quantify the effect of various perturbations to statistical models. We use the proposed sensitivity analysis procedure to systematically investigate the tenability of the non-ignorable missing at random (NMAR) assumption. Simulation studies are conducted to evaluate our methods, and a dataset is analyzed to illustrate the use of our diagnostic measures.
Sensitivity analysis for contagion effects in social networks
VanderWeele, Tyler J.
2014-01-01
Analyses of social network data have suggested that obesity, smoking, happiness and loneliness all travel through social networks. Individuals exert “contagion effects” on one another through social ties and association. These analyses have come under critique because of the possibility that homophily from unmeasured factors may explain these statistical associations and because similar findings can be obtained when the same methodology is applied to height, acne and head-aches, for which the conclusion of contagion effects seems somewhat less plausible. We use sensitivity analysis techniques to assess the extent to which supposed contagion effects for obesity, smoking, happiness and loneliness might be explained away by homophily or confounding and the extent to which the critique using analysis of data on height, acne and head-aches is relevant. Sensitivity analyses suggest that contagion effects for obesity and smoking cessation are reasonably robust to possible latent homophily or environmental confounding; those for happiness and loneliness are somewhat less so. Supposed effects for height, acne and head-aches are all easily explained away by latent homophily and confounding. The methodology that has been employed in past studies for contagion effects in social networks, when used in conjunction with sensitivity analysis, may prove useful in establishing social influence for various behaviors and states. The sensitivity analysis approach can be used to address the critique of latent homophily as a possible explanation of associations interpreted as contagion effects. PMID:25580037
Sensitivity Analysis of a Horizontal Earth Electrode under Impulse ...
This paper presents the sensitivity analysis of an earthing conductor under the influence of impulse current arising from a lightning stroke. The approach is based on the 2nd order finite difference time domain (FDTD). The earthing conductor is regarded as a lossy transmission line where it is divided into series connected ...
Beyond the GUM: variance-based sensitivity analysis in metrology
Lira, I
2016-01-01
Variance-based sensitivity analysis is a well established tool for evaluating the contribution of the uncertainties in the inputs to the uncertainty in the output of a general mathematical model. While the literature on this subject is quite extensive, it has not found widespread use in metrological applications. In this article we present a succinct review of the fundamentals of sensitivity analysis, in a form that should be useful to most people familiarized with the Guide to the Expression of Uncertainty in Measurement (GUM). Through two examples, it is shown that in linear measurement models, no new knowledge is gained by using sensitivity analysis that is not already available after the terms in the so-called ‘law of propagation of uncertainties’ have been computed. However, if the model behaves non-linearly in the neighbourhood of the best estimates of the input quantities—and if these quantities are assumed to be statistically independent—sensitivity analysis is definitely advantageous for gaining insight into how they can be ranked according to their importance in establishing the uncertainty of the measurand. (paper)
Sensitivity analysis of the Ohio phosphorus risk index
The Phosphorus (P) Index is a widely used tool for assessing the vulnerability of agricultural fields to P loss; yet, few of the P Indices developed in the U.S. have been evaluated for their accuracy. Sensitivity analysis is one approach that can be used prior to calibration and field-scale testing ...
Sensitivity analysis for oblique incidence reflectometry using Monte Carlo simulations
Kamran, Faisal; Andersen, Peter E.
2015-01-01
profiles. This article presents a sensitivity analysis of the technique in turbid media. Monte Carlo simulations are used to investigate the technique and its potential to distinguish the small changes between different levels of scattering. We present various regions of the dynamic range of optical...
Omitted Variable Sensitivity Analysis with the Annotated Love Plot
Hansen, Ben B.; Fredrickson, Mark M.
2014-01-01
The goal of this research is to make sensitivity analysis accessible not only to empirical researchers but also to the various stakeholders for whom educational evaluations are conducted. To do this it derives anchors for the omitted variable (OV)-program participation association intrinsically, using the Love plot to present a wide range of…
Weighting-Based Sensitivity Analysis in Causal Mediation Studies
Hong, Guanglei; Qin, Xu; Yang, Fan
2018-01-01
Through a sensitivity analysis, the analyst attempts to determine whether a conclusion of causal inference could be easily reversed by a plausible violation of an identification assumption. Analytic conclusions that are harder to alter by such a violation are expected to add a higher value to scientific knowledge about causality. This article…
Sensitivity analysis of railpad parameters on vertical railway track dynamics
Oregui Echeverria-Berreyarza, M.; Nunez Vicencio, Alfredo; Dollevoet, R.P.B.J.; Li, Z.
2016-01-01
This paper presents a sensitivity analysis of railpad parameters on vertical railway track dynamics, incorporating the nonlinear behavior of the fastening (i.e., downward forces compress the railpad whereas upward forces are resisted by the clamps). For this purpose, solid railpads, rail-railpad
Methods for global sensitivity analysis in life cycle assessment
Groen, Evelyne A.; Bokkers, Eddy; Heijungs, Reinout; Boer, de Imke J.M.
2017-01-01
Purpose: Input parameters required to quantify environmental impact in life cycle assessment (LCA) can be uncertain due to e.g. temporal variability or unknowns about the true value of emission factors. Uncertainty of environmental impact can be analysed by means of a global sensitivity analysis to
Sensitivity analysis on ultimate strength of aluminium stiffened panels
Rigo, P.; Sarghiuta, R.; Estefen, S.
2003-01-01
This paper presents the results of an extensive sensitivity analysis carried out by the Committee III.1 "Ultimate Strength" of ISSC?2003 in the framework of a benchmark on the ultimate strength of aluminium stiffened panels. Previously, different benchmarks were presented by ISSC committees on ul...
Sensitivity and specificity of coherence and phase synchronization analysis
Winterhalder, Matthias; Schelter, Bjoern; Kurths, Juergen; Schulze-Bonhage, Andreas; Timmer, Jens
2006-01-01
In this Letter, we show that coherence and phase synchronization analysis are sensitive but not specific in detecting the correct class of underlying dynamics. We propose procedures to increase specificity and demonstrate the power of the approach by application to paradigmatic dynamic model systems
Sensitivity Analysis of Structures by Virtual Distortion Method
Gierlinski, J.T.; Holnicki-Szulc, J.; Sørensen, John Dalsgaard
1991-01-01
are used in structural optimization, see Haftka [4]. The recently developed Virtual Distortion Method (VDM) is a numerical technique which offers an efficient approach to calculation of the sensitivity derivatives. This method has been orginally applied to structural remodelling and collapse analysis, see...
Design tradeoff studies and sensitivity analysis. Appendix B
1979-05-25
The results of the design trade-off studies and the sensitivity analysis of Phase I of the Near Term Hybrid Vehicle (NTHV) Program are presented. The effects of variations in the design of the vehicle body, propulsion systems, and other components on vehicle power, weight, cost, and fuel economy and an optimized hybrid vehicle design are discussed. (LCL)
Sensitivity analysis and power for instrumental variable studies.
Wang, Xuran; Jiang, Yang; Zhang, Nancy R; Small, Dylan S
2018-03-31
In observational studies to estimate treatment effects, unmeasured confounding is often a concern. The instrumental variable (IV) method can control for unmeasured confounding when there is a valid IV. To be a valid IV, a variable needs to be independent of unmeasured confounders and only affect the outcome through affecting the treatment. When applying the IV method, there is often concern that a putative IV is invalid to some degree. We present an approach to sensitivity analysis for the IV method which examines the sensitivity of inferences to violations of IV validity. Specifically, we consider sensitivity when the magnitude of association between the putative IV and the unmeasured confounders and the direct effect of the IV on the outcome are limited in magnitude by a sensitivity parameter. Our approach is based on extending the Anderson-Rubin test and is valid regardless of the strength of the instrument. A power formula for this sensitivity analysis is presented. We illustrate its usage via examples about Mendelian randomization studies and its implications via a comparison of using rare versus common genetic variants as instruments. © 2018, The International Biometric Society.
Ismail, A.; Hassan, Noor I.
2013-09-01
Cancer is one of the principal causes of death in Malaysia. This study was performed to determine the pattern of rate of cancer deaths at a public hospital in Malaysia over an 11 year period from year 2001 to 2011, to determine the best fitted model of forecasting the rate of cancer deaths using Univariate Modeling and to forecast the rates for the next two years (2012 to 2013). The medical records of the death of patients with cancer admitted at this Hospital over 11 year's period were reviewed, with a total of 663 cases. The cancers were classified according to 10th Revision International Classification of Diseases (ICD-10). Data collected include socio-demographic background of patients such as registration number, age, gender, ethnicity, ward and diagnosis. Data entry and analysis was accomplished using SPSS 19.0 and Minitab 16.0. The five Univariate Models used were Naïve with Trend Model, Average Percent Change Model (ACPM), Single Exponential Smoothing, Double Exponential Smoothing and Holt's Method. The overall 11 years rate of cancer deaths showed that at this hospital, Malay patients have the highest percentage (88.10%) compared to other ethnic groups with males (51.30%) higher than females. Lung and breast cancer have the most number of cancer deaths among gender. About 29.60% of the patients who died due to cancer were aged 61 years old and above. The best Univariate Model used for forecasting the rate of cancer deaths is Single Exponential Smoothing Technique with alpha of 0.10. The forecast for the rate of cancer deaths shows a horizontally or flat value. The forecasted mortality trend remains at 6.84% from January 2012 to December 2013. All the government and private sectors and non-governmental organizations need to highlight issues on cancer especially lung and breast cancers to the public through campaigns using mass media, media electronics, posters and pamphlets in the attempt to decrease the rate of cancer deaths in Malaysia.
Sensitivity analysis of LOFT L2-5 test calculations
Prosek, Andrej
2014-01-01
The uncertainty quantification of best-estimate code predictions is typically accompanied by a sensitivity analysis, in which the influence of the individual contributors to uncertainty is determined. The objective of this study is to demonstrate the improved fast Fourier transform based method by signal mirroring (FFTBM-SM) for the sensitivity analysis. The sensitivity study was performed for the LOFT L2-5 test, which simulates the large break loss of coolant accident. There were 14 participants in the BEMUSE (Best Estimate Methods-Uncertainty and Sensitivity Evaluation) programme, each performing a reference calculation and 15 sensitivity runs of the LOFT L2-5 test. The important input parameters varied were break area, gap conductivity, fuel conductivity, decay power etc. For the influence of input parameters on the calculated results the FFTBM-SM was used. The only difference between FFTBM-SM and original FFTBM is that in the FFTBM-SM the signals are symmetrized to eliminate the edge effect (the so called edge is the difference between the first and last data point of one period of the signal) in calculating average amplitude. It is very important to eliminate unphysical contribution to the average amplitude, which is used as a figure of merit for input parameter influence on output parameters. The idea is to use reference calculation as 'experimental signal', 'sensitivity run' as 'calculated signal', and average amplitude as figure of merit for sensitivity instead for code accuracy. The larger is the average amplitude the larger is the influence of varied input parameter. The results show that with FFTBM-SM the analyst can get good picture of the contribution of the parameter variation to the results. They show when the input parameters are influential and how big is this influence. FFTBM-SM could be also used to quantify the influence of several parameter variations on the results. However, the influential parameters could not be
Harper, W.V.; Gupta, S.K.
1983-10-01
A computer code was used to study steady-state flow for a hypothetical borehole scenario. The model consists of three coupled equations with only eight parameters and three dependent variables. This study focused on steady-state flow as the performance measure of interest. Two different approaches to sensitivity/uncertainty analysis were used on this code. One approach, based on Latin Hypercube Sampling (LHS), is a statistical sampling method, whereas, the second approach is based on the deterministic evaluation of sensitivities. The LHS technique is easy to apply and should work well for codes with a moderate number of parameters. Of deterministic techniques, the direct method is preferred when there are many performance measures of interest and a moderate number of parameters. The adjoint method is recommended when there are a limited number of performance measures and an unlimited number of parameters. This unlimited number of parameters capability can be extremely useful for finite element or finite difference codes with a large number of grid blocks. The Office of Nuclear Waste Isolation will use the technique most appropriate for an individual situation. For example, the adjoint method may be used to reduce the scope to a size that can be readily handled by a technique such as LHS. Other techniques for sensitivity/uncertainty analysis, e.g., kriging followed by conditional simulation, will be used also. 15 references, 4 figures, 9 tables
Sensitivity and uncertainty analysis of NET/ITER shielding blankets
Hogenbirk, A.; Gruppelaar, H.; Verschuur, K.A.
1990-09-01
Results are presented of sensitivity and uncertainty calculations based upon the European fusion file (EFF-1). The effect of uncertainties in Fe, Cr and Ni cross sections on the nuclear heating in the coils of a NET/ITER shielding blanket has been studied. The analysis has been performed for the total cross section as well as partial cross sections. The correct expression for the sensitivity profile was used, including the gain term. The resulting uncertainty in the nuclear heating lies between 10 and 20 per cent. (author). 18 refs.; 2 figs.; 2 tabs
Sensitivity analysis of critical experiments with evaluated nuclear data libraries
Fujiwara, D.; Kosaka, S.
2008-01-01
Criticality benchmark testing was performed with evaluated nuclear data libraries for thermal, low-enriched uranium fuel rod applications. C/E values for k eff were calculated with the continuous-energy Monte Carlo code MVP2 and its libraries generated from Endf/B-VI.8, Endf/B-VII.0, JENDL-3.3 and JEFF-3.1. Subsequently, the observed k eff discrepancies between libraries were decomposed to specify the source of difference in the nuclear data libraries using sensitivity analysis technique. The obtained sensitivity profiles are also utilized to estimate the adequacy of cold critical experiments to the boiling water reactor under hot operating condition. (authors)
Importance measures in global sensitivity analysis of nonlinear models
Homma, Toshimitsu; Saltelli, Andrea
1996-01-01
The present paper deals with a new method of global sensitivity analysis of nonlinear models. This is based on a measure of importance to calculate the fractional contribution of the input parameters to the variance of the model prediction. Measures of importance in sensitivity analysis have been suggested by several authors, whose work is reviewed in this article. More emphasis is given to the developments of sensitivity indices by the Russian mathematician I.M. Sobol'. Given that Sobol' treatment of the measure of importance is the most general, his formalism is employed throughout this paper where conceptual and computational improvements of the method are presented. The computational novelty of this study is the introduction of the 'total effect' parameter index. This index provides a measure of the total effect of a given parameter, including all the possible synergetic terms between that parameter and all the others. Rank transformation of the data is also introduced in order to increase the reproducibility of the method. These methods are tested on a few analytical and computer models. The main conclusion of this work is the identification of a sensitivity analysis methodology which is both flexible, accurate and informative, and which can be achieved at reasonable computational cost
Rethinking Sensitivity Analysis of Nuclear Simulations with Topology
Dan Maljovec; Bei Wang; Paul Rosen; Andrea Alfonsi; Giovanni Pastore; Cristian Rabiti; Valerio Pascucci
2016-01-01
In nuclear engineering, understanding the safety margins of the nuclear reactor via simulations is arguably of paramount importance in predicting and preventing nuclear accidents. It is therefore crucial to perform sensitivity analysis to understand how changes in the model inputs affect the outputs. Modern nuclear simulation tools rely on numerical representations of the sensitivity information -- inherently lacking in visual encodings -- offering limited effectiveness in communicating and exploring the generated data. In this paper, we design a framework for sensitivity analysis and visualization of multidimensional nuclear simulation data using partition-based, topology-inspired regression models and report on its efficacy. We rely on the established Morse-Smale regression technique, which allows us to partition the domain into monotonic regions where easily interpretable linear models can be used to assess the influence of inputs on the output variability. The underlying computation is augmented with an intuitive and interactive visual design to effectively communicate sensitivity information to the nuclear scientists. Our framework is being deployed into the multi-purpose probabilistic risk assessment and uncertainty quantification framework RAVEN (Reactor Analysis and Virtual Control Environment). We evaluate our framework using an simulation dataset studying nuclear fuel performance.
Prior Sensitivity Analysis in Default Bayesian Structural Equation Modeling.
van Erp, Sara; Mulder, Joris; Oberski, Daniel L
2017-11-27
Bayesian structural equation modeling (BSEM) has recently gained popularity because it enables researchers to fit complex models and solve some of the issues often encountered in classical maximum likelihood estimation, such as nonconvergence and inadmissible solutions. An important component of any Bayesian analysis is the prior distribution of the unknown model parameters. Often, researchers rely on default priors, which are constructed in an automatic fashion without requiring substantive prior information. However, the prior can have a serious influence on the estimation of the model parameters, which affects the mean squared error, bias, coverage rates, and quantiles of the estimates. In this article, we investigate the performance of three different default priors: noninformative improper priors, vague proper priors, and empirical Bayes priors-with the latter being novel in the BSEM literature. Based on a simulation study, we find that these three default BSEM methods may perform very differently, especially with small samples. A careful prior sensitivity analysis is therefore needed when performing a default BSEM analysis. For this purpose, we provide a practical step-by-step guide for practitioners to conducting a prior sensitivity analysis in default BSEM. Our recommendations are illustrated using a well-known case study from the structural equation modeling literature, and all code for conducting the prior sensitivity analysis is available in the online supplemental materials. (PsycINFO Database Record (c) 2017 APA, all rights reserved).
A global sensitivity analysis approach for morphogenesis models
Boas, Sonja E. M.
2015-11-21
Background Morphogenesis is a developmental process in which cells organize into shapes and patterns. Complex, non-linear and multi-factorial models with images as output are commonly used to study morphogenesis. It is difficult to understand the relation between the uncertainty in the input and the output of such ‘black-box’ models, giving rise to the need for sensitivity analysis tools. In this paper, we introduce a workflow for a global sensitivity analysis approach to study the impact of single parameters and the interactions between them on the output of morphogenesis models. Results To demonstrate the workflow, we used a published, well-studied model of vascular morphogenesis. The parameters of this cellular Potts model (CPM) represent cell properties and behaviors that drive the mechanisms of angiogenic sprouting. The global sensitivity analysis correctly identified the dominant parameters in the model, consistent with previous studies. Additionally, the analysis provided information on the relative impact of single parameters and of interactions between them. This is very relevant because interactions of parameters impede the experimental verification of the predicted effect of single parameters. The parameter interactions, although of low impact, provided also new insights in the mechanisms of in silico sprouting. Finally, the analysis indicated that the model could be reduced by one parameter. Conclusions We propose global sensitivity analysis as an alternative approach to study the mechanisms of morphogenesis. Comparison of the ranking of the impact of the model parameters to knowledge derived from experimental data and from manipulation experiments can help to falsify models and to find the operand mechanisms in morphogenesis. The workflow is applicable to all ‘black-box’ models, including high-throughput in vitro models in which output measures are affected by a set of experimental perturbations.
A global sensitivity analysis approach for morphogenesis models.
Boas, Sonja E M; Navarro Jimenez, Maria I; Merks, Roeland M H; Blom, Joke G
2015-11-21
Morphogenesis is a developmental process in which cells organize into shapes and patterns. Complex, non-linear and multi-factorial models with images as output are commonly used to study morphogenesis. It is difficult to understand the relation between the uncertainty in the input and the output of such 'black-box' models, giving rise to the need for sensitivity analysis tools. In this paper, we introduce a workflow for a global sensitivity analysis approach to study the impact of single parameters and the interactions between them on the output of morphogenesis models. To demonstrate the workflow, we used a published, well-studied model of vascular morphogenesis. The parameters of this cellular Potts model (CPM) represent cell properties and behaviors that drive the mechanisms of angiogenic sprouting. The global sensitivity analysis correctly identified the dominant parameters in the model, consistent with previous studies. Additionally, the analysis provided information on the relative impact of single parameters and of interactions between them. This is very relevant because interactions of parameters impede the experimental verification of the predicted effect of single parameters. The parameter interactions, although of low impact, provided also new insights in the mechanisms of in silico sprouting. Finally, the analysis indicated that the model could be reduced by one parameter. We propose global sensitivity analysis as an alternative approach to study the mechanisms of morphogenesis. Comparison of the ranking of the impact of the model parameters to knowledge derived from experimental data and from manipulation experiments can help to falsify models and to find the operand mechanisms in morphogenesis. The workflow is applicable to all 'black-box' models, including high-throughput in vitro models in which output measures are affected by a set of experimental perturbations.
Anisotropic analysis for seismic sensitivity of groundwater monitoring wells
Pan, Y.; Hsu, K.
2011-12-01
Taiwan is located at the boundaries of Eurasian Plate and the Philippine Sea Plate. The movement of plate causes crustal uplift and lateral deformation to lead frequent earthquakes in the vicinity of Taiwan. The change of groundwater level trigged by earthquake has been observed and studied in Taiwan for many years. The change of groundwater may appear in oscillation and step changes. The former is caused by seismic waves. The latter is caused by the volumetric strain and reflects the strain status. Since the setting of groundwater monitoring well is easier and cheaper than the setting of strain gauge, the groundwater measurement may be used as a indication of stress. This research proposes the concept of seismic sensitivity of groundwater monitoring well and apply to DonHer station in Taiwan. Geostatistical method is used to analysis the anisotropy of seismic sensitivity. GIS is used to map the sensitive area of the existing groundwater monitoring well.
Sensitivity analysis of predictive models with an automated adjoint generator
Pin, F.G.; Oblow, E.M.
1987-01-01
The adjoint method is a well established sensitivity analysis methodology that is particularly efficient in large-scale modeling problems. The coefficients of sensitivity of a given response with respect to every parameter involved in the modeling code can be calculated from the solution of a single adjoint run of the code. Sensitivity coefficients provide a quantitative measure of the importance of the model data in calculating the final results. The major drawback of the adjoint method is the requirement for calculations of very large numbers of partial derivatives to set up the adjoint equations of the model. ADGEN is a software system that has been designed to eliminate this drawback and automatically implement the adjoint formulation in computer codes. The ADGEN system will be described and its use for improving performance assessments and predictive simulations will be discussed. 8 refs., 1 fig
Sensitivity analysis of time-dependent laminar flows
Hristova, H.; Etienne, S.; Pelletier, D.; Borggaard, J.
2004-01-01
This paper presents a general sensitivity equation method (SEM) for time dependent incompressible laminar flows. The SEM accounts for complex parameter dependence and is suitable for a wide range of problems. The formulation is verified on a problem with a closed form solution obtained by the method of manufactured solution. Systematic grid convergence studies confirm the theoretical rates of convergence in both space and time. The methodology is then applied to pulsatile flow around a square cylinder. Computations show that the flow starts with symmetrical vortex shedding followed by a transition to the traditional Von Karman street (alternate vortex shedding). Simulations show that the transition phase manifests itself earlier in the sensitivity fields than in the flow field itself. Sensitivities are then demonstrated for fast evaluation of nearby flows and uncertainty analysis. (author)
Computational Methods for Sensitivity and Uncertainty Analysis in Criticality Safety
Broadhead, B.L.; Childs, R.L.; Rearden, B.T.
1999-01-01
Interest in the sensitivity methods that were developed and widely used in the 1970s (the FORSS methodology at ORNL among others) has increased recently as a result of potential use in the area of criticality safety data validation procedures to define computational bias, uncertainties and area(s) of applicability. Functional forms of the resulting sensitivity coefficients can be used as formal parameters in the determination of applicability of benchmark experiments to their corresponding industrial application areas. In order for these techniques to be generally useful to the criticality safety practitioner, the procedures governing their use had to be updated and simplified. This paper will describe the resulting sensitivity analysis tools that have been generated for potential use by the criticality safety community
Parameter uncertainty effects on variance-based sensitivity analysis
Yu, W.; Harris, T.J.
2009-01-01
In the past several years there has been considerable commercial and academic interest in methods for variance-based sensitivity analysis. The industrial focus is motivated by the importance of attributing variance contributions to input factors. A more complete understanding of these relationships enables companies to achieve goals related to quality, safety and asset utilization. In a number of applications, it is possible to distinguish between two types of input variables-regressive variables and model parameters. Regressive variables are those that can be influenced by process design or by a control strategy. With model parameters, there are typically no opportunities to directly influence their variability. In this paper, we propose a new method to perform sensitivity analysis through a partitioning of the input variables into these two groupings: regressive variables and model parameters. A sequential analysis is proposed, where first an sensitivity analysis is performed with respect to the regressive variables. In the second step, the uncertainty effects arising from the model parameters are included. This strategy can be quite useful in understanding process variability and in developing strategies to reduce overall variability. When this method is used for nonlinear models which are linear in the parameters, analytical solutions can be utilized. In the more general case of models that are nonlinear in both the regressive variables and the parameters, either first order approximations can be used, or numerically intensive methods must be used
Understanding dynamics using sensitivity analysis: caveat and solution
2011-01-01
Background Parametric sensitivity analysis (PSA) has become one of the most commonly used tools in computational systems biology, in which the sensitivity coefficients are used to study the parametric dependence of biological models. As many of these models describe dynamical behaviour of biological systems, the PSA has subsequently been used to elucidate important cellular processes that regulate this dynamics. However, in this paper, we show that the PSA coefficients are not suitable in inferring the mechanisms by which dynamical behaviour arises and in fact it can even lead to incorrect conclusions. Results A careful interpretation of parametric perturbations used in the PSA is presented here to explain the issue of using this analysis in inferring dynamics. In short, the PSA coefficients quantify the integrated change in the system behaviour due to persistent parametric perturbations, and thus the dynamical information of when a parameter perturbation matters is lost. To get around this issue, we present a new sensitivity analysis based on impulse perturbations on system parameters, which is named impulse parametric sensitivity analysis (iPSA). The inability of PSA and the efficacy of iPSA in revealing mechanistic information of a dynamical system are illustrated using two examples involving switch activation. Conclusions The interpretation of the PSA coefficients of dynamical systems should take into account the persistent nature of parametric perturbations involved in the derivation of this analysis. The application of PSA to identify the controlling mechanism of dynamical behaviour can be misleading. By using impulse perturbations, introduced at different times, the iPSA provides the necessary information to understand how dynamics is achieved, i.e. which parameters are essential and when they become important. PMID:21406095
Schlink, U.
1996-12-31
The work evaluates specifically the nuisance data provided by the measuring station in the centre of Leipig during the period from 1980 to 1993, with the aim to develop an algorithm for making very short-term forecasts of excessive nuisances. Forecasting was to be univariate, i.e., based exclusively on the half-hourly readings of SO{sub 2} concentrations taken in the past. As shown by Fourier analysis, there exist three main and mutually independent spectral regions: the high-frequency sector (period < 12 hours) of unstable irregularities, the seasonal sector with the periods of 24 and 12 hours, and the low-frequency sector (period > 24 hours). After breaking the measuring series up into components, the low-frequency sector is termed trend component, or trend for short. For obtaining the components, a Kalman filter is used. It was found that smog episodes are most adequately described by the trend component. This is therefore more closely investigated. The phase representation then shows characteristic trajectories of the trends. (orig./KW) [Deutsch] In der vorliegende Arbeit wurden speziell die Immissionsdaten der Messstation Leipzig-Mitte des Zeitraumes 1980-1993 mit dem Ziel der Erstellung eines Algorithmus fuer die Kuerzestfristprognose von Ueberschreitungssituationen untersucht. Die Prognosestellung sollte allein anhand der in der Vergangenheit registrierten Halbstundenwerte der SO{sub 2}-Konzentration, also univariat erfolgen. Wie die Fourieranalyse zeigt, gibt es drei wesentliche und voneinander unabhaengige Spektralbereiche: Den hochfrequenten Bereich (Periode <12 Stunden) der instabilen Irregularitaeten, den saisonalen Anteil mit den Perioden von 24 und 12 Stunden und den niedrigfrequenten Bereich (Periode >24 Stunden). Letzterer wird nach einer Zerlegung der Messreihe in Komponenten als Trendkomponente (oder kurz Trend) bezeichnet. Fuer die Komponentenzerlegung wird ein Kalman-Filter verwendet. Es stellt sich heraus, dass Smogepisoden am deutlichsten
Sensitivity Analysis of Deviation Source for Fast Assembly Precision Optimization
Jianjun Tang
2014-01-01
Full Text Available Assembly precision optimization of complex product has a huge benefit in improving the quality of our products. Due to the impact of a variety of deviation source coupling phenomena, the goal of assembly precision optimization is difficult to be confirmed accurately. In order to achieve optimization of assembly precision accurately and rapidly, sensitivity analysis of deviation source is proposed. First, deviation source sensitivity is defined as the ratio of assembly dimension variation and deviation source dimension variation. Second, according to assembly constraint relations, assembly sequences and locating, deviation transmission paths are established by locating the joints between the adjacent parts, and establishing each part’s datum reference frame. Third, assembly multidimensional vector loops are created using deviation transmission paths, and the corresponding scalar equations of each dimension are established. Then, assembly deviation source sensitivity is calculated by using a first-order Taylor expansion and matrix transformation method. Finally, taking assembly precision optimization of wing flap rocker as an example, the effectiveness and efficiency of the deviation source sensitivity analysis method are verified.
Sensitivity analysis for improving nanomechanical photonic transducers biosensors
Fariña, D; Álvarez, M; Márquez, S; Lechuga, L M; Dominguez, C
2015-01-01
The achievement of high sensitivity and highly integrated transducers is one of the main challenges in the development of high-throughput biosensors. The aim of this study is to improve the final sensitivity of an opto-mechanical device to be used as a reliable biosensor. We report the analysis of the mechanical and optical properties of optical waveguide microcantilever transducers, and their dependency on device design and dimensions. The selected layout (geometry) based on two butt-coupled misaligned waveguides displays better sensitivities than an aligned one. With this configuration, we find that an optimal microcantilever thickness range between 150 nm and 400 nm would increase both microcantilever bending during the biorecognition process and increase optical sensitivity to 4.8 × 10 −2 nm −1 , an order of magnitude higher than other similar opto-mechanical devices. Moreover, the analysis shows that a single mode behaviour of the propagating radiation is required to avoid modal interference that could misinterpret the readout signal. (paper)
Improving the performance of univariate control charts for abnormal detection and classification
Yiakopoulos, Christos; Koutsoudaki, Maria; Gryllias, Konstantinos; Antoniadis, Ioannis
2017-03-01
Bearing failures in rotating machinery can cause machine breakdown and economical loss, if no effective actions are taken on time. Therefore, it is of prime importance to detect accurately the presence of faults, especially at their early stage, to prevent sequent damage and reduce costly downtime. The machinery fault diagnosis follows a roadmap of data acquisition, feature extraction and diagnostic decision making, in which mechanical vibration fault feature extraction is the foundation and the key to obtain an accurate diagnostic result. A challenge in this area is the selection of the most sensitive features for various types of fault, especially when the characteristics of failures are difficult to be extracted. Thus, a plethora of complex data-driven fault diagnosis methods are fed by prominent features, which are extracted and reduced through traditional or modern algorithms. Since most of the available datasets are captured during normal operating conditions, the last decade a number of novelty detection methods, able to work when only normal data are available, have been developed. In this study, a hybrid method combining univariate control charts and a feature extraction scheme is introduced focusing towards an abnormal change detection and classification, under the assumption that measurements under normal operating conditions of the machinery are available. The feature extraction method integrates the morphological operators and the Morlet wavelets. The effectiveness of the proposed methodology is validated on two different experimental cases with bearing faults, demonstrating that the proposed approach can improve the fault detection and classification performance of conventional control charts.
Gerstl, S.A.W.
1980-01-01
SENSIT computes the sensitivity and uncertainty of a calculated integral response (such as a dose rate) due to input cross sections and their uncertainties. Sensitivity profiles are computed for neutron and gamma-ray reaction cross sections of standard multigroup cross section sets and for secondary energy distributions (SEDs) of multigroup scattering matrices. In the design sensitivity mode, SENSIT computes changes in an integral response due to design changes and gives the appropriate sensitivity coefficients. Cross section uncertainty analyses are performed for three types of input data uncertainties: cross-section covariance matrices for pairs of multigroup reaction cross sections, spectral shape uncertainty parameters for secondary energy distributions (integral SED uncertainties), and covariance matrices for energy-dependent response functions. For all three types of data uncertainties SENSIT computes the resulting variance and estimated standard deviation in an integral response of interest, on the basis of generalized perturbation theory. SENSIT attempts to be more comprehensive than earlier sensitivity analysis codes, such as SWANLAKE.
Least Squares Shadowing sensitivity analysis of chaotic limit cycle oscillations
Wang, Qiqi, E-mail: qiqi@mit.edu; Hu, Rui, E-mail: hurui@mit.edu; Blonigan, Patrick, E-mail: blonigan@mit.edu
2014-06-15
The adjoint method, among other sensitivity analysis methods, can fail in chaotic dynamical systems. The result from these methods can be too large, often by orders of magnitude, when the result is the derivative of a long time averaged quantity. This failure is known to be caused by ill-conditioned initial value problems. This paper overcomes this failure by replacing the initial value problem with the well-conditioned “least squares shadowing (LSS) problem”. The LSS problem is then linearized in our sensitivity analysis algorithm, which computes a derivative that converges to the derivative of the infinitely long time average. We demonstrate our algorithm in several dynamical systems exhibiting both periodic and chaotic oscillations.
Therapeutic Implications from Sensitivity Analysis of Tumor Angiogenesis Models
Poleszczuk, Jan; Hahnfeldt, Philip; Enderling, Heiko
2015-01-01
Anti-angiogenic cancer treatments induce tumor starvation and regression by targeting the tumor vasculature that delivers oxygen and nutrients. Mathematical models prove valuable tools to study the proof-of-concept, efficacy and underlying mechanisms of such treatment approaches. The effects of parameter value uncertainties for two models of tumor development under angiogenic signaling and anti-angiogenic treatment are studied. Data fitting is performed to compare predictions of both models and to obtain nominal parameter values for sensitivity analysis. Sensitivity analysis reveals that the success of different cancer treatments depends on tumor size and tumor intrinsic parameters. In particular, we show that tumors with ample vascular support can be successfully targeted with conventional cytotoxic treatments. On the other hand, tumors with curtailed vascular support are not limited by their growth rate and therefore interruption of neovascularization emerges as the most promising treatment target. PMID:25785600
Global sensitivity analysis of multiscale properties of porous materials
Um, Kimoon; Zhang, Xuan; Katsoulakis, Markos; Plechac, Petr; Tartakovsky, Daniel M.
2018-02-01
Ubiquitous uncertainty about pore geometry inevitably undermines the veracity of pore- and multi-scale simulations of transport phenomena in porous media. It raises two fundamental issues: sensitivity of effective material properties to pore-scale parameters and statistical parameterization of Darcy-scale models that accounts for pore-scale uncertainty. Homogenization-based maps of pore-scale parameters onto their Darcy-scale counterparts facilitate both sensitivity analysis (SA) and uncertainty quantification. We treat uncertain geometric characteristics of a hierarchical porous medium as random variables to conduct global SA and to derive probabilistic descriptors of effective diffusion coefficients and effective sorption rate. Our analysis is formulated in terms of solute transport diffusing through a fluid-filled pore space, while sorbing to the solid matrix. Yet it is sufficiently general to be applied to other multiscale porous media phenomena that are amenable to homogenization.
Sensitivity analysis overlaps of friction elements in cartridge seals
Žmindák Milan
2018-01-01
Full Text Available Cartridge seals are self-contained units consisting of a shaft sleeve, seals, and gland plate. The applications of mechanical seals are numerous. The most common example of application is in bearing production for automobile industry. This paper deals with the sensitivity analysis of overlaps friction elements in cartridge seal and their influence on the friction torque sealing and compressive force. Furthermore, it describes materials for the manufacture of sealings, approaches usually used to solution of hyperelastic materials by FEM and short introduction into the topic wheel bearings. The practical part contains one of the approach for measurement friction torque, which results were used to specifying the methodology and precision of FEM calculation realized by software ANSYS WORKBENCH. This part also contains the sensitivity analysis of overlaps friction elements.
An overview of the design and analysis of simulation experiments for sensitivity analysis
Kleijnen, J.P.C.
2005-01-01
Sensitivity analysis may serve validation, optimization, and risk analysis of simulation models. This review surveys 'classic' and 'modern' designs for experiments with simulation models. Classic designs were developed for real, non-simulated systems in agriculture, engineering, etc. These designs
Probabilistic Sensitivities for Fatigue Analysis of Turbine Engine Disks
Harry R. Millwater; R. Wesley Osborn
2006-01-01
A methodology is developed and applied that determines the sensitivities of the probability-of-fracture of a gas turbine disk fatigue analysis with respect to the parameters of the probability distributions describing the random variables. The disk material is subject to initial anomalies, in either low- or high-frequency quantities, such that commonly used materials (titanium, nickel, powder nickel) and common damage mechanisms (inherent defects or su...
Influence analysis to assess sensitivity of the dropout process
Molenberghs, Geert; Verbeke, Geert; Thijs, Herbert; Lesaffre, Emmanuel; Kenward, Michael
2001-01-01
Diggle and Kenward (Appl. Statist. 43 (1994) 49) proposed a selection model for continuous longitudinal data subject to possible non-random dropout. It has provoked a large debate about the role for such models. The original enthusiasm was followed by skepticism about the strong but untestable assumption upon which this type of models invariably rests. Since then, the view has emerged that these models should ideally be made part of a sensitivity analysis. One of their examples is a set of da...
Synthesis, Characterization, and Sensitivity Analysis of Urea Nitrate (UN)
2015-04-01
determined. From the results of the study, UN is safe to store under normal operating conditions. 15. SUBJECT TERMS urea, nitrate , sensitivity, thermal ...HNO3). Due to its simple composition, ease of manufacture, and higher detonation parameters than ammonium nitrate , it has become one of the...an H50 value of 10.054 ± 0.620 inches. 5. Conclusions From the results of the thermal analysis study, it can be concluded that urea nitrate is
Applications of the TSUNAMI sensitivity and uncertainty analysis methodology
Rearden, Bradley T.; Hopper, Calvin M.; Elam, Karla R.; Goluoglu, Sedat; Parks, Cecil V.
2003-01-01
The TSUNAMI sensitivity and uncertainty analysis tools under development for the SCALE code system have recently been applied in four criticality safety studies. TSUNAMI is used to identify applicable benchmark experiments for criticality code validation, assist in the design of new critical experiments for a particular need, reevaluate previously computed computational biases, and assess the validation coverage and propose a penalty for noncoverage for a specific application. (author)
Sensitivity Analysis of Launch Vehicle Debris Risk Model
Gee, Ken; Lawrence, Scott L.
2010-01-01
As part of an analysis of the loss of crew risk associated with an ascent abort system for a manned launch vehicle, a model was developed to predict the impact risk of the debris resulting from an explosion of the launch vehicle on the crew module. The model consisted of a debris catalog describing the number, size and imparted velocity of each piece of debris, a method to compute the trajectories of the debris and a method to calculate the impact risk given the abort trajectory of the crew module. The model provided a point estimate of the strike probability as a function of the debris catalog, the time of abort and the delay time between the abort and destruction of the launch vehicle. A study was conducted to determine the sensitivity of the strike probability to the various model input parameters and to develop a response surface model for use in the sensitivity analysis of the overall ascent abort risk model. The results of the sensitivity analysis and the response surface model are presented in this paper.
Global sensitivity analysis using a Gaussian Radial Basis Function metamodel
Wu, Zeping; Wang, Donghui; Okolo N, Patrick; Hu, Fan; Zhang, Weihua
2016-01-01
Sensitivity analysis plays an important role in exploring the actual impact of adjustable parameters on response variables. Amongst the wide range of documented studies on sensitivity measures and analysis, Sobol' indices have received greater portion of attention due to the fact that they can provide accurate information for most models. In this paper, a novel analytical expression to compute the Sobol' indices is derived by introducing a method which uses the Gaussian Radial Basis Function to build metamodels of computationally expensive computer codes. Performance of the proposed method is validated against various analytical functions and also a structural simulation scenario. Results demonstrate that the proposed method is an efficient approach, requiring a computational cost of one to two orders of magnitude less when compared to the traditional Quasi Monte Carlo-based evaluation of Sobol' indices. - Highlights: • RBF based sensitivity analysis method is proposed. • Sobol' decomposition of Gaussian RBF metamodel is obtained. • Sobol' indices of Gaussian RBF metamodel are derived based on the decomposition. • The efficiency of proposed method is validated by some numerical examples.
Systemization of burnup sensitivity analysis code (2) (Contract research)
Tatsumi, Masahiro; Hyoudou, Hideaki
2008-08-01
Towards the practical use of fast reactors, it is a very important subject to improve prediction accuracy for neutronic properties in LMFBR cores from the viewpoint of improvements on plant economic efficiency with rationally high performance cores and that on reliability and safety margins. A distinct improvement on accuracy in nuclear core design has been accomplished by the development of adjusted nuclear library using the cross-section adjustment method, in which the results of critical experiments of JUPITER and so on are reflected. In the design of large LMFBR cores, however, it is important to accurately estimate not only neutronic characteristics, for example, reaction rate distribution and control rod worth but also burnup characteristics, for example, burnup reactivity loss, breeding ratio and so on. For this purpose, it is desired to improve prediction accuracy of burnup characteristics using the data widely obtained in actual core such as the experimental fast reactor 'JOYO'. The analysis of burnup characteristic is needed to effectively use burnup characteristics data in the actual cores based on the cross-section adjustment method. So far, a burnup sensitivity analysis code, SAGEP-BURN, has been developed and confirmed its effectiveness. However, there is a problem that analysis sequence become inefficient because of a big burden to users due to complexity of the theory of burnup sensitivity and limitation of the system. It is also desired to rearrange the system for future revision since it is becoming difficult to implement new functions in the existing large system. It is not sufficient to unify each computational component for the following reasons: the computational sequence may be changed for each item being analyzed or for purpose such as interpretation of physical meaning. Therefore, it is needed to systemize the current code for burnup sensitivity analysis with component blocks of functionality that can be divided or constructed on occasion
Sensitivity analysis in multiple imputation in effectiveness studies of psychotherapy.
Crameri, Aureliano; von Wyl, Agnes; Koemeda, Margit; Schulthess, Peter; Tschuschke, Volker
2015-01-01
The importance of preventing and treating incomplete data in effectiveness studies is nowadays emphasized. However, most of the publications focus on randomized clinical trials (RCT). One flexible technique for statistical inference with missing data is multiple imputation (MI). Since methods such as MI rely on the assumption of missing data being at random (MAR), a sensitivity analysis for testing the robustness against departures from this assumption is required. In this paper we present a sensitivity analysis technique based on posterior predictive checking, which takes into consideration the concept of clinical significance used in the evaluation of intra-individual changes. We demonstrate the possibilities this technique can offer with the example of irregular longitudinal data collected with the Outcome Questionnaire-45 (OQ-45) and the Helping Alliance Questionnaire (HAQ) in a sample of 260 outpatients. The sensitivity analysis can be used to (1) quantify the degree of bias introduced by missing not at random data (MNAR) in a worst reasonable case scenario, (2) compare the performance of different analysis methods for dealing with missing data, or (3) detect the influence of possible violations to the model assumptions (e.g., lack of normality). Moreover, our analysis showed that ratings from the patient's and therapist's version of the HAQ could significantly improve the predictive value of the routine outcome monitoring based on the OQ-45. Since analysis dropouts always occur, repeated measurements with the OQ-45 and the HAQ analyzed with MI are useful to improve the accuracy of outcome estimates in quality assurance assessments and non-randomized effectiveness studies in the field of outpatient psychotherapy.
B1 -sensitivity analysis of quantitative magnetization transfer imaging.
Boudreau, Mathieu; Stikov, Nikola; Pike, G Bruce
2018-01-01
To evaluate the sensitivity of quantitative magnetization transfer (qMT) fitted parameters to B 1 inaccuracies, focusing on the difference between two categories of T 1 mapping techniques: B 1 -independent and B 1 -dependent. The B 1 -sensitivity of qMT was investigated and compared using two T 1 measurement methods: inversion recovery (IR) (B 1 -independent) and variable flip angle (VFA), B 1 -dependent). The study was separated into four stages: 1) numerical simulations, 2) sensitivity analysis of the Z-spectra, 3) healthy subjects at 3T, and 4) comparison using three different B 1 imaging techniques. For typical B 1 variations in the brain at 3T (±30%), the simulations resulted in errors of the pool-size ratio (F) ranging from -3% to 7% for VFA, and -40% to > 100% for IR, agreeing with the Z-spectra sensitivity analysis. In healthy subjects, pooled whole-brain Pearson correlation coefficients for F (comparing measured double angle and nominal flip angle B 1 maps) were ρ = 0.97/0.81 for VFA/IR. This work describes the B 1 -sensitivity characteristics of qMT, demonstrating that it varies substantially on the B 1 -dependency of the T 1 mapping method. Particularly, the pool-size ratio is more robust against B 1 inaccuracies if VFA T 1 mapping is used, so much so that B 1 mapping could be omitted without substantially biasing F. Magn Reson Med 79:276-285, 2018. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.
Probability and sensitivity analysis of machine foundation and soil interaction
Králik J., jr.
2009-06-01
Full Text Available This paper deals with the possibility of the sensitivity and probabilistic analysis of the reliability of the machine foundation depending on variability of the soil stiffness, structure geometry and compressor operation. The requirements to design of the foundation under rotating machines increased due to development of calculation method and computer tools. During the structural design process, an engineer has to consider problems of the soil-foundation and foundation-machine interaction from the safety, reliability and durability of structure point of view. The advantages and disadvantages of the deterministic and probabilistic analysis of the machine foundation resistance are discussed. The sensitivity of the machine foundation to the uncertainties of the soil properties due to longtime rotating movement of machine is not negligible for design engineers. On the example of compressor foundation and turbine fy. SIEMENS AG the affectivity of the probabilistic design methodology was presented. The Latin Hypercube Sampling (LHS simulation method for the analysis of the compressor foundation reliability was used on program ANSYS. The 200 simulations for five load cases were calculated in the real time on PC. The probabilistic analysis gives us more complex information about the soil-foundation-machine interaction as the deterministic analysis.
Global Sensitivity Analysis for multivariate output using Polynomial Chaos Expansion
Garcia-Cabrejo, Oscar; Valocchi, Albert
2014-01-01
Many mathematical and computational models used in engineering produce multivariate output that shows some degree of correlation. However, conventional approaches to Global Sensitivity Analysis (GSA) assume that the output variable is scalar. These approaches are applied on each output variable leading to a large number of sensitivity indices that shows a high degree of redundancy making the interpretation of the results difficult. Two approaches have been proposed for GSA in the case of multivariate output: output decomposition approach [9] and covariance decomposition approach [14] but they are computationally intensive for most practical problems. In this paper, Polynomial Chaos Expansion (PCE) is used for an efficient GSA with multivariate output. The results indicate that PCE allows efficient estimation of the covariance matrix and GSA on the coefficients in the approach defined by Campbell et al. [9], and the development of analytical expressions for the multivariate sensitivity indices defined by Gamboa et al. [14]. - Highlights: • PCE increases computational efficiency in 2 approaches of GSA of multivariate output. • Efficient estimation of covariance matrix of output from coefficients of PCE. • Efficient GSA on coefficients of orthogonal decomposition of the output using PCE. • Analytical expressions of multivariate sensitivity indices from coefficients of PCE
Parametric Sensitivity Analysis of the WAVEWATCH III Model
Beng-Chun Lee
2009-01-01
Full Text Available The parameters in numerical wave models need to be calibrated be fore a model can be applied to a specific region. In this study, we selected the 8 most important parameters from the source term of the WAVEWATCH III model and subjected them to sensitivity analysis to evaluate the sensitivity of the WAVEWATCH III model to the selected parameters to determine how many of these parameters should be considered for further discussion, and to justify the significance priority of each parameter. After ranking each parameter by sensitivity and assessing their cumulative impact, we adopted the ARS method to search for the optimal values of those parameters to which the WAVEWATCH III model is most sensitive by comparing modeling results with ob served data at two data buoys off the coast of north eastern Taiwan; the goal being to find optimal parameter values for improved modeling of wave development. The procedure adopting optimal parameters in wave simulations did improve the accuracy of the WAVEWATCH III model in comparison to default runs based on field observations at two buoys.
ADGEN: a system for automated sensitivity analysis of predictive models
Pin, F.G.; Horwedel, J.E.; Oblow, E.M.; Lucius, J.L.
1987-01-01
A system that can automatically enhance computer codes with a sensitivity calculation capability is presented. With this new system, named ADGEN, rapid and cost-effective calculation of sensitivities can be performed in any FORTRAN code for all input data or parameters. The resulting sensitivities can be used in performance assessment studies related to licensing or interactions with the public to systematically and quantitatively prove the relative importance of each of the system parameters in calculating the final performance results. A general procedure calling for the systematic use of sensitivities in assessment studies is presented. The procedure can be used in modeling and model validation studies to avoid over modeling, in site characterization planning to avoid over collection of data, and in performance assessments to determine the uncertainties on the final calculated results. The added capability to formally perform the inverse problem, i.e., to determine the input data or parameters on which to focus to determine the input data or parameters on which to focus additional research or analysis effort in order to improve the uncertainty of the final results, is also discussed. 7 references, 2 figures
ADGEN: a system for automated sensitivity analysis of predictive models
Pin, F.G.; Horwedel, J.E.; Oblow, E.M.; Lucius, J.L.
1986-09-01
A system that can automatically enhance computer codes with a sensitivity calculation capability is presented. With this new system, named ADGEN, rapid and cost-effective calculation of sensitivities can be performed in any FORTRAN code for all input data or parameters. The resulting sensitivities can be used in performance assessment studies related to licensing or interactions with the public to systematically and quantitatively prove the relative importance of each of the system parameters in calculating the final performance results. A general procedure calling for the systematic use of sensitivities in assessment studies is presented. The procedure can be used in modelling and model validation studies to avoid ''over modelling,'' in site characterization planning to avoid ''over collection of data,'' and in performance assessment to determine the uncertainties on the final calculated results. The added capability to formally perform the inverse problem, i.e., to determine the input data or parameters on which to focus additional research or analysis effort in order to improve the uncertainty of the final results, is also discussed
Sensitivity analysis: Interaction of DOE SNF and packaging materials
Anderson, P.A.; Kirkham, R.J.; Shaber, E.L.
1999-01-01
A sensitivity analysis was conducted to evaluate the technical issues pertaining to possible destructive interactions between spent nuclear fuels (SNFs) and the stainless steel canisters. When issues are identified through such an analysis, they provide the technical basis for answering what if questions and, if needed, for conducting additional analyses, testing, or other efforts to resolve them in order to base the licensing on solid technical grounds. The analysis reported herein systematically assessed the chemical and physical properties and the potential interactions of the materials that comprise typical US Department of Energy (DOE) SNFs and the stainless steel canisters in which they will be stored, transported, and placed in a geologic repository for final disposition. The primary focus in each step of the analysis was to identify any possible phenomena that could potentially compromise the structural integrity of the canisters and to assess their thermodynamic feasibility
Linear regression and sensitivity analysis in nuclear reactor design
Kumar, Akansha; Tsvetkov, Pavel V.; McClarren, Ryan G.
2015-01-01
Highlights: • Presented a benchmark for the applicability of linear regression to complex systems. • Applied linear regression to a nuclear reactor power system. • Performed neutronics, thermal–hydraulics, and energy conversion using Brayton’s cycle for the design of a GCFBR. • Performed detailed sensitivity analysis to a set of parameters in a nuclear reactor power system. • Modeled and developed reactor design using MCNP, regression using R, and thermal–hydraulics in Java. - Abstract: The paper presents a general strategy applicable for sensitivity analysis (SA), and uncertainity quantification analysis (UA) of parameters related to a nuclear reactor design. This work also validates the use of linear regression (LR) for predictive analysis in a nuclear reactor design. The analysis helps to determine the parameters on which a LR model can be fit for predictive analysis. For those parameters, a regression surface is created based on trial data and predictions are made using this surface. A general strategy of SA to determine and identify the influential parameters those affect the operation of the reactor is mentioned. Identification of design parameters and validation of linearity assumption for the application of LR of reactor design based on a set of tests is performed. The testing methods used to determine the behavior of the parameters can be used as a general strategy for UA, and SA of nuclear reactor models, and thermal hydraulics calculations. A design of a gas cooled fast breeder reactor (GCFBR), with thermal–hydraulics, and energy transfer has been used for the demonstration of this method. MCNP6 is used to simulate the GCFBR design, and perform the necessary criticality calculations. Java is used to build and run input samples, and to extract data from the output files of MCNP6, and R is used to perform regression analysis and other multivariate variance, and analysis of the collinearity of data
Mixed kernel function support vector regression for global sensitivity analysis
Cheng, Kai; Lu, Zhenzhou; Wei, Yuhao; Shi, Yan; Zhou, Yicheng
2017-11-01
Global sensitivity analysis (GSA) plays an important role in exploring the respective effects of input variables on an assigned output response. Amongst the wide sensitivity analyses in literature, the Sobol indices have attracted much attention since they can provide accurate information for most models. In this paper, a mixed kernel function (MKF) based support vector regression (SVR) model is employed to evaluate the Sobol indices at low computational cost. By the proposed derivation, the estimation of the Sobol indices can be obtained by post-processing the coefficients of the SVR meta-model. The MKF is constituted by the orthogonal polynomials kernel function and Gaussian radial basis kernel function, thus the MKF possesses both the global characteristic advantage of the polynomials kernel function and the local characteristic advantage of the Gaussian radial basis kernel function. The proposed approach is suitable for high-dimensional and non-linear problems. Performance of the proposed approach is validated by various analytical functions and compared with the popular polynomial chaos expansion (PCE). Results demonstrate that the proposed approach is an efficient method for global sensitivity analysis.
Optimizing human activity patterns using global sensitivity analysis.
Fairchild, Geoffrey; Hickmann, Kyle S; Mniszewski, Susan M; Del Valle, Sara Y; Hyman, James M
2014-12-01
Implementing realistic activity patterns for a population is crucial for modeling, for example, disease spread, supply and demand, and disaster response. Using the dynamic activity simulation engine, DASim, we generate schedules for a population that capture regular (e.g., working, eating, and sleeping) and irregular activities (e.g., shopping or going to the doctor). We use the sample entropy (SampEn) statistic to quantify a schedule's regularity for a population. We show how to tune an activity's regularity by adjusting SampEn, thereby making it possible to realistically design activities when creating a schedule. The tuning process sets up a computationally intractable high-dimensional optimization problem. To reduce the computational demand, we use Bayesian Gaussian process regression to compute global sensitivity indices and identify the parameters that have the greatest effect on the variance of SampEn. We use the harmony search (HS) global optimization algorithm to locate global optima. Our results show that HS combined with global sensitivity analysis can efficiently tune the SampEn statistic with few search iterations. We demonstrate how global sensitivity analysis can guide statistical emulation and global optimization algorithms to efficiently tune activities and generate realistic activity patterns. Though our tuning methods are applied to dynamic activity schedule generation, they are general and represent a significant step in the direction of automated tuning and optimization of high-dimensional computer simulations.
Combinatorial bounds on the α-divergence of univariate mixture models
Nielsen, Frank; Sun, Ke
2017-01-01
We derive lower- and upper-bounds of α-divergence between univariate mixture models with components in the exponential family. Three pairs of bounds are presented in order with increasing quality and increasing computational cost. They are verified
Sensitivity analysis practices: Strategies for model-based inference
Saltelli, Andrea; Ratto, Marco; Tarantola, Stefano; Campolongo, Francesca
2006-01-01
Fourteen years after Science's review of sensitivity analysis (SA) methods in 1989 (System analysis at molecular scale, by H. Rabitz) we search Science Online to identify and then review all recent articles having 'sensitivity analysis' as a keyword. In spite of the considerable developments which have taken place in this discipline, of the good practices which have emerged, and of existing guidelines for SA issued on both sides of the Atlantic, we could not find in our review other than very primitive SA tools, based on 'one-factor-at-a-time' (OAT) approaches. In the context of model corroboration or falsification, we demonstrate that this use of OAT methods is illicit and unjustified, unless the model under analysis is proved to be linear. We show that available good practices, such as variance based measures and others, are able to overcome OAT shortcomings and easy to implement. These methods also allow the concept of factors importance to be defined rigorously, thus making the factors importance ranking univocal. We analyse the requirements of SA in the context of modelling, and present best available practices on the basis of an elementary model. We also point the reader to available recipes for a rigorous SA
Sensitivity analysis practices: Strategies for model-based inference
Saltelli, Andrea [Institute for the Protection and Security of the Citizen (IPSC), European Commission, Joint Research Centre, TP 361, 21020 Ispra (Vatican City State, Holy See,) (Italy)]. E-mail: andrea.saltelli@jrc.it; Ratto, Marco [Institute for the Protection and Security of the Citizen (IPSC), European Commission, Joint Research Centre, TP 361, 21020 Ispra (VA) (Italy); Tarantola, Stefano [Institute for the Protection and Security of the Citizen (IPSC), European Commission, Joint Research Centre, TP 361, 21020 Ispra (VA) (Italy); Campolongo, Francesca [Institute for the Protection and Security of the Citizen (IPSC), European Commission, Joint Research Centre, TP 361, 21020 Ispra (VA) (Italy)
2006-10-15
Fourteen years after Science's review of sensitivity analysis (SA) methods in 1989 (System analysis at molecular scale, by H. Rabitz) we search Science Online to identify and then review all recent articles having 'sensitivity analysis' as a keyword. In spite of the considerable developments which have taken place in this discipline, of the good practices which have emerged, and of existing guidelines for SA issued on both sides of the Atlantic, we could not find in our review other than very primitive SA tools, based on 'one-factor-at-a-time' (OAT) approaches. In the context of model corroboration or falsification, we demonstrate that this use of OAT methods is illicit and unjustified, unless the model under analysis is proved to be linear. We show that available good practices, such as variance based measures and others, are able to overcome OAT shortcomings and easy to implement. These methods also allow the concept of factors importance to be defined rigorously, thus making the factors importance ranking univocal. We analyse the requirements of SA in the context of modelling, and present best available practices on the basis of an elementary model. We also point the reader to available recipes for a rigorous SA.
Regional and parametric sensitivity analysis of Sobol' indices
Wei, Pengfei; Lu, Zhenzhou; Song, Jingwen
2015-01-01
Nowadays, utilizing the Monte Carlo estimators for variance-based sensitivity analysis has gained sufficient popularity in many research fields. These estimators are usually based on n+2 sample matrices well designed for computing both the main and total effect indices, where n is the input dimension. The aim of this paper is to use such n+2 sample matrices to investigate how the main and total effect indices change when the uncertainty of the model inputs are reduced. For this purpose, the regional main and total effect functions are defined for measuring the changes on the main and total effect indices when the distribution range of one input is reduced, and the parametric main and total effect functions are introduced to quantify the residual main and total effect indices due to the reduced variance of one input. Monte Carlo estimators are derived for all the developed sensitivity concepts based on the n+2 samples matrices originally used for computing the main and total effect indices, thus no extra computational cost is introduced. The Ishigami function, a nonlinear model and a planar ten-bar structure are utilized for illustrating the developed sensitivity concepts, and for demonstrating the efficiency and accuracy of the derived Monte Carlo estimators. - Highlights: • The regional main and total effect functions are developed. • The parametric main and total effect functions are introduced. • The proposed sensitivity functions are all generalizations of Sobol' indices. • The Monte Carlo estimators are derived for the four sensitivity functions. • The computational cost of the estimators is the same as that of Sobol' indices
A sensitivity analysis of regional and small watershed hydrologic models
Ambaruch, R.; Salomonson, V. V.; Simmons, J. W.
1975-01-01
Continuous simulation models of the hydrologic behavior of watersheds are important tools in several practical applications such as hydroelectric power planning, navigation, and flood control. Several recent studies have addressed the feasibility of using remote earth observations as sources of input data for hydrologic models. The objective of the study reported here was to determine how accurately remotely sensed measurements must be to provide inputs to hydrologic models of watersheds, within the tolerances needed for acceptably accurate synthesis of streamflow by the models. The study objective was achieved by performing a series of sensitivity analyses using continuous simulation models of three watersheds. The sensitivity analysis showed quantitatively how variations in each of 46 model inputs and parameters affect simulation accuracy with respect to five different performance indices.
Stochastic sensitivity analysis and Langevin simulation for neural network learning
Koda, Masato
1997-01-01
A comprehensive theoretical framework is proposed for the learning of a class of gradient-type neural networks with an additive Gaussian white noise process. The study is based on stochastic sensitivity analysis techniques, and formal expressions are obtained for stochastic learning laws in terms of functional derivative sensitivity coefficients. The present method, based on Langevin simulation techniques, uses only the internal states of the network and ubiquitous noise to compute the learning information inherent in the stochastic correlation between noise signals and the performance functional. In particular, the method does not require the solution of adjoint equations of the back-propagation type. Thus, the present algorithm has the potential for efficiently learning network weights with significantly fewer computations. Application to an unfolded multi-layered network is described, and the results are compared with those obtained by using a back-propagation method
An easily implemented static condensation method for structural sensitivity analysis
Gangadharan, S. N.; Haftka, R. T.; Nikolaidis, E.
1990-01-01
A black-box approach to static condensation for sensitivity analysis is presented with illustrative examples of a cube and a car structure. The sensitivity of the structural response with respect to joint stiffness parameter is calculated using the direct method, forward-difference, and central-difference schemes. The efficiency of the various methods for identifying joint stiffness parameters from measured static deflections of these structures is compared. The results indicate that the use of static condensation can reduce computation times significantly and the black-box approach is only slightly less efficient than the standard implementation of static condensation. The ease of implementation of the black-box approach recommends it for use with general-purpose finite element codes that do not have a built-in facility for static condensation.
Comparison of different Methods for Univariate Time Series Imputation in R
Moritz, Steffen; Sardá, Alexis; Bartz-Beielstein, Thomas; Zaefferer, Martin; Stork, Jörg
2015-01-01
Missing values in datasets are a well-known problem and there are quite a lot of R packages offering imputation functions. But while imputation in general is well covered within R, it is hard to find functions for imputation of univariate time series. The problem is, most standard imputation techniques can not be applied directly. Most algorithms rely on inter-attribute correlations, while univariate time series imputation needs to employ time dependencies. This paper provides an overview of ...
Nuclear data sensitivity/uncertainty analysis for XT-ADS
Sugawara, Takanori; Sarotto, Massimo; Stankovskiy, Alexey; Van den Eynde, Gert
2011-01-01
Highlights: → The sensitivity and uncertainty analyses were performed to comprehend the reliability of the XT-ADS neutronic design. → The uncertainties deduced from the covariance data for the XT-ADS criticality were 0.94%, 1.9% and 1.1% by the SCALE 44-group, TENDL-2009 and JENDL-3.3 data, respectively. → When the target accuracy of 0.3%Δk for the criticality was considered, the uncertainties did not satisfy it. → To achieve this accuracy, the uncertainties should be improved by experiments under an adequate condition. - Abstract: The XT-ADS, an accelerator-driven system for an experimental demonstration, has been investigated in the framework of IP EUROTRANS FP6 project. In this study, the sensitivity and uncertainty analyses were performed to comprehend the reliability of the XT-ADS neutronic design. For the sensitivity analysis, it was found that the sensitivity coefficients were significantly different by changing the geometry models and calculation codes. For the uncertainty analysis, it was confirmed that the uncertainties deduced from the covariance data varied significantly by changing them. The uncertainties deduced from the covariance data for the XT-ADS criticality were 0.94%, 1.9% and 1.1% by the SCALE 44-group, TENDL-2009 and JENDL-3.3 data, respectively. When the target accuracy of 0.3%Δk for the criticality was considered, the uncertainties did not satisfy it. To achieve this accuracy, the uncertainties should be improved by experiments under an adequate condition.
Uncertainty and sensitivity analysis of the nuclear fuel thermal behavior
Boulore, A., E-mail: antoine.boulore@cea.fr [Commissariat a l' Energie Atomique (CEA), DEN, Fuel Research Department, 13108 Saint-Paul-lez-Durance (France); Struzik, C. [Commissariat a l' Energie Atomique (CEA), DEN, Fuel Research Department, 13108 Saint-Paul-lez-Durance (France); Gaudier, F. [Commissariat a l' Energie Atomique (CEA), DEN, Systems and Structure Modeling Department, 91191 Gif-sur-Yvette (France)
2012-12-15
Highlights: Black-Right-Pointing-Pointer A complete quantitative method for uncertainty propagation and sensitivity analysis is applied. Black-Right-Pointing-Pointer The thermal conductivity of UO{sub 2} is modeled as a random variable. Black-Right-Pointing-Pointer The first source of uncertainty is the linear heat rate. Black-Right-Pointing-Pointer The second source of uncertainty is the thermal conductivity of the fuel. - Abstract: In the global framework of nuclear fuel behavior simulation, the response of the models describing the physical phenomena occurring during the irradiation in reactor is mainly conditioned by the confidence in the calculated temperature of the fuel. Amongst all parameters influencing the temperature calculation in our fuel rod simulation code (METEOR V2), several sources of uncertainty have been identified as being the most sensitive: thermal conductivity of UO{sub 2}, radial distribution of power in the fuel pellet, local linear heat rate in the fuel rod, geometry of the pellet and thermal transfer in the gap. Expert judgment and inverse methods have been used to model the uncertainty of these parameters using theoretical distributions and correlation matrices. Propagation of these uncertainties in the METEOR V2 code using the URANIE framework and a Monte-Carlo technique has been performed in different experimental irradiations of UO{sub 2} fuel. At every time step of the simulated experiments, we get a temperature statistical distribution which results from the initial distributions of the uncertain parameters. We then can estimate confidence intervals of the calculated temperature. In order to quantify the sensitivity of the calculated temperature to each of the uncertain input parameters and data, we have also performed a sensitivity analysis using the Sobol' indices at first order.
Biosphere dose conversion Factor Importance and Sensitivity Analysis
M. Wasiolek
2004-01-01
This report presents importance and sensitivity analysis for the environmental radiation model for Yucca Mountain, Nevada (ERMYN). ERMYN is a biosphere model supporting the total system performance assessment (TSPA) for the license application (LA) for the Yucca Mountain repository. This analysis concerns the output of the model, biosphere dose conversion factors (BDCFs) for the groundwater, and the volcanic ash exposure scenarios. It identifies important processes and parameters that influence the BDCF values and distributions, enhances understanding of the relative importance of the physical and environmental processes on the outcome of the biosphere model, includes a detailed pathway analysis for key radionuclides, and evaluates the appropriateness of selected parameter values that are not site-specific or have large uncertainty
A framework for sensitivity analysis of decision trees.
Kamiński, Bogumił; Jakubczyk, Michał; Szufel, Przemysław
2018-01-01
In the paper, we consider sequential decision problems with uncertainty, represented as decision trees. Sensitivity analysis is always a crucial element of decision making and in decision trees it often focuses on probabilities. In the stochastic model considered, the user often has only limited information about the true values of probabilities. We develop a framework for performing sensitivity analysis of optimal strategies accounting for this distributional uncertainty. We design this robust optimization approach in an intuitive and not overly technical way, to make it simple to apply in daily managerial practice. The proposed framework allows for (1) analysis of the stability of the expected-value-maximizing strategy and (2) identification of strategies which are robust with respect to pessimistic/optimistic/mode-favoring perturbations of probabilities. We verify the properties of our approach in two cases: (a) probabilities in a tree are the primitives of the model and can be modified independently; (b) probabilities in a tree reflect some underlying, structural probabilities, and are interrelated. We provide a free software tool implementing the methods described.
Sensitivity Analysis of OECD Benchmark Tests in BISON
Swiler, Laura Painton [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Gamble, Kyle [Idaho National Lab. (INL), Idaho Falls, ID (United States); Schmidt, Rodney C. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Williamson, Richard [Idaho National Lab. (INL), Idaho Falls, ID (United States)
2015-09-01
This report summarizes a NEAMS (Nuclear Energy Advanced Modeling and Simulation) project focused on sensitivity analysis of a fuels performance benchmark problem. The benchmark problem was defined by the Uncertainty Analysis in Modeling working group of the Nuclear Science Committee, part of the Nuclear Energy Agency of the Organization for Economic Cooperation and Development (OECD ). The benchmark problem involv ed steady - state behavior of a fuel pin in a Pressurized Water Reactor (PWR). The problem was created in the BISON Fuels Performance code. Dakota was used to generate and analyze 300 samples of 17 input parameters defining core boundary conditions, manuf acturing tolerances , and fuel properties. There were 24 responses of interest, including fuel centerline temperatures at a variety of locations and burnup levels, fission gas released, axial elongation of the fuel pin, etc. Pearson and Spearman correlatio n coefficients and Sobol' variance - based indices were used to perform the sensitivity analysis. This report summarizes the process and presents results from this study.
SENSITIVITY ANALYSIS FOR SALTSTONE DISPOSAL UNIT COLUMN DEGRADATION ANALYSES
Flach, G.
2014-10-28
PORFLOW related analyses supporting a Sensitivity Analysis for Saltstone Disposal Unit (SDU) column degradation were performed. Previous analyses, Flach and Taylor 2014, used a model in which the SDU columns degraded in a piecewise manner from the top and bottom simultaneously. The current analyses employs a model in which all pieces of the column degrade at the same time. Information was extracted from the analyses which may be useful in determining the distribution of Tc-99 in the various SDUs throughout time and in determining flow balances for the SDUs.
Sensitivity analysis and design optimization through automatic differentiation
Hovland, Paul D; Norris, Boyana; Strout, Michelle Mills; Bhowmick, Sanjukta; Utke, Jean
2005-01-01
Automatic differentiation is a technique for transforming a program or subprogram that computes a function, including arbitrarily complex simulation codes, into one that computes the derivatives of that function. We describe the implementation and application of automatic differentiation tools. We highlight recent advances in the combinatorial algorithms and compiler technology that underlie successful implementation of automatic differentiation tools. We discuss applications of automatic differentiation in design optimization and sensitivity analysis. We also describe ongoing research in the design of language-independent source transformation infrastructures for automatic differentiation algorithms
Analysis of Hydrological Sensitivity for Flood Risk Assessment
Sanjay Kumar Sharma
2018-02-01
Full Text Available In order for the Indian government to maximize Integrated Water Resource Management (IWRM, the Brahmaputra River has played an important role in the undertaking of the Pilot Basin Study (PBS due to the Brahmaputra River’s annual regional flooding. The selected Kulsi River—a part of Brahmaputra sub-basin—experienced severe floods in 2007 and 2008. In this study, the Rainfall-Runoff-Inundation (RRI hydrological model was used to simulate the recent historical flood in order to understand and improve the integrated flood risk management plan. The ultimate objective was to evaluate the sensitivity of hydrologic simulation using different Digital Elevation Model (DEM resources, coupled with DEM smoothing techniques, with a particular focus on the comparison of river discharge and flood inundation extent. As a result, the sensitivity analysis showed that, among the input parameters, the RRI model is highly sensitive to Manning’s roughness coefficient values for flood plains, followed by the source of the DEM, and then soil depth. After optimizing its parameters, the simulated inundation extent showed that the smoothing filter was more influential than its simulated discharge at the outlet. Finally, the calibrated and validated RRI model simulations agreed well with the observed discharge and the Moderate Imaging Spectroradiometer (MODIS-detected flood extents.
Sensitivity analysis for the effects of multiple unmeasured confounders.
Groenwold, Rolf H H; Sterne, Jonathan A C; Lawlor, Debbie A; Moons, Karel G M; Hoes, Arno W; Tilling, Kate
2016-09-01
Observational studies are prone to (unmeasured) confounding. Sensitivity analysis of unmeasured confounding typically focuses on a single unmeasured confounder. The purpose of this study was to assess the impact of multiple (possibly weak) unmeasured confounders. Simulation studies were performed based on parameters estimated from the British Women's Heart and Health Study, including 28 measured confounders and assuming no effect of ascorbic acid intake on mortality. In addition, 25, 50, or 100 unmeasured confounders were simulated, with various mutual correlations and correlations with measured confounders. The correlated unmeasured confounders did not need to be strongly associated with exposure and outcome to substantially bias the exposure-outcome association at interest, provided that there are sufficiently many unmeasured confounders. Correlations between unmeasured confounders, in addition to the strength of their relationship with exposure and outcome, are key drivers of the magnitude of unmeasured confounding and should be considered in sensitivity analyses. However, if the unmeasured confounders are correlated with measured confounders, the bias yielded by unmeasured confounders is partly removed through adjustment for the measured confounders. Discussions of the potential impact of unmeasured confounding in observational studies, and sensitivity analyses to examine this, should focus on the potential for the joint effect of multiple unmeasured confounders to bias results. Copyright © 2016 Elsevier Inc. All rights reserved.
High order effects in cross section sensitivity analysis
Greenspan, E.; Karni, Y.; Gilai, D.
1978-01-01
Two types of high order effects associated with perturbations in the flux shape are considered: Spectral Fine Structure Effects (SFSE) and non-linearity between changes in performance parameters and data uncertainties. SFSE are investigated in Part I using a simple single resonance model. Results obtained for each of the resolved and for representative unresolved resonances of 238 U in a ZPR-6/7 like environment indicate that SFSE can have a significant contribution to the sensitivity of group constants to resonance parameters. Methods to account for SFSE both for the propagation of uncertainties and for the adjustment of nuclear data are discussed. A Second Order Sensitivity Theory (SOST) is presented, and its accuracy relative to that of the first order sensitivity theory and of the direct substitution method is investigated in Part II. The investigation is done for the non-linear problem of the effect of changes in the 297 keV sodium minimum cross section on the transport of neutrons in a deep-penetration problem. It is found that the SOST provides a satisfactory accuracy for cross section uncertainty analysis. For the same degree of accuracy, the SOST can be significantly more efficient than the direct substitution method
Accuracy and sensitivity analysis on seismic anisotropy parameter estimation
Yan, Fuyong; Han, De-Hua
2018-04-01
There is significant uncertainty in measuring the Thomsen’s parameter δ in laboratory even though the dimensions and orientations of the rock samples are known. It is expected that more challenges will be encountered in the estimating of the seismic anisotropy parameters from field seismic data. Based on Monte Carlo simulation of vertical transversely isotropic layer cake model using the database of laboratory anisotropy measurement from the literature, we apply the commonly used quartic non-hyperbolic reflection moveout equation to estimate the seismic anisotropy parameters and test its accuracy and sensitivities to the source-receive offset, vertical interval velocity error and time picking error. The testing results show that the methodology works perfectly for noise-free synthetic data with short spread length. However, this method is extremely sensitive to the time picking error caused by mild random noises, and it requires the spread length to be greater than the depth of the reflection event. The uncertainties increase rapidly for the deeper layers and the estimated anisotropy parameters can be very unreliable for a layer with more than five overlain layers. It is possible that an isotropic formation can be misinterpreted as a strong anisotropic formation. The sensitivity analysis should provide useful guidance on how to group the reflection events and build a suitable geological model for anisotropy parameter inversion.
A global sensitivity analysis of crop virtual water content
Tamea, S.; Tuninetti, M.; D'Odorico, P.; Laio, F.; Ridolfi, L.
2015-12-01
The concepts of virtual water and water footprint are becoming widely used in the scientific literature and they are proving their usefulness in a number of multidisciplinary contexts. With such growing interest a measure of data reliability (and uncertainty) is becoming pressing but, as of today, assessments of data sensitivity to model parameters, performed at the global scale, are not known. This contribution aims at filling this gap. Starting point of this study is the evaluation of the green and blue virtual water content (VWC) of four staple crops (i.e. wheat, rice, maize, and soybean) at a global high resolution scale. In each grid cell, the crop VWC is given by the ratio between the total crop evapotranspiration over the growing season and the crop actual yield, where evapotranspiration is determined with a detailed daily soil water balance and actual yield is estimated using country-based data, adjusted to account for spatial variability. The model provides estimates of the VWC at a 5x5 arc minutes and it improves on previous works by using the newest available data and including multi-cropping practices in the evaluation. The model is then used as the basis for a sensitivity analysis, in order to evaluate the role of model parameters in affecting the VWC and to understand how uncertainties in input data propagate and impact the VWC accounting. In each cell, small changes are exerted to one parameter at a time, and a sensitivity index is determined as the ratio between the relative change of VWC and the relative change of the input parameter with respect to its reference value. At the global scale, VWC is found to be most sensitive to the planting date, with a positive (direct) or negative (inverse) sensitivity index depending on the typical season of crop planting date. VWC is also markedly dependent on the length of the growing period, with an increase in length always producing an increase of VWC, but with higher spatial variability for rice than for
J Ghanbari
2017-06-01
Full Text Available Introduction Cumin is one of the most important medicinal plants in Iran and today, it is in the second level of popularity between spices in the world after black pepper. Cumin is an aromatic plant used as flavoring and seasoning agent in foods. Cumin seeds have been found to possess significant biological and have been used for treatment of toothache, dyspepsia, diarrhoea, epilepsy and jaundice. Knowledge of GEI is advantageous to have a cultivar that gives consistently high yield in a broad range of environments and to increase efficiency of breeding program and selection of best genotypes. A genotype that has stable trait expression across environments contributes little to GEI and its performance should be more predictable from the main several statistical methods have been proposed for stability analysis, with the aim of explaining the information contained in the GEI. Regression technique was proposed by Finlay and Wilkinson (1963 and was improved by Eberhart and Russell (1966. Generally, genotype stability was estimated by the slope of and deviation from the regression line for each of the genotypes. This is a popular method in stability analysis and has been applied in many crops. Non-parametric methods (rank mean (R, standard deviation rank (SDR and yield index ratio (YIR, environmental variance (S2i and genotypic variation coefficient (CVi Wricke's ecovalence and Shukla's stability variance (Shukla, 1972 have been used to determine genotype-by-environment interaction in many studies. This study was aimed to evaluate the ecotype × sowing date interaction in cumin and to evaluation of genotypic response of cumin to different sowing dates using univariate stability parameters. Materials and Methods In order to study of ecotype × sowing date interaction, different cumin ecotypes: Semnan, Fars, Yazd, Golestan, Khorasan-Razavi, Khorasan-Shomali, Khorasan-Jonoubi, Isfahan and Kerman in 5 different sowing dates (26th December, 10th January
Hohn, M. Ed; Nuhfer, E.B.; Vinopal, R.J.; Klanderman, D.S.
1980-01-01
Classifying very fine-grained rocks through fabric elements provides information about depositional environments, but is subject to the biases of visual taxonomy. To evaluate the statistical significance of an empirical classification of very fine-grained rocks, samples from Devonian shales in four cored wells in West Virginia and Virginia were measured for 15 variables: quartz, illite, pyrite and expandable clays determined by X-ray diffraction; total sulfur, organic content, inorganic carbon, matrix density, bulk density, porosity, silt, as well as density, sonic travel time, resistivity, and ??-ray response measured from well logs. The four lithologic types comprised: (1) sharply banded shale, (2) thinly laminated shale, (3) lenticularly laminated shale, and (4) nonbanded shale. Univariate and multivariate analyses of variance showed that the lithologic classification reflects significant differences for the variables measured, difference that can be detected independently of stratigraphic effects. Little-known statistical methods found useful in this work included: the multivariate analysis of variance with more than one effect, simultaneous plotting of samples and variables on canonical variates, and the use of parametric ANOVA and MANOVA on ranked data. ?? 1980 Plenum Publishing Corporation.
Stress assessment based on EEG univariate features and functional connectivity measures.
Alonso, J F; Romero, S; Ballester, M R; Antonijoan, R M; Mañanas, M A
2015-07-01
The biological response to stress originates in the brain but involves different biochemical and physiological effects. Many common clinical methods to assess stress are based on the presence of specific hormones and on features extracted from different signals, including electrocardiogram, blood pressure, skin temperature, or galvanic skin response. The aim of this paper was to assess stress using EEG-based variables obtained from univariate analysis and functional connectivity evaluation. Two different stressors, the Stroop test and sleep deprivation, were applied to 30 volunteers to find common EEG patterns related to stress effects. Results showed a decrease of the high alpha power (11 to 12 Hz), an increase in the high beta band (23 to 36 Hz, considered a busy brain indicator), and a decrease in the approximate entropy. Moreover, connectivity showed that the high beta coherence and the interhemispheric nonlinear couplings, measured by the cross mutual information function, increased significantly for both stressors, suggesting that useful stress indexes may be obtained from EEG-based features.
Variance-based sensitivity analysis for wastewater treatment plant modelling.
Cosenza, Alida; Mannina, Giorgio; Vanrolleghem, Peter A; Neumann, Marc B
2014-02-01
Global sensitivity analysis (GSA) is a valuable tool to support the use of mathematical models that characterise technical or natural systems. In the field of wastewater modelling, most of the recent applications of GSA use either regression-based methods, which require close to linear relationships between the model outputs and model factors, or screening methods, which only yield qualitative results. However, due to the characteristics of membrane bioreactors (MBR) (non-linear kinetics, complexity, etc.) there is an interest to adequately quantify the effects of non-linearity and interactions. This can be achieved with variance-based sensitivity analysis methods. In this paper, the Extended Fourier Amplitude Sensitivity Testing (Extended-FAST) method is applied to an integrated activated sludge model (ASM2d) for an MBR system including microbial product formation and physical separation processes. Twenty-one model outputs located throughout the different sections of the bioreactor and 79 model factors are considered. Significant interactions among the model factors are found. Contrary to previous GSA studies for ASM models, we find the relationship between variables and factors to be non-linear and non-additive. By analysing the pattern of the variance decomposition along the plant, the model factors having the highest variance contributions were identified. This study demonstrates the usefulness of variance-based methods in membrane bioreactor modelling where, due to the presence of membranes and different operating conditions than those typically found in conventional activated sludge systems, several highly non-linear effects are present. Further, the obtained results highlight the relevant role played by the modelling approach for MBR taking into account simultaneously biological and physical processes. © 2013.
DDASAC, Double-Precision Differential or Algebraic Sensitivity Analysis
Caracotsios, M.; Stewart, W.E.; Petzold, L.
1997-01-01
1 - Description of program or function: DDASAC solves nonlinear initial-value problems involving stiff implicit systems of ordinary differential and algebraic equations. Purely algebraic nonlinear systems can also be solved, given an initial guess within the region of attraction of a solution. Options include automatic reconciliation of inconsistent initial states and derivatives, automatic initial step selection, direct concurrent parametric sensitivity analysis, and stopping at a prescribed value of any user-defined functional of the current solution vector. Local error control (in the max-norm or the 2-norm) is provided for the state vector and can include the sensitivities on request. 2 - Method of solution: Reconciliation of initial conditions is done with a damped Newton algorithm adapted from Bain and Stewart (1991). Initial step selection is done by the first-order algorithm of Shampine (1987), extended here to differential-algebraic equation systems. The solution is continued with the DASSL predictor- corrector algorithm (Petzold 1983, Brenan et al. 1989) with the initial acceleration phase detected and with row scaling of the Jacobian added. The backward-difference formulas for the predictor and corrector are expressed in divide-difference form, and the fixed-leading-coefficient form of the corrector (Jackson and Sacks-Davis 1980, Brenan et al. 1989) is used. Weights for error tests are updated in each step with the user's tolerances at the predicted state. Sensitivity analysis is performed directly on the corrector equations as given by Catacotsios and Stewart (1985) and is extended here to the initialization when needed. 3 - Restrictions on the complexity of the problem: This algorithm, like DASSL, performs well on differential-algebraic systems of index 0 and 1 but not on higher-index systems; see Brenan et al. (1989). The user assigns the work array lengths and the output unit. The machine number range and precision are determined at run time by a
Uncertainty and sensitivity analysis of environmental transport models
Margulies, T.S.; Lancaster, L.E.
1985-01-01
An uncertainty and sensitivity analysis has been made of the CRAC-2 (Calculations of Reactor Accident Consequences) atmospheric transport and deposition models. Robustness and uncertainty aspects of air and ground deposited material and the relative contribution of input and model parameters were systematically studied. The underlying data structures were investigated using a multiway layout of factors over specified ranges generated via a Latin hypercube sampling scheme. The variables selected in our analysis include: weather bin, dry deposition velocity, rain washout coefficient/rain intensity, duration of release, heat content, sigma-z (vertical) plume dispersion parameter, sigma-y (crosswind) plume dispersion parameter, and mixing height. To determine the contributors to the output variability (versus distance from the site) step-wise regression analyses were performed on transformations of the spatial concentration patterns simulated. 27 references, 2 figures, 3 tables
Cross-covariance based global dynamic sensitivity analysis
Shi, Yan; Lu, Zhenzhou; Li, Zhao; Wu, Mengmeng
2018-02-01
For identifying the cross-covariance source of dynamic output at each time instant for structural system involving both input random variables and stochastic processes, a global dynamic sensitivity (GDS) technique is proposed. The GDS considers the effect of time history inputs on the dynamic output. In the GDS, the cross-covariance decomposition is firstly developed to measure the contribution of the inputs to the output at different time instant, and an integration of the cross-covariance change over the specific time interval is employed to measure the whole contribution of the input to the cross-covariance of output. Then, the GDS main effect indices and the GDS total effect indices can be easily defined after the integration, and they are effective in identifying the important inputs and the non-influential inputs on the cross-covariance of output at each time instant, respectively. The established GDS analysis model has the same form with the classical ANOVA when it degenerates to the static case. After degeneration, the first order partial effect can reflect the individual effects of inputs to the output variance, and the second order partial effect can reflect the interaction effects to the output variance, which illustrates the consistency of the proposed GDS indices and the classical variance-based sensitivity indices. The MCS procedure and the Kriging surrogate method are developed to solve the proposed GDS indices. Several examples are introduced to illustrate the significance of the proposed GDS analysis technique and the effectiveness of the proposed solution.
Complex finite element sensitivity method for creep analysis
Gomez-Farias, Armando; Montoya, Arturo; Millwater, Harry
2015-01-01
The complex finite element method (ZFEM) has been extended to perform sensitivity analysis for mechanical and structural systems undergoing creep deformation. ZFEM uses a complex finite element formulation to provide shape, material, and loading derivatives of the system response, providing an insight into the essential factors which control the behavior of the system as a function of time. A complex variable-based quadrilateral user element (UEL) subroutine implementing the power law creep constitutive formulation was incorporated within the Abaqus commercial finite element software. The results of the complex finite element computations were verified by comparing them to the reference solution for the steady-state creep problem of a thick-walled cylinder in the power law creep range. A practical application of the ZFEM implementation to creep deformation analysis is the calculation of the skeletal point of a notched bar test from a single ZFEM run. In contrast, the standard finite element procedure requires multiple runs. The value of the skeletal point is that it identifies the location where the stress state is accurate, regardless of the certainty of the creep material properties. - Highlights: • A novel finite element sensitivity method (ZFEM) for creep was introduced. • ZFEM has the capability to calculate accurate partial derivatives. • ZFEM can be used for identification of the skeletal point of creep structures. • ZFEM can be easily implemented in a commercial software, e.g. Abaqus. • ZFEM results were shown to be in excellent agreement with analytical solutions
Overview of hybrid subspace methods for uncertainty quantification, sensitivity analysis
Abdel-Khalik, Hany S.; Bang, Youngsuk; Wang, Congjian
2013-01-01
Highlights: ► We overview the state-of-the-art in uncertainty quantification and sensitivity analysis. ► We overview new developments in above areas using hybrid methods. ► We give a tutorial introduction to above areas and the new developments. ► Hybrid methods address the explosion in dimensionality in nonlinear models. ► Representative numerical experiments are given. -- Abstract: The role of modeling and simulation has been heavily promoted in recent years to improve understanding of complex engineering systems. To realize the benefits of modeling and simulation, concerted efforts in the areas of uncertainty quantification and sensitivity analysis are required. The manuscript intends to serve as a pedagogical presentation of the material to young researchers and practitioners with little background on the subjects. We believe this is important as the role of these subjects is expected to be integral to the design, safety, and operation of existing as well as next generation reactors. In addition to covering the basics, an overview of the current state-of-the-art will be given with particular emphasis on the challenges pertaining to nuclear reactor modeling. The second objective will focus on presenting our own development of hybrid subspace methods intended to address the explosion in the computational overhead required when handling real-world complex engineering systems.
Control strategies and sensitivity analysis of anthroponotic visceral leishmaniasis model.
Zamir, Muhammad; Zaman, Gul; Alshomrani, Ali Saleh
2017-12-01
This study proposes a mathematical model of Anthroponotic visceral leishmaniasis epidemic with saturated infection rate and recommends different control strategies to manage the spread of this disease in the community. To do this, first, a model formulation is presented to support these strategies, with quantifications of transmission and intervention parameters. To understand the nature of the initial transmission of the disease, the reproduction number [Formula: see text] is obtained by using the next-generation method. On the basis of sensitivity analysis of the reproduction number [Formula: see text], four different control strategies are proposed for managing disease transmission. For quantification of the prevalence period of the disease, a numerical simulation for each strategy is performed and a detailed summary is presented. Disease-free state is obtained with the help of control strategies. The threshold condition for globally asymptotic stability of the disease-free state is found, and it is ascertained that the state is globally stable. On the basis of sensitivity analysis of the reproduction number, it is shown that the disease can be eradicated by using the proposed strategies.
Sensitivity Analysis in Observational Research: Introducing the E-Value.
VanderWeele, Tyler J; Ding, Peng
2017-08-15
Sensitivity analysis is useful in assessing how robust an association is to potential unmeasured or uncontrolled confounding. This article introduces a new measure called the "E-value," which is related to the evidence for causality in observational studies that are potentially subject to confounding. The E-value is defined as the minimum strength of association, on the risk ratio scale, that an unmeasured confounder would need to have with both the treatment and the outcome to fully explain away a specific treatment-outcome association, conditional on the measured covariates. A large E-value implies that considerable unmeasured confounding would be needed to explain away an effect estimate. A small E-value implies little unmeasured confounding would be needed to explain away an effect estimate. The authors propose that in all observational studies intended to produce evidence for causality, the E-value be reported or some other sensitivity analysis be used. They suggest calculating the E-value for both the observed association estimate (after adjustments for measured confounders) and the limit of the confidence interval closest to the null. If this were to become standard practice, the ability of the scientific community to assess evidence from observational studies would improve considerably, and ultimately, science would be strengthened.
Nordic reference study on uncertainty and sensitivity analysis
Hirschberg, S.; Jacobsson, P.; Pulkkinen, U.; Porn, K.
1989-01-01
This paper provides a review of the first phase of Nordic reference study on uncertainty and sensitivity analysis. The main objective of this study is to use experiences form previous Nordic Benchmark Exercises and reference studies concerning critical modeling issues such as common cause failures and human interactions, and to demonstrate the impact of associated uncertainties on the uncertainty of the investigated accident sequence. This has been done independently by three working groups which used different approaches to modeling and to uncertainty analysis. The estimated uncertainty interval for the analyzed accident sequence is large. Also the discrepancies between the groups are substantial but can be explained. Sensitivity analyses which have been carried out concern e.g. use of different CCF-quantification models, alternative handling of CCF-data, time windows for operator actions and time dependences in phase mission operation, impact of state-of-knowledge dependences and ranking of dominating uncertainty contributors. Specific findings with respect to these issues are summarized in the paper
Simple Sensitivity Analysis for Orion Guidance Navigation and Control
Pressburger, Tom; Hoelscher, Brian; Martin, Rodney; Sricharan, Kumar
2013-01-01
The performance of Orion flight software, especially its GNC software, is being analyzed by running Monte Carlo simulations of Orion spacecraft flights. The simulated performance is analyzed for conformance with flight requirements, expressed as performance constraints. Flight requirements include guidance (e.g. touchdown distance from target) and control (e.g., control saturation) as well as performance (e.g., heat load constraints). The Monte Carlo simulations disperse hundreds of simulation input variables, for everything from mass properties to date of launch. We describe in this paper a sensitivity analysis tool ("Critical Factors Tool" or CFT) developed to find the input variables or pairs of variables which by themselves significantly influence satisfaction of requirements or significantly affect key performance metrics (e.g., touchdown distance from target). Knowing these factors can inform robustness analysis, can inform where engineering resources are most needed, and could even affect operations. The contributions of this paper include the introduction of novel sensitivity measures, such as estimating success probability, and a technique for determining whether pairs of factors are interacting dependently or independently. The tool found that input variables such as moments, mass, thrust dispersions, and date of launch were found to be significant factors for success of various requirements. Examples are shown in this paper as well as a summary and physics discussion of EFT-1 driving factors that the tool found.
Deterministic sensitivity analysis for the numerical simulation of contaminants transport
Marchand, E.
2007-12-01
The questions of safety and uncertainty are central to feasibility studies for an underground nuclear waste storage site, in particular the evaluation of uncertainties about safety indicators which are due to uncertainties concerning properties of the subsoil or of the contaminants. The global approach through probabilistic Monte Carlo methods gives good results, but it requires a large number of simulations. The deterministic method investigated here is complementary. Based on the Singular Value Decomposition of the derivative of the model, it gives only local information, but it is much less demanding in computing time. The flow model follows Darcy's law and the transport of radionuclides around the storage site follows a linear convection-diffusion equation. Manual and automatic differentiation are compared for these models using direct and adjoint modes. A comparative study of both probabilistic and deterministic approaches for the sensitivity analysis of fluxes of contaminants through outlet channels with respect to variations of input parameters is carried out with realistic data provided by ANDRA. Generic tools for sensitivity analysis and code coupling are developed in the Caml language. The user of these generic platforms has only to provide the specific part of the application in any language of his choice. We also present a study about two-phase air/water partially saturated flows in hydrogeology concerning the limitations of the Richards approximation and of the global pressure formulation used in petroleum engineering. (author)
Sensitivity Analysis of a Riparian Vegetation Growth Model
Michael Nones
2016-11-01
Full Text Available The paper presents a sensitivity analysis of two main parameters used in a mathematic model able to evaluate the effects of changing hydrology on the growth of riparian vegetation along rivers and its effects on the cross-section width. Due to a lack of data in existing literature, in a past study the schematization proposed here was applied only to two large rivers, assuming steady conditions for the vegetational carrying capacity and coupling the vegetal model with a 1D description of the river morphology. In this paper, the limitation set by steady conditions is overcome, imposing the vegetational evolution dependent upon the initial plant population and the growth rate, which represents the potential growth of the overall vegetation along the watercourse. The sensitivity analysis shows that, regardless of the initial population density, the growth rate can be considered the main parameter defining the development of riparian vegetation, but it results site-specific effects, with significant differences for large and small rivers. Despite the numerous simplifications adopted and the small database analyzed, the comparison between measured and computed river widths shows a quite good capability of the model in representing the typical interactions between riparian vegetation and water flow occurring along watercourses. After a thorough calibration, the relatively simple structure of the code permits further developments and applications to a wide range of alluvial rivers.
Sensitivity analysis of numerical model of prestressed concrete containment
Bílý, Petr, E-mail: petr.bily@fsv.cvut.cz; Kohoutková, Alena, E-mail: akohout@fsv.cvut.cz
2015-12-15
Graphical abstract: - Highlights: • FEM model of prestressed concrete containment with steel liner was created. • Sensitivity analysis of changes in geometry and loads was conducted. • Steel liner and temperature effects are the most important factors. • Creep and shrinkage parameters are essential for the long time analysis. • Prestressing schedule is a key factor in the early stages. - Abstract: Safety is always the main consideration in the design of containment of nuclear power plant. However, efficiency of the design process should be also taken into consideration. Despite the advances in computational abilities in recent years, simplified analyses may be found useful for preliminary scoping or trade studies. In the paper, a study on sensitivity of finite element model of prestressed concrete containment to changes in geometry, loads and other factors is presented. Importance of steel liner, reinforcement, prestressing process, temperature changes, nonlinearity of materials as well as density of finite elements mesh is assessed in the main stages of life cycle of the containment. Although the modeling adjustments have not produced any significant changes in computation time, it was found that in some cases simplified modeling process can lead to significant reduction of work time without degradation of the results.
Procedures for uncertainty and sensitivity analysis in repository performance assessment
Poern, K.; Aakerlund, O.
1985-10-01
The objective of the project was mainly a literature study of available methods for the treatment of parameter uncertainty propagation and sensitivity aspects in complete models such as those concerning geologic disposal of radioactive waste. The study, which has run parallel with the development of a code package (PROPER) for computer assisted analysis of function, also aims at the choice of accurate, cost-affective methods for uncertainty and sensitivity analysis. Such a choice depends on several factors like the number of input parameters, the capacity of the model and the computer reresources required to use the model. Two basic approaches are addressed in the report. In one of these the model of interest is directly simulated by an efficient sampling technique to generate an output distribution. Applying the other basic method the model is replaced by an approximating analytical response surface, which is then used in the sampling phase or in moment matching to generate the output distribution. Both approaches are illustrated by simple examples in the report. (author)
Global sensitivity analysis for models with spatially dependent outputs
Iooss, B.; Marrel, A.; Jullien, M.; Laurent, B.
2011-01-01
The global sensitivity analysis of a complex numerical model often calls for the estimation of variance-based importance measures, named Sobol' indices. Meta-model-based techniques have been developed in order to replace the CPU time-expensive computer code with an inexpensive mathematical function, which predicts the computer code output. The common meta-model-based sensitivity analysis methods are well suited for computer codes with scalar outputs. However, in the environmental domain, as in many areas of application, the numerical model outputs are often spatial maps, which may also vary with time. In this paper, we introduce an innovative method to obtain a spatial map of Sobol' indices with a minimal number of numerical model computations. It is based upon the functional decomposition of the spatial output onto a wavelet basis and the meta-modeling of the wavelet coefficients by the Gaussian process. An analytical example is presented to clarify the various steps of our methodology. This technique is then applied to a real hydrogeological case: for each model input variable, a spatial map of Sobol' indices is thus obtained. (authors)
Multivariate Sensitivity Analysis of Time-of-Flight Sensor Fusion
Schwarz, Sebastian; Sjöström, Mårten; Olsson, Roger
2014-09-01
Obtaining three-dimensional scenery data is an essential task in computer vision, with diverse applications in various areas such as manufacturing and quality control, security and surveillance, or user interaction and entertainment. Dedicated Time-of-Flight sensors can provide detailed scenery depth in real-time and overcome short-comings of traditional stereo analysis. Nonetheless, they do not provide texture information and have limited spatial resolution. Therefore such sensors are typically combined with high resolution video sensors. Time-of-Flight Sensor Fusion is a highly active field of research. Over the recent years, there have been multiple proposals addressing important topics such as texture-guided depth upsampling and depth data denoising. In this article we take a step back and look at the underlying principles of ToF sensor fusion. We derive the ToF sensor fusion error model and evaluate its sensitivity to inaccuracies in camera calibration and depth measurements. In accordance with our findings, we propose certain courses of action to ensure high quality fusion results. With this multivariate sensitivity analysis of the ToF sensor fusion model, we provide an important guideline for designing, calibrating and running a sophisticated Time-of-Flight sensor fusion capture systems.
Hydrocoin level 3 - Testing methods for sensitivity/uncertainty analysis
Grundfelt, B.; Lindbom, B.; Larsson, A.; Andersson, K.
1991-01-01
The HYDROCOIN study is an international cooperative project for testing groundwater hydrology modelling strategies for performance assessment of nuclear waste disposal. The study was initiated in 1984 by the Swedish Nuclear Power Inspectorate and the technical work was finalised in 1987. The participating organisations are regulatory authorities as well as implementing organisations in 10 countries. The study has been performed at three levels aimed at studying computer code verification, model validation and sensitivity/uncertainty analysis respectively. The results from the first two levels, code verification and model validation, have been published in reports in 1988 and 1990 respectively. This paper focuses on some aspects of the results from Level 3, sensitivity/uncertainty analysis, for which a final report is planned to be published during 1990. For Level 3, seven test cases were defined. Some of these aimed at exploring the uncertainty associated with the modelling results by simply varying parameter values and conceptual assumptions. In other test cases statistical sampling methods were applied. One of the test cases dealt with particle tracking and the uncertainty introduced by this type of post processing. The amount of results available is substantial although unevenly spread over the test cases. It has not been possible to cover all aspects of the results in this paper. Instead, the different methods applied will be illustrated by some typical analyses. 4 figs., 9 refs
Thermodynamics-based Metabolite Sensitivity Analysis in metabolic networks.
Kiparissides, A; Hatzimanikatis, V
2017-01-01
The increasing availability of large metabolomics datasets enhances the need for computational methodologies that can organize the data in a way that can lead to the inference of meaningful relationships. Knowledge of the metabolic state of a cell and how it responds to various stimuli and extracellular conditions can offer significant insight in the regulatory functions and how to manipulate them. Constraint based methods, such as Flux Balance Analysis (FBA) and Thermodynamics-based flux analysis (TFA), are commonly used to estimate the flow of metabolites through genome-wide metabolic networks, making it possible to identify the ranges of flux values that are consistent with the studied physiological and thermodynamic conditions. However, unless key intracellular fluxes and metabolite concentrations are known, constraint-based models lead to underdetermined problem formulations. This lack of information propagates as uncertainty in the estimation of fluxes and basic reaction properties such as the determination of reaction directionalities. Therefore, knowledge of which metabolites, if measured, would contribute the most to reducing this uncertainty can significantly improve our ability to define the internal state of the cell. In the present work we combine constraint based modeling, Design of Experiments (DoE) and Global Sensitivity Analysis (GSA) into the Thermodynamics-based Metabolite Sensitivity Analysis (TMSA) method. TMSA ranks metabolites comprising a metabolic network based on their ability to constrain the gamut of possible solutions to a limited, thermodynamically consistent set of internal states. TMSA is modular and can be applied to a single reaction, a metabolic pathway or an entire metabolic network. This is, to our knowledge, the first attempt to use metabolic modeling in order to provide a significance ranking of metabolites to guide experimental measurements. Copyright © 2016 International Metabolic Engineering Society. Published by Elsevier
Sensitivity analysis for modules for various biosphere types
Karlsson, Sara; Bergstroem, U.; Rosen, K.
2000-09-01
This study presents the results of a sensitivity analysis for the modules developed earlier for calculation of ecosystem specific dose conversion factors (EDFs). The report also includes a comparison between the probabilistically calculated mean values of the EDFs and values gained in deterministic calculations. An overview of the distribution of radionuclides between different environmental parts in the models is also presented. The radionuclides included in the study were 36 Cl, 59 Ni, 93 Mo, 129 I, 135 Cs, 237 Np and 239 Pu, sel to represent various behaviour in the biosphere and some are of particular importance from the dose point of view. The deterministic and probabilistic EDFs showed a good agreement, for most nuclides and modules. Exceptions from this occurred if very skew distributions were used for parameters of importance for the results. Only a minor amount of the released radionuclides were present in the model compartments for all modules, except for the agricultural land module. The differences between the radionuclides were not pronounced which indicates that nuclide specific parameters were of minor importance for the retention of radionuclides for the simulated time period of 10 000 years in those modules. The results from the agricultural land module showed a different pattern. Large amounts of the radionuclides were present in the solid fraction of the saturated soil zone. The high retention within this compartment makes the zone a potential source for future exposure. Differences between the nuclides due to element specific Kd-values could be seen. The amount of radionuclides present in the upper soil layer, which is the most critical zone for exposure to humans, was less then 1% for all studied radionuclides. The sensitivity analysis showed that the physical/chemical parameters were the most important in most modules in contrast to the dominance of biological parameters in the uncertainty analysis. The only exception was the well module where
Sensitivity analysis of the terrestrial food chain model FOOD III
Zach, Reto.
1980-10-01
As a first step in constructing a terrestrial food chain model suitable for long-term waste management situations, a numerical sensitivity analysis of FOOD III was carried out to identify important model parameters. The analysis involved 42 radionuclides, four pathways, 14 food types, 93 parameters and three percentages of parameter variation. We also investigated the importance of radionuclides, pathways and food types. The analysis involved a simple contamination model to render results from individual pathways comparable. The analysis showed that radionuclides vary greatly in their dose contribution to each of the four pathways, but relative contributions to each pathway are very similar. Man's and animals' drinking water pathways are much more important than the leaf and root pathways. However, this result depends on the contamination model used. All the pathways contain unimportant food types. Considering the number of parameters involved, FOOD III has too many different food types. Many of the parameters of the leaf and root pathway are important. However, this is true for only a few of the parameters of animals' drinking water pathway, and for neither of the two parameters of mans' drinking water pathway. The radiological decay constant increases the variability of these results. The dose factor is consistently the most important variable, and it explains most of the variability of radionuclide doses within pathways. Consideration of the variability of dose factors is important in contemporary as well as long-term waste management assessment models, if realistic estimates are to be made. (auth)
Uncertainty and sensitivity analysis in nuclear accident consequence assessment
Karlberg, Olof.
1989-01-01
This report contains the results of a four year project in research contracts with the Nordic Cooperation in Nuclear Safety and the National Institute for Radiation Protection. An uncertainty/sensitivity analysis methodology consisting of Latin Hypercube sampling and regression analysis was applied to an accident consequence model. A number of input parameters were selected and the uncertainties related to these parameter were estimated within a Nordic group of experts. Individual doses, collective dose, health effects and their related uncertainties were then calculated for three release scenarios and for a representative sample of meteorological situations. From two of the scenarios the acute phase after an accident were simulated and from one the long time consequences. The most significant parameters were identified. The outer limits of the calculated uncertainty distributions are large and will grow to several order of magnitudes for the low probability consequences. The uncertainty in the expectation values are typical a factor 2-5 (1 Sigma). The variation in the model responses due to the variation of the weather parameters is fairly equal to the parameter uncertainty induced variation. The most important parameters showed out to be different for each pathway of exposure, which could be expected. However, the overall most important parameters are the wet deposition coefficient and the shielding factors. A general discussion of the usefulness of uncertainty analysis in consequence analysis is also given. (au)
Robust and sensitive analysis of mouse knockout phenotypes.
Natasha A Karp
Full Text Available A significant challenge of in-vivo studies is the identification of phenotypes with a method that is robust and reliable. The challenge arises from practical issues that lead to experimental designs which are not ideal. Breeding issues, particularly in the presence of fertility or fecundity problems, frequently lead to data being collected in multiple batches. This problem is acute in high throughput phenotyping programs. In addition, in a high throughput environment operational issues lead to controls not being measured on the same day as knockouts. We highlight how application of traditional methods, such as a Student's t-Test or a 2-way ANOVA, in these situations give flawed results and should not be used. We explore the use of mixed models using worked examples from Sanger Mouse Genome Project focusing on Dual-Energy X-Ray Absorptiometry data for the analysis of mouse knockout data and compare to a reference range approach. We show that mixed model analysis is more sensitive and less prone to artefacts allowing the discovery of subtle quantitative phenotypes essential for correlating a gene's function to human disease. We demonstrate how a mixed model approach has the additional advantage of being able to include covariates, such as body weight, to separate effect of genotype from these covariates. This is a particular issue in knockout studies, where body weight is a common phenotype and will enhance the precision of assigning phenotypes and the subsequent selection of lines for secondary phenotyping. The use of mixed models with in-vivo studies has value not only in improving the quality and sensitivity of the data analysis but also ethically as a method suitable for small batches which reduces the breeding burden of a colony. This will reduce the use of animals, increase throughput, and decrease cost whilst improving the quality and depth of knowledge gained.
Iman, R.L.; Helton, J.C.
1985-01-01
Probabilistic Risk Assessment (PRA) is playing an increasingly important role in the nuclear reactor regulatory process. The assessment of uncertainties associated with PRA results is widely recognized as an important part of the analysis process. One of the major criticisms of the Reactor Safety Study was that its representation of uncertainty was inadequate. The desire for the capability to treat uncertainties with the MELCOR risk code being developed at Sandia National Laboratories is indicative of the current interest in this topic. However, as yet, uncertainty analysis and sensitivity analysis in the context of PRA is a relatively immature field. In this paper, available methods for uncertainty analysis and sensitivity analysis in a PRA are reviewed. This review first treats methods for use with individual components of a PRA and then considers how these methods could be combined in the performance of a complete PRA. In the context of this paper, the goal of uncertainty analysis is to measure the imprecision in PRA outcomes of interest, and the goal of sensitivity analysis is to identify the major contributors to this imprecision. There are a number of areas that must be considered in uncertainty analysis and sensitivity analysis for a PRA: (1) information, (2) systems analysis, (3) thermal-hydraulic phenomena/fission product behavior, (4) health and economic consequences, and (5) display of results. Each of these areas and the synthesis of them into a complete PRA are discussed
Sensitivity Analysis Of Financing Demand In Syariah Banking
DR. HJ. ROSYETTI
2017-11-01
Full Text Available This study aims to analyze the Sensitivity of Demand Financing in syariah banking with a focus on the elasticity of financing demand income elasticity and cross elasticity. The type of data used in this study is secondary data quantitative and time series obtained from the publication of BPS BI and OJK. The data analysis technique begins by estimating multiple linear regression equations using the Eviews Application further measuring the sensitivity using elasticity. The research variables consist of revenue gross domestic product and conventional bank interest rate as independent variables and demand for financing as a dependent variable. The results obtained for the results gross domestic product and interest rate of conventional banks simultaneously affect the demand for financing in Islamic banking with a significant level of 5 obtained probability value F statistic amp945 005. Partially revenue share and gross domestic product have a significant effect on demand for financing. While the variable interest rate of conventional banks partially does not have a significant effect on demand for financing in Islamic banking. The ability of the three independent variables to explain the dependent variable of 99.06 the rest of 0.04 influenced by other factors outside this study. The sensitive value of demand for financing in syariah banking during the observation period was 3.94 amp400P 1 so that it can be said that demand for financing in syariah banking is elastic. The elasticity of income demand for financing in syariah banking during the observation period of 3.08 amp400I 1 is categorized as luxuries goods. The cross elasticity value of financing demand in syariah banking during the observation period is 0.52 or positive amp400C 0 it can be categorized that the interest rate of a conventional bank is a substitute of profit sharing.
Mostafa Nejadhadad
2017-11-01
Full Text Available A geochemical exploration program was applied to recognize the anomalous geochemical haloes at the Ravanj lead mine, Delijan, Iran. Sampling of unweathered rocks were undertaken across rock exposures on a 10 × 10 meter grid (n = 302 as well as the accessible parts of underground mine A (n = 42. First, the threshold values of all elements were determined using the cut-off values used in the exploratory data analysis (EDA method. Then, for further studies, elements with lognormal distributions (Pb, Zn, Ag, As, Cd, Co, Cu, Sb, S, Sr, Th, Ba, Bi, Fe, Ni and Mn were selected. Robustness against outliers is achieved by application of central log ratio transformation to address the closure problems with compositional data prior to principle components analysis (PCA. Results of these analyses show that, in the Ravanj deposit, Pb mineralization is characterized by a Pb-Ba-Ag-Sb ± Zn ± Cd association. The supra-mineralization haloes are characterized by barite and tetrahedrite in a Ba- Th- Ag- Cu- Sb- As- Sr association and sub-mineralization haloes are comprised of pyrite and tetrahedrite, probably reflecting a Fe-Cu-As-Bi-Ni-Co-Mo-Mn association. Using univariate and multivariate geostatistical analyses (e.g., EDA and robust PCA, four anomalies were detected and mapped in Block A of the Ravanj deposit. Anomalies 1 and 2 are around the ancient orebodies. Anomaly 3 is located in a thin bedded limestone-shale intercalation unit that does not show significant mineralization. Drilling of the fourth anomaly suggested a low grade, non-economic Pb mineralization.
Simplified procedures for fast reactor fuel cycle and sensitivity analysis
Badruzzaman, A.
1979-01-01
The Continuous Slowing Down-Integral Transport Theory has been extended to perform criticality calculations in a Fast Reactor Core-blanket system achieving excellent prediction of the spectrum and the eigenvalue. The integral transport parameters did not need recalculation with source iteration and were found to be relatively constant with exposure. Fuel cycle parameters were accurately predicted when these were not varied, thus reducing a principal potential penalty of the Intergal Transport approach where considerable effort may be required to calculate transport parameters in more complicated geometries. The small variation of the spectrum in the central core region, and its weak dependence on exposure for both this region, the core blanket interface and blanket region led to the extension and development of inexpensive simplified procedures to complement exact methods. These procedures gave accurate predictions of the key fuel cycle parameters such as cost and their sensitivity to variation in spectrum-averaged and multigroup cross sections. They also predicted the implications of design variation on these parameters very well. The accuracy of these procedures and their use in analyzing a wide variety of sensitivities demonstrate the potential utility of survey calculations in Fast Reactor analysis and fuel management
Relative sensitivity analysis of the predictive properties of sloppy models.
Myasnikova, Ekaterina; Spirov, Alexander
2018-01-25
Commonly among the model parameters characterizing complex biological systems are those that do not significantly influence the quality of the fit to experimental data, so-called "sloppy" parameters. The sloppiness can be mathematically expressed through saturating response functions (Hill's, sigmoid) thereby embodying biological mechanisms responsible for the system robustness to external perturbations. However, if a sloppy model is used for the prediction of the system behavior at the altered input (e.g. knock out mutations, natural expression variability), it may demonstrate the poor predictive power due to the ambiguity in the parameter estimates. We introduce a method of the predictive power evaluation under the parameter estimation uncertainty, Relative Sensitivity Analysis. The prediction problem is addressed in the context of gene circuit models describing the dynamics of segmentation gene expression in Drosophila embryo. Gene regulation in these models is introduced by a saturating sigmoid function of the concentrations of the regulatory gene products. We show how our approach can be applied to characterize the essential difference between the sensitivity properties of robust and non-robust solutions and select among the existing solutions those providing the correct system behavior at any reasonable input. In general, the method allows to uncover the sources of incorrect predictions and proposes the way to overcome the estimation uncertainties.
Sensitivity analysis of energy demands on performance of CCHP system
Li, C.Z.; Shi, Y.M.; Huang, X.H.
2008-01-01
Sensitivity analysis of energy demands is carried out in this paper to study their influence on performance of CCHP system. Energy demand is a very important and complex factor in the optimization model of CCHP system. Average, uncertainty and historical peaks are adopted to describe energy demands. The mix-integer nonlinear programming model (MINLP) which can reflect the three aspects of energy demands is established. Numerical studies are carried out based on energy demands of a hotel and a hospital. The influence of average, uncertainty and peaks of energy demands on optimal facility scheme and economic advantages of CCHP system are investigated. The optimization results show that the optimal GT's capacity and economy of CCHP system mainly lie on the average energy demands. Sum of capacities of GB and HE is equal to historical heating demand peaks, and sum of capacities of AR and ER are equal to historical cooling demand peaks. Maximum of PG is sensitive with historical peaks of energy demands and not influenced by uncertainty of energy demands, while the corresponding influence on DH is adverse
Ensemble Solar Forecasting Statistical Quantification and Sensitivity Analysis: Preprint
Cheung, WanYin; Zhang, Jie; Florita, Anthony; Hodge, Bri-Mathias; Lu, Siyuan; Hamann, Hendrik F.; Sun, Qian; Lehman, Brad
2015-12-08
Uncertainties associated with solar forecasts present challenges to maintain grid reliability, especially at high solar penetrations. This study aims to quantify the errors associated with the day-ahead solar forecast parameters and the theoretical solar power output for a 51-kW solar power plant in a utility area in the state of Vermont, U.S. Forecasts were generated by three numerical weather prediction (NWP) models, including the Rapid Refresh, the High Resolution Rapid Refresh, and the North American Model, and a machine-learning ensemble model. A photovoltaic (PV) performance model was adopted to calculate theoretical solar power generation using the forecast parameters (e.g., irradiance, cell temperature, and wind speed). Errors of the power outputs were quantified using statistical moments and a suite of metrics, such as the normalized root mean squared error (NRMSE). In addition, the PV model's sensitivity to different forecast parameters was quantified and analyzed. Results showed that the ensemble model yielded forecasts in all parameters with the smallest NRMSE. The NRMSE of solar irradiance forecasts of the ensemble NWP model was reduced by 28.10% compared to the best of the three NWP models. Further, the sensitivity analysis indicated that the errors of the forecasted cell temperature attributed only approximately 0.12% to the NRMSE of the power output as opposed to 7.44% from the forecasted solar irradiance.
A Sensitivity Analysis Approach to Identify Key Environmental Performance Factors
Xi Yu
2014-01-01
Full Text Available Life cycle assessment (LCA is widely used in design phase to reduce the product’s environmental impacts through the whole product life cycle (PLC during the last two decades. The traditional LCA is restricted to assessing the environmental impacts of a product and the results cannot reflect the effects of changes within the life cycle. In order to improve the quality of ecodesign, it is a growing need to develop an approach which can reflect the changes between the design parameters and product’s environmental impacts. A sensitivity analysis approach based on LCA and ecodesign is proposed in this paper. The key environmental performance factors which have significant influence on the products’ environmental impacts can be identified by analyzing the relationship between environmental impacts and the design parameters. Users without much environmental knowledge can use this approach to determine which design parameter should be first considered when (redesigning a product. A printed circuit board (PCB case study is conducted; eight design parameters are chosen to be analyzed by our approach. The result shows that the carbon dioxide emission during the PCB manufacture is highly sensitive to the area of PCB panel.
Sensitivity Study on Analysis of Reactor Containment Response to LOCA
Chung, Ku Young; Sung, Key Yong
2010-01-01
As a reactor containment vessel is the final barrier to the release of radioactive material during design basis accidents (DBAs), its structural integrity must be maintained by withstanding the high pressure conditions resulting from DBAs. To verify the structural integrity of the containment, response analyses are performed to get the pressure transient inside the containment after DBAs, including loss of coolant accidents (LOCAs). The purpose of this study is to give regulative insights into the importance of input variables in the analysis of containment responses to a large break LOCA (LBLOCA). For the sensitivity study, a LBLOCA in Kori 3 and 4 nuclear power plant (NPP) is analyzed by CONTEMPT-LT computer code
Sensitivity Study on Analysis of Reactor Containment Response to LOCA
Chung, Ku Young; Sung, Key Yong [Korea Institute of Nuclear Safety, Daejeon (Korea, Republic of)
2010-10-15
As a reactor containment vessel is the final barrier to the release of radioactive material during design basis accidents (DBAs), its structural integrity must be maintained by withstanding the high pressure conditions resulting from DBAs. To verify the structural integrity of the containment, response analyses are performed to get the pressure transient inside the containment after DBAs, including loss of coolant accidents (LOCAs). The purpose of this study is to give regulative insights into the importance of input variables in the analysis of containment responses to a large break LOCA (LBLOCA). For the sensitivity study, a LBLOCA in Kori 3 and 4 nuclear power plant (NPP) is analyzed by CONTEMPT-LT computer code
Emissivity compensated spectral pyrometry—algorithm and sensitivity analysis
Hagqvist, Petter; Sikström, Fredrik; Christiansson, Anna-Karin; Lennartson, Bengt
2014-01-01
In order to solve the problem of non-contact temperature measurements on an object with varying emissivity, a new method is herein described and evaluated. The method uses spectral radiance measurements and converts them to temperature readings. It proves to be resilient towards changes in spectral emissivity and tolerates noisy spectral measurements. It is based on an assumption of smooth changes in emissivity and uses historical values of spectral emissivity and temperature for estimating current spectral emissivity. The algorithm, its constituent steps and accompanying parameters are described and discussed. A thorough sensitivity analysis of the method is carried out through simulations. No rigorous instrument calibration is needed for the presented method and it is therefore industrially tractable. (paper)
Sensitive Spectroscopic Analysis of Biomarkers in Exhaled Breath
Bicer, A.; Bounds, J.; Zhu, F.; Kolomenskii, A. A.; Kaya, N.; Aluauee, E.; Amani, M.; Schuessler, H. A.
2018-06-01
We have developed a novel optical setup which is based on a high finesse cavity and absorption laser spectroscopy in the near-IR spectral region. In pilot experiments, spectrally resolved absorption measurements of biomarkers in exhaled breath, such as methane and acetone, were carried out using cavity ring-down spectroscopy (CRDS). With a 172-cm-long cavity, an efficient optical path of 132 km was achieved. The CRDS technique is well suited for such measurements due to its high sensitivity and good spectral resolution. The detection limits for methane of 8 ppbv and acetone of 2.1 ppbv with spectral sampling of 0.005 cm-1 were achieved, which allowed to analyze multicomponent gas mixtures and to observe absorption peaks of 12CH4 and 13CH4. Further improvements of the technique have the potential to realize diagnostics of health conditions based on a multicomponent analysis of breath samples.
Sequence length variation, indel costs, and congruence in sensitivity analysis
Aagesen, Lone; Petersen, Gitte; Seberg, Ole
2005-01-01
The behavior of two topological and four character-based congruence measures was explored using different indel treatments in three empirical data sets, each with different alignment difficulties. The analyses were done using direct optimization within a sensitivity analysis framework in which...... the cost of indels was varied. Indels were treated either as a fifth character state, or strings of contiguous gaps were considered single events by using linear affine gap cost. Congruence consistently improved when indels were treated as single events, but no congruence measure appeared as the obviously...... preferable one. However, when combining enough data, all congruence measures clearly tended to select the same alignment cost set as the optimal one. Disagreement among congruence measures was mostly caused by a dominant fragment or a data partition that included all or most of the length variation...
Forecasting electricity spot-prices using linear univariate time-series models
Cuaresma, Jesus Crespo; Hlouskova, Jaroslava; Kossmeier, Stephan; Obersteiner, Michael
2004-01-01
This paper studies the forecasting abilities of a battery of univariate models on hourly electricity spot prices, using data from the Leipzig Power Exchange. The specifications studied include autoregressive models, autoregressive-moving average models and unobserved component models. The results show that specifications, where each hour of the day is modelled separately present uniformly better forecasting properties than specifications for the whole time-series, and that the inclusion of simple probabilistic processes for the arrival of extreme price events can lead to improvements in the forecasting abilities of univariate models for electricity spot prices. (Author)
Global sensitivity analysis using low-rank tensor approximations
Konakli, Katerina; Sudret, Bruno
2016-01-01
In the context of global sensitivity analysis, the Sobol' indices constitute a powerful tool for assessing the relative significance of the uncertain input parameters of a model. We herein introduce a novel approach for evaluating these indices at low computational cost, by post-processing the coefficients of polynomial meta-models belonging to the class of low-rank tensor approximations. Meta-models of this class can be particularly efficient in representing responses of high-dimensional models, because the number of unknowns in their general functional form grows only linearly with the input dimension. The proposed approach is validated in example applications, where the Sobol' indices derived from the meta-model coefficients are compared to reference indices, the latter obtained by exact analytical solutions or Monte-Carlo simulation with extremely large samples. Moreover, low-rank tensor approximations are confronted to the popular polynomial chaos expansion meta-models in case studies that involve analytical rank-one functions and finite-element models pertinent to structural mechanics and heat conduction. In the examined applications, indices based on the novel approach tend to converge faster to the reference solution with increasing size of the experimental design used to build the meta-model. - Highlights: • A new method is proposed for global sensitivity analysis of high-dimensional models. • Low-rank tensor approximations (LRA) are used as a meta-modeling technique. • Analytical formulas for the Sobol' indices in terms of LRA coefficients are derived. • The accuracy and efficiency of the approach is illustrated in application examples. • LRA-based indices are compared to indices based on polynomial chaos expansions.
Sensitivity analysis on various parameters for lattice analysis of DUPIC fuel with WIMS-AECL code
Roh, Gyu Hong; Choi, Hang Bok; Park, Jee Won [Korea Atomic Energy Research Institute, Taejon (Korea, Republic of)
1997-12-31
The code WIMS-AECL has been used for the lattice analysis of DUPIC fuel. The lattice parameters calculated by the code is sensitive to the choice of number of parameters, such as the number of tracking lines, number of condensed groups, mesh spacing in the moderator region, other parameters vital to the calculation of probabilities and burnup analysis. We have studied this sensitivity with respect to these parameters and recommend their proper values which are necessary for carrying out the lattice analysis of DUPIC fuel.
Sensitivity analysis on various parameters for lattice analysis of DUPIC fuel with WIMS-AECL code
Roh, Gyu Hong; Choi, Hang Bok; Park, Jee Won [Korea Atomic Energy Research Institute, Taejon (Korea, Republic of)
1998-12-31
The code WIMS-AECL has been used for the lattice analysis of DUPIC fuel. The lattice parameters calculated by the code is sensitive to the choice of number of parameters, such as the number of tracking lines, number of condensed groups, mesh spacing in the moderator region, other parameters vital to the calculation of probabilities and burnup analysis. We have studied this sensitivity with respect to these parameters and recommend their proper values which are necessary for carrying out the lattice analysis of DUPIC fuel.
An Overview of the Design and Analysis of Simulation Experiments for Sensitivity Analysis
Kleijnen, J.P.C.
2004-01-01
Sensitivity analysis may serve validation, optimization, and risk analysis of simulation models.This review surveys classic and modern designs for experiments with simulation models.Classic designs were developed for real, non-simulated systems in agriculture, engineering, etc.These designs assume a
Relative performance of academic departments using DEA with sensitivity analysis.
Tyagi, Preeti; Yadav, Shiv Prasad; Singh, S P
2009-05-01
The process of liberalization and globalization of Indian economy has brought new opportunities and challenges in all areas of human endeavor including education. Educational institutions have to adopt new strategies to make best use of the opportunities and counter the challenges. One of these challenges is how to assess the performance of academic programs based on multiple criteria. Keeping this in view, this paper attempts to evaluate the performance efficiencies of 19 academic departments of IIT Roorkee (India) through data envelopment analysis (DEA) technique. The technique has been used to assess the performance of academic institutions in a number of countries like USA, UK, Australia, etc. But we are using it first time in Indian context to the best of our knowledge. Applying DEA models, we calculate technical, pure technical and scale efficiencies and identify the reference sets for inefficient departments. Input and output projections are also suggested for inefficient departments to reach the frontier. Overall performance, research performance and teaching performance are assessed separately using sensitivity analysis.
Umesh P. Agarwal; Richard S. Reiner; Sally A. Ralph
2010-01-01
Two new methods based on FTâRaman spectroscopy, one simple, based on band intensity ratio, and the other using a partial least squares (PLS) regression model, are proposed to determine cellulose I crystallinity. In the simple method, crystallinity in cellulose I samples was determined based on univariate regression that was first developed using the Raman band...
Combinatorial bounds on the α-divergence of univariate mixture models
Nielsen, Frank
2017-06-20
We derive lower- and upper-bounds of α-divergence between univariate mixture models with components in the exponential family. Three pairs of bounds are presented in order with increasing quality and increasing computational cost. They are verified empirically through simulated Gaussian mixture models. The presented methodology generalizes to other divergence families relying on Hellinger-type integrals.
PDASAC, Partial Differential Sensitivity Analysis of Stiff System
Caracotsios, M.; Stewart, W.E.
2001-01-01
1 - Description of program or function: PDASAC solves stiff, nonlinear initial-boundary-value problems in a timelike dimension t and a space dimension x. Plane, circular cylindrical or spherical boundaries can be handled. Mixed-order systems of partial differential and algebraic equations can be analyzed with members of order or 0 or 1 in t, 0, 1 or 2 in x. Parametric sensitivities of the calculated states are computed simultaneously on request, via the Jacobian of the state equations. Initial and boundary conditions are efficiently reconciled. Local error control (in the max-norm or the 2-norm) is provided for the state vector and can include the parametric sensitivities if desired. 2 - Method of solution: The method of lines is used, with a user- selected x-grid and a minimum-bandwidth finite-difference approximations of the x-derivatives. Starting conditions are reconciled with a damped Newton algorithm adapted from Bain and Stewart (1991). Initial step selection is done by the first-order algorithms of Shampine (1987), extended here to differential- algebraic equation systems. The solution is continued with the DASSL predictor-corrector algorithm (Petzold 1983, Brenan et al. 1989) with the initial acceleration phase deleted and with row scaling of the Jacobian added. The predictor and corrector are expressed in divided-difference form, with the fixed-leading-coefficient form of corrector (Jackson and Sacks-Davis 1989; Brenan et al. 1989). Weights for the error tests are updated in each step with the user's tolerances at the predicted state. Sensitivity analysis is performed directly on the corrector equations of Caracotsios and Stewart (1985) and is extended here to the initialization when needed. 3 - Restrictions on the complexity of the problem: This algorithm, like DASSL, performs well on differential-algebraic equation systems of index 0 and 1 but not on higher-index systems; see Brenan et al. (1989). The user assigned the work array lengths and the output
Sensitivity Analysis for Steady State Groundwater Flow Using Adjoint Operators
Sykes, J. F.; Wilson, J. L.; Andrews, R. W.
1985-03-01
Adjoint sensitivity theory is currently being considered as a potential method for calculating the sensitivity of nuclear waste repository performance measures to the parameters of the system. For groundwater flow systems, performance measures of interest include piezometric heads in the vicinity of a waste site, velocities or travel time in aquifers, and mass discharge to biosphere points. The parameters include recharge-discharge rates, prescribed boundary heads or fluxes, formation thicknesses, and hydraulic conductivities. The derivative of a performance measure with respect to the system parameters is usually taken as a measure of sensitivity. To calculate sensitivities, adjoint sensitivity equations are formulated from the equations describing the primary problem. The solution of the primary problem and the adjoint sensitivity problem enables the determination of all of the required derivatives and hence related sensitivity coefficients. In this study, adjoint sensitivity theory is developed for equations of two-dimensional steady state flow in a confined aquifer. Both the primary flow equation and the adjoint sensitivity equation are solved using the Galerkin finite element method. The developed computer code is used to investigate the regional flow parameters of the Leadville Formation of the Paradox Basin in Utah. The results illustrate the sensitivity of calculated local heads to the boundary conditions. Alternatively, local velocity related performance measures are more sensitive to hydraulic conductivities.
Shrivastava, Manish [Pacific Northwest National Laboratory, Richland Washington USA; Zhao, Chun [Pacific Northwest National Laboratory, Richland Washington USA; Easter, Richard C. [Pacific Northwest National Laboratory, Richland Washington USA; Qian, Yun [Pacific Northwest National Laboratory, Richland Washington USA; Zelenyuk, Alla [Pacific Northwest National Laboratory, Richland Washington USA; Fast, Jerome D. [Pacific Northwest National Laboratory, Richland Washington USA; Liu, Ying [Pacific Northwest National Laboratory, Richland Washington USA; Zhang, Qi [Department of Environmental Toxicology, University of California Davis, California USA; Guenther, Alex [Department of Earth System Science, University of California, Irvine California USA
2016-04-08
We investigate the sensitivity of secondary organic aerosol (SOA) loadings simulated by a regional chemical transport model to 7 selected tunable model parameters: 4 involving emissions of anthropogenic and biogenic volatile organic compounds, anthropogenic semi-volatile and intermediate volatility organics (SIVOCs), and NOx, 2 involving dry deposition of SOA precursor gases, and one involving particle-phase transformation of SOA to low volatility. We adopt a quasi-Monte Carlo sampling approach to effectively sample the high-dimensional parameter space, and perform a 250 member ensemble of simulations using a regional model, accounting for some of the latest advances in SOA treatments based on our recent work. We then conduct a variance-based sensitivity analysis using the generalized linear model method to study the responses of simulated SOA loadings to the tunable parameters. Analysis of SOA variance from all 250 simulations shows that the volatility transformation parameter, which controls whether particle-phase transformation of SOA from semi-volatile SOA to non-volatile is on or off, is the dominant contributor to variance of simulated surface-level daytime SOA (65% domain average contribution). We also split the simulations into 2 subsets of 125 each, depending on whether the volatility transformation is turned on/off. For each subset, the SOA variances are dominated by the parameters involving biogenic VOC and anthropogenic SIVOC emissions. Furthermore, biogenic VOC emissions have a larger contribution to SOA variance when the SOA transformation to non-volatile is on, while anthropogenic SIVOC emissions have a larger contribution when the transformation is off. NOx contributes less than 4.3% to SOA variance, and this low contribution is mainly attributed to dominance of intermediate to high NOx conditions throughout the simulated domain. The two parameters related to dry deposition of SOA precursor gases also have very low contributions to SOA variance
Aboagye-Sarfo, Patrick; Mai, Qun; Sanfilippo, Frank M; Preen, David B; Stewart, Louise M; Fatovich, Daniel M
2015-10-01
To develop multivariate vector-ARMA (VARMA) forecast models for predicting emergency department (ED) demand in Western Australia (WA) and compare them to the benchmark univariate autoregressive moving average (ARMA) and Winters' models. Seven-year monthly WA state-wide public hospital ED presentation data from 2006/07 to 2012/13 were modelled. Graphical and VARMA modelling methods were used for descriptive analysis and model fitting. The VARMA models were compared to the benchmark univariate ARMA and Winters' models to determine their accuracy to predict ED demand. The best models were evaluated by using error correction methods for accuracy. Descriptive analysis of all the dependent variables showed an increasing pattern of ED use with seasonal trends over time. The VARMA models provided a more precise and accurate forecast with smaller confidence intervals and better measures of accuracy in predicting ED demand in WA than the ARMA and Winters' method. VARMA models are a reliable forecasting method to predict ED demand for strategic planning and resource allocation. While the ARMA models are a closely competing alternative, they under-estimated future ED demand. Copyright © 2015 Elsevier Inc. All rights reserved.
An Application of Monte-Carlo-Based Sensitivity Analysis on the Overlap in Discriminant Analysis
S. Razmyan
2012-01-01
Full Text Available Discriminant analysis (DA is used for the measurement of estimates of a discriminant function by minimizing their group misclassifications to predict group membership of newly sampled data. A major source of misclassification in DA is due to the overlapping of groups. The uncertainty in the input variables and model parameters needs to be properly characterized in decision making. This study combines DEA-DA with a sensitivity analysis approach to an assessment of the influence of banks’ variables on the overall variance in overlap in a DA in order to determine which variables are most significant. A Monte-Carlo-based sensitivity analysis is considered for computing the set of first-order sensitivity indices of the variables to estimate the contribution of each uncertain variable. The results show that the uncertainties in the loans granted and different deposit variables are more significant than uncertainties in other banks’ variables in decision making.
Sensitivity analysis of alkaline plume modelling: influence of mineralogy
Gaboreau, S.; Claret, F.; Marty, N.; Burnol, A.; Tournassat, C.; Gaucher, E.C.; Munier, I.; Michau, N.; Cochepin, B.
2010-01-01
Document available in extended abstract form only. In the context of a disposal facility for radioactive waste in clayey geological formation, an important modelling effort has been carried out in order to predict the time evolution of interacting cement based (concrete or cement) and clay (argillites and bentonite) materials. The high number of modelling input parameters associated with non negligible uncertainties makes often difficult the interpretation of modelling results. As a consequence, it is necessary to carry out sensitivity analysis on main modelling parameters. In a recent study, Marty et al. (2009) could demonstrate that numerical mesh refinement and consideration of dissolution/precipitation kinetics have a marked effect on (i) the time necessary to numerically clog the initial porosity and (ii) on the final mineral assemblage at the interface. On the contrary, these input parameters have little effect on the extension of the alkaline pH plume. In the present study, we propose to investigate the effects of the considered initial mineralogy on the principal simulation outputs: (1) the extension of the high pH plume, (2) the time to clog the porosity and (3) the alteration front in the clay barrier (extension and nature of mineralogy changes). This was done through sensitivity analysis on both concrete composition and clay mineralogical assemblies since in most published studies, authors considered either only one composition per materials or simplified mineralogy in order to facilitate or to reduce their calculation times. 1D Cartesian reactive transport models were run in order to point out the importance of (1) the crystallinity of concrete phases, (2) the type of clayey materials and (3) the choice of secondary phases that are allowed to precipitate during calculations. Two concrete materials with either nanocrystalline or crystalline phases were simulated in contact with two clayey materials (smectite MX80 or Callovo- Oxfordian argillites). Both
Heo, Jaeseok; Kim, Kyung Doo
2015-01-01
Statistical approaches to uncertainty quantification and sensitivity analysis are very important in estimating the safety margins for an engineering design application. This paper presents a system analysis and optimization toolkit developed by Korea Atomic Energy Research Institute (KAERI), which includes multiple packages of the sensitivity analysis and uncertainty quantification algorithms. In order to reduce the computing demand, multiple compute resources including multiprocessor computers and a network of workstations are simultaneously used. A Graphical User Interface (GUI) was also developed within the parallel computing framework for users to readily employ the toolkit for an engineering design and optimization problem. The goal of this work is to develop a GUI framework for engineering design and scientific analysis problems by implementing multiple packages of system analysis methods in the parallel computing toolkit. This was done by building an interface between an engineering simulation code and the system analysis software packages. The methods and strategies in the framework were designed to exploit parallel computing resources such as those found in a desktop multiprocessor workstation or a network of workstations. Available approaches in the framework include statistical and mathematical algorithms for use in science and engineering design problems. Currently the toolkit has 6 modules of the system analysis methodologies: deterministic and probabilistic approaches of data assimilation, uncertainty propagation, Chi-square linearity test, sensitivity analysis, and FFTBM
Heo, Jaeseok; Kim, Kyung Doo [KAERI, Daejeon (Korea, Republic of)
2015-05-15
Statistical approaches to uncertainty quantification and sensitivity analysis are very important in estimating the safety margins for an engineering design application. This paper presents a system analysis and optimization toolkit developed by Korea Atomic Energy Research Institute (KAERI), which includes multiple packages of the sensitivity analysis and uncertainty quantification algorithms. In order to reduce the computing demand, multiple compute resources including multiprocessor computers and a network of workstations are simultaneously used. A Graphical User Interface (GUI) was also developed within the parallel computing framework for users to readily employ the toolkit for an engineering design and optimization problem. The goal of this work is to develop a GUI framework for engineering design and scientific analysis problems by implementing multiple packages of system analysis methods in the parallel computing toolkit. This was done by building an interface between an engineering simulation code and the system analysis software packages. The methods and strategies in the framework were designed to exploit parallel computing resources such as those found in a desktop multiprocessor workstation or a network of workstations. Available approaches in the framework include statistical and mathematical algorithms for use in science and engineering design problems. Currently the toolkit has 6 modules of the system analysis methodologies: deterministic and probabilistic approaches of data assimilation, uncertainty propagation, Chi-square linearity test, sensitivity analysis, and FFTBM.
ECOS - analysis of sensitivity to database and input parameters
Sumerling, T.J.; Jones, C.H.
1986-06-01
The sensitivity of doses calculated by the generic biosphere code ECOS to parameter changes has been investigated by the authors for the Department of the Environment as part of its radioactive waste management research programme. The sensitivity of results to radionuclide dependent parameters has been tested by specifying reasonable parameter ranges and performing code runs for best estimate, upper-bound and lower-bound parameter values. The work indicates that doses are most sensitive to scenario parameters: geosphere input fractions, area of contaminated land, land use and diet, flux of contaminated waters and water use. Recommendations are made based on the results of sensitivity. (author)
A Sensitivity Analysis of fMRI Balloon Model
Zayane, Chadia
2015-04-22
Functional magnetic resonance imaging (fMRI) allows the mapping of the brain activation through measurements of the Blood Oxygenation Level Dependent (BOLD) contrast. The characterization of the pathway from the input stimulus to the output BOLD signal requires the selection of an adequate hemodynamic model and the satisfaction of some specific conditions while conducting the experiment and calibrating the model. This paper, focuses on the identifiability of the Balloon hemodynamic model. By identifiability, we mean the ability to estimate accurately the model parameters given the input and the output measurement. Previous studies of the Balloon model have somehow added knowledge either by choosing prior distributions for the parameters, freezing some of them, or looking for the solution as a projection on a natural basis of some vector space. In these studies, the identification was generally assessed using event-related paradigms. This paper justifies the reasons behind the need of adding knowledge, choosing certain paradigms, and completing the few existing identifiability studies through a global sensitivity analysis of the Balloon model in the case of blocked design experiment.
Methods and computer codes for probabilistic sensitivity and uncertainty analysis
Vaurio, J.K.
1985-01-01
This paper describes the methods and applications experience with two computer codes that are now available from the National Energy Software Center at Argonne National Laboratory. The purpose of the SCREEN code is to identify a group of most important input variables of a code that has many (tens, hundreds) input variables with uncertainties, and do this without relying on judgment or exhaustive sensitivity studies. Purpose of the PROSA-2 code is to propagate uncertainties and calculate the distributions of interesting output variable(s) of a safety analysis code using response surface techniques, based on the same runs used for screening. Several applications are discussed, but the codes are generic, not tailored to any specific safety application code. They are compatible in terms of input/output requirements but also independent of each other, e.g., PROSA-2 can be used without first using SCREEN if a set of important input variables has first been selected by other methods. Also, although SCREEN can select cases to be run (by random sampling), a user can select cases by other methods if he so prefers, and still use the rest of SCREEN for identifying important input variables
Nonparametric Bounds and Sensitivity Analysis of Treatment Effects
Richardson, Amy; Hudgens, Michael G.; Gilbert, Peter B.; Fine, Jason P.
2015-01-01
This paper considers conducting inference about the effect of a treatment (or exposure) on an outcome of interest. In the ideal setting where treatment is assigned randomly, under certain assumptions the treatment effect is identifiable from the observable data and inference is straightforward. However, in other settings such as observational studies or randomized trials with noncompliance, the treatment effect is no longer identifiable without relying on untestable assumptions. Nonetheless, the observable data often do provide some information about the effect of treatment, that is, the parameter of interest is partially identifiable. Two approaches are often employed in this setting: (i) bounds are derived for the treatment effect under minimal assumptions, or (ii) additional untestable assumptions are invoked that render the treatment effect identifiable and then sensitivity analysis is conducted to assess how inference about the treatment effect changes as the untestable assumptions are varied. Approaches (i) and (ii) are considered in various settings, including assessing principal strata effects, direct and indirect effects and effects of time-varying exposures. Methods for drawing formal inference about partially identified parameters are also discussed. PMID:25663743
Impact Responses and Parameters Sensitivity Analysis of Electric Wheelchairs
Song Wang
2018-06-01
Full Text Available The shock and vibration of electric wheelchairs undergoing road irregularities is inevitable. The road excitation causes the uneven magnetic gap of the motor, and the harmful vibration decreases the recovery rate of rehabilitation patients. To effectively suppress the shock and vibration, this paper introduces the DA (dynamic absorber to the electric wheelchair. Firstly, a vibration model of the human-wheelchair system with the DA was created. The models of the road excitation for wheelchairs going up a step and going down a step were proposed, respectively. To reasonably evaluate the impact level of the human-wheelchair system undergoing the step–road transition, evaluation indexes were given. Moreover, the created vibration model and the road–step model were validated via tests. Then, to reveal the vibration suppression performance of the DA, the impact responses and the amplitude frequency characteristics were numerically simulated and compared. Finally, a sensitivity analysis of the impact responses to the tire static radius r and the characteristic parameters was carried out. The results show that the DA can effectively suppress the shock and vibration of the human-wheelchair system. Moreover, for the electric wheelchair going up a step and going down a step, there are some differences in the vibration behaviors.
A Sensitivity Analysis of fMRI Balloon Model
Zayane, Chadia; Laleg-Kirati, Taous-Meriem
2015-01-01
Functional magnetic resonance imaging (fMRI) allows the mapping of the brain activation through measurements of the Blood Oxygenation Level Dependent (BOLD) contrast. The characterization of the pathway from the input stimulus to the output BOLD signal requires the selection of an adequate hemodynamic model and the satisfaction of some specific conditions while conducting the experiment and calibrating the model. This paper, focuses on the identifiability of the Balloon hemodynamic model. By identifiability, we mean the ability to estimate accurately the model parameters given the input and the output measurement. Previous studies of the Balloon model have somehow added knowledge either by choosing prior distributions for the parameters, freezing some of them, or looking for the solution as a projection on a natural basis of some vector space. In these studies, the identification was generally assessed using event-related paradigms. This paper justifies the reasons behind the need of adding knowledge, choosing certain paradigms, and completing the few existing identifiability studies through a global sensitivity analysis of the Balloon model in the case of blocked design experiment.
Sensitivity analysis on parameters and processes affecting vapor intrusion risk
Picone, Sara
2012-03-30
A one-dimensional numerical model was developed and used to identify the key processes controlling vapor intrusion risks by means of a sensitivity analysis. The model simulates the fate of a dissolved volatile organic compound present below the ventilated crawl space of a house. In contrast to the vast majority of previous studies, this model accounts for vertical variation of soil water saturation and includes aerobic biodegradation. The attenuation factor (ratio between concentration in the crawl space and source concentration) and the characteristic time to approach maximum concentrations were calculated and compared for a variety of scenarios. These concepts allow an understanding of controlling mechanisms and aid in the identification of critical parameters to be collected for field situations. The relative distance of the source to the nearest gas-filled pores of the unsaturated zone is the most critical parameter because diffusive contaminant transport is significantly slower in water-filled pores than in gas-filled pores. Therefore, attenuation factors decrease and characteristic times increase with increasing relative distance of the contaminant dissolved source to the nearest gas diffusion front. Aerobic biodegradation may decrease the attenuation factor by up to three orders of magnitude. Moreover, the occurrence of water table oscillations is of importance. Dynamic processes leading to a retreating water table increase the attenuation factor by two orders of magnitude because of the enhanced gas phase diffusion. © 2012 SETAC.
Sensitivity study of CFD turbulent models for natural convection analysis
Yu sun, Park
2007-01-01
The buoyancy driven convective flow fields are steady circulatory flows which were made between surfaces maintained at two fixed temperatures. They are ubiquitous in nature and play an important role in many engineering applications. Application of a natural convection can reduce the costs and efforts remarkably. This paper focuses on the sensitivity study of turbulence analysis using CFD (Computational Fluid Dynamics) for a natural convection in a closed rectangular cavity. Using commercial CFD code, FLUENT and various turbulent models were applied to the turbulent flow. Results from each CFD model will be compared each other in the viewpoints of grid resolution and flow characteristics. It has been showed that: -) obtaining general flow characteristics is possible with relatively coarse grid; -) there is no significant difference between results from finer grid resolutions than grid with y + + is defined as y + = ρ*u*y/μ, u being the wall friction velocity, y being the normal distance from the center of the cell to the wall, ρ and μ being respectively the fluid density and the fluid viscosity; -) the K-ε models show a different flow characteristic from K-ω models or from the Reynolds Stress Model (RSM); and -) the y + parameter is crucial for the selection of the appropriate turbulence model to apply within the simulation
Multi-scale sensitivity analysis of pile installation using DEM
Esposito, Ricardo Gurevitz; Velloso, Raquel Quadros; , Eurípedes do Amaral Vargas, Jr.; Danziger, Bernadete Ragoni
2017-12-01
The disturbances experienced by the soil due to the pile installation and dynamic soil-structure interaction still present major challenges to foundation engineers. These phenomena exhibit complex behaviors, difficult to measure in physical tests and to reproduce in numerical models. Due to the simplified approach used by the discrete element method (DEM) to simulate large deformations and nonlinear stress-dilatancy behavior of granular soils, the DEM consists of an excellent tool to investigate these processes. This study presents a sensitivity analysis of the effects of introducing a single pile using the PFC2D software developed by Itasca Co. The different scales investigated in these simulations include point and shaft resistance, alterations in porosity and stress fields and particles displacement. Several simulations were conducted in order to investigate the effects of different numerical approaches showing indications that the method of installation and particle rotation could influence greatly in the conditions around the numerical pile. Minor effects were also noted due to change in penetration velocity and pile-soil friction. The difference in behavior of a moving and a stationary pile shows good qualitative agreement with previous experimental results indicating the necessity of realizing a force equilibrium process prior to any load-test to be simulated.
Sensitivity Analysis for the CLIC Damping Ring Inductive Adder
Holma, Janne
2012-01-01
The CLIC study is exploring the scheme for an electron-positron collider with high luminosity and a nominal centre-of-mass energy of 3 TeV. The CLIC pre-damping rings and damping rings will produce, through synchrotron radiation, ultra-low emittance beam with high bunch charge, necessary for the luminosity performance of the collider. To limit the beam emittance blow-up due to oscillations, the pulse generators for the damping ring kickers must provide extremely flat, high-voltage pulses. The specifications for the extraction kickers of the CLIC damping rings are particularly demanding: the flattop of the output pulse must be 160 ns duration, 12.5 kV and 250 A, with a combined ripple and droop of not more than ±0.02 %. An inductive adder allows the use of different modulation techniques and is therefore a very promising approach to meeting the specifications. PSpice has been utilised to carry out a sensitivity analysis of the predicted output pulse to the value of both individual and groups of circuit compon...
Considering Respiratory Tract Infections and Antimicrobial Sensitivity: An Exploratory Analysis
Amin, R.
2009-01-01
Full Text Available This study was conducted to observe the sensitivity and resistance of status of antibiotics for respiratory tract infection (RTI. Throat swab culture and sensitivity report of 383 patients revealed sensitivity profiles were observed with amoxycillin (7.9%, penicillin (33.7%, ampicillin (36.6%, co-trimoxazole (46.5%, azithromycin (53.5%, erythromycin (57.4%, cephalexin (69.3%, gentamycin (78.2%, ciprofloxacin (80.2%, cephradine (81.2%, ceftazidime (93.1%, ceftriaxone (93.1%. Sensitivity to cefuroxime was reported 93.1% cases. Resistance was found with amoxycillin (90.1%, ampicillin (64.1%, penicillin (61.4%, co-trimoxazole (43.6%, erythromycin (39.6%, and azithromycin (34.7%. Cefuroxime demonstrates high level of sensitivity than other antibiotics and supports its consideration with patients with upper RTI.
Sensitivity analysis: Theory and practical application in safety cases
Kuhlmann, Sebastian; Plischke, Elmar; Roehlig, Klaus-Juergen; Becker, Dirk-Alexander
2014-01-01
The projects described here aim at deriving an adaptive and stepwise approach to sensitivity analysis (SA). Since the appropriateness of a single SA method strongly depends on the nature of the model under study, a top-down approach (from simple to sophisticated methods) is suggested. If simple methods explain the model behaviour sufficiently well then there is no need for applying more sophisticated ones and the SA procedure can be considered complete. The procedure is developed and tested using a model for a LLW/ILW repository in salt. Additionally, a new model for the disposal of HLW in rock salt will be available soon for SA studies within the MOSEL/NUMSA projects. This model will address special characteristics of waste disposal in undisturbed rock salt, especially the case of total confinement, resulting in a zero release which is indeed the objective of radioactive waste disposal. A high proportion of zero-output realisations causes many SA methods to fail, so special treatment is needed and has to be developed. Furthermore, the HLW disposal model will be used as a first test case for applying the procedure described above, which was and is being derived using the LLW/ILW model. How to treat dependencies in the input, model conservatism and time-dependent outputs will be addressed in the future project programme: - If correlations or, more generally, dependencies between input parameters exist, the question arises about the deeper meaning of sensitivity results in such cases: A strict separation between inputs, internal states and outputs is no longer possible. Such correlations (or dependencies) might have different reasons. In some cases correlated input parameters might have a common physically (well-)known fundamental cause but there are reasons why this fundamental cause cannot or should not be integrated into the model, i.e. the cause might generate a very complex model which cannot be calculated in appropriate time. In other cases the correlation may
Uncertainty and sensitivity analysis in a Probabilistic Safety Analysis level-1
Nunez Mc Leod, Jorge E.; Rivera, Selva S.
1996-01-01
A methodology for sensitivity and uncertainty analysis, applicable to a Probabilistic Safety Assessment Level I has been presented. The work contents are: correct association of distributions to parameters, importance and qualification of expert opinions, generations of samples according to sample sizes, and study of the relationships among system variables and systems response. A series of statistical-mathematical techniques are recommended along the development of the analysis methodology, as well as different graphical visualization for the control of the study. (author)
Economic impact analysis for global warming: Sensitivity analysis for cost and benefit estimates
Ierland, E.C. van; Derksen, L.
1994-01-01
Proper policies for the prevention or mitigation of the effects of global warming require profound analysis of the costs and benefits of alternative policy strategies. Given the uncertainty about the scientific aspects of the process of global warming, in this paper a sensitivity analysis for the impact of various estimates of costs and benefits of greenhouse gas reduction strategies is carried out to analyze the potential social and economic impacts of climate change
R package imputeTestbench to compare imputations methods for univariate time series
Bokde, Neeraj; Kulat, Kishore; Beck, Marcus W; Asencio-Cortés, Gualberto
2016-01-01
This paper describes the R package imputeTestbench that provides a testbench for comparing imputation methods for missing data in univariate time series. The imputeTestbench package can be used to simulate the amount and type of missing data in a complete dataset and compare filled data using different imputation methods. The user has the option to simulate missing data by removing observations completely at random or in blocks of different sizes. Several default imputation methods are includ...
Chek, Mohd Zaki Awang; Ahmad, Abu Bakar; Ridzwan, Ahmad Nur Azam Ahmad; Jelas, Imran Md.; Jamal, Nur Faezah; Ismail, Isma Liana; Zulkifli, Faiz; Noor, Syamsul Ikram Mohd
2012-09-01
The main objective of this study is to forecast the future claims amount of Invalidity Pension Scheme (IPS). All data were derived from SOCSO annual reports from year 1972 - 2010. These claims consist of all claims amount from 7 benefits offered by SOCSO such as Invalidity Pension, Invalidity Grant, Survivors Pension, Constant Attendance Allowance, Rehabilitation, Funeral and Education. Prediction of future claims of Invalidity Pension Scheme will be made using Univariate Forecasting Models to predict the future claims among workforce in Malaysia.
Pasta, D J; Taylor, J L; Henning, J M
1999-01-01
Decision-analytic models are frequently used to evaluate the relative costs and benefits of alternative therapeutic strategies for health care. Various types of sensitivity analysis are used to evaluate the uncertainty inherent in the models. Although probabilistic sensitivity analysis is more difficult theoretically and computationally, the results can be much more powerful and useful than deterministic sensitivity analysis. The authors show how a Monte Carlo simulation can be implemented using standard software to perform a probabilistic sensitivity analysis incorporating the bootstrap. The method is applied to a decision-analytic model evaluating the cost-effectiveness of Helicobacter pylori eradication. The necessary steps are straightforward and are described in detail. The use of the bootstrap avoids certain difficulties encountered with theoretical distributions. The probabilistic sensitivity analysis provided insights into the decision-analytic model beyond the traditional base-case and deterministic sensitivity analyses and should become the standard method for assessing sensitivity.
Sorption of redox-sensitive elements: critical analysis
Strickert, R.G.
1980-12-01
The redox-sensitive elements (Tc, U, Np, Pu) discussed in this report are of interest to nuclear waste management due to their long-lived isotopes which have a potential radiotoxic effect on man. In their lower oxidation states these elements have been shown to be highly adsorbed by geologic materials occurring under reducing conditions. Experimental research conducted in recent years, especially through the Waste Isolation Safety Assessment Program (WISAP) and Waste/Rock Interaction Technology (WRIT) program, has provided extensive information on the mechanisms of retardation. In general, ion-exchange probably plays a minor role in the sorption behavior of cations of the above three actinide elements. Formation of anionic complexes of the oxidized states with common ligands (OH - , CO -- 3 ) is expected to reduce adsorption by ion exchange further. Pertechnetate also exhibits little ion-exchange sorption by geologic media. In the reduced (IV) state, all of the elements are highly charged and it appears that they form a very insoluble compound (oxide, hydroxide, etc.) or undergo coprecipitation or are incorporated into minerals. The exact nature of the insoluble compounds and the effect of temperature, pH, pe, other chemical species, and other parameters are currently being investigated. Oxidation states other than Tc (IV,VII), U(IV,VI), Np(IV,V), and Pu(IV,V) are probably not important for the geologic repository environment expected, but should be considered especially when extreme conditions exist (radiation, temperature, etc.). Various experimental techniques such as oxidation-state analysis of tracer-level isotopes, redox potential measurement and control, pH measurement, and solid phase identification have been used to categorize the behavior of the various valence states
Sorption of redox-sensitive elements: critical analysis
Strickert, R.G.
1980-12-01
The redox-sensitive elements (Tc, U, Np, Pu) discussed in this report are of interest to nuclear waste management due to their long-lived isotopes which have a potential radiotoxic effect on man. In their lower oxidation states these elements have been shown to be highly adsorbed by geologic materials occurring under reducing conditions. Experimental research conducted in recent years, especially through the Waste Isolation Safety Assessment Program (WISAP) and Waste/Rock Interaction Technology (WRIT) program, has provided extensive information on the mechanisms of retardation. In general, ion-exchange probably plays a minor role in the sorption behavior of cations of the above three actinide elements. Formation of anionic complexes of the oxidized states with common ligands (OH/sup -/, CO/sup - -//sub 3/) is expected to reduce adsorption by ion exchange further. Pertechnetate also exhibits little ion-exchange sorption by geologic media. In the reduced (IV) state, all of the elements are highly charged and it appears that they form a very insoluble compound (oxide, hydroxide, etc.) or undergo coprecipitation or are incorporated into minerals. The exact nature of the insoluble compounds and the effect of temperature, pH, pe, other chemical species, and other parameters are currently being investigated. Oxidation states other than Tc (IV,VII), U(IV,VI), Np(IV,V), and Pu(IV,V) are probably not important for the geologic repository environment expected, but should be considered especially when extreme conditions exist (radiation, temperature, etc.). Various experimental techniques such as oxidation-state analysis of tracer-level isotopes, redox potential measurement and control, pH measurement, and solid phase identification have been used to categorize the behavior of the various valence states.
Analysis of Sea Ice Cover Sensitivity in Global Climate Model
V. P. Parhomenko
2014-01-01
Full Text Available The paper presents joint calculations using a 3D atmospheric general circulation model, an ocean model, and a sea ice evolution model. The purpose of the work is to analyze a seasonal and annual evolution of sea ice, long-term variability of a model ice cover, and its sensitivity to some parameters of model as well to define atmosphere-ice-ocean interaction.Results of 100 years simulations of Arctic basin sea ice evolution are analyzed. There are significant (about 0.5 m inter-annual fluctuations of an ice cover.The ice - atmosphere sensible heat flux reduced by 10% leads to the growth of average sea ice thickness within the limits of 0.05 m – 0.1 m. However in separate spatial points the thickness decreases up to 0.5 m. An analysis of the seasonably changing average ice thickness with decreasing, as compared to the basic variant by 0.05 of clear sea ice albedo and that of snow shows the ice thickness reduction in a range from 0.2 m up to 0.6 m, and the change maximum falls for the summer season of intensive melting. The spatial distribution of ice thickness changes shows, that on the large part of the Arctic Ocean there was a reduction of ice thickness down to 1 m. However, there is also an area of some increase of the ice layer basically in a range up to 0.2 m (Beaufort Sea. The 0.05 decrease of sea ice snow albedo leads to reduction of average ice thickness approximately by 0.2 m, and this value slightly depends on a season. In the following experiment the ocean – ice thermal interaction influence on the ice cover is estimated. It is carried out by increase of a heat flux from ocean to the bottom surface of sea ice by 2 W/sq. m in comparison with base variant. The analysis demonstrates, that the average ice thickness reduces in a range from 0.2 m to 0.35 m. There are small seasonal changes of this value.The numerical experiments results have shown, that an ice cover and its seasonal evolution rather strongly depend on varied parameters
Sensitivity analysis of hybrid power systems using Power Pinch Analysis considering Feed-in Tariff
Mohammad Rozali, Nor Erniza; Wan Alwi, Sharifah Rafidah; Manan, Zainuddin Abdul; Klemeš, Jiří Jaromír
2016-01-01
Feed-in Tariff (FiT) has been one of the most effective policies in accelerating the development of renewable energy (RE) projects. The amount of RE electricity in the FiT purchase agreement is an important decision that has to be made by the RE project developers. They have to consider various crucial factors associated with RE system operation as well as its stochastic nature. The presented work aims to assess the sensitivity and profitability of a hybrid power system (HPS) in cases of RE system failure or shutdown. The amount of RE electricity for the FiT purchase agreement in various scenarios was determined using a novel tool called On-Grid Problem Table based on the Power Pinch Analysis (PoPA). A sensitivity table has also been introduced to assist planners to evaluate the effects of the RE system's failure on the profitability of the HPS. This table offers insights on the variance of the RE electricity. The sensitivity analysis of various possible scenarios shows that the RE projects can still provide financial benefits via the FiT, despite the losses incurred from the penalty levied. - Highlights: • A Power Pinch Analysis (PoPA) tool to assess the economics of an HPS with FiT. • The new On-Grid Problem Table for targeting the available RE electricity for FiT sale. • A sensitivity table showing the effect of RE electricity changes on the HPS profitability.
Global sensitivity analysis in stochastic simulators of uncertain reaction networks
Navarro, Marí a; Le Maitre, Olivier; Knio, Omar
2016-01-01
sources are independent, and considers the Poisson processes in the random-time-change representation of the state dynamics as the fundamental objects governing the inherent stochasticity. A sampling algorithm is proposed to perform the global sensitivity
Spatiotemporal sensitivity analysis of vertical transport of pesticides in soil
Environmental fate and transport processes are influenced by many factors. Simulation models that mimic these processes often have complex implementations, which can lead to over-parameterization. Sensitivity analyses are subsequently used to identify critical parameters whose un...
SBML-SAT: a systems biology markup language (SBML) based sensitivity analysis tool.
Zi, Zhike; Zheng, Yanan; Rundell, Ann E; Klipp, Edda
2008-08-15
It has long been recognized that sensitivity analysis plays a key role in modeling and analyzing cellular and biochemical processes. Systems biology markup language (SBML) has become a well-known platform for coding and sharing mathematical models of such processes. However, current SBML compatible software tools are limited in their ability to perform global sensitivity analyses of these models. This work introduces a freely downloadable, software package, SBML-SAT, which implements algorithms for simulation, steady state analysis, robustness analysis and local and global sensitivity analysis for SBML models. This software tool extends current capabilities through its execution of global sensitivity analyses using multi-parametric sensitivity analysis, partial rank correlation coefficient, SOBOL's method, and weighted average of local sensitivity analyses in addition to its ability to handle systems with discontinuous events and intuitive graphical user interface. SBML-SAT provides the community of systems biologists a new tool for the analysis of their SBML models of biochemical and cellular processes.
Andronis, L; Barton, P; Bryan, S
2009-06-01
To determine how we define good practice in sensitivity analysis in general and probabilistic sensitivity analysis (PSA) in particular, and to what extent it has been adhered to in the independent economic evaluations undertaken for the National Institute for Health and Clinical Excellence (NICE) over recent years; to establish what policy impact sensitivity analysis has in the context of NICE, and policy-makers' views on sensitivity analysis and uncertainty, and what use is made of sensitivity analysis in policy decision-making. Three major electronic databases, MEDLINE, EMBASE and the NHS Economic Evaluation Database, were searched from inception to February 2008. The meaning of 'good practice' in the broad area of sensitivity analysis was explored through a review of the literature. An audit was undertaken of the 15 most recent NICE multiple technology appraisal judgements and their related reports to assess how sensitivity analysis has been undertaken by independent academic teams for NICE. A review of the policy and guidance documents issued by NICE aimed to assess the policy impact of the sensitivity analysis and the PSA in particular. Qualitative interview data from NICE Technology Appraisal Committee members, collected as part of an earlier study, were also analysed to assess the value attached to the sensitivity analysis components of the economic analyses conducted for NICE. All forms of sensitivity analysis, notably both deterministic and probabilistic approaches, have their supporters and their detractors. Practice in relation to univariate sensitivity analysis is highly variable, with considerable lack of clarity in relation to the methods used and the basis of the ranges employed. In relation to PSA, there is a high level of variability in the form of distribution used for similar parameters, and the justification for such choices is rarely given. Virtually all analyses failed to consider correlations within the PSA, and this is an area of concern
Linear Parametric Sensitivity Analysis of the Constraint Coefficient Matrix in Linear Programs
R.A. Zuidwijk (Rob)
2005-01-01
textabstractSensitivity analysis is used to quantify the impact of changes in the initial data of linear programs on the optimal value. In particular, parametric sensitivity analysis involves a perturbation analysis in which the effects of small changes of some or all of the initial data on an
Sensitivity analysis of dynamic characteristic of the fixture based on design variables
Wang Dongsheng; Nong Shaoning; Zhang Sijian; Ren Wanfa
2002-01-01
The research on the sensitivity analysis is dealt with of structural natural frequencies to structural design parameters. A typical fixture for vibration test is designed. Using I-DEAS Finite Element programs, the sensitivity of its natural frequency to design parameters is analyzed by Matrix Perturbation Method. The research result shows that the sensitivity analysis is a fast and effective dynamic re-analysis method to dynamic design and parameters modification of complex structures such as fixtures
Depletion GPT-free sensitivity analysis for reactor eigenvalue problems
Kennedy, C.; Abdel-Khalik, H.
2013-01-01
This manuscript introduces a novel approach to solving depletion perturbation theory problems without the need to set up or solve the generalized perturbation theory (GPT) equations. The approach, hereinafter denoted generalized perturbation theory free (GPT-Free), constructs a reduced order model (ROM) using methods based in perturbation theory and computes response sensitivity profiles in a manner that is independent of the number or type of responses, allowing for an efficient computation of sensitivities when many responses are required. Moreover, the reduction error from using the ROM is quantified in the GPT-Free approach by means of a Wilks' order statistics error metric denoted the K-metric. Traditional GPT has been recognized as the most computationally efficient approach for performing sensitivity analyses of models with many input parameters, e.g. when forward sensitivity analyses are computationally intractable. However, most neutronics codes that can solve the fundamental (homogenous) adjoint eigenvalue problem do not have GPT capabilities unless envisioned during code development. The GPT-Free approach addresses this limitation by requiring only the ability to compute the fundamental adjoint. This manuscript demonstrates the GPT-Free approach for depletion reactor calculations performed in SCALE6 using the 7x7 UAM assembly model. A ROM is developed for the assembly over a time horizon of 990 days. The approach both calculates the reduction error over the lifetime of the simulation using the K-metric and benchmarks the obtained sensitivities using sample calculations. (authors)
Global sensitivity analysis using sparse grid interpolation and polynomial chaos
Buzzard, Gregery T.
2012-01-01
Sparse grid interpolation is widely used to provide good approximations to smooth functions in high dimensions based on relatively few function evaluations. By using an efficient conversion from the interpolating polynomial provided by evaluations on a sparse grid to a representation in terms of orthogonal polynomials (gPC representation), we show how to use these relatively few function evaluations to estimate several types of sensitivity coefficients and to provide estimates on local minima and maxima. First, we provide a good estimate of the variance-based sensitivity coefficients of Sobol' (1990) [1] and then use the gradient of the gPC representation to give good approximations to the derivative-based sensitivity coefficients described by Kucherenko and Sobol' (2009) [2]. Finally, we use the package HOM4PS-2.0 given in Lee et al. (2008) [3] to determine the critical points of the interpolating polynomial and use these to determine the local minima and maxima of this polynomial. - Highlights: ► Efficient estimation of variance-based sensitivity coefficients. ► Efficient estimation of derivative-based sensitivity coefficients. ► Use of homotopy methods for approximation of local maxima and minima.
Di Maio, Francesco, E-mail: francesco.dimaio@polimi.it [Energy Department, Politecnico di Milano, Via La Masa 34, 20156 Milano (Italy); Nicola, Giancarlo [Energy Department, Politecnico di Milano, Via La Masa 34, 20156 Milano (Italy); Zio, Enrico [Energy Department, Politecnico di Milano, Via La Masa 34, 20156 Milano (Italy); Chair on System Science and Energetic Challenge Fondation EDF, Ecole Centrale Paris and Supelec, Paris (France); Yu, Yu [School of Nuclear Science and Engineering, North China Electric Power University, 102206 Beijing (China)
2015-08-15
Highlights: • Uncertainties of TH codes affect the system failure probability quantification. • We present Finite Mixture Models (FMMs) for sensitivity analysis of TH codes. • FMMs approximate the pdf of the output of a TH code with a limited number of simulations. • The approach is tested on a Passive Containment Cooling System of an AP1000 reactor. • The novel approach overcomes the results of a standard variance decomposition method. - Abstract: For safety analysis of Nuclear Power Plants (NPPs), Best Estimate (BE) Thermal Hydraulic (TH) codes are used to predict system response in normal and accidental conditions. The assessment of the uncertainties of TH codes is a critical issue for system failure probability quantification. In this paper, we consider passive safety systems of advanced NPPs and present a novel approach of Sensitivity Analysis (SA). The approach is based on Finite Mixture Models (FMMs) to approximate the probability density function (i.e., the uncertainty) of the output of the passive safety system TH code with a limited number of simulations. We propose a novel Sensitivity Analysis (SA) method for keeping the computational cost low: an Expectation Maximization (EM) algorithm is used to calculate the saliency of the TH code input variables for identifying those that most affect the system functional failure. The novel approach is compared with a standard variance decomposition method on a case study considering a Passive Containment Cooling System (PCCS) of an Advanced Pressurized reactor AP1000.
Adjoint sensitivity analysis of the thermomechanical behavior of repositories
Wilson, J.L.; Thompson, B.M.
1984-01-01
The adjoint sensitivity method is applied to thermomechanical models for the first time. The method provides an efficient and inexpensive answer to the question: how sensitive are thermomechanical predictions to assumed parameters. The answer is exact, in the sense that it yields exact derivatives of response measures to parameters, and approximate, in the sense that projections of the response fo other parameter assumptions are only first order correct. The method is applied to linear finite element models of thermomechanical behavior. Extensions to more complicated models are straight-forward but often laborious. An illustration of the method with a two-dimensional repository corridor model reveals that the chosen stress response measure was most sensitive to Poisson's ratio for the rock matrix
Hattori, Satoshi; Zhou, Xiao-Hua
2018-02-10
Publication bias is one of the most important issues in meta-analysis. For standard meta-analyses to examine intervention effects, the funnel plot and the trim-and-fill method are simple and widely used techniques for assessing and adjusting for the influence of publication bias, respectively. However, their use may be subjective and can then produce misleading insights. To make a more objective inference for publication bias, various sensitivity analysis methods have been proposed, including the Copas selection model. For meta-analysis of diagnostic studies evaluating a continuous biomarker, the summary receiver operating characteristic (sROC) curve is a very useful method in the presence of heterogeneous cutoff values. To our best knowledge, no methods are available for evaluation of influence of publication bias on estimation of the sROC curve. In this paper, we introduce a Copas-type selection model for meta-analysis of diagnostic studies and propose a sensitivity analysis method for publication bias. Our method enables us to assess the influence of publication bias on the estimation of the sROC curve and then judge whether the result of the meta-analysis is sufficiently confident or should be interpreted with much caution. We illustrate our proposed method with real data. Copyright © 2017 John Wiley & Sons, Ltd.
Time course analysis of baroreflex sensitivity during postural stress
Westerhof, Berend E.; Gisolf, Janneke; Karemaker, John M.; Wesseling, Karel H.; Secher, Niels H.; van Lieshout, Johannes J.
2006-01-01
Postural stress requires immediate autonomic nervous action to maintain blood pressure. We determined time-domain cardiac baroreflex sensitivity (BRS) and time delay (tau) between systolic blood pressure and interbeat interval variations during stepwise changes in the angle of vertical body axis
Comparative Analysis of Intercultural Sensitivity among Teachers Working with Refugees
Strekalova-Hughes, Ekaterina
2017-01-01
The unprecedented global refugee crisis and the accompanying political discourse places added pressures on teachers working with children who are refugees in resettling countries. Given the increased chances of having a refugee child in one's classroom, it is critical to explore how interculturally sensitive teachers are and if working with…
Fecal bacteria source characterization and sensitivity analysis of SWAT 2005
The Soil and Water Assessment Tool (SWAT) version 2005 includes a microbial sub-model to simulate fecal bacteria transport at the watershed scale. The objectives of this study were to demonstrate methods to characterize fecal coliform bacteria (FCB) source loads and to assess the model sensitivity t...
Sensitivity Analysis of the Critical Speed in Railway Vehicle Dynamics
Bigoni, Daniele; True, Hans; Engsig-Karup, Allan Peter
2013-01-01
-axle Cooperrider bogie, in order to study the sensitivity of the critical speed with respect to suspension parameters. The importance of a certain suspension component is expressed by the variance in critical speed that is ascribable to it. This proves to be useful in the identification of parameters for which...
Smart optimisation and sensitivity analysis in water distribution systems
Page, Philip R
2015-12-01
Full Text Available optimisation of a water distribution system by keeping the average pressure unchanged as water demands change, by changing the speed of the pumps. Another application area considered, using the same mathematical notions, is the study of the sensitivity...
Zhong Wu
2017-04-01
Full Text Available Since AASHTO released the Mechanistic-Empirical Pavement Design Guide (MEPDG for public review in 2004, many highway research agencies have performed sensitivity analyses using the prototype MEPDG design software. The information provided by the sensitivity analysis is essential for design engineers to better understand the MEPDG design models and to identify important input parameters for pavement design. In literature, different studies have been carried out based on either local or global sensitivity analysis methods, and sensitivity indices have been proposed for ranking the importance of the input parameters. In this paper, a regional sensitivity analysis method, Monte Carlo filtering (MCF, is presented. The MCF method maintains many advantages of the global sensitivity analysis, while focusing on the regional sensitivity of the MEPDG model near the design criteria rather than the entire problem domain. It is shown that the information obtained from the MCF method is more helpful and accurate in guiding design engineers in pavement design practices. To demonstrate the proposed regional sensitivity method, a typical three-layer flexible pavement structure was analyzed at input level 3. A detailed procedure to generate Monte Carlo runs using the AASHTOWare Pavement ME Design software was provided. The results in the example show that the sensitivity ranking of the input parameters in this study reasonably matches with that in a previous study under a global sensitivity analysis. Based on the analysis results, the strengths, practical issues, and applications of the MCF method were further discussed.
Univariate and Cross Tabulation Analysis of Construction Accidents in the Aegean Region
BARADAN, Selim; AKBOĞA, Özge; ÇETİNKAYA, Ufuk; USMEN, Mümtaz A.
2016-01-01
It is crucial toinvestigate case studies and analyze accident statistics to establish safetyand health culture in the construction industry, which exhibits high fatalityrates. However, it is difficult to find reliable and accurate constructionaccidents data in Turkeydue to inadequate accident reporting and recordkeeping system, which hindersstatistical safety research. Therefore, an independent database was generatedby using inspection reports in this research study. Data mining was performed...
Handbook of univariate and multivariate data analysis and interpretation with SPSS
Ho, Robert
2006-01-01
Many statistics texts tend to focus more on the theory and mathematics underlying statistical tests than on their applications and interpretation. This can leave readers with little understanding of how to apply statistical tests or how to interpret their findings. While the SPSS statistical software has done much to alleviate the frustrations of social science professionals and students who must analyze data, they still face daunting challenges in selecting the proper tests, executing the tests, and interpreting the test results.With emphasis firmly on such practical matters, this handbook se
Y. Tang
2007-01-01
Full Text Available This study seeks to identify sensitivity tools that will advance our understanding of lumped hydrologic models for the purposes of model improvement, calibration efficiency and improved measurement schemes. Four sensitivity analysis methods were tested: (1 local analysis using parameter estimation software (PEST, (2 regional sensitivity analysis (RSA, (3 analysis of variance (ANOVA, and (4 Sobol's method. The methods' relative efficiencies and effectiveness have been analyzed and compared. These four sensitivity methods were applied to the lumped Sacramento soil moisture accounting model (SAC-SMA coupled with SNOW-17. Results from this study characterize model sensitivities for two medium sized watersheds within the Juniata River Basin in Pennsylvania, USA. Comparative results for the 4 sensitivity methods are presented for a 3-year time series with 1 h, 6 h, and 24 h time intervals. The results of this study show that model parameter sensitivities are heavily impacted by the choice of analysis method as well as the model time interval. Differences between the two adjacent watersheds also suggest strong influences of local physical characteristics on the sensitivity methods' results. This study also contributes a comprehensive assessment of the repeatability, robustness, efficiency, and ease-of-implementation of the four sensitivity methods. Overall ANOVA and Sobol's method were shown to be superior to RSA and PEST. Relative to one another, ANOVA has reduced computational requirements and Sobol's method yielded more robust sensitivity rankings.
Sensitivity Analysis of Transonic Flow over J-78 Wings
Alexander Kuzmin
2015-01-01
Full Text Available 3D transonic flow over swept and unswept wings with an J-78 airfoil at spanwise sections is studied numerically at negative and vanishing angles of attack. Solutions of the unsteady Reynolds-averaged Navier-Stokes equations are obtained with a finite-volume solver on unstructured meshes. The numerical simulation shows that adverse Mach numbers, at which the lift coefficient is highly sensitive to small perturbations, are larger than those obtained earlier for 2D flow. Due to the larger Mach numbers, there is an onset of self-exciting oscillations of shock waves on the wings. The swept wing exhibits a higher sensitivity to variations of the Mach number than the unswept one.
Sensitive Detection of Deliquescent Bacterial Capsules through Nanomechanical Analysis.
Nguyen, Song Ha; Webb, Hayden K
2015-10-20
Encapsulated bacteria usually exhibit strong resistance to a wide range of sterilization methods, and are often virulent. Early detection of encapsulation can be crucial in microbial pathology. This work demonstrates a fast and sensitive method for the detection of encapsulated bacterial cells. Nanoindentation force measurements were used to confirm the presence of deliquescent bacterial capsules surrounding bacterial cells. Force/distance approach curves contained characteristic linear-nonlinear-linear domains, indicating cocompression of the capsular layer and cell, indentation of the capsule, and compression of the cell alone. This is a sensitive method for the detection and verification of the encapsulation status of bacterial cells. Given that this method was successful in detecting the nanomechanical properties of two different layers of cell material, i.e. distinguishing between the capsule and the remainder of the cell, further development may potentially lead to the ability to analyze even thinner cellular layers, e.g. lipid bilayers.
Long vs. short-term energy storage:sensitivity analysis.
Schoenung, Susan M. (Longitude 122 West, Inc., Menlo Park, CA); Hassenzahl, William V. (,Advanced Energy Analysis, Piedmont, CA)
2007-07-01
This report extends earlier work to characterize long-duration and short-duration energy storage technologies, primarily on the basis of life-cycle cost, and to investigate sensitivities to various input assumptions. Another technology--asymmetric lead-carbon capacitors--has also been added. Energy storage technologies are examined for three application categories--bulk energy storage, distributed generation, and power quality--with significant variations in discharge time and storage capacity. Sensitivity analyses include cost of electricity and natural gas, and system life, which impacts replacement costs and capital carrying charges. Results are presented in terms of annual cost, $/kW-yr. A major variable affecting system cost is hours of storage available for discharge.
Analytical sensitivity analysis of geometric errors in a three axis machine tool
Park, Sung Ryung; Yang, Seung Han
2012-01-01
In this paper, an analytical method is used to perform a sensitivity analysis of geometric errors in a three axis machine tool. First, an error synthesis model is constructed for evaluating the position volumetric error due to the geometric errors, and then an output variable is defined, such as the magnitude of the position volumetric error. Next, the global sensitivity analysis is executed using an analytical method. Finally, the sensitivity indices are calculated using the quantitative values of the geometric errors
System reliability assessment via sensitivity analysis in the Markov chain scheme
Gandini, A.
1988-01-01
Methods for reliability sensitivity analysis in the Markov chain scheme are presented, together with a new formulation which makes use of Generalized Perturbation Theory (GPT) methods. As well known, sensitivity methods are fundamental in system risk analysis, since they allow to identify important components, so to assist the analyst in finding weaknesses in design and operation and in suggesting optimal modifications for system upgrade. The relationship between the GPT sensitivity expression and the Birnbaum importance is also given [fr
Analysis of the individual radio sensitivity of breast cancer patients
Auer, Judith
2013-01-01
Individual radiosensitivity has a crucial impact on radiotherapy related side effects. A prediction of individual radiosensitivity could avoid these side effects. Our aim was to study a breast cancer collective for its variation of individual radiosensitivity. Peripheral blood samples were obtained from 129 individuals. 67 breast cancer patients and 62 healthy and age matched individuals were looked at and their individual radiosensitivity was estimated by a 3-color Fluorescence in situ hybridization approach. Blood samples were obtained (i) before starting adjuvant radiotherapy and were in vitro irradiated by 2 Gy; (ii) after 5 single doses of 1.8 Gy and after 72 h had elapsed. DNA of lymphocytes was probed with whole chromosome painting for chromosomes 1, 2 and 4. The rate of breaks per metaphase was analyzed and used as a predictor of individual radiosensitivity. Breast cancer patients were distinctly more radio-sensitive compared to healthy controls. Additionally the distribution of the cancer patients' radiosensitivity was broader. A subgroup of 9 rather radio-sensitive and 9 rather radio-resistant patients was identified. A subgroup of patients aged between 40 and 50 was distinctly more radio-sensitive than younger or older patients. The in vivo irradiation approach was not applicable to detect individual radiosensitivity. In the breast cancer collective a distinctly resistant and sensitive subgroup is identified, which could be subject for treatment adjustment. Especially in the range of age 40 to 50 patients have an increased radiosensitivity. An in vivo irradiation in a breast cancer collective is not suitable to estimate individual radiosensitivity due to a low deposed dose.
3.8 Proposed approach to uncertainty quantification and sensitivity analysis in the next PA
Flach, Greg [Savannah River Site (SRS), Aiken, SC (United States). Savannah River National Lab. (SRNL); Wohlwend, Jen [Savannah River Site (SRS), Aiken, SC (United States). Savannah River National Lab. (SRNL)
2017-10-02
This memorandum builds upon Section 3.8 of SRNL (2016) and Flach (2017) by defining key error analysis, uncertainty quantification, and sensitivity analysis concepts and terms, in preparation for the next E-Area Performance Assessment (WSRC 2008) revision.
Supplementary Material for: A global sensitivity analysis approach for morphogenesis models
Boas, Sonja; Navarro, Marí a; Merks, Roeland; Blom, Joke
2015-01-01
) represent cell properties and behaviors that drive the mechanisms of angiogenic sprouting. The global sensitivity analysis correctly identified the dominant parameters in the model, consistent with previous studies. Additionally, the analysis provided
ALVIN, Diffusion and Integral Data Comparison and Sensitivity Analysis
Harris, D.R.; Reupke, W.A.; Wilson, W.B.
1982-01-01
1 - Description of problem or function: ALVIN analyzes the consistency of a set of differential and integral nuclear data, adjusts the differential nuclear data to improve agreement with integral observations, and identifies inconsistent data. ALVIN also computes required sensitivities and related quantities such as sensitivity profiles. 2 - Method of solution: Linear perturbation theory is used for the sensitivity calculations. Data consistency and adjustment computations use least squares techniques. 3 - Restrictions on the complexity of the problem: The DAFT2 consistency and adjustment subroutine treats fully or partially correlated differential and integral parameters, but only as many as the order of the largest matrix that can be inverted. The DAFT3 consistency and adjustment subroutine treats arbitrarily large differential data sets, but only if they are uncorrelated with the integral data. Due to the current dimensions of some arrays, maxima of 75 spatial mesh points, 41 groups, and 6. order Legendre polynomials are allowed. This can be changed by increasing the dimensions of the LCM arrays and the arrays in the labeled COMMON block S1 and blank COMMON
Analysis of Consumers' Preferences and Price Sensitivity to Native Chickens.
Lee, Min-A; Jung, Yoojin; Jo, Cheorun; Park, Ji-Young; Nam, Ki-Chang
2017-01-01
This study analyzed consumers' preferences and price sensitivity to native chickens. A survey was conducted from Jan 6 to 17, 2014, and data were collected from consumers (n=500) living in Korea. Statistical analyses evaluated the consumption patterns of native chickens, preference marketing for native chicken breeds which will be newly developed, and price sensitivity measurement (PSM). Of the subjects who preferred broilers, 24.3% do not purchase native chickens because of the dryness and tough texture, while those who preferred native chickens liked their chewy texture (38.2%). Of the total subjects, 38.2% preferred fried native chickens (38.2%) for processed food, 38.4% preferred direct sales for native chicken distribution, 51.0% preferred native chickens to be slaughtered in specialty stores, and 32.4% wanted easy access to native chickens. Additionally, the price stress range (PSR) was 50 won and the point of marginal cheapness (PMC) and point of marginal expensiveness (PME) were 6,980 won and 12,300 won, respectively. Evaluation of the segmentation market revealed that consumers who prefer broiler to native chicken breeds were more sensitive to the chicken price. To accelerate the consumption of newly developed native chicken meat, it is necessary to develop a texture that each consumer needs, to increase the accessibility of native chickens, and to have diverse menus and recipes as well as reasonable pricing for native chickens.
Global sensitivity analysis of DRAINMOD-FOREST, an integrated forest ecosystem model
Shiying Tian; Mohamed A. Youssef; Devendra M. Amatya; Eric D. Vance
2014-01-01
Global sensitivity analysis is a useful tool to understand process-based ecosystem models by identifying key parameters and processes controlling model predictions. This study reported a comprehensive global sensitivity analysis for DRAINMOD-FOREST, an integrated model for simulating water, carbon (C), and nitrogen (N) cycles and plant growth in lowland forests. The...
Personalization of models with many model parameters : an efficient sensitivity analysis approach
Donders, W.P.; Huberts, W.; van de Vosse, F.N.; Delhaas, T.
2015-01-01
Uncertainty quantification and global sensitivity analysis are indispensable for patient-specific applications of models that enhance diagnosis or aid decision-making. Variance-based sensitivity analysis methods, which apportion each fraction of the output uncertainty (variance) to the effects of
Survey of sampling-based methods for uncertainty and sensitivity analysis
Helton, J.C.; Johnson, J.D.; Sallaberry, C.J.; Storlie, C.B.
2006-01-01
Sampling-based methods for uncertainty and sensitivity analysis are reviewed. The following topics are considered: (i) definition of probability distributions to characterize epistemic uncertainty in analysis inputs (ii) generation of samples from uncertain analysis inputs (iii) propagation of sampled inputs through an analysis (iv) presentation of uncertainty analysis results, and (v) determination of sensitivity analysis results. Special attention is given to the determination of sensitivity analysis results, with brief descriptions and illustrations given for the following procedures/techniques: examination of scatterplots, correlation analysis, regression analysis, partial correlation analysis, rank transformations, statistical tests for patterns based on gridding, entropy tests for patterns based on gridding, nonparametric regression analysis, squared rank differences/rank correlation coefficient test, two-dimensional Kolmogorov-Smirnov test, tests for patterns based on distance measures, top down coefficient of concordance, and variance decomposition
Survey of sampling-based methods for uncertainty and sensitivity analysis.
Johnson, Jay Dean; Helton, Jon Craig; Sallaberry, Cedric J. PhD. (.; .); Storlie, Curt B. (Colorado State University, Fort Collins, CO)
2006-06-01
Sampling-based methods for uncertainty and sensitivity analysis are reviewed. The following topics are considered: (1) Definition of probability distributions to characterize epistemic uncertainty in analysis inputs, (2) Generation of samples from uncertain analysis inputs, (3) Propagation of sampled inputs through an analysis, (4) Presentation of uncertainty analysis results, and (5) Determination of sensitivity analysis results. Special attention is given to the determination of sensitivity analysis results, with brief descriptions and illustrations given for the following procedures/techniques: examination of scatterplots, correlation analysis, regression analysis, partial correlation analysis, rank transformations, statistical tests for patterns based on gridding, entropy tests for patterns based on gridding, nonparametric regression analysis, squared rank differences/rank correlation coefficient test, two dimensional Kolmogorov-Smirnov test, tests for patterns based on distance measures, top down coefficient of concordance, and variance decomposition.
Wind Speed Prediction Using a Univariate ARIMA Model and a Multivariate NARX Model
Erasmo Cadenas
2016-02-01
Full Text Available Two on step ahead wind speed forecasting models were compared. A univariate model was developed using a linear autoregressive integrated moving average (ARIMA. This method’s performance is well studied for a large number of prediction problems. The other is a multivariate model developed using a nonlinear autoregressive exogenous artificial neural network (NARX. This uses the variables: barometric pressure, air temperature, wind direction and solar radiation or relative humidity, as well as delayed wind speed. Both models were developed from two databases from two sites: an hourly average measurements database from La Mata, Oaxaca, Mexico, and a ten minute average measurements database from Metepec, Hidalgo, Mexico. The main objective was to compare the impact of the various meteorological variables on the performance of the multivariate model of wind speed prediction with respect to the high performance univariate linear model. The NARX model gave better results with improvements on the ARIMA model of between 5.5% and 10. 6% for the hourly database and of between 2.3% and 12.8% for the ten minute database for mean absolute error and mean squared error, respectively.
Code development for eigenvalue total sensitivity analysis and total uncertainty analysis
Wan, Chenghui; Cao, Liangzhi; Wu, Hongchun; Zu, Tiejun; Shen, Wei
2015-01-01
Highlights: • We develop a new code for total sensitivity and uncertainty analysis. • The implicit effects of cross sections can be considered. • The results of our code agree well with TSUNAMI-1D. • Detailed analysis for origins of implicit effects is performed. - Abstract: The uncertainties of multigroup cross sections notably impact eigenvalue of neutron-transport equation. We report on a total sensitivity analysis and total uncertainty analysis code named UNICORN that has been developed by applying the direct numerical perturbation method and statistical sampling method. In order to consider the contributions of various basic cross sections and the implicit effects which are indirect results of multigroup cross sections through resonance self-shielding calculation, an improved multigroup cross-section perturbation model is developed. The DRAGON 4.0 code, with application of WIMSD-4 format library, is used by UNICORN to carry out the resonance self-shielding and neutron-transport calculations. In addition, the bootstrap technique has been applied to the statistical sampling method in UNICORN to obtain much steadier and more reliable uncertainty results. The UNICORN code has been verified against TSUNAMI-1D by analyzing the case of TMI-1 pin-cell. The numerical results show that the total uncertainty of eigenvalue caused by cross sections can reach up to be about 0.72%. Therefore the contributions of the basic cross sections and their implicit effects are not negligible
Probability density adjoint for sensitivity analysis of the Mean of Chaos
Blonigan, Patrick J., E-mail: blonigan@mit.edu; Wang, Qiqi, E-mail: qiqi@mit.edu
2014-08-01
Sensitivity analysis, especially adjoint based sensitivity analysis, is a powerful tool for engineering design which allows for the efficient computation of sensitivities with respect to many parameters. However, these methods break down when used to compute sensitivities of long-time averaged quantities in chaotic dynamical systems. This paper presents a new method for sensitivity analysis of ergodic chaotic dynamical systems, the density adjoint method. The method involves solving the governing equations for the system's invariant measure and its adjoint on the system's attractor manifold rather than in phase-space. This new approach is derived for and demonstrated on one-dimensional chaotic maps and the three-dimensional Lorenz system. It is found that the density adjoint computes very finely detailed adjoint distributions and accurate sensitivities, but suffers from large computational costs.
Improved Extreme Learning Machine based on the Sensitivity Analysis
Cui, Licheng; Zhai, Huawei; Wang, Benchao; Qu, Zengtang
2018-03-01
Extreme learning machine and its improved ones is weak in some points, such as computing complex, learning error and so on. After deeply analyzing, referencing the importance of hidden nodes in SVM, an novel analyzing method of the sensitivity is proposed which meets people’s cognitive habits. Based on these, an improved ELM is proposed, it could remove hidden nodes before meeting the learning error, and it can efficiently manage the number of hidden nodes, so as to improve the its performance. After comparing tests, it is better in learning time, accuracy and so on.
Soliman, Essam S; Moawed, Sherif A; Hassan, Rania A
2017-08-01
Birds litter contains unutilized nitrogen in the form of uric acid that is converted into ammonia; a fact that does not only affect poultry performance but also has a negative effect on people's health around the farm and contributes in the environmental degradation. The influence of microclimatic ammonia emissions on Ross and Hubbard broilers reared in different housing systems at two consecutive seasons (fall and winter) was evaluated using a discriminant function analysis to differentiate between Ross and Hubbard breeds. A total number of 400 air samples were collected and analyzed for ammonia levels during the experimental period. Data were analyzed using univariate and multivariate statistical methods. Ammonia levels were significantly higher (p0.05) were found between the two farms in body weight, body weight gain, feed intake, feed conversion ratio, and performance index (PI) of broilers. Body weight; weight gain and PI had increased values (pbroiler breed. Ammonia emissions were positively (although weekly) correlated with the ambient relative humidity (r=0.383; p0.05). Test of significance of discriminant function analysis did not show a classification based on the studied traits suggesting that they cannot been used as predictor variables. The percentage of correct classification was 52% and it was improved after deletion of highly correlated traits to 57%. The study revealed that broiler's growth was negatively affected by increased microclimatic ammonia concentrations and recommended the analysis of broilers' growth performance parameters data using multivariate discriminant function analysis.
Comparison of global sensitivity analysis methods – Application to fuel behavior modeling
Ikonen, Timo, E-mail: timo.ikonen@vtt.fi
2016-02-15
Highlights: • Several global sensitivity analysis methods are compared. • The methods’ applicability to nuclear fuel performance simulations is assessed. • The implications of large input uncertainties and complex models are discussed. • Alternative strategies to perform sensitivity analyses are proposed. - Abstract: Fuel performance codes have two characteristics that make their sensitivity analysis challenging: large uncertainties in input parameters and complex, non-linear and non-additive structure of the models. The complex structure of the code leads to interactions between inputs that show as cross terms in the sensitivity analysis. Due to the large uncertainties of the inputs these interactions are significant, sometimes even dominating the sensitivity analysis. For the same reason, standard linearization techniques do not usually perform well in the analysis of fuel performance codes. More sophisticated methods are typically needed in the analysis. To this end, we compare the performance of several sensitivity analysis methods in the analysis of a steady state FRAPCON simulation. The comparison of importance rankings obtained with the various methods shows that even the simplest methods can be sufficient for the analysis of fuel maximum temperature. However, the analysis of the gap conductance requires more powerful methods that take into account the interactions of the inputs. In some cases, moment-independent methods are needed. We also investigate the computational cost of the various methods and present recommendations as to which methods to use in the analysis.
Variance decomposition-based sensitivity analysis via neural networks
Marseguerra, Marzio; Masini, Riccardo; Zio, Enrico; Cojazzi, Giacomo
2003-01-01
This paper illustrates a method for efficiently performing multiparametric sensitivity analyses of the reliability model of a given system. These analyses are of great importance for the identification of critical components in highly hazardous plants, such as the nuclear or chemical ones, thus providing significant insights for their risk-based design and management. The technique used to quantify the importance of a component parameter with respect to the system model is based on a classical decomposition of the variance. When the model of the system is realistically complicated (e.g. by aging, stand-by, maintenance, etc.), its analytical evaluation soon becomes impractical and one is better off resorting to Monte Carlo simulation techniques which, however, could be computationally burdensome. Therefore, since the variance decomposition method requires a large number of system evaluations, each one to be performed by Monte Carlo, the need arises for possibly substituting the Monte Carlo simulation model with a fast, approximated, algorithm. Here we investigate an approach which makes use of neural networks appropriately trained on the results of a Monte Carlo system reliability/availability evaluation to quickly provide with reasonable approximation, the values of the quantities of interest for the sensitivity analyses. The work was a joint effort between the Department of Nuclear Engineering of the Polytechnic of Milan, Italy, and the Institute for Systems, Informatics and Safety, Nuclear Safety Unit of the Joint Research Centre in Ispra, Italy which sponsored the project
Developing optical traps for ultra-sensitive analysis
Zhao, X.; Vieira, D.J.; Guckert, R.; Crane, S.
1998-01-01
The authors describe the coupling of a magneto-optical trap to a mass separator for the ultra-sensitive detection of selected radioactive species. As a proof of principle test, they have demonstrated the trapping of ∼ 6 million 82 Rb (t 1/2 = 75 s) atoms using an ion implantation and heated foil release method for introducing the sample into a trapping cell with minimal gas loading. Gamma-ray counting techniques were used to determine the efficiencies of each step in the process. By far the weakest step in the process is the efficiency of the optical trap itself (0.3%). Further improvements in the quality of the nonstick dryfilm coating on the inside of the trapping cell and the possible use of larger diameter laser beams are indicated. In the presence of a large background of scattered light, this initial work achieved a detection sensitivity of ∼ 4,000 trapped atoms. Improved detection schemes using a pulsed trap and gated photon detection method are outlined. Application of this technology to the areas of environmental monitoring and nuclear proliferation are foreseen
Iris Recognition for Partially Occluded Images: Methodology and Sensitivity Analysis
Poursaberi A
2007-01-01
Full Text Available Accurate iris detection is a crucial part of an iris recognition system. One of the main issues in iris segmentation is coping with occlusion that happens due to eyelids and eyelashes. In the literature, some various methods have been suggested to solve the occlusion problem. In this paper, two different segmentations of iris are presented. In the first algorithm, a circle is located around the pupil with an appropriate diameter. The iris area encircled by the circular boundary is used for recognition purposes then. In the second method, again a circle is located around the pupil with a larger diameter. This time, however, only the lower part of the encircled iris area is utilized for individual recognition. Wavelet-based texture features are used in the process. Hamming and harmonic mean distance classifiers are exploited as a mixed classifier in suggested algorithm. It is observed that relying on a smaller but more reliable part of the iris, though reducing the net amount of information, improves the overall performance. Experimental results on CASIA database show that our method has a promising performance with an accuracy of 99.31%. The sensitivity of the proposed method is analyzed versus contrast, illumination, and noise as well, where lower sensitivity to all factors is observed when the lower half of the iris is used for recognition.
A Flow-Sensitive Analysis of Privacy Properties
Nielson, Hanne Riis; Nielson, Flemming
2007-01-01
that information I send to some service never is leaked to another service? - unless I give my permission? We shall develop a static program analysis for the pi- calculus and show how it can be used to give privacy guarantees like the ones requested above. The analysis records the explicit information flow...
A method of the sensitivity analysis of build-up and decay of actinides
Mitani, Hiroshi; Koyama, Kinji; Kuroi, Hideo
1977-07-01
To make sensitivity analysis of build-up and decay of actinides, mathematical methods related to this problem have been investigated in detail. Application of time-dependent perturbation technique and Bateman method to sensitivity analysis is mainly studied. For the purpose, a basic equation and its adjoint equation for build-up and decay of actinides are systematically solved by introducing Laplace and modified Laplace transforms and their convolution theorems. Then, the mathematical method of sensitivity analyses is formulated by the above technique; its physical significance is also discussed. Finally, application of eigenvalue-method is investigated. Sensitivity coefficients can be directly calculated by this method. (auth.)
Best estimate analysis of LOFT L2-5 with CATHARE: uncertainty and sensitivity analysis
JOUCLA, Jerome; PROBST, Pierre [Institute for Radiological Protection and Nuclear Safety, Fontenay-aux-Roses (France); FOUET, Fabrice [APTUS, Versailles (France)
2008-07-01
The revision of the 10 CFR50.46 in 1988 has made possible the use of best-estimate codes. They may be used in safety demonstration and licensing, provided that uncertainties are added to the relevant output parameters before comparing them with the acceptance criteria. In the safety analysis of the large break loss of coolant accident, it was agreed that the 95. percentile estimated with a high degree of confidence should be lower than the acceptance criteria. It appeared necessary to IRSN, technical support of the French Safety Authority, to get more insight into these strategies which are being developed not only in thermal-hydraulics but in other fields such as in neutronics. To estimate the 95. percentile with a high confidence level, we propose to use rank statistics or bootstrap. Toward the objective of assessing uncertainty, it is useful to determine and to classify the main input parameters. We suggest approximating the code by a surrogate model, the Kriging model, which will be used to make a sensitivity analysis with the SOBOL methodology. This paper presents the application of two new methodologies of how to make the uncertainty and sensitivity analysis on the maximum peak cladding temperature of the LOFT L2-5 test with the CATHARE code. (authors)
A framework for 2-stage global sensitivity analysis of GastroPlus™ compartmental models.
Scherholz, Megerle L; Forder, James; Androulakis, Ioannis P
2018-04-01
Parameter sensitivity and uncertainty analysis for physiologically based pharmacokinetic (PBPK) models are becoming an important consideration for regulatory submissions, requiring further evaluation to establish the need for global sensitivity analysis. To demonstrate the benefits of an extensive analysis, global sensitivity was implemented for the GastroPlus™ model, a well-known commercially available platform, using four example drugs: acetaminophen, risperidone, atenolol, and furosemide. The capabilities of GastroPlus were expanded by developing an integrated framework to automate the GastroPlus graphical user interface with AutoIt and for execution of the sensitivity analysis in MATLAB ® . Global sensitivity analysis was performed in two stages using the Morris method to screen over 50 parameters for significant factors followed by quantitative assessment of variability using Sobol's sensitivity analysis. The 2-staged approach significantly reduced computational cost for the larger model without sacrificing interpretation of model behavior, showing that the sensitivity results were well aligned with the biopharmaceutical classification system. Both methods detected nonlinearities and parameter interactions that would have otherwise been missed by local approaches. Future work includes further exploration of how the input domain influences the calculated global sensitivity measures as well as extending the framework to consider a whole-body PBPK model.
Meta-analysis of the relative sensitivity of semi-natural vegetation species to ozone
Hayes, F.; Jones, M.L.M.; Mills, G.; Ashmore, M.
2007-01-01
This study identified 83 species from existing publications suitable for inclusion in a database of sensitivity of species to ozone (OZOVEG database). An index, the relative sensitivity to ozone, was calculated for each species based on changes in biomass in order to test for species traits associated with ozone sensitivity. Meta-analysis of the ozone sensitivity data showed a wide inter-specific range in response to ozone. Some relationships in comparison to plant physiological and ecological characteristics were identified. Plants of the therophyte lifeform were particularly sensitive to ozone. Species with higher mature leaf N concentration were more sensitive to ozone than those with lower leaf N concentration. Some relationships between relative sensitivity to ozone and Ellenberg habitat requirements were also identified. In contrast, no relationships between relative sensitivity to ozone and mature leaf P concentration, Grime's CSR strategy, leaf longevity, flowering season, stomatal density and maximum altitude were found. The relative sensitivity of species and relationships with plant characteristics identified in this study could be used to predict sensitivity to ozone of untested species and communities. - Meta-analysis of the relative sensitivity of semi-natural vegetation species to ozone showed some relationships with physiological and ecological characteristics
Uncertainty and sensitivity analysis using probabilistic system assessment code. 1
Honma, Toshimitsu; Sasahara, Takashi.
1993-10-01
This report presents the results obtained when applying the probabilistic system assessment code under development to the PSACOIN Level 0 intercomparison exercise organized by the Probabilistic System Assessment Code User Group in the Nuclear Energy Agency (NEA) of OECD. This exercise is one of a series designed to compare and verify probabilistic codes in the performance assessment of geological radioactive waste disposal facilities. The computations were performed using the Monte Carlo sampling code PREP and post-processor code USAMO. The submodels in the waste disposal system were described and coded with the specification of the exercise. Besides the results required for the exercise, further additional uncertainty and sensitivity analyses were performed and the details of these are also included. (author)
Sensitivity, uncertainty, and importance analysis of a risk assessment
Andsten, R.S.; Vaurio, J.K.
1992-01-01
In this paper a number of supplementary studies and applications associated with probabilistic safety assessment (PSA) are described, including sensitivity and importance evaluations of failures, errors, systems, and groups of components. The main purpose is to illustrate the usefulness of a PSA for making decisions about safety improvements, training, allowed outage times, and test intervals. A useful measure of uncertainty importance is presented, and it points out areas needing development, such as reactor vessel aging phenomena, for reducing overall uncertainty. A time-dependent core damage frequency is also presented, illustrating the impact of testing scenarios and intervals. Tea methods and applications presented are based on the Level 1 PSA carried out for the internal initiating event of the Loviisa 1 nuclear power station. Steam generator leakages and associated operator actions are major contributors to the current core-damage frequency estimate of 2 x10 -4 /yr. The results are used to improve the plant and procedures and to guide future improvements
Models for patients' recruitment in clinical trials and sensitivity analysis.
Mijoule, Guillaume; Savy, Stéphanie; Savy, Nicolas
2012-07-20
Taking a decision on the feasibility and estimating the duration of patients' recruitment in a clinical trial are very important but very hard questions to answer, mainly because of the huge variability of the system. The more elaborated works on this topic are those of Anisimov and co-authors, where they investigate modelling of the enrolment period by using Gamma-Poisson processes, which allows to develop statistical tools that can help the manager of the clinical trial to answer these questions and thus help him to plan the trial. The main idea is to consider an ongoing study at an intermediate time, denoted t(1). Data collected on [0,t(1)] allow to calibrate the parameters of the model, which are then used to make predictions on what will happen after t(1). This method allows us to estimate the probability of ending the trial on time and give possible corrective actions to the trial manager especially regarding how many centres have to be open to finish on time. In this paper, we investigate a Pareto-Poisson model, which we compare with the Gamma-Poisson one. We will discuss the accuracy of the estimation of the parameters and compare the models on a set of real case data. We make the comparison on various criteria : the expected recruitment duration, the quality of fitting to the data and its sensitivity to parameter errors. We discuss the influence of the centres opening dates on the estimation of the duration. This is a very important question to deal with in the setting of our data set. In fact, these dates are not known. For this discussion, we consider a uniformly distributed approach. Finally, we study the sensitivity of the expected duration of the trial with respect to the parameters of the model : we calculate to what extent an error on the estimation of the parameters generates an error in the prediction of the duration.
Comprehensive mechanisms for combustion chemistry: Experiment, modeling, and sensitivity analysis
Dryer, F.L.; Yetter, R.A. [Princeton Univ., NJ (United States)
1993-12-01
This research program is an integrated experimental/numerical effort to study pyrolysis and oxidation reactions and mechanisms for small-molecule hydrocarbon structures under conditions representative of combustion environments. The experimental aspects of the work are conducted in large diameter flow reactors, at pressures from one to twenty atmospheres, temperatures from 550 K to 1200 K, and with observed reaction times from 10{sup {minus}2} to 5 seconds. Gas sampling of stable reactant, intermediate, and product species concentrations provides not only substantial definition of the phenomenology of reaction mechanisms, but a significantly constrained set of kinetic information with negligible diffusive coupling. Analytical techniques used for detecting hydrocarbons and carbon oxides include gas chromatography (GC), and gas infrared (NDIR) and FTIR methods are utilized for continuous on-line sample detection of light absorption measurements of OH have also been performed in an atmospheric pressure flow reactor (APFR), and a variable pressure flow (VPFR) reactor is presently being instrumented to perform optical measurements of radicals and highly reactive molecular intermediates. The numerical aspects of the work utilize zero and one-dimensional pre-mixed, detailed kinetic studies, including path, elemental gradient sensitivity, and feature sensitivity analyses. The program emphasizes the use of hierarchical mechanistic construction to understand and develop detailed kinetic mechanisms. Numerical studies are utilized for guiding experimental parameter selections, for interpreting observations, for extending the predictive range of mechanism constructs, and to study the effects of diffusive transport coupling on reaction behavior in flames. Modeling using well defined and validated mechanisms for the CO/H{sub 2}/oxidant systems.
Linear Parametric Sensitivity Analysis of the Constraint Coefficient Matrix in Linear Programs
Zuidwijk, Rob
2005-01-01
textabstractSensitivity analysis is used to quantify the impact of changes in the initial data of linear programs on the optimal value. In particular, parametric sensitivity analysis involves a perturbation analysis in which the effects of small changes of some or all of the initial data on an optimal solution are investigated, and the optimal solution is studied on a so-called critical range of the initial data, in which certain properties such as the optimal basis in linear programming are ...
Nguyen, Duc T.; Storaasli, Olaf O.; Qin, Jiangning; Qamar, Ramzi
1994-01-01
An automatic differentiation tool (ADIFOR) is incorporated into a finite element based structural analysis program for shape and non-shape design sensitivity analysis of structural systems. The entire analysis and sensitivity procedures are parallelized and vectorized for high performance computation. Small scale examples to verify the accuracy of the proposed program and a medium scale example to demonstrate the parallel vector performance on multiple CRAY C90 processors are included.
Sfetsos, A. [7 Pirsou Str., Athens (Greece); Coonick, A.H. [Imperial Coll. of Science Technology and Medicine, Dept. of Electrical and Electronic Engineering, London (United Kingdom)
2000-07-01
This paper introduces a new approach for the forecasting of mean hourly global solar radiation received by a horizontal surface. In addition to the traditional linear methods, several artificial-intelligence-based techniques are studied. These include linear, feed-forward, recurrent Elman and Radial Basis neural networks alongside the adaptive neuro-fuzzy inference scheme. The problem is examined initially for the univariate case, and is extended to include additional meteorological parameters in the process of estimating the optimum model. The results indicate that the developed artificial intelligence models predict the solar radiation time series more effectively compared to the conventional procedures based on the clearness index. The forecasting ability of some models can be further enhanced with the use of additional meteorological parameters. (Author)
Sensitivity analysis techniques applied to a system of hyperbolic conservation laws
Weirs, V. Gregory; Kamm, James R.; Swiler, Laura P.; Tarantola, Stefano; Ratto, Marco; Adams, Brian M.; Rider, William J.; Eldred, Michael S.
2012-01-01
Sensitivity analysis is comprised of techniques to quantify the effects of the input variables on a set of outputs. In particular, sensitivity indices can be used to infer which input parameters most significantly affect the results of a computational model. With continually increasing computing power, sensitivity analysis has become an important technique by which to understand the behavior of large-scale computer simulations. Many sensitivity analysis methods rely on sampling from distributions of the inputs. Such sampling-based methods can be computationally expensive, requiring many evaluations of the simulation; in this case, the Sobol' method provides an easy and accurate way to compute variance-based measures, provided a sufficient number of model evaluations are available. As an alternative, meta-modeling approaches have been devised to approximate the response surface and estimate various measures of sensitivity. In this work, we consider a variety of sensitivity analysis methods, including different sampling strategies, different meta-models, and different ways of evaluating variance-based sensitivity indices. The problem we consider is the 1-D Riemann problem. By a careful choice of inputs, discontinuous solutions are obtained, leading to discontinuous response surfaces; such surfaces can be particularly problematic for meta-modeling approaches. The goal of this study is to compare the estimated sensitivity indices with exact values and to evaluate the convergence of these estimates with increasing samples sizes and under an increasing number of meta-model evaluations. - Highlights: ► Sensitivity analysis techniques for a model shock physics problem are compared. ► The model problem and the sensitivity analysis problem have exact solutions. ► Subtle details of the method for computing sensitivity indices can affect the results.
Abdel Samee Nagwan M
2012-08-01
Full Text Available Abstract Background Discovering new biomarkers has a great role in improving early diagnosis of Hepatocellular carcinoma (HCC. The experimental determination of biomarkers needs a lot of time and money. This motivates this work to use in-silico prediction of biomarkers to reduce the number of experiments required for detecting new ones. This is achieved by extracting the most representative genes in microarrays of HCC. Results In this work, we provide a method for extracting the differential expressed genes, up regulated ones, that can be considered candidate biomarkers in high throughput microarrays of HCC. We examine the power of several gene selection methods (such as Pearson’s correlation coefficient, Cosine coefficient, Euclidean distance, Mutual information and Entropy with different estimators in selecting informative genes. A biological interpretation of the highly ranked genes is done using KEGG (Kyoto Encyclopedia of Genes and Genomes pathways, ENTREZ and DAVID (Database for Annotation, Visualization, and Integrated Discovery databases. The top ten genes selected using Pearson’s correlation coefficient and Cosine coefficient contained six genes that have been implicated in cancer (often multiple cancers genesis in previous studies. A fewer number of genes were obtained by the other methods (4 genes using Mutual information, 3genes using Euclidean distance and only one gene using Entropy. A better result was obtained by the utilization of a hybrid approach based on intersecting the highly ranked genes in the output of all investigated methods. This hybrid combination yielded seven genes (2 genes for HCC and 5 genes in different types of cancer in the top ten genes of the list of intersected genes. Conclusions To strengthen the effectiveness of the univariate selection methods, we propose a hybrid approach by intersecting several of these methods in a cascaded manner. This approach surpasses all of univariate selection methods when
Parametric Sensitivity Analysis of Oscillatory Delay Systems with an Application to Gene Regulation.
Ingalls, Brian; Mincheva, Maya; Roussel, Marc R
2017-07-01
A parametric sensitivity analysis for periodic solutions of delay-differential equations is developed. Because phase shifts cause the sensitivity coefficients of a periodic orbit to diverge, we focus on sensitivities of the extrema, from which amplitude sensitivities are computed, and of the period. Delay-differential equations are often used to model gene expression networks. In these models, the parametric sensitivities of a particular genotype define the local geometry of the evolutionary landscape. Thus, sensitivities can be used to investigate directions of gradual evolutionary change. An oscillatory protein synthesis model whose properties are modulated by RNA interference is used as an example. This model consists of a set of coupled delay-differential equations involving three delays. Sensitivity analyses are carried out at several operating points. Comments on the evolutionary implications of the results are offered.
Spiessl, Sabine; Becker, Dirk-Alexander
2017-06-01
Sensitivity analysis is a mathematical means for analysing the sensitivities of a computational model to variations of its input parameters. Thus, it is a tool for managing parameter uncertainties. It is often performed probabilistically as global sensitivity analysis, running the model a large number of times with different parameter value combinations. Going along with the increase of computer capabilities, global sensitivity analysis has been a field of mathematical research for some decades. In the field of final repository modelling, probabilistic analysis is regarded a key element of a modern safety case. An appropriate uncertainty and sensitivity analysis can help identify parameters that need further dedicated research to reduce the overall uncertainty, generally leads to better system understanding and can thus contribute to building confidence in the models. The purpose of the project described here was to systematically investigate different numerical and graphical techniques of sensitivity analysis with typical repository models, which produce a distinctly right-skewed and tailed output distribution and can exhibit a highly nonlinear, non-monotonic or even non-continuous behaviour. For the investigations presented here, three test models were defined that describe generic, but typical repository systems. A number of numerical and graphical sensitivity analysis methods were selected for investigation and, in part, modified or adapted. Different sampling methods were applied to produce various parameter samples of different sizes and many individual runs with the test models were performed. The results were evaluated with the different methods of sensitivity analysis. On this basis the methods were compared and assessed. This report gives an overview of the background and the applied methods. The results obtained for three typical test models are presented and explained; conclusions in view of practical applications are drawn. At the end, a recommendation
Spiessl, Sabine; Becker, Dirk-Alexander
2017-06-15
Sensitivity analysis is a mathematical means for analysing the sensitivities of a computational model to variations of its input parameters. Thus, it is a tool for managing parameter uncertainties. It is often performed probabilistically as global sensitivity analysis, running the model a large number of times with different parameter value combinations. Going along with the increase of computer capabilities, global sensitivity analysis has been a field of mathematical research for some decades. In the field of final repository modelling, probabilistic analysis is regarded a key element of a modern safety case. An appropriate uncertainty and sensitivity analysis can help identify parameters that need further dedicated research to reduce the overall uncertainty, generally leads to better system understanding and can thus contribute to building confidence in the models. The purpose of the project described here was to systematically investigate different numerical and graphical techniques of sensitivity analysis with typical repository models, which produce a distinctly right-skewed and tailed output distribution and can exhibit a highly nonlinear, non-monotonic or even non-continuous behaviour. For the investigations presented here, three test models were defined that describe generic, but typical repository systems. A number of numerical and graphical sensitivity analysis methods were selected for investigation and, in part, modified or adapted. Different sampling methods were applied to produce various parameter samples of different sizes and many individual runs with the test models were performed. The results were evaluated with the different methods of sensitivity analysis. On this basis the methods were compared and assessed. This report gives an overview of the background and the applied methods. The results obtained for three typical test models are presented and explained; conclusions in view of practical applications are drawn. At the end, a recommendation
Lu Yichi; Mohanty, Sitakanta
2001-01-01
The Fourier Amplitude Sensitivity Test (FAST) method has been used to perform a sensitivity analysis of a computer model developed for conducting total system performance assessment of the proposed high-level nuclear waste repository at Yucca Mountain, Nevada, USA. The computer model has a large number of random input parameters with assigned probability density functions, which may or may not be uniform, for representing data uncertainty. The FAST method, which was previously applied to models with parameters represented by the uniform probability distribution function only, has been modified to be applied to models with nonuniform probability distribution functions. Using an example problem with a small input parameter set, several aspects of the FAST method, such as the effects of integer frequency sets and random phase shifts in the functional transformations, and the number of discrete sampling points (equivalent to the number of model executions) on the ranking of the input parameters have been investigated. Because the number of input parameters of the computer model under investigation is too large to be handled by the FAST method, less important input parameters were first screened out using the Morris method. The FAST method was then used to rank the remaining parameters. The validity of the parameter ranking by the FAST method was verified using the conditional complementary cumulative distribution function (CCDF) of the output. The CCDF results revealed that the introduction of random phase shifts into the functional transformations, proposed by previous investigators to disrupt the repetitiveness of search curves, does not necessarily improve the sensitivity analysis results because it destroys the orthogonality of the trigonometric functions, which is required for Fourier analysis
Sensitivity Analysis of the Bone Fracture Risk Model
Lewandowski, Beth; Myers, Jerry; Sibonga, Jean Diane
2017-01-01
Introduction: The probability of bone fracture during and after spaceflight is quantified to aid in mission planning, to determine required astronaut fitness standards and training requirements and to inform countermeasure research and design. Probability is quantified with a probabilistic modeling approach where distributions of model parameter values, instead of single deterministic values, capture the parameter variability within the astronaut population and fracture predictions are probability distributions with a mean value and an associated uncertainty. Because of this uncertainty, the model in its current state cannot discern an effect of countermeasures on fracture probability, for example between use and non-use of bisphosphonates or between spaceflight exercise performed with the Advanced Resistive Exercise Device (ARED) or on devices prior to installation of ARED on the International Space Station. This is thought to be due to the inability to measure key contributors to bone strength, for example, geometry and volumetric distributions of bone mass, with areal bone mineral density (BMD) measurement techniques. To further the applicability of model, we performed a parameter sensitivity study aimed at identifying those parameter uncertainties that most effect the model forecasts in order to determine what areas of the model needed enhancements for reducing uncertainty. Methods: The bone fracture risk model (BFxRM), originally published in (Nelson et al) is a probabilistic model that can assess the risk of astronaut bone fracture. This is accomplished by utilizing biomechanical models to assess the applied loads; utilizing models of spaceflight BMD loss in at-risk skeletal locations; quantifying bone strength through a relationship between areal BMD and bone failure load; and relating fracture risk index (FRI), the ratio of applied load to bone strength, to fracture probability. There are many factors associated with these calculations including
Sensitivity of PPI analysis to differences in noise reduction strategies.
Barton, M; Marecek, R; Rektor, I; Filip, P; Janousova, E; Mikl, M
2015-09-30
In some fields of fMRI data analysis, using correct methods for dealing with noise is crucial for achieving meaningful results. This paper provides a quantitative assessment of the effects of different preprocessing and noise filtering strategies on psychophysiological interactions (PPI) methods for analyzing fMRI data where noise management has not yet been established. Both real and simulated fMRI data were used to assess these effects. Four regions of interest (ROIs) were chosen for the PPI analysis on the basis of their engagement during two tasks. PPI analysis was performed for 32 different preprocessing and analysis settings, which included data filtering with RETROICOR or no such filtering; different filtering of the ROI "seed" signal with a nuisance data-driven time series; and the involvement of these data-driven time series in the subsequent PPI GLM analysis. The extent of the statistically significant results was quantified at the group level using simple descriptive statistics. Simulated data were generated to assess statistical improvement of different filtering strategies. We observed that different approaches for dealing with noise in PPI analysis yield differing results in real data. In simulated data, we found RETROICOR, seed signal filtering and the addition of data-driven covariates to the PPI design matrix significantly improves results. We recommend the use of RETROICOR, and data-driven filtering of the whole data, or alternatively, seed signal filtering with data-driven signals and the addition of data-driven covariates to the PPI design matrix. Copyright © 2015 Elsevier B.V. All rights reserved.
Kim, Min-Uk; Moon, Kyong Whan; Sohn, Jong-Ryeul; Byeon, Sang-Hoon
2018-05-18
We studied sensitive weather variables for consequence analysis, in the case of chemical leaks on the user side of offsite consequence analysis (OCA) tools. We used OCA tools Korea Offsite Risk Assessment (KORA) and Areal Location of Hazardous Atmospheres (ALOHA) in South Korea and the United States, respectively. The chemicals used for this analysis were 28% ammonia (NH₃), 35% hydrogen chloride (HCl), 50% hydrofluoric acid (HF), and 69% nitric acid (HNO₃). The accident scenarios were based on leakage accidents in storage tanks. The weather variables were air temperature, wind speed, humidity, and atmospheric stability. Sensitivity analysis was performed using the Statistical Package for the Social Sciences (SPSS) program for dummy regression analysis. Sensitivity analysis showed that impact distance was not sensitive to humidity. Impact distance was most sensitive to atmospheric stability, and was also more sensitive to air temperature than wind speed, according to both the KORA and ALOHA tools. Moreover, the weather variables were more sensitive in rural conditions than in urban conditions, with the ALOHA tool being more influenced by weather variables than the KORA tool. Therefore, if using the ALOHA tool instead of the KORA tool in rural conditions, users should be careful not to cause any differences in impact distance due to input errors of weather variables, with the most sensitive one being atmospheric stability.
Min-Uk Kim
2018-05-01
Full Text Available We studied sensitive weather variables for consequence analysis, in the case of chemical leaks on the user side of offsite consequence analysis (OCA tools. We used OCA tools Korea Offsite Risk Assessment (KORA and Areal Location of Hazardous Atmospheres (ALOHA in South Korea and the United States, respectively. The chemicals used for this analysis were 28% ammonia (NH3, 35% hydrogen chloride (HCl, 50% hydrofluoric acid (HF, and 69% nitric acid (HNO3. The accident scenarios were based on leakage accidents in storage tanks. The weather variables were air temperature, wind speed, humidity, and atmospheric stability. Sensitivity analysis was performed using the Statistical Package for the Social Sciences (SPSS program for dummy regression analysis. Sensitivity analysis showed that impact distance was not sensitive to humidity. Impact distance was most sensitive to atmospheric stability, and was also more sensitive to air temperature than wind speed, according to both the KORA and ALOHA tools. Moreover, the weather variables were more sensitive in rural conditions than in urban conditions, with the ALOHA tool being more influenced by weather variables than the KORA tool. Therefore, if using the ALOHA tool instead of the KORA tool in rural conditions, users should be careful not to cause any differences in impact distance due to input errors of weather variables, with the most sensitive one being atmospheric stability.
Shotgun lipidomic analysis of chemically sulfated sterols compromises analytical sensitivity
Casanovas, Albert; Hannibal-Bach, Hans Kristian; Jensen, Ole Nørregaard
2014-01-01
Shotgun lipidomics affords comprehensive and quantitative analysis of lipid species in cells and tissues at high-throughput [1 5]. The methodology is based on direct infusion of lipid extracts by electrospray ionization (ESI) combined with tandem mass spectrometry (MS/MS) and/or high resolution F...... low ionization efficiency in ESI [7]. For this reason, chemical derivatization procedures including acetylation [8] or sulfation [9] are commonly implemented to facilitate ionization, detection and quantification of sterols for global lipidome analysis [1-3, 10]....
Justification of investment projects of biogas systems by the sensitivity analysis
Perebijnos Vasilij Ivanovich
2015-06-01
Full Text Available Methodical features of sensitivity analysis application for evaluation of biogas plants investment projects are shown in the article. Risk factors of the indicated investment projects have been studied. Methodical basis for the use of sensitivity analysis and calculation of elasticity coefficient has been worked out. Calculation of sensitivity analysis and elasticity coefficient of three biogas plants projects, which differ in direction of biogas transformation: use in co-generation plant, application of biomethane as motor fuel and resulting carbon dioxide as marketable product, has been made. Factors strongly affecting projects efficiency have been revealed.
Jeon, Jun-Seo; Lee, Seung-Rae; Pasquinelli, Lisa
2015-01-01
., it is getting more attention as these issues are gradually alleviated. In this study, a sensitivity analysis of recovery efficiency in two cases of HT-ATES system with a single well is conducted to select key parameters. For a fractional factorial design used to choose input parameters with uniformity...... with Smoothly Clopped Absolute Deviation Penalty, is utilized. Finally, the sensitivity analysis is performed based on the variation decomposition. According to the result of sensitivity analysis, the most important input variables are selected and confirmed to consider the interaction effects for each case...
Sensitivity analysis in the WWTP modelling community – new opportunities and applications
Sin, Gürkan; Ruano, M.V.; Neumann, Marc B.
2010-01-01
design (BSM1 plant layout) using Standardized Regression Coefficients (SRC) and (ii) Applying sensitivity analysis to help fine-tuning a fuzzy controller for a BNPR plant using Morris Screening. The results obtained from each case study are then critically discussed in view of practical applications......A mainstream viewpoint on sensitivity analysis in the wastewater modelling community is that it is a first-order differential analysis of outputs with respect to the parameters – typically obtained by perturbing one parameter at a time with a small factor. An alternative viewpoint on sensitivity...
Contribution to the sample mean plot for graphical and numerical sensitivity analysis
Bolado-Lavin, R.; Castaings, W.; Tarantola, S.
2009-01-01
The contribution to the sample mean plot, originally proposed by Sinclair, is revived and further developed as practical tool for global sensitivity analysis. The potentials of this simple and versatile graphical tool are discussed. Beyond the qualitative assessment provided by this approach, a statistical test is proposed for sensitivity analysis. A case study that simulates the transport of radionuclides through the geosphere from an underground disposal vault containing nuclear waste is considered as a benchmark. The new approach is tested against a very efficient sensitivity analysis method based on state dependent parameter meta-modelling
Sensitive KIT D816V mutation analysis of blood as a diagnostic test in mastocytosis
Kielsgaard Kristensen, Thomas; Vestergaard, Hanne; Bindslev-Jensen, Carsten
2014-01-01
The recent progress in sensitive KIT D816V mutation analysis suggests that mutation analysis of peripheral blood (PB) represents a promising diagnostic test in mastocytosis. However, there is a need for systematic assessment of the analytical sensitivity and specificity of the approach in order...... to establish its value in clinical use. We therefore evaluated sensitive KIT D816V mutation analysis of PB as a diagnostic test in an entire case-series of adults with mastocytosis. We demonstrate for the first time that by using a sufficiently sensitive KIT D816V mutation analysis, it is possible to detect...... the mutation in PB in nearly all adult mastocytosis patients. The mutation was detected in PB in 78 of 83 systemic mastocytosis (94%) and 3 of 4 cutaneous mastocytosis patients (75%). The test was 100% specific as determined by analysis of clinically relevant control patients who all tested negative. Mutation...
Sensitivity Analysis of Reactor Regulating System for SMART
Jeon, Yu Lim; Kang, Han Ok; Lee, Seong Wook; Park, Cheon Tae
2009-01-01
The integral reactor technology is one of the Small and Medium sized Reactor (SMR) which has recently come into a spotlight due to its suitability for various fields. SMART (System integrated Modular Advanced ReacTor), a small sized integral type PWR with a rated thermal power of 330MWt is one of the advanced SMR. SMART developed by the Korea Atomic Energy Research Institute (KAERI), has a capacity to provide 40,000 m3 per day of potable water and 90 MW of electricity (Chang et al., 2000). Figure 1 shows the SMART which adopts a sensible mixture of new innovative design features and proven technologies aimed at achieving highly enhanced safety and improved economics. Design features contributing to a safety enhancement are basically inherent safety improving features and passive safety features. Fundamental thermal-hydraulic experiments were carried out during the design concepts development to assure the fundamental behavior of major concepts of the SMART systems. A TASS/SMR is a suitable code for accident and performance analyses of SMART. In this paper, we proposed a new power control logic for stable operating outputs of Reactor Regulating System (RRS) of SMART. We analyzed the sensitivity of operating parameter for various operating conditions
Sensitivity Analysis for Atmospheric Infrared Sounder (AIRS) CO2 Retrieval
Gat, Ilana
2012-01-01
The Atmospheric Infrared Sounder (AIRS) is a thermal infrared sensor able to retrieve the daily atmospheric state globally for clear as well as partially cloudy field-of-views. The AIRS spectrometer has 2378 channels sensing from 15.4 micrometers to 3.7 micrometers, of which a small subset in the 15 micrometers region has been selected, to date, for CO2 retrieval. To improve upon the current retrieval method, we extended the retrieval calculations to include a prior estimate component and developed a channel ranking system to optimize the channels and number of channels used. The channel ranking system uses a mathematical formalism to rapidly process and assess the retrieval potential of large numbers of channels. Implementing this system, we identifed a larger optimized subset of AIRS channels that can decrease retrieval errors and minimize the overall sensitivity to other iridescent contributors, such as water vapor, ozone, and atmospheric temperature. This methodology selects channels globally by accounting for the latitudinal, longitudinal, and seasonal dependencies of the subset. The new methodology increases accuracy in AIRS CO2 as well as other retrievals and enables the extension of retrieved CO2 vertical profiles to altitudes ranging from the lower troposphere to upper stratosphere. The extended retrieval method for CO2 vertical profile estimation using a maximum-likelihood estimation method. We use model data to demonstrate the beneficial impact of the extended retrieval method using the new channel ranking system on CO2 retrieval.
Seismic hazard analysis. Application of methodology, results, and sensitivity studies
Bernreuter, D.L.
1981-10-01
As part of the Site Specific Spectra Project, this report seeks to identify the sources of and minimize uncertainty in estimates of seismic hazards in the Eastern United States. Findings are being used by the Nuclear Regulatory Commission to develop a synthesis among various methods that can be used in evaluating seismic hazard at the various plants in the Eastern United States. In this volume, one of a five-volume series, we discuss the application of the probabilistic approach using expert opinion. The seismic hazard is developed at nine sites in the Central and Northeastern United States, and both individual experts' and synthesis results are obtained. We also discuss and evaluate the ground motion models used to develop the seismic hazard at the various sites, analyzing extensive sensitivity studies to determine the important parameters and the significance of uncertainty in them. Comparisons are made between probabilistic and real spectra for a number of Eastern earthquakes. The uncertainty in the real spectra is examined as a function of the key earthquake source parameters. In our opinion, the single most important conclusion of this study is that the use of expert opinion to supplement the sparse data available on Eastern United States earthquakes is a viable approach for determining estimated seismic hazard in this region of the country. (author)
Regional sensitivity analysis using revised mean and variance ratio functions
Wei, Pengfei; Lu, Zhenzhou; Ruan, Wenbin; Song, Jingwen
2014-01-01
The variance ratio function, derived from the contribution to sample variance (CSV) plot, is a regional sensitivity index for studying how much the output deviates from the original mean of model output when the distribution range of one input is reduced and to measure the contribution of different distribution ranges of each input to the variance of model output. In this paper, the revised mean and variance ratio functions are developed for quantifying the actual change of the model output mean and variance, respectively, when one reduces the range of one input. The connection between the revised variance ratio function and the original one is derived and discussed. It is shown that compared with the classical variance ratio function, the revised one is more suitable to the evaluation of model output variance due to reduced ranges of model inputs. A Monte Carlo procedure, which needs only a set of samples for implementing it, is developed for efficiently computing the revised mean and variance ratio functions. The revised mean and variance ratio functions are compared with the classical ones by using the Ishigami function. At last, they are applied to a planar 10-bar structure
Sensitivity analysis of uranium solubility under strongly oxidizing conditions
Liu, L.; Neretnieks, I.
1999-01-01
To evaluate the effect of geochemical conditions in the repository on the solubility of uranium under strongly oxidizing conditions, a mathematical model has been developed to determine the solubility, by utilizing a set of nonlinear algebraic equations to describe the chemical equilibria in the groundwater environment. The model takes into account the predominant precipitation-dissolution reactions, hydrolysis reactions and complexation reactions that may occur under strongly oxidizing conditions. The model also includes the solubility-limiting solids induced by the presence of carbonate, phosphate, silicate, calcium, and sodium in the groundwater. The thermodynamic equilibrium constants used in the solubility calculations are essentially taken from the NEA Thermochemical Data Base of Uranium, with some modification and some uranium minerals added, such as soddyite, rutherfordite, uranophane, uranyl orthophosphate, and becquerelite. By applying this model, the sensitivities of uranium solubility to variations in the concentrations of various groundwater component species are systematically investigated. The results show that the total analytical concentrations of carbonate, phosphate, silicate, and calcium in deep groundwater play the most important role in determining the solubility of uranium under strongly oxidizing conditions
Sensitivity Analysis of Mixed Models for Incomplete Longitudinal Data
Xu, Shu; Blozis, Shelley A.
2011-01-01
Mixed models are used for the analysis of data measured over time to study population-level change and individual differences in change characteristics. Linear and nonlinear functions may be used to describe a longitudinal response, individuals need not be observed at the same time points, and missing data, assumed to be missing at random (MAR),…
Analysis and implementation of packet preemption for Time Sensitive Networks
Zhou, Zifan; Yan, Ying; Ruepp, Sarah Renée
2017-01-01
level simulation in Riverbed Modeler. The simulation is complemented by numerical analysis which provides the average queuing delay for both types of traffic (preemptable and express). Furthermore, the paper describes an approach to implement the packet preemption solution on an FPGA in VHDL, which...... illustrates the complexity of hardware implementation....
A simple, sensitive graphical method of treating thermogravimetric analysis data
Abraham Broido
1969-01-01
Thermogravimetric Analysis (TGA) is finding increasing utility in investigations of the pyrolysis and combustion behavior of materuals. Although a theoretical treatment of the TGA behavior of an idealized reaction is relatively straight-forward, major complications can be introduced when the reactions are complex, e.g., in the pyrolysis of cellulose, and when...
Xiao-meng Song
2013-01-01
Full Text Available Parameter identification, model calibration, and uncertainty quantification are important steps in the model-building process, and are necessary for obtaining credible results and valuable information. Sensitivity analysis of hydrological model is a key step in model uncertainty quantification, which can identify the dominant parameters, reduce the model calibration uncertainty, and enhance the model optimization efficiency. There are, however, some shortcomings in classical approaches, including the long duration of time and high computation cost required to quantitatively assess the sensitivity of a multiple-parameter hydrological model. For this reason, a two-step statistical evaluation framework using global techniques is presented. It is based on (1 a screening method (Morris for qualitative ranking of parameters, and (2 a variance-based method integrated with a meta-model for quantitative sensitivity analysis, i.e., the Sobol method integrated with the response surface model (RSMSobol. First, the Morris screening method was used to qualitatively identify the parameters' sensitivity, and then ten parameters were selected to quantify the sensitivity indices. Subsequently, the RSMSobol method was used to quantify the sensitivity, i.e., the first-order and total sensitivity indices based on the response surface model (RSM were calculated. The RSMSobol method can not only quantify the sensitivity, but also reduce the computational cost, with good accuracy compared to the classical approaches. This approach will be effective and reliable in the global sensitivity analysis of a complex large-scale distributed hydrological model.
Malaguerra, Flavio; Chambon, Julie Claire Claudia; Bjerg, Poul Løgstrup
2011-01-01
experiments of complete trichloroethene (TCE) degradation in natural sediments. Global sensitivity analysis was performed using the Morris method and Sobol sensitivity indices to identify the most influential model parameters. Results show that the sulfate concentration and fermentation kinetics are the most...
We present a multi-faceted sensitivity analysis of a spatially explicit, individual-based model (IBM) (HexSim) of a threatened species, the Northern Spotted Owl (Strix occidentalis caurina) on a national forest in Washington, USA. Few sensitivity analyses have been conducted on ...
Sensitivity Analysis of an Agent-Based Model of Culture's Consequences for Trade
Burgers, S.L.G.E.; Jonker, C.M.; Hofstede, G.J.; Verwaart, D.
2010-01-01
This paper describes the analysis of an agent-based model’s sensitivity to changes in parameters that describe the agents’ cultural background, relational parameters, and parameters of the decision functions. As agent-based models may be very sensitive to small changes in parameter values, it is of
Reliability analysis of a sensitive and independent stabilometry parameter set.
Nagymáté, Gergely; Orlovits, Zsanett; Kiss, Rita M
2018-01-01
Recent studies have suggested reduced independent and sensitive parameter sets for stabilometry measurements based on correlation and variance analyses. However, the reliability of these recommended parameter sets has not been studied in the literature or not in every stance type used in stabilometry assessments, for example, single leg stances. The goal of this study is to evaluate the test-retest reliability of different time-based and frequency-based parameters that are calculated from the center of pressure (CoP) during bipedal and single leg stance for 30- and 60-second measurement intervals. Thirty healthy subjects performed repeated standing trials in a bipedal stance with eyes open and eyes closed conditions and in a single leg stance with eyes open for 60 seconds. A force distribution measuring plate was used to record the CoP. The reliability of the CoP parameters was characterized by using the intraclass correlation coefficient (ICC), standard error of measurement (SEM), minimal detectable change (MDC), coefficient of variation (CV) and CV compliance rate (CVCR). Based on the ICC, SEM and MDC results, many parameters yielded fair to good reliability values, while the CoP path length yielded the highest reliability (smallest ICC > 0.67 (0.54-0.79), largest SEM% = 19.2%). Usually, frequency type parameters and extreme value parameters yielded poor reliability values. There were differences in the reliability of the maximum CoP velocity (better with 30 seconds) and mean power frequency (better with 60 seconds) parameters between the different sampling intervals.
Reliability analysis of a sensitive and independent stabilometry parameter set
Nagymáté, Gergely; Orlovits, Zsanett
2018-01-01
Recent studies have suggested reduced independent and sensitive parameter sets for stabilometry measurements based on correlation and variance analyses. However, the reliability of these recommended parameter sets has not been studied in the literature or not in every stance type used in stabilometry assessments, for example, single leg stances. The goal of this study is to evaluate the test-retest reliability of different time-based and frequency-based parameters that are calculated from the center of pressure (CoP) during bipedal and single leg stance for 30- and 60-second measurement intervals. Thirty healthy subjects performed repeated standing trials in a bipedal stance with eyes open and eyes closed conditions and in a single leg stance with eyes open for 60 seconds. A force distribution measuring plate was used to record the CoP. The reliability of the CoP parameters was characterized by using the intraclass correlation coefficient (ICC), standard error of measurement (SEM), minimal detectable change (MDC), coefficient of variation (CV) and CV compliance rate (CVCR). Based on the ICC, SEM and MDC results, many parameters yielded fair to good reliability values, while the CoP path length yielded the highest reliability (smallest ICC > 0.67 (0.54–0.79), largest SEM% = 19.2%). Usually, frequency type parameters and extreme value parameters yielded poor reliability values. There were differences in the reliability of the maximum CoP velocity (better with 30 seconds) and mean power frequency (better with 60 seconds) parameters between the different sampling intervals. PMID:29664938
Exploratory market structure analysis. Topology-sensitive methodology.
Mazanec, Josef
1999-01-01
Given the recent abundance of brand choice data from scanner panels market researchers have neglected the measurement and analysis of perceptions. Heterogeneity of perceptions is still a largely unexplored issue in market structure and segmentation studies. Over the last decade various parametric approaches toward modelling segmented perception-preference structures such as combined MDS and Latent Class procedures have been introduced. These methods, however, are not taylored for qualitative ...
Development of Ultra-sensitive Laser Spectroscopic Analysis Technology
Cha, H. K.; Kim, D. H.; Song, K. S. (and others)
2007-04-15
Laser spectroscopic analysis technology has three distinct merits in detecting various nuclides found in nuclear fields. High selectivity originated from small bandwidth of tunable lasers makes it possible to distinguish various kinds of isotopes and isomers. High intensity of focused laser beam makes it possible to analyze ultratrace amount. Remote delivery of laser beam improves safety of workers who are exposed in dangerous environment. Also it can be applied to remote sensing of environment pollution.
Development of Ultra-sensitive Laser Spectroscopic Analysis Technology
Cha, H. K.; Kim, D. H.; Song, K. S.
2007-04-01
Laser spectroscopic analysis technology has three distinct merits in detecting various nuclides found in nuclear fields. High selectivity originated from small bandwidth of tunable lasers makes it possible to distinguish various kinds of isotopes and isomers. High intensity of focused laser beam makes it possible to analyze ultratrace amount. Remote delivery of laser beam improves safety of workers who are exposed in dangerous environment. Also it can be applied to remote sensing of environment pollution
Optimal Design, Reliability And Sensitivity Analysis Of Foundation Plate
Tvrdá Katarína
2015-12-01
Full Text Available This paper deals with the optimal design of thickness of a plate rested on Winkler’s foundation. First order method was used for the optimization, while maintaining different restrictive conditions. The aim is to obtain a minimum volume of the foundation plate. At the end some probabilistic and safety analysis of the deflection of the foundation using LHS Monte Carlo method are presented.
Xi, Qing; Li, Zhao-Fu; Luo, Chuan
2014-05-01
Sensitivity analysis of hydrology and water quality parameters has a great significance for integrated model's construction and application. Based on AnnAGNPS model's mechanism, terrain, hydrology and meteorology, field management, soil and other four major categories of 31 parameters were selected for the sensitivity analysis in Zhongtian river watershed which is a typical small watershed of hilly region in the Taihu Lake, and then used the perturbation method to evaluate the sensitivity of the parameters to the model's simulation results. The results showed that: in the 11 terrain parameters, LS was sensitive to all the model results, RMN, RS and RVC were generally sensitive and less sensitive to the output of sediment but insensitive to the remaining results. For hydrometeorological parameters, CN was more sensitive to runoff and sediment and relatively sensitive for the rest results. In field management, fertilizer and vegetation parameters, CCC, CRM and RR were less sensitive to sediment and particulate pollutants, the six fertilizer parameters (FR, FD, FID, FOD, FIP, FOP) were particularly sensitive for nitrogen and phosphorus nutrients. For soil parameters, K is quite sensitive to all the results except the runoff, the four parameters of the soil's nitrogen and phosphorus ratio (SONR, SINR, SOPR, SIPR) were less sensitive to the corresponding results. The simulation and verification results of runoff in Zhongtian watershed show a good accuracy with the deviation less than 10% during 2005- 2010. Research results have a direct reference value on AnnAGNPS model's parameter selection and calibration adjustment. The runoff simulation results of the study area also proved that the sensitivity analysis was practicable to the parameter's adjustment and showed the adaptability to the hydrology simulation in the Taihu Lake basin's hilly region and provide reference for the model's promotion in China.
Geramizadeh, Maryam; Katoozian, Hamidreza; Amid, Reza; Kadkhodazadeh, Mahdi
2018-04-01
This study aimed to optimize the thread depth and pitch of a recently designed dental implant to provide uniform stress distribution by means of a response surface optimization method available in finite element (FE) software. The sensitivity of simulation to different mechanical parameters was also evaluated. A three-dimensional model of a tapered dental implant with micro-threads in the upper area and V-shaped threads in the rest of the body was modeled and analyzed using finite element analysis (FEA). An axial load of 100 N was applied to the top of the implants. The model was optimized for thread depth and pitch to determine the optimal stress distribution. In this analysis, micro-threads had 0.25 to 0.3 mm depth and 0.27 to 0.33 mm pitch, and V-shaped threads had 0.405 to 0.495 mm depth and 0.66 to 0.8 mm pitch. The optimized depth and pitch were 0.307 and 0.286 mm for micro-threads and 0.405 and 0.808 mm for V-shaped threads, respectively. In this design, the most effective parameters on stress distribution were the depth and pitch of the micro-threads based on sensitivity analysis results. Based on the results of this study, the optimal implant design has micro-threads with 0.307 and 0.286 mm depth and pitch, respectively, in the upper area and V-shaped threads with 0.405 and 0.808 mm depth and pitch in the rest of the body. These results indicate that micro-thread parameters have a greater effect on stress and strain values.
Kuramoto, S. Janet; Stuart, Elizabeth A.
2013-01-01
Despite that randomization is the gold standard for estimating causal relationships, many questions in prevention science are left to be answered through non-experimental studies often because randomization is either infeasible or unethical. While methods such as propensity score matching can adjust for observed confounding, unobserved confounding is the Achilles heel of most non-experimental studies. This paper describes and illustrates seven sensitivity analysis techniques that assess the sensitivity of study results to an unobserved confounder. These methods were categorized into two groups to reflect differences in their conceptualization of sensitivity analysis, as well as their targets of interest. As a motivating example we examine the sensitivity of the association between maternal suicide and offspring’s risk for suicide attempt hospitalization. While inferences differed slightly depending on the type of sensitivity analysis conducted, overall the association between maternal suicide and offspring’s hospitalization for suicide attempt was found to be relatively robust to an unobserved confounder. The ease of implementation and the insight these analyses provide underscores sensitivity analysis techniques as an important tool for non-experimental studies. The implementation of sensitivity analysis can help increase confidence in results from non-experimental studies and better inform prevention researchers and policymakers regarding potential intervention targets. PMID:23408282
Liu, Weiwei; Kuramoto, S Janet; Stuart, Elizabeth A
2013-12-01
Despite the fact that randomization is the gold standard for estimating causal relationships, many questions in prevention science are often left to be answered through nonexperimental studies because randomization is either infeasible or unethical. While methods such as propensity score matching can adjust for observed confounding, unobserved confounding is the Achilles heel of most nonexperimental studies. This paper describes and illustrates seven sensitivity analysis techniques that assess the sensitivity of study results to an unobserved confounder. These methods were categorized into two groups to reflect differences in their conceptualization of sensitivity analysis, as well as their targets of interest. As a motivating example, we examine the sensitivity of the association between maternal suicide and offspring's risk for suicide attempt hospitalization. While inferences differed slightly depending on the type of sensitivity analysis conducted, overall, the association between maternal suicide and offspring's hospitalization for suicide attempt was found to be relatively robust to an unobserved confounder. The ease of implementation and the insight these analyses provide underscores sensitivity analysis techniques as an important tool for nonexperimental studies. The implementation of sensitivity analysis can help increase confidence in results from nonexperimental studies and better inform prevention researchers and policy makers regarding potential intervention targets.
Cacuci, D.G.
1984-07-01
This report presents a self-contained mathematical formalism for deterministic sensitivity analysis of two-phase flow systems, a detailed application to sensitivity analysis of the homogeneous equilibrium model of two-phase flow, and a representative application to sensitivity analysis of a model (simulating pump-trip-type accidents in BWRs) where a transition between single phase and two phase occurs. The rigor and generality of this sensitivity analysis formalism stem from the use of Gateaux (G-) differentials. This report highlights the major aspects of deterministic (forward and adjoint) sensitivity analysis, including derivation of the forward sensitivity equations, derivation of sensitivity expressions in terms of adjoint functions, explicit construction of the adjoint system satisfied by these adjoint functions, determination of the characteristics of this adjoint system, and demonstration that these characteristics are the same as those of the original quasilinear two-phase flow equations. This proves that whenever the original two-phase flow problem is solvable, the adjoint system is also solvable and, in principle, the same numerical methods can be used to solve both the original and adjoint equations
Sensitivity analysis of Takagi-Sugeno-Kang rainfall-runoff fuzzy models
A. P. Jacquin
2009-01-01
Full Text Available This paper is concerned with the sensitivity analysis of the model parameters of the Takagi-Sugeno-Kang fuzzy rainfall-runoff models previously developed by the authors. These models are classified in two types of fuzzy models, where the first type is intended to account for the effect of changes in catchment wetness and the second type incorporates seasonality as a source of non-linearity. The sensitivity analysis is performed using two global sensitivity analysis methods, namely Regional Sensitivity Analysis and Sobol's variance decomposition. The data of six catchments from different geographical locations and sizes are used in the sensitivity analysis. The sensitivity of the model parameters is analysed in terms of several measures of goodness of fit, assessing the model performance from different points of view. These measures include the Nash-Sutcliffe criteria, volumetric errors and peak errors. The results show that the sensitivity of the model parameters depends on both the catchment type and the measure used to assess the model performance.
Sensitivity and uncertainty analysis for fission product decay heat calculations
Rebah, J.; Lee, Y.K.; Nimal, J.C.; Nimal, B.; Luneville, L.; Duchemin, B.
1994-01-01
The calculated uncertainty in decay heat due to the uncertainty in basic nuclear data given in the CEA86 Library, is presented. Uncertainties in summation calculation arise from several sources: fission product yields, half-lives and average decay energies. The correlation between basic data is taken into account. The uncertainty analysis were obtained for thermal-neutron-induced fission of U235 and Pu239 in the case of burst fission and irradiation time. The calculated decay heat in this study is compared with experimental results and with new calculation using the JEF2 Library. (from authors) 6 figs., 19 refs
Spacing Sensitivity Analysis of HLW Intermediate Storage Facility
Youn, Bum Soo; Lee, Kwang Ho
2010-01-01
Currently, South Korea's spent fuels are stored in its temporary storage within the plant. But the temporary storage is expected to be reaching saturation soon. For the effective management of spent fuel wastes, the need for intermediate storage facility is a desperate position. However, the research for the intermediate storage facility for waste has not made active so far. In addition, in case of foreign countries it is mostly treated confidentially and the information isn't easy to collect. Therefore, the purpose of this study is creating the basic thermal analysis data for the waste storage facility that will be valuable in the future
How often do sensitivity analyses for economic parameters change cost-utility analysis conclusions?
Schackman, Bruce R; Gold, Heather Taffet; Stone, Patricia W; Neumann, Peter J
2004-01-01
There is limited evidence about the extent to which sensitivity analysis has been used in the cost-effectiveness literature. Sensitivity analyses for health-related QOL (HR-QOL), cost and discount rate economic parameters are of particular interest because they measure the effects of methodological and estimation uncertainties. To investigate the use of sensitivity analyses in the pharmaceutical cost-utility literature in order to test whether a change in economic parameters could result in a different conclusion regarding the cost effectiveness of the intervention analysed. Cost-utility analyses of pharmaceuticals identified in a prior comprehensive audit (70 articles) were reviewed and further audited. For each base case for which sensitivity analyses were reported (n = 122), up to two sensitivity analyses for HR-QOL (n = 133), cost (n = 99), and discount rate (n = 128) were examined. Article mentions of thresholds for acceptable cost-utility ratios were recorded (total 36). Cost-utility ratios were denominated in US dollars for the year reported in each of the original articles in order to determine whether a different conclusion would have been indicated at the time the article was published. Quality ratings from the original audit for articles where sensitivity analysis results crossed the cost-utility ratio threshold above the base-case result were compared with those that did not. The most frequently mentioned cost-utility thresholds were $US20,000/QALY, $US50,000/QALY, and $US100,000/QALY. The proportions of sensitivity analyses reporting quantitative results that crossed the threshold above the base-case results (or where the sensitivity analysis result was dominated) were 31% for HR-QOL sensitivity analyses, 20% for cost-sensitivity analyses, and 15% for discount-rate sensitivity analyses. Almost half of the discount-rate sensitivity analyses did not report quantitative results. Articles that reported sensitivity analyses where results crossed the cost
Vanderweele, Tyler J; Arah, Onyebuchi A
2011-01-01
Uncontrolled confounding in observational studies gives rise to biased effect estimates. Sensitivity analysis techniques can be useful in assessing the magnitude of these biases. In this paper, we use the potential outcomes framework to derive a general class of sensitivity-analysis formulas for outcomes, treatments, and measured and unmeasured confounding variables that may be categorical or continuous. We give results for additive, risk-ratio and odds-ratio scales. We show that these results encompass a number of more specific sensitivity-analysis methods in the statistics and epidemiology literature. The applicability, usefulness, and limits of the bias-adjustment formulas are discussed. We illustrate the sensitivity-analysis techniques that follow from our results by applying them to 3 different studies. The bias formulas are particularly simple and easy to use in settings in which the unmeasured confounding variable is binary with constant effect on the outcome across treatment levels.
Least squares shadowing sensitivity analysis of a modified Kuramoto–Sivashinsky equation
Blonigan, Patrick J.; Wang, Qiqi
2014-01-01
Highlights: •Modifying the Kuramoto–Sivashinsky equation and changing its boundary conditions make it an ergodic dynamical system. •The modified Kuramoto–Sivashinsky equation exhibits distinct dynamics for three different ranges of system parameters. •Least squares shadowing sensitivity analysis computes accurate gradients for a wide range of system parameters. - Abstract: Computational methods for sensitivity analysis are invaluable tools for scientists and engineers investigating a wide range of physical phenomena. However, many of these methods fail when applied to chaotic systems, such as the Kuramoto–Sivashinsky (K–S) equation, which models a number of different chaotic systems found in nature. The following paper discusses the application of a new sensitivity analysis method developed by the authors to a modified K–S equation. We find that least squares shadowing sensitivity analysis computes accurate gradients for solutions corresponding to a wide range of system parameters
Sensitivity and uncertainty analysis applied to a repository in rock salt
Polle, A.N.
1996-12-01
This document describes the sensitivity and uncertainty analysis with UNCSAM, as applied to a repository in rock salt for the EVEREST project. UNCSAM is a dedicated software package for sensitivity and uncertainty analysis, which was already used within the preceding PROSA project. The use of UNCSAM provides a flexible interface to EMOS ECN by substituting the sampled values in the various input files to be used by EMOS ECN ; the model calculations for this repository were performed with the EMOS ECN code. Preceding the sensitivity and uncertainty analysis, a number of preparations has been carried out to facilitate EMOS ECN with the probabilistic input data. For post-processing the EMOS ECN results, the characteristic output signals were processed. For the sensitivity and uncertainty analysis with UNCSAM the stochastic input, i.e. sampled values, and the output for the various EMOS ECN runs have been analyzed. (orig.)
The application of sensitivity analysis to models of large scale physiological systems
Leonard, J. I.
1974-01-01
A survey of the literature of sensitivity analysis as it applies to biological systems is reported as well as a brief development of sensitivity theory. A simple population model and a more complex thermoregulatory model illustrate the investigatory techniques and interpretation of parameter sensitivity analysis. The role of sensitivity analysis in validating and verifying models, and in identifying relative parameter influence in estimating errors in model behavior due to uncertainty in input data is presented. This analysis is valuable to the simulationist and the experimentalist in allocating resources for data collection. A method for reducing highly complex, nonlinear models to simple linear algebraic models that could be useful for making rapid, first order calculations of system behavior is presented.
Prunescu, Remus Mihail; Sin, Gürkan
2014-01-01
This study presents the uncertainty and sensitivity analysis of a lignocellulosic enzymatic hydrolysis model considering both model and feed parameters as sources of uncertainty. The dynamic model is parametrized for accommodating various types of biomass, and different enzymatic complexes...
May Day: A computer code to perform uncertainty and sensitivity analysis. Manuals
Bolado, R.; Alonso, A.; Moya, J.M.
1996-07-01
The computer program May Day was developed to carry out the uncertainty and sensitivity analysis in the evaluation of radioactive waste storage. The May Day was made by the Polytechnical University of Madrid. (Author)
Pi Ting; Zhang Yunqing; Chen Liping
2012-01-01
Design sensitivity analysis of flexible multibody systems is important in optimizing the performance of mechanical systems. The choice of coordinates to describe the motion of multibody systems has a great influence on the efficiency and accuracy of both the dynamic and sensitivity analysis. In the flexible multibody system dynamics, both the floating frame of reference formulation (FFRF) and absolute nodal coordinate formulation (ANCF) are frequently utilized to describe flexibility, however, only the former has been used in design sensitivity analysis. In this article, ANCF, which has been recently developed and focuses on modeling of beams and plates in large deformation problems, is extended into design sensitivity analysis of flexible multibody systems. The Motion equations of a constrained flexible multibody system are expressed as a set of index-3 differential algebraic equations (DAEs), in which the element elastic forces are defined using nonlinear strain-displacement relations. Both the direct differentiation method and adjoint variable method are performed to do sensitivity analysis and the related dynamic and sensitivity equations are integrated with HHT-I3 algorithm. In this paper, a new method to deduce system sensitivity equations is proposed. With this approach, the system sensitivity equations are constructed by assembling the element sensitivity equations with the help of invariant matrices, which results in the advantage that the complex symbolic differentiation of the dynamic equations is avoided when the flexible multibody system model is changed. Besides that, the dynamic and sensitivity equations formed with the proposed method can be efficiently integrated using HHT-I3 method, which makes the efficiency of the direct differentiation method comparable to that of the adjoint variable method when the number of design variables is not extremely large. All these improvements greatly enhance the application value of the direct differentiation
Sensitivity analysis of FDS 6 results for nuclear power plants
Alvear, Daniel; Puente, Eduardo; Abreu, Orlando [Cantabria Univ., Santander (Spain). Group GIDAI - Fire Safety-Research and Technology; Peco, Julian [Consejo de Seguridad Nuclear, Madrid (Spain)
2015-12-15
The Spanish standard ''Instruction IS-30, Rev. 1'' (February 21, 2013) allows the new approaches of risk informed performance based design (PBD) The Spanish standard ''Instruction IS-30, rev. 1'' (February 21, 2013) for demonstrating the safe shutdown capability in case of fire in nuclear power plants. In this sense, fire computer models have become an interesting tool to study real fire scenarios. Such models use a set of input parameters that define the features of the physical domain, material, radiation, turbulence, etc. This paper analyses the impact of the grid size and different sub-models of the fire simulation code FDS, version 6 with the objective to evaluate and define their relative weight in the final simulation results. For the grid size analysis, two different scale scenarios were selected, the bench scale test PENLIGHT and a large-scale test similar to Appendix B of NUREG - 1934 (17 m x 10 m x 4.6 m, with an ignition source of 2 MW and 16 cable trays). For the sub-model analysis, the PRS-INT4 real scale configuration of the INTEGRAL experimental campaign of the international OECD PRISME Project has been used. The results offer relevant data for users and show the critical parameters that must be selected properly to guarantee the quality of the simulations.
Sensitivity analysis of characteristics of spent mixed oxide fuel
Hagura, Naoto; Yoshida, Tadashi
2008-01-01
Prediction error was evaluated for decay heat and nuclide generation in spent mixed oxide (MOX) fuels on the basis of error files in JENDL-3.3. This computational analysis was performed using SWAT code system, ORIGEN2 code, and ERRORJ code. The results of nuclide generation error evaluation were compared with some discrepancies in the calculated values to experimental values (C/E ratio) which were already published and were obtained by analysis of post irradiated experiments (PIE) data. Though the discrepancies of some C/E values, especially those of americium and curium isotopes, ranged from a half to twice, the present error evaluation based on the error file of nuclide generation became 10% or less. We conclude that the discrepancy between calculation and the PIE data is almost factor 5 larger than that evaluated from the covariance data in JENDL-3.3. Therefore the practical error value of total decay heat should be 20% or more on 1 σ basis. (authors)
Sensitivity analysis of characteristics of spent mixed oxide fuel
Hagura, Naoto; Yoshida, Tadashi [Musashi Institute of Technology, 1-28-1 Tamazutsumi, Setagaya, Tokyo 158-8557 (Japan)
2008-07-01
Prediction error was evaluated for decay heat and nuclide generation in spent mixed oxide (MOX) fuels on the basis of error files in JENDL-3.3. This computational analysis was performed using SWAT code system, ORIGEN2 code, and ERRORJ code. The results of nuclide generation error evaluation were compared with some discrepancies in the calculated values to experimental values (C/E ratio) which were already published and were obtained by analysis of post irradiated experiments (PIE) data. Though the discrepancies of some C/E values, especially those of americium and curium isotopes, ranged from a half to twice, the present error evaluation based on the error file of nuclide generation became 10% or less. We conclude that the discrepancy between calculation and the PIE data is almost factor 5 larger than that evaluated from the covariance data in JENDL-3.3. Therefore the practical error value of total decay heat should be 20% or more on 1 sigma basis. (authors)
Analysis of Publically Available Skin Sensitization Data from REACH Registrations 2008–2014
Luechtefeld, Thomas; Maertens, Alexandra; Russo, Daniel P.; Rovida, Costanza; Zhu, Hao; Hartung, Thomas
2017-01-01
Summary The public data on skin sensitization from REACH registrations already included 19,111 studies on skin sensitization in December 2014, making it the largest repository of such data so far (1,470 substances with mouse LLNA, 2,787 with GPMT, 762 with both in vivo and in vitro and 139 with only in vitro data). 21% were classified as sensitizers. The extracted skin sensitization data was analyzed to identify relationships in skin sensitization guidelines, visualize structural relationships of sensitizers, and build models to predict sensitization. A chemical with molecular weight > 500 Da is generally considered non-sensitizing owing to low bioavailability, but 49 sensitizing chemicals with a molecular weight > 500 Da were found. A chemical similarity map was produced using PubChem’s 2D Tanimoto similarity metric and Gephi force layout visualization. Nine clusters of chemicals were identified by Blondel’s module recognition algorithm revealing wide module-dependent variation. Approximately 31% of mapped chemicals are Michael’s acceptors but alone this does not imply skin sensitization. A simple sensitization model using molecular weight and five ToxTree structural alerts showed a balanced accuracy of 65.8% (specificity 80.4%, sensitivity 51.4%), demonstrating that structural alerts have information value. A simple variant of k-nearest neighbors outperformed the ToxTree approach even at 75% similarity threshold (82% balanced accuracy at 0.95 threshold). At higher thresholds, the balanced accuracy increased. Lower similarity thresholds decrease sensitivity faster than specificity. This analysis scopes the landscape of chemical skin sensitization, demonstrating the value of large public datasets for health hazard prediction. PMID:26863411
Sobieszczanski-Sobieski, Jaroslaw
1988-01-01
Optimization by decomposition, complex system sensitivity analysis, and a rapid growth of disciplinary sensitivity analysis are some of the recent developments that hold promise of a quantum jump in the support engineers receive from computers in the quantitative aspects of design. Review of the salient points of these techniques is given and illustrated by examples from aircraft design as a process that combines the best of human intellect and computer power to manipulate data.
Sensitivity analysis in Gaussian Bayesian networks using a symbolic-numerical technique
Castillo, Enrique; Kjaerulff, Uffe
2003-01-01
The paper discusses the problem of sensitivity analysis in Gaussian Bayesian networks. The algebraic structure of the conditional means and variances, as rational functions involving linear and quadratic functions of the parameters, are used to simplify the sensitivity analysis. In particular the probabilities of conditional variables exceeding given values and related probabilities are analyzed. Two examples of application are used to illustrate all the concepts and methods
Emre Sert
2017-06-01
In summary, within the scope of this work, unlike the previous studies, experiments involving physical tests (i.e. tilt table, fishhook and cornering and numerical calculations are included. In addition, verification of the virtual model, parametric sensitivity analysis and the comparison of the virtual test and the physical test is performed. Because of the vigorous verification, sensitivity analysis and validation process, the results can be more reliable compared to previous studies.
A Sensitivity Analysis On The Springback Behavior Of The Unconstrained Bending Problem
Meinders, T.; Konter, A.W.A.; Meijers, S.E.; Atzema, E.H.; Kappert, H.
2005-01-01
Sheet metal forming software is commonly used in the automotive and sheet metal sectors to support the design stage. However, the ability of the currently available software to accurately predict springback is limited. A sensitivity analysis of the springback behavior of a simple product is performed to gain more knowledge into the various factors contributing to the predictability of springback. The sensitivity analysis comprises both numerical and physical aspects and the most important results are reported in this paper
Baradez, Marc-Olivier; Biziato, Daniela; Hassan, Enas; Marshall, Damian
2018-01-01
Cell therapies offer unquestionable promises for the treatment, and in some cases even the cure, of complex diseases. As we start to see more of these therapies gaining market authorization, attention is turning to the bioprocesses used for their manufacture, in particular the challenge of gaining higher levels of process control to help regulate cell behavior, manage process variability, and deliver product of a consistent quality. Many processes already incorporate the measurement of key markers such as nutrient consumption, metabolite production, and cell concentration, but these are often performed off-line and only at set time points in the process. Having the ability to monitor these markers in real-time using in-line sensors would offer significant advantages, allowing faster decision-making and a finer level of process control. In this study, we use Raman spectroscopy as an in-line optical sensor for bioprocess monitoring of an autologous T-cell immunotherapy model produced in a stirred tank bioreactor system. Using reference datasets generated on a standard bioanalyzer, we develop chemometric models from the Raman spectra for glucose, glutamine, lactate, and ammonia. These chemometric models can accurately monitor donor-specific increases in nutrient consumption and metabolite production as the primary T-cell transition from a recovery phase and begin proliferating. Using a univariate modeling approach, we then show how changes in peak intensity within the Raman spectra can be correlated with cell concentration and viability. These models, which act as surrogate markers, can be used to monitor cell behavior including cell proliferation rates, proliferative capacity, and transition of the cells to a quiescent phenotype. Finally, using the univariate models, we also demonstrate how Raman spectroscopy can be applied for real-time monitoring. The ability to measure these key parameters using an in-line Raman optical sensor makes it possible to have immediate
Baradez, Marc-Olivier; Biziato, Daniela; Hassan, Enas; Marshall, Damian
2018-01-01
Cell therapies offer unquestionable promises for the treatment, and in some cases even the cure, of complex diseases. As we start to see more of these therapies gaining market authorization, attention is turning to the bioprocesses used for their manufacture, in particular the challenge of gaining higher levels of process control to help regulate cell behavior, manage process variability, and deliver product of a consistent quality. Many processes already incorporate the measurement of key markers such as nutrient consumption, metabolite production, and cell concentration, but these are often performed off-line and only at set time points in the process. Having the ability to monitor these markers in real-time using in-line sensors would offer significant advantages, allowing faster decision-making and a finer level of process control. In this study, we use Raman spectroscopy as an in-line optical sensor for bioprocess monitoring of an autologous T-cell immunotherapy model produced in a stirred tank bioreactor system. Using reference datasets generated on a standard bioanalyzer, we develop chemometric models from the Raman spectra for glucose, glutamine, lactate, and ammonia. These chemometric models can accurately monitor donor-specific increases in nutrient consumption and metabolite production as the primary T-cell transition from a recovery phase and begin proliferating. Using a univariate modeling approach, we then show how changes in peak intensity within the Raman spectra can be correlated with cell concentration and viability. These models, which act as surrogate markers, can be used to monitor cell behavior including cell proliferation rates, proliferative capacity, and transition of the cells to a quiescent phenotype. Finally, using the univariate models, we also demonstrate how Raman spectroscopy can be applied for real-time monitoring. The ability to measure these key parameters using an in-line Raman optical sensor makes it possible to have immediate
Marc-Olivier Baradez
2018-03-01
Full Text Available Cell therapies offer unquestionable promises for the treatment, and in some cases even the cure, of complex diseases. As we start to see more of these therapies gaining market authorization, attention is turning to the bioprocesses used for their manufacture, in particular the challenge of gaining higher levels of process control to help regulate cell behavior, manage process variability, and deliver product of a consistent quality. Many processes already incorporate the measurement of key markers such as nutrient consumption, metabolite production, and cell concentration, but these are often performed off-line and only at set time points in the process. Having the ability to monitor these markers in real-time using in-line sensors would offer significant advantages, allowing faster decision-making and a finer level of process control. In this study, we use Raman spectroscopy as an in-line optical sensor for bioprocess monitoring of an autologous T-cell immunotherapy model produced in a stirred tank bioreactor system. Using reference datasets generated on a standard bioanalyzer, we develop chemometric models from the Raman spectra for glucose, glutamine, lactate, and ammonia. These chemometric models can accurately monitor donor-specific increases in nutrient consumption and metabolite production as the primary T-cell transition from a recovery phase and begin proliferating. Using a univariate modeling approach, we then show how changes in peak intensity within the Raman spectra can be correlated with cell concentration and viability. These models, which act as surrogate markers, can be used to monitor cell behavior including cell proliferation rates, proliferative capacity, and transition of the cells to a quiescent phenotype. Finally, using the univariate models, we also demonstrate how Raman spectroscopy can be applied for real-time monitoring. The ability to measure these key parameters using an in-line Raman optical sensor makes it possible
Song, Kee Nam
1998-01-01
The elastic formula of leaf type hold down spring(HDS) assembly is verified by comparing the values of elastic stiffness with the characteristic test results of the HDS's specimens. The comparisons show that the derived elastic stiffness formula is useful in reliably estimating the elastic stiffness of leaf type HDS assembly. The elastic stiffness sensitivity of leaf type HDS assembly is analyzed using the formula and its gradient vectors obtained from the mid-point formula. As a result of sensitivity analysis, the elastic stiffness sensitivity with respect to each design variable is quantified and design variables of large sensitivity are identified. Among the design variables, leaf thickness is identified as the most sensitive design variable to the elastic of leaf type HDS assembly. In addition, the elastic stiffness sensitivity, with respect to design variable, is in power-law type correlation to the base thickness of the leaf. (author)
Sensitivity analysis methods and a biosphere test case implemented in EIKOS
Ekstroem, P.A.; Broed, R.
2006-05-01
Computer-based models can be used to approximate real life processes. These models are usually based on mathematical equations, which are dependent on several variables. The predictive capability of models is therefore limited by the uncertainty in the value of these. Sensitivity analysis is used to apportion the relative importance each uncertain input parameter has on the output variation. Sensitivity analysis is therefore an essential tool in simulation modelling and for performing risk assessments. Simple sensitivity analysis techniques based on fitting the output to a linear equation are often used, for example correlation or linear regression coefficients. These methods work well for linear models, but for non-linear models their sensitivity estimations are not accurate. Usually models of complex natural systems are non-linear. Within the scope of this work, various sensitivity analysis methods, which can cope with linear, non-linear, as well as non-monotone problems, have been implemented, in a software package, EIKOS, written in Matlab language. The following sensitivity analysis methods are supported by EIKOS: Pearson product moment correlation coefficient (CC), Spearman Rank Correlation Coefficient (RCC), Partial (Rank) Correlation Coefficients (PCC), Standardized (Rank) Regression Coefficients (SRC), Sobol' method, Jansen's alternative, Extended Fourier Amplitude Sensitivity Test (EFAST) as well as the classical FAST method and the Smirnov and the Cramer-von Mises tests. A graphical user interface has also been developed, from which the user easily can load or call the model and perform a sensitivity analysis as well as uncertainty analysis. The implemented sensitivity analysis methods has been benchmarked with well-known test functions and compared with other sensitivity analysis software, with successful results. An illustration of the applicability of EIKOS is added to the report. The test case used is a landscape model consisting of several linked
Sensitivity analysis methods and a biosphere test case implemented in EIKOS
Ekstroem, P.A.; Broed, R. [Facilia AB, Stockholm, (Sweden)
2006-05-15
Computer-based models can be used to approximate real life processes. These models are usually based on mathematical equations, which are dependent on several variables. The predictive capability of models is therefore limited by the uncertainty in the value of these. Sensitivity analysis is used to apportion the relative importance each uncertain input parameter has on the output variation. Sensitivity analysis is therefore an essential tool in simulation modelling and for performing risk assessments. Simple sensitivity analysis techniques based on fitting the output to a linear equation are often used, for example correlation or linear regression coefficients. These methods work well for linear models, but for non-linear models their sensitivity estimations are not accurate. Usually models of complex natural systems are non-linear. Within the scope of this work, various sensitivity analysis methods, which can cope with linear, non-linear, as well as non-monotone problems, have been implemented, in a software package, EIKOS, written in Matlab language. The following sensitivity analysis methods are supported by EIKOS: Pearson product moment correlation coefficient (CC), Spearman Rank Correlation Coefficient (RCC), Partial (Rank) Correlation Coefficients (PCC), Standardized (Rank) Regression Coefficients (SRC), Sobol' method, Jansen's alternative, Extended Fourier Amplitude Sensitivity Test (EFAST) as well as the classical FAST method and the Smirnov and the Cramer-von Mises tests. A graphical user interface has also been developed, from which the user easily can load or call the model and perform a sensitivity analysis as well as uncertainty analysis. The implemented sensitivity analysis methods has been benchmarked with well-known test functions and compared with other sensitivity analysis software, with successful results. An illustration of the applicability of EIKOS is added to the report. The test case used is a landscape model consisting of several
Study plan for the sensitivity analysis of the Terrain-Responsive Atmospheric Code (TRAC)
Restrepo, L.F.; Deitesfeld, C.A.
1987-01-01
Rocky Flats Plant, Golden, Colorado is presently developing a computer code to model the dispersion of potential or actual releases of radioactive or toxic materials to the environment, along with the public consequences from these releases. The model, the Terrain-Responsive Atmospheric Code (TRAC), considers several complex features which could affect the overall dispersion and consequences. To help validate TRAC, a sensitivity analysis is being planned to determine how sensitive the model's solutions are to input variables. This report contains a brief description of the code, along with a list of tasks and resources needed to complete the sensitivity analysis
Sensitivity analysis of an environmental model: an application of different analysis methods
Campolongo, Francesca; Saltelli, Andrea
1997-01-01
A parametric sensitivity analysis (SA) was conducted on a well known model for the production of a key sulphur bearing compound from algal biota. The model is of interest because of the climatic relevance of the gas (dimethylsulphide, DMS), an initiator of cloud particles. A screening test at low sample size is applied first (Morris method) followed by a computationally intensive variance based measure. Standardised regression coefficients are also computed. The various SA measures are compared with each other, and the use of bootstrap is suggested to extract empirical confidence bounds on the SA estimators. For some of the input factors, investigators guess about the parameters relevance was confirmed; for some others, the results shed new light on the system mechanism and on the data parametrisation
Sensitivity analysis for decision-making using the MORE method-A Pareto approach
Ravalico, Jakin K.; Maier, Holger R.; Dandy, Graeme C.
2009-01-01
Integrated Assessment Modelling (IAM) incorporates knowledge from different disciplines to provide an overarching assessment of the impact of different management decisions. The complex nature of these models, which often include non-linearities and feedback loops, requires special attention for sensitivity analysis. This is especially true when the models are used to form the basis of management decisions, where it is important to assess how sensitive the decisions being made are to changes in model parameters. This research proposes an extension to the Management Option Rank Equivalence (MORE) method of sensitivity analysis; a new method of sensitivity analysis developed specifically for use in IAM and decision-making. The extension proposes using a multi-objective Pareto optimal search to locate minimum combined parameter changes that result in a change in the preferred management option. It is demonstrated through a case study of the Namoi River, where results show that the extension to MORE is able to provide sensitivity information for individual parameters that takes into account simultaneous variations in all parameters. Furthermore, the increased sensitivities to individual parameters that are discovered when joint parameter variation is taken into account shows the importance of ensuring that any sensitivity analysis accounts for these changes.
Global sensitivity analysis applied to drying models for one or a population of granules
Mortier, Severine Therese F. C.; Gernaey, Krist; Thomas, De Beer
2014-01-01
The development of mechanistic models for pharmaceutical processes is of increasing importance due to a noticeable shift toward continuous production in the industry. Sensitivity analysis is a powerful tool during the model building process. A global sensitivity analysis (GSA), exploring sensitiv......The development of mechanistic models for pharmaceutical processes is of increasing importance due to a noticeable shift toward continuous production in the industry. Sensitivity analysis is a powerful tool during the model building process. A global sensitivity analysis (GSA), exploring...... sensitivity in a broad parameter space, is performed to detect the most sensitive factors in two models, that is, one for drying of a single granule and one for the drying of a population of granules [using population balance model (PBM)], which was extended by including the gas velocity as extra input...... compared to our earlier work. beta(2) was found to be the most important factor for the single particle model which is useful information when performing model calibration. For the PBM-model, the granule radius and gas temperature were found to be most sensitive. The former indicates that granulator...
Developing a univariate approach to phase-I monitoring of fuzzy quality profiles
Kazem Noghondarian
2012-10-01
Full Text Available In many real-world applications, the quality of a process or a particular product can be characterized by a functional relationship called profile. A profile builds the relationships between a response quality characteristic and one or more explanatory variables. Monitoring the quality of a profile is implemented to understand and to verify the stability of this functional relationship over time. In some real applications, a fuzzy linear regression model can represent the profile adequately where the response quality characteristic is fuzzy. The purpose of this paper is to develop an approach for monitoring process/product profiles in fuzzy environment. A model in fuzzy linear regression is developed to construct the quality profiles by using linear programming and then fuzzy individuals and moving-range (I-MR control charts are developed to monitor both intercept and slope of fuzzy profiles to achieve an in-control process. A case study in customer satisfaction is presented to show the application of our approach and to express the sensitivity analysis of parameters for building a fuzzy profile.
Batista Braga, Jez Willian; Trevizan, Lilian Cristina; Nunes, Lidiane Cristina; Aparecida Rufini, Iolanda; Santos, Dario; Krug, Francisco Jose
2010-01-01
The application of laser induced breakdown spectrometry (LIBS) aiming the direct analysis of plant materials is a great challenge that still needs efforts for its development and validation. In this way, a series of experimental approaches has been carried out in order to show that LIBS can be used as an alternative method to wet acid digestions based methods for analysis of agricultural and environmental samples. The large amount of information provided by LIBS spectra for these complex samples increases the difficulties for selecting the most appropriated wavelengths for each analyte. Some applications have suggested that improvements in both accuracy and precision can be achieved by the application of multivariate calibration in LIBS data when compared to the univariate regression developed with line emission intensities. In the present work, the performance of univariate and multivariate calibration, based on partial least squares regression (PLSR), was compared for analysis of pellets of plant materials made from an appropriate mixture of cryogenically ground samples with cellulose as the binding agent. The development of a specific PLSR model for each analyte and the selection of spectral regions containing only lines of the analyte of interest were the best conditions for the analysis. In this particular application, these models showed a similar performance, but PLSR seemed to be more robust due to a lower occurrence of outliers in comparison to the univariate method. Data suggests that efforts dealing with sample presentation and fitness of standards for LIBS analysis must be done in order to fulfill the boundary conditions for matrix independent development and validation.
Sensitivity analysis of GHG emissions from biofuels in Canada
2006-01-01
This report identified key factors influencing the life-cycle greenhouse gas (GHG) emissions of ethanol and biodiesel production pathways in Canada. The report was prepared for use by policy makers in order to facilitate decision making that positively impacts the lifecycle GHG performance of renewable fuels. Four ethanol production pathways were considered: (1) ethanol production from corn; (2) ethanol production from wheat in conventional starch ethanol facilities; (3) ethanol produced from wheat straw using lignocellulosic technology; and (4) ethanol from sugar cane imported into Canada. For the pathway analysis, ethanols were blended at low levels with sulphur gasoline or used as E85 with low levels of gasoline. All ethanol scenarios were modelled for light duty vehicles. Results of the study demonstrated that all 4 pathways showed significant reductions in GHG emissions when compared to low sulphur gasoline. Differences in vehicle operation emissions between gasoline and ethanol-blended gasoline were related to a combination of the difference in the carbon content per unit of energy and the energy efficiency improvement. The study examined land use changes and feedstock production as well as all other lifecycle processes for diesel, canola, soy, palm, tallow, tallow grease, and yellow grease. A variety of transportation distances were considered. It was concluded that the alternative uses of co-products such as combustion to provide thermal energy resulted in improved GHG results. 17 refs., 117 tabs., 13 figs
Lee, Tae Hee; Yoo, Jung Hun; Choi, Hyeong Cheol
2002-01-01
A finite element package is often used as a daily design tool for engineering designers in order to analyze and improve the design. The finite element analysis can provide the responses of a system for given design variables. Although finite element analysis can quite well provide the structural behaviors for given design variables, it cannot provide enough information to improve the design such as design sensitivity coefficients. Design sensitivity analysis is an essential step to predict the change in responses due to a change in design variables and to optimize a system with the aid of the gradient-based optimization techniques. To develop a numerical method of design sensitivity analysis, analytical derivatives that are based on analytical differentiation of the continuous or discrete finite element equations are effective but analytical derivatives are difficult because of the lack of internal information of the commercial finite element package such as shape functions. Therefore, design sensitivity analysis outside of the finite element package is necessary for practical application in an industrial setting. In this paper, the semi-analytic method for design sensitivity analysis is used for the development of the design sensitivity module outside of a commercial finite element package of ANSYS. The direct differentiation method is employed to compute the design derivatives of the response and the pseudo-load for design sensitivity analysis is effectively evaluated by using the design variation of the related internal nodal forces. Especially, we suggest an effective method for stress and nonlinear design sensitivity analyses that is independent of the commercial finite element package is also discussed. Numerical examples are illustrated to show the accuracy and efficiency of the developed method and to provide insights for implementation of the suggested method into other commercial finite element packages
Caton, R. G.; Colman, J. J.; Parris, R. T.; Nickish, L.; Bullock, G.
2017-12-01
The Air Force Research Laboratory, in collaboration with NorthWest Research Associates, is developing advanced software capabilities for high fidelity simulations of high frequency (HF) sky wave propagation and performance analysis of HF systems. Based on the HiCIRF (High-frequency Channel Impulse Response Function) platform [Nickisch et. al, doi:10.1029/2011RS004928], the new Air Force Coverage Analysis Program (AFCAP) provides the modular capabilities necessary for a comprehensive sensitivity study of the large number of variables which define simulations of HF propagation modes. In this paper, we report on an initial exercise of AFCAP to analyze the sensitivities of the tool to various environmental and geophysical parameters. Through examination of the channel scattering function and amplitude-range-Doppler output on two-way propagation paths with injected target signals, we will compare simulated returns over a range of geophysical conditions as well as varying definitions for environmental noise, meteor clutter, and sea state models for Bragg backscatter. We also investigate the impacts of including clutter effects due to field-aligned backscatter from small scale ionization structures at varied levels of severity as defined by the climatologically WideBand Model (WBMOD). In the absence of additional user provided information, AFCAP relies on International Reference Ionosphere (IRI) model to define the ionospheric state for use in 2D ray tracing algorithms. Because the AFCAP architecture includes the option for insertion of a user defined gridded ionospheric representation, we compare output from the tool using the IRI and ionospheric definitions from assimilative models such as GPSII (GPS Ionospheric Inversion).
Soliman, Essam S.; Moawed, Sherif A.; Hassan, Rania A.
2017-01-01
Background and Aim: Birds litter contains unutilized nitrogen in the form of uric acid that is converted into ammonia; a fact that does not only affect poultry performance but also has a negative effect on people’s health around the farm and contributes in the environmental degradation. The influence of microclimatic ammonia emissions on Ross and Hubbard broilers reared in different housing systems at two consecutive seasons (fall and winter) was evaluated using a discriminant function analysis to differentiate between Ross and Hubbard breeds. Materials and Methods: A total number of 400 air samples were collected and analyzed for ammonia levels during the experimental period. Data were analyzed using univariate and multivariate statistical methods. Results: Ammonia levels were significantly higher (p0.05) were found between the two farms in body weight, body weight gain, feed intake, feed conversion ratio, and performance index (PI) of broilers. Body weight; weight gain and PI had increased values (pbroiler breed. Ammonia emissions were positively (although weekly) correlated with the ambient relative humidity (r=0.383; p0.05). Test of significance of discriminant function analysis did not show a classification based on the studied traits suggesting that they cannot been used as predictor variables. The percentage of correct classification was 52% and it was improved after deletion of highly correlated traits to 57%. Conclusion: The study revealed that broiler’s growth was negatively affected by increased microclimatic ammonia concentrations and recommended the analysis of broilers’ growth performance parameters data using multivariate discriminant function analysis. PMID:28919677
Code development of total sensitivity and uncertainty analysis for reactor physics calculations
Wan, C.; Cao, L.; Wu, H.; Zu, T.; Shen, W.
2015-01-01
Sensitivity and uncertainty analysis are essential parts for reactor system to perform risk and policy analysis. In this study, total sensitivity and corresponding uncertainty analysis for responses of neutronics calculations have been accomplished and developed the S&U analysis code named UNICORN. The UNICORN code can consider the implicit effects of multigroup cross sections on the responses. The UNICORN code has been applied to typical pin-cell case in this paper, and can be proved correct by comparison the results with those of the TSUNAMI-1D code. (author)
Code development of total sensitivity and uncertainty analysis for reactor physics calculations
Wan, C.; Cao, L.; Wu, H.; Zu, T., E-mail: chenghuiwan@stu.xjtu.edu.cn, E-mail: caolz@mail.xjtu.edu.cn, E-mail: hongchun@mail.xjtu.edu.cn, E-mail: tiejun@mail.xjtu.edu.cn [Xi' an Jiaotong Univ., School of Nuclear Science and Technology, Xi' an (China); Shen, W., E-mail: Wei.Shen@cnsc-ccsn.gc.ca [Xi' an Jiaotong Univ., School of Nuclear Science and Technology, Xi' an (China); Canadian Nuclear Safety Commission, Ottawa, ON (Canada)
2015-07-01
Sensitivity and uncertainty analysis are essential parts for reactor system to perform risk and policy analysis. In this study, total sensitivity and corresponding uncertainty analysis for responses of neutronics calculations have been accomplished and developed the S&U analysis code named UNICORN. The UNICORN code can consider the implicit effects of multigroup cross sections on the responses. The UNICORN code has been applied to typical pin-cell case in this paper, and can be proved correct by comparison the results with those of the TSUNAMI-1D code. (author)
Fernando Cervantes-Sanchez
2016-01-01
Full Text Available This paper presents a novel method for improving the training step of the single-scale Gabor filters by using the Boltzmann univariate marginal distribution algorithm (BUMDA in X-ray angiograms. Since the single-scale Gabor filters (SSG are governed by three parameters, the optimal selection of the SSG parameters is highly desirable in order to maximize the detection performance of coronary arteries while reducing the computational time. To obtain the best set of parameters for the SSG, the area (Az under the receiver operating characteristic curve is used as fitness function. Moreover, to classify vessel and nonvessel pixels from the Gabor filter response, the interclass variance thresholding method has been adopted. The experimental results using the proposed method obtained the highest detection rate with Az=0.9502 over a training set of 40 images and Az=0.9583 with a test set of 40 images. In addition, the experimental results of vessel segmentation provided an accuracy of 0.944 with the test set of angiograms.
Abusam, A.A.A.; Keesman, K.J.; Straten, van G.; Spanjers, H.; Meinema, K.
2001-01-01
This paper demonstrates the application of the factorial sensitivity analysis methodology in studying the influence of variations in stoichiometric, kinetic and operating parameters on the performance indices of an oxidation ditch simulation model (benchmark). Factorial sensitivity analysis