Lamb wave-based damage quantification and probability of detection modeling for fatigue life assessment of riveted lap joint

NASA Astrophysics Data System (ADS)

He, Jingjing; Wang, Dengjiang; Zhang, Weifang

2015-03-01

This study presents an experimental and modeling study for damage detection and quantification in riveted lap joints. Embedded lead zirconate titanate piezoelectric (PZT) ceramic wafer-type sensors are employed to perform in-situ non-destructive testing during fatigue cyclical loading. A multi-feature integration method is developed to quantify the crack size using signal features of correlation coefficient, amplitude change, and phase change. In addition, probability of detection (POD) model is constructed to quantify the reliability of the developed sizing method. Using the developed crack size quantification method and the resulting POD curve, probabilistic fatigue life prediction can be performed to provide comprehensive information for decision-making. The effectiveness of the overall methodology is demonstrated and validated using several aircraft lap joint specimens from different manufactures and under different loading conditions.

The NASA Langley Multidisciplinary Uncertainty Quantification Challenge

NASA Technical Reports Server (NTRS)

Crespo, Luis G.; Kenny, Sean P.; Giesy, Daniel P.

2014-01-01

This paper presents the formulation of an uncertainty quantification challenge problem consisting of five subproblems. These problems focus on key aspects of uncertainty characterization, sensitivity analysis, uncertainty propagation, extreme-case analysis, and robust design.

A DDDAS Framework for Volcanic Ash Propagation and Hazard Analysis

DTIC Science & Technology

2012-01-01

probability distribution for the input variables (for example, Hermite polynomials for normally distributed parameters, or Legendre for uniformly...parameters and windfields will drive our simulations. We will use uncertainty quantification methodology – polynomial chaos quadrature in combination...quantification methodology ? polynomial chaos quadrature in combination with data integration to complete the DDDAS loop. 15. SUBJECT TERMS 16. SECURITY

Installation Restoration Program. Phase II: Stage 1 Problem Confirmation Study, Duluth International Airport, Duluth, Minnesota.

DTIC Science & Technology

1984-10-01

8 iii "i t-. Table of Contents (cont.) Section Title Page -APPENDIX A Acronyms, Definitions, Nomenclature and Units of Measure B Scope of Work, Task...Identification/Records Search Phase II - Problem Confirmation and Quantification Phase III - Technology Base Development Phase IV - Corrective Action Only...Problem Identification/Records Search Phase II - Problem Confirmation and Quantification Phase III - Technology Base Development Phase IV - Corrective

Robust approaches to quantification of margin and uncertainty for sparse data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hund, Lauren; Schroeder, Benjamin B.; Rumsey, Kelin

Characterizing the tails of probability distributions plays a key role in quantification of margins and uncertainties (QMU), where the goal is characterization of low probability, high consequence events based on continuous measures of performance. When data are collected using physical experimentation, probability distributions are typically fit using statistical methods based on the collected data, and these parametric distributional assumptions are often used to extrapolate about the extreme tail behavior of the underlying probability distribution. In this project, we character- ize the risk associated with such tail extrapolation. Specifically, we conducted a scaling study to demonstrate the large magnitude of themore » risk; then, we developed new methods for communicat- ing risk associated with tail extrapolation from unvalidated statistical models; lastly, we proposed a Bayesian data-integration framework to mitigate tail extrapolation risk through integrating ad- ditional information. We conclude that decision-making using QMU is a complex process that cannot be achieved using statistical analyses alone.« less

Confidence estimation for quantitative photoacoustic imaging

NASA Astrophysics Data System (ADS)

Gröhl, Janek; Kirchner, Thomas; Maier-Hein, Lena

2018-02-01

Quantification of photoacoustic (PA) images is one of the major challenges currently being addressed in PA research. Tissue properties can be quantified by correcting the recorded PA signal with an estimation of the corresponding fluence. Fluence estimation itself, however, is an ill-posed inverse problem which usually needs simplifying assumptions to be solved with state-of-the-art methods. These simplifications, as well as noise and artifacts in PA images reduce the accuracy of quantitative PA imaging (PAI). This reduction in accuracy is often localized to image regions where the assumptions do not hold true. This impedes the reconstruction of functional parameters when averaging over entire regions of interest (ROI). Averaging over a subset of voxels with a high accuracy would lead to an improved estimation of such parameters. To achieve this, we propose a novel approach to the local estimation of confidence in quantitative reconstructions of PA images. It makes use of conditional probability densities to estimate confidence intervals alongside the actual quantification. It encapsulates an estimation of the errors introduced by fluence estimation as well as signal noise. We validate the approach using Monte Carlo generated data in combination with a recently introduced machine learning-based approach to quantitative PAI. Our experiments show at least a two-fold improvement in quantification accuracy when evaluating on voxels with high confidence instead of thresholding signal intensity.

Probabilistic risk analysis of building contamination.

PubMed

Bolster, D T; Tartakovsky, D M

2008-10-01

We present a general framework for probabilistic risk assessment (PRA) of building contamination. PRA provides a powerful tool for the rigorous quantification of risk in contamination of building spaces. A typical PRA starts by identifying relevant components of a system (e.g. ventilation system components, potential sources of contaminants, remediation methods) and proceeds by using available information and statistical inference to estimate the probabilities of their failure. These probabilities are then combined by means of fault-tree analyses to yield probabilistic estimates of the risk of system failure (e.g. building contamination). A sensitivity study of PRAs can identify features and potential problems that need to be addressed with the most urgency. Often PRAs are amenable to approximations, which can significantly simplify the approach. All these features of PRA are presented in this paper via a simple illustrative example, which can be built upon in further studies. The tool presented here can be used to design and maintain adequate ventilation systems to minimize exposure of occupants to contaminants.

GC-MS quantification of suspected volatile allergens in fragrances. 2. Data treatment strategies and method performances.

PubMed

Bassereau, Maud; Chaintreau, Alain; Duperrex, Stéphanie; Joulain, Daniel; Leijs, Hans; Loesing, Gerd; Owen, Neil; Sherlock, Alan; Schippa, Christine; Thorel, Pierre-Jean; Vey, Matthias

2007-01-10

The performances of the GC-MS determination of suspected allergens in fragrance concentrates have been investigated. The limit of quantification was experimentally determined (10 mg/L), and the variability was investigated for three different data treatment strategies: (1) two columns and three quantification ions; (2) two columns and one quantification ion; and (3) one column and three quantification ions. The first strategy best minimizes the risk of determination bias due to coelutions. This risk was evaluated by calculating the probability of coeluting a suspected allergen with perfume constituents exhibiting ions in common. For hydroxycitronellal, when using a two-column strategy, this may statistically occur more than once every 36 analyses for one ion or once every 144 analyses for three ions in common.

A Bayesian Approach to Evaluating Consistency between Climate Model Output and Observations

NASA Astrophysics Data System (ADS)

Braverman, A. J.; Cressie, N.; Teixeira, J.

2010-12-01

Like other scientific and engineering problems that involve physical modeling of complex systems, climate models can be evaluated and diagnosed by comparing their output to observations of similar quantities. Though the global remote sensing data record is relatively short by climate research standards, these data offer opportunities to evaluate model predictions in new ways. For example, remote sensing data are spatially and temporally dense enough to provide distributional information that goes beyond simple moments to allow quantification of temporal and spatial dependence structures. In this talk, we propose a new method for exploiting these rich data sets using a Bayesian paradigm. For a collection of climate models, we calculate posterior probabilities its members best represent the physical system each seeks to reproduce. The posterior probability is based on the likelihood that a chosen summary statistic, computed from observations, would be obtained when the model's output is considered as a realization from a stochastic process. By exploring how posterior probabilities change with different statistics, we may paint a more quantitative and complete picture of the strengths and weaknesses of the models relative to the observations. We demonstrate our method using model output from the CMIP archive, and observations from NASA's Atmospheric Infrared Sounder.

A Posteriori Error Analysis and Uncertainty Quantification for Adaptive Multiscale Operator Decomposition Methods for Multiphysics Problems

DTIC Science & Technology

2014-04-01

Barrier methods for critical exponent problems in geometric analysis and mathematical physics, J. Erway and M. Holst, Submitted for publication ...TR-14-33 A Posteriori Error Analysis and Uncertainty Quantification for Adaptive Multiscale Operator Decomposition Methods for Multiphysics...Problems Approved for public release, distribution is unlimited. April 2014 HDTRA1-09-1-0036 Donald Estep and Michael

Dual Approach To Superquantile Estimation And Applications To Density Fitting

DTIC Science & Technology

2016-06-01

incorporate additional constraints to improve the fidelity of density estimates in tail regions. We limit our investigation to data with heavy tails, where...samples of various heavy -tailed distributions. 14. SUBJECT TERMS probability density estimation, epi-splines, optimization, risk quantification...limit our investigation to data with heavy tails, where risk quantification is typically the most difficult. Demonstrations are provided in the form of

Quantification of network structural dissimilarities.

PubMed

Schieber, Tiago A; Carpi, Laura; Díaz-Guilera, Albert; Pardalos, Panos M; Masoller, Cristina; Ravetti, Martín G

2017-01-09

Identifying and quantifying dissimilarities among graphs is a fundamental and challenging problem of practical importance in many fields of science. Current methods of network comparison are limited to extract only partial information or are computationally very demanding. Here we propose an efficient and precise measure for network comparison, which is based on quantifying differences among distance probability distributions extracted from the networks. Extensive experiments on synthetic and real-world networks show that this measure returns non-zero values only when the graphs are non-isomorphic. Most importantly, the measure proposed here can identify and quantify structural topological differences that have a practical impact on the information flow through the network, such as the presence or absence of critical links that connect or disconnect connected components.

Efficient uncertainty quantification in fully-integrated surface and subsurface hydrologic simulations

NASA Astrophysics Data System (ADS)

Miller, K. L.; Berg, S. J.; Davison, J. H.; Sudicky, E. A.; Forsyth, P. A.

2018-01-01

Although high performance computers and advanced numerical methods have made the application of fully-integrated surface and subsurface flow and transport models such as HydroGeoSphere common place, run times for large complex basin models can still be on the order of days to weeks, thus, limiting the usefulness of traditional workhorse algorithms for uncertainty quantification (UQ) such as Latin Hypercube simulation (LHS) or Monte Carlo simulation (MCS), which generally require thousands of simulations to achieve an acceptable level of accuracy. In this paper we investigate non-intrusive polynomial chaos for uncertainty quantification, which in contrast to random sampling methods (e.g., LHS and MCS), represents a model response of interest as a weighted sum of polynomials over the random inputs. Once a chaos expansion has been constructed, approximating the mean, covariance, probability density function, cumulative distribution function, and other common statistics as well as local and global sensitivity measures is straightforward and computationally inexpensive, thus making PCE an attractive UQ method for hydrologic models with long run times. Our polynomial chaos implementation was validated through comparison with analytical solutions as well as solutions obtained via LHS for simple numerical problems. It was then used to quantify parametric uncertainty in a series of numerical problems with increasing complexity, including a two-dimensional fully-saturated, steady flow and transient transport problem with six uncertain parameters and one quantity of interest; a one-dimensional variably-saturated column test involving transient flow and transport, four uncertain parameters, and two quantities of interest at 101 spatial locations and five different times each (1010 total); and a three-dimensional fully-integrated surface and subsurface flow and transport problem for a small test catchment involving seven uncertain parameters and three quantities of interest at 241 different times each. Numerical experiments show that polynomial chaos is an effective and robust method for quantifying uncertainty in fully-integrated hydrologic simulations, which provides a rich set of features and is computationally efficient. Our approach has the potential for significant speedup over existing sampling based methods when the number of uncertain model parameters is modest ( ≤ 20). To our knowledge, this is the first implementation of the algorithm in a comprehensive, fully-integrated, physically-based three-dimensional hydrosystem model.

Stochastic approach for radionuclides quantification

NASA Astrophysics Data System (ADS)

Clement, A.; Saurel, N.; Perrin, G.

2018-01-01

Gamma spectrometry is a passive non-destructive assay used to quantify radionuclides present in more or less complex objects. Basic methods using empirical calibration with a standard in order to quantify the activity of nuclear materials by determining the calibration coefficient are useless on non-reproducible, complex and single nuclear objects such as waste packages. Package specifications as composition or geometry change from one package to another and involve a high variability of objects. Current quantification process uses numerical modelling of the measured scene with few available data such as geometry or composition. These data are density, material, screen, geometric shape, matrix composition, matrix and source distribution. Some of them are strongly dependent on package data knowledge and operator backgrounds. The French Commissariat à l'Energie Atomique (CEA) is developing a new methodology to quantify nuclear materials in waste packages and waste drums without operator adjustment and internal package configuration knowledge. This method suggests combining a global stochastic approach which uses, among others, surrogate models available to simulate the gamma attenuation behaviour, a Bayesian approach which considers conditional probability densities of problem inputs, and Markov Chains Monte Carlo algorithms (MCMC) which solve inverse problems, with gamma ray emission radionuclide spectrum, and outside dimensions of interest objects. The methodology is testing to quantify actinide activity in different kind of matrix, composition, and configuration of sources standard in terms of actinide masses, locations and distributions. Activity uncertainties are taken into account by this adjustment methodology.

Uncertainty propagation of p-boxes using sparse polynomial chaos expansions

NASA Astrophysics Data System (ADS)

Schöbi, Roland; Sudret, Bruno

2017-06-01

In modern engineering, physical processes are modelled and analysed using advanced computer simulations, such as finite element models. Furthermore, concepts of reliability analysis and robust design are becoming popular, hence, making efficient quantification and propagation of uncertainties an important aspect. In this context, a typical workflow includes the characterization of the uncertainty in the input variables. In this paper, input variables are modelled by probability-boxes (p-boxes), accounting for both aleatory and epistemic uncertainty. The propagation of p-boxes leads to p-boxes of the output of the computational model. A two-level meta-modelling approach is proposed using non-intrusive sparse polynomial chaos expansions to surrogate the exact computational model and, hence, to facilitate the uncertainty quantification analysis. The capabilities of the proposed approach are illustrated through applications using a benchmark analytical function and two realistic engineering problem settings. They show that the proposed two-level approach allows for an accurate estimation of the statistics of the response quantity of interest using a small number of evaluations of the exact computational model. This is crucial in cases where the computational costs are dominated by the runs of high-fidelity computational models.

Uncertainty propagation of p-boxes using sparse polynomial chaos expansions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Schöbi, Roland, E-mail: schoebi@ibk.baug.ethz.ch; Sudret, Bruno, E-mail: sudret@ibk.baug.ethz.ch

2017-06-15

In modern engineering, physical processes are modelled and analysed using advanced computer simulations, such as finite element models. Furthermore, concepts of reliability analysis and robust design are becoming popular, hence, making efficient quantification and propagation of uncertainties an important aspect. In this context, a typical workflow includes the characterization of the uncertainty in the input variables. In this paper, input variables are modelled by probability-boxes (p-boxes), accounting for both aleatory and epistemic uncertainty. The propagation of p-boxes leads to p-boxes of the output of the computational model. A two-level meta-modelling approach is proposed using non-intrusive sparse polynomial chaos expansions tomore » surrogate the exact computational model and, hence, to facilitate the uncertainty quantification analysis. The capabilities of the proposed approach are illustrated through applications using a benchmark analytical function and two realistic engineering problem settings. They show that the proposed two-level approach allows for an accurate estimation of the statistics of the response quantity of interest using a small number of evaluations of the exact computational model. This is crucial in cases where the computational costs are dominated by the runs of high-fidelity computational models.« less

On the complex quantification of risk: systems-based perspective on terrorism.

PubMed

Haimes, Yacov Y

2011-08-01

This article highlights the complexity of the quantification of the multidimensional risk function, develops five systems-based premises on quantifying the risk of terrorism to a threatened system, and advocates the quantification of vulnerability and resilience through the states of the system. The five premises are: (i) There exists interdependence between a specific threat to a system by terrorist networks and the states of the targeted system, as represented through the system's vulnerability, resilience, and criticality-impact. (ii) A specific threat, its probability, its timing, the states of the targeted system, and the probability of consequences can be interdependent. (iii) The two questions in the risk assessment process: "What is the likelihood?" and "What are the consequences?" can be interdependent. (iv) Risk management policy options can reduce both the likelihood of a threat to a targeted system and the associated likelihood of consequences by changing the states (including both vulnerability and resilience) of the system. (v) The quantification of risk to a vulnerable system from a specific threat must be built on a systemic and repeatable modeling process, by recognizing that the states of the system constitute an essential step to construct quantitative metrics of the consequences based on intelligence gathering, expert evidence, and other qualitative information. The fact that the states of all systems are functions of time (among other variables) makes the time frame pivotal in each component of the process of risk assessment, management, and communication. Thus, risk to a system, caused by an initiating event (e.g., a threat) is a multidimensional function of the specific threat, its probability and time frame, the states of the system (representing vulnerability and resilience), and the probabilistic multidimensional consequences. © 2011 Society for Risk Analysis.

Information theoretic quantification of diagnostic uncertainty.

PubMed

Westover, M Brandon; Eiseman, Nathaniel A; Cash, Sydney S; Bianchi, Matt T

2012-01-01

Diagnostic test interpretation remains a challenge in clinical practice. Most physicians receive training in the use of Bayes' rule, which specifies how the sensitivity and specificity of a test for a given disease combine with the pre-test probability to quantify the change in disease probability incurred by a new test result. However, multiple studies demonstrate physicians' deficiencies in probabilistic reasoning, especially with unexpected test results. Information theory, a branch of probability theory dealing explicitly with the quantification of uncertainty, has been proposed as an alternative framework for diagnostic test interpretation, but is even less familiar to physicians. We have previously addressed one key challenge in the practical application of Bayes theorem: the handling of uncertainty in the critical first step of estimating the pre-test probability of disease. This essay aims to present the essential concepts of information theory to physicians in an accessible manner, and to extend previous work regarding uncertainty in pre-test probability estimation by placing this type of uncertainty within a principled information theoretic framework. We address several obstacles hindering physicians' application of information theoretic concepts to diagnostic test interpretation. These include issues of terminology (mathematical meanings of certain information theoretic terms differ from clinical or common parlance) as well as the underlying mathematical assumptions. Finally, we illustrate how, in information theoretic terms, one can understand the effect on diagnostic uncertainty of considering ranges instead of simple point estimates of pre-test probability.

Vulnerability inducing technologies: An initial appreciation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Reinhardt, G.C.

The arms control community continues to act as though vulnerability were directly proportional to numbers of nuclear weapons, however rapidly they would voice their rejection of such a proposition if it were placed before them in an intellectual forum. Such neglect in matching action to knowledge is a well-known human phenomenon, but in this case it is particularly troublesome. START manages to reduce the numbers of weapons to just the right extent to encourage damage limiting. The present number of nuclear weapons on either side probably provides a robust deterrent; six thousand probably does not. To make matters worse, wemore » live in a period of burgeoning technical expansion, so that even with the best intent on both sides, new technology threatens to cause new vulnerabilities in strategic systems. To pin a shorthand label on the problem, we will refer to vulnerability inducing technology as ''VIT.'' In order to appreciate VIT, we will make a rough quantification of its consequences. This will at least provide some incentive for further study because the consequences are grave indeed. 2 tabs.« less

Uncertainty quantification of crustal scale thermo-chemical properties in Southeast Australia

NASA Astrophysics Data System (ADS)

Mather, B.; Moresi, L. N.; Rayner, P. J.

2017-12-01

The thermo-chemical properties of the crust are essential to understanding the mechanical and thermal state of the lithosphere. The uncertainties associated with these parameters are connected to the available geophysical observations and a priori information to constrain the objective function. Often, it is computationally efficient to reduce the parameter space by mapping large portions of the crust into lithologies that have assumed homogeneity. However, the boundaries of these lithologies are, in themselves, uncertain and should also be included in the inverse problem. We assimilate geological uncertainties from an a priori geological model of Southeast Australia with geophysical uncertainties from S-wave tomography and 174 heat flow observations within an adjoint inversion framework. This reduces the computational cost of inverting high dimensional probability spaces, compared to probabilistic inversion techniques that operate in the `forward' mode, but at the sacrifice of uncertainty and covariance information. We overcome this restriction using a sensitivity analysis, that perturbs our observations and a priori information within their probability distributions, to estimate the posterior uncertainty of thermo-chemical parameters in the crust.

Traits Without Borders: Integrating Functional Diversity Across Scales.

PubMed

Carmona, Carlos P; de Bello, Francesco; Mason, Norman W H; Lepš, Jan

2016-05-01

Owing to the conceptual complexity of functional diversity (FD), a multitude of different methods are available for measuring it, with most being operational at only a small range of spatial scales. This causes uncertainty in ecological interpretations and limits the potential to generalize findings across studies or compare patterns across scales. We solve this problem by providing a unified framework expanding on and integrating existing approaches. The framework, based on trait probability density (TPD), is the first to fully implement the Hutchinsonian concept of the niche as a probabilistic hypervolume in estimating FD. This novel approach could revolutionize FD-based research by allowing quantification of the various FD components from organismal to macroecological scales, and allowing seamless transitions between scales. Copyright © 2016 Elsevier Ltd. All rights reserved.

AVQS: attack route-based vulnerability quantification scheme for smart grid.

PubMed

Ko, Jongbin; Lim, Hyunwoo; Lee, Seokjun; Shon, Taeshik

2014-01-01

A smart grid is a large, consolidated electrical grid system that includes heterogeneous networks and systems. Based on the data, a smart grid system has a potential security threat in its network connectivity. To solve this problem, we develop and apply a novel scheme to measure the vulnerability in a smart grid domain. Vulnerability quantification can be the first step in security analysis because it can help prioritize the security problems. However, existing vulnerability quantification schemes are not suitable for smart grid because they do not consider network vulnerabilities. We propose a novel attack route-based vulnerability quantification scheme using a network vulnerability score and an end-to-end security score, depending on the specific smart grid network environment to calculate the vulnerability score for a particular attack route. To evaluate the proposed approach, we derive several attack scenarios from the advanced metering infrastructure domain. The experimental results of the proposed approach and the existing common vulnerability scoring system clearly show that we need to consider network connectivity for more optimized vulnerability quantification.

Dynamic remapping decisions in multi-phase parallel computations

NASA Technical Reports Server (NTRS)

Nicol, D. M.; Reynolds, P. F., Jr.

1986-01-01

The effectiveness of any given mapping of workload to processors in a parallel system is dependent on the stochastic behavior of the workload. Program behavior is often characterized by a sequence of phases, with phase changes occurring unpredictably. During a phase, the behavior is fairly stable, but may become quite different during the next phase. Thus a workload assignment generated for one phase may hinder performance during the next phase. We consider the problem of deciding whether to remap a paralled computation in the face of uncertainty in remapping's utility. Fundamentally, it is necessary to balance the expected remapping performance gain against the delay cost of remapping. This paper treats this problem formally by constructing a probabilistic model of a computation with at most two phases. We use stochastic dynamic programming to show that the remapping decision policy which minimizes the expected running time of the computation has an extremely simple structure: the optimal decision at any step is followed by comparing the probability of remapping gain against a threshold. This theoretical result stresses the importance of detecting a phase change, and assessing the possibility of gain from remapping. We also empirically study the sensitivity of optimal performance to imprecise decision threshold. Under a wide range of model parameter values, we find nearly optimal performance if remapping is chosen simply when the gain probability is high. These results strongly suggest that except in extreme cases, the remapping decision problem is essentially that of dynamically determining whether gain can be achieved by remapping after a phase change; precise quantification of the decision model parameters is not necessary.

Elicitation of quantitative data from a heterogeneous expert panel: formal process and application in animal health.

PubMed

Van der Fels-Klerx, Ine H J; Goossens, Louis H J; Saatkamp, Helmut W; Horst, Suzan H S

2002-02-01

This paper presents a protocol for a formal expert judgment process using a heterogeneous expert panel aimed at the quantification of continuous variables. The emphasis is on the process's requirements related to the nature of expertise within the panel, in particular the heterogeneity of both substantive and normative expertise. The process provides the opportunity for interaction among the experts so that they fully understand and agree upon the problem at hand, including qualitative aspects relevant to the variables of interest, prior to the actual quantification task. Individual experts' assessments on the variables of interest, cast in the form of subjective probability density functions, are elicited with a minimal demand for normative expertise. The individual experts' assessments are aggregated into a single probability density function per variable, thereby weighting the experts according to their expertise. Elicitation techniques proposed include the Delphi technique for the qualitative assessment task and the ELI method for the actual quantitative assessment task. Appropriately, the Classical model was used to weight the experts' assessments in order to construct a single distribution per variable. Applying this model, the experts' quality typically was based on their performance on seed variables. An application of the proposed protocol in the broad and multidisciplinary field of animal health is presented. Results of this expert judgment process showed that the proposed protocol in combination with the proposed elicitation and analysis techniques resulted in valid data on the (continuous) variables of interest. In conclusion, the proposed protocol for a formal expert judgment process aimed at the elicitation of quantitative data from a heterogeneous expert panel provided satisfactory results. Hence, this protocol might be useful for expert judgment studies in other broad and/or multidisciplinary fields of interest.

Applied Mathematics in EM Studies with Special Emphasis on an Uncertainty Quantification and 3-D Integral Equation Modelling

NASA Astrophysics Data System (ADS)

Pankratov, Oleg; Kuvshinov, Alexey

2016-01-01

Despite impressive progress in the development and application of electromagnetic (EM) deterministic inverse schemes to map the 3-D distribution of electrical conductivity within the Earth, there is one question which remains poorly addressed—uncertainty quantification of the recovered conductivity models. Apparently, only an inversion based on a statistical approach provides a systematic framework to quantify such uncertainties. The Metropolis-Hastings (M-H) algorithm is the most popular technique for sampling the posterior probability distribution that describes the solution of the statistical inverse problem. However, all statistical inverse schemes require an enormous amount of forward simulations and thus appear to be extremely demanding computationally, if not prohibitive, if a 3-D set up is invoked. This urges development of fast and scalable 3-D modelling codes which can run large-scale 3-D models of practical interest for fractions of a second on high-performance multi-core platforms. But, even with these codes, the challenge for M-H methods is to construct proposal functions that simultaneously provide a good approximation of the target density function while being inexpensive to be sampled. In this paper we address both of these issues. First we introduce a variant of the M-H method which uses information about the local gradient and Hessian of the penalty function. This, in particular, allows us to exploit adjoint-based machinery that has been instrumental for the fast solution of deterministic inverse problems. We explain why this modification of M-H significantly accelerates sampling of the posterior probability distribution. In addition we show how Hessian handling (inverse, square root) can be made practicable by a low-rank approximation using the Lanczos algorithm. Ultimately we discuss uncertainty analysis based on stochastic inversion results. In addition, we demonstrate how this analysis can be performed within a deterministic approach. In the second part, we summarize modern trends in the development of efficient 3-D EM forward modelling schemes with special emphasis on recent advances in the integral equation approach.

Patterns of cross-contamination in a multispecies population genomic project: detection, quantification, impact, and solutions.

PubMed

Ballenghien, Marion; Faivre, Nicolas; Galtier, Nicolas

2017-03-29

Contamination is a well-known but often neglected problem in molecular biology. Here, we investigated the prevalence of cross-contamination among 446 samples from 116 distinct species of animals, which were processed in the same laboratory and subjected to subcontracted transcriptome sequencing. Using cytochrome oxidase 1 as a barcode, we identified a minimum of 782 events of between-species contamination, with approximately 80% of our samples being affected. An analysis of laboratory metadata revealed a strong effect of the sequencing center: nearly all the detected events of between-species contamination involved species that were sent the same day to the same company. We introduce new methods to address the amount of within-species, between-individual contamination, and to correct for this problem when calling genotypes from base read counts. We report evidence for pervasive within-species contamination in this data set, and show that classical population genomic statistics, such as synonymous diversity, the ratio of non-synonymous to synonymous diversity, inbreeding coefficient F IT , and Tajima's D, are sensitive to this problem to various extents. Control analyses suggest that our published results are probably robust to the problem of contamination. Recommendations on how to prevent or avoid contamination in large-scale population genomics/molecular ecology are provided based on this analysis.

Model for spectral and chromatographic data

DOEpatents

Jarman, Kristin [Richland, WA; Willse, Alan [Richland, WA; Wahl, Karen [Richland, WA; Wahl, Jon [Richland, WA

2002-11-26

A method and apparatus using a spectral analysis technique are disclosed. In one form of the invention, probabilities are selected to characterize the presence (and in another form, also a quantification of a characteristic) of peaks in an indexed data set for samples that match a reference species, and other probabilities are selected for samples that do not match the reference species. An indexed data set is acquired for a sample, and a determination is made according to techniques exemplified herein as to whether the sample matches or does not match the reference species. When quantification of peak characteristics is undertaken, the model is appropriately expanded, and the analysis accounts for the characteristic model and data. Further techniques are provided to apply the methods and apparatuses to process control, cluster analysis, hypothesis testing, analysis of variance, and other procedures involving multiple comparisons of indexed data.

A framework for assessing the uncertainty in wave energy delivery to targeted subsurface formations

NASA Astrophysics Data System (ADS)

Karve, Pranav M.; Kallivokas, Loukas F.; Manuel, Lance

2016-02-01

Stress wave stimulation of geological formations has potential applications in petroleum engineering, hydro-geology, and environmental engineering. The stimulation can be applied using wave sources whose spatio-temporal characteristics are designed to focus the emitted wave energy into the target region. Typically, the design process involves numerical simulations of the underlying wave physics, and assumes a perfect knowledge of the material properties and the overall geometry of the geostructure. In practice, however, precise knowledge of the properties of the geological formations is elusive, and quantification of the reliability of a deterministic approach is crucial for evaluating the technical and economical feasibility of the design. In this article, we discuss a methodology that could be used to quantify the uncertainty in the wave energy delivery. We formulate the wave propagation problem for a two-dimensional, layered, isotropic, elastic solid truncated using hybrid perfectly-matched-layers (PMLs), and containing a target elastic or poroelastic inclusion. We define a wave motion metric to quantify the amount of the delivered wave energy. We, then, treat the material properties of the layers as random variables, and perform a first-order uncertainty analysis of the formation to compute the probabilities of failure to achieve threshold values of the motion metric. We illustrate the uncertainty quantification procedure using synthetic data.

AVQS: Attack Route-Based Vulnerability Quantification Scheme for Smart Grid

PubMed Central

Lim, Hyunwoo; Lee, Seokjun; Shon, Taeshik

2014-01-01

A smart grid is a large, consolidated electrical grid system that includes heterogeneous networks and systems. Based on the data, a smart grid system has a potential security threat in its network connectivity. To solve this problem, we develop and apply a novel scheme to measure the vulnerability in a smart grid domain. Vulnerability quantification can be the first step in security analysis because it can help prioritize the security problems. However, existing vulnerability quantification schemes are not suitable for smart grid because they do not consider network vulnerabilities. We propose a novel attack route-based vulnerability quantification scheme using a network vulnerability score and an end-to-end security score, depending on the specific smart grid network environment to calculate the vulnerability score for a particular attack route. To evaluate the proposed approach, we derive several attack scenarios from the advanced metering infrastructure domain. The experimental results of the proposed approach and the existing common vulnerability scoring system clearly show that we need to consider network connectivity for more optimized vulnerability quantification. PMID:25152923

A short course on measure and probability theories

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pebay, Philippe Pierre

2004-02-01

This brief Introduction to Measure Theory, and its applications to Probabilities, corresponds to the lecture notes of a seminar series given at Sandia National Laboratories in Livermore, during the spring of 2003. The goal of these seminars was to provide a minimal background to Computational Combustion scientists interested in using more advanced stochastic concepts and methods, e.g., in the context of uncertainty quantification. Indeed, most mechanical engineering curricula do not provide students with formal training in the field of probability, and even in less in measure theory. However, stochastic methods have been used more and more extensively in the pastmore » decade, and have provided more successful computational tools. Scientists at the Combustion Research Facility of Sandia National Laboratories have been using computational stochastic methods for years. Addressing more and more complex applications, and facing difficult problems that arose in applications showed the need for a better understanding of theoretical foundations. This is why the seminar series was launched, and these notes summarize most of the concepts which have been discussed. The goal of the seminars was to bring a group of mechanical engineers and computational combustion scientists to a full understanding of N. WIENER'S polynomial chaos theory. Therefore, these lectures notes are built along those lines, and are not intended to be exhaustive. In particular, the author welcomes any comments or criticisms.« less

Most Probable Number Rapid Viability PCR Method to Detect Viable Spores of Bacillus anthracis in Swab Samples

DOE Office of Scientific and Technical Information (OSTI.GOV)

Letant, S E; Kane, S R; Murphy, G A

2008-05-30

This note presents a comparison of Most-Probable-Number Rapid Viability (MPN-RV) PCR and traditional culture methods for the quantification of Bacillus anthracis Sterne spores in macrofoam swabs generated by the Centers for Disease Control and Prevention (CDC) for a multi-center validation study aimed at testing environmental swab processing methods for recovery, detection, and quantification of viable B. anthracis spores from surfaces. Results show that spore numbers provided by the MPN RV-PCR method were in statistical agreement with the CDC conventional culture method for all three levels of spores tested (10{sup 4}, 10{sup 2}, and 10 spores) even in the presence ofmore » dirt. In addition to detecting low levels of spores in environmental conditions, the MPN RV-PCR method is specific, and compatible with automated high-throughput sample processing and analysis protocols.« less

Sensitivity Analysis and Uncertainty Quantification for the LAMMPS Molecular Dynamics Simulation Code

DOE Office of Scientific and Technical Information (OSTI.GOV)

Picard, Richard Roy; Bhat, Kabekode Ghanasham

2017-07-18

We examine sensitivity analysis and uncertainty quantification for molecular dynamics simulation. Extreme (large or small) output values for the LAMMPS code often occur at the boundaries of input regions, and uncertainties in those boundary values are overlooked by common SA methods. Similarly, input values for which code outputs are consistent with calibration data can also occur near boundaries. Upon applying approaches in the literature for imprecise probabilities (IPs), much more realistic results are obtained than for the complacent application of standard SA and code calibration.

Quantification and Multi-purpose Allocation of Water Resources in a Dual-reservoir System

NASA Astrophysics Data System (ADS)

Salami, Y. D.

2017-12-01

Transboundary rivers that run through separate water management jurisdictions sometimes experience competitive water usage. Where the river has multiple existing or planned dams along its course, quantification and efficient allocation of water for such purposes as hydropower generation, irrigation for agriculture, and water supply can be a challenge. This problem is even more pronounced when large parts of the river basin are located in semi-arid regions known for water insecurity, poor crop yields from irrigation scheme failures, and human population displacement arising from water-related conflict. This study seeks to mitigate the impacts of such factors on the Kainji-Jebba dual-reservoir system located along the Niger River in Africa by seasonally quantifying and efficiently apportioning water to all stipulated uses of both dams thereby improving operational policy and long-term water security. Historical storage fluctuations (18 km3 to 5 km3) and flows into and out of both reservoirs were analyzed for relationships to such things as surrounding catchment contribution, dam operational policies, irrigation and hydropower requirements, etc. Optimum values of the aforementioned parameters were then determined by simulations based upon hydrological contributions and withdrawals and worst case scenarios of natural and anthropogenic conditions (like annual probability of reservoir depletion) affecting water availability and allocation. Finally, quantification and optimized allocation of water was done based on needs for hydropower, irrigation for agriculture, water supply, and storage evacuation for flood control. Results revealed that water supply potential increased by 69%, average agricultural yield improved by 36%, and hydropower generation increased by 54% and 66% at the upstream and downstream dams respectively. Lessons learned from this study may help provide a robust and practical means of water resources management in similar river basins and multi-reservoir systems.

Quantile-based bias correction and uncertainty quantification of extreme event attribution statements

DOE PAGES

Jeon, Soyoung; Paciorek, Christopher J.; Wehner, Michael F.

2016-02-16

Extreme event attribution characterizes how anthropogenic climate change may have influenced the probability and magnitude of selected individual extreme weather and climate events. Attribution statements often involve quantification of the fraction of attributable risk (FAR) or the risk ratio (RR) and associated confidence intervals. Many such analyses use climate model output to characterize extreme event behavior with and without anthropogenic influence. However, such climate models may have biases in their representation of extreme events. To account for discrepancies in the probabilities of extreme events between observational datasets and model datasets, we demonstrate an appropriate rescaling of the model output basedmore » on the quantiles of the datasets to estimate an adjusted risk ratio. Our methodology accounts for various components of uncertainty in estimation of the risk ratio. In particular, we present an approach to construct a one-sided confidence interval on the lower bound of the risk ratio when the estimated risk ratio is infinity. We demonstrate the methodology using the summer 2011 central US heatwave and output from the Community Earth System Model. In this example, we find that the lower bound of the risk ratio is relatively insensitive to the magnitude and probability of the actual event.« less

Direct measurement of the free energy of aging hard sphere colloidal glasses.

PubMed

Zargar, Rojman; Nienhuis, Bernard; Schall, Peter; Bonn, Daniel

2013-06-21

The nature of the glass transition is one of the most important unsolved problems in condensed matter physics. The difference between glasses and liquids is believed to be caused by very large free energy barriers for particle rearrangements; however, so far it has not been possible to confirm this experimentally. We provide the first quantitative determination of the free energy for an aging hard sphere colloidal glass. The determination of the free energy allows for a number of new insights in the glass transition, notably the quantification of the strong spatial and temporal heterogeneity in the free energy. A study of the local minima of the free energy reveals that the observed variations are directly related to the rearrangements of the particles. Our main finding is that the probability of particle rearrangements shows a power law dependence on the free energy changes associated with the rearrangements similar to the Gutenberg-Richter law in seismology.

Spatial Uncertainty Modeling of Fuzzy Information in Images for Pattern Classification

PubMed Central

Pham, Tuan D.

2014-01-01

The modeling of the spatial distribution of image properties is important for many pattern recognition problems in science and engineering. Mathematical methods are needed to quantify the variability of this spatial distribution based on which a decision of classification can be made in an optimal sense. However, image properties are often subject to uncertainty due to both incomplete and imprecise information. This paper presents an integrated approach for estimating the spatial uncertainty of vagueness in images using the theory of geostatistics and the calculus of probability measures of fuzzy events. Such a model for the quantification of spatial uncertainty is utilized as a new image feature extraction method, based on which classifiers can be trained to perform the task of pattern recognition. Applications of the proposed algorithm to the classification of various types of image data suggest the usefulness of the proposed uncertainty modeling technique for texture feature extraction. PMID:25157744

Quo vadis plant hormone analysis?

PubMed

Tarkowská, Danuše; Novák, Ondřej; Floková, Kristýna; Tarkowski, Petr; Turečková, Veronika; Grúz, Jiří; Rolčík, Jakub; Strnad, Miroslav

2014-07-01

Plant hormones act as chemical messengers in the regulation of myriads of physiological processes that occur in plants. To date, nine groups of plant hormones have been identified and more will probably be discovered. Furthermore, members of each group may participate in the regulation of physiological responses in planta both alone and in concert with members of either the same group or other groups. The ideal way to study biochemical processes involving these signalling molecules is 'hormone profiling', i.e. quantification of not only the hormones themselves, but also their biosynthetic precursors and metabolites in plant tissues. However, this is highly challenging since trace amounts of all of these substances are present in highly complex plant matrices. Here, we review advances, current trends and future perspectives in the analysis of all currently known plant hormones and the associated problems of extracting them from plant tissues and separating them from the numerous potentially interfering compounds.

Random Predictor Models for Rigorous Uncertainty Quantification: Part 2

NASA Technical Reports Server (NTRS)

Crespo, Luis G.; Kenny, Sean P.; Giesy, Daniel P.

2015-01-01

This and a companion paper propose techniques for constructing parametric mathematical models describing key features of the distribution of an output variable given input-output data. By contrast to standard models, which yield a single output value at each value of the input, Random Predictors Models (RPMs) yield a random variable at each value of the input. Optimization-based strategies for calculating RPMs having a polynomial dependency on the input and a linear dependency on the parameters are proposed. These formulations yield RPMs having various levels of fidelity in which the mean, the variance, and the range of the model's parameter, thus of the output, are prescribed. As such they encompass all RPMs conforming to these prescriptions. The RPMs are optimal in the sense that they yield the tightest predictions for which all (or, depending on the formulation, most) of the observations are less than a fixed number of standard deviations from the mean prediction. When the data satisfies mild stochastic assumptions, and the optimization problem(s) used to calculate the RPM is convex (or, when its solution coincides with the solution to an auxiliary convex problem), the model's reliability, which is the probability that a future observation would be within the predicted ranges, is bounded rigorously.

Random Predictor Models for Rigorous Uncertainty Quantification: Part 1

NASA Technical Reports Server (NTRS)

Crespo, Luis G.; Kenny, Sean P.; Giesy, Daniel P.

2015-01-01

This and a companion paper propose techniques for constructing parametric mathematical models describing key features of the distribution of an output variable given input-output data. By contrast to standard models, which yield a single output value at each value of the input, Random Predictors Models (RPMs) yield a random variable at each value of the input. Optimization-based strategies for calculating RPMs having a polynomial dependency on the input and a linear dependency on the parameters are proposed. These formulations yield RPMs having various levels of fidelity in which the mean and the variance of the model's parameters, thus of the predicted output, are prescribed. As such they encompass all RPMs conforming to these prescriptions. The RPMs are optimal in the sense that they yield the tightest predictions for which all (or, depending on the formulation, most) of the observations are less than a fixed number of standard deviations from the mean prediction. When the data satisfies mild stochastic assumptions, and the optimization problem(s) used to calculate the RPM is convex (or, when its solution coincides with the solution to an auxiliary convex problem), the model's reliability, which is the probability that a future observation would be within the predicted ranges, can be bounded tightly and rigorously.

Introduction of Mass Spectrometry in an First-Semester General Chemistry Laboratory Course: Quantification of Mtbe or Dmso in Water

ERIC Educational Resources Information Center

Solow, Mike

2004-01-01

Quantification of a contaminant in water provides the first-year general chemistry students with a tangible application of mass spectrometry. The relevance of chemistry to assessing and solving environmental problems is highlighted for students when they perform mass spectroscopy experiments.

How to Make Data a Blessing to Parametric Uncertainty Quantification and Reduction?

NASA Astrophysics Data System (ADS)

Ye, M.; Shi, X.; Curtis, G. P.; Kohler, M.; Wu, J.

2013-12-01

In a Bayesian point of view, probability of model parameters and predictions are conditioned on data used for parameter inference and prediction analysis. It is critical to use appropriate data for quantifying parametric uncertainty and its propagation to model predictions. However, data are always limited and imperfect. When a dataset cannot properly constrain model parameters, it may lead to inaccurate uncertainty quantification. While in this case data appears to be a curse to uncertainty quantification, a comprehensive modeling analysis may help understand the cause and characteristics of parametric uncertainty and thus turns data into a blessing. In this study, we illustrate impacts of data on uncertainty quantification and reduction using an example of surface complexation model (SCM) developed to simulate uranyl (U(VI)) adsorption. The model includes two adsorption sites, referred to as strong and weak sites. The amount of uranium adsorption on these sites determines both the mean arrival time and the long tail of the breakthrough curves. There is one reaction on the weak site but two reactions on the strong site. The unknown parameters include fractions of the total surface site density of the two sites and surface complex formation constants of the three reactions. A total of seven experiments were conducted with different geochemical conditions to estimate these parameters. The experiments with low initial concentration of U(VI) result in a large amount of parametric uncertainty. A modeling analysis shows that it is because the experiments cannot distinguish the relative adsorption affinity of the strong and weak sites on uranium adsorption. Therefore, the experiments with high initial concentration of U(VI) are needed, because in the experiments the strong site is nearly saturated and the weak site can be determined. The experiments with high initial concentration of U(VI) are a blessing to uncertainty quantification, and the experiments with low initial concentration help modelers turn a curse into a blessing. The data impacts on uncertainty quantification and reduction are quantified using probability density functions of model parameters obtained from Markov Chain Monte Carlo simulation using the DREAM algorithm. This study provides insights to model calibration, uncertainty quantification, experiment design, and data collection in groundwater reactive transport modeling and other environmental modeling.

The Efficacy of Using Diagrams When Solving Probability Word Problems in College

ERIC Educational Resources Information Center

Beitzel, Brian D.; Staley, Richard K.

2015-01-01

Previous experiments have shown a deleterious effect of visual representations on college students' ability to solve total- and joint-probability word problems. The present experiments used conditional-probability problems, known to be more difficult than total- and joint-probability problems. The diagram group was instructed in how to use tree…

QUANTIFICATION OF FLUOROTELOMER-BASED CHEMICALS IN MAMMALIAN MATRICES BY MONITORING PERFLUOROALKYL CHAIN FRAGMENTS WITH GC/MS

EPA Science Inventory

Perfluorocarboxylic acids (PFCAs), namely perfluorooctanoic acid (PFOA) and perfluorononanoic acid (PFNA), have been identified as persistent, bioaccurnulative and potentially toxic compounds. The structural analog, 8-2 fluorotelomer alcohol (8-2 FTOH) is considered the probable ...

Goal-Oriented Probability Density Function Methods for Uncertainty Quantification

DTIC Science & Technology

2015-12-11

approximations or data-driven approaches. We investigated the accuracy of analytical tech- niques based Kubo -Van Kampen operator cumulant expansions for...analytical techniques based Kubo -Van Kampen operator cumulant expansions for Langevin equations driven by fractional Brownian motion and other noises

Modelling detection probabilities to evaluate management and control tools for an invasive species

USGS Publications Warehouse

Christy, M.T.; Yackel Adams, A.A.; Rodda, G.H.; Savidge, J.A.; Tyrrell, C.L.

2010-01-01

For most ecologists, detection probability (p) is a nuisance variable that must be modelled to estimate the state variable of interest (i.e. survival, abundance, or occupancy). However, in the realm of invasive species control, the rate of detection and removal is the rate-limiting step for management of this pervasive environmental problem. For strategic planning of an eradication (removal of every individual), one must identify the least likely individual to be removed, and determine the probability of removing it. To evaluate visual searching as a control tool for populations of the invasive brown treesnake Boiga irregularis, we designed a mark-recapture study to evaluate detection probability as a function of time, gender, size, body condition, recent detection history, residency status, searcher team and environmental covariates. We evaluated these factors using 654 captures resulting from visual detections of 117 snakes residing in a 5-ha semi-forested enclosure on Guam, fenced to prevent immigration and emigration of snakes but not their prey. Visual detection probability was low overall (= 0??07 per occasion) but reached 0??18 under optimal circumstances. Our results supported sex-specific differences in detectability that were a quadratic function of size, with both small and large females having lower detection probabilities than males of those sizes. There was strong evidence for individual periodic changes in detectability of a few days duration, roughly doubling detection probability (comparing peak to non-elevated detections). Snakes in poor body condition had estimated mean detection probabilities greater than snakes with high body condition. Search teams with high average detection rates exhibited detection probabilities about twice that of search teams with low average detection rates. Surveys conducted with bright moonlight and strong wind gusts exhibited moderately decreased probabilities of detecting snakes. Synthesis and applications. By emphasizing and modelling detection probabilities, we now know: (i) that eradication of this species by searching is possible, (ii) how much searching effort would be required, (iii) under what environmental conditions searching would be most efficient, and (iv) several factors that are likely to modulate this quantification when searching is applied to new areas. The same approach can be use for evaluation of any control technology or population monitoring programme. ?? 2009 The Authors. Journal compilation ?? 2009 British Ecological Society.

Application of Gaussian Process Modeling to Analysis of Functional Unreliability

DOE Office of Scientific and Technical Information (OSTI.GOV)

R. Youngblood

2014-06-01

This paper applies Gaussian Process (GP) modeling to analysis of the functional unreliability of a “passive system.” GPs have been used widely in many ways [1]. The present application uses a GP for emulation of a system simulation code. Such an emulator can be applied in several distinct ways, discussed below. All applications illustrated in this paper have precedents in the literature; the present paper is an application of GP technology to a problem that was originally analyzed [2] using neural networks (NN), and later [3, 4] by a method called “Alternating Conditional Expectations” (ACE). This exercise enables a multifacetedmore » comparison of both the processes and the results. Given knowledge of the range of possible values of key system variables, one could, in principle, quantify functional unreliability by sampling from their joint probability distribution, and performing a system simulation for each sample to determine whether the function succeeded for that particular setting of the variables. Using previously available system simulation codes, such an approach is generally impractical for a plant-scale problem. It has long been recognized, however, that a well-trained code emulator or surrogate could be used in a sampling process to quantify certain performance metrics, even for plant-scale problems. “Response surfaces” were used for this many years ago. But response surfaces are at their best for smoothly varying functions; in regions of parameter space where key system performance metrics may behave in complex ways, or even exhibit discontinuities, response surfaces are not the best available tool. This consideration was one of several that drove the work in [2]. In the present paper, (1) the original quantification of functional unreliability using NN [2], and later ACE [3], is reprised using GP; (2) additional information provided by the GP about uncertainty in the limit surface, generally unavailable in other representations, is discussed; (3) a simple forensic exercise is performed, analogous to the inverse problem of code calibration, but with an accident management spin: given an observation about containment pressure, what can we say about the system variables? References 1. For an introduction to GPs, see (for example) Gaussian Processes for Machine Learning, C. E. Rasmussen and C. K. I. Williams (MIT, 2006). 2. Reliability Quantification of Advanced Reactor Passive Safety Systems, J. J. Vandenkieboom, PhD Thesis (University of Michigan, 1996). 3. Z. Cui, J. C. Lee, J. J. Vandenkieboom, and R. W. Youngblood, “Unreliability Quantification of a Containment Cooling System through ACE and ANN Algorithms,” Trans. Am. Nucl. Soc. 85, 178 (2001). 4. Risk and Safety Analysis of Nuclear Systems, J. C. Lee and N. J. McCormick (Wiley, 2011). See especially §11.2.4.« less

Forward and inverse uncertainty quantification using multilevel Monte Carlo algorithms for an elliptic non-local equation

DOE PAGES

Jasra, Ajay; Law, Kody J. H.; Zhou, Yan

2016-01-01

Our paper considers uncertainty quantification for an elliptic nonlocal equation. In particular, it is assumed that the parameters which define the kernel in the nonlocal operator are uncertain and a priori distributed according to a probability measure. It is shown that the induced probability measure on some quantities of interest arising from functionals of the solution to the equation with random inputs is well-defined,s as is the posterior distribution on parameters given observations. As the elliptic nonlocal equation cannot be solved approximate posteriors are constructed. The multilevel Monte Carlo (MLMC) and multilevel sequential Monte Carlo (MLSMC) sampling algorithms are usedmore » for a priori and a posteriori estimation, respectively, of quantities of interest. Furthermore, these algorithms reduce the amount of work to estimate posterior expectations, for a given level of error, relative to Monte Carlo and i.i.d. sampling from the posterior at a given level of approximation of the solution of the elliptic nonlocal equation.« less

Forward and inverse uncertainty quantification using multilevel Monte Carlo algorithms for an elliptic non-local equation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jasra, Ajay; Law, Kody J. H.; Zhou, Yan

Our paper considers uncertainty quantification for an elliptic nonlocal equation. In particular, it is assumed that the parameters which define the kernel in the nonlocal operator are uncertain and a priori distributed according to a probability measure. It is shown that the induced probability measure on some quantities of interest arising from functionals of the solution to the equation with random inputs is well-defined,s as is the posterior distribution on parameters given observations. As the elliptic nonlocal equation cannot be solved approximate posteriors are constructed. The multilevel Monte Carlo (MLMC) and multilevel sequential Monte Carlo (MLSMC) sampling algorithms are usedmore » for a priori and a posteriori estimation, respectively, of quantities of interest. Furthermore, these algorithms reduce the amount of work to estimate posterior expectations, for a given level of error, relative to Monte Carlo and i.i.d. sampling from the posterior at a given level of approximation of the solution of the elliptic nonlocal equation.« less

Prioritizing Project Risks Using AHP

DOE Office of Scientific and Technical Information (OSTI.GOV)

Thibadeau, Barbara M

2007-01-01

This essay introduces the Analytic Hierarchy Process (AHP) as a method by which to rank project risks, in terms of importance as well as likelihood. AHP is way to handle quantifiable and/or intangible criteria in the decision making process. It is a multi-objective multi-criteria decision-making approach that is based on the idea of pair-wise comparisons of alternatives with respect to a given criterion (e.g., which alternative, A or B, is preferred and by how much more is it preferred) or with respect to an objective (e.g., which is more important, A or B, and by how much more is itmore » important). This approach was pioneered by Thomas Saaty in the late 1970's. It has been suggested that a successful project is one that successfully manages risk and that project management is the management of uncertainty. Risk management relies on the quantification of uncertainty which, in turn, is predicated upon the accuracy of probabilistic approaches (in terms of likelihood as well as magnitude). In many cases, the appropriate probability distribution (or probability value) is unknown. And, researchers have shown that probability values are not made very accurately, that the use of verbal expressions is not a suitable alternative, that there is great variability in the use and interpretation of these values and that there is a great reluctance to assign them in the first place. Data from an ongoing project is used to show that AHP can be used to obtain these values, thus overcoming some of the problems associated with the direct assignment of discrete probability values. A novel method by which to calculate the consistency of the data is introduced. The AHP approach is easily implemented and, typically, offers results that are consistent with the decision maker's intuition.« less

P values are only an index to evidence: 20th- vs. 21st-century statistical science.

PubMed

Burnham, K P; Anderson, D R

2014-03-01

Early statistical methods focused on pre-data probability statements (i.e., data as random variables) such as P values; these are not really inferences nor are P values evidential. Statistical science clung to these principles throughout much of the 20th century as a wide variety of methods were developed for special cases. Looking back, it is clear that the underlying paradigm (i.e., testing and P values) was weak. As Kuhn (1970) suggests, new paradigms have taken the place of earlier ones: this is a goal of good science. New methods have been developed and older methods extended and these allow proper measures of strength of evidence and multimodel inference. It is time to move forward with sound theory and practice for the difficult practical problems that lie ahead. Given data the useful foundation shifts to post-data probability statements such as model probabilities (Akaike weights) or related quantities such as odds ratios and likelihood intervals. These new methods allow formal inference from multiple models in the a prior set. These quantities are properly evidential. The past century was aimed at finding the "best" model and making inferences from it. The goal in the 21st century is to base inference on all the models weighted by their model probabilities (model averaging). Estimates of precision can include model selection uncertainty leading to variances conditional on the model set. The 21st century will be about the quantification of information, proper measures of evidence, and multi-model inference. Nelder (1999:261) concludes, "The most important task before us in developing statistical science is to demolish the P-value culture, which has taken root to a frightening extent in many areas of both pure and applied science and technology".

Bayesian Hierarchical Grouping: perceptual grouping as mixture estimation

PubMed Central

Froyen, Vicky; Feldman, Jacob; Singh, Manish

2015-01-01

We propose a novel framework for perceptual grouping based on the idea of mixture models, called Bayesian Hierarchical Grouping (BHG). In BHG we assume that the configuration of image elements is generated by a mixture of distinct objects, each of which generates image elements according to some generative assumptions. Grouping, in this framework, means estimating the number and the parameters of the mixture components that generated the image, including estimating which image elements are “owned” by which objects. We present a tractable implementation of the framework, based on the hierarchical clustering approach of Heller and Ghahramani (2005). We illustrate it with examples drawn from a number of classical perceptual grouping problems, including dot clustering, contour integration, and part decomposition. Our approach yields an intuitive hierarchical representation of image elements, giving an explicit decomposition of the image into mixture components, along with estimates of the probability of various candidate decompositions. We show that BHG accounts well for a diverse range of empirical data drawn from the literature. Because BHG provides a principled quantification of the plausibility of grouping interpretations over a wide range of grouping problems, we argue that it provides an appealing unifying account of the elusive Gestalt notion of Prägnanz. PMID:26322548

Quantification and Segmentation of Brain Tissues from MR Images: A Probabilistic Neural Network Approach

PubMed Central

Wang, Yue; Adalý, Tülay; Kung, Sun-Yuan; Szabo, Zsolt

2007-01-01

This paper presents a probabilistic neural network based technique for unsupervised quantification and segmentation of brain tissues from magnetic resonance images. It is shown that this problem can be solved by distribution learning and relaxation labeling, resulting in an efficient method that may be particularly useful in quantifying and segmenting abnormal brain tissues where the number of tissue types is unknown and the distributions of tissue types heavily overlap. The new technique uses suitable statistical models for both the pixel and context images and formulates the problem in terms of model-histogram fitting and global consistency labeling. The quantification is achieved by probabilistic self-organizing mixtures and the segmentation by a probabilistic constraint relaxation network. The experimental results show the efficient and robust performance of the new algorithm and that it outperforms the conventional classification based approaches. PMID:18172510

Probability 1/e

ERIC Educational Resources Information Center

Koo, Reginald; Jones, Martin L.

2011-01-01

Quite a number of interesting problems in probability feature an event with probability equal to 1/e. This article discusses three such problems and attempts to explain why this probability occurs with such frequency.

MOLD SPECIFIC QUANTITATIVE PCR: THE EMERGING STANDARD IN MOLD ANALYSIS

EPA Science Inventory

Today I will talk about the use of quantitative or Real time PCR for the standardized identification and quantification of molds. There are probably at least 100,000 species of molds or fungi. But there are actually about 100 typically found indoors. Some pose a threat to human...

DOE Office of Scientific and Technical Information (OSTI.GOV)

Curry, J J; Gallagher, D W; Modarres, M

Appendices are presented concerning isolation condenser makeup; vapor suppression system; station air system; reactor building closed cooling water system; turbine building secondary closed water system; service water system; emergency service water system; fire protection system; emergency ac power; dc power system; event probability estimation; methodology of accident sequence quantification; and assignment of dominant sequences to release categories.

Patterns of Hits to the Nearshore from a Major Fairway in the Gulf of Finland

NASA Astrophysics Data System (ADS)

Viikmae, B.; Soomere, T.

2012-04-01

The Baltic Sea hosts one of the heaviest ship traffic in the world. Although relatively small in size, still up to 15% of the world's cargo is transported along its numerous fairways. The largest threat to the environment is oil transportation that has increased more than by a factor of two in 2000-2008 and a 40% increase is expected by the year 2015. One of the major marine highways in the European waters enters the Baltic Sea through the Danish Straits, crosses the Baltic Proper and stretches through the Gulf of Finland to Saint Petersburg. An area that is highly vulnerable to ship pollution is the nearshore that usually has the largest ecological value. While the probability of coastal pollution for open ocean coasts can be reduced by shifting ship routes farther offshore, the problem for narrow bays, like the Gulf of Finland, is how to minimize the probability of hitting any of the coasts. To a certain extent, light can be shed to this problem by means of quantification of the offshore areas in terms of their ability to serve a danger to coastal environment if pollution would happen in these areas. A convenient way to address this problem is to use statistical analysis of a large number of Lagrangian trajectories of test particles representing the potential pollution and passively carried by surface currents. In this paper, we make and attempt to quantify the link between potential sources of pollution along an existing fairway and the sections of the coast reached by current-driven pollution. The problem is analysed by means of considering hits to the nearshore from a major fairway in the Gulf of Finland and by making sure whether certain parts of the coast are hit by pollution particles most frequently and whether or not these pollution particles stem from certain specific parts of the fairway. Trajectories are simulated in the Gulf of Finland by placing tracers among a line that follows one of the major fairways from the Baltic Proper to Saint Petersburg. The probabilities for the hit to different parts of the nearshore and the ability of different sections of the fairway to provide coastal pollution have extensive seasonal variability. The potential impact of the fairway is roughly proportional to its distance from the nearest coast. A short section of the fairway to the south of Vyborg and a segment to the west of Tallinn are the most probable sources of coastal pollution. The most frequently hit coastal areas are short fragments between Hanko and Helsinki, the NE coast of the gulf to the south of Vyborg, and longer segments from Tallinn to Hiiumaa on the southern coast of the gulf.

Dermatologic radiotherapy and thyroid cancer. Dose measurements and risk quantification.

PubMed

Goldschmidt, H; Gorson, R O; Lassen, M

1983-05-01

Thyroid doses for various dermatologic radiation techniques were measured with thermoluminescent dosimeters and ionization rate meters in an Alderson-Rando anthropomorphic phantom. The effects of changes in radiation quality and of the use or nonuse of treatment cones and thyroid shields were evaluated in detail. The results indicate that the potential risk of radiogenic thyroid cancer is very small when proper radiation protection measures are used. The probability of radiogenic thyroid cancer developing and the potential mortality risk were assessed quantitatively for each measurement. The quantification of radiation risks allows comparisons with risks of other therapeutic modalities and the common hazards of daily life.

Quantitative histogram analysis of images

NASA Astrophysics Data System (ADS)

Holub, Oliver; Ferreira, Sérgio T.

2006-11-01

A routine for histogram analysis of images has been written in the object-oriented, graphical development environment LabVIEW. The program converts an RGB bitmap image into an intensity-linear greyscale image according to selectable conversion coefficients. This greyscale image is subsequently analysed by plots of the intensity histogram and probability distribution of brightness, and by calculation of various parameters, including average brightness, standard deviation, variance, minimal and maximal brightness, mode, skewness and kurtosis of the histogram and the median of the probability distribution. The program allows interactive selection of specific regions of interest (ROI) in the image and definition of lower and upper threshold levels (e.g., to permit the removal of a constant background signal). The results of the analysis of multiple images can be conveniently saved and exported for plotting in other programs, which allows fast analysis of relatively large sets of image data. The program file accompanies this manuscript together with a detailed description of two application examples: The analysis of fluorescence microscopy images, specifically of tau-immunofluorescence in primary cultures of rat cortical and hippocampal neurons, and the quantification of protein bands by Western-blot. The possibilities and limitations of this kind of analysis are discussed. Program summaryTitle of program: HAWGC Catalogue identifier: ADXG_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/ADXG_v1_0 Program obtainable from: CPC Program Library, Queen's University of Belfast, N. Ireland Computers: Mobile Intel Pentium III, AMD Duron Installations: No installation necessary—Executable file together with necessary files for LabVIEW Run-time engine Operating systems or monitors under which the program has been tested: WindowsME/2000/XP Programming language used: LabVIEW 7.0 Memory required to execute with typical data:˜16MB for starting and ˜160MB used for loading of an image No. of bits in a word: 32 No. of processors used: 1 Has the code been vectorized or parallelized?: No No of lines in distributed program, including test data, etc.:138 946 No. of bytes in distributed program, including test data, etc.:15 166 675 Distribution format: tar.gz Nature of physical problem: Quantification of image data (e.g., for discrimination of molecular species in gels or fluorescent molecular probes in cell cultures) requires proprietary or complex software packages, which might not include the relevant statistical parameters or make the analysis of multiple images a tedious procedure for the general user. Method of solution: Tool for conversion of RGB bitmap image into luminance-linear image and extraction of luminance histogram, probability distribution, and statistical parameters (average brightness, standard deviation, variance, minimal and maximal brightness, mode, skewness and kurtosis of histogram and median of probability distribution) with possible selection of region of interest (ROI) and lower and upper threshold levels. Restrictions on the complexity of the problem: Does not incorporate application-specific functions (e.g., morphometric analysis) Typical running time: Seconds (depending on image size and processor speed) Unusual features of the program: None

Measurement of absolute gamma emission probabilities

NASA Astrophysics Data System (ADS)

Sumithrarachchi, Chandana S.; Rengan, Krish; Griffin, Henry C.

2003-06-01

The energies and emission probabilities (intensities) of gamma-rays emitted in radioactive decays of particular nuclides are the most important characteristics by which to quantify mixtures of radionuclides. Often, quantification is limited by uncertainties in measured intensities. A technique was developed to reduce these uncertainties. The method involves obtaining a pure sample of a nuclide using radiochemical techniques, and using appropriate fractions for beta and gamma measurements. The beta emission rates were measured using a liquid scintillation counter, and the gamma emission rates were measured with a high-purity germanium detector. Results were combined to obtain absolute gamma emission probabilities. All sources of uncertainties greater than 0.1% were examined. The method was tested with 38Cl and 88Rb.

A machine learning approach for efficient uncertainty quantification using multiscale methods

NASA Astrophysics Data System (ADS)

Chan, Shing; Elsheikh, Ahmed H.

2018-02-01

Several multiscale methods account for sub-grid scale features using coarse scale basis functions. For example, in the Multiscale Finite Volume method the coarse scale basis functions are obtained by solving a set of local problems over dual-grid cells. We introduce a data-driven approach for the estimation of these coarse scale basis functions. Specifically, we employ a neural network predictor fitted using a set of solution samples from which it learns to generate subsequent basis functions at a lower computational cost than solving the local problems. The computational advantage of this approach is realized for uncertainty quantification tasks where a large number of realizations has to be evaluated. We attribute the ability to learn these basis functions to the modularity of the local problems and the redundancy of the permeability patches between samples. The proposed method is evaluated on elliptic problems yielding very promising results.

Recent application of quantification II in Japanese medical research.

PubMed Central

Suzuki, T; Kudo, A

1979-01-01

Hayashi's Quantification II is a method of multivariate discrimination analysis to manipulate attribute data as predictor variables. It is very useful in the medical research field for estimation, diagnosis, prognosis, evaluation of epidemiological factors, and other problems based on multiplicity of attribute data. In Japan, this method is so well known that most of the computer program packages include the Hayashi Quantification, but it seems to be yet unfamiliar with the method for researchers outside Japan. In view of this situation, we introduced 19 selected articles of recent applications of the Quantification II in Japanese medical research. In reviewing these papers, special mention is made to clarify how the researchers were satisfied with findings provided by the method. At the same time, some recommendations are made about terminology and program packages. Also a brief discussion of the background of the quantification methods is given with special reference to the Behaviormetric Society of Japan. PMID:540587

Data-driven probability concentration and sampling on manifold

DOE Office of Scientific and Technical Information (OSTI.GOV)

Soize, C., E-mail: christian.soize@univ-paris-est.fr; Ghanem, R., E-mail: ghanem@usc.edu

2016-09-15

A new methodology is proposed for generating realizations of a random vector with values in a finite-dimensional Euclidean space that are statistically consistent with a dataset of observations of this vector. The probability distribution of this random vector, while a priori not known, is presumed to be concentrated on an unknown subset of the Euclidean space. A random matrix is introduced whose columns are independent copies of the random vector and for which the number of columns is the number of data points in the dataset. The approach is based on the use of (i) the multidimensional kernel-density estimation methodmore » for estimating the probability distribution of the random matrix, (ii) a MCMC method for generating realizations for the random matrix, (iii) the diffusion-maps approach for discovering and characterizing the geometry and the structure of the dataset, and (iv) a reduced-order representation of the random matrix, which is constructed using the diffusion-maps vectors associated with the first eigenvalues of the transition matrix relative to the given dataset. The convergence aspects of the proposed methodology are analyzed and a numerical validation is explored through three applications of increasing complexity. The proposed method is found to be robust to noise levels and data complexity as well as to the intrinsic dimension of data and the size of experimental datasets. Both the methodology and the underlying mathematical framework presented in this paper contribute new capabilities and perspectives at the interface of uncertainty quantification, statistical data analysis, stochastic modeling and associated statistical inverse problems.« less

Descriptive and Experimental Analyses of Potential Precursors to Problem Behavior

PubMed Central

Borrero, Carrie S.W; Borrero, John C

2008-01-01

We conducted descriptive observations of severe problem behavior for 2 individuals with autism to identify precursors to problem behavior. Several comparative probability analyses were conducted in addition to lag-sequential analyses using the descriptive data. Results of the descriptive analyses showed that the probability of the potential precursor was greater given problem behavior compared to the unconditional probability of the potential precursor. Results of the lag-sequential analyses showed a marked increase in the probability of a potential precursor in the 1-s intervals immediately preceding an instance of problem behavior, and that the probability of problem behavior was highest in the 1-s intervals immediately following an instance of the precursor. We then conducted separate functional analyses of problem behavior and the precursor to identify respective operant functions. Results of the functional analyses showed that both problem behavior and the precursor served the same operant functions. These results replicate prior experimental analyses on the relation between problem behavior and precursors and extend prior research by illustrating a quantitative method to identify precursors to more severe problem behavior. PMID:18468281

Ponder This

ERIC Educational Resources Information Center

Yevdokimov, Oleksiy

2009-01-01

This article presents a problem set which includes a selection of probability problems. Probability theory started essentially as an empirical science and developed on the mathematical side later. The problems featured in this article demonstrate diversity of ideas and different concepts of probability, in particular, they refer to Laplace and…

Discriminability limits in spatio-temporal stereo block matching.

PubMed

Jain, Ankit K; Nguyen, Truong Q

2014-05-01

Disparity estimation is a fundamental task in stereo imaging and is a well-studied problem. Recently, methods have been adapted to the video domain where motion is used as a matching criterion to help disambiguate spatially similar candidates. In this paper, we analyze the validity of the underlying assumptions of spatio-temporal disparity estimation, and determine the extent to which motion aids the matching process. By analyzing the error signal for spatio-temporal block matching under the sum of squared differences criterion and treating motion as a stochastic process, we determine the probability of a false match as a function of image features, motion distribution, image noise, and number of frames in the spatio-temporal patch. This performance quantification provides insight into when spatio-temporal matching is most beneficial in terms of the scene and motion, and can be used as a guide to select parameters for stereo matching algorithms. We validate our results through simulation and experiments on stereo video.

Modeling Dental Health Care Workers' Risk of Occupational Infection from Bloodborne Pathogens.

ERIC Educational Resources Information Center

Capilouto, Eli; And Others

1990-01-01

The brief paper offers a model which permits quantification of the dental health care workers' risk of occupationally acquiring infection from bloodborne pathogens such as human immunodeficiency virus and hepatitis B virus. The model incorporates five parameters such as the probability that any individual patient is infected and number of patients…

Dakota Graphical User Interface v. 1.0

DOE Office of Scientific and Technical Information (OSTI.GOV)

Friedman-Hill, Ernest; Glickman, Matthew; Gibson, Marcus

Graphical analysis environment for Sandia’s Dakota software for optimization and uncertainty quantification. The Dakota GUI is an interactive graphical analysis environment for creating, running, and interpreting Dakota optimization and uncertainty quantification studies. It includes problem (Dakota study) set-up, option specification, simulation interfacing, analysis execution, and results visualization. Through the use of wizards, templates, and views, Dakota GUI helps uses navigate Dakota’s complex capability landscape.

Automated reference-free detection of motion artifacts in magnetic resonance images.

PubMed

Küstner, Thomas; Liebgott, Annika; Mauch, Lukas; Martirosian, Petros; Bamberg, Fabian; Nikolaou, Konstantin; Yang, Bin; Schick, Fritz; Gatidis, Sergios

2018-04-01

Our objectives were to provide an automated method for spatially resolved detection and quantification of motion artifacts in MR images of the head and abdomen as well as a quality control of the trained architecture. T1-weighted MR images of the head and the upper abdomen were acquired in 16 healthy volunteers under rest and under motion. Images were divided into overlapping patches of different sizes achieving spatial separation. Using these patches as input data, a convolutional neural network (CNN) was trained to derive probability maps for the presence of motion artifacts. A deep visualization offers a human-interpretable quality control of the trained CNN. Results were visually assessed on probability maps and as classification accuracy on a per-patch, per-slice and per-volunteer basis. On visual assessment, a clear difference of probability maps was observed between data sets with and without motion. The overall accuracy of motion detection on a per-patch/per-volunteer basis reached 97%/100% in the head and 75%/100% in the abdomen, respectively. Automated detection of motion artifacts in MRI is feasible with good accuracy in the head and abdomen. The proposed method provides quantification and localization of artifacts as well as a visualization of the learned content. It may be extended to other anatomic areas and used for quality assurance of MR images.

Model Update of a Micro Air Vehicle (MAV) Flexible Wing Frame with Uncertainty Quantification

NASA Technical Reports Server (NTRS)

Reaves, Mercedes C.; Horta, Lucas G.; Waszak, Martin R.; Morgan, Benjamin G.

2004-01-01

This paper describes a procedure to update parameters in the finite element model of a Micro Air Vehicle (MAV) to improve displacement predictions under aerodynamics loads. Because of fabrication, materials, and geometric uncertainties, a statistical approach combined with Multidisciplinary Design Optimization (MDO) is used to modify key model parameters. Static test data collected using photogrammetry are used to correlate with model predictions. Results show significant improvements in model predictions after parameters are updated; however, computed probabilities values indicate low confidence in updated values and/or model structure errors. Lessons learned in the areas of wing design, test procedures, modeling approaches with geometric nonlinearities, and uncertainties quantification are all documented.

The Process of Probability Problem Solving: Use of External Visual Representations

ERIC Educational Resources Information Center

Zahner, Doris; Corter, James E.

2010-01-01

We investigate the role of external inscriptions, particularly those of a spatial or visual nature, in the solution of probability word problems. We define a taxonomy of external visual representations used in probability problem solving that includes "pictures," "spatial reorganization of the given information," "outcome listings," "contingency…

Uncertainty Quantification of the FUN3D-Predicted NASA CRM Flutter Boundary

NASA Technical Reports Server (NTRS)

Stanford, Bret K.; Massey, Steven J.

2017-01-01

A nonintrusive point collocation method is used to propagate parametric uncertainties of the flexible Common Research Model, a generic transport configuration, through the unsteady aeroelastic CFD solver FUN3D. A range of random input variables are considered, including atmospheric flow variables, structural variables, and inertial (lumped mass) variables. UQ results are explored for a range of output metrics (with a focus on dynamic flutter stability), for both subsonic and transonic Mach numbers, for two different CFD mesh refinements. A particular focus is placed on computing failure probabilities: the probability that the wing will flutter within the flight envelope.

Movement patterns and study area boundaries: Influences on survival estimation in capture-mark-recapture studies

USGS Publications Warehouse

Horton, G.E.; Letcher, B.H.

2008-01-01

The inability to account for the availability of individuals in the study area during capture-mark-recapture (CMR) studies and the resultant confounding of parameter estimates can make correct interpretation of CMR model parameter estimates difficult. Although important advances based on the Cormack-Jolly-Seber (CJS) model have resulted in estimators of true survival that work by unconfounding either death or recapture probability from availability for capture in the study area, these methods rely on the researcher's ability to select a method that is correctly matched to emigration patterns in the population. If incorrect assumptions regarding site fidelity (non-movement) are made, it may be difficult or impossible as well as costly to change the study design once the incorrect assumption is discovered. Subtleties in characteristics of movement (e.g. life history-dependent emigration, nomads vs territory holders) can lead to mixtures in the probability of being available for capture among members of the same population. The result of these mixtures may be only a partial unconfounding of emigration from other CMR model parameters. Biologically-based differences in individual movement can combine with constraints on study design to further complicate the problem. Because of the intricacies of movement and its interaction with other parameters in CMR models, quantification of and solutions to these problems are needed. Based on our work with stream-dwelling populations of Atlantic salmon Salmo salar, we used a simulation approach to evaluate existing CMR models under various mixtures of movement probabilities. The Barker joint data model provided unbiased estimates of true survival under all conditions tested. The CJS and robust design models provided similarly unbiased estimates of true survival but only when emigration information could be incorporated directly into individual encounter histories. For the robust design model, Markovian emigration (future availability for capture depends on an individual's current location) was a difficult emigration pattern to detect unless survival and especially recapture probability were high. Additionally, when local movement was high relative to study area boundaries and movement became more diffuse (e.g. a random walk), local movement and permanent emigration were difficult to distinguish and had consequences for correctly interpreting the survival parameter being estimated (apparent survival vs true survival). ?? 2008 The Authors.

Overview of the AVT-191 Project to Assess Sensitivity Analysis and Uncertainty Quantification Methods for Military Vehicle Design

NASA Technical Reports Server (NTRS)

Benek, John A.; Luckring, James M.

2017-01-01

A NATO symposium held in 2008 identified many promising sensitivity analysis and un-certainty quantification technologies, but the maturity and suitability of these methods for realistic applications was not known. The STO Task Group AVT-191 was established to evaluate the maturity and suitability of various sensitivity analysis and uncertainty quantification methods for application to realistic problems of interest to NATO. The program ran from 2011 to 2015, and the work was organized into four discipline-centric teams: external aerodynamics, internal aerodynamics, aeroelasticity, and hydrodynamics. This paper presents an overview of the AVT-191 program content.

Summary Findings from the AVT-191 Project to Assess Sensitivity Analysis and Uncertainty Quantification Methods for Military Vehicle Design

NASA Technical Reports Server (NTRS)

Benek, John A.; Luckring, James M.

2017-01-01

A NATO symposium held in Greece in 2008 identified many promising sensitivity analysis and uncertainty quantification technologies, but the maturity and suitability of these methods for realistic applications was not clear. The NATO Science and Technology Organization, Task Group AVT-191 was established to evaluate the maturity and suitability of various sensitivity analysis and uncertainty quantification methods for application to realistic vehicle development problems. The program ran from 2011 to 2015, and the work was organized into four discipline-centric teams: external aerodynamics, internal aerodynamics, aeroelasticity, and hydrodynamics. This paper summarizes findings and lessons learned from the task group.

Systematic Errors in Peptide and Protein Identification and Quantification by Modified Peptides*

PubMed Central

Bogdanow, Boris; Zauber, Henrik; Selbach, Matthias

2016-01-01

The principle of shotgun proteomics is to use peptide mass spectra in order to identify corresponding sequences in a protein database. The quality of peptide and protein identification and quantification critically depends on the sensitivity and specificity of this assignment process. Many peptides in proteomic samples carry biochemical modifications, and a large fraction of unassigned spectra arise from modified peptides. Spectra derived from modified peptides can erroneously be assigned to wrong amino acid sequences. However, the impact of this problem on proteomic data has not yet been investigated systematically. Here we use combinations of different database searches to show that modified peptides can be responsible for 20–50% of false positive identifications in deep proteomic data sets. These false positive hits are particularly problematic as they have significantly higher scores and higher intensities than other false positive matches. Furthermore, these wrong peptide assignments lead to hundreds of false protein identifications and systematic biases in protein quantification. We devise a “cleaned search” strategy to address this problem and show that this considerably improves the sensitivity and specificity of proteomic data. In summary, we show that modified peptides cause systematic errors in peptide and protein identification and quantification and should therefore be considered to further improve the quality of proteomic data annotation. PMID:27215553

WE-H-207A-06: Hypoxia Quantification in Static PET Images: The Signal in the Noise

DOE Office of Scientific and Technical Information (OSTI.GOV)

Keller, H; Yeung, I; Milosevic, M

2016-06-15

Purpose: Quantification of hypoxia from PET images is of considerable clinical interest. In the absence of dynamic PET imaging the hypoxic fraction (HF) of a tumor has to be estimated from voxel values of activity concentration of a radioactive hypoxia tracer. This work is part of an effort to standardize quantification of tumor hypoxic fraction from PET images. Methods: A simple hypoxia imaging model in the tumor was developed. The distribution of the tracer activity was described as the sum of two different probability distributions, one for the normoxic (and necrotic), the other for the hypoxic voxels. The widths ofmore » the distributions arise due to variability of the transport, tumor tissue inhomogeneity, tracer binding kinetics, and due to PET image noise. Quantification of HF was performed for various levels of variability using two different methodologies: a) classification thresholds between normoxic and hypoxic voxels based on a non-hypoxic surrogate (muscle), and b) estimation of the (posterior) probability distributions based on maximizing likelihood optimization that does not require a surrogate. Data from the hypoxia imaging model and from 27 cervical cancer patients enrolled in a FAZA PET study were analyzed. Results: In the model, where the true value of HF is known, thresholds usually underestimate the value for large variability. For the patients, a significant uncertainty of the HF values (an average intra-patient range of 17%) was caused by spatial non-uniformity of image noise which is a hallmark of all PET images. Maximum likelihood estimation (MLE) is able to directly optimize for the weights of both distributions, however, may suffer from poor optimization convergence. For some patients, MLE-based HF values showed significant differences to threshold-based HF-values. Conclusion: HF-values depend critically on the magnitude of the different sources of tracer uptake variability. A measure of confidence should also be reported.« less

Multi-level damage identification with response reconstruction

NASA Astrophysics Data System (ADS)

Zhang, Chao-Dong; Xu, You-Lin

2017-10-01

Damage identification through finite element (FE) model updating usually forms an inverse problem. Solving the inverse identification problem for complex civil structures is very challenging since the dimension of potential damage parameters in a complex civil structure is often very large. Aside from enormous computation efforts needed in iterative updating, the ill-condition and non-global identifiability features of the inverse problem probably hinder the realization of model updating based damage identification for large civil structures. Following a divide-and-conquer strategy, a multi-level damage identification method is proposed in this paper. The entire structure is decomposed into several manageable substructures and each substructure is further condensed as a macro element using the component mode synthesis (CMS) technique. The damage identification is performed at two levels: the first is at macro element level to locate the potentially damaged region and the second is over the suspicious substructures to further locate as well as quantify the damage severity. In each level's identification, the damage searching space over which model updating is performed is notably narrowed down, not only reducing the computation amount but also increasing the damage identifiability. Besides, the Kalman filter-based response reconstruction is performed at the second level to reconstruct the response of the suspicious substructure for exact damage quantification. Numerical studies and laboratory tests are both conducted on a simply supported overhanging steel beam for conceptual verification. The results demonstrate that the proposed multi-level damage identification via response reconstruction does improve the identification accuracy of damage localization and quantization considerably.

Bayesian Methods for Effective Field Theories

NASA Astrophysics Data System (ADS)

Wesolowski, Sarah

Microscopic predictions of the properties of atomic nuclei have reached a high level of precision in the past decade. This progress mandates improved uncertainty quantification (UQ) for a robust comparison of experiment with theory. With the uncertainty from many-body methods under control, calculations are now sensitive to the input inter-nucleon interactions. These interactions include parameters that must be fit to experiment, inducing both uncertainty from the fit and from missing physics in the operator structure of the Hamiltonian. Furthermore, the implementation of the inter-nucleon interactions is not unique, which presents the additional problem of assessing results using different interactions. Effective field theories (EFTs) take advantage of a separation of high- and low-energy scales in the problem to form a power-counting scheme that allows the organization of terms in the Hamiltonian based on their expected contribution to observable predictions. This scheme gives a natural framework for quantification of uncertainty due to missing physics. The free parameters of the EFT, called the low-energy constants (LECs), must be fit to data, but in a properly constructed EFT these constants will be natural-sized, i.e., of order unity. The constraints provided by the EFT, namely the size of the systematic uncertainty from truncation of the theory and the natural size of the LECs, are assumed information even before a calculation is performed or a fit is done. Bayesian statistical methods provide a framework for treating uncertainties that naturally incorporates prior information as well as putting stochastic and systematic uncertainties on an equal footing. For EFT UQ Bayesian methods allow the relevant EFT properties to be incorporated quantitatively as prior probability distribution functions (pdfs). Following the logic of probability theory, observable quantities and underlying physical parameters such as the EFT breakdown scale may be expressed as pdfs that incorporate the prior pdfs. Problems of model selection, such as distinguishing between competing EFT implementations, are also natural in a Bayesian framework. In this thesis we focus on two complementary topics for EFT UQ using Bayesian methods--quantifying EFT truncation uncertainty and parameter estimation for LECs. Using the order-by-order calculations and underlying EFT constraints as prior information, we show how to estimate EFT truncation uncertainties. We then apply the result to calculating truncation uncertainties on predictions of nucleon-nucleon scattering in chiral effective field theory. We apply model-checking diagnostics to our calculations to ensure that the statistical model of truncation uncertainty produces consistent results. A framework for EFT parameter estimation based on EFT convergence properties and naturalness is developed which includes a series of diagnostics to ensure the extraction of the maximum amount of available information from data to estimate LECs with minimal bias. We develop this framework using model EFTs and apply it to the problem of extrapolating lattice quantum chromodynamics results for the nucleon mass. We then apply aspects of the parameter estimation framework to perform case studies in chiral EFT parameter estimation, investigating a possible operator redundancy at fourth order in the chiral expansion and the appropriate inclusion of truncation uncertainty in estimating LECs.

WHAMII - An enumeration and insertion procedure with binomial bounds for the stochastic time-constrained traveling salesman problem

NASA Technical Reports Server (NTRS)

Dahl, Roy W.; Keating, Karen; Salamone, Daryl J.; Levy, Laurence; Nag, Barindra; Sanborn, Joan A.

1987-01-01

This paper presents an algorithm (WHAMII) designed to solve the Artificial Intelligence Design Challenge at the 1987 AIAA Guidance, Navigation and Control Conference. The problem under consideration is a stochastic generalization of the traveling salesman problem in which travel costs can incur a penalty with a given probability. The variability in travel costs leads to a probability constraint with respect to violating the budget allocation. Given the small size of the problem (eleven cities), an approach is considered that combines partial tour enumeration with a heuristic city insertion procedure. For computational efficiency during both the enumeration and insertion procedures, precalculated binomial probabilities are used to determine an upper bound on the actual probability of violating the budget constraint for each tour. The actual probability is calculated for the final best tour, and additional insertions are attempted until the actual probability exceeds the bound.

Uncertainty quantification of voice signal production mechanical model and experimental updating

NASA Astrophysics Data System (ADS)

Cataldo, E.; Soize, C.; Sampaio, R.

2013-11-01

The aim of this paper is to analyze the uncertainty quantification in a voice production mechanical model and update the probability density function corresponding to the tension parameter using the Bayes method and experimental data. Three parameters are considered uncertain in the voice production mechanical model used: the tension parameter, the neutral glottal area and the subglottal pressure. The tension parameter of the vocal folds is mainly responsible for the changing of the fundamental frequency of a voice signal, generated by a mechanical/mathematical model for producing voiced sounds. The three uncertain parameters are modeled by random variables. The probability density function related to the tension parameter is considered uniform and the probability density functions related to the neutral glottal area and the subglottal pressure are constructed using the Maximum Entropy Principle. The output of the stochastic computational model is the random voice signal and the Monte Carlo method is used to solve the stochastic equations allowing realizations of the random voice signals to be generated. For each realization of the random voice signal, the corresponding realization of the random fundamental frequency is calculated and the prior pdf of this random fundamental frequency is then estimated. Experimental data are available for the fundamental frequency and the posterior probability density function of the random tension parameter is then estimated using the Bayes method. In addition, an application is performed considering a case with a pathology in the vocal folds. The strategy developed here is important mainly due to two things. The first one is related to the possibility of updating the probability density function of a parameter, the tension parameter of the vocal folds, which cannot be measured direct and the second one is related to the construction of the likelihood function. In general, it is predefined using the known pdf. Here, it is constructed in a new and different manner, using the own system considered.

Life-long stability of neurons: a century of research on neurogenesis, neuronal death and neuron quantification in adult CNS.

PubMed

Turlejski, Kris; Djavadian, Ruzanna

2002-01-01

In this chapter we provide an extensive review of 100 years of research on the stability of neurons in the mammalian brain, with special emphasis on humans. Although Cajal formulated the Neuronal Doctrine, he was wrong in his beliefs that adult neurogenesis did not occur and adult neurons are dying throughout life. These two beliefs became accepted "common knowledge" and have shaped much of neuroscience research and provided much of the basis for clinical treatment of age-related brain diseases. In this review, we consider adult neurogenesis from a historical and evolutionary perspective. It is concluded, that while adult neurogenesis is a factor in the dynamics of the dentate gyrus and olfactory bulb, it is probably not a major factor during the life-span in most brain areas. Likewise, the acceptance of neuronal death as an explanation for normal age-related senility is challenged with evidence collected over the last fifty years. Much of the problem in changing this common belief of dying neurons was the inadequacies of neuronal counting methods. In this review we discuss in detail implications of recent improvements in neuronal quantification. We conclude: First, age-related neuronal atrophy is the major factor in functional deterioration of existing neurons and could be slowed down, or even reversed by various pharmacological interventions. Second, in most cases neuronal degeneration during aging is a pathology that in principle may be avoided. Third, loss of myelin and of the white matter is more frequent and important than the limited neuronal death in normal aging.

Approximate Quantification in Young, Healthy Older Adults', and Alzheimer Patients

ERIC Educational Resources Information Center

Gandini, Delphine; Lemaire, Patrick; Michel, Bernard Francois

2009-01-01

Forty young adults, 40 healthy older adults, and 39 probable AD patients were asked to estimate small (e.g., 25) and large (e.g., 60) collections of dots in a choice condition and in two no-choice conditions. Participants could choose between benchmark and anchoring strategies on each collection of dots in the choice condition and were required to…

Predictive Game Theory

NASA Technical Reports Server (NTRS)

Wolpert, David H.

2005-01-01

Probability theory governs the outcome of a game; there is a distribution over mixed strat.'s, not a single "equilibrium". To predict a single mixed strategy must use our loss function (external to the game's players. Provides a quantification of any strategy's rationality. Prove rationality falls as cost of computation rises (for players who have not previously interacted). All extends to games with varying numbers of players.

Challenges in leveraging existing human performance data for quantifying the IDHEAS HRA method

DOE PAGES

Liao, Huafei N.; Groth, Katrina; Stevens-Adams, Susan

2015-07-29

Our article documents an exploratory study for collecting and using human performance data to inform human error probability (HEP) estimates for a new human reliability analysis (HRA) method, the IntegrateD Human Event Analysis System (IDHEAS). The method was based on cognitive models and mechanisms underlying human behaviour and employs a framework of 14 crew failure modes (CFMs) to represent human failures typical for human performance in nuclear power plant (NPP) internal, at-power events [1]. A decision tree (DT) was constructed for each CFM to assess the probability of the CFM occurring in different contexts. Data needs for IDHEAS quantification aremore » discussed. Then, the data collection framework and process is described and how the collected data were used to inform HEP estimation is illustrated with two examples. Next, five major technical challenges are identified for leveraging human performance data for IDHEAS quantification. Furthermore, these challenges reflect the data needs specific to IDHEAS. More importantly, they also represent the general issues with current human performance data and can provide insight for a path forward to support HRA data collection, use, and exchange for HRA method development, implementation, and validation.« less

MSblender: A probabilistic approach for integrating peptide identifications from multiple database search engines.

PubMed

Kwon, Taejoon; Choi, Hyungwon; Vogel, Christine; Nesvizhskii, Alexey I; Marcotte, Edward M

2011-07-01

Shotgun proteomics using mass spectrometry is a powerful method for protein identification but suffers limited sensitivity in complex samples. Integrating peptide identifications from multiple database search engines is a promising strategy to increase the number of peptide identifications and reduce the volume of unassigned tandem mass spectra. Existing methods pool statistical significance scores such as p-values or posterior probabilities of peptide-spectrum matches (PSMs) from multiple search engines after high scoring peptides have been assigned to spectra, but these methods lack reliable control of identification error rates as data are integrated from different search engines. We developed a statistically coherent method for integrative analysis, termed MSblender. MSblender converts raw search scores from search engines into a probability score for every possible PSM and properly accounts for the correlation between search scores. The method reliably estimates false discovery rates and identifies more PSMs than any single search engine at the same false discovery rate. Increased identifications increment spectral counts for most proteins and allow quantification of proteins that would not have been quantified by individual search engines. We also demonstrate that enhanced quantification contributes to improve sensitivity in differential expression analyses.

MSblender: a probabilistic approach for integrating peptide identifications from multiple database search engines

PubMed Central

Kwon, Taejoon; Choi, Hyungwon; Vogel, Christine; Nesvizhskii, Alexey I.; Marcotte, Edward M.

2011-01-01

Shotgun proteomics using mass spectrometry is a powerful method for protein identification but suffers limited sensitivity in complex samples. Integrating peptide identifications from multiple database search engines is a promising strategy to increase the number of peptide identifications and reduce the volume of unassigned tandem mass spectra. Existing methods pool statistical significance scores such as p-values or posterior probabilities of peptide-spectrum matches (PSMs) from multiple search engines after high scoring peptides have been assigned to spectra, but these methods lack reliable control of identification error rates as data are integrated from different search engines. We developed a statistically coherent method for integrative analysis, termed MSblender. MSblender converts raw search scores from search engines into a probability score for all possible PSMs and properly accounts for the correlation between search scores. The method reliably estimates false discovery rates and identifies more PSMs than any single search engine at the same false discovery rate. Increased identifications increment spectral counts for all detected proteins and allow quantification of proteins that would not have been quantified by individual search engines. We also demonstrate that enhanced quantification contributes to improve sensitivity in differential expression analyses. PMID:21488652

Uncertainty Quantification for Polynomial Systems via Bernstein Expansions

NASA Technical Reports Server (NTRS)

Crespo, Luis G.; Kenny, Sean P.; Giesy, Daniel P.

2012-01-01

This paper presents a unifying framework to uncertainty quantification for systems having polynomial response metrics that depend on both aleatory and epistemic uncertainties. The approach proposed, which is based on the Bernstein expansions of polynomials, enables bounding the range of moments and failure probabilities of response metrics as well as finding supersets of the extreme epistemic realizations where the limits of such ranges occur. These bounds and supersets, whose analytical structure renders them free of approximation error, can be made arbitrarily tight with additional computational effort. Furthermore, this framework enables determining the importance of particular uncertain parameters according to the extent to which they affect the first two moments of response metrics and failure probabilities. This analysis enables determining the parameters that should be considered uncertain as well as those that can be assumed to be constants without incurring significant error. The analytical nature of the approach eliminates the numerical error that characterizes the sampling-based techniques commonly used to propagate aleatory uncertainties as well as the possibility of under predicting the range of the statistic of interest that may result from searching for the best- and worstcase epistemic values via nonlinear optimization or sampling.

Final Report: Quantification of Uncertainty in Extreme Scale Computations (QUEST)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Marzouk, Youssef; Conrad, Patrick; Bigoni, Daniele

QUEST (\\url{www.quest-scidac.org}) is a SciDAC Institute that is focused on uncertainty quantification (UQ) in large-scale scientific computations. Our goals are to (1) advance the state of the art in UQ mathematics, algorithms, and software; and (2) provide modeling, algorithmic, and general UQ expertise, together with software tools, to other SciDAC projects, thereby enabling and guiding a broad range of UQ activities in their respective contexts. QUEST is a collaboration among six institutions (Sandia National Laboratories, Los Alamos National Laboratory, the University of Southern California, Massachusetts Institute of Technology, the University of Texas at Austin, and Duke University) with a historymore » of joint UQ research. Our vision encompasses all aspects of UQ in leadership-class computing. This includes the well-founded setup of UQ problems; characterization of the input space given available data/information; local and global sensitivity analysis; adaptive dimensionality and order reduction; forward and inverse propagation of uncertainty; handling of application code failures, missing data, and hardware/software fault tolerance; and model inadequacy, comparison, validation, selection, and averaging. The nature of the UQ problem requires the seamless combination of data, models, and information across this landscape in a manner that provides a self-consistent quantification of requisite uncertainties in predictions from computational models. Accordingly, our UQ methods and tools span an interdisciplinary space across applied math, information theory, and statistics. The MIT QUEST effort centers on statistical inference and methods for surrogate or reduced-order modeling. MIT personnel have been responsible for the development of adaptive sampling methods, methods for approximating computationally intensive models, and software for both forward uncertainty propagation and statistical inverse problems. A key software product of the MIT QUEST effort is the MIT Uncertainty Quantification library, called MUQ (\\url{muq.mit.edu}).« less

Quantifying errors without random sampling.

PubMed

Phillips, Carl V; LaPole, Luwanna M

2003-06-12

All quantifications of mortality, morbidity, and other health measures involve numerous sources of error. The routine quantification of random sampling error makes it easy to forget that other sources of error can and should be quantified. When a quantification does not involve sampling, error is almost never quantified and results are often reported in ways that dramatically overstate their precision. We argue that the precision implicit in typical reporting is problematic and sketch methods for quantifying the various sources of error, building up from simple examples that can be solved analytically to more complex cases. There are straightforward ways to partially quantify the uncertainty surrounding a parameter that is not characterized by random sampling, such as limiting reported significant figures. We present simple methods for doing such quantifications, and for incorporating them into calculations. More complicated methods become necessary when multiple sources of uncertainty must be combined. We demonstrate that Monte Carlo simulation, using available software, can estimate the uncertainty resulting from complicated calculations with many sources of uncertainty. We apply the method to the current estimate of the annual incidence of foodborne illness in the United States. Quantifying uncertainty from systematic errors is practical. Reporting this uncertainty would more honestly represent study results, help show the probability that estimated values fall within some critical range, and facilitate better targeting of further research.

Variations on Bayesian Prediction and Inference

DTIC Science & Technology

2016-05-09

inference 2.2.1 Background There are a number of statistical inference problems that are not generally formulated via a full probability model...problem of inference about an unknown parameter, the Bayesian approach requires a full probability 1. REPORT DATE (DD-MM-YYYY) 4. TITLE AND...the problem of inference about an unknown parameter, the Bayesian approach requires a full probability model/likelihood which can be an obstacle

Distributed Immune Systems for Wireless Network Information Assurance

DTIC Science & Technology

2010-04-26

ratio test (SPRT), where the goal is to optimize a hypothesis testing problem given a trade-off between the probability of errors and the...using cumulative sum (CUSUM) and Girshik-Rubin-Shiryaev (GRSh) statistics. In sequential versions of the problem the sequential probability ratio ...the more complicated problems, in particular those where no clear mean can be established. We developed algorithms based on the sequential probability

Uncertainty quantification metrics for whole product life cycle cost estimates in aerospace innovation

NASA Astrophysics Data System (ADS)

Schwabe, O.; Shehab, E.; Erkoyuncu, J.

2015-08-01

The lack of defensible methods for quantifying cost estimate uncertainty over the whole product life cycle of aerospace innovations such as propulsion systems or airframes poses a significant challenge to the creation of accurate and defensible cost estimates. Based on the axiomatic definition of uncertainty as the actual prediction error of the cost estimate, this paper provides a comprehensive overview of metrics used for the uncertainty quantification of cost estimates based on a literature review, an evaluation of publicly funded projects such as part of the CORDIS or Horizon 2020 programs, and an analysis of established approaches used by organizations such NASA, the U.S. Department of Defence, the ESA, and various commercial companies. The metrics are categorized based on their foundational character (foundations), their use in practice (state-of-practice), their availability for practice (state-of-art) and those suggested for future exploration (state-of-future). Insights gained were that a variety of uncertainty quantification metrics exist whose suitability depends on the volatility of available relevant information, as defined by technical and cost readiness level, and the number of whole product life cycle phases the estimate is intended to be valid for. Information volatility and number of whole product life cycle phases can hereby be considered as defining multi-dimensional probability fields admitting various uncertainty quantification metric families with identifiable thresholds for transitioning between them. The key research gaps identified were the lacking guidance grounded in theory for the selection of uncertainty quantification metrics and lacking practical alternatives to metrics based on the Central Limit Theorem. An innovative uncertainty quantification framework consisting of; a set-theory based typology, a data library, a classification system, and a corresponding input-output model are put forward to address this research gap as the basis for future work in this field.

An Inverse Problem for a Class of Conditional Probability Measure-Dependent Evolution Equations

PubMed Central

Mirzaev, Inom; Byrne, Erin C.; Bortz, David M.

2016-01-01

We investigate the inverse problem of identifying a conditional probability measure in measure-dependent evolution equations arising in size-structured population modeling. We formulate the inverse problem as a least squares problem for the probability measure estimation. Using the Prohorov metric framework, we prove existence and consistency of the least squares estimates and outline a discretization scheme for approximating a conditional probability measure. For this scheme, we prove general method stability. The work is motivated by Partial Differential Equation (PDE) models of flocculation for which the shape of the post-fragmentation conditional probability measure greatly impacts the solution dynamics. To illustrate our methodology, we apply the theory to a particular PDE model that arises in the study of population dynamics for flocculating bacterial aggregates in suspension, and provide numerical evidence for the utility of the approach. PMID:28316360

Topics in inference and decision-making with partial knowledge

NASA Technical Reports Server (NTRS)

Safavian, S. Rasoul; Landgrebe, David

1990-01-01

Two essential elements needed in the process of inference and decision-making are prior probabilities and likelihood functions. When both of these components are known accurately and precisely, the Bayesian approach provides a consistent and coherent solution to the problems of inference and decision-making. In many situations, however, either one or both of the above components may not be known, or at least may not be known precisely. This problem of partial knowledge about prior probabilities and likelihood functions is addressed. There are at least two ways to cope with this lack of precise knowledge: robust methods, and interval-valued methods. First, ways of modeling imprecision and indeterminacies in prior probabilities and likelihood functions are examined; then how imprecision in the above components carries over to the posterior probabilities is examined. Finally, the problem of decision making with imprecise posterior probabilities and the consequences of such actions are addressed. Application areas where the above problems may occur are in statistical pattern recognition problems, for example, the problem of classification of high-dimensional multispectral remote sensing image data.

Uncertainty quantification tools for multiphase gas-solid flow simulations using MFIX

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fox, Rodney O.; Passalacqua, Alberto

2016-02-01

Computational fluid dynamics (CFD) has been widely studied and used in the scientific community and in the industry. Various models were proposed to solve problems in different areas. However, all models deviate from reality. Uncertainty quantification (UQ) process evaluates the overall uncertainties associated with the prediction of quantities of interest. In particular it studies the propagation of input uncertainties to the outputs of the models so that confidence intervals can be provided for the simulation results. In the present work, a non-intrusive quadrature-based uncertainty quantification (QBUQ) approach is proposed. The probability distribution function (PDF) of the system response can bemore » then reconstructed using extended quadrature method of moments (EQMOM) and extended conditional quadrature method of moments (ECQMOM). The report first explains the theory of QBUQ approach, including methods to generate samples for problems with single or multiple uncertain input parameters, low order statistics, and required number of samples. Then methods for univariate PDF reconstruction (EQMOM) and multivariate PDF reconstruction (ECQMOM) are explained. The implementation of QBUQ approach into the open-source CFD code MFIX is discussed next. At last, QBUQ approach is demonstrated in several applications. The method is first applied to two examples: a developing flow in a channel with uncertain viscosity, and an oblique shock problem with uncertain upstream Mach number. The error in the prediction of the moment response is studied as a function of the number of samples, and the accuracy of the moments required to reconstruct the PDF of the system response is discussed. The QBUQ approach is then demonstrated by considering a bubbling fluidized bed as example application. The mean particle size is assumed to be the uncertain input parameter. The system is simulated with a standard two-fluid model with kinetic theory closures for the particulate phase implemented into MFIX. The effect of uncertainty on the disperse-phase volume fraction, on the phase velocities and on the pressure drop inside the fluidized bed are examined, and the reconstructed PDFs are provided for the three quantities studied. Then the approach is applied to a bubbling fluidized bed with two uncertain parameters, particle-particle and particle-wall restitution coefficients. Contour plots of the mean and standard deviation of solid volume fraction, solid phase velocities and gas pressure are provided. The PDFs of the response are reconstructed using EQMOM with appropriate kernel density functions. The simulation results are compared to experimental data provided by the 2013 NETL small-scale challenge problem. Lastly, the proposed procedure is demonstrated by considering a riser of a circulating fluidized bed as an example application. The mean particle size is considered to be the uncertain input parameter. Contour plots of the mean and standard deviation of solid volume fraction, solid phase velocities, and granular temperature are provided. Mean values and confidence intervals of the quantities of interest are compared to the experiment results. The univariate and bivariate PDF reconstructions of the system response are performed using EQMOM and ECQMOM.« less

Prevalence and Correlates of Sleep Problems in Adult Israeli Jews Exposed to Actual or Threatened Terrorist or Rocket Attacks

PubMed Central

Palmieri, Patrick A.; Chipman, Katie J.; Canetti, Daphna; Johnson, Robert J.; Hobfoll, Stevan E.

2010-01-01

Study Objectives: To estimate the prevalence of, and to identify correlates of clinically significant sleep problems in adult Israeli citizens exposed to chronic terrorism and war trauma or threat thereof. Methods: A population-based, cross-sectional study of 1001 adult Israeli citizens interviewed by phone between July 15 and August 26, 2008. The phone survey was conducted in Hebrew and assessed demographics, trauma/stressor exposure, probable posttraumatic stress disorder (PTSD), probable depression, and sleep problems. Probable PTSD and depression were assessed with the PTSD Symptom Scale (PSS) and Patient Health Questionnaire (PHQ-9), respectively, following DSM-IV diagnostic criteria. Sleep problems in the past month were assessed with the Pittsburgh Sleep Quality Index (PSQI), on which a global composite score ≥ 6 indicates a clinical-level sleep problem. Results: Prevalence of probable PTSD and depression was 5.5% and 5.8%, respectively. Prevalence of clinically significant sleep problems was 37.4% overall, but was significantly higher for probable PTSD (81.8%) and probable depression (79.3%) subgroups. Independent correlates of poor sleep included being female, older, less educated, experiencing major life stressors, and experiencing psychosocial resource loss. Psychosocial resource loss due to terrorist attacks emerged as the strongest potentially modifiable risk factor for sleep problems. Conclusions: Sleep problems are common among Israeli adults living under chronic traumatic threat and trauma exposure. Given the continuing threat of war, interventions that bolster psychosocial resources may play an important role in preventing or alleviating sleep problems in this population. Citation: Palmieri PA; Chipman KJ; Canetti D; Johnson RJ; Hobfoll SE. Prevalence and correlates of sleep problems in adult Israeli Jews exposed to actual or threatened terrorist or rocket attacks. J Clin Sleep Med 2010;6(6):557-564. PMID:21206544

Implement Method for Automated Testing of Markov Chain Convergence into INVERSE for ORNL12-RS-108J: Advanced Multi-Dimensional Forward and Inverse Modeling

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bledsoe, Keith C.

2015-04-01

The DiffeRential Evolution Adaptive Metropolis (DREAM) method is a powerful optimization/uncertainty quantification tool used to solve inverse transport problems in Los Alamos National Laboratory’s INVERSE code system. The DREAM method has been shown to be adept at accurate uncertainty quantification, but it can be very computationally demanding. Previously, the DREAM method in INVERSE performed a user-defined number of particle transport calculations. This placed a burden on the user to guess the number of calculations that would be required to accurately solve any given problem. This report discusses a new approach that has been implemented into INVERSE, the Gelman-Rubin convergence metric.more » This metric automatically detects when an appropriate number of transport calculations have been completed and the uncertainty in the inverse problem has been accurately calculated. In a test problem with a spherical geometry, this method was found to decrease the number of transport calculations (and thus time required) to solve a problem by an average of over 90%. In a cylindrical test geometry, a 75% decrease was obtained.« less

California's High School Dropouts: Examining the Fiscal Consequences

ERIC Educational Resources Information Center

Stuit, David A.; Springer, Jeffrey A.

2010-01-01

This report analyzes the economic and social costs of the high school dropout problem in California from the perspective of a state taxpayer. The authors' analysis considers the consequences of this problem in terms of labor market, tax revenue, public health, and incarceration costs. The authors' quantification of these costs reveals the sizeable…

Transfer of Solutions to Conditional Probability Problems: Effects of Example Problem Format, Solution Format, and Problem Context

ERIC Educational Resources Information Center

Chow, Alan F.; Van Haneghan, James P.

2016-01-01

This study reports the results of a study examining how easily students are able to transfer frequency solutions to conditional probability problems to novel situations. University students studied either a problem solved using the traditional Bayes formula format or using a natural frequency (tree diagram) format. In addition, the example problem…

Risk Quantification of Systems Engineering Documents Improves Probability of DOD Project Success

DTIC Science & Technology

2009-09-01

comprehensive risk model for DoD milestone review documentation as well as recommended changes to the Capability Maturity Model Integration ( CMMI ) Project...Milestone Documentation, Project Planning, Rational Frame, Political Frame, CMMI Project Planning Process Area, CMMI Risk Management Process Area...well as recommended changes to the Capability Maturity Model Integration ( CMMI ) Project Planning and Risk Management process areas. The intent is to

The dimerization equilibrium of a ClC Cl−/H+ antiporter in lipid bilayers

PubMed Central

Chadda, Rahul; Krishnamani, Venkatramanan; Mersch, Kacey; Wong, Jason; Brimberry, Marley; Chadda, Ankita; Kolmakova-Partensky, Ludmila; Friedman, Larry J; Gelles, Jeff; Robertson, Janice L

2016-01-01

Interactions between membrane protein interfaces in lipid bilayers play an important role in membrane protein folding but quantification of the strength of these interactions has been challenging. Studying dimerization of ClC-type transporters offers a new approach to the problem, as individual subunits adopt a stable and functionally verifiable fold that constrains the system to two states – monomer or dimer. Here, we use single-molecule photobleaching analysis to measure the probability of ClC-ec1 subunit capture into liposomes during extrusion of large, multilamellar membranes. The capture statistics describe a monomer to dimer transition that is dependent on the subunit/lipid mole fraction density and follows an equilibrium dimerization isotherm. This allows for the measurement of the free energy of ClC-ec1 dimerization in lipid bilayers, revealing that it is one of the strongest membrane protein complexes measured so far, and introduces it as new type of dimerization model to investigate the physical forces that drive membrane protein association in membranes. DOI: http://dx.doi.org/10.7554/eLife.17438.001 PMID:27484630

Gain Switching for a Detection System to Accommodate a Newly Developed MALDI-Based Quantification Method

NASA Astrophysics Data System (ADS)

Ahn, Sung Hee; Hyeon, Taeghwan; Kim, Myung Soo; Moon, Jeong Hee

2017-09-01

In matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF), matrix-derived ions are routinely deflected away to avoid problems with ion detection. This, however, limits the use of a quantification method that utilizes the analyte-to-matrix ion abundance ratio. In this work, we will show that it is possible to measure this ratio by a minor instrumental modification of a simple form of MALDI-TOF. This involves detector gain switching. [Figure not available: see fulltext.

Numerical study of the influence of surface reaction probabilities on reactive species in an rf atmospheric pressure plasma containing humidity

NASA Astrophysics Data System (ADS)

Schröter, Sandra; Gibson, Andrew R.; Kushner, Mark J.; Gans, Timo; O'Connell, Deborah

2018-01-01

The quantification and control of reactive species (RS) in atmospheric pressure plasmas (APPs) is of great interest for their technological applications, in particular in biomedicine. Of key importance in simulating the densities of these species are fundamental data on their production and destruction. In particular, data concerning particle-surface reaction probabilities in APPs are scarce, with most of these probabilities measured in low-pressure systems. In this work, the role of surface reaction probabilities, γ, of reactive neutral species (H, O and OH) on neutral particle densities in a He-H2O radio-frequency micro APP jet (COST-μ APPJ) are investigated using a global model. It is found that the choice of γ, particularly for low-mass species having large diffusivities, such as H, can change computed species densities significantly. The importance of γ even at elevated pressures offers potential for tailoring the RS composition of atmospheric pressure microplasmas by choosing different wall materials or plasma geometries.

Quantifying the probability of record-setting heat events in the historical record and at different levels of climate forcing

NASA Astrophysics Data System (ADS)

Diffenbaugh, N. S.

2017-12-01

Severe heat provides one of the most direct, acute, and rapidly changing impacts of climate on people and ecostystems. Theory, historical observations, and climate model simulations all suggest that global warming should increase the probability of hot events that fall outside of our historical experience. Given the acutre impacts of extreme heat, quantifying the probability of historically unprecedented hot events at different levels of climate forcing is critical for climate adaptation and mitigation decisions. However, in practice that quantification presents a number of methodological challenges. This presentation will review those methodological challenges, including the limitations of the observational record and of climate model fidelity. The presentation will detail a comprehensive approach to addressing these challenges. It will then demonstrate the application of that approach to quantifying uncertainty in the probability of record-setting hot events in the current climate, as well as periods with lower and higher greenhouse gas concentrations than the present.

Quantification of effective exoelectrogens by most probable number (MPN) in a microbial fuel cell.

PubMed

Heidrich, Elizabeth S; Curtis, Thomas P; Woodcock, Stephen; Dolfing, Jan

2016-10-01

The objective of this work was to quantify the number of exoelectrogens in wastewater capable of producing current in a microbial fuel cell by adapting the classical most probable number (MPN) methodology using current production as end point. Inoculating a series of microbial fuel cells with various dilutions of domestic wastewater and with acetate as test substrate yielded an apparent number of exoelectrogens of 17perml. Using current as a proxy for activity the apparent exoelectrogen growth rate was 0.03h(-1). With starch or wastewater as more complex test substrates similar apparent growth rates were obtained, but the apparent MPN based numbers of exoelectrogens in wastewater were significantly lower, probably because in contrast to acetate, complex substrates require complex food chains to deliver the electrons to the electrodes. Consequently, the apparent MPN is a function of the combined probabilities of members of the food chain being present. Copyright © 2016 The Author(s). Published by Elsevier Ltd.. All rights reserved.

GMO quantification: valuable experience and insights for the future.

PubMed

Milavec, Mojca; Dobnik, David; Yang, Litao; Zhang, Dabing; Gruden, Kristina; Zel, Jana

2014-10-01

Cultivation and marketing of genetically modified organisms (GMOs) have been unevenly adopted worldwide. To facilitate international trade and to provide information to consumers, labelling requirements have been set up in many countries. Quantitative real-time polymerase chain reaction (qPCR) is currently the method of choice for detection, identification and quantification of GMOs. This has been critically assessed and the requirements for the method performance have been set. Nevertheless, there are challenges that should still be highlighted, such as measuring the quantity and quality of DNA, and determining the qPCR efficiency, possible sequence mismatches, characteristics of taxon-specific genes and appropriate units of measurement, as these remain potential sources of measurement uncertainty. To overcome these problems and to cope with the continuous increase in the number and variety of GMOs, new approaches are needed. Statistical strategies of quantification have already been proposed and expanded with the development of digital PCR. The first attempts have been made to use new generation sequencing also for quantitative purposes, although accurate quantification of the contents of GMOs using this technology is still a challenge for the future, and especially for mixed samples. New approaches are needed also for the quantification of stacks, and for potential quantification of organisms produced by new plant breeding techniques.

Prizes in Cereal Boxes: An Application of Probability.

ERIC Educational Resources Information Center

Litwiller, Bonnie H.; Duncan, David R.

1992-01-01

Presents four cases of real-world probabilistic situations to promote more effective teaching of probability. Calculates the probability of obtaining six of six different prizes successively in six, seven, eight, and nine boxes of cereal, generalizes the problem to n boxes of cereal, and offers suggestions to extend the problem. (MDH)

Ergot alkaloids in Norwegian wild grasses: a mass spectrometric approach.

PubMed

Uhlig, Silvio; Vikøren, Turid; Ivanova, Lada; Handeland, Kjell

2007-01-01

Ergot alkaloids are mycotoxins which are produced among fungi in the family Clavicipitaceae. Poisoning with ergot alkaloids is an important veterinary problem in animal husbandry and has recently also been recognised in wild animals. While the poisoning syndrome observed in domestic animals such as cattle, horses and sheep is usually caused by endophyte-infected grass, the recently observed ergotism among Norwegian cervids is probably due to infection of wild grasses with Claviceps. Mass spectrometry is today the method of choice for the rapid qualitative and quantitative determination of many natural compounds. This study uses tandem quadrupole mass spectrometry as well as ion trap mass spectrometry in connection with electrospray(+) ionisation for the quantification, screening and fragmentation of ergot alkaloids in extracts from Claviceps sclerotia that had been picked from wild grasses from several locations in Norway. Ergotamine, ergovaline, ergonovine and ergocryptine were available as standards and were quantified in the extracts, while ergocrystine, ergocornine, ergonine/ergosine, lysergic acid and lysergol were identified on the basis of their molecular weights and semi-quantified. Ergocrystine dominated the alkaloid spectrum of most extracts. Levels of the quantified alkaloids were in the range 0.2-9300 microg/g. Several unknown ergot alkaloids were found in the extracts. MS(n) experiments identified some as simple lysergic acid amide derivatives, while othes are probably related to ergocrystine and ergocryptine by dehydration, dehydrogenation and/or amino acid substitution at R(1) of the peptide moiety.

On "black swans" and "perfect storms": risk analysis and management when statistics are not enough.

PubMed

Paté-Cornell, Elisabeth

2012-11-01

Two images, "black swans" and "perfect storms," have struck the public's imagination and are used--at times indiscriminately--to describe the unthinkable or the extremely unlikely. These metaphors have been used as excuses to wait for an accident to happen before taking risk management measures, both in industry and government. These two images represent two distinct types of uncertainties (epistemic and aleatory). Existing statistics are often insufficient to support risk management because the sample may be too small and the system may have changed. Rationality as defined by the von Neumann axioms leads to a combination of both types of uncertainties into a single probability measure--Bayesian probability--and accounts only for risk aversion. Yet, the decisionmaker may also want to be ambiguity averse. This article presents an engineering risk analysis perspective on the problem, using all available information in support of proactive risk management decisions and considering both types of uncertainty. These measures involve monitoring of signals, precursors, and near-misses, as well as reinforcement of the system and a thoughtful response strategy. It also involves careful examination of organizational factors such as the incentive system, which shape human performance and affect the risk of errors. In all cases, including rare events, risk quantification does not allow "prediction" of accidents and catastrophes. Instead, it is meant to support effective risk management rather than simply reacting to the latest events and headlines. © 2012 Society for Risk Analysis.

Structuring as an Aid to Performance in Base-Rate Problems.

DTIC Science & Technology

1988-06-01

Design. All subjects were given two base-rate problems, here called the Lightbulb problem (adapted from Lyon & Slovic, 1976) and the Dyslexia problem; both...are shown in Table 1. Approximately half the subjects received the Lightbulb problem first; the others received the Dyslexia problem first. The two...probability that this bulb is really defective? [the child really has dyslexia ]? You can probably give a good estimate if you think hard and carefully

Quantification of Vibrio species in oysters from the Gulf of Mexico with two procedures based on MPN and PCR.

PubMed

Barrera-Escorcia, Guadalupe; Wong-Chang, Irma; Fernández-Rendón, Carlos Leopoldo; Botello, Alfonso Vázquez; Gómez-Gil, Bruno; Lizárraga-Partida, Marcial Leonardo

2016-11-01

Oysters can accumulate potentially pathogenic water bacteria. The objective of this study was to compare two procedures to quantify Vibrio species present in oysters to determine the most sensitive method. We analyzed oyster samples from the Gulf of Mexico, commercialized in Mexico City. The samples were inoculated in tubes with alkaline peptone water (APW), based on three tubes and four dilutions (10 -1 to 10 -4 ). From these tubes, the first quantification of Vibrio species was performed (most probable number (MPN) from tubes) and bacteria were inoculated by streaking on thiosulfate-citrate-bile salts-sucrose (TCBS) petri dishes. Colonies were isolated for a second quantification (MPN from dishes). Polymerase chain reaction (PCR) was used to determine species with specific primers: ompW for Vibrio cholerae, tlh for Vibrio parahaemolyticus, and VvhA for Vibrio vulnificus. Simultaneously, the sanitary quality of oysters was determined. The quantification of V. parahaemolyticus was significantly higher in APW tubes than in TCBS dishes. Regarding V. vulnificus counts, the differences among both approaches were not significant. In contrast, the MPNs of V. cholerae obtained from dishes were higher than from tubes. The quantification of MPNs through PCR of V. parahaemolyticus and V. vulnificus obtained from APW was sensitive and recommendable for the detection of both species. In contrast, to quantify V. cholerae, it was necessary to isolate colonies on TCBS prior PCR. Culturing in APW at 42 °C could be an alternative to avoid colony isolation. The MPNs of V. cholerae from dishes was associated with the bad sanitary quality of the samples.

Hydrologic Impacts of Climate Change: Quantification of Uncertainties (Alexander von Humboldt Medal Lecture)

NASA Astrophysics Data System (ADS)

Mujumdar, Pradeep P.

2014-05-01

Climate change results in regional hydrologic change. The three prominent signals of global climate change, viz., increase in global average temperatures, rise in sea levels and change in precipitation patterns convert into signals of regional hydrologic change in terms of modifications in water availability, evaporative water demand, hydrologic extremes of floods and droughts, water quality, salinity intrusion in coastal aquifers, groundwater recharge and other related phenomena. A major research focus in hydrologic sciences in recent years has been assessment of impacts of climate change at regional scales. An important research issue addressed in this context deals with responses of water fluxes on a catchment scale to the global climatic change. A commonly adopted methodology for assessing the regional hydrologic impacts of climate change is to use the climate projections provided by the General Circulation Models (GCMs) for specified emission scenarios in conjunction with the process-based hydrologic models to generate the corresponding hydrologic projections. The scaling problem arising because of the large spatial scales at which the GCMs operate compared to those required in distributed hydrologic models, and their inability to satisfactorily simulate the variables of interest to hydrology are addressed by downscaling the GCM simulations to hydrologic scales. Projections obtained with this procedure are burdened with a large uncertainty introduced by the choice of GCMs and emission scenarios, small samples of historical data against which the models are calibrated, downscaling methods used and other sources. Development of methodologies to quantify and reduce such uncertainties is a current area of research in hydrology. In this presentation, an overview of recent research carried out by the author's group on assessment of hydrologic impacts of climate change addressing scale issues and quantification of uncertainties is provided. Methodologies developed with conditional random fields, Dempster-Shafer theory, possibility theory, imprecise probabilities and non-stationary extreme value theory are discussed. Specific applications on uncertainty quantification in impacts on streamflows, evaporative water demands, river water quality and urban flooding are presented. A brief discussion on detection and attribution of hydrologic change at river basin scales, contribution of landuse change and likely alterations in return levels of hydrologic extremes is also provided.

Some Open Issues on Rockfall Hazard Analysis in Fractured Rock Mass: Problems and Prospects

NASA Astrophysics Data System (ADS)

Ferrero, Anna Maria; Migliazza, Maria Rita; Pirulli, Marina; Umili, Gessica

2016-09-01

Risk is part of every sector of engineering design. It is a consequence of the uncertainties connected with the cognitive boundaries and with the natural variability of the relevant variables. In soil and rock engineering, in particular, uncertainties are linked to geometrical and mechanical aspects and the model used for the problem schematization. While the uncertainties due to the cognitive gaps could be filled by improving the quality of numerical codes and measuring instruments, nothing can be done to remove the randomness of natural variables, except defining their variability with stochastic approaches. Probabilistic analyses represent a useful tool to run parametric analyses and to identify the more significant aspects of a given phenomenon: They can be used for a rational quantification and mitigation of risk. The connection between the cognitive level and the probability of failure is at the base of the determination of hazard, which is often quantified through the assignment of safety factors. But these factors suffer from conceptual limits, which can be only overcome by adopting mathematical techniques with sound bases, not so used up to now (Einstein et al. in rock mechanics in civil and environmental engineering, CRC Press, London, 3-13, 2010; Brown in J Rock Mech Geotech Eng 4(3):193-204, 2012). The present paper describes the problems and the more reliable techniques used to quantify the uncertainties that characterize the large number of parameters that are involved in rock slope hazard assessment through a real case specifically related to rockfall. Limits of the existing approaches and future developments of the research are also provided.

Investigating the Relationship between Conceptual and Procedural Errors in the Domain of Probability Problem-Solving.

ERIC Educational Resources Information Center

O'Connell, Ann Aileen

The relationships among types of errors observed during probability problem solving were studied. Subjects were 50 graduate students in an introductory probability and statistics course. Errors were classified as text comprehension, conceptual, procedural, and arithmetic. Canonical correlation analysis was conducted on the frequencies of specific…

Round robin test on quantification of amyloid-β 1-42 in cerebrospinal fluid by mass spectrometry.

PubMed

Pannee, Josef; Gobom, Johan; Shaw, Leslie M; Korecka, Magdalena; Chambers, Erin E; Lame, Mary; Jenkins, Rand; Mylott, William; Carrillo, Maria C; Zegers, Ingrid; Zetterberg, Henrik; Blennow, Kaj; Portelius, Erik

2016-01-01

Cerebrospinal fluid (CSF) amyloid-β 1-42 (Aβ42) is an important biomarker for Alzheimer's disease, both in diagnostics and to monitor disease-modifying therapies. However, there is a great need for standardization of methods used for quantification. To overcome problems associated with immunoassays, liquid chromatography-tandem mass spectrometry (LC-MS/MS) has emerged as a critical orthogonal alternative. We compared results for CSF Aβ42 quantification in a round robin study performed in four laboratories using similar sample preparation methods and LC-MS instrumentation. The LC-MS results showed excellent correlation between laboratories (r(2) >0.98), high analytical precision, and good correlation with enzyme-linked immunosorbent assay (r(2) >0.85). The use of a common reference sample further decreased interlaboratory variation. Our results indicate that LC-MS is suitable for absolute quantification of Aβ42 in CSF and highlight the importance of developing a certified reference material. Copyright © 2016 The Alzheimer's Association. Published by Elsevier Inc. All rights reserved.

A Constrained Genetic Algorithm with Adaptively Defined Fitness Function in MRS Quantification

NASA Astrophysics Data System (ADS)

Papakostas, G. A.; Karras, D. A.; Mertzios, B. G.; Graveron-Demilly, D.; van Ormondt, D.

MRS Signal quantification is a rather involved procedure and has attracted the interest of the medical engineering community, regarding the development of computationally efficient methodologies. Significant contributions based on Computational Intelligence tools, such as Neural Networks (NNs), demonstrated a good performance but not without drawbacks already discussed by the authors. On the other hand preliminary application of Genetic Algorithms (GA) has already been reported in the literature by the authors regarding the peak detection problem encountered in MRS quantification using the Voigt line shape model. This paper investigates a novel constrained genetic algorithm involving a generic and adaptively defined fitness function which extends the simple genetic algorithm methodology in case of noisy signals. The applicability of this new algorithm is scrutinized through experimentation in artificial MRS signals interleaved with noise, regarding its signal fitting capabilities. Although extensive experiments with real world MRS signals are necessary, the herein shown performance illustrates the method's potential to be established as a generic MRS metabolites quantification procedure.

Learning and Information Approaches for Inference in Dynamic Data-Driven Geophysical Applications

NASA Astrophysics Data System (ADS)

Ravela, S.

2015-12-01

Many Geophysical inference problems are characterized by non-linear processes, high-dimensional models and complex uncertainties. A dynamic coupling between models, estimation, and sampling is typically sought to efficiently characterize and reduce uncertainty. This process is however fraught with several difficulties. Among them, the key difficulties are the ability to deal with model errors, efficacy of uncertainty quantification and data assimilation. In this presentation, we present three key ideas from learning and intelligent systems theory and apply them to two geophysical applications. The first idea is the use of Ensemble Learning to compensate for model error, the second is to develop tractable Information Theoretic Learning to deal with non-Gaussianity in inference, and the third is a Manifold Resampling technique for effective uncertainty quantification. We apply these methods, first to the development of a cooperative autonomous observing system using sUAS for studying coherent structures. We apply this to Second, we apply this to the problem of quantifying risk from hurricanes and storm surges in a changing climate. Results indicate that learning approaches can enable new effectiveness in cases where standard approaches to model reduction, uncertainty quantification and data assimilation fail.

Multiobjective fuzzy stochastic linear programming problems with inexact probability distribution

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hamadameen, Abdulqader Othman; Zainuddin, Zaitul Marlizawati

This study deals with multiobjective fuzzy stochastic linear programming problems with uncertainty probability distribution which are defined as fuzzy assertions by ambiguous experts. The problem formulation has been presented and the two solutions strategies are; the fuzzy transformation via ranking function and the stochastic transformation when α{sup –}. cut technique and linguistic hedges are used in the uncertainty probability distribution. The development of Sen’s method is employed to find a compromise solution, supported by illustrative numerical example.

Propagating Mixed Uncertainties in Cyber Attacker Payoffs: Exploration of Two-Phase Monte Carlo Sampling and Probability Bounds Analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chatterjee, Samrat; Tipireddy, Ramakrishna; Oster, Matthew R.

Securing cyber-systems on a continual basis against a multitude of adverse events is a challenging undertaking. Game-theoretic approaches, that model actions of strategic decision-makers, are increasingly being applied to address cybersecurity resource allocation challenges. Such game-based models account for multiple player actions and represent cyber attacker payoffs mostly as point utility estimates. Since a cyber-attacker’s payoff generation mechanism is largely unknown, appropriate representation and propagation of uncertainty is a critical task. In this paper we expand on prior work and focus on operationalizing the probabilistic uncertainty quantification framework, for a notional cyber system, through: 1) representation of uncertain attacker andmore » system-related modeling variables as probability distributions and mathematical intervals, and 2) exploration of uncertainty propagation techniques including two-phase Monte Carlo sampling and probability bounds analysis.« less

Exact and Approximate Probabilistic Symbolic Execution

NASA Technical Reports Server (NTRS)

Luckow, Kasper; Pasareanu, Corina S.; Dwyer, Matthew B.; Filieri, Antonio; Visser, Willem

2014-01-01

Probabilistic software analysis seeks to quantify the likelihood of reaching a target event under uncertain environments. Recent approaches compute probabilities of execution paths using symbolic execution, but do not support nondeterminism. Nondeterminism arises naturally when no suitable probabilistic model can capture a program behavior, e.g., for multithreading or distributed systems. In this work, we propose a technique, based on symbolic execution, to synthesize schedulers that resolve nondeterminism to maximize the probability of reaching a target event. To scale to large systems, we also introduce approximate algorithms to search for good schedulers, speeding up established random sampling and reinforcement learning results through the quantification of path probabilities based on symbolic execution. We implemented the techniques in Symbolic PathFinder and evaluated them on nondeterministic Java programs. We show that our algorithms significantly improve upon a state-of- the-art statistical model checking algorithm, originally developed for Markov Decision Processes.

Uncertainty quantification analysis of the dynamics of an electrostatically actuated microelectromechanical switch model

NASA Astrophysics Data System (ADS)

Snow, Michael G.; Bajaj, Anil K.

2015-08-01

This work presents an uncertainty quantification (UQ) analysis of a comprehensive model for an electrostatically actuated microelectromechanical system (MEMS) switch. The goal is to elucidate the effects of parameter variations on certain key performance characteristics of the switch. A sufficiently detailed model of the electrostatically actuated switch in the basic configuration of a clamped-clamped beam is developed. This multi-physics model accounts for various physical effects, including the electrostatic fringing field, finite length of electrodes, squeeze film damping, and contact between the beam and the dielectric layer. The performance characteristics of immediate interest are the static and dynamic pull-in voltages for the switch. Numerical approaches for evaluating these characteristics are developed and described. Using Latin Hypercube Sampling and other sampling methods, the model is evaluated to find these performance characteristics when variability in the model's geometric and physical parameters is specified. Response surfaces of these results are constructed via a Multivariate Adaptive Regression Splines (MARS) technique. Using a Direct Simulation Monte Carlo (DSMC) technique on these response surfaces gives smooth probability density functions (PDFs) of the outputs characteristics when input probability characteristics are specified. The relative variation in the two pull-in voltages due to each of the input parameters is used to determine the critical parameters.

Global Sensitivity Analysis and Estimation of Model Error, Toward Uncertainty Quantification in Scramjet Computations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Huan, Xun; Safta, Cosmin; Sargsyan, Khachik

The development of scramjet engines is an important research area for advancing hypersonic and orbital flights. Progress toward optimal engine designs requires accurate flow simulations together with uncertainty quantification. However, performing uncertainty quantification for scramjet simulations is challenging due to the large number of uncertain parameters involved and the high computational cost of flow simulations. These difficulties are addressed in this paper by developing practical uncertainty quantification algorithms and computational methods, and deploying them in the current study to large-eddy simulations of a jet in crossflow inside a simplified HIFiRE Direct Connect Rig scramjet combustor. First, global sensitivity analysis ismore » conducted to identify influential uncertain input parameters, which can help reduce the system’s stochastic dimension. Second, because models of different fidelity are used in the overall uncertainty quantification assessment, a framework for quantifying and propagating the uncertainty due to model error is presented. In conclusion, these methods are demonstrated on a nonreacting jet-in-crossflow test problem in a simplified scramjet geometry, with parameter space up to 24 dimensions, using static and dynamic treatments of the turbulence subgrid model, and with two-dimensional and three-dimensional geometries.« less

Global Sensitivity Analysis and Estimation of Model Error, Toward Uncertainty Quantification in Scramjet Computations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Huan, Xun; Safta, Cosmin; Sargsyan, Khachik

The development of scramjet engines is an important research area for advancing hypersonic and orbital flights. Progress toward optimal engine designs requires accurate flow simulations together with uncertainty quantification. However, performing uncertainty quantification for scramjet simulations is challenging due to the large number of uncertain parameters involved and the high computational cost of flow simulations. These difficulties are addressed in this paper by developing practical uncertainty quantification algorithms and computational methods, and deploying them in the current study to large-eddy simulations of a jet in crossflow inside a simplified HIFiRE Direct Connect Rig scramjet combustor. First, global sensitivity analysis ismore » conducted to identify influential uncertain input parameters, which can help reduce the system’s stochastic dimension. Second, because models of different fidelity are used in the overall uncertainty quantification assessment, a framework for quantifying and propagating the uncertainty due to model error is presented. Finally, these methods are demonstrated on a nonreacting jet-in-crossflow test problem in a simplified scramjet geometry, with parameter space up to 24 dimensions, using static and dynamic treatments of the turbulence subgrid model, and with two-dimensional and three-dimensional geometries.« less

Global Sensitivity Analysis and Estimation of Model Error, Toward Uncertainty Quantification in Scramjet Computations

NASA Astrophysics Data System (ADS)

Huan, Xun; Safta, Cosmin; Sargsyan, Khachik; Geraci, Gianluca; Eldred, Michael S.; Vane, Zachary P.; Lacaze, Guilhem; Oefelein, Joseph C.; Najm, Habib N.

2018-03-01

The development of scramjet engines is an important research area for advancing hypersonic and orbital flights. Progress toward optimal engine designs requires accurate flow simulations together with uncertainty quantification. However, performing uncertainty quantification for scramjet simulations is challenging due to the large number of uncertain parameters involved and the high computational cost of flow simulations. These difficulties are addressed in this paper by developing practical uncertainty quantification algorithms and computational methods, and deploying them in the current study to large-eddy simulations of a jet in crossflow inside a simplified HIFiRE Direct Connect Rig scramjet combustor. First, global sensitivity analysis is conducted to identify influential uncertain input parameters, which can help reduce the systems stochastic dimension. Second, because models of different fidelity are used in the overall uncertainty quantification assessment, a framework for quantifying and propagating the uncertainty due to model error is presented. These methods are demonstrated on a nonreacting jet-in-crossflow test problem in a simplified scramjet geometry, with parameter space up to 24 dimensions, using static and dynamic treatments of the turbulence subgrid model, and with two-dimensional and three-dimensional geometries.

Global Sensitivity Analysis and Estimation of Model Error, Toward Uncertainty Quantification in Scramjet Computations

DOE PAGES

Huan, Xun; Safta, Cosmin; Sargsyan, Khachik; ...

2018-02-09

The development of scramjet engines is an important research area for advancing hypersonic and orbital flights. Progress toward optimal engine designs requires accurate flow simulations together with uncertainty quantification. However, performing uncertainty quantification for scramjet simulations is challenging due to the large number of uncertain parameters involved and the high computational cost of flow simulations. These difficulties are addressed in this paper by developing practical uncertainty quantification algorithms and computational methods, and deploying them in the current study to large-eddy simulations of a jet in crossflow inside a simplified HIFiRE Direct Connect Rig scramjet combustor. First, global sensitivity analysis ismore » conducted to identify influential uncertain input parameters, which can help reduce the system’s stochastic dimension. Second, because models of different fidelity are used in the overall uncertainty quantification assessment, a framework for quantifying and propagating the uncertainty due to model error is presented. In conclusion, these methods are demonstrated on a nonreacting jet-in-crossflow test problem in a simplified scramjet geometry, with parameter space up to 24 dimensions, using static and dynamic treatments of the turbulence subgrid model, and with two-dimensional and three-dimensional geometries.« less

DECHADE: DEtecting slight Changes with HArd DEcisions in Wireless Sensor Networks

NASA Astrophysics Data System (ADS)

Ciuonzo, D.; Salvo Rossi, P.

2018-07-01

This paper focuses on the problem of change detection through a Wireless Sensor Network (WSN) whose nodes report only binary decisions (on the presence/absence of a certain event to be monitored), due to bandwidth/energy constraints. The resulting problem can be modelled as testing the equality of samples drawn from independent Bernoulli probability mass functions, when the bit probabilities under both hypotheses are not known. Both One-Sided (OS) and Two-Sided (TS) tests are considered, with reference to: (i) identical bit probability (a homogeneous scenario), (ii) different per-sensor bit probabilities (a non-homogeneous scenario) and (iii) regions with identical bit probability (a block-homogeneous scenario) for the observed samples. The goal is to provide a systematic framework collecting a plethora of viable detectors (designed via theoretically founded criteria) which can be used for each instance of the problem. Finally, verification of the derived detectors in two relevant WSN-related problems is provided to show the appeal of the proposed framework.

Probabilistic risk assessment for a loss of coolant accident in McMaster Nuclear Reactor and application of reliability physics model for modeling human reliability

NASA Astrophysics Data System (ADS)

Ha, Taesung

A probabilistic risk assessment (PRA) was conducted for a loss of coolant accident, (LOCA) in the McMaster Nuclear Reactor (MNR). A level 1 PRA was completed including event sequence modeling, system modeling, and quantification. To support the quantification of the accident sequence identified, data analysis using the Bayesian method and human reliability analysis (HRA) using the accident sequence evaluation procedure (ASEP) approach were performed. Since human performance in research reactors is significantly different from that in power reactors, a time-oriented HRA model (reliability physics model) was applied for the human error probability (HEP) estimation of the core relocation. This model is based on two competing random variables: phenomenological time and performance time. The response surface and direct Monte Carlo simulation with Latin Hypercube sampling were applied for estimating the phenomenological time, whereas the performance time was obtained from interviews with operators. An appropriate probability distribution for the phenomenological time was assigned by statistical goodness-of-fit tests. The human error probability (HEP) for the core relocation was estimated from these two competing quantities: phenomenological time and operators' performance time. The sensitivity of each probability distribution in human reliability estimation was investigated. In order to quantify the uncertainty in the predicted HEPs, a Bayesian approach was selected due to its capability of incorporating uncertainties in model itself and the parameters in that model. The HEP from the current time-oriented model was compared with that from the ASEP approach. Both results were used to evaluate the sensitivity of alternative huinan reliability modeling for the manual core relocation in the LOCA risk model. This exercise demonstrated the applicability of a reliability physics model supplemented with a. Bayesian approach for modeling human reliability and its potential usefulness of quantifying model uncertainty as sensitivity analysis in the PRA model.

Uncertainty quantification for PZT bimorph actuators

NASA Astrophysics Data System (ADS)

Bravo, Nikolas; Smith, Ralph C.; Crews, John

2018-03-01

In this paper, we discuss the development of a high fidelity model for a PZT bimorph actuator used for micro-air vehicles, which includes the Robobee. We developed a high-fidelity model for the actuator using the homogenized energy model (HEM) framework, which quantifies the nonlinear, hysteretic, and rate-dependent behavior inherent to PZT in dynamic operating regimes. We then discussed an inverse problem on the model. We included local and global sensitivity analysis of the parameters in the high-fidelity model. Finally, we will discuss the results of Bayesian inference and uncertainty quantification on the HEM.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhu, Lin; Dai, Zhenxue; Gong, Huili

Understanding the heterogeneity arising from the complex architecture of sedimentary sequences in alluvial fans is challenging. This study develops a statistical inverse framework in a multi-zone transition probability approach for characterizing the heterogeneity in alluvial fans. An analytical solution of the transition probability matrix is used to define the statistical relationships among different hydrofacies and their mean lengths, integral scales, and volumetric proportions. A statistical inversion is conducted to identify the multi-zone transition probability models and estimate the optimal statistical parameters using the modified Gauss–Newton–Levenberg–Marquardt method. The Jacobian matrix is computed by the sensitivity equation method, which results in anmore » accurate inverse solution with quantification of parameter uncertainty. We use the Chaobai River alluvial fan in the Beijing Plain, China, as an example for elucidating the methodology of alluvial fan characterization. The alluvial fan is divided into three sediment zones. In each zone, the explicit mathematical formulations of the transition probability models are constructed with optimized different integral scales and volumetric proportions. The hydrofacies distributions in the three zones are simulated sequentially by the multi-zone transition probability-based indicator simulations. Finally, the result of this study provides the heterogeneous structure of the alluvial fan for further study of flow and transport simulations.« less

Statistic inversion of multi-zone transition probability models for aquifer characterization in alluvial fans

DOE PAGES

Zhu, Lin; Dai, Zhenxue; Gong, Huili; ...

2015-06-12

Understanding the heterogeneity arising from the complex architecture of sedimentary sequences in alluvial fans is challenging. This study develops a statistical inverse framework in a multi-zone transition probability approach for characterizing the heterogeneity in alluvial fans. An analytical solution of the transition probability matrix is used to define the statistical relationships among different hydrofacies and their mean lengths, integral scales, and volumetric proportions. A statistical inversion is conducted to identify the multi-zone transition probability models and estimate the optimal statistical parameters using the modified Gauss–Newton–Levenberg–Marquardt method. The Jacobian matrix is computed by the sensitivity equation method, which results in anmore » accurate inverse solution with quantification of parameter uncertainty. We use the Chaobai River alluvial fan in the Beijing Plain, China, as an example for elucidating the methodology of alluvial fan characterization. The alluvial fan is divided into three sediment zones. In each zone, the explicit mathematical formulations of the transition probability models are constructed with optimized different integral scales and volumetric proportions. The hydrofacies distributions in the three zones are simulated sequentially by the multi-zone transition probability-based indicator simulations. Finally, the result of this study provides the heterogeneous structure of the alluvial fan for further study of flow and transport simulations.« less

New Ways Of Doing Business (NWODB) cost quantification analysis

NASA Technical Reports Server (NTRS)

Hamaker, Joseph W.; Rosmait, Russell L.

1992-01-01

The cost of designing, producing, and operating typical aerospace flight hardware is necessarily more expensive than most other human endeavors. Because of the more stringent environment of space, hardware designed to operate there will probably always be more expensive than similar hardware which is designed for less taxing environments. It is the thesis of this study that there are very significant improvements that can be made in the cost of aerospace flight hardware.

Recursive recovery of Markov transition probabilities from boundary value data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Patch, Sarah Kathyrn

1994-04-01

In an effort to mathematically describe the anisotropic diffusion of infrared radiation in biological tissue Gruenbaum posed an anisotropic diffusion boundary value problem in 1989. In order to accommodate anisotropy, he discretized the temporal as well as the spatial domain. The probabilistic interpretation of the diffusion equation is retained; radiation is assumed to travel according to a random walk (of sorts). In this random walk the probabilities with which photons change direction depend upon their previous as well as present location. The forward problem gives boundary value data as a function of the Markov transition probabilities. The inverse problem requiresmore » finding the transition probabilities from boundary value data. Problems in the plane are studied carefully in this thesis. Consistency conditions amongst the data are derived. These conditions have two effects: they prohibit inversion of the forward map but permit smoothing of noisy data. Next, a recursive algorithm which yields a family of solutions to the inverse problem is detailed. This algorithm takes advantage of all independent data and generates a system of highly nonlinear algebraic equations. Pluecker-Grassmann relations are instrumental in simplifying the equations. The algorithm is used to solve the 4 x 4 problem. Finally, the smallest nontrivial problem in three dimensions, the 2 x 2 x 2 problem, is solved.« less

A fuzzy Bayesian network approach to quantify the human behaviour during an evacuation

NASA Astrophysics Data System (ADS)

Ramli, Nurulhuda; Ghani, Noraida Abdul; Ahmad, Nazihah

2016-06-01

Bayesian Network (BN) has been regarded as a successful representation of inter-relationship of factors affecting human behavior during an emergency. This paper is an extension of earlier work of quantifying the variables involved in the BN model of human behavior during an evacuation using a well-known direct probability elicitation technique. To overcome judgment bias and reduce the expert's burden in providing precise probability values, a new approach for the elicitation technique is required. This study proposes a new fuzzy BN approach for quantifying human behavior during an evacuation. Three major phases of methodology are involved, namely 1) development of qualitative model representing human factors during an evacuation, 2) quantification of BN model using fuzzy probability and 3) inferencing and interpreting the BN result. A case study of three inter-dependencies of human evacuation factors such as danger assessment ability, information about the threat and stressful conditions are used to illustrate the application of the proposed method. This approach will serve as an alternative to the conventional probability elicitation technique in understanding the human behavior during an evacuation.

Fault tree analysis for urban flooding.

PubMed

ten Veldhuis, J A E; Clemens, F H L R; van Gelder, P H A J M

2009-01-01

Traditional methods to evaluate flood risk generally focus on heavy storm events as the principal cause of flooding. Conversely, fault tree analysis is a technique that aims at modelling all potential causes of flooding. It quantifies both overall flood probability and relative contributions of individual causes of flooding. This paper presents a fault model for urban flooding and an application to the case of Haarlem, a city of 147,000 inhabitants. Data from a complaint register, rainfall gauges and hydrodynamic model calculations are used to quantify probabilities of basic events in the fault tree. This results in a flood probability of 0.78/week for Haarlem. It is shown that gully pot blockages contribute to 79% of flood incidents, whereas storm events contribute only 5%. This implies that for this case more efficient gully pot cleaning is a more effective strategy to reduce flood probability than enlarging drainage system capacity. Whether this is also the most cost-effective strategy can only be decided after risk assessment has been complemented with a quantification of consequences of both types of events. To do this will be the next step in this study.

When Best Intentions Go Awry: The Failures of Concrete Representations to Help Solve Probability Word Problems

ERIC Educational Resources Information Center

Beitzel, Brian D.; Staley, Richard K.; DuBois, Nelson F.

2011-01-01

Previous research has cast doubt on the efficacy of utilizing external representations as an aid to solving word problems. The present study replicates previous findings that concrete representations hinder college students' ability to solve probability word problems, and extends those findings to apply to a multimedia instructional context. Our…

Mapping proteins in the presence of paralogs using units of coevolution

PubMed Central

2013-01-01

Background We study the problem of mapping proteins between two protein families in the presence of paralogs. This problem occurs as a difficult subproblem in coevolution-based computational approaches for protein-protein interaction prediction. Results Similar to prior approaches, our method is based on the idea that coevolution implies equal rates of sequence evolution among the interacting proteins, and we provide a first attempt to quantify this notion in a formal statistical manner. We call the units that are central to this quantification scheme the units of coevolution. A unit consists of two mapped protein pairs and its score quantifies the coevolution of the pairs. This quantification allows us to provide a maximum likelihood formulation of the paralog mapping problem and to cast it into a binary quadratic programming formulation. Conclusion CUPID, our software tool based on a Lagrangian relaxation of this formulation, makes it, for the first time, possible to compute state-of-the-art quality pairings in a few minutes of runtime. In summary, we suggest a novel alternative to the earlier available approaches, which is statistically sound and computationally feasible. PMID:24564758

Multilevel and quasi-Monte Carlo methods for uncertainty quantification in particle travel times through random heterogeneous porous media

NASA Astrophysics Data System (ADS)

Crevillén-García, D.; Power, H.

2017-08-01

In this study, we apply four Monte Carlo simulation methods, namely, Monte Carlo, quasi-Monte Carlo, multilevel Monte Carlo and multilevel quasi-Monte Carlo to the problem of uncertainty quantification in the estimation of the average travel time during the transport of particles through random heterogeneous porous media. We apply the four methodologies to a model problem where the only input parameter, the hydraulic conductivity, is modelled as a log-Gaussian random field by using direct Karhunen-Loéve decompositions. The random terms in such expansions represent the coefficients in the equations. Numerical calculations demonstrating the effectiveness of each of the methods are presented. A comparison of the computational cost incurred by each of the methods for three different tolerances is provided. The accuracy of the approaches is quantified via the mean square error.

Multilevel and quasi-Monte Carlo methods for uncertainty quantification in particle travel times through random heterogeneous porous media.

PubMed

Crevillén-García, D; Power, H

2017-08-01

In this study, we apply four Monte Carlo simulation methods, namely, Monte Carlo, quasi-Monte Carlo, multilevel Monte Carlo and multilevel quasi-Monte Carlo to the problem of uncertainty quantification in the estimation of the average travel time during the transport of particles through random heterogeneous porous media. We apply the four methodologies to a model problem where the only input parameter, the hydraulic conductivity, is modelled as a log-Gaussian random field by using direct Karhunen-Loéve decompositions. The random terms in such expansions represent the coefficients in the equations. Numerical calculations demonstrating the effectiveness of each of the methods are presented. A comparison of the computational cost incurred by each of the methods for three different tolerances is provided. The accuracy of the approaches is quantified via the mean square error.

Multilevel and quasi-Monte Carlo methods for uncertainty quantification in particle travel times through random heterogeneous porous media

PubMed Central

Power, H.

2017-01-01

In this study, we apply four Monte Carlo simulation methods, namely, Monte Carlo, quasi-Monte Carlo, multilevel Monte Carlo and multilevel quasi-Monte Carlo to the problem of uncertainty quantification in the estimation of the average travel time during the transport of particles through random heterogeneous porous media. We apply the four methodologies to a model problem where the only input parameter, the hydraulic conductivity, is modelled as a log-Gaussian random field by using direct Karhunen–Loéve decompositions. The random terms in such expansions represent the coefficients in the equations. Numerical calculations demonstrating the effectiveness of each of the methods are presented. A comparison of the computational cost incurred by each of the methods for three different tolerances is provided. The accuracy of the approaches is quantified via the mean square error. PMID:28878974

Remote Sensing and Problems of the Hydrosphere

NASA Technical Reports Server (NTRS)

Goldberg, E. D. (Editor)

1979-01-01

A discussion of freshwater and marine systems is presented including areas of the classification of lakes, identification and quantification of major functional groups of phytoplankton, sources and sinks of biochemical factors, and temporal and regional variability of surface features. Atmospheric processes linked to hydrospheric process through the transfer of matter via aerosols and gases are discussed. Particle fluxes to the aquatic environment and global geochemical problems are examined.

A Bayes network approach to uncertainty quantification in hierarchically developed computational models

DOE PAGES

Urbina, Angel; Mahadevan, Sankaran; Paez, Thomas L.

2012-03-01

Here, performance assessment of complex systems is ideally accomplished through system-level testing, but because they are expensive, such tests are seldom performed. On the other hand, for economic reasons, data from tests on individual components that are parts of complex systems are more readily available. The lack of system-level data leads to a need to build computational models of systems and use them for performance prediction in lieu of experiments. Because their complexity, models are sometimes built in a hierarchical manner, starting with simple components, progressing to collections of components, and finally, to the full system. Quantification of uncertainty inmore » the predicted response of a system model is required in order to establish confidence in the representation of actual system behavior. This paper proposes a framework for the complex, but very practical problem of quantification of uncertainty in system-level model predictions. It is based on Bayes networks and uses the available data at multiple levels of complexity (i.e., components, subsystem, etc.). Because epistemic sources of uncertainty were shown to be secondary, in this application, aleatoric only uncertainty is included in the present uncertainty quantification. An example showing application of the techniques to uncertainty quantification of measures of response of a real, complex aerospace system is included.« less

Two-Way Tables: Issues at the Heart of Statistics and Probability for Students and Teachers

ERIC Educational Resources Information Center

Watson, Jane; Callingham, Rosemary

2014-01-01

Some problems exist at the intersection of statistics and probability, creating a dilemma in relation to the best approach to assist student understanding. Such is the case with problems presented in two-way tables representing conditional information. The difficulty can be confounded if the context within which the problem is set is one where…

An efficient assisted history matching and uncertainty quantification workflow using Gaussian processes proxy models and variogram based sensitivity analysis: GP-VARS

NASA Astrophysics Data System (ADS)

Rana, Sachin; Ertekin, Turgay; King, Gregory R.

2018-05-01

Reservoir history matching is frequently viewed as an optimization problem which involves minimizing misfit between simulated and observed data. Many gradient and evolutionary strategy based optimization algorithms have been proposed to solve this problem which typically require a large number of numerical simulations to find feasible solutions. Therefore, a new methodology referred to as GP-VARS is proposed in this study which uses forward and inverse Gaussian processes (GP) based proxy models combined with a novel application of variogram analysis of response surface (VARS) based sensitivity analysis to efficiently solve high dimensional history matching problems. Empirical Bayes approach is proposed to optimally train GP proxy models for any given data. The history matching solutions are found via Bayesian optimization (BO) on forward GP models and via predictions of inverse GP model in an iterative manner. An uncertainty quantification method using MCMC sampling in conjunction with GP model is also presented to obtain a probabilistic estimate of reservoir properties and estimated ultimate recovery (EUR). An application of the proposed GP-VARS methodology on PUNQ-S3 reservoir is presented in which it is shown that GP-VARS provides history match solutions in approximately four times less numerical simulations as compared to the differential evolution (DE) algorithm. Furthermore, a comparison of uncertainty quantification results obtained by GP-VARS, EnKF and other previously published methods shows that the P50 estimate of oil EUR obtained by GP-VARS is in close agreement to the true values for the PUNQ-S3 reservoir.

Familiarity with and uptake of alternative methods to control sheep gastro-intestinal parasites on farms in England.

PubMed

Moore, Hope; Pandolfi, Fanny; Kyriazakis, Ilias

2016-05-15

A questionnaire was distributed electronically amongst sheep farmers in England; it aimed to provide a quantification of current anthelmintic practices, farmer awareness of the issue of anthelmintic resistance (AR) and the uptake, awareness and opinions surrounding conventional and alternative methods of nematode control. The majority of farmers relied on several anthelmintics and used faecal egg counts to identify worm problems. Although farmers were aware of the issue of AR amongst helminth parasites in the UK, there was a disconnection between such awareness and on farm problems and practice of nematode control. Grazing management was used by 52% of responders, while breeding for resistance and bioactive forages by 22 and 18% respectively. Farms with more than 500 ewes, and farmers who felt nematodes were a problem, had a higher probability of using selective breeding. Farmers who considered their wormer effective, had a qualification in agriculture and whose staff did not include any family members, were more likely to use bioactive forages; the opposite was the case if farmers dosed their lambs frequently. Amongst the alternatives, highest preference was for selective breeding and vaccination, if the latter was to become commercially available, with more respondents having a preference for breeding than actually using it. Several barriers to the uptake of an alternative were identified, the most influential factor being the cost to set it up and the length of time for which it would remain effective. The disconnection between awareness of AR and practice of nematode control on farm reinforces the need for emphasising the links between the causes of AR and the consequences of strategies to address its challenge. Copyright © 2016 Elsevier B.V. All rights reserved.

Enhancing sparsity of Hermite polynomial expansions by iterative rotations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yang, Xiu; Lei, Huan; Baker, Nathan A.

2016-02-01

Compressive sensing has become a powerful addition to uncertainty quantification in recent years. This paper identifies new bases for random variables through linear mappings such that the representation of the quantity of interest is more sparse with new basis functions associated with the new random variables. This sparsity increases both the efficiency and accuracy of the compressive sensing-based uncertainty quantification method. Specifically, we consider rotation- based linear mappings which are determined iteratively for Hermite polynomial expansions. We demonstrate the effectiveness of the new method with applications in solving stochastic partial differential equations and high-dimensional (O(100)) problems.

Risk assessment for parasites in cultures of Diplodus puntazzo (Sparidae) in the Western Mediterranean: prospects of cross infection with Sparus aurata.

PubMed

Sánchez-García, Neus; Raga, Juan Antonio; Montero, Francisco E

2014-08-29

The sharpsnout seabream Diplodus puntazzo is of interest in Mediterranean fish farming. Disease is an important problem because parasites can spread quickly in culture conditions and fish often develop high parasite burdens. Here we assess the risk that documented parasites pose to the sustainability of D. puntazzo farming. This study specifically considers metazoan and protist parasites recorded from wild and farmed D. puntazzo in scientific literature. Risk assessment studies involve the identification, characterization and qualitative quantification of the risk in question (parasitoses in this case) and the probability of establishment. We considered the parasite species which may be difficult to manage as a priority for research into potential management strategies. Those parasites which could be transmitted from cultures of gilthead seabream (Sparus aurata) were also included in this study. Four groups of parasites represented a risk to D. puntazzo farming, ranging from moderate to high: Ciliophora, Myxozoa, Monogenea and Copepoda. Three parasite species were considered high risk to D. puntazzo cultures: Amyloodinium sp., Cryptocaryon sp. and Enteromyxum leei. These species were responsible for high mortalities in cultures of these and other fish species. In addition Sparicotyle chrysophrii, Caligus ligusticus and Gnathia vorax entail a moderate risk to D. puntazzo Mediterranean farms. No important episodes have been related to caligids and isopods in Mediterranean sparids, nevertheless they should be properly managed to prevent future problems. Copyright © 2014 Elsevier B.V. All rights reserved.

Installation Restoration Program. Phase 2. Confirmation/Quantification. Stage 1. Hancock Field, New York and HQTAC, Langley AFB, Virginia

DTIC Science & Technology

1984-10-01

contamination resulting from previous waste disposal practices at Hancock Field .. o Recommend measures to mitigate adverse impacts at identified...best well to use in judging water quality impacts caused by the disposal activities. Slug tests (Hvorslev, 1951) were performed at each of the four... impact future samplings because this water will probably become mixed in the aquifer before the next sample round and if some remains . near the well

Installation Restoration Program. Phase 2. Confirmation/Quantification. Stage 1. Reese Air Force Base, Lubbock, Texas. Volume 2. Appendices

DTIC Science & Technology

1988-04-01

epidemiological studies ; pending resolution of essentiality in human diet; EPA has not regulated arsenic as a carcinogen in drinking water 3ICadmium B1...Probable human carcinogen based upon sufficient evidence in epidemiological studies ; not regulated as a carcinogen in drinking water because there is...Carcinogenic in animal studies ; because of the extensive negative epidemiological evidence, EPA has proposed to regu- late lead in drinking water based on

Spatial context learning approach to automatic segmentation of pleural effusion in chest computed tomography images

NASA Astrophysics Data System (ADS)

Mansoor, Awais; Casas, Rafael; Linguraru, Marius G.

2016-03-01

Pleural effusion is an abnormal collection of fluid within the pleural cavity. Excessive accumulation of pleural fluid is an important bio-marker for various illnesses, including congestive heart failure, pneumonia, metastatic cancer, and pulmonary embolism. Quantification of pleural effusion can be indicative of the progression of disease as well as the effectiveness of any treatment being administered. Quantification, however, is challenging due to unpredictable amounts and density of fluid, complex topology of the pleural cavity, and the similarity in texture and intensity of pleural fluid to the surrounding tissues in computed tomography (CT) scans. Herein, we present an automated method for the segmentation of pleural effusion in CT scans based on spatial context information. The method consists of two stages: first, a probabilistic pleural effusion map is created using multi-atlas segmentation. The probabilistic map assigns a priori probabilities to the presence of pleural uid at every location in the CT scan. Second, a statistical pattern classification approach is designed to annotate pleural regions using local descriptors based on a priori probabilities, geometrical, and spatial features. Thirty seven CT scans from a diverse patient population containing confirmed cases of minimal to severe amounts of pleural effusion were used to validate the proposed segmentation method. An average Dice coefficient of 0.82685 and Hausdorff distance of 16.2155 mm was obtained.

Impact of adherence on duration of virological suppression among patients receiving combination antiretroviral therapy.

PubMed

Raboud, J M; Harris, M; Rae, S; Montaner, J S G

2002-04-01

To assess the effect of adherence to antiretroviral therapy on the duration of virological suppression after controlling for whether or not the patient ever attained a plasma viral load below the limit of detection of sensitive HIV-1 RNA assays. Data were combined from three randomized, blinded clinical trials (INCAS, AVANTI-2, and AVANTI-3) that compared the antiviral effects of two- and three-drug antiretroviral regimens. Virological suppression was defined as maintaining a plasma viral load below 1000 copies/mL. Adherence was defined prospectively and measured by patient self-report. Adherence did not have a major impact on the probability of achieving virological suppression for patients receiving dual therapy. However, for patients receiving triple therapy, adherence increased the probability of virological suppression, whether the plasma viral load nadir was above or below the lower limit of quantification. Compared to adherent patients with a plasma viral load nadir below the lower limit of quantification, the relative risk of virological failure was 3.0 for non-adherent patients with a nadir below the limit, 18.1 for adherent patients with a nadir above the limit, and 32.1 for non-adherent patients with a nadir above the limit. For patients receiving current three-drug antiretroviral regimens, adherence to therapy and plasma viral load nadir are important factors determining the duration of virological suppression.

The DNA database search controversy revisited: bridging the Bayesian-frequentist gap.

PubMed

Storvik, Geir; Egeland, Thore

2007-09-01

Two different quantities have been suggested for quantification of evidence in cases where a suspect is found by a search through a database of DNA profiles. The likelihood ratio, typically motivated from a Bayesian setting, is preferred by most experts in the field. The so-called np rule has been suggested through frequentist arguments and has been suggested by the American National Research Council and Stockmarr (1999, Biometrics55, 671-677). The two quantities differ substantially and have given rise to the DNA database search controversy. Although several authors have criticized the different approaches, a full explanation of why these differences appear is still lacking. In this article we show that a P-value in a frequentist hypothesis setting is approximately equal to the result of the np rule. We argue, however, that a more reasonable procedure in this case is to use conditional testing, in which case a P-value directly related to posterior probabilities and the likelihood ratio is obtained. This way of viewing the problem bridges the gap between the Bayesian and frequentist approaches. At the same time it indicates that the np rule should not be used to quantify evidence.

Biparametric complexities and generalized Planck radiation law

NASA Astrophysics Data System (ADS)

Puertas-Centeno, David; Toranzo, I. V.; Dehesa, J. S.

2017-12-01

Complexity theory embodies some of the hardest, most fundamental and most challenging open problems in modern science. The very term complexity is very elusive, so the main goal of this theory is to find meaningful quantifiers for it. In fact, we need various measures to take into account the multiple facets of this term. Here, some biparametric Crámer-Rao and Heisenberg-Rényi measures of complexity of continuous probability distributions are defined and discussed. Then, they are applied to blackbody radiation at temperature T in a d-dimensional universe. It is found that these dimensionless quantities do not depend on T nor on any physical constants. So, they have a universal character in the sense that they only depend on spatial dimensionality. To determine these complexity quantifiers, we have calculated their dispersion (typical deviations) and entropy (Rényi entropies and the generalized Fisher information) constituents. They are found to have a temperature-dependent behavior similar to the celebrated Wien’s displacement law of the dominant frequency ν_max at which the spectrum reaches its maximum. Moreover, they allow us to gain insights into new aspects of the d-dimensional blackbody spectrum and the quantification of quantum effects associated with space dimensionality.

Using Predictive Analytics to Detect Major Problems in Department of Defense Acquisition Programs

DTIC Science & Technology

2012-03-01

research is focused on three questions. First, can we predict the contractor provided estimate at complete (EAC)? Second, can we use those predictions to...develop an algorithm to determine if a problem will occur in an acquisition program or sub-program? Lastly, can we provide the probability of a problem...more than doubling the probability of a problem occurrence compared to current tools in the cost community. Though program managers can use this

Perceived risk associated with ecstasy use: a latent class analysis approach

PubMed Central

Martins, SS; Carlson, RG; Alexandre, PK; Falck, RS

2011-01-01

This study aims to define categories of perceived health problems among ecstasy users based on observed clustering of their perceptions of ecstasy-related health problems. Data from a community sample of ecstasy users (n=402) aged 18 to 30, in Ohio, was used in this study. Data was analyzed via Latent Class Analysis (LCA) and Regression. This study identified five different subgroups of ecstasy users based on their perceptions of health problems they associated with their ecstasy use. Almost one third of the sample (28.9%) belonged to a class with “low level of perceived problems” (Class 4). About one fourth (25.6%) of the sample (Class 2), had high probabilities of “perceiving problems on sexual-related items”, but generally low or moderate probabilities of perceiving problems in other areas. Roughly one-fifth of the sample (21.1%, Class 1) had moderate probabilities of perceiving ecstasy health-related problems in all areas. A small proportion of respondents (11.9%, Class 5) had high probabilities of reporting “perceived memory and cognitive problems, and of perceiving “ecstasy related-problems in all areas” (12.4%, Class 3). A large proportion of ecstasy users perceive either low or moderate risk associated with their ecstasy use. It is important to further investigate whether lower levels of risk perception are associated with persistence of ecstasy use. PMID:21296504

Charts designate probable future oceanographic research fields

NASA Technical Reports Server (NTRS)

1968-01-01

Charts outline the questions and problems of oceanographic research in the future. NASA uses the charts to estimate the probable requirements for instrumentation carried by satellites engaged in cooperative programs with other agencies concerned with identification, analysis, and solution of many of these problems.

Pigeons, Facebook and the Birthday Problem

ERIC Educational Resources Information Center

Russell, Matthew

2013-01-01

The unexpectedness of the birthday problem has long been used by teachers of statistics in discussing basic probability calculation. An activity is described that engages students in understanding probability and sampling using the popular Facebook social networking site. (Contains 2 figures and 1 table.)

An optimal repartitioning decision policy

NASA Technical Reports Server (NTRS)

Nicol, D. M.; Reynolds, P. F., Jr.

1986-01-01

A central problem to parallel processing is the determination of an effective partitioning of workload to processors. The effectiveness of any given partition is dependent on the stochastic nature of the workload. The problem of determining when and if the stochastic behavior of the workload has changed enough to warrant the calculation of a new partition is treated. The problem is modeled as a Markov decision process, and an optimal decision policy is derived. Quantification of this policy is usually intractable. A heuristic policy which performs nearly optimally is investigated empirically. The results suggest that the detection of change is the predominant issue in this problem.

Could our pretest probabilities become evidence based? A prospective survey of hospital practice.

PubMed

Richardson, W Scott; Polashenski, Walter A; Robbins, Brett W

2003-03-01

We sought to measure the proportion of patients on our clinical service who presented with clinical problems for which research evidence was available to inform estimates of pretest probability. We also aimed to discern whether any of this evidence was of sufficient quality that we would want to use it for clinical decision making. Prospective, consecutive case series and literature survey. Inpatient medical service of a university-affiliated Veterans' Affairs hospital in south Texas. Patients admitted during the 3 study months for diagnostic evaluation. Patients' active clinical problems were identified prospectively and recorded at the time of discharge, transfer, or death. We electronically searched medline and hand-searched bibliographies to find citations that reported research evidence about the frequency of underlying diseases that cause these clinical problems. We critically appraised selected citations and ranked them on a hierarchy of evidence. We admitted 122 patients for diagnostic evaluation, in whom we identified 45 different principal clinical problems. For 35 of the 45 problems (78%; 95% confidence interval [95% CI], 66% to 90%), we found citations that qualified as disease probability evidence. Thus, 111 of our 122 patients (91%; 95% CI, 86% to 96%) had clinical problems for which evidence was available in the medical literature. During 3 months on our hospital medicine service, almost all of the patients admitted for diagnostic evaluation had clinical problems for which evidence is available to guide our estimates of pretest probability. If confirmed by others, these data suggest that clinicians' pretest probabilities could become evidence based.

A Facile and Sensitive Method for Quantification of Cyclic Nucleotide Monophosphates in Mammalian Organs: Basal Levels of Eight cNMPs and Identification of 2',3'-cIMP

PubMed Central

Jia, Xin; Fontaine, Benjamin M.; Strobel, Fred; Weinert, Emily E.

2014-01-01

A sensitive, versatile and economical method to extract and quantify cyclic nucleotide monophosphates (cNMPs) using LC-MS/MS, including both 3',5'-cNMPs and 2',3'-cNMPs, in mammalian tissues and cellular systems has been developed. Problems, such as matrix effects from complex biological samples, are addressed and have been optimized. This protocol allows for comparison of multiple cNMPs in the same system and was used to examine the relationship between tissue levels of cNMPs in a panel of rat organs. In addition, the study reports the first identification and quantification of 2',3'-cIMP. The developed method will allow for quantification of cNMPs levels in cells and tissues with varying disease states, which will provide insight into the role(s) and interplay of cNMP signalling pathways. PMID:25513747

A facile and sensitive method for quantification of cyclic nucleotide monophosphates in mammalian organs: basal levels of eight cNMPs and identification of 2',3'-cIMP.

PubMed

Jia, Xin; Fontaine, Benjamin M; Strobel, Fred; Weinert, Emily E

2014-12-12

A sensitive, versatile and economical method to extract and quantify cyclic nucleotide monophosphates (cNMPs) using LC-MS/MS, including both 3',5'-cNMPs and 2',3'-cNMPs, in mammalian tissues and cellular systems has been developed. Problems, such as matrix effects from complex biological samples, are addressed and have been optimized. This protocol allows for comparison of multiple cNMPs in the same system and was used to examine the relationship between tissue levels of cNMPs in a panel of rat organs. In addition, the study reports the first identification and quantification of 2',3'-cIMP. The developed method will allow for quantification of cNMPs levels in cells and tissues with varying disease states, which will provide insight into the role(s) and interplay of cNMP signalling pathways.

Connected component analysis of review-SEM images for sub-10nm node process verification

NASA Astrophysics Data System (ADS)

Halder, Sandip; Leray, Philippe; Sah, Kaushik; Cross, Andrew; Parisi, Paolo

2017-03-01

Analysis of hotspots is becoming more and more critical as we scale from node to node. To define true process windows at sub-14 nm technology nodes, often defect inspections are being included to weed out design weak spots (often referred to as hotspots). Defect inspection sub 28 nm nodes is a two pass process. Defect locations identified by optical inspection tools need to be reviewed by review-SEM's to understand exactly which feature is failing in the region flagged by the optical tool. The images grabbed by the review-SEM tool are used for classification but rarely for quantification. The goal of this paper is to see if the thousands of review-SEM images which are existing can be used for quantification and further analysis. More specifically we address the SEM quantification problem with connected component analysis.

Multi-tissue partial volume quantification in multi-contrast MRI using an optimised spectral unmixing approach.

PubMed

Collewet, Guylaine; Moussaoui, Saïd; Deligny, Cécile; Lucas, Tiphaine; Idier, Jérôme

2018-06-01

Multi-tissue partial volume estimation in MRI images is investigated with a viewpoint related to spectral unmixing as used in hyperspectral imaging. The main contribution of this paper is twofold. It firstly proposes a theoretical analysis of the statistical optimality conditions of the proportion estimation problem, which in the context of multi-contrast MRI data acquisition allows to appropriately set the imaging sequence parameters. Secondly, an efficient proportion quantification algorithm based on the minimisation of a penalised least-square criterion incorporating a regularity constraint on the spatial distribution of the proportions is proposed. Furthermore, the resulting developments are discussed using empirical simulations. The practical usefulness of the spectral unmixing approach for partial volume quantification in MRI is illustrated through an application to food analysis on the proving of a Danish pastry. Copyright © 2018 Elsevier Inc. All rights reserved.

Caregiver burden in Alzheimer's disease patients in Spain.

PubMed

Peña-Longobardo, Luz María; Oliva-Moreno, Juan

2015-01-01

Alzheimer's disease constitutes one of the leading causes of burden of disease, and it is the third leading disease in terms of economic and social costs. To analyze the burden and problems borne by informal caregivers of patients who suffer from Alzheimer's disease in Spain. We used the Survey on Disabilities, Autonomy and Dependency to obtain information on the characteristics of disabled people with Alzheimer's disease and the individuals who provide them with personal care. Additionally, statistical multivariate analyses using probit models were performed to analyze the burden placed on caregivers in terms of health, professional, and leisure/social aspects. 46% of informal caregivers suffered from health-related problems as a result of providing care, 90% had leisure-related problems, and 75% of caregivers under 65 years old admitted to suffering from problems related to their professional lives. The probability of a problem arising for an informal caregiver was positively associated with the degree of dependency of the person cared for. In the case of caring for a greatly dependent person, the probability of suffering from health-related problems was 22% higher, the probability of professional problems was 18% higher, and there was a 10% greater probability of suffering from leisure-related problems compared to non-dependents. The results show a part of the large hidden cost for society in terms of problems related to the burden lessened by the caregivers. This information should be a useful tool for designing policies focused toward supporting caregivers and improving their welfare.

A collision probability analysis of the double-heterogeneity problem

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hebert, A.

1993-10-01

A practical collision probability model is presented for the description of geometries with many levels of heterogeneity. Regular regions of the macrogeometry are assumed to contain a stochastic mixture of spherical grains or cylindrical tubes. Simple expressions for the collision probabilities in the global geometry are obtained as a function of the collision probabilities in the macro- and microgeometries. This model was successfully implemented in the collision probability kernel of the APOLLO-1, APOLLO-2, and DRAGON lattice codes for the description of a broad range of reactor physics problems. Resonance self-shielding and depletion calculations in the microgeometries are possible because eachmore » microregion is explicitly represented.« less

Probability Distributions for Random Quantum Operations

NASA Astrophysics Data System (ADS)

Schultz, Kevin

Motivated by uncertainty quantification and inference of quantum information systems, in this work we draw connections between the notions of random quantum states and operations in quantum information with probability distributions commonly encountered in the field of orientation statistics. This approach identifies natural sample spaces and probability distributions upon these spaces that can be used in the analysis, simulation, and inference of quantum information systems. The theory of exponential families on Stiefel manifolds provides the appropriate generalization to the classical case. Furthermore, this viewpoint motivates a number of additional questions into the convex geometry of quantum operations relative to both the differential geometry of Stiefel manifolds as well as the information geometry of exponential families defined upon them. In particular, we draw on results from convex geometry to characterize which quantum operations can be represented as the average of a random quantum operation. This project was supported by the Intelligence Advanced Research Projects Activity via Department of Interior National Business Center Contract Number 2012-12050800010.

A novel approach to estimate the eruptive potential and probability in open conduit volcanoes

PubMed Central

De Gregorio, Sofia; Camarda, Marco

2016-01-01

In open conduit volcanoes, volatile-rich magma continuously enters into the feeding system nevertheless the eruptive activity occurs intermittently. From a practical perspective, the continuous steady input of magma in the feeding system is not able to produce eruptive events alone, but rather surplus of magma inputs are required to trigger the eruptive activity. The greater the amount of surplus of magma within the feeding system, the higher is the eruptive probability.Despite this observation, eruptive potential evaluations are commonly based on the regular magma supply, and in eruptive probability evaluations, generally any magma input has the same weight. Conversely, herein we present a novel approach based on the quantification of surplus of magma progressively intruded in the feeding system. To quantify the surplus of magma, we suggest to process temporal series of measurable parameters linked to the magma supply. We successfully performed a practical application on Mt Etna using the soil CO2 flux recorded over ten years. PMID:27456812

A novel approach to estimate the eruptive potential and probability in open conduit volcanoes.

PubMed

De Gregorio, Sofia; Camarda, Marco

2016-07-26

In open conduit volcanoes, volatile-rich magma continuously enters into the feeding system nevertheless the eruptive activity occurs intermittently. From a practical perspective, the continuous steady input of magma in the feeding system is not able to produce eruptive events alone, but rather surplus of magma inputs are required to trigger the eruptive activity. The greater the amount of surplus of magma within the feeding system, the higher is the eruptive probability.Despite this observation, eruptive potential evaluations are commonly based on the regular magma supply, and in eruptive probability evaluations, generally any magma input has the same weight. Conversely, herein we present a novel approach based on the quantification of surplus of magma progressively intruded in the feeding system. To quantify the surplus of magma, we suggest to process temporal series of measurable parameters linked to the magma supply. We successfully performed a practical application on Mt Etna using the soil CO2 flux recorded over ten years.

Isotope-coded ESI-enhancing derivatization reagents for differential analysis, quantification and profiling of metabolites in biological samples by LC/MS: A review.

PubMed

Higashi, Tatsuya; Ogawa, Shoujiro

2016-10-25

The analysis of the qualitative and quantitative changes of metabolites in body fluids and tissues yields valuable information for the diagnosis, pathological analysis and treatment of many diseases. Recently, liquid chromatography/electrospray ionization-(tandem) mass spectrometry [LC/ESI-MS(/MS)] has been widely used for these purposes due to the high separation capability of LC, broad coverage of ESI for various compounds and high specificity of MS(/MS). However, there are still two major problems to be solved regarding the biological sample analysis; lack of sensitivity and limited availability of stable isotope-labeled analogues (internal standards, ISs) for most metabolites. Stable isotope-coded derivatization (ICD) can be the answer for these problems. By the ICD, different isotope-coded moieties are introduced to the metabolites and one of the resulting derivatives can serve as the IS, which minimize the matrix effects. Furthermore, the derivatization can improve the ESI efficiency, fragmentation property in the MS/MS and chromatographic behavior of the metabolites, which lead to a high sensitivity and specificity in the various detection modes. Based on this background, this article reviews the recently-reported isotope-coded ESI-enhancing derivatization (ICEED) reagents, which are key components for the ICD-based LC/MS(/MS) studies, and their applications to the detection, identification, quantification and profiling of metabolites in human and animal samples. The LC/MS(/MS) using the ICEED reagents is the powerful method especially for the differential analysis (relative quantification) of metabolites in two comparative samples, simultaneous quantification of multiple metabolites whose stable isotope-labeled ISs are not available, and submetabolome profiling. Copyright © 2016 Elsevier B.V. All rights reserved.

Fitness Probability Distribution of Bit-Flip Mutation.

PubMed

Chicano, Francisco; Sutton, Andrew M; Whitley, L Darrell; Alba, Enrique

2015-01-01

Bit-flip mutation is a common mutation operator for evolutionary algorithms applied to optimize functions over binary strings. In this paper, we develop results from the theory of landscapes and Krawtchouk polynomials to exactly compute the probability distribution of fitness values of a binary string undergoing uniform bit-flip mutation. We prove that this probability distribution can be expressed as a polynomial in p, the probability of flipping each bit. We analyze these polynomials and provide closed-form expressions for an easy linear problem (Onemax), and an NP-hard problem, MAX-SAT. We also discuss a connection of the results with runtime analysis.

Extended Importance Sampling for Reliability Analysis under Evidence Theory

NASA Astrophysics Data System (ADS)

Yuan, X. K.; Chen, B.; Zhang, B. Q.

2018-05-01

In early engineering practice, the lack of data and information makes uncertainty difficult to deal with. However, evidence theory has been proposed to handle uncertainty with limited information as an alternative way to traditional probability theory. In this contribution, a simulation-based approach, called ‘Extended importance sampling’, is proposed based on evidence theory to handle problems with epistemic uncertainty. The proposed approach stems from the traditional importance sampling for reliability analysis under probability theory, and is developed to handle the problem with epistemic uncertainty. It first introduces a nominal instrumental probability density function (PDF) for every epistemic uncertainty variable, and thus an ‘equivalent’ reliability problem under probability theory is obtained. Then the samples of these variables are generated in a way of importance sampling. Based on these samples, the plausibility and belief (upper and lower bounds of probability) can be estimated. It is more efficient than direct Monte Carlo simulation. Numerical and engineering examples are given to illustrate the efficiency and feasible of the proposed approach.

Efficient marginalization to compute protein posterior probabilities from shotgun mass spectrometry data

PubMed Central

Serang, Oliver; MacCoss, Michael J.; Noble, William Stafford

2010-01-01

The problem of identifying proteins from a shotgun proteomics experiment has not been definitively solved. Identifying the proteins in a sample requires ranking them, ideally with interpretable scores. In particular, “degenerate” peptides, which map to multiple proteins, have made such a ranking difficult to compute. The problem of computing posterior probabilities for the proteins, which can be interpreted as confidence in a protein’s presence, has been especially daunting. Previous approaches have either ignored the peptide degeneracy problem completely, addressed it by computing a heuristic set of proteins or heuristic posterior probabilities, or by estimating the posterior probabilities with sampling methods. We present a probabilistic model for protein identification in tandem mass spectrometry that recognizes peptide degeneracy. We then introduce graph-transforming algorithms that facilitate efficient computation of protein probabilities, even for large data sets. We evaluate our identification procedure on five different well-characterized data sets and demonstrate our ability to efficiently compute high-quality protein posteriors. PMID:20712337

Teaching Classic Probability Problems With Modern Digital Tools

ERIC Educational Resources Information Center

Abramovich, Sergei; Nikitin, Yakov Yu.

2017-01-01

This article is written to share teaching ideas about using commonly available computer applications--a spreadsheet, "The Geometer's Sketchpad", and "Wolfram Alpha"--to explore three classic and historically significant problems from the probability theory. These ideas stem from the authors' work with prospective economists,…

Some New Twists to Problems Involving the Gaussian Probability Integral

NASA Technical Reports Server (NTRS)

Simon, Marvin K.; Divsalar, Dariush

1997-01-01

Using an alternate form of the Gaussian probability integral discovered a number of years ago, it is shown that the solution to a number of previously considered communication problems can be simplified and in some cases made more accurate(i.e., exact rather than bounded).

A Tale of Two Probabilities

ERIC Educational Resources Information Center

Falk, Ruma; Kendig, Keith

2013-01-01

Two contestants debate the notorious probability problem of the sex of the second child. The conclusions boil down to explication of the underlying scenarios and assumptions. Basic principles of probability theory are highlighted.

Multiple-solution problems in a statistics classroom: an example

NASA Astrophysics Data System (ADS)

Chu, Chi Wing; Chan, Kevin L. T.; Chan, Wai-Sum; Kwong, Koon-Shing

2017-11-01

The mathematics education literature shows that encouraging students to develop multiple solutions for given problems has a positive effect on students' understanding and creativity. In this paper, we present an example of multiple-solution problems in statistics involving a set of non-traditional dice. In particular, we consider the exact probability mass distribution for the sum of face values. Four different ways of solving the problem are discussed. The solutions span various basic concepts in different mathematical disciplines (sample space in probability theory, the probability generating function in statistics, integer partition in basic combinatorics and individual risk model in actuarial science) and thus promotes upper undergraduate students' awareness of knowledge connections between their courses. All solutions of the example are implemented using the R statistical software package.

Quantum probability and Hilbert's sixth problem

NASA Astrophysics Data System (ADS)

Accardi, Luigi

2018-04-01

With the birth of quantum mechanics, the two disciplines that Hilbert proposed to axiomatize, probability and mechanics, became entangled and a new probabilistic model arose in addition to the classical one. Thus, to meet Hilbert's challenge, an axiomatization should account deductively for the basic features of all three disciplines. This goal was achieved within the framework of quantum probability. The present paper surveys the quantum probabilistic axiomatization. This article is part of the themed issue `Hilbert's sixth problem'.

Statistical hydrodynamics and related problems in spaces of probability measures

NASA Astrophysics Data System (ADS)

Dostoglou, Stamatios

2017-11-01

A rigorous theory of statistical solutions of the Navier-Stokes equations, suitable for exploring Kolmogorov's ideas, has been developed by M.I. Vishik and A.V. Fursikov, culminating in their monograph "Mathematical problems of Statistical Hydromechanics." We review some progress made in recent years following this approach, with emphasis on problems concerning the correlation of velocities and corresponding questions in the space of probability measures on Hilbert spaces.

In situ spectroradiometric quantification of ERTS data

NASA Technical Reports Server (NTRS)

Yost, E. (Principal Investigator)

1972-01-01

The author has identified the following significant results. Additive color photographic analysis of ERTS-1 multispectral imagery indicates that the presence of soil moisture in playas (desert dry lakes) can be readily detected from space. Time sequence additive color presentations in which 600-700 nm bands taken at three successive 18-day cycles show that changes in soil moisture of playas with time can be detected as unique color signatures and can probably be quantitatively measured using photographic images of multispectral scanner data.

Installation Restoration Program Phase 2. Confirmation/Quantification Stage 2. Bulk Fuel Storage Area Fuel Spill Investigation.

DTIC Science & Technology

1987-10-01

discharged from these wells was containerized and transported to the base oil separator plant for treatment. It is estimated that approximately 25 percent...and 29). The fly ash is probably associated with the power plant tc the west of the Bulk Fuel Storage Area. Just below the fill, at 13 to 15 feet, is...been widely used in petroleum refineries and fuel terminals in response to similar spill impact situations. Although the collect ion/recov- ery

Quantification of free fatty acids in human stratum corneum using tandem mass spectrometry and surrogate analyte approach.

PubMed

Dapic, Irena; Kobetic, Renata; Brkljacic, Lidija; Kezic, Sanja; Jakasa, Ivone

2018-02-01

The free fatty acids (FFAs) are one of the major components of the lipids in the stratum corneum (SC), the uppermost layer of the skin. Relative composition of FFAs has been proposed as a biomarker of the skin barrier status in patients with atopic dermatitis (AD). Here, we developed an LC-ESI-MS/MS method for simultaneous quantification of a range of FFAs with long and very long chain length in the SC collected by adhesive tape (D-Squame). The method, based on derivatization with 2-bromo-1-methylpyridinium iodide and 3-carbinol-1-methylpyridinium iodide, allowed highly sensitive detection and quantification of FFAs using multiple reaction monitoring. For the quantification, we applied a surrogate analyte approach and internal standardization using isotope labeled derivatives of FFAs. Adhesive tapes showed the presence of several FFAs, which are also present in the SC, a problem encountered in previous studies. Therefore, the levels of FFAs in the SC were corrected using C12:0, which was present on the adhesive tape, but not detected in the SC. The method was applied to SC samples from patients with atopic dermatitis and healthy subjects. Quantification using multiple reaction monitoring allowed sufficient sensitivity to analyze FFAs of chain lengths C16-C28 in the SC collected on only one tape strip. Copyright © 2017 John Wiley & Sons, Ltd.

RNA-Skim: a rapid method for RNA-Seq quantification at transcript level

PubMed Central

Zhang, Zhaojun; Wang, Wei

2014-01-01

Motivation: RNA-Seq technique has been demonstrated as a revolutionary means for exploring transcriptome because it provides deep coverage and base pair-level resolution. RNA-Seq quantification is proven to be an efficient alternative to Microarray technique in gene expression study, and it is a critical component in RNA-Seq differential expression analysis. Most existing RNA-Seq quantification tools require the alignments of fragments to either a genome or a transcriptome, entailing a time-consuming and intricate alignment step. To improve the performance of RNA-Seq quantification, an alignment-free method, Sailfish, has been recently proposed to quantify transcript abundances using all k-mers in the transcriptome, demonstrating the feasibility of designing an efficient alignment-free method for transcriptome quantification. Even though Sailfish is substantially faster than alternative alignment-dependent methods such as Cufflinks, using all k-mers in the transcriptome quantification impedes the scalability of the method. Results: We propose a novel RNA-Seq quantification method, RNA-Skim, which partitions the transcriptome into disjoint transcript clusters based on sequence similarity, and introduces the notion of sig-mers, which are a special type of k-mers uniquely associated with each cluster. We demonstrate that the sig-mer counts within a cluster are sufficient for estimating transcript abundances with accuracy comparable with any state-of-the-art method. This enables RNA-Skim to perform transcript quantification on each cluster independently, reducing a complex optimization problem into smaller optimization tasks that can be run in parallel. As a result, RNA-Skim uses <4% of the k-mers and <10% of the CPU time required by Sailfish. It is able to finish transcriptome quantification in <10 min per sample by using just a single thread on a commodity computer, which represents >100 speedup over the state-of-the-art alignment-based methods, while delivering comparable or higher accuracy. Availability and implementation: The software is available at http://www.csbio.unc.edu/rs. Contact: weiwang@cs.ucla.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:24931995

Probability in Action: The Red Traffic Light

ERIC Educational Resources Information Center

Shanks, John A.

2007-01-01

Emphasis on problem solving in mathematics has gained considerable attention in recent years. While statistics teaching has always been problem driven, the same cannot be said for the teaching of probability where discrete examples involving coins and playing cards are often the norm. This article describes an application of simple probability…

Automatic Item Generation of Probability Word Problems

ERIC Educational Resources Information Center

Holling, Heinz; Bertling, Jonas P.; Zeuch, Nina

2009-01-01

Mathematical word problems represent a common item format for assessing student competencies. Automatic item generation (AIG) is an effective way of constructing many items with predictable difficulties, based on a set of predefined task parameters. The current study presents a framework for the automatic generation of probability word problems…

Logic, probability, and human reasoning.

PubMed

Johnson-Laird, P N; Khemlani, Sangeet S; Goodwin, Geoffrey P

2015-04-01

This review addresses the long-standing puzzle of how logic and probability fit together in human reasoning. Many cognitive scientists argue that conventional logic cannot underlie deductions, because it never requires valid conclusions to be withdrawn - not even if they are false; it treats conditional assertions implausibly; and it yields many vapid, although valid, conclusions. A new paradigm of probability logic allows conclusions to be withdrawn and treats conditionals more plausibly, although it does not address the problem of vapidity. The theory of mental models solves all of these problems. It explains how people reason about probabilities and postulates that the machinery for reasoning is itself probabilistic. Recent investigations accordingly suggest a way to integrate probability and deduction. Copyright © 2015 Elsevier Ltd. All rights reserved.

A Posteriori Error Analysis and Uncertainty Quantification for Adaptive Multiscale Operator Decomposition Methods for Multiphysics Problems

DTIC Science & Technology

2013-06-24

Barrier methods for critical exponent problems in geometric analysis and mathematical physics, J. Erway and M. Hoist, Submitted for publication . • Finite...1996. [20] C. LANCZOS, Linear Differential Operators, Dover Publications , Mineola, NY, 1997. [21] G.I. MARCHUK, Adjoint Equations and Analysis of...NUMBER(S) 16. SECURITY CLASSIFICATION OF: 19b. TELEPHONE NUMBER (Include area code) The public reporting burden for this collection of information is

Robust high-resolution quantification of time signals encoded by in vivo magnetic resonance spectroscopy

NASA Astrophysics Data System (ADS)

Belkić, Dževad; Belkić, Karen

2018-01-01

This paper on molecular imaging emphasizes improving specificity of magnetic resonance spectroscopy (MRS) for early cancer diagnostics by high-resolution data analysis. Sensitivity of magnetic resonance imaging (MRI) is excellent, but specificity is insufficient. Specificity is improved with MRS by going beyond morphology to assess the biochemical content of tissue. This is contingent upon accurate data quantification of diagnostically relevant biomolecules. Quantification is spectral analysis which reconstructs chemical shifts, amplitudes and relaxation times of metabolites. Chemical shifts inform on electronic shielding of resonating nuclei bound to different molecular compounds. Oscillation amplitudes in time signals retrieve the abundance of MR sensitive nuclei whose number is proportional to metabolite concentrations. Transverse relaxation times, the reciprocal of decay probabilities of resonances, arise from spin-spin coupling and reflect local field inhomogeneities. In MRS single voxels are used. For volumetric coverage, multi-voxels are employed within a hybrid of MRS and MRI called magnetic resonance spectroscopic imaging (MRSI). Common to MRS and MRSI is encoding of time signals and subsequent spectral analysis. Encoded data do not provide direct clinical information. Spectral analysis of time signals can yield the quantitative information, of which metabolite concentrations are the most clinically important. This information is equivocal with standard data analysis through the non-parametric, low-resolution fast Fourier transform and post-processing via fitting. By applying the fast Padé transform (FPT) with high-resolution, noise suppression and exact quantification via quantum mechanical signal processing, advances are made, presented herein, focusing on four areas of critical public health importance: brain, prostate, breast and ovarian cancers.

Perceived health problems in swimmers according to the chemical treatment of water in swimming pools.

PubMed

Fernández-Luna, Álvaro; Burillo, Pablo; Felipe, José Luis; del Corral, Julio; García-Unanue, Jorge; Gallardo, Leonor

2016-01-01

The objective of this study was to determine which chemical treatment used for disinfecting water in indoor swimming pools had the least impact on users' perceptions of health problems, and which generated the greatest satisfaction with the quality of the water. A survey on satisfaction and perceived health problems was given to 1001 users at 20 indoor swimming pools which used different water treatment methods [chlorine, bromine, ozone, ultraviolet lamps (UV) and salt electrolysis]. The findings suggest that there is a greater probability of perceived health problems, such as eye and skin irritation, respiratory problems and skin dryness, in swimming pools treated with chlorine than in swimming pools using other chemical treatment methods. Pools treated with bromine have similar, although slightly better, results. Other factors, such as age, gender, time of day of use (morning and afternoon) and type of user (competitive and recreational), can also affect the probability of suffering health problems. For all of the above, using combined treatment methods as ozone and UV, or salt electrolysis produces a lower probability of perceived health problems and greater satisfaction.

[Analysis of free foetal DNA in maternal plasma using STR loci].

PubMed

Vodicka, R; Vrtel, R; Procházka, M; Santavá, A; Dusek, L; Vrbická, D; Singh, R; Krejciríková, E; Schneiderová, E; Santavý, J

2006-01-01

Problems of maternal and foetal genotype differentiation of maternal plasma in pregnant women are solved generally by real-time systems. In this case the specific probes are used to distinguish particular genotype. Mostly gonosomal sequences are utilised to recognise the male foetus. This work describes possibilities in free foetal DNA detection and quantification by STR. Artificial genotype mixtures ranging from 0,2 % to 100 % to simulate maternal and paternal genotypes and 27 DNA samples from pregnant women in different stage of pregnancy were used for DNA quantification and detection. Foetal genotype was confirmed by biological father genotyping. The detection was performed in STR from 21st chromosome Down syndrome (DS) responsible region by innovated (I) QF PCR which allows to reveal and quantify even very rare DNA mosaics. The STR quantification was assessed in artificial mixtures of genotypes and discriminability of particular genotypes was on the level of few percent. Foetal DNA was detected in 74 % of tested samples. The IQF PCR application in quantification and differentiation between maternal and foetal genotypes by STR loci could have importance in non-invasive prenatal diagnostics as another possible marker for DS risk assessment.

Microstructural Quantification, Property Prediction, and Stochastic Reconstruction of Heterogeneous Materials Using Limited X-Ray Tomography Data

NASA Astrophysics Data System (ADS)

Li, Hechao

An accurate knowledge of the complex microstructure of a heterogeneous material is crucial for quantitative structure-property relations establishment and its performance prediction and optimization. X-ray tomography has provided a non-destructive means for microstructure characterization in both 3D and 4D (i.e., structural evolution over time). Traditional reconstruction algorithms like filtered-back-projection (FBP) method or algebraic reconstruction techniques (ART) require huge number of tomographic projections and segmentation process before conducting microstructural quantification. This can be quite time consuming and computationally intensive. In this thesis, a novel procedure is first presented that allows one to directly extract key structural information in forms of spatial correlation functions from limited x-ray tomography data. The key component of the procedure is the computation of a "probability map", which provides the probability of an arbitrary point in the material system belonging to specific phase. The correlation functions of interest are then readily computed from the probability map. Using effective medium theory, accurate predictions of physical properties (e.g., elastic moduli) can be obtained. Secondly, a stochastic optimization procedure that enables one to accurately reconstruct material microstructure from a small number of x-ray tomographic projections (e.g., 20 - 40) is presented. Moreover, a stochastic procedure for multi-modal data fusion is proposed, where both X-ray projections and correlation functions computed from limited 2D optical images are fused to accurately reconstruct complex heterogeneous materials in 3D. This multi-modal reconstruction algorithm is proved to be able to integrate the complementary data to perform an excellent optimization procedure, which indicates its high efficiency in using limited structural information. Finally, the accuracy of the stochastic reconstruction procedure using limited X-ray projection data is ascertained by analyzing the microstructural degeneracy and the roughness of energy landscape associated with different number of projections. Ground-state degeneracy of a microstructure is found to decrease with increasing number of projections, which indicates a higher probability that the reconstructed configurations match the actual microstructure. The roughness of energy landscape can also provide information about the complexity and convergence behavior of the reconstruction for given microstructures and projection number.

User Guidelines and Best Practices for CASL VUQ Analysis Using Dakota

DOE Office of Scientific and Technical Information (OSTI.GOV)

Adams, Brian M.; Coleman, Kayla; Hooper, Russell W.

2016-10-04

In general, Dakota is the Consortium for Advanced Simulation of Light Water Reactors (CASL) delivery vehicle for verification, validation, and uncertainty quantification (VUQ) algorithms. It permits ready application of the VUQ methods described above to simulation codes by CASL researchers, code developers, and application engineers. More specifically, the CASL VUQ Strategy [33] prescribes the use of Predictive Capability Maturity Model (PCMM) assessments [37]. PCMM is an expert elicitation tool designed to characterize and communicate completeness of the approaches used for computational model definition, verification, validation, and uncertainty quantification associated with an intended application. Exercising a computational model with the methodsmore » in Dakota will yield, in part, evidence for a predictive capability maturity model (PCMM) assessment. Table 1.1 summarizes some key predictive maturity related activities (see details in [33]), with examples of how Dakota fits in. This manual offers CASL partners a guide to conducting Dakota-based VUQ studies for CASL problems. It motivates various classes of Dakota methods and includes examples of their use on representative application problems. On reading, a CASL analyst should understand why and how to apply Dakota to a simulation problem.« less

Assessing future vent opening locations at the Somma-Vesuvio volcanic complex: 2. Probability maps of the caldera for a future Plinian/sub-Plinian event with uncertainty quantification

NASA Astrophysics Data System (ADS)

Tadini, A.; Bevilacqua, A.; Neri, A.; Cioni, R.; Aspinall, W. P.; Bisson, M.; Isaia, R.; Mazzarini, F.; Valentine, G. A.; Vitale, S.; Baxter, P. J.; Bertagnini, A.; Cerminara, M.; de Michieli Vitturi, M.; Di Roberto, A.; Engwell, S.; Esposti Ongaro, T.; Flandoli, F.; Pistolesi, M.

2017-06-01

In this study, we combine reconstructions of volcanological data sets and inputs from a structured expert judgment to produce a first long-term probability map for vent opening location for the next Plinian or sub-Plinian eruption of Somma-Vesuvio. In the past, the volcano has exhibited significant spatial variability in vent location; this can exert a significant control on where hazards materialize (particularly of pyroclastic density currents). The new vent opening probability mapping has been performed through (i) development of spatial probability density maps with Gaussian kernel functions for different data sets and (ii) weighted linear combination of these spatial density maps. The epistemic uncertainties affecting these data sets were quantified explicitly with expert judgments and implemented following a doubly stochastic approach. Various elicitation pooling metrics and subgroupings of experts and target questions were tested to evaluate the robustness of outcomes. Our findings indicate that (a) Somma-Vesuvio vent opening probabilities are distributed inside the whole caldera, with a peak corresponding to the area of the present crater, but with more than 50% probability that the next vent could open elsewhere within the caldera; (b) there is a mean probability of about 30% that the next vent will open west of the present edifice; (c) there is a mean probability of about 9.5% that the next medium-large eruption will enlarge the present Somma-Vesuvio caldera, and (d) there is a nonnegligible probability (mean value of 6-10%) that the next Plinian or sub-Plinian eruption will have its initial vent opening outside the present Somma-Vesuvio caldera.

Approximation of the ruin probability using the scaled Laplace transform inversion

PubMed Central

Mnatsakanov, Robert M.; Sarkisian, Khachatur; Hakobyan, Artak

2015-01-01

The problem of recovering the ruin probability in the classical risk model based on the scaled Laplace transform inversion is studied. It is shown how to overcome the problem of evaluating the ruin probability at large values of an initial surplus process. Comparisons of proposed approximations with the ones based on the Laplace transform inversions using a fixed Talbot algorithm as well as on the ones using the Trefethen–Weideman–Schmelzer and maximum entropy methods are presented via a simulation study. PMID:26752796

Decision analysis with approximate probabilities

NASA Technical Reports Server (NTRS)

Whalen, Thomas

1992-01-01

This paper concerns decisions under uncertainty in which the probabilities of the states of nature are only approximately known. Decision problems involving three states of nature are studied. This is due to the fact that some key issues do not arise in two-state problems, while probability spaces with more than three states of nature are essentially impossible to graph. The primary focus is on two levels of probabilistic information. In one level, the three probabilities are separately rounded to the nearest tenth. This can lead to sets of rounded probabilities which add up to 0.9, 1.0, or 1.1. In the other level, probabilities are rounded to the nearest tenth in such a way that the rounded probabilities are forced to sum to 1.0. For comparison, six additional levels of probabilistic information, previously analyzed, were also included in the present analysis. A simulation experiment compared four criteria for decisionmaking using linearly constrained probabilities (Maximin, Midpoint, Standard Laplace, and Extended Laplace) under the eight different levels of information about probability. The Extended Laplace criterion, which uses a second order maximum entropy principle, performed best overall.

Birthday and birthmate problems: misconceptions of probability among psychology undergraduates and casino visitors and personnel.

PubMed

Voracek, Martin; Tran, Ulrich S; Formann, Anton K

2008-02-01

Subjective estimates and associated confidence ratings for the solutions of some classic occupancy problems were studied in samples of 721 psychology undergraduates, 39 casino visitors, and 34 casino employees. On tasks varying the classic birthday problem, i.e., the probability P for any coincidence among N individuals sharing the same birthday, clear majorities of respondents markedly overestimated N, given P, and markedly underestimated P, given N. Respondents did notedly better on tasks varying the birthmate problem, i.e., P for the specific coincidence among N individuals of having a birthday today. Psychology students and women did better on both task types, but were less confident about their estimates than casino visitors or per sonnel and men. Several further person variables, such as indicators of topical knowledge and familiarity, were associated with better and more confident performance on birthday problems, but not on birthmate problems. Likewise, higher confidence ratings were related to subjective estimates that were closer to the solutions of birthday problems, but not of birthmate problems. Implications of and possible explanations for these findings, study limitations, directions for further inquiry, and the real-world relevance of ameliorating misconceptions of probability are discussed.

Introduction to Sustainable Urban Engineering - National Perspective - Measuring the Magnitude of the Problem

EPA Science Inventory

This seminar will present previous work on the Tool for the Reduction and Assessment and of Chemical and other environmental Impacts (TRACI) along with interim research on the quantification of land use modifications for comprehensive impact assessment. Various research options ...

Plausible inference: A multi-valued logic for problem solving

NASA Technical Reports Server (NTRS)

Friedman, L.

1979-01-01

A new logic is developed which permits continuously variable strength of belief in the truth of assertions. Four inference rules result, with formal logic as a limiting case. Quantification of belief is defined. Propagation of belief to linked assertions results from dependency-based techniques of truth maintenance so that local consistency is achieved or contradiction discovered in problem solving. Rules for combining, confirming, or disconfirming beliefs are given, and several heuristics are suggested that apply to revising already formed beliefs in the light of new evidence. The strength of belief that results in such revisions based on conflicting evidence are a highly subjective phenomenon. Certain quantification rules appear to reflect an orderliness in the subjectivity. Several examples of reasoning by plausible inference are given, including a legal example and one from robot learning. Propagation of belief takes place in directions forbidden in formal logic and this results in conclusions becoming possible for a given set of assertions that are not reachable by formal logic.

Imaging evaluation of non-alcoholic fatty liver disease: focused on quantification.

PubMed

Lee, Dong Ho

2017-12-01

Non-alcoholic fatty liver disease (NAFLD) has been an emerging major health problem, and the most common cause of chronic liver disease in Western countries. Traditionally, liver biopsy has been gold standard method for quantification of hepatic steatosis. However, its invasive nature with potential complication as well as measurement variability are major problem. Thus, various imaging studies have been used for evaluation of hepatic steatosis. Ultrasonography provides fairly good accuracy to detect moderate-to-severe degree hepatic steatosis, but limited accuracy for mild steatosis. Operator-dependency and subjective/qualitative nature of examination are another major drawbacks of ultrasonography. Computed tomography can be considered as an unsuitable imaging modality for evaluation of NAFLD due to potential risk of radiation exposure and limited accuracy in detecting mild steatosis. Both magnetic resonance spectroscopy and magnetic resonance imaging using chemical shift technique provide highly accurate and reproducible diagnostic performance for evaluating NAFLD, and therefore, have been used in many clinical trials as a non-invasive reference of standard method.

Performance of uncertainty quantification methodologies and linear solvers in cardiovascular simulations

NASA Astrophysics Data System (ADS)

Seo, Jongmin; Schiavazzi, Daniele; Marsden, Alison

2017-11-01

Cardiovascular simulations are increasingly used in clinical decision making, surgical planning, and disease diagnostics. Patient-specific modeling and simulation typically proceeds through a pipeline from anatomic model construction using medical image data to blood flow simulation and analysis. To provide confidence intervals on simulation predictions, we use an uncertainty quantification (UQ) framework to analyze the effects of numerous uncertainties that stem from clinical data acquisition, modeling, material properties, and boundary condition selection. However, UQ poses a computational challenge requiring multiple evaluations of the Navier-Stokes equations in complex 3-D models. To achieve efficiency in UQ problems with many function evaluations, we implement and compare a range of iterative linear solver and preconditioning techniques in our flow solver. We then discuss applications to patient-specific cardiovascular simulation and how the problem/boundary condition formulation in the solver affects the selection of the most efficient linear solver. Finally, we discuss performance improvements in the context of uncertainty propagation. Support from National Institute of Health (R01 EB018302) is greatly appreciated.

Imaging evaluation of non-alcoholic fatty liver disease: focused on quantification

PubMed Central

2017-01-01

Non-alcoholic fatty liver disease (NAFLD) has been an emerging major health problem, and the most common cause of chronic liver disease in Western countries. Traditionally, liver biopsy has been gold standard method for quantification of hepatic steatosis. However, its invasive nature with potential complication as well as measurement variability are major problem. Thus, various imaging studies have been used for evaluation of hepatic steatosis. Ultrasonography provides fairly good accuracy to detect moderate-to-severe degree hepatic steatosis, but limited accuracy for mild steatosis. Operator-dependency and subjective/qualitative nature of examination are another major drawbacks of ultrasonography. Computed tomography can be considered as an unsuitable imaging modality for evaluation of NAFLD due to potential risk of radiation exposure and limited accuracy in detecting mild steatosis. Both magnetic resonance spectroscopy and magnetic resonance imaging using chemical shift technique provide highly accurate and reproducible diagnostic performance for evaluating NAFLD, and therefore, have been used in many clinical trials as a non-invasive reference of standard method. PMID:28994271

Decision theory for computing variable and value ordering decisions for scheduling problems

NASA Technical Reports Server (NTRS)

Linden, Theodore A.

1993-01-01

Heuristics that guide search are critical when solving large planning and scheduling problems, but most variable and value ordering heuristics are sensitive to only one feature of the search state. One wants to combine evidence from all features of the search state into a subjective probability that a value choice is best, but there has been no solid semantics for merging evidence when it is conceived in these terms. Instead, variable and value ordering decisions should be viewed as problems in decision theory. This led to two key insights: (1) The fundamental concept that allows heuristic evidence to be merged is the net incremental utility that will be achieved by assigning a value to a variable. Probability distributions about net incremental utility can merge evidence from the utility function, binary constraints, resource constraints, and other problem features. The subjective probability that a value is the best choice is then derived from probability distributions about net incremental utility. (2) The methods used for rumor control in Bayesian Networks are the primary way to prevent cycling in the computation of probable net incremental utility. These insights lead to semantically justifiable ways to compute heuristic variable and value ordering decisions that merge evidence from all available features of the search state.

Driving under the influence of cannabis: pitfalls, validation, and quality control of a UPLC-MS/MS method for the quantification of tetrahydrocannabinol in oral fluid collected with StatSure, Quantisal, or Certus collector.

PubMed

Wille, Sarah M R; Di Fazio, Vincent; Ramírez-Fernandez, Maria del Mar; Kummer, Natalie; Samyn, Nele

2013-02-01

"Driving under the influence of drugs" (DUID) has a large impact on the worldwide mortality risk. Therefore, DUID legislations based on impairment or analytical limits are adopted. Drug detection in oral fluid is of interest due to the ease of sampling during roadside controls. The prevalence of Δ9-tetrahydrocannabinol (THC) in seriously injured drivers ranges from 0.5% to 7.6% in Europe. For these reasons, the quantification of THC in oral fluid collected with 3 alternative on-site collectors is presented and discussed in this publication. An ultra-performance liquid chromatography-mass spectrometric quantification method for THC in oral fluid samples collected with the StatSure (Diagnostic Systems), Quantisal (Immunalysis), and Certus (Concateno) devices was validated according to the international guidelines. Small sample volumes of 100-200 μL were extracted using hexane. Special attention was paid to factors such as matrix effects, THC adsorption onto the collector, and stability in the collection fluid. A relatively high-throughput analysis was developed and validated according to ISO 17025 requirements. Although the effects of the matrix on the quantification could be minimized using a deuterated internal standard, and stability was acceptable according the validation data, adsorption of THC onto the collectors was a problem. For the StatSure device, THC was totally recovered from the collector pad after storage for 24 hours at room temperature or 7 days at 4°C. A loss of 15%-25% was observed for the Quantisal collector, whereas the recovery from the Certus device was irreproducible (relative standard deviation, 44%-85%) and low (29%-80%). During the roadside setting, a practical problem arose: small volumes of oral fluid (eg, 300 μL) were collected. However, THC was easily detected and concentrations ranged from 8 to 922 ng/mL in neat oral fluid. A relatively high-throughput analysis (40 samples in 4 hours) adapted for routine DUID analysis was developed and validated for THC quantification in oral fluid samples collected from drivers under the influence of cannabis.

Recent advances in hopanoids analysis: Quantification protocols overview, main research targets and selected problems of complex data exploration.

PubMed

Zarzycki, Paweł K; Portka, Joanna K

2015-09-01

Pentacyclic triterpenoids, particularly hopanoids, are organism-specific compounds and are generally considered as useful biomarkers that allow fingerprinting and classification of biological, environmental and geological samples. Simultaneous quantification of various hopanoids together with battery of related non-polar and low-molecular mass compounds may provide principal information for geochemical and environmental research focusing on both modern and ancient investigations. Target compounds can be derived from microbial biomass, water columns, sediments, coals, crude fossils or rocks. This create number of analytical problems due to different composition of the analytical matrix and interfering compounds and therefore, proper optimization of quantification protocols for such biomarkers is still the challenge. In this work we summarizing typical analytical protocols that were recently applied for quantification of hopanoids like compounds from different samples. Main steps including components of interest extraction, pre-purification, fractionation, derivatization and quantification involving gas (1D and 2D) as well as liquid separation techniques (liquid-liquid extraction, solid-phase extraction, planar and low resolution column chromatography, high-performance liquid chromatography) are described and discussed from practical point of view, mainly based on the experimental papers that were published within last two years, where significant increase in hopanoids research was noticed. The second aim of this review is to describe the latest research trends concerning determination of hopanoids and related low-molecular mass lipids analyzed in various samples including sediments, rocks, coals, crude oils and plant fossils as well as stromatolites and microbial biomass cultivated under different conditions. It has been found that majority of the most recent papers are based on uni- or bivariate approach for complex data analysis. Data interpretation involves number of physicochemical parameters and hopanoids quantities or given biomarkers mass ratios derived from high-throughput separation and detection systems, typically GC-MS and HPLC-MS. Based on quantitative data reported in recently published experimental works it has been demonstrated that multivariate data analysis using e.g. principal components computations may significantly extend our knowledge concerning proper biomarkers selection and samples classification by means of hopanoids and related non-polar compounds. Copyright © 2015 Elsevier Ltd. All rights reserved.

Secondary School Students' Reasoning about Conditional Probability, Samples, and Sampling Procedures

ERIC Educational Resources Information Center

Prodromou, Theodosia

2016-01-01

In the Australian mathematics curriculum, Year 12 students (aged 16-17) are asked to solve conditional probability problems that involve the representation of the problem situation with two-way tables or three-dimensional diagrams and consider sampling procedures that result in different correct answers. In a small exploratory study, we…

Learning Probabilities in Computer Engineering by Using a Competency- and Problem-Based Approach

ERIC Educational Resources Information Center

Khoumsi, Ahmed; Hadjou, Brahim

2005-01-01

Our department has redesigned its electrical and computer engineering programs by adopting a learning methodology based on competence development, problem solving, and the realization of design projects. In this article, we show how this pedagogical approach has been successfully used for learning probabilities and their application to computer…

Avoiding the ensemble decorrelation problem using member-by-member post-processing

NASA Astrophysics Data System (ADS)

Van Schaeybroeck, Bert; Vannitsem, Stéphane

2014-05-01

Forecast calibration or post-processing has become a standard tool in atmospheric and climatological science due to the presence of systematic initial condition and model errors. For ensemble forecasts the most competitive methods derive from the assumption of a fixed ensemble distribution. However, when independently applying such 'statistical' methods at different locations, lead times or for multiple variables the correlation structure for individual ensemble members is destroyed. Instead of reastablishing the correlation structure as in Schefzik et al. (2013) we instead propose a calibration method that avoids such problem by correcting each ensemble member individually. Moreover, we analyse the fundamental mechanisms by which the probabilistic ensemble skill can be enhanced. In terms of continuous ranked probability score, our member-by-member approach amounts to skill gain that extends for lead times far beyond the error doubling time and which is as good as the one of the most competitive statistical approach, non-homogeneous Gaussian regression (Gneiting et al. 2005). Besides the conservation of correlation structure, additional benefits arise including the fact that higher-order ensemble moments like kurtosis and skewness are inherited from the uncorrected forecasts. Our detailed analysis is performed in the context of the Kuramoto-Sivashinsky equation and different simple models but the results extent succesfully to the ensemble forecast of the European Centre for Medium-Range Weather Forecasts (Van Schaeybroeck and Vannitsem, 2013, 2014) . References [1] Gneiting, T., Raftery, A. E., Westveld, A., Goldman, T., 2005: Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation. Mon. Weather Rev. 133, 1098-1118. [2] Schefzik, R., T.L. Thorarinsdottir, and T. Gneiting, 2013: Uncertainty Quantification in Complex Simulation Models Using Ensemble Copula Coupling. To appear in Statistical Science 28. [3] Van Schaeybroeck, B., and S. Vannitsem, 2013: Reliable probabilities through statistical post-processing of ensemble forecasts. Proceedings of the European Conference on Complex Systems 2012, Springer proceedings on complexity, XVI, p. 347-352. [4] Van Schaeybroeck, B., and S. Vannitsem, 2014: Ensemble post-processing using member-by-member approaches: theoretical aspects, under review.

Generation of monoclonal pan-hemagglutinin antibodies for the quantification of multiple strains of influenza

PubMed Central

Zou, Wei; Marcil, Anne; Paquet, Eric; Gadoury, Christine; Jaentschke, Bozena; Li, Xuguang; Petiot, Emma; Durocher, Yves; Baardsnes, Jason; Rosa-Calatrava, Manuel; Ansorge, Sven; Kamen, Amine A.

2017-01-01

Vaccination is the most effective course of action to prevent influenza. About 150 million doses of influenza vaccines were distributed for the 2015–2016 season in the USA alone according to the Centers for Disease Control and Prevention. Vaccine dosage is calculated based on the concentration of hemagglutinin (HA), the main surface glycoprotein expressed by influenza which varies from strain to strain. Therefore yearly-updated strain-specific antibodies and calibrating antigens are required. Preparing these quantification reagents can take up to three months and significantly slows down the release of new vaccine lots. Therefore, to circumvent the need for strain-specific sera, two anti-HA monoclonal antibodies (mAbs) against a highly conserved sequence have been produced by immunizing mice with a novel peptide-conjugate. Immunoblots demonstrate that 40 strains of influenza encompassing HA subtypes H1 to H13, as well as B strains from the Yamagata and Victoria lineage were detected when the two mAbs are combined to from a pan-HA mAb cocktail. Quantification using this pan-HA mAbs cocktail was achieved in a dot blot assay and results correlated with concentrations measured in a hemagglutination assay with a coefficient of correlation of 0.80. A competitive ELISA was also optimised with purified viral-like particles. Regardless of the quantification method used, pan-HA antibodies can be employed to accelerate process development when strain-specific antibodies are not available, and represent a valuable tool in case of pandemics. These antibodies were also expressed in CHO cells to facilitate large-scale production using bioreactor technologies which might be required to meet industrial needs for quantification reagents. Finally, a simulation model was created to predict the binding affinity of the two anti-HA antibodies to the amino acids composing the highly conserved epitope; different probabilities of interaction between a given amino acid and the antibodies might explain the affinity of each antibody against different influenza strains. PMID:28662134

An agglomerative hierarchical clustering approach to visualisation in Bayesian clustering problems

PubMed Central

Dawson, Kevin J.; Belkhir, Khalid

2009-01-01

Clustering problems (including the clustering of individuals into outcrossing populations, hybrid generations, full-sib families and selfing lines) have recently received much attention in population genetics. In these clustering problems, the parameter of interest is a partition of the set of sampled individuals, - the sample partition. In a fully Bayesian approach to clustering problems of this type, our knowledge about the sample partition is represented by a probability distribution on the space of possible sample partitions. Since the number of possible partitions grows very rapidly with the sample size, we can not visualise this probability distribution in its entirety, unless the sample is very small. As a solution to this visualisation problem, we recommend using an agglomerative hierarchical clustering algorithm, which we call the exact linkage algorithm. This algorithm is a special case of the maximin clustering algorithm that we introduced previously. The exact linkage algorithm is now implemented in our software package Partition View. The exact linkage algorithm takes the posterior co-assignment probabilities as input, and yields as output a rooted binary tree, - or more generally, a forest of such trees. Each node of this forest defines a set of individuals, and the node height is the posterior co-assignment probability of this set. This provides a useful visual representation of the uncertainty associated with the assignment of individuals to categories. It is also a useful starting point for a more detailed exploration of the posterior distribution in terms of the co-assignment probabilities. PMID:19337306

Newsvendor problem under complete uncertainty: a case of innovative products.

PubMed

Gaspars-Wieloch, Helena

2017-01-01

The paper presents a new scenario-based decision rule for the classical version of the newsvendor problem (NP) under complete uncertainty (i.e. uncertainty with unknown probabilities). So far, NP has been analyzed under uncertainty with known probabilities or under uncertainty with partial information (probabilities known incompletely). The novel approach is designed for the sale of new, innovative products, where it is quite complicated to define probabilities or even probability-like quantities, because there are no data available for forecasting the upcoming demand via statistical analysis. The new procedure described in the contribution is based on a hybrid of Hurwicz and Bayes decision rules. It takes into account the decision maker's attitude towards risk (measured by coefficients of optimism and pessimism) and the dispersion (asymmetry, range, frequency of extremes values) of payoffs connected with particular order quantities. It does not require any information about the probability distribution.

DNA Identification of Skeletal Remains from World War II Mass Graves Uncovered in Slovenia

PubMed Central

Marjanović, Damir; Durmić-Pašić, Adaleta; Bakal, Narcisa; Haverić, Sanin; Kalamujić, Belma; Kovačević, Lejla; Ramić, Jasmin; Pojskić, Naris; Škaro, Vedrana; Projić, Petar; Bajrović, Kasim; Hadžiselimović, Rifat; Drobnič, Katja; Huffine, Ed; Davoren, Jon; Primorac, Dragan

2007-01-01

Aim To present the joint effort of three institutions in the identification of human remains from the World War II found in two mass graves in the area of Škofja Loka, Slovenia. Methods The remains of 27 individuals were found in two small and closely located mass graves. The DNA was isolated from bone and teeth samples using either standard phenol/chloroform alcohol extraction or optimized Qiagen DNA extraction procedure. Some recovered samples required the employment of additional DNA purification methods, such as N-buthanol treatment. QuantifilerTM Human DNA Quantification Kit was used for DNA quantification. PowerPlex 16 kit was used to simultaneously amplify 15 short tandem repeat (STR) loci. Matching probabilities were estimated using the DNA View program. Results Out of all processed samples, 15 remains were fully profiled at all 15 STR loci. The other 12 profiles were partial. The least successful profile included 13 loci. Also, 69 referent samples (buccal swabs) from potential living relatives were collected and profiled. Comparison of victims' profile against referent samples database resulted in 4 strong matches. In addition, 5 other profiles were matched to certain referent samples with lower probability. Conclusion Our results show that more than 6 decades after the end of the World War II, DNA analysis may significantly contribute to the identification of the remains from that period. Additional analysis of Y-STRs and mitochondrial DNA (mtDNA) markers will be performed in the second phase of the identification project. PMID:17696306

Optimal Information Processing in Biochemical Networks

NASA Astrophysics Data System (ADS)

Wiggins, Chris

2012-02-01

A variety of experimental results over the past decades provide examples of near-optimal information processing in biological networks, including in biochemical and transcriptional regulatory networks. Computing information-theoretic quantities requires first choosing or computing the joint probability distribution describing multiple nodes in such a network --- for example, representing the probability distribution of finding an integer copy number of each of two interacting reactants or gene products while respecting the `intrinsic' small copy number noise constraining information transmission at the scale of the cell. I'll given an overview of some recent analytic and numerical work facilitating calculation of such joint distributions and the associated information, which in turn makes possible numerical optimization of information flow in models of noisy regulatory and biochemical networks. Illustrating cases include quantification of form-function relations, ideal design of regulatory cascades, and response to oscillatory driving.

The place of probability in Hilbert's axiomatization of physics, ca. 1900-1928

NASA Astrophysics Data System (ADS)

Verburgt, Lukas M.

2016-02-01

Although it has become a common place to refer to the 'sixth problem' of Hilbert's (1900) Paris lecture as the starting point for modern axiomatized probability theory, his own views on probability have received comparatively little explicit attention. The central aim of this paper is to provide a detailed account of this topic in light of the central observation that the development of Hilbert's project of the axiomatization of physics went hand-in-hand with a redefinition of the status of probability theory and the meaning of probability. Where Hilbert first regarded the theory as a mathematizable physical discipline and later approached it as a 'vague' mathematical application in physics, he eventually understood probability, first, as a feature of human thought and, then, as an implicitly defined concept without a fixed physical interpretation. It thus becomes possible to suggest that Hilbert came to question, from the early 1920s on, the very possibility of achieving the goal of the axiomatization of probability as described in the 'sixth problem' of 1900.

Quantification of cracks in concrete bridge decks in Ohio District 3.

DOT National Transportation Integrated Search

2012-02-01

The development of cracks in reinforced bridge decks is a critical problem, not only in Ohio state, but the whole of United States. Many bridge decks constructed within the last 10 years in Ohio have already shown varying levels and patterns of crack...

Quantification of cracks in concrete bridge decks in Ohio district 3.

DOT National Transportation Integrated Search

2012-02-01

The development of cracks in reinforced bridge decks is a critical problem, not only in Ohio state, but the whole of United States. Many bridge decks constructed within the last 10 years in Ohio have already shown varying levels and patterns of crack...

Estimation of dimensions and orientation of multiple riverine dune generations using spectral moments

NASA Astrophysics Data System (ADS)

Lisimenka, Aliaksandr; Kubicki, Adam

2017-02-01

A new spectral analysis technique is proposed for rhythmic bedform quantification, based on the 2D Fourier transform involving the calculation of a set of low-order spectral moments. The approach provides a tool for efficient quantification of bedform length and height as well as spatial crest-line alignment. Contrary to the conventional method, it not only describes the most energetic component of an undulating seabed surface but also retrieves information on its secondary structure without application of any band-pass filter of which the upper and lower cut-off frequencies are a priori unknown. Validation is based on bathymetric data collected in the main Vistula River mouth area (Przekop Wisły), Poland. This revealed two generations (distinct groups) of dunes which are migrating seawards along distinct paths, probably related to the hydrological regime of the river. The data enable the identification of dune divergence and convergence zones. The approach proved successful in the parameterisation of topographic roughness, an essential aspect in numerical modelling studies.

Comparison of the efficiency between two sampling plans for aflatoxins analysis in maize

PubMed Central

Mallmann, Adriano Olnei; Marchioro, Alexandro; Oliveira, Maurício Schneider; Rauber, Ricardo Hummes; Dilkin, Paulo; Mallmann, Carlos Augusto

2014-01-01

Variance and performance of two sampling plans for aflatoxins quantification in maize were evaluated. Eight lots of maize were sampled using two plans: manual, using sampling spear for kernels; and automatic, using a continuous flow to collect milled maize. Total variance and sampling, preparation, and analysis variance were determined and compared between plans through multifactor analysis of variance. Four theoretical distribution models were used to compare aflatoxins quantification distributions in eight maize lots. The acceptance and rejection probabilities for a lot under certain aflatoxin concentration were determined using variance and the information on the selected distribution model to build the operational characteristic curves (OC). Sampling and total variance were lower at the automatic plan. The OC curve from the automatic plan reduced both consumer and producer risks in comparison to the manual plan. The automatic plan is more efficient than the manual one because it expresses more accurately the real aflatoxin contamination in maize. PMID:24948911

Hazards of solid waste management: bioethical problems, principles, and priorities

PubMed Central

Maxey, Margaret N.

1978-01-01

The putative hazards of solid waste management cannot be evaluated without placing the problem within a cultural climate of crisis where some persons consider such by-products of “high, hard technology” to have raised unresolved moral and ethical issues. In order to assist scientific and technical efforts to protect public health and safety, a bioethical perspective requires us to examine three controversial aspects of policy-making about public safety. Failure to recognize the qualitative difference between two cognitive activities—risk-measurements (objective, scientific probabilities) and safety-judgments (subjective, shifting value priorities)—has had three unfortunate consequences. Sophisticated methods of risk analysis have been applied in a piecemeal, haphazard, ad hoc fashion within traditional institutions with the false expectation that incremental risk-reducing programs automatically ensure public health and safety. Ethical priorities require, first and foremost, a whole new field of data arranged for comparable risk-analyses. Critics of cost/risk/benefit quantifications attack the absurdity of “putting a price on human life” but have not been confronted with its threefold ethical justification. The widening discrepancy in risk-perceptions and loss of mutual confidence between scientific experts and ordinary citizens has placed a burden of social responsibility on members of the scientific and technical community to engage in more effective public education through the political process, notwithstanding advocates of a nonscientific adversary process. The urgency of effective public education has been demonstrated by the extent to which we have lost our historically balanced judgment about the alleged environmental hazards posed by advanced technology. PMID:738238

Quantification and propagation of disciplinary uncertainty via Bayesian statistics

NASA Astrophysics Data System (ADS)

Mantis, George Constantine

2002-08-01

Several needs exist in the military, commercial, and civil sectors for new hypersonic systems. These needs remain unfulfilled, due in part to the uncertainty encountered in designing these systems. This uncertainty takes a number of forms, including disciplinary uncertainty, that which is inherent in the analytical tools utilized during the design process. Yet, few efforts to date empower the designer with the means to account for this uncertainty within the disciplinary analyses. In the current state-of-the-art in design, the effects of this unquantifiable uncertainty significantly increase the risks associated with new design efforts. Typically, the risk proves too great to allow a given design to proceed beyond the conceptual stage. To that end, the research encompasses the formulation and validation of a new design method, a systematic process for probabilistically assessing the impact of disciplinary uncertainty. The method implements Bayesian Statistics theory to quantify this source of uncertainty, and propagate its effects to the vehicle system level. Comparison of analytical and physical data for existing systems, modeled a priori in the given analysis tools, leads to quantification of uncertainty in those tools' calculation of discipline-level metrics. Then, after exploration of the new vehicle's design space, the quantified uncertainty is propagated probabilistically through the design space. This ultimately results in the assessment of the impact of disciplinary uncertainty on the confidence in the design solution: the final shape and variability of the probability functions defining the vehicle's system-level metrics. Although motivated by the hypersonic regime, the proposed treatment of uncertainty applies to any class of aerospace vehicle, just as the problem itself affects the design process of any vehicle. A number of computer programs comprise the environment constructed for the implementation of this work. Application to a single-stage-to-orbit (SSTO) reusable launch vehicle concept, developed by the NASA Langley Research Center under the Space Launch Initiative, provides the validation case for this work, with the focus placed on economics, aerothermodynamics, propulsion, and structures metrics. (Abstract shortened by UMI.)

A Comprehensive Probability Project for the Upper Division One-Semester Probability Course Using Yahtzee

ERIC Educational Resources Information Center

Wilson, Jason; Lawman, Joshua; Murphy, Rachael; Nelson, Marissa

2011-01-01

This article describes a probability project used in an upper division, one-semester probability course with third-semester calculus and linear algebra prerequisites. The student learning outcome focused on developing the skills necessary for approaching project-sized math/stat application problems. These skills include appropriately defining…

Evaluation of Mathematical Self-Explanations with LSA in a Counterintuitive Problem of Probabilities

ERIC Educational Resources Information Center

Guiu, Jordi Maja

2012-01-01

In this paper different type of mathematical explanations are presented in relation to the mathematical problem of probabilities Monty Hall (card version) and the computational tool Latent Semantic Analyses (LSA) is used. At the moment the results in the literature about this computational tool to study texts show that this technique is…

The Classicist and the Frequentist Approach to Probability within a "TinkerPlots2" Combinatorial Problem

ERIC Educational Resources Information Center

Prodromou, Theodosia

2012-01-01

This article seeks to address a pedagogical theory of introducing the classicist and the frequentist approach to probability, by investigating important elements in 9th grade students' learning process while working with a "TinkerPlots2" combinatorial problem. Results from this research study indicate that, after the students had seen…

Computer-Based Graphical Displays for Enhancing Mental Animation and Improving Reasoning in Novice Learning of Probability

ERIC Educational Resources Information Center

Kaplan, Danielle E.; Wu, Erin Chia-ling

2006-01-01

Our research suggests static and animated graphics can lead to more animated thinking and more correct problem solving in computer-based probability learning. Pilot software modules were developed for graduate online statistics courses and representation research. A study with novice graduate student statisticians compared problem solving in five…

A MATLAB implementation of the minimum relative entropy method for linear inverse problems

NASA Astrophysics Data System (ADS)

Neupauer, Roseanna M.; Borchers, Brian

2001-08-01

The minimum relative entropy (MRE) method can be used to solve linear inverse problems of the form Gm= d, where m is a vector of unknown model parameters and d is a vector of measured data. The MRE method treats the elements of m as random variables, and obtains a multivariate probability density function for m. The probability density function is constrained by prior information about the upper and lower bounds of m, a prior expected value of m, and the measured data. The solution of the inverse problem is the expected value of m, based on the derived probability density function. We present a MATLAB implementation of the MRE method. Several numerical issues arise in the implementation of the MRE method and are discussed here. We present the source history reconstruction problem from groundwater hydrology as an example of the MRE implementation.

Generating probabilistic Boolean networks from a prescribed transition probability matrix.

PubMed

Ching, W-K; Chen, X; Tsing, N-K

2009-11-01

Probabilistic Boolean networks (PBNs) have received much attention in modeling genetic regulatory networks. A PBN can be regarded as a Markov chain process and is characterised by a transition probability matrix. In this study, the authors propose efficient algorithms for constructing a PBN when its transition probability matrix is given. The complexities of the algorithms are also analysed. This is an interesting inverse problem in network inference using steady-state data. The problem is important as most microarray data sets are assumed to be obtained from sampling the steady-state.

Neuroimaging Characteristics of Small-Vessel Disease in Older Adults with Normal Cognition, Mild Cognitive Impairment, and Alzheimer Disease.

PubMed

Mimenza-Alvarado, Alberto; Aguilar-Navarro, Sara G; Yeverino-Castro, Sara; Mendoza-Franco, César; Ávila-Funes, José Alberto; Román, Gustavo C

2018-01-01

Cerebral small-vessel disease (SVD) represents the most frequent type of vascular brain lesions, often coexisting with Alzheimer disease (AD). By quantifying white matter hyperintensities (WMH) and hippocampal and parietal atrophy, we aimed to describe the prevalence and severity of SVD among older adults with normal cognition (NC), mild cognitive impairment (MCI), and probable AD and to describe associated risk factors. This study included 105 older adults evaluated with magnetic resonance imaging and clinical and neuropsychological tests. We used the Fazekas scale (FS) for quantification of WMH, the Scheltens scale (SS) for hippocampal atrophy, and the Koedam scale (KS) for parietal atrophy. Logistic regression models were performed to determine the association between FS, SS, and KS scores and the presence of NC, MCI, or probable AD. Compared to NC subjects, SVD was more prevalent in MCI and probable AD subjects. After adjusting for confounding factors, logistic regression showed a positive association between higher scores on the FS and probable AD (OR = 7.6, 95% CI 2.7-20, p < 0.001). With the use of the SS and KS (OR = 4.5, 95% CI 3.5-58, p = 0.003 and OR = 8.9, 95% CI 1-72, p = 0.04, respectively), the risk also remained significant for probable AD. These results suggest an association between severity of vascular brain lesions and neurodegeneration.

Bird Radar Validation in the Field by Time-Referencing Line-Transect Surveys

PubMed Central

Dokter, Adriaan M.; Baptist, Martin J.; Ens, Bruno J.; Krijgsveld, Karen L.; van Loon, E. Emiel

2013-01-01

Track-while-scan bird radars are widely used in ornithological studies, but often the precise detection capabilities of these systems are unknown. Quantification of radar performance is essential to avoid observational biases, which requires practical methods for validating a radar’s detection capability in specific field settings. In this study a method to quantify the detection capability of a bird radar is presented, as well a demonstration of this method in a case study. By time-referencing line-transect surveys, visually identified birds were automatically linked to individual tracks using their transect crossing time. Detection probabilities were determined as the fraction of the total set of visual observations that could be linked to radar tracks. To avoid ambiguities in assigning radar tracks to visual observations, the observer’s accuracy in determining a bird’s transect crossing time was taken into account. The accuracy was determined by examining the effect of a time lag applied to the visual observations on the number of matches found with radar tracks. Effects of flight altitude, distance, surface substrate and species size on the detection probability by the radar were quantified in a marine intertidal study area. Detection probability varied strongly with all these factors, as well as species-specific flight behaviour. The effective detection range for single birds flying at low altitude for an X-band marine radar based system was estimated at ∼1.5 km. Within this range the fraction of individual flying birds that were detected by the radar was 0.50±0.06 with a detection bias towards higher flight altitudes, larger birds and high tide situations. Besides radar validation, which we consider essential when quantification of bird numbers is important, our method of linking radar tracks to ground-truthed field observations can facilitate species-specific studies using surveillance radars. The methodology may prove equally useful for optimising tracking algorithms. PMID:24066103

Bird radar validation in the field by time-referencing line-transect surveys.

PubMed

Dokter, Adriaan M; Baptist, Martin J; Ens, Bruno J; Krijgsveld, Karen L; van Loon, E Emiel

2013-01-01

Track-while-scan bird radars are widely used in ornithological studies, but often the precise detection capabilities of these systems are unknown. Quantification of radar performance is essential to avoid observational biases, which requires practical methods for validating a radar's detection capability in specific field settings. In this study a method to quantify the detection capability of a bird radar is presented, as well a demonstration of this method in a case study. By time-referencing line-transect surveys, visually identified birds were automatically linked to individual tracks using their transect crossing time. Detection probabilities were determined as the fraction of the total set of visual observations that could be linked to radar tracks. To avoid ambiguities in assigning radar tracks to visual observations, the observer's accuracy in determining a bird's transect crossing time was taken into account. The accuracy was determined by examining the effect of a time lag applied to the visual observations on the number of matches found with radar tracks. Effects of flight altitude, distance, surface substrate and species size on the detection probability by the radar were quantified in a marine intertidal study area. Detection probability varied strongly with all these factors, as well as species-specific flight behaviour. The effective detection range for single birds flying at low altitude for an X-band marine radar based system was estimated at ~1.5 km. Within this range the fraction of individual flying birds that were detected by the radar was 0.50 ± 0.06 with a detection bias towards higher flight altitudes, larger birds and high tide situations. Besides radar validation, which we consider essential when quantification of bird numbers is important, our method of linking radar tracks to ground-truthed field observations can facilitate species-specific studies using surveillance radars. The methodology may prove equally useful for optimising tracking algorithms.

Multi-fidelity methods for uncertainty quantification in transport problems

NASA Astrophysics Data System (ADS)

Tartakovsky, G.; Yang, X.; Tartakovsky, A. M.; Barajas-Solano, D. A.; Scheibe, T. D.; Dai, H.; Chen, X.

2016-12-01

We compare several multi-fidelity approaches for uncertainty quantification in flow and transport simulations that have a lower computational cost than the standard Monte Carlo method. The cost reduction is achieved by combining a small number of high-resolution (high-fidelity) simulations with a large number of low-resolution (low-fidelity) simulations. We propose a new method, a re-scaled Multi Level Monte Carlo (rMLMC) method. The rMLMC is based on the idea that the statistics of quantities of interest depends on scale/resolution. We compare rMLMC with existing multi-fidelity methods such as Multi Level Monte Carlo (MLMC) and reduced basis methods and discuss advantages of each approach.

Quantification of genomic relationship from DNA pooled samples

USDA-ARS?s Scientific Manuscript database

Use of DNA pooling for GWAS has been demonstrated to reduce genotypic costs up to 90% while achieving similar power to individual genotyping. Recent work has focused on use of DNA pooling to inform problems in genomic prediction. This study is designed to demonstrate the efficacy of estimating genom...

Flood probability quantification for road infrastructure: Data-driven spatial-statistical approach and case study applications.

PubMed

Kalantari, Zahra; Cavalli, Marco; Cantone, Carolina; Crema, Stefano; Destouni, Georgia

2017-03-01

Climate-driven increase in the frequency of extreme hydrological events is expected to impose greater strain on the built environment and major transport infrastructure, such as roads and railways. This study develops a data-driven spatial-statistical approach to quantifying and mapping the probability of flooding at critical road-stream intersection locations, where water flow and sediment transport may accumulate and cause serious road damage. The approach is based on novel integration of key watershed and road characteristics, including also measures of sediment connectivity. The approach is concretely applied to and quantified for two specific study case examples in southwest Sweden, with documented road flooding effects of recorded extreme rainfall. The novel contributions of this study in combining a sediment connectivity account with that of soil type, land use, spatial precipitation-runoff variability and road drainage in catchments, and in extending the connectivity measure use for different types of catchments, improve the accuracy of model results for road flood probability. Copyright © 2016 Elsevier B.V. All rights reserved.

Two-dimensional fuzzy fault tree analysis for chlorine release from a chlor-alkali industry using expert elicitation.

PubMed

Renjith, V R; Madhu, G; Nayagam, V Lakshmana Gomathi; Bhasi, A B

2010-11-15

The hazards associated with major accident hazard (MAH) industries are fire, explosion and toxic gas releases. Of these, toxic gas release is the worst as it has the potential to cause extensive fatalities. Qualitative and quantitative hazard analyses are essential for the identification and quantification of these hazards related to chemical industries. Fault tree analysis (FTA) is an established technique in hazard identification. This technique has the advantage of being both qualitative and quantitative, if the probabilities and frequencies of the basic events are known. This paper outlines the estimation of the probability of release of chlorine from storage and filling facility of chlor-alkali industry using FTA. An attempt has also been made to arrive at the probability of chlorine release using expert elicitation and proven fuzzy logic technique for Indian conditions. Sensitivity analysis has been done to evaluate the percentage contribution of each basic event that could lead to chlorine release. Two-dimensional fuzzy fault tree analysis (TDFFTA) has been proposed for balancing the hesitation factor involved in expert elicitation. Copyright © 2010 Elsevier B.V. All rights reserved.

What Can Quantum Optics Say about Computational Complexity Theory?

NASA Astrophysics Data System (ADS)

Rahimi-Keshari, Saleh; Lund, Austin P.; Ralph, Timothy C.

2015-02-01

Considering the problem of sampling from the output photon-counting probability distribution of a linear-optical network for input Gaussian states, we obtain results that are of interest from both quantum theory and the computational complexity theory point of view. We derive a general formula for calculating the output probabilities, and by considering input thermal states, we show that the output probabilities are proportional to permanents of positive-semidefinite Hermitian matrices. It is believed that approximating permanents of complex matrices in general is a #P-hard problem. However, we show that these permanents can be approximated with an algorithm in the BPPNP complexity class, as there exists an efficient classical algorithm for sampling from the output probability distribution. We further consider input squeezed-vacuum states and discuss the complexity of sampling from the probability distribution at the output.

Probabilistic methods for sensitivity analysis and calibration in the NASA challenge problem

DOE PAGES

Safta, Cosmin; Sargsyan, Khachik; Najm, Habib N.; ...

2015-01-01

In this study, a series of algorithms are proposed to address the problems in the NASA Langley Research Center Multidisciplinary Uncertainty Quantification Challenge. A Bayesian approach is employed to characterize and calibrate the epistemic parameters based on the available data, whereas a variance-based global sensitivity analysis is used to rank the epistemic and aleatory model parameters. A nested sampling of the aleatory–epistemic space is proposed to propagate uncertainties from model parameters to output quantities of interest.

Probabilistic methods for sensitivity analysis and calibration in the NASA challenge problem

DOE Office of Scientific and Technical Information (OSTI.GOV)

Safta, Cosmin; Sargsyan, Khachik; Najm, Habib N.

In this study, a series of algorithms are proposed to address the problems in the NASA Langley Research Center Multidisciplinary Uncertainty Quantification Challenge. A Bayesian approach is employed to characterize and calibrate the epistemic parameters based on the available data, whereas a variance-based global sensitivity analysis is used to rank the epistemic and aleatory model parameters. A nested sampling of the aleatory–epistemic space is proposed to propagate uncertainties from model parameters to output quantities of interest.

Uncertainty Reduction using Bayesian Inference and Sensitivity Analysis: A Sequential Approach to the NASA Langley Uncertainty Quantification Challenge

NASA Technical Reports Server (NTRS)

Sankararaman, Shankar

2016-01-01

This paper presents a computational framework for uncertainty characterization and propagation, and sensitivity analysis under the presence of aleatory and epistemic un- certainty, and develops a rigorous methodology for efficient refinement of epistemic un- certainty by identifying important epistemic variables that significantly affect the overall performance of an engineering system. The proposed methodology is illustrated using the NASA Langley Uncertainty Quantification Challenge (NASA-LUQC) problem that deals with uncertainty analysis of a generic transport model (GTM). First, Bayesian inference is used to infer subsystem-level epistemic quantities using the subsystem-level model and corresponding data. Second, tools of variance-based global sensitivity analysis are used to identify four important epistemic variables (this limitation specified in the NASA-LUQC is reflective of practical engineering situations where not all epistemic variables can be refined due to time/budget constraints) that significantly affect system-level performance. The most significant contribution of this paper is the development of the sequential refine- ment methodology, where epistemic variables for refinement are not identified all-at-once. Instead, only one variable is first identified, and then, Bayesian inference and global sensi- tivity calculations are repeated to identify the next important variable. This procedure is continued until all 4 variables are identified and the refinement in the system-level perfor- mance is computed. The advantages of the proposed sequential refinement methodology over the all-at-once uncertainty refinement approach are explained, and then applied to the NASA Langley Uncertainty Quantification Challenge problem.

State-specific catalytic recombination boundary condition for DSMC methods in aerospace applications

NASA Astrophysics Data System (ADS)

Bariselli, F.; Torres, E.; Magin, T. E.

2016-11-01

Accurate characterization of the hypersonic flow around a vehicle during its atmospheric entry is important for a precise quantification of heat flux margins. In some cases, exothermic reactions promoted by the catalytic properties of the surface material can significantly contribute to the overall heat flux. In this work, the effect of catalytic recombination of atomic nitrogen is examined within the framework of a state-specific DSMC implementation. State-to-state reaction cross sections are derived from a detailed quantum-chemical database for the N2(v, J) + N system. A coarse-grain model is used to reduce the number of internal states and state-specific reactions to a manageable level. The catalytic boundary condition is based on an phenomenological approach and the state-specific surface recombination probabilities can be imposed by the user. This can represent an important aspect in modelling catalysis, since experiments and molecular dynamics suggest that only part of the chemical energy is absorbed by the wall, with the formed molecules leaving the surface in an excited state. The implementation is verified in a simplified geometrical configuration by comparing the numerical results with an analytical solution, developed for a 1D diffusion problem in a binary mixture. Then, the effect of catalysis in a hypersonic flow along the stagnation line of a blunt body is studied.

Funding for the 2ND IAEA technical meeting on fusion data processing, validation and analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Greenwald, Martin

The International Atomic Energy Agency (IAEA) will organize the second Technical Meeting on Fusion Da Processing, Validation and Analysis from 30 May to 02 June, 2017, in Cambridge, MA USA. The meeting w be hosted by the MIT Plasma Science and Fusion Center (PSFC). The objective of the meeting is to provide a platform where a set of topics relevant to fusion data processing, validation and analysis are discussed with the view of extrapolation needs to next step fusion devices such as ITER. The validation and analysis of experimental data obtained from diagnostics used to characterize fusion plasmas are crucialmore » for a knowledge based understanding of the physical processes governing the dynamics of these plasmas. The meeting will aim at fostering, in particular, discussions of research and development results that set out or underline trends observed in the current major fusion confinement devices. General information on the IAEA, including its mission and organization, can be found at the IAEA websit Uncertainty quantification (UQ) Model selection, validation, and verification (V&V) Probability theory and statistical analysis Inverse problems & equilibrium reconstru ction Integrated data analysis Real time data analysis Machine learning Signal/image proc essing & pattern recognition Experimental design and synthetic diagnostics Data management« less

Animal reintroductions: an innovative assessment of survival

USGS Publications Warehouse

Muths, Erin L.; Bailey, Larissa L.; Watry, Mary Kay

2014-01-01

Quantitative evaluations of reintroductions are infrequent and assessments of milestones reached before a project is completed, or abandoned due to lack of funding, are rare. However, such assessments, which are promoted in adaptive management frameworks, are critical. Quantification can provide defensible estimates of biological success, such as the number of survivors from a released cohort, with associated cost per animal. It is unlikely that the global issues of endangered wildlife and population declines will abate, therefore, assurance colonies and reintroductions are likely to become more common. If such endeavors are to be successful biologically or achieve adequate funding, implementation must be more rigorous and accountable. We use a novel application of a multistate, robust design capture-recapture model to estimate survival of reintroduced tadpoles through metamorphosis (i.e., the number of individuals emerging from the pond) and thereby provide a quantitative measure of effort and success for an "in progress" reintroduction of toads. Our data also suggest that tadpoles released at later developmental stages have an increased probability of survival and that eggs laid in the wild hatched at higher rates than eggs laid by captive toads. We illustrate how an interim assessment can identify problems, highlight successes, and provide information for use in adjusting the effort or implementing a Decision-Theoretic adaptive management strategy.

Absolute quantification of Medicago truncatula sucrose synthase isoforms and N-metabolism enzymes in symbiotic root nodules and the detection of novel nodule phosphoproteins by mass spectrometry

PubMed Central

Wienkoop, Stefanie; Larrainzar, Estíbaliz; Glinski, Mirko; González, Esther M.; Arrese-Igor, Cesar; Weckwerth, Wolfram

2008-01-01

Mass spectrometry (MS) has become increasingly important for tissue specific protein quantification at the isoform level, as well as for the analysis of protein post-translational regulation mechanisms and turnover rates. Thanks to the development of high accuracy mass spectrometers, peptide sequencing without prior knowledge of the amino acid sequence—de novo sequencing—can be performed. In this work, absolute quantification of a set of key enzymes involved in carbon and nitrogen metabolism in Medicago truncatula ‘Jemalong A17’ root nodules is presented. Among them, sucrose synthase (SuSy; EC 2.4.1.13), one of the central enzymes in sucrose cleavage in root nodules, has been further characterized and the relative phosphorylation state of the three most abundant isoforms has been quantified. De novo sequencing provided sequence information of a so far unidentified peptide, most probably belonging to SuSy2, the second most abundant isoform in M. truncatula root nodules. TiO2-phosphopeptide enrichment led to the identification of not only a phosphorylation site at Ser11 in SuSy1, but also of several novel phosphorylation sites present in other root nodule proteins such as alkaline invertase (AI; EC 3.2.1.26) and an RNA-binding protein. PMID:18772307

Adaptive polynomial chaos techniques for uncertainty quantification of a gas cooled fast reactor transient

DOE Office of Scientific and Technical Information (OSTI.GOV)

Perko, Z.; Gilli, L.; Lathouwers, D.

2013-07-01

Uncertainty quantification plays an increasingly important role in the nuclear community, especially with the rise of Best Estimate Plus Uncertainty methodologies. Sensitivity analysis, surrogate models, Monte Carlo sampling and several other techniques can be used to propagate input uncertainties. In recent years however polynomial chaos expansion has become a popular alternative providing high accuracy at affordable computational cost. This paper presents such polynomial chaos (PC) methods using adaptive sparse grids and adaptive basis set construction, together with an application to a Gas Cooled Fast Reactor transient. Comparison is made between a new sparse grid algorithm and the traditionally used techniquemore » proposed by Gerstner. An adaptive basis construction method is also introduced and is proved to be advantageous both from an accuracy and a computational point of view. As a demonstration the uncertainty quantification of a 50% loss of flow transient in the GFR2400 Gas Cooled Fast Reactor design was performed using the CATHARE code system. The results are compared to direct Monte Carlo sampling and show the superior convergence and high accuracy of the polynomial chaos expansion. Since PC techniques are easy to implement, they can offer an attractive alternative to traditional techniques for the uncertainty quantification of large scale problems. (authors)« less

A contemporary approach to the problem of determining physical parameters according to the results of measurements

NASA Technical Reports Server (NTRS)

Elyasberg, P. Y.

1979-01-01

The shortcomings of the classical approach are set forth, and the newer methods resulting from these shortcomings are explained. The problem was approached with the assumption that the probabilities of error were known, as well as without knowledge of the distribution of the probabilities of error. The advantages of the newer approach are discussed.

Probability estimation with machine learning methods for dichotomous and multicategory outcome: theory.

PubMed

Kruppa, Jochen; Liu, Yufeng; Biau, Gérard; Kohler, Michael; König, Inke R; Malley, James D; Ziegler, Andreas

2014-07-01

Probability estimation for binary and multicategory outcome using logistic and multinomial logistic regression has a long-standing tradition in biostatistics. However, biases may occur if the model is misspecified. In contrast, outcome probabilities for individuals can be estimated consistently with machine learning approaches, including k-nearest neighbors (k-NN), bagged nearest neighbors (b-NN), random forests (RF), and support vector machines (SVM). Because machine learning methods are rarely used by applied biostatisticians, the primary goal of this paper is to explain the concept of probability estimation with these methods and to summarize recent theoretical findings. Probability estimation in k-NN, b-NN, and RF can be embedded into the class of nonparametric regression learning machines; therefore, we start with the construction of nonparametric regression estimates and review results on consistency and rates of convergence. In SVMs, outcome probabilities for individuals are estimated consistently by repeatedly solving classification problems. For SVMs we review classification problem and then dichotomous probability estimation. Next we extend the algorithms for estimating probabilities using k-NN, b-NN, and RF to multicategory outcomes and discuss approaches for the multicategory probability estimation problem using SVM. In simulation studies for dichotomous and multicategory dependent variables we demonstrate the general validity of the machine learning methods and compare it with logistic regression. However, each method fails in at least one simulation scenario. We conclude with a discussion of the failures and give recommendations for selecting and tuning the methods. Applications to real data and example code are provided in a companion article (doi:10.1002/bimj.201300077). © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

UQ for Decision Making: How (at least five) Kinds of Probability Might Come Into Play

NASA Astrophysics Data System (ADS)

Smith, L. A.

2013-12-01

In 1959 IJ Good published the discussion "Kinds of Probability" in Science. Good identified (at least) five kinds. The need for (at least) a sixth kind of probability when quantifying uncertainty in the context of climate science is discussed. This discussion brings out the differences in weather-like forecasting tasks and climate-links tasks, with a focus on the effective use both of science and of modelling in support of decision making. Good also introduced the idea of a "Dynamic probability" a probability one expects to change without any additional empirical evidence; the probabilities assigned by a chess playing program when it is only half thorough its analysis being an example. This case is contrasted with the case of "Mature probabilities" where a forecast algorithm (or model) has converged on its asymptotic probabilities and the question hinges in whether or not those probabilities are expected to change significantly before the event in question occurs, even in the absence of new empirical evidence. If so, then how might one report and deploy such immature probabilities in scientific-support of decision-making rationally? Mature Probability is suggested as a useful sixth kind, although Good would doubtlessly argue that we can get by with just one, effective communication with decision makers may be enhanced by speaking as if the others existed. This again highlights the distinction between weather-like contexts and climate-like contexts. In the former context one has access to a relevant climatology (a relevant, arguably informative distribution prior to any model simulations), in the latter context that information is not available although one can fall back on the scientific basis upon which the model itself rests, and estimate the probability that the model output is in fact misinformative. This subjective "probability of a big surprise" is one way to communicate the probability of model-based information holding in practice, the probability that the information the model-based probability is conditioned on holds. It is argued that no model-based climate-like probability forecast is complete without a quantitative estimate of its own irrelevance, and that the clear identification of model-based probability forecasts as mature or immature, are critical elements for maintaining the credibility of science-based decision support, and can shape uncertainty quantification more widely.

Problem Solving, Patterns, Probability, Pascal, and Palindromes.

ERIC Educational Resources Information Center

Hylton-Lindsay, Althea Antoinette

2003-01-01

Presents a problem-solving activity, the birth order problem, and several solution-seeking strategies. Includes responses of current and prospective teachers and a comparison of various strategies. (YDS)

University Students’ Conceptual Knowledge of Randomness and Probability in the Contexts of Evolution and Mathematics

PubMed Central

Fiedler, Daniela; Tröbst, Steffen; Harms, Ute

2017-01-01

Students of all ages face severe conceptual difficulties regarding key aspects of evolution—the central, unifying, and overarching theme in biology. Aspects strongly related to abstract “threshold” concepts like randomness and probability appear to pose particular difficulties. A further problem is the lack of an appropriate instrument for assessing students’ conceptual knowledge of randomness and probability in the context of evolution. To address this problem, we have developed two instruments, Randomness and Probability Test in the Context of Evolution (RaProEvo) and Randomness and Probability Test in the Context of Mathematics (RaProMath), that include both multiple-choice and free-response items. The instruments were administered to 140 university students in Germany, then the Rasch partial-credit model was applied to assess them. The results indicate that the instruments generate reliable and valid inferences about students’ conceptual knowledge of randomness and probability in the two contexts (which are separable competencies). Furthermore, RaProEvo detected significant differences in knowledge of randomness and probability, as well as evolutionary theory, between biology majors and preservice biology teachers. PMID:28572180

Prevalence and quantification of Listeria monocytogenes in chicken offal at the retail level in Malaysia.

PubMed

Kuan, C H; Goh, S G; Loo, Y Y; Chang, W S; Lye, Y L; Puspanadan, S; Tang, J Y H; Nakaguchi, Y; Nishibuchi, M; Mahyudin, N A; Radu, S

2013-06-01

A total of 216 chicken offal samples (chicken liver = 72; chicken heart = 72; chicken gizzard = 72) from wet markets and hypermarkets in Selangor, Malaysia, were examined for the presence and density of Listeria monocytogenes by using a combination of the most probable number and PCR method. The prevalence of L. monocytogenes in 216 chicken offal samples examined was 26.39%, and among the positive samples, the chicken gizzard showed the highest percentage at 33.33% compared with chicken liver (25.00%) and chicken heart (20.83%). The microbial load of L. monocytogenes in chicken offal samples ranged from <3 to 93.0 most probable number per gram. The presence of L. monocytogenes in chicken offal samples may indicate that chicken offal can act as a possible vehicle for the occurrence of foodborne listeriosis. Hence, there is a need to investigate the biosafety level of chicken offal in Malaysia.

Propensity, Probability, and Quantum Theory

NASA Astrophysics Data System (ADS)

Ballentine, Leslie E.

2016-08-01

Quantum mechanics and probability theory share one peculiarity. Both have well established mathematical formalisms, yet both are subject to controversy about the meaning and interpretation of their basic concepts. Since probability plays a fundamental role in QM, the conceptual problems of one theory can affect the other. We first classify the interpretations of probability into three major classes: (a) inferential probability, (b) ensemble probability, and (c) propensity. Class (a) is the basis of inductive logic; (b) deals with the frequencies of events in repeatable experiments; (c) describes a form of causality that is weaker than determinism. An important, but neglected, paper by P. Humphreys demonstrated that propensity must differ mathematically, as well as conceptually, from probability, but he did not develop a theory of propensity. Such a theory is developed in this paper. Propensity theory shares many, but not all, of the axioms of probability theory. As a consequence, propensity supports the Law of Large Numbers from probability theory, but does not support Bayes theorem. Although there are particular problems within QM to which any of the classes of probability may be applied, it is argued that the intrinsic quantum probabilities (calculated from a state vector or density matrix) are most naturally interpreted as quantum propensities. This does not alter the familiar statistical interpretation of QM. But the interpretation of quantum states as representing knowledge is untenable. Examples show that a density matrix fails to represent knowledge.

Sequential Probability Ratio Test for Collision Avoidance Maneuver Decisions

NASA Technical Reports Server (NTRS)

Carpenter, J. Russell; Markley, F. Landis

2010-01-01

When facing a conjunction between space objects, decision makers must chose whether to maneuver for collision avoidance or not. We apply a well-known decision procedure, the sequential probability ratio test, to this problem. We propose two approaches to the problem solution, one based on a frequentist method, and the other on a Bayesian method. The frequentist method does not require any prior knowledge concerning the conjunction, while the Bayesian method assumes knowledge of prior probability densities. Our results show that both methods achieve desired missed detection rates, but the frequentist method's false alarm performance is inferior to the Bayesian method's

Calculation of transmission probability by solving an eigenvalue problem

NASA Astrophysics Data System (ADS)

Bubin, Sergiy; Varga, Kálmán

2010-11-01

The electron transmission probability in nanodevices is calculated by solving an eigenvalue problem. The eigenvalues are the transmission probabilities and the number of nonzero eigenvalues is equal to the number of open quantum transmission eigenchannels. The number of open eigenchannels is typically a few dozen at most, thus the computational cost amounts to the calculation of a few outer eigenvalues of a complex Hermitian matrix (the transmission matrix). The method is implemented on a real space grid basis providing an alternative to localized atomic orbital based quantum transport calculations. Numerical examples are presented to illustrate the efficiency of the method.

Markovian Search Games in Heterogeneous Spaces

DOE Office of Scientific and Technical Information (OSTI.GOV)

Griffin, Christopher H

2009-01-01

We consider how to search for a mobile evader in a large heterogeneous region when sensors are used for detection. Sensors are modeled using probability of detection. Due to environmental effects, this probability will not be constant over the entire region. We map this problem to a graph search problem and, even though deterministic graph search is NP-complete, we derive a tractable, optimal, probabilistic search strategy. We do this by defining the problem as a differential game played on a Markov chain. We prove that this strategy is optimal in the sense of Nash. Simulations of an example problem illustratemore » our approach and verify our claims.« less

Climate sensitivity estimated from temperature reconstructions of the Last Glacial Maximum

NASA Astrophysics Data System (ADS)

Schmittner, A.; Urban, N.; Shakun, J. D.; Mahowald, N. M.; Clark, P. U.; Bartlein, P. J.; Mix, A. C.; Rosell-Melé, A.

2011-12-01

In 1959 IJ Good published the discussion "Kinds of Probability" in Science. Good identified (at least) five kinds. The need for (at least) a sixth kind of probability when quantifying uncertainty in the context of climate science is discussed. This discussion brings out the differences in weather-like forecasting tasks and climate-links tasks, with a focus on the effective use both of science and of modelling in support of decision making. Good also introduced the idea of a "Dynamic probability" a probability one expects to change without any additional empirical evidence; the probabilities assigned by a chess playing program when it is only half thorough its analysis being an example. This case is contrasted with the case of "Mature probabilities" where a forecast algorithm (or model) has converged on its asymptotic probabilities and the question hinges in whether or not those probabilities are expected to change significantly before the event in question occurs, even in the absence of new empirical evidence. If so, then how might one report and deploy such immature probabilities in scientific-support of decision-making rationally? Mature Probability is suggested as a useful sixth kind, although Good would doubtlessly argue that we can get by with just one, effective communication with decision makers may be enhanced by speaking as if the others existed. This again highlights the distinction between weather-like contexts and climate-like contexts. In the former context one has access to a relevant climatology (a relevant, arguably informative distribution prior to any model simulations), in the latter context that information is not available although one can fall back on the scientific basis upon which the model itself rests, and estimate the probability that the model output is in fact misinformative. This subjective "probability of a big surprise" is one way to communicate the probability of model-based information holding in practice, the probability that the information the model-based probability is conditioned on holds. It is argued that no model-based climate-like probability forecast is complete without a quantitative estimate of its own irrelevance, and that the clear identification of model-based probability forecasts as mature or immature, are critical elements for maintaining the credibility of science-based decision support, and can shape uncertainty quantification more widely.

Re-assessment of road accident data-analysis policy : applying theory from involuntary, high-consequence, low-probability events like nuclear power plant meltdowns to voluntary, low-consequence, high-probability events like traffic accidents

DOT National Transportation Integrated Search

2002-02-01

This report examines the literature on involuntary, high-consequence, low-probability (IHL) events like nuclear power plant meltdowns to determine what can be applied to the problem of voluntary, low-consequence high-probability (VLH) events like tra...

Advanced Stochastic Collocation Methods for Polynomial Chaos in RAVEN

NASA Astrophysics Data System (ADS)

Talbot, Paul W.

As experiment complexity in fields such as nuclear engineering continually increases, so does the demand for robust computational methods to simulate them. In many simulations, input design parameters and intrinsic experiment properties are sources of uncertainty. Often small perturbations in uncertain parameters have significant impact on the experiment outcome. For instance, in nuclear fuel performance, small changes in fuel thermal conductivity can greatly affect maximum stress on the surrounding cladding. The difficulty quantifying input uncertainty impact in such systems has grown with the complexity of numerical models. Traditionally, uncertainty quantification has been approached using random sampling methods like Monte Carlo. For some models, the input parametric space and corresponding response output space is sufficiently explored with few low-cost calculations. For other models, it is computationally costly to obtain good understanding of the output space. To combat the expense of random sampling, this research explores the possibilities of using advanced methods in Stochastic Collocation for generalized Polynomial Chaos (SCgPC) as an alternative to traditional uncertainty quantification techniques such as Monte Carlo (MC) and Latin Hypercube Sampling (LHS) methods for applications in nuclear engineering. We consider traditional SCgPC construction strategies as well as truncated polynomial spaces using Total Degree and Hyperbolic Cross constructions. We also consider applying anisotropy (unequal treatment of different dimensions) to the polynomial space, and offer methods whereby optimal levels of anisotropy can be approximated. We contribute development to existing adaptive polynomial construction strategies. Finally, we consider High-Dimensional Model Reduction (HDMR) expansions, using SCgPC representations for the subspace terms, and contribute new adaptive methods to construct them. We apply these methods on a series of models of increasing complexity. We use analytic models of various levels of complexity, then demonstrate performance on two engineering-scale problems: a single-physics nuclear reactor neutronics problem, and a multiphysics fuel cell problem coupling fuels performance and neutronics. Lastly, we demonstrate sensitivity analysis for a time-dependent fuels performance problem. We demonstrate the application of all the algorithms in RAVEN, a production-level uncertainty quantification framework.

Bayesian approach to inverse statistical mechanics.

PubMed

Habeck, Michael

2014-05-01

Inverse statistical mechanics aims to determine particle interactions from ensemble properties. This article looks at this inverse problem from a Bayesian perspective and discusses several statistical estimators to solve it. In addition, a sequential Monte Carlo algorithm is proposed that draws the interaction parameters from their posterior probability distribution. The posterior probability involves an intractable partition function that is estimated along with the interactions. The method is illustrated for inverse problems of varying complexity, including the estimation of a temperature, the inverse Ising problem, maximum entropy fitting, and the reconstruction of molecular interaction potentials.

Bayesian approach to inverse statistical mechanics

NASA Astrophysics Data System (ADS)

Habeck, Michael

2014-05-01

Inverse statistical mechanics aims to determine particle interactions from ensemble properties. This article looks at this inverse problem from a Bayesian perspective and discusses several statistical estimators to solve it. In addition, a sequential Monte Carlo algorithm is proposed that draws the interaction parameters from their posterior probability distribution. The posterior probability involves an intractable partition function that is estimated along with the interactions. The method is illustrated for inverse problems of varying complexity, including the estimation of a temperature, the inverse Ising problem, maximum entropy fitting, and the reconstruction of molecular interaction potentials.

Probability in High Dimension

DTIC Science & Technology

2014-06-30

b 1 , . . . , b0m, bm)  fm(b0) + Pm i=1 1bi 6=b0 i 1b i 6=b j for j<i. 4.8 ( Travelling salesman problem ). Let X 1 , . . . ,Xn be i.i.d. points that...are uniformly distributed in the unit square [0, 1]2. We think of Xi as the location of city i. The goal of the travelling salesman problem is to find... salesman problem , . . . • Probability in Banach spaces: probabilistic limit theorems for Banach- valued random variables, empirical processes, local

Cosmological measure with volume averaging and the vacuum energy problem

NASA Astrophysics Data System (ADS)

Astashenok, Artyom V.; del Popolo, Antonino

2012-04-01

In this paper, we give a possible solution to the cosmological constant problem. It is shown that the traditional approach, based on volume weighting of probabilities, leads to an incoherent conclusion: the probability that a randomly chosen observer measures Λ = 0 is exactly equal to 1. Using an alternative, volume averaging measure, instead of volume weighting can explain why the cosmological constant is non-zero.

Inverse models: A necessary next step in ground-water modeling

USGS Publications Warehouse

Poeter, E.P.; Hill, M.C.

1997-01-01

Inverse models using, for example, nonlinear least-squares regression, provide capabilities that help modelers take full advantage of the insight available from ground-water models. However, lack of information about the requirements and benefits of inverse models is an obstacle to their widespread use. This paper presents a simple ground-water flow problem to illustrate the requirements and benefits of the nonlinear least-squares repression method of inverse modeling and discusses how these attributes apply to field problems. The benefits of inverse modeling include: (1) expedited determination of best fit parameter values; (2) quantification of the (a) quality of calibration, (b) data shortcomings and needs, and (c) confidence limits on parameter estimates and predictions; and (3) identification of issues that are easily overlooked during nonautomated calibration.Inverse models using, for example, nonlinear least-squares regression, provide capabilities that help modelers take full advantage of the insight available from ground-water models. However, lack of information about the requirements and benefits of inverse models is an obstacle to their widespread use. This paper presents a simple ground-water flow problem to illustrate the requirements and benefits of the nonlinear least-squares regression method of inverse modeling and discusses how these attributes apply to field problems. The benefits of inverse modeling include: (1) expedited determination of best fit parameter values; (2) quantification of the (a) quality of calibration, (b) data shortcomings and needs, and (c) confidence limits on parameter estimates and predictions; and (3) identification of issues that are easily overlooked during nonautomated calibration.

It's all about gains: Risk preferences in problem gambling.

PubMed

Ring, Patrick; Probst, Catharina C; Neyse, Levent; Wolff, Stephan; Kaernbach, Christian; van Eimeren, Thilo; Camerer, Colin F; Schmidt, Ulrich

2018-06-07

Problem gambling is a serious socioeconomic problem involving high individual and social costs. In this article, we study risk preferences of problem gamblers including their risk attitudes in the gain and loss domains, their weighting of probabilities, and their degree of loss aversion. Our findings indicate that problem gamblers are systematically more risk taking and less sensitive toward changes in probabilities in the gain domain only. Neither their risk attitudes in the loss domain nor their degree of loss aversion are significantly different from the controls. Additional evidence for a similar degree of sensitivity toward negative outcomes is gained from skin conductance data-a psychophysiological marker for emotional arousal-in a threat-of-shock task. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Quantification, Risk, and the Rhetoric of Higher Education Management

ERIC Educational Resources Information Center

Dale, Leigh

2012-01-01

This essay is a contribution to the literature of critique of higher education policy and management. It offers a general discussion of problems arising from the proliferation of mechanisms of audit and quality assurance within the context of concerns about dependence on quantitative measures of values. It argues that uncritical dependence on…

Installation Restoration Program. Phase 2. Confirmation/Quantification. Stage 1

DTIC Science & Technology

1986-03-28

Comftin,,,on Fvvem in.eea),a n dftl by bloe * numri ~A Problem Confirmation Study was conducted at Burlington Air National Guard BasE in Vermont. The...cont’d) 3. Characteristics(cont’d)- Biological Agent ( ) Volatile (4f Toxic (). Reactive ( ) 4. Toxicity: TLVs IDLHs 5. Special Hazards 6. Acute

Visible and near-infrared instruments for detection and quantification of individual sprouted wheat kernels

USDA-ARS?s Scientific Manuscript database

Pre-harvest sprouting of wheat kernels within the grain head presents serious problems as it can greatly affect end use quality. Functional properties of wheat flour made from sprouted wheat result in poor dough and bread-making quality. This research examined the ability of two instruments to estim...

Immunochemical Detection Methods for Gluten in Food Products: Where Do We Go from Here?

PubMed

Slot, I D Bruins; van der Fels-Klerx, H J; Bremer, M G E G; Hamer, R J

2016-11-17

Accurate and reliable quantification methods for gluten in food are necessary to ensure proper product labeling and thus safeguard the gluten sensitive consumer against exposure. Immunochemical detection is the method of choice, as it is sensitive, rapid and relatively easy to use. Although a wide range of detection kits are commercially available, there are still many difficulties in gluten detection that have not yet been overcome. This review gives an overview of the currently commercially available immunochemical detection methods, and discusses the problems that still exist in gluten detection in food. The largest problems are encountered in the extraction of gluten from food matrices, the choice of epitopes targeted by the detection method, and the use of a standardized reference material. By comparing the available techniques with the unmet needs in gluten detection, the possible benefit of a new multiplex immunoassay is investigated. This detection method would allow for the detection and quantification of multiple harmful gluten peptides at once and would, therefore, be a logical advancement in gluten detection in food.

A Generalized Perturbation Theory Solver In Rattlesnake Based On PETSc With Application To TREAT Steady State Uncertainty Quantification

DOE Office of Scientific and Technical Information (OSTI.GOV)

Schunert, Sebastian; Wang, Congjian; Wang, Yaqi

Rattlesnake and MAMMOTH are the designated TREAT analysis tools currently being developed at the Idaho National Laboratory. Concurrent with development of the multi-physics, multi-scale capabilities, sensitivity analysis and uncertainty quantification (SA/UQ) capabilities are required for predicitive modeling of the TREAT reactor. For steady-state SA/UQ, that is essential for setting initial conditions for the transients, generalized perturbation theory (GPT) will be used. This work describes the implementation of a PETSc based solver for the generalized adjoint equations that constitute a inhomogeneous, rank deficient problem. The standard approach is to use an outer iteration strategy with repeated removal of the fundamental modemore » contamination. The described GPT algorithm directly solves the GPT equations without the need of an outer iteration procedure by using Krylov subspaces that are orthogonal to the operator’s nullspace. Three test problems are solved and provide sufficient verification for the Rattlesnake’s GPT capability. We conclude with a preliminary example evaluating the impact of the Boron distribution in the TREAT reactor using perturbation theory.« less

A method of decision analysis quantifying the effects of age and comorbidities on the probability of deriving significant benefit from medical treatments

PubMed Central

Bean, Nigel G.; Ruberu, Ravi P.

2017-01-01

Background The external validity, or generalizability, of trials and guidelines has been considered poor in the context of multiple morbidity. How multiple morbidity might affect the magnitude of benefit of a given treatment, and thereby external validity, has had little study. Objective To provide a method of decision analysis to quantify the effects of age and comorbidity on the probability of deriving a given magnitude of treatment benefit. Design We developed a method to calculate probabilistically the effect of all of a patient’s comorbidities on their underlying utility, or well-being, at a future time point. From this, we derived a distribution of possible magnitudes of treatment benefit at that future time point. We then expressed this distribution as the probability of deriving at least a given magnitude of treatment benefit. To demonstrate the applicability of this method of decision analysis, we applied it to the treatment of hypercholesterolaemia in a geriatric population of 50 individuals. We highlighted the results of four of these individuals. Results This method of analysis provided individualized quantifications of the effect of age and comorbidity on the probability of treatment benefit. The average probability of deriving a benefit, of at least 50% of the magnitude of benefit available to an individual without comorbidity, was only 0.8%. Conclusion The effects of age and comorbidity on the probability of deriving significant treatment benefits can be quantified for any individual. Even without consideration of other factors affecting external validity, these effects may be sufficient to guide decision-making. PMID:29090189

Parameter Analysis of the VPIN (Volume synchronized Probability of Informed Trading) Metric

DOE Office of Scientific and Technical Information (OSTI.GOV)

Song, Jung Heon; Wu, Kesheng; Simon, Horst D.

2014-03-01

VPIN (Volume synchronized Probability of Informed trading) is a leading indicator of liquidity-induced volatility. It is best known for having produced a signal more than hours before the Flash Crash of 2010. On that day, the market saw the biggest one-day point decline in the Dow Jones Industrial Average, which culminated to the market value of $1 trillion disappearing, but only to recover those losses twenty minutes later (Lauricella 2010). The computation of VPIN requires the user to set up a handful of free parameters. The values of these parameters significantly affect the effectiveness of VPIN as measured by themore » false positive rate (FPR). An earlier publication reported that a brute-force search of simple parameter combinations yielded a number of parameter combinations with FPR of 7%. This work is a systematic attempt to find an optimal parameter set using an optimization package, NOMAD (Nonlinear Optimization by Mesh Adaptive Direct Search) by Audet, le digabel, and tribes (2009) and le digabel (2011). We have implemented a number of techniques to reduce the computation time with NOMAD. Tests show that we can reduce the FPR to only 2%. To better understand the parameter choices, we have conducted a series of sensitivity analysis via uncertainty quantification on the parameter spaces using UQTK (Uncertainty Quantification Toolkit). Results have shown dominance of 2 parameters in the computation of FPR. Using the outputs from NOMAD optimization and sensitivity analysis, We recommend A range of values for each of the free parameters that perform well on a large set of futures trading records.« less

StakeMeter: value-based stakeholder identification and quantification framework for value-based software systems.

PubMed

Babar, Muhammad Imran; Ghazali, Masitah; Jawawi, Dayang N A; Bin Zaheer, Kashif

2015-01-01

Value-based requirements engineering plays a vital role in the development of value-based software (VBS). Stakeholders are the key players in the requirements engineering process, and the selection of critical stakeholders for the VBS systems is highly desirable. Based on the stakeholder requirements, the innovative or value-based idea is realized. The quality of the VBS system is associated with the concrete set of valuable requirements, and the valuable requirements can only be obtained if all the relevant valuable stakeholders participate in the requirements elicitation phase. The existing value-based approaches focus on the design of the VBS systems. However, the focus on the valuable stakeholders and requirements is inadequate. The current stakeholder identification and quantification (SIQ) approaches are neither state-of-the-art nor systematic for the VBS systems. The existing approaches are time-consuming, complex and inconsistent which makes the initiation process difficult. Moreover, the main motivation of this research is that the existing SIQ approaches do not provide the low level implementation details for SIQ initiation and stakeholder metrics for quantification. Hence, keeping in view the existing SIQ problems, this research contributes in the form of a new SIQ framework called 'StakeMeter'. The StakeMeter framework is verified and validated through case studies. The proposed framework provides low-level implementation guidelines, attributes, metrics, quantification criteria and application procedure as compared to the other methods. The proposed framework solves the issues of stakeholder quantification or prioritization, higher time consumption, complexity, and process initiation. The framework helps in the selection of highly critical stakeholders for the VBS systems with less judgmental error.

Uncertainty Quantification of CFD Data Generated for a Model Scramjet Isolator Flowfield

NASA Technical Reports Server (NTRS)

Baurle, R. A.; Axdahl, E. L.

2017-01-01

Computational fluid dynamics is now considered to be an indispensable tool for the design and development of scramjet engine components. Unfortunately, the quantification of uncertainties is rarely addressed with anything other than sensitivity studies, so the degree of confidence associated with the numerical results remains exclusively with the subject matter expert that generated them. This practice must be replaced with a formal uncertainty quantification process for computational fluid dynamics to play an expanded role in the system design, development, and flight certification process. Given the limitations of current hypersonic ground test facilities, this expanded role is believed to be a requirement by some in the hypersonics community if scramjet engines are to be given serious consideration as a viable propulsion system. The present effort describes a simple, relatively low cost, nonintrusive approach to uncertainty quantification that includes the basic ingredients required to handle both aleatoric (random) and epistemic (lack of knowledge) sources of uncertainty. The nonintrusive nature of the approach allows the computational fluid dynamicist to perform the uncertainty quantification with the flow solver treated as a "black box". Moreover, a large fraction of the process can be automated, allowing the uncertainty assessment to be readily adapted into the engineering design and development workflow. In the present work, the approach is applied to a model scramjet isolator problem where the desire is to validate turbulence closure models in the presence of uncertainty. In this context, the relevant uncertainty sources are determined and accounted for to allow the analyst to delineate turbulence model-form errors from other sources of uncertainty associated with the simulation of the facility flow.

Aspects of posttraumatic stress disorder in long-term testicular cancer survivors: cross-sectional and longitudinal findings.

PubMed

Dahl, Alv A; Østby-Deglum, Marie; Oldenburg, Jan; Bremnes, Roy; Dahl, Olav; Klepp, Olbjørn; Wist, Erik; Fosså, Sophie D

2016-10-01

The purpose of this research is to study the prevalence of posttraumatic stress disorder (PTSD) and variables associated with PTSD in Norwegian long-term testicular cancer survivors (TCSs) both cross-sectionally and longitudinally. At a mean of 11 years after diagnosis, 1418 TCSs responded to a mailed questionnaire, and at a mean of 19 years after diagnosis, 1046 of them responded again to a modified questionnaire. Posttraumatic symptoms related to testicular cancer were self-rated with the Impact of Event Scale (IES) at the 11-year study only. An IES total score ≥35 defined Full PTSD, and a score 26-34 identified Partial PTSD, and the combination of Full and Partial PTSD defined Probable PTSD. At the 11-year study, 4.5 % had Full PTSD, 6.4 % had Partial PTSD, and 10.9 % Probable had PTSD. At both studies, socio-demographic variables, somatic health, anxiety/depression, chronic fatigue, and neurotoxic adverse effects were significantly associated with Probable PTSD in bivariate analyses. Probable anxiety disorder, poor self-rated health, and neurotoxicity remained significant with Probable PTSD in multivariate analyses at the 11-year study. In bivariate analyses, probable PTSD at that time significantly predicted socio-demographic variables, somatic health, anxiety/depression, chronic fatigue, and neurotoxicity among participants of the 19-year study, but only probable anxiety disorder remained significant in multivariable analysis. In spite of excellent prognosis, 10.9 % of long-term testicular cancer survivors had Probable PTSD at a mean of 11 years after diagnosis. Probable PTSD was significantly associated with a broad range of problems both at that time and was predictive of considerable problems at a mean of 19 year postdiagnosis. Among long-term testicular cancer survivors, 10.9 % have Probable PTSD with many associated problems, and therefore health personnel should explore stress symptoms at follow-up since efficient treatments are available.

Seismic waveform inversion using neural networks

NASA Astrophysics Data System (ADS)

De Wit, R. W.; Trampert, J.

2012-12-01

Full waveform tomography aims to extract all available information on Earth structure and seismic sources from seismograms. The strongly non-linear nature of this inverse problem is often addressed through simplifying assumptions for the physical theory or data selection, thus potentially neglecting valuable information. Furthermore, the assessment of the quality of the inferred model is often lacking. This calls for the development of methods that fully appreciate the non-linear nature of the inverse problem, whilst providing a quantification of the uncertainties in the final model. We propose to invert seismic waveforms in a fully non-linear way by using artificial neural networks. Neural networks can be viewed as powerful and flexible non-linear filters. They are very common in speech, handwriting and pattern recognition. Mixture Density Networks (MDN) allow us to obtain marginal posterior probability density functions (pdfs) of all model parameters, conditioned on the data. An MDN can approximate an arbitrary conditional pdf as a linear combination of Gaussian kernels. Seismograms serve as input, Earth structure parameters are the so-called targets and network training aims to learn the relationship between input and targets. The network is trained on a large synthetic data set, which we construct by drawing many random Earth models from a prior model pdf and solving the forward problem for each of these models, thus generating synthetic seismograms. As a first step, we aim to construct a 1D Earth model. Training sets are constructed using the Mineos package, which computes synthetic seismograms in a spherically symmetric non-rotating Earth by summing normal modes. We train a network on the body waveforms present in these seismograms. Once the network has been trained, it can be presented with new unseen input data, in our case the body waves in real seismograms. We thus obtain the posterior pdf which represents our final state of knowledge given the information in the training set and the real data.

Assessment and quantification of patient set-up errors in nasopharyngeal cancer patients and their biological and dosimetric impact in terms of generalized equivalent uniform dose (gEUD), tumour control probability (TCP) and normal tissue complication probability (NTCP).

PubMed

Boughalia, A; Marcie, S; Fellah, M; Chami, S; Mekki, F

2015-06-01

The aim of this study is to assess and quantify patients' set-up errors using an electronic portal imaging device and to evaluate their dosimetric and biological impact in terms of generalized equivalent uniform dose (gEUD) on predictive models, such as the tumour control probability (TCP) and the normal tissue complication probability (NTCP). 20 patients treated for nasopharyngeal cancer were enrolled in the radiotherapy-oncology department of HCA. Systematic and random errors were quantified. The dosimetric and biological impact of these set-up errors on the target volume and the organ at risk (OARs) coverage were assessed using calculation of dose-volume histogram, gEUD, TCP and NTCP. For this purpose, an in-house software was developed and used. The standard deviations (1SDs) of the systematic set-up and random set-up errors were calculated for the lateral and subclavicular fields and gave the following results: ∑ = 0.63 ± (0.42) mm and σ = 3.75 ± (0.79) mm, respectively. Thus a planning organ at risk volume (PRV) margin of 3 mm was defined around the OARs, and a 5-mm margin used around the clinical target volume. The gEUD, TCP and NTCP calculations obtained with and without set-up errors have shown increased values for tumour, where ΔgEUD (tumour) = 1.94% Gy (p = 0.00721) and ΔTCP = 2.03%. The toxicity of OARs was quantified using gEUD and NTCP. The values of ΔgEUD (OARs) vary from 0.78% to 5.95% in the case of the brainstem and the optic chiasm, respectively. The corresponding ΔNTCP varies from 0.15% to 0.53%, respectively. The quantification of set-up errors has a dosimetric and biological impact on the tumour and on the OARs. The developed in-house software using the concept of gEUD, TCP and NTCP biological models has been successfully used in this study. It can be used also to optimize the treatment plan established for our patients. The gEUD, TCP and NTCP may be more suitable tools to assess the treatment plans before treating the patients.

Association between sleep problems and symptoms of attention-deficit/hyperactivity disorder in young adults.

PubMed

Gau, Susan S F; Kessler, Ronald C; Tseng, Wan-Ling; Wu, Yu-Yu; Chiu, Yen-Nan; Yeh, Chin-Bin; Hwu, Hai-Gwo

2007-02-01

To examine the association between sleep-related problems and symptoms of attention-deficit/hyperactivity disorder (ADHD) in a community sample of young adults in Taiwan. A college-based cross-sectional survey. Two thousand two hundred eighty-four first-year college students (aged 18-20) in a university in Taiwan. Each student completed a questionnaire regarding sleep schedule (self-estimated total sleep duration and sleep need), sleep problems (dyssomnia, parasomnia, and snoring), and the Chinese version of the Adult ADHD Self-Report Scale. Subjects were grouped separately for the inattention and hyperactivity subscales into highly likely ADHD (2.3%, 0.7%), probable ADHD (21.3%, 5.7%), and probably non-ADHD (76.4%, 93.6%) groups according to the scoring scheme of the subscales of the Adult ADHD Self-Report Scale. Results showed that, for both inattention and hyperactivity symptoms, the highly likely ADHD and probable ADHD groups were more likely than the non-ADHD group to have a variety of current and lifetime sleep problems. No significant difference in sleep problems was found between the highly likely ADHD and probable ADHD groups. Inattention, but not hyperactivity, was associated with greater sleep need and greater difference between sleep need and self-estimated nocturnal sleep duration. Hyperactivity, but not inattention, was associated with decreased nocturnal sleep duration. Consistent with prior findings from children and adolescents, ADHD symptoms in young adults are related to sleep problems. Further studies on adults with ADHD should help to refine our understanding of the causal basis for any implications of this association.

Prediction of shock initiation thresholds and ignition probability of polymer-bonded explosives using mesoscale simulations

NASA Astrophysics Data System (ADS)

Kim, Seokpum; Wei, Yaochi; Horie, Yasuyuki; Zhou, Min

2018-05-01

The design of new materials requires establishment of macroscopic measures of material performance as functions of microstructure. Traditionally, this process has been an empirical endeavor. An approach to computationally predict the probabilistic ignition thresholds of polymer-bonded explosives (PBXs) using mesoscale simulations is developed. The simulations explicitly account for microstructure, constituent properties, and interfacial responses and capture processes responsible for the development of hotspots and damage. The specific mechanisms tracked include viscoelasticity, viscoplasticity, fracture, post-fracture contact, frictional heating, and heat conduction. The probabilistic analysis uses sets of statistically similar microstructure samples to directly mimic relevant experiments for quantification of statistical variations of material behavior due to inherent material heterogeneities. The particular thresholds and ignition probabilities predicted are expressed in James type and Walker-Wasley type relations, leading to the establishment of explicit analytical expressions for the ignition probability as function of loading. Specifically, the ignition thresholds corresponding to any given level of ignition probability and ignition probability maps are predicted for PBX 9404 for the loading regime of Up = 200-1200 m/s where Up is the particle speed. The predicted results are in good agreement with available experimental measurements. A parametric study also shows that binder properties can significantly affect the macroscopic ignition behavior of PBXs. The capability to computationally predict the macroscopic engineering material response relations out of material microstructures and basic constituent and interfacial properties lends itself to the design of new materials as well as the analysis of existing materials.

A linear programming model for protein inference problem in shotgun proteomics.

PubMed

Huang, Ting; He, Zengyou

2012-11-15

Assembling peptides identified from tandem mass spectra into a list of proteins, referred to as protein inference, is an important issue in shotgun proteomics. The objective of protein inference is to find a subset of proteins that are truly present in the sample. Although many methods have been proposed for protein inference, several issues such as peptide degeneracy still remain unsolved. In this article, we present a linear programming model for protein inference. In this model, we use a transformation of the joint probability that each peptide/protein pair is present in the sample as the variable. Then, both the peptide probability and protein probability can be expressed as a formula in terms of the linear combination of these variables. Based on this simple fact, the protein inference problem is formulated as an optimization problem: minimize the number of proteins with non-zero probabilities under the constraint that the difference between the calculated peptide probability and the peptide probability generated from peptide identification algorithms should be less than some threshold. This model addresses the peptide degeneracy issue by forcing some joint probability variables involving degenerate peptides to be zero in a rigorous manner. The corresponding inference algorithm is named as ProteinLP. We test the performance of ProteinLP on six datasets. Experimental results show that our method is competitive with the state-of-the-art protein inference algorithms. The source code of our algorithm is available at: https://sourceforge.net/projects/prolp/. zyhe@dlut.edu.cn. Supplementary data are available at Bioinformatics Online.

Kolmogorov complexity, statistical regularization of inverse problems, and Birkhoff's formalization of beauty

NASA Astrophysics Data System (ADS)

Kreinovich, Vladik; Longpre, Luc; Koshelev, Misha

1998-09-01

Most practical applications of statistical methods are based on the implicit assumption that if an event has a very small probability, then it cannot occur. For example, the probability that a kettle placed on a cold stove would start boiling by itself is not 0, it is positive, but it is so small, that physicists conclude that such an event is simply impossible. This assumption is difficult to formalize in traditional probability theory, because this theory only describes measures on sets and does not allow us to divide functions into 'random' and non-random ones. This distinction was made possible by the idea of algorithmic randomness, introduce by Kolmogorov and his student Martin- Loef in the 1960s. We show that this idea can also be used for inverse problems. In particular, we prove that for every probability measure, the corresponding set of random functions is compact, and, therefore, the corresponding restricted inverse problem is well-defined. The resulting techniques turns out to be interestingly related with the qualitative esthetic measure introduced by G. Birkhoff as order/complexity.

Individual heterogeneity and identifiability in capture-recapture models

USGS Publications Warehouse

Link, W.A.

2004-01-01

Individual heterogeneity in detection probabilities is a far more serious problem for capture-recapture modeling than has previously been recognized. In this note, I illustrate that population size is not an identifiable parameter under the general closed population mark-recapture model Mh. The problem of identifiability is obvious if the population includes individuals with pi = 0, but persists even when it is assumed that individual detection probabilities are bounded away from zero. Identifiability may be attained within parametric families of distributions for pi, but not among parametric families of distributions. Consequently, in the presence of individual heterogeneity in detection probability, capture-recapture analysis is strongly model dependent.

Sedimentation in Hot Creek in vicinity of Hot Creek Fish Hatchery, Mono County, California

USGS Publications Warehouse

Burkham, D.E.

1978-01-01

An accumulation of fine-grained sediment in Hot Creek downstream from Hot Creek Fish Hatchery, Mono County, Calif., created concern that the site may be deteriorating as a habitat for trout. The accumulation is a phenomenon that probably occurs naturally in the problem reach. Fluctuation in the weather probably is the basic cause of the deposition of fine-grained sediment that has occurred since about 1970. Man 's activities and the Hot Creek Fish Hatchery may have contributed to the problem; the significance of these factors, however, probably was magnified because of drought conditions in 1975-77. (Woodard-USGS)

Quantification of extracellular matrix expansion by CMR in infiltrative heart disease.

PubMed

Mongeon, François-Pierre; Jerosch-Herold, Michael; Coelho-Filho, Otávio Rizzi; Blankstein, Ron; Falk, Rodney H; Kwong, Raymond Y

2012-09-01

The aim of this study was to perform direct quantification of myocardial extracellular volume fraction (ECF) with T1-weighted cardiac magnetic resonance (CMR) imaging in patients suspected to have infiltrative heart disease. Infiltrative heart disease refers to accumulation of abnormal substances within the myocardium. Qualitative assessment of late gadolinium enhancement (LGE) remains the most commonly used method for CMR evaluation of patients suspected with myocardial infiltration. This technique is widely available and can be performed in a reproducible and standardized manner. However, the degree of extracellular matrix expansion due to myocardial infiltration in the intercellular space has, to date, not been amenable to noninvasive quantification with LGE. We performed 3-T CMR in 38 patients (mean age 68 ± 15 years) who were referred for assessment of infiltrative heart disease and also in 9 healthy volunteers as control subjects. The T1 quantification by Look-Locker gradient-echo before and after contrast determined segmental myocardial partition coefficients. The ECF was obtained by referencing the tissue partition coefficient for gadolinium to the plasma volume fraction in blood, derived from serum hematocrit. Cine CMR and LGE imaging in matching locations were also performed. Seventeen patients (45%) had cardiac amyloidosis (CA) (biopsy-confirmed or clinically highly probable), 20 (53%) had a non-amyloid cardiomyopathy, and 1 had lysosomal storage disease. Median global ECF was substantially higher in CA patients (0.49) compared with non-amyloid cardiomyopathy patients (0.33, p < 0.0001) and volunteers (0.24, p = 0.0001). The ECF strongly correlated with visually assessed segmental LGE (r = 0.80, p < 0.0001) and LV mass index (r = 0.69, p < 0.0001), reflecting severity of myocardial infiltration. In patients with CA, ECF was highest in segments with LGE, although it remained elevated in segments without qualitative LGE. The CMR ECF quantification identified substantial expansion of the interstitial space in patients with CA compared with volunteers. Further studies using this technique for diagnosis and assessment of the severity of myocardial infiltration are warranted. Copyright © 2012 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.

Scalable Methods for Uncertainty Quantification, Data Assimilation and Target Accuracy Assessment for Multi-Physics Advanced Simulation of Light Water Reactors

NASA Astrophysics Data System (ADS)

Khuwaileh, Bassam

High fidelity simulation of nuclear reactors entails large scale applications characterized with high dimensionality and tremendous complexity where various physics models are integrated in the form of coupled models (e.g. neutronic with thermal-hydraulic feedback). Each of the coupled modules represents a high fidelity formulation of the first principles governing the physics of interest. Therefore, new developments in high fidelity multi-physics simulation and the corresponding sensitivity/uncertainty quantification analysis are paramount to the development and competitiveness of reactors achieved through enhanced understanding of the design and safety margins. Accordingly, this dissertation introduces efficient and scalable algorithms for performing efficient Uncertainty Quantification (UQ), Data Assimilation (DA) and Target Accuracy Assessment (TAA) for large scale, multi-physics reactor design and safety problems. This dissertation builds upon previous efforts for adaptive core simulation and reduced order modeling algorithms and extends these efforts towards coupled multi-physics models with feedback. The core idea is to recast the reactor physics analysis in terms of reduced order models. This can be achieved via identifying the important/influential degrees of freedom (DoF) via the subspace analysis, such that the required analysis can be recast by considering the important DoF only. In this dissertation, efficient algorithms for lower dimensional subspace construction have been developed for single physics and multi-physics applications with feedback. Then the reduced subspace is used to solve realistic, large scale forward (UQ) and inverse problems (DA and TAA). Once the elite set of DoF is determined, the uncertainty/sensitivity/target accuracy assessment and data assimilation analysis can be performed accurately and efficiently for large scale, high dimensional multi-physics nuclear engineering applications. Hence, in this work a Karhunen-Loeve (KL) based algorithm previously developed to quantify the uncertainty for single physics models is extended for large scale multi-physics coupled problems with feedback effect. Moreover, a non-linear surrogate based UQ approach is developed, used and compared to performance of the KL approach and brute force Monte Carlo (MC) approach. On the other hand, an efficient Data Assimilation (DA) algorithm is developed to assess information about model's parameters: nuclear data cross-sections and thermal-hydraulics parameters. Two improvements are introduced in order to perform DA on the high dimensional problems. First, a goal-oriented surrogate model can be used to replace the original models in the depletion sequence (MPACT -- COBRA-TF - ORIGEN). Second, approximating the complex and high dimensional solution space with a lower dimensional subspace makes the sampling process necessary for DA possible for high dimensional problems. Moreover, safety analysis and design optimization depend on the accurate prediction of various reactor attributes. Predictions can be enhanced by reducing the uncertainty associated with the attributes of interest. Accordingly, an inverse problem can be defined and solved to assess the contributions from sources of uncertainty; and experimental effort can be subsequently directed to further improve the uncertainty associated with these sources. In this dissertation a subspace-based gradient-free and nonlinear algorithm for inverse uncertainty quantification namely the Target Accuracy Assessment (TAA) has been developed and tested. The ideas proposed in this dissertation were first validated using lattice physics applications simulated using SCALE6.1 package (Pressurized Water Reactor (PWR) and Boiling Water Reactor (BWR) lattice models). Ultimately, the algorithms proposed her were applied to perform UQ and DA for assembly level (CASL progression problem number 6) and core wide problems representing Watts Bar Nuclear 1 (WBN1) for cycle 1 of depletion (CASL Progression Problem Number 9) modeled via simulated using VERA-CS which consists of several multi-physics coupled models. The analysis and algorithms developed in this dissertation were encoded and implemented in a newly developed tool kit algorithms for Reduced Order Modeling based Uncertainty/Sensitivity Estimator (ROMUSE).

Reconciliation of Decision-Making Heuristics Based on Decision Trees Topologies and Incomplete Fuzzy Probabilities Sets

PubMed Central

Doubravsky, Karel; Dohnal, Mirko

2015-01-01

Complex decision making tasks of different natures, e.g. economics, safety engineering, ecology and biology, are based on vague, sparse, partially inconsistent and subjective knowledge. Moreover, decision making economists / engineers are usually not willing to invest too much time into study of complex formal theories. They require such decisions which can be (re)checked by human like common sense reasoning. One important problem related to realistic decision making tasks are incomplete data sets required by the chosen decision making algorithm. This paper presents a relatively simple algorithm how some missing III (input information items) can be generated using mainly decision tree topologies and integrated into incomplete data sets. The algorithm is based on an easy to understand heuristics, e.g. a longer decision tree sub-path is less probable. This heuristic can solve decision problems under total ignorance, i.e. the decision tree topology is the only information available. But in a practice, isolated information items e.g. some vaguely known probabilities (e.g. fuzzy probabilities) are usually available. It means that a realistic problem is analysed under partial ignorance. The proposed algorithm reconciles topology related heuristics and additional fuzzy sets using fuzzy linear programming. The case study, represented by a tree with six lotteries and one fuzzy probability, is presented in details. PMID:26158662

Reconciliation of Decision-Making Heuristics Based on Decision Trees Topologies and Incomplete Fuzzy Probabilities Sets.

PubMed

Doubravsky, Karel; Dohnal, Mirko

2015-01-01

Complex decision making tasks of different natures, e.g. economics, safety engineering, ecology and biology, are based on vague, sparse, partially inconsistent and subjective knowledge. Moreover, decision making economists / engineers are usually not willing to invest too much time into study of complex formal theories. They require such decisions which can be (re)checked by human like common sense reasoning. One important problem related to realistic decision making tasks are incomplete data sets required by the chosen decision making algorithm. This paper presents a relatively simple algorithm how some missing III (input information items) can be generated using mainly decision tree topologies and integrated into incomplete data sets. The algorithm is based on an easy to understand heuristics, e.g. a longer decision tree sub-path is less probable. This heuristic can solve decision problems under total ignorance, i.e. the decision tree topology is the only information available. But in a practice, isolated information items e.g. some vaguely known probabilities (e.g. fuzzy probabilities) are usually available. It means that a realistic problem is analysed under partial ignorance. The proposed algorithm reconciles topology related heuristics and additional fuzzy sets using fuzzy linear programming. The case study, represented by a tree with six lotteries and one fuzzy probability, is presented in details.

A short note on probability in clinical medicine.

PubMed

Upshur, Ross E G

2013-06-01

Probability claims are ubiquitous in clinical medicine, yet exactly how clinical events relate to interpretations of probability has been not been well explored. This brief essay examines the major interpretations of probability and how these interpretations may account for the probabilistic nature of clinical events. It is argued that there are significant problems with the unquestioned application of interpretation of probability to clinical events. The essay concludes by suggesting other avenues to understand uncertainty in clinical medicine. © 2013 John Wiley & Sons Ltd.

An Improved Hierarchical Genetic Algorithm for Sheet Cutting Scheduling with Process Constraints

PubMed Central

Rao, Yunqing; Qi, Dezhong; Li, Jinling

2013-01-01

For the first time, an improved hierarchical genetic algorithm for sheet cutting problem which involves n cutting patterns for m non-identical parallel machines with process constraints has been proposed in the integrated cutting stock model. The objective of the cutting scheduling problem is minimizing the weighted completed time. A mathematical model for this problem is presented, an improved hierarchical genetic algorithm (ant colony—hierarchical genetic algorithm) is developed for better solution, and a hierarchical coding method is used based on the characteristics of the problem. Furthermore, to speed up convergence rates and resolve local convergence issues, a kind of adaptive crossover probability and mutation probability is used in this algorithm. The computational result and comparison prove that the presented approach is quite effective for the considered problem. PMID:24489491

An improved hierarchical genetic algorithm for sheet cutting scheduling with process constraints.

PubMed

Rao, Yunqing; Qi, Dezhong; Li, Jinling

2013-01-01

For the first time, an improved hierarchical genetic algorithm for sheet cutting problem which involves n cutting patterns for m non-identical parallel machines with process constraints has been proposed in the integrated cutting stock model. The objective of the cutting scheduling problem is minimizing the weighted completed time. A mathematical model for this problem is presented, an improved hierarchical genetic algorithm (ant colony--hierarchical genetic algorithm) is developed for better solution, and a hierarchical coding method is used based on the characteristics of the problem. Furthermore, to speed up convergence rates and resolve local convergence issues, a kind of adaptive crossover probability and mutation probability is used in this algorithm. The computational result and comparison prove that the presented approach is quite effective for the considered problem.

Quantification of mRNA expression by competitive PCR using non-homologous competitors containing a shifted restriction site

PubMed Central

Watzinger, Franz; Hörth, Elfriede; Lion, Thomas

2001-01-01

Despite the recent introduction of real-time PCR methods, competitive PCR techniques continue to play an important role in nucleic acid quantification because of the significantly lower cost of equipment and consumables. Here we describe a shifted restriction-site competitive PCR (SRS-cPCR) assay based on a modified type of competitor. The competitor fragments are designed to contain a recognition site for a restriction endonuclease that is also present in the target sequence to be quantified, but in a different position. Upon completion of the PCR, the amplicons are digested in the same tube with a single restriction enzyme, without the need to purify PCR products. The generated competitor- and target-specific restriction fragments display different sizes, and can be readily separated by electrophoresis and quantified by image analysis. Suboptimal digestion affects competitor- and target-derived amplicons to the same extent, thus eliminating the problem of incorrect quantification as a result of incomplete digestion of PCR products. We have established optimized conditions for a panel of 20 common restriction endonucleases permitting efficient digestion in PCR buffer. It is possible, therefore, to find a suitable restriction site for competitive PCR in virtually any sequence of interest. The assay presented is inexpensive, widely applicable, and permits reliable and accurate quantification of nucleic acid targets. PMID:11376164

Inverse regression-based uncertainty quantification algorithms for high-dimensional models: Theory and practice

NASA Astrophysics Data System (ADS)

Li, Weixuan; Lin, Guang; Li, Bing

2016-09-01

Many uncertainty quantification (UQ) approaches suffer from the curse of dimensionality, that is, their computational costs become intractable for problems involving a large number of uncertainty parameters. In these situations, the classic Monte Carlo often remains the preferred method of choice because its convergence rate O (n - 1 / 2), where n is the required number of model simulations, does not depend on the dimension of the problem. However, many high-dimensional UQ problems are intrinsically low-dimensional, because the variation of the quantity of interest (QoI) is often caused by only a few latent parameters varying within a low-dimensional subspace, known as the sufficient dimension reduction (SDR) subspace in the statistics literature. Motivated by this observation, we propose two inverse regression-based UQ algorithms (IRUQ) for high-dimensional problems. Both algorithms use inverse regression to convert the original high-dimensional problem to a low-dimensional one, which is then efficiently solved by building a response surface for the reduced model, for example via the polynomial chaos expansion. The first algorithm, which is for the situations where an exact SDR subspace exists, is proved to converge at rate O (n-1), hence much faster than MC. The second algorithm, which doesn't require an exact SDR, employs the reduced model as a control variate to reduce the error of the MC estimate. The accuracy gain could still be significant, depending on how well the reduced model approximates the original high-dimensional one. IRUQ also provides several additional practical advantages: it is non-intrusive; it does not require computing the high-dimensional gradient of the QoI; and it reports an error bar so the user knows how reliable the result is.

Intelligent systems for the autonomous exploration of Titan and Enceladus

NASA Astrophysics Data System (ADS)

Furfaro, Roberto; Lunine, Jonathan I.; Kargel, Jeffrey S.; Fink, Wolfgang

2008-04-01

Future planetary exploration of the outer satellites of the Solar System will require higher levels of onboard automation, including autonomous determination of sites where the probability of significant scientific findings is highest. Generally, the level of needed automation is heavily influenced by the distance between Earth and the robotic explorer(s) (e.g. spacecraft(s), rover(s), and balloon(s)). Therefore, planning missions to the outer satellites mandates the analysis, design and integration within the mission architecture of semi- and/or completely autonomous intelligence systems. Such systems should (1) include software packages that enable fully automated and comprehensive identification, characterization, and quantification of feature information within an operational region with subsequent target prioritization and selection for close-up reexamination; and (2) integrate existing information with acquired, "in transit" spatial and temporal sensor data to automatically perform intelligent planetary reconnaissance, which includes identification of sites with the highest potential to yield significant geological and astrobiological information. In this paper we review and compare some of the available Artificial Intelligence (AI) schemes and their adaptation to the problem of designing expert systems for onboard-based, autonomous science to be performed in the course of outer satellites exploration. More specifically, the fuzzy-logic framework proposed is analyzed in some details to show the effectiveness of such a scheme when applied to the problem of designing expert systems capable of identifying and further exploring regions on Titan and/or Enceladus that have the highest potential to yield evidence for past or present life. Based on available information (e.g., Cassini data), the current knowledge and understanding of Titan and Enceladus environments is evaluated to define a path for the design of a fuzzy-based system capable of reasoning over collected data and capable of providing the inference required to autonomously optimize future outer satellites explorations.

Using multidimensional scaling to quantify similarity in visual search and beyond

PubMed Central

Godwin, Hayward J.; Fitzsimmons, Gemma; Robbins, Arryn; Menneer, Tamaryn; Goldinger, Stephen D.

2017-01-01

Visual search is one of the most widely studied topics in vision science, both as an independent topic of interest, and as a tool for studying attention and visual cognition. A wide literature exists that seeks to understand how people find things under varying conditions of difficulty and complexity, and in situations ranging from the mundane (e.g., looking for one’s keys) to those with significant societal importance (e.g., baggage or medical screening). A primary determinant of the ease and probability of success during search are the similarity relationships that exist in the search environment, such as the similarity between the background and the target, or the likeness of the non-targets to one another. A sense of similarity is often intuitive, but it is seldom quantified directly. This presents a problem in that similarity relationships are imprecisely specified, limiting the capacity of the researcher to examine adequately their influence. In this article, we present a novel approach to overcoming this problem that combines multidimensional scaling (MDS) analyses with behavioral and eye-tracking measurements. We propose a method whereby MDS can be repurposed to successfully quantify the similarity of experimental stimuli, thereby opening up theoretical questions in visual search and attention that cannot currently be addressed. These quantifications, in conjunction with behavioral and oculomotor measures, allow for critical observations about how similarity affects performance, information selection, and information processing. We provide a demonstration and tutorial of the approach, identify documented examples of its use, discuss how complementary computer vision methods could also be adopted, and close with a discussion of potential avenues for future application of this technique. PMID:26494381

Link importance incorporated failure probability measuring solution for multicast light-trees in elastic optical networks

NASA Astrophysics Data System (ADS)

Li, Xin; Zhang, Lu; Tang, Ying; Huang, Shanguo

2018-03-01

The light-tree-based optical multicasting (LT-OM) scheme provides a spectrum- and energy-efficient method to accommodate emerging multicast services. Some studies focus on the survivability technologies for LTs against a fixed number of link failures, such as single-link failure. However, a few studies involve failure probability constraints when building LTs. It is worth noting that each link of an LT plays different important roles under failure scenarios. When calculating the failure probability of an LT, the importance of its every link should be considered. We design a link importance incorporated failure probability measuring solution (LIFPMS) for multicast LTs under independent failure model and shared risk link group failure model. Based on the LIFPMS, we put forward the minimum failure probability (MFP) problem for the LT-OM scheme. Heuristic approaches are developed to address the MFP problem in elastic optical networks. Numerical results show that the LIFPMS provides an accurate metric for calculating the failure probability of multicast LTs and enhances the reliability of the LT-OM scheme while accommodating multicast services.

Uncertainty, ensembles and air quality dispersion modeling: applications and challenges

NASA Astrophysics Data System (ADS)

Dabberdt, Walter F.; Miller, Erik

The past two decades have seen significant advances in mesoscale meteorological modeling research and applications, such as the development of sophisticated and now widely used advanced mesoscale prognostic models, large eddy simulation models, four-dimensional data assimilation, adjoint models, adaptive and targeted observational strategies, and ensemble and probabilistic forecasts. Some of these advances are now being applied to urban air quality modeling and applications. Looking forward, it is anticipated that the high-priority air quality issues for the near-to-intermediate future will likely include: (1) routine operational forecasting of adverse air quality episodes; (2) real-time high-level support to emergency response activities; and (3) quantification of model uncertainty. Special attention is focused here on the quantification of model uncertainty through the use of ensemble simulations. Application to emergency-response dispersion modeling is illustrated using an actual event that involved the accidental release of the toxic chemical oleum. Both surface footprints of mass concentration and the associated probability distributions at individual receptors are seen to provide valuable quantitative indicators of the range of expected concentrations and their associated uncertainty.

A quantitative PCR approach for quantification of functional genes involved in the degradation of polycyclic aromatic hydrocarbons in contaminated soils.

PubMed

Shahsavari, Esmaeil; Aburto-Medina, Arturo; Taha, Mohamed; Ball, Andrew S

2016-01-01

Polycyclic aromatic hydrocarbons (PAHs) are major pollutants globally and due to their carcinogenic and mutagenic properties their clean-up is paramount. Bioremediation or using PAH degrading microorganisms (mainly bacteria) to degrade the pollutants represents cheap, effective methods. These PAH degraders harbor functional genes which help microorganisms use PAHs as source of food and energy. Most probable number (MPN) and plate counting methods are widely used for counting PAHs degraders; however, as culture based methods only count a small fraction (<1%) of microorganisms capable of carrying out PAH degradation, the use of culture-independent methodologies is desirable.•This protocol presents a robust, rapid and sensitive qPCR method for the quantification of the functional genes involved in the degradation of PAHs in soil samples.•This protocol enables us to screen a vast number of PAH contaminated soil samples in few hours.•This protocol provides valuable information about the natural attenuation potential of contaminated soil and can be used to monitor the bioremediation process.

QUANTIFICATION AND INTERPRETATION OF TOTAL PETROLEUM HYDROCARBONS IN SEDIMENT SAMPLES BY A GC/MS METHOD AND COMPARISON WITH EPA 418.1 AND A RAPID FIELD METHOD

EPA Science Inventory

ABSTRACT: Total Petroleum hydrocarbons (TPH) as a lumped parameter can be easily and rapidly measured or monitored. Despite interpretational problems, it has become an accepted regulatory benchmark used widely to evaluate the extent of petroleum product contamination. Three cu...

The effects of cracks on the quantification of the cancellous bone fabric tensor in fossil and archaeological specimens: a simulation study.

PubMed

Bishop, Peter J; Clemente, Christofer J; Hocknull, Scott A; Barrett, Rod S; Lloyd, David G

2017-03-01

Cancellous bone is very sensitive to its prevailing mechanical environment, and study of its architecture has previously aided interpretations of locomotor biomechanics in extinct animals or archaeological populations. However, quantification of architectural features may be compromised by poor preservation in fossil and archaeological specimens, such as post mortem cracking or fracturing. In this study, the effects of post mortem cracks on the quantification of cancellous bone fabric were investigated through the simulation of cracks in otherwise undamaged modern bone samples. The effect on both scalar (degree of fabric anisotropy, fabric elongation index) and vector (principal fabric directions) variables was assessed through comparing the results of architectural analyses of cracked vs. non-cracked samples. Error was found to decrease as the relative size of the crack decreased, and as the orientation of the crack approached the orientation of the primary fabric direction. However, even in the best-case scenario simulated, error remained substantial, with at least 18% of simulations showing a > 10% error when scalar variables were considered, and at least 6.7% of simulations showing a > 10° error when vector variables were considered. As a 10% (scalar) or 10° (vector) difference is probably too large for reliable interpretation of a fossil or archaeological specimen, these results suggest that cracks should be avoided if possible when analysing cancellous bone architecture in such specimens. © 2016 Anatomical Society.

Development of a rapid method to quantify Salmonella Typhimurium using a combination of MPN with qPCR and a shortened time incubation.

PubMed

Kim, Sun Ae; Park, Si Hong; Lee, Sang In; Ricke, Steven C

2017-08-01

A novel method was developed for the specific quantification of S. Typhimurium using a most-probable-number (MPN) combined with qPCR and a shortened incubation time (MPN-qPCR-SIT). For S. Typhimurium enumeration, dilutions of samples were transferred into three wells on a microtiter plate and the plate was incubated for 4 h. The S. Typhimurium presence in the wells was identified using a qPCR and populations were determined based on an MPN calculation. The R 2 between the MPN-qPCR-SIT and conventional MPN exhibited a high level of correlation (0.9335-0.9752), suggesting that the MPN-qPCR-SIT offers a reliable alternative method for S. Typhimurium quantification. Although plating and qPCR were limited in their ability to detect low levels of S. Typhimurium (e.g. 0.18 log MPN/ml), these levels could be successfully detected with the MPN-qPCR-SIT. Chicken breast samples inoculated with S. Typhimurium were incubated at 0, 4, and 24 h and incubated samples were subjected to microbiome analysis. Levels of Salmonella and Enterobacteriaceae increased significantly with incubation time. The obvious benefits of the MPN-qPCR-SIT are: 1) a further confirmation step is not required, 2) the detection limit is as low as conventional MPN, but 3) is more rapid, requiring approximately 7 h to simultaneously complete quantification. Copyright © 2017 Elsevier Ltd. All rights reserved.

Uncertainty Quantification of Evapotranspiration and Infiltration from Modeling and Historic Time Series at the Savannah River F-Area

NASA Astrophysics Data System (ADS)

Faybishenko, B.; Flach, G. P.

2012-12-01

The objectives of this presentation are: (a) to illustrate the application of Monte Carlo and fuzzy-probabilistic approaches for uncertainty quantification (UQ) in predictions of potential evapotranspiration (PET), actual evapotranspiration (ET), and infiltration (I), using uncertain hydrological or meteorological time series data, and (b) to compare the results of these calculations with those from field measurements at the U.S. Department of Energy Savannah River Site (SRS), near Aiken, South Carolina, USA. The UQ calculations include the evaluation of aleatory (parameter uncertainty) and epistemic (model) uncertainties. The effect of aleatory uncertainty is expressed by assigning the probability distributions of input parameters, using historical monthly averaged data from the meteorological station at the SRS. The combined effect of aleatory and epistemic uncertainties on the UQ of PET, ET, and Iis then expressed by aggregating the results of calculations from multiple models using a p-box and fuzzy numbers. The uncertainty in PETis calculated using the Bair-Robertson, Blaney-Criddle, Caprio, Hargreaves-Samani, Hamon, Jensen-Haise, Linacre, Makkink, Priestly-Taylor, Penman, Penman-Monteith, Thornthwaite, and Turc models. Then, ET is calculated from the modified Budyko model, followed by calculations of I from the water balance equation. We show that probabilistic and fuzzy-probabilistic calculations using multiple models generate the PET, ET, and Idistributions, which are well within the range of field measurements. We also show that a selection of a subset of models can be used to constrain the uncertainty quantification of PET, ET, and I.

Systematic influences of gamma-ray spectrometry data near the decision threshold for radioactivity measurements in the environment.

PubMed

Zorko, Benjamin; Korun, Matjaž; Mora Canadas, Juan Carlos; Nicoulaud-Gouin, Valerie; Chyly, Pavol; Blixt Buhr, Anna Maria; Lager, Charlotte; Aquilonius, Karin; Krajewski, Pawel

2016-07-01

Several methods for reporting outcomes of gamma-ray spectrometric measurements of environmental samples for dose calculations are presented and discussed. The measurement outcomes can be reported as primary measurement results, primary measurement results modified according to the quantification limit, best estimates obtained by the Bayesian posterior (ISO 11929), best estimates obtained by the probability density distribution resembling shifting, and the procedure recommended by the European Commission (EC). The annual dose is calculated from the arithmetic average using any of these five procedures. It was shown that the primary measurement results modified according to the quantification limit could lead to an underestimation of the annual dose. On the other hand the best estimates lead to an overestimation of the annual dose. The annual doses calculated from the measurement outcomes obtained according to the EC's recommended procedure, which does not cope with the uncertainties, fluctuate between an under- and overestimation, depending on the frequency of the measurement results that are larger than the limit of detection. In the extreme case, when no measurement results above the detection limit occur, the average over primary measurement results modified according to the quantification limit underestimates the average over primary measurement results for about 80%. The average over best estimates calculated according the procedure resembling shifting overestimates the average over primary measurement results for 35%, the average obtained by the Bayesian posterior for 85% and the treatment according to the EC recommendation for 89%. Copyright © 2016 Elsevier Ltd. All rights reserved.

Quantitative characterization of fatty liver disease using x-ray scattering

NASA Astrophysics Data System (ADS)

Elsharkawy, Wafaa B.; Elshemey, Wael M.

2013-11-01

Nonalcoholic fatty liver disease (NAFLD) is a dynamic condition in which fat abnormally accumulates within the hepatocytes. It is believed to be a marker of risk of later chronic liver diseases, such as liver cirrhosis and carcinoma. The fat content in liver biopsies determines its validity for liver transplantation. Transplantation of livers with severe NAFLD is associated with a high risk of primary non-function. Moreover, NAFLD is recognized as a clinically important feature that influences patient morbidity and mortality after hepatic resection. Unfortunately, there is a lack in a precise, reliable and reproducible method for quantification of NAFLD. This work suggests a method for the quantification of NAFLD. The method is based on the fact that fatty liver tissue would have a characteristic x-ray scattering profile with a relatively intense fat peak at a momentum transfer value of 1.1 nm-1 compared to a soft tissue peak at 1.6 nm-1. The fat content in normal and fatty liver is plotted against three profile characterization parameters (ratio of peak intensities, ratio of area under peaks and ratio of area under fat peak to total profile area) for measured and Monte Carlo simulated x-ray scattering profiles. Results show a high linear dependence (R2>0.9) of the characterization parameters on the liver fat content with a reported high correlation coefficient (>0.9) between measured and simulated data. These results indicate that the current method probably offers reliable quantification of fatty liver disease.

Technology-enhanced Interactive Teaching of Marginal, Joint and Conditional Probabilities: The Special Case of Bivariate Normal Distribution

PubMed Central

Dinov, Ivo D.; Kamino, Scott; Bhakhrani, Bilal; Christou, Nicolas

2014-01-01

Summary Data analysis requires subtle probability reasoning to answer questions like What is the chance of event A occurring, given that event B was observed? This generic question arises in discussions of many intriguing scientific questions such as What is the probability that an adolescent weighs between 120 and 140 pounds given that they are of average height? and What is the probability of (monetary) inflation exceeding 4% and housing price index below 110? To address such problems, learning some applied, theoretical or cross-disciplinary probability concepts is necessary. Teaching such courses can be improved by utilizing modern information technology resources. Students’ understanding of multivariate distributions, conditional probabilities, correlation and causation can be significantly strengthened by employing interactive web-based science educational resources. Independent of the type of a probability course (e.g. majors, minors or service probability course, rigorous measure-theoretic, applied or statistics course) student motivation, learning experiences and knowledge retention may be enhanced by blending modern technological tools within the classical conceptual pedagogical models. We have designed, implemented and disseminated a portable open-source web-application for teaching multivariate distributions, marginal, joint and conditional probabilities using the special case of bivariate Normal distribution. A real adolescent height and weight dataset is used to demonstrate the classroom utilization of the new web-application to address problems of parameter estimation, univariate and multivariate inference. PMID:25419016

Technology-enhanced Interactive Teaching of Marginal, Joint and Conditional Probabilities: The Special Case of Bivariate Normal Distribution.

PubMed

Dinov, Ivo D; Kamino, Scott; Bhakhrani, Bilal; Christou, Nicolas

2013-01-01

Data analysis requires subtle probability reasoning to answer questions like What is the chance of event A occurring, given that event B was observed? This generic question arises in discussions of many intriguing scientific questions such as What is the probability that an adolescent weighs between 120 and 140 pounds given that they are of average height? and What is the probability of (monetary) inflation exceeding 4% and housing price index below 110? To address such problems, learning some applied, theoretical or cross-disciplinary probability concepts is necessary. Teaching such courses can be improved by utilizing modern information technology resources. Students' understanding of multivariate distributions, conditional probabilities, correlation and causation can be significantly strengthened by employing interactive web-based science educational resources. Independent of the type of a probability course (e.g. majors, minors or service probability course, rigorous measure-theoretic, applied or statistics course) student motivation, learning experiences and knowledge retention may be enhanced by blending modern technological tools within the classical conceptual pedagogical models. We have designed, implemented and disseminated a portable open-source web-application for teaching multivariate distributions, marginal, joint and conditional probabilities using the special case of bivariate Normal distribution. A real adolescent height and weight dataset is used to demonstrate the classroom utilization of the new web-application to address problems of parameter estimation, univariate and multivariate inference.

Multiscale/Multifunctional Probabilistic Composite Fatigue

NASA Technical Reports Server (NTRS)

Chamis, Christos C.

2010-01-01

A multilevel (multiscale/multifunctional) evaluation is demonstrated by applying it to three different sample problems. These problems include the probabilistic evaluation of a space shuttle main engine blade, an engine rotor and an aircraft wing. The results demonstrate that the blade will fail at the highest probability path, the engine two-stage rotor will fail by fracture at the rim and the aircraft wing will fail at 109 fatigue cycles with a probability of 0.9967.

Pitfalls and Precautions When Using Predicted Failure Data for Quantitative Analysis of Safety Risk for Human Rated Launch Vehicles

NASA Technical Reports Server (NTRS)

Hatfield, Glen S.; Hark, Frank; Stott, James

2016-01-01

Launch vehicle reliability analysis is largely dependent upon using predicted failure rates from data sources such as MIL-HDBK-217F. Reliability prediction methodologies based on component data do not take into account risks attributable to manufacturing, assembly, and process controls. These sources often dominate component level reliability or risk of failure probability. While consequences of failure is often understood in assessing risk, using predicted values in a risk model to estimate the probability of occurrence will likely underestimate the risk. Managers and decision makers often use the probability of occurrence in determining whether to accept the risk or require a design modification. Due to the absence of system level test and operational data inherent in aerospace applications, the actual risk threshold for acceptance may not be appropriately characterized for decision making purposes. This paper will establish a method and approach to identify the pitfalls and precautions of accepting risk based solely upon predicted failure data. This approach will provide a set of guidelines that may be useful to arrive at a more realistic quantification of risk prior to acceptance by a program.

Quantification of EEG reactivity in comatose patients

PubMed Central

Hermans, Mathilde C.; Westover, M. Brandon; van Putten, Michel J.A.M.; Hirsch, Lawrence J.; Gaspard, Nicolas

2016-01-01

Objective EEG reactivity is an important predictor of outcome in comatose patients. However, visual analysis of reactivity is prone to subjectivity and may benefit from quantitative approaches. Methods In EEG segments recorded during reactivity testing in 59 comatose patients, 13 quantitative EEG parameters were used to compare the spectral characteristics of 1-minute segments before and after the onset of stimulation (spectral temporal symmetry). Reactivity was quantified with probability values estimated using combinations of these parameters. The accuracy of probability values as a reactivity classifier was evaluated against the consensus assessment of three expert clinical electroencephalographers using visual analysis. Results The binary classifier assessing spectral temporal symmetry in four frequency bands (delta, theta, alpha and beta) showed best accuracy (Median AUC: 0.95) and was accompanied by substantial agreement with the individual opinion of experts (Gwet’s AC1: 65–70%), at least as good as inter-expert agreement (AC1: 55%). Probability values also reflected the degree of reactivity, as measured by the inter-experts’ agreement regarding reactivity for each individual case. Conclusion Automated quantitative EEG approaches based on probabilistic description of spectral temporal symmetry reliably quantify EEG reactivity. Significance Quantitative EEG may be useful for evaluating reactivity in comatose patients, offering increased objectivity. PMID:26183757

Brain tumor segmentation from multimodal magnetic resonance images via sparse representation.

PubMed

Li, Yuhong; Jia, Fucang; Qin, Jing

2016-10-01

Accurately segmenting and quantifying brain gliomas from magnetic resonance (MR) images remains a challenging task because of the large spatial and structural variability among brain tumors. To develop a fully automatic and accurate brain tumor segmentation algorithm, we present a probabilistic model of multimodal MR brain tumor segmentation. This model combines sparse representation and the Markov random field (MRF) to solve the spatial and structural variability problem. We formulate the tumor segmentation problem as a multi-classification task by labeling each voxel as the maximum posterior probability. We estimate the maximum a posteriori (MAP) probability by introducing the sparse representation into a likelihood probability and a MRF into the prior probability. Considering the MAP as an NP-hard problem, we convert the maximum posterior probability estimation into a minimum energy optimization problem and employ graph cuts to find the solution to the MAP estimation. Our method is evaluated using the Brain Tumor Segmentation Challenge 2013 database (BRATS 2013) and obtained Dice coefficient metric values of 0.85, 0.75, and 0.69 on the high-grade Challenge data set, 0.73, 0.56, and 0.54 on the high-grade Challenge LeaderBoard data set, and 0.84, 0.54, and 0.57 on the low-grade Challenge data set for the complete, core, and enhancing regions. The experimental results show that the proposed algorithm is valid and ranks 2nd compared with the state-of-the-art tumor segmentation algorithms in the MICCAI BRATS 2013 challenge. Copyright © 2016 Elsevier B.V. All rights reserved.

The maximum entropy method of moments and Bayesian probability theory

NASA Astrophysics Data System (ADS)

Bretthorst, G. Larry

2013-08-01

The problem of density estimation occurs in many disciplines. For example, in MRI it is often necessary to classify the types of tissues in an image. To perform this classification one must first identify the characteristics of the tissues to be classified. These characteristics might be the intensity of a T1 weighted image and in MRI many other types of characteristic weightings (classifiers) may be generated. In a given tissue type there is no single intensity that characterizes the tissue, rather there is a distribution of intensities. Often this distributions can be characterized by a Gaussian, but just as often it is much more complicated. Either way, estimating the distribution of intensities is an inference problem. In the case of a Gaussian distribution, one must estimate the mean and standard deviation. However, in the Non-Gaussian case the shape of the density function itself must be inferred. Three common techniques for estimating density functions are binned histograms [1, 2], kernel density estimation [3, 4], and the maximum entropy method of moments [5, 6]. In the introduction, the maximum entropy method of moments will be reviewed. Some of its problems and conditions under which it fails will be discussed. Then in later sections, the functional form of the maximum entropy method of moments probability distribution will be incorporated into Bayesian probability theory. It will be shown that Bayesian probability theory solves all of the problems with the maximum entropy method of moments. One gets posterior probabilities for the Lagrange multipliers, and, finally, one can put error bars on the resulting estimated density function.

Conceptual, Methodological, and Ethical Problems in Communicating Uncertainty in Clinical Evidence

PubMed Central

Han, Paul K. J.

2014-01-01

The communication of uncertainty in clinical evidence is an important endeavor that poses difficult conceptual, methodological, and ethical problems. Conceptual problems include logical paradoxes in the meaning of probability and “ambiguity”— second-order uncertainty arising from the lack of reliability, credibility, or adequacy of probability information. Methodological problems include questions about optimal methods for representing fundamental uncertainties and for communicating these uncertainties in clinical practice. Ethical problems include questions about whether communicating uncertainty enhances or diminishes patient autonomy and produces net benefits or harms. This article reviews the limited but growing literature on these problems and efforts to address them and identifies key areas of focus for future research. It is argued that the critical need moving forward is for greater conceptual clarity and consistent representational methods that make the meaning of various uncertainties understandable, and for clinical interventions to support patients in coping with uncertainty in decision making. PMID:23132891

Hamster Math: Authentic Experiences in Data Collection.

ERIC Educational Resources Information Center

Jorgensen, Beth

1996-01-01

Describes the data collection and interpretation project of primary grade students involving predicting, graphing, estimating, measuring, number problem construction, problem solving, and probability. (MKR)

Probability and Visual Aids for Assessing Intervention Effectiveness in Single-Case Designs: A Field Test.

PubMed

Manolov, Rumen; Jamieson, Matthew; Evans, Jonathan J; Sierra, Vicenta

2015-09-01

Single-case data analysis still relies heavily on visual inspection, and, at the same time, it is not clear to what extent the results of different quantitative procedures converge in identifying an intervention effect and its magnitude when applied to the same data; this is the type of evidence provided here for two procedures. One of the procedures, included due to the importance of providing objective criteria to visual analysts, is a visual aid fitting and projecting split-middle trend while taking into account data variability. The other procedure converts several different metrics into probabilities making their results comparable. In the present study, we expore to what extend these two procedures coincide in the magnitude of intervention effect taking place in a set of studies stemming from a recent meta-analysis. The procedures concur to a greater extent with the values of the indices computed and with each other and, to a lesser extent, with our own visual analysis. For distinguishing smaller from larger effects, the probability-based approach seems somewhat better suited. Moreover, the results of the field test suggest that the latter is a reasonably good mechanism for translating different metrics into similar labels. User friendly R code is provided for promoting the use of the visual aid, together with a quantification based on nonoverlap and the label provided by the probability approach. © The Author(s) 2015.

StakeMeter: Value-Based Stakeholder Identification and Quantification Framework for Value-Based Software Systems

PubMed Central

Babar, Muhammad Imran; Ghazali, Masitah; Jawawi, Dayang N. A.; Zaheer, Kashif Bin

2015-01-01

Value-based requirements engineering plays a vital role in the development of value-based software (VBS). Stakeholders are the key players in the requirements engineering process, and the selection of critical stakeholders for the VBS systems is highly desirable. Based on the stakeholder requirements, the innovative or value-based idea is realized. The quality of the VBS system is associated with the concrete set of valuable requirements, and the valuable requirements can only be obtained if all the relevant valuable stakeholders participate in the requirements elicitation phase. The existing value-based approaches focus on the design of the VBS systems. However, the focus on the valuable stakeholders and requirements is inadequate. The current stakeholder identification and quantification (SIQ) approaches are neither state-of-the-art nor systematic for the VBS systems. The existing approaches are time-consuming, complex and inconsistent which makes the initiation process difficult. Moreover, the main motivation of this research is that the existing SIQ approaches do not provide the low level implementation details for SIQ initiation and stakeholder metrics for quantification. Hence, keeping in view the existing SIQ problems, this research contributes in the form of a new SIQ framework called ‘StakeMeter’. The StakeMeter framework is verified and validated through case studies. The proposed framework provides low-level implementation guidelines, attributes, metrics, quantification criteria and application procedure as compared to the other methods. The proposed framework solves the issues of stakeholder quantification or prioritization, higher time consumption, complexity, and process initiation. The framework helps in the selection of highly critical stakeholders for the VBS systems with less judgmental error. PMID:25799490

Quantification of susceptibility change at high-concentrated SPIO-labeled target by characteristic phase gradient recognition.

PubMed

Zhu, Haitao; Nie, Binbin; Liu, Hua; Guo, Hua; Demachi, Kazuyuki; Sekino, Masaki; Shan, Baoci

2016-05-01

Phase map cross-correlation detection and quantification may produce highlighted signal at superparamagnetic iron oxide nanoparticles, and distinguish them from other hypointensities. The method may quantify susceptibility change by performing least squares analysis between a theoretically generated magnetic field template and an experimentally scanned phase image. Because characteristic phase recognition requires the removal of phase wrap and phase background, additional steps of phase unwrapping and filtering may increase the chance of computing error and enlarge the inconsistence among algorithms. To solve problem, phase gradient cross-correlation and quantification method is developed by recognizing characteristic phase gradient pattern instead of phase image because phase gradient operation inherently includes unwrapping and filtering functions. However, few studies have mentioned the detectable limit of currently used phase gradient calculation algorithms. The limit may lead to an underestimation of large magnetic susceptibility change caused by high-concentrated iron accumulation. In this study, mathematical derivation points out the value of maximum detectable phase gradient calculated by differential chain algorithm in both spatial and Fourier domain. To break through the limit, a modified quantification method is proposed by using unwrapped forward differentiation for phase gradient generation. The method enlarges the detectable range of phase gradient measurement and avoids the underestimation of magnetic susceptibility. Simulation and phantom experiments were used to quantitatively compare different methods. In vivo application performs MRI scanning on nude mice implanted by iron-labeled human cancer cells. Results validate the limit of detectable phase gradient and the consequent susceptibility underestimation. Results also demonstrate the advantage of unwrapped forward differentiation compared with differential chain algorithms for susceptibility quantification at high-concentrated iron accumulation. Copyright © 2015 Elsevier Inc. All rights reserved.

Problems of Indian Children.

ERIC Educational Resources Information Center

Linton, Marigold

Previous approaches to the learning problems of American Indian children are viewed as inadequate. An alternative is suggested which emphasizes the problem solution strategies which these children bring to the school situation. Solutions were analyzed in terms of: (1) their probability; (2) their efficiency at permitting a present problem to be…

Superposition Quantification

NASA Astrophysics Data System (ADS)

Chang, Li-Na; Luo, Shun-Long; Sun, Yuan

2017-11-01

The principle of superposition is universal and lies at the heart of quantum theory. Although ever since the inception of quantum mechanics a century ago, superposition has occupied a central and pivotal place, rigorous and systematic studies of the quantification issue have attracted significant interests only in recent years, and many related problems remain to be investigated. In this work we introduce a figure of merit which quantifies superposition from an intuitive and direct perspective, investigate its fundamental properties, connect it to some coherence measures, illustrate it through several examples, and apply it to analyze wave-particle duality. Supported by Science Challenge Project under Grant No. TZ2016002, Laboratory of Computational Physics, Institute of Applied Physics and Computational Mathematics, Beijing, Key Laboratory of Random Complex Structures and Data Science, Chinese Academy of Sciences, Grant under No. 2008DP173182

Bayesian Treatment of Uncertainty in Environmental Modeling: Optimization, Sampling and Data Assimilation Using the DREAM Software Package

NASA Astrophysics Data System (ADS)

Vrugt, J. A.

2012-12-01

In the past decade much progress has been made in the treatment of uncertainty in earth systems modeling. Whereas initial approaches has focused mostly on quantification of parameter and predictive uncertainty, recent methods attempt to disentangle the effects of parameter, forcing (input) data, model structural and calibration data errors. In this talk I will highlight some of our recent work involving theory, concepts and applications of Bayesian parameter and/or state estimation. In particular, new methods for sequential Monte Carlo (SMC) and Markov Chain Monte Carlo (MCMC) simulation will be presented with emphasis on massively parallel distributed computing and quantification of model structural errors. The theoretical and numerical developments will be illustrated using model-data synthesis problems in hydrology, hydrogeology and geophysics.

The impossibility of probabilities

NASA Astrophysics Data System (ADS)

Zimmerman, Peter D.

2017-11-01

This paper discusses the problem of assigning probabilities to the likelihood of nuclear terrorism events, in particular examining the limitations of using Bayesian priors for this purpose. It suggests an alternate approach to analyzing the threat of nuclear terrorism.

An empirical study of the effect of granting multiple tries for online homework

NASA Astrophysics Data System (ADS)

Kortemeyer, Gerd

2015-07-01

When deploying online homework in physics courses, an important consideration is how many tries learners should be allowed to solve numerical free-response problems. While on the one hand, this number should be large enough to allow learners mastery of concepts and avoid copying, on the other hand, granting too many allowed tries encourages counter-productive behavior. We investigate data from an introductory calculus-based physics course that allowed different numbers of tries in different semesters. It turns out that the probabilities for successfully completing or abandoning problems during a particular try are independent of the number of tries already made, which indicates that students do not learn from their earlier tries. We also find that the probability for successfully completing a problem during a particular try decreases with the number of allowed tries, likely due to increased carelessness or guessing, while the probability to give up on a problem after a particular try is largely independent of the number of allowed tries. These findings lead to a mathematical model for learner usage of multiple tries, which predicts an optimum number of five allowed tries.

Meaner king uses biased bases

DOE Office of Scientific and Technical Information (OSTI.GOV)

Reimpell, Michael; Werner, Reinhard F.

2007-06-15

The mean king problem is a quantum mechanical retrodiction problem, in which Alice has to name the outcome of an ideal measurement made in one of several different orthonormal bases. Alice is allowed to prepare the state of the system and to do a final measurement, possibly including an entangled copy. However, Alice gains knowledge about which basis was measured only after she no longer has access to the quantum system or its copy. We give a necessary and sufficient condition on the bases, for Alice to have a strategy to solve this problem, without assuming that the bases aremore » mutually unbiased. The condition requires the existence of an overall joint probability distribution for random variables, whose marginal pair distributions are fixed as the transition probability matrices of the given bases. In particular, in the qubit case the problem is decided by Bell's original three variable inequality. In the standard setting of mutually unbiased bases, when they do exist, Alice can always succeed. However, for randomly chosen bases her success probability rapidly goes to zero with increasing dimension.« less

Meaner king uses biased bases

NASA Astrophysics Data System (ADS)

Reimpell, Michael; Werner, Reinhard F.

2007-06-01

The mean king problem is a quantum mechanical retrodiction problem, in which Alice has to name the outcome of an ideal measurement made in one of several different orthonormal bases. Alice is allowed to prepare the state of the system and to do a final measurement, possibly including an entangled copy. However, Alice gains knowledge about which basis was measured only after she no longer has access to the quantum system or its copy. We give a necessary and sufficient condition on the bases, for Alice to have a strategy to solve this problem, without assuming that the bases are mutually unbiased. The condition requires the existence of an overall joint probability distribution for random variables, whose marginal pair distributions are fixed as the transition probability matrices of the given bases. In particular, in the qubit case the problem is decided by Bell’s original three variable inequality. In the standard setting of mutually unbiased bases, when they do exist, Alice can always succeed. However, for randomly chosen bases her success probability rapidly goes to zero with increasing dimension.

Dispersal and individual quality in a long lived species

USGS Publications Warehouse

Cam, E.; Monnat, J.-Y.; Royle, J. Andrew

2004-01-01

The idea of differences in individual quality has been put forward in numerous long-term studies in long-lived species to explain differences in lifetime production among individuals. Despite the important role of individual heterogeneity in vital rates in demography, population dynamics and life history theory, the idea of 'individual quality' is elusive. It is sometimes assumed to be a static or dynamic individual characteristic. When considered as a dynamic trait, it is sometimes assumed to vary deterministically or stochastically, or to be confounded with the characteristics of the habitat. We addressed heterogeneity in reproductive performance among individuals established in higher-quality habitat in a long-lived seabird species. We used approaches to statistical inference based on individual random effects permitting quantification of heterogeneity in populations and assessment of individual variation from the population mean. We found evidence of heterogeneity in breeding probability, not success probability. We assessed the influence of dispersal on individual reproductive potential. Dispersal is likely to be destabilizing in species with high site and mate fidelity. We detected heterogeneity after dispersal, not before. Individuals may perform well regardless of quality before destabilization, including those that recruited in higher-quality habitat by chance, but only higher-quality individuals may be able to overcome the consequences of dispersal. Importantly, results differed when accounting for individual heterogeneity (an increase in mean breeding probability when individuals dispersed), or not (a decrease in mean breeding probability). In the latter case, the decrease in mean breeding probability may result from a substantial decrease in breeding probability in a few individuals and a slight increase in others. In other words, the pattern observed at the population mean level may not reflect what happens in the majority of individuals.

Error Patterns in Ordering Fractions among At-Risk Fourth-Grade Students

PubMed Central

Malone, Amelia S.; Fuchs, Lynn S.

2016-01-01

The 3 purposes of this study were to: (a) describe fraction ordering errors among at-risk 4th-grade students; (b) assess the effect of part-whole understanding and accuracy of fraction magnitude estimation on the probability of committing errors; and (c) examine the effect of students' ability to explain comparing problems on the probability of committing errors. Students (n = 227) completed a 9-item ordering test. A high proportion (81%) of problems were completed incorrectly. Most (65% of) errors were due to students misapplying whole number logic to fractions. Fraction-magnitude estimation skill, but not part-whole understanding, significantly predicted the probability of committing this type of error. Implications for practice are discussed. PMID:26966153

The complexity of divisibility.

PubMed

Bausch, Johannes; Cubitt, Toby

2016-09-01

We address two sets of long-standing open questions in linear algebra and probability theory, from a computational complexity perspective: stochastic matrix divisibility, and divisibility and decomposability of probability distributions. We prove that finite divisibility of stochastic matrices is an NP-complete problem, and extend this result to nonnegative matrices, and completely-positive trace-preserving maps, i.e. the quantum analogue of stochastic matrices. We further prove a complexity hierarchy for the divisibility and decomposability of probability distributions, showing that finite distribution divisibility is in P, but decomposability is NP-hard. For the former, we give an explicit polynomial-time algorithm. All results on distributions extend to weak-membership formulations, proving that the complexity of these problems is robust to perturbations.

Combinatorial optimization problem solution based on improved genetic algorithm

NASA Astrophysics Data System (ADS)

Zhang, Peng

2017-08-01

Traveling salesman problem (TSP) is a classic combinatorial optimization problem. It is a simplified form of many complex problems. In the process of study and research, it is understood that the parameters that affect the performance of genetic algorithm mainly include the quality of initial population, the population size, and crossover probability and mutation probability values. As a result, an improved genetic algorithm for solving TSP problems is put forward. The population is graded according to individual similarity, and different operations are performed to different levels of individuals. In addition, elitist retention strategy is adopted at each level, and the crossover operator and mutation operator are improved. Several experiments are designed to verify the feasibility of the algorithm. Through the experimental results analysis, it is proved that the improved algorithm can improve the accuracy and efficiency of the solution.

Multi-Scale/Multi-Functional Probabilistic Composite Fatigue

NASA Technical Reports Server (NTRS)

Chamis, Christos C.

2008-01-01

A multi-level (multi-scale/multi-functional) evaluation is demonstrated by applying it to three different sample problems. These problems include the probabilistic evaluation of a space shuttle main engine blade, an engine rotor and an aircraft wing. The results demonstrate that the blade will fail at the highest probability path, the engine two-stage rotor will fail by fracture at the rim and the aircraft wing will fail at 109 fatigue cycles with a probability of 0.9967.

Characterization of drop aerodynamic fragmentation in the bag and sheet-thinning regimes by crossed-beam, two-view, digital in-line holography

DOE PAGES

Guildenbecher, Daniel R.; Gao, Jian; Chen, Jun; ...

2017-04-19

When a spherical liquid drop is subjected to a step change in relative gas velocity, aerodynamic forces lead to drop deformation and possible breakup into a number of secondary fragments. In order to investigate this flow, a digital in-line holography (DIH) diagnostic is proposed which enables rapid quantification of spatial statistics with limited experimental repetition. To overcome the high uncertainty in the depth direction experienced in previous applications of DIH, a crossed-beam, two-view configuration is introduced. With appropriate calibration, this diagnostic is shown to provide accurate quantification of fragment sizes, three-dimensional positions and three-component velocities in a large measurement volume.more » We apply these capabilities in order to investigate the aerodynamic breakup of drops at two non-dimensional Weber numbers, We, corresponding to the bag (We = 14) and sheet-thinning (We = 55) regimes. Ensemble average results show the evolution of fragment size and velocity statistics during the course of breakup. Our results indicate that mean fragment sizes increase throughout the course of breakup. For the bag breakup case, the evolution of a multi-mode fragment size probability density is observed. This is attributed to separate fragmentation mechanisms for the bag and rim structures. In contrast, for the sheet-thinning case, the fragment size probability density shows only one distinct peak indicating a single fragmentation mechanism. Compared to previous related investigations of this flow, many orders of magnitude more fragments are measured per condition, resulting in a significant improvement in data fidelity. For this reason, this experimental dataset is likely to provide new opportunities for detailed validation of analytic and computational models of this flow.« less

Probabilistic Risk Assessment of Hydraulic Fracturing in Unconventional Reservoirs by Means of Fault Tree Analysis: An Initial Discussion

NASA Astrophysics Data System (ADS)

Rodak, C. M.; McHugh, R.; Wei, X.

2016-12-01

The development and combination of horizontal drilling and hydraulic fracturing has unlocked unconventional hydrocarbon reserves around the globe. These advances have triggered a number of concerns regarding aquifer contamination and over-exploitation, leading to scientific studies investigating potential risks posed by directional hydraulic fracturing activities. These studies, balanced with potential economic benefits of energy production, are a crucial source of information for communities considering the development of unconventional reservoirs. However, probabilistic quantification of the overall risk posed by hydraulic fracturing at the system level are rare. Here we present the concept of fault tree analysis to determine the overall probability of groundwater contamination or over-exploitation, broadly referred to as the probability of failure. The potential utility of fault tree analysis for the quantification and communication of risks is approached with a general application. However, the fault tree design is robust and can handle various combinations of regional-specific data pertaining to relevant spatial scales, geological conditions, and industry practices where available. All available data are grouped into quantity and quality-based impacts and sub-divided based on the stage of the hydraulic fracturing process in which the data is relevant as described by the USEPA. Each stage is broken down into the unique basic events required for failure; for example, to quantify the risk of an on-site spill we must consider the likelihood, magnitude, composition, and subsurface transport of the spill. The structure of the fault tree described above can be used to render a highly complex system of variables into a straightforward equation for risk calculation based on Boolean logic. This project shows the utility of fault tree analysis for the visual communication of the potential risks of hydraulic fracturing activities on groundwater resources.

Quantification of atopy and the probability of rhinitis in preschool children: a population-based birth cohort study.

PubMed

Marinho, S; Simpson, A; Söderström, L; Woodcock, A; Ahlstedt, S; Custovic, A

2007-12-01

Atopy quantification using IgE levels/skin test diameter (SPT-MWD) may better predict the expression of rhinitis than using atopy as a dichotomous variable. To investigate the association between the presence, temporal pattern and severity of rhinitis in preschool children and specific IgE levels/SPT-MWDs. Children were followed prospectively to age 5 years in a whole-population birth cohort study. We administered questionnaires (n = 815), skin prick tested children (n = 717) and measured specific serum IgE (n = 478) to inhalant and food allergens. Main outcomes were current rhinitis (CR) and current rhinoconjunctivitis (CRC). The prevalence of CR and CRC was 26.1% and 12.1%, respectively. The risk of CR and CRC increased significantly with increasing IgE to grass, mite and cat; CRC was also associated with increasing IgE to dog and peanut. Similarly, increasing SPT-MWDs to inhalant allergens were significantly associated with CR and CRC. This association was also shown for grass within the group of atopic children. Perennial and seasonal rhinitis were associated with increasing IgE/SPT-MWD to mite and grass, respectively. Moderate/severe rhinitis was associated with increasing IgE/SPT-MWD to grass. In a multivariate analysis, increasing levels of IgE/SPT-MWD to grass were the strongest independent predictors of both CR (for IgE: OR 1.42, 95% CI 1.23-1.64, P < 0.001) and CRC (for IgE: 1.51, 1.30-1.76, P < 0.001). The probability of CR/CRC increases with increasing specific IgE levels or SPT-MWD. With respect to allergic rhinitis, the absolute levels of specific IgE antibody or the size of SPT wheal offer more information than just the presence/absence of sensitization.

Optimal estimation for discrete time jump processes

NASA Technical Reports Server (NTRS)

Vaca, M. V.; Tretter, S. A.

1977-01-01

Optimum estimates of nonobservable random variables or random processes which influence the rate functions of a discrete time jump process (DTJP) are obtained. The approach is based on the a posteriori probability of a nonobservable event expressed in terms of the a priori probability of that event and of the sample function probability of the DTJP. A general representation for optimum estimates and recursive equations for minimum mean squared error (MMSE) estimates are obtained. MMSE estimates are nonlinear functions of the observations. The problem of estimating the rate of a DTJP when the rate is a random variable with a probability density function of the form cx super K (l-x) super m and show that the MMSE estimates are linear in this case. This class of density functions explains why there are insignificant differences between optimum unconstrained and linear MMSE estimates in a variety of problems.

Optimal estimation for discrete time jump processes

NASA Technical Reports Server (NTRS)

Vaca, M. V.; Tretter, S. A.

1978-01-01

Optimum estimates of nonobservable random variables or random processes which influence the rate functions of a discrete time jump process (DTJP) are derived. The approach used is based on the a posteriori probability of a nonobservable event expressed in terms of the a priori probability of that event and of the sample function probability of the DTJP. Thus a general representation is obtained for optimum estimates, and recursive equations are derived for minimum mean-squared error (MMSE) estimates. In general, MMSE estimates are nonlinear functions of the observations. The problem is considered of estimating the rate of a DTJP when the rate is a random variable with a beta probability density function and the jump amplitudes are binomially distributed. It is shown that the MMSE estimates are linear. The class of beta density functions is rather rich and explains why there are insignificant differences between optimum unconstrained and linear MMSE estimates in a variety of problems.

A closer look at the probabilities of the notorious three prisoners.

PubMed

Falk, R

1992-06-01

The "problem of three prisoners", a counterintuitive teaser, is analyzed. It is representative of a class of probability puzzles where the correct solution depends on explication of underlying assumptions. Spontaneous beliefs concerning the problem and intuitive heuristics are reviewed. The psychological background of these beliefs is explored. Several attempts to find a simple criterion to predict whether and how the probability of the target event will change as a result of obtaining evidence are examined. However, despite the psychological appeal of these attempts, none proves to be valid in general. A necessary and sufficient condition for change in the probability of the target event, following observation of new data, is proposed. That criterion is an extension of the likelihood-ratio principle (which holds in the case of only two complementary alternatives) to any number of alternatives. Some didactic implications concerning the significance of the chance set-up and reliance on analogies are discussed.

Probability machines: consistent probability estimation using nonparametric learning machines.

PubMed

Malley, J D; Kruppa, J; Dasgupta, A; Malley, K G; Ziegler, A

2012-01-01

Most machine learning approaches only provide a classification for binary responses. However, probabilities are required for risk estimation using individual patient characteristics. It has been shown recently that every statistical learning machine known to be consistent for a nonparametric regression problem is a probability machine that is provably consistent for this estimation problem. The aim of this paper is to show how random forests and nearest neighbors can be used for consistent estimation of individual probabilities. Two random forest algorithms and two nearest neighbor algorithms are described in detail for estimation of individual probabilities. We discuss the consistency of random forests, nearest neighbors and other learning machines in detail. We conduct a simulation study to illustrate the validity of the methods. We exemplify the algorithms by analyzing two well-known data sets on the diagnosis of appendicitis and the diagnosis of diabetes in Pima Indians. Simulations demonstrate the validity of the method. With the real data application, we show the accuracy and practicality of this approach. We provide sample code from R packages in which the probability estimation is already available. This means that all calculations can be performed using existing software. Random forest algorithms as well as nearest neighbor approaches are valid machine learning methods for estimating individual probabilities for binary responses. Freely available implementations are available in R and may be used for applications.

Reasoning and choice in the Monty Hall Dilemma (MHD): implications for improving Bayesian reasoning

PubMed Central

Tubau, Elisabet; Aguilar-Lleyda, David; Johnson, Eric D.

2015-01-01

The Monty Hall Dilemma (MHD) is a two-step decision problem involving counterintuitive conditional probabilities. The first choice is made among three equally probable options, whereas the second choice takes place after the elimination of one of the non-selected options which does not hide the prize. Differing from most Bayesian problems, statistical information in the MHD has to be inferred, either by learning outcome probabilities or by reasoning from the presented sequence of events. This often leads to suboptimal decisions and erroneous probability judgments. Specifically, decision makers commonly develop a wrong intuition that final probabilities are equally distributed, together with a preference for their first choice. Several studies have shown that repeated practice enhances sensitivity to the different reward probabilities, but does not facilitate correct Bayesian reasoning. However, modest improvements in probability judgments have been observed after guided explanations. To explain these dissociations, the present review focuses on two types of causes producing the observed biases: Emotional-based choice biases and cognitive limitations in understanding probabilistic information. Among the latter, we identify a crucial cause for the universal difficulty in overcoming the equiprobability illusion: Incomplete representation of prior and conditional probabilities. We conclude that repeated practice and/or high incentives can be effective for overcoming choice biases, but promoting an adequate partitioning of possibilities seems to be necessary for overcoming cognitive illusions and improving Bayesian reasoning. PMID:25873906

Solving inverse problem for Markov chain model of customer lifetime value using flower pollination algorithm

NASA Astrophysics Data System (ADS)

Al-Ma'shumah, Fathimah; Permana, Dony; Sidarto, Kuntjoro Adji

2015-12-01

Customer Lifetime Value is an important and useful concept in marketing. One of its benefits is to help a company for budgeting marketing expenditure for customer acquisition and customer retention. Many mathematical models have been introduced to calculate CLV considering the customer retention/migration classification scheme. A fairly new class of these models which will be described in this paper uses Markov Chain Models (MCM). This class of models has the major advantage for its flexibility to be modified to several different cases/classification schemes. In this model, the probabilities of customer retention and acquisition play an important role. From Pfeifer and Carraway, 2000, the final formula of CLV obtained from MCM usually contains nonlinear form of the transition probability matrix. This nonlinearity makes the inverse problem of CLV difficult to solve. This paper aims to solve this inverse problem, yielding the approximate transition probabilities for the customers, by applying metaheuristic optimization algorithm developed by Yang, 2013, Flower Pollination Algorithm. The major interpretation of obtaining the transition probabilities are to set goals for marketing teams in keeping the relative frequencies of customer acquisition and customer retention.

Entropy based quantification of Ki-67 positive cell images and its evaluation by a reader study

NASA Astrophysics Data System (ADS)

Niazi, M. Khalid Khan; Pennell, Michael; Elkins, Camille; Hemminger, Jessica; Jin, Ming; Kirby, Sean; Kurt, Habibe; Miller, Barrie; Plocharczyk, Elizabeth; Roth, Rachel; Ziegler, Rebecca; Shana'ah, Arwa; Racke, Fred; Lozanski, Gerard; Gurcan, Metin N.

2013-03-01

Presence of Ki-67, a nuclear protein, is typically used to measure cell proliferation. The quantification of the Ki-67 proliferation index is performed visually by the pathologist; however, this is subject to inter- and intra-reader variability. Automated techniques utilizing digital image analysis by computers have emerged. The large variations in specimen preparation, staining, and imaging as well as true biological heterogeneity of tumor tissue often results in variable intensities in Ki-67 stained images. These variations affect the performance of currently developed methods. To optimize the segmentation of Ki-67 stained cells, one should define a data dependent transformation that will account for these color variations instead of defining a fixed linear transformation to separate different hues. To address these issues in images of tissue stained with Ki-67, we propose a methodology that exploits the intrinsic properties of CIE L∗a∗b∗ color space to translate this complex problem into an automatic entropy based thresholding problem. The developed method was evaluated through two reader studies with pathology residents and expert hematopathologists. Agreement between the proposed method and the expert pathologists was good (CCC = 0.80).

Optical tomographic imaging for breast cancer detection

NASA Astrophysics Data System (ADS)

Cong, Wenxiang; Intes, Xavier; Wang, Ge

2017-09-01

Diffuse optical breast imaging utilizes near-infrared (NIR) light propagation through tissues to assess the optical properties of tissues for the identification of abnormal tissue. This optical imaging approach is sensitive, cost-effective, and does not involve any ionizing radiation. However, the image reconstruction of diffuse optical tomography (DOT) is a nonlinear inverse problem and suffers from severe illposedness due to data noise, NIR light scattering, and measurement incompleteness. An image reconstruction method is proposed for the detection of breast cancer. This method splits the image reconstruction problem into the localization of abnormal tissues and quantification of absorption variations. The localization of abnormal tissues is performed based on a well-posed optimization model, which can be solved via a differential evolution optimization method to achieve a stable reconstruction. The quantification of abnormal absorption is then determined in localized regions of relatively small extents, in which a potential tumor might be. Consequently, the number of unknown absorption variables can be greatly reduced to overcome the underdetermined nature of DOT. Numerical simulation experiments are performed to verify merits of the proposed method, and the results show that the image reconstruction method is stable and accurate for the identification of abnormal tissues, and robust against the measurement noise of data.

First quantification of relationship between dune orientation and sediment availability, Olympia Undae, Mars

NASA Astrophysics Data System (ADS)

Fernandez-Cascales, Laura; Lucas, Antoine; Rodriguez, Sébastien; Gao, Xin; Spiga, Aymeric; Narteau, Clément

2018-05-01

Dunes provide unique information about wind regimes on planetary bodies where there is no direct meteorological data. At the eastern margin of Olympia Undae on Mars, dune orientation is measured from satellite imagery and sediment cover is estimated using the high contrast between the dune material and substrate. The analysis of these data provide the first quantification of relationship between sediment availability and dune orientation. Abrupt and smooth dune reorientations are associated with inward and outward dynamics of dunes approaching and ejecting from major sedimentary bodies, respectively. These reorientation patterns along sediment transport pathways are interpreted using a new generation dune model based on the coexistence of two dune growth mechanisms. This model also permits solving of the inverse problem of predicting the wind regime from dune orientation. For bidirectional wind regimes, solutions of this inverse problem show substantial differences in the distributions of sediment flux orientation, which can be attributed to atmospheric flow variations induced by changes in albedo at the boundaries of major dune fields. Then, we conclude that relationships between sediment cover and dune orientation can be used to constrain wind regime and dune field development on Mars and other planetary surfaces.

On uncertainty quantification in hydrogeology and hydrogeophysics

NASA Astrophysics Data System (ADS)

Linde, Niklas; Ginsbourger, David; Irving, James; Nobile, Fabio; Doucet, Arnaud

2017-12-01

Recent advances in sensor technologies, field methodologies, numerical modeling, and inversion approaches have contributed to unprecedented imaging of hydrogeological properties and detailed predictions at multiple temporal and spatial scales. Nevertheless, imaging results and predictions will always remain imprecise, which calls for appropriate uncertainty quantification (UQ). In this paper, we outline selected methodological developments together with pioneering UQ applications in hydrogeology and hydrogeophysics. The applied mathematics and statistics literature is not easy to penetrate and this review aims at helping hydrogeologists and hydrogeophysicists to identify suitable approaches for UQ that can be applied and further developed to their specific needs. To bypass the tremendous computational costs associated with forward UQ based on full-physics simulations, we discuss proxy-modeling strategies and multi-resolution (Multi-level Monte Carlo) methods. We consider Bayesian inversion for non-linear and non-Gaussian state-space problems and discuss how Sequential Monte Carlo may become a practical alternative. We also describe strategies to account for forward modeling errors in Bayesian inversion. Finally, we consider hydrogeophysical inversion, where petrophysical uncertainty is often ignored leading to overconfident parameter estimation. The high parameter and data dimensions encountered in hydrogeological and geophysical problems make UQ a complicated and important challenge that has only been partially addressed to date.

Low order models for uncertainty quantification in acoustic propagation problems

NASA Astrophysics Data System (ADS)

Millet, Christophe

2016-11-01

Long-range sound propagation problems are characterized by both a large number of length scales and a large number of normal modes. In the atmosphere, these modes are confined within waveguides causing the sound to propagate through multiple paths to the receiver. For uncertain atmospheres, the modes are described as random variables. Concise mathematical models and analysis reveal fundamental limitations in classical projection techniques due to different manifestations of the fact that modes that carry small variance can have important effects on the large variance modes. In the present study, we propose a systematic strategy for obtaining statistically accurate low order models. The normal modes are sorted in decreasing Sobol indices using asymptotic expansions, and the relevant modes are extracted using a modified iterative Krylov-based method. The statistics of acoustic signals are computed by decomposing the original pulse into a truncated sum of modal pulses that can be described by a stationary phase method. As the low-order acoustic model preserves the overall structure of waveforms under perturbations of the atmosphere, it can be applied to uncertainty quantification. The result of this study is a new algorithm which applies on the entire phase space of acoustic fields.

Optimization of Systems with Uncertainty: Initial Developments for Performance, Robustness and Reliability Based Designs

NASA Technical Reports Server (NTRS)

Crespo, Luis G.; Bushnell, Dennis M. (Technical Monitor)

2002-01-01

This paper presents a study on the optimization of systems with structured uncertainties, whose inputs and outputs can be exhaustively described in the probabilistic sense. By propagating the uncertainty from the input to the output in the space of the probability density functions and the moments, optimization problems that pursue performance, robustness and reliability based designs are studied. Be specifying the desired outputs in terms of desired probability density functions and then in terms of meaningful probabilistic indices, we settle a computationally viable framework for solving practical optimization problems. Applications to static optimization and stability control are used to illustrate the relevance of incorporating uncertainty in the early stages of the design. Several examples that admit a full probabilistic description of the output in terms of the design variables and the uncertain inputs are used to elucidate the main features of the generic problem and its solution. Extensions to problems that do not admit closed form solutions are also evaluated. Concrete evidence of the importance of using a consistent probabilistic formulation of the optimization problem and a meaningful probabilistic description of its solution is provided in the examples. In the stability control problem the analysis shows that standard deterministic approaches lead to designs with high probability of running into instability. The implementation of such designs can indeed have catastrophic consequences.

Probability and Quantum Paradigms: the Interplay

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kracklauer, A. F.

Since the introduction of Born's interpretation of quantum wave functions as yielding the probability density of presence, Quantum Theory and Probability have lived in a troubled symbiosis. Problems arise with this interpretation because quantum probabilities exhibit features alien to usual probabilities, namely non Boolean structure and non positive-definite phase space probability densities. This has inspired research into both elaborate formulations of Probability Theory and alternate interpretations for wave functions. Herein the latter tactic is taken and a suggested variant interpretation of wave functions based on photo detection physics proposed, and some empirical consequences are considered. Although incomplete in a fewmore » details, this variant is appealing in its reliance on well tested concepts and technology.« less

Probability and Quantum Paradigms: the Interplay

NASA Astrophysics Data System (ADS)

Kracklauer, A. F.

2007-12-01

Since the introduction of Born's interpretation of quantum wave functions as yielding the probability density of presence, Quantum Theory and Probability have lived in a troubled symbiosis. Problems arise with this interpretation because quantum probabilities exhibit features alien to usual probabilities, namely non Boolean structure and non positive-definite phase space probability densities. This has inspired research into both elaborate formulations of Probability Theory and alternate interpretations for wave functions. Herein the latter tactic is taken and a suggested variant interpretation of wave functions based on photo detection physics proposed, and some empirical consequences are considered. Although incomplete in a few details, this variant is appealing in its reliance on well tested concepts and technology.

Applications of the Galton Watson process to human DNA evolution and demography

NASA Astrophysics Data System (ADS)

Neves, Armando G. M.; Moreira, Carlos H. C.

2006-08-01

We show that the problem of existence of a mitochondrial Eve can be understood as an application of the Galton-Watson process and presents interesting analogies with critical phenomena in Statistical Mechanics. In the approximation of small survival probability, and assuming limited progeny, we are able to find for a genealogic tree the maximum and minimum survival probabilities over all probability distributions for the number of children per woman constrained to a given mean. As a consequence, we can relate existence of a mitochondrial Eve to quantitative demographic data of early mankind. In particular, we show that a mitochondrial Eve may exist even in an exponentially growing population, provided that the mean number of children per woman Nbar is constrained to a small range depending on the probability p that a child is a female. Assuming that the value p≈0.488 valid nowadays has remained fixed for thousands of generations, the range where a mitochondrial Eve occurs with sizeable probability is 2.0492

Computational nuclear quantum many-body problem: The UNEDF project

NASA Astrophysics Data System (ADS)

Bogner, S.; Bulgac, A.; Carlson, J.; Engel, J.; Fann, G.; Furnstahl, R. J.; Gandolfi, S.; Hagen, G.; Horoi, M.; Johnson, C.; Kortelainen, M.; Lusk, E.; Maris, P.; Nam, H.; Navratil, P.; Nazarewicz, W.; Ng, E.; Nobre, G. P. A.; Ormand, E.; Papenbrock, T.; Pei, J.; Pieper, S. C.; Quaglioni, S.; Roche, K. J.; Sarich, J.; Schunck, N.; Sosonkina, M.; Terasaki, J.; Thompson, I.; Vary, J. P.; Wild, S. M.

2013-10-01

The UNEDF project was a large-scale collaborative effort that applied high-performance computing to the nuclear quantum many-body problem. The primary focus of the project was on constructing, validating, and applying an optimized nuclear energy density functional, which entailed a wide range of pioneering developments in microscopic nuclear structure and reactions, algorithms, high-performance computing, and uncertainty quantification. UNEDF demonstrated that close associations among nuclear physicists, mathematicians, and computer scientists can lead to novel physics outcomes built on algorithmic innovations and computational developments. This review showcases a wide range of UNEDF science results to illustrate this interplay.

The Effect of the Ill-posed Problem on Quantitative Error Assessment in Digital Image Correlation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lehoucq, R. B.; Reu, P. L.; Turner, D. Z.

Here, this work explores the effect of the ill-posed problem on uncertainty quantification for motion estimation using digital image correlation (DIC) (Sutton et al. 2009). We develop a correction factor for standard uncertainty estimates based on the cosine of the angle between the true motion and the image gradients, in an integral sense over a subregion of the image. This correction factor accounts for variability in the DIC solution previously unaccounted for when considering only image noise, interpolation bias, contrast, and the software settings such as subset size and spacing.

The Effect of the Ill-posed Problem on Quantitative Error Assessment in Digital Image Correlation

DOE PAGES

Lehoucq, R. B.; Reu, P. L.; Turner, D. Z.

2017-11-27

Here, this work explores the effect of the ill-posed problem on uncertainty quantification for motion estimation using digital image correlation (DIC) (Sutton et al. 2009). We develop a correction factor for standard uncertainty estimates based on the cosine of the angle between the true motion and the image gradients, in an integral sense over a subregion of the image. This correction factor accounts for variability in the DIC solution previously unaccounted for when considering only image noise, interpolation bias, contrast, and the software settings such as subset size and spacing.

Problems in radiation transfer in astrophysics: An escape probability treatment of line overlap and a model of masers around VX Sgr

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lockett, P.B.

1989-01-01

The escape probability formalism is used in this dissertation to treat two problems in astrophysical radiative transfer. The first problem concerns line overlap, which occurs when two or more spectral lines lie close enough together that there is a significant probability that a photon emitted in one of the lines can be absorbed in another. The second problem involves creating a detailed model of the masers around the supergiant star, VX Sgr. The author has developed an escape probability procedure that accounts for the effects of line overlap by integrating the amount of absorption in each of the overlapping lines.more » This method was used to test the accuracy of a simpler escape probability formalism developed by Elitzur and Netzer that utilized rectangular line profiles. Good agreement between the two methods was found for a wide range of physical conditions. The more accurate method was also used to examine the effects of line overlap of the far infrared lines of the OH molecule. This overlap did have important effects on the level populations and could cause maser emission. He has also developed a detailed model of the OH 1612 and water masers around VX Sgr. He found that the masers can be adequately explained using reasonable estimates for the physical parameters. He also was able to provide a tighter constraint on the highly uncertain mass loss rate from the star. He had less success modeling the SiO masers. His explanation will require a more exact method of treating the many levels involved and also a more accurate knowledge of the relevant physical input parameters.« less

Problems in radiative transfer in astrophysics: An escape probability treatment of line overlap and a model of the masers around VX Sgr

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lockett, P.B.

1989-01-01

The escape probability formalism is used to treat two problems in astrophysical radiative transfer. The first problem concerns line overlap, which occurs when two or more spectral lines lie close enough together that there is a significant probability that a photon emitted in one of the lines can be absorbed in another. The second problem involved creating a detailed model of the masers around the supergiant star, VX Sgr. An escape probability procedure was developed that accounts for the effects of line overlap by integrating the amount of absorption in each of the overlapping lines. This method was used tomore » test the accuracy of a simpler escape probability formalism developed by Elitzur and Netzer that utilized rectangular line profiles. Good agreement between the two methods was found for a wide range of physical conditions. The more accurate method was also used to examine the effects of line overlap of the far infrared lines of the OH molecule. This overlap did have important effects on the level populations and could cause maser emission. A detailed model of the OH 1612 and water masers around VX Sgr were also developed. The masers can be adequately explained using reasonable estimates for the physical parameters. It is possible to provide a tighter constraint on the highly uncertain mass loss rate from the star. Modeling the SiO masers was less successful. Their explanation will require a more exact method of treating the many levels involved and also a more accurate knowledge of the relevant physical input parameters.« less

Uncertainty Quantification in Remaining Useful Life of Aerospace Components using State Space Models and Inverse FORM

NASA Technical Reports Server (NTRS)

Sankararaman, Shankar; Goebel, Kai

2013-01-01

This paper investigates the use of the inverse first-order reliability method (inverse- FORM) to quantify the uncertainty in the remaining useful life (RUL) of aerospace components. The prediction of remaining useful life is an integral part of system health prognosis, and directly helps in online health monitoring and decision-making. However, the prediction of remaining useful life is affected by several sources of uncertainty, and therefore it is necessary to quantify the uncertainty in the remaining useful life prediction. While system parameter uncertainty and physical variability can be easily included in inverse-FORM, this paper extends the methodology to include: (1) future loading uncertainty, (2) process noise; and (3) uncertainty in the state estimate. The inverse-FORM method has been used in this paper to (1) quickly obtain probability bounds on the remaining useful life prediction; and (2) calculate the entire probability distribution of remaining useful life prediction, and the results are verified against Monte Carlo sampling. The proposed methodology is illustrated using a numerical example.

Probabilistically Perfect Cloning of Two Pure States: Geometric Approach.

PubMed

Yerokhin, V; Shehu, A; Feldman, E; Bagan, E; Bergou, J A

2016-05-20

We solve the long-standing problem of making n perfect clones from m copies of one of two known pure states with minimum failure probability in the general case where the known states have arbitrary a priori probabilities. The solution emerges from a geometric formulation of the problem. This formulation reveals that cloning converges to state discrimination followed by state preparation as the number of clones goes to infinity. The convergence exhibits a phenomenon analogous to a second-order symmetry-breaking phase transition.

A Probability Problem from Real Life: The Tire Exploded.

ERIC Educational Resources Information Center

Bartlett, Albert A.

1993-01-01

Discusses the probability of seeing a tire explode or disintegrate while traveling down the highway. Suggests that a person observing 10 hours a day would see a failure on the average of once every 300 years. (MVL)

Measurement of 8-oxo-7,8-dihydro-2'-deoxyguanosine and 8-oxo-7,8-dihydro-guanosine in cerebrospinal fluid by ultra performance liquid chromatography-tandem mass spectrometry.

PubMed

Weimann, Allan; Simonsen, Anja Hviid; Poulsen, Henrik E

2018-01-15

Increased levels of nucleosides modified by oxidation in human cerebrospinal fluid (CSF) have several times been reported in Alzheimer patients and patients suffering from Parkinson's disease. The focus has especially been on nucleosides containing the 8-hydroxylation of guanine. Only few reports on quantification of the ribonucleoside 8-oxo-7,8-dihydro-guanosine (8oxoGuo) in CSF have been published, whereas more have been published on the quantification of the deoxy-ribonucleoside 8-oxo-7,8-dihydro-2'-deoxyguanosine (8oxodGuo). The reports on the quantification of 8oxodGuo concentrations in CSF report absolute concentrations varying by a factor >10 5 in healthy humans. This could indicate that there is a serious specificity problem in some of the methods. In this paper an isotope-dilution UPLC-MS/MS method with high specificity and sensitivity for the quantification of 8oxoGuo and 8oxodGuo in CSF is presented. LLOQ for the two analytes is determined to 4pM and 2pM, respectively. The calibration curves has been tested to be linear in the range from 4 to 3,000pM for 8oxoGuo and between 2 and 3,000pM for 8oxodGuo. Using a weighting factor of 1/x the correlation coefficient "r" for both analytes is >0.999. Copyright © 2017 Elsevier B.V. All rights reserved.

Nondestructive Analysis of Tumor-Associated Membrane Protein Integrating Imaging and Amplified Detection in situ Based on Dual-Labeled DNAzyme.

PubMed

Chen, Xiaoxia; Zhao, Jing; Chen, Tianshu; Gao, Tao; Zhu, Xiaoli; Li, Genxi

2018-01-01

Comprehensive analysis of the expression level and location of tumor-associated membrane proteins (TMPs) is of vital importance for the profiling of tumor cells. Currently, two kinds of independent techniques, i.e. ex situ detection and in situ imaging, are usually required for the quantification and localization of TMPs respectively, resulting in some inevitable problems. Methods: Herein, based on a well-designed and fluorophore-labeled DNAzyme, we develop an integrated and facile method, in which imaging and quantification of TMPs in situ are achieved simultaneously in a single system. The labeled DNAzyme not only produces localized fluorescence for the visualization of TMPs but also catalyzes the cleavage of a substrate to produce quantitative fluorescent signals that can be collected from solution for the sensitive detection of TMPs. Results: Results from the DNAzyme-based in situ imaging and quantification of TMPs match well with traditional immunofluorescence and western blotting. In addition to the advantage of two-in-one, the DNAzyme-based method is highly sensitivity, allowing the detection of TMPs in only 100 cells. Moreover, the method is nondestructive. Cells after analysis could retain their physiological activity and could be cultured for other applications. Conclusion: The integrated system provides solid results for both imaging and quantification of TMPs, making it a competitive method over some traditional techniques for the analysis of TMPs, which offers potential application as a toolbox in the future.

7 CFR 4285.70 - Evaluation criteria.

Code of Federal Regulations, 2012 CFR

2012-01-01

...) Adequacy, soundness, and appropriateness of the proposed approach to solve the identified problem. (30%) (3) Feasibility and probability of success of project solving the problem. (10%) (4) Qualifications, experience in...

7 CFR 4285.70 - Evaluation criteria.

Code of Federal Regulations, 2013 CFR

2013-01-01

...) Adequacy, soundness, and appropriateness of the proposed approach to solve the identified problem. (30%) (3) Feasibility and probability of success of project solving the problem. (10%) (4) Qualifications, experience in...

7 CFR 4285.70 - Evaluation criteria.

Code of Federal Regulations, 2014 CFR

2014-01-01

...) Adequacy, soundness, and appropriateness of the proposed approach to solve the identified problem. (30%) (3) Feasibility and probability of success of project solving the problem. (10%) (4) Qualifications, experience in...

7 CFR 4285.70 - Evaluation criteria.

Code of Federal Regulations, 2011 CFR

2011-01-01

...) Adequacy, soundness, and appropriateness of the proposed approach to solve the identified problem. (30%) (3) Feasibility and probability of success of project solving the problem. (10%) (4) Qualifications, experience in...

7 CFR 4285.70 - Evaluation criteria.

Code of Federal Regulations, 2010 CFR

2010-01-01

...) Adequacy, soundness, and appropriateness of the proposed approach to solve the identified problem. (30%) (3) Feasibility and probability of success of project solving the problem. (10%) (4) Qualifications, experience in... proposal demonstrates the following: (1) Focus on a practical solution to a significant problem involving...

Synconset Waves and Chains: Spiking Onsets in Synchronous Populations Predict and Are Predicted by Network Structure

PubMed Central

Raghavan, Mohan; Amrutur, Bharadwaj; Narayanan, Rishikesh; Sikdar, Sujit Kumar

2013-01-01

Synfire waves are propagating spike packets in synfire chains, which are feedforward chains embedded in random networks. Although synfire waves have proved to be effective quantification for network activity with clear relations to network structure, their utilities are largely limited to feedforward networks with low background activity. To overcome these shortcomings, we describe a novel generalisation of synfire waves, and define ‘synconset wave’ as a cascade of first spikes within a synchronisation event. Synconset waves would occur in ‘synconset chains’, which are feedforward chains embedded in possibly heavily recurrent networks with heavy background activity. We probed the utility of synconset waves using simulation of single compartment neuron network models with biophysically realistic conductances, and demonstrated that the spread of synconset waves directly follows from the network connectivity matrix and is modulated by top-down inputs and the resultant oscillations. Such synconset profiles lend intuitive insights into network organisation in terms of connection probabilities between various network regions rather than an adjacency matrix. To test this intuition, we develop a Bayesian likelihood function that quantifies the probability that an observed synfire wave was caused by a given network. Further, we demonstrate it's utility in the inverse problem of identifying the network that caused a given synfire wave. This method was effective even in highly subsampled networks where only a small subset of neurons were accessible, thus showing it's utility in experimental estimation of connectomes in real neuronal-networks. Together, we propose synconset chains/waves as an effective framework for understanding the impact of network structure on function, and as a step towards developing physiology-driven network identification methods. Finally, as synconset chains extend the utilities of synfire chains to arbitrary networks, we suggest utilities of our framework to several aspects of network physiology including cell assemblies, population codes, and oscillatory synchrony. PMID:24116018

Early histological, hormonal, and molecular changes during pineapple (Ananas comosus (L.) Merrill) artificial flowering induction.

PubMed

Espinosa, Maita Eulalia Ávila; Moreira, Rafael Oliveira; Lima, André Almeida; Ságio, Solange Aparecida; Barreto, Horllys Gomes; Luiz, Sara Lazara Pérez; Abreu, Carlos Eduardo Aragón; Yanes-Paz, Ermis; Ruíz, Yanelis Capdesuñer; González-Olmedo, Justo Lorenzo; Chalfun-Júnior, Antonio

2017-02-01

Natural flowering can cause serious scheduling problems in the pineapple (Ananas comosus) industry and increase harvest costs. Pineapple flowering is thought to be triggered by increased ethylene levels and artificial forcing of pineapple flowering is a common practice to promote flowering synchronisation. However, little is known about the early hormonal and molecular changes of pineapple flowering induction and development. Here, we aimed to analyse the molecular, hormonal, and histological changes during artificial pineapple flowering by Ethrel ® 48 treatment. Histological analyses of the shoot apical meristem, leaf gibberellic acid (GA 3 ), and ethylene quantification were carried out during the first 72h after Ethrel ® 48 treatment. Expression profiles from ethylene biosynthesis (AcACS2 and AcACO1), gibberellin metabolism (AcGA2-ox1 and AcDELLA1), and flower development (FT-like gene (AcFT), LFY-like gene (AcLFY), and a PISTILLATA-like gene (AcPI)) genes were analysed during the first 24h after Ethrel ® 48 treatment. Differentiation processes of the shoot apical meristem into flower buds were already present in the first 72h after Ethrel ® 48 treatment. Ethrel ® 48 lead to a reduction in GA 3 levels, probably triggered by elevated ethylene levels and the positive regulation AcGA2-ox1. AcLFY activation upon Ethrel ® 48 may also have contributed to the reduction of GA 3 levels and, along with the up-regulation of AcPI, are probably associated with the flower induction activation. AcFT and AcDELLA1 do not seem to be regulated by GA 3 and ethylene. Decreased GA 3 and increased ethylene levels suggest an accumulation of AcDELLA1, which may display an important role in pineapple flowering induction. Thus, this study shows that molecular, hormonal, and histological changes are present right after Ethrel ® 48 treatment, providing new insights into how pineapple flowering occurs under natural conditions. Copyright © 2016 Elsevier GmbH. All rights reserved.

A Spreadsheet Simulation of the Monty Hall Problem

ERIC Educational Resources Information Center

Patterson, Mike C.; Harmel, Bob; Friesen, Dan

2010-01-01

The "Monty Hall" problem or "Three Door" problem--where a person chooses one of three doors in hope of winning a valuable prize but is subsequently offered the choice of changing his or her selection--is a well known and often discussed probability problem. In this paper, the structure, history, and ultimate solution of the…

Development and validation of an UPLC-MS/MS method for the quantification of tamoxifen and its main metabolites in human scalp hair.

PubMed

Drooger, Jan C; Jager, Agnes; Lam, Mei-Ho; den Boer, Mathilda D; Sleijfer, Stefan; Mathijssen, Ron H J; de Bruijn, Peter

2015-10-10

The aim of this study was to validate an earlier developed high-performance highly sensitive ultra performance liquid chromatography/tandem mass spectrometry (UPLC-MS/MS) method for quantification of tamoxifen and its three main metabolites (N-desmethyl-tamoxifen, 4-hydroxy-tamoxifen and 4-hydroxy-N-desmethyl-tamoxifen) in scalp hair. This non-invasive method might, by segmental analysis of hair, be useful in the determination of the concentration of drugs and its metabolites over time, which can be used to study a wide variety of clinical relevant questions. Hair samples (150-300 hair strands, cut as close to the scalp as possible from the posterior vertex region of the head) were collected from female patients taking tamoxifen 20mg daily (n=19). The analytes were extracted using a liquid-liquid extraction procedure with carbonate buffer at pH 8.8 and a mixture of n-hexane/isopropranol method, followed by UPLC-MS/MS chromatography, based on an earlier validated method. The calibration curves were linear in the range of 1.00-200 pmol for tamoxifen and N-desmethyl-tamoxifen, with lower limit of quantitation of 1.00 pmol and 0.100-20.0 pmol with lower limit of quantitation of 0.100 pmol for endoxifen and 4-hydroxy-tamoxifen. Assay performance was fair with a within-run and between-run variability less than 9.24 at the three quality control samples and less than 15.7 for the lower limit of quantitation. Importantly, a steep linear decline was observed from distal to proximal hair segments. Probably, this is due to UV exposure as we showed degradation of tamoxifen and its metabolites after exposure to UV-light. Furthermore, higher concentrations of tamoxifen were found in black hair samples compared to blond and brown hair samples. We conclude that measurement of the concentration of tamoxifen and its main metabolites in hair is possible, with the selective, sensitive, accurate and precise UPLC-MS/MS method. However, for tamoxifen, it seems not possible to determine exposure over time with segmental analysis of hair, probably largely due to the effect of UV irradiation. Further research should therefore focus on quantification of other anticancer drugs, in segmented scalp hair, that are less sensitive to UV irradiation. Copyright © 2015 Elsevier B.V. All rights reserved.

A Measure Approximation for Distributionally Robust PDE-Constrained Optimization Problems

DOE PAGES

Kouri, Drew Philip

2017-12-19

In numerous applications, scientists and engineers acquire varied forms of data that partially characterize the inputs to an underlying physical system. This data is then used to inform decisions such as controls and designs. Consequently, it is critical that the resulting control or design is robust to the inherent uncertainties associated with the unknown probabilistic characterization of the model inputs. Here in this work, we consider optimal control and design problems constrained by partial differential equations with uncertain inputs. We do not assume a known probabilistic model for the inputs, but rather we formulate the problem as a distributionally robustmore » optimization problem where the outer minimization problem determines the control or design, while the inner maximization problem determines the worst-case probability measure that matches desired characteristics of the data. We analyze the inner maximization problem in the space of measures and introduce a novel measure approximation technique, based on the approximation of continuous functions, to discretize the unknown probability measure. Finally, we prove consistency of our approximated min-max problem and conclude with numerical results.« less

A simple dilute and shoot methodology for the identification and quantification of illegal insulin.

PubMed

Vanhee, Celine; Janvier, Steven; Moens, Goedele; Deconinck, Eric; Courselle, Patricia

2016-10-01

The occurrence of illegal medicines is a well-established global problem and concerns mostly small molecules. However, due to the advances in genomics and recombinant expression technologies there is an increased development of polypeptide therapeutics. Insulin is one of the best known polypeptide drug, and illegal versions of this medicine led to lethal incidents in the past. Therefore, it is crucial for the public health sector to develop reliable, efficient, cheap, unbiased and easily applicable active pharmaceutical ingredient (API) identification and quantification strategies for routine analysis of suspected illegal insulins. Here we demonstrate that our combined label-free full scan approach is not only able to distinguish between all those different versions of insulin and the insulins originating from different species, but also able to chromatographically separate human insulin and insulin lispro in conditions that are compatible with mass spectrometry (MS). Additionally, we were also able to selectively quantify the different insulins, including human insulin and insulin lispro according to the validation criteria, put forward by the United Nations (UN), for the analysis of seized illicit drugs. The proposed identification and quantification method is currently being used in our official medicines control laboratory to analyze insulins retrieved from the illegal market.

Immunohistochemistry as an Important Tool in Biomarkers Detection and Clinical Practice

PubMed Central

de Matos, Leandro Luongo; Trufelli, Damila Cristina; de Matos, Maria Graciela Luongo; da Silva Pinhal, Maria Aparecida

2010-01-01

The immunohistochemistry technique is used in the search for cell or tissue antigens that range from amino acids and proteins to infectious agents and specific cellular populations. The technique comprises two phases: (1) slides preparation and stages involved for the reaction; (2) interpretation and quantification of the obtained expression. Immunohistochemistry is an important tool for scientific research and also a complementary technique for the elucidation of differential diagnoses which are not determinable by conventional analysis with hematoxylin and eosin. In the last couple of decades there has been an exponential increase in publications on immunohistochemistry and immunocytochemistry techniques. This review covers the immunohistochemistry technique; its history, applications, importance, limitations, difficulties, problems and some aspects related to results interpretation and quantification. Future developments on the immunohistochemistry technique and its expression quantification should not be disseminated in two languages—that of the pathologist and another of clinician or surgeon. The scientific, diagnostic and prognostic applications of this methodology must be explored in a bid to benefit of patient. In order to achieve this goal a collaboration and pooling of knowledge from both of these valuable medical areas is vital PMID:20212918

Gambling and problem gambling among recently sentenced women in new zealand prisons.

PubMed

Abbott, Max W; McKenna, Brian G

2005-01-01

Ninety-four recently sentenced women prisoners were interviewed to assess aspects of their gambling involvement, problem gambling and relationships between gambling and criminal offending. A third of the women, on the basis of their SOGS-R scores, were assessed as lifetime probable pathological gamblers and just under a quarter were assessed as probable pathological gamblers during the 6 months prior to imprisonment. For women prisoners, a preference for non-casino gaming machines and housie were predictive of problem gambling. Relative to non-problem gamblers, problem gamblers experienced higher rates of childhood conduct disorder and current non-psychotic mental disorder. Just over a quarter of prisoners and a half of the problem gamblers had committed a crime to obtain money to gamble. Few women said their early offending or convictions related to gambling. It was concluded that most women were "criminals first and problem gamblers second" rather than people whose offending careers commenced as a consequence of problem gambling. However, the extent of problem gambling-related offending among the women prisoners highlights the potential for comprehensive assessment and treatment programs in prison to reduce recidivism and other adverse impacts of problem gambling and gambling-related offending.

Kant and the scientific study of consciousness.

PubMed

Sturm, Thomas; Wunderlich, Falk

2010-01-01

We argue that Kant's views about consciousness, the mind-body problem and the status of psychology as a science all differ drastically from the way in which these topics are conjoined in present debates about the prominent idea of a science of consciousness. Kant never used the concept of consciousness in the now dominant sense of phenomenal qualia; his discussions of the mind-body problem center not on the reducibility of mental properties but of substances; and his views about the possibility of psychology as a science did not employ the requirement of a mechanistic explanation, but of a quantification of phenomena. This shows strikingly how deeply philosophical problems and conceptions can change even if they look similar on the surface.

Inference of emission rates from multiple sources using Bayesian probability theory.

PubMed

Yee, Eugene; Flesch, Thomas K

2010-03-01

The determination of atmospheric emission rates from multiple sources using inversion (regularized least-squares or best-fit technique) is known to be very susceptible to measurement and model errors in the problem, rendering the solution unusable. In this paper, a new perspective is offered for this problem: namely, it is argued that the problem should be addressed as one of inference rather than inversion. Towards this objective, Bayesian probability theory is used to estimate the emission rates from multiple sources. The posterior probability distribution for the emission rates is derived, accounting fully for the measurement errors in the concentration data and the model errors in the dispersion model used to interpret the data. The Bayesian inferential methodology for emission rate recovery is validated against real dispersion data, obtained from a field experiment involving various source-sensor geometries (scenarios) consisting of four synthetic area sources and eight concentration sensors. The recovery of discrete emission rates from three different scenarios obtained using Bayesian inference and singular value decomposition inversion are compared and contrasted.

Framing From Experience: Cognitive Processes and Predictions of Risky Choice.

PubMed

Gonzalez, Cleotilde; Mehlhorn, Katja

2016-07-01

A framing bias shows risk aversion in problems framed as "gains" and risk seeking in problems framed as "losses," even when these are objectively equivalent and probabilities and outcomes values are explicitly provided. We test this framing bias in situations where decision makers rely on their own experience, sampling the problem's options (safe and risky) and seeing the outcomes before making a choice. In Experiment 1, we replicate the framing bias in description-based decisions and find risk indifference in gains and losses in experience-based decisions. Predictions of an Instance-Based Learning model suggest that objective probabilities as well as the number of samples taken are factors that contribute to the lack of framing effect. We test these two factors in Experiment 2 and find no framing effect when a few samples are taken but when large samples are taken, the framing effect appears regardless of the objective probability values. Implications of behavioral results and cognitive modeling are discussed. Copyright © 2015 Cognitive Science Society, Inc.

Quantification of Liver Iron with MRI: State of the Art and Remaining Challenges

PubMed Central

Hernando, Diego; Levin, Yakir S; Sirlin, Claude B; Reeder, Scott B

2015-01-01

Liver iron overload is the histological hallmark of hereditary hemochromatosis and transfusional hemosiderosis, and can also occur in chronic hepatopathies. Iron overload can result in liver damage, with the eventual development of cirrhosis, liver failure and hepatocellular carcinoma. Assessment of liver iron levels is necessary for detection and quantitative staging of iron overload, and monitoring of iron-reducing treatments. This article discusses the need for non-invasive assessment of liver iron, and reviews qualitative and quantitative methods with a particular emphasis on MRI. Specific MRI methods for liver iron quantification include signal intensity ratio as well as R2 and R2* relaxometry techniques. Methods that are in clinical use, as well as their limitations, are described. Remaining challenges, unsolved problems, and emerging techniques to provide improved characterization of liver iron deposition are discussed. PMID:24585403

Quantification of functional genes from procaryotes in soil by PCR.

PubMed

Sharma, Shilpi; Radl, Viviane; Hai, Brigitte; Kloos, Karin; Fuka, Mirna Mrkonjic; Engel, Marion; Schauss, Kristina; Schloter, Michael

2007-03-01

Controlling turnover processes and fluxes in soils and other environments requires information about the gene pool and possibilities for its in situ induction. Therefore in the recent years there has been a growing interest in genes and transcripts coding for metabolic enzymes. Besides questions addressing redundancy and diversity, more and more attention is given on the abundance of specific DNA and mRNA in the different habitats. This review will describe several PCR techniques that are suitable for quantification of functional genes and transcripts such as MPN-PCR, competitive PCR and real-time PCR. The advantages and disadvantages of the mentioned methods are discussed. In addition, the problems of quantitative extraction of nucleic acid and substances that inhibit polymerase are described. Finally, some examples from recent papers are given to demonstrate the applicability and usefulness of the different approaches.

Risk-based decision making to manage water quality failures caused by combined sewer overflows

NASA Astrophysics Data System (ADS)

Sriwastava, A. K.; Torres-Matallana, J. A.; Tait, S.; Schellart, A.

2017-12-01

Regulatory authorities set certain environmental permit for water utilities such that the combined sewer overflows (CSO) managed by these companies conform to the regulations. These utility companies face the risk of paying penalty or negative publicity in case they breach the environmental permit. These risks can be addressed by designing appropriate solutions such as investing in additional infrastructure which improve the system capacity and reduce the impact of CSO spills. The performance of these solutions is often estimated using urban drainage models. Hence, any uncertainty in these models can have a significant effect on the decision making process. This study outlines a risk-based decision making approach to address water quality failure caused by CSO spills. A calibrated lumped urban drainage model is used to simulate CSO spill quality in Haute-Sûre catchment in Luxembourg. Uncertainty in rainfall and model parameters is propagated through Monte Carlo simulations to quantify uncertainty in the concentration of ammonia in the CSO spill. A combination of decision alternatives such as the construction of a storage tank at the CSO and the reduction in the flow contribution of catchment surfaces are selected as planning measures to avoid the water quality failure. Failure is defined as exceedance of a concentration-duration based threshold based on Austrian emission standards for ammonia (De Toffol, 2006) with a certain frequency. For each decision alternative, uncertainty quantification results into a probability distribution of the number of annual CSO spill events which exceed the threshold. For each alternative, a buffered failure probability as defined in Rockafellar & Royset (2010), is estimated. Buffered failure probability (pbf) is a conservative estimate of failure probability (pf), however, unlike failure probability, it includes information about the upper tail of the distribution. A pareto-optimal set of solutions is obtained by performing mean- pbf optimization. The effectiveness of using buffered failure probability compared to the failure probability is tested by comparing the solutions obtained by using mean-pbf and mean-pf optimizations.

Monte Carlo, Probability, Algebra, and Pi.

ERIC Educational Resources Information Center

Hinders, Duane C.

1981-01-01

The uses of random number generators are illustrated in three ways: (1) the solution of a probability problem using a coin; (2) the solution of a system of simultaneous linear equations using a die; and (3) the approximation of pi using darts. (MP)

Lung sound analysis for wheeze episode detection.

PubMed

Jain, Abhishek; Vepa, Jithendra

2008-01-01

Listening and interpreting lung sounds by a stethoscope had been an important component of screening and diagnosing lung diseases. However this practice has always been vulnerable to poor audibility, inter-observer variations (between different physicians) and poor reproducibility. Thus computerized analysis of lung sounds for objective diagnosis of lung diseases is seen as a probable aid. In this paper we aim at automatic analysis of lung sounds for wheeze episode detection and quantification. The proposed algorithm integrates and analyses the set of parameters based on ATS (American Thoracic Society) definition of wheezes. It is very robust, computationally simple and yielded sensitivity of 84% and specificity of 86%.

Understanding Pre-Quantitative Risk in Projects

NASA Technical Reports Server (NTRS)

Cooper, Lynne P.

2011-01-01

Standard approaches to risk management in projects depend on the ability of teams to identify risks and quantify the probabilities and consequences of these risks (e.g., the 5 x 5 risk matrix). However, long before quantification does - or even can - occur, and long after, teams make decisions based on their pre-quantitative understanding of risk. These decisions can have long-lasting impacts on the project. While significant research has looked at the process of how to quantify risk, our understanding of how teams conceive of and manage pre-quantitative risk is lacking. This paper introduces the concept of pre-quantitative risk and discusses the implications of addressing pre-quantitative risk in projects.

Statistical Inference in Graphical Models

DTIC Science & Technology

2008-06-17

fuse probability theory and graph theory in such a way as to permit efficient rep- resentation and computation with probability distributions. They...message passing. 59 viii 1. INTRODUCTION In approaching real-world problems, we often need to deal with uncertainty. Probability and statis- tics provide a...dynamic programming methods. However, for many sensors of interest, the signal-to-noise ratio does not allow such a treatment. Another source of

Design and simulation of stratified probability digital receiver with application to the multipath communication

NASA Technical Reports Server (NTRS)

Deal, J. H.

1975-01-01

One approach to the problem of simplifying complex nonlinear filtering algorithms is through using stratified probability approximations where the continuous probability density functions of certain random variables are represented by discrete mass approximations. This technique is developed in this paper and used to simplify the filtering algorithms developed for the optimum receiver for signals corrupted by both additive and multiplicative noise.

FDA 101: Dietary Supplements

MedlinePlus

... too good to be true, it probably is. Report Problems Adverse effects with dietary supplements should be ... immediately. Both of you are then encouraged to report this problem to FDA. For information on how ...

Quantification and study of the L-DOPA decarboxylase expression in gastric adenocarcinoma cells treated with chemotherapeutic substances.

PubMed

Korbakis, Dimitrios; Fragoulis, Emmanuel G; Scorilas, Andreas

2013-03-01

3,4-Dihydroxy-L-phenylalanine decarboxylase (DDC) is an enzyme implicated in the biosynthetic pathways of the neurotransmitters dopamine and probably serotonin. DDC gene expression has been studied in numerous malignancies and the corresponding data have shown remarkable alterations in the mRNA and/or protein levels encoded by the gene. The aim of this study was to examine any modulations in the DDC mRNA levels in gastric cancer cells after their treatment with the chemotherapeutic agents 5-fluorouracil, leucovorin, irinotecan, etoposide, cisplatin, and taxol. The sensitivity of the AGS gastric adenocarcinoma cells to the antineoplastic drugs was evaluated using the MTT assay. Total RNA was extracted and reverse transcribed into cDNA. A highly sensitive quantitative real-time PCR methodology was developed for the quantification of DDC mRNA. GAPDH was used as a housekeeping gene. Relative quantification analysis was carried out using the comparative C T method ((Equation is included in full-text article.)). The treatment of AGS cells with several concentrations of various broadly used anticancer drugs resulted in significant modulations of the DDC mRNA levels compared with those in the untreated cells in a time-specific and drug-specific manner. Generally, DDC expression levels appeared to decrease after three time periods of exposure to the selected chemotherapeutic agents, suggesting a characteristic DDC mRNA expression profile that is possibly related to the mechanism of each drug. Our experimental data show that the DDC gene might serve as a new potential molecular biomarker predicting treatment response in gastric cancer cells.

Influence of Temperature and Predation on Survival of Salmonella enterica Serovar Typhimurium and Expression of invA in Soil and Manure-Amended Soil▿

PubMed Central

García, R.; Bælum, J.; Fredslund, L.; Santorum, P.; Jacobsen, C. S.

2010-01-01

The effects of three temperatures (5, 15, and 25°C) on the survival of Salmonella enterica serovar Typhimurium in topsoil were investigated in small microcosms by three different techniques: plate counting, invA gene quantification, and invA mRNA quantification. Differences in survival were related to the effect of protozoan predation. Tetracycline-resistant Salmonella serovar Typhimurium was inoculated into soil and manure-amended soil at 1.5 × 108 cells g soil−1. Population densities were determined by plate counting and by molecular methods and monitored for 42 days. Simultaneous extraction of RNA and DNA, followed by quantitative PCR, was used to investigate invA gene levels and expression. Analysis by these three techniques showed that Salmonella serovar Typhimurium survived better at 5°C. Comparing DNA and CFU levels, significantly higher values were determined by DNA-based techniques. invA mRNA levels showed a fast decrease in activity, with no detectable mRNA after an incubation period of less than 4 days in any of the soil scenarios. A negative correlation was found between Salmonella serovar Typhimurium CFU levels and protozoan most probable numbers, and we propose the role of the predator-prey interaction as a factor to explain the die-off of the introduced strain by both culture- and DNA quantification-based methods. The results indicate that temperature, manure, and protozoan predation are important factors influencing the survival of Salmonella serovar Typhimurium in soil. PMID:20562283

A General Uncertainty Quantification Methodology for Cloud Microphysical Property Retrievals

NASA Astrophysics Data System (ADS)

Tang, Q.; Xie, S.; Chen, X.; Zhao, C.

2014-12-01

The US Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program provides long-term (~20 years) ground-based cloud remote sensing observations. However, there are large uncertainties in the retrieval products of cloud microphysical properties based on the active and/or passive remote-sensing measurements. To address this uncertainty issue, a DOE Atmospheric System Research scientific focus study, Quantification of Uncertainties in Cloud Retrievals (QUICR), has been formed. In addition to an overview of recent progress of QUICR, we will demonstrate the capacity of an observation-based general uncertainty quantification (UQ) methodology via the ARM Climate Research Facility baseline cloud microphysical properties (MICROBASE) product. This UQ method utilizes the Karhunen-Loéve expansion (KLE) and Central Limit Theorems (CLT) to quantify the retrieval uncertainties from observations and algorithm parameters. The input perturbations are imposed on major modes to take into account the cross correlations between input data, which greatly reduces the dimension of random variables (up to a factor of 50) and quantifies vertically resolved full probability distribution functions of retrieved quantities. Moreover, this KLE/CLT approach has the capability of attributing the uncertainties in the retrieval output to individual uncertainty source and thus sheds light on improving the retrieval algorithm and observations. We will present the results of a case study for the ice water content at the Southern Great Plains during an intensive observing period on March 9, 2000. This work is performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.

Accuracy Quantification of the Loci-CHEM Code for Chamber Wall Heat Transfer in a GO2/GH2 Single Element Injector Model Problem

NASA Technical Reports Server (NTRS)

West, Jeff; Westra, Doug; Lin, Jeff; Tucker, Kevin

2006-01-01

A robust rocket engine combustor design and development process must include tools which can accurately predict the multi-dimensional thermal environments imposed on solid surfaces by the hot combustion products. Currently, empirical methods used in the design process are typically one dimensional and do not adequately account for the heat flux rise rate in the near-injector region of the chamber. Computational Fluid Dynamics holds promise to meet the design tool requirement, but requires accuracy quantification, or validation, before it can be confidently applied in the design process. This effort presents the beginning of such a validation process for the Loci-CHEM CFD code. The model problem examined here is a gaseous oxygen (GO2)/gaseous hydrogen (GH2) shear coaxial single element injector operating at a chamber pressure of 5.42 MPa. The GO2/GH2 propellant combination in this geometry represents one the simplest rocket model problems and is thus foundational to subsequent validation efforts for more complex injectors. Multiple steady state solutions have been produced with Loci-CHEM employing different hybrid grids and two-equation turbulence models. Iterative convergence for each solution is demonstrated via mass conservation, flow variable monitoring at discrete flow field locations as a function of solution iteration and overall residual performance. A baseline hybrid was used and then locally refined to demonstrate grid convergence. Solutions were obtained with three variations of the k-omega turbulence model.

Engendering drug problems: Materialising gender in the DUDIT and other screening and diagnostic 'apparatuses'.

PubMed

Dwyer, Robyn; Fraser, Suzanne

2017-06-01

It is widely accepted that alcohol and other drug consumption is profoundly gendered. Just where this gendering is occurring, however, remains the subject of debate. We contend that one important and overlooked site where the gendering of substance consumption and addiction is taking place is through AOD research itself: in particular, through the addiction screening and diagnostic tools designed to measure and track substance consumption and problems within populations. These tools establish key criteria and set numerical threshold scores for the identification of problems. In many of these tools, separate threshold scores for women and men are established or recommended. Drawing on Karen Barad's concept of post-humanist performativity, in this article we examine the ways in which gender itself is being materialised by these apparatuses of measurement. We focus primarily on the Drug Use Disorders Identification Test (DUDIT) tool as an exemplar of gendering processes that operate across addiction tools more broadly. We consider gendering processes operating through tools questions themselves and we also examine the quantification and legitimation processes used in establishing gender difference and the implications these have for women. We find tools rely on and reproduce narrow and marginalising assumptions about women as essentially fragile and vulnerable and simultaneously reinforce normative expectations that women sacrifice pleasure. The seemingly objective and neutral quantification processes operating in tools naturalise gender as they enact it. Copyright © 2017 Elsevier B.V. All rights reserved.

Accuracy Quantification of the Loci-CHEM Code for Chamber Wall Heat Fluxes in a G02/GH2 Single Element Injector Model Problem

NASA Technical Reports Server (NTRS)

West, Jeff; Westra, Doug; Lin, Jeff; Tucker, Kevin

2006-01-01

A robust rocket engine combustor design and development process must include tools which can accurately predict the multi-dimensional thermal environments imposed on solid surfaces by the hot combustion products. Currently, empirical methods used in the design process are typically one dimensional and do not adequately account for the heat flux rise rate in the near-injector region of the chamber. Computational Fluid Dynamics holds promise to meet the design tool requirement, but requires accuracy quantification, or validation, before it can be confidently applied in the design process. This effort presents the beginning of such a validation process for the Loci- CHEM CPD code. The model problem examined here is a gaseous oxygen (GO2)/gaseous hydrogen (GH2) shear coaxial single element injector operating at a chamber pressure of 5.42 MPa. The GO2/GH2 propellant combination in this geometry represents one the simplest rocket model problems and is thus foundational to subsequent validation efforts for more complex injectors. Multiple steady state solutions have been produced with Loci-CHEM employing different hybrid grids and two-equation turbulence models. Iterative convergence for each solution is demonstrated via mass conservation, flow variable monitoring at discrete flow field locations as a function of solution iteration and overall residual performance. A baseline hybrid grid was used and then locally refined to demonstrate grid convergence. Solutions were also obtained with three variations of the k-omega turbulence model.

Estimating rare events in biochemical systems using conditional sampling.

PubMed

Sundar, V S

2017-01-28

The paper focuses on development of variance reduction strategies to estimate rare events in biochemical systems. Obtaining this probability using brute force Monte Carlo simulations in conjunction with the stochastic simulation algorithm (Gillespie's method) is computationally prohibitive. To circumvent this, important sampling tools such as the weighted stochastic simulation algorithm and the doubly weighted stochastic simulation algorithm have been proposed. However, these strategies require an additional step of determining the important region to sample from, which is not straightforward for most of the problems. In this paper, we apply the subset simulation method, developed as a variance reduction tool in the context of structural engineering, to the problem of rare event estimation in biochemical systems. The main idea is that the rare event probability is expressed as a product of more frequent conditional probabilities. These conditional probabilities are estimated with high accuracy using Monte Carlo simulations, specifically the Markov chain Monte Carlo method with the modified Metropolis-Hastings algorithm. Generating sample realizations of the state vector using the stochastic simulation algorithm is viewed as mapping the discrete-state continuous-time random process to the standard normal random variable vector. This viewpoint opens up the possibility of applying more sophisticated and efficient sampling schemes developed elsewhere to problems in stochastic chemical kinetics. The results obtained using the subset simulation method are compared with existing variance reduction strategies for a few benchmark problems, and a satisfactory improvement in computational time is demonstrated.

Assessment and quantification of patient set-up errors in nasopharyngeal cancer patients and their biological and dosimetric impact in terms of generalized equivalent uniform dose (gEUD), tumour control probability (TCP) and normal tissue complication probability (NTCP)

PubMed Central

Marcie, S; Fellah, M; Chami, S; Mekki, F

2015-01-01

Objective: The aim of this study is to assess and quantify patients' set-up errors using an electronic portal imaging device and to evaluate their dosimetric and biological impact in terms of generalized equivalent uniform dose (gEUD) on predictive models, such as the tumour control probability (TCP) and the normal tissue complication probability (NTCP). Methods: 20 patients treated for nasopharyngeal cancer were enrolled in the radiotherapy–oncology department of HCA. Systematic and random errors were quantified. The dosimetric and biological impact of these set-up errors on the target volume and the organ at risk (OARs) coverage were assessed using calculation of dose–volume histogram, gEUD, TCP and NTCP. For this purpose, an in-house software was developed and used. Results: The standard deviations (1SDs) of the systematic set-up and random set-up errors were calculated for the lateral and subclavicular fields and gave the following results: ∑ = 0.63 ± (0.42) mm and σ = 3.75 ± (0.79) mm, respectively. Thus a planning organ at risk volume (PRV) margin of 3 mm was defined around the OARs, and a 5-mm margin used around the clinical target volume. The gEUD, TCP and NTCP calculations obtained with and without set-up errors have shown increased values for tumour, where ΔgEUD (tumour) = 1.94% Gy (p = 0.00721) and ΔTCP = 2.03%. The toxicity of OARs was quantified using gEUD and NTCP. The values of ΔgEUD (OARs) vary from 0.78% to 5.95% in the case of the brainstem and the optic chiasm, respectively. The corresponding ΔNTCP varies from 0.15% to 0.53%, respectively. Conclusion: The quantification of set-up errors has a dosimetric and biological impact on the tumour and on the OARs. The developed in-house software using the concept of gEUD, TCP and NTCP biological models has been successfully used in this study. It can be used also to optimize the treatment plan established for our patients. Advances in knowledge: The gEUD, TCP and NTCP may be more suitable tools to assess the treatment plans before treating the patients. PMID:25882689

Hydrologic drought prediction under climate change: Uncertainty modeling with Dempster-Shafer and Bayesian approaches

NASA Astrophysics Data System (ADS)

Raje, Deepashree; Mujumdar, P. P.

2010-09-01

Representation and quantification of uncertainty in climate change impact studies are a difficult task. Several sources of uncertainty arise in studies of hydrologic impacts of climate change, such as those due to choice of general circulation models (GCMs), scenarios and downscaling methods. Recently, much work has focused on uncertainty quantification and modeling in regional climate change impacts. In this paper, an uncertainty modeling framework is evaluated, which uses a generalized uncertainty measure to combine GCM, scenario and downscaling uncertainties. The Dempster-Shafer (D-S) evidence theory is used for representing and combining uncertainty from various sources. A significant advantage of the D-S framework over the traditional probabilistic approach is that it allows for the allocation of a probability mass to sets or intervals, and can hence handle both aleatory or stochastic uncertainty, and epistemic or subjective uncertainty. This paper shows how the D-S theory can be used to represent beliefs in some hypotheses such as hydrologic drought or wet conditions, describe uncertainty and ignorance in the system, and give a quantitative measurement of belief and plausibility in results. The D-S approach has been used in this work for information synthesis using various evidence combination rules having different conflict modeling approaches. A case study is presented for hydrologic drought prediction using downscaled streamflow in the Mahanadi River at Hirakud in Orissa, India. Projections of n most likely monsoon streamflow sequences are obtained from a conditional random field (CRF) downscaling model, using an ensemble of three GCMs for three scenarios, which are converted to monsoon standardized streamflow index (SSFI-4) series. This range is used to specify the basic probability assignment (bpa) for a Dempster-Shafer structure, which represents uncertainty associated with each of the SSFI-4 classifications. These uncertainties are then combined across GCMs and scenarios using various evidence combination rules given by the D-S theory. A Bayesian approach is also presented for this case study, which models the uncertainty in projected frequencies of SSFI-4 classifications by deriving a posterior distribution for the frequency of each classification, using an ensemble of GCMs and scenarios. Results from the D-S and Bayesian approaches are compared, and relative merits of each approach are discussed. Both approaches show an increasing probability of extreme, severe and moderate droughts and decreasing probability of normal and wet conditions in Orissa as a result of climate change.

Invoking Arbitrary Units Is Not a Solution to the Problem of Quantification in the Social Sciences

ERIC Educational Resources Information Center

Barrett, Paul

2011-01-01

The article by Stephen Humphry (this issue) is a technical tour de force. At one level, the author marvels at the ingenuity and sophisticated logic and argument on display. This is impressive work and thinking whichever way one looks at it. However, after twice re-reading the manuscript, the same question arises on the author's mind: What exactly…

Game Theory and Uncertainty Quantification for Cyber Defense Applications

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chatterjee, Samrat; Halappanavar, Mahantesh; Tipireddy, Ramakrishna

Cyber-system defenders face the challenging task of protecting critical assets and information continually against multiple types of malicious attackers. Defenders typically operate within resource constraints while attackers operate at relatively low costs. As a result, design and development of resilient cyber-systems that can support mission goals under attack while accounting for the dynamics between attackers and defenders is an important research problem.

Inverse regression-based uncertainty quantification algorithms for high-dimensional models: Theory and practice

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Weixuan; Lin, Guang; Li, Bing

2016-09-01

A well-known challenge in uncertainty quantification (UQ) is the "curse of dimensionality". However, many high-dimensional UQ problems are essentially low-dimensional, because the randomness of the quantity of interest (QoI) is caused only by uncertain parameters varying within a low-dimensional subspace, known as the sufficient dimension reduction (SDR) subspace. Motivated by this observation, we propose and demonstrate in this paper an inverse regression-based UQ approach (IRUQ) for high-dimensional problems. Specifically, we use an inverse regression procedure to estimate the SDR subspace and then convert the original problem to a low-dimensional one, which can be efficiently solved by building a response surface model such as a polynomial chaos expansion. The novelty and advantages of the proposed approach is seen in its computational efficiency and practicality. Comparing with Monte Carlo, the traditionally preferred approach for high-dimensional UQ, IRUQ with a comparable cost generally gives much more accurate solutions even for high-dimensional problems, and even when the dimension reduction is not exactly sufficient. Theoretically, IRUQ is proved to converge twice as fast as the approach it uses seeking the SDR subspace. For example, while a sliced inverse regression method converges to the SDR subspace at the rate ofmore » $$O(n^{-1/2})$$, the corresponding IRUQ converges at $$O(n^{-1})$$. IRUQ also provides several desired conveniences in practice. It is non-intrusive, requiring only a simulator to generate realizations of the QoI, and there is no need to compute the high-dimensional gradient of the QoI. Finally, error bars can be derived for the estimation results reported by IRUQ.« less

Points on the Path to Probability.

ERIC Educational Resources Information Center

Kiernan, James F.

2001-01-01

Presents the problem of points and the development of the binomial triangle, or Pascal's triangle. Examines various attempts to solve this problem to give students insight into the nature of mathematical discovery. (KHR)

Gardner's Two Children Problems and Variations: Puzzles with Conditional Probability and Sample Spaces

ERIC Educational Resources Information Center

Taylor, Wendy; Stacey, Kaye

2014-01-01

This article presents "The Two Children Problem," published by Martin Gardner, who wrote a famous and widely-read math puzzle column in the magazine "Scientific American," and a problem presented by puzzler Gary Foshee. This paper explains the paradox of Problems 2 and 3 and many other variations of the theme. Then the authors…

Math Ties: Problem Solving, Logic Teasers, and Math Puzzles All "Tied" to the Math Curriculum. Book A1.

ERIC Educational Resources Information Center

Santi, Terri

This book contains a classroom-tested approach to the teaching of problem solving to all students in Grades 4-6, regardless of ability. Information on problem solving in general is provided, then mathematical problems on logic, whole numbers, number theory, fractions, decimals, geometry, ratio, proportion, percent, probability, sets, and…

Predicting non-square 2D dice probabilities

NASA Astrophysics Data System (ADS)

Pender, G. A. T.; Uhrin, M.

2014-07-01

The prediction of the final state probabilities of a general cuboid randomly thrown onto a surface is a problem that naturally arises in the minds of men and women familiar with regular cubic dice and the basic concepts of probability. Indeed, it was considered by Newton in 1664 (Newton 1967 The Mathematical Papers of Issac Newton vol I (Cambridge: Cambridge University Press) pp 60-1). In this paper we make progress on the 2D problem (which can be realized in 3D by considering a long cuboid, or alternatively a rectangular cross-sectioned dreidel). For the two-dimensional case we suggest that the ratio of the probabilities of landing on each of the two sides is given by \\frac{\\sqrt{{{k}^{2}}+{{l}^{2}}}-k}{\\sqrt{{{k}^{2}}+{{l}^{2}}}-l}\\frac{arctan \\frac{l}{k}}{arctan \\frac{k}{l}} where k and l are the lengths of the two sides. We test this theory both experimentally and computationally, and find good agreement between our theory, experimental and computational results. Our theory is known, from its derivation, to be an approximation for particularly bouncy or ‘grippy’ surfaces where the die rolls through many revolutions before settling. On real surfaces we would expect (and we observe) that the true probability ratio for a 2D die is a somewhat closer to unity than predicted by our theory. This problem may also have wider relevance in the testing of physics engines.

Reference genes for real-time PCR quantification of messenger RNAs and microRNAs in mouse model of obesity.

PubMed

Matoušková, Petra; Bártíková, Hana; Boušová, Iva; Hanušová, Veronika; Szotáková, Barbora; Skálová, Lenka

2014-01-01

Obesity and metabolic syndrome is increasing health problem worldwide. Among other ways, nutritional intervention using phytochemicals is important method for treatment and prevention of this disease. Recent studies have shown that certain phytochemicals could alter the expression of specific genes and microRNAs (miRNAs) that play a fundamental role in the pathogenesis of obesity. For study of the obesity and its treatment, monosodium glutamate (MSG)-injected mice with developed central obesity, insulin resistance and liver lipid accumulation are frequently used animal models. To understand the mechanism of phytochemicals action in obese animals, the study of selected genes expression together with miRNA quantification is extremely important. For this purpose, real-time quantitative PCR is a sensitive and reproducible method, but it depends on proper normalization entirely. The aim of present study was to identify the appropriate reference genes for mRNA and miRNA quantification in MSG mice treated with green tea catechins, potential anti-obesity phytochemicals. Two sets of reference genes were tested: first set contained seven commonly used genes for normalization of messenger RNA, the second set of candidate reference genes included ten small RNAs for normalization of miRNA. The expression stability of these reference genes were tested upon treatment of mice with catechins using geNorm, NormFinder and BestKeeper algorithms. Selected normalizers for mRNA quantification were tested and validated on expression of quinone oxidoreductase, biotransformation enzyme known to be modified by catechins. The effect of selected normalizers for miRNA quantification was tested on two obesity- and diabetes- related miRNAs, miR-221 and miR-29b, respectively. Finally, the combinations of B2M/18S/HPRT1 and miR-16/sno234 were validated as optimal reference genes for mRNA and miRNA quantification in liver and 18S/RPlP0/HPRT1 and sno234/miR-186 in small intestine of MSG mice. These reference genes will be used for mRNA and miRNA normalization in further study of green tea catechins action in obese mice.

Quantification of the vertical translocation rate of soil solid-phase material by the magnetic tracer method

NASA Astrophysics Data System (ADS)

Zhidkin, A. P.; Gennadiev, A. N.

2016-07-01

Approaches to the quantification of the vertical translocation rate of soil solid-phase material by the magnetic tracer method have been developed; the tracer penetration depth and rate have been determined, as well as the radial distribution of the tracer in chernozems (Chernozems) and dark gray forest soils (Luvisols) of Belgorod oblast under natural steppe and forest vegetation and in arable lands under agricultural use of different durations. It has been found that the penetration depth of spherical magnetic particles (SMPs) during their 150-year-occurrence in soils of a forest plot is 68 cm under forest, 58 cm on a 100-year old plowland, and only 49 cm on a 150-year-old plowland. In the chernozems of the steppe plot, the penetration depth of SMPs exceeds the studied depth of 70 cm both under natural vegetation and on the plowlands. The penetration rates of SMPs deep into the soil vary significantly among the key plots: 0.92-1.32 mm/year on the forest plot and 1.47-1.63 mm/year on the steppe plot, probably because of the more active recent turbation activity of soil animals.

Methyl angolensate changes in Khaya ivorensis after fungal infection.

PubMed

Reche, Karine V G; de Souza, Gezimar D; Trapp, Marília A; Rodrigues-Filho, Edson; Silva, Sebastião C; Fernandes, João B; Vieira, Paulo C; Muller, Manfred W; da Silva, M Fátima das G F

2009-12-01

Khaya ivorensis with and without symptoms of stem and branch cankers, caused by Botryosphaeria rhodina were examined in order to determine whether the secondary metabolites in this plant were associated with a chemical defense response. This study provides evidence that the limonoid methyl angolensate (MA) is present at higher concentrations in K. ivorensis with symptoms of stem cankers rather than in the plants without symptoms. A rapid, sensitive and selective HPLC-ESI-MS/MS method (using selected reaction monitoring--SRM--mode) was developed for quantification of MA in all aerials parts of such plants, with a good linearity over a range of 0.1-20.0 g/kg, with r(2)>0.996+/-6.1%. The limits of detection (LOD) and quantification (LOQ) were less than 0.03 g/kg and 0.08 g/kg, respectively. Relative Standard Deviations (RSDs) ranged from 1.7% to 19.2% for all matrices. While the MA concentration did not change in the stem bark, its amounts increased nearly fourfold in stems and by 20% in leaves, when plants with symptoms were compared with those without symptoms. These data suggest that MA plays a role in plant-pathogen interactions, probably as a phytoanticipin.

MR-Consistent Simultaneous Reconstruction of Attenuation and Activity for Non-TOF PET/MR

NASA Astrophysics Data System (ADS)

Heußer, Thorsten; Rank, Christopher M.; Freitag, Martin T.; Dimitrakopoulou-Strauss, Antonia; Schlemmer, Heinz-Peter; Beyer, Thomas; Kachelrieß, Marc

2016-10-01

Attenuation correction (AC) is required for accurate quantification of the reconstructed activity distribution in positron emission tomography (PET). For simultaneous PET/magnetic resonance (MR), however, AC is challenging, since the MR images do not provide direct information on the attenuating properties of the underlying tissue. Standard MR-based AC does not account for the presence of bone and thus leads to an underestimation of the activity distribution. To improve quantification for non-time-of-flight PET/MR, we propose an algorithm which simultaneously reconstructs activity and attenuation distribution from the PET emission data using available MR images as anatomical prior information. The MR information is used to derive voxel-dependent expectations on the attenuation coefficients. The expectations are modeled using Gaussian-like probability functions. An iterative reconstruction scheme incorporating the prior information on the attenuation coefficients is used to update attenuation and activity distribution in an alternating manner. We tested and evaluated the proposed algorithm for simulated 3D PET data of the head and the pelvis region. Activity deviations were below 5% in soft tissue and lesions compared to the ground truth whereas standard MR-based AC resulted in activity underestimation values of up to 12%.

Current role of liquid chromatography-mass spectrometry in clinical and forensic toxicology.

PubMed

Maurer, Hans H

2007-08-01

This paper reviews multi-analyte single-stage and tandem liquid chromatography-mass spectrometry (LC-MS) procedures using different mass analyzers (quadrupole, ion trap, time-of-flight) for screening, identification, and/or quantification of drugs, poisons, and/or their metabolites in blood, plasma, serum, or urine published after 2004. Basic information about the biosample assayed, work-up, LC column, mobile phase, ionization type, mass spectral detection mode, and validation data of each procedure is summarized in tables. The following analytes are covered: drugs of abuse, analgesics, opioids, sedative-hypnotics, benzodiazepines, antidepressants including selective-serotonin reuptake inhibitors (SSRIs), herbal phenalkylamines (ephedrines), oral antidiabetics, antiarrhythmics and other cardiovascular drugs, antiretroviral drugs, toxic alkaloids, quaternary ammonium drugs and herbicides, and dialkylphosphate pesticides. The pros and cons of the reviewed procedures are critically discussed, particularly, the need for studies on matrix effects, selectivity, analyte stability, and the use of stable-isotope labeled internal standards instead of unlabeled therapeutic drugs. In conclusion, LC-MS will probably become a gold standard for detection of very low concentrations particularly in alternative matrices and for quantification in clinical and forensic toxicology. However, some drawbacks still need to be addressed and finally overcome.

Estimating Whether Replacing Time in Active Outdoor Play and Sedentary Video Games With Active Video Games Influences Youth's Mental Health.

PubMed

Janssen, Ian

2016-11-01

The primary objective was to use isotemporal substitution models to estimate whether replacing time spent in sedentary video games (SVGs) and active outdoor play (AOP) with active video games (AVGs) would be associated with changes in youth's mental health. A representative sample of 20,122 Canadian youth in Grades 6-10 was studied. The exposure variables were average hours/day spent playing AVGs, SVGs, and AOP. The outcomes consisted of a negative and internalizing mental health indicator (emotional problems), a positive and internalizing mental health indicator (life satisfaction), and a positive and externalizing mental health indicator (prosocial behavior). Isotemporal substitution models estimated the extent to which replacing time spent in SVGs and AOP with an equivalent amount of time in AVGs had on the mental health indicators. Replacing 1 hour/day of SVGs with 1 hour/day of AVGs was associated with a 6% (95% confidence interval: 3%-9%) reduced probability of high emotional problems, a 4% (2%-7%) increased probability of high life satisfaction, and a 13% (9%-16%) increased probability of high prosocial behavior. Replacing 1 hour/day of AOP with 1 hour/day of AVGs was associated with a 7% (3%-11%) increased probability of high emotional problems, a 3% (1%-5%) reduced probability of high life satisfaction, and a 6% (2%-9%) reduced probability of high prosocial behavior. Replacing SVGs with AVGs was associated with more preferable mental health indicators. Conversely, replacing AOP with AVGs was associated with more deleterious mental health indicators. Copyright © 2016 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.

Quantification of EEG reactivity in comatose patients.

PubMed

Hermans, Mathilde C; Westover, M Brandon; van Putten, Michel J A M; Hirsch, Lawrence J; Gaspard, Nicolas

2016-01-01

EEG reactivity is an important predictor of outcome in comatose patients. However, visual analysis of reactivity is prone to subjectivity and may benefit from quantitative approaches. In EEG segments recorded during reactivity testing in 59 comatose patients, 13 quantitative EEG parameters were used to compare the spectral characteristics of 1-minute segments before and after the onset of stimulation (spectral temporal symmetry). Reactivity was quantified with probability values estimated using combinations of these parameters. The accuracy of probability values as a reactivity classifier was evaluated against the consensus assessment of three expert clinical electroencephalographers using visual analysis. The binary classifier assessing spectral temporal symmetry in four frequency bands (delta, theta, alpha and beta) showed best accuracy (Median AUC: 0.95) and was accompanied by substantial agreement with the individual opinion of experts (Gwet's AC1: 65-70%), at least as good as inter-expert agreement (AC1: 55%). Probability values also reflected the degree of reactivity, as measured by the inter-experts' agreement regarding reactivity for each individual case. Automated quantitative EEG approaches based on probabilistic description of spectral temporal symmetry reliably quantify EEG reactivity. Quantitative EEG may be useful for evaluating reactivity in comatose patients, offering increased objectivity. Copyright © 2015 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

A Comparative Study of Involvement and Motivation among Casino Gamblers.

PubMed

Lee, Choong-Ki; Lee, Bongkoo; Bernhard, Bo Jason; Lee, Tae Kyung

2009-09-01

The purpose of this paper is to investigate three different types of gamblers (which we label "non-problem", "some problem", and "probable pathological gamblers") to determine differences in involvement and motivation, as well as differences in demographic and behavioral variables. The analysis takes advantage of a unique opportunity to sample on-site at a major casino in South Korea, and the resulting purposive sample yielded 180 completed questionnaires in each of the three groups, for a total number of 540. Factor analysis, analysis of variance (ANOVA) and Duncan tests, and Chi-square tests are employed to analyze the data collected from the survey. Findings from ANOVA tests indicate that involvement factors of importance/self-expression, pleasure/interest, and centrality derived from the factor analysis were significantly different among these three types of gamblers. The "probable pathological" and "some problem" gamblers were found to have similar degrees of involvement, and higher degrees of involvement than the non-problem gamblers. The tests also reveal that motivational factors of escape, socialization, winning, and exploring scenery were significantly different among these three types of gamblers. When looking at motivations to visit the casino, "probable pathological" gamblers were more likely to seek winning, the "some problem" group appeared to be more likely to seek escape, and the "non-problem" gamblers indicate that their motivations to visit centered around explorations of scenery and culture in the surrounding casino area. The tools for exploring motivations and involvements of gambling provide valuable and discerning information about the entire spectrum of gamblers.

Sexual abuse, residential schooling and probable pathological gambling among Indigenous Peoples.

PubMed

Dion, Jacinthe; Cantinotti, Michael; Ross, Amélie; Collin-Vézina, Delphine

2015-06-01

Sexual abuse leads to short-term and long-lasting pervasive outcomes, including addictions. Among Indigenous Peoples, sexual abuse experienced in the context of residential schooling may have led to unresolved grief that is contributing to social problems, such as pathological (disordered) gambling. The aim of this study is to investigate the link between child sexual abuse, residential schooling and probable pathological gambling. The participants were 358 Indigenous persons (54.2% women) aged between 18 and 87 years, from two communities and two semi-urban centers in Quebec (Canada). Probable pathological gambling was evaluated using the South Oaks Gambling Screen (SOGS), and sexual abuse and residential schooling were assessed with dichotomous questions (yes/no). The results indicate an 8.7% past-year prevalence rate of pathological gambling problems among participants, which is high compared with the general Canadian population. Moreover, 35.4% were sexually abused, while 28.1% reported having been schooled in a residential setting. The results of a logistic regression also indicate that experiences of child sexual abuse and residential schooling are associated with probable pathological gambling among Indigenous Peoples. These findings underscore the importance of using an ecological approach when treating gambling, to address childhood traumas alongside current addiction problems. Copyright © 2015 Elsevier Ltd. All rights reserved.

An Alternative Approach to the Total Probability Formula. Classroom Notes

ERIC Educational Resources Information Center

Wu, Dane W. Wu; Bangerter, Laura M.

2004-01-01

Given a set of urns, each filled with a mix of black chips and white chips, what is the probability of drawing a black chip from the last urn after some sequential random shifts of chips among the urns? The Total Probability Formula (TPF) is the common tool to solve such a problem. However, when the number of urns is more than two and the number…

First-passage problems: A probabilistic dynamic analysis for degraded structures

NASA Technical Reports Server (NTRS)

Shiao, Michael C.; Chamis, Christos C.

1990-01-01

Structures subjected to random excitations with uncertain system parameters degraded by surrounding environments (a random time history) are studied. Methods are developed to determine the statistics of dynamic responses, such as the time-varying mean, the standard deviation, the autocorrelation functions, and the joint probability density function of any response and its derivative. Moreover, the first-passage problems with deterministic and stationary/evolutionary random barriers are evaluated. The time-varying (joint) mean crossing rate and the probability density function of the first-passage time for various random barriers are derived.

Common quandaries and their practical solutions in Bayesian network modeling

Treesearch

Bruce G. Marcot

2017-01-01

Use and popularity of Bayesian network (BN) modeling has greatly expanded in recent years, but many common problems remain. Here, I summarize key problems in BN model construction and interpretation,along with suggested practical solutions. Problems in BN model construction include parameterizing probability values, variable definition, complex network structures,...

7 CFR Appendix to Subpart - Imported Fire Ant

Code of Federal Regulations, 2010 CFR

2010-01-01

... or plastic containers with drain holes prior to immersion Immerse soil balls and containers, singly... determine the probable source of the problem and to ensure that the problem is resolved. If the problem is... fiberglass, glass, or plastic in such a way that IFA is physically excluded and cannot become established...

An Empirical Bayes Estimate of Multinomial Probabilities.

DTIC Science & Technology

1982-02-01

multinomial probabilities has been considered from a decision theoretic point of view by Steinhaus (1957), Trybula (1958) and Rutkowska (1977). In a recent...variate Rypergeometric and Multinomial Distributions," Zastosowania Matematyki, 16, 9-21. Steinhaus , H. (1957), "The Problem of Estimation." Annals of

Probability bounds analysis for nonlinear population ecology models.

PubMed

Enszer, Joshua A; Andrei Măceș, D; Stadtherr, Mark A

2015-09-01

Mathematical models in population ecology often involve parameters that are empirically determined and inherently uncertain, with probability distributions for the uncertainties not known precisely. Propagating such imprecise uncertainties rigorously through a model to determine their effect on model outputs can be a challenging problem. We illustrate here a method for the direct propagation of uncertainties represented by probability bounds though nonlinear, continuous-time, dynamic models in population ecology. This makes it possible to determine rigorous bounds on the probability that some specified outcome for a population is achieved, which can be a core problem in ecosystem modeling for risk assessment and management. Results can be obtained at a computational cost that is considerably less than that required by statistical sampling methods such as Monte Carlo analysis. The method is demonstrated using three example systems, with focus on a model of an experimental aquatic food web subject to the effects of contamination by ionic liquids, a new class of potentially important industrial chemicals. Copyright © 2015. Published by Elsevier Inc.

A new exact method for line radiative transfer

NASA Astrophysics Data System (ADS)

Elitzur, Moshe; Asensio Ramos, Andrés

2006-01-01

We present a new method, the coupled escape probability (CEP), for exact calculation of line emission from multi-level systems, solving only algebraic equations for the level populations. The CEP formulation of the classical two-level problem is a set of linear equations, and we uncover an exact analytic expression for the emission from two-level optically thick sources that holds as long as they are in the `effectively thin' regime. In a comparative study of a number of standard problems, the CEP method outperformed the leading line transfer methods by substantial margins. The algebraic equations employed by our new method are already incorporated in numerous codes based on the escape probability approximation. All that is required for an exact solution with these existing codes is to augment the expression for the escape probability with simple zone-coupling terms. As an application, we find that standard escape probability calculations generally produce the correct cooling emission by the CII 158-μm line but not by the 3P lines of OI.

Gambling, Delay, and Probability Discounting in Adults With and Without ADHD.

PubMed

Dai, Zhijie; Harrow, Sarah-Eve; Song, Xianwen; Rucklidge, Julia J; Grace, Randolph C

2016-11-01

We investigated the relationship between impulsivity, as measured by delay and probability discounting, and gambling-related cognitions and behavior in adults with and without ADHD. Adults who met Diagnostic and Statistical Manual of Mental Disorders (4th ed.; DSM-IV) diagnostic criteria for ADHD (n = 31) and controls (n = 29) were recruited from the community. All completed an interview that included an assessment of psychiatric disorders, gambling questionnaires, and simulated gambling, delay, and probability discounting tasks. The ADHD group was more likely to meet the criteria for problem gambling and was more impulsive than controls based on a composite discounting measure. ADHD symptoms were correlated with gambling-related cognitions and behavior. Probability, but not delay discounting, explained significant variance in gambling-related measures after controlling for ADHD symptoms. Results confirm an association between adult ADHD and gambling, and suggest that the facets of impulsivity related to risk proneness may be an independent risk factor for problem gambling in this population. © The Author(s) 2013.

Social interaction as a heuristic for combinatorial optimization problems

NASA Astrophysics Data System (ADS)

Fontanari, José F.

2010-11-01

We investigate the performance of a variant of Axelrod’s model for dissemination of culture—the Adaptive Culture Heuristic (ACH)—on solving an NP-Complete optimization problem, namely, the classification of binary input patterns of size F by a Boolean Binary Perceptron. In this heuristic, N agents, characterized by binary strings of length F which represent possible solutions to the optimization problem, are fixed at the sites of a square lattice and interact with their nearest neighbors only. The interactions are such that the agents’ strings (or cultures) become more similar to the low-cost strings of their neighbors resulting in the dissemination of these strings across the lattice. Eventually the dynamics freezes into a homogeneous absorbing configuration in which all agents exhibit identical solutions to the optimization problem. We find through extensive simulations that the probability of finding the optimal solution is a function of the reduced variable F/N1/4 so that the number of agents must increase with the fourth power of the problem size, N∝F4 , to guarantee a fixed probability of success. In this case, we find that the relaxation time to reach an absorbing configuration scales with F6 which can be interpreted as the overall computational cost of the ACH to find an optimal set of weights for a Boolean binary perceptron, given a fixed probability of success.

Solving probability reasoning based on DNA strand displacement and probability modules.

PubMed

Zhang, Qiang; Wang, Xiaobiao; Wang, Xiaojun; Zhou, Changjun

2017-12-01

In computation biology, DNA strand displacement technology is used to simulate the computation process and has shown strong computing ability. Most researchers use it to solve logic problems, but it is only rarely used in probabilistic reasoning. To process probabilistic reasoning, a conditional probability derivation model and total probability model based on DNA strand displacement were established in this paper. The models were assessed through the game "read your mind." It has been shown to enable the application of probabilistic reasoning in genetic diagnosis. Copyright © 2017 Elsevier Ltd. All rights reserved.

Origin of probabilities and their application to the multiverse

NASA Astrophysics Data System (ADS)

Albrecht, Andreas; Phillips, Daniel

2014-12-01

We argue using simple models that all successful practical uses of probabilities originate in quantum fluctuations in the microscopic physical world around us, often propagated to macroscopic scales. Thus we claim there is no physically verified fully classical theory of probability. We comment on the general implications of this view, and specifically question the application of purely classical probabilities to cosmology in cases where key questions are known to have no quantum answer. We argue that the ideas developed here may offer a way out of the notorious measure problems of eternal inflation.

Consecutive rebounds in plasma viral load are associated with virological failure at 52 weeks among HIV-infected patients.

PubMed

Raboud, Janet M; Rae, Sandra; Woods, Ryan; Harris, Marianne; Montaner, Julio S G

2002-08-16

To describe the characteristics and predictors of transient plasma viral load (pVL) rebounds among patients on stable antiretroviral therapy and to determine the effect of one or more pVL rebounds on virological response at week 52. Individual data were combined from 358 patients from the INCAS, AVANTI-2 and AVANTI-3 studies. Logistic regression models were used to determine the relationship between the magnitude of an increase in pVL and the probability of returning to the lower limit of quantification (LLOQ: 20-50 copies/ml) and to determine the odds of virological success at 52 weeks associated with single and consecutive pVL rebounds. A group of 165 patients achieved a pVL nadir < LLOQ; of these, 85 patients experienced pVL rebounds within 52 weeks. The probability of a pVL rebound was greater among patients who did not adhere to treatment (68% vs 49%; P < 0.05). The probability of reachieving virological suppression after a pVL rebound was not associated with the magnitude of the rebound [odds ratio (OR), 0.86; P = 0.56] but was associated with triple therapy (OR, 2.22; P = 0.06) or non-adherence (OR, 0.40; P = 0.04). The probability of virological success at week 52 was not associated with an isolated pVL rebound but was less likely after detectable pVL at two consecutive visits. An isolated pVL rebound was not associated with virological success at 52 weeks but rebounds at two consecutive visits decreased the probability of later virological success. Given their high risk of short-term virological failure, patients who present with consecutive detectable pVL measurements following complete suppression should be considered ideal candidates for intervention studies.

A 30-year history of earthquake crisis communication in California and lessons for the future

NASA Astrophysics Data System (ADS)

Jones, L.

2015-12-01

The first statement from the US Geological Survey to the California Office of Emergency Services quantifying the probability of a possible future earthquake was made in October 1985 about the probability (approximately 5%) that a M4.7 earthquake located directly beneath the Coronado Bay Bridge in San Diego would be a foreshock to a larger earthquake. In the next 30 years, publication of aftershock advisories have become routine and formal statements about the probability of a larger event have been developed in collaboration with the California Earthquake Prediction Evaluation Council (CEPEC) and sent to CalOES more than a dozen times. Most of these were subsequently released to the public. These communications have spanned a variety of approaches, with and without quantification of the probabilities, and using different ways to express the spatial extent and the magnitude distribution of possible future events. The USGS is re-examining its approach to aftershock probability statements and to operational earthquake forecasting with the goal of creating pre-vetted automated statements that can be released quickly after significant earthquakes. All of the previous formal advisories were written during the earthquake crisis. The time to create and release a statement became shorter with experience from the first public advisory (to the 1988 Lake Elsman earthquake) that was released 18 hours after the triggering event, but was never completed in less than 2 hours. As was done for the Parkfield experiment, the process will be reviewed by CEPEC and NEPEC (National Earthquake Prediction Evaluation Council) so the statements can be sent to the public automatically. This talk will review the advisories, the variations in wording and the public response and compare this with social science research about successful crisis communication, to create recommendations for future advisories

Probabilistic cluster labeling of imagery data

NASA Technical Reports Server (NTRS)

Chittineni, C. B. (Principal Investigator)

1980-01-01

The problem of obtaining the probabilities of class labels for the clusters using spectral and spatial information from a given set of labeled patterns and their neighbors is considered. A relationship is developed between class and clusters conditional densities in terms of probabilities of class labels for the clusters. Expressions are presented for updating the a posteriori probabilities of the classes of a pixel using information from its local neighborhood. Fixed-point iteration schemes are developed for obtaining the optimal probabilities of class labels for the clusters. These schemes utilize spatial information and also the probabilities of label imperfections. Experimental results from the processing of remotely sensed multispectral scanner imagery data are presented.

New methods for image collection and analysis in scanning Auger microscopy

NASA Technical Reports Server (NTRS)

Browning, R.

1985-01-01

While scanning Auger micrographs are used extensively for illustrating the stoichiometry of complex surfaces and for indicating areas of interest for fine point Auger spectroscopy, there are many problems in the quantification and analysis of Auger images. These problems include multiple contrast mechanisms and the lack of meaningful relationships with other Auger data. Collection of multielemental Auger images allows some new approaches to image analysis and presentation. Information about the distribution and quantity of elemental combinations at a surface are retrievable, and particular combinations of elements can be imaged, such as alloy phases. Results from the precipitate hardened alloy Al-2124 illustrate multispectral Auger imaging.

Metrics on the relative spacecraft motion invariant manifold.

PubMed

Gurfil, P; Kholshevnikov, Konstantin V

2005-12-01

This paper establishes a methodology for obtaining the general solution to the spacecraft relative motion problem by utilizing Cartesian configuration space in conjunction with classical orbital elements. The geometry of the relative motion configuration space is analyzed, and the relative motion invariant manifold is determined. Most importantly, the geometric structure of the relative motion problem is used to derive useful metrics for quantification of the minimum, maximum, and mean distance between spacecraft for commensurable and non-commensurable mean motions. A number of analytic solutions, as well as useful examples, are provided, illustrating the calculated bounds. A few particular cases are given that yield simple solutions.

Math Ties: Problem Solving, Logic Teasers, and Math Puzzles All "Tied" To the Math Curriculum. Book B1.

ERIC Educational Resources Information Center

Santi, Terri

This book contains a classroom-tested approach to the teaching of problem solving to all students in Grades 6-8, regardless of ability. Information on problem solving in general is provided, then mathematical problems on logic, exponents, fractions, pre-algebra, algebra, geometry, number theory, set theory, ratio, proportion, percent, probability,…

Gambling and problem gambling among young adolescents in Great Britain.

PubMed

Forrest, David; McHale, Ian G

2012-12-01

International evidence suggests that problem gambling tends to be 2-4 times higher among adolescents as among adults and this proves to be true of Great Britain according to the latest adolescent prevalence survey. 8,958 British children (11-15) were surveyed in 201 schools during late 2008 and 2009. The questionnaire included a standard screen, DSM-IV-MR-J, to test for problem gambling. Our regression models explore influences of demographic, home and school characteristics on probabilities (both unconditional and conditional on being a gambler) of a child testing positive for problem gambling. More than 20% of children participated in gambling and, of these, nearly 8% tested positive. Age-group prevalence of problem gambling was 1.9%, compared with 0.6-0.9% in the most recent official adult surveys. Boys were much more likely than girls to gamble and to exhibit symptoms of problem gambling if they did. Generally, home characteristics, particularly parental attitude and example, dominated school characteristics in accounting for risks. Unanticipated findings included significantly elevated probabilities of problem gambling among Asian children and among children who live in a home without siblings. Child income was also a potent predictor of gambling and problem gambling.

Mayer control problem with probabilistic uncertainty on initial positions

NASA Astrophysics Data System (ADS)

Marigonda, Antonio; Quincampoix, Marc

2018-03-01

In this paper we introduce and study an optimal control problem in the Mayer's form in the space of probability measures on Rn endowed with the Wasserstein distance. Our aim is to study optimality conditions when the knowledge of the initial state and velocity is subject to some uncertainty, which are modeled by a probability measure on Rd and by a vector-valued measure on Rd, respectively. We provide a characterization of the value function of such a problem as unique solution of an Hamilton-Jacobi-Bellman equation in the space of measures in a suitable viscosity sense. Some applications to a pursuit-evasion game with uncertainty in the state space is also discussed, proving the existence of a value for the game.

A multilevel finite element method for Fredholm integral eigenvalue problems

NASA Astrophysics Data System (ADS)

Xie, Hehu; Zhou, Tao

2015-12-01

In this work, we proposed a multigrid finite element (MFE) method for solving the Fredholm integral eigenvalue problems. The main motivation for such studies is to compute the Karhunen-Loève expansions of random fields, which play an important role in the applications of uncertainty quantification. In our MFE framework, solving the eigenvalue problem is converted to doing a series of integral iterations and eigenvalue solving in the coarsest mesh. Then, any existing efficient integration scheme can be used for the associated integration process. The error estimates are provided, and the computational complexity is analyzed. It is noticed that the total computational work of our method is comparable with a single integration step in the finest mesh. Several numerical experiments are presented to validate the efficiency of the proposed numerical method.

Probability weighted moments: Definition and relation to parameters of several distributions expressable in inverse form

USGS Publications Warehouse

Greenwood, J. Arthur; Landwehr, J. Maciunas; Matalas, N.C.; Wallis, J.R.

1979-01-01

Distributions whose inverse forms are explicitly defined, such as Tukey's lambda, may present problems in deriving their parameters by more conventional means. Probability weighted moments are introduced and shown to be potentially useful in expressing the parameters of these distributions.

Teaching Probability in Intermediate Grades

ERIC Educational Resources Information Center

Engel, Arthur

1971-01-01

A discussion of the importance and procedures for including probability in the elementary through secondary mathematics curriculum is presented. Many examples and problems are presented which the author feels students can understand and will be motivated to do. Random digits, Monte Carlo methods, combinatorial theory, and Markov chains are…

From Dualism to Unity in Quantum Physics

NASA Astrophysics Data System (ADS)

Landé, Alfred

2016-02-01

Preface; Introduction; 1. Causality, chance, continuity; 2. States, observables, probabilities; 3. The metric law of probabilities; 4. Quantum dynamics; 5. Quantum fact and fiction; Retrospect. From dualism to unity, from positivism to realism; Appendix 1. Survey of elementary postulates; Appendix 2. Two problems of uniqueness; References; Index.

Ultrafast gas chromatography method with direct injection for the quantitative determination of benzene, toluene, ethylbenzene, and xylenes in commercial gasoline.

PubMed

Miranda, Nahieh Toscano; Sequinel, Rodrigo; Hatanaka, Rafael Rodrigues; de Oliveira, José Eduardo; Flumignan, Danilo Luiz

2017-04-01

Benzene, toluene, ethylbenzene, and xylenes are some of the most hazardous constituents found in commercial gasoline samples; therefore, these components must be monitored to avoid toxicological problems. We propose a new routine method of ultrafast gas chromatography coupled to flame ionization detection for the direct determination of benzene, toluene, ethylbenzene, and xylenes in commercial gasoline. This method is based on external standard calibration to quantify each compound, including the validation step of the study of linearity, detection and quantification limits, precision, and accuracy. The time of analysis was less than 3.2 min, with quantitative statements regarding the separation and quantification of all compounds in commercial gasoline samples. Ultrafast gas chromatography is a promising alternative method to official analytical techniques. Government laboratories could consider using this method for quality control. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Quantifying Motor Impairment in Movement Disorders.

PubMed

FitzGerald, James J; Lu, Zhongjiao; Jareonsettasin, Prem; Antoniades, Chrystalina A

2018-01-01

Until recently the assessment of many movement disorders has relied on clinical rating scales that despite careful design are inherently subjective and non-linear. This makes accurate and truly observer-independent quantification difficult and limits the use of sensitive parametric statistical methods. At last, devices capable of measuring neurological problems quantitatively are becoming readily available. Examples include the use of oculometers to measure eye movements and accelerometers to measure tremor. Many applications are being developed for use on smartphones. The benefits include not just more accurate disease quantification, but also consistency of data for longitudinal studies, accurate stratification of patients for entry into trials, and the possibility of automated data capture for remote follow-up. In this mini review, we will look at movement disorders with a particular focus on Parkinson's disease, describe some of the limitations of existing clinical evaluation tools, and illustrate the ways in which objective metrics have already been successful.

Probabilistic Methods for Uncertainty Propagation Applied to Aircraft Design

NASA Technical Reports Server (NTRS)

Green, Lawrence L.; Lin, Hong-Zong; Khalessi, Mohammad R.

2002-01-01

Three methods of probabilistic uncertainty propagation and quantification (the method of moments, Monte Carlo simulation, and a nongradient simulation search method) are applied to an aircraft analysis and conceptual design program to demonstrate design under uncertainty. The chosen example problems appear to have discontinuous design spaces and thus these examples pose difficulties for many popular methods of uncertainty propagation and quantification. However, specific implementation features of the first and third methods chosen for use in this study enable successful propagation of small uncertainties through the program. Input uncertainties in two configuration design variables are considered. Uncertainties in aircraft weight are computed. The effects of specifying required levels of constraint satisfaction with specified levels of input uncertainty are also demonstrated. The results show, as expected, that the designs under uncertainty are typically heavier and more conservative than those in which no input uncertainties exist.

Raman spectroscopy for DNA quantification in cell nucleus.

PubMed

Okotrub, K A; Surovtsev, N V; Semeshin, V F; Omelyanchuk, L V

2015-01-01

Here we demonstrate the feasibility of a novel approach to quantify DNA in cell nuclei. This approach is based on spectroscopy analysis of Raman light scattering, and avoids the problem of nonstoichiometric binding of dyes to DNA, as it directly measures the signal from DNA. Quantitative analysis of nuclear DNA contribution to Raman spectrum could be reliably performed using intensity of a phosphate mode at 1096 cm(-1) . When compared to the known DNA standards from cells of different animals, our results matched those values at error of 10%. We therefore suggest that this approach will be useful to expand the list of DNA standards, to properly adjust the duration of hydrolysis in Feulgen staining, to assay the applicability of fuchsines for DNA quantification, as well as to measure DNA content in cells with complex hydrolysis patterns, when Feulgen densitometry is inappropriate. © 2014 International Society for Advancement of Cytometry.

Information transduction capacity reduces the uncertainties in annotation-free isoform discovery and quantification

PubMed Central

Deng, Yue; Bao, Feng; Yang, Yang; Ji, Xiangyang; Du, Mulong; Zhang, Zhengdong

2017-01-01

Abstract The automated transcript discovery and quantification of high-throughput RNA sequencing (RNA-seq) data are important tasks of next-generation sequencing (NGS) research. However, these tasks are challenging due to the uncertainties that arise in the inference of complete splicing isoform variants from partially observed short reads. Here, we address this problem by explicitly reducing the inherent uncertainties in a biological system caused by missing information. In our approach, the RNA-seq procedure for transforming transcripts into short reads is considered an information transmission process. Consequently, the data uncertainties are substantially reduced by exploiting the information transduction capacity of information theory. The experimental results obtained from the analyses of simulated datasets and RNA-seq datasets from cell lines and tissues demonstrate the advantages of our method over state-of-the-art competitors. Our algorithm is an open-source implementation of MaxInfo. PMID:28911101

Electromagnetic Modeling, Optimization and Uncertainty Quantification for Antenna and Radar Systems Surfaces Scattering and Energy Absorption

DTIC Science & Technology

2017-03-06

design of antenna and radar systems, energy absorption and scattering by rough-surfaces. This work has lead to significant new methodologies , including...problems in the field of electromagnetic propagation and scattering, with applicability to design of antenna and radar systems, energy absorption...and scattering by rough-surfaces. This work has lead to significant new methodologies , including introduction of a certain Windowed Green Function

Library Management: Quantifying Goals. Papers Presented at the Fourth Annual Library Science Institute (Indiana State University, Terre Haute, Indiana, April 27-29, 1972). Changing Concept Series No. 2.

ERIC Educational Resources Information Center

Kim, Choong H., Ed.; And Others

Recognizing that the rational determination of priority goals for library services is only possible on the basis of objectively defined quantities, these papers discuss the problems of such quantification. The lead paper reviews current thought on the subject, noting the lack of practical techniques for the disciplined measurement of library…

Maximum Entropy/Optimal Projection (MEOP) control design synthesis: Optimal quantification of the major design tradeoffs

NASA Technical Reports Server (NTRS)

Hyland, D. C.; Bernstein, D. S.

1987-01-01

The underlying philosophy and motivation of the optimal projection/maximum entropy (OP/ME) stochastic modeling and reduced control design methodology for high order systems with parameter uncertainties are discussed. The OP/ME design equations for reduced-order dynamic compensation including the effect of parameter uncertainties are reviewed. The application of the methodology to several Large Space Structures (LSS) problems of representative complexity is illustrated.

On the Structure of a Best Possible Crossover Selection Strategy in Genetic Algorithms

NASA Astrophysics Data System (ADS)

Lässig, Jörg; Hoffmann, Karl Heinz

The paper considers the problem of selecting individuals in the current population in genetic algorithms for crossover to find a solution with high fitness for a given optimization problem. Many different schemes have been described in the literature as possible strategies for this task but so far comparisons have been predominantly empirical. It is shown that if one wishes to maximize any linear function of the final state probabilities, e.g. the fitness of the best individual in the final population of the algorithm, then a best probability distribution for selecting an individual in each generation is a rectangular distribution over the individuals sorted in descending sequence by their fitness values. This means uniform probabilities have to be assigned to a group of the best individuals of the population but probabilities equal to zero to individuals with lower fitness, assuming that the probability distribution to choose individuals from the current population can be chosen independently for each iteration and each individual. This result is then generalized also to typical practically applied performance measures, such as maximizing the expected fitness value of the best individual seen in any generation.

Tug-Of-War Model for Two-Bandit Problem

NASA Astrophysics Data System (ADS)

Kim, Song-Ju; Aono, Masashi; Hara, Masahiko

The amoeba of the true slime mold Physarum polycephalum shows high computational capabilities. In the so-called amoeba-based computing, some computing tasks including combinatorial optimization are performed by the amoeba instead of a digital computer. We expect that there must be problems living organisms are good at solving. The “multi-armed bandit problem” would be the one of such problems. Consider a number of slot machines. Each of the machines has an arm which gives a player a reward with a certain probability when pulled. The problem is to determine the optimal strategy for maximizing the total reward sum after a certain number of trials. To maximize the total reward sum, it is necessary to judge correctly and quickly which machine has the highest reward probability. Therefore, the player should explore many machines to gather much knowledge on which machine is the best, but should not fail to exploit the reward from the known best machine. We consider that living organisms follow some efficient method to solve the problem.

The Framing of Decisions and the Psychology of Choice.

ERIC Educational Resources Information Center

Tversky, Amos; Kahneman, Daniel

1981-01-01

Presents evidence that the psychological principles that govern the perception of decision problems and evaluation of probabilities and outcomes produce predictable shifts of preference when the same problem is framed in different ways. (Author/SK)

Hydrogeophysical Assessment of Aquifer Uncertainty Using Simulated Annealing driven MRF-Based Stochastic Joint Inversion

NASA Astrophysics Data System (ADS)

Oware, E. K.

2017-12-01

Geophysical quantification of hydrogeological parameters typically involve limited noisy measurements coupled with inadequate understanding of the target phenomenon. Hence, a deterministic solution is unrealistic in light of the largely uncertain inputs. Stochastic imaging (SI), in contrast, provides multiple equiprobable realizations that enable probabilistic assessment of aquifer properties in a realistic manner. Generation of geologically realistic prior models is central to SI frameworks. Higher-order statistics for representing prior geological features in SI are, however, usually borrowed from training images (TIs), which may produce undesirable outcomes if the TIs are unpresentatitve of the target structures. The Markov random field (MRF)-based SI strategy provides a data-driven alternative to TI-based SI algorithms. In the MRF-based method, the simulation of spatial features is guided by Gibbs energy (GE) minimization. Local configurations with smaller GEs have higher likelihood of occurrence and vice versa. The parameters of the Gibbs distribution for computing the GE are estimated from the hydrogeophysical data, thereby enabling the generation of site-specific structures in the absence of reliable TIs. In Metropolis-like SI methods, the variance of the transition probability controls the jump-size. The procedure is a standard Markov chain Monte Carlo (McMC) method when a constant variance is assumed, and becomes simulated annealing (SA) when the variance (cooling temperature) is allowed to decrease gradually with time. We observe that in certain problems, the large variance typically employed at the beginning to hasten burn-in may be unideal for sampling at the equilibrium state. The powerfulness of SA stems from its flexibility to adaptively scale the variance at different stages of the sampling. Degeneration of results were reported in a previous implementation of the MRF-based SI strategy based on a constant variance. Here, we present an updated version of the algorithm based on SA that appears to resolve the degeneration problem with seemingly improved results. We illustrate the performance of the SA version with a joint inversion of time-lapse concentration and electrical resistivity measurements in a hypothetical trinary hydrofacies aquifer characterization problem.

Target Coverage in Wireless Sensor Networks with Probabilistic Sensors

PubMed Central

Shan, Anxing; Xu, Xianghua; Cheng, Zongmao

2016-01-01

Sensing coverage is a fundamental problem in wireless sensor networks (WSNs), which has attracted considerable attention. Conventional research on this topic focuses on the 0/1 coverage model, which is only a coarse approximation to the practical sensing model. In this paper, we study the target coverage problem, where the objective is to find the least number of sensor nodes in randomly-deployed WSNs based on the probabilistic sensing model. We analyze the joint detection probability of target with multiple sensors. Based on the theoretical analysis of the detection probability, we formulate the minimum ϵ-detection coverage problem. We prove that the minimum ϵ-detection coverage problem is NP-hard and present an approximation algorithm called the Probabilistic Sensor Coverage Algorithm (PSCA) with provable approximation ratios. To evaluate our design, we analyze the performance of PSCA theoretically and also perform extensive simulations to demonstrate the effectiveness of our proposed algorithm. PMID:27618902

Bell-Boole Inequality: Nonlocality or Probabilistic Incompatibility of Random Variables?

NASA Astrophysics Data System (ADS)

Khrennikov, Andrei

2008-06-01

The main aim of this report is to inform the quantum information community about investigations on the problem of probabilistic compatibility of a family of random variables: a possibility to realize such a family on the basis of a single probability measure (to construct a single Kolmogorov probability space). These investigations were started hundred of years ago by J. Boole (who invented Boolean algebras). The complete solution of the problem was obtained by Soviet mathematician Vorobjev in 60th. Surprisingly probabilists and statisticians obtained inequalities for probabilities and correlations among which one can find the famous Bell’s inequality and its generalizations. Such inequalities appeared simply as constraints for probabilistic compatibility. In this framework one can not see a priori any link to such problems as nonlocality and “death of reality” which are typically linked to Bell’s type inequalities in physical literature. We analyze the difference between positions of mathematicians and quantum physicists. In particular, we found that one of the most reasonable explanations of probabilistic incompatibility is mixing in Bell’s type inequalities statistical data from a number of experiments performed under different experimental contexts.

What Does a Random Line Look Like: An Experimental Study

ERIC Educational Resources Information Center

Turner, Nigel E.; Liu, Eleanor; Toneatto, Tony

2011-01-01

The study examined the perception of random lines by people with gambling problems compared to people without gambling problems. The sample consisted of 67 probable pathological gamblers and 46 people without gambling problems. Participants completed a number of questionnaires about their gambling and were then presented with a series of random…

7 CFR 301.81-10 - Costs and charges.

Code of Federal Regulations, 2010 CFR

2010-01-01

... not remove burlap wrap or plastic containers with drain holes prior to immersion Immerse soil balls... determine the probable source of the problem and to ensure that the problem is resolved. If the problem is... fiberglass, glass, or plastic in such a way that IFA is physically excluded and cannot become established...

Relative Contributions of Three Descriptive Methods: Implications for Behavioral Assessment

ERIC Educational Resources Information Center

Pence, Sacha T.; Roscoe, Eileen M.; Bourret, Jason C.; Ahearn, William H.

2009-01-01

This study compared the outcomes of three descriptive analysis methods--the ABC method, the conditional probability method, and the conditional and background probability method--to each other and to the results obtained from functional analyses. Six individuals who had been diagnosed with developmental delays and exhibited problem behavior…

Multilevel sequential Monte Carlo samplers

DOE PAGES

Beskos, Alexandros; Jasra, Ajay; Law, Kody; ...

2016-08-24

Here, we study the approximation of expectations w.r.t. probability distributions associated to the solution of partial differential equations (PDEs); this scenario appears routinely in Bayesian inverse problems. In practice, one often has to solve the associated PDE numerically, using, for instance finite element methods and leading to a discretisation bias, with the step-size level h L. In addition, the expectation cannot be computed analytically and one often resorts to Monte Carlo methods. In the context of this problem, it is known that the introduction of the multilevel Monte Carlo (MLMC) method can reduce the amount of computational effort to estimate expectations, for a given level of error. This is achieved via a telescoping identity associated to a Monte Carlo approximation of a sequence of probability distributions with discretisation levelsmore » $${\\infty}$$ >h 0>h 1 ...>h L. In many practical problems of interest, one cannot achieve an i.i.d. sampling of the associated sequence of probability distributions. A sequential Monte Carlo (SMC) version of the MLMC method is introduced to deal with this problem. In conclusion, it is shown that under appropriate assumptions, the attractive property of a reduction of the amount of computational effort to estimate expectations, for a given level of error, can be maintained within the SMC context.« less

Optimization and Verification of Droplet Digital PCR Even-Specific Methods for the Quantification of GM Maize DAS1507 and NK603.

PubMed

Grelewska-Nowotko, Katarzyna; Żurawska-Zajfert, Magdalena; Żmijewska, Ewelina; Sowa, Sławomir

2018-05-01

In recent years, digital polymerase chain reaction (dPCR), a new molecular biology technique, has been gaining in popularity. Among many other applications, this technique can also be used for the detection and quantification of genetically modified organisms (GMOs) in food and feed. It might replace the currently widely used real-time PCR method (qPCR), by overcoming problems related to the PCR inhibition and the requirement of certified reference materials to be used as a calibrant. In theory, validated qPCR methods can be easily transferred to the dPCR platform. However, optimization of the PCR conditions might be necessary. In this study, we report the transfer of two validated qPCR methods for quantification of maize DAS1507 and NK603 events to the droplet dPCR (ddPCR) platform. After some optimization, both methods have been verified according to the guidance of the European Network of GMO Laboratories (ENGL) on analytical method verification (ENGL working group on "Method Verification." (2011) Verification of Analytical Methods for GMO Testing When Implementing Interlaboratory Validated Methods). Digital PCR methods performed equally or better than the qPCR methods. Optimized ddPCR methods confirm their suitability for GMO determination in food and feed.

Reliable quantification of phthalates in environmental matrices (air, water, sludge, sediment and soil): a review.

PubMed

Net, Sopheak; Delmont, Anne; Sempéré, Richard; Paluselli, Andrea; Ouddane, Baghdad

2015-05-15

Because of their widespread application, phthalates or phthalic acid esters (PAEs) are ubiquitous in the environment. Their presence has attracted considerable attention due to their potential impacts on ecosystem functioning and on public health, so their quantification has become a necessity. Various extraction procedures as well as gas/liquid chromatography and mass spectrometry detection techniques are found as suitable for reliable detection of such compounds. However, PAEs are ubiquitous in the laboratory environment including ambient air, reagents, sampling equipment, and various analytical devices, that induces difficult analysis of real samples with a low PAE background. Therefore, accurate PAE analysis in environmental matrices is a challenging task. This paper reviews the extensive literature data on the techniques for PAE quantification in natural media. Sampling, sample extraction/pretreatment and detection for quantifying PAEs in different environmental matrices (air, water, sludge, sediment and soil) have been reviewed and compared. The concept of "green analytical chemistry" for PAE determination is also discussed. Moreover useful information about the material preparation and the procedures of quality control and quality assurance are presented to overcome the problem of sample contamination and these encountered due to matrix effects in order to avoid overestimating PAE concentrations in the environment. Copyright © 2015 Elsevier B.V. All rights reserved.

Recommended Immunological Strategies to Screen for Botulinum Neurotoxin-Containing Samples.

PubMed

Simon, Stéphanie; Fiebig, Uwe; Liu, Yvonne; Tierney, Rob; Dano, Julie; Worbs, Sylvia; Endermann, Tanja; Nevers, Marie-Claire; Volland, Hervé; Sesardic, Dorothea; Dorner, Martin B

2015-11-26

Botulinum neurotoxins (BoNTs) cause the life-threatening neurological illness botulism in humans and animals and are divided into seven serotypes (BoNT/A-G), of which serotypes A, B, E, and F cause the disease in humans. BoNTs are classified as "category A" bioterrorism threat agents and are relevant in the context of the Biological Weapons Convention. An international proficiency test (PT) was conducted to evaluate detection, quantification and discrimination capabilities of 23 expert laboratories from the health, food and security areas. Here we describe three immunological strategies that proved to be successful for the detection and quantification of BoNT/A, B, and E considering the restricted sample volume (1 mL) distributed. To analyze the samples qualitatively and quantitatively, the first strategy was based on sensitive immunoenzymatic and immunochromatographic assays for fast qualitative and quantitative analyses. In the second approach, a bead-based suspension array was used for screening followed by conventional ELISA for quantification. In the third approach, an ELISA plate format assay was used for serotype specific immunodetection of BoNT-cleaved substrates, detecting the activity of the light chain, rather than the toxin protein. The results provide guidance for further steps in quality assurance and highlight problems to address in the future.

Recommended Immunological Strategies to Screen for Botulinum Neurotoxin-Containing Samples

PubMed Central

Simon, Stéphanie; Fiebig, Uwe; Liu, Yvonne; Tierney, Rob; Dano, Julie; Worbs, Sylvia; Endermann, Tanja; Nevers, Marie-Claire; Volland, Hervé; Sesardic, Dorothea; Dorner, Martin B.

2015-01-01

Botulinum neurotoxins (BoNTs) cause the life-threatening neurological illness botulism in humans and animals and are divided into seven serotypes (BoNT/A–G), of which serotypes A, B, E, and F cause the disease in humans. BoNTs are classified as “category A” bioterrorism threat agents and are relevant in the context of the Biological Weapons Convention. An international proficiency test (PT) was conducted to evaluate detection, quantification and discrimination capabilities of 23 expert laboratories from the health, food and security areas. Here we describe three immunological strategies that proved to be successful for the detection and quantification of BoNT/A, B, and E considering the restricted sample volume (1 mL) distributed. To analyze the samples qualitatively and quantitatively, the first strategy was based on sensitive immunoenzymatic and immunochromatographic assays for fast qualitative and quantitative analyses. In the second approach, a bead-based suspension array was used for screening followed by conventional ELISA for quantification. In the third approach, an ELISA plate format assay was used for serotype specific immunodetection of BoNT-cleaved substrates, detecting the activity of the light chain, rather than the toxin protein. The results provide guidance for further steps in quality assurance and highlight problems to address in the future. PMID:26703727

Quantitative detection of the potato cyst nematode, Globodera pallida, and the beet cyst nematode, Heterodera schachtii, using Real-Time PCR with SYBR green I dye.

PubMed

Madani, Mehrdad; Subbotin, Sergei A; Moens, Maurice

2005-04-01

The potato cyst nematode Globodera pallida and the beet cyst nematode Heterodera schachtii are major nematode pests in world agriculture. Precise identification and knowledge about the number of nematodes in field soil are necessary to develop effective integrated pest control. Here we report the results of the Real-Time PCR assay for the rapid detection and quantification of G. pallida and H. schachtii. Using species specific primers and SYBR green I dye, we were able to detect a single second stage juvenile of cyst forming nematodes in samples. The specificity of the reaction was confirmed by the lack of amplification of DNAs from other Heterodera or Globodera species. Validation tests showed a rather high correlation between real numbers of second stage juveniles in a sample and expected numbers detected by Real-Time PCR. Reasons for observed differences in sensitivity and reliability of quantification detection for two species as well as other problems of Real-Time PCR are discussed. The Real-Time PCR assay with SYBR green I dye targeting fragments of the ITS-rDNA provided a sensitive means for the rapid and simultaneous detection and quantification of juveniles of these pests.

A Comparative Study of Involvement and Motivation among Casino Gamblers

PubMed Central

Lee, Choong-Ki; Lee, BongKoo; Bernhard, Bo Jason

2009-01-01

Objective The purpose of this paper is to investigate three different types of gamblers (which we label "non-problem", "some problem", and "probable pathological gamblers") to determine differences in involvement and motivation, as well as differences in demographic and behavioral variables. Methods The analysis takes advantage of a unique opportunity to sample on-site at a major casino in South Korea, and the resulting purposive sample yielded 180 completed questionnaires in each of the three groups, for a total number of 540. Factor analysis, analysis of variance (ANOVA) and Duncan tests, and Chi-square tests are employed to analyze the data collected from the survey. Results Findings from ANOVA tests indicate that involvement factors of importance/self-expression, pleasure/interest, and centrality derived from the factor analysis were significantly different among these three types of gamblers. The "probable pathological" and "some problem" gamblers were found to have similar degrees of involvement, and higher degrees of involvement than the non-problem gamblers. The tests also reveal that motivational factors of escape, socialization, winning, and exploring scenery were significantly different among these three types of gamblers. When looking at motivations to visit the casino, "probable pathological" gamblers were more likely to seek winning, the "some problem" group appeared to be more likely to seek escape, and the "non-problem" gamblers indicate that their motivations to visit centered around explorations of scenery and culture in the surrounding casino area. Conclusion The tools for exploring motivations and involvements of gambling provide valuable and discerning information about the entire spectrum of gamblers. PMID:20046388

Estimating uncertainty of Full Waveform Inversion with Ensemble-based methods

NASA Astrophysics Data System (ADS)

Thurin, J.; Brossier, R.; Métivier, L.

2017-12-01

Uncertainty estimation is one key feature of tomographic applications for robust interpretation. However, this information is often missing in the frame of large scale linearized inversions, and only the results at convergence are shown, despite the ill-posed nature of the problem. This issue is common in the Full Waveform Inversion community.While few methodologies have already been proposed in the literature, standard FWI workflows do not include any systematic uncertainty quantifications methods yet, but often try to assess the result's quality through cross-comparison with other results from seismic or comparison with other geophysical data. With the development of large seismic networks/surveys, the increase in computational power and the more and more systematic application of FWI, it is crucial to tackle this problem and to propose robust and affordable workflows, in order to address the uncertainty quantification problem faced for near surface targets, crustal exploration, as well as regional and global scales.In this work (Thurin et al., 2017a,b), we propose an approach which takes advantage of the Ensemble Transform Kalman Filter (ETKF) proposed by Bishop et al., (2001), in order to estimate a low-rank approximation of the posterior covariance matrix of the FWI problem, allowing us to evaluate some uncertainty information of the solution. Instead of solving the FWI problem through a Bayesian inversion with the ETKF, we chose to combine a conventional FWI, based on local optimization, and the ETKF strategies. This scheme allows combining the efficiency of local optimization for solving large scale inverse problems and make the sampling of the local solution space possible thanks to its embarrassingly parallel property. References:Bishop, C. H., Etherton, B. J. and Majumdar, S. J., 2001. Adaptive sampling with the ensemble transform Kalman filter. Part I: Theoretical aspects. Monthly weather review, 129(3), 420-436.Thurin, J., Brossier, R. and Métivier, L. 2017,a.: Ensemble-Based Uncertainty Estimation in Full Waveform Inversion. 79th EAGE Conference and Exhibition 2017, (12 - 15 June, 2017)Thurin, J., Brossier, R. and Métivier, L. 2017,b.: An Ensemble-Transform Kalman Filter - Full Waveform Inversion scheme for Uncertainty estimation; SEG Technical Program Expanded Abstracts 2012

Characterization of the exogenous insert and development of event-specific PCR detection methods for genetically modified Huanong No. 1 papaya.

PubMed

Guo, Jinchao; Yang, Litao; Liu, Xin; Guan, Xiaoyan; Jiang, Lingxi; Zhang, Dabing

2009-08-26

Genetically modified (GM) papaya (Carica papaya L.), Huanong No. 1, was approved for commercialization in Guangdong province, China in 2006, and the development of the Huanong No. 1 papaya detection method is necessary for implementing genetically modified organism (GMO) labeling regulations. In this study, we reported the characterization of the exogenous integration of GM Huanong No. 1 papaya by means of conventional polymerase chain reaction (PCR) and thermal asymmetric interlaced (TAIL)-PCR strategies. The results suggested that one intact copy of the initial construction was integrated in the papaya genome and which probably resulted in one deletion (38 bp in size) of the host genomic DNA. Also, one unintended insertion of a 92 bp truncated NptII fragment was observed at the 5' end of the exogenous insert. Furthermore, we revealed its 5' and 3' flanking sequences between the insert DNA and the papaya genomic DNA, and developed the event-specific qualitative and quantitative PCR assays for GM Huanong No. 1 papaya based on the 5' integration flanking sequence. The relative limit of detection (LOD) of the qualitative PCR assay was about 0.01% in 100 ng of total papaya genomic DNA, corresponding to about 25 copies of papaya haploid genome. In the quantitative PCR, the limits of detection and quantification (LOD and LOQ) were as low as 12.5 and 25 copies of papaya haploid genome, respectively. In practical sample quantification, the quantified biases between the test and true values of three samples ranged from 0.44% to 4.41%. Collectively, we proposed that all of these results are useful for the identification and quantification of Huanong No. 1 papaya and its derivates.

Stationary plasma thruster evaluation in Russia

NASA Technical Reports Server (NTRS)

Brophy, John R.

1992-01-01

A team of electric propulsion specialists from U.S. government laboratories experimentally evaluated the performance of a 1.35-kW Stationary Plasma Thruster (SPT) at the Scientific Research Institute of Thermal Processes in Moscow and at 'Fakel' Enterprise in Kaliningrad, Russia. The evaluation was performed using a combination of U.S. and Russian instrumentation and indicated that the actual performance of the thruster appears to be close to the claimed performance. The claimed performance was a specific impulse of 16,000 m/s, an overall efficiency of 50 percent, and an input power of 1.35 kW, and is superior to the performance of western electric thrusters at this specific impulse. The unique performance capabilities of the stationary plasma thruster, along with claims that more than fifty of the 660-W thrusters have been flown in space on Russian spacecraft, attracted the interest of western spacecraft propulsion specialists. A two-phase program was initiated to evaluate the stationary plasma thruster performance and technology. The first phase of this program, to experimentally evaluate the performance of the thruster with U.S. instrumentation in Russia, is described in this report. The second phase objective is to determine the suitability of the stationary plasma thruster technology for use on western spacecraft. This will be accomplished by bringing stationary plasma thrusters to the U.S. for quantification of thruster erosion rates, measurements of the performance variation as a function of long-duration operation, quantification of the exhaust beam divergence angle, and determination of the non-propellant efflux from the thruster. These issues require quantification in order to maximize the probability for user application of the SPT technology and significantly increase the propulsion capabilities of U.S. spacecraft.

Estimating inbreeding rates in natural populations: Addressing the problem of incomplete pedigrees

USGS Publications Warehouse

Miller, Mark P.; Haig, Susan M.; Ballou, Jonathan D.; Steel, E. Ashley

2017-01-01

Understanding and estimating inbreeding is essential for managing threatened and endangered wildlife populations. However, determination of inbreeding rates in natural populations is confounded by incomplete parentage information. We present an approach for quantifying inbreeding rates for populations with incomplete parentage information. The approach exploits knowledge of pedigree configurations that lead to inbreeding coefficients of F = 0.25 and F = 0.125, allowing for quantification of Pr(I|k): the probability of observing pedigree I given the fraction of known parents (k). We developed analytical expressions under simplifying assumptions that define properties and behavior of inbreeding rate estimators for varying values of k. We demonstrated that inbreeding is overestimated if Pr(I|k) is not taken into consideration and that bias is primarily influenced by k. By contrast, our new estimator, incorporating Pr(I|k), is unbiased over a wide range of values of kthat may be observed in empirical studies. Stochastic computer simulations that allowed complex inter- and intragenerational inbreeding produced similar results. We illustrate the effects that accounting for Pr(I|k) can have in empirical data by revisiting published analyses of Arabian oryx (Oryx leucoryx) and Red deer (Cervus elaphus). Our results demonstrate that incomplete pedigrees are not barriers for quantifying inbreeding in wild populations. Application of our approach will permit a better understanding of the role that inbreeding plays in the dynamics of populations of threatened and endangered species and may help refine our understanding of inbreeding avoidance mechanisms in the wild.

Geosimulation of urban growth and demographic decline in the Ruhr: a case study for 2025 using the artificial intelligence of cells and agents

NASA Astrophysics Data System (ADS)

Rienow, Andreas; Stenger, Dirk

2014-07-01

The Ruhr is an "old acquaintance" in the discourse of urban decline in old industrialized cities. The agglomeration has to struggle with archetypical problems of former monofunctional manufacturing cities. Surprisingly, the image of a shrinking city has to be refuted if you shift the focus from socioeconomic wealth to its morphological extension. Thus, it is the objective of this study to meet the challenge of modeling urban sprawl and demographic decline by combining two artificial intelligent solutions: The popular urban cellular automaton SLEUTH simulates urban growth using four simple but effective growth rules. In order to improve its performance, SLEUTH has been modified among others by combining it with a robust probability map based on support vector machines. Additionally, a complex multi-agent system is developed to simulate residential mobility in a shrinking city agglomeration: residential mobility and the housing market of shrinking city systems focuses on the dynamic of interregional housing markets implying the development of potential dwelling areas. The multi-agent system comprises the simulation of population patterns, housing prices, and housing demand in shrinking city agglomerations. Both models are calibrated and validated regarding their localization and quantification performance. Subsequently, the urban landscape configuration and composition of the Ruhr 2025 are simulated. A simple spatial join is used to combine the results serving as valuable inputs for future regional planning in the context of multifarious demographic change and preceding urban growth.

Existence, uniqueness and regularity of a time-periodic probability density distribution arising in a sedimentation-diffusion problem

NASA Technical Reports Server (NTRS)

Nitsche, Ludwig C.; Nitsche, Johannes M.; Brenner, Howard

1988-01-01

The sedimentation and diffusion of a nonneutrally buoyant Brownian particle in vertical fluid-filled cylinder of finite length which is instantaneously inverted at regular intervals are investigated analytically. A one-dimensional convective-diffusive equation is derived to describe the temporal and spatial evolution of the probability density; a periodicity condition is formulated; the applicability of Fredholm theory is established; and the parameter-space regions are determined within which the existence and uniqueness of solutions are guaranteed. Numerical results for sample problems are presented graphically and briefly characterized.

Gambling and problem gambling among recently sentenced male prisoners in four New Zealand prisons.

PubMed

Abbott, Max W; McKenna, Brian G; Giles, Lynne C

2005-01-01

Recently sentenced inmates in four New Zealand male prisons (N = 357) were interviewed to assess their gambling involvement, problem gambling and criminal offending. Frequent participation in and high expenditure on continuous forms of gambling prior to imprisonment were reported. Nineteen percent said they had been in prison for a gambling-related offence and most of this offending was property-related and non-violent. On the basis of their SOGS-R scores, 21% were lifetime probable pathological gamblers and 16% were probable pathological gamblers during the six months prior to imprisonment. Of the "current" problem gamblers, 51% reported gambling-related offending and 35% had been imprisoned for a crime of this type. Gambling-related offending increased with problem gambling severity. However, only five percent of problem gamblers said their early offending was gambling-related. The large majority reported other types of offending at this time. Few men had sought or received help for gambling problems prior to imprisonment or during their present incarceration. This highlights the potential for assessment and treatment programs in prison to reduce recidivism and adverse effects of problem gambling and gambling-related offending.

Discrete particle swarm optimization to solve multi-objective limited-wait hybrid flow shop scheduling problem

NASA Astrophysics Data System (ADS)

Santosa, B.; Siswanto, N.; Fiqihesa

2018-04-01

This paper proposes a discrete Particle Swam Optimization (PSO) to solve limited-wait hybrid flowshop scheduing problem with multi objectives. Flow shop schedulimg represents the condition when several machines are arranged in series and each job must be processed at each machine with same sequence. The objective functions are minimizing completion time (makespan), total tardiness time, and total machine idle time. Flow shop scheduling model always grows to cope with the real production system accurately. Since flow shop scheduling is a NP-Hard problem then the most suitable method to solve is metaheuristics. One of metaheuristics algorithm is Particle Swarm Optimization (PSO), an algorithm which is based on the behavior of a swarm. Originally, PSO was intended to solve continuous optimization problems. Since flow shop scheduling is a discrete optimization problem, then, we need to modify PSO to fit the problem. The modification is done by using probability transition matrix mechanism. While to handle multi objectives problem, we use Pareto Optimal (MPSO). The results of MPSO is better than the PSO because the MPSO solution set produced higher probability to find the optimal solution. Besides the MPSO solution set is closer to the optimal solution

Estimation of distribution overlap of urn models.

PubMed

Hampton, Jerrad; Lladser, Manuel E

2012-01-01

A classical problem in statistics is estimating the expected coverage of a sample, which has had applications in gene expression, microbial ecology, optimization, and even numismatics. Here we consider a related extension of this problem to random samples of two discrete distributions. Specifically, we estimate what we call the dissimilarity probability of a sample, i.e., the probability of a draw from one distribution not being observed in [Formula: see text] draws from another distribution. We show our estimator of dissimilarity to be a [Formula: see text]-statistic and a uniformly minimum variance unbiased estimator of dissimilarity over the largest appropriate range of [Formula: see text]. Furthermore, despite the non-Markovian nature of our estimator when applied sequentially over [Formula: see text], we show it converges uniformly in probability to the dissimilarity parameter, and we present criteria when it is approximately normally distributed and admits a consistent jackknife estimator of its variance. As proof of concept, we analyze V35 16S rRNA data to discern between various microbial environments. Other potential applications concern any situation where dissimilarity of two discrete distributions may be of interest. For instance, in SELEX experiments, each urn could represent a random RNA pool and each draw a possible solution to a particular binding site problem over that pool. The dissimilarity of these pools is then related to the probability of finding binding site solutions in one pool that are absent in the other.

Computing rates of Markov models of voltage-gated ion channels by inverting partial differential equations governing the probability density functions of the conducting and non-conducting states.

PubMed

Tveito, Aslak; Lines, Glenn T; Edwards, Andrew G; McCulloch, Andrew

2016-07-01

Markov models are ubiquitously used to represent the function of single ion channels. However, solving the inverse problem to construct a Markov model of single channel dynamics from bilayer or patch-clamp recordings remains challenging, particularly for channels involving complex gating processes. Methods for solving the inverse problem are generally based on data from voltage clamp measurements. Here, we describe an alternative approach to this problem based on measurements of voltage traces. The voltage traces define probability density functions of the functional states of an ion channel. These probability density functions can also be computed by solving a deterministic system of partial differential equations. The inversion is based on tuning the rates of the Markov models used in the deterministic system of partial differential equations such that the solution mimics the properties of the probability density function gathered from (pseudo) experimental data as well as possible. The optimization is done by defining a cost function to measure the difference between the deterministic solution and the solution based on experimental data. By evoking the properties of this function, it is possible to infer whether the rates of the Markov model are identifiable by our method. We present applications to Markov model well-known from the literature. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Advanced reliability methods for structural evaluation

NASA Technical Reports Server (NTRS)

Wirsching, P. H.; Wu, Y.-T.

1985-01-01

Fast probability integration (FPI) methods, which can yield approximate solutions to such general structural reliability problems as the computation of the probabilities of complicated functions of random variables, are known to require one-tenth the computer time of Monte Carlo methods for a probability level of 0.001; lower probabilities yield even more dramatic differences. A strategy is presented in which a computer routine is run k times with selected perturbed values of the variables to obtain k solutions for a response variable Y. An approximating polynomial is fit to the k 'data' sets, and FPI methods are employed for this explicit form.

The Everett-Wheeler interpretation and the open future

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sudbery, Anthony

2011-03-28

I discuss the meaning of probability in the Everett-Wheeler interpretation of quantum mechanics, together with the problem of defining histories. To resolve these, I propose an understanding of probability arising from a form of temporal logic: the probability of a future-tense proposition is identified with its truth value in a many-valued and context-dependent logic. In short, probability is degree of truth. These ideas relate to traditional naive ideas of time and chance. Indeed, I argue that Everettian quantum mechanics is the only form of scientific theory that truly incorporates the perception that the future is open.

Quantum computing and probability.

PubMed

Ferry, David K

2009-11-25

Over the past two decades, quantum computing has become a popular and promising approach to trying to solve computationally difficult problems. Missing in many descriptions of quantum computing is just how probability enters into the process. Here, we discuss some simple examples of how uncertainty and probability enter, and how this and the ideas of quantum computing challenge our interpretations of quantum mechanics. It is found that this uncertainty can lead to intrinsic decoherence, and this raises challenges for error correction.

Probabilistic seismic hazard analyses for ground motions and fault displacement at Yucca Mountain, Nevada

USGS Publications Warehouse

Stepp, J.C.; Wong, I.; Whitney, J.; Quittmeyer, R.; Abrahamson, N.; Toro, G.; Young, S.R.; Coppersmith, K.; Savy, J.; Sullivan, T.

2001-01-01

Probabilistic seismic hazard analyses were conducted to estimate both ground motion and fault displacement hazards at the potential geologic repository for spent nuclear fuel and high-level radioactive waste at Yucca Mountain, Nevada. The study is believed to be the largest and most comprehensive analyses ever conducted for ground-shaking hazard and is a first-of-a-kind assessment of probabilistic fault displacement hazard. The major emphasis of the study was on the quantification of epistemic uncertainty. Six teams of three experts performed seismic source and fault displacement evaluations, and seven individual experts provided ground motion evaluations. State-of-the-practice expert elicitation processes involving structured workshops, consensus identification of parameters and issues to be evaluated, common sharing of data and information, and open exchanges about the basis for preliminary interpretations were implemented. Ground-shaking hazard was computed for a hypothetical rock outcrop at -300 m, the depth of the potential waste emplacement drifts, at the designated design annual exceedance probabilities of 10-3 and 10-4. The fault displacement hazard was calculated at the design annual exceedance probabilities of 10-4 and 10-5.

Pitfalls and Precautions When Using Predicted Failure Data for Quantitative Analysis of Safety Risk for Human Rated Launch Vehicles

NASA Technical Reports Server (NTRS)

Hatfield, Glen S.; Hark, Frank; Stott, James

2016-01-01

Launch vehicle reliability analysis is largely dependent upon using predicted failure rates from data sources such as MIL-HDBK-217F. Reliability prediction methodologies based on component data do not take into account system integration risks such as those attributable to manufacturing and assembly. These sources often dominate component level risk. While consequence of failure is often understood, using predicted values in a risk model to estimate the probability of occurrence may underestimate the actual risk. Managers and decision makers use the probability of occurrence to influence the determination whether to accept the risk or require a design modification. The actual risk threshold for acceptance may not be fully understood due to the absence of system level test data or operational data. This paper will establish a method and approach to identify the pitfalls and precautions of accepting risk based solely upon predicted failure data. This approach will provide a set of guidelines that may be useful to arrive at a more realistic quantification of risk prior to acceptance by a program.

Beyond the cortical column: abundance and physiology of horizontal connections imply a strong role for inputs from the surround.

PubMed

Boucsein, Clemens; Nawrot, Martin P; Schnepel, Philipp; Aertsen, Ad

2011-01-01

Current concepts of cortical information processing and most cortical network models largely rest on the assumption that well-studied properties of local synaptic connectivity are sufficient to understand the generic properties of cortical networks. This view seems to be justified by the observation that the vertical connectivity within local volumes is strong, whereas horizontally, the connection probability between pairs of neurons drops sharply with distance. Recent neuroanatomical studies, however, have emphasized that a substantial fraction of synapses onto neocortical pyramidal neurons stems from cells outside the local volume. Here, we discuss recent findings on the signal integration from horizontal inputs, showing that they could serve as a substrate for reliable and temporally precise signal propagation. Quantification of connection probabilities and parameters of synaptic physiology as a function of lateral distance indicates that horizontal projections constitute a considerable fraction, if not the majority, of inputs from within the cortical network. Taking these non-local horizontal inputs into account may dramatically change our current view on cortical information processing.

Probabilistic determination of probe locations from distance data

PubMed Central

Xu, Xiao-Ping; Slaughter, Brian D.; Volkmann, Niels

2013-01-01

Distance constraints, in principle, can be employed to determine information about the location of probes within a three-dimensional volume. Traditional methods for locating probes from distance constraints involve optimization of scoring functions that measure how well the probe location fits the distance data, exploring only a small subset of the scoring function landscape in the process. These methods are not guaranteed to find the global optimum and provide no means to relate the identified optimum to all other optima in scoring space. Here, we introduce a method for the location of probes from distance information that is based on probability calculus. This method allows exploration of the entire scoring space by directly combining probability functions representing the distance data and information about attachment sites. The approach is guaranteed to identify the global optimum and enables the derivation of confidence intervals for the probe location as well as statistical quantification of ambiguities. We apply the method to determine the location of a fluorescence probe using distances derived by FRET and show that the resulting location matches that independently derived by electron microscopy. PMID:23770585

Enhanced intelligent water drops algorithm for multi-depot vehicle routing problem

PubMed Central

Akutsah, Francis; Olusanya, Micheal O.; Adewumi, Aderemi O.

2018-01-01

The intelligent water drop algorithm is a swarm-based metaheuristic algorithm, inspired by the characteristics of water drops in the river and the environmental changes resulting from the action of the flowing river. Since its appearance as an alternative stochastic optimization method, the algorithm has found applications in solving a wide range of combinatorial and functional optimization problems. This paper presents an improved intelligent water drop algorithm for solving multi-depot vehicle routing problems. A simulated annealing algorithm was introduced into the proposed algorithm as a local search metaheuristic to prevent the intelligent water drop algorithm from getting trapped into local minima and also improve its solution quality. In addition, some of the potential problematic issues associated with using simulated annealing that include high computational runtime and exponential calculation of the probability of acceptance criteria, are investigated. The exponential calculation of the probability of acceptance criteria for the simulated annealing based techniques is computationally expensive. Therefore, in order to maximize the performance of the intelligent water drop algorithm using simulated annealing, a better way of calculating the probability of acceptance criteria is considered. The performance of the proposed hybrid algorithm is evaluated by using 33 standard test problems, with the results obtained compared with the solutions offered by four well-known techniques from the subject literature. Experimental results and statistical tests show that the new method possesses outstanding performance in terms of solution quality and runtime consumed. In addition, the proposed algorithm is suitable for solving large-scale problems. PMID:29554662

Enhanced intelligent water drops algorithm for multi-depot vehicle routing problem.

PubMed

Ezugwu, Absalom E; Akutsah, Francis; Olusanya, Micheal O; Adewumi, Aderemi O

2018-01-01

The intelligent water drop algorithm is a swarm-based metaheuristic algorithm, inspired by the characteristics of water drops in the river and the environmental changes resulting from the action of the flowing river. Since its appearance as an alternative stochastic optimization method, the algorithm has found applications in solving a wide range of combinatorial and functional optimization problems. This paper presents an improved intelligent water drop algorithm for solving multi-depot vehicle routing problems. A simulated annealing algorithm was introduced into the proposed algorithm as a local search metaheuristic to prevent the intelligent water drop algorithm from getting trapped into local minima and also improve its solution quality. In addition, some of the potential problematic issues associated with using simulated annealing that include high computational runtime and exponential calculation of the probability of acceptance criteria, are investigated. The exponential calculation of the probability of acceptance criteria for the simulated annealing based techniques is computationally expensive. Therefore, in order to maximize the performance of the intelligent water drop algorithm using simulated annealing, a better way of calculating the probability of acceptance criteria is considered. The performance of the proposed hybrid algorithm is evaluated by using 33 standard test problems, with the results obtained compared with the solutions offered by four well-known techniques from the subject literature. Experimental results and statistical tests show that the new method possesses outstanding performance in terms of solution quality and runtime consumed. In addition, the proposed algorithm is suitable for solving large-scale problems.

Ultra-Scalable Algorithms for Large-Scale Uncertainty Quantification in Inverse Wave Propagation

DTIC Science & Technology

2016-03-04

53] N. Petra , J. Martin , G. Stadler, and O. Ghattas, A computational framework for infinite-dimensional Bayesian inverse problems: Part II...positions: Alen Alexanderian (NC State), Tan Bui-Thanh (UT-Austin), Carsten Burstedde (University of Bonn), Noemi Petra (UC Merced), Georg Stalder (NYU), Hari...Baltimore, MD, Nov. 2002. SC2002 Best Technical Paper Award. [3] A. Alexanderian, N. Petra , G. Stadler, and O. Ghattas, A-optimal design of exper

New simple and low-cost methods for periodic checks of Cyclone® Plus Storage Phosphor System.

PubMed

Edalucci, Elisabetta; Maffione, Anna Margherita; Fornasier, Maria Rosa; De Denaro, Mario; Scian, Giovanni; Dore, Franca; Rubello, Domenico

2017-01-01

The recent large use of the Cyclone® Plus Storage Phosphor System, especially in European countries, as imaging system for quantification of radiochemical purity of radiopharmaceuticals raised the problem of setting the periodic controls as required by European Legislation. We described simple, low-cost methods for Cyclone® Plus quality controls, which can be useful to evaluate the performance measurement of this imaging system.

Magnetic Resonance Imaging Quantification of Liver Iron

PubMed Central

Sirlin, Claude B.; Reeder, Scott B.

2011-01-01

Iron overload is the histological hallmark of genetic hemochromatosis and transfusional hemosiderosis but also may occur in chronic hepatopathies. This article provides an overview of iron deposition and diseases where liver iron overload is clinically relevant. Next, this article reviews why quantitative non-invasive biomarkers of liver iron would be beneficial. Finally, we describe current state of the art methods for quantifying iron with MRI and review remaining challenges and unsolved problems, PMID:21094445

Outcome Measures for Heavy Menstrual Bleeding

PubMed Central

Quinn, Stephen D; Higham, Jenny

2016-01-01

Heavy menstrual bleeding is one of the most commonly encountered gynecological problems. While accurate objective quantification of menstrual blood loss is of value in the research setting, it is the subjective assessment of blood loss that is of greater importance when assessing the severity of heavy menstrual bleeding and any subsequent response to treatment. In this review the various approaches to objective, subjective and semi-subjective assessment of menstrual blood loss will be discussed. PMID:26693585

Multi-Scale Fusion of Information for Uncertainty Quantification and Management in Large-Scale Simulations

DTIC Science & Technology

2015-12-02

simplification of the equations but at the expense of introducing modeling errors. We have shown that the Wick solutions have accuracy comparable to...the system of equations for the coefficients of formal power series solutions . Moreover, the structure of this propagator is seemingly universal, i.e...the problem of computing the numerical solution to kinetic partial differential equa- tions involving many phase variables. These types of equations

A comparison of numerical solutions of partial differential equations with probabilistic and possibilistic parameters for the quantification of uncertainty in subsurface solute transport.

PubMed

Zhang, Kejiang; Achari, Gopal; Li, Hua

2009-11-03

Traditionally, uncertainty in parameters are represented as probabilistic distributions and incorporated into groundwater flow and contaminant transport models. With the advent of newer uncertainty theories, it is now understood that stochastic methods cannot properly represent non random uncertainties. In the groundwater flow and contaminant transport equations, uncertainty in some parameters may be random, whereas those of others may be non random. The objective of this paper is to develop a fuzzy-stochastic partial differential equation (FSPDE) model to simulate conditions where both random and non random uncertainties are involved in groundwater flow and solute transport. Three potential solution techniques namely, (a) transforming a probability distribution to a possibility distribution (Method I) then a FSPDE becomes a fuzzy partial differential equation (FPDE), (b) transforming a possibility distribution to a probability distribution (Method II) and then a FSPDE becomes a stochastic partial differential equation (SPDE), and (c) the combination of Monte Carlo methods and FPDE solution techniques (Method III) are proposed and compared. The effects of these three methods on the predictive results are investigated by using two case studies. The results show that the predictions obtained from Method II is a specific case of that got from Method I. When an exact probabilistic result is needed, Method II is suggested. As the loss or gain of information during a probability-possibility (or vice versa) transformation cannot be quantified, their influences on the predictive results is not known. Thus, Method III should probably be preferred for risk assessments.

Bayesian analysis of rare events

NASA Astrophysics Data System (ADS)

Straub, Daniel; Papaioannou, Iason; Betz, Wolfgang

2016-06-01

In many areas of engineering and science there is an interest in predicting the probability of rare events, in particular in applications related to safety and security. Increasingly, such predictions are made through computer models of physical systems in an uncertainty quantification framework. Additionally, with advances in IT, monitoring and sensor technology, an increasing amount of data on the performance of the systems is collected. This data can be used to reduce uncertainty, improve the probability estimates and consequently enhance the management of rare events and associated risks. Bayesian analysis is the ideal method to include the data into the probabilistic model. It ensures a consistent probabilistic treatment of uncertainty, which is central in the prediction of rare events, where extrapolation from the domain of observation is common. We present a framework for performing Bayesian updating of rare event probabilities, termed BUS. It is based on a reinterpretation of the classical rejection-sampling approach to Bayesian analysis, which enables the use of established methods for estimating probabilities of rare events. By drawing upon these methods, the framework makes use of their computational efficiency. These methods include the First-Order Reliability Method (FORM), tailored importance sampling (IS) methods and Subset Simulation (SuS). In this contribution, we briefly review these methods in the context of the BUS framework and investigate their applicability to Bayesian analysis of rare events in different settings. We find that, for some applications, FORM can be highly efficient and is surprisingly accurate, enabling Bayesian analysis of rare events with just a few model evaluations. In a general setting, BUS implemented through IS and SuS is more robust and flexible.

Assessing the Effect of Language Demand in Bundles of Math Word Problems

ERIC Educational Resources Information Center

Banks, Kathleen; Jeddeeni, Ahmad; Walker, Cindy M.

2016-01-01

Differential bundle functioning (DBF) analyses were conducted to determine whether seventh and eighth grade second language learners (SLLs) had lower probabilities of answering bundles of math word problems correctly that had heavy language demands, when compared to non-SLLs of equal math proficiency. Math word problems on each of four test forms…

The Effect of Shift Work on Quality and Stability of Marital Relations.

ERIC Educational Resources Information Center

White, Lynn; Keith, Bruce

1990-01-01

Interviewed a national panel of 1,668 married women and men in 1980 and 1983 to assess effects of shift work on 6 measures of marital quality (marital happiness, interaction, disagreements, general problems, sexual problems, and child-related problems) and probability of divorce. Results suggested that shift work has a modest but general adverse…

Crib Work--An Evaluation of a Problem-Based Learning Experiment: Preliminary Results

ERIC Educational Resources Information Center

Walsh, Vonda K.; Bush, H. Francis

2013-01-01

Problem-based learning has been proven to be successful in both medical colleges and physics classes, but not uniformly across all disciplines. A college course in probability and statistics was used as a setting to test the effectiveness of problem-based learning when applied to homework. This paper compares the performances of the students from…

Minimal entropy probability paths between genome families.

PubMed

Ahlbrandt, Calvin; Benson, Gary; Casey, William

2004-05-01

We develop a metric for probability distributions with applications to biological sequence analysis. Our distance metric is obtained by minimizing a functional defined on the class of paths over probability measures on N categories. The underlying mathematical theory is connected to a constrained problem in the calculus of variations. The solution presented is a numerical solution, which approximates the true solution in a set of cases called rich paths where none of the components of the path is zero. The functional to be minimized is motivated by entropy considerations, reflecting the idea that nature might efficiently carry out mutations of genome sequences in such a way that the increase in entropy involved in transformation is as small as possible. We characterize sequences by frequency profiles or probability vectors, in the case of DNA where N is 4 and the components of the probability vector are the frequency of occurrence of each of the bases A, C, G and T. Given two probability vectors a and b, we define a distance function based as the infimum of path integrals of the entropy function H( p) over all admissible paths p(t), 0 < or = t< or =1, with p(t) a probability vector such that p(0)=a and p(1)=b. If the probability paths p(t) are parameterized as y(s) in terms of arc length s and the optimal path is smooth with arc length L, then smooth and "rich" optimal probability paths may be numerically estimated by a hybrid method of iterating Newton's method on solutions of a two point boundary value problem, with unknown distance L between the abscissas, for the Euler-Lagrange equations resulting from a multiplier rule for the constrained optimization problem together with linear regression to improve the arc length estimate L. Matlab code for these numerical methods is provided which works only for "rich" optimal probability vectors. These methods motivate a definition of an elementary distance function which is easier and faster to calculate, works on non-rich vectors, does not involve variational theory and does not involve differential equations, but is a better approximation of the minimal entropy path distance than the distance //b-a//(2). We compute minimal entropy distance matrices for examples of DNA myostatin genes and amino-acid sequences across several species. Output tree dendograms for our minimal entropy metric are compared with dendograms based on BLAST and BLAST identity scores.

Problem of quality assurance during metal constructions welding via robotic technological complexes

NASA Astrophysics Data System (ADS)

Fominykh, D. S.; Rezchikov, A. F.; Kushnikov, V. A.; Ivashchenko, V. A.; Bogomolov, A. S.; Filimonyuk, L. Yu; Dolinina, O. N.; Kushnikov, O. V.; Shulga, T. E.; Tverdokhlebov, V. A.

2018-05-01

The problem of minimizing the probability for critical combinations of events that lead to a loss in welding quality via robotic process automation is examined. The problem is formulated, models and algorithms for its solution are developed. The problem is solved by minimizing the criterion characterizing the losses caused by defective products. Solving the problem may enhance the quality and accuracy of operations performed and reduce the losses caused by defective product

Transactional Problem Content in Cost Discounting: Parallel Effects for Probability and Delay

ERIC Educational Resources Information Center

Jones, Stephen; Oaksford, Mike

2011-01-01

Four experiments investigated the effects of transactional content on temporal and probabilistic discounting of costs. Kusev, van Schaik, Ayton, Dent, and Chater (2009) have shown that content other than gambles can alter decision-making behavior even when associated value and probabilities are held constant. Transactions were hypothesized to lead…

Probability & Perception: The Representativeness Heuristic in Action

ERIC Educational Resources Information Center

Lu, Yun; Vasko, Francis J.; Drummond, Trevor J.; Vasko, Lisa E.

2014-01-01

If the prospective students of probability lack a background in mathematical proofs, hands-on classroom activities may work well to help them to learn to analyze problems correctly. For example, students may physically roll a die twice to count and compare the frequency of the sequences. Tools such as graphing calculators or Microsoft Excel®…

University Students' Conceptual Knowledge of Randomness and Probability in the Contexts of Evolution and Mathematics

ERIC Educational Resources Information Center

Fiedler, Daniela; Tröbst, Steffen; Harms, Ute

2017-01-01

Students of all ages face severe conceptual difficulties regarding key aspects of evolution-- the central, unifying, and overarching theme in biology. Aspects strongly related to abstract "threshold" concepts like randomness and probability appear to pose particular difficulties. A further problem is the lack of an appropriate instrument…

Temporal scaling in information propagation.

PubMed

Huang, Junming; Li, Chao; Wang, Wen-Qiang; Shen, Hua-Wei; Li, Guojie; Cheng, Xue-Qi

2014-06-18

For the study of information propagation, one fundamental problem is uncovering universal laws governing the dynamics of information propagation. This problem, from the microscopic perspective, is formulated as estimating the propagation probability that a piece of information propagates from one individual to another. Such a propagation probability generally depends on two major classes of factors: the intrinsic attractiveness of information and the interactions between individuals. Despite the fact that the temporal effect of attractiveness is widely studied, temporal laws underlying individual interactions remain unclear, causing inaccurate prediction of information propagation on evolving social networks. In this report, we empirically study the dynamics of information propagation, using the dataset from a population-scale social media website. We discover a temporal scaling in information propagation: the probability a message propagates between two individuals decays with the length of time latency since their latest interaction, obeying a power-law rule. Leveraging the scaling law, we further propose a temporal model to estimate future propagation probabilities between individuals, reducing the error rate of information propagation prediction from 6.7% to 2.6% and improving viral marketing with 9.7% incremental customers.