Designing management strategies for carbon dioxide storage and utilization under uncertainty using inexact modelling

NASA Astrophysics Data System (ADS)

Wang, Yu; Fan, Jie; Xu, Ye; Sun, Wei; Chen, Dong

2017-06-01

Effective application of carbon capture, utilization and storage (CCUS) systems could help to alleviate the influence of climate change by reducing carbon dioxide (CO2) emissions. The research objective of this study is to develop an equilibrium chance-constrained programming model with bi-random variables (ECCP model) for supporting the CCUS management system under random circumstances. The major advantage of the ECCP model is that it tackles random variables as bi-random variables with a normal distribution, where the mean values follow a normal distribution. This could avoid irrational assumptions and oversimplifications in the process of parameter design and enrich the theory of stochastic optimization. The ECCP model is solved by an equilibrium change-constrained programming algorithm, which provides convenience for decision makers to rank the solution set using the natural order of real numbers. The ECCP model is applied to a CCUS management problem, and the solutions could be useful in helping managers to design and generate rational CO2-allocation patterns under complexities and uncertainties.

Log-normal distribution from a process that is not multiplicative but is additive.

PubMed

Mouri, Hideaki

2013-10-01

The central limit theorem ensures that a sum of random variables tends to a Gaussian distribution as their total number tends to infinity. However, for a class of positive random variables, we find that the sum tends faster to a log-normal distribution. Although the sum tends eventually to a Gaussian distribution, the distribution of the sum is always close to a log-normal distribution rather than to any Gaussian distribution if the summands are numerous enough. This is in contrast to the current consensus that any log-normal distribution is due to a product of random variables, i.e., a multiplicative process, or equivalently to nonlinearity of the system. In fact, the log-normal distribution is also observable for a sum, i.e., an additive process that is typical of linear systems. We show conditions for such a sum, an analytical example, and an application to random scalar fields such as those of turbulence.

An Introduction to Recursive Partitioning: Rationale, Application, and Characteristics of Classification and Regression Trees, Bagging, and Random Forests

ERIC Educational Resources Information Center

Strobl, Carolin; Malley, James; Tutz, Gerhard

2009-01-01

Recursive partitioning methods have become popular and widely used tools for nonparametric regression and classification in many scientific fields. Especially random forests, which can deal with large numbers of predictor variables even in the presence of complex interactions, have been applied successfully in genetics, clinical medicine, and…

Application of the theory of reasoned action to environmental behaviors

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

Duquette, R.D.

The applicability of Ajzen and Fishbein's Theory of Reasoned Action (1980) to environmental behaviors was examined. Trained interviewers conducted a telephone survey employing random digit dialing with random selection of individuals within households; 388 individuals completed interviews. A preliminary study was conducted to identify salient outcomes (advantages and disadvantages), referents (individuals or groups), and activities associated with protecting the environment. The main study questionnaire was based upon the most frequently identified outcomes, referents, and activities, using the procedures of Ajzen and Fishbein (1980). A pilot test indicated that the ..cap alpha.. coefficients of all subscales were greater than .70. Inmore » addition to the theory variables, the external variables of occupation and education were assessed. The relationships between the theory variables were examined using correlational and multiple regression techniques. Though weaker than in previous studies, all the theoretical relationships were in the hypothesized direction. Analysis of variance, used to examine the external variables, found significant differences among occupational groups and educational levels with regard to intention to protect the environment. Polluters scored lower on intention than individuals with non-polluting or not applicable occupations. Individuals with a high school diploma or less were lower on intention and were significantly less favorable toward protecting the environment than those with some college or a college degree.« less

Decisions with Uncertain Consequences—A Total Ordering on Loss-Distributions

PubMed Central

König, Sandra; Schauer, Stefan

2016-01-01

Decisions are often based on imprecise, uncertain or vague information. Likewise, the consequences of an action are often equally unpredictable, thus putting the decision maker into a twofold jeopardy. Assuming that the effects of an action can be modeled by a random variable, then the decision problem boils down to comparing different effects (random variables) by comparing their distribution functions. Although the full space of probability distributions cannot be ordered, a properly restricted subset of distributions can be totally ordered in a practically meaningful way. We call these loss-distributions, since they provide a substitute for the concept of loss-functions in decision theory. This article introduces the theory behind the necessary restrictions and the hereby constructible total ordering on random loss variables, which enables decisions under uncertainty of consequences. Using data obtained from simulations, we demonstrate the practical applicability of our approach. PMID:28030572

Learning planar Ising models

DOE PAGES

Johnson, Jason K.; Oyen, Diane Adele; Chertkov, Michael; ...

2016-12-01

Inference and learning of graphical models are both well-studied problems in statistics and machine learning that have found many applications in science and engineering. However, exact inference is intractable in general graphical models, which suggests the problem of seeking the best approximation to a collection of random variables within some tractable family of graphical models. In this paper, we focus on the class of planar Ising models, for which exact inference is tractable using techniques of statistical physics. Based on these techniques and recent methods for planarity testing and planar embedding, we propose a greedy algorithm for learning the bestmore » planar Ising model to approximate an arbitrary collection of binary random variables (possibly from sample data). Given the set of all pairwise correlations among variables, we select a planar graph and optimal planar Ising model defined on this graph to best approximate that set of correlations. Finally, we demonstrate our method in simulations and for two applications: modeling senate voting records and identifying geo-chemical depth trends from Mars rover data.« less

Learning planar Ising models

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

Johnson, Jason K.; Oyen, Diane Adele; Chertkov, Michael

Inference and learning of graphical models are both well-studied problems in statistics and machine learning that have found many applications in science and engineering. However, exact inference is intractable in general graphical models, which suggests the problem of seeking the best approximation to a collection of random variables within some tractable family of graphical models. In this paper, we focus on the class of planar Ising models, for which exact inference is tractable using techniques of statistical physics. Based on these techniques and recent methods for planarity testing and planar embedding, we propose a greedy algorithm for learning the bestmore » planar Ising model to approximate an arbitrary collection of binary random variables (possibly from sample data). Given the set of all pairwise correlations among variables, we select a planar graph and optimal planar Ising model defined on this graph to best approximate that set of correlations. Finally, we demonstrate our method in simulations and for two applications: modeling senate voting records and identifying geo-chemical depth trends from Mars rover data.« less

Resistance controllability and variability improvement in a TaO{sub x}-based resistive memory for multilevel storage application

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

Prakash, A., E-mail: amitknp@postech.ac.kr, E-mail: amit.knp02@gmail.com, E-mail: hwanghs@postech.ac.kr; Song, J.; Hwang, H., E-mail: amitknp@postech.ac.kr, E-mail: amit.knp02@gmail.com, E-mail: hwanghs@postech.ac.kr

In order to obtain reliable multilevel cell (MLC) characteristics, resistance controllability between the different resistance levels is required especially in resistive random access memory (RRAM), which is prone to resistance variability mainly due to its intrinsic random nature of defect generation and filament formation. In this study, we have thoroughly investigated the multilevel resistance variability in a TaO{sub x}-based nanoscale (<30 nm) RRAM operated in MLC mode. It is found that the resistance variability not only depends on the conductive filament size but also is a strong function of oxygen vacancy concentration in it. Based on the gained insights through experimentalmore » observations and simulation, it is suggested that forming thinner but denser conductive filament may greatly improve the temporal resistance variability even at low operation current despite the inherent stochastic nature of resistance switching process.« less

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.

On the Extraction of Components and the Applicability of the Factor Model.

ERIC Educational Resources Information Center

Dziuban, Charles D.; Harris, Chester W.

A reanalysis of Shaycroft's matrix of intercorrelations of 10 test variables plus 4 random variables is discussed. Three different procedures were used in the reanalysis: (1) Image Component Analysis, (2) Uniqueness Rescaling Factor Analysis, and (3) Alpha Factor Analysis. The results of these analyses are presented in tables. It is concluded from…

Hoeffding Type Inequalities and their Applications in Statistics and Operations Research

NASA Astrophysics Data System (ADS)

Daras, Tryfon

2007-09-01

Large Deviation theory is the branch of Probability theory that deals with rare events. Sometimes, these events can be described by the sum of random variables that deviates from its mean more than a "normal" amount. A precise calculation of the probabilities of such events turns out to be crucial in a variety of different contents (e.g. in Probability Theory, Statistics, Operations Research, Statistical Physics, Financial Mathematics e.t.c.). Recent applications of the theory deal with random walks in random environments, interacting diffusions, heat conduction, polymer chains [1]. In this paper we prove an inequality of exponential type, namely theorem 2.1, which gives a large deviation upper bound for a specific sequence of r.v.s. Inequalities of this type have many applications in Combinatorics [2]. The inequality generalizes already proven results of this type, in the case of symmetric probability measures. We get as consequences to the inequality: (a) large deviations upper bounds for exchangeable Bernoulli sequences of random variables, generalizing results proven for independent and identically distributed Bernoulli sequences of r.v.s. and (b) a general form of Bernstein's inequality. We compare the inequality with large deviation results already proven by the author and try to see its advantages. Finally, using the inequality, we solve one of the basic problems of Operations Research (bin packing problem) in the case of exchangeable r.v.s.

Estimating Individual Influences of Behavioral Intentions: An Application of Random-Effects Modeling to the Theory of Reasoned Action.

ERIC Educational Resources Information Center

Hedeker, Donald; And Others

1996-01-01

Methods are proposed and described for estimating the degree to which relations among variables vary at the individual level. As an example, M. Fishbein and I. Ajzen's theory of reasoned action is examined. This article illustrates the use of empirical Bayes methods based on a random-effects regression model to estimate individual influences…

Practice schedule and acquisition, retention, and transfer of a throwing task in 6-yr.-old children.

PubMed

Granda Vera, Juan; Montilla, Mariano Medina

2003-06-01

Earlier studies have raised questions about the usefulness of variable and random practice in learning motor tasks so this study was designed to investigate the effects of contextual interference in young children, and specifically to evaluate the effectiveness of variable or random practice structure in 6-yr.-old boys and girls. Participants on a variable practice schedule showed better performances than those on a blocked schedule. The differences between the two groups were significant in the acquisition, retention, and transfer phases. These results support the hypothesis that contextual interference enhances skill learning. Because the study involved groups of young children in the setting of their normally scheduled physical education class, the practical applications of the results are evident.

Circularly-symmetric complex normal ratio distribution for scalar transmissibility functions. Part I: Fundamentals

NASA Astrophysics Data System (ADS)

Yan, Wang-Ji; Ren, Wei-Xin

2016-12-01

Recent advances in signal processing and structural dynamics have spurred the adoption of transmissibility functions in academia and industry alike. Due to the inherent randomness of measurement and variability of environmental conditions, uncertainty impacts its applications. This study is focused on statistical inference for raw scalar transmissibility functions modeled as complex ratio random variables. The goal is achieved through companion papers. This paper (Part I) is dedicated to dealing with a formal mathematical proof. New theorems on multivariate circularly-symmetric complex normal ratio distribution are proved on the basis of principle of probabilistic transformation of continuous random vectors. The closed-form distributional formulas for multivariate ratios of correlated circularly-symmetric complex normal random variables are analytically derived. Afterwards, several properties are deduced as corollaries and lemmas to the new theorems. Monte Carlo simulation (MCS) is utilized to verify the accuracy of some representative cases. This work lays the mathematical groundwork to find probabilistic models for raw scalar transmissibility functions, which are to be expounded in detail in Part II of this study.

Effects of "Like Type" Sex Pairings between Applicants-Principals and Type of Focal Position Considered at the Screening Stage of the Selection Process

ERIC Educational Resources Information Center

Young, I. Phillip

2005-01-01

This study addresses the screening decisions for a national random sample of high school principals as viewed from the attraction-similarity theory of interpersonal perceptions. Independent variables are the sex of principals, sex of applicants, and the type of focal positions sought by hypothetical job applicants (teacher or counselor). Dependent…

Testing concordance of instrumental variable effects in generalized linear models with application to Mendelian randomization

PubMed Central

Dai, James Y.; Chan, Kwun Chuen Gary; Hsu, Li

2014-01-01

Instrumental variable regression is one way to overcome unmeasured confounding and estimate causal effect in observational studies. Built on structural mean models, there has been considerale work recently developed for consistent estimation of causal relative risk and causal odds ratio. Such models can sometimes suffer from identification issues for weak instruments. This hampered the applicability of Mendelian randomization analysis in genetic epidemiology. When there are multiple genetic variants available as instrumental variables, and causal effect is defined in a generalized linear model in the presence of unmeasured confounders, we propose to test concordance between instrumental variable effects on the intermediate exposure and instrumental variable effects on the disease outcome, as a means to test the causal effect. We show that a class of generalized least squares estimators provide valid and consistent tests of causality. For causal effect of a continuous exposure on a dichotomous outcome in logistic models, the proposed estimators are shown to be asymptotically conservative. When the disease outcome is rare, such estimators are consistent due to the log-linear approximation of the logistic function. Optimality of such estimators relative to the well-known two-stage least squares estimator and the double-logistic structural mean model is further discussed. PMID:24863158

Simple, Efficient Estimators of Treatment Effects in Randomized Trials Using Generalized Linear Models to Leverage Baseline Variables

PubMed Central

Rosenblum, Michael; van der Laan, Mark J.

2010-01-01

Models, such as logistic regression and Poisson regression models, are often used to estimate treatment effects in randomized trials. These models leverage information in variables collected before randomization, in order to obtain more precise estimates of treatment effects. However, there is the danger that model misspecification will lead to bias. We show that certain easy to compute, model-based estimators are asymptotically unbiased even when the working model used is arbitrarily misspecified. Furthermore, these estimators are locally efficient. As a special case of our main result, we consider a simple Poisson working model containing only main terms; in this case, we prove the maximum likelihood estimate of the coefficient corresponding to the treatment variable is an asymptotically unbiased estimator of the marginal log rate ratio, even when the working model is arbitrarily misspecified. This is the log-linear analog of ANCOVA for linear models. Our results demonstrate one application of targeted maximum likelihood estimation. PMID:20628636

Using Multigroup-Multiphase Latent State-Trait Models to Study Treatment-Induced Changes in Intra-Individual State Variability: An Application to Smokers' Affect.

PubMed

Geiser, Christian; Griffin, Daniel; Shiffman, Saul

2016-01-01

Sometimes, researchers are interested in whether an intervention, experimental manipulation, or other treatment causes changes in intra-individual state variability. The authors show how multigroup-multiphase latent state-trait (MG-MP-LST) models can be used to examine treatment effects with regard to both mean differences and differences in state variability. The approach is illustrated based on a randomized controlled trial in which N = 338 smokers were randomly assigned to nicotine replacement therapy (NRT) vs. placebo prior to quitting smoking. We found that post quitting, smokers in both the NRT and placebo group had significantly reduced intra-individual affect state variability with respect to the affect items calm and content relative to the pre-quitting phase. This reduction in state variability did not differ between the NRT and placebo groups, indicating that quitting smoking may lead to a stabilization of individuals' affect states regardless of whether or not individuals receive NRT.

Using Multigroup-Multiphase Latent State-Trait Models to Study Treatment-Induced Changes in Intra-Individual State Variability: An Application to Smokers' Affect

PubMed Central

Geiser, Christian; Griffin, Daniel; Shiffman, Saul

2016-01-01

Sometimes, researchers are interested in whether an intervention, experimental manipulation, or other treatment causes changes in intra-individual state variability. The authors show how multigroup-multiphase latent state-trait (MG-MP-LST) models can be used to examine treatment effects with regard to both mean differences and differences in state variability. The approach is illustrated based on a randomized controlled trial in which N = 338 smokers were randomly assigned to nicotine replacement therapy (NRT) vs. placebo prior to quitting smoking. We found that post quitting, smokers in both the NRT and placebo group had significantly reduced intra-individual affect state variability with respect to the affect items calm and content relative to the pre-quitting phase. This reduction in state variability did not differ between the NRT and placebo groups, indicating that quitting smoking may lead to a stabilization of individuals' affect states regardless of whether or not individuals receive NRT. PMID:27499744

Pilot Study on the Applicability of Variance Reduction Techniques to the Simulation of a Stochastic Combat Model

DTIC Science & Technology

1987-09-01

inverse transform method to obtain unit-mean exponential random variables, where Vi is the jth random number in the sequence of a stream of uniform random...numbers. The inverse transform method is discussed in the simulation textbooks listed in the reference section of this thesis. X(b,c,d) = - P(b,c,d...Defender ,C * P(b,c,d) We again use the inverse transform method to obtain the conditions for an interim event to occur and to induce the change in

A Fast Numerical Method for Max-Convolution and the Application to Efficient Max-Product Inference in Bayesian Networks.

PubMed

Serang, Oliver

2015-08-01

Observations depending on sums of random variables are common throughout many fields; however, no efficient solution is currently known for performing max-product inference on these sums of general discrete distributions (max-product inference can be used to obtain maximum a posteriori estimates). The limiting step to max-product inference is the max-convolution problem (sometimes presented in log-transformed form and denoted as "infimal convolution," "min-convolution," or "convolution on the tropical semiring"), for which no O(k log(k)) method is currently known. Presented here is an O(k log(k)) numerical method for estimating the max-convolution of two nonnegative vectors (e.g., two probability mass functions), where k is the length of the larger vector. This numerical max-convolution method is then demonstrated by performing fast max-product inference on a convolution tree, a data structure for performing fast inference given information on the sum of n discrete random variables in O(nk log(nk)log(n)) steps (where each random variable has an arbitrary prior distribution on k contiguous possible states). The numerical max-convolution method can be applied to specialized classes of hidden Markov models to reduce the runtime of computing the Viterbi path from nk(2) to nk log(k), and has potential application to the all-pairs shortest paths problem.

[Topical hemostatic devices in surgery: between science and marketing].

PubMed

González, Héctor Daniel; Figueras Felip, Joan

2009-06-01

Topical hemostatic agents have been used in surgery with varying degrees of success. These agents include oxidized cellulose, absorbable gelatin sponges, microfibrillar collagen and fibrin seals. Fibrin seals have become widely used as they improve perioperative hemostasis, reduce the need for red blood cell transfusions and prevent biliary leaks. Their widespread use, however, contrasts with the scarcity of data from controlled studies to support their clinical effectiveness. Therefore, a prospective, randomized, controlled, single-center study was performed in 300 patients who underwent elective hepatectomy, with and without application of fibrin seal on the raw liver surface. None of the variables evaluated (blood loss, transfusions, biliary fistulas and postoperative results) differed between the two groups. We conclude that the application of fibrin seal does not seem justified and that discontinuing its routine use would substantially reduce costs. The use of a new agent, Tachosil, is supported by a single multicenter, prospective, randomized, controlled trial, which is limited by the small number of patients and by the fact that the variable analyzed--time from application to hemostasis--may not be clinically relevant.

Modeling Errors in Daily Precipitation Measurements: Additive or Multiplicative?

NASA Technical Reports Server (NTRS)

Tian, Yudong; Huffman, George J.; Adler, Robert F.; Tang, Ling; Sapiano, Matthew; Maggioni, Viviana; Wu, Huan

2013-01-01

The definition and quantification of uncertainty depend on the error model used. For uncertainties in precipitation measurements, two types of error models have been widely adopted: the additive error model and the multiplicative error model. This leads to incompatible specifications of uncertainties and impedes intercomparison and application.In this letter, we assess the suitability of both models for satellite-based daily precipitation measurements in an effort to clarify the uncertainty representation. Three criteria were employed to evaluate the applicability of either model: (1) better separation of the systematic and random errors; (2) applicability to the large range of variability in daily precipitation; and (3) better predictive skills. It is found that the multiplicative error model is a much better choice under all three criteria. It extracted the systematic errors more cleanly, was more consistent with the large variability of precipitation measurements, and produced superior predictions of the error characteristics. The additive error model had several weaknesses, such as non constant variance resulting from systematic errors leaking into random errors, and the lack of prediction capability. Therefore, the multiplicative error model is a better choice.

Simultaneous escaping of explicit and hidden free energy barriers: application of the orthogonal space random walk strategy in generalized ensemble based conformational sampling.

PubMed

Zheng, Lianqing; Chen, Mengen; Yang, Wei

2009-06-21

To overcome the pseudoergodicity problem, conformational sampling can be accelerated via generalized ensemble methods, e.g., through the realization of random walks along prechosen collective variables, such as spatial order parameters, energy scaling parameters, or even system temperatures or pressures, etc. As usually observed, in generalized ensemble simulations, hidden barriers are likely to exist in the space perpendicular to the collective variable direction and these residual free energy barriers could greatly abolish the sampling efficiency. This sampling issue is particularly severe when the collective variable is defined in a low-dimension subset of the target system; then the "Hamiltonian lagging" problem, which reveals the fact that necessary structural relaxation falls behind the move of the collective variable, may be likely to occur. To overcome this problem in equilibrium conformational sampling, we adopted the orthogonal space random walk (OSRW) strategy, which was originally developed in the context of free energy simulation [L. Zheng, M. Chen, and W. Yang, Proc. Natl. Acad. Sci. U.S.A. 105, 20227 (2008)]. Thereby, generalized ensemble simulations can simultaneously escape both the explicit barriers along the collective variable direction and the hidden barriers that are strongly coupled with the collective variable move. As demonstrated in our model studies, the present OSRW based generalized ensemble treatments show improved sampling capability over the corresponding classical generalized ensemble treatments.

Musk as a Pheromone? Didactic Exercise.

ERIC Educational Resources Information Center

Bersted, Chris T.

A classroom/laboratory exercise has been used to introduce college students to factorial research designs, differentiate between interpretations for experimental and quasi-experimental variables, and exemplify application of laboratory research methods to test practical questions (advertising claims). The exercise involves having randomly divided…

Time-variant random interval natural frequency analysis of structures

NASA Astrophysics Data System (ADS)

Wu, Binhua; Wu, Di; Gao, Wei; Song, Chongmin

2018-02-01

This paper presents a new robust method namely, unified interval Chebyshev-based random perturbation method, to tackle hybrid random interval structural natural frequency problem. In the proposed approach, random perturbation method is implemented to furnish the statistical features (i.e., mean and standard deviation) and Chebyshev surrogate model strategy is incorporated to formulate the statistical information of natural frequency with regards to the interval inputs. The comprehensive analysis framework combines the superiority of both methods in a way that computational cost is dramatically reduced. This presented method is thus capable of investigating the day-to-day based time-variant natural frequency of structures accurately and efficiently under concrete intrinsic creep effect with probabilistic and interval uncertain variables. The extreme bounds of the mean and standard deviation of natural frequency are captured through the embedded optimization strategy within the analysis procedure. Three particularly motivated numerical examples with progressive relationship in perspective of both structure type and uncertainty variables are demonstrated to justify the computational applicability, accuracy and efficiency of the proposed method.

A comparison of three random effects approaches to analyze repeated bounded outcome scores with an application in a stroke revalidation study.

PubMed

Molas, Marek; Lesaffre, Emmanuel

2008-12-30

Discrete bounded outcome scores (BOS), i.e. discrete measurements that are restricted on a finite interval, often occur in practice. Examples are compliance measures, quality of life measures, etc. In this paper we examine three related random effects approaches to analyze longitudinal studies with a BOS as response: (1) a linear mixed effects (LM) model applied to a logistic transformed modified BOS; (2) a model assuming that the discrete BOS is a coarsened version of a latent random variable, which after a logistic-normal transformation, satisfies an LM model; and (3) a random effects probit model. We consider also the extension whereby the variability of the BOS is allowed to depend on covariates. The methods are contrasted using a simulation study and on a longitudinal project, which documents stroke rehabilitation in four European countries using measures of motor and functional recovery. Copyright 2008 John Wiley & Sons, Ltd.

Key-Generation Algorithms for Linear Piece In Hand Matrix Method

NASA Astrophysics Data System (ADS)

Tadaki, Kohtaro; Tsujii, Shigeo

The linear Piece In Hand (PH, for short) matrix method with random variables was proposed in our former work. It is a general prescription which can be applicable to any type of multivariate public-key cryptosystems for the purpose of enhancing their security. Actually, we showed, in an experimental manner, that the linear PH matrix method with random variables can certainly enhance the security of HFE against the Gröbner basis attack, where HFE is one of the major variants of multivariate public-key cryptosystems. In 1998 Patarin, Goubin, and Courtois introduced the plus method as a general prescription which aims to enhance the security of any given MPKC, just like the linear PH matrix method with random variables. In this paper we prove the equivalence between the plus method and the primitive linear PH matrix method, which is introduced by our previous work to explain the notion of the PH matrix method in general in an illustrative manner and not for a practical use to enhance the security of any given MPKC. Based on this equivalence, we show that the linear PH matrix method with random variables has the substantial advantage over the plus method with respect to the security enhancement. In the linear PH matrix method with random variables, the three matrices, including the PH matrix, play a central role in the secret-key and public-key. In this paper, we clarify how to generate these matrices and thus present two probabilistic polynomial-time algorithms to generate these matrices. In particular, the second one has a concise form, and is obtained as a byproduct of the proof of the equivalence between the plus method and the primitive linear PH matrix method.

Generating variable and random schedules of reinforcement using Microsoft Excel macros.

PubMed

Bancroft, Stacie L; Bourret, Jason C

2008-01-01

Variable reinforcement schedules are used to arrange the availability of reinforcement following varying response ratios or intervals of time. Random reinforcement schedules are subtypes of variable reinforcement schedules that can be used to arrange the availability of reinforcement at a constant probability across number of responses or time. Generating schedule values for variable and random reinforcement schedules can be difficult. The present article describes the steps necessary to write macros in Microsoft Excel that will generate variable-ratio, variable-interval, variable-time, random-ratio, random-interval, and random-time reinforcement schedule values.

Statistical optics

NASA Astrophysics Data System (ADS)

Goodman, J. W.

This book is based on the thesis that some training in the area of statistical optics should be included as a standard part of any advanced optics curriculum. Random variables are discussed, taking into account definitions of probability and random variables, distribution functions and density functions, an extension to two or more random variables, statistical averages, transformations of random variables, sums of real random variables, Gaussian random variables, complex-valued random variables, and random phasor sums. Other subjects examined are related to random processes, some first-order properties of light waves, the coherence of optical waves, some problems involving high-order coherence, effects of partial coherence on imaging systems, imaging in the presence of randomly inhomogeneous media, and fundamental limits in photoelectric detection of light. Attention is given to deterministic versus statistical phenomena and models, the Fourier transform, and the fourth-order moment of the spectrum of a detected speckle image.

Generating Variable and Random Schedules of Reinforcement Using Microsoft Excel Macros

PubMed Central

Bancroft, Stacie L; Bourret, Jason C

2008-01-01

Variable reinforcement schedules are used to arrange the availability of reinforcement following varying response ratios or intervals of time. Random reinforcement schedules are subtypes of variable reinforcement schedules that can be used to arrange the availability of reinforcement at a constant probability across number of responses or time. Generating schedule values for variable and random reinforcement schedules can be difficult. The present article describes the steps necessary to write macros in Microsoft Excel that will generate variable-ratio, variable-interval, variable-time, random-ratio, random-interval, and random-time reinforcement schedule values. PMID:18595286

Probabilistic structural analysis methods for improving Space Shuttle engine reliability

NASA Technical Reports Server (NTRS)

Boyce, L.

1989-01-01

Probabilistic structural analysis methods are particularly useful in the design and analysis of critical structural components and systems that operate in very severe and uncertain environments. These methods have recently found application in space propulsion systems to improve the structural reliability of Space Shuttle Main Engine (SSME) components. A computer program, NESSUS, based on a deterministic finite-element program and a method of probabilistic analysis (fast probability integration) provides probabilistic structural analysis for selected SSME components. While computationally efficient, it considers both correlated and nonnormal random variables as well as an implicit functional relationship between independent and dependent variables. The program is used to determine the response of a nickel-based superalloy SSME turbopump blade. Results include blade tip displacement statistics due to the variability in blade thickness, modulus of elasticity, Poisson's ratio or density. Modulus of elasticity significantly contributed to blade tip variability while Poisson's ratio did not. Thus, a rational method for choosing parameters to be modeled as random is provided.

Estimation and classification by sigmoids based on mutual information

NASA Technical Reports Server (NTRS)

Baram, Yoram

1994-01-01

An estimate of the probability density function of a random vector is obtained by maximizing the mutual information between the input and the output of a feedforward network of sigmoidal units with respect to the input weights. Classification problems can be solved by selecting the class associated with the maximal estimated density. Newton's s method, applied to an estimated density, yields a recursive maximum likelihood estimator, consisting of a single internal layer of sigmoids, for a random variable or a random sequence. Applications to the diamond classification and to the prediction of a sun-spot process are demonstrated.

Probabilistic finite elements for transient analysis in nonlinear continua

NASA Technical Reports Server (NTRS)

Liu, W. K.; Belytschko, T.; Mani, A.

1985-01-01

The probabilistic finite element method (PFEM), which is a combination of finite element methods and second-moment analysis, is formulated for linear and nonlinear continua with inhomogeneous random fields. Analogous to the discretization of the displacement field in finite element methods, the random field is also discretized. The formulation is simplified by transforming the correlated variables to a set of uncorrelated variables through an eigenvalue orthogonalization. Furthermore, it is shown that a reduced set of the uncorrelated variables is sufficient for the second-moment analysis. Based on the linear formulation of the PFEM, the method is then extended to transient analysis in nonlinear continua. The accuracy and efficiency of the method is demonstrated by application to a one-dimensional, elastic/plastic wave propagation problem. The moments calculated compare favorably with those obtained by Monte Carlo simulation. Also, the procedure is amenable to implementation in deterministic FEM based computer programs.

Uncertainty Quantification in Simulations of Epidemics Using Polynomial Chaos

PubMed Central

Santonja, F.; Chen-Charpentier, B.

2012-01-01

Mathematical models based on ordinary differential equations are a useful tool to study the processes involved in epidemiology. Many models consider that the parameters are deterministic variables. But in practice, the transmission parameters present large variability and it is not possible to determine them exactly, and it is necessary to introduce randomness. In this paper, we present an application of the polynomial chaos approach to epidemiological mathematical models based on ordinary differential equations with random coefficients. Taking into account the variability of the transmission parameters of the model, this approach allows us to obtain an auxiliary system of differential equations, which is then integrated numerically to obtain the first-and the second-order moments of the output stochastic processes. A sensitivity analysis based on the polynomial chaos approach is also performed to determine which parameters have the greatest influence on the results. As an example, we will apply the approach to an obesity epidemic model. PMID:22927889

The living Drake equation of the Tau Zero Foundation

NASA Astrophysics Data System (ADS)

Maccone, Claudio

2011-03-01

The living Drake equation is our statistical generalization of the Drake equation such that it can take into account any number of factors. This new result opens up the possibility to enrich the equation by inserting more new factors as long as the scientific learning increases. The adjective "Living" refers just to this continuous enrichment of the Drake equation and is the goal of a new research project that the Tau Zero Foundation has entrusted to this author as the discoverer of the statistical Drake equation described hereafter. From a simple product of seven positive numbers, the Drake equation is now turned into the product of seven positive random variables. We call this "the Statistical Drake Equation". The mathematical consequences of this transformation are then derived. The proof of our results is based on the Central Limit Theorem (CLT) of Statistics. In loose terms, the CLT states that the sum of any number of independent random variables, each of which may be arbitrarily distributed, approaches a Gaussian (i.e. normal) random variable. This is called the Lyapunov form of the CLT, or the Lindeberg form of the CLT, depending on the mathematical constraints assumed on the third moments of the various probability distributions. In conclusion, we show that: The new random variable N, yielding the number of communicating civilizations in the Galaxy, follows the lognormal distribution. Then, the mean value, standard deviation, mode, median and all the moments of this lognormal N can be derived from the means and standard deviations of the seven input random variables. In fact, the seven factors in the ordinary Drake equation now become seven independent positive random variables. The probability distribution of each random variable may be arbitrary. The CLT in the so-called Lyapunov or Lindeberg forms (that both do not assume the factors to be identically distributed) allows for that. In other words, the CLT "translates" into our statistical Drake equation by allowing an arbitrary probability distribution for each factor. This is both physically realistic and practically very useful, of course. An application of our statistical Drake equation then follows. The (average) distance between any two neighbouring and communicating civilizations in the Galaxy may be shown to be inversely proportional to the cubic root of N. Then, this distance now becomes a new random variable. We derive the relevant probability density function, apparently previously unknown (dubbed "Maccone distribution" by Paul Davies). Data Enrichment Principle. It should be noticed that any positive number of random variables in the statistical Drake equation is compatible with the CLT. So, our generalization allows for many more factors to be added in the future as long as more refined scientific knowledge about each factor will be known to the scientists. This capability to make room for more future factors in the statistical Drake equation we call the "Data Enrichment Principle", and regard as the key to more profound, future results in Astrobiology and SETI.

Quantum random bit generation using energy fluctuations in stimulated Raman scattering.

PubMed

Bustard, Philip J; England, Duncan G; Nunn, Josh; Moffatt, Doug; Spanner, Michael; Lausten, Rune; Sussman, Benjamin J

2013-12-02

Random number sequences are a critical resource in modern information processing systems, with applications in cryptography, numerical simulation, and data sampling. We introduce a quantum random number generator based on the measurement of pulse energy quantum fluctuations in Stokes light generated by spontaneously-initiated stimulated Raman scattering. Bright Stokes pulse energy fluctuations up to five times the mean energy are measured with fast photodiodes and converted to unbiased random binary strings. Since the pulse energy is a continuous variable, multiple bits can be extracted from a single measurement. Our approach can be generalized to a wide range of Raman active materials; here we demonstrate a prototype using the optical phonon line in bulk diamond.

What weather variables are important in predicting heat-related mortality? A new application of statistical learning methods

PubMed Central

Zhang, Kai; Li, Yun; Schwartz, Joel D.; O'Neill, Marie S.

2014-01-01

Hot weather increases risk of mortality. Previous studies used different sets of weather variables to characterize heat stress, resulting in variation in heat-mortality- associations depending on the metric used. We employed a statistical learning method – random forests – to examine which of various weather variables had the greatest impact on heat-related mortality. We compiled a summertime daily weather and mortality counts dataset from four U.S. cities (Chicago, IL; Detroit, MI; Philadelphia, PA; and Phoenix, AZ) from 1998 to 2006. A variety of weather variables were ranked in predicting deviation from typical daily all-cause and cause-specific death counts. Ranks of weather variables varied with city and health outcome. Apparent temperature appeared to be the most important predictor of heat-related mortality for all-cause mortality. Absolute humidity was, on average, most frequently selected one of the top variables for all-cause mortality and seven cause-specific mortality categories. Our analysis affirms that apparent temperature is a reasonable variable for activating heat alerts and warnings, which are commonly based on predictions of total mortality in next few days. Additionally, absolute humidity should be included in future heat-health studies. Finally, random forests can be used to guide choice of weather variables in heat epidemiology studies. PMID:24834832

wayGoo recommender system: personalized recommendations for events scheduling, based on static and real-time information

NASA Astrophysics Data System (ADS)

Thanos, Konstantinos-Georgios; Thomopoulos, Stelios C. A.

2016-05-01

wayGoo is a fully functional application whose main functionalities include content geolocation, event scheduling, and indoor navigation. However, significant information about events do not reach users' attention, either because of the size of this information or because some information comes from real - time data sources. The purpose of this work is to facilitate event management operations by prioritizing the presented events, based on users' interests using both, static and real - time data. Through the wayGoo interface, users select conceptual topics that are interesting for them. These topics constitute a browsing behavior vector which is used for learning users' interests implicitly, without being intrusive. Then, the system estimates user preferences and return an events list sorted from the most preferred one to the least. User preferences are modeled via a Naïve Bayesian Network which consists of: a) the `decision' random variable corresponding to users' decision on attending an event, b) the `distance' random variable, modeled by a linear regression that estimates the probability that the distance between a user and each event destination is not discouraging, ` the seat availability' random variable, modeled by a linear regression, which estimates the probability that the seat availability is encouraging d) and the `relevance' random variable, modeled by a clustering - based collaborative filtering, which determines the relevance of each event users' interests. Finally, experimental results show that the proposed system contribute essentially to assisting users in browsing and selecting events to attend.

A random walk model for evaluating clinical trials involving serial observations.

PubMed

Hopper, J L; Young, G P

1988-05-01

For clinical trials where the variable of interest is ordered and categorical (for example, disease severity, symptom scale), and where measurements are taken at intervals, it might be possible to achieve a greater discrimination between the efficacy of treatments by modelling each patient's progress as a stochastic process. The random walk is a simple, easily interpreted model that can be fitted by maximum likelihood using a maximization routine with inference based on standard likelihood theory. In general the model can allow for randomly censored data, incorporates measured prognostic factors, and inference is conditional on the (possibly non-random) allocation of patients. Tests of fit and of model assumptions are proposed, and application to two therapeutic trials of gastroenterological disorders are presented. The model gave measures of the rate of, and variability in, improvement for patients under different treatments. A small simulation study suggested that the model is more powerful than considering the difference between initial and final scores, even when applied to data generated by a mechanism other than the random walk model assumed in the analysis. It thus provides a useful additional statistical method for evaluating clinical trials.

An uncertainty model of acoustic metamaterials with random parameters

NASA Astrophysics Data System (ADS)

He, Z. C.; Hu, J. Y.; Li, Eric

2018-01-01

Acoustic metamaterials (AMs) are man-made composite materials. However, the random uncertainties are unavoidable in the application of AMs due to manufacturing and material errors which lead to the variance of the physical responses of AMs. In this paper, an uncertainty model based on the change of variable perturbation stochastic finite element method (CVPS-FEM) is formulated to predict the probability density functions of physical responses of AMs with random parameters. Three types of physical responses including the band structure, mode shapes and frequency response function of AMs are studied in the uncertainty model, which is of great interest in the design of AMs. In this computation, the physical responses of stochastic AMs are expressed as linear functions of the pre-defined random parameters by using the first-order Taylor series expansion and perturbation technique. Then, based on the linear function relationships of parameters and responses, the probability density functions of the responses can be calculated by the change-of-variable technique. Three numerical examples are employed to demonstrate the effectiveness of the CVPS-FEM for stochastic AMs, and the results are validated by Monte Carlo method successfully.

Soil variability in engineering applications

NASA Astrophysics Data System (ADS)

Vessia, Giovanna

2014-05-01

Natural geomaterials, as soils and rocks, show spatial variability and heterogeneity of physical and mechanical properties. They can be measured by in field and laboratory testing. The heterogeneity concerns different values of litho-technical parameters pertaining similar lithological units placed close to each other. On the contrary, the variability is inherent to the formation and evolution processes experienced by each geological units (homogeneous geomaterials on average) and captured as a spatial structure of fluctuation of physical property values about their mean trend, e.g. the unit weight, the hydraulic permeability, the friction angle, the cohesion, among others. The preceding spatial variations shall be managed by engineering models to accomplish reliable designing of structures and infrastructures. Materon (1962) introduced the Geostatistics as the most comprehensive tool to manage spatial correlation of parameter measures used in a wide range of earth science applications. In the field of the engineering geology, Vanmarcke (1977) developed the first pioneering attempts to describe and manage the inherent variability in geomaterials although Terzaghi (1943) already highlighted that spatial fluctuations of physical and mechanical parameters used in geotechnical designing cannot be neglected. A few years later, Mandelbrot (1983) and Turcotte (1986) interpreted the internal arrangement of geomaterial according to Fractal Theory. In the same years, Vanmarcke (1983) proposed the Random Field Theory providing mathematical tools to deal with inherent variability of each geological units or stratigraphic succession that can be resembled as one material. In this approach, measurement fluctuations of physical parameters are interpreted through the spatial variability structure consisting in the correlation function and the scale of fluctuation. Fenton and Griffiths (1992) combined random field simulation with the finite element method to produce the Random Finite Element Method (RFEM). This method has been used to investigate the random behavior of soils in the context of a variety of classical geotechnical problems. Afterward, some following studies collected the worldwide variability values of many technical parameters of soils (Phoon and Kulhawy 1999a) and their spatial correlation functions (Phoon and Kulhawy 1999b). In Italy, Cherubini et al. (2007) calculated the spatial variability structure of sandy and clayey soils from the standard cone penetration test readings. The large extent of the worldwide measured spatial variability of soils and rocks heavily affects the reliability of geotechnical designing as well as other uncertainties introduced by testing devices and engineering models. So far, several methods have been provided to deal with the preceding sources of uncertainties in engineering designing models (e.g. First Order Reliability Method, Second Order Reliability Method, Response Surface Method, High Dimensional Model Representation, etc.). Nowadays, the efforts in this field have been focusing on (1) measuring spatial variability of different rocks and soils and (2) developing numerical models that take into account the spatial variability as additional physical variable. References Cherubini C., Vessia G. and Pula W. 2007. Statistical soil characterization of Italian sites for reliability analyses. Proc. 2nd Int. Workshop. on Characterization and Engineering Properties of Natural Soils, 3-4: 2681-2706. Griffiths D.V. and Fenton G.A. 1993. Seepage beneath water retaining structures founded on spatially random soil, Géotechnique, 43(6): 577-587. Mandelbrot B.B. 1983. The Fractal Geometry of Nature. San Francisco: W H Freeman. Matheron G. 1962. Traité de Géostatistique appliquée. Tome 1, Editions Technip, Paris, 334 p. Phoon K.K. and Kulhawy F.H. 1999a. Characterization of geotechnical variability. Can Geotech J, 36(4): 612-624. Phoon K.K. and Kulhawy F.H. 1999b. Evaluation of geotechnical property variability. Can Geotech J, 36(4): 625-639. Terzaghi K. 1943. Theoretical Soil Mechanics. New York: John Wiley and Sons. Turcotte D.L. 1986. Fractals and fragmentation. J Geophys Res, 91: 1921-1926. Vanmarcke E.H. 1977. Probabilistic modeling of soil profiles. J Geotech Eng Div, ASCE, 103: 1227-1246. Vanmarcke E.H. 1983. Random fields: analysis and synthesis. MIT Press, Cambridge.

Describing temporal variability of the mean Estonian precipitation series in climate time scale

NASA Astrophysics Data System (ADS)

Post, P.; Kärner, O.

2009-04-01

Applicability of the random walk type models to represent the temporal variability of various atmospheric temperature series has been successfully demonstrated recently (e.g. Kärner, 2002). Main problem in the temperature modeling is connected to the scale break in the generally self similar air temperature anomaly series (Kärner, 2005). The break separates short-range strong non-stationarity from nearly stationary longer range variability region. This is an indication of the fact that several geophysical time series show a short-range non-stationary behaviour and a stationary behaviour in longer range (Davis et al., 1996). In order to model series like that the choice of time step appears to be crucial. To characterize the long-range variability we can neglect the short-range non-stationary fluctuations, provided that we are able to model properly the long-range tendencies. The structure function (Monin and Yaglom, 1975) was used to determine an approximate segregation line between the short and the long scale in terms of modeling. The longer scale can be called climate one, because such models are applicable in scales over some decades. In order to get rid of the short-range fluctuations in daily series the variability can be examined using sufficiently long time step. In the present paper, we show that the same philosophy is useful to find a model to represent a climate-scale temporal variability of the Estonian daily mean precipitation amount series over 45 years (1961-2005). Temporal variability of the obtained daily time series is examined by means of an autoregressive and integrated moving average (ARIMA) family model of the type (0,1,1). This model is applicable for daily precipitation simulating if to select an appropriate time step that enables us to neglet the short-range non-stationary fluctuations. A considerably longer time step than one day (30 days) is used in the current paper to model the precipitation time series variability. Each ARIMA (0,1,1) model can be interpreted to be consisting of random walk in a noisy environment (Box and Jenkins, 1976). The fitted model appears to be weakly non-stationary, that gives us the possibility to use stationary approximation if only the noise component from that sum of white noise and random walk is exploited. We get a convenient routine to generate a stationary precipitation climatology with a reasonable accuracy, since the noise component variance is much larger than the dispersion of the random walk generator. This interpretation emphasizes dominating role of a random component in the precipitation series. The result is understandable due to a small territory of Estonia that is situated in the mid-latitude cyclone track. References Box, J.E.P. and G. Jenkins 1976: Time Series Analysis, Forecasting and Control (revised edn.), Holden Day San Francisco, CA, 575 pp. Davis, A., Marshak, A., Wiscombe, W. and R. Cahalan 1996: Multifractal characterizations of intermittency in nonstationary geophysical signals and fields.in G. Trevino et al. (eds) Current Topics in Nonsstationarity Analysis. World-Scientific, Singapore, 97-158. Kärner, O. 2002: On nonstationarity and antipersistency in global temperature series. J. Geophys. Res. D107; doi:10.1029/2001JD002024. Kärner, O. 2005: Some examples on negative feedback in the Earth climate system. Centr. European J. Phys. 3; 190-208. Monin, A.S. and A.M. Yaglom 1975: Statistical Fluid Mechanics, Vol 2. Mechanics of Turbulence , MIT Press Boston Mass, 886 pp.

The Statistical Drake Equation

NASA Astrophysics Data System (ADS)

Maccone, Claudio

2010-12-01

We provide the statistical generalization of the Drake equation. From a simple product of seven positive numbers, the Drake equation is now turned into the product of seven positive random variables. We call this "the Statistical Drake Equation". The mathematical consequences of this transformation are then derived. The proof of our results is based on the Central Limit Theorem (CLT) of Statistics. In loose terms, the CLT states that the sum of any number of independent random variables, each of which may be ARBITRARILY distributed, approaches a Gaussian (i.e. normal) random variable. This is called the Lyapunov Form of the CLT, or the Lindeberg Form of the CLT, depending on the mathematical constraints assumed on the third moments of the various probability distributions. In conclusion, we show that: The new random variable N, yielding the number of communicating civilizations in the Galaxy, follows the LOGNORMAL distribution. Then, as a consequence, the mean value of this lognormal distribution is the ordinary N in the Drake equation. The standard deviation, mode, and all the moments of this lognormal N are also found. The seven factors in the ordinary Drake equation now become seven positive random variables. The probability distribution of each random variable may be ARBITRARY. The CLT in the so-called Lyapunov or Lindeberg forms (that both do not assume the factors to be identically distributed) allows for that. In other words, the CLT "translates" into our statistical Drake equation by allowing an arbitrary probability distribution for each factor. This is both physically realistic and practically very useful, of course. An application of our statistical Drake equation then follows. The (average) DISTANCE between any two neighboring and communicating civilizations in the Galaxy may be shown to be inversely proportional to the cubic root of N. Then, in our approach, this distance becomes a new random variable. We derive the relevant probability density function, apparently previously unknown and dubbed "Maccone distribution" by Paul Davies. DATA ENRICHMENT PRINCIPLE. It should be noticed that ANY positive number of random variables in the Statistical Drake Equation is compatible with the CLT. So, our generalization allows for many more factors to be added in the future as long as more refined scientific knowledge about each factor will be known to the scientists. This capability to make room for more future factors in the statistical Drake equation, we call the "Data Enrichment Principle," and we regard it as the key to more profound future results in the fields of Astrobiology and SETI. Finally, a practical example is given of how our statistical Drake equation works numerically. We work out in detail the case, where each of the seven random variables is uniformly distributed around its own mean value and has a given standard deviation. For instance, the number of stars in the Galaxy is assumed to be uniformly distributed around (say) 350 billions with a standard deviation of (say) 1 billion. Then, the resulting lognormal distribution of N is computed numerically by virtue of a MathCad file that the author has written. This shows that the mean value of the lognormal random variable N is actually of the same order as the classical N given by the ordinary Drake equation, as one might expect from a good statistical generalization.

Partial Identification of Treatment Effects: Applications to Generalizability

ERIC Educational Resources Information Center

Chan, Wendy

2016-01-01

Results from large-scale evaluation studies form the foundation of evidence-based policy. The randomized experiment is often considered the gold standard among study designs because the causal impact of a treatment or intervention can be assessed without threats of confounding from external variables. Policy-makers have become increasingly…

Statistically extracted fundamental watershed variables for estimating the loads of total nitrogen in small streams

USGS Publications Warehouse

Kronholm, Scott C.; Capel, Paul D.; Terziotti, Silvia

2016-01-01

Accurate estimation of total nitrogen loads is essential for evaluating conditions in the aquatic environment. Extrapolation of estimates beyond measured streams will greatly expand our understanding of total nitrogen loading to streams. Recursive partitioning and random forest regression were used to assess 85 geospatial, environmental, and watershed variables across 636 small (<585 km2) watersheds to determine which variables are fundamentally important to the estimation of annual loads of total nitrogen. Initial analysis led to the splitting of watersheds into three groups based on predominant land use (agricultural, developed, and undeveloped). Nitrogen application, agricultural and developed land area, and impervious or developed land in the 100-m stream buffer were commonly extracted variables by both recursive partitioning and random forest regression. A series of multiple linear regression equations utilizing the extracted variables were created and applied to the watersheds. As few as three variables explained as much as 76 % of the variability in total nitrogen loads for watersheds with predominantly agricultural land use. Catchment-scale national maps were generated to visualize the total nitrogen loads and yields across the USA. The estimates provided by these models can inform water managers and help identify areas where more in-depth monitoring may be beneficial.

Recourse-based facility-location problems in hybrid uncertain environment.

PubMed

Wang, Shuming; Watada, Junzo; Pedrycz, Witold

2010-08-01

The objective of this paper is to study facility-location problems in the presence of a hybrid uncertain environment involving both randomness and fuzziness. A two-stage fuzzy-random facility-location model with recourse (FR-FLMR) is developed in which both the demands and costs are assumed to be fuzzy-random variables. The bounds of the optimal objective value of the two-stage FR-FLMR are derived. As, in general, the fuzzy-random parameters of the FR-FLMR can be regarded as continuous fuzzy-random variables with an infinite number of realizations, the computation of the recourse requires solving infinite second-stage programming problems. Owing to this requirement, the recourse function cannot be determined analytically, and, hence, the model cannot benefit from the use of techniques of classical mathematical programming. In order to solve the location problems of this nature, we first develop a technique of fuzzy-random simulation to compute the recourse function. The convergence of such simulation scenarios is discussed. In the sequel, we propose a hybrid mutation-based binary ant-colony optimization (MBACO) approach to the two-stage FR-FLMR, which comprises the fuzzy-random simulation and the simplex algorithm. A numerical experiment illustrates the application of the hybrid MBACO algorithm. The comparison shows that the hybrid MBACO finds better solutions than the one using other discrete metaheuristic algorithms, such as binary particle-swarm optimization, genetic algorithm, and tabu search.

Stochastic reduced order models for inverse problems under uncertainty

PubMed Central

Warner, James E.; Aquino, Wilkins; Grigoriu, Mircea D.

2014-01-01

This work presents a novel methodology for solving inverse problems under uncertainty using stochastic reduced order models (SROMs). Given statistical information about an observed state variable in a system, unknown parameters are estimated probabilistically through the solution of a model-constrained, stochastic optimization problem. The point of departure and crux of the proposed framework is the representation of a random quantity using a SROM - a low dimensional, discrete approximation to a continuous random element that permits e cient and non-intrusive stochastic computations. Characterizing the uncertainties with SROMs transforms the stochastic optimization problem into a deterministic one. The non-intrusive nature of SROMs facilitates e cient gradient computations for random vector unknowns and relies entirely on calls to existing deterministic solvers. Furthermore, the method is naturally extended to handle multiple sources of uncertainty in cases where state variable data, system parameters, and boundary conditions are all considered random. The new and widely-applicable SROM framework is formulated for a general stochastic optimization problem in terms of an abstract objective function and constraining model. For demonstration purposes, however, we study its performance in the specific case of inverse identification of random material parameters in elastodynamics. We demonstrate the ability to efficiently recover random shear moduli given material displacement statistics as input data. We also show that the approach remains effective for the case where the loading in the problem is random as well. PMID:25558115

Improving multilevel Monte Carlo for stochastic differential equations with application to the Langevin equation

PubMed Central

Müller, Eike H.; Scheichl, Rob; Shardlow, Tony

2015-01-01

This paper applies several well-known tricks from the numerical treatment of deterministic differential equations to improve the efficiency of the multilevel Monte Carlo (MLMC) method for stochastic differential equations (SDEs) and especially the Langevin equation. We use modified equations analysis as an alternative to strong-approximation theory for the integrator, and we apply this to introduce MLMC for Langevin-type equations with integrators based on operator splitting. We combine this with extrapolation and investigate the use of discrete random variables in place of the Gaussian increments, which is a well-known technique for the weak approximation of SDEs. We show that, for small-noise problems, discrete random variables can lead to an increase in efficiency of almost two orders of magnitude for practical levels of accuracy. PMID:27547075

Improving multilevel Monte Carlo for stochastic differential equations with application to the Langevin equation.

PubMed

Müller, Eike H; Scheichl, Rob; Shardlow, Tony

2015-04-08

This paper applies several well-known tricks from the numerical treatment of deterministic differential equations to improve the efficiency of the multilevel Monte Carlo (MLMC) method for stochastic differential equations (SDEs) and especially the Langevin equation. We use modified equations analysis as an alternative to strong-approximation theory for the integrator, and we apply this to introduce MLMC for Langevin-type equations with integrators based on operator splitting. We combine this with extrapolation and investigate the use of discrete random variables in place of the Gaussian increments, which is a well-known technique for the weak approximation of SDEs. We show that, for small-noise problems, discrete random variables can lead to an increase in efficiency of almost two orders of magnitude for practical levels of accuracy.

A random utility model of delay discounting and its application to people with externalizing psychopathology.

PubMed

Dai, Junyi; Gunn, Rachel L; Gerst, Kyle R; Busemeyer, Jerome R; Finn, Peter R

2016-10-01

Previous studies have demonstrated that working memory capacity plays a central role in delay discounting in people with externalizing psychopathology. These studies used a hyperbolic discounting model, and its single parameter-a measure of delay discounting-was estimated using the standard method of searching for indifference points between intertemporal options. However, there are several problems with this approach. First, the deterministic perspective on delay discounting underlying the indifference point method might be inappropriate. Second, the estimation procedure using the R2 measure often leads to poor model fit. Third, when parameters are estimated using indifference points only, much of the information collected in a delay discounting decision task is wasted. To overcome these problems, this article proposes a random utility model of delay discounting. The proposed model has 2 parameters, 1 for delay discounting and 1 for choice variability. It was fit to choice data obtained from a recently published data set using both maximum-likelihood and Bayesian parameter estimation. As in previous studies, the delay discounting parameter was significantly associated with both externalizing problems and working memory capacity. Furthermore, choice variability was also found to be significantly associated with both variables. This finding suggests that randomness in decisions may be a mechanism by which externalizing problems and low working memory capacity are associated with poor decision making. The random utility model thus has the advantage of disclosing the role of choice variability, which had been masked by the traditional deterministic model. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

A Comparison of Four Approaches to Account for Method Effects in Latent State-Trait Analyses

ERIC Educational Resources Information Center

Geiser, Christian; Lockhart, Ginger

2012-01-01

Latent state-trait (LST) analysis is frequently applied in psychological research to determine the degree to which observed scores reflect stable person-specific effects, effects of situations and/or person-situation interactions, and random measurement error. Most LST applications use multiple repeatedly measured observed variables as indicators…

An Entropy-Based Measure of Dependence between Two Groups of Random Variables. Research Report. ETS RR-07-20

ERIC Educational Resources Information Center

Kong, Nan

2007-01-01

In multivariate statistics, the linear relationship among random variables has been fully explored in the past. This paper looks into the dependence of one group of random variables on another group of random variables using (conditional) entropy. A new measure, called the K-dependence coefficient or dependence coefficient, is defined using…

Improvements in sub-grid, microphysics averages using quadrature based approaches

NASA Astrophysics Data System (ADS)

Chowdhary, K.; Debusschere, B.; Larson, V. E.

2013-12-01

Sub-grid variability in microphysical processes plays a critical role in atmospheric climate models. In order to account for this sub-grid variability, Larson and Schanen (2013) propose placing a probability density function on the sub-grid cloud microphysics quantities, e.g. autoconversion rate, essentially interpreting the cloud microphysics quantities as a random variable in each grid box. Random sampling techniques, e.g. Monte Carlo and Latin Hypercube, can be used to calculate statistics, e.g. averages, on the microphysics quantities, which then feed back into the model dynamics on the coarse scale. We propose an alternate approach using numerical quadrature methods based on deterministic sampling points to compute the statistical moments of microphysics quantities in each grid box. We have performed a preliminary test on the Kessler autoconversion formula, and, upon comparison with Latin Hypercube sampling, our approach shows an increased level of accuracy with a reduction in sample size by almost two orders of magnitude. Application to other microphysics processes is the subject of ongoing research.

Hierarchical Bayesian spatial models for predicting multiple forest variables using waveform LiDAR, hyperspectral imagery, and large inventory datasets

USGS Publications Warehouse

Finley, Andrew O.; Banerjee, Sudipto; Cook, Bruce D.; Bradford, John B.

2013-01-01

In this paper we detail a multivariate spatial regression model that couples LiDAR, hyperspectral and forest inventory data to predict forest outcome variables at a high spatial resolution. The proposed model is used to analyze forest inventory data collected on the US Forest Service Penobscot Experimental Forest (PEF), ME, USA. In addition to helping meet the regression model's assumptions, results from the PEF analysis suggest that the addition of multivariate spatial random effects improves model fit and predictive ability, compared with two commonly applied modeling approaches. This improvement results from explicitly modeling the covariation among forest outcome variables and spatial dependence among observations through the random effects. Direct application of such multivariate models to even moderately large datasets is often computationally infeasible because of cubic order matrix algorithms involved in estimation. We apply a spatial dimension reduction technique to help overcome this computational hurdle without sacrificing richness in modeling.

Probabilistic structural analysis methods and applications

NASA Technical Reports Server (NTRS)

Cruse, T. A.; Wu, Y.-T.; Dias, B.; Rajagopal, K. R.

1988-01-01

An advanced algorithm for simulating the probabilistic distribution of structural responses due to statistical uncertainties in loads, geometry, material properties, and boundary conditions is reported. The method effectively combines an advanced algorithm for calculating probability levels for multivariate problems (fast probability integration) together with a general-purpose finite-element code for stress, vibration, and buckling analysis. Application is made to a space propulsion system turbine blade for which the geometry and material properties are treated as random variables.

Bayesian statistics and Monte Carlo methods

NASA Astrophysics Data System (ADS)

Koch, K. R.

2018-03-01

The Bayesian approach allows an intuitive way to derive the methods of statistics. Probability is defined as a measure of the plausibility of statements or propositions. Three rules are sufficient to obtain the laws of probability. If the statements refer to the numerical values of variables, the so-called random variables, univariate and multivariate distributions follow. They lead to the point estimation by which unknown quantities, i.e. unknown parameters, are computed from measurements. The unknown parameters are random variables, they are fixed quantities in traditional statistics which is not founded on Bayes' theorem. Bayesian statistics therefore recommends itself for Monte Carlo methods, which generate random variates from given distributions. Monte Carlo methods, of course, can also be applied in traditional statistics. The unknown parameters, are introduced as functions of the measurements, and the Monte Carlo methods give the covariance matrix and the expectation of these functions. A confidence region is derived where the unknown parameters are situated with a given probability. Following a method of traditional statistics, hypotheses are tested by determining whether a value for an unknown parameter lies inside or outside the confidence region. The error propagation of a random vector by the Monte Carlo methods is presented as an application. If the random vector results from a nonlinearly transformed vector, its covariance matrix and its expectation follow from the Monte Carlo estimate. This saves a considerable amount of derivatives to be computed, and errors of the linearization are avoided. The Monte Carlo method is therefore efficient. If the functions of the measurements are given by a sum of two or more random vectors with different multivariate distributions, the resulting distribution is generally not known. TheMonte Carlo methods are then needed to obtain the covariance matrix and the expectation of the sum.

Packet Randomized Experiments for Eliminating Classes of Confounders

PubMed Central

Pavela, Greg; Wiener, Howard; Fontaine, Kevin R.; Fields, David A.; Voss, Jameson D.; Allison, David B.

2014-01-01

Background Although randomization is considered essential for causal inference, it is often not possible to randomize in nutrition and obesity research. To address this, we develop a framework for an experimental design—packet randomized experiments (PREs), which improves causal inferences when randomization on a single treatment variable is not possible. This situation arises when subjects are randomly assigned to a condition (such as a new roommate) which varies in one characteristic of interest (such as weight), but also varies across many others. There has been no general discussion of this experimental design, including its strengths, limitations, and statistical properties. As such, researchers are left to develop and apply PREs on an ad hoc basis, limiting its potential to improve causal inferences among nutrition and obesity researchers. Methods We introduce PREs as an intermediary design between randomized controlled trials and observational studies. We review previous research that used the PRE design and describe its application in obesity-related research, including random roommate assignments, heterochronic parabiosis, and the quasi-random assignment of subjects to geographic areas. We then provide a statistical framework to control for potential packet-level confounders not accounted for by randomization. Results PREs have successfully been used to improve causal estimates of the effect of roommates, altitude, and breastfeeding on weight outcomes. When certain assumptions are met, PREs can asymptotically control for packet-level characteristics. This has the potential to statistically estimate the effect of a single treatment even when randomization to a single treatment did not occur. Conclusions Applying PREs to obesity-related research will improve decisions about clinical, public health, and policy actions insofar as it offers researchers new insight into cause and effect relationships among variables. PMID:25444088

Random mechanics: Nonlinear vibrations, turbulences, seisms, swells, fatigue

NASA Astrophysics Data System (ADS)

Kree, P.; Soize, C.

The random modeling of physical phenomena, together with probabilistic methods for the numerical calculation of random mechanical forces, are analytically explored. Attention is given to theoretical examinations such as probabilistic concepts, linear filtering techniques, and trajectory statistics. Applications of the methods to structures experiencing atmospheric turbulence, the quantification of turbulence, and the dynamic responses of the structures are considered. A probabilistic approach is taken to study the effects of earthquakes on structures and to the forces exerted by ocean waves on marine structures. Theoretical analyses by means of vector spaces and stochastic modeling are reviewed, as are Markovian formulations of Gaussian processes and the definition of stochastic differential equations. Finally, random vibrations with a variable number of links and linear oscillators undergoing the square of Gaussian processes are investigated.

A biorthogonal decomposition for the identification and simulation of non-stationary and non-Gaussian random fields

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

Zentner, I.; Ferré, G., E-mail: gregoire.ferre@ponts.org; Poirion, F.

2016-06-01

In this paper, a new method for the identification and simulation of non-Gaussian and non-stationary stochastic fields given a database is proposed. It is based on two successive biorthogonal decompositions aiming at representing spatio–temporal stochastic fields. The proposed double expansion allows to build the model even in the case of large-size problems by separating the time, space and random parts of the field. A Gaussian kernel estimator is used to simulate the high dimensional set of random variables appearing in the decomposition. The capability of the method to reproduce the non-stationary and non-Gaussian features of random phenomena is illustrated bymore » applications to earthquakes (seismic ground motion) and sea states (wave heights).« less

Average inactivity time model, associated orderings and reliability properties

NASA Astrophysics Data System (ADS)

Kayid, M.; Izadkhah, S.; Abouammoh, A. M.

2018-02-01

In this paper, we introduce and study a new model called 'average inactivity time model'. This new model is specifically applicable to handle the heterogeneity of the time of the failure of a system in which some inactive items exist. We provide some bounds for the mean average inactivity time of a lifespan unit. In addition, we discuss some dependence structures between the average variable and the mixing variable in the model when original random variable possesses some aging behaviors. Based on the conception of the new model, we introduce and study a new stochastic order. Finally, to illustrate the concept of the model, some interesting reliability problems are reserved.

Finding Relevant Parameters for the Thin-film Photovoltaic Cells Production Process with the Application of Data Mining Methods.

PubMed

Ulaczyk, Jan; Morawiec, Krzysztof; Zabierowski, Paweł; Drobiazg, Tomasz; Barreau, Nicolas

2017-09-01

A data mining approach is proposed as a useful tool for the control parameters analysis of the 3-stage CIGSe photovoltaic cell production process, in order to find variables that are the most relevant for cell electric parameters and efficiency. The analysed data set consists of stage duration times, heater power values as well as temperatures for the element sources and the substrate - there are 14 variables per sample in total. The most relevant variables of the process have been found based on the so-called random forest analysis with the application of the Boruta algorithm. 118 CIGSe samples, prepared at Institut des Matériaux Jean Rouxel, were analysed. The results are close to experimental knowledge on the CIGSe cells production process. They bring new evidence to production parameters of new cells and further research. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Effects of Various Cryotherapy Applications on Postoperative Pain in Molar Teeth with Symptomatic Apical Periodontitis: A Preliminary Randomized Prospective Clinical Trial.

PubMed

Gundogdu, Eyup Candas; Arslan, Hakan

2018-03-01

The purpose of the study was to evaluate the effects of intracanal, intraoral, and extraoral cryotherapy on postoperative pain in molar teeth with symptomatic apical periodontitis. A total of 100 patients were randomly distributed into 4 groups: control (without cryotherapy application), intracanal cryotherapy application, intraoral cryotherapy application, and extraoral cryotherapy application. The postoperative pain of the patients was recorded at the first, third, fifth, and seventh days. The data were statistically analyzed by using linear regression, χ 2 , one-way analysis of variance, Tukey post hoc, and Kruskal-Wallis H tests (P = .05). There were no statistically significant differences among the groups in terms of demographic data (P > .05). The preoperative pain levels and preoperative visual analogue scale (VAS) scores of pain on percussion were similar among the groups (P > .05). The linear regression analysis demonstrated that group variable had the most significant effect on postoperative pain at day 1 (P < .001) among the other variables (group, age, gender, tooth number, preoperative pain levels, and VAS scores of pain on percussion). When compared with the control group, all the cryotherapy groups exhibited less percussion pain and less postoperative pain at the first, third, fifth, and seventh days (P < .05). Within the study limitations, all the cryotherapy applications (intracanal, intraoral, and extraoral) resulted in lower postoperative pain levels and lower VAS scores of pain on percussion versus those of the control group. Copyright © 2017 American Association of Endodontists. Published by Elsevier Inc. All rights reserved.

Contextuality in canonical systems of random variables

NASA Astrophysics Data System (ADS)

Dzhafarov, Ehtibar N.; Cervantes, Víctor H.; Kujala, Janne V.

2017-10-01

Random variables representing measurements, broadly understood to include any responses to any inputs, form a system in which each of them is uniquely identified by its content (that which it measures) and its context (the conditions under which it is recorded). Two random variables are jointly distributed if and only if they share a context. In a canonical representation of a system, all random variables are binary, and every content-sharing pair of random variables has a unique maximal coupling (the joint distribution imposed on them so that they coincide with maximal possible probability). The system is contextual if these maximal couplings are incompatible with the joint distributions of the context-sharing random variables. We propose to represent any system of measurements in a canonical form and to consider the system contextual if and only if its canonical representation is contextual. As an illustration, we establish a criterion for contextuality of the canonical system consisting of all dichotomizations of a single pair of content-sharing categorical random variables. This article is part of the themed issue `Second quantum revolution: foundational questions'.

Extreme value analysis in biometrics.

PubMed

Hüsler, Jürg

2009-04-01

We review some approaches of extreme value analysis in the context of biometrical applications. The classical extreme value analysis is based on iid random variables. Two different general methods are applied, which will be discussed together with biometrical examples. Different estimation, testing, goodness-of-fit procedures for applications are discussed. Furthermore, some non-classical situations are considered where the data are possibly dependent, where a non-stationary behavior is observed in the data or where the observations are not univariate. A few open problems are also stated.

Effect of multiplicative noise on stationary stochastic process

NASA Astrophysics Data System (ADS)

Kargovsky, A. V.; Chikishev, A. Yu.; Chichigina, O. A.

2018-03-01

An open system that can be analyzed using the Langevin equation with multiplicative noise is considered. The stationary state of the system results from a balance of deterministic damping and random pumping simulated as noise with controlled periodicity. The dependence of statistical moments of the variable that characterizes the system on parameters of the problem is studied. A nontrivial decrease in the mean value of the main variable with an increase in noise stochasticity is revealed. Applications of the results in several physical, chemical, biological, and technical problems of natural and humanitarian sciences are discussed.

A Dasymetric-Based Monte Carlo Simulation Approach to the Probabilistic Analysis of Spatial Variables

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

Morton, April M; Piburn, Jesse O; McManamay, Ryan A

2017-01-01

Monte Carlo simulation is a popular numerical experimentation technique used in a range of scientific fields to obtain the statistics of unknown random output variables. Despite its widespread applicability, it can be difficult to infer required input probability distributions when they are related to population counts unknown at desired spatial resolutions. To overcome this challenge, we propose a framework that uses a dasymetric model to infer the probability distributions needed for a specific class of Monte Carlo simulations which depend on population counts.

Multimedia Learning and Individual Differences: Mediating the Effects of Working Memory Capacity with Segmentation

ERIC Educational Resources Information Center

Lusk, Danielle L.; Evans, Amber D.; Jeffrey, Thomas R.; Palmer, Keith R.; Wikstrom, Chris S.; Doolittle, Peter E.

2009-01-01

Research in multimedia learning lacks an emphasis on individual difference variables, such as working memory capacity (WMC). The effects of WMC and the segmentation of multimedia instruction were examined by assessing the recall and application of low (n = 66) and high (n = 67) working memory capacity students randomly assigned to either a…

Can Financial Need Analysis be Simplified?

ERIC Educational Resources Information Center

Orwig, M. D.; Jones, Paul K.

This paper examines the problem of collecting financial data on aid applicants. A 10% sample (12,383) of student records was taken from the 1968-69 alphabetic history file for the ACT Student Need Analysis Service. Random sub-samples were taken in certain phases of the study. A relatively small number of financial variables were found to predict…

Latent class instrumental variables: A clinical and biostatistical perspective

PubMed Central

Baker, Stuart G.; Kramer, Barnett S.; Lindeman, Karen S.

2015-01-01

In some two-arm randomized trials, some participants receive the treatment assigned to the other arm as a result of technical problems, refusal of a treatment invitation, or a choice of treatment in an encouragement design. In some before-and-after studies, the availability of a new treatment changes from one time period to this next. Under assumptions that are often reasonable, the latent class instrumental variable (IV) method estimates the effect of treatment received in the aforementioned scenarios involving all-or-none compliance and all-or-none availability. Key aspects are four initial latent classes (sometimes called principal strata) based on treatment received if in each randomization group or time period, the exclusion restriction assumption (in which randomization group or time period is an instrumental variable), the monotonicity assumption (which drops an implausible latent class from the analysis), and the estimated effect of receiving treatment in one latent class (sometimes called efficacy, the local average treatment effect, or the complier average causal effect). Since its independent formulations in the biostatistics and econometrics literatures, the latent class IV method (which has no well-established name) has gained increasing popularity. We review the latent class IV method from a clinical and biostatistical perspective, focusing on underlying assumptions, methodological extensions, and applications in our fields of obstetrics and cancer research. PMID:26239275

[Ethyl chloride aerosol spray for local anesthesia before arterial puncture: randomized placebo-controlled trial].

PubMed

Ballesteros-Peña, Sendoa; Fernández-Aedo, Irrintzi; Vallejo-De la Hoz, Gorka

2017-06-01

To compare the efficacy of an ethyl chloride aerosol spray to a placebo spray applied in the emergency department to the skin to reduce pain from arterial puncture for blood gas analysis. Single-blind, randomized placebo-controlled trial in an emergency department of Hospital de Basurto in Bilbao, Spain. We included 126 patients for whom arterial blood gas analysis had been ordered. They were randomly assigned to receive application of the experimental ethyl chloride spray (n=66) or a placebo aerosol spray of a solution of alcohol in water (n=60). The assigned spray was applied just before arterial puncture. The main outcome variable was pain intensity reported on an 11-point numeric rating scale. The median (interquartile range) pain level was 2 (1-5) in the experimental arm and 2 (1-4.5) in the placebo arm (P=.72). Topical application of an ethyl chloride spray did not reduce pain caused by arterial puncture.

Maximum-entropy probability distributions under Lp-norm constraints

NASA Technical Reports Server (NTRS)

Dolinar, S.

1991-01-01

Continuous probability density functions and discrete probability mass functions are tabulated which maximize the differential entropy or absolute entropy, respectively, among all probability distributions with a given L sub p norm (i.e., a given pth absolute moment when p is a finite integer) and unconstrained or constrained value set. Expressions for the maximum entropy are evaluated as functions of the L sub p norm. The most interesting results are obtained and plotted for unconstrained (real valued) continuous random variables and for integer valued discrete random variables. The maximum entropy expressions are obtained in closed form for unconstrained continuous random variables, and in this case there is a simple straight line relationship between the maximum differential entropy and the logarithm of the L sub p norm. Corresponding expressions for arbitrary discrete and constrained continuous random variables are given parametrically; closed form expressions are available only for special cases. However, simpler alternative bounds on the maximum entropy of integer valued discrete random variables are obtained by applying the differential entropy results to continuous random variables which approximate the integer valued random variables in a natural manner. All the results are presented in an integrated framework that includes continuous and discrete random variables, constraints on the permissible value set, and all possible values of p. Understanding such as this is useful in evaluating the performance of data compression schemes.

Parameter identification using a creeping-random-search algorithm

NASA Technical Reports Server (NTRS)

Parrish, R. V.

1971-01-01

A creeping-random-search algorithm is applied to different types of problems in the field of parameter identification. The studies are intended to demonstrate that a random-search algorithm can be applied successfully to these various problems, which often cannot be handled by conventional deterministic methods, and, also, to introduce methods that speed convergence to an extremal of the problem under investigation. Six two-parameter identification problems with analytic solutions are solved, and two application problems are discussed in some detail. Results of the study show that a modified version of the basic creeping-random-search algorithm chosen does speed convergence in comparison with the unmodified version. The results also show that the algorithm can successfully solve problems that contain limits on state or control variables, inequality constraints (both independent and dependent, and linear and nonlinear), or stochastic models.

Inference for binomial probability based on dependent Bernoulli random variables with applications to meta‐analysis and group level studies

PubMed Central

Bakbergenuly, Ilyas; Morgenthaler, Stephan

2016-01-01

We study bias arising as a result of nonlinear transformations of random variables in random or mixed effects models and its effect on inference in group‐level studies or in meta‐analysis. The findings are illustrated on the example of overdispersed binomial distributions, where we demonstrate considerable biases arising from standard log‐odds and arcsine transformations of the estimated probability p^, both for single‐group studies and in combining results from several groups or studies in meta‐analysis. Our simulations confirm that these biases are linear in ρ, for small values of ρ, the intracluster correlation coefficient. These biases do not depend on the sample sizes or the number of studies K in a meta‐analysis and result in abysmal coverage of the combined effect for large K. We also propose bias‐correction for the arcsine transformation. Our simulations demonstrate that this bias‐correction works well for small values of the intraclass correlation. The methods are applied to two examples of meta‐analyses of prevalence. PMID:27192062

Application Performance Analysis and Efficient Execution on Systems with multi-core CPUs, GPUs and MICs: A Case Study with Microscopy Image Analysis

PubMed Central

Teodoro, George; Kurc, Tahsin; Andrade, Guilherme; Kong, Jun; Ferreira, Renato; Saltz, Joel

2015-01-01

We carry out a comparative performance study of multi-core CPUs, GPUs and Intel Xeon Phi (Many Integrated Core-MIC) with a microscopy image analysis application. We experimentally evaluate the performance of computing devices on core operations of the application. We correlate the observed performance with the characteristics of computing devices and data access patterns, computation complexities, and parallelization forms of the operations. The results show a significant variability in the performance of operations with respect to the device used. The performances of operations with regular data access are comparable or sometimes better on a MIC than that on a GPU. GPUs are more efficient than MICs for operations that access data irregularly, because of the lower bandwidth of the MIC for random data accesses. We propose new performance-aware scheduling strategies that consider variabilities in operation speedups. Our scheduling strategies significantly improve application performance compared to classic strategies in hybrid configurations. PMID:28239253

On the minimum of independent geometrically distributed random variables

NASA Technical Reports Server (NTRS)

Ciardo, Gianfranco; Leemis, Lawrence M.; Nicol, David

1994-01-01

The expectations E(X(sub 1)), E(Z(sub 1)), and E(Y(sub 1)) of the minimum of n independent geometric, modifies geometric, or exponential random variables with matching expectations differ. We show how this is accounted for by stochastic variability and how E(X(sub 1))/E(Y(sub 1)) equals the expected number of ties at the minimum for the geometric random variables. We then introduce the 'shifted geometric distribution' and show that there is a unique value of the shift for which the individual shifted geometric and exponential random variables match expectations both individually and in the minimums.

Students' Misconceptions about Random Variables

ERIC Educational Resources Information Center

Kachapova, Farida; Kachapov, Ilias

2012-01-01

This article describes some misconceptions about random variables and related counter-examples, and makes suggestions about teaching initial topics on random variables in general form instead of doing it separately for discrete and continuous cases. The focus is on post-calculus probability courses. (Contains 2 figures.)

Extending existing structural identifiability analysis methods to mixed-effects models.

PubMed

Janzén, David L I; Jirstrand, Mats; Chappell, Michael J; Evans, Neil D

2018-01-01

The concept of structural identifiability for state-space models is expanded to cover mixed-effects state-space models. Two methods applicable for the analytical study of the structural identifiability of mixed-effects models are presented. The two methods are based on previously established techniques for non-mixed-effects models; namely the Taylor series expansion and the input-output form approach. By generating an exhaustive summary, and by assuming an infinite number of subjects, functions of random variables can be derived which in turn determine the distribution of the system's observation function(s). By considering the uniqueness of the analytical statistical moments of the derived functions of the random variables, the structural identifiability of the corresponding mixed-effects model can be determined. The two methods are applied to a set of examples of mixed-effects models to illustrate how they work in practice. Copyright © 2017 Elsevier Inc. All rights reserved.

Variable-bias coin tossing

NASA Astrophysics Data System (ADS)

Colbeck, Roger; Kent, Adrian

2006-03-01

Alice is a charismatic quantum cryptographer who believes her parties are unmissable; Bob is a (relatively) glamorous string theorist who believes he is an indispensable guest. To prevent possibly traumatic collisions of self-perception and reality, their social code requires that decisions about invitation or acceptance be made via a cryptographically secure variable-bias coin toss (VBCT). This generates a shared random bit by the toss of a coin whose bias is secretly chosen, within a stipulated range, by one of the parties; the other party learns only the random bit. Thus one party can secretly influence the outcome, while both can save face by blaming any negative decisions on bad luck. We describe here some cryptographic VBCT protocols whose security is guaranteed by quantum theory and the impossibility of superluminal signaling, setting our results in the context of a general discussion of secure two-party computation. We also briefly discuss other cryptographic applications of VBCT.

The Use of Attitude Segmentation in Selecting Market Targets and Choosing a New Product Name: Application to an Automated Teller System.

ERIC Educational Resources Information Center

Mauldin, Charles R.; And Others

Ninety-six subjects were randomly chosen from 386 bank customers who responded to a questionnaire using subjective variables to segment or label respondents. A review of subjective segmentation studies revealed that the studies can be divided into three approaches--benefit segmentation, attitude segmentation, and life style segmentation. Choosing…

The epistemic and aleatory uncertainties of the ETAS-type models: an application to the Central Italy seismicity.

PubMed

Lombardi, A M

2017-09-18

Stochastic models provide quantitative evaluations about the occurrence of earthquakes. A basic component of this type of models are the uncertainties in defining main features of an intrinsically random process. Even if, at a very basic level, any attempting to distinguish between types of uncertainty is questionable, an usual way to deal with this topic is to separate epistemic uncertainty, due to lack of knowledge, from aleatory variability, due to randomness. In the present study this problem is addressed in the narrow context of short-term modeling of earthquakes and, specifically, of ETAS modeling. By mean of an application of a specific version of the ETAS model to seismicity of Central Italy, recently struck by a sequence with a main event of Mw6.5, the aleatory and epistemic (parametric) uncertainty are separated and quantified. The main result of the paper is that the parametric uncertainty of the ETAS-type model, adopted here, is much lower than the aleatory variability in the process. This result points out two main aspects: an analyst has good chances to set the ETAS-type models, but he may retrospectively describe and forecast the earthquake occurrences with still limited precision and accuracy.

Investigation of advanced UQ for CRUD prediction with VIPRE.

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

Eldred, Michael Scott

2011-09-01

This document summarizes the results from a level 3 milestone study within the CASL VUQ effort. It demonstrates the application of 'advanced UQ,' in particular dimension-adaptive p-refinement for polynomial chaos and stochastic collocation. The study calculates statistics for several quantities of interest that are indicators for the formation of CRUD (Chalk River unidentified deposit), which can lead to CIPS (CRUD induced power shift). Stochastic expansion methods are attractive methods for uncertainty quantification due to their fast convergence properties. For smooth functions (i.e., analytic, infinitely-differentiable) in L{sup 2} (i.e., possessing finite variance), exponential convergence rates can be obtained under order refinementmore » for integrated statistical quantities of interest such as mean, variance, and probability. Two stochastic expansion methods are of interest: nonintrusive polynomial chaos expansion (PCE), which computes coefficients for a known basis of multivariate orthogonal polynomials, and stochastic collocation (SC), which forms multivariate interpolation polynomials for known coefficients. Within the DAKOTA project, recent research in stochastic expansion methods has focused on automated polynomial order refinement ('p-refinement') of expansions to support scalability to higher dimensional random input spaces [4, 3]. By preferentially refining only in the most important dimensions of the input space, the applicability of these methods can be extended from O(10{sup 0})-O(10{sup 1}) random variables to O(10{sup 2}) and beyond, depending on the degree of anisotropy (i.e., the extent to which randominput variables have differing degrees of influence on the statistical quantities of interest (QOIs)). Thus, the purpose of this study is to investigate the application of these adaptive stochastic expansion methods to the analysis of CRUD using the VIPRE simulation tools for two different plant models of differing random dimension, anisotropy, and smoothness.« less

Finite-sample corrected generalized estimating equation of population average treatment effects in stepped wedge cluster randomized trials.

PubMed

Scott, JoAnna M; deCamp, Allan; Juraska, Michal; Fay, Michael P; Gilbert, Peter B

2017-04-01

Stepped wedge designs are increasingly commonplace and advantageous for cluster randomized trials when it is both unethical to assign placebo, and it is logistically difficult to allocate an intervention simultaneously to many clusters. We study marginal mean models fit with generalized estimating equations for assessing treatment effectiveness in stepped wedge cluster randomized trials. This approach has advantages over the more commonly used mixed models that (1) the population-average parameters have an important interpretation for public health applications and (2) they avoid untestable assumptions on latent variable distributions and avoid parametric assumptions about error distributions, therefore, providing more robust evidence on treatment effects. However, cluster randomized trials typically have a small number of clusters, rendering the standard generalized estimating equation sandwich variance estimator biased and highly variable and hence yielding incorrect inferences. We study the usual asymptotic generalized estimating equation inferences (i.e., using sandwich variance estimators and asymptotic normality) and four small-sample corrections to generalized estimating equation for stepped wedge cluster randomized trials and for parallel cluster randomized trials as a comparison. We show by simulation that the small-sample corrections provide improvement, with one correction appearing to provide at least nominal coverage even with only 10 clusters per group. These results demonstrate the viability of the marginal mean approach for both stepped wedge and parallel cluster randomized trials. We also study the comparative performance of the corrected methods for stepped wedge and parallel designs, and describe how the methods can accommodate interval censoring of individual failure times and incorporate semiparametric efficient estimators.

Monte Carlo investigation of thrust imbalance of solid rocket motor pairs

NASA Technical Reports Server (NTRS)

Sforzini, R. H.; Foster, W. A., Jr.

1976-01-01

The Monte Carlo method of statistical analysis is used to investigate the theoretical thrust imbalance of pairs of solid rocket motors (SRMs) firing in parallel. Sets of the significant variables are selected using a random sampling technique and the imbalance calculated for a large number of motor pairs using a simplified, but comprehensive, model of the internal ballistics. The treatment of burning surface geometry allows for the variations in the ovality and alignment of the motor case and mandrel as well as those arising from differences in the basic size dimensions and propellant properties. The analysis is used to predict the thrust-time characteristics of 130 randomly selected pairs of Titan IIIC SRMs. A statistical comparison of the results with test data for 20 pairs shows the theory underpredicts the standard deviation in maximum thrust imbalance by 20% with variability in burning times matched within 2%. The range in thrust imbalance of Space Shuttle type SRM pairs is also estimated using applicable tolerances and variabilities and a correction factor based on the Titan IIIC analysis.

A Multivariate Randomization Text of Association Applied to Cognitive Test Results

NASA Technical Reports Server (NTRS)

Ahumada, Albert; Beard, Bettina

2009-01-01

Randomization tests provide a conceptually simple, distribution-free way to implement significance testing. We have applied this method to the problem of evaluating the significance of the association among a number (k) of variables. The randomization method was the random re-ordering of k-1 of the variables. The criterion variable was the value of the largest eigenvalue of the correlation matrix.

Polymorphous computing fabric

DOEpatents

Wolinski, Christophe Czeslaw [Los Alamos, NM; Gokhale, Maya B [Los Alamos, NM; McCabe, Kevin Peter [Los Alamos, NM

2011-01-18

Fabric-based computing systems and methods are disclosed. A fabric-based computing system can include a polymorphous computing fabric that can be customized on a per application basis and a host processor in communication with said polymorphous computing fabric. The polymorphous computing fabric includes a cellular architecture that can be highly parameterized to enable a customized synthesis of fabric instances for a variety of enhanced application performances thereof. A global memory concept can also be included that provides the host processor random access to all variables and instructions associated with the polymorphous computing fabric.

Record statistics of a strongly correlated time series: random walks and Lévy flights

NASA Astrophysics Data System (ADS)

Godrèche, Claude; Majumdar, Satya N.; Schehr, Grégory

2017-08-01

We review recent advances on the record statistics of strongly correlated time series, whose entries denote the positions of a random walk or a Lévy flight on a line. After a brief survey of the theory of records for independent and identically distributed random variables, we focus on random walks. During the last few years, it was indeed realized that random walks are a very useful ‘laboratory’ to test the effects of correlations on the record statistics. We start with the simple one-dimensional random walk with symmetric jumps (both continuous and discrete) and discuss in detail the statistics of the number of records, as well as of the ages of the records, i.e. the lapses of time between two successive record breaking events. Then we review the results that were obtained for a wide variety of random walk models, including random walks with a linear drift, continuous time random walks, constrained random walks (like the random walk bridge) and the case of multiple independent random walkers. Finally, we discuss further observables related to records, like the record increments, as well as some questions raised by physical applications of record statistics, like the effects of measurement error and noise.

Empirical study of seven data mining algorithms on different characteristics of datasets for biomedical classification applications.

PubMed

Zhang, Yiyan; Xin, Yi; Li, Qin; Ma, Jianshe; Li, Shuai; Lv, Xiaodan; Lv, Weiqi

2017-11-02

Various kinds of data mining algorithms are continuously raised with the development of related disciplines. The applicable scopes and their performances of these algorithms are different. Hence, finding a suitable algorithm for a dataset is becoming an important emphasis for biomedical researchers to solve practical problems promptly. In this paper, seven kinds of sophisticated active algorithms, namely, C4.5, support vector machine, AdaBoost, k-nearest neighbor, naïve Bayes, random forest, and logistic regression, were selected as the research objects. The seven algorithms were applied to the 12 top-click UCI public datasets with the task of classification, and their performances were compared through induction and analysis. The sample size, number of attributes, number of missing values, and the sample size of each class, correlation coefficients between variables, class entropy of task variable, and the ratio of the sample size of the largest class to the least class were calculated to character the 12 research datasets. The two ensemble algorithms reach high accuracy of classification on most datasets. Moreover, random forest performs better than AdaBoost on the unbalanced dataset of the multi-class task. Simple algorithms, such as the naïve Bayes and logistic regression model are suitable for a small dataset with high correlation between the task and other non-task attribute variables. K-nearest neighbor and C4.5 decision tree algorithms perform well on binary- and multi-class task datasets. Support vector machine is more adept on the balanced small dataset of the binary-class task. No algorithm can maintain the best performance in all datasets. The applicability of the seven data mining algorithms on the datasets with different characteristics was summarized to provide a reference for biomedical researchers or beginners in different fields.

Continuous-Time Random Walk with multi-step memory: an application to market dynamics

NASA Astrophysics Data System (ADS)

Gubiec, Tomasz; Kutner, Ryszard

2017-11-01

An extended version of the Continuous-Time Random Walk (CTRW) model with memory is herein developed. This memory involves the dependence between arbitrary number of successive jumps of the process while waiting times between jumps are considered as i.i.d. random variables. This dependence was established analyzing empirical histograms for the stochastic process of a single share price on a market within the high frequency time scale. Then, it was justified theoretically by considering bid-ask bounce mechanism containing some delay characteristic for any double-auction market. Our model appeared exactly analytically solvable. Therefore, it enables a direct comparison of its predictions with their empirical counterparts, for instance, with empirical velocity autocorrelation function. Thus, the present research significantly extends capabilities of the CTRW formalism. Contribution to the Topical Issue "Continuous Time Random Walk Still Trendy: Fifty-year History, Current State and Outlook", edited by Ryszard Kutner and Jaume Masoliver.

Demonstration of the Application of Composite Load Spectra (CLS) and Probabilistic Structural Analysis (PSAM) Codes to SSME Heat Exchanger Turnaround Vane

NASA Technical Reports Server (NTRS)

Rajagopal, Kadambi R.; DebChaudhury, Amitabha; Orient, George

2000-01-01

This report describes a probabilistic structural analysis performed to determine the probabilistic structural response under fluctuating random pressure loads for the Space Shuttle Main Engine (SSME) turnaround vane. It uses a newly developed frequency and distance dependent correlation model that has features to model the decay phenomena along the flow and across the flow with the capability to introduce a phase delay. The analytical results are compared using two computer codes SAFER (Spectral Analysis of Finite Element Responses) and NESSUS (Numerical Evaluation of Stochastic Structures Under Stress) and with experimentally observed strain gage data. The computer code NESSUS with an interface to a sub set of Composite Load Spectra (CLS) code is used for the probabilistic analysis. A Fatigue code was used to calculate fatigue damage due to the random pressure excitation. The random variables modeled include engine system primitive variables that influence the operating conditions, convection velocity coefficient, stress concentration factor, structural damping, and thickness of the inner and outer vanes. The need for an appropriate correlation model in addition to magnitude of the PSD is emphasized. The study demonstrates that correlation characteristics even under random pressure loads are capable of causing resonance like effects for some modes. The study identifies the important variables that contribute to structural alternate stress response and drive the fatigue damage for the new design. Since the alternate stress for the new redesign is less than the endurance limit for the material, the damage due high cycle fatigue is negligible.

Latent class instrumental variables: a clinical and biostatistical perspective.

PubMed

Baker, Stuart G; Kramer, Barnett S; Lindeman, Karen S

2016-01-15

In some two-arm randomized trials, some participants receive the treatment assigned to the other arm as a result of technical problems, refusal of a treatment invitation, or a choice of treatment in an encouragement design. In some before-and-after studies, the availability of a new treatment changes from one time period to this next. Under assumptions that are often reasonable, the latent class instrumental variable (IV) method estimates the effect of treatment received in the aforementioned scenarios involving all-or-none compliance and all-or-none availability. Key aspects are four initial latent classes (sometimes called principal strata) based on treatment received if in each randomization group or time period, the exclusion restriction assumption (in which randomization group or time period is an instrumental variable), the monotonicity assumption (which drops an implausible latent class from the analysis), and the estimated effect of receiving treatment in one latent class (sometimes called efficacy, the local average treatment effect, or the complier average causal effect). Since its independent formulations in the biostatistics and econometrics literatures, the latent class IV method (which has no well-established name) has gained increasing popularity. We review the latent class IV method from a clinical and biostatistical perspective, focusing on underlying assumptions, methodological extensions, and applications in our fields of obstetrics and cancer research. Copyright © 2015 John Wiley & Sons, Ltd.

Probabilistic evaluation of SSME structural components

NASA Astrophysics Data System (ADS)

Rajagopal, K. R.; Newell, J. F.; Ho, H.

1991-05-01

The application is described of Composite Load Spectra (CLS) and Numerical Evaluation of Stochastic Structures Under Stress (NESSUS) family of computer codes to the probabilistic structural analysis of four Space Shuttle Main Engine (SSME) space propulsion system components. These components are subjected to environments that are influenced by many random variables. The applications consider a wide breadth of uncertainties encountered in practice, while simultaneously covering a wide area of structural mechanics. This has been done consistent with the primary design requirement for each component. The probabilistic application studies are discussed using finite element models that have been typically used in the past in deterministic analysis studies.

Application of random effects to the study of resource selection by animals

USGS Publications Warehouse

Gillies, C.S.; Hebblewhite, M.; Nielsen, S.E.; Krawchuk, M.A.; Aldridge, Cameron L.; Frair, J.L.; Saher, D.J.; Stevens, C.E.; Jerde, C.L.

2006-01-01

1. Resource selection estimated by logistic regression is used increasingly in studies to identify critical resources for animal populations and to predict species occurrence.2. Most frequently, individual animals are monitored and pooled to estimate population-level effects without regard to group or individual-level variation. Pooling assumes that both observations and their errors are independent, and resource selection is constant given individual variation in resource availability.3. Although researchers have identified ways to minimize autocorrelation, variation between individuals caused by differences in selection or available resources, including functional responses in resource selection, have not been well addressed.4. Here we review random-effects models and their application to resource selection modelling to overcome these common limitations. We present a simple case study of an analysis of resource selection by grizzly bears in the foothills of the Canadian Rocky Mountains with and without random effects.5. Both categorical and continuous variables in the grizzly bear model differed in interpretation, both in statistical significance and coefficient sign, depending on how a random effect was included. We used a simulation approach to clarify the application of random effects under three common situations for telemetry studies: (a) discrepancies in sample sizes among individuals; (b) differences among individuals in selection where availability is constant; and (c) differences in availability with and without a functional response in resource selection.6. We found that random intercepts accounted for unbalanced sample designs, and models with random intercepts and coefficients improved model fit given the variation in selection among individuals and functional responses in selection. Our empirical example and simulations demonstrate how including random effects in resource selection models can aid interpretation and address difficult assumptions limiting their generality. This approach will allow researchers to appropriately estimate marginal (population) and conditional (individual) responses, and account for complex grouping, unbalanced sample designs and autocorrelation.

Application of random effects to the study of resource selection by animals.

PubMed

Gillies, Cameron S; Hebblewhite, Mark; Nielsen, Scott E; Krawchuk, Meg A; Aldridge, Cameron L; Frair, Jacqueline L; Saher, D Joanne; Stevens, Cameron E; Jerde, Christopher L

2006-07-01

1. Resource selection estimated by logistic regression is used increasingly in studies to identify critical resources for animal populations and to predict species occurrence. 2. Most frequently, individual animals are monitored and pooled to estimate population-level effects without regard to group or individual-level variation. Pooling assumes that both observations and their errors are independent, and resource selection is constant given individual variation in resource availability. 3. Although researchers have identified ways to minimize autocorrelation, variation between individuals caused by differences in selection or available resources, including functional responses in resource selection, have not been well addressed. 4. Here we review random-effects models and their application to resource selection modelling to overcome these common limitations. We present a simple case study of an analysis of resource selection by grizzly bears in the foothills of the Canadian Rocky Mountains with and without random effects. 5. Both categorical and continuous variables in the grizzly bear model differed in interpretation, both in statistical significance and coefficient sign, depending on how a random effect was included. We used a simulation approach to clarify the application of random effects under three common situations for telemetry studies: (a) discrepancies in sample sizes among individuals; (b) differences among individuals in selection where availability is constant; and (c) differences in availability with and without a functional response in resource selection. 6. We found that random intercepts accounted for unbalanced sample designs, and models with random intercepts and coefficients improved model fit given the variation in selection among individuals and functional responses in selection. Our empirical example and simulations demonstrate how including random effects in resource selection models can aid interpretation and address difficult assumptions limiting their generality. This approach will allow researchers to appropriately estimate marginal (population) and conditional (individual) responses, and account for complex grouping, unbalanced sample designs and autocorrelation.

The Benefits of College Athletic Success: An Application of the Propensity Score Design with Instrumental Variables. NBER Working Paper No. 18196

ERIC Educational Resources Information Center

Anderson, Michael L.

2012-01-01

Spending on big-time college athletics is often justified on the grounds that athletic success attracts students and raises donations. Testing this claim has proven difficult because success is not randomly assigned. We exploit data on bookmaker spreads to estimate the probability of winning each game for college football teams. We then condition…

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.

Modelling and control algorithms of the cross conveyors line with multiengine variable speed drives

NASA Astrophysics Data System (ADS)

Cheremushkina, M. S.; Baburin, S. V.

2017-02-01

The paper deals with the actual problem of developing the control algorithm that meets the technical requirements of the mine belt conveyors, and enables energy and resource savings taking into account a random sort of traffic. The most effective method of solution of these tasks is the construction of control systems with the use of variable speed drives for asynchronous motors. The authors designed the mathematical model of the system ‘variable speed multiengine drive - conveyor - control system of conveyors’ that takes into account the dynamic processes occurring in the elements of the transport system, provides an assessment of the energy efficiency of application the developed algorithms, which allows one to reduce the dynamic overload in the belt to 15-20%.

Simulation of Crack Propagation in Engine Rotating Components under Variable Amplitude Loading

NASA Technical Reports Server (NTRS)

Bonacuse, P. J.; Ghosn, L. J.; Telesman, J.; Calomino, A. M.; Kantzos, P.

1998-01-01

The crack propagation life of tested specimens has been repeatedly shown to strongly depend on the loading history. Overloads and extended stress holds at temperature can either retard or accelerate the crack growth rate. Therefore, to accurately predict the crack propagation life of an actual component, it is essential to approximate the true loading history. In military rotorcraft engine applications, the loading profile (stress amplitudes, temperature, and number of excursions) can vary significantly depending on the type of mission flown. To accurately assess the durability of a fleet of engines, the crack propagation life distribution of a specific component should account for the variability in the missions performed (proportion of missions flown and sequence). In this report, analytical and experimental studies are described that calibrate/validate the crack propagation prediction capability ]or a disk alloy under variable amplitude loading. A crack closure based model was adopted to analytically predict the load interaction effects. Furthermore, a methodology has been developed to realistically simulate the actual mission mix loading on a fleet of engines over their lifetime. A sequence of missions is randomly selected and the number of repeats of each mission in the sequence is determined assuming a Poisson distributed random variable with a given mean occurrence rate. Multiple realizations of random mission histories are generated in this manner and are used to produce stress, temperature, and time points for fracture mechanics calculations. The result is a cumulative distribution of crack propagation lives for a given, life limiting, component location. This information can be used to determine a safe retirement life or inspection interval for the given location.

Covariance Bell inequalities

NASA Astrophysics Data System (ADS)

Pozsgay, Victor; Hirsch, Flavien; Branciard, Cyril; Brunner, Nicolas

2017-12-01

We introduce Bell inequalities based on covariance, one of the most common measures of correlation. Explicit examples are discussed, and violations in quantum theory are demonstrated. A crucial feature of these covariance Bell inequalities is their nonlinearity; this has nontrivial consequences for the derivation of their local bound, which is not reached by deterministic local correlations. For our simplest inequality, we derive analytically tight bounds for both local and quantum correlations. An interesting application of covariance Bell inequalities is that they can act as "shared randomness witnesses": specifically, the value of the Bell expression gives device-independent lower bounds on both the dimension and the entropy of the shared random variable in a local model.

Estimate variable importance for recurrent event outcomes with an application to identify hypoglycemia risk factors.

PubMed

Duan, Ran; Fu, Haoda

2015-08-30

Recurrent event data are an important data type for medical research. In particular, many safety endpoints are recurrent outcomes, such as hypoglycemic events. For such a situation, it is important to identify the factors causing these events and rank these factors by their importance. Traditional model selection methods are not able to provide variable importance in this context. Methods that are able to evaluate the variable importance, such as gradient boosting and random forest algorithms, cannot directly be applied to recurrent events data. In this paper, we propose a two-step method that enables us to evaluate the variable importance for recurrent events data. We evaluated the performance of our proposed method by simulations and applied it to a data set from a diabetes study. Copyright © 2015 John Wiley & Sons, Ltd.

Computational simulation of probabilistic lifetime strength for aerospace materials subjected to high temperature, mechanical fatigue, creep and thermal fatigue

NASA Technical Reports Server (NTRS)

Boyce, Lola; Bast, Callie C.; Trimble, Greg A.

1992-01-01

This report presents the results of a fourth year effort of a research program, conducted for NASA-LeRC by the University of Texas at San Antonio (UTSA). The research included on-going development of methodology that provides probabilistic lifetime strength of aerospace materials via computational simulation. A probabilistic material strength degradation model, in the form of a randomized multifactor interaction equation, is postulated for strength degradation of structural components of aerospace propulsion systems subject to a number of effects or primitive variables. These primitive variables may include high temperature, fatigue or creep. In most cases, strength is reduced as a result of the action of a variable. This multifactor interaction strength degradation equation has been randomized and is included in the computer program, PROMISS. Also included in the research is the development of methodology to calibrate the above-described constitutive equation using actual experimental materials data together with regression analysis of that data, thereby predicting values for the empirical material constants for each effect or primitive variable. This regression methodology is included in the computer program, PROMISC. Actual experimental materials data were obtained from industry and the open literature for materials typically for applications in aerospace propulsion system components. Material data for Inconel 718 has been analyzed using the developed methodology.

Computational simulation of probabilistic lifetime strength for aerospace materials subjected to high temperature, mechanical fatigue, creep, and thermal fatigue

NASA Technical Reports Server (NTRS)

Boyce, Lola; Bast, Callie C.; Trimble, Greg A.

1992-01-01

The results of a fourth year effort of a research program conducted for NASA-LeRC by The University of Texas at San Antonio (UTSA) are presented. The research included on-going development of methodology that provides probabilistic lifetime strength of aerospace materials via computational simulation. A probabilistic material strength degradation model, in the form of a randomized multifactor interaction equation, is postulated for strength degradation of structural components of aerospace propulsion systems subjected to a number of effects or primitive variables. These primitive variables may include high temperature, fatigue, or creep. In most cases, strength is reduced as a result of the action of a variable. This multifactor interaction strength degradation equation was randomized and is included in the computer program, PROMISC. Also included in the research is the development of methodology to calibrate the above-described constitutive equation using actual experimental materials data together with regression analysis of that data, thereby predicting values for the empirical material constants for each effect or primitive variable. This regression methodology is included in the computer program, PROMISC. Actual experimental materials data were obtained from industry and the open literature for materials typically for applications in aerospace propulsion system components. Material data for Inconel 718 was analyzed using the developed methodology.

True Randomness from Big Data.

PubMed

Papakonstantinou, Periklis A; Woodruff, David P; Yang, Guang

2016-09-26

Generating random bits is a difficult task, which is important for physical systems simulation, cryptography, and many applications that rely on high-quality random bits. Our contribution is to show how to generate provably random bits from uncertain events whose outcomes are routinely recorded in the form of massive data sets. These include scientific data sets, such as in astronomics, genomics, as well as data produced by individuals, such as internet search logs, sensor networks, and social network feeds. We view the generation of such data as the sampling process from a big source, which is a random variable of size at least a few gigabytes. Our view initiates the study of big sources in the randomness extraction literature. Previous approaches for big sources rely on statistical assumptions about the samples. We introduce a general method that provably extracts almost-uniform random bits from big sources and extensively validate it empirically on real data sets. The experimental findings indicate that our method is efficient enough to handle large enough sources, while previous extractor constructions are not efficient enough to be practical. Quality-wise, our method at least matches quantum randomness expanders and classical world empirical extractors as measured by standardized tests.

True Randomness from Big Data

NASA Astrophysics Data System (ADS)

Papakonstantinou, Periklis A.; Woodruff, David P.; Yang, Guang

2016-09-01

Generating random bits is a difficult task, which is important for physical systems simulation, cryptography, and many applications that rely on high-quality random bits. Our contribution is to show how to generate provably random bits from uncertain events whose outcomes are routinely recorded in the form of massive data sets. These include scientific data sets, such as in astronomics, genomics, as well as data produced by individuals, such as internet search logs, sensor networks, and social network feeds. We view the generation of such data as the sampling process from a big source, which is a random variable of size at least a few gigabytes. Our view initiates the study of big sources in the randomness extraction literature. Previous approaches for big sources rely on statistical assumptions about the samples. We introduce a general method that provably extracts almost-uniform random bits from big sources and extensively validate it empirically on real data sets. The experimental findings indicate that our method is efficient enough to handle large enough sources, while previous extractor constructions are not efficient enough to be practical. Quality-wise, our method at least matches quantum randomness expanders and classical world empirical extractors as measured by standardized tests.

True Randomness from Big Data

PubMed Central

Papakonstantinou, Periklis A.; Woodruff, David P.; Yang, Guang

2016-01-01

Generating random bits is a difficult task, which is important for physical systems simulation, cryptography, and many applications that rely on high-quality random bits. Our contribution is to show how to generate provably random bits from uncertain events whose outcomes are routinely recorded in the form of massive data sets. These include scientific data sets, such as in astronomics, genomics, as well as data produced by individuals, such as internet search logs, sensor networks, and social network feeds. We view the generation of such data as the sampling process from a big source, which is a random variable of size at least a few gigabytes. Our view initiates the study of big sources in the randomness extraction literature. Previous approaches for big sources rely on statistical assumptions about the samples. We introduce a general method that provably extracts almost-uniform random bits from big sources and extensively validate it empirically on real data sets. The experimental findings indicate that our method is efficient enough to handle large enough sources, while previous extractor constructions are not efficient enough to be practical. Quality-wise, our method at least matches quantum randomness expanders and classical world empirical extractors as measured by standardized tests. PMID:27666514

Comparative study of feature selection with ensemble learning using SOM variants

NASA Astrophysics Data System (ADS)

Filali, Ameni; Jlassi, Chiraz; Arous, Najet

2017-03-01

Ensemble learning has succeeded in the growth of stability and clustering accuracy, but their runtime prohibits them from scaling up to real-world applications. This study deals the problem of selecting a subset of the most pertinent features for every cluster from a dataset. The proposed method is another extension of the Random Forests approach using self-organizing maps (SOM) variants to unlabeled data that estimates the out-of-bag feature importance from a set of partitions. Every partition is created using a various bootstrap sample and a random subset of the features. Then, we show that the process internal estimates are used to measure variable pertinence in Random Forests are also applicable to feature selection in unsupervised learning. This approach aims to the dimensionality reduction, visualization and cluster characterization at the same time. Hence, we provide empirical results on nineteen benchmark data sets indicating that RFS can lead to significant improvement in terms of clustering accuracy, over several state-of-the-art unsupervised methods, with a very limited subset of features. The approach proves promise to treat with very broad domains.

Biologically-variable rhythmic auditory cues are superior to isochronous cues in fostering natural gait variability in Parkinson's disease.

PubMed

Dotov, D G; Bayard, S; Cochen de Cock, V; Geny, C; Driss, V; Garrigue, G; Bardy, B; Dalla Bella, S

2017-01-01

Rhythmic auditory cueing improves certain gait symptoms of Parkinson's disease (PD). Cues are typically stimuli or beats with a fixed inter-beat interval. We show that isochronous cueing has an unwanted side-effect in that it exacerbates one of the motor symptoms characteristic of advanced PD. Whereas the parameters of the stride cycle of healthy walkers and early patients possess a persistent correlation in time, or long-range correlation (LRC), isochronous cueing renders stride-to-stride variability random. Random stride cycle variability is also associated with reduced gait stability and lack of flexibility. To investigate how to prevent patients from acquiring a random stride cycle pattern, we tested rhythmic cueing which mimics the properties of variability found in healthy gait (biological variability). PD patients (n=19) and age-matched healthy participants (n=19) walked with three rhythmic cueing stimuli: isochronous, with random variability, and with biological variability (LRC). Synchronization was not instructed. The persistent correlation in gait was preserved only with stimuli with biological variability, equally for patients and controls (p's<0.05). In contrast, cueing with isochronous or randomly varying inter-stimulus/beat intervals removed the LRC in the stride cycle. Notably, the individual's tendency to synchronize steps with beats determined the amount of negative effects of isochronous and random cues (p's<0.05) but not the positive effect of biological variability. Stimulus variability and patients' propensity to synchronize play a critical role in fostering healthier gait dynamics during cueing. The beneficial effects of biological variability provide useful guidelines for improving existing cueing treatments. Copyright © 2016 Elsevier B.V. All rights reserved.

Analysis of causality from observational studies and its application in clinical research in Intensive Care Medicine.

PubMed

Coscia Requena, C; Muriel, A; Peñuelas, O

2018-02-28

Random allocation of treatment or intervention is the key feature of clinical trials and divides patients into treatment groups that are approximately balanced for baseline, and therefore comparable covariates except for the variable treatment of the study. However, in observational studies, where treatment allocation is not random, patients in the treatment and control groups often differ in covariates that are related to intervention variables. These imbalances in covariates can lead to biased estimates of the treatment effect. However, randomized clinical trials are sometimes not feasible for ethical, logistical, economic or other reasons. To resolve these situations, interest in the field of clinical research has grown in designing studies that are most similar to randomized experiments using observational (i.e. non-random) data. Observational studies using propensity score analysis methods have been increasing in the scientific papers of Intensive Care. Propensity score analyses attempt to control for confounding in non-experimental studies by adjusting for the likelihood that a given patient is exposed. However, studies with propensity indexes may be confusing, and intensivists are not familiar with this methodology and may not fully understand the importance of this technique. The objectives of this review are: to describe the fundamentals of propensity index methods; to present the techniques to adequately evaluate propensity index models; to discuss the advantages and disadvantages of these techniques. Copyright © 2018 Elsevier España, S.L.U. y SEMICYUC. All rights reserved.

Initial development of a device for controlling manually applied forces.

PubMed

Waddington, Gordon S; Adams, Roger D

2007-05-01

In both simulation and manual therapy studies, substantial variability has been shown when therapists attempt to replicate an applied force. Knowledge about the forces employed during treatment could reduce this variability. In the current project, a prototype for a mobilizing device incorporating a dynamometer was constructed. The prototype device was built around a conventional "hand-grip" dynamometer to give dial visibility during application of mobilizing forces and a moulded handle was used to increase the hand contact surface during force application. The variability of the mobilization forces produced was measured, and ratings of comfort during a simulated spinal mobilization technique were obtained from therapists. Thirty physiotherapists were randomly allocated to apply either: (i) their own estimate of a grade III mobilization force using their hands in a pisiform grip or (ii) a 100N force with the manual therapy dynamometer, and to rate comfort during the performance of both techniques on a 100mm visual analogue scale. Variance in dynamometer-dial-guided force application was always significantly less than the variance in therapist-concept-guided force application. Repeated-measures tests showed that the mean force produced at grade III was not significantly different from 100N, but physiotherapist comfort ratings were found to be significantly greater (P<0.01) when the manual therapy dynamometer was used. Manually applied force variability was significantly less and therapist comfort greater when using a device with visual access to a dial giving immediate force readout.

An instrumental variable random-coefficients model for binary outcomes

PubMed Central

Chesher, Andrew; Rosen, Adam M

2014-01-01

In this paper, we study a random-coefficients model for a binary outcome. We allow for the possibility that some or even all of the explanatory variables are arbitrarily correlated with the random coefficients, thus permitting endogeneity. We assume the existence of observed instrumental variables Z that are jointly independent with the random coefficients, although we place no structure on the joint determination of the endogenous variable X and instruments Z, as would be required for a control function approach. The model fits within the spectrum of generalized instrumental variable models, and we thus apply identification results from our previous studies of such models to the present context, demonstrating their use. Specifically, we characterize the identified set for the distribution of random coefficients in the binary response model with endogeneity via a collection of conditional moment inequalities, and we investigate the structure of these sets by way of numerical illustration. PMID:25798048

Polynomial chaos expansion with random and fuzzy variables

NASA Astrophysics Data System (ADS)

Jacquelin, E.; Friswell, M. I.; Adhikari, S.; Dessombz, O.; Sinou, J.-J.

2016-06-01

A dynamical uncertain system is studied in this paper. Two kinds of uncertainties are addressed, where the uncertain parameters are described through random variables and/or fuzzy variables. A general framework is proposed to deal with both kinds of uncertainty using a polynomial chaos expansion (PCE). It is shown that fuzzy variables may be expanded in terms of polynomial chaos when Legendre polynomials are used. The components of the PCE are a solution of an equation that does not depend on the nature of uncertainty. Once this equation is solved, the post-processing of the data gives the moments of the random response when the uncertainties are random or gives the response interval when the variables are fuzzy. With the PCE approach, it is also possible to deal with mixed uncertainty, when some parameters are random and others are fuzzy. The results provide a fuzzy description of the response statistical moments.

Inference for binomial probability based on dependent Bernoulli random variables with applications to meta-analysis and group level studies.

PubMed

Bakbergenuly, Ilyas; Kulinskaya, Elena; Morgenthaler, Stephan

2016-07-01

We study bias arising as a result of nonlinear transformations of random variables in random or mixed effects models and its effect on inference in group-level studies or in meta-analysis. The findings are illustrated on the example of overdispersed binomial distributions, where we demonstrate considerable biases arising from standard log-odds and arcsine transformations of the estimated probability p̂, both for single-group studies and in combining results from several groups or studies in meta-analysis. Our simulations confirm that these biases are linear in ρ, for small values of ρ, the intracluster correlation coefficient. These biases do not depend on the sample sizes or the number of studies K in a meta-analysis and result in abysmal coverage of the combined effect for large K. We also propose bias-correction for the arcsine transformation. Our simulations demonstrate that this bias-correction works well for small values of the intraclass correlation. The methods are applied to two examples of meta-analyses of prevalence. © 2016 The Authors. Biometrical Journal Published by Wiley-VCH Verlag GmbH & Co. KGaA.

Computer simulation of random variables and vectors with arbitrary probability distribution laws

NASA Technical Reports Server (NTRS)

Bogdan, V. M.

1981-01-01

Assume that there is given an arbitrary n-dimensional probability distribution F. A recursive construction is found for a sequence of functions x sub 1 = f sub 1 (U sub 1, ..., U sub n), ..., x sub n = f sub n (U sub 1, ..., U sub n) such that if U sub 1, ..., U sub n are independent random variables having uniform distribution over the open interval (0,1), then the joint distribution of the variables x sub 1, ..., x sub n coincides with the distribution F. Since uniform independent random variables can be well simulated by means of a computer, this result allows one to simulate arbitrary n-random variables if their joint probability distribution is known.

Electrical Switching of Perovskite Thin-Film Resistors

NASA Technical Reports Server (NTRS)

Liu, Shangqing; Wu, Juan; Ignatiev, Alex

2010-01-01

Electronic devices that exploit electrical switching of physical properties of thin films of perovskite materials (especially colossal magnetoresistive materials) have been invented. Unlike some related prior devices, these devices function at room temperature and do not depend on externally applied magnetic fields. Devices of this type can be designed to function as sensors (exhibiting varying electrical resistance in response to varying temperature, magnetic field, electric field, and/or mechanical pressure) and as elements of electronic memories. The underlying principle is that the application of one or more short electrical pulse(s) can induce a reversible, irreversible, or partly reversible change in the electrical, thermal, mechanical, and magnetic properties of a thin perovskite film. The energy in the pulse must be large enough to induce the desired change but not so large as to destroy the film. Depending on the requirements of a specific application, the pulse(s) can have any of a large variety of waveforms (e.g., square, triangular, or sine) and be of positive, negative, or alternating polarity. In some applications, it could be necessary to use multiple pulses to induce successive incremental physical changes. In one class of applications, electrical pulses of suitable shapes, sizes, and polarities are applied to vary the detection sensitivities of sensors. Another class of applications arises in electronic circuits in which certain resistance values are required to be variable: Incorporating the affected resistors into devices of the present type makes it possible to control their resistances electrically over wide ranges, and the lifetimes of electrically variable resistors exceed those of conventional mechanically variable resistors. Another and potentially the most important class of applications is that of resistance-based nonvolatile-memory devices, such as a resistance random access memory (RRAM) described in the immediately following article, Electrically Variable Resistive Memory Devices (MFS-32511-1).

Measures of Residual Risk with Connections to Regression, Risk Tracking, Surrogate Models, and Ambiguity

DTIC Science & Technology

2015-01-07

vector that helps to manage , predict, and mitigate the risk in the original variable. Residual risk can be exemplified as a quantification of the improved... the random variable of interest is viewed in concert with a related random vector that helps to manage , predict, and mitigate the risk in the original...measures of risk. They view a random variable of interest in concert with an auxiliary random vector that helps to manage , predict and mitigate the risk

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

Dondero, Rachel Elizabeth

The increased use of Field Programmable Gate Arrays (FPGAs) in critical systems brings new challenges in securing the diversely programmable fabric from cyber-attacks. FPGAs are an inexpensive, efficient, and flexible alternative to Application Specific Integrated Circuits (ASICs), which are becoming increasingly expensive and impractical for low volume manufacturing as technology nodes continue to shrink. Unfortunately, FPGAs are not designed for high security applications, and their high-flexibility lends itself to low security and vulnerability to malicious attacks. Similar to securing an ASIC’s functionality, FPGA programmers can exploit the inherent randomness introduced into hardware structures during fabrication for security applications. Physically Unclonablemore » Functions (PUFs) are one such solution that uses the die specific variability in hardware fabrication for both secret key generation and verification. PUFs strive to be random, unique, and reliable. Throughout recent years many PUF structures have been presented to try and maximize these three design constraints, reliability being the most difficult of the three to achieve. This thesis presents a new PUF structure that combines two elementary PUF concepts (a bi-stable SRAM PUF and a delay-based arbiter PUF) to create a PUF with increased reliability, while maintaining both random and unique qualities. Properties of the new PUF will be discussed as well as the various design modifications that can be made to tweak the desired performance and overhead.« less

SETI and SEH (Statistical Equation for Habitables)

NASA Astrophysics Data System (ADS)

Maccone, Claudio

2011-01-01

The statistics of habitable planets may be based on a set of ten (and possibly more) astrobiological requirements first pointed out by Stephen H. Dole in his book "Habitable planets for man" (1964). In this paper, we first provide the statistical generalization of the original and by now too simplistic Dole equation. In other words, a product of ten positive numbers is now turned into the product of ten positive random variables. This we call the SEH, an acronym standing for "Statistical Equation for Habitables". The mathematical structure of the SEH is then derived. The proof is based on the central limit theorem (CLT) of Statistics. In loose terms, the CLT states that the sum of any number of independent random variables, each of which may be arbitrarily distributed, approaches a Gaussian (i.e. normal) random variable. This is called the Lyapunov form of the CLT, or the Lindeberg form of the CLT, depending on the mathematical constraints assumed on the third moments of the various probability distributions. In conclusion, we show that The new random variable NHab, yielding the number of habitables (i.e. habitable planets) in the Galaxy, follows the lognormal distribution. By construction, the mean value of this lognormal distribution is the total number of habitable planets as given by the statistical Dole equation. But now we also derive the standard deviation, the mode, the median and all the moments of this new lognormal NHab random variable. The ten (or more) astrobiological factors are now positive random variables. The probability distribution of each random variable may be arbitrary. The CLT in the so-called Lyapunov or Lindeberg forms (that both do not assume the factors to be identically distributed) allows for that. In other words, the CLT "translates" into our SEH by allowing an arbitrary probability distribution for each factor. This is both astrobiologically realistic and useful for any further investigations. An application of our SEH then follows. The (average) distancebetween any two nearby habitable planets in the Galaxy may be shown to be inversely proportional to the cubic root of NHab. Then, in our approach, this distance becomes a new random variable. We derive the relevant probability density function, apparently previously unknown and dubbed "Maccone distribution" by Paul Davies in 2008. Data Enrichment Principle. It should be noticed that ANY positive number of random variables in the SEH is compatible with the CLT. So, our generalization allows for many more factors to be added in the future as long as more refined scientific knowledge about each factor will be known to the scientists. This capability to make room for more future factors in the SEH we call the "Data Enrichment Principle", and we regard it as the key to more profound future results in the fields of Astrobiology and SETI. A practical example is then given of how our SEH works numerically. We work out in detail the case where each of the ten random variables is uniformly distributed around its own mean value as given by Dole back in 1964 and has an assumed standard deviation of 10%. The conclusion is that the average number of habitable planets in the Galaxy should be around 100 million±200 million, and the average distance in between any couple of nearby habitable planets should be about 88 light years±40 light years. Finally, we match our SEH results against the results of the Statistical Drake Equation that we introduced in our 2008 IAC presentation. As expected, the number of currently communicating ET civilizations in the Galaxy turns out to be much smaller than the number of habitable planets (about 10,000 against 100 million, i.e. one ET civilization out of 10,000 habitable planets). And the average distance between any two nearby habitable planets turns out to be much smaller than the average distance between any two neighboring ET civilizations: 88 light years vs. 2000 light years, respectively. This means an ET average distance about 20 times higher than the average distance between any couple of adjacent habitable planets.

Raw and Central Moments of Binomial Random Variables via Stirling Numbers

ERIC Educational Resources Information Center

Griffiths, Martin

2013-01-01

We consider here the problem of calculating the moments of binomial random variables. It is shown how formulae for both the raw and the central moments of such random variables may be obtained in a recursive manner utilizing Stirling numbers of the first kind. Suggestions are also provided as to how students might be encouraged to explore this…

About the cumulants of periodic signals

NASA Astrophysics Data System (ADS)

Barrau, Axel; El Badaoui, Mohammed

2018-01-01

This note studies cumulants of time series. These functions originating from the probability theory being commonly used as features of deterministic signals, their classical properties are examined in this modified framework. We show additivity of cumulants, ensured in the case of independent random variables, requires here a different hypothesis. Practical applications are proposed, in particular an analysis of the failure of the JADE algorithm to separate some specific periodic signals.

Applications of Probabilistic Combiners on Linear Feedback Shift Register Sequences

DTIC Science & Technology

2016-12-01

on the resulting output strings show a drastic increase in complexity, while simultaneously passing the stringent randomness tests required by the...a three-variable function. Our tests on the resulting output strings show a drastic increase in complex- ity, while simultaneously passing the...10001101 01000010 11101001 Decryption of a message that has been encrypted using bitwise XOR is quite simple. Since each bit is its own additive inverse

Efficacy of laser phototherapy in comparison to topical clobetasol for the treatment of oral lichen planus: a randomized controlled trial

NASA Astrophysics Data System (ADS)

Dillenburg, Caroline Siviero; Martins, Marco Antonio Trevizani; Munerato, Maria Cristina; Marques, Márcia Martins; Carrard, Vinícius Coelho; Filho, Manoel Sant'Ana; Castilho, Rogério Moraes; Martins, Manoela Domingues

2014-06-01

Oral lichen planus (OLP) is a relatively common chronic mucocutaneous inflammatory disease and a search for novel therapeutic options has been performed. We sought to compare the efficacy of laser phototherapy (LPT) to topical clobetasol propionate 0.05% for the treatment of atrophic and erosive OLP. Forty-two patients with atrophic/erosive OLP were randomly allocated to two groups: clobetasol group (n=21): application of topical clobetasol propionate gel (0.05%) three times a day; LPT group (n=21): application of laser irradiation using InGaAlP diode laser three times a week. Evaluations were performed once a week during treatment (Days 7, 14, 21, and 30) and in four weeks (Day 60) and eight weeks (Day 90) after treatment. At the end of treatment (Day 30), significant reductions in all variables were found in both groups. The LPT group had a higher percentage of complete lesion resolution. At follow-up periods (Days 60 and 90), the LPT group maintained the clinical pattern seen at Day 30, with no recurrence of the lesions, whereas the clobetasol group exhibited worsening for all variables analyzed. These findings suggest that the LPT proved more effective than topical clobetasol 0.05% for the treatment of OLP.

A Random Variable Related to the Inversion Vector of a Partial Random Permutation

ERIC Educational Resources Information Center

Laghate, Kavita; Deshpande, M. N.

2005-01-01

In this article, we define the inversion vector of a permutation of the integers 1, 2,..., n. We set up a particular kind of permutation, called a partial random permutation. The sum of the elements of the inversion vector of such a permutation is a random variable of interest.

Random forests for classification in ecology

USGS Publications Warehouse

Cutler, D.R.; Edwards, T.C.; Beard, K.H.; Cutler, A.; Hess, K.T.; Gibson, J.; Lawler, J.J.

2007-01-01

Classification procedures are some of the most widely used statistical methods in ecology. Random forests (RF) is a new and powerful statistical classifier that is well established in other disciplines but is relatively unknown in ecology. Advantages of RF compared to other statistical classifiers include (1) very high classification accuracy; (2) a novel method of determining variable importance; (3) ability to model complex interactions among predictor variables; (4) flexibility to perform several types of statistical data analysis, including regression, classification, survival analysis, and unsupervised learning; and (5) an algorithm for imputing missing values. We compared the accuracies of RF and four other commonly used statistical classifiers using data on invasive plant species presence in Lava Beds National Monument, California, USA, rare lichen species presence in the Pacific Northwest, USA, and nest sites for cavity nesting birds in the Uinta Mountains, Utah, USA. We observed high classification accuracy in all applications as measured by cross-validation and, in the case of the lichen data, by independent test data, when comparing RF to other common classification methods. We also observed that the variables that RF identified as most important for classifying invasive plant species coincided with expectations based on the literature. ?? 2007 by the Ecological Society of America.

Bounds for the price of discrete arithmetic Asian options

NASA Astrophysics Data System (ADS)

Vanmaele, M.; Deelstra, G.; Liinev, J.; Dhaene, J.; Goovaerts, M. J.

2006-01-01

In this paper the pricing of European-style discrete arithmetic Asian options with fixed and floating strike is studied by deriving analytical lower and upper bounds. In our approach we use a general technique for deriving upper (and lower) bounds for stop-loss premiums of sums of dependent random variables, as explained in Kaas et al. (Ins. Math. Econom. 27 (2000) 151-168), and additionally, the ideas of Rogers and Shi (J. Appl. Probab. 32 (1995) 1077-1088) and of Nielsen and Sandmann (J. Financial Quant. Anal. 38(2) (2003) 449-473). We are able to create a unifying framework for European-style discrete arithmetic Asian options through these bounds, that generalizes several approaches in the literature as well as improves the existing results. We obtain analytical and easily computable bounds. The aim of the paper is to formulate an advice of the appropriate choice of the bounds given the parameters, investigate the effect of different conditioning variables and compare their efficiency numerically. Several sets of numerical results are included. We also discuss hedging using these bounds. Moreover, our methods are applicable to a wide range of (pricing) problems involving a sum of dependent random variables.

A Geometrical Framework for Covariance Matrices of Continuous and Categorical Variables

ERIC Educational Resources Information Center

Vernizzi, Graziano; Nakai, Miki

2015-01-01

It is well known that a categorical random variable can be represented geometrically by a simplex. Accordingly, several measures of association between categorical variables have been proposed and discussed in the literature. Moreover, the standard definitions of covariance and correlation coefficient for continuous random variables have been…

The quotient of normal random variables and application to asset price fat tails

NASA Astrophysics Data System (ADS)

Caginalp, Carey; Caginalp, Gunduz

2018-06-01

The quotient of random variables with normal distributions is examined and proven to have power law decay, with density f(x) ≃f0x-2, with the coefficient depending on the means and variances of the numerator and denominator and their correlation. We also obtain the conditional probability densities for each of the four quadrants given by the signs of the numerator and denominator for arbitrary correlation ρ ∈ [ - 1 , 1) . For ρ = - 1 we obtain a particularly simple closed form solution for all x ∈ R. The results are applied to a basic issue in economics and finance, namely the density of relative price changes. Classical finance stipulates a normal distribution of relative price changes, though empirical studies suggest a power law at the tail end. By considering the supply and demand in a basic price change model, we prove that the relative price change has density that decays with an x-2 power law. Various parameter limits are established.

The Lambert Way to Gaussianize Heavy-Tailed Data with the Inverse of Tukey's h Transformation as a Special Case

PubMed Central

Goerg, Georg M.

2015-01-01

I present a parametric, bijective transformation to generate heavy tail versions of arbitrary random variables. The tail behavior of this heavy tail Lambert  W × F X random variable depends on a tail parameter δ ≥ 0: for δ = 0, Y ≡ X, for δ > 0 Y has heavier tails than X. For X being Gaussian it reduces to Tukey's h distribution. The Lambert W function provides an explicit inverse transformation, which can thus remove heavy tails from observed data. It also provides closed-form expressions for the cumulative distribution (cdf) and probability density function (pdf). As a special case, these yield analytic expression for Tukey's h pdf and cdf. Parameters can be estimated by maximum likelihood and applications to S&P 500 log-returns demonstrate the usefulness of the presented methodology. The R package LambertW implements most of the introduced methodology and is publicly available on CRAN. PMID:26380372

Design of state-feedback controllers including sensitivity reduction, with applications to precision pointing

NASA Technical Reports Server (NTRS)

Hadass, Z.

1974-01-01

The design procedure of feedback controllers was described and the considerations for the selection of the design parameters were given. The frequency domain properties of single-input single-output systems using state feedback controllers are analyzed, and desirable phase and gain margin properties are demonstrated. Special consideration is given to the design of controllers for tracking systems, especially those designed to track polynomial commands. As an example, a controller was designed for a tracking telescope with a polynomial tracking requirement and some special features such as actuator saturation and multiple measurements, one of which is sampled. The resulting system has a tracking performance comparing favorably with a much more complicated digital aided tracker. The parameter sensitivity reduction was treated by considering the variable parameters as random variables. A performance index is defined as a weighted sum of the state and control convariances that sum from both the random system disturbances and the parameter uncertainties, and is minimized numerically by adjusting a set of free parameters.

CALCULATION OF NONLINEAR CONFIDENCE AND PREDICTION INTERVALS FOR GROUND-WATER FLOW MODELS.

USGS Publications Warehouse

Cooley, Richard L.; Vecchia, Aldo V.

1987-01-01

A method is derived to efficiently compute nonlinear confidence and prediction intervals on any function of parameters derived as output from a mathematical model of a physical system. The method is applied to the problem of obtaining confidence and prediction intervals for manually-calibrated ground-water flow models. To obtain confidence and prediction intervals resulting from uncertainties in parameters, the calibrated model and information on extreme ranges and ordering of the model parameters within one or more independent groups are required. If random errors in the dependent variable are present in addition to uncertainties in parameters, then calculation of prediction intervals also requires information on the extreme range of error expected. A simple Monte Carlo method is used to compute the quantiles necessary to establish probability levels for the confidence and prediction intervals. Application of the method to a hypothetical example showed that inclusion of random errors in the dependent variable in addition to uncertainties in parameters can considerably widen the prediction intervals.

Signal and noise extraction from analog memory elements for neuromorphic computing.

PubMed

Gong, N; Idé, T; Kim, S; Boybat, I; Sebastian, A; Narayanan, V; Ando, T

2018-05-29

Dense crossbar arrays of non-volatile memory (NVM) can potentially enable massively parallel and highly energy-efficient neuromorphic computing systems. The key requirements for the NVM elements are continuous (analog-like) conductance tuning capability and switching symmetry with acceptable noise levels. However, most NVM devices show non-linear and asymmetric switching behaviors. Such non-linear behaviors render separation of signal and noise extremely difficult with conventional characterization techniques. In this study, we establish a practical methodology based on Gaussian process regression to address this issue. The methodology is agnostic to switching mechanisms and applicable to various NVM devices. We show tradeoff between switching symmetry and signal-to-noise ratio for HfO 2 -based resistive random access memory. Then, we characterize 1000 phase-change memory devices based on Ge 2 Sb 2 Te 5 and separate total variability into device-to-device variability and inherent randomness from individual devices. These results highlight the usefulness of our methodology to realize ideal NVM devices for neuromorphic computing.

Multidimensional density shaping by sigmoids.

PubMed

Roth, Z; Baram, Y

1996-01-01

An estimate of the probability density function of a random vector is obtained by maximizing the output entropy of a feedforward network of sigmoidal units with respect to the input weights. Classification problems can be solved by selecting the class associated with the maximal estimated density. Newton's optimization method, applied to the estimated density, yields a recursive estimator for a random variable or a random sequence. A constrained connectivity structure yields a linear estimator, which is particularly suitable for "real time" prediction. A Gaussian nonlinearity yields a closed-form solution for the network's parameters, which may also be used for initializing the optimization algorithm when other nonlinearities are employed. A triangular connectivity between the neurons and the input, which is naturally suggested by the statistical setting, reduces the number of parameters. Applications to classification and forecasting problems are demonstrated.

Data Applicability of Heritage and New Hardware For Launch Vehicle Reliability Models

NASA Technical Reports Server (NTRS)

Al Hassan, Mohammad; Novack, Steven

2015-01-01

Bayesian reliability requires the development of a prior distribution to represent degree of belief about the value of a parameter (such as a component's failure rate) before system specific data become available from testing or operations. Generic failure data are often provided in reliability databases as point estimates (mean or median). A component's failure rate is considered a random variable where all possible values are represented by a probability distribution. The applicability of the generic data source is a significant source of uncertainty that affects the spread of the distribution. This presentation discusses heuristic guidelines for quantifying uncertainty due to generic data applicability when developing prior distributions mainly from reliability predictions.

Multilevel Model Prediction

ERIC Educational Resources Information Center

Frees, Edward W.; Kim, Jee-Seon

2006-01-01

Multilevel models are proven tools in social research for modeling complex, hierarchical systems. In multilevel modeling, statistical inference is based largely on quantification of random variables. This paper distinguishes among three types of random variables in multilevel modeling--model disturbances, random coefficients, and future response…

Regression Discontinuity for Causal Effect Estimation in Epidemiology.

PubMed

Oldenburg, Catherine E; Moscoe, Ellen; Bärnighausen, Till

Regression discontinuity analyses can generate estimates of the causal effects of an exposure when a continuously measured variable is used to assign the exposure to individuals based on a threshold rule. Individuals just above the threshold are expected to be similar in their distribution of measured and unmeasured baseline covariates to individuals just below the threshold, resulting in exchangeability. At the threshold exchangeability is guaranteed if there is random variation in the continuous assignment variable, e.g., due to random measurement error. Under exchangeability, causal effects can be identified at the threshold. The regression discontinuity intention-to-treat (RD-ITT) effect on an outcome can be estimated as the difference in the outcome between individuals just above (or below) versus just below (or above) the threshold. This effect is analogous to the ITT effect in a randomized controlled trial. Instrumental variable methods can be used to estimate the effect of exposure itself utilizing the threshold as the instrument. We review the recent epidemiologic literature reporting regression discontinuity studies and find that while regression discontinuity designs are beginning to be utilized in a variety of applications in epidemiology, they are still relatively rare, and analytic and reporting practices vary. Regression discontinuity has the potential to greatly contribute to the evidence base in epidemiology, in particular on the real-life and long-term effects and side-effects of medical treatments that are provided based on threshold rules - such as treatments for low birth weight, hypertension or diabetes.

A Review of Spectral Methods for Variable Amplitude Fatigue Prediction and New Results

NASA Technical Reports Server (NTRS)

Larsen, Curtis E.; Irvine, Tom

2013-01-01

A comprehensive review of the available methods for estimating fatigue damage from variable amplitude loading is presented. The dependence of fatigue damage accumulation on power spectral density (psd) is investigated for random processes relevant to real structures such as in offshore or aerospace applications. Beginning with the Rayleigh (or narrow band) approximation, attempts at improved approximations or corrections to the Rayleigh approximation are examined by comparison to rainflow analysis of time histories simulated from psd functions representative of simple theoretical and real world applications. Spectral methods investigated include corrections by Wirsching and Light, Ortiz and Chen, the Dirlik formula, and the Single-Moment method, among other more recent proposed methods. Good agreement is obtained between the spectral methods and the time-domain rainflow identification for most cases, with some limitations. Guidelines are given for using the several spectral methods to increase confidence in the damage estimate.

Selection of specific interactors from phage display library based on sea lamprey variable lymphocyte receptor sequences.

PubMed

Wezner-Ptasinska, Magdalena; Otlewski, Jacek

2015-12-01

Variable lymphocyte receptors (VLRs) are non-immunoglobulin components of adaptive immunity in jawless vertebrates. These proteins composed of leucine-rich repeat modules offer some advantages over antibodies in target binding and therefore are attractive candidates for biotechnological applications. In this paper we report the design and characterization of a phage display library based on a previously proposed dVLR scaffold containing six LRR modules [Wezner-Ptasinska et al., 2011]. Our library was designed based on a consensus approach in which the randomization scheme reflects the frequencies of amino acids naturally occurring in respective positions responsible for antigen recognition. We demonstrate general applicability of the scaffold by selecting dVLRs specific for lysozyme and S100A7 protein with KD values in the micromolar range. The dVLR library could be used as a convenient alternative to antibodies for effective isolation of high affinity binders.

Assessing the accuracy and stability of variable selection methods for random forest modeling in ecology.

PubMed

Fox, Eric W; Hill, Ryan A; Leibowitz, Scott G; Olsen, Anthony R; Thornbrugh, Darren J; Weber, Marc H

2017-07-01

Random forest (RF) modeling has emerged as an important statistical learning method in ecology due to its exceptional predictive performance. However, for large and complex ecological data sets, there is limited guidance on variable selection methods for RF modeling. Typically, either a preselected set of predictor variables are used or stepwise procedures are employed which iteratively remove variables according to their importance measures. This paper investigates the application of variable selection methods to RF models for predicting probable biological stream condition. Our motivating data set consists of the good/poor condition of n = 1365 stream survey sites from the 2008/2009 National Rivers and Stream Assessment, and a large set (p = 212) of landscape features from the StreamCat data set as potential predictors. We compare two types of RF models: a full variable set model with all 212 predictors and a reduced variable set model selected using a backward elimination approach. We assess model accuracy using RF's internal out-of-bag estimate, and a cross-validation procedure with validation folds external to the variable selection process. We also assess the stability of the spatial predictions generated by the RF models to changes in the number of predictors and argue that model selection needs to consider both accuracy and stability. The results suggest that RF modeling is robust to the inclusion of many variables of moderate to low importance. We found no substantial improvement in cross-validated accuracy as a result of variable reduction. Moreover, the backward elimination procedure tended to select too few variables and exhibited numerous issues such as upwardly biased out-of-bag accuracy estimates and instabilities in the spatial predictions. We use simulations to further support and generalize results from the analysis of real data. A main purpose of this work is to elucidate issues of model selection bias and instability to ecologists interested in using RF to develop predictive models with large environmental data sets.

Vector solution for the mean electromagnetic fields in a layer of random particles

NASA Technical Reports Server (NTRS)

Lang, R. H.; Seker, S. S.; Levine, D. M.

1986-01-01

The mean electromagnetic fields are found in a layer of randomly oriented particles lying over a half space. A matrix-dyadic formulation of Maxwell's equations is employed in conjunction with the Foldy-Lax approximation to obtain equations for the mean fields. A two variable perturbation procedure, valid in the limit of small fractional volume, is then used to derive uncoupled equations for the slowly varying amplitudes of the mean wave. These equations are solved to obtain explicit expressions for the mean electromagnetic fields in the slab region in the general case of arbitrarily oriented particles and arbitrary polarization of the incident radiation. Numerical examples are given for the application to remote sensing of vegetation.

Mean convergence theorems and weak laws of large numbers for weighted sums of random variables under a condition of weighted integrability

NASA Astrophysics Data System (ADS)

Ordóñez Cabrera, Manuel; Volodin, Andrei I.

2005-05-01

From the classical notion of uniform integrability of a sequence of random variables, a new concept of integrability (called h-integrability) is introduced for an array of random variables, concerning an array of constantsE We prove that this concept is weaker than other previous related notions of integrability, such as Cesàro uniform integrability [Chandra, Sankhya Ser. A 51 (1989) 309-317], uniform integrability concerning the weights [Ordóñez Cabrera, Collect. Math. 45 (1994) 121-132] and Cesàro [alpha]-integrability [Chandra and Goswami, J. Theoret. ProbabE 16 (2003) 655-669]. Under this condition of integrability and appropriate conditions on the array of weights, mean convergence theorems and weak laws of large numbers for weighted sums of an array of random variables are obtained when the random variables are subject to some special kinds of dependence: (a) rowwise pairwise negative dependence, (b) rowwise pairwise non-positive correlation, (c) when the sequence of random variables in every row is [phi]-mixing. Finally, we consider the general weak law of large numbers in the sense of Gut [Statist. Probab. Lett. 14 (1992) 49-52] under this new condition of integrability for a Banach space setting.

Controlled assessment of the efficacy of occlusal stabilization splints on sleep bruxism.

PubMed

van der Zaag, Jacques; Lobbezoo, Frank; Wicks, Darrel J; Visscher, Corine M; Hamburger, Hans L; Naeije, Machiel

2005-01-01

To assess the efficacy of occlusal stabilization splints in the management of sleep bruxism (SB) in a double-blind, parallel, controlled, randomized clinical trial. Twenty-one participants were randomly assigned to an occlusal splint group (n = 11; mean age = 34.2 +/- 13.1 years) or a palatal splint (ie, an acrylic palatal coverage) group (n = 10; mean age = 34.9 +/- 11.2 years). Two polysomnographic recordings that included bilateral masseter electromyographic activity were made: one prior to treatment, the other after a treatment period of 4 weeks. The number of bruxism episodes per hour of sleep (Epi/h), the number of bursts per hour (Bur/h), and the bruxism time index (ie, the percentage of total sleep time spent bruxing) were established as outcome variables at a 10% maximum voluntary contraction threshold level. A general linear model was used to test both the effects between splint groups and within the treatment phase as well as their interaction for each outcome variable. Neither occlusal stabilization splints nor palatal splints had an influence on the SB outcome variables or on the sleep variables measured on a group level. In individual cases, variable outcomes were found: Some patients had an increase (33% to 48% of the cases), while others showed no change (33% to 48%) or a decrease (19% to 29%) in SB outcome variables. The absence of significant group effects of splints in the management of SB indicates that caution is required when splints are indicated, apart from their role in the protection against dental wear. The application of splints should therefore be considered at the individual patient level.

An application of the theory of planned behavior--a randomized controlled food safety pilot intervention for young adults.

PubMed

Milton, Alyssa C; Mullan, Barbara A

2012-03-01

Approximately 48 million Americans are affected by foodborne illness each year. Evidence suggests that the application of health psychology theory to food safety interventions can increase behaviors that reduce the incidence of illness such as adequately keeping hands, surfaces and equipment clean. This aim of this pilot study was to be the first to explore the effectiveness of a food safety intervention based on the Theory of Planned Behavior (TPB). Young adult participants (N = 45) were randomly allocated to intervention, general control or mere measurement control conditions. Food safety observations and TPB measures were taken at baseline and at 4-week follow-up. Within and between group differences on target variables were considered and regression analyses were conducted to determine the relationship between condition, behavior and the TPB intention constructs; attitude, subjective norm, perceived behavioral control (PBC). TPB variables at baseline predicted observed food safety behaviors. At follow-up, the intervention led to significant increases in PBC (p = .024) and observed behaviors (p = .001) compared to both control conditions. Furthermore, correlations were found between observed and self-reported behaviors (p = .008). The pilot intervention supports the utility of the TPB as a method of improving food safety behavior. Changes in TPB cognitions appear to be best translated to behavior via behavioral intentions and PBC. Further research should be conducted to increase effectiveness of translating TPB variables to food safety behaviors. The additional finding of a correlation between self-reported and observed behavior also has implications for future research as it provides evidence toward the construct validity of self-reported behavioral measures.

Security of BB84 with weak randomness and imperfect qubit encoding

NASA Astrophysics Data System (ADS)

Zhao, Liang-Yuan; Yin, Zhen-Qiang; Li, Hong-Wei; Chen, Wei; Fang, Xi; Han, Zheng-Fu; Huang, Wei

2018-03-01

The main threats for the well-known Bennett-Brassard 1984 (BB84) practical quantum key distribution (QKD) systems are that its encoding is inaccurate and measurement device may be vulnerable to particular attacks. Thus, a general physical model or security proof to tackle these loopholes simultaneously and quantitatively is highly desired. Here we give a framework on the security of BB84 when imperfect qubit encoding and vulnerability of measurement device are both considered. In our analysis, the potential attacks to measurement device are generalized by the recently proposed weak randomness model which assumes the input random numbers are partially biased depending on a hidden variable planted by an eavesdropper. And the inevitable encoding inaccuracy is also introduced here. From a fundamental view, our work reveals the potential information leakage due to encoding inaccuracy and weak randomness input. For applications, our result can be viewed as a useful tool to quantitatively evaluate the security of a practical QKD system.

Descriptive parameter for photon trajectories in a turbid medium

NASA Astrophysics Data System (ADS)

Gandjbakhche, Amir H.; Weiss, George H.

2000-06-01

In many applications of laser techniques for diagnostic or therapeutic purposes it is necessary to be able to characterize photon trajectories to know which parts of the tissue are being interrogated. In this paper, we consider the cw reflectance experiment on a semi-infinite medium with uniform optical parameters and having a planar interface. The analysis is carried out in terms of a continuous-time random walk and the relation between the occupancy of a plane parallel to the surface to the maximum depth reached by the random walker is studied. The first moment of the ratio of average depth to the average maximum depth yields information about the volume of tissue interrogated as well as giving some indication of the region of tissue that gets the most light. We have also calculated the standard deviation of this random variable. It is not large enough to qualitatively affect information contained in the first moment.

Design of Probabilistic Random Forests with Applications to Anticancer Drug Sensitivity Prediction

PubMed Central

Rahman, Raziur; Haider, Saad; Ghosh, Souparno; Pal, Ranadip

2015-01-01

Random forests consisting of an ensemble of regression trees with equal weights are frequently used for design of predictive models. In this article, we consider an extension of the methodology by representing the regression trees in the form of probabilistic trees and analyzing the nature of heteroscedasticity. The probabilistic tree representation allows for analytical computation of confidence intervals (CIs), and the tree weight optimization is expected to provide stricter CIs with comparable performance in mean error. We approached the ensemble of probabilistic trees’ prediction from the perspectives of a mixture distribution and as a weighted sum of correlated random variables. We applied our methodology to the drug sensitivity prediction problem on synthetic and cancer cell line encyclopedia dataset and illustrated that tree weights can be selected to reduce the average length of the CI without increase in mean error. PMID:27081304

General Exact Solution to the Problem of the Probability Density for Sums of Random Variables

NASA Astrophysics Data System (ADS)

Tribelsky, Michael I.

2002-07-01

The exact explicit expression for the probability density pN(x) for a sum of N random, arbitrary correlated summands is obtained. The expression is valid for any number N and any distribution of the random summands. Most attention is paid to application of the developed approach to the case of independent and identically distributed summands. The obtained results reproduce all known exact solutions valid for the, so called, stable distributions of the summands. It is also shown that if the distribution is not stable, the profile of pN(x) may be divided into three parts, namely a core (small x), a tail (large x), and a crossover from the core to the tail (moderate x). The quantitative description of all three parts as well as that for the entire profile is obtained. A number of particular examples are considered in detail.

General exact solution to the problem of the probability density for sums of random variables.

PubMed

Tribelsky, Michael I

2002-08-12

The exact explicit expression for the probability density p(N)(x) for a sum of N random, arbitrary correlated summands is obtained. The expression is valid for any number N and any distribution of the random summands. Most attention is paid to application of the developed approach to the case of independent and identically distributed summands. The obtained results reproduce all known exact solutions valid for the, so called, stable distributions of the summands. It is also shown that if the distribution is not stable, the profile of p(N)(x) may be divided into three parts, namely a core (small x), a tail (large x), and a crossover from the core to the tail (moderate x). The quantitative description of all three parts as well as that for the entire profile is obtained. A number of particular examples are considered in detail.

Design approaches to experimental mediation☆

PubMed Central

Pirlott, Angela G.; MacKinnon, David P.

2016-01-01

Identifying causal mechanisms has become a cornerstone of experimental social psychology, and editors in top social psychology journals champion the use of mediation methods, particularly innovative ones when possible (e.g. Halberstadt, 2010, Smith, 2012). Commonly, studies in experimental social psychology randomly assign participants to levels of the independent variable and measure the mediating and dependent variables, and the mediator is assumed to causally affect the dependent variable. However, participants are not randomly assigned to levels of the mediating variable(s), i.e., the relationship between the mediating and dependent variables is correlational. Although researchers likely know that correlational studies pose a risk of confounding, this problem seems forgotten when thinking about experimental designs randomly assigning participants to levels of the independent variable and measuring the mediator (i.e., “measurement-of-mediation” designs). Experimentally manipulating the mediator provides an approach to solving these problems, yet these methods contain their own set of challenges (e.g., Bullock, Green, & Ha, 2010). We describe types of experimental manipulations targeting the mediator (manipulations demonstrating a causal effect of the mediator on the dependent variable and manipulations targeting the strength of the causal effect of the mediator) and types of experimental designs (double randomization, concurrent double randomization, and parallel), provide published examples of the designs, and discuss the strengths and challenges of each design. Therefore, the goals of this paper include providing a practical guide to manipulation-of-mediator designs in light of their challenges and encouraging researchers to use more rigorous approaches to mediation because manipulation-of-mediator designs strengthen the ability to infer causality of the mediating variable on the dependent variable. PMID:27570259

Design approaches to experimental mediation.

PubMed

Pirlott, Angela G; MacKinnon, David P

2016-09-01

Identifying causal mechanisms has become a cornerstone of experimental social psychology, and editors in top social psychology journals champion the use of mediation methods, particularly innovative ones when possible (e.g. Halberstadt, 2010, Smith, 2012). Commonly, studies in experimental social psychology randomly assign participants to levels of the independent variable and measure the mediating and dependent variables, and the mediator is assumed to causally affect the dependent variable. However, participants are not randomly assigned to levels of the mediating variable(s), i.e., the relationship between the mediating and dependent variables is correlational. Although researchers likely know that correlational studies pose a risk of confounding, this problem seems forgotten when thinking about experimental designs randomly assigning participants to levels of the independent variable and measuring the mediator (i.e., "measurement-of-mediation" designs). Experimentally manipulating the mediator provides an approach to solving these problems, yet these methods contain their own set of challenges (e.g., Bullock, Green, & Ha, 2010). We describe types of experimental manipulations targeting the mediator (manipulations demonstrating a causal effect of the mediator on the dependent variable and manipulations targeting the strength of the causal effect of the mediator) and types of experimental designs (double randomization, concurrent double randomization, and parallel), provide published examples of the designs, and discuss the strengths and challenges of each design. Therefore, the goals of this paper include providing a practical guide to manipulation-of-mediator designs in light of their challenges and encouraging researchers to use more rigorous approaches to mediation because manipulation-of-mediator designs strengthen the ability to infer causality of the mediating variable on the dependent variable.

Probabilistic models for reactive behaviour in heterogeneous condensed phase media

NASA Astrophysics Data System (ADS)

Baer, M. R.; Gartling, D. K.; DesJardin, P. E.

2012-02-01

This work presents statistically-based models to describe reactive behaviour in heterogeneous energetic materials. Mesoscale effects are incorporated in continuum-level reactive flow descriptions using probability density functions (pdfs) that are associated with thermodynamic and mechanical states. A generalised approach is presented that includes multimaterial behaviour by treating the volume fraction as a random kinematic variable. Model simplifications are then sought to reduce the complexity of the description without compromising the statistical approach. Reactive behaviour is first considered for non-deformable media having a random temperature field as an initial state. A pdf transport relationship is derived and an approximate moment approach is incorporated in finite element analysis to model an example application whereby a heated fragment impacts a reactive heterogeneous material which leads to a delayed cook-off event. Modelling is then extended to include deformation effects associated with shock loading of a heterogeneous medium whereby random variables of strain, strain-rate and temperature are considered. A demonstrative mesoscale simulation of a non-ideal explosive is discussed that illustrates the joint statistical nature of the strain and temperature fields during shock loading to motivate the probabilistic approach. This modelling is derived in a Lagrangian framework that can be incorporated in continuum-level shock physics analysis. Future work will consider particle-based methods for a numerical implementation of this modelling approach.

Fatigue Tests with Random Flight Simulation Loading

NASA Technical Reports Server (NTRS)

Schijve, J.

1972-01-01

Crack propagation was studied in a full-scale wing structure under different simulated flight conditions. Omission of low-amplitude gust cycles had a small effect on the crack rate. Truncation of the infrequently occurring high-amplitude gust cycles to a lower level had a noticeably accelerating effect on crack growth. The application of fail-safe load (100 percent limit load) effectively stopped subsequent crack growth under resumed flight-simulation loading. In another flight-simulation test series on sheet specimens, the variables studied are the design stress level and the cyclic frequency of the random gust loading. Inflight mean stresses vary from 5.5 to 10.0 kg/sq mm. The effect of the stress level is larger for the 2024 alloy than for the 7075 alloy. Three frequencies were employed: namely, 10 cps, 1 cps, and 0.1 cps. The frequency effect was small. The advantages and limitations of flight-simulation tests are compared with those of alternative test procedures such as constant-amplitude tests, program tests, and random-load tests. Various testing purposes are considered. The variables of flight-simulation tests are listed and their effects are discussed. A proposal is made for performing systematic flight-simulation tests in such a way that the compiled data may be used as a source of reference.

Comparison of an indirect tri-planar myofascial release (MFR) technique and a hot pack for increasing range of motion.

PubMed

Kain, Jay; Martorello, Laura; Swanson, Edward; Sego, Sandra

2011-01-01

The purpose of the randomized clinical study was to scientifically assess which intervention increases passive range of motion most effectively: the indirect tri-planar myofascial release (MFR) technique or the application of hot packs for gleno-humeral joint flexion, extension, and abduction. A total of 31 participants from a sample of convenience were randomly assigned to examine whether or not MFR was as effective in increasing range of motion as hot packs. The sample consisted of students at American International College. Students were randomly assigned to two groups: hot pack application (N=13) or MFR technique (N=18). The independent variable was the intervention, either the tri-planar MFR technique or the hot pack application. Group one received the indirect tri-planar MFR technique once for 3min. Group two received one hot pack application for 20min. The dependent variables, passive gleno-humeral shoulder range of motion in shoulder flexion, shoulder extension, and shoulder abduction, were taken pre- and post-intervention for both groups. Data was analyzed through the use of a two-way factorial design with mixed-factors ANOVA. Prior to conducting the study, inter-rater reliability was established using three testers for goniometric measures. A 2 (type of intervention: hot packs or MFR) by 2 (pre-test or post-test) mixed-factors ANOVA was calculated. Significant increases in range of motion were found for flexion, extension and abduction when comparing pre-test scores to post-test scores. The results of the ANOVA showed that for passive range of motion no differences were found for flexion, extension and abduction between the effectiveness of hot packs and MFR. For each of the dependent variables measured, MFR was shown to be as effective as hot packs in increasing range of motion, supporting the hypothesis. Since there was no significant difference between the types of intervention, both the hot pack application and the MFR technique were found to be equally effective in increasing passive range of motion of the joint in flexion, extension, and abduction of the gleno-humeral joint. The indirect tri-planar intervention could be considered more effective as an intervention in terms of time spent with a patient and the number of patients seen in a 20-min period. No equipment is required to carry out the MFR intervention, whereby using a hot pack requires the hot pack, towels, and a hydraculator unit with the use of the indirect tri-planar intervention, a therapist could treat four to five patients in the time it would take for one standard hot pack treatment of 20min, less the hands-on intervention of the therapist. Copyright © 2009 Elsevier Ltd. All rights reserved.

Transport of Charged Particles in Turbulent Magnetic Fields

NASA Astrophysics Data System (ADS)

Parashar, T.; Subedi, P.; Sonsrettee, W.; Blasi, P.; Ruffolo, D. J.; Matthaeus, W. H.; Montgomery, D.; Chuychai, P.; Dmitruk, P.; Wan, M.; Chhiber, R.

2017-12-01

Magnetic fields permeate the Universe. They are found in planets, stars, galaxies, and the intergalactic medium. The magnetic field found in these astrophysical systems are usually chaotic, disordered, and turbulent. The investigation of the transport of cosmic rays in magnetic turbulence is a subject of considerable interest. One of the important aspects of cosmic ray transport is to understand their diffusive behavior and to calculate the diffusion coefficient in the presence of these turbulent fields. Research has most frequently concentrated on determining the diffusion coefficient in the presence of a mean magnetic field. Here, we will particularly focus on calculating diffusion coefficients of charged particles and magnetic field lines in a fully three-dimensional isotropic turbulent magnetic field with no mean field, which may be pertinent to many astrophysical situations. For charged particles in isotropic turbulence we identify different ranges of particle energy depending upon the ratio of the Larmor radius of the charged particle to the characteristic outer length scale of the turbulence. Different theoretical models are proposed to calculate the diffusion coefficient, each applicable to a distinct range of particle energies. The theoretical ideas are tested against results of detailed numerical experiments using Monte-Carlo simulations of particle propagation in stochastic magnetic fields. We also discuss two different methods of generating random magnetic field to study charged particle propagation using numerical simulation. One method is the usual way of generating random fields with a specified power law in wavenumber space, using Gaussian random variables. Turbulence, however, is non-Gaussian, with variability that comes in bursts called intermittency. We therefore devise a way to generate synthetic intermittent fields which have many properties of realistic turbulence. Possible applications of such synthetically generated intermittent fields are discussed.

An analytic solution of the stochastic storage problem applicable to soil water

USGS Publications Warehouse

Milly, P.C.D.

1993-01-01

The accumulation of soil water during rainfall events and the subsequent depletion of soil water by evaporation between storms can be described, to first order, by simple accounting models. When the alternating supplies (precipitation) and demands (potential evaporation) are viewed as random variables, it follows that soil-water storage, evaporation, and runoff are also random variables. If the forcing (supply and demand) processes are stationary for a sufficiently long period of time, an asymptotic regime should eventually be reached where the probability distribution functions of storage, evaporation, and runoff are stationary and uniquely determined by the distribution functions of the forcing. Under the assumptions that the potential evaporation rate is constant, storm arrivals are Poisson-distributed, rainfall is instantaneous, and storm depth follows an exponential distribution, it is possible to derive the asymptotic distributions of storage, evaporation, and runoff analytically for a simple balance model. A particular result is that the fraction of rainfall converted to runoff is given by (1 - R−1)/(eα(1−R−1) − R−1), in which R is the ratio of mean potential evaporation to mean rainfall and a is the ratio of soil water-holding capacity to mean storm depth. The problem considered here is analogous to the well-known problem of storage in a reservoir behind a dam, for which the present work offers a new solution for reservoirs of finite capacity. A simple application of the results of this analysis suggests that random, intraseasonal fluctuations of precipitation cannot by themselves explain the observed dependence of the annual water balance on annual totals of precipitation and potential evaporation.

A novel recursive Fourier transform for nonuniform sampled signals: application to heart rate variability spectrum estimation.

PubMed

Holland, Alexander; Aboy, Mateo

2009-07-01

We present a novel method to iteratively calculate discrete Fourier transforms for discrete time signals with sample time intervals that may be widely nonuniform. The proposed recursive Fourier transform (RFT) does not require interpolation of the samples to uniform time intervals, and each iterative transform update of N frequencies has computational order N. Because of the inherent non-uniformity in the time between successive heart beats, an application particularly well suited for this transform is power spectral density (PSD) estimation for heart rate variability. We compare RFT based spectrum estimation with Lomb-Scargle Transform (LST) based estimation. PSD estimation based on the LST also does not require uniform time samples, but the LST has a computational order greater than Nlog(N). We conducted an assessment study involving the analysis of quasi-stationary signals with various levels of randomly missing heart beats. Our results indicate that the RFT leads to comparable estimation performance to the LST with significantly less computational overhead and complexity for applications requiring iterative spectrum estimations.

Experimental Investigations of Non-Stationary Properties In Radiometer Receivers Using Measurements of Multiple Calibration References

NASA Technical Reports Server (NTRS)

Racette, Paul; Lang, Roger; Zhang, Zhao-Nan; Zacharias, David; Krebs, Carolyn A. (Technical Monitor)

2002-01-01

Radiometers must be periodically calibrated because the receiver response fluctuates. Many techniques exist to correct for the time varying response of a radiometer receiver. An analytical technique has been developed that uses generalized least squares regression (LSR) to predict the performance of a wide variety of calibration algorithms. The total measurement uncertainty including the uncertainty of the calibration can be computed using LSR. The uncertainties of the calibration samples used in the regression are based upon treating the receiver fluctuations as non-stationary processes. Signals originating from the different sources of emission are treated as simultaneously existing random processes. Thus, the radiometer output is a series of samples obtained from these random processes. The samples are treated as random variables but because the underlying processes are non-stationary the statistics of the samples are treated as non-stationary. The statistics of the calibration samples depend upon the time for which the samples are to be applied. The statistics of the random variables are equated to the mean statistics of the non-stationary processes over the interval defined by the time of calibration sample and when it is applied. This analysis opens the opportunity for experimental investigation into the underlying properties of receiver non stationarity through the use of multiple calibration references. In this presentation we will discuss the application of LSR to the analysis of various calibration algorithms, requirements for experimental verification of the theory, and preliminary results from analyzing experiment measurements.

Compliance-Effect Correlation Bias in Instrumental Variables Estimators

ERIC Educational Resources Information Center

Reardon, Sean F.

2010-01-01

Instrumental variable estimators hold the promise of enabling researchers to estimate the effects of educational treatments that are not (or cannot be) randomly assigned but that may be affected by randomly assigned interventions. Examples of the use of instrumental variables in such cases are increasingly common in educational and social science…

Multivariate Analysis and Its Applications

DTIC Science & Technology

1989-02-14

defined in situations where measurements are taken on natural clusters of individuals like brothers in a family. A number of problems arise in the study of...intraclass correlations. How do we estimate it when observations are available on clusters of different sizes? How do we test the hypothesis that the...the random variable y(X) = #I X + G2X 2 + ... + GmX m , follows an exponential distribution with mean unity. Such a class of life distributions, has a

Random analysis of bearing capacity of square footing using the LAS procedure

NASA Astrophysics Data System (ADS)

Kawa, Marek; Puła, Wojciech; Suska, Michał

2016-09-01

In the present paper, a three-dimensional problem of bearing capacity of square footing on random soil medium is analyzed. The random fields of strength parameters c and φ are generated using LAS procedure (Local Average Subdivision, Fenton and Vanmarcke 1990). The procedure used is re-implemented by the authors in Mathematica environment in order to combine it with commercial program. Since the procedure is still tested the random filed has been assumed as one-dimensional: the strength properties of soil are random in vertical direction only. Individual realizations of bearing capacity boundary-problem with strength parameters of medium defined the above procedure are solved using FLAC3D Software. The analysis is performed for two qualitatively different cases, namely for the purely cohesive and cohesive-frictional soils. For the latter case the friction angle and cohesion have been assumed as independent random variables. For these two cases the random square footing bearing capacity results have been obtained for the range of fluctuation scales from 0.5 m to 10 m. Each time 1000 Monte Carlo realizations have been performed. The obtained results allow not only the mean and variance but also the probability density function to be estimated. An example of application of this function for reliability calculation has been presented in the final part of the paper.

A random forest algorithm for nowcasting of intense precipitation events

NASA Astrophysics Data System (ADS)

Das, Saurabh; Chakraborty, Rohit; Maitra, Animesh

2017-09-01

Automatic nowcasting of convective initiation and thunderstorms has potential applications in several sectors including aviation planning and disaster management. In this paper, random forest based machine learning algorithm is tested for nowcasting of convective rain with a ground based radiometer. Brightness temperatures measured at 14 frequencies (7 frequencies in 22-31 GHz band and 7 frequencies in 51-58 GHz bands) are utilized as the inputs of the model. The lower frequency band is associated to the water vapor absorption whereas the upper frequency band relates to the oxygen absorption and hence, provide information on the temperature and humidity of the atmosphere. Synthetic minority over-sampling technique is used to balance the data set and 10-fold cross validation is used to assess the performance of the model. Results indicate that random forest algorithm with fixed alarm generation time of 30 min and 60 min performs quite well (probability of detection of all types of weather condition ∼90%) with low false alarms. It is, however, also observed that reducing the alarm generation time improves the threat score significantly and also decreases false alarms. The proposed model is found to be very sensitive to the boundary layer instability as indicated by the variable importance measure. The study shows the suitability of a random forest algorithm for nowcasting application utilizing a large number of input parameters from diverse sources and can be utilized in other forecasting problems.

A Two-Stage Estimation Method for Random Coefficient Differential Equation Models with Application to Longitudinal HIV Dynamic Data.

PubMed

Fang, Yun; Wu, Hulin; Zhu, Li-Xing

2011-07-01

We propose a two-stage estimation method for random coefficient ordinary differential equation (ODE) models. A maximum pseudo-likelihood estimator (MPLE) is derived based on a mixed-effects modeling approach and its asymptotic properties for population parameters are established. The proposed method does not require repeatedly solving ODEs, and is computationally efficient although it does pay a price with the loss of some estimation efficiency. However, the method does offer an alternative approach when the exact likelihood approach fails due to model complexity and high-dimensional parameter space, and it can also serve as a method to obtain the starting estimates for more accurate estimation methods. In addition, the proposed method does not need to specify the initial values of state variables and preserves all the advantages of the mixed-effects modeling approach. The finite sample properties of the proposed estimator are studied via Monte Carlo simulations and the methodology is also illustrated with application to an AIDS clinical data set.

A probabilistic bridge safety evaluation against floods.

PubMed

Liao, Kuo-Wei; Muto, Yasunori; Chen, Wei-Lun; Wu, Bang-Ho

2016-01-01

To further capture the influences of uncertain factors on river bridge safety evaluation, a probabilistic approach is adopted. Because this is a systematic and nonlinear problem, MPP-based reliability analyses are not suitable. A sampling approach such as a Monte Carlo simulation (MCS) or importance sampling is often adopted. To enhance the efficiency of the sampling approach, this study utilizes Bayesian least squares support vector machines to construct a response surface followed by an MCS, providing a more precise safety index. Although there are several factors impacting the flood-resistant reliability of a bridge, previous experiences and studies show that the reliability of the bridge itself plays a key role. Thus, the goal of this study is to analyze the system reliability of a selected bridge that includes five limit states. The random variables considered here include the water surface elevation, water velocity, local scour depth, soil property and wind load. Because the first three variables are deeply affected by river hydraulics, a probabilistic HEC-RAS-based simulation is performed to capture the uncertainties in those random variables. The accuracy and variation of our solutions are confirmed by a direct MCS to ensure the applicability of the proposed approach. The results of a numerical example indicate that the proposed approach can efficiently provide an accurate bridge safety evaluation and maintain satisfactory variation.

Reliability Coupled Sensitivity Based Design Approach for Gravity Retaining Walls

NASA Astrophysics Data System (ADS)

Guha Ray, A.; Baidya, D. K.

2012-09-01

Sensitivity analysis involving different random variables and different potential failure modes of a gravity retaining wall focuses on the fact that high sensitivity of a particular variable on a particular mode of failure does not necessarily imply a remarkable contribution to the overall failure probability. The present paper aims at identifying a probabilistic risk factor ( R f ) for each random variable based on the combined effects of failure probability ( P f ) of each mode of failure of a gravity retaining wall and sensitivity of each of the random variables on these failure modes. P f is calculated by Monte Carlo simulation and sensitivity analysis of each random variable is carried out by F-test analysis. The structure, redesigned by modifying the original random variables with the risk factors, is safe against all the variations of random variables. It is observed that R f for friction angle of backfill soil ( φ 1 ) increases and cohesion of foundation soil ( c 2 ) decreases with an increase of variation of φ 1 , while R f for unit weights ( γ 1 and γ 2 ) for both soil and friction angle of foundation soil ( φ 2 ) remains almost constant for variation of soil properties. The results compared well with some of the existing deterministic and probabilistic methods and found to be cost-effective. It is seen that if variation of φ 1 remains within 5 %, significant reduction in cross-sectional area can be achieved. But if the variation is more than 7-8 %, the structure needs to be modified. Finally design guidelines for different wall dimensions, based on the present approach, are proposed.

A randomized controlled trial of mindfulness-based stress reduction for women with early-stage breast cancer receiving radiotherapy.

PubMed

Henderson, Virginia P; Massion, Ann O; Clemow, Lynn; Hurley, Thomas G; Druker, Susan; Hébert, James R

2013-09-01

To testthe relative effectiveness of a mindfulness-based stress reduction program (MBSR) compared with a nutrition education intervention (NEP) and usual care (UC) in women with newly diagnosed early-stage breast cancer (BrCA)undergoing radiotherapy. Datawere available from a randomized controlled trialof 172 women, 20 to 65 years old, with stage I or II BrCA. Data from women completing the 8-week MBSR program plus 3 additional sessions focuses on special needs associated with BrCA were compared to women receiving attention control NEP and UC. Follow-up was performed at 3 post-intervention points: 4 months, and 1 and 2 years. Standardized, validated self-administered questionnaires were used to assess psychosocial variables. Descriptive analyses compared women by randomization assignment. Regression analyses, incorporating both intention-to-treat and post hoc multivariable approaches, were used to control for potential confounding variables. A subset of 120 women underwent radiotherapy; 77 completed treatment prior to the study, and 40 had radiotherapy during the MBSR intervention. Women who actively received radiotherapy (art) while participating in the MBSR intervention (MBSR-art) experienced a significant (P < .05) improvement in 16 psychosocial variables compared with the NEP-art, UC-art, or both at 4 months. These included health-related, BrCA-specific quality of life and psychosocial coping, which were the primary outcomes, and secondary measures, including meaningfulness, helplessness, cognitive avoidance, depression, paranoid ideation, hostility, anxiety, global severity, anxious preoccupation, and emotional control. MBSR appears to facilitate psychosocial adjustment in BrCA patients receiving radiotherapy, suggesting applicability for MBSR as adjunctive therapy in oncological practice.

Anderson localization for radial tree-like random quantum graphs

NASA Astrophysics Data System (ADS)

Hislop, Peter D.; Post, Olaf

We prove that certain random models associated with radial, tree-like, rooted quantum graphs exhibit Anderson localization at all energies. The two main examples are the random length model (RLM) and the random Kirchhoff model (RKM). In the RLM, the lengths of each generation of edges form a family of independent, identically distributed random variables (iid). For the RKM, the iid random variables are associated with each generation of vertices and moderate the current flow through the vertex. We consider extensions to various families of decorated graphs and prove stability of localization with respect to decoration. In particular, we prove Anderson localization for the random necklace model.

Unbiased split variable selection for random survival forests using maximally selected rank statistics.

PubMed

Wright, Marvin N; Dankowski, Theresa; Ziegler, Andreas

2017-04-15

The most popular approach for analyzing survival data is the Cox regression model. The Cox model may, however, be misspecified, and its proportionality assumption may not always be fulfilled. An alternative approach for survival prediction is random forests for survival outcomes. The standard split criterion for random survival forests is the log-rank test statistic, which favors splitting variables with many possible split points. Conditional inference forests avoid this split variable selection bias. However, linear rank statistics are utilized by default in conditional inference forests to select the optimal splitting variable, which cannot detect non-linear effects in the independent variables. An alternative is to use maximally selected rank statistics for the split point selection. As in conditional inference forests, splitting variables are compared on the p-value scale. However, instead of the conditional Monte-Carlo approach used in conditional inference forests, p-value approximations are employed. We describe several p-value approximations and the implementation of the proposed random forest approach. A simulation study demonstrates that unbiased split variable selection is possible. However, there is a trade-off between unbiased split variable selection and runtime. In benchmark studies of prediction performance on simulated and real datasets, the new method performs better than random survival forests if informative dichotomous variables are combined with uninformative variables with more categories and better than conditional inference forests if non-linear covariate effects are included. In a runtime comparison, the method proves to be computationally faster than both alternatives, if a simple p-value approximation is used. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

A selective review of the first 20 years of instrumental variables models in health-services research and medicine.

PubMed

Cawley, John

2015-01-01

The method of instrumental variables (IV) is useful for estimating causal effects. Intuitively, it exploits exogenous variation in the treatment, sometimes called natural experiments or instruments. This study reviews the literature in health-services research and medical research that applies the method of instrumental variables, documents trends in its use, and offers examples of various types of instruments. A literature search of the PubMed and EconLit research databases for English-language journal articles published after 1990 yielded a total of 522 original research articles. Citations counts for each article were derived from the Web of Science. A selective review was conducted, with articles prioritized based on number of citations, validity and power of the instrument, and type of instrument. The average annual number of papers in health services research and medical research that apply the method of instrumental variables rose from 1.2 in 1991-1995 to 41.8 in 2006-2010. Commonly-used instruments (natural experiments) in health and medicine are relative distance to a medical care provider offering the treatment and the medical care provider's historic tendency to administer the treatment. Less common but still noteworthy instruments include randomization of treatment for reasons other than research, randomized encouragement to undertake the treatment, day of week of admission as an instrument for waiting time for surgery, and genes as an instrument for whether the respondent has a heritable condition. The use of the method of IV has increased dramatically in the past 20 years, and a wide range of instruments have been used. Applications of the method of IV have in several cases upended conventional wisdom that was based on correlations and led to important insights about health and healthcare. Future research should pursue new applications of existing instruments and search for new instruments that are powerful and valid.

Simulation of multivariate stationary stochastic processes using dimension-reduction representation methods

NASA Astrophysics Data System (ADS)

Liu, Zhangjun; Liu, Zenghui; Peng, Yongbo

2018-03-01

In view of the Fourier-Stieltjes integral formula of multivariate stationary stochastic processes, a unified formulation accommodating spectral representation method (SRM) and proper orthogonal decomposition (POD) is deduced. By introducing random functions as constraints correlating the orthogonal random variables involved in the unified formulation, the dimension-reduction spectral representation method (DR-SRM) and the dimension-reduction proper orthogonal decomposition (DR-POD) are addressed. The proposed schemes are capable of representing the multivariate stationary stochastic process with a few elementary random variables, bypassing the challenges of high-dimensional random variables inherent in the conventional Monte Carlo methods. In order to accelerate the numerical simulation, the technique of Fast Fourier Transform (FFT) is integrated with the proposed schemes. For illustrative purposes, the simulation of horizontal wind velocity field along the deck of a large-span bridge is proceeded using the proposed methods containing 2 and 3 elementary random variables. Numerical simulation reveals the usefulness of the dimension-reduction representation methods.

Operating Room Time Savings with the Use of Splint Packs: A Randomized Controlled Trial

PubMed Central

Gonzalez, Tyler A.; Bluman, Eric M.; Palms, David; Smith, Jeremy T.; Chiodo, Christopher P.

2016-01-01

Background: The most expensive variable in the operating room (OR) is time. Lean Process Management is being used in the medical field to improve efficiency in the OR. Streamlining individual processes within the OR is crucial to a comprehensive time saving and cost-cutting health care strategy. At our institution, one hour of OR time costs approximately $500, exclusive of supply and personnel costs. Commercially prepared splint packs (SP) contain all components necessary for plaster-of-Paris short-leg splint application and have the potential to decrease splint application time and overall costs by making it a more lean process. We conducted a randomized controlled trial comparing OR time savings between SP use and bulk supply (BS) splint application. Methods: Fifty consecutive adult operative patients on whom post-operative short-leg splint immobilization was indicated were randomized to either a control group using BS or an experimental group using SP. One orthopaedic surgeon (EMB) prepared and applied all of the splints in a standardized fashion. Retrieval time, preparation time, splint application time, and total splinting time for both groups were measured and statistically analyzed. Results: The retrieval time, preparation time and total splinting time were significantly less (p<0.001) in the SP group compared with the BS group. There was no significant difference in application time between the SP group and BS group. Conclusion: The use of SP made the process of splinting more lean. This has resulted in an average of 2 minutes 52 seconds saved in total splinting time compared to BS, making it an effective cost-cutting and time saving technique. For high volume ORs, use of splint packs may contribute to substantial time and cost savings without impacting patient safety. PMID:26894212

Hydrocortisone Cream to Reduce Perineal Pain after Vaginal Birth: A Randomized Controlled Trial.

PubMed

Manfre, Margaret; Adams, Donita; Callahan, Gloria; Gould, Patricia; Lang, Susan; McCubbins, Holly; Mintz, Amy; Williams, Sommer; Bishard, Mark; Dempsey, Amy; Chulay, Marianne

2015-01-01

To determine if the use of hydrocortisone cream decreases perineal pain in the immediate postpartum period. This was a randomized controlled trial (RCT), crossover study design, with each participant serving as their own control. Participants received three different methods for perineal pain management at three sequential perineal pain treatments after birth: two topical creams (corticosteroid; placebo) and a control treatment (no cream application). Treatment order was randomly assigned, with participants and investigators blinded to cream type. The primary dependent variable was the change in perineal pain levels (posttest minus pretest pain levels) immediately before and 30 to 60 minutes after perineal pain treatments. Data were analyzed with analysis of variance, with p < 0.05 considered significant. A total of 27 participants completed all three perineal pain treatments over a 12-hour period. A reduction in pain was found after application of both the topical creams, with average perineal pain change scores of -4.8 ± 8.4 mm after treatment with hydrocortisone cream (N = 27) and -6.7 ± 13.0 mm after treatment with the placebo cream (N = 27). Changes in pain scores with no cream application were 1.2 ± 10.5 mm (N = 27). Analysis of variance found a significant difference between treatment groups (F2,89 = 3.6, p = 0.03), with both cream treatments having significantly better pain reduction than the control, no cream treatment (hydrocortisone vs. no cream, p = 0.04; placebo cream vs. no cream, p = 0.01). There were no differences in perineal pain reduction between the two cream treatments (p = .54). This RCT found that the application of either hydrocortisone cream or placebo cream provided significantly better pain relief than no cream application.

Random Survival Forest in practice: a method for modelling complex metabolomics data in time to event analysis.

PubMed

Dietrich, Stefan; Floegel, Anna; Troll, Martina; Kühn, Tilman; Rathmann, Wolfgang; Peters, Anette; Sookthai, Disorn; von Bergen, Martin; Kaaks, Rudolf; Adamski, Jerzy; Prehn, Cornelia; Boeing, Heiner; Schulze, Matthias B; Illig, Thomas; Pischon, Tobias; Knüppel, Sven; Wang-Sattler, Rui; Drogan, Dagmar

2016-10-01

The application of metabolomics in prospective cohort studies is statistically challenging. Given the importance of appropriate statistical methods for selection of disease-associated metabolites in highly correlated complex data, we combined random survival forest (RSF) with an automated backward elimination procedure that addresses such issues. Our RSF approach was illustrated with data from the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam study, with concentrations of 127 serum metabolites as exposure variables and time to development of type 2 diabetes mellitus (T2D) as outcome variable. Out of this data set, Cox regression with a stepwise selection method was recently published. Replication of methodical comparison (RSF and Cox regression) was conducted in two independent cohorts. Finally, the R-code for implementing the metabolite selection procedure into the RSF-syntax is provided. The application of the RSF approach in EPIC-Potsdam resulted in the identification of 16 incident T2D-associated metabolites which slightly improved prediction of T2D when used in addition to traditional T2D risk factors and also when used together with classical biomarkers. The identified metabolites partly agreed with previous findings using Cox regression, though RSF selected a higher number of highly correlated metabolites. The RSF method appeared to be a promising approach for identification of disease-associated variables in complex data with time to event as outcome. The demonstrated RSF approach provides comparable findings as the generally used Cox regression, but also addresses the problem of multicollinearity and is suitable for high-dimensional data. © The Author 2016; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association.

Generating Variable and Random Schedules of Reinforcement Using Microsoft Excel Macros

ERIC Educational Resources Information Center

Bancroft, Stacie L.; Bourret, Jason C.

2008-01-01

Variable reinforcement schedules are used to arrange the availability of reinforcement following varying response ratios or intervals of time. Random reinforcement schedules are subtypes of variable reinforcement schedules that can be used to arrange the availability of reinforcement at a constant probability across number of responses or time.…

Optimization Of Mean-Semivariance-Skewness Portfolio Selection Model In Fuzzy Random Environment

NASA Astrophysics Data System (ADS)

Chatterjee, Amitava; Bhattacharyya, Rupak; Mukherjee, Supratim; Kar, Samarjit

2010-10-01

The purpose of the paper is to construct a mean-semivariance-skewness portfolio selection model in fuzzy random environment. The objective is to maximize the skewness with predefined maximum risk tolerance and minimum expected return. Here the security returns in the objectives and constraints are assumed to be fuzzy random variables in nature and then the vagueness of the fuzzy random variables in the objectives and constraints are transformed into fuzzy variables which are similar to trapezoidal numbers. The newly formed fuzzy model is then converted into a deterministic optimization model. The feasibility and effectiveness of the proposed method is verified by numerical example extracted from Bombay Stock Exchange (BSE). The exact parameters of fuzzy membership function and probability density function are obtained through fuzzy random simulating the past dates.

A comparison of rule-based and machine learning approaches for classifying patient portal messages.

PubMed

Cronin, Robert M; Fabbri, Daniel; Denny, Joshua C; Rosenbloom, S Trent; Jackson, Gretchen Purcell

2017-09-01

Secure messaging through patient portals is an increasingly popular way that consumers interact with healthcare providers. The increasing burden of secure messaging can affect clinic staffing and workflows. Manual management of portal messages is costly and time consuming. Automated classification of portal messages could potentially expedite message triage and delivery of care. We developed automated patient portal message classifiers with rule-based and machine learning techniques using bag of words and natural language processing (NLP) approaches. To evaluate classifier performance, we used a gold standard of 3253 portal messages manually categorized using a taxonomy of communication types (i.e., main categories of informational, medical, logistical, social, and other communications, and subcategories including prescriptions, appointments, problems, tests, follow-up, contact information, and acknowledgement). We evaluated our classifiers' accuracies in identifying individual communication types within portal messages with area under the receiver-operator curve (AUC). Portal messages often contain more than one type of communication. To predict all communication types within single messages, we used the Jaccard Index. We extracted the variables of importance for the random forest classifiers. The best performing approaches to classification for the major communication types were: logistic regression for medical communications (AUC: 0.899); basic (rule-based) for informational communications (AUC: 0.842); and random forests for social communications and logistical communications (AUCs: 0.875 and 0.925, respectively). The best performing classification approach of classifiers for individual communication subtypes was random forests for Logistical-Contact Information (AUC: 0.963). The Jaccard Indices by approach were: basic classifier, Jaccard Index: 0.674; Naïve Bayes, Jaccard Index: 0.799; random forests, Jaccard Index: 0.859; and logistic regression, Jaccard Index: 0.861. For medical communications, the most predictive variables were NLP concepts (e.g., Temporal_Concept, which maps to 'morning', 'evening' and Idea_or_Concept which maps to 'appointment' and 'refill'). For logistical communications, the most predictive variables contained similar numbers of NLP variables and words (e.g., Telephone mapping to 'phone', 'insurance'). For social and informational communications, the most predictive variables were words (e.g., social: 'thanks', 'much', informational: 'question', 'mean'). This study applies automated classification methods to the content of patient portal messages and evaluates the application of NLP techniques on consumer communications in patient portal messages. We demonstrated that random forest and logistic regression approaches accurately classified the content of portal messages, although the best approach to classification varied by communication type. Words were the most predictive variables for classification of most communication types, although NLP variables were most predictive for medical communication types. As adoption of patient portals increases, automated techniques could assist in understanding and managing growing volumes of messages. Further work is needed to improve classification performance to potentially support message triage and answering. Copyright © 2017 Elsevier B.V. All rights reserved.

Random Variables: Simulations and Surprising Connections.

ERIC Educational Resources Information Center

Quinn, Robert J.; Tomlinson, Stephen

1999-01-01

Features activities for advanced second-year algebra students in grades 11 and 12. Introduces three random variables and considers an empirical and theoretical probability for each. Uses coins, regular dice, decahedral dice, and calculators. (ASK)

Binomial leap methods for simulating stochastic chemical kinetics.

PubMed

Tian, Tianhai; Burrage, Kevin

2004-12-01

This paper discusses efficient simulation methods for stochastic chemical kinetics. Based on the tau-leap and midpoint tau-leap methods of Gillespie [D. T. Gillespie, J. Chem. Phys. 115, 1716 (2001)], binomial random variables are used in these leap methods rather than Poisson random variables. The motivation for this approach is to improve the efficiency of the Poisson leap methods by using larger stepsizes. Unlike Poisson random variables whose range of sample values is from zero to infinity, binomial random variables have a finite range of sample values. This probabilistic property has been used to restrict possible reaction numbers and to avoid negative molecular numbers in stochastic simulations when larger stepsize is used. In this approach a binomial random variable is defined for a single reaction channel in order to keep the reaction number of this channel below the numbers of molecules that undergo this reaction channel. A sampling technique is also designed for the total reaction number of a reactant species that undergoes two or more reaction channels. Samples for the total reaction number are not greater than the molecular number of this species. In addition, probability properties of the binomial random variables provide stepsize conditions for restricting reaction numbers in a chosen time interval. These stepsize conditions are important properties of robust leap control strategies. Numerical results indicate that the proposed binomial leap methods can be applied to a wide range of chemical reaction systems with very good accuracy and significant improvement on efficiency over existing approaches. (c) 2004 American Institute of Physics.

Random field theory to interpret the spatial variability of lacustrine soils

NASA Astrophysics Data System (ADS)

Russo, Savino; Vessia, Giovanna

2015-04-01

The lacustrine soils are quaternary soils, dated from Pleistocene to Holocene periods, generated in low-energy depositional environments and characterized by soil mixture of clays, sands and silts with alternations of finer and coarser grain size layers. They are often met at shallow depth filling several tens of meters of tectonic or erosive basins typically placed in internal Appenine areas. The lacustrine deposits are often locally interbedded by detritic soils resulting from the failure of surrounding reliefs. Their heterogeneous lithology is associated with high spatial variability of physical and mechanical properties both along horizontal and vertical directions. The deterministic approach is still commonly adopted to accomplish the mechanical characterization of these heterogeneous soils where undisturbed sampling is practically not feasible (if the incoherent fraction is prevalent) or not spatially representative (if the cohesive fraction prevails). The deterministic approach consists on performing in situ tests, like Standard Penetration Tests (SPT) or Cone Penetration Tests (CPT) and deriving design parameters through "expert judgment" interpretation of the measure profiles. These readings of tip and lateral resistances (Rp and RL respectively) are almost continuous but highly variable in soil classification according to Schmertmann (1978). Thus, neglecting the spatial variability cannot be the best strategy to estimated spatial representative values of physical and mechanical parameters of lacustrine soils to be used for engineering applications. Hereafter, a method to draw the spatial variability structure of the aforementioned measure profiles is presented. It is based on the theory of the Random Fields (Vanmarcke 1984) applied to vertical readings of Rp measures from mechanical CPTs. The proposed method relies on the application of the regression analysis, by which the spatial mean trend and fluctuations about this trend are derived. Moreover, the scale of fluctuation is calculated to measure the maximum length beyond which profiles of measures are independent. The spatial mean trend can be used to identify "quasi-homogeneous" soil layers where the standard deviation and the scale of fluctuation can be calculated. In this study, five Rp profiles performed in the lacustrine deposits of the high River Pescara Valley have been analyzed. There, silty clay deposits with thickness ranging from a few meters to about 60m, and locally rich in sands and peats, are investigated. In this study, vertical trends of Rp profiles have been derived to be converted into design parameter mean trends. Furthermore, the variability structure derived from Rp readings can be propagated to design parameters to calculate the "characteristic values" requested by the European building codes. References Schmertmann J.H. 1978. Guidelines for Cone Penetration Test, Performance and Design. Report No. FHWA-TS-78-209, U.S. Department of Transportation, Washington, D.C., pp. 145. Vanmarcke E.H. 1984. Random Fields, analysis and synthesis. Cambridge (USA): MIT Press.

A new randomized Kaczmarz based kernel canonical correlation analysis algorithm with applications to information retrieval.

PubMed

Cai, Jia; Tang, Yi

2018-02-01

Canonical correlation analysis (CCA) is a powerful statistical tool for detecting the linear relationship between two sets of multivariate variables. Kernel generalization of it, namely, kernel CCA is proposed to describe nonlinear relationship between two variables. Although kernel CCA can achieve dimensionality reduction results for high-dimensional data feature selection problem, it also yields the so called over-fitting phenomenon. In this paper, we consider a new kernel CCA algorithm via randomized Kaczmarz method. The main contributions of the paper are: (1) A new kernel CCA algorithm is developed, (2) theoretical convergence of the proposed algorithm is addressed by means of scaled condition number, (3) a lower bound which addresses the minimum number of iterations is presented. We test on both synthetic dataset and several real-world datasets in cross-language document retrieval and content-based image retrieval to demonstrate the effectiveness of the proposed algorithm. Numerical results imply the performance and efficiency of the new algorithm, which is competitive with several state-of-the-art kernel CCA methods. Copyright © 2017 Elsevier Ltd. All rights reserved.

A randomized trial of Korodin Herz-Kreislauf-Tropfen as add-on treatment in older patients with orthostatic hypotension.

PubMed

Kroll, M; Ring, C; Gaus, W; Hempel, B

2005-06-01

In a randomized, double-blind, placebo-controlled, parallel group, phase III clinical trial efficacy and safety of Korodin, a combination of natural D-camphor and an extract from fresh crataegus berries, was investigated in patients 50 years and older with orthostatic hypotension. At visit 1 eligibility of patients was checked and a placebo medication was given to all patients. At visit 2 orthostatic hypotension had to be reconfirmed, then the patient was randomized either to Korodin or placebo, study medication (25 drops) was applied once and then outcome was measured. After 7 days of home treatment with daily 3 x 25 drops outcome was measured at visit 3. Systolic blood pressure (SBP), diastolic blood pressure (DBP) and heart rate (HR) were documented 10, 5, 2 and 0 min before as well as 1, 3, 5, 8, and 10 min after getting in the upright position at visit 1, at visit 2 before and after application of study medication and at visit 3. Primary outcome was the change of mean arterial blood pressure (MAP) from just before standing up to the nadir within the first 3 min after standing up. Secondary outcome variables were SBP, DBP, HR, quality of life (SF-12) and seven typical signs and symptoms of orthostatic hypotension. The study was performed in a rehabilitation clinic and in two doctor's practices in Germany from November 2002 to May 2003. During this time, 57 patients were admitted to the study, 39 patients were eligible and randomized, 38 patients were treated according to protocol and evaluated, 21 patients with Korodin and 17 patients with placebo. After a single application the median decrease of MAP was 11.4 mmHg for Korodin and 14.0 mmHg for placebo. Compared to baseline, the median MAP improved 4.3 mmHg for Korodin and 0.3 mmHg for placebo. After 1 week of treatment the decrease of median MAP after standing up was 9.3 mmHg for Korodin and 13.3 mmHg for placebo. Compared to baseline, the improvement was 5.9 mmHg for Korodin and 1.6 mmHg for placebo. Efficacy of 1 week treatment was significant. For the single application a superiority of Korodin over placebo was seen; however, it was not significant. All secondary outcome variables confirmed these findings, except for the physical summary score in the quality of life evaluation (SF-12 questionnaire). Only one adverse event occurred, but this was not serious and without relationship to the study medication. The other safety variables (SBP, DBP, HR, ECG, physical examination) did not show any problems. This study demonstrates that Korodin is efficacious for orthostatic hypotension in patients over 50 years.

Do bioclimate variables improve performance of climate envelope models?

USGS Publications Warehouse

Watling, James I.; Romañach, Stephanie S.; Bucklin, David N.; Speroterra, Carolina; Brandt, Laura A.; Pearlstine, Leonard G.; Mazzotti, Frank J.

2012-01-01

Climate envelope models are widely used to forecast potential effects of climate change on species distributions. A key issue in climate envelope modeling is the selection of predictor variables that most directly influence species. To determine whether model performance and spatial predictions were related to the selection of predictor variables, we compared models using bioclimate variables with models constructed from monthly climate data for twelve terrestrial vertebrate species in the southeastern USA using two different algorithms (random forests or generalized linear models), and two model selection techniques (using uncorrelated predictors or a subset of user-defined biologically relevant predictor variables). There were no differences in performance between models created with bioclimate or monthly variables, but one metric of model performance was significantly greater using the random forest algorithm compared with generalized linear models. Spatial predictions between maps using bioclimate and monthly variables were very consistent using the random forest algorithm with uncorrelated predictors, whereas we observed greater variability in predictions using generalized linear models.

Investigating Factorial Invariance of Latent Variables Across Populations When Manifest Variables Are Missing Completely

PubMed Central

Widaman, Keith F.; Grimm, Kevin J.; Early, Dawnté R.; Robins, Richard W.; Conger, Rand D.

2013-01-01

Difficulties arise in multiple-group evaluations of factorial invariance if particular manifest variables are missing completely in certain groups. Ad hoc analytic alternatives can be used in such situations (e.g., deleting manifest variables), but some common approaches, such as multiple imputation, are not viable. At least 3 solutions to this problem are viable: analyzing differing sets of variables across groups, using pattern mixture approaches, and a new method using random number generation. The latter solution, proposed in this article, is to generate pseudo-random normal deviates for all observations for manifest variables that are missing completely in a given sample and then to specify multiple-group models in a way that respects the random nature of these values. An empirical example is presented in detail comparing the 3 approaches. The proposed solution can enable quantitative comparisons at the latent variable level between groups using programs that require the same number of manifest variables in each group. PMID:24019738

A unified approach for squeal instability analysis of disc brakes with two types of random-fuzzy uncertainties

NASA Astrophysics Data System (ADS)

Lü, Hui; Shangguan, Wen-Bin; Yu, Dejie

2017-09-01

Automotive brake systems are always subjected to various types of uncertainties and two types of random-fuzzy uncertainties may exist in the brakes. In this paper, a unified approach is proposed for squeal instability analysis of disc brakes with two types of random-fuzzy uncertainties. In the proposed approach, two uncertainty analysis models with mixed variables are introduced to model the random-fuzzy uncertainties. The first one is the random and fuzzy model, in which random variables and fuzzy variables exist simultaneously and independently. The second one is the fuzzy random model, in which uncertain parameters are all treated as random variables while their distribution parameters are expressed as fuzzy numbers. Firstly, the fuzziness is discretized by using α-cut technique and the two uncertainty analysis models are simplified into random-interval models. Afterwards, by temporarily neglecting interval uncertainties, the random-interval models are degraded into random models, in which the expectations, variances, reliability indexes and reliability probabilities of system stability functions are calculated. And then, by reconsidering the interval uncertainties, the bounds of the expectations, variances, reliability indexes and reliability probabilities are computed based on Taylor series expansion. Finally, by recomposing the analysis results at each α-cut level, the fuzzy reliability indexes and probabilities can be obtained, by which the brake squeal instability can be evaluated. The proposed approach gives a general framework to deal with both types of random-fuzzy uncertainties that may exist in the brakes and its effectiveness is demonstrated by numerical examples. It will be a valuable supplement to the systematic study of brake squeal considering uncertainty.

Compensation for Lithography Induced Process Variations during Physical Design

NASA Astrophysics Data System (ADS)

Chin, Eric Yiow-Bing

This dissertation addresses the challenge of designing robust integrated circuits in the deep sub micron regime in the presence of lithography process variability. By extending and combining existing process and circuit analysis techniques, flexible software frameworks are developed to provide detailed studies of circuit performance in the presence of lithography variations such as focus and exposure. Applications of these software frameworks to select circuits demonstrate the electrical impact of these variations and provide insight into variability aware compact models that capture the process dependent circuit behavior. These variability aware timing models abstract lithography variability from the process level to the circuit level and are used to estimate path level circuit performance with high accuracy with very little overhead in runtime. The Interconnect Variability Characterization (IVC) framework maps lithography induced geometrical variations at the interconnect level to electrical delay variations. This framework is applied to one dimensional repeater circuits patterned with both 90nm single patterning and 32nm double patterning technologies, under the presence of focus, exposure, and overlay variability. Studies indicate that single and double patterning layouts generally exhibit small variations in delay (between 1--3%) due to self compensating RC effects associated with dense layouts and overlay errors for layouts without self-compensating RC effects. The delay response of each double patterned interconnect structure is fit with a second order polynomial model with focus, exposure, and misalignment parameters with 12 coefficients and residuals of less than 0.1ps. The IVC framework is also applied to a repeater circuit with cascaded interconnect structures to emulate more complex layout scenarios, and it is observed that the variations on each segment average out to reduce the overall delay variation. The Standard Cell Variability Characterization (SCVC) framework advances existing layout-level lithography aware circuit analysis by extending it to cell-level applications utilizing a physically accurate approach that integrates process simulation, compact transistor models, and circuit simulation to characterize electrical cell behavior. This framework is applied to combinational and sequential cells in the Nangate 45nm Open Cell Library, and the timing response of these cells to lithography focus and exposure variations demonstrate Bossung like behavior. This behavior permits the process parameter dependent response to be captured in a nine term variability aware compact model based on Bossung fitting equations. For a two input NAND gate, the variability aware compact model captures the simulated response to an accuracy of 0.3%. The SCVC framework is also applied to investigate advanced process effects including misalignment and layout proximity. The abstraction of process variability from the layout level to the cell level opens up an entire new realm of circuit analysis and optimization and provides a foundation for path level variability analysis without the computationally expensive costs associated with joint process and circuit simulation. The SCVC framework is used with slight modification to illustrate the speedup and accuracy tradeoffs of using compact models. With variability aware compact models, the process dependent performance of a three stage logic circuit can be estimated to an accuracy of 0.7% with a speedup of over 50,000. Path level variability analysis also provides an accurate estimate (within 1%) of ring oscillator period in well under a second. Another significant advantage of variability aware compact models is that they can be easily incorporated into existing design methodologies for design optimization. This is demonstrated by applying cell swapping on a logic circuit to reduce the overall delay variability along a circuit path. By including these variability aware compact models in cell characterization libraries, design metrics such as circuit timing, power, area, and delay variability can be quickly assessed to optimize for the correct balance of all design metrics, including delay variability. Deterministic lithography variations can be easily captured using the variability aware compact models described in this dissertation. However, another prominent source of variability is random dopant fluctuations, which affect transistor threshold voltage and in turn circuit performance. The SCVC framework is utilized to investigate the interactions between deterministic lithography variations and random dopant fluctuations. Monte Carlo studies show that the output delay distribution in the presence of random dopant fluctuations is dependent on lithography focus and exposure conditions, with a 3.6 ps change in standard deviation across the focus exposure process window. This indicates that the electrical impact of random variations is dependent on systematic lithography variations, and this dependency should be included for precise analysis.

Purposeful Variable Selection and Stratification to Impute Missing FAST Data in Trauma Research

PubMed Central

Fuchs, Paul A.; del Junco, Deborah J.; Fox, Erin E.; Holcomb, John B.; Rahbar, Mohammad H.; Wade, Charles A.; Alarcon, Louis H.; Brasel, Karen J.; Bulger, Eileen M.; Cohen, Mitchell J.; Myers, John G.; Muskat, Peter; Phelan, Herb A.; Schreiber, Martin A.; Cotton, Bryan A.

2013-01-01

Background The Focused Assessment with Sonography for Trauma (FAST) exam is an important variable in many retrospective trauma studies. The purpose of this study was to devise an imputation method to overcome missing data for the FAST exam. Due to variability in patients’ injuries and trauma care, these data are unlikely to be missing completely at random (MCAR), raising concern for validity when analyses exclude patients with missing values. Methods Imputation was conducted under a less restrictive, more plausible missing at random (MAR) assumption. Patients with missing FAST exams had available data on alternate, clinically relevant elements that were strongly associated with FAST results in complete cases, especially when considered jointly. Subjects with missing data (32.7%) were divided into eight mutually exclusive groups based on selected variables that both described the injury and were associated with missing FAST values. Additional variables were selected within each group to classify missing FAST values as positive or negative, and correct FAST exam classification based on these variables was determined for patients with non-missing FAST values. Results Severe head/neck injury (odds ratio, OR=2.04), severe extremity injury (OR=4.03), severe abdominal injury (OR=1.94), no injury (OR=1.94), other abdominal injury (OR=0.47), other head/neck injury (OR=0.57) and other extremity injury (OR=0.45) groups had significant ORs for missing data; the other group odds ratio was not significant (OR=0.84). All 407 missing FAST values were imputed, with 109 classified as positive. Correct classification of non-missing FAST results using the alternate variables was 87.2%. Conclusions Purposeful imputation for missing FAST exams based on interactions among selected variables assessed by simple stratification may be a useful adjunct to sensitivity analysis in the evaluation of imputation strategies under different missing data mechanisms. This approach has the potential for widespread application in clinical and translational research and validation is warranted. Level of Evidence Level II Prognostic or Epidemiological PMID:23778515

Instrumentation of the variable-angle magneto-optic ellipsometer and its application to M-O media and other non-magnetic films

NASA Technical Reports Server (NTRS)

Zhou, Andy F.; Erwin, J. Kevin; Mansuripur, M.

1992-01-01

A new and comprehensive dielectric tensor characterization instrument is presented for characterization of magneto-optical recording media and non-magnetic thin films. Random and systematic errors of the system are studied. A series of TbFe, TbFeCo, and Co/Pt samples with different composition and thicknesses are characterized for their optical and magneto-optical properties. The optical properties of several non-magnetic films are also measured.

A Bayesian Approach to Identifying Structural Nonlinearity using Free-Decay Response: Application to Damage Detection in Composites

DTIC Science & Technology

2010-03-03

obtainable while for the free-decay problem we simply have to include the initial conditions as random variables to be predicted. A different approach that...important and useful properties of MLEs is that, under regularity conditions , they are asymptotically unbiased and possess the minimum possible...becomes pLðzjh;s2G;MiÞ (i.e. the likelihood is conditional on the specified model). However, in this work we will only consider a single model and drop the

Selection of Variables in Cluster Analysis: An Empirical Comparison of Eight Procedures

ERIC Educational Resources Information Center

Steinley, Douglas; Brusco, Michael J.

2008-01-01

Eight different variable selection techniques for model-based and non-model-based clustering are evaluated across a wide range of cluster structures. It is shown that several methods have difficulties when non-informative variables (i.e., random noise) are included in the model. Furthermore, the distribution of the random noise greatly impacts the…

Multiple Imputation For Combined-Survey Estimation With Incomplete Regressors In One But Not Both Surveys

PubMed Central

Rendall, Michael S.; Ghosh-Dastidar, Bonnie; Weden, Margaret M.; Baker, Elizabeth H.; Nazarov, Zafar

2013-01-01

Within-survey multiple imputation (MI) methods are adapted to pooled-survey regression estimation where one survey has more regressors, but typically fewer observations, than the other. This adaptation is achieved through: (1) larger numbers of imputations to compensate for the higher fraction of missing values; (2) model-fit statistics to check the assumption that the two surveys sample from a common universe; and (3) specificying the analysis model completely from variables present in the survey with the larger set of regressors, thereby excluding variables never jointly observed. In contrast to the typical within-survey MI context, cross-survey missingness is monotonic and easily satisfies the Missing At Random (MAR) assumption needed for unbiased MI. Large efficiency gains and substantial reduction in omitted variable bias are demonstrated in an application to sociodemographic differences in the risk of child obesity estimated from two nationally-representative cohort surveys. PMID:24223447

Two-Part and Related Regression Models for Longitudinal Data

PubMed Central

Farewell, V.T.; Long, D.L.; Tom, B.D.M.; Yiu, S.; Su, L.

2017-01-01

Statistical models that involve a two-part mixture distribution are applicable in a variety of situations. Frequently, the two parts are a model for the binary response variable and a model for the outcome variable that is conditioned on the binary response. Two common examples are zero-inflated or hurdle models for count data and two-part models for semicontinuous data. Recently, there has been particular interest in the use of these models for the analysis of repeated measures of an outcome variable over time. The aim of this review is to consider motivations for the use of such models in this context and to highlight the central issues that arise with their use. We examine two-part models for semicontinuous and zero-heavy count data, and we also consider models for count data with a two-part random effects distribution. PMID:28890906

A Tutorial in Bayesian Potential Outcomes Mediation Analysis.

PubMed

Miočević, Milica; Gonzalez, Oscar; Valente, Matthew J; MacKinnon, David P

2018-01-01

Statistical mediation analysis is used to investigate intermediate variables in the relation between independent and dependent variables. Causal interpretation of mediation analyses is challenging because randomization of subjects to levels of the independent variable does not rule out the possibility of unmeasured confounders of the mediator to outcome relation. Furthermore, commonly used frequentist methods for mediation analysis compute the probability of the data given the null hypothesis, which is not the probability of a hypothesis given the data as in Bayesian analysis. Under certain assumptions, applying the potential outcomes framework to mediation analysis allows for the computation of causal effects, and statistical mediation in the Bayesian framework gives indirect effects probabilistic interpretations. This tutorial combines causal inference and Bayesian methods for mediation analysis so the indirect and direct effects have both causal and probabilistic interpretations. Steps in Bayesian causal mediation analysis are shown in the application to an empirical example.

A linear programming approach to max-sum problem: a review.

PubMed

Werner, Tomás

2007-07-01

The max-sum labeling problem, defined as maximizing a sum of binary (i.e., pairwise) functions of discrete variables, is a general NP-hard optimization problem with many applications, such as computing the MAP configuration of a Markov random field. We review a not widely known approach to the problem, developed by Ukrainian researchers Schlesinger et al. in 1976, and show how it contributes to recent results, most importantly, those on the convex combination of trees and tree-reweighted max-product. In particular, we review Schlesinger et al.'s upper bound on the max-sum criterion, its minimization by equivalent transformations, its relation to the constraint satisfaction problem, the fact that this minimization is dual to a linear programming relaxation of the original problem, and the three kinds of consistency necessary for optimality of the upper bound. We revisit problems with Boolean variables and supermodular problems. We describe two algorithms for decreasing the upper bound. We present an example application for structural image analysis.

Practical secure quantum communications

NASA Astrophysics Data System (ADS)

Diamanti, Eleni

2015-05-01

We review recent advances in the field of quantum cryptography, focusing in particular on practical implementations of two central protocols for quantum network applications, namely key distribution and coin flipping. The former allows two parties to share secret messages with information-theoretic security, even in the presence of a malicious eavesdropper in the communication channel, which is impossible with classical resources alone. The latter enables two distrustful parties to agree on a random bit, again with information-theoretic security, and with a cheating probability lower than the one that can be reached in a classical scenario. Our implementations rely on continuous-variable technology for quantum key distribution and on a plug and play discrete-variable system for coin flipping, and necessitate a rigorous security analysis adapted to the experimental schemes and their imperfections. In both cases, we demonstrate the protocols with provable security over record long distances in optical fibers and assess the performance of our systems as well as their limitations. The reported advances offer a powerful toolbox for practical applications of secure communications within future quantum networks.

Digital video technologies and their network requirements

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

R. P. Tsang; H. Y. Chen; J. M. Brandt

1999-11-01

Coded digital video signals are considered to be one of the most difficult data types to transport due to their real-time requirements and high bit rate variability. In this study, the authors discuss the coding mechanisms incorporated by the major compression standards bodies, i.e., JPEG and MPEG, as well as more advanced coding mechanisms such as wavelet and fractal techniques. The relationship between the applications which use these coding schemes and their network requirements are the major focus of this study. Specifically, the authors relate network latency, channel transmission reliability, random access speed, buffering and network bandwidth with the variousmore » coding techniques as a function of the applications which use them. Such applications include High-Definition Television, Video Conferencing, Computer-Supported Collaborative Work (CSCW), and Medical Imaging.« less

Sampling-Based Stochastic Sensitivity Analysis Using Score Functions for RBDO Problems with Correlated Random Variables

DTIC Science & Technology

2010-08-01

a collection of information if it does not display a currently valid OMB control number. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS. a ...SECURITY CLASSIFICATION OF: This study presents a methodology for computing stochastic sensitivities with respect to the design variables, which are the...Random Variables Report Title ABSTRACT This study presents a methodology for computing stochastic sensitivities with respect to the design variables

Fluid Structure Interaction in a Turbine Blade

NASA Technical Reports Server (NTRS)

Gorla, Rama S. R.

2004-01-01

An unsteady, three dimensional Navier-Stokes solution in rotating frame formulation for turbomachinery applications is presented. Casting the governing equations in a rotating frame enabled the freezing of grid motion and resulted in substantial savings in computer time. The turbine blade was computationally simulated and probabilistically evaluated in view of several uncertainties in the aerodynamic, structural, material and thermal variables that govern the turbine blade. The interconnection between the computational fluid dynamics code and finite element structural analysis code was necessary to couple the thermal profiles with the structural design. The stresses and their variations were evaluated at critical points on the Turbine blade. Cumulative distribution functions and sensitivity factors were computed for stress responses due to aerodynamic, geometric, mechanical and thermal random variables.

ARIMA representation for daily solar irradiance and surface air temperature time series

NASA Astrophysics Data System (ADS)

Kärner, Olavi

2009-06-01

Autoregressive integrated moving average (ARIMA) models are used to compare long-range temporal variability of the total solar irradiance (TSI) at the top of the atmosphere (TOA) and surface air temperature series. The comparison shows that one and the same type of the model is applicable to represent the TSI and air temperature series. In terms of the model type surface air temperature imitates closely that for the TSI. This may mean that currently no other forcing to the climate system is capable to change the random walk type variability established by the varying activity of the rotating Sun. The result should inspire more detailed examination of the dependence of various climate series on short-range fluctuations of TSI.

Reward and uncertainty in exploration programs

NASA Technical Reports Server (NTRS)

Kaufman, G. M.; Bradley, P. G.

1971-01-01

A set of variables which are crucial to the economic outcome of petroleum exploration are discussed. These are treated as random variables; the values they assume indicate the number of successes that occur in a drilling program and determine, for a particular discovery, the unit production cost and net economic return if that reservoir is developed. In specifying the joint probability law for those variables, extreme and probably unrealistic assumptions are made. In particular, the different random variables are assumed to be independently distributed. Using postulated probability functions and specified parameters, values are generated for selected random variables, such as reservoir size. From this set of values the economic magnitudes of interest, net return and unit production cost are computed. This constitutes a single trial, and the procedure is repeated many times. The resulting histograms approximate the probability density functions of the variables which describe the economic outcomes of an exploratory drilling program.

A single-loop optimization method for reliability analysis with second order uncertainty

NASA Astrophysics Data System (ADS)

Xie, Shaojun; Pan, Baisong; Du, Xiaoping

2015-08-01

Reliability analysis may involve random variables and interval variables. In addition, some of the random variables may have interval distribution parameters owing to limited information. This kind of uncertainty is called second order uncertainty. This article develops an efficient reliability method for problems involving the three aforementioned types of uncertain input variables. The analysis produces the maximum and minimum reliability and is computationally demanding because two loops are needed: a reliability analysis loop with respect to random variables and an interval analysis loop for extreme responses with respect to interval variables. The first order reliability method and nonlinear optimization are used for the two loops, respectively. For computational efficiency, the two loops are combined into a single loop by treating the Karush-Kuhn-Tucker (KKT) optimal conditions of the interval analysis as constraints. Three examples are presented to demonstrate the proposed method.

A randomized controlled trial comparing periodic mask CPAP with physiotherapy after abdominal surgery.

PubMed

Denehy, L; Carroll, S; Ntoumenopoulos, G; Jenkins, S

2001-01-01

Physiotherapists use a variety of techniques aimed at improving lung volumes and secretion clearance in patients after surgery. Periodic continuous positive airway pressure (PCPAP) is used to treat patients following elective upper abdominal surgery. However, the optimal method of application has not been identified, more specifically, the dosage of application of PCPAP. The present randomized controlled trial compared the effects of two dosages of PCPAP application and 'traditional' physiotherapy upon functional residual capacity (FRC), vital capacity (VC), oxyhaemoglobin saturation (SpO2), incidence of post-operative pulmonary complications and length of stay with a control group receiving 'traditional' physiotherapy only. Fifty-seven subjects were randomly allocated to one of three groups. All groups received 'traditional' physiotherapy twice daily for a minimum of three post-operative days. In addition, two groups received PCPAP for 15 or 30 minutes, four times per day, for three days. Fifty subjects (39 male; 11 female) completed the study. There were no significant differences in any variables between the three groups. The overall incidence of post-operative pulmonary complications was 22% in the control group, 11% and 6% in the PCPAP 15-minute and PCPAP 30-minute groups, respectively. Length of hospital stay was not significantly different between the groups but for subjects who developed post-operative pulmonary complications, the length of stay was significantly greater (Z = -2.32; p = 0.021). The addition of PCPAP to a traditional physiotherapy post-operative treatment regimen after upper abdominal surgery did not significantly affect physiological or clinical outcomes.

Fitting and Calibrating a Multilevel Mixed-Effects Stem Taper Model for Maritime Pine in NW Spain

PubMed Central

Arias-Rodil, Manuel; Castedo-Dorado, Fernando; Cámara-Obregón, Asunción; Diéguez-Aranda, Ulises

2015-01-01

Stem taper data are usually hierarchical (several measurements per tree, and several trees per plot), making application of a multilevel mixed-effects modelling approach essential. However, correlation between trees in the same plot/stand has often been ignored in previous studies. Fitting and calibration of a variable-exponent stem taper function were conducted using data from 420 trees felled in even-aged maritime pine (Pinus pinaster Ait.) stands in NW Spain. In the fitting step, the tree level explained much more variability than the plot level, and therefore calibration at plot level was omitted. Several stem heights were evaluated for measurement of the additional diameter needed for calibration at tree level. Calibration with an additional diameter measured at between 40 and 60% of total tree height showed the greatest improvement in volume and diameter predictions. If additional diameter measurement is not available, the fixed-effects model fitted by the ordinary least squares technique should be used. Finally, we also evaluated how the expansion of parameters with random effects affects the stem taper prediction, as we consider this a key question when applying the mixed-effects modelling approach to taper equations. The results showed that correlation between random effects should be taken into account when assessing the influence of random effects in stem taper prediction. PMID:26630156

Cooperative Convex Optimization in Networked Systems: Augmented Lagrangian Algorithms With Directed Gossip Communication

NASA Astrophysics Data System (ADS)

Jakovetic, Dusan; Xavier, João; Moura, José M. F.

2011-08-01

We study distributed optimization in networked systems, where nodes cooperate to find the optimal quantity of common interest, x=x^\\star. The objective function of the corresponding optimization problem is the sum of private (known only by a node,) convex, nodes' objectives and each node imposes a private convex constraint on the allowed values of x. We solve this problem for generic connected network topologies with asymmetric random link failures with a novel distributed, decentralized algorithm. We refer to this algorithm as AL-G (augmented Lagrangian gossiping,) and to its variants as AL-MG (augmented Lagrangian multi neighbor gossiping) and AL-BG (augmented Lagrangian broadcast gossiping.) The AL-G algorithm is based on the augmented Lagrangian dual function. Dual variables are updated by the standard method of multipliers, at a slow time scale. To update the primal variables, we propose a novel, Gauss-Seidel type, randomized algorithm, at a fast time scale. AL-G uses unidirectional gossip communication, only between immediate neighbors in the network and is resilient to random link failures. For networks with reliable communication (i.e., no failures,) the simplified, AL-BG (augmented Lagrangian broadcast gossiping) algorithm reduces communication, computation and data storage cost. We prove convergence for all proposed algorithms and demonstrate by simulations the effectiveness on two applications: l_1-regularized logistic regression for classification and cooperative spectrum sensing for cognitive radio networks.

Testing statistical self-similarity in the topology of river networks

USGS Publications Warehouse

Troutman, Brent M.; Mantilla, Ricardo; Gupta, Vijay K.

2010-01-01

Recent work has demonstrated that the topological properties of real river networks deviate significantly from predictions of Shreve's random model. At the same time the property of mean self-similarity postulated by Tokunaga's model is well supported by data. Recently, a new class of network model called random self-similar networks (RSN) that combines self-similarity and randomness has been introduced to replicate important topological features observed in real river networks. We investigate if the hypothesis of statistical self-similarity in the RSN model is supported by data on a set of 30 basins located across the continental United States that encompass a wide range of hydroclimatic variability. We demonstrate that the generators of the RSN model obey a geometric distribution, and self-similarity holds in a statistical sense in 26 of these 30 basins. The parameters describing the distribution of interior and exterior generators are tested to be statistically different and the difference is shown to produce the well-known Hack's law. The inter-basin variability of RSN parameters is found to be statistically significant. We also test generator dependence on two climatic indices, mean annual precipitation and radiative index of dryness. Some indication of climatic influence on the generators is detected, but this influence is not statistically significant with the sample size available. Finally, two key applications of the RSN model to hydrology and geomorphology are briefly discussed.

Fuzzy Stochastic Petri Nets for Modeling Biological Systems with Uncertain Kinetic Parameters

PubMed Central

Liu, Fei; Heiner, Monika; Yang, Ming

2016-01-01

Stochastic Petri nets (SPNs) have been widely used to model randomness which is an inherent feature of biological systems. However, for many biological systems, some kinetic parameters may be uncertain due to incomplete, vague or missing kinetic data (often called fuzzy uncertainty), or naturally vary, e.g., between different individuals, experimental conditions, etc. (often called variability), which has prevented a wider application of SPNs that require accurate parameters. Considering the strength of fuzzy sets to deal with uncertain information, we apply a specific type of stochastic Petri nets, fuzzy stochastic Petri nets (FSPNs), to model and analyze biological systems with uncertain kinetic parameters. FSPNs combine SPNs and fuzzy sets, thereby taking into account both randomness and fuzziness of biological systems. For a biological system, SPNs model the randomness, while fuzzy sets model kinetic parameters with fuzzy uncertainty or variability by associating each parameter with a fuzzy number instead of a crisp real value. We introduce a simulation-based analysis method for FSPNs to explore the uncertainties of outputs resulting from the uncertainties associated with input parameters, which works equally well for bounded and unbounded models. We illustrate our approach using a yeast polarization model having an infinite state space, which shows the appropriateness of FSPNs in combination with simulation-based analysis for modeling and analyzing biological systems with uncertain information. PMID:26910830

Context-invariant quasi hidden variable (qHV) modelling of all joint von Neumann measurements for an arbitrary Hilbert space

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

Loubenets, Elena R.

We prove the existence for each Hilbert space of the two new quasi hidden variable (qHV) models, statistically noncontextual and context-invariant, reproducing all the von Neumann joint probabilities via non-negative values of real-valued measures and all the quantum product expectations—via the qHV (classical-like) average of the product of the corresponding random variables. In a context-invariant model, a quantum observable X can be represented by a variety of random variables satisfying the functional condition required in quantum foundations but each of these random variables equivalently models X under all joint von Neumann measurements, regardless of their contexts. The proved existence ofmore » this model negates the general opinion that, in terms of random variables, the Hilbert space description of all the joint von Neumann measurements for dimH≥3 can be reproduced only contextually. The existence of a statistically noncontextual qHV model, in particular, implies that every N-partite quantum state admits a local quasi hidden variable model introduced in Loubenets [J. Math. Phys. 53, 022201 (2012)]. The new results of the present paper point also to the generality of the quasi-classical probability model proposed in Loubenets [J. Phys. A: Math. Theor. 45, 185306 (2012)].« less

Reducing seed dependent variability of non-uniformly sampled multidimensional NMR data

NASA Astrophysics Data System (ADS)

Mobli, Mehdi

2015-07-01

The application of NMR spectroscopy to study the structure, dynamics and function of macromolecules requires the acquisition of several multidimensional spectra. The one-dimensional NMR time-response from the spectrometer is extended to additional dimensions by introducing incremented delays in the experiment that cause oscillation of the signal along "indirect" dimensions. For a given dimension the delay is incremented at twice the rate of the maximum frequency (Nyquist rate). To achieve high-resolution requires acquisition of long data records sampled at the Nyquist rate. This is typically a prohibitive step due to time constraints, resulting in sub-optimal data records to the detriment of subsequent analyses. The multidimensional NMR spectrum itself is typically sparse, and it has been shown that in such cases it is possible to use non-Fourier methods to reconstruct a high-resolution multidimensional spectrum from a random subset of non-uniformly sampled (NUS) data. For a given acquisition time, NUS has the potential to improve the sensitivity and resolution of a multidimensional spectrum, compared to traditional uniform sampling. The improvements in sensitivity and/or resolution achieved by NUS are heavily dependent on the distribution of points in the random subset acquired. Typically, random points are selected from a probability density function (PDF) weighted according to the NMR signal envelope. In extreme cases as little as 1% of the data is subsampled. The heavy under-sampling can result in poor reproducibility, i.e. when two experiments are carried out where the same number of random samples is selected from the same PDF but using different random seeds. Here, a jittered sampling approach is introduced that is shown to improve random seed dependent reproducibility of multidimensional spectra generated from NUS data, compared to commonly applied NUS methods. It is shown that this is achieved due to the low variability of the inherent sensitivity of the random subset chosen from a given PDF. Finally, it is demonstrated that metrics used to find optimal NUS distributions are heavily dependent on the inherent sensitivity of the random subset, and such optimisation is therefore less critical when using the proposed sampling scheme.

Existence and Optimality Conditions for Risk-Averse PDE-Constrained Optimization

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

Kouri, Drew Philip; Surowiec, Thomas M.

Uncertainty is ubiquitous in virtually all engineering applications, and, for such problems, it is inadequate to simulate the underlying physics without quantifying the uncertainty in unknown or random inputs, boundary and initial conditions, and modeling assumptions. Here in this paper, we introduce a general framework for analyzing risk-averse optimization problems constrained by partial differential equations (PDEs). In particular, we postulate conditions on the random variable objective function as well as the PDE solution that guarantee existence of minimizers. Furthermore, we derive optimality conditions and apply our results to the control of an environmental contaminant. Lastly, we introduce a new riskmore » measure, called the conditional entropic risk, that fuses desirable properties from both the conditional value-at-risk and the entropic risk measures.« less

Existence and Optimality Conditions for Risk-Averse PDE-Constrained Optimization

DOE PAGES

Kouri, Drew Philip; Surowiec, Thomas M.

2018-06-05

Uncertainty is ubiquitous in virtually all engineering applications, and, for such problems, it is inadequate to simulate the underlying physics without quantifying the uncertainty in unknown or random inputs, boundary and initial conditions, and modeling assumptions. Here in this paper, we introduce a general framework for analyzing risk-averse optimization problems constrained by partial differential equations (PDEs). In particular, we postulate conditions on the random variable objective function as well as the PDE solution that guarantee existence of minimizers. Furthermore, we derive optimality conditions and apply our results to the control of an environmental contaminant. Lastly, we introduce a new riskmore » measure, called the conditional entropic risk, that fuses desirable properties from both the conditional value-at-risk and the entropic risk measures.« less

Scaling of Directed Dynamical Small-World Networks with Random Responses

NASA Astrophysics Data System (ADS)

Zhu, Chen-Ping; Xiong, Shi-Jie; Tian, Ying-Jie; Li, Nan; Jiang, Ke-Sheng

2004-05-01

A dynamical model of small-world networks, with directed links which describe various correlations in social and natural phenomena, is presented. Random responses of sites to the input message are introduced to simulate real systems. The interplay of these ingredients results in the collective dynamical evolution of a spinlike variable S(t) of the whole network. The global average spreading length s and average spreading time s are found to scale as p-αln(N with different exponents. Meanwhile, S(t) behaves in a duple scaling form for N≫N*: S˜f(p-βqγt˜), where p and q are rewiring and external parameters, α, β, and γ are scaling exponents, and f(t˜) is a universal function. Possible applications of the model are discussed.

Properties of behavior under different random ratio and random interval schedules: A parametric study.

PubMed

Dembo, M; De Penfold, J B; Ruiz, R; Casalta, H

1985-03-01

Four pigeons were trained to peck a key under different values of a temporally defined independent variable (T) and different probabilities of reinforcement (p). Parameter T is a fixed repeating time cycle and p the probability of reinforcement for the first response of each cycle T. Two dependent variables were used: mean response rate and mean postreinforcement pause. For all values of p a critical value for the independent variable T was found (T=1 sec) in which marked changes took place in response rate and postreinforcement pauses. Behavior typical of random ratio schedules was obtained at T 1 sec and behavior typical of random interval schedules at T 1 sec. Copyright © 1985. Published by Elsevier B.V.

Methods for sample size determination in cluster randomized trials

PubMed Central

Rutterford, Clare; Copas, Andrew; Eldridge, Sandra

2015-01-01

Background: The use of cluster randomized trials (CRTs) is increasing, along with the variety in their design and analysis. The simplest approach for their sample size calculation is to calculate the sample size assuming individual randomization and inflate this by a design effect to account for randomization by cluster. The assumptions of a simple design effect may not always be met; alternative or more complicated approaches are required. Methods: We summarise a wide range of sample size methods available for cluster randomized trials. For those familiar with sample size calculations for individually randomized trials but with less experience in the clustered case, this manuscript provides formulae for a wide range of scenarios with associated explanation and recommendations. For those with more experience, comprehensive summaries are provided that allow quick identification of methods for a given design, outcome and analysis method. Results: We present first those methods applicable to the simplest two-arm, parallel group, completely randomized design followed by methods that incorporate deviations from this design such as: variability in cluster sizes; attrition; non-compliance; or the inclusion of baseline covariates or repeated measures. The paper concludes with methods for alternative designs. Conclusions: There is a large amount of methodology available for sample size calculations in CRTs. This paper gives the most comprehensive description of published methodology for sample size calculation and provides an important resource for those designing these trials. PMID:26174515

Uncertain dynamic analysis for rigid-flexible mechanisms with random geometry and material properties

NASA Astrophysics Data System (ADS)

Wu, Jinglai; Luo, Zhen; Zhang, Nong; Zhang, Yunqing; Walker, Paul D.

2017-02-01

This paper proposes an uncertain modelling and computational method to analyze dynamic responses of rigid-flexible multibody systems (or mechanisms) with random geometry and material properties. Firstly, the deterministic model for the rigid-flexible multibody system is built with the absolute node coordinate formula (ANCF), in which the flexible parts are modeled by using ANCF elements, while the rigid parts are described by ANCF reference nodes (ANCF-RNs). Secondly, uncertainty for the geometry of rigid parts is expressed as uniform random variables, while the uncertainty for the material properties of flexible parts is modeled as a continuous random field, which is further discretized to Gaussian random variables using a series expansion method. Finally, a non-intrusive numerical method is developed to solve the dynamic equations of systems involving both types of random variables, which systematically integrates the deterministic generalized-α solver with Latin Hypercube sampling (LHS) and Polynomial Chaos (PC) expansion. The benchmark slider-crank mechanism is used as a numerical example to demonstrate the characteristics of the proposed method.

GLOBAL REFERENCE ATMOSPHERIC MODELS FOR AEROASSIST APPLICATIONS

NASA Technical Reports Server (NTRS)

Duvall, Aleta; Justus, C. G.; Keller, Vernon W.

2005-01-01

Aeroassist is a broad category of advanced transportation technology encompassing aerocapture, aerobraking, aeroentry, precision landing, hazard detection and avoidance, and aerogravity assist. The eight destinations in the Solar System with sufficient atmosphere to enable aeroassist technology are Venus, Earth, Mars, Jupiter, Saturn, Uranus, Neptune, and Saturn's moon Titan. Engineering-level atmospheric models for five of these targets - Earth, Mars, Titan, Neptune, and Venus - have been developed at NASA's Marshall Space Flight Center. These models are useful as tools in mission planning and systems analysis studies associated with aeroassist applications. The series of models is collectively named the Global Reference Atmospheric Model or GRAM series. An important capability of all the models in the GRAM series is their ability to simulate quasi-random perturbations for Monte Carlo analysis in developing guidance, navigation and control algorithms, for aerothermal design, and for other applications sensitive to atmospheric variability. Recent example applications are discussed.

The Significance of an Excess in a Counting Experiment: Assessing the Impact of Systematic Uncertainties and the Case with a Gaussian Background

NASA Astrophysics Data System (ADS)

Vianello, Giacomo

2018-05-01

Several experiments in high-energy physics and astrophysics can be treated as on/off measurements, where an observation potentially containing a new source or effect (“on” measurement) is contrasted with a background-only observation free of the effect (“off” measurement). In counting experiments, the significance of the new source or effect can be estimated with a widely used formula from Li & Ma, which assumes that both measurements are Poisson random variables. In this paper we study three other cases: (i) the ideal case where the background measurement has no uncertainty, which can be used to study the maximum sensitivity that an instrument can achieve, (ii) the case where the background estimate b in the off measurement has an additional systematic uncertainty, and (iii) the case where b is a Gaussian random variable instead of a Poisson random variable. The latter case applies when b comes from a model fitted on archival or ancillary data, or from the interpolation of a function fitted on data surrounding the candidate new source/effect. Practitioners typically use a formula that is only valid when b is large and when its uncertainty is very small, while we derive a general formula that can be applied in all regimes. We also develop simple methods that can be used to assess how much an estimate of significance is sensitive to systematic uncertainties on the efficiency or on the background. Examples of applications include the detection of short gamma-ray bursts and of new X-ray or γ-ray sources. All the techniques presented in this paper are made available in a Python code that is ready to use.

Optimal auxiliary-covariate-based two-phase sampling design for semiparametric efficient estimation of a mean or mean difference, with application to clinical trials.

PubMed

Gilbert, Peter B; Yu, Xuesong; Rotnitzky, Andrea

2014-03-15

To address the objective in a clinical trial to estimate the mean or mean difference of an expensive endpoint Y, one approach employs a two-phase sampling design, wherein inexpensive auxiliary variables W predictive of Y are measured in everyone, Y is measured in a random sample, and the semiparametric efficient estimator is applied. This approach is made efficient by specifying the phase two selection probabilities as optimal functions of the auxiliary variables and measurement costs. While this approach is familiar to survey samplers, it apparently has seldom been used in clinical trials, and several novel results practicable for clinical trials are developed. We perform simulations to identify settings where the optimal approach significantly improves efficiency compared to approaches in current practice. We provide proofs and R code. The optimality results are developed to design an HIV vaccine trial, with objective to compare the mean 'importance-weighted' breadth (Y) of the T-cell response between randomized vaccine groups. The trial collects an auxiliary response (W) highly predictive of Y and measures Y in the optimal subset. We show that the optimal design-estimation approach can confer anywhere between absent and large efficiency gain (up to 24 % in the examples) compared to the approach with the same efficient estimator but simple random sampling, where greater variability in the cost-standardized conditional variance of Y given W yields greater efficiency gains. Accurate estimation of E[Y | W] is important for realizing the efficiency gain, which is aided by an ample phase two sample and by using a robust fitting method. Copyright © 2013 John Wiley & Sons, Ltd.

Optimal Auxiliary-Covariate Based Two-Phase Sampling Design for Semiparametric Efficient Estimation of a Mean or Mean Difference, with Application to Clinical Trials

PubMed Central

Gilbert, Peter B.; Yu, Xuesong; Rotnitzky, Andrea

2014-01-01

To address the objective in a clinical trial to estimate the mean or mean difference of an expensive endpoint Y, one approach employs a two-phase sampling design, wherein inexpensive auxiliary variables W predictive of Y are measured in everyone, Y is measured in a random sample, and the semi-parametric efficient estimator is applied. This approach is made efficient by specifying the phase-two selection probabilities as optimal functions of the auxiliary variables and measurement costs. While this approach is familiar to survey samplers, it apparently has seldom been used in clinical trials, and several novel results practicable for clinical trials are developed. Simulations are performed to identify settings where the optimal approach significantly improves efficiency compared to approaches in current practice. Proofs and R code are provided. The optimality results are developed to design an HIV vaccine trial, with objective to compare the mean “importance-weighted” breadth (Y) of the T cell response between randomized vaccine groups. The trial collects an auxiliary response (W) highly predictive of Y, and measures Y in the optimal subset. We show that the optimal design-estimation approach can confer anywhere between absent and large efficiency gain (up to 24% in the examples) compared to the approach with the same efficient estimator but simple random sampling, where greater variability in the cost-standardized conditional variance of Y given W yields greater efficiency gains. Accurate estimation of E[Y∣W] is important for realizing the efficiency gain, which is aided by an ample phase-two sample and by using a robust fitting method. PMID:24123289

Do little interactions get lost in dark random forests?

PubMed

Wright, Marvin N; Ziegler, Andreas; König, Inke R

2016-03-31

Random forests have often been claimed to uncover interaction effects. However, if and how interaction effects can be differentiated from marginal effects remains unclear. In extensive simulation studies, we investigate whether random forest variable importance measures capture or detect gene-gene interactions. With capturing interactions, we define the ability to identify a variable that acts through an interaction with another one, while detection is the ability to identify an interaction effect as such. Of the single importance measures, the Gini importance captured interaction effects in most of the simulated scenarios, however, they were masked by marginal effects in other variables. With the permutation importance, the proportion of captured interactions was lower in all cases. Pairwise importance measures performed about equal, with a slight advantage for the joint variable importance method. However, the overall fraction of detected interactions was low. In almost all scenarios the detection fraction in a model with only marginal effects was larger than in a model with an interaction effect only. Random forests are generally capable of capturing gene-gene interactions, but current variable importance measures are unable to detect them as interactions. In most of the cases, interactions are masked by marginal effects and interactions cannot be differentiated from marginal effects. Consequently, caution is warranted when claiming that random forests uncover interactions.

The influence of an uncertain force environment on reshaping trial-to-trial motor variability.

PubMed

Izawa, Jun; Yoshioka, Toshinori; Osu, Rieko

2014-09-10

Motor memory is updated to generate ideal movements in a novel environment. When the environment changes every trial randomly, how does the brain incorporate this uncertainty into motor memory? To investigate how the brain adapts to an uncertain environment, we considered a reach adaptation protocol where individuals practiced moving in a force field where a noise was injected. After they had adapted, we measured the trial-to-trial variability in the temporal profiles of the produced hand force. We found that the motor variability was significantly magnified by the adaptation to the random force field. Temporal profiles of the motor variance were significantly dissociable between two different types of random force fields experienced. A model-based analysis suggests that the variability is generated by noise in the gains of the internal model. It further suggests that the trial-to-trial motor variability magnified by the adaptation in a random force field is generated by the uncertainty of the internal model formed in the brain as a result of the adaptation.

The behaviour of random forest permutation-based variable importance measures under predictor correlation.

PubMed

Nicodemus, Kristin K; Malley, James D; Strobl, Carolin; Ziegler, Andreas

2010-02-27

Random forests (RF) have been increasingly used in applications such as genome-wide association and microarray studies where predictor correlation is frequently observed. Recent works on permutation-based variable importance measures (VIMs) used in RF have come to apparently contradictory conclusions. We present an extended simulation study to synthesize results. In the case when both predictor correlation was present and predictors were associated with the outcome (HA), the unconditional RF VIM attributed a higher share of importance to correlated predictors, while under the null hypothesis that no predictors are associated with the outcome (H0) the unconditional RF VIM was unbiased. Conditional VIMs showed a decrease in VIM values for correlated predictors versus the unconditional VIMs under HA and was unbiased under H0. Scaled VIMs were clearly biased under HA and H0. Unconditional unscaled VIMs are a computationally tractable choice for large datasets and are unbiased under the null hypothesis. Whether the observed increased VIMs for correlated predictors may be considered a "bias" - because they do not directly reflect the coefficients in the generating model - or if it is a beneficial attribute of these VIMs is dependent on the application. For example, in genetic association studies, where correlation between markers may help to localize the functionally relevant variant, the increased importance of correlated predictors may be an advantage. On the other hand, we show examples where this increased importance may result in spurious signals.

Factors related to reduction in the consumption of fast food: application of the theory-based approaches.

PubMed

Zeinab, Jalambadani; Gholamreza, Garmaroudi; Mehdi, Yaseri; Mahmood, Tavousi; Korush, Jafarian

2017-09-21

The Trans-Theoretical model (TTM) and Theory of Planned Behaviour (TPB) may be promising models for understanding and predicting reduction in the consumption of fast food. The aim of this study was to examine the applicability of the Trans-Theoretical model (TTM) and the additional predictive role of the subjective norms and perceived behavioural control in predicting reduction consumption of fast food in obese Iranian adolescent girls. A cross sectional study design was conducted among twelve randomly selected schools in Sabzevar, Iran from 2015 to 2017. Four hundred eighty five randomly selected students consented to participate in the study. Hierarchical regression models used to predict the role of important variables that can influence the reduction in the consumption of fast food among students. using SPSS version 22. Variables Perceived behavioural control (r=0.58, P<0.001), Subjective norms (r=0.51, P<0.001), self-efficacy (r=0.49, P<0.001), decisional balance (pros) (r=0.29, P<0.001), decisional balance (cons) (r=0.25, P<0.001), stage of change (r=0.38, P<0.001), were significantly and positively correlated while experiential processes of change (r=0.08, P=0.135) and behavioural processes of change (r=0.09, P=0.145), were not significant. The study demonstrated that the TTM (except the experiential and behavioural processes of change) focusing on the perceived behavioural control and subjective norms are useful models for reduction in the consumption of fast food.

Spatio-temporal modelling of wind speed variations and extremes in the Caribbean and the Gulf of Mexico

NASA Astrophysics Data System (ADS)

Rychlik, Igor; Mao, Wengang

2018-02-01

The wind speed variability in the North Atlantic has been successfully modelled using a spatio-temporal transformed Gaussian field. However, this type of model does not correctly describe the extreme wind speeds attributed to tropical storms and hurricanes. In this study, the transformed Gaussian model is further developed to include the occurrence of severe storms. In this new model, random components are added to the transformed Gaussian field to model rare events with extreme wind speeds. The resulting random field is locally stationary and homogeneous. The localized dependence structure is described by time- and space-dependent parameters. The parameters have a natural physical interpretation. To exemplify its application, the model is fitted to the ECMWF ERA-Interim reanalysis data set. The model is applied to compute long-term wind speed distributions and return values, e.g., 100- or 1000-year extreme wind speeds, and to simulate random wind speed time series at a fixed location or spatio-temporal wind fields around that location.

Multimode resource-constrained multiple project scheduling problem under fuzzy random environment and its application to a large scale hydropower construction project.

PubMed

Xu, Jiuping; Feng, Cuiying

2014-01-01

This paper presents an extension of the multimode resource-constrained project scheduling problem for a large scale construction project where multiple parallel projects and a fuzzy random environment are considered. By taking into account the most typical goals in project management, a cost/weighted makespan/quality trade-off optimization model is constructed. To deal with the uncertainties, a hybrid crisp approach is used to transform the fuzzy random parameters into fuzzy variables that are subsequently defuzzified using an expected value operator with an optimistic-pessimistic index. Then a combinatorial-priority-based hybrid particle swarm optimization algorithm is developed to solve the proposed model, where the combinatorial particle swarm optimization and priority-based particle swarm optimization are designed to assign modes to activities and to schedule activities, respectively. Finally, the results and analysis of a practical example at a large scale hydropower construction project are presented to demonstrate the practicality and efficiency of the proposed model and optimization method.

Multimode Resource-Constrained Multiple Project Scheduling Problem under Fuzzy Random Environment and Its Application to a Large Scale Hydropower Construction Project

PubMed Central

Xu, Jiuping

2014-01-01

This paper presents an extension of the multimode resource-constrained project scheduling problem for a large scale construction project where multiple parallel projects and a fuzzy random environment are considered. By taking into account the most typical goals in project management, a cost/weighted makespan/quality trade-off optimization model is constructed. To deal with the uncertainties, a hybrid crisp approach is used to transform the fuzzy random parameters into fuzzy variables that are subsequently defuzzified using an expected value operator with an optimistic-pessimistic index. Then a combinatorial-priority-based hybrid particle swarm optimization algorithm is developed to solve the proposed model, where the combinatorial particle swarm optimization and priority-based particle swarm optimization are designed to assign modes to activities and to schedule activities, respectively. Finally, the results and analysis of a practical example at a large scale hydropower construction project are presented to demonstrate the practicality and efficiency of the proposed model and optimization method. PMID:24550708

The challenge for genetic epidemiologists: how to analyze large numbers of SNPs in relation to complex diseases.

PubMed

Heidema, A Geert; Boer, Jolanda M A; Nagelkerke, Nico; Mariman, Edwin C M; van der A, Daphne L; Feskens, Edith J M

2006-04-21

Genetic epidemiologists have taken the challenge to identify genetic polymorphisms involved in the development of diseases. Many have collected data on large numbers of genetic markers but are not familiar with available methods to assess their association with complex diseases. Statistical methods have been developed for analyzing the relation between large numbers of genetic and environmental predictors to disease or disease-related variables in genetic association studies. In this commentary we discuss logistic regression analysis, neural networks, including the parameter decreasing method (PDM) and genetic programming optimized neural networks (GPNN) and several non-parametric methods, which include the set association approach, combinatorial partitioning method (CPM), restricted partitioning method (RPM), multifactor dimensionality reduction (MDR) method and the random forests approach. The relative strengths and weaknesses of these methods are highlighted. Logistic regression and neural networks can handle only a limited number of predictor variables, depending on the number of observations in the dataset. Therefore, they are less useful than the non-parametric methods to approach association studies with large numbers of predictor variables. GPNN on the other hand may be a useful approach to select and model important predictors, but its performance to select the important effects in the presence of large numbers of predictors needs to be examined. Both the set association approach and random forests approach are able to handle a large number of predictors and are useful in reducing these predictors to a subset of predictors with an important contribution to disease. The combinatorial methods give more insight in combination patterns for sets of genetic and/or environmental predictor variables that may be related to the outcome variable. As the non-parametric methods have different strengths and weaknesses we conclude that to approach genetic association studies using the case-control design, the application of a combination of several methods, including the set association approach, MDR and the random forests approach, will likely be a useful strategy to find the important genes and interaction patterns involved in complex diseases.

Tacholess order-tracking approach for wind turbine gearbox fault detection

NASA Astrophysics Data System (ADS)

Wang, Yi; Xie, Yong; Xu, Guanghua; Zhang, Sicong; Hou, Chenggang

2017-09-01

Monitoring of wind turbines under variable-speed operating conditions has become an important issue in recent years. The gearbox of a wind turbine is the most important transmission unit; it generally exhibits complex vibration signatures due to random variations in operating conditions. Spectral analysis is one of the main approaches in vibration signal processing. However, spectral analysis is based on a stationary assumption and thus inapplicable to the fault diagnosis of wind turbines under variable-speed operating conditions. This constraint limits the application of spectral analysis to wind turbine diagnosis in industrial applications. Although order-tracking methods have been proposed for wind turbine fault detection in recent years, current methods are only applicable to cases in which the instantaneous shaft phase is available. For wind turbines with limited structural spaces, collecting phase signals with tachometers or encoders is difficult. In this study, a tacholess order-tracking method for wind turbines is proposed to overcome the limitations of traditional techniques. The proposed method extracts the instantaneous phase from the vibration signal, resamples the signal at equiangular increments, and calculates the order spectrum for wind turbine fault identification. The effectiveness of the proposed method is experimentally validated with the vibration signals of wind turbines.

Machine learning search for variable stars

NASA Astrophysics Data System (ADS)

Pashchenko, Ilya N.; Sokolovsky, Kirill V.; Gavras, Panagiotis

2018-04-01

Photometric variability detection is often considered as a hypothesis testing problem: an object is variable if the null hypothesis that its brightness is constant can be ruled out given the measurements and their uncertainties. The practical applicability of this approach is limited by uncorrected systematic errors. We propose a new variability detection technique sensitive to a wide range of variability types while being robust to outliers and underestimated measurement uncertainties. We consider variability detection as a classification problem that can be approached with machine learning. Logistic Regression (LR), Support Vector Machines (SVM), k Nearest Neighbours (kNN), Neural Nets (NN), Random Forests (RF), and Stochastic Gradient Boosting classifier (SGB) are applied to 18 features (variability indices) quantifying scatter and/or correlation between points in a light curve. We use a subset of Optical Gravitational Lensing Experiment phase two (OGLE-II) Large Magellanic Cloud (LMC) photometry (30 265 light curves) that was searched for variability using traditional methods (168 known variable objects) as the training set and then apply the NN to a new test set of 31 798 OGLE-II LMC light curves. Among 205 candidates selected in the test set, 178 are real variables, while 13 low-amplitude variables are new discoveries. The machine learning classifiers considered are found to be more efficient (select more variables and fewer false candidates) compared to traditional techniques using individual variability indices or their linear combination. The NN, SGB, SVM, and RF show a higher efficiency compared to LR and kNN.

Comonotonic bounds on the survival probabilities in the Lee-Carter model for mortality projection

NASA Astrophysics Data System (ADS)

Denuit, Michel; Dhaene, Jan

2007-06-01

In the Lee-Carter framework, future survival probabilities are random variables with an intricate distribution function. In large homogeneous portfolios of life annuities, value-at-risk or conditional tail expectation of the total yearly payout of the company are approximately equal to the corresponding quantities involving random survival probabilities. This paper aims to derive some bounds in the increasing convex (or stop-loss) sense on these random survival probabilities. These bounds are obtained with the help of comonotonic upper and lower bounds on sums of correlated random variables.

Variance approach for multi-objective linear programming with fuzzy random of objective function coefficients

NASA Astrophysics Data System (ADS)

Indarsih, Indrati, Ch. Rini

2016-02-01

In this paper, we define variance of the fuzzy random variables through alpha level. We have a theorem that can be used to know that the variance of fuzzy random variables is a fuzzy number. We have a multi-objective linear programming (MOLP) with fuzzy random of objective function coefficients. We will solve the problem by variance approach. The approach transform the MOLP with fuzzy random of objective function coefficients into MOLP with fuzzy of objective function coefficients. By weighted methods, we have linear programming with fuzzy coefficients and we solve by simplex method for fuzzy linear programming.

A web application for moderation training: Initial results of a randomized clinical trial1

PubMed Central

Hester, Reid K.; Delaney, Harold D.; Campbell, William; Handmaker, Nancy

2009-01-01

Eighty four heavy drinkers who responded to a newspaper recruitment ad were randomly assigned to receive either: a) training in a moderate drinking protocol via an Internet-based program (www.moderatedrinking.com) and use of the online resources of Moderation Management (MM) (www.moderation.org) or b) use of the online resources of MM alone. Follow-ups are being conducted at 3, 6, and 12 months. Results of the recently completed 3 month follow-up (86% follow-up) indicated both groups significantly reduced their drinking based on these variables: standard drinks per week; percent days abstinent; and mean BAC per drinking day. Both groups also significantly reduced their alcohol-related problems. Relative to the control group the experimental group had better outcomes on percent days abstinent and log Drinks per Drinking Day. These short-term outcome data provide evidence for the effectiveness of both the moderate drinking web application and of the resources available online at MM in helping heavy drinkers reduce their drinking and alcohol-related problems. PMID:19339137

Exploiting Data Missingness in Bayesian Network Modeling

NASA Astrophysics Data System (ADS)

Rodrigues de Morais, Sérgio; Aussem, Alex

This paper proposes a framework built on the use of Bayesian networks (BN) for representing statistical dependencies between the existing random variables and additional dummy boolean variables, which represent the presence/absence of the respective random variable value. We show how augmenting the BN with these additional variables helps pinpoint the mechanism through which missing data contributes to the classification task. The missing data mechanism is thus explicitly taken into account to predict the class variable using the data at hand. Extensive experiments on synthetic and real-world incomplete data sets reveals that the missingness information improves classification accuracy.

State Estimation Using Dependent Evidence Fusion: Application to Acoustic Resonance-Based Liquid Level Measurement.

PubMed

Xu, Xiaobin; Li, Zhenghui; Li, Guo; Zhou, Zhe

2017-04-21

Estimating the state of a dynamic system via noisy sensor measurement is a common problem in sensor methods and applications. Most state estimation methods assume that measurement noise and state perturbations can be modeled as random variables with known statistical properties. However in some practical applications, engineers can only get the range of noises, instead of the precise statistical distributions. Hence, in the framework of Dempster-Shafer (DS) evidence theory, a novel state estimatation method by fusing dependent evidence generated from state equation, observation equation and the actual observations of the system states considering bounded noises is presented. It can be iteratively implemented to provide state estimation values calculated from fusion results at every time step. Finally, the proposed method is applied to a low-frequency acoustic resonance level gauge to obtain high-accuracy measurement results.

Variability in prefrontal hemodynamic response during exposure to repeated self-selected music excerpts, a near-infrared spectroscopy study.

PubMed

Moghimi, Saba; Schudlo, Larissa; Chau, Tom; Guerguerian, Anne-Marie

2015-01-01

Music-induced brain activity modulations in areas involved in emotion regulation may be useful in achieving therapeutic outcomes. Clinical applications of music may involve prolonged or repeated exposures to music. However, the variability of the observed brain activity patterns in repeated exposures to music is not well understood. We hypothesized that multiple exposures to the same music would elicit more consistent activity patterns than exposure to different music. In this study, the temporal and spatial variability of cerebral prefrontal hemodynamic response was investigated across multiple exposures to self-selected musical excerpts in 10 healthy adults. The hemodynamic changes were measured using prefrontal cortex near infrared spectroscopy and represented by instantaneous phase values. Based on spatial and temporal characteristics of these observed hemodynamic changes, we defined a consistency index to represent variability across these domains. The consistency index across repeated exposures to the same piece of music was compared to the consistency index corresponding to prefrontal activity from randomly matched non-identical musical excerpts. Consistency indexes were significantly different for identical versus non-identical musical excerpts when comparing a subset of repetitions. When all four exposures were compared, no significant difference was observed between the consistency indexes of randomly matched non-identical musical excerpts and the consistency index corresponding to repetitions of the same musical excerpts. This observation suggests the existence of only partial consistency between repeated exposures to the same musical excerpt, which may stem from the role of the prefrontal cortex in regulating other cognitive and emotional processes.

Application of random forests methods to diabetic retinopathy classification analyses.

PubMed

Casanova, Ramon; Saldana, Santiago; Chew, Emily Y; Danis, Ronald P; Greven, Craig M; Ambrosius, Walter T

2014-01-01

Diabetic retinopathy (DR) is one of the leading causes of blindness in the United States and world-wide. DR is a silent disease that may go unnoticed until it is too late for effective treatment. Therefore, early detection could improve the chances of therapeutic interventions that would alleviate its effects. Graded fundus photography and systemic data from 3443 ACCORD-Eye Study participants were used to estimate Random Forest (RF) and logistic regression classifiers. We studied the impact of sample size on classifier performance and the possibility of using RF generated class conditional probabilities as metrics describing DR risk. RF measures of variable importance are used to detect factors that affect classification performance. Both types of data were informative when discriminating participants with or without DR. RF based models produced much higher classification accuracy than those based on logistic regression. Combining both types of data did not increase accuracy but did increase statistical discrimination of healthy participants who subsequently did or did not have DR events during four years of follow-up. RF variable importance criteria revealed that microaneurysms counts in both eyes seemed to play the most important role in discrimination among the graded fundus variables, while the number of medicines and diabetes duration were the most relevant among the systemic variables. We have introduced RF methods to DR classification analyses based on fundus photography data. In addition, we propose an approach to DR risk assessment based on metrics derived from graded fundus photography and systemic data. Our results suggest that RF methods could be a valuable tool to diagnose DR diagnosis and evaluate its progression.

Variability in Prefrontal Hemodynamic Response during Exposure to Repeated Self-Selected Music Excerpts, a Near-Infrared Spectroscopy Study

PubMed Central

Moghimi, Saba; Schudlo, Larissa; Chau, Tom; Guerguerian, Anne-Marie

2015-01-01

Music-induced brain activity modulations in areas involved in emotion regulation may be useful in achieving therapeutic outcomes. Clinical applications of music may involve prolonged or repeated exposures to music. However, the variability of the observed brain activity patterns in repeated exposures to music is not well understood. We hypothesized that multiple exposures to the same music would elicit more consistent activity patterns than exposure to different music. In this study, the temporal and spatial variability of cerebral prefrontal hemodynamic response was investigated across multiple exposures to self-selected musical excerpts in 10 healthy adults. The hemodynamic changes were measured using prefrontal cortex near infrared spectroscopy and represented by instantaneous phase values. Based on spatial and temporal characteristics of these observed hemodynamic changes, we defined a consistency index to represent variability across these domains. The consistency index across repeated exposures to the same piece of music was compared to the consistency index corresponding to prefrontal activity from randomly matched non-identical musical excerpts. Consistency indexes were significantly different for identical versus non-identical musical excerpts when comparing a subset of repetitions. When all four exposures were compared, no significant difference was observed between the consistency indexes of randomly matched non-identical musical excerpts and the consistency index corresponding to repetitions of the same musical excerpts. This observation suggests the existence of only partial consistency between repeated exposures to the same musical excerpt, which may stem from the role of the prefrontal cortex in regulating other cognitive and emotional processes. PMID:25837268

Random Forest Application for NEXRAD Radar Data Quality Control

NASA Astrophysics Data System (ADS)

Keem, M.; Seo, B. C.; Krajewski, W. F.

2017-12-01

Identification and elimination of non-meteorological radar echoes (e.g., returns from ground, wind turbines, and biological targets) are the basic data quality control steps before radar data use in quantitative applications (e.g., precipitation estimation). Although WSR-88Ds' recent upgrade to dual-polarization has enhanced this quality control and echo classification, there are still challenges to detect some non-meteorological echoes that show precipitation-like characteristics (e.g., wind turbine or anomalous propagation clutter embedded in rain). With this in mind, a new quality control method using Random Forest is proposed in this study. This classification algorithm is known to produce reliable results with less uncertainty. The method introduces randomness into sampling and feature selections and integrates consequent multiple decision trees. The multidimensional structure of the trees can characterize the statistical interactions of involved multiple features in complex situations. The authors explore the performance of Random Forest method for NEXRAD radar data quality control. Training datasets are selected using several clear cases of precipitation and non-precipitation (but with some non-meteorological echoes). The model is structured using available candidate features (from the NEXRAD data) such as horizontal reflectivity, differential reflectivity, differential phase shift, copolar correlation coefficient, and their horizontal textures (e.g., local standard deviation). The influence of each feature on classification results are quantified by variable importance measures that are automatically estimated by the Random Forest algorithm. Therefore, the number and types of features in the final forest can be examined based on the classification accuracy. The authors demonstrate the capability of the proposed approach using several cases ranging from distinct to complex rain/no-rain events and compare the performance with the existing algorithms (e.g., MRMS). They also discuss operational feasibility based on the observed strength and weakness of the method.

Assessing the accuracy and stability of variable selection ...

EPA Pesticide Factsheets

Random forest (RF) modeling has emerged as an important statistical learning method in ecology due to its exceptional predictive performance. However, for large and complex ecological datasets there is limited guidance on variable selection methods for RF modeling. Typically, either a preselected set of predictor variables are used, or stepwise procedures are employed which iteratively add/remove variables according to their importance measures. This paper investigates the application of variable selection methods to RF models for predicting probable biological stream condition. Our motivating dataset consists of the good/poor condition of n=1365 stream survey sites from the 2008/2009 National Rivers and Stream Assessment, and a large set (p=212) of landscape features from the StreamCat dataset. Two types of RF models are compared: a full variable set model with all 212 predictors, and a reduced variable set model selected using a backwards elimination approach. We assess model accuracy using RF's internal out-of-bag estimate, and a cross-validation procedure with validation folds external to the variable selection process. We also assess the stability of the spatial predictions generated by the RF models to changes in the number of predictors, and argue that model selection needs to consider both accuracy and stability. The results suggest that RF modeling is robust to the inclusion of many variables of moderate to low importance. We found no substanti

Variability and reliability analysis in self-assembled multichannel carbon nanotube field-effect transistors

NASA Astrophysics Data System (ADS)

Hu, Zhaoying; Tulevski, George S.; Hannon, James B.; Afzali, Ali; Liehr, Michael; Park, Hongsik

2015-06-01

Carbon nanotubes (CNTs) have been widely studied as a channel material of scaled transistors for high-speed and low-power logic applications. In order to have sufficient drive current, it is widely assumed that CNT-based logic devices will have multiple CNTs in each channel. Understanding the effects of the number of CNTs on device performance can aid in the design of CNT field-effect transistors (CNTFETs). We have fabricated multi-CNT-channel CNTFETs with an 80-nm channel length using precise self-assembly methods. We describe compact statistical models and Monte Carlo simulations to analyze failure probability and the variability of the on-state current and threshold voltage. The results show that multichannel CNTFETs are more resilient to process variation and random environmental fluctuations than single-CNT devices.

Reducing The Risk Of Fires In Conveyor Transport

NASA Astrophysics Data System (ADS)

Cheremushkina, M. S.; Poddubniy, D. A.

2017-01-01

The paper deals with the actual problem of increasing the safety of operation of belt conveyors in mines. Was developed the control algorithm that meets the technical requirements of the mine belt conveyors, reduces the risk of fires of conveyors belt, and enables energy and resource savings taking into account random sort of traffic. The most effective method of decision such tasks is the construction of control systems with the use of variable speed drives for asynchronous motors. Was designed the mathematical model of the system "variable speed multiengine drive - conveyor - control system of conveyors", that takes into account the dynamic processes occurring in the elements of the transport system, provides an assessment of the energy efficiency of application the developed algorithms, which allows to reduce the dynamic overload in the belt to (15-20)%.

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

Dayman, Ken J; Ade, Brian J; Weber, Charles F

High-dimensional, nonlinear function estimation using large datasets is a current area of interest in the machine learning community, and applications may be found throughout the analytical sciences, where ever-growing datasets are making more information available to the analyst. In this paper, we leverage the existing relevance vector machine, a sparse Bayesian version of the well-studied support vector machine, and expand the method to include integrated feature selection and automatic function shaping. These innovations produce an algorithm that is able to distinguish variables that are useful for making predictions of a response from variables that are unrelated or confusing. We testmore » the technology using synthetic data, conduct initial performance studies, and develop a model capable of making position-independent predictions of the coreaveraged burnup using a single specimen drawn randomly from a nuclear reactor core.« less

Topical Oil Application and Trans-Epidermal Water Loss in Preterm Very Low Birth Weight Infants-A Randomized Trial.

PubMed

Nangia, Sushma; Paul, Vinod Kumar; Deorari, Ashok Kumar; Sreenivas, V; Agarwal, Ramesh; Chawla, Deepak

2015-12-01

Topical emollient application reduces trans-epidermal water loss (TEWL) in preterm neonates. Coconut oil used traditionally for infant massage in India has not been evaluated for the same. Very low birth weight (VLBW) neonates were randomized at 12 h of age to Oil (n = 37) or Control (n = 37) groups. Oil group neonates received twice-daily coconut oil application without massage, and Control group received standard care. TEWL was measured every 12 h using an evaporimeter till Day 7 when skin swabs were obtained for bacterial growth and skin condition was assessed using a validated score. Birth weight (g; mean ± SD: 1213 + 214 vs. 1164 + 208, p = 0.31), gestation [week; median (interquartile range): 32 (31-33) vs. 32 (29-33), p = 0.10] and other baseline variables were comparable. TEWL was significantly reduced (g/m(2)/h, mean difference: -6.80, 95% confidence interval: -3.48, -10.15; p < 0.01) with better skin condition and lower bacterial growth in the Oil group (20% vs. 60%, p < 0.01). Coconut oil application reduced TEWL without increasing skin colonization in VLBW neonates. NCT01758068. © The Author [2015]. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Nonrecurrence and Bell-like inequalities

NASA Astrophysics Data System (ADS)

Danforth, Douglas G.

2017-12-01

The general class, Λ, of Bell hidden variables is composed of two subclasses ΛR and ΛN such that ΛR⋃ΛN = Λ and ΛR∩ ΛN = {}. The class ΛN is very large and contains random variables whose domain is the continuum, the reals. There are an uncountable infinite number of reals. Every instance of a real random variable is unique. The probability of two instances being equal is zero, exactly zero. ΛN induces sample independence. All correlations are context dependent but not in the usual sense. There is no "spooky action at a distance". Random variables, belonging to ΛN, are independent from one experiment to the next. The existence of the class ΛN makes it impossible to derive any of the standard Bell inequalities used to define quantum entanglement.

Perturbed effects at radiation physics

NASA Astrophysics Data System (ADS)

Külahcı, Fatih; Şen, Zekâi

2013-09-01

Perturbation methodology is applied in order to assess the linear attenuation coefficient, mass attenuation coefficient and cross-section behavior with random components in the basic variables such as the radiation amounts frequently used in the radiation physics and chemistry. Additionally, layer attenuation coefficient (LAC) and perturbed LAC (PLAC) are proposed for different contact materials. Perturbation methodology provides opportunity to obtain results with random deviations from the average behavior of each variable that enters the whole mathematical expression. The basic photon intensity variation expression as the inverse exponential power law (as Beer-Lambert's law) is adopted for perturbation method exposition. Perturbed results are presented not only in terms of the mean but additionally the standard deviation and the correlation coefficients. Such perturbation expressions provide one to assess small random variability in basic variables.

Standard errors and confidence intervals for variable importance in random forest regression, classification, and survival.

PubMed

Ishwaran, Hemant; Lu, Min

2018-06-04

Random forests are a popular nonparametric tree ensemble procedure with broad applications to data analysis. While its widespread popularity stems from its prediction performance, an equally important feature is that it provides a fully nonparametric measure of variable importance (VIMP). A current limitation of VIMP, however, is that no systematic method exists for estimating its variance. As a solution, we propose a subsampling approach that can be used to estimate the variance of VIMP and for constructing confidence intervals. The method is general enough that it can be applied to many useful settings, including regression, classification, and survival problems. Using extensive simulations, we demonstrate the effectiveness of the subsampling estimator and in particular find that the delete-d jackknife variance estimator, a close cousin, is especially effective under low subsampling rates due to its bias correction properties. These 2 estimators are highly competitive when compared with the .164 bootstrap estimator, a modified bootstrap procedure designed to deal with ties in out-of-sample data. Most importantly, subsampling is computationally fast, thus making it especially attractive for big data settings. Copyright © 2018 John Wiley & Sons, Ltd.

Effects of Random Phase Shifts and Carrier Drift on the Resolution Properties of Linear Step Frequency Pulse Trains.

DTIC Science & Technology

1985-12-01

complex representation, the difference is 2 f a(t)-ga(t) ( t (3) ’- . where fa (t)=f(t)+jf(t), ga(t)-g(t)+jg(t), and f(t) and g(t) are the Hilbert ...percentage drift (PERCNT) represent reasonable values for".’. those variables Thf heart of both routines is a modified algorithm given by Dorn and Greenberg ...Application. N.Y.: Academic Press, 1967. -, - 4. Dorn, William S. and Greenberg , Herbert J. Mathematics and Compu- ting: with Fortran Programming. New York

Assessing the accuracy and stability of variable selection methods for random forest modeling in ecology

EPA Science Inventory

Random forest (RF) modeling has emerged as an important statistical learning method in ecology due to its exceptional predictive performance. However, for large and complex ecological datasets there is limited guidance on variable selection methods for RF modeling. Typically, e...

The influence of personality type on decision making in the physical therapy admission process.

PubMed

Bezner, J R; Boucher, B K

2001-01-01

The purpose of this study was to identify the personality types of physical therapy (PT) interviewers and applicants, using the Personality Styles (PS) assessment tool, and to determine whether an interview team's personality type influences the rating score given. The PS was validated in a study of 298 students who completed the Myers-Briggs Type Indicator (MBTI) Form G and a PS assessment. By chi-square analysis the PS model appears to be a valid representation of the MBTI (chi 2 = 86.62, p < 0.00001). Subjects for the interview portion of the study were 282 student applicants, 19 faculty, and 47 clinicians from two PT programs. A randomly assigned faculty/clinician team interviewed each applicant. Two one-way ANOVAs were performed with interview score as the dependent variable and 1) applicant personality type in relation to faculty/clinician team (same, different from both, like one) and 2) applicant personality type as the independent variables. Internal consistency of the interview rating form was alpha = 0.89. Mean interview score was 33.97/42 (SD 4.59). Interview scores were not significantly different between applicants who interviewed with clinician/faculty teams that were "like" compared with "not like" the applicants (F0.864; p = 0.423), but were significantly different between applicants with different PS personality types (F3.159; p = 0.026). Although personality type of the interview team did not impact the score given, thereby refuting the presence of interviewer bias, the rating scores did vary according to personality type of the applicant, suggesting a possible stereotyping bias in the criteria used to rate applicants.

Benford's law and continuous dependent random variables

NASA Astrophysics Data System (ADS)

Becker, Thealexa; Burt, David; Corcoran, Taylor C.; Greaves-Tunnell, Alec; Iafrate, Joseph R.; Jing, Joy; Miller, Steven J.; Porfilio, Jaclyn D.; Ronan, Ryan; Samranvedhya, Jirapat; Strauch, Frederick W.; Talbut, Blaine

2018-01-01

Many mathematical, man-made and natural systems exhibit a leading-digit bias, where a first digit (base 10) of 1 occurs not 11% of the time, as one would expect if all digits were equally likely, but rather 30%. This phenomenon is known as Benford's Law. Analyzing which datasets adhere to Benford's Law and how quickly Benford behavior sets in are the two most important problems in the field. Most previous work studied systems of independent random variables, and relied on the independence in their analyses. Inspired by natural processes such as particle decay, we study the dependent random variables that emerge from models of decomposition of conserved quantities. We prove that in many instances the distribution of lengths of the resulting pieces converges to Benford behavior as the number of divisions grow, and give several conjectures for other fragmentation processes. The main difficulty is that the resulting random variables are dependent. We handle this by using tools from Fourier analysis and irrationality exponents to obtain quantified convergence rates as well as introducing and developing techniques to measure and control the dependencies. The construction of these tools is one of the major motivations of this work, as our approach can be applied to many other dependent systems. As an example, we show that the n ! entries in the determinant expansions of n × n matrices with entries independently drawn from nice random variables converges to Benford's Law.

Breakthrough in current-in-plane tunneling measurement precision by application of multi-variable fitting algorithm.

PubMed

Cagliani, Alberto; Østerberg, Frederik W; Hansen, Ole; Shiv, Lior; Nielsen, Peter F; Petersen, Dirch H

2017-09-01

We present a breakthrough in micro-four-point probe (M4PP) metrology to substantially improve precision of transmission line (transfer length) type measurements by application of advanced electrode position correction. In particular, we demonstrate this methodology for the M4PP current-in-plane tunneling (CIPT) technique. The CIPT method has been a crucial tool in the development of magnetic tunnel junction (MTJ) stacks suitable for magnetic random-access memories for more than a decade. On two MTJ stacks, the measurement precision of resistance-area product and tunneling magnetoresistance was improved by up to a factor of 3.5 and the measurement reproducibility by up to a factor of 17, thanks to our improved position correction technique.

A Unifying Probability Example.

ERIC Educational Resources Information Center

Maruszewski, Richard F., Jr.

2002-01-01

Presents an example from probability and statistics that ties together several topics including the mean and variance of a discrete random variable, the binomial distribution and its particular mean and variance, the sum of independent random variables, the mean and variance of the sum, and the central limit theorem. Uses Excel to illustrate these…

Variable selection with random forest: Balancing stability, performance, and interpretation in ecological and environmental modeling

EPA Science Inventory

Random forest (RF) is popular in ecological and environmental modeling, in part, because of its insensitivity to correlated predictors and resistance to overfitting. Although variable selection has been proposed to improve both performance and interpretation of RF models, it is u...

Random effects coefficient of determination for mixed and meta-analysis models

PubMed Central

Demidenko, Eugene; Sargent, James; Onega, Tracy

2011-01-01

The key feature of a mixed model is the presence of random effects. We have developed a coefficient, called the random effects coefficient of determination, Rr2, that estimates the proportion of the conditional variance of the dependent variable explained by random effects. This coefficient takes values from 0 to 1 and indicates how strong the random effects are. The difference from the earlier suggested fixed effects coefficient of determination is emphasized. If Rr2 is close to 0, there is weak support for random effects in the model because the reduction of the variance of the dependent variable due to random effects is small; consequently, random effects may be ignored and the model simplifies to standard linear regression. The value of Rr2 apart from 0 indicates the evidence of the variance reduction in support of the mixed model. If random effects coefficient of determination is close to 1 the variance of random effects is very large and random effects turn into free fixed effects—the model can be estimated using the dummy variable approach. We derive explicit formulas for Rr2 in three special cases: the random intercept model, the growth curve model, and meta-analysis model. Theoretical results are illustrated with three mixed model examples: (1) travel time to the nearest cancer center for women with breast cancer in the U.S., (2) cumulative time watching alcohol related scenes in movies among young U.S. teens, as a risk factor for early drinking onset, and (3) the classic example of the meta-analysis model for combination of 13 studies on tuberculosis vaccine. PMID:23750070

Correlated resistive/capacitive state variability in solid TiO2 based memory devices

NASA Astrophysics Data System (ADS)

Li, Qingjiang; Salaoru, Iulia; Khiat, Ali; Xu, Hui; Prodromakis, Themistoklis

2017-05-01

In this work, we experimentally demonstrated the correlated resistive/capacitive switching and state variability in practical TiO2 based memory devices. Based on filamentary functional mechanism, we argue that the impedance state variability stems from the randomly distributed defects inside the oxide bulk. Finally, our assumption was verified via a current percolation circuit model, by taking into account of random defects distribution and coexistence of memristor and memcapacitor.

Algebraic Functions of H-Functions with Specific Dependency Structure.

DTIC Science & Technology

1984-05-01

a study of its characteristic function. Such analysis is reproduced in books by Springer (17), Anderson (23), Feller (34,35), Mood and Graybill (52...following linearity property for expectations of jointly distributed random variables is derived. r 1 Theorem 1.1: If X and Y are real random variables...appear in American Journal of Mathematical and Management Science. 13. Mathai, A.M., and R.K. Saxena, "On linear combinations of stochastic variables

Subharmonic response of a single-degree-of-freedom nonlinear vibro-impact system to a narrow-band random excitation.

PubMed

Haiwu, Rong; Wang, Xiangdong; Xu, Wei; Fang, Tong

2009-08-01

The subharmonic response of single-degree-of-freedom nonlinear vibro-impact oscillator with a one-sided barrier to narrow-band random excitation is investigated. The narrow-band random excitation used here is a filtered Gaussian white noise. The analysis is based on a special Zhuravlev transformation, which reduces the system to one without impacts, or velocity jumps, thereby permitting the applications of asymptotic averaging over the "fast" variables. The averaged stochastic equations are solved exactly by the method of moments for the mean-square response amplitude for the case of linear system with zero offset. A perturbation-based moment closure scheme is proposed and the formula of the mean-square amplitude is obtained approximately for the case of linear system with nonzero offset. The perturbation-based moment closure scheme is used once again to obtain the algebra equation of the mean-square amplitude of the response for the case of nonlinear system. The effects of damping, detuning, nonlinear intensity, bandwidth, and magnitudes of random excitations are analyzed. The theoretical analyses are verified by numerical results. Theoretical analyses and numerical simulations show that the peak amplitudes may be strongly reduced at large detunings or large nonlinear intensity.

A Novel Weighted Kernel PCA-Based Method for Optimization and Uncertainty Quantification

NASA Astrophysics Data System (ADS)

Thimmisetty, C.; Talbot, C.; Chen, X.; Tong, C. H.

2016-12-01

It has been demonstrated that machine learning methods can be successfully applied to uncertainty quantification for geophysical systems through the use of the adjoint method coupled with kernel PCA-based optimization. In addition, it has been shown through weighted linear PCA how optimization with respect to both observation weights and feature space control variables can accelerate convergence of such methods. Linear machine learning methods, however, are inherently limited in their ability to represent features of non-Gaussian stochastic random fields, as they are based on only the first two statistical moments of the original data. Nonlinear spatial relationships and multipoint statistics leading to the tortuosity characteristic of channelized media, for example, are captured only to a limited extent by linear PCA. With the aim of coupling the kernel-based and weighted methods discussed, we present a novel mathematical formulation of kernel PCA, Weighted Kernel Principal Component Analysis (WKPCA), that both captures nonlinear relationships and incorporates the attribution of significance levels to different realizations of the stochastic random field of interest. We also demonstrate how new instantiations retaining defining characteristics of the random field can be generated using Bayesian methods. In particular, we present a novel WKPCA-based optimization method that minimizes a given objective function with respect to both feature space random variables and observation weights through which optimal snapshot significance levels and optimal features are learned. We showcase how WKPCA can be applied to nonlinear optimal control problems involving channelized media, and in particular demonstrate an application of the method to learning the spatial distribution of material parameter values in the context of linear elasticity, and discuss further extensions of the method to stochastic inversion.

The Statistical Fermi Paradox

NASA Astrophysics Data System (ADS)

Maccone, C.

In this paper is provided the statistical generalization of the Fermi paradox. The statistics of habitable planets may be based on a set of ten (and possibly more) astrobiological requirements first pointed out by Stephen H. Dole in his book Habitable planets for man (1964). The statistical generalization of the original and by now too simplistic Dole equation is provided by replacing a product of ten positive numbers by the product of ten positive random variables. This is denoted the SEH, an acronym standing for “Statistical Equation for Habitables”. The proof in this paper is based on the Central Limit Theorem (CLT) of Statistics, stating that the sum of any number of independent random variables, each of which may be ARBITRARILY distributed, approaches a Gaussian (i.e. normal) random variable (Lyapunov form of the CLT). It is then shown that: 1. The new random variable NHab, yielding the number of habitables (i.e. habitable planets) in the Galaxy, follows the log- normal distribution. By construction, the mean value of this log-normal distribution is the total number of habitable planets as given by the statistical Dole equation. 2. The ten (or more) astrobiological factors are now positive random variables. The probability distribution of each random variable may be arbitrary. The CLT in the so-called Lyapunov or Lindeberg forms (that both do not assume the factors to be identically distributed) allows for that. In other words, the CLT "translates" into the SEH by allowing an arbitrary probability distribution for each factor. This is both astrobiologically realistic and useful for any further investigations. 3. By applying the SEH it is shown that the (average) distance between any two nearby habitable planets in the Galaxy may be shown to be inversely proportional to the cubic root of NHab. This distance is denoted by new random variable D. The relevant probability density function is derived, which was named the "Maccone distribution" by Paul Davies in 2008. 4. A practical example is then given of how the SEH works numerically. Each of the ten random variables is uniformly distributed around its own mean value as given by Dole (1964) and a standard deviation of 10% is assumed. The conclusion is that the average number of habitable planets in the Galaxy should be around 100 million ±200 million, and the average distance in between any two nearby habitable planets should be about 88 light years ±40 light years. 5. The SEH results are matched against the results of the Statistical Drake Equation from reference 4. As expected, the number of currently communicating ET civilizations in the Galaxy turns out to be much smaller than the number of habitable planets (about 10,000 against 100 million, i.e. one ET civilization out of 10,000 habitable planets). The average distance between any two nearby habitable planets is much smaller that the average distance between any two neighbouring ET civilizations: 88 light years vs. 2000 light years, respectively. This means an ET average distance about 20 times higher than the average distance between any pair of adjacent habitable planets. 6. Finally, a statistical model of the Fermi Paradox is derived by applying the above results to the coral expansion model of Galactic colonization. The symbolic manipulator "Macsyma" is used to solve these difficult equations. A new random variable Tcol, representing the time needed to colonize a new planet is introduced, which follows the lognormal distribution, Then the new quotient random variable Tcol/D is studied and its probability density function is derived by Macsyma. Finally a linear transformation of random variables yields the overall time TGalaxy needed to colonize the whole Galaxy. We believe that our mathematical work in deriving this STATISTICAL Fermi Paradox is highly innovative and fruitful for the future.

Application and testing of a procedure to evaluate transferability of habitat suitability criteria

USGS Publications Warehouse

Thomas, Jeff A.; Bovee, Ken D.

1993-01-01

A procedure designed to test the transferability of habitat suitability criteria was evaluated in the Cache la Poudre River, Colorado. Habitat suitability criteria were developed for active adult and juvenile rainbow trout in the South Platte River, Colorado. These criteria were tested by comparing microhabitat use predicted from the criteria with observed microhabitat use by adult rainbow trout in the Cache la Poudre River. A one-sided X2 test, using counts of occupied and unoccupied cells in each suitability classification, was used to test for non-random selection for optimum habitat use over usable habitat and for suitable over unsuitable habitat. Criteria for adult rainbow trout were judged to be transferable to the Cache la Poudre River, but juvenile criteria (applied to adults) were not transferable. Random subsampling of occupied and unoccupied cells was conducted to determine the effect of sample size on the reliability of the test procedure. The incidence of type I and type II errors increased rapidly as the sample size was reduced below 55 occupied and 200 unoccupied cells. Recommended modifications to the procedure included the adoption of a systematic or randomized sampling design and direct measurement of microhabitat variables. With these modifications, the procedure is economical, simple and reliable. Use of the procedure as a quality assurance device in routine applications of the instream flow incremental methodology was encouraged.

Effects of self-management health information technology on glycaemic control for patients with diabetes: a meta-analysis of randomized controlled trials.

PubMed

Tao, Da; Or, Calvin Kl

2013-04-01

We conducted a systematic review and meta-analysis of randomized controlled trials (RCTs) which had evaluated self-management health information technology (SMHIT) for glycaemic control in patients with diabetes. A total of 43 RCTs was identified, which reported on 52 control-intervention comparisons. The glycosylated haemoglobin (HbA 1c ) data were pooled using a random effects meta-analysis method, followed by a meta-regression and subgroup analyses to examine the effects of a set of moderators. The meta-analysis showed that use of SMHITs was associated with a significant reduction in HbA 1c compared to usual care, with a pooled standardized mean difference of -0.30% (95% CI -0.39 to -0.21, P < 0.001). Sample size, age, study setting, type of application and method of data entry significantly moderated the effects of SMHIT use. The review supports the use of SMHITs as a self-management approach to improve glycaemic control. The effect of SMHIT use is significantly greater when the technology is a web-based application, when a mechanism for patients' health data entry is provided (manual or automatic) and when the technology is operated in the home or without location restrictions. Integrating these variables into the design of SMHITs may augment the effectiveness of the interventions. © SAGE Publications Ltd, 2013.

Random parameter models for accident prediction on two-lane undivided highways in India.

PubMed

Dinu, R R; Veeraragavan, A

2011-02-01

Generalized linear modeling (GLM), with the assumption of Poisson or negative binomial error structure, has been widely employed in road accident modeling. A number of explanatory variables related to traffic, road geometry, and environment that contribute to accident occurrence have been identified and accident prediction models have been proposed. The accident prediction models reported in literature largely employ the fixed parameter modeling approach, where the magnitude of influence of an explanatory variable is considered to be fixed for any observation in the population. Similar models have been proposed for Indian highways too, which include additional variables representing traffic composition. The mixed traffic on Indian highways comes with a lot of variability within, ranging from difference in vehicle types to variability in driver behavior. This could result in variability in the effect of explanatory variables on accidents across locations. Random parameter models, which can capture some of such variability, are expected to be more appropriate for the Indian situation. The present study is an attempt to employ random parameter modeling for accident prediction on two-lane undivided rural highways in India. Three years of accident history, from nearly 200 km of highway segments, is used to calibrate and validate the models. The results of the analysis suggest that the model coefficients for traffic volume, proportion of cars, motorized two-wheelers and trucks in traffic, and driveway density and horizontal and vertical curvatures are randomly distributed across locations. The paper is concluded with a discussion on modeling results and the limitations of the present study. Copyright © 2010 Elsevier Ltd. All rights reserved.

Maximum-Entropy Inference with a Programmable Annealer

PubMed Central

Chancellor, Nicholas; Szoke, Szilard; Vinci, Walter; Aeppli, Gabriel; Warburton, Paul A.

2016-01-01

Optimisation problems typically involve finding the ground state (i.e. the minimum energy configuration) of a cost function with respect to many variables. If the variables are corrupted by noise then this maximises the likelihood that the solution is correct. The maximum entropy solution on the other hand takes the form of a Boltzmann distribution over the ground and excited states of the cost function to correct for noise. Here we use a programmable annealer for the information decoding problem which we simulate as a random Ising model in a field. We show experimentally that finite temperature maximum entropy decoding can give slightly better bit-error-rates than the maximum likelihood approach, confirming that useful information can be extracted from the excited states of the annealer. Furthermore we introduce a bit-by-bit analytical method which is agnostic to the specific application and use it to show that the annealer samples from a highly Boltzmann-like distribution. Machines of this kind are therefore candidates for use in a variety of machine learning applications which exploit maximum entropy inference, including language processing and image recognition. PMID:26936311

Bias, Confounding, and Interaction: Lions and Tigers, and Bears, Oh My!

PubMed

Vetter, Thomas R; Mascha, Edward J

2017-09-01

Epidemiologists seek to make a valid inference about the causal effect between an exposure and a disease in a specific population, using representative sample data from a specific population. Clinical researchers likewise seek to make a valid inference about the association between an intervention and outcome(s) in a specific population, based upon their randomly collected, representative sample data. Both do so by using the available data about the sample variable to make a valid estimate about its corresponding or underlying, but unknown population parameter. Random error in an experiment can be due to the natural, periodic fluctuation or variation in the accuracy or precision of virtually any data sampling technique or health measurement tool or scale. In a clinical research study, random error can be due to not only innate human variability but also purely chance. Systematic error in an experiment arises from an innate flaw in the data sampling technique or measurement instrument. In the clinical research setting, systematic error is more commonly referred to as systematic bias. The most commonly encountered types of bias in anesthesia, perioperative, critical care, and pain medicine research include recall bias, observational bias (Hawthorne effect), attrition bias, misclassification or informational bias, and selection bias. A confounding variable is a factor associated with both the exposure of interest and the outcome of interest. A confounding variable (confounding factor or confounder) is a variable that correlates (positively or negatively) with both the exposure and outcome. Confounding is typically not an issue in a randomized trial because the randomized groups are sufficiently balanced on all potential confounding variables, both observed and nonobserved. However, confounding can be a major problem with any observational (nonrandomized) study. Ignoring confounding in an observational study will often result in a "distorted" or incorrect estimate of the association or treatment effect. Interaction among variables, also known as effect modification, exists when the effect of 1 explanatory variable on the outcome depends on the particular level or value of another explanatory variable. Bias and confounding are common potential explanations for statistically significant associations between exposure and outcome when the true relationship is noncausal. Understanding interactions is vital to proper interpretation of treatment effects. These complex concepts should be consistently and appropriately considered whenever one is not only designing but also analyzing and interpreting data from a randomized trial or observational study.

Random dopant fluctuations and statistical variability in n-channel junctionless FETs

NASA Astrophysics Data System (ADS)

Akhavan, N. D.; Umana-Membreno, G. A.; Gu, R.; Antoszewski, J.; Faraone, L.

2018-01-01

The influence of random dopant fluctuations on the statistical variability of the electrical characteristics of n-channel silicon junctionless nanowire transistor (JNT) has been studied using three dimensional quantum simulations based on the non-equilibrium Green’s function (NEGF) formalism. Average randomly distributed body doping densities of 2 × 1019, 6 × 1019 and 1 × 1020 cm-3 have been considered employing an atomistic model for JNTs with gate lengths of 5, 10 and 15 nm. We demonstrate that by properly adjusting the doping density in the JNT, a near ideal statistical variability and electrical performance can be achieved, which can pave the way for the continuation of scaling in silicon CMOS technology.

A New Method of Random Environmental Walking for Assessing Behavioral Preferences for Different Lighting Applications

PubMed Central

Patching, Geoffrey R.; Rahm, Johan; Jansson, Märit; Johansson, Maria

2017-01-01

Accurate assessment of people’s preferences for different outdoor lighting applications is increasingly considered important in the development of new urban environments. Here a new method of random environmental walking is proposed to complement current methods of assessing urban lighting applications, such as self-report questionnaires. The procedure involves participants repeatedly walking between different lighting applications by random selection of a lighting application and preferred choice or by random selection of a lighting application alone. In this manner, participants are exposed to all lighting applications of interest more than once and participants’ preferences for the different lighting applications are reflected in the number of times they walk to each lighting application. On the basis of an initial simulation study, to explore the feasibility of this approach, a comprehensive field test was undertaken. The field test included random environmental walking and collection of participants’ subjective ratings of perceived pleasantness (PP), perceived quality, perceived strength, and perceived flicker of four lighting applications. The results indicate that random environmental walking can reveal participants’ preferences for different lighting applications that, in the present study, conformed to participants’ ratings of PP and perceived quality of the lighting applications. As a complement to subjectively stated environmental preferences, random environmental walking has the potential to expose behavioral preferences for different lighting applications. PMID:28337163

Measures of Residual Risk with Connections to Regression, Risk Tracking, Surrogate Models, and Ambiguity

DTIC Science & Technology

2015-04-15

manage , predict, and mitigate the risk in the original variable. Residual risk can be exemplified as a quantification of the improved... the random variable of interest is viewed in concert with a related random vector that helps to manage , predict, and mitigate the risk in the original... manage , predict and mitigate the risk in the original variable. Residual risk can be exemplified as a quantification of the improved situation faced

A Strategy to Use Soft Data Effectively in Randomized Controlled Clinical Trials.

ERIC Educational Resources Information Center

Kraemer, Helena Chmura; Thiemann, Sue

1989-01-01

Sees soft data, measures having substantial intrasubject variability due to errors of measurement or response inconsistency, as important measures of response in randomized clinical trials. Shows that using intensive design and slope of response on time as outcome measure maximizes sample retention and decreases within-group variability, thus…

Bayesian approach to non-Gaussian field statistics for diffusive broadband terahertz pulses.

PubMed

Pearce, Jeremy; Jian, Zhongping; Mittleman, Daniel M

2005-11-01

We develop a closed-form expression for the probability distribution function for the field components of a diffusive broadband wave propagating through a random medium. We consider each spectral component to provide an individual observation of a random variable, the configurationally averaged spectral intensity. Since the intensity determines the variance of the field distribution at each frequency, this random variable serves as the Bayesian prior that determines the form of the non-Gaussian field statistics. This model agrees well with experimental results.

A posteriori noise estimation in variable data sets. With applications to spectra and light curves

NASA Astrophysics Data System (ADS)

Czesla, S.; Molle, T.; Schmitt, J. H. M. M.

2018-01-01

Most physical data sets contain a stochastic contribution produced by measurement noise or other random sources along with the signal. Usually, neither the signal nor the noise are accurately known prior to the measurement so that both have to be estimated a posteriori. We have studied a procedure to estimate the standard deviation of the stochastic contribution assuming normality and independence, requiring a sufficiently well-sampled data set to yield reliable results. This procedure is based on estimating the standard deviation in a sample of weighted sums of arbitrarily sampled data points and is identical to the so-called DER_SNR algorithm for specific parameter settings. To demonstrate the applicability of our procedure, we present applications to synthetic data, high-resolution spectra, and a large sample of space-based light curves and, finally, give guidelines to apply the procedure in situation not explicitly considered here to promote its adoption in data analysis.

Debates—Stochastic subsurface hydrology from theory to practice: The relevance of stochastic subsurface hydrology to practical problems of contaminant transport and remediation. What is characterization and stochastic theory good for?

NASA Astrophysics Data System (ADS)

Fiori, A.; Cvetkovic, V.; Dagan, G.; Attinger, S.; Bellin, A.; Dietrich, P.; Zech, A.; Teutsch, G.

2016-12-01

The emergence of stochastic subsurface hydrology stemmed from the realization that the random spatial variability of aquifer properties has a profound impact on solute transport. The last four decades witnessed a tremendous expansion of the discipline, many fundamental processes and principal mechanisms being identified. However, the research findings have not impacted significantly the application in practice, for several reasons which are discussed. The paper discusses the current status of stochastic subsurface hydrology, the relevance of the scientific results for applications and it also provides a perspective to a few possible future directions. In particular, we discuss how the transfer of knowledge can be facilitated by identifying clear goals for characterization and modeling application, relying on recent recent advances in research in these areas.

Optimal estimation of spatially variable recharge and transmissivity fields under steady-state groundwater flow. Part 1. Theory

NASA Astrophysics Data System (ADS)

Graham, Wendy D.; Tankersley, Claude D.

1994-05-01

Stochastic methods are used to analyze two-dimensional steady groundwater flow subject to spatially variable recharge and transmissivity. Approximate partial differential equations are developed for the covariances and cross-covariances between the random head, transmissivity and recharge fields. Closed-form solutions of these equations are obtained using Fourier transform techniques. The resulting covariances and cross-covariances can be incorporated into a Bayesian conditioning procedure which provides optimal estimates of the recharge, transmissivity and head fields given available measurements of any or all of these random fields. Results show that head measurements contain valuable information for estimating the random recharge field. However, when recharge is treated as a spatially variable random field, the value of head measurements for estimating the transmissivity field can be reduced considerably. In a companion paper, the method is applied to a case study of the Upper Floridan Aquifer in NE Florida.

Random Effects: Variance Is the Spice of Life.

PubMed

Jupiter, Daniel C

Covariates in regression analyses allow us to understand how independent variables of interest impact our dependent outcome variable. Often, we consider fixed effects covariates (e.g., gender or diabetes status) for which we examine subjects at each value of the covariate. We examine both men and women and, within each gender, examine both diabetic and nondiabetic patients. Occasionally, however, we consider random effects covariates for which we do not examine subjects at every value. For example, we examine patients from only a sample of hospitals and, within each hospital, examine both diabetic and nondiabetic patients. The random sampling of hospitals is in contrast to the complete coverage of all genders. In this column I explore the differences in meaning and analysis when thinking about fixed and random effects variables. Copyright © 2016 American College of Foot and Ankle Surgeons. Published by Elsevier Inc. All rights reserved.

Sampling Strategies for Evaluating the Rate of Adventitious Transgene Presence in Non-Genetically Modified Crop Fields.

PubMed

Makowski, David; Bancal, Rémi; Bensadoun, Arnaud; Monod, Hervé; Messéan, Antoine

2017-09-01

According to E.U. regulations, the maximum allowable rate of adventitious transgene presence in non-genetically modified (GM) crops is 0.9%. We compared four sampling methods for the detection of transgenic material in agricultural non-GM maize fields: random sampling, stratified sampling, random sampling + ratio reweighting, random sampling + regression reweighting. Random sampling involves simply sampling maize grains from different locations selected at random from the field concerned. The stratified and reweighting sampling methods make use of an auxiliary variable corresponding to the output of a gene-flow model (a zero-inflated Poisson model) simulating cross-pollination as a function of wind speed, wind direction, and distance to the closest GM maize field. With the stratified sampling method, an auxiliary variable is used to define several strata with contrasting transgene presence rates, and grains are then sampled at random from each stratum. With the two methods involving reweighting, grains are first sampled at random from various locations within the field, and the observations are then reweighted according to the auxiliary variable. Data collected from three maize fields were used to compare the four sampling methods, and the results were used to determine the extent to which transgene presence rate estimation was improved by the use of stratified and reweighting sampling methods. We found that transgene rate estimates were more accurate and that substantially smaller samples could be used with sampling strategies based on an auxiliary variable derived from a gene-flow model. © 2017 Society for Risk Analysis.

Random variable transformation for generalized stochastic radiative transfer in finite participating slab media

NASA Astrophysics Data System (ADS)

El-Wakil, S. A.; Sallah, M.; El-Hanbaly, A. M.

2015-10-01

The stochastic radiative transfer problem is studied in a participating planar finite continuously fluctuating medium. The problem is considered for specular- and diffusly-reflecting boundaries with linear anisotropic scattering. Random variable transformation (RVT) technique is used to get the complete average for the solution functions, that are represented by the probability-density function (PDF) of the solution process. In the RVT algorithm, a simple integral transformation to the input stochastic process (the extinction function of the medium) is applied. This linear transformation enables us to rewrite the stochastic transport equations in terms of the optical random variable (x) and the optical random thickness (L). Then the transport equation is solved deterministically to get a closed form for the solution as a function of x and L. So, the solution is used to obtain the PDF of the solution functions applying the RVT technique among the input random variable (L) and the output process (the solution functions). The obtained averages of the solution functions are used to get the complete analytical averages for some interesting physical quantities, namely, reflectivity and transmissivity at the medium boundaries. In terms of the average reflectivity and transmissivity, the average of the partial heat fluxes for the generalized problem with internal source of radiation are obtained and represented graphically.

Image discrimination models predict detection in fixed but not random noise

NASA Technical Reports Server (NTRS)

Ahumada, A. J. Jr; Beard, B. L.; Watson, A. B. (Principal Investigator)

1997-01-01

By means of a two-interval forced-choice procedure, contrast detection thresholds for an aircraft positioned on a simulated airport runway scene were measured with fixed and random white-noise masks. The term fixed noise refers to a constant, or unchanging, noise pattern for each stimulus presentation. The random noise was either the same or different in the two intervals. Contrary to simple image discrimination model predictions, the same random noise condition produced greater masking than the fixed noise. This suggests that observers seem unable to hold a new noisy image for comparison. Also, performance appeared limited by internal process variability rather than by external noise variability, since similar masking was obtained for both random noise types.

Finding the Best-Fit Polynomial Approximation in Evaluating Drill Data: the Application of a Generalized Inverse Matrix / Poszukiwanie Najlepszej ZGODNOŚCI W PRZYBLIŻENIU Wielomianowym Wykorzystanej do Oceny Danych Z ODWIERTÓW - Zastosowanie UOGÓLNIONEJ Macierzy Odwrotnej

NASA Astrophysics Data System (ADS)

Karakus, Dogan

2013-12-01

In mining, various estimation models are used to accurately assess the size and the grade distribution of an ore body. The estimation of the positional properties of unknown regions using random samples with known positional properties was first performed using polynomial approximations. Although the emergence of computer technologies and statistical evaluation of random variables after the 1950s rendered the polynomial approximations less important, theoretically the best surface passing through the random variables can be expressed as a polynomial approximation. In geoscience studies, in which the number of random variables is high, reliable solutions can be obtained only with high-order polynomials. Finding the coefficients of these types of high-order polynomials can be computationally intensive. In this study, the solution coefficients of high-order polynomials were calculated using a generalized inverse matrix method. A computer algorithm was developed to calculate the polynomial degree giving the best regression between the values obtained for solutions of different polynomial degrees and random observational data with known values, and this solution was tested with data derived from a practical application. In this application, the calorie values for data from 83 drilling points in a coal site located in southwestern Turkey were used, and the results are discussed in the context of this study. W górnictwie wykorzystuje się rozmaite modele estymacji do dokładnego określenia wielkości i rozkładu zawartości pierwiastka użytecznego w rudzie. Estymację położenia i właściwości skał w nieznanych obszarach z wykorzystaniem próbek losowych o znanym położeniu przeprowadzano na początku z wykorzystaniem przybliżenia wielomianowego. Pomimo tego, że rozwój technik komputerowych i statystycznych metod ewaluacji próbek losowych sprawiły, że po roku 1950 metody przybliżenia wielomianowego straciły na znaczeniu, nadal teoretyczna powierzchnia najlepszej zgodności przechodząca przez zmienne losowe wyrażana jest właśnie poprzez przybliżenie wielomianowe. W geofizyce, gdzie liczba próbek losowych jest zazwyczaj bardzo wysoka, wiarygodne rozwiązania uzyskać można jedynie przy wykorzystaniu wielomianów wyższych stopni. Określenie współczynników w tego typu wielomia nach jest skomplikowaną procedurą obliczeniową. W pracy tej poszukiwane współczynniki wielomianu wyższych stopni obliczono przy zastosowaniu metody uogólnionej macierzy odwrotnej. Opracowano odpowiedni algorytm komputerowy do obliczania stopnia wielomianu, zapewniający najlepszą regresję pomiędzy wartościami otrzymanymi z rozwiązań bazujących na wielomianach różnych stopni i losowymi danymi z obserwacji, o znanych wartościach. Rozwiązanie to przetestowano z użyciem danych uzyskanych z zastosowań praktycznych. W tym zastosowaniu użyto danych o wartości opałowej pochodzących z 83 odwiertów wykonanych w zagłębiu węglowym w południowo- zachodniej Turcji, wyniki obliczeń przedyskutowano w kontekście zagadnień uwzględnionych w niniejszej pracy.

Optimal Subset Selection of Time-Series MODIS Images and Sample Data Transfer with Random Forests for Supervised Classification Modelling

PubMed Central

Zhou, Fuqun; Zhang, Aining

2016-01-01

Nowadays, various time-series Earth Observation data with multiple bands are freely available, such as Moderate Resolution Imaging Spectroradiometer (MODIS) datasets including 8-day composites from NASA, and 10-day composites from the Canada Centre for Remote Sensing (CCRS). It is challenging to efficiently use these time-series MODIS datasets for long-term environmental monitoring due to their vast volume and information redundancy. This challenge will be greater when Sentinel 2–3 data become available. Another challenge that researchers face is the lack of in-situ data for supervised modelling, especially for time-series data analysis. In this study, we attempt to tackle the two important issues with a case study of land cover mapping using CCRS 10-day MODIS composites with the help of Random Forests’ features: variable importance, outlier identification. The variable importance feature is used to analyze and select optimal subsets of time-series MODIS imagery for efficient land cover mapping, and the outlier identification feature is utilized for transferring sample data available from one year to an adjacent year for supervised classification modelling. The results of the case study of agricultural land cover classification at a regional scale show that using only about a half of the variables we can achieve land cover classification accuracy close to that generated using the full dataset. The proposed simple but effective solution of sample transferring could make supervised modelling possible for applications lacking sample data. PMID:27792152

Improved Horvitz-Thompson Estimation of Model Parameters from Two-phase Stratified Samples: Applications in Epidemiology

PubMed Central

Breslow, Norman E.; Lumley, Thomas; Ballantyne, Christie M; Chambless, Lloyd E.; Kulich, Michal

2009-01-01

The case-cohort study involves two-phase sampling: simple random sampling from an infinite super-population at phase one and stratified random sampling from a finite cohort at phase two. Standard analyses of case-cohort data involve solution of inverse probability weighted (IPW) estimating equations, with weights determined by the known phase two sampling fractions. The variance of parameter estimates in (semi)parametric models, including the Cox model, is the sum of two terms: (i) the model based variance of the usual estimates that would be calculated if full data were available for the entire cohort; and (ii) the design based variance from IPW estimation of the unknown cohort total of the efficient influence function (IF) contributions. This second variance component may be reduced by adjusting the sampling weights, either by calibration to known cohort totals of auxiliary variables correlated with the IF contributions or by their estimation using these same auxiliary variables. Both adjustment methods are implemented in the R survey package. We derive the limit laws of coefficients estimated using adjusted weights. The asymptotic results suggest practical methods for construction of auxiliary variables that are evaluated by simulation of case-cohort samples from the National Wilms Tumor Study and by log-linear modeling of case-cohort data from the Atherosclerosis Risk in Communities Study. Although not semiparametric efficient, estimators based on adjusted weights may come close to achieving full efficiency within the class of augmented IPW estimators. PMID:20174455

Moderation analysis with missing data in the predictors.

PubMed

Zhang, Qian; Wang, Lijuan

2017-12-01

The most widely used statistical model for conducting moderation analysis is the moderated multiple regression (MMR) model. In MMR modeling, missing data could pose a challenge, mainly because the interaction term is a product of two or more variables and thus is a nonlinear function of the involved variables. In this study, we consider a simple MMR model, where the effect of the focal predictor X on the outcome Y is moderated by a moderator U. The primary interest is to find ways of estimating and testing the moderation effect with the existence of missing data in X. We mainly focus on cases when X is missing completely at random (MCAR) and missing at random (MAR). Three methods are compared: (a) Normal-distribution-based maximum likelihood estimation (NML); (b) Normal-distribution-based multiple imputation (NMI); and (c) Bayesian estimation (BE). Via simulations, we found that NML and NMI could lead to biased estimates of moderation effects under MAR missingness mechanism. The BE method outperformed NMI and NML for MMR modeling with missing data in the focal predictor, missingness depending on the moderator and/or auxiliary variables, and correctly specified distributions for the focal predictor. In addition, more robust BE methods are needed in terms of the distribution mis-specification problem of the focal predictor. An empirical example was used to illustrate the applications of the methods with a simple sensitivity analysis. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Optimal Subset Selection of Time-Series MODIS Images and Sample Data Transfer with Random Forests for Supervised Classification Modelling.

PubMed

Zhou, Fuqun; Zhang, Aining

2016-10-25

Nowadays, various time-series Earth Observation data with multiple bands are freely available, such as Moderate Resolution Imaging Spectroradiometer (MODIS) datasets including 8-day composites from NASA, and 10-day composites from the Canada Centre for Remote Sensing (CCRS). It is challenging to efficiently use these time-series MODIS datasets for long-term environmental monitoring due to their vast volume and information redundancy. This challenge will be greater when Sentinel 2-3 data become available. Another challenge that researchers face is the lack of in-situ data for supervised modelling, especially for time-series data analysis. In this study, we attempt to tackle the two important issues with a case study of land cover mapping using CCRS 10-day MODIS composites with the help of Random Forests' features: variable importance, outlier identification. The variable importance feature is used to analyze and select optimal subsets of time-series MODIS imagery for efficient land cover mapping, and the outlier identification feature is utilized for transferring sample data available from one year to an adjacent year for supervised classification modelling. The results of the case study of agricultural land cover classification at a regional scale show that using only about a half of the variables we can achieve land cover classification accuracy close to that generated using the full dataset. The proposed simple but effective solution of sample transferring could make supervised modelling possible for applications lacking sample data.

Comparison of Random Forest and Parametric Imputation Models for Imputing Missing Data Using MICE: A CALIBER Study

PubMed Central

Shah, Anoop D.; Bartlett, Jonathan W.; Carpenter, James; Nicholas, Owen; Hemingway, Harry

2014-01-01

Multivariate imputation by chained equations (MICE) is commonly used for imputing missing data in epidemiologic research. The “true” imputation model may contain nonlinearities which are not included in default imputation models. Random forest imputation is a machine learning technique which can accommodate nonlinearities and interactions and does not require a particular regression model to be specified. We compared parametric MICE with a random forest-based MICE algorithm in 2 simulation studies. The first study used 1,000 random samples of 2,000 persons drawn from the 10,128 stable angina patients in the CALIBER database (Cardiovascular Disease Research using Linked Bespoke Studies and Electronic Records; 2001–2010) with complete data on all covariates. Variables were artificially made “missing at random,” and the bias and efficiency of parameter estimates obtained using different imputation methods were compared. Both MICE methods produced unbiased estimates of (log) hazard ratios, but random forest was more efficient and produced narrower confidence intervals. The second study used simulated data in which the partially observed variable depended on the fully observed variables in a nonlinear way. Parameter estimates were less biased using random forest MICE, and confidence interval coverage was better. This suggests that random forest imputation may be useful for imputing complex epidemiologic data sets in which some patients have missing data. PMID:24589914

Comparison of random forest and parametric imputation models for imputing missing data using MICE: a CALIBER study.

PubMed

Shah, Anoop D; Bartlett, Jonathan W; Carpenter, James; Nicholas, Owen; Hemingway, Harry

2014-03-15

Multivariate imputation by chained equations (MICE) is commonly used for imputing missing data in epidemiologic research. The "true" imputation model may contain nonlinearities which are not included in default imputation models. Random forest imputation is a machine learning technique which can accommodate nonlinearities and interactions and does not require a particular regression model to be specified. We compared parametric MICE with a random forest-based MICE algorithm in 2 simulation studies. The first study used 1,000 random samples of 2,000 persons drawn from the 10,128 stable angina patients in the CALIBER database (Cardiovascular Disease Research using Linked Bespoke Studies and Electronic Records; 2001-2010) with complete data on all covariates. Variables were artificially made "missing at random," and the bias and efficiency of parameter estimates obtained using different imputation methods were compared. Both MICE methods produced unbiased estimates of (log) hazard ratios, but random forest was more efficient and produced narrower confidence intervals. The second study used simulated data in which the partially observed variable depended on the fully observed variables in a nonlinear way. Parameter estimates were less biased using random forest MICE, and confidence interval coverage was better. This suggests that random forest imputation may be useful for imputing complex epidemiologic data sets in which some patients have missing data.

"Congratulations, you have been randomized into the control group!(?)": issues to consider when recruiting schools for matched-pair randomized control trials of prevention programs.

PubMed

Ji, Peter; DuBois, David L; Flay, Brian R; Brechling, Vanessa

2008-03-01

Recruiting schools into a matched-pair randomized control trial (MP-RCT) to evaluate the efficacy of a school-level prevention program presents challenges for researchers. We considered which of 2 procedures would be most effective for recruiting schools into the study and assigning them to conditions. In 1 procedure (recruit and match/randomize), we would recruit schools and match them prior to randomization, and in the other (match/randomize and recruitment), we would match schools and randomize them prior to recruitment. We considered how each procedure impacted the randomization process and our ability to recruit schools into the study. After implementing the selected procedure, the equivalence of both treatment and control group schools and the participating and nonparticipating schools on school demographic variables was evaluated. We decided on the recruit and match/randomize procedure because we thought it would provide the opportunity to build rapport with the schools and prepare them for the randomization process, thereby increasing the likelihood that they would accept their randomly assigned conditions. Neither the treatment and control group schools nor the participating and nonparticipating schools exhibited statistically significant differences from each other on any of the school demographic variables. Recruitment of schools prior to matching and randomization in an MP-RCT may facilitate the recruitment of schools and thus enhance both the statistical power and the representativeness of study findings. Future research would benefit from the consideration of a broader range of variables (eg, readiness to implement a comprehensive prevention program) both in matching schools and in evaluating their representativeness to nonparticipating schools.

Random effects coefficient of determination for mixed and meta-analysis models.

PubMed

Demidenko, Eugene; Sargent, James; Onega, Tracy

2012-01-01

The key feature of a mixed model is the presence of random effects. We have developed a coefficient, called the random effects coefficient of determination, [Formula: see text], that estimates the proportion of the conditional variance of the dependent variable explained by random effects. This coefficient takes values from 0 to 1 and indicates how strong the random effects are. The difference from the earlier suggested fixed effects coefficient of determination is emphasized. If [Formula: see text] is close to 0, there is weak support for random effects in the model because the reduction of the variance of the dependent variable due to random effects is small; consequently, random effects may be ignored and the model simplifies to standard linear regression. The value of [Formula: see text] apart from 0 indicates the evidence of the variance reduction in support of the mixed model. If random effects coefficient of determination is close to 1 the variance of random effects is very large and random effects turn into free fixed effects-the model can be estimated using the dummy variable approach. We derive explicit formulas for [Formula: see text] in three special cases: the random intercept model, the growth curve model, and meta-analysis model. Theoretical results are illustrated with three mixed model examples: (1) travel time to the nearest cancer center for women with breast cancer in the U.S., (2) cumulative time watching alcohol related scenes in movies among young U.S. teens, as a risk factor for early drinking onset, and (3) the classic example of the meta-analysis model for combination of 13 studies on tuberculosis vaccine.

Oscillating flow loss test results in Stirling engine heat exchangers

NASA Technical Reports Server (NTRS)

Koester, G.; Howell, S.; Wood, G.; Miller, E.; Gedeon, D.

1990-01-01

The results are presented for a test program designed to generate a database of oscillating flow loss information that is applicable to Stirling engine heat exchangers. The tests were performed on heater/cooler tubes of various lengths and entrance/exit configurations, on stacked and sintered screen regenerators of various wire diameters and on Brunswick and Metex random fiber regenerators. The test results were performed over a range of oscillating flow parameters consistent with Stirling engine heat exchanger experience. The tests were performed on the Sunpower oscillating flow loss rig which is based on a variable stroke and variable frequency linear drive motor. In general, the results are presented by comparing the measured oscillating flow losses to the calculated flow losses. The calculated losses are based on the cycle integration of steady flow friction factors and entrance/exit loss coefficients.

Combining remotely sensed and other measurements for hydrologic areal averages

NASA Technical Reports Server (NTRS)

Johnson, E. R.; Peck, E. L.; Keefer, T. N.

1982-01-01

A method is described for combining measurements of hydrologic variables of various sampling geometries and measurement accuracies to produce an estimated mean areal value over a watershed and a measure of the accuracy of the mean areal value. The method provides a means to integrate measurements from conventional hydrological networks and remote sensing. The resulting areal averages can be used to enhance a wide variety of hydrological applications including basin modeling. The correlation area method assigns weights to each available measurement (point, line, or areal) based on the area of the basin most accurately represented by the measurement. The statistical characteristics of the accuracy of the various measurement technologies and of the random fields of the hydrologic variables used in the study (water equivalent of the snow cover and soil moisture) required to implement the method are discussed.

ESTIMATING PERSON-CENTERED TREATMENT (PeT) EFFECTS USING INSTRUMENTAL VARIABLES: AN APPLICATION TO EVALUATING PROSTATE CANCER TREATMENTS

PubMed Central

BASU, ANIRBAN

2014-01-01

SUMMARY This paper builds on the methods of local instrumental variables developed by Heckman and Vytlacil (1999, 2001, 2005) to estimate person-centered treatment (PeT) effects that are conditioned on the person’s observed characteristics and averaged over the potential conditional distribution of unobserved characteristics that lead them to their observed treatment choices. PeT effects are more individualized than conditional treatment effects from a randomized setting with the same observed characteristics. PeT effects can be easily aggregated to construct any of the mean treatment effect parameters and, more importantly, are well suited to comprehend individual-level treatment effect heterogeneity. The paper presents the theory behind PeT effects, and applies it to study the variation in individual-level comparative effects of prostate cancer treatments on overall survival and costs. PMID:25620844

Measurement Model Nonlinearity in Estimation of Dynamical Systems

NASA Astrophysics Data System (ADS)

Majji, Manoranjan; Junkins, J. L.; Turner, J. D.

2012-06-01

The role of nonlinearity of the measurement model and its interactions with the uncertainty of measurements and geometry of the problem is studied in this paper. An examination of the transformations of the probability density function in various coordinate systems is presented for several astrodynamics applications. Smooth and analytic nonlinear functions are considered for the studies on the exact transformation of uncertainty. Special emphasis is given to understanding the role of change of variables in the calculus of random variables. The transformation of probability density functions through mappings is shown to provide insight in to understanding the evolution of uncertainty in nonlinear systems. Examples are presented to highlight salient aspects of the discussion. A sequential orbit determination problem is analyzed, where the transformation formula provides useful insights for making the choice of coordinates for estimation of dynamic systems.

Kinesio Taping effects on knee extension force among soccer players

PubMed Central

Serra, Maysa V. G. B.; Vieira, Edgar R.; Brunt, Denis; Goethel, Márcio F.; Gonçalves, Mauro; Quemelo, Paulo R. V.

2015-01-01

Background: Kinesio Taping (KT) is widely used, however the effects of KT on muscle activation and force are contradictory. Objective: To evaluate the effects of KT on knee extension force in soccer players. Method: This is a clinical trial study design. Thirty-four subjects performed two maximal isometric voluntary contractions of the lower limbs pre, immediately post, and 24 hours after tape application on the lower limbs. Both lower limbs were taped, using K-Tape and 3M Micropore tape randomly on the right and left thighs of the participants. Isometric knee extension force was measured for dominant side using a strain gauge. The following variables were assessed: peak force, time to peak force, rate of force development until peak force, time to peak rate of force development, and 200 ms pulse. Results: There were no statistically significant differences in the variables assessed between KT and Micropore conditions (F=0.645, p=0.666) or among testing sessions (pre, post, and 24h after) (F=0.528, p=0.868), and there was no statistical significance (F=0.271, p=0.986) for interaction between tape conditions and testing session. Conclusion: KT did not affect the force-related measures assessed immediately and 24 hours after the KT application compared with Micropore application, during maximal isometric voluntary knee extension. PMID:25789557

Kinesio Taping effects on knee extension force among soccer players.

PubMed

Serra, Maysa V G B; Vieira, Edgar R; Brunt, Denis; Goethel, Márcio F; Gonçalves, Mauro; Quemelo, Paulo R V

2015-01-01

Kinesio Taping (KT) is widely used, however the effects of KT on muscle activation and force are contradictory. To evaluate the effects of KT on knee extension force in soccer players. This is a clinical trial study design. Thirty-four subjects performed two maximal isometric voluntary contractions of the lower limbs pre, immediately post, and 24 hours after tape application on the lower limbs. Both lower limbs were taped, using K-Tape and 3M Micropore tape randomly on the right and left thighs of the participants. Isometric knee extension force was measured for dominant side using a strain gauge. The following variables were assessed: peak force, time to peak force, rate of force development until peak force, time to peak rate of force development, and 200 ms pulse. There were no statistically significant differences in the variables assessed between KT and Micropore conditions (F=0.645, p=0.666) or among testing sessions (pre, post, and 24h after) (F=0.528, p=0.868), and there was no statistical significance (F=0.271, p=0.986) for interaction between tape conditions and testing session. KT did not affect the force-related measures assessed immediately and 24 hours after the KT application compared with Micropore application, during maximal isometric voluntary knee extension.

Isolation of a pH-Sensitive IgNAR Variable Domain from a Yeast-Displayed, Histidine-Doped Master Library.

PubMed

Könning, Doreen; Zielonka, Stefan; Sellmann, Carolin; Schröter, Christian; Grzeschik, Julius; Becker, Stefan; Kolmar, Harald

2016-04-01

In recent years, engineering of pH-sensitivity into antibodies as well as antibody-derived fragments has become more and more attractive for biomedical and biotechnological applications. Herein, we report the isolation of the first pH-sensitive IgNAR variable domain (vNAR), which was isolated from a yeast-displayed, semi-synthetic master library. This strategy enables the direct identification of pH-dependent binders from a histidine-enriched CDR3 library. Displayed vNAR variants contained two histidine substitutions on average at random positions in their 12-residue CDR3 loop. Upon screening of seven rounds against the proof-of-concept target EpCAM (selection for binding at pH 7.4 and decreased binding at pH 6.0), a single clone was obtained that showed specific and pH-dependent binding as characterized by yeast surface display and biolayer interferometry. Potential applications for such pH-dependent vNAR domains include their employment in tailored affinity chromatography, enabling mild elution protocols. Moreover, utilizing a master library for the isolation of pH-sensitive vNAR variants may be a generic strategy to obtain binding entities with prescribed characteristics for applications in biotechnology, diagnostics, and therapy.

Reliability Sensitivity Analysis and Design Optimization of Composite Structures Based on Response Surface Methodology

NASA Technical Reports Server (NTRS)

Rais-Rohani, Masoud

2003-01-01

This report discusses the development and application of two alternative strategies in the form of global and sequential local response surface (RS) techniques for the solution of reliability-based optimization (RBO) problems. The problem of a thin-walled composite circular cylinder under axial buckling instability is used as a demonstrative example. In this case, the global technique uses a single second-order RS model to estimate the axial buckling load over the entire feasible design space (FDS) whereas the local technique uses multiple first-order RS models with each applied to a small subregion of FDS. Alternative methods for the calculation of unknown coefficients in each RS model are explored prior to the solution of the optimization problem. The example RBO problem is formulated as a function of 23 uncorrelated random variables that include material properties, thickness and orientation angle of each ply, cylinder diameter and length, as well as the applied load. The mean values of the 8 ply thicknesses are treated as independent design variables. While the coefficients of variation of all random variables are held fixed, the standard deviations of ply thicknesses can vary during the optimization process as a result of changes in the design variables. The structural reliability analysis is based on the first-order reliability method with reliability index treated as the design constraint. In addition to the probabilistic sensitivity analysis of reliability index, the results of the RBO problem are presented for different combinations of cylinder length and diameter and laminate ply patterns. The two strategies are found to produce similar results in terms of accuracy with the sequential local RS technique having a considerably better computational efficiency.

Application of Random Forests Methods to Diabetic Retinopathy Classification Analyses

PubMed Central

Casanova, Ramon; Saldana, Santiago; Chew, Emily Y.; Danis, Ronald P.; Greven, Craig M.; Ambrosius, Walter T.

2014-01-01

Background Diabetic retinopathy (DR) is one of the leading causes of blindness in the United States and world-wide. DR is a silent disease that may go unnoticed until it is too late for effective treatment. Therefore, early detection could improve the chances of therapeutic interventions that would alleviate its effects. Methodology Graded fundus photography and systemic data from 3443 ACCORD-Eye Study participants were used to estimate Random Forest (RF) and logistic regression classifiers. We studied the impact of sample size on classifier performance and the possibility of using RF generated class conditional probabilities as metrics describing DR risk. RF measures of variable importance are used to detect factors that affect classification performance. Principal Findings Both types of data were informative when discriminating participants with or without DR. RF based models produced much higher classification accuracy than those based on logistic regression. Combining both types of data did not increase accuracy but did increase statistical discrimination of healthy participants who subsequently did or did not have DR events during four years of follow-up. RF variable importance criteria revealed that microaneurysms counts in both eyes seemed to play the most important role in discrimination among the graded fundus variables, while the number of medicines and diabetes duration were the most relevant among the systemic variables. Conclusions and Significance We have introduced RF methods to DR classification analyses based on fundus photography data. In addition, we propose an approach to DR risk assessment based on metrics derived from graded fundus photography and systemic data. Our results suggest that RF methods could be a valuable tool to diagnose DR diagnosis and evaluate its progression. PMID:24940623

Modeling Randomness in Judging Rating Scales with a Random-Effects Rating Scale Model

ERIC Educational Resources Information Center

Wang, Wen-Chung; Wilson, Mark; Shih, Ching-Lin

2006-01-01

This study presents the random-effects rating scale model (RE-RSM) which takes into account randomness in the thresholds over persons by treating them as random-effects and adding a random variable for each threshold in the rating scale model (RSM) (Andrich, 1978). The RE-RSM turns out to be a special case of the multidimensional random…

A new method for reconstruction of solar irradiance

NASA Astrophysics Data System (ADS)

Privalsky, Victor

2018-07-01

The purpose of this research is to show how time series should be reconstructed using an example with the data on total solar irradiation (TSI) of the Earth and on sunspot numbers (SSN) since 1749. The traditional approach through regression equation(s) is designed for time-invariant vectors of random variables and is not applicable to time series, which present random functions of time. The autoregressive reconstruction (ARR) method suggested here requires fitting a multivariate stochastic difference equation to the target/proxy time series. The reconstruction is done through the scalar equation for the target time series with the white noise term excluded. The time series approach is shown to provide a better reconstruction of TSI than the correlation/regression method. A reconstruction criterion is introduced which allows one to define in advance the achievable level of success in the reconstruction. The conclusion is that time series, including the total solar irradiance, cannot be reconstructed properly if the data are not treated as sample records of random processes and analyzed in both time and frequency domains.

A comparative study: classification vs. user-based collaborative filtering for clinical prediction.

PubMed

Hao, Fang; Blair, Rachael Hageman

2016-12-08

Recommender systems have shown tremendous value for the prediction of personalized item recommendations for individuals in a variety of settings (e.g., marketing, e-commerce, etc.). User-based collaborative filtering is a popular recommender system, which leverages an individuals' prior satisfaction with items, as well as the satisfaction of individuals that are "similar". Recently, there have been applications of collaborative filtering based recommender systems for clinical risk prediction. In these applications, individuals represent patients, and items represent clinical data, which includes an outcome. Application of recommender systems to a problem of this type requires the recasting a supervised learning problem as unsupervised. The rationale is that patients with similar clinical features carry a similar disease risk. As the "Big Data" era progresses, it is likely that approaches of this type will be reached for as biomedical data continues to grow in both size and complexity (e.g., electronic health records). In the present study, we set out to understand and assess the performance of recommender systems in a controlled yet realistic setting. User-based collaborative filtering recommender systems are compared to logistic regression and random forests with different types of imputation and varying amounts of missingness on four different publicly available medical data sets: National Health and Nutrition Examination Survey (NHANES, 2011-2012 on Obesity), Study to Understand Prognoses Preferences Outcomes and Risks of Treatment (SUPPORT), chronic kidney disease, and dermatology data. We also examined performance using simulated data with observations that are Missing At Random (MAR) or Missing Completely At Random (MCAR) under various degrees of missingness and levels of class imbalance in the response variable. Our results demonstrate that user-based collaborative filtering is consistently inferior to logistic regression and random forests with different imputations on real and simulated data. The results warrant caution for the collaborative filtering for the purpose of clinical risk prediction when traditional classification is feasible and practical. CF may not be desirable in datasets where classification is an acceptable alternative. We describe some natural applications related to "Big Data" where CF would be preferred and conclude with some insights as to why caution may be warranted in this context.

Randomized trial of intermittent or continuous amnioinfusion for variable decelerations.

PubMed

Rinehart, B K; Terrone, D A; Barrow, J H; Isler, C M; Barrilleaux, P S; Roberts, W E

2000-10-01

To determine whether continuous or intermittent bolus amnioinfusion is more effective in relieving variable decelerations. Patients with repetitive variable decelerations were randomized to an intermittent bolus or continuous amnioinfusion. The intermittent bolus infusion group received boluses of 500 mL of normal saline, each over 30 minutes, with boluses repeated if variable decelerations recurred. The continuous infusion group received a bolus infusion of 500 mL of normal saline over 30 minutes and then 3 mL per minute until delivery occurred. The ability of the amnioinfusion to abolish variable decelerations was analyzed, as were maternal demographic and pregnancy outcome variables. Power analysis indicated that 64 patients would be required. Thirty-five patients were randomized to intermittent infusion and 30 to continuous infusion. There were no differences between groups in terms of maternal demographics, gestational age, delivery mode, neonatal outcome, median time to resolution of variable decelerations, or the number of times variable decelerations recurred. The median volume infused in the intermittent infusion group (500 mL) was significantly less than that in the continuous infusion group (905 mL, P =.003). Intermittent bolus amnioinfusion is as effective as continuous infusion in relieving variable decelerations in labor. Further investigation is necessary to determine whether either of these techniques is associated with increased occurrence of rare complications such as cord prolapse or uterine rupture.

Multiobjective optimization in structural design with uncertain parameters and stochastic processes

NASA Technical Reports Server (NTRS)

Rao, S. S.

1984-01-01

The application of multiobjective optimization techniques to structural design problems involving uncertain parameters and random processes is studied. The design of a cantilever beam with a tip mass subjected to a stochastic base excitation is considered for illustration. Several of the problem parameters are assumed to be random variables and the structural mass, fatigue damage, and negative of natural frequency of vibration are considered for minimization. The solution of this three-criteria design problem is found by using global criterion, utility function, game theory, goal programming, goal attainment, bounded objective function, and lexicographic methods. It is observed that the game theory approach is superior in finding a better optimum solution, assuming the proper balance of the various objective functions. The procedures used in the present investigation are expected to be useful in the design of general dynamic systems involving uncertain parameters, stochastic process, and multiple objectives.

Achieving flexible low-scattering metasurface based on randomly distribution of meta-elements.

PubMed

Zhao, Junming; Sima, Boyu; Jia, Nan; Wang, Cheng; Zhu, Bo; Jiang, Tian; Feng, Yijun

2016-11-28

In the paper, a flexible low-scattering metasurface is proposed and realized. The layout is composed of similar "#" shaped elements with variable sizes which are randomly distributed along the surface. The various dimensions of the meta-elements lead to different reflection phases for the meta-elements with respect to the incident plane wave, resulting a diffuse reflection surface and exhibiting a broadband backward low-scattering property. In consideration of the flexibility, metasurfaces composed of printed metallic element films attaching with flexible substrate are designed, fabricated and measured in microwave domain. The measurement results show that 10dB radar cross section (RCS) reduction is obtained across the X-band by coating them to either metallic plates or metallic cylinders with only 1/8 working wavelength thickness. We think that the proposed flexible metasurface is applicable to other frequency bands and can be applied in EM stealth technology.

Decision tree modeling using R.

PubMed

Zhang, Zhongheng

2016-08-01

In machine learning field, decision tree learner is powerful and easy to interpret. It employs recursive binary partitioning algorithm that splits the sample in partitioning variable with the strongest association with the response variable. The process continues until some stopping criteria are met. In the example I focus on conditional inference tree, which incorporates tree-structured regression models into conditional inference procedures. While growing a single tree is subject to small changes in the training data, random forests procedure is introduced to address this problem. The sources of diversity for random forests come from the random sampling and restricted set of input variables to be selected. Finally, I introduce R functions to perform model based recursive partitioning. This method incorporates recursive partitioning into conventional parametric model building.

Efficacy of topical Aloe vera in patients with oral lichen planus: a randomized double-blind study.

PubMed

Salazar-Sánchez, N; López-Jornet, P; Camacho-Alonso, F; Sánchez-Siles, M

2010-11-01

  Different treatments have been used in application to symptomatic oral lichen planus (OLP), with variable results, perhaps caused by the refractory nature of the disease. The objective of this study was to evaluate the efficacy of the topical application of aloe vera (AV) in OLP compared with placebo. A total of 64 patients with OLP were randomized in a double-blind study to either AV (32 patients) or placebo (32 patients), at a dose of 0.4 ml (70% concentration) three times a day. A Visual Analog Scale was used for rating pain, with the application of a clinical scale for scoring the lesions, the Oral Health Impact Profile 49 (OHIP-49), and the Hospital Anxiety-Depression (HAD) scale. The patients were evaluated after 6 and 12 weeks. No statistically significant differences were recorded between both groups in relation to pain after 6 and 12 weeks. In the AV group, complete pain remission was achieved in 31.2% of the cases after 6 weeks, and in 61% after 12 weeks. In the placebo group, these percentages were 17.2% and 41.6%, respectively. There were no adverse effects in any of the groups. In relation to quality of life, significant differences were observed between the two groups in the psychological disability domain and total OHIP-49 score. The topical application of AV improves the total quality of life score in patients with OLP. © 2010 John Wiley & Sons A/S.

What explains usage of mobile physician-rating apps? Results from a web-based questionnaire.

PubMed

Bidmon, Sonja; Terlutter, Ralf; Röttl, Johanna

2014-06-11

Consumers are increasingly accessing health-related information via mobile devices. Recently, several apps to rate and locate physicians have been released in the United States and Germany. However, knowledge about what kinds of variables explain usage of mobile physician-rating apps is still lacking. This study analyzes factors influencing the adoption of and willingness to pay for mobile physician-rating apps. A structural equation model was developed based on the Technology Acceptance Model and the literature on health-related information searches and usage of mobile apps. Relationships in the model were analyzed for moderating effects of physician-rating website (PRW) usage. A total of 1006 randomly selected German patients who had visited a general practitioner at least once in the 3 months before the beginning of the survey were randomly selected and surveyed. A total of 958 usable questionnaires were analyzed by partial least squares path modeling and moderator analyses. The suggested model yielded a high model fit. We found that perceived ease of use (PEOU) of the Internet to gain health-related information, the sociodemographic variables age and gender, and the psychographic variables digital literacy, feelings about the Internet and other Web-based applications in general, patients' value of health-related knowledgeability, as well as the information-seeking behavior variables regarding the amount of daily private Internet use for health-related information, frequency of using apps for health-related information in the past, and attitude toward PRWs significantly affected the adoption of mobile physician-rating apps. The sociodemographic variable age, but not gender, and the psychographic variables feelings about the Internet and other Web-based applications in general and patients' value of health-related knowledgeability, but not digital literacy, were significant predictors of willingness to pay. Frequency of using apps for health-related information in the past and attitude toward PRWs, but not the amount of daily Internet use for health-related information, were significant predictors of willingness to pay. The perceived usefulness of the Internet to gain health-related information and the amount of daily Internet use in general did not have any significant effect on both of the endogenous variables. The moderation analysis with the group comparisons for users and nonusers of PRWs revealed that the attitude toward PRWs had significantly more impact on the adoption and willingness to pay for mobile physician-rating apps in the nonuser group. Important variables that contribute to the adoption of a mobile physician-rating app and the willingness to pay for it were identified. The results of this study are important for researchers because they can provide important insights about the variables that influence the acceptance of apps that allow for ratings of physicians. They are also useful for creators of mobile physician-rating apps because they can help tailor mobile physician-rating apps to the consumers' characteristics and needs.

What Explains Usage of Mobile Physician-Rating Apps? Results From a Web-Based Questionnaire

PubMed Central

Terlutter, Ralf; Röttl, Johanna

2014-01-01

Background Consumers are increasingly accessing health-related information via mobile devices. Recently, several apps to rate and locate physicians have been released in the United States and Germany. However, knowledge about what kinds of variables explain usage of mobile physician-rating apps is still lacking. Objective This study analyzes factors influencing the adoption of and willingness to pay for mobile physician-rating apps. A structural equation model was developed based on the Technology Acceptance Model and the literature on health-related information searches and usage of mobile apps. Relationships in the model were analyzed for moderating effects of physician-rating website (PRW) usage. Methods A total of 1006 randomly selected German patients who had visited a general practitioner at least once in the 3 months before the beginning of the survey were randomly selected and surveyed. A total of 958 usable questionnaires were analyzed by partial least squares path modeling and moderator analyses. Results The suggested model yielded a high model fit. We found that perceived ease of use (PEOU) of the Internet to gain health-related information, the sociodemographic variables age and gender, and the psychographic variables digital literacy, feelings about the Internet and other Web-based applications in general, patients’ value of health-related knowledgeability, as well as the information-seeking behavior variables regarding the amount of daily private Internet use for health-related information, frequency of using apps for health-related information in the past, and attitude toward PRWs significantly affected the adoption of mobile physician-rating apps. The sociodemographic variable age, but not gender, and the psychographic variables feelings about the Internet and other Web-based applications in general and patients’ value of health-related knowledgeability, but not digital literacy, were significant predictors of willingness to pay. Frequency of using apps for health-related information in the past and attitude toward PRWs, but not the amount of daily Internet use for health-related information, were significant predictors of willingness to pay. The perceived usefulness of the Internet to gain health-related information and the amount of daily Internet use in general did not have any significant effect on both of the endogenous variables. The moderation analysis with the group comparisons for users and nonusers of PRWs revealed that the attitude toward PRWs had significantly more impact on the adoption and willingness to pay for mobile physician-rating apps in the nonuser group. Conclusions Important variables that contribute to the adoption of a mobile physician-rating app and the willingness to pay for it were identified. The results of this study are important for researchers because they can provide important insights about the variables that influence the acceptance of apps that allow for ratings of physicians. They are also useful for creators of mobile physician-rating apps because they can help tailor mobile physician-rating apps to the consumers’ characteristics and needs. PMID:24918859

Design for cyclic loading endurance of composites

NASA Technical Reports Server (NTRS)

Shiao, Michael C.; Murthy, Pappu L. N.; Chamis, Christos C.; Liaw, Leslie D. G.

1993-01-01

The application of the computer code IPACS (Integrated Probabilistic Assessment of Composite Structures) to aircraft wing type structures is described. The code performs a complete probabilistic analysis for composites taking into account the uncertainties in geometry, boundary conditions, material properties, laminate lay-ups, and loads. Results of the analysis are presented in terms of cumulative distribution functions (CDF) and probability density function (PDF) of the fatigue life of a wing type composite structure under different hygrothermal environments subjected to the random pressure. The sensitivity of the fatigue life to a number of critical structural/material variables is also computed from the analysis.

Irregularity, volatility, risk, and financial market time series

PubMed Central

Pincus, Steve; Kalman, Rudolf E.

2004-01-01

The need to assess subtle, potentially exploitable changes in serial structure is paramount in the analysis of financial data. Herein, we demonstrate the utility of approximate entropy (ApEn), a model-independent measure of sequential irregularity, toward this goal, by several distinct applications. We consider both empirical data and models, including composite indices (Standard and Poor's 500 and Hang Seng), individual stock prices, the random-walk hypothesis, and the Black–Scholes and fractional Brownian motion models. Notably, ApEn appears to be a potentially useful marker of system stability, with rapid increases possibly foreshadowing significant changes in a financial variable. PMID:15358860

Formal Models of Hardware and Their Applications to VLSI Design Automation.

DTIC Science & Technology

1986-12-24

ORGANIZATION Universitv of Southern’iaplcbe ralifnrni Offico of ’,aval "esearch 6c. ADDRESS (City. State and ZIP Code) 7b. ADDRESS (City. Stote and ZIP Code...Di’f-i2C-33-K-O147 8.ADESS IXity, State and ZIP Coda, 10 SOURCE OF FUNDING NODS US fr-," esearch C-f-ice PORM POET TS OKUI 2..Fc 2~1ELEMENT No NO. NO...are classified as belonging to one of six different types. The dimensions of the routing channel are defined as functions of these random variables

Kinematic Methods of Designing Free Form Shells

NASA Astrophysics Data System (ADS)

Korotkiy, V. A.; Khmarova, L. I.

2017-11-01

The geometrical shell model is formed in light of the set requirements expressed through surface parameters. The shell is modelled using the kinematic method according to which the shell is formed as a continuous one-parameter set of curves. The authors offer a kinematic method based on the use of second-order curves with a variable eccentricity as a form-making element. Additional guiding ruled surfaces are used to control the designed surface form. The authors made a software application enabling to plot a second-order curve specified by a random set of five coplanar points and tangents.

Redshift data and statistical inference

NASA Technical Reports Server (NTRS)

Newman, William I.; Haynes, Martha P.; Terzian, Yervant

1994-01-01

Frequency histograms and the 'power spectrum analysis' (PSA) method, the latter developed by Yu & Peebles (1969), have been widely employed as techniques for establishing the existence of periodicities. We provide a formal analysis of these two classes of methods, including controlled numerical experiments, to better understand their proper use and application. In particular, we note that typical published applications of frequency histograms commonly employ far greater numbers of class intervals or bins than is advisable by statistical theory sometimes giving rise to the appearance of spurious patterns. The PSA method generates a sequence of random numbers from observational data which, it is claimed, is exponentially distributed with unit mean and variance, essentially independent of the distribution of the original data. We show that the derived random processes is nonstationary and produces a small but systematic bias in the usual estimate of the mean and variance. Although the derived variable may be reasonably described by an exponential distribution, the tail of the distribution is far removed from that of an exponential, thereby rendering statistical inference and confidence testing based on the tail of the distribution completely unreliable. Finally, we examine a number of astronomical examples wherein these methods have been used giving rise to widespread acceptance of statistically unconfirmed conclusions.

Comparing two-zone models of dust exposure.

PubMed

Jones, Rachael M; Simmons, Catherine E; Boelter, Fred W

2011-09-01

The selection and application of mathematical models to work tasks is challenging. Previously, we developed and evaluated a semi-empirical two-zone model that predicts time-weighted average (TWA) concentrations (Ctwa) of dust emitted during the sanding of drywall joint compound. Here, we fit the emission rate and random air speed variables of a mechanistic two-zone model to testing event data and apply and evaluate the model using data from two field studies. We found that the fitted random air speed values and emission rate were sensitive to (i) the size of the near-field and (ii) the objective function used for fitting, but this did not substantially impact predicted dust Ctwa. The mechanistic model predictions were lower than the semi-empirical model predictions and measured respirable dust Ctwa at Site A but were within an acceptable range. At Site B, a 10.5 m3 room, the mechanistic model did not capture the observed difference between PBZ and area Ctwa. The model predicted uniform mixing and predicted dust Ctwa up to an order of magnitude greater than was measured. We suggest that applications of the mechanistic model be limited to contexts where the near-field volume is very small relative to the far-field volume.

Sleep Telemedicine: An Emerging Field's Latest Frontier.

PubMed

Zia, Subaila; Fields, Barry G

2016-06-01

There is a widening gap between sleep provider access and patient demand for it. An American Academy of Sleep Medicine position paper recently recognized sleep telemedicine as one tool to narrow that divide. We define the term sleep telemedicine as the use of sleep-related medical information exchanged from one site to another via electronic communications to improve a patient's health. Applicable data transfer methods include telephone, video, smartphone applications, and the Internet. Their usefulness for the treatment of insomnia and sleep-disordered breathing is highlighted. Sleep telemedicine programs range in complexity from telephone-based patient feedback systems to comprehensive treatment pathways incorporating real-time video, telephone, and the Internet. While large, randomized trials are lacking, smaller studies comparing telemedicine with in-person care suggest noninferiority in terms of patient satisfaction, adherence to treatment, and symptomatic improvement. Sleep telemedicine is feasible from a technological and quality-driven perspective, but cost uncertainties, complex reimbursement structures, and variable licensing rules remain significant challenges to its feasibility on a larger scale. As legislative reform pends, larger randomized trials are needed to elucidate impact on patient outcomes, cost, and health-care system accessibility. Published by Elsevier Inc.

A bioavailable strontium isoscape for Western Europe: A machine learning approach

PubMed Central

von Holstein, Isabella C. C.; Laffoon, Jason E.; Willmes, Malte; Liu, Xiao-Ming; Davies, Gareth R.

2018-01-01

Strontium isotope ratios (87Sr/86Sr) are gaining considerable interest as a geolocation tool and are now widely applied in archaeology, ecology, and forensic research. However, their application for provenance requires the development of baseline models predicting surficial 87Sr/86Sr variations (“isoscapes”). A variety of empirically-based and process-based models have been proposed to build terrestrial 87Sr/86Sr isoscapes but, in their current forms, those models are not mature enough to be integrated with continuous-probability surface models used in geographic assignment. In this study, we aim to overcome those limitations and to predict 87Sr/86Sr variations across Western Europe by combining process-based models and a series of remote-sensing geospatial products into a regression framework. We find that random forest regression significantly outperforms other commonly used regression and interpolation methods, and efficiently predicts the multi-scale patterning of 87Sr/86Sr variations by accounting for geological, geomorphological and atmospheric controls. Random forest regression also provides an easily interpretable and flexible framework to integrate different types of environmental auxiliary variables required to model the multi-scale patterning of 87Sr/86Sr variability. The method is transferable to different scales and resolutions and can be applied to the large collection of geospatial data available at local and global levels. The isoscape generated in this study provides the most accurate 87Sr/86Sr predictions in bioavailable strontium for Western Europe (R2 = 0.58 and RMSE = 0.0023) to date, as well as a conservative estimate of spatial uncertainty by applying quantile regression forest. We anticipate that the method presented in this study combined with the growing numbers of bioavailable 87Sr/86Sr data and satellite geospatial products will extend the applicability of the 87Sr/86Sr geo-profiling tool in provenance applications. PMID:29847595

Reliability analysis of structures under periodic proof tests in service

NASA Technical Reports Server (NTRS)

Yang, J.-N.

1976-01-01

A reliability analysis of structures subjected to random service loads and periodic proof tests treats gust loads and maneuver loads as random processes. Crack initiation, crack propagation, and strength degradation are treated as the fatigue process. The time to fatigue crack initiation and ultimate strength are random variables. Residual strength decreases during crack propagation, so that failure rate increases with time. When a structure fails under periodic proof testing, a new structure is built and proof-tested. The probability of structural failure in service is derived from treatment of all the random variables, strength degradations, service loads, proof tests, and the renewal of failed structures. Some numerical examples are worked out.

Smooth conditional distribution function and quantiles under random censorship.

PubMed

Leconte, Eve; Poiraud-Casanova, Sandrine; Thomas-Agnan, Christine

2002-09-01

We consider a nonparametric random design regression model in which the response variable is possibly right censored. The aim of this paper is to estimate the conditional distribution function and the conditional alpha-quantile of the response variable. We restrict attention to the case where the response variable as well as the explanatory variable are unidimensional and continuous. We propose and discuss two classes of estimators which are smooth with respect to the response variable as well as to the covariate. Some simulations demonstrate that the new methods have better mean square error performances than the generalized Kaplan-Meier estimator introduced by Beran (1981) and considered in the literature by Dabrowska (1989, 1992) and Gonzalez-Manteiga and Cadarso-Suarez (1994).

Treatment of peri-implantitis: clinical outcome of chloramine as an adjunctive to non-surgical therapy, a randomized clinical trial.

PubMed

Roos-Jansåker, Ann-Marie; Almhöjd, Ulrica S; Jansson, Henrik

2017-01-01

To evaluate the adjunctive clinical effects of a chloramine to non-surgical treatment of peri-implantitis. Eighteen individuals diagnosed with peri-implantitis (clinical signs of inflammation and progressive bone loss) on at least two implants were included. Clinical variables; plaque accumulation (Pl), probing depth (PD), clinical attachment level (CAL) and bleeding on probing (BoP), were recorded at baseline and at 3-month follow-up. Primary clinical efficacy variable was the change in the number of sites with BoP. The implants were randomized into two different treatment groups: test and control. Both implants received supra- and submucosal debridement by ultrasonic instrumentation supplemented with hand instruments. The implants assigned to the test group first received local applications of a chloramine gel (Perisolv ™ ; RLS Global AB, Gothenburg, Sweden) followed by mechanical instrumentation. The oral hygiene was checked at 6 weeks. After 3 months, implants of both groups showed statistically significant reduction (P < 0.001) in the number of BoP-positive sites compared with baseline. The reduction of BoP-positive sites in the test group changed from 0.97 (SD ± 0.12) to 0.38 (SD ± 0.46), and in the control group from 0.97 (SD ± 0.12) to 0.31 (SD ± 0.42). Between-group comparisons revealed no statistically significant differences at baseline and after 3 months, for BoP or any of the other variables. In the present randomized clinical trial of peri-implantitis therapy; non-surgical mechanical debridement with adjunctive use of a chloramine is equally effective in the reduction of mucosal inflammation as conventional non-surgical mechanical debridement up to 3 months. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Azimuthal Dependence of the Ground Motion Variability from Scenario Modeling of the 2014 Mw6.0 South Napa, California, Earthquake Using an Advanced Kinematic Source Model

NASA Astrophysics Data System (ADS)

Gallovič, F.

2017-09-01

Strong ground motion simulations require physically plausible earthquake source model. Here, I present the application of such a kinematic model introduced originally by Ruiz et al. (Geophys J Int 186:226-244, 2011). The model is constructed to inherently provide synthetics with the desired omega-squared spectral decay in the full frequency range. The source is composed of randomly distributed overlapping subsources with fractal number-size distribution. The position of the subsources can be constrained by prior knowledge of major asperities (stemming, e.g., from slip inversions), or can be completely random. From earthquake physics point of view, the model includes positive correlation between slip and rise time as found in dynamic source simulations. Rupture velocity and rise time follows local S-wave velocity profile, so that the rupture slows down and rise times increase close to the surface, avoiding unrealistically strong ground motions. Rupture velocity can also have random variations, which result in irregular rupture front while satisfying the causality principle. This advanced kinematic broadband source model is freely available and can be easily incorporated into any numerical wave propagation code, as the source is described by spatially distributed slip rate functions, not requiring any stochastic Green's functions. The source model has been previously validated against the observed data due to the very shallow unilateral 2014 Mw6 South Napa, California, earthquake; the model reproduces well the observed data including the near-fault directivity (Seism Res Lett 87:2-14, 2016). The performance of the source model is shown here on the scenario simulations for the same event. In particular, synthetics are compared with existing ground motion prediction equations (GMPEs), emphasizing the azimuthal dependence of the between-event ground motion variability. I propose a simple model reproducing the azimuthal variations of the between-event ground motion variability, providing an insight into possible refinement of GMPEs' functional forms.

Automatic identification of variables in epidemiological datasets using logic regression.

PubMed

Lorenz, Matthias W; Abdi, Negin Ashtiani; Scheckenbach, Frank; Pflug, Anja; Bülbül, Alpaslan; Catapano, Alberico L; Agewall, Stefan; Ezhov, Marat; Bots, Michiel L; Kiechl, Stefan; Orth, Andreas

2017-04-13

For an individual participant data (IPD) meta-analysis, multiple datasets must be transformed in a consistent format, e.g. using uniform variable names. When large numbers of datasets have to be processed, this can be a time-consuming and error-prone task. Automated or semi-automated identification of variables can help to reduce the workload and improve the data quality. For semi-automation high sensitivity in the recognition of matching variables is particularly important, because it allows creating software which for a target variable presents a choice of source variables, from which a user can choose the matching one, with only low risk of having missed a correct source variable. For each variable in a set of target variables, a number of simple rules were manually created. With logic regression, an optimal Boolean combination of these rules was searched for every target variable, using a random subset of a large database of epidemiological and clinical cohort data (construction subset). In a second subset of this database (validation subset), this optimal combination rules were validated. In the construction sample, 41 target variables were allocated on average with a positive predictive value (PPV) of 34%, and a negative predictive value (NPV) of 95%. In the validation sample, PPV was 33%, whereas NPV remained at 94%. In the construction sample, PPV was 50% or less in 63% of all variables, in the validation sample in 71% of all variables. We demonstrated that the application of logic regression in a complex data management task in large epidemiological IPD meta-analyses is feasible. However, the performance of the algorithm is poor, which may require backup strategies.

Random elements on lattices: Review and statistical applications

NASA Astrophysics Data System (ADS)

Potocký, Rastislav; Villarroel, Claudia Navarro; Sepúlveda, Maritza; Luna, Guillermo; Stehlík, Milan

2017-07-01

We discuss important contributions to random elements on lattices. We relate to both algebraic and probabilistic properties. Several applications and concepts are discussed, e.g. positive dependence, Random walks and distributions on lattices, Super-lattices, learning. The application to Chilean Ecology is given.

AUTOCLASSIFICATION OF THE VARIABLE 3XMM SOURCES USING THE RANDOM FOREST MACHINE LEARNING ALGORITHM

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

Farrell, Sean A.; Murphy, Tara; Lo, Kitty K., E-mail: s.farrell@physics.usyd.edu.au

In the current era of large surveys and massive data sets, autoclassification of astrophysical sources using intelligent algorithms is becoming increasingly important. In this paper we present the catalog of variable sources in the Third XMM-Newton Serendipitous Source catalog (3XMM) autoclassified using the Random Forest machine learning algorithm. We used a sample of manually classified variable sources from the second data release of the XMM-Newton catalogs (2XMMi-DR2) to train the classifier, obtaining an accuracy of ∼92%. We also evaluated the effectiveness of identifying spurious detections using a sample of spurious sources, achieving an accuracy of ∼95%. Manual investigation of amore » random sample of classified sources confirmed these accuracy levels and showed that the Random Forest machine learning algorithm is highly effective at automatically classifying 3XMM sources. Here we present the catalog of classified 3XMM variable sources. We also present three previously unidentified unusual sources that were flagged as outlier sources by the algorithm: a new candidate supergiant fast X-ray transient, a 400 s X-ray pulsar, and an eclipsing 5 hr binary system coincident with a known Cepheid.« less

A Probabilistic Design Method Applied to Smart Composite Structures

NASA Technical Reports Server (NTRS)

Shiao, Michael C.; Chamis, Christos C.

1995-01-01

A probabilistic design method is described and demonstrated using a smart composite wing. Probabilistic structural design incorporates naturally occurring uncertainties including those in constituent (fiber/matrix) material properties, fabrication variables, structure geometry and control-related parameters. Probabilistic sensitivity factors are computed to identify those parameters that have a great influence on a specific structural reliability. Two performance criteria are used to demonstrate this design methodology. The first criterion requires that the actuated angle at the wing tip be bounded by upper and lower limits at a specified reliability. The second criterion requires that the probability of ply damage due to random impact load be smaller than an assigned value. When the relationship between reliability improvement and the sensitivity factors is assessed, the results show that a reduction in the scatter of the random variable with the largest sensitivity factor (absolute value) provides the lowest failure probability. An increase in the mean of the random variable with a negative sensitivity factor will reduce the failure probability. Therefore, the design can be improved by controlling or selecting distribution parameters associated with random variables. This can be implemented during the manufacturing process to obtain maximum benefit with minimum alterations.

Extended q -Gaussian and q -exponential distributions from gamma random variables

NASA Astrophysics Data System (ADS)

Budini, Adrián A.

2015-05-01

The family of q -Gaussian and q -exponential probability densities fit the statistical behavior of diverse complex self-similar nonequilibrium systems. These distributions, independently of the underlying dynamics, can rigorously be obtained by maximizing Tsallis "nonextensive" entropy under appropriate constraints, as well as from superstatistical models. In this paper we provide an alternative and complementary scheme for deriving these objects. We show that q -Gaussian and q -exponential random variables can always be expressed as a function of two statistically independent gamma random variables with the same scale parameter. Their shape index determines the complexity q parameter. This result also allows us to define an extended family of asymmetric q -Gaussian and modified q -exponential densities, which reduce to the standard ones when the shape parameters are the same. Furthermore, we demonstrate that a simple change of variables always allows relating any of these distributions with a beta stochastic variable. The extended distributions are applied in the statistical description of different complex dynamics such as log-return signals in financial markets and motion of point defects in a fluid flow.

Optimal allocation of testing resources for statistical simulations

NASA Astrophysics Data System (ADS)

Quintana, Carolina; Millwater, Harry R.; Singh, Gulshan; Golden, Patrick

2015-07-01

Statistical estimates from simulation involve uncertainty caused by the variability in the input random variables due to limited data. Allocating resources to obtain more experimental data of the input variables to better characterize their probability distributions can reduce the variance of statistical estimates. The methodology proposed determines the optimal number of additional experiments required to minimize the variance of the output moments given single or multiple constraints. The method uses multivariate t-distribution and Wishart distribution to generate realizations of the population mean and covariance of the input variables, respectively, given an amount of available data. This method handles independent and correlated random variables. A particle swarm method is used for the optimization. The optimal number of additional experiments per variable depends on the number and variance of the initial data, the influence of the variable in the output function and the cost of each additional experiment. The methodology is demonstrated using a fretting fatigue example.

Effect of platelet-rich fibrin on frequency of alveolar osteitis following mandibular third molar surgery: a double-blinded randomized clinical trial.

PubMed

Eshghpour, Majid; Dastmalchi, Parisa; Nekooei, Amir Hossein; Nejat, AmirHossein

2014-08-01

To evaluate the effectiveness of platelet-rich fibrin (PRF) in preventing the development of alveolar osteitis (AO). In a double-blinded study, patients with bilateral impacted mandibular third molars underwent surgical extractions, with one socket receiving PRF and the other one serving as a control. The surgeon and patient were unaware of the study or control side. The predictor variable was the PRF application and was categorized as PRF and non-PRF. The outcome variable was the development of AO during the first postoperative week. Other study variables included age, gender, smoking status, irrigation volume, extraction difficulty, surgeon experience, and number of anesthetic cartridges. Data were analyzed using χ(2) and t tests, with the significance level set at a P value less than .05. Seventy-eight patients (mean age, 25 yr) underwent 156 impacted third molar surgeries. The overall frequency of AO was 14.74% for all surgeries. The frequency of AO in the PRF group was significantly lower than in the non-PRF group (odds ratio = 0.44; P < .05). Based on the results of the present study, PRF application may decrease the risk of AO development after mandibular third molar surgery. Copyright © 2014 American Association of Oral and Maxillofacial Surgeons. Published by Elsevier Inc. All rights reserved.

Effect of topical application of dipyrone on dental sensitivity reduction after in-office dental bleaching: A randomized, triple-blind multicenter clinical trial.

PubMed

Rezende, Márcia; Chemin, Kaprice; Vaez, Savil Costa; Peixoto, Aline Carvalho; Rabelo, Jéssica de Freitas; Braga, Stella Sueli Lourenço; Faria-E-Silva, André Luis; Silva, Gisele Rodrigues da; Soares, Carlos José; Loguercio, Alessandro D; Reis, Alessandra

2018-05-01

Tooth sensitivity commonly occurs during and immediately after dental bleaching. The authors conducted a trial to compare tooth sensitivity after in-office bleaching after the use of either a topical dipyrone or placebo gel. A split-mouth, triple-blind, randomized, multicenter clinical trial was conducted among 120 healthy adults having teeth that were shade A2 or darker. The facial tooth surfaces of the right or left sides of the maxillary arch of each patient were randomly assigned to receive either topical dipyrone or placebo gel before 2 in-office bleaching sessions (35% hydrogen peroxide) separated by 2 weeks. Visual analog and numerical rating scales were used to record tooth sensitivity during and up to 48 hours after bleaching. Tooth color change from baseline to 1 month after bleaching was measured with shade guide and spectrophotometer measures. The primary outcome variable was absolute risk of tooth sensitivity. An intention-to-treat analysis was used to analyze data from all patients who were randomly assigned to receive the dipyrone and placebo gels. No statically significant difference was found in the absolute risk of tooth sensitivity between the dipyrone and placebo gels (83% and 90%, respectively, P = .09; relative risk, 0.92; 95% confidence interval, 0.8 to 1.0). A whitening effect was observed in both groups with no statistically significant difference (P > .05) between them. No adverse effects were observed. Topical use of dipyrone gel before tooth bleaching, at the levels used in this study, did not reduce the risk or intensity of bleaching-induced tooth sensitivity. Topical application of dipyrone gel does not reduce bleaching-induced tooth sensitivity. Copyright © 2018 American Dental Association. Published by Elsevier Inc. All rights reserved.

[The Brazilian Hospital Information System and the acute myocardial infarction hospital care].

PubMed

Escosteguy, Claudia Caminha; Portela, Margareth Crisóstomo; Medronho, Roberto de Andrade; de Vasconcellos, Maurício Teixeira Leite

2002-08-01

To analyze the applicability of the Brazilian Unified Health System's national hospital database to evaluate the quality of acute myocardial infarction hospital care. It was evaluated 1,936 hospital admission forms having acute myocardial infarction (AMI) as primary diagnosis in the municipal district of Rio de Janeiro, Brazil, in 1997. Data was collected from the national hospital database. A stratified random sampling of 391 medical records was also evaluated. AMI diagnosis agreement followed the literature criteria. Variable accuracy analysis was performed using kappa index agreement. The quality of AMI diagnosis registered in hospital admission forms was satisfactory according to the gold standard of the literature. In general, the accuracy of the variables demographics (sex, age group), process (medical procedures and interventions), and outcome (hospital death) was satisfactory. The accuracy of demographics and outcome variables was higher than the one of process variables. Under registration of secondary diagnosis was high in the forms and it was the main limiting factor. Given the study findings and the widespread availability of the national hospital database, it is pertinent its use as an instrument in the evaluation of the quality of AMI medical care.

Probabilistic graphs as a conceptual and computational tool in hydrology and water management

NASA Astrophysics Data System (ADS)

Schoups, Gerrit

2014-05-01

Originally developed in the fields of machine learning and artificial intelligence, probabilistic graphs constitute a general framework for modeling complex systems in the presence of uncertainty. The framework consists of three components: 1. Representation of the model as a graph (or network), with nodes depicting random variables in the model (e.g. parameters, states, etc), which are joined together by factors. Factors are local probabilistic or deterministic relations between subsets of variables, which, when multiplied together, yield the joint distribution over all variables. 2. Consistent use of probability theory for quantifying uncertainty, relying on basic rules of probability for assimilating data into the model and expressing unknown variables as a function of observations (via the posterior distribution). 3. Efficient, distributed approximation of the posterior distribution using general-purpose algorithms that exploit model structure encoded in the graph. These attributes make probabilistic graphs potentially useful as a conceptual and computational tool in hydrology and water management (and beyond). Conceptually, they can provide a common framework for existing and new probabilistic modeling approaches (e.g. by drawing inspiration from other fields of application), while computationally they can make probabilistic inference feasible in larger hydrological models. The presentation explores, via examples, some of these benefits.

Probabilities and statistics for backscatter estimates obtained by a scatterometer

NASA Technical Reports Server (NTRS)

Pierson, Willard J., Jr.

1989-01-01

Methods for the recovery of winds near the surface of the ocean from measurements of the normalized radar backscattering cross section must recognize and make use of the statistics (i.e., the sampling variability) of the backscatter measurements. Radar backscatter values from a scatterometer are random variables with expected values given by a model. A model relates backscatter to properties of the waves on the ocean, which are in turn generated by the winds in the atmospheric marine boundary layer. The effective wind speed and direction at a known height for a neutrally stratified atmosphere are the values to be recovered from the model. The probability density function for the backscatter values is a normal probability distribution with the notable feature that the variance is a known function of the expected value. The sources of signal variability, the effects of this variability on the wind speed estimation, and criteria for the acceptance or rejection of models are discussed. A modified maximum likelihood method for estimating wind vectors is described. Ways to make corrections for the kinds of errors found for the Seasat SASS model function are described, and applications to a new scatterometer are given.

Evaluating physical habitat and water chemistry data from statewide stream monitoring programs to establish least-impacted conditions in Washington State

USGS Publications Warehouse

Wilmoth, Siri K.; Irvine, Kathryn M.; Larson, Chad

2015-01-01

Various GIS-generated land-use predictor variables, physical habitat metrics, and water chemistry variables from 75 reference streams and 351 randomly sampled sites throughout Washington State were evaluated for effectiveness at discriminating reference from random sites within level III ecoregions. A combination of multivariate clustering and ordination techniques were used. We describe average observed conditions for a subset of predictor variables as well as proposing statistical criteria for establishing reference conditions for stream habitat in Washington. Using these criteria, we determined whether any of the random sites met expectations for reference condition and whether any of the established reference sites failed to meet expectations for reference condition. Establishing these criteria will set a benchmark from which future data will be compared.

Non-manipulation quantitative designs.

PubMed

Rumrill, Phillip D

2004-01-01

The article describes non-manipulation quantitative designs of two types, correlational and causal comparative studies. Both of these designs are characterized by the absence of random assignment of research participants to conditions or groups and non-manipulation of the independent variable. Without random selection or manipulation of the independent variable, no attempt is made to draw causal inferences regarding relationships between independent and dependent variables. Nonetheless, non-manipulation studies play an important role in rehabilitation research, as described in this article. Examples from the contemporary rehabilitation literature are included. Copyright 2004 IOS Press

Morinda citrifolia (Noni) as an Anti-Inflammatory Treatment in Women with Primary Dysmenorrhoea: A Randomised Double-Blind Placebo-Controlled Trial.

PubMed

Fletcher, H M; Dawkins, J; Rattray, C; Wharfe, G; Reid, M; Gordon-Strachan, G

2013-01-01

Introduction. Noni (Morinda citrifolia) has been used for many years as an anti-inflammatory agent. We tested the efficacy of Noni in women with dysmenorrhea. Method. We did a prospective randomized double-blind placebo-controlled trial in 100 university students of 18 years and older over three menstrual cycles. Patients were invited to participate and randomly assigned to receive 400 mg Noni capsules or placebo. They were assessed for baseline demographic variables such as age, parity, and BMI. They were also assessed before and after treatment, for pain, menstrual blood loss, and laboratory variables: ESR, hemoglobin, and packed cell volume. Results. Of the 1027 women screened, 100 eligible women were randomized. Of the women completing the study, 42 women were randomized to Noni and 38 to placebo. There were no significant differences in any of the variables at randomization. There were also no significant differences in mean bleeding score or pain score at randomization. Both bleeding and pain scores gradually improved in both groups as the women were observed over three menstrual cycles; however, the improvement was not significantly different in the Noni group when compared to the controls. Conclusion. Noni did not show a reduction in menstrual pain or bleeding when compared to placebo.

Morinda citrifolia (Noni) as an Anti-Inflammatory Treatment in Women with Primary Dysmenorrhoea: A Randomised Double-Blind Placebo-Controlled Trial

PubMed Central

Fletcher, H. M.; Dawkins, J.; Rattray, C.; Wharfe, G.; Reid, M.; Gordon-Strachan, G.

2013-01-01

Introduction. Noni (Morinda citrifolia) has been used for many years as an anti-inflammatory agent. We tested the efficacy of Noni in women with dysmenorrhea. Method. We did a prospective randomized double-blind placebo-controlled trial in 100 university students of 18 years and older over three menstrual cycles. Patients were invited to participate and randomly assigned to receive 400 mg Noni capsules or placebo. They were assessed for baseline demographic variables such as age, parity, and BMI. They were also assessed before and after treatment, for pain, menstrual blood loss, and laboratory variables: ESR, hemoglobin, and packed cell volume. Results. Of the 1027 women screened, 100 eligible women were randomized. Of the women completing the study, 42 women were randomized to Noni and 38 to placebo. There were no significant differences in any of the variables at randomization. There were also no significant differences in mean bleeding score or pain score at randomization. Both bleeding and pain scores gradually improved in both groups as the women were observed over three menstrual cycles; however, the improvement was not significantly different in the Noni group when compared to the controls. Conclusion. Noni did not show a reduction in menstrual pain or bleeding when compared to placebo. PMID:23431314

Variable density randomized stack of spirals (VDR-SoS) for compressive sensing MRI.

PubMed

Valvano, Giuseppe; Martini, Nicola; Landini, Luigi; Santarelli, Maria Filomena

2016-07-01

To develop a 3D sampling strategy based on a stack of variable density spirals for compressive sensing MRI. A random sampling pattern was obtained by rotating each spiral by a random angle and by delaying for few time steps the gradient waveforms of the different interleaves. A three-dimensional (3D) variable sampling density was obtained by designing different variable density spirals for each slice encoding. The proposed approach was tested with phantom simulations up to a five-fold undersampling factor. Fully sampled 3D dataset of a human knee, and of a human brain, were obtained from a healthy volunteer. The proposed approach was tested with off-line reconstructions of the knee dataset up to a four-fold acceleration and compared with other noncoherent trajectories. The proposed approach outperformed the standard stack of spirals for various undersampling factors. The level of coherence and the reconstruction quality of the proposed approach were similar to those of other trajectories that, however, require 3D gridding for the reconstruction. The variable density randomized stack of spirals (VDR-SoS) is an easily implementable trajectory that could represent a valid sampling strategy for 3D compressive sensing MRI. It guarantees low levels of coherence without requiring 3D gridding. Magn Reson Med 76:59-69, 2016. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.

High throughput nonparametric probability density estimation.

PubMed

Farmer, Jenny; Jacobs, Donald

2018-01-01

In high throughput applications, such as those found in bioinformatics and finance, it is important to determine accurate probability distribution functions despite only minimal information about data characteristics, and without using human subjectivity. Such an automated process for univariate data is implemented to achieve this goal by merging the maximum entropy method with single order statistics and maximum likelihood. The only required properties of the random variables are that they are continuous and that they are, or can be approximated as, independent and identically distributed. A quasi-log-likelihood function based on single order statistics for sampled uniform random data is used to empirically construct a sample size invariant universal scoring function. Then a probability density estimate is determined by iteratively improving trial cumulative distribution functions, where better estimates are quantified by the scoring function that identifies atypical fluctuations. This criterion resists under and over fitting data as an alternative to employing the Bayesian or Akaike information criterion. Multiple estimates for the probability density reflect uncertainties due to statistical fluctuations in random samples. Scaled quantile residual plots are also introduced as an effective diagnostic to visualize the quality of the estimated probability densities. Benchmark tests show that estimates for the probability density function (PDF) converge to the true PDF as sample size increases on particularly difficult test probability densities that include cases with discontinuities, multi-resolution scales, heavy tails, and singularities. These results indicate the method has general applicability for high throughput statistical inference.

High throughput nonparametric probability density estimation

PubMed Central

Farmer, Jenny

2018-01-01

In high throughput applications, such as those found in bioinformatics and finance, it is important to determine accurate probability distribution functions despite only minimal information about data characteristics, and without using human subjectivity. Such an automated process for univariate data is implemented to achieve this goal by merging the maximum entropy method with single order statistics and maximum likelihood. The only required properties of the random variables are that they are continuous and that they are, or can be approximated as, independent and identically distributed. A quasi-log-likelihood function based on single order statistics for sampled uniform random data is used to empirically construct a sample size invariant universal scoring function. Then a probability density estimate is determined by iteratively improving trial cumulative distribution functions, where better estimates are quantified by the scoring function that identifies atypical fluctuations. This criterion resists under and over fitting data as an alternative to employing the Bayesian or Akaike information criterion. Multiple estimates for the probability density reflect uncertainties due to statistical fluctuations in random samples. Scaled quantile residual plots are also introduced as an effective diagnostic to visualize the quality of the estimated probability densities. Benchmark tests show that estimates for the probability density function (PDF) converge to the true PDF as sample size increases on particularly difficult test probability densities that include cases with discontinuities, multi-resolution scales, heavy tails, and singularities. These results indicate the method has general applicability for high throughput statistical inference. PMID:29750803

Solution and reasoning reuse in space planning and scheduling applications

NASA Technical Reports Server (NTRS)

Verfaillie, Gerard; Schiex, Thomas

1994-01-01

In the space domain, as in other domains, the CSP (Constraint Satisfaction Problems) techniques are increasingly used to represent and solve planning and scheduling problems. But these techniques have been developed to solve CSP's which are composed of fixed sets of variables and constraints, whereas many planning and scheduling problems are dynamic. It is therefore important to develop methods which allow a new solution to be rapidly found, as close as possible to the previous one, when some variables or constraints are added or removed. After presenting some existing approaches, this paper proposes a simple and efficient method, which has been developed on the basis of the dynamic backtracking algorithm. This method allows previous solution and reasoning to be reused in the framework of a CSP which is close to the previous one. Some experimental results on general random CSPs and on operation scheduling problems for remote sensing satellites are given.

Considerations of multiple imputation approaches for handling missing data in clinical trials.

PubMed

Quan, Hui; Qi, Li; Luo, Xiaodong; Darchy, Loic

2018-07-01

Missing data exist in all clinical trials and missing data issue is a very serious issue in terms of the interpretability of the trial results. There is no universally applicable solution for all missing data problems. Methods used for handling missing data issue depend on the circumstances particularly the assumptions on missing data mechanisms. In recent years, if the missing at random mechanism cannot be assumed, conservative approaches such as the control-based and returning to baseline multiple imputation approaches are applied for dealing with the missing data issues. In this paper, we focus on the variability in data analysis of these approaches. As demonstrated by examples, the choice of the variability can impact the conclusion of the analysis. Besides the methods for continuous endpoints, we also discuss methods for binary and time to event endpoints as well as consideration for non-inferiority assessment. Copyright © 2018. Published by Elsevier Inc.

A double hit model for the distribution of time to AIDS onset

NASA Astrophysics Data System (ADS)

Chillale, Nagaraja Rao

2013-09-01

Incubation time is a key epidemiologic descriptor of an infectious disease. In the case of HIV infection this is a random variable and is probably the longest one. The probability distribution of incubation time is the major determinant of the relation between the incidences of HIV infection and its manifestation to Aids. This is also one of the key factors used for accurate estimation of AIDS incidence in a region. The present article i) briefly reviews the work done, points out uncertainties in estimation of AIDS onset time and stresses the need for its precise estimation, ii) highlights some of the modelling features of onset distribution including immune failure mechanism, and iii) proposes a 'Double Hit' model for the distribution of time to AIDS onset in the cases of (a) independent and (b) dependent time variables of the two markers and examined the applicability of a few standard probability models.

Power calculator for instrumental variable analysis in pharmacoepidemiology

PubMed Central

Walker, Venexia M; Davies, Neil M; Windmeijer, Frank; Burgess, Stephen; Martin, Richard M

2017-01-01

Abstract Background Instrumental variable analysis, for example with physicians’ prescribing preferences as an instrument for medications issued in primary care, is an increasingly popular method in the field of pharmacoepidemiology. Existing power calculators for studies using instrumental variable analysis, such as Mendelian randomization power calculators, do not allow for the structure of research questions in this field. This is because the analysis in pharmacoepidemiology will typically have stronger instruments and detect larger causal effects than in other fields. Consequently, there is a need for dedicated power calculators for pharmacoepidemiological research. Methods and Results The formula for calculating the power of a study using instrumental variable analysis in the context of pharmacoepidemiology is derived before being validated by a simulation study. The formula is applicable for studies using a single binary instrument to analyse the causal effect of a binary exposure on a continuous outcome. An online calculator, as well as packages in both R and Stata, are provided for the implementation of the formula by others. Conclusions The statistical power of instrumental variable analysis in pharmacoepidemiological studies to detect a clinically meaningful treatment effect is an important consideration. Research questions in this field have distinct structures that must be accounted for when calculating power. The formula presented differs from existing instrumental variable power formulae due to its parametrization, which is designed specifically for ease of use by pharmacoepidemiologists. PMID:28575313

LQTA-QSAR: a new 4D-QSAR methodology.

PubMed

Martins, João Paulo A; Barbosa, Euzébio G; Pasqualoto, Kerly F M; Ferreira, Márcia M C

2009-06-01

A novel 4D-QSAR approach which makes use of the molecular dynamics (MD) trajectories and topology information retrieved from the GROMACS package is presented in this study. This new methodology, named LQTA-QSAR (LQTA, Laboratório de Quimiometria Teórica e Aplicada), has a module (LQTAgrid) that calculates intermolecular interaction energies at each grid point considering probes and all aligned conformations resulting from MD simulations. These interaction energies are the independent variables or descriptors employed in a QSAR analysis. The comparison of the proposed methodology to other 4D-QSAR and CoMFA formalisms was performed using a set of forty-seven glycogen phosphorylase b inhibitors (data set 1) and a set of forty-four MAP p38 kinase inhibitors (data set 2). The QSAR models for both data sets were built using the ordered predictor selection (OPS) algorithm for variable selection. Model validation was carried out applying y-randomization and leave-N-out cross-validation in addition to the external validation. PLS models for data set 1 and 2 provided the following statistics: q(2) = 0.72, r(2) = 0.81 for 12 variables selected and 2 latent variables and q(2) = 0.82, r(2) = 0.90 for 10 variables selected and 5 latent variables, respectively. Visualization of the descriptors in 3D space was successfully interpreted from the chemical point of view, supporting the applicability of this new approach in rational drug design.

Random Item IRT Models

ERIC Educational Resources Information Center

De Boeck, Paul

2008-01-01

It is common practice in IRT to consider items as fixed and persons as random. Both, continuous and categorical person parameters are most often random variables, whereas for items only continuous parameters are used and they are commonly of the fixed type, although exceptions occur. It is shown in the present article that random item parameters…

Single-dose intra-alveolar chlorhexidine gel application, easier surgeries, and younger ages are associated with reduced dry socket risk.

PubMed

Haraji, Afshin; Rakhshan, Vahid

2014-02-01

Although dry socket (DS) is commonly investigated, many of its risk factors remain highly controversial. In addition, few studies are available to show the preventive effect of chlorhexidine gel on DS. Moreover, multivariable analyses of DS risk factors are scarce, and their interactions have not been assessed previously. Therefore, the simultaneous effect of chlorhexidine gel and 4 DS risk factors and their interactions were analyzed within a multivariable framework. Using a split-mouth randomized clinical trial design, the investigators enrolled a cohort of patients requiring extraction of 2 mandibular third molars. The primary predictor variable was extraction socket treatment status, classified as experimental or standard. Experimental treatment was the insertion of chlorhexidine gel (0.2%) into the extraction socket. Each patient had 1 third molar randomly selected as the treatment site. The contralateral third molar served as the control socket and was treated in the usual manner. The primary outcome variable was DS status, present or absent, assessed on postoperative day 3. Other study variables were categorized as demographic, smoking, and surgical difficulty according to the Pederson scale. Appropriate bivariate and multiple logistic regression statistics were used to measure the association between risk for DS and chlorhexidine gel use, age, gender, smoking, and surgical difficulty and their interactions (α = 0.05). The sample consisted of 90 bilateral extraction sockets in 45 patients (24 men; 21 smokers; mean age, 21.1 ± 2.7 yr). Regression analysis showed that when other factors and their interactions were controlled for, chlorhexidine gel application lowered the risk of DS (odds ratio [OR] = 0.05; P = .004). Increasing age (OR = 2.9; P = .030) was associated with an increased risk for DS. A similar association existed between increased difficulty level of extraction and DS risk (OR = 3.8; P = .051). The effect of gender was marginally significant (P = .091), whereas smoking did not have a significant influence (P = .4). Intra-alveolar application of chlorhexidine gel and practicing less traumatic surgeries are advocated, particularly in older patients. Smoking seems unlikely to affect DS frequency. The role of gender is inconclusive. Copyright © 2014 American Association of Oral and Maxillofacial Surgeons. Published by Elsevier Inc. All rights reserved.

Is the Non-Dipole Magnetic Field Random?

NASA Technical Reports Server (NTRS)

Walker, Andrew D.; Backus, George E.

1996-01-01

Statistical modelling of the Earth's magnetic field B has a long history. In particular, the spherical harmonic coefficients of scalar fields derived from B can be treated as Gaussian random variables. In this paper, we give examples of highly organized fields whose spherical harmonic coefficients pass tests for independent Gaussian random variables. The fact that coefficients at some depth may be usefully summarized as independent samples from a normal distribution need not imply that there really is some physical, random process at that depth. In fact, the field can be extremely structured and still be regarded for some purposes as random. In this paper, we examined the radial magnetic field B(sub r) produced by the core, but the results apply to any scalar field on the core-mantle boundary (CMB) which determines B outside the CMB.

Implications of Heterogeneity of Treatment Effect for Reporting and Analysis of Randomized Trials in Critical Care.

PubMed

Iwashyna, Theodore J; Burke, James F; Sussman, Jeremy B; Prescott, Hallie C; Hayward, Rodney A; Angus, Derek C

2015-11-01

Randomized clinical trials (RCTs) are conducted to guide clinicians' selection of therapies for individual patients. Currently, RCTs in critical care often report an overall mean effect and selected individual subgroups. Yet work in other fields suggests that such reporting practices can be improved. Specifically, this Critical Care Perspective reviews recent work on so-called "heterogeneity of treatment effect" (HTE) by baseline risk and extends that work to examine its applicability to trials of acute respiratory failure and severe sepsis. Because patients in RCTs in critical care medicine-and patients in intensive care units-have wide variability in their risk of death, these patients will have wide variability in the absolute benefit that they can derive from a given therapy. If the side effects of the therapy are not perfectly collinear with the treatment benefits, this will result in HTE, where different patients experience quite different expected benefits of a therapy. We use simulations of RCTs to demonstrate that such HTE could result in apparent paradoxes, including: (1) positive trials of therapies that are beneficial overall but consistently harm or have little benefit to low-risk patients who met enrollment criteria, and (2) overall negative trials of therapies that still consistently benefit high-risk patients. We further show that these results persist even in the presence of causes of death unmodified by the treatment under study. These results have implications for reporting and analyzing RCT data, both to better understand how our therapies work and to improve the bedside applicability of RCTs. We suggest a plan for measurement in future RCTs in the critically ill.

Benefits of a self-myofascial release program on health-related quality of life in people with fibromyalgia: a randomized controlled trial.

PubMed

Ceca, Diego; Elvira, Laura; Guzmán, José F; Pablos, Ana

2017-01-01

Fibromyalgia (FM) is a disease with symptoms that significantly limit the life of affected patients. Earlier studies have shown that the application of self-myofascial release provides benefits in variables such as fatigue, range of motion (ROM) or perceived muscle pain in a healthy population. Despite this, the self-myofascial release technique has not yet been used in people with FM. This study aimed to find out the benefits of applying a self-myofascial release program on health-related quality of life in people with FM. Sixty-six participants with FM were randomized into two groups, intervention (N.=33) and control (N.=33). The intervention group (IG) participated in the self-myofascial release program for twenty weeks. The study assessed the impact of a self-myofascial release program on cervical spine, shoulder and hip ROM and self-reported disease impact. Two measurements were performed, one at baseline (preintervention) and one postintervention. Two-way mixed-effect (between-within) ANOVA was used for the statistical analysis. Significant changes (P<0.05) were achieved between the two measurements and between groups for final Fibromyalgia Impact Questionnaire (FIQ-S) Score and for five of its seven subscales, including: days per week feeling good, pain intensity, fatigue, stiffness and depression/sadness, as well as all the ROM variables evaluated (neck flexion, neck extension, lateral neck flexion and rotation (bilateral), shoulder flexion and abduction and hip abduction) excluding hip flexion. The application of a self-myofascial release program can improve the health-related quality of life of people with FM, provided that regular, structured practice is carried out.

Microstructure and Mechanical Property of Glutaraldehyde-Treated Porcine Pulmonary Ligament.

PubMed

Chen, Huan; Zhao, Xuefeng; Berwick, Zachary C; Krieger, Joshua F; Chambers, Sean; Kassab, Ghassan S

2016-06-01

There is a significant need for fixed biological tissues with desired structural and material constituents for tissue engineering applications. Here, we introduce the lung ligament as a fixed biological material that may have clinical utility for tissue engineering. To characterize the lung tissue for potential clinical applications, we studied glutaraldehyde-treated porcine pulmonary ligament (n = 11) with multiphoton microscopy (MPM) and conducted biaxial planar experiments to characterize the mechanical property of the tissue. The MPM imaging revealed that there are generally two families of collagen fibers distributed in two distinct layers: The first family largely aligns along the longitudinal direction with a mean angle of θ = 10.7 ± 9.3 deg, while the second one exhibits a random distribution with a mean θ = 36.6 ± 27.4. Elastin fibers appear in some intermediate sublayers with a random orientation distribution with a mean θ = 39.6 ± 23 deg. Based on the microstructural observation, a microstructure-based constitutive law was proposed to model the elastic property of the tissue. The material parameters were identified by fitting the model to the biaxial stress-strain data of specimens, and good fitting quality was achieved. The parameter e0 (which denotes the strain beyond which the collagen can withstand tension) of glutaraldehyde-treated tissues demonstrated low variability implying a relatively consistent collagen undulation in different samples, while the stiffness parameters for elastin and collagen fibers showed relatively greater variability. The fixed tissues presented a smaller e0 than that of fresh specimen, confirming that glutaraldehyde crosslinking increases the mechanical strength of collagen-based biomaterials. The present study sheds light on the biomechanics of glutaraldehyde-treated porcine pulmonary ligament that may be a candidate for tissue engineering.

Generalized radiative transfer theory for scattering by particles in an absorbing gas: Addressing both spatial and spectral integration in multi-angle remote sensing of optically thin aerosol layers

NASA Astrophysics Data System (ADS)

Davis, Anthony B.; Xu, Feng; Diner, David J.

2018-01-01

We demonstrate the computational advantage gained by introducing non-exponential transmission laws into radiative transfer theory for two specific situations. One is the problem of spatial integration over a large domain where the scattering particles cluster randomly in a medium uniformly filled with an absorbing gas, and only a probabilistic description of the variability is available. The increasingly important application here is passive atmospheric profiling using oxygen absorption in the visible/near-IR spectrum. The other scenario is spectral integration over a region where the absorption cross-section of a spatially uniform gas varies rapidly and widely and, moreover, there are scattering particles embedded in the gas that are distributed uniformly, or not. This comes up in many applications, O2 A-band profiling being just one instance. We bring a common framework to solve these problems both efficiently and accurately that is grounded in the recently developed theory of Generalized Radiative Transfer (GRT). In GRT, the classic exponential law of transmission is replaced by one with a slower power-law decay that accounts for the unresolved spectral or spatial variability. Analytical results are derived in the single-scattering limit that applies to optically thin aerosol layers. In spectral integration, a modest gain in accuracy is obtained. As for spatial integration of near-monochromatic radiance, we find that, although both continuum and in-band radiances are affected by moderate levels of sub-pixel variability, only extreme variability will affect in-band/continuum ratios.

Are glucose levels, glucose variability and autonomic control influenced by inspiratory muscle exercise in patients with type 2 diabetes? Study protocol for a randomized controlled trial.

PubMed

Schein, Aso; Correa, Aps; Casali, Karina Rabello; Schaan, Beatriz D

2016-01-20

Physical exercise reduces glucose levels and glucose variability in patients with type 2 diabetes. Acute inspiratory muscle exercise has been shown to reduce these parameters in a small group of patients with type 2 diabetes, but these results have yet to be confirmed in a well-designed study. The aim of this study is to investigate the effect of acute inspiratory muscle exercise on glucose levels, glucose variability, and cardiovascular autonomic function in patients with type 2 diabetes. This study will use a randomized clinical trial crossover design. A total of 14 subjects will be recruited and randomly allocated to two groups to perform acute inspiratory muscle loading at 2 % of maximal inspiratory pressure (PImax, placebo load) or 60 % of PImax (experimental load). Inspiratory muscle training could be a novel exercise modality to be used to decrease glucose levels and glucose variability. ClinicalTrials.gov NCT02292810 .

Parallelization of a spatial random field characterization process using the Method of Anchored Distributions and the HTCondor high throughput computing system

NASA Astrophysics Data System (ADS)

Osorio-Murillo, C. A.; Over, M. W.; Frystacky, H.; Ames, D. P.; Rubin, Y.

2013-12-01

A new software application called MAD# has been coupled with the HTCondor high throughput computing system to aid scientists and educators with the characterization of spatial random fields and enable understanding the spatial distribution of parameters used in hydrogeologic and related modeling. MAD# is an open source desktop software application used to characterize spatial random fields using direct and indirect information through Bayesian inverse modeling technique called the Method of Anchored Distributions (MAD). MAD relates indirect information with a target spatial random field via a forward simulation model. MAD# executes inverse process running the forward model multiple times to transfer information from indirect information to the target variable. MAD# uses two parallelization profiles according to computational resources available: one computer with multiple cores and multiple computers - multiple cores through HTCondor. HTCondor is a system that manages a cluster of desktop computers for submits serial or parallel jobs using scheduling policies, resources monitoring, job queuing mechanism. This poster will show how MAD# reduces the time execution of the characterization of random fields using these two parallel approaches in different case studies. A test of the approach was conducted using 1D problem with 400 cells to characterize saturated conductivity, residual water content, and shape parameters of the Mualem-van Genuchten model in four materials via the HYDRUS model. The number of simulations evaluated in the inversion was 10 million. Using the one computer approach (eight cores) were evaluated 100,000 simulations in 12 hours (10 million - 1200 hours approximately). In the evaluation on HTCondor, 32 desktop computers (132 cores) were used, with a processing time of 60 hours non-continuous in five days. HTCondor reduced the processing time for uncertainty characterization by a factor of 20 (1200 hours reduced to 60 hours.)

Large-Scale Uncertainty and Error Analysis for Time-dependent Fluid/Structure Interactions in Wind Turbine Applications

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

Alonso, Juan J.; Iaccarino, Gianluca

2013-08-25

The following is the final report covering the entire period of this aforementioned grant, June 1, 2011 - May 31, 2013 for the portion of the effort corresponding to Stanford University (SU). SU has partnered with Sandia National Laboratories (PI: Mike S. Eldred) and Purdue University (PI: Dongbin Xiu) to complete this research project and this final report includes those contributions made by the members of the team at Stanford. Dr. Eldred is continuing his contributions to this project under a no-cost extension and his contributions to the overall effort will be detailed at a later time (once his effortmore » has concluded) on a separate project submitted by Sandia National Laboratories. At Stanford, the team is made up of Profs. Alonso, Iaccarino, and Duraisamy, post-doctoral researcher Vinod Lakshminarayan, and graduate student Santiago Padron. At Sandia National Laboratories, the team includes Michael Eldred, Matt Barone, John Jakeman, and Stefan Domino, and at Purdue University, we have Prof. Dongbin Xiu as our main collaborator. The overall objective of this project was to develop a novel, comprehensive methodology for uncertainty quantification by combining stochastic expansions (nonintrusive polynomial chaos and stochastic collocation), the adjoint approach, and fusion with experimental data to account for aleatory and epistemic uncertainties from random variable, random field, and model form sources. The expected outcomes of this activity were detailed in the proposal and are repeated here to set the stage for the results that we have generated during the time period of execution of this project: 1. The rigorous determination of an error budget comprising numerical errors in physical space and statistical errors in stochastic space and its use for optimal allocation of resources; 2. A considerable increase in efficiency when performing uncertainty quantification with a large number of uncertain variables in complex non-linear multi-physics problems; 3. A solution to the long-time integration problem of spectral chaos approaches; 4. A rigorous methodology to account for aleatory and epistemic uncertainties, to emphasize the most important variables via dimension reduction and dimension-adaptive refinement, and to support fusion with experimental data using Bayesian inference; 5. The application of novel methodologies to time-dependent reliability studies in wind turbine applications including a number of efforts relating to the uncertainty quantification in vertical-axis wind turbine applications. In this report, we summarize all accomplishments in the project (during the time period specified) focusing on advances in UQ algorithms and deployment efforts to the wind turbine application area. Detailed publications in each of these areas have also been completed and are available from the respective conference proceedings and journals as detailed in a later section.« less

An Integrated Probabilistic-Fuzzy Assessment of Uncertainty Associated with Human Health Risk to MSW Landfill Leachate Contamination

NASA Astrophysics Data System (ADS)

Mishra, H.; Karmakar, S.; Kumar, R.

2016-12-01

Risk assessment will not remain simple when it involves multiple uncertain variables. Uncertainties in risk assessment majorly results from (1) the lack of knowledge of input variable (mostly random), and (2) data obtained from expert judgment or subjective interpretation of available information (non-random). An integrated probabilistic-fuzzy health risk approach has been proposed for simultaneous treatment of random and non-random uncertainties associated with input parameters of health risk model. The LandSim 2.5, a landfill simulator, has been used to simulate the Turbhe landfill (Navi Mumbai, India) activities for various time horizons. Further the LandSim simulated six heavy metals concentration in ground water have been used in the health risk model. The water intake, exposure duration, exposure frequency, bioavailability and average time are treated as fuzzy variables, while the heavy metals concentration and body weight are considered as probabilistic variables. Identical alpha-cut and reliability level are considered for fuzzy and probabilistic variables respectively and further, uncertainty in non-carcinogenic human health risk is estimated using ten thousand Monte-Carlo simulations (MCS). This is the first effort in which all the health risk variables have been considered as non-deterministic for the estimation of uncertainty in risk output. The non-exceedance probability of Hazard Index (HI), summation of hazard quotients, of heavy metals of Co, Cu, Mn, Ni, Zn and Fe for male and female population have been quantified and found to be high (HI>1) for all the considered time horizon, which evidently shows possibility of adverse health effects on the population residing near Turbhe landfill.

Optimizing a Sensor Network with Data from Hazard Mapping Demonstrated in a Heavy-Vehicle Manufacturing Facility.

PubMed

Berman, Jesse D; Peters, Thomas M; Koehler, Kirsten A

2018-05-28

To design a method that uses preliminary hazard mapping data to optimize the number and location of sensors within a network for a long-term assessment of occupational concentrations, while preserving temporal variability, accuracy, and precision of predicted hazards. Particle number concentrations (PNCs) and respirable mass concentrations (RMCs) were measured with direct-reading instruments in a large heavy-vehicle manufacturing facility at 80-82 locations during 7 mapping events, stratified by day and season. Using kriged hazard mapping, a statistical approach identified optimal orders for removing locations to capture temporal variability and high prediction precision of PNC and RMC concentrations. We compared optimal-removal, random-removal, and least-optimal-removal orders to bound prediction performance. The temporal variability of PNC was found to be higher than RMC with low correlation between the two particulate metrics (ρ = 0.30). Optimal-removal orders resulted in more accurate PNC kriged estimates (root mean square error [RMSE] = 49.2) at sample locations compared with random-removal order (RMSE = 55.7). For estimates at locations having concentrations in the upper 10th percentile, the optimal-removal order preserved average estimated concentrations better than random- or least-optimal-removal orders (P < 0.01). However, estimated average concentrations using an optimal-removal were not statistically different than random-removal when averaged over the entire facility. No statistical difference was observed for optimal- and random-removal methods for RMCs that were less variable in time and space than PNCs. Optimized removal performed better than random-removal in preserving high temporal variability and accuracy of hazard map for PNC, but not for the more spatially homogeneous RMC. These results can be used to reduce the number of locations used in a network of static sensors for long-term monitoring of hazards in the workplace, without sacrificing prediction performance.

Extracting random numbers from quantum tunnelling through a single diode.

PubMed

Bernardo-Gavito, Ramón; Bagci, Ibrahim Ethem; Roberts, Jonathan; Sexton, James; Astbury, Benjamin; Shokeir, Hamzah; McGrath, Thomas; Noori, Yasir J; Woodhead, Christopher S; Missous, Mohamed; Roedig, Utz; Young, Robert J

2017-12-19

Random number generation is crucial in many aspects of everyday life, as online security and privacy depend ultimately on the quality of random numbers. Many current implementations are based on pseudo-random number generators, but information security requires true random numbers for sensitive applications like key generation in banking, defence or even social media. True random number generators are systems whose outputs cannot be determined, even if their internal structure and response history are known. Sources of quantum noise are thus ideal for this application due to their intrinsic uncertainty. In this work, we propose using resonant tunnelling diodes as practical true random number generators based on a quantum mechanical effect. The output of the proposed devices can be directly used as a random stream of bits or can be further distilled using randomness extraction algorithms, depending on the application.

Variable Selection in the Presence of Missing Data: Imputation-based Methods.

PubMed

Zhao, Yize; Long, Qi

2017-01-01

Variable selection plays an essential role in regression analysis as it identifies important variables that associated with outcomes and is known to improve predictive accuracy of resulting models. Variable selection methods have been widely investigated for fully observed data. However, in the presence of missing data, methods for variable selection need to be carefully designed to account for missing data mechanisms and statistical techniques used for handling missing data. Since imputation is arguably the most popular method for handling missing data due to its ease of use, statistical methods for variable selection that are combined with imputation are of particular interest. These methods, valid used under the assumptions of missing at random (MAR) and missing completely at random (MCAR), largely fall into three general strategies. The first strategy applies existing variable selection methods to each imputed dataset and then combine variable selection results across all imputed datasets. The second strategy applies existing variable selection methods to stacked imputed datasets. The third variable selection strategy combines resampling techniques such as bootstrap with imputation. Despite recent advances, this area remains under-developed and offers fertile ground for further research.

PRP For the Treatment of Cartilage Pathology

PubMed Central

Kon, Elizaveta; Filardo, Giuseppe; Di Matteo, Berardo; Marcacci, Maurilio

2013-01-01

In recent years biological strategies are being more widely used to treat cartilage lesions. One of the most exploited novel treatments is Platelet-rich Plasma (PRP), whose high content of growth factors is supposed to determine a regenerative stimulus to cartilaginous tissue. Despite many promising in vitro and in vivo studies, when discussing clinical application a clear indication for the use of PRP cannot be assessed. There are initial encouraging clinical data, but only a few randomized controlled trials have been published, so it is not possible to fully endorse this kind of approach for the treatment of cartilage pathology. Furthermore, study comparison is very difficult due to the great variability in PRP preparation methods, cell content and concentration, storage modalities, activation methods and even application protocols. These factors partially explain the lack of high quality controlled trials up to now. This paper discusses the main aspects concerning the basic biology of PRP, the principal sources of variability, and summarizes the available literature on PRP use, both in surgical and conservative treatments. Based on current evidence, PRP treatment should only be indicated for low-grade cartilage degeneration and in case of failure of more traditional conservative approaches. PMID:23730375

Towards practical application of sensors for monitoring animal health; design and validation of a model to detect ketosis.

PubMed

Steensels, Machteld; Maltz, Ephraim; Bahr, Claudia; Berckmans, Daniel; Antler, Aharon; Halachmi, Ilan

2017-05-01

The objective of this study was to design and validate a mathematical model to detect post-calving ketosis. The validation was conducted in four commercial dairy farms in Israel, on a total of 706 multiparous Holstein dairy cows: 203 cows clinically diagnosed with ketosis and 503 healthy cows. A logistic binary regression model was developed, where the dependent variable is categorical (healthy/diseased) and a set of explanatory variables were measured with existing commercial sensors: rumination duration, activity and milk yield of each individual cow. In a first validation step (within-farm), the model was calibrated on the database of each farm separately. Two thirds of the sick cows and an equal number of healthy cows were randomly selected for model validation. The remaining one third of the cows, which did not participate in the model validation, were used for model calibration. In order to overcome the random selection effect, this procedure was repeated 100 times. In a second (between-farms) validation step, the model was calibrated on one farm and validated on another farm. Within-farm accuracy, ranging from 74 to 79%, was higher than between-farm accuracy, ranging from 49 to 72%, in all farms. The within-farm sensitivities ranged from 78 to 90%, and specificities ranged from 71 to 74%. The between-farms sensitivities ranged from 65 to 95%. The developed model can be improved in future research, by employing other variables that can be added; or by exploring other models to achieve greater sensitivity and specificity.

Observational studies of patients in the emergency department: a comparison of 4 sampling methods.

PubMed

Valley, Morgan A; Heard, Kennon J; Ginde, Adit A; Lezotte, Dennis C; Lowenstein, Steven R

2012-08-01

We evaluate the ability of 4 sampling methods to generate representative samples of the emergency department (ED) population. We analyzed the electronic records of 21,662 consecutive patient visits at an urban, academic ED. From this population, we simulated different models of study recruitment in the ED by using 2 sample sizes (n=200 and n=400) and 4 sampling methods: true random, random 4-hour time blocks by exact sample size, random 4-hour time blocks by a predetermined number of blocks, and convenience or "business hours." For each method and sample size, we obtained 1,000 samples from the population. Using χ(2) tests, we measured the number of statistically significant differences between the sample and the population for 8 variables (age, sex, race/ethnicity, language, triage acuity, arrival mode, disposition, and payer source). Then, for each variable, method, and sample size, we compared the proportion of the 1,000 samples that differed from the overall ED population to the expected proportion (5%). Only the true random samples represented the population with respect to sex, race/ethnicity, triage acuity, mode of arrival, language, and payer source in at least 95% of the samples. Patient samples obtained using random 4-hour time blocks and business hours sampling systematically differed from the overall ED patient population for several important demographic and clinical variables. However, the magnitude of these differences was not large. Common sampling strategies selected for ED-based studies may affect parameter estimates for several representative population variables. However, the potential for bias for these variables appears small. Copyright © 2012. Published by Mosby, Inc.

Statistical Validation of a Web-Based GIS Application and Its Applicability to Cardiovascular-Related Studies.

PubMed

Lee, Jae Eun; Sung, Jung Hye; Malouhi, Mohamad

2015-12-22

There is abundant evidence that neighborhood characteristics are significantly linked to the health of the inhabitants of a given space within a given time frame. This study is to statistically validate a web-based GIS application designed to support cardiovascular-related research developed by the NIH funded Research Centers in Minority Institutions (RCMI) Translational Research Network (RTRN) Data Coordinating Center (DCC) and discuss its applicability to cardiovascular studies. Geo-referencing, geocoding and geospatial analyses were conducted for 500 randomly selected home addresses in a U.S. southeastern Metropolitan area. The correlation coefficient, factor analysis and Cronbach's alpha (α) were estimated to quantify measures of the internal consistency, reliability and construct/criterion/discriminant validity of the cardiovascular-related geospatial variables (walk score, number of hospitals, fast food restaurants, parks and sidewalks). Cronbach's α for CVD GEOSPATIAL variables was 95.5%, implying successful internal consistency. Walk scores were significantly correlated with number of hospitals (r = 0.715; p < 0.0001), fast food restaurants (r = 0.729; p < 0.0001), parks (r = 0.773; p < 0.0001) and sidewalks (r = 0.648; p < 0.0001) within a mile from homes. It was also significantly associated with diversity index (r = 0.138, p = 0.0023), median household incomes (r = -0.181; p < 0.0001), and owner occupied rates (r = -0.440; p < 0.0001). However, its non-significant correlation was found with median age, vulnerability, unemployment rate, labor force, and population growth rate. Our data demonstrates that geospatial data generated by the web-based application were internally consistent and demonstrated satisfactory validity. Therefore, the GIS application may be useful to apply to cardiovascular-related studies aimed to investigate potential impact of geospatial factors on diseases and/or the long-term effect of clinical trials.

Method Improving Reading Comprehension In Primary Education Program Students

NASA Astrophysics Data System (ADS)

Rohana

2018-01-01

This study aims to determine the influence of reading comprehension skills of English for PGSD students through the application of SQ3R learning method. The type of this research is Pre-Experimental research because it is not yet a real experiment, there are external variables that influence the formation of a dependent variable, this is because there is no control variable and the sample is not chosen randomly. The research design is used is one-group pretest-post-test design involving one group that is an experimental group. In this design, the observation is done twice before and after the experiment. Observations made before the experiment (O1) are called pretests and the post-experimental observation (O2) is called posttest. The difference between O1 and O2 ie O2 - O1 is the effect of the treatment. The results showed that there was an improvement in reading comprehension skills of PGSD students in Class M.4.3 using SQ3R method, and better SQ3R enabling SQ3R to improve English comprehension skills.

Inter-rater reliability of output measures for a posture matching assessment approach: a pilot study with food service workers.

PubMed

Cann, A P; Connolly, M; Ruuska, R; MacNeil, M; Birmingham, T B; Vandervoort, A A; Callaghan, J P

2008-04-01

Despite the ongoing health problem of repetitive strain injuries, there are few tools currently available for ergonomic applications evaluating cumulative loading that have well-documented evidence of reliability and validity. The purpose of this study was to determine the inter-rater reliability of a posture matching based analysis tool (3DMatch, University of Waterloo) for predicting cumulative and peak spinal loads. A total of 30 food service workers were each videotaped for a 1-h period while performing typical work activities and a single work task was randomly selected from each for analysis by two raters. Inter-rater reliability was determined using intraclass correlation coefficients (ICC) model 2,1 and standard errors of measurement for cumulative and peak spinal and shoulder loading variables across all subjects. Overall, 85.5% of variables had moderate to excellent inter-rater reliability, with ICCs ranging from 0.30-0.99 for all cumulative and peak loading variables. 3DMatch was found to be a reliable ergonomic tool when more than one rater is involved.

Solar F10.7 radiation - A short term model for Space Station applications

NASA Technical Reports Server (NTRS)

Vedder, John D.; Tabor, Jill L.

1991-01-01

A new method is described for statistically modeling the F10.7 component of solar radiation for 91-day intervals. The resulting model represents this component of the solar flux as a quasi-exponentially correlated, Weibull distributed random variable, and thereby demonstrates excellent agreement with observed F10.7 data. Values of the F10.7 flux are widely used in models of the earth's upper atmosphere because of its high correlation with density fluctuations due to solar heating effects. Because of the direct relation between atmospheric density and drag, a realistic model of the short term fluctuation of the F10.7 flux is important for the design and operation of Space Station Freedom. The method of modeling this flux described in this report should therefore be useful for a variety of Space Station applications.

Stochastic Formal Correctness of Numerical Algorithms

NASA Technical Reports Server (NTRS)

Daumas, Marc; Lester, David; Martin-Dorel, Erik; Truffert, Annick

2009-01-01

We provide a framework to bound the probability that accumulated errors were never above a given threshold on numerical algorithms. Such algorithms are used for example in aircraft and nuclear power plants. This report contains simple formulas based on Levy's and Markov's inequalities and it presents a formal theory of random variables with a special focus on producing concrete results. We selected four very common applications that fit in our framework and cover the common practices of systems that evolve for a long time. We compute the number of bits that remain continuously significant in the first two applications with a probability of failure around one out of a billion, where worst case analysis considers that no significant bit remains. We are using PVS as such formal tools force explicit statement of all hypotheses and prevent incorrect uses of theorems.

Truly random number generation: an example

NASA Astrophysics Data System (ADS)

Frauchiger, Daniela; Renner, Renato

2013-10-01

Randomness is crucial for a variety of applications, ranging from gambling to computer simulations, and from cryptography to statistics. However, many of the currently used methods for generating randomness do not meet the criteria that are necessary for these applications to work properly and safely. A common problem is that a sequence of numbers may look random but nevertheless not be truly random. In fact, the sequence may pass all standard statistical tests and yet be perfectly predictable. This renders it useless for many applications. For example, in cryptography, the predictability of a "andomly" chosen password is obviously undesirable. Here, we review a recently developed approach to generating true | and hence unpredictable | randomness.

Variable versus conventional lung protective mechanical ventilation during open abdominal surgery: study protocol for a randomized controlled trial.

PubMed

Spieth, Peter M; Güldner, Andreas; Uhlig, Christopher; Bluth, Thomas; Kiss, Thomas; Schultz, Marcus J; Pelosi, Paolo; Koch, Thea; Gama de Abreu, Marcelo

2014-05-02

General anesthesia usually requires mechanical ventilation, which is traditionally accomplished with constant tidal volumes in volume- or pressure-controlled modes. Experimental studies suggest that the use of variable tidal volumes (variable ventilation) recruits lung tissue, improves pulmonary function and reduces systemic inflammatory response. However, it is currently not known whether patients undergoing open abdominal surgery might benefit from intraoperative variable ventilation. The PROtective VARiable ventilation trial ('PROVAR') is a single center, randomized controlled trial enrolling 50 patients who are planning for open abdominal surgery expected to last longer than 3 hours. PROVAR compares conventional (non-variable) lung protective ventilation (CV) with variable lung protective ventilation (VV) regarding pulmonary function and inflammatory response. The primary endpoint of the study is the forced vital capacity on the first postoperative day. Secondary endpoints include further lung function tests, plasma cytokine levels, spatial distribution of ventilation assessed by means of electrical impedance tomography and postoperative pulmonary complications. We hypothesize that VV improves lung function and reduces systemic inflammatory response compared to CV in patients receiving mechanical ventilation during general anesthesia for open abdominal surgery longer than 3 hours. PROVAR is the first randomized controlled trial aiming at intra- and postoperative effects of VV on lung function. This study may help to define the role of VV during general anesthesia requiring mechanical ventilation. Clinicaltrials.gov NCT01683578 (registered on September 3 3012).

A Random Variable Transformation Process.

ERIC Educational Resources Information Center

Scheuermann, Larry

1989-01-01

Provides a short BASIC program, RANVAR, which generates random variates for various theoretical probability distributions. The seven variates include: uniform, exponential, normal, binomial, Poisson, Pascal, and triangular. (MVL)

A Cautious Note on Auxiliary Variables That Can Increase Bias in Missing Data Problems.

PubMed

Thoemmes, Felix; Rose, Norman

2014-01-01

The treatment of missing data in the social sciences has changed tremendously during the last decade. Modern missing data techniques such as multiple imputation and full-information maximum likelihood are used much more frequently. These methods assume that data are missing at random. One very common approach to increase the likelihood that missing at random is achieved consists of including many covariates as so-called auxiliary variables. These variables are either included based on data considerations or in an inclusive fashion; that is, taking all available auxiliary variables. In this article, we point out that there are some instances in which auxiliary variables exhibit the surprising property of increasing bias in missing data problems. In a series of focused simulation studies, we highlight some situations in which this type of biasing behavior can occur. We briefly discuss possible ways how one can avoid selecting bias-inducing covariates as auxiliary variables.

Detecting Random, Partially Random, and Nonrandom Minnesota Multiphasic Personality Inventory-2 Protocols

ERIC Educational Resources Information Center

Pinsoneault, Terry B.

2007-01-01

The ability of the Minnesota Multiphasic Personality Inventory-2 (MMPI-2; J. N. Butcher et al., 2001) validity scales to detect random, partially random, and nonrandom MMPI-2 protocols was investigated. Investigations included the Variable Response Inconsistency scale (VRIN), F, several potentially useful new F and VRIN subscales, and F-sub(b) - F…

Uncertainty in Random Forests: What does it mean in a spatial context?

NASA Astrophysics Data System (ADS)

Klump, Jens; Fouedjio, Francky

2017-04-01

Geochemical surveys are an important part of exploration for mineral resources and in environmental studies. The samples and chemical analyses are often laborious and difficult to obtain and therefore come at a high cost. As a consequence, these surveys are characterised by datasets with large numbers of variables but relatively few data points when compared to conventional big data problems. With more remote sensing platforms and sensor networks being deployed, large volumes of auxiliary data of the surveyed areas are becoming available. The use of these auxiliary data has the potential to improve the prediction of chemical element concentrations over the whole study area. Kriging is a well established geostatistical method for the prediction of spatial data but requires significant pre-processing and makes some basic assumptions about the underlying distribution of the data. Some machine learning algorithms, on the other hand, may require less data pre-processing and are non-parametric. In this study we used a dataset provided by Kirkwood et al. [1] to explore the potential use of Random Forest in geochemical mapping. We chose Random Forest because it is a well understood machine learning method and has the advantage that it provides us with a measure of uncertainty. By comparing Random Forest to Kriging we found that both methods produced comparable maps of estimated values for our variables of interest. Kriging outperformed Random Forest for variables of interest with relatively strong spatial correlation. The measure of uncertainty provided by Random Forest seems to be quite different to the measure of uncertainty provided by Kriging. In particular, the lack of spatial context can give misleading results in areas without ground truth data. In conclusion, our preliminary results show that the model driven approach in geostatistics gives us more reliable estimates for our target variables than Random Forest for variables with relatively strong spatial correlation. However, in cases of weak spatial correlation Random Forest, as a nonparametric method, may give the better results once we have a better understanding of the meaning of its uncertainty measures in a spatial context. References [1] Kirkwood, C., M. Cave, D. Beamish, S. Grebby, and A. Ferreira (2016), A machine learning approach to geochemical mapping, Journal of Geochemical Exploration, 163, 28-40, doi:10.1016/j.gexplo.2016.05.003.

Honest Importance Sampling with Multiple Markov Chains

PubMed Central

Tan, Aixin; Doss, Hani; Hobert, James P.

2017-01-01

Importance sampling is a classical Monte Carlo technique in which a random sample from one probability density, π1, is used to estimate an expectation with respect to another, π. The importance sampling estimator is strongly consistent and, as long as two simple moment conditions are satisfied, it obeys a central limit theorem (CLT). Moreover, there is a simple consistent estimator for the asymptotic variance in the CLT, which makes for routine computation of standard errors. Importance sampling can also be used in the Markov chain Monte Carlo (MCMC) context. Indeed, if the random sample from π1 is replaced by a Harris ergodic Markov chain with invariant density π1, then the resulting estimator remains strongly consistent. There is a price to be paid however, as the computation of standard errors becomes more complicated. First, the two simple moment conditions that guarantee a CLT in the iid case are not enough in the MCMC context. Second, even when a CLT does hold, the asymptotic variance has a complex form and is difficult to estimate consistently. In this paper, we explain how to use regenerative simulation to overcome these problems. Actually, we consider a more general set up, where we assume that Markov chain samples from several probability densities, π1, …, πk, are available. We construct multiple-chain importance sampling estimators for which we obtain a CLT based on regeneration. We show that if the Markov chains converge to their respective target distributions at a geometric rate, then under moment conditions similar to those required in the iid case, the MCMC-based importance sampling estimator obeys a CLT. Furthermore, because the CLT is based on a regenerative process, there is a simple consistent estimator of the asymptotic variance. We illustrate the method with two applications in Bayesian sensitivity analysis. The first concerns one-way random effects models under different priors. The second involves Bayesian variable selection in linear regression, and for this application, importance sampling based on multiple chains enables an empirical Bayes approach to variable selection. PMID:28701855

Honest Importance Sampling with Multiple Markov Chains.

PubMed

Tan, Aixin; Doss, Hani; Hobert, James P

2015-01-01

Importance sampling is a classical Monte Carlo technique in which a random sample from one probability density, π 1 , is used to estimate an expectation with respect to another, π . The importance sampling estimator is strongly consistent and, as long as two simple moment conditions are satisfied, it obeys a central limit theorem (CLT). Moreover, there is a simple consistent estimator for the asymptotic variance in the CLT, which makes for routine computation of standard errors. Importance sampling can also be used in the Markov chain Monte Carlo (MCMC) context. Indeed, if the random sample from π 1 is replaced by a Harris ergodic Markov chain with invariant density π 1 , then the resulting estimator remains strongly consistent. There is a price to be paid however, as the computation of standard errors becomes more complicated. First, the two simple moment conditions that guarantee a CLT in the iid case are not enough in the MCMC context. Second, even when a CLT does hold, the asymptotic variance has a complex form and is difficult to estimate consistently. In this paper, we explain how to use regenerative simulation to overcome these problems. Actually, we consider a more general set up, where we assume that Markov chain samples from several probability densities, π 1 , …, π k , are available. We construct multiple-chain importance sampling estimators for which we obtain a CLT based on regeneration. We show that if the Markov chains converge to their respective target distributions at a geometric rate, then under moment conditions similar to those required in the iid case, the MCMC-based importance sampling estimator obeys a CLT. Furthermore, because the CLT is based on a regenerative process, there is a simple consistent estimator of the asymptotic variance. We illustrate the method with two applications in Bayesian sensitivity analysis. The first concerns one-way random effects models under different priors. The second involves Bayesian variable selection in linear regression, and for this application, importance sampling based on multiple chains enables an empirical Bayes approach to variable selection.

Regulation of Spatiotemporal Patterns by Biological Variability: General Principles and Applications to Dictyostelium discoideum

PubMed Central

Grace, Miriam; Hütt, Marc-Thorsten

2015-01-01

Spatiotemporal patterns often emerge from local interactions in a self-organizing fashion. In biology, the resulting patterns are also subject to the influence of the systematic differences between the system’s constituents (biological variability). This regulation of spatiotemporal patterns by biological variability is the topic of our review. We discuss several examples of correlations between cell properties and the self-organized spatiotemporal patterns, together with their relevance for biology. Our guiding, illustrative example will be spiral waves of cAMP in a colony of Dictyostelium discoideum cells. Analogous processes take place in diverse situations (such as cardiac tissue, where spiral waves occur in potentially fatal ventricular fibrillation) so a deeper understanding of this additional layer of self-organized pattern formation would be beneficial to a wide range of applications. One of the most striking differences between pattern-forming systems in physics or chemistry and those in biology is the potential importance of variability. In the former, system components are essentially identical with random fluctuations determining the details of the self-organization process and the resulting patterns. In biology, due to variability, the properties of potentially very few cells can have a driving influence on the resulting asymptotic collective state of the colony. Variability is one means of implementing a few-element control on the collective mode. Regulatory architectures, parameters of signaling cascades, and properties of structure formation processes can be "reverse-engineered" from observed spatiotemporal patterns, as different types of regulation and forms of interactions between the constituents can lead to markedly different correlations. The power of this biology-inspired view of pattern formation lies in building a bridge between two scales: the patterns as a collective state of a very large number of cells on the one hand, and the internal parameters of the single cells on the other. PMID:26562406

Reliability Overhaul Model

DTIC Science & Technology

1989-08-01

Random variables for the conditional exponential distribution are generated using the inverse transform method. C1) Generate U - UCO,i) (2) Set s - A ln...e - [(x+s - 7)/ n] 0 + [Cx-T)/n]0 c. Random variables from the conditional weibull distribution are generated using the inverse transform method. C1...using a standard normal transformation and the inverse transform method. B - 3 APPENDIX 3 DISTRIBUTIONS SUPPORTED BY THE MODEL (1) Generate Y - PCX S

Performance of DS/SSMA (Direct-Sequence Spread-Spectrum Multiple-Access) Communications in Impulsive Channels.

DTIC Science & Technology

1986-11-01

mother and my brother. Their support and encouragement made this research exciting and enjoyable. I am grateful to my advisor, Professor H. Vincent Poor...the model. The m! M A variance of a random variable with density given by (A. 1) is a2 KmC 2 2A(I+l’)• (A.2) With the variance of the random variable

On Probability Domains IV

NASA Astrophysics Data System (ADS)

Frič, Roman; Papčo, Martin

2017-12-01

Stressing a categorical approach, we continue our study of fuzzified domains of probability, in which classical random events are replaced by measurable fuzzy random events. In operational probability theory (S. Bugajski) classical random variables are replaced by statistical maps (generalized distribution maps induced by random variables) and in fuzzy probability theory (S. Gudder) the central role is played by observables (maps between probability domains). We show that to each of the two generalized probability theories there corresponds a suitable category and the two resulting categories are dually equivalent. Statistical maps and observables become morphisms. A statistical map can send a degenerated (pure) state to a non-degenerated one —a quantum phenomenon and, dually, an observable can map a crisp random event to a genuine fuzzy random event —a fuzzy phenomenon. The dual equivalence means that the operational probability theory and the fuzzy probability theory coincide and the resulting generalized probability theory has two dual aspects: quantum and fuzzy. We close with some notes on products and coproducts in the dual categories.

Bayesian random-effect model for predicting outcome fraught with heterogeneity--an illustration with episodes of 44 patients with intractable epilepsy.

PubMed

Yen, A M-F; Liou, H-H; Lin, H-L; Chen, T H-H

2006-01-01

The study aimed to develop a predictive model to deal with data fraught with heterogeneity that cannot be explained by sampling variation or measured covariates. The random-effect Poisson regression model was first proposed to deal with over-dispersion for data fraught with heterogeneity after making allowance for measured covariates. Bayesian acyclic graphic model in conjunction with Markov Chain Monte Carlo (MCMC) technique was then applied to estimate the parameters of both relevant covariates and random effect. Predictive distribution was then generated to compare the predicted with the observed for the Bayesian model with and without random effect. Data from repeated measurement of episodes among 44 patients with intractable epilepsy were used as an illustration. The application of Poisson regression without taking heterogeneity into account to epilepsy data yielded a large value of heterogeneity (heterogeneity factor = 17.90, deviance = 1485, degree of freedom (df) = 83). After taking the random effect into account, the value of heterogeneity factor was greatly reduced (heterogeneity factor = 0.52, deviance = 42.5, df = 81). The Pearson chi2 for the comparison between the expected seizure frequencies and the observed ones at two and three months of the model with and without random effect were 34.27 (p = 1.00) and 1799.90 (p < 0.0001), respectively. The Bayesian acyclic model using the MCMC method was demonstrated to have great potential for disease prediction while data show over-dispersion attributed either to correlated property or to subject-to-subject variability.

Application of positive airway pressure in restoring pulmonary function and thoracic mobility in the postoperative period of bariatric surgery: a randomized clinical trial

PubMed Central

Brigatto, Patrícia; Carbinatto, Jéssica C.; Costa, Carolina M.; Montebelo, Maria I. L.; Rasera-Júnior, Irineu; Pazzianotto-Forti, Eli M.

2014-01-01

Objective: To evaluate whether the application of bilevel positive airway pressure in the postoperative period of bariatric surgery might be more effective in restoring lung volume and capacity and thoracic mobility than the separate application of expiratory and inspiratory positive pressure. Method: Sixty morbidly obese adult subjects who were hospitalized for bariatric surgery and met the predefined inclusion criteria were evaluated. The pulmonary function and thoracic mobility were preoperatively assessed by spirometry and cirtometry and reevaluated on the 1st postoperative day. After preoperative evaluation, the subjects were randomized and allocated into groups: EPAP Group (n=20), IPPB Group (n=20) and BIPAP Group (n=20), then received the corresponding intervention: positive expiratory pressure (EPAP), inspiratory positive pressure breathing (IPPB) or bilevel inspiratory positive airway pressure (BIPAP), in 6 sets of 15 breaths or 30 minutes twice a day in the immediate postoperative period and on the 1st postoperative day, in addition to conventional physical therapy. Results: There was a significant postoperative reduction in spirometric variables (p<0.05), regardless of the technique used, with no significant difference among the techniques (p>0.05). Thoracic mobility was preserved only in group BIPAP (p>0.05), but no significant difference was found in the comparison among groups (p>0.05). Conclusion: The application of positive pressure does not seem to be effective in restoring lung function after bariatric surgery, but the use of bilevel positive pressure can preserve thoracic mobility, although this technique was not superior to the other techniques. PMID:25590448

Tests of Hypotheses Arising In the Correlated Random Coefficient Model*

PubMed Central

Heckman, James J.; Schmierer, Daniel

2010-01-01

This paper examines the correlated random coefficient model. It extends the analysis of Swamy (1971), who pioneered the uncorrelated random coefficient model in economics. We develop the properties of the correlated random coefficient model and derive a new representation of the variance of the instrumental variable estimator for that model. We develop tests of the validity of the correlated random coefficient model against the null hypothesis of the uncorrelated random coefficient model. PMID:21170148

Latin Hypercube Sampling (LHS) UNIX Library/Standalone

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

2004-05-13

The LHS UNIX Library/Standalone software provides the capability to draw random samples from over 30 distribution types. It performs the sampling by a stratified sampling method called Latin Hypercube Sampling (LHS). Multiple distributions can be sampled simultaneously, with user-specified correlations amongst the input distributions, LHS UNIX Library/ Standalone provides a way to generate multi-variate samples. The LHS samples can be generated either as a callable library (e.g., from within the DAKOTA software framework) or as a standalone capability. LHS UNIX Library/Standalone uses the Latin Hypercube Sampling method (LHS) to generate samples. LHS is a constrained Monte Carlo sampling scheme. Inmore » LHS, the range of each variable is divided into non-overlapping intervals on the basis of equal probability. A sample is selected at random with respect to the probability density in each interval, If multiple variables are sampled simultaneously, then values obtained for each are paired in a random manner with the n values of the other variables. In some cases, the pairing is restricted to obtain specified correlations amongst the input variables. Many simulation codes have input parameters that are uncertain and can be specified by a distribution, To perform uncertainty analysis and sensitivity analysis, random values are drawn from the input parameter distributions, and the simulation is run with these values to obtain output values. If this is done repeatedly, with many input samples drawn, one can build up a distribution of the output as well as examine correlations between input and output variables.« less

Assessing differences in groups randomized by recruitment chain in a respondent-driven sample of Seattle-area injection drug users.

PubMed

Burt, Richard D; Thiede, Hanne

2014-11-01

Respondent-driven sampling (RDS) is a form of peer-based study recruitment and analysis that incorporates features designed to limit and adjust for biases in traditional snowball sampling. It is being widely used in studies of hidden populations. We report an empirical evaluation of RDS's consistency and variability, comparing groups recruited contemporaneously, by identical methods and using identical survey instruments. We randomized recruitment chains from the RDS-based 2012 National HIV Behavioral Surveillance survey of injection drug users in the Seattle area into two groups and compared them in terms of sociodemographic characteristics, drug-associated risk behaviors, sexual risk behaviors, human immunodeficiency virus (HIV) status and HIV testing frequency. The two groups differed in five of the 18 variables examined (P ≤ .001): race (e.g., 60% white vs. 47%), gender (52% male vs. 67%), area of residence (32% downtown Seattle vs. 44%), an HIV test in the previous 12 months (51% vs. 38%). The difference in serologic HIV status was particularly pronounced (4% positive vs. 18%). In four further randomizations, differences in one to five variables attained this level of significance, although the specific variables involved differed. We found some material differences between the randomized groups. Although the variability of the present study was less than has been reported in serial RDS surveys, these findings indicate caution in the interpretation of RDS results. Copyright © 2014 Elsevier Inc. All rights reserved.

Sampling in health geography: reconciling geographical objectives and probabilistic methods. An example of a health survey in Vientiane (Lao PDR)

PubMed Central

Vallée, Julie; Souris, Marc; Fournet, Florence; Bochaton, Audrey; Mobillion, Virginie; Peyronnie, Karine; Salem, Gérard

2007-01-01

Background Geographical objectives and probabilistic methods are difficult to reconcile in a unique health survey. Probabilistic methods focus on individuals to provide estimates of a variable's prevalence with a certain precision, while geographical approaches emphasise the selection of specific areas to study interactions between spatial characteristics and health outcomes. A sample selected from a small number of specific areas creates statistical challenges: the observations are not independent at the local level, and this results in poor statistical validity at the global level. Therefore, it is difficult to construct a sample that is appropriate for both geographical and probability methods. Methods We used a two-stage selection procedure with a first non-random stage of selection of clusters. Instead of randomly selecting clusters, we deliberately chose a group of clusters, which as a whole would contain all the variation in health measures in the population. As there was no health information available before the survey, we selected a priori determinants that can influence the spatial homogeneity of the health characteristics. This method yields a distribution of variables in the sample that closely resembles that in the overall population, something that cannot be guaranteed with randomly-selected clusters, especially if the number of selected clusters is small. In this way, we were able to survey specific areas while minimising design effects and maximising statistical precision. Application We applied this strategy in a health survey carried out in Vientiane, Lao People's Democratic Republic. We selected well-known health determinants with unequal spatial distribution within the city: nationality and literacy. We deliberately selected a combination of clusters whose distribution of nationality and literacy is similar to the distribution in the general population. Conclusion This paper describes the conceptual reasoning behind the construction of the survey sample and shows that it can be advantageous to choose clusters using reasoned hypotheses, based on both probability and geographical approaches, in contrast to a conventional, random cluster selection strategy. PMID:17543100

Empowering Lay-Counsellors with Technology: Masivukeni, a Standardized Multimedia Counselling Support Tool to Deliver ART Counselling.

PubMed

Gouse, H; Robbins, R N; Mellins, C A; Kingon, A; Rowe, J; Henry, M; Remien, R H; Pearson, A; Victor, F; Joska, J A

2018-05-19

Lay-counsellors in resource-limited settings convey critical HIV- and ART-information, and face challenges including limited training and variable application of counselling. This study explored lay-counsellors and Department of Health (DoH) perspectives on the utility of a multimedia adherence counselling program. Masivukeni, an mHealth application that provides scaffolding for delivering standardized ART counselling was used in a 3-year randomized control trail at two primary health care clinics in Cape Town, South Africa. In this programmatic and descriptive narrative report, we describe the application; lay-counsellors' response to open-ended questions regarding their experience with using Masivukeni; and perspectives of the City of Cape Town and Western Cape Government DoH, obtained through ongoing engagements and feedback sessions. Counsellors reported Masivukeni empowered them to provide high quality counselling. DoH indicated strong support for a future implementation study assessing feasibility for larger scale roll-out. Masivukeni has potential as a counselling tool in resource-limited settings.

Compositions, Random Sums and Continued Random Fractions of Poisson and Fractional Poisson Processes

NASA Astrophysics Data System (ADS)

Orsingher, Enzo; Polito, Federico

2012-08-01

In this paper we consider the relation between random sums and compositions of different processes. In particular, for independent Poisson processes N α ( t), N β ( t), t>0, we have that N_{α}(N_{β}(t)) stackrel{d}{=} sum_{j=1}^{N_{β}(t)} Xj, where the X j s are Poisson random variables. We present a series of similar cases, where the outer process is Poisson with different inner processes. We highlight generalisations of these results where the external process is infinitely divisible. A section of the paper concerns compositions of the form N_{α}(tauk^{ν}), ν∈(0,1], where tauk^{ν} is the inverse of the fractional Poisson process, and we show how these compositions can be represented as random sums. Furthermore we study compositions of the form Θ( N( t)), t>0, which can be represented as random products. The last section is devoted to studying continued fractions of Cauchy random variables with a Poisson number of levels. We evaluate the exact distribution and derive the scale parameter in terms of ratios of Fibonacci numbers.

Effects of manual lymph drainage on cardiac autonomic tone in healthy subjects.

PubMed

Kim, Sung-Joong; Kwon, Oh-Yun; Yi, Chung-Hwi

2009-01-01

This study was designed to investigate the effects of manual lymph drainage on the cardiac autonomic tone. Thirty-two healthy male subjects were randomly assigned to manual lymph drainage (MLD) (experimental) and rest (control) groups. Electrocardiogram (ECG) parameters were recorded with bipolar electrocardiography using standard limb lead positions. The pressure-pain threshold (PPT) was quantitatively measured using an algometer. Heart rate variability differed significantly between the experimental and control groups (p < 0.05), but the PPT in the upper trapezius muscle did not (p > 0.05). These findings indicate that the application of MLD was effective in reducing the activity of the sympathetic nervous system.

Dynamic analyses, FPGA implementation and engineering applications of multi-butterfly chaotic attractors generated from generalised Sprott C system

NASA Astrophysics Data System (ADS)

Lai, Qiang; Zhao, Xiao-Wen; Rajagopal, Karthikeyan; Xu, Guanghui; Akgul, Akif; Guleryuz, Emre

2018-01-01

This paper considers the generation of multi-butterfly chaotic attractors from a generalised Sprott C system with multiple non-hyperbolic equilibria. The system is constructed by introducing an additional variable whose derivative has a switching function to the Sprott C system. It is numerically found that the system creates two-, three-, four-, five-butterfly attractors and any other multi-butterfly attractors. First, the dynamic analyses of multi-butterfly chaotic attractors are presented. Secondly, the field programmable gate array implementation, electronic circuit realisation and random number generator are done with the multi-butterfly chaotic attractors.

Occurrence probability of structured motifs in random sequences.

PubMed

Robin, S; Daudin, J-J; Richard, H; Sagot, M-F; Schbath, S

2002-01-01

The problem of extracting from a set of nucleic acid sequences motifs which may have biological function is more and more important. In this paper, we are interested in particular motifs that may be implicated in the transcription process. These motifs, called structured motifs, are composed of two ordered parts separated by a variable distance and allowing for substitutions. In order to assess their statistical significance, we propose approximations of the probability of occurrences of such a structured motif in a given sequence. An application of our method to evaluate candidate promoters in E. coli and B. subtilis is presented. Simulations show the goodness of the approximations.

Random forests as cumulative effects models: A case study of lakes and rivers in Muskoka, Canada.

PubMed

Jones, F Chris; Plewes, Rachel; Murison, Lorna; MacDougall, Mark J; Sinclair, Sarah; Davies, Christie; Bailey, John L; Richardson, Murray; Gunn, John

2017-10-01

Cumulative effects assessment (CEA) - a type of environmental appraisal - lacks effective methods for modeling cumulative effects, evaluating indicators of ecosystem condition, and exploring the likely outcomes of development scenarios. Random forests are an extension of classification and regression trees, which model response variables by recursive partitioning. Random forests were used to model a series of candidate ecological indicators that described lakes and rivers from a case study watershed (The Muskoka River Watershed, Canada). Suitability of the candidate indicators for use in cumulative effects assessment and watershed monitoring was assessed according to how well they could be predicted from natural habitat features and how sensitive they were to human land-use. The best models explained 75% of the variation in a multivariate descriptor of lake benthic-macroinvertebrate community structure, and 76% of the variation in the conductivity of river water. Similar results were obtained by cross-validation. Several candidate indicators detected a simulated doubling of urban land-use in their catchments, and a few were able to detect a simulated doubling of agricultural land-use. The paper demonstrates that random forests can be used to describe the combined and singular effects of multiple stressors and natural environmental factors, and furthermore, that random forests can be used to evaluate the performance of monitoring indicators. The numerical methods presented are applicable to any ecosystem and indicator type, and therefore represent a step forward for CEA. Crown Copyright © 2017. Published by Elsevier Ltd. All rights reserved.

Linear combinations come alive in crossover designs.

PubMed

Shuster, Jonathan J

2017-10-30

Before learning anything about statistical inference in beginning service courses in biostatistics, students learn how to calculate the mean and variance of linear combinations of random variables. Practical precalculus examples of the importance of these exercises can be helpful for instructors, the target audience of this paper. We shall present applications to the "1-sample" and "2-sample" methods for randomized short-term 2-treatment crossover studies, where patients experience both treatments in random order with a "washout" between the active treatment periods. First, we show that the 2-sample method is preferred as it eliminates "conditional bias" when sample sizes by order differ and produces a smaller variance. We also demonstrate that it is usually advisable to use the differences in posttests (ignoring baseline and post washout values) rather than the differences between the changes in treatment from the start of the period to the end of the period ("delta of delta"). Although the intent is not to provide a definitive discussion of crossover designs, we provide a section and references to excellent alternative methods, where instructors can provide motivation to students to explore the topic in greater detail in future readings or courses. Copyright © 2017 John Wiley & Sons, Ltd.

A Permutation-Randomization Approach to Test the Spatial Distribution of Plant Diseases.

PubMed

Lione, G; Gonthier, P

2016-01-01

The analysis of the spatial distribution of plant diseases requires the availability of trustworthy geostatistical methods. The mean distance tests (MDT) are here proposed as a series of permutation and randomization tests to assess the spatial distribution of plant diseases when the variable of phytopathological interest is categorical. A user-friendly software to perform the tests is provided. Estimates of power and type I error, obtained with Monte Carlo simulations, showed the reliability of the MDT (power > 0.80; type I error < 0.05). A biological validation on the spatial distribution of spores of two fungal pathogens causing root rot on conifers was successfully performed by verifying the consistency between the MDT responses and previously published data. An application of the MDT was carried out to analyze the relation between the plantation density and the distribution of the infection of Gnomoniopsis castanea, an emerging fungal pathogen causing nut rot on sweet chestnut. Trees carrying nuts infected by the pathogen were randomly distributed in areas with different plantation densities, suggesting that the distribution of G. castanea was not related to the plantation density. The MDT could be used to analyze the spatial distribution of plant diseases both in agricultural and natural ecosystems.

The Bayesian group lasso for confounded spatial data

USGS Publications Warehouse

Hefley, Trevor J.; Hooten, Mevin B.; Hanks, Ephraim M.; Russell, Robin E.; Walsh, Daniel P.

2017-01-01

Generalized linear mixed models for spatial processes are widely used in applied statistics. In many applications of the spatial generalized linear mixed model (SGLMM), the goal is to obtain inference about regression coefficients while achieving optimal predictive ability. When implementing the SGLMM, multicollinearity among covariates and the spatial random effects can make computation challenging and influence inference. We present a Bayesian group lasso prior with a single tuning parameter that can be chosen to optimize predictive ability of the SGLMM and jointly regularize the regression coefficients and spatial random effect. We implement the group lasso SGLMM using efficient Markov chain Monte Carlo (MCMC) algorithms and demonstrate how multicollinearity among covariates and the spatial random effect can be monitored as a derived quantity. To test our method, we compared several parameterizations of the SGLMM using simulated data and two examples from plant ecology and disease ecology. In all examples, problematic levels multicollinearity occurred and influenced sampling efficiency and inference. We found that the group lasso prior resulted in roughly twice the effective sample size for MCMC samples of regression coefficients and can have higher and less variable predictive accuracy based on out-of-sample data when compared to the standard SGLMM.

Ratio index variables or ANCOVA? Fisher's cats revisited.

PubMed

Tu, Yu-Kang; Law, Graham R; Ellison, George T H; Gilthorpe, Mark S

2010-01-01

Over 60 years ago Ronald Fisher demonstrated a number of potential pitfalls with statistical analyses using ratio variables. Nonetheless, these pitfalls are largely overlooked in contemporary clinical and epidemiological research, which routinely uses ratio variables in statistical analyses. This article aims to demonstrate how very different findings can be generated as a result of less than perfect correlations among the data used to generate ratio variables. These imperfect correlations result from measurement error and random biological variation. While the former can often be reduced by improvements in measurement, random biological variation is difficult to estimate and eliminate in observational studies. Moreover, wherever the underlying biological relationships among epidemiological variables are unclear, and hence the choice of statistical model is also unclear, the different findings generated by different analytical strategies can lead to contradictory conclusions. Caution is therefore required when interpreting analyses of ratio variables whenever the underlying biological relationships among the variables involved are unspecified or unclear. (c) 2009 John Wiley & Sons, Ltd.

Predicting CD4 count changes among patients on antiretroviral treatment: Application of data mining techniques.

PubMed

Kebede, Mihiretu; Zegeye, Desalegn Tigabu; Zeleke, Berihun Megabiaw

2017-12-01

To monitor the progress of therapy and disease progression, periodic CD4 counts are required throughout the course of HIV/AIDS care and support. The demand for CD4 count measurement is increasing as ART programs expand over the last decade. This study aimed to predict CD4 count changes and to identify the predictors of CD4 count changes among patients on ART. A cross-sectional study was conducted at the University of Gondar Hospital from 3,104 adult patients on ART with CD4 counts measured at least twice (baseline and most recent). Data were retrieved from the HIV care clinic electronic database and patients` charts. Descriptive data were analyzed by SPSS version 20. Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology was followed to undertake the study. WEKA version 3.8 was used to conduct a predictive data mining. Before building the predictive data mining models, information gain values and correlation-based Feature Selection methods were used for attribute selection. Variables were ranked according to their relevance based on their information gain values. J48, Neural Network, and Random Forest algorithms were experimented to assess model accuracies. The median duration of ART was 191.5 weeks. The mean CD4 count change was 243 (SD 191.14) cells per microliter. Overall, 2427 (78.2%) patients had their CD4 counts increased by at least 100 cells per microliter, while 4% had a decline from the baseline CD4 value. Baseline variables including age, educational status, CD8 count, ART regimen, and hemoglobin levels predicted CD4 count changes with predictive accuracies of J48, Neural Network, and Random Forest being 87.1%, 83.5%, and 99.8%, respectively. Random Forest algorithm had a superior performance accuracy level than both J48 and Artificial Neural Network. The precision, sensitivity and recall values of Random Forest were also more than 99%. Nearly accurate prediction results were obtained using Random Forest algorithm. This algorithm could be used in a low-resource setting to build a web-based prediction model for CD4 count changes. Copyright © 2017 Elsevier B.V. All rights reserved.

Estimating the impact of mineral aerosols on crop yields in food insecure regions using statistical crop models

NASA Astrophysics Data System (ADS)

Hoffman, A.; Forest, C. E.; Kemanian, A.

2016-12-01

A significant number of food-insecure nations exist in regions of the world where dust plays a large role in the climate system. While the impacts of common climate variables (e.g. temperature, precipitation, ozone, and carbon dioxide) on crop yields are relatively well understood, the impact of mineral aerosols on yields have not yet been thoroughly investigated. This research aims to develop the data and tools to progress our understanding of mineral aerosol impacts on crop yields. Suspended dust affects crop yields by altering the amount and type of radiation reaching the plant, modifying local temperature and precipitation. While dust events (i.e. dust storms) affect crop yields by depleting the soil of nutrients or by defoliation via particle abrasion. The impact of dust on yields is modeled statistically because we are uncertain which impacts will dominate the response on national and regional scales considered in this study. Multiple linear regression is used in a number of large-scale statistical crop modeling studies to estimate yield responses to various climate variables. In alignment with previous work, we develop linear crop models, but build upon this simple method of regression with machine-learning techniques (e.g. random forests) to identify important statistical predictors and isolate how dust affects yields on the scales of interest. To perform this analysis, we develop a crop-climate dataset for maize, soybean, groundnut, sorghum, rice, and wheat for the regions of West Africa, East Africa, South Africa, and the Sahel. Random forest regression models consistently model historic crop yields better than the linear models. In several instances, the random forest models accurately capture the temperature and precipitation threshold behavior in crops. Additionally, improving agricultural technology has caused a well-documented positive trend that dominates time series of global and regional yields. This trend is often removed before regression with traditional crop models, but likely at the cost of removing climate information. Our random forest models consistently discover the positive trend without removing any additional data. The application of random forests as a statistical crop model provides insight into understanding the impact of dust on yields in marginal food producing regions.

Screening large-scale association study data: exploiting interactions using random forests.

PubMed

Lunetta, Kathryn L; Hayward, L Brooke; Segal, Jonathan; Van Eerdewegh, Paul

2004-12-10

Genome-wide association studies for complex diseases will produce genotypes on hundreds of thousands of single nucleotide polymorphisms (SNPs). A logical first approach to dealing with massive numbers of SNPs is to use some test to screen the SNPs, retaining only those that meet some criterion for further study. For example, SNPs can be ranked by p-value, and those with the lowest p-values retained. When SNPs have large interaction effects but small marginal effects in a population, they are unlikely to be retained when univariate tests are used for screening. However, model-based screens that pre-specify interactions are impractical for data sets with thousands of SNPs. Random forest analysis is an alternative method that produces a single measure of importance for each predictor variable that takes into account interactions among variables without requiring model specification. Interactions increase the importance for the individual interacting variables, making them more likely to be given high importance relative to other variables. We test the performance of random forests as a screening procedure to identify small numbers of risk-associated SNPs from among large numbers of unassociated SNPs using complex disease models with up to 32 loci, incorporating both genetic heterogeneity and multi-locus interaction. Keeping other factors constant, if risk SNPs interact, the random forest importance measure significantly outperforms the Fisher Exact test as a screening tool. As the number of interacting SNPs increases, the improvement in performance of random forest analysis relative to Fisher Exact test for screening also increases. Random forests perform similarly to the univariate Fisher Exact test as a screening tool when SNPs in the analysis do not interact. In the context of large-scale genetic association studies where unknown interactions exist among true risk-associated SNPs or SNPs and environmental covariates, screening SNPs using random forest analyses can significantly reduce the number of SNPs that need to be retained for further study compared to standard univariate screening methods.

Random variability explains apparent global clustering of large earthquakes

USGS Publications Warehouse

Michael, A.J.

2011-01-01

The occurrence of 5 Mw ≥ 8.5 earthquakes since 2004 has created a debate over whether or not we are in a global cluster of large earthquakes, temporarily raising risks above long-term levels. I use three classes of statistical tests to determine if the record of M ≥ 7 earthquakes since 1900 can reject a null hypothesis of independent random events with a constant rate plus localized aftershock sequences. The data cannot reject this null hypothesis. Thus, the temporal distribution of large global earthquakes is well-described by a random process, plus localized aftershocks, and apparent clustering is due to random variability. Therefore the risk of future events has not increased, except within ongoing aftershock sequences, and should be estimated from the longest possible record of events.

Probabilistic evaluation of fuselage-type composite structures

NASA Technical Reports Server (NTRS)

Shiao, Michael C.; Chamis, Christos C.

1992-01-01

A methodology is developed to computationally simulate the uncertain behavior of composite structures. The uncertain behavior includes buckling loads, natural frequencies, displacements, stress/strain etc., which are the consequences of the random variation (scatter) of the primitive (independent random) variables in the constituent, ply, laminate and structural levels. This methodology is implemented in the IPACS (Integrated Probabilistic Assessment of Composite Structures) computer code. A fuselage-type composite structure is analyzed to demonstrate the code's capability. The probability distribution functions of the buckling loads, natural frequency, displacement, strain and stress are computed. The sensitivity of each primitive (independent random) variable to a given structural response is also identified from the analyses.

Bayesian dynamic modeling of time series of dengue disease case counts.

PubMed

Martínez-Bello, Daniel Adyro; López-Quílez, Antonio; Torres-Prieto, Alexander

2017-07-01

The aim of this study is to model the association between weekly time series of dengue case counts and meteorological variables, in a high-incidence city of Colombia, applying Bayesian hierarchical dynamic generalized linear models over the period January 2008 to August 2015. Additionally, we evaluate the model's short-term performance for predicting dengue cases. The methodology shows dynamic Poisson log link models including constant or time-varying coefficients for the meteorological variables. Calendar effects were modeled using constant or first- or second-order random walk time-varying coefficients. The meteorological variables were modeled using constant coefficients and first-order random walk time-varying coefficients. We applied Markov Chain Monte Carlo simulations for parameter estimation, and deviance information criterion statistic (DIC) for model selection. We assessed the short-term predictive performance of the selected final model, at several time points within the study period using the mean absolute percentage error. The results showed the best model including first-order random walk time-varying coefficients for calendar trend and first-order random walk time-varying coefficients for the meteorological variables. Besides the computational challenges, interpreting the results implies a complete analysis of the time series of dengue with respect to the parameter estimates of the meteorological effects. We found small values of the mean absolute percentage errors at one or two weeks out-of-sample predictions for most prediction points, associated with low volatility periods in the dengue counts. We discuss the advantages and limitations of the dynamic Poisson models for studying the association between time series of dengue disease and meteorological variables. The key conclusion of the study is that dynamic Poisson models account for the dynamic nature of the variables involved in the modeling of time series of dengue disease, producing useful models for decision-making in public health.

Financial Management of a Large Multi-site Randomized Clinical Trial

PubMed Central

Sheffet, Alice J.; Flaxman, Linda; Tom, MeeLee; Hughes, Susan E.; Longbottom, Mary E.; Howard, Virginia J.; Marler, John R.; Brott, Thomas G.

2014-01-01

Background The Carotid Revascularization Endarterectomy versus Stenting Trial (CREST) received five years’ funding ($21,112,866) from the National Institutes of Health to compare carotid stenting to surgery for stroke prevention in 2,500 randomized participants at 40 sites. Aims Herein we evaluate the change in the CREST budget from a fixed to variable-cost model and recommend strategies for the financial management of large-scale clinical trials. Methods Projections of the original grant’s fixed-cost model were compared to the actual costs of the revised variable-cost model. The original grant’s fixed-cost budget included salaries, fringe benefits, and other direct and indirect costs. For the variable-cost model, the costs were actual payments to the clinical sites and core centers based upon actual trial enrollment. We compared annual direct and indirect costs and per-patient cost for both the fixed and variable models. Differences between clinical site and core center expenditures were also calculated. Results Using a variable-cost budget for clinical sites, funding was extended by no-cost extension from five to eight years. Randomizing sites tripled from 34 to 109. Of the 2,500 targeted sample size, 138 (5.5%) were randomized during the first five years and 1,387 (55.5%) during the no-cost extension. The actual per-patient costs of the variable model were 9% ($13,845) of the projected per-patient costs ($152,992) of the fixed model. Conclusions Performance-based budgets conserve funding, promote compliance, and allow for additional sites at modest additional cost. Costs of large-scale clinical trials can thus be reduced through effective management without compromising scientific integrity. PMID:24661748

Financial management of a large multisite randomized clinical trial.

PubMed

Sheffet, Alice J; Flaxman, Linda; Tom, MeeLee; Hughes, Susan E; Longbottom, Mary E; Howard, Virginia J; Marler, John R; Brott, Thomas G

2014-08-01

The Carotid Revascularization Endarterectomy versus Stenting Trial (CREST) received five years' funding ($21 112 866) from the National Institutes of Health to compare carotid stenting to surgery for stroke prevention in 2500 randomized participants at 40 sites. Herein we evaluate the change in the CREST budget from a fixed to variable-cost model and recommend strategies for the financial management of large-scale clinical trials. Projections of the original grant's fixed-cost model were compared to the actual costs of the revised variable-cost model. The original grant's fixed-cost budget included salaries, fringe benefits, and other direct and indirect costs. For the variable-cost model, the costs were actual payments to the clinical sites and core centers based upon actual trial enrollment. We compared annual direct and indirect costs and per-patient cost for both the fixed and variable models. Differences between clinical site and core center expenditures were also calculated. Using a variable-cost budget for clinical sites, funding was extended by no-cost extension from five to eight years. Randomizing sites tripled from 34 to 109. Of the 2500 targeted sample size, 138 (5·5%) were randomized during the first five years and 1387 (55·5%) during the no-cost extension. The actual per-patient costs of the variable model were 9% ($13 845) of the projected per-patient costs ($152 992) of the fixed model. Performance-based budgets conserve funding, promote compliance, and allow for additional sites at modest additional cost. Costs of large-scale clinical trials can thus be reduced through effective management without compromising scientific integrity. © 2014 The Authors. International Journal of Stroke © 2014 World Stroke Organization.

Android Application Model of "Suami Siaga Plus" as an Innovation in Birth Preparedness and Complication Readiness (BP/CR) Intervention.

PubMed

Santoso, Hanna Yuanita Dana; Supriyana, Supriyana; Bahiyatun, Bahiyatun; Widyawati, Melyana Nurul; Fatmasari, Diyah; Sudiyono, Sudiyono; Widyastari, Dyah Anantalia; Sinaga, Doni Marisi

2017-03-01

Objective: WHO recommends Mobile health , a practice of medicine and public health supported by mobile devices, to improve community health status and change people's behavior for the health purposes. The present study sought to examine the effectiveness of the android application program of Suami Siaga Plus in increasing husband's scores in birth preparedness and complication readiness (BP/CR) intervention. Materials and methods: It was a randomized controlled trial with pretest-posttest design. A total of 38 couples of husbands and pregnant women from three health centers at three sub districts in Semarang was selected by proportional systematic random sampling technique and equally distributed into control and intervention group. A questionnaire related to BP/CR published by JHPIEGO was employed in data collection. Statistical analysis was performed to obtain the frequency distribution and percentage of the variables, and also to assess the mean difference of BP/CR score of husbands. Results: Husbands' knowledge of key danger signs and five standard elements in BP/CR practices in both intervention and control group increased after counseling. Moreover, the proportion of husbands who understand the key danger signs during pregnancy was higher among those who were exposed by Suami Siaga Plus application delivered via mobile phone. Counseling only increased husbands' score from 61.5 to 62.6 (2%), whilst the combination of counseling and the application boosted 20% of husbands' score from 60.4 to 72.9 (p-value 0.000). Conclusion: A combination of counseling and Suami Siaga Plus application significantly improves husbands and wives' score on BP/CR compared to those who received counseling only. The data suggests the application would be able to suppress the three delays, which in turn can reduce the maternal mortality rate. The study results could be important information for the Department of Health and health professionals to use android application program , in particular to the husband whose wife is in pregnancy, childbirth and postpartum periods.

Application of Monte Carlo techniques to optimization of high-energy beam transport in a stochastic environment

NASA Technical Reports Server (NTRS)

Parrish, R. V.; Dieudonne, J. E.; Filippas, T. A.

1971-01-01

An algorithm employing a modified sequential random perturbation, or creeping random search, was applied to the problem of optimizing the parameters of a high-energy beam transport system. The stochastic solution of the mathematical model for first-order magnetic-field expansion allows the inclusion of state-variable constraints, and the inclusion of parameter constraints allowed by the method of algorithm application eliminates the possibility of infeasible solutions. The mathematical model and the algorithm were programmed for a real-time simulation facility; thus, two important features are provided to the beam designer: (1) a strong degree of man-machine communication (even to the extent of bypassing the algorithm and applying analog-matching techniques), and (2) extensive graphics for displaying information concerning both algorithm operation and transport-system behavior. Chromatic aberration was also included in the mathematical model and in the optimization process. Results presented show this method as yielding better solutions (in terms of resolutions) to the particular problem than those of a standard analog program as well as demonstrating flexibility, in terms of elements, constraints, and chromatic aberration, allowed by user interaction with both the algorithm and the stochastic model. Example of slit usage and a limited comparison of predicted results and actual results obtained with a 600 MeV cyclotron are given.

Fixed-Rate Compressed Floating-Point Arrays.

PubMed

Lindstrom, Peter

2014-12-01

Current compression schemes for floating-point data commonly take fixed-precision values and compress them to a variable-length bit stream, complicating memory management and random access. We present a fixed-rate, near-lossless compression scheme that maps small blocks of 4(d) values in d dimensions to a fixed, user-specified number of bits per block, thereby allowing read and write random access to compressed floating-point data at block granularity. Our approach is inspired by fixed-rate texture compression methods widely adopted in graphics hardware, but has been tailored to the high dynamic range and precision demands of scientific applications. Our compressor is based on a new, lifted, orthogonal block transform and embedded coding, allowing each per-block bit stream to be truncated at any point if desired, thus facilitating bit rate selection using a single compression scheme. To avoid compression or decompression upon every data access, we employ a software write-back cache of uncompressed blocks. Our compressor has been designed with computational simplicity and speed in mind to allow for the possibility of a hardware implementation, and uses only a small number of fixed-point arithmetic operations per compressed value. We demonstrate the viability and benefits of lossy compression in several applications, including visualization, quantitative data analysis, and numerical simulation.

Low-rank separated representation surrogates of high-dimensional stochastic functions: Application in Bayesian inference

NASA Astrophysics Data System (ADS)

Validi, AbdoulAhad

2014-03-01

This study introduces a non-intrusive approach in the context of low-rank separated representation to construct a surrogate of high-dimensional stochastic functions, e.g., PDEs/ODEs, in order to decrease the computational cost of Markov Chain Monte Carlo simulations in Bayesian inference. The surrogate model is constructed via a regularized alternative least-square regression with Tikhonov regularization using a roughening matrix computing the gradient of the solution, in conjunction with a perturbation-based error indicator to detect optimal model complexities. The model approximates a vector of a continuous solution at discrete values of a physical variable. The required number of random realizations to achieve a successful approximation linearly depends on the function dimensionality. The computational cost of the model construction is quadratic in the number of random inputs, which potentially tackles the curse of dimensionality in high-dimensional stochastic functions. Furthermore, this vector-valued separated representation-based model, in comparison to the available scalar-valued case, leads to a significant reduction in the cost of approximation by an order of magnitude equal to the vector size. The performance of the method is studied through its application to three numerical examples including a 41-dimensional elliptic PDE and a 21-dimensional cavity flow.

A syringe-sharing model for the spread of HIV: application to Omsk, Western Siberia.

PubMed

Artzrouni, Marc; Leonenko, Vasiliy N; Mara, Thierry A

2017-03-01

A system of two differential equations is used to model the transmission dynamics of human immunodeficiency virus between 'persons who inject drugs' (PWIDs) and their syringes. Our vector-borne disease model hinges on a metaphorical urn from which PWIDs draw syringes at random which may or may not be infected and may or may not result in one of the two agents becoming infected. The model's parameters are estimated with data mostly from the city of Omsk in Western Siberia. A linear trend in PWID prevalence in Omsk could only be fitted by considering a time-dependent version of the model captured through a secular decrease in the probability that PWIDs decide to share a syringe. A global sensitivity analysis is performed with 14 parameters considered random variables in order to assess their impact on average numbers infected over a 50-year projection. With obvious intervention implications the drug injection rate and the probability of syringe-cleansing are the only parameters whose coefficients of correlations with numbers of infected PWIDs and infected syringes have an absolute value close to or larger than 0.40. © The authors 2015. Published by Oxford University Press on behalf of the Institute of Mathematics and its Applications. All rights reserved.

Elastase-Sensitive Elastomeric Scaffolds with Variable Anisotropy for Soft Tissue Engineering

PubMed Central

Guan, Jianjun; Fujimoto, Kazuro L.; Wagner, William R.

2010-01-01

Purpose To develop elastase-sensitive polyurethane scaffolds that would be applicable to the engineering of mechanically active soft tissues. Methods A polyurethane containing an elastase-sensitive peptide sequence was processed into scaffolds by thermally induced phase separation. Processing conditions were manipulated to alter scaffold properties and anisotropy. The scaffold’s mechanical properties, degradation, and cytocompatibility using muscle-derived stem cells were characterized. Scaffold in vivo degradation was evaluated by subcutaneous implantation. Results When heat transfer was multidirectional, scaffolds had randomly oriented pores. Imposition of a heat transfer gradient resulted in oriented pores. Both scaffolds were flexible and relatively strong with mechanical properties dependent upon fabrication conditions such as solvent type, polymer concentration and quenching temperature. Oriented scaffolds exhibited anisotropic mechanical properties with greater tensile strength in the orientation direction. These scaffolds also supported muscle-derived stem cell growth more effectively than random scaffolds. The scaffolds expressed over 40% weight loss after 56 days in elastase containing buffer. Elastase-sensitive scaffolds were complete degraded after 8 weeks subcutaneous implantation in rats, markedly faster than similar polyurethanes that did not contain the peptide sequence. Conclusion The elastase-sensitive polyurethane scaffolds showed promise for application in soft tissue engineering where controlling scaffold mechanical properties and pore architecture are desirable. PMID:18509596

Strategic Use of Random Subsample Replication and a Coefficient of Factor Replicability

ERIC Educational Resources Information Center

Katzenmeyer, William G.; Stenner, A. Jackson

1975-01-01

The problem of demonstrating replicability of factor structure across random variables is addressed. Procedures are outlined which combine the use of random subsample replication strategies with the correlations between factor score estimates across replicate pairs to generate a coefficient of replicability and confidence intervals associated with…

Simulation of the Effects of Random Measurement Errors

ERIC Educational Resources Information Center

Kinsella, I. A.; Hannaidh, P. B. O.

1978-01-01

Describes a simulation method for measurement of errors that requires calculators and tables of random digits. Each student simulates the random behaviour of the component variables in the function and by combining the results of all students, the outline of the sampling distribution of the function can be obtained. (GA)

Exploring prediction uncertainty of spatial data in geostatistical and machine learning Approaches

NASA Astrophysics Data System (ADS)

Klump, J. F.; Fouedjio, F.

2017-12-01

Geostatistical methods such as kriging with external drift as well as machine learning techniques such as quantile regression forest have been intensively used for modelling spatial data. In addition to providing predictions for target variables, both approaches are able to deliver a quantification of the uncertainty associated with the prediction at a target location. Geostatistical approaches are, by essence, adequate for providing such prediction uncertainties and their behaviour is well understood. However, they often require significant data pre-processing and rely on assumptions that are rarely met in practice. Machine learning algorithms such as random forest regression, on the other hand, require less data pre-processing and are non-parametric. This makes the application of machine learning algorithms to geostatistical problems an attractive proposition. The objective of this study is to compare kriging with external drift and quantile regression forest with respect to their ability to deliver reliable prediction uncertainties of spatial data. In our comparison we use both simulated and real world datasets. Apart from classical performance indicators, comparisons make use of accuracy plots, probability interval width plots, and the visual examinations of the uncertainty maps provided by the two approaches. By comparing random forest regression to kriging we found that both methods produced comparable maps of estimated values for our variables of interest. However, the measure of uncertainty provided by random forest seems to be quite different to the measure of uncertainty provided by kriging. In particular, the lack of spatial context can give misleading results in areas without ground truth data. These preliminary results raise questions about assessing the risks associated with decisions based on the predictions from geostatistical and machine learning algorithms in a spatial context, e.g. mineral exploration.

Shake It Off: A Randomized Pilot Study of the Effect of Whole Body Vibration on Pain in Healing Burn Wounds.

PubMed

Ray, Juliet J; Alvarez, Angel D; Ulbrich, Sondra L; Lessner-Eisenberg, Sharon; Satahoo, Shevonne S; Meizoso, Jonathan P; Karcutskie, Charles A; Mundra, Leela S; Namias, Nicholas; Pizano, Louis R; Schulman, Carl I

Whole body vibration (WBV) has been shown to improve strength in extremities with healed burn wounds. We hypothesize that WBV reduces pain during rehabilitation compared to standard therapy alone. Patients with ≥1% TBSA burn to one or more extremities from October 2014 to December 2015 were randomized to vibration (VIBE) or control. Each burned extremity was tested separately within the assigned group. Patients underwent one to three therapy sessions (S1, S2, S3) consisting of five upper and/or lower extremity exercises with or without WBV. Pain was assessed pre-, mid-, and postsession on a scale of 1 to 10. Mean pain scores at S1 to S3 were compared between groups with paired samples t-tests. An independent t-test was used to compare differences in pain scores between groups. Continuous variables were compared using a t-test or Mann-Whitney U test, and categorical variables were compared using a χ or Fisher's exact test, as appropriate. Forty-eight randomized test extremities (VIBE = 26, control = 22) were analyzed from a total of 31 subjects. There were no significant differences between groups in age, gender, overall TBSA, TBSA in the test extremity, pain medication use before therapy session, or skin grafting before therapy session. At S1, S2, and S3, there was a statistically significant decrease in mid- and postsession pain compared to presession pain in VIBE vs controls. Exposure to WBV decreased pain during and after physical therapy. This modality may be applicable to a variety of soft tissue injuries and warrants additional investigation.

Stochastic analysis of uncertain thermal parameters for random thermal regime of frozen soil around a single freezing pipe

NASA Astrophysics Data System (ADS)

Wang, Tao; Zhou, Guoqing; Wang, Jianzhou; Zhou, Lei

2018-03-01

The artificial ground freezing method (AGF) is widely used in civil and mining engineering, and the thermal regime of frozen soil around the freezing pipe affects the safety of design and construction. The thermal parameters can be truly random due to heterogeneity of the soil properties, which lead to the randomness of thermal regime of frozen soil around the freezing pipe. The purpose of this paper is to study the one-dimensional (1D) random thermal regime problem on the basis of a stochastic analysis model and the Monte Carlo (MC) method. Considering the uncertain thermal parameters of frozen soil as random variables, stochastic processes and random fields, the corresponding stochastic thermal regime of frozen soil around a single freezing pipe are obtained and analyzed. Taking the variability of each stochastic parameter into account individually, the influences of each stochastic thermal parameter on stochastic thermal regime are investigated. The results show that the mean temperatures of frozen soil around the single freezing pipe with three analogy method are the same while the standard deviations are different. The distributions of standard deviation have a great difference at different radial coordinate location and the larger standard deviations are mainly at the phase change area. The computed data with random variable method and stochastic process method have a great difference from the measured data while the computed data with random field method well agree with the measured data. Each uncertain thermal parameter has a different effect on the standard deviation of frozen soil temperature around the single freezing pipe. These results can provide a theoretical basis for the design and construction of AGF.

Compiling probabilistic, bio-inspired circuits on a field programmable analog array

PubMed Central

Marr, Bo; Hasler, Jennifer

2014-01-01

A field programmable analog array (FPAA) is presented as an energy and computational efficiency engine: a mixed mode processor for which functions can be compiled at significantly less energy costs using probabilistic computing circuits. More specifically, it will be shown that the core computation of any dynamical system can be computed on the FPAA at significantly less energy per operation than a digital implementation. A stochastic system that is dynamically controllable via voltage controlled amplifier and comparator thresholds is implemented, which computes Bernoulli random variables. From Bernoulli variables it is shown exponentially distributed random variables, and random variables of an arbitrary distribution can be computed. The Gillespie algorithm is simulated to show the utility of this system by calculating the trajectory of a biological system computed stochastically with this probabilistic hardware where over a 127X performance improvement over current software approaches is shown. The relevance of this approach is extended to any dynamical system. The initial circuits and ideas for this work were generated at the 2008 Telluride Neuromorphic Workshop. PMID:24847199

Robustness-Based Design Optimization Under Data Uncertainty

NASA Technical Reports Server (NTRS)

Zaman, Kais; McDonald, Mark; Mahadevan, Sankaran; Green, Lawrence

2010-01-01

This paper proposes formulations and algorithms for design optimization under both aleatory (i.e., natural or physical variability) and epistemic uncertainty (i.e., imprecise probabilistic information), from the perspective of system robustness. The proposed formulations deal with epistemic uncertainty arising from both sparse and interval data without any assumption about the probability distributions of the random variables. A decoupled approach is proposed in this paper to un-nest the robustness-based design from the analysis of non-design epistemic variables to achieve computational efficiency. The proposed methods are illustrated for the upper stage design problem of a two-stage-to-orbit (TSTO) vehicle, where the information on the random design inputs are only available as sparse point and/or interval data. As collecting more data reduces uncertainty but increases cost, the effect of sample size on the optimality and robustness of the solution is also studied. A method is developed to determine the optimal sample size for sparse point data that leads to the solutions of the design problem that are least sensitive to variations in the input random variables.

Time-lapse culture with morphokinetic embryo selection improves pregnancy and live birth chances and reduces early pregnancy loss: a meta-analysis.

PubMed

Pribenszky, Csaba; Nilselid, Anna-Maria; Montag, Markus

2017-11-01

Embryo evaluation and selection is fundamental in clinical IVF. Time-lapse follow-up of embryo development comprises undisturbed culture and the application of the visual information to support embryo evaluation. A meta-analysis of randomized controlled trials was carried out to study whether time-lapse monitoring with the prospective use of a morphokinetic algorithm for selection of embryos improves overall clinical outcome (pregnancy, early pregnancy loss, stillbirth and live birth rate) compared with embryo selection based on single time-point morphology in IVF cycles. The meta-analysis of five randomized controlled trials (n = 1637) showed that the application of time-lapse monitoring was associated with a significantly higher ongoing clinical pregnancy rate (51.0% versus 39.9%), with a pooled odds ratio of 1.542 (P < 0.001), significantly lower early pregnancy loss (15.3% versus 21.3%; OR: 0.662; P = 0.019) and a significantly increased live birth rate (44.2% versus 31.3%; OR 1.668; P = 0.009). Difference in stillbirth was not significant between groups (4.7% versus 2.4%). Quality of the evidence was moderate to low owing to inconsistencies across the studies. Selective application and variability were also limitations. Although time-lapse is shown to significantly improve overall clinical outcome, further high-quality evidence is needed before universal conclusions can be drawn. Copyright © 2017 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Optimal timing in biological processes

USGS Publications Warehouse

Williams, B.K.; Nichols, J.D.

1984-01-01

A general approach for obtaining solutions to a class of biological optimization problems is provided. The general problem is one of determining the appropriate time to take some action, when the action can be taken only once during some finite time frame. The approach can also be extended to cover a number of other problems involving animal choice (e.g., mate selection, habitat selection). Returns (assumed to index fitness) are treated as random variables with time-specific distributions, and can be either observable or unobservable at the time action is taken. In the case of unobservable returns, the organism is assumed to base decisions on some ancillary variable that is associated with returns. Optimal policies are derived for both situations and their properties are discussed. Various extensions are also considered, including objective functions based on functions of returns other than the mean, nonmonotonic relationships between the observable variable and returns; possible death of the organism before action is taken; and discounting of future returns. A general feature of the optimal solutions for many of these problems is that an organism should be very selective (i.e., should act only when returns or expected returns are relatively high) at the beginning of the time frame and should become less and less selective as time progresses. An example of the application of optimal timing to a problem involving the timing of bird migration is discussed, and a number of other examples for which the approach is applicable are described.

Hybrid modeling of spatial continuity for application to numerical inverse problems

USGS Publications Warehouse

Friedel, Michael J.; Iwashita, Fabio

2013-01-01

A novel two-step modeling approach is presented to obtain optimal starting values and geostatistical constraints for numerical inverse problems otherwise characterized by spatially-limited field data. First, a type of unsupervised neural network, called the self-organizing map (SOM), is trained to recognize nonlinear relations among environmental variables (covariates) occurring at various scales. The values of these variables are then estimated at random locations across the model domain by iterative minimization of SOM topographic error vectors. Cross-validation is used to ensure unbiasedness and compute prediction uncertainty for select subsets of the data. Second, analytical functions are fit to experimental variograms derived from original plus resampled SOM estimates producing model variograms. Sequential Gaussian simulation is used to evaluate spatial uncertainty associated with the analytical functions and probable range for constraining variables. The hybrid modeling of spatial continuity is demonstrated using spatially-limited hydrologic measurements at different scales in Brazil: (1) physical soil properties (sand, silt, clay, hydraulic conductivity) in the 42 km2 Vargem de Caldas basin; (2) well yield and electrical conductivity of groundwater in the 132 km2 fractured crystalline aquifer; and (3) specific capacity, hydraulic head, and major ions in a 100,000 km2 transboundary fractured-basalt aquifer. These results illustrate the benefits of exploiting nonlinear relations among sparse and disparate data sets for modeling spatial continuity, but the actual application of these spatial data to improve numerical inverse modeling requires testing.

Serum Testosterone (T) Level Variability in T Gel-Treated Older Hypogonadal Men: Treatment Monitoring Implications

PubMed Central

Pak, Youngju; Wang, Christina; Liu, Peter Y.; Bhasin, Shalender; Gill, Thomas M.; Matsumoto, Alvin M.; Pahor, Marco; Surampudi, Prasanth; Snyder, Peter J.

2015-01-01

Context: The optimal frequency for on-treatment serum T measurement used for dose adjustment after transdermal T gel application is unknown, especially in older men with thinner skin and slower metabolic clearance. Objectives: The objectives of the study was to determine the variability of postgel application serum T concentrations and assess whether single levels are reflective of average serum T concentrations over 24 hours (Cavg0–24). Design: This was a double-blinded, placebo-controlled randomized trial. Setting: The study was conducted at five academic centers. Participants: Forty-seven symptomatic men 65 years old or older with an average of two morning T concentration less than 275 ng/dL participated in the study. Intervention(s): Transdermal T or placebo gel was applied for 120 ± 14 days. Monthly dose adjustments were made if necessary to target serum T between 400 and 500 to 800 ng/dL. Main Outcome Measures: Variability of serum T 2 hours after the gel application on two outpatient visits and at multiple time points over 24 hours during the inpatient day was measured. Results: On-treatment T levels varied substantially on the 2 ambulatory days and over 24 hours during the inpatient day. Ambulatory 2-hour postapplication T levels did not correlate significantly with either 2-hour postapplication serum T or Cavg0–24 measured during the inpatient day. Only 22.2% of men receiving T had a Cavg0–24 within the target range of 500–800 ng/dL; 81.5% had a Cavg0–24 within the broader 300–1000 ng/dL range. Conclusion: Large within-individual variations in serum T after T gel application render ambulatory 2-hour postapplication T level a poor indicator of average serum T on another day. Our data point out the limitations of dose adjustments based on a single postapplication serum T measurement. PMID:26120790

Information content of MOPITT CO profile retrievals: Temporal and geographical variability

NASA Astrophysics Data System (ADS)

Deeter, M. N.; Edwards, D. P.; Gille, J. C.; Worden, H. M.

2015-12-01

Satellite measurements of tropospheric carbon monoxide (CO) enable a wide array of applications including studies of air quality and pollution transport. The MOPITT (Measurements of Pollution in the Troposphere) instrument on the Earth Observing System Terra platform has been measuring CO concentrations globally since March 2000. As indicated by the Degrees of Freedom for Signal (DFS), the standard metric for trace-gas retrieval information content, MOPITT retrieval performance varies over a wide range. We show that both instrumental and geophysical effects yield significant geographical and temporal variability in MOPITT DFS values. Instrumental radiance uncertainties, which describe random errors (or "noise") in the calibrated radiances, vary over long time scales (e.g., months to years) and vary between the four detector elements of MOPITT's linear detector array. MOPITT retrieval performance depends on several factors including thermal contrast, fine-scale variability of surface properties, and CO loading. The relative importance of these various effects is highly variable, as demonstrated by analyses of monthly mean DFS values for the United States and the Amazon Basin. An understanding of the geographical and temporal variability of MOPITT retrieval performance is potentially valuable to data users seeking to limit the influence of the a priori through data filtering. To illustrate, it is demonstrated that calculated regional-average CO mixing ratios may be improved by excluding observations from a subset of pixels in MOPITT's linear detector array.

Confounding, causality, and confusion: the role of intermediate variables in interpreting observational studies in obstetrics.

PubMed

Ananth, Cande V; Schisterman, Enrique F

2017-08-01

Prospective and retrospective cohorts and case-control studies are some of the most important study designs in epidemiology because, under certain assumptions, they can mimic a randomized trial when done well. These assumptions include, but are not limited to, properly accounting for 2 important sources of bias: confounding and selection bias. While not adjusting the causal association for an intermediate variable will yield an unbiased estimate of the exposure-outcome's total causal effect, it is often that obstetricians will want to adjust for an intermediate variable to assess if the intermediate is the underlying driver of the association. Such a practice must be weighed in light of the underlying research question and whether such an adjustment is necessary should be carefully considered. Gestational age is, by far, the most commonly encountered variable in obstetrics that is often mislabeled as a confounder when, in fact, it may be an intermediate. If, indeed, gestational age is an intermediate but if mistakenly labeled as a confounding variable and consequently adjusted in an analysis, the conclusions can be unexpected. The implications of this overadjustment of an intermediate as though it were a confounder can render an otherwise persuasive study downright meaningless. This commentary provides an exposition of confounding bias, collider stratification, and selection biases, with applications in obstetrics and perinatal epidemiology. Copyright © 2017 Elsevier Inc. All rights reserved.

The role of kinesiotaping combined with botulinum toxin to reduce plantar flexors spasticity after stroke.

PubMed

Karadag-Saygi, Evrim; Cubukcu-Aydoseli, Koza; Kablan, Nilufer; Ofluoglu, Demet

2010-01-01

To evaluate the effect of kinesiotaping as an adjuvant therapy to botulinum toxin A (BTX-A) injection in lower extremity spasticity. This is a single-center, randomized, and double-blind study. Twenty hemiplegic patients with spastic equinus foot were enrolled into the study and randomized into 2 groups. The first group (n=10) received BTX-A injection and kinesiotaping, and the second group (n=10) received BTX-A injection and sham-taping. Clinical assessment was done before injection and at 2 weeks and 1, 3, and 6 months. Outcome measures were modified Ashworth scale (MAS), passive ankle dorsiflexion, gait velocity, and step length. Improvement was recorded in both kinesiotaping and sham groups for all outcome variables. No significant difference was found between groups other than passive range of motion (ROM), which was found to have increased more in the kinesiotaping group at 2 weeks. There is no clear benefit in adjuvant kinesiotaping application with botulinum toxin for correction of spastic equinus in stroke.

Application of multivariable search techniques to the optimization of airfoils in a low speed nonlinear inviscid flow field

NASA Technical Reports Server (NTRS)

Hague, D. S.; Merz, A. W.

1975-01-01

Multivariable search techniques are applied to a particular class of airfoil optimization problems. These are the maximization of lift and the minimization of disturbance pressure magnitude in an inviscid nonlinear flow field. A variety of multivariable search techniques contained in an existing nonlinear optimization code, AESOP, are applied to this design problem. These techniques include elementary single parameter perturbation methods, organized search such as steepest-descent, quadratic, and Davidon methods, randomized procedures, and a generalized search acceleration technique. Airfoil design variables are seven in number and define perturbations to the profile of an existing NACA airfoil. The relative efficiency of the techniques are compared. It is shown that elementary one parameter at a time and random techniques compare favorably with organized searches in the class of problems considered. It is also shown that significant reductions in disturbance pressure magnitude can be made while retaining reasonable lift coefficient values at low free stream Mach numbers.

A universal self-charging system driven by random biomechanical energy for sustainable operation of mobile electronics

NASA Astrophysics Data System (ADS)

Niu, Simiao; Wang, Xiaofeng; Yi, Fang; Zhou, Yu Sheng; Wang, Zhong Lin

2015-12-01

Human biomechanical energy is characterized by fluctuating amplitudes and variable low frequency, and an effective utilization of such energy cannot be achieved by classical energy-harvesting technologies. Here we report a high-efficient self-charging power system for sustainable operation of mobile electronics exploiting exclusively human biomechanical energy, which consists of a high-output triboelectric nanogenerator, a power management circuit to convert the random a.c. energy to d.c. electricity at 60% efficiency, and an energy storage device. With palm tapping as the only energy source, this power unit provides a continuous d.c. electricity of 1.044 mW (7.34 W m-3) in a regulated and managed manner. This self-charging unit can be universally applied as a standard `infinite-lifetime' power source for continuously driving numerous conventional electronics, such as thermometers, electrocardiograph system, pedometers, wearable watches, scientific calculators and wireless radio-frequency communication system, which indicates the immediate and broad applications in personal sensor systems and internet of things.

Investigating the origins of high multilevel resistive switching in forming free Ti/TiO2-x-based memory devices through experiments and simulations

NASA Astrophysics Data System (ADS)

Bousoulas, P.; Giannopoulos, I.; Asenov, P.; Karageorgiou, I.; Tsoukalas, D.

2017-03-01

Although multilevel capability is probably the most important property of resistive random access memory (RRAM) technology, it is vulnerable to reliability issues due to the stochastic nature of conducting filament (CF) creation. As a result, the various resistance states cannot be clearly distinguished, which leads to memory capacity failure. In this work, due to the gradual resistance switching pattern of TiO2-x-based RRAM devices, we demonstrate at least six resistance states with distinct memory margin and promising temporal variability. It is shown that the formation of small CFs with high density of oxygen vacancies enhances the uniformity of the switching characteristics in spite of the random nature of the switching effect. Insight into the origin of the gradual resistance modulation mechanisms is gained by the application of a trap-assisted-tunneling model together with numerical simulations of the filament formation physical processes.

A universal self-charging system driven by random biomechanical energy for sustainable operation of mobile electronics.

PubMed

Niu, Simiao; Wang, Xiaofeng; Yi, Fang; Zhou, Yu Sheng; Wang, Zhong Lin

2015-12-11

Human biomechanical energy is characterized by fluctuating amplitudes and variable low frequency, and an effective utilization of such energy cannot be achieved by classical energy-harvesting technologies. Here we report a high-efficient self-charging power system for sustainable operation of mobile electronics exploiting exclusively human biomechanical energy, which consists of a high-output triboelectric nanogenerator, a power management circuit to convert the random a.c. energy to d.c. electricity at 60% efficiency, and an energy storage device. With palm tapping as the only energy source, this power unit provides a continuous d.c. electricity of 1.044 mW (7.34 W m(-3)) in a regulated and managed manner. This self-charging unit can be universally applied as a standard 'infinite-lifetime' power source for continuously driving numerous conventional electronics, such as thermometers, electrocardiograph system, pedometers, wearable watches, scientific calculators and wireless radio-frequency communication system, which indicates the immediate and broad applications in personal sensor systems and internet of things.

The arcsine is asinine: the analysis of proportions in ecology.

PubMed

Warton, David I; Hui, Francis K C

2011-01-01

The arcsine square root transformation has long been standard procedure when analyzing proportional data in ecology, with applications in data sets containing binomial and non-binomial response variables. Here, we argue that the arcsine transform should not be used in either circumstance. For binomial data, logistic regression has greater interpretability and higher power than analyses of transformed data. However, it is important to check the data for additional unexplained variation, i.e., overdispersion, and to account for it via the inclusion of random effects in the model if found. For non-binomial data, the arcsine transform is undesirable on the grounds of interpretability, and because it can produce nonsensical predictions. The logit transformation is proposed as an alternative approach to address these issues. Examples are presented in both cases to illustrate these advantages, comparing various methods of analyzing proportions including untransformed, arcsine- and logit-transformed linear models and logistic regression (with or without random effects). Simulations demonstrate that logistic regression usually provides a gain in power over other methods.

Sustained State-Independent Quantum Contextual Correlations from a Single Ion

NASA Astrophysics Data System (ADS)

Leupold, F. M.; Malinowski, M.; Zhang, C.; Negnevitsky, V.; Alonso, J.; Home, J. P.; Cabello, A.

2018-05-01

We use a single trapped-ion qutrit to demonstrate the quantum-state-independent violation of noncontextuality inequalities using a sequence of randomly chosen quantum nondemolition projective measurements. We concatenate 53 ×106 sequential measurements of 13 observables, and unambiguously violate an optimal noncontextual bound. We use the same data set to characterize imperfections including signaling and repeatability of the measurements. The experimental sequence was generated in real time with a quantum random number generator integrated into our control system to select the subsequent observable with a latency below 50 μ s , which can be used to constrain contextual hidden-variable models that might describe our results. The state-recycling experimental procedure is resilient to noise and independent of the qutrit state, substantiating the fact that the contextual nature of quantum physics is connected to measurements and not necessarily to designated states. The use of extended sequences of quantum nondemolition measurements finds applications in the fields of sensing and quantum information.

Using maximum entropy modeling for optimal selection of sampling sites for monitoring networks

USGS Publications Warehouse

Stohlgren, Thomas J.; Kumar, Sunil; Barnett, David T.; Evangelista, Paul H.

2011-01-01

Environmental monitoring programs must efficiently describe state shifts. We propose using maximum entropy modeling to select dissimilar sampling sites to capture environmental variability at low cost, and demonstrate a specific application: sample site selection for the Central Plains domain (453,490 km2) of the National Ecological Observatory Network (NEON). We relied on four environmental factors: mean annual temperature and precipitation, elevation, and vegetation type. A “sample site” was defined as a 20 km × 20 km area (equal to NEON’s airborne observation platform [AOP] footprint), within which each 1 km2 cell was evaluated for each environmental factor. After each model run, the most environmentally dissimilar site was selected from all potential sample sites. The iterative selection of eight sites captured approximately 80% of the environmental envelope of the domain, an improvement over stratified random sampling and simple random designs for sample site selection. This approach can be widely used for cost-efficient selection of survey and monitoring sites.

Semiparametric methods for estimation of a nonlinear exposure-outcome relationship using instrumental variables with application to Mendelian randomization.

PubMed

Staley, James R; Burgess, Stephen

2017-05-01

Mendelian randomization, the use of genetic variants as instrumental variables (IV), can test for and estimate the causal effect of an exposure on an outcome. Most IV methods assume that the function relating the exposure to the expected value of the outcome (the exposure-outcome relationship) is linear. However, in practice, this assumption may not hold. Indeed, often the primary question of interest is to assess the shape of this relationship. We present two novel IV methods for investigating the shape of the exposure-outcome relationship: a fractional polynomial method and a piecewise linear method. We divide the population into strata using the exposure distribution, and estimate a causal effect, referred to as a localized average causal effect (LACE), in each stratum of population. The fractional polynomial method performs metaregression on these LACE estimates. The piecewise linear method estimates a continuous piecewise linear function, the gradient of which is the LACE estimate in each stratum. Both methods were demonstrated in a simulation study to estimate the true exposure-outcome relationship well, particularly when the relationship was a fractional polynomial (for the fractional polynomial method) or was piecewise linear (for the piecewise linear method). The methods were used to investigate the shape of relationship of body mass index with systolic blood pressure and diastolic blood pressure. © 2017 The Authors Genetic Epidemiology Published by Wiley Periodicals, Inc.

Applying an Ensemble Classification Tree Approach to the Prediction of Completion of a 12-Step Facilitation Intervention with Stimulant Abusers

PubMed Central

Doyle, Suzanne R.; Donovan, Dennis M.

2014-01-01

Aims The purpose of this study was to explore the selection of predictor variables in the evaluation of drug treatment completion using an ensemble approach with classification trees. The basic methodology is reviewed and the subagging procedure of random subsampling is applied. Methods Among 234 individuals with stimulant use disorders randomized to a 12-Step facilitative intervention shown to increase stimulant use abstinence, 67.52% were classified as treatment completers. A total of 122 baseline variables were used to identify factors associated with completion. Findings The number of types of self-help activity involvement prior to treatment was the predominant predictor. Other effective predictors included better coping self-efficacy for substance use in high-risk situations, more days of prior meeting attendance, greater acceptance of the Disease model, higher confidence for not resuming use following discharge, lower ASI Drug and Alcohol composite scores, negative urine screens for cocaine or marijuana, and fewer employment problems. Conclusions The application of an ensemble subsampling regression tree method utilizes the fact that classification trees are unstable but, on average, produce an improved prediction of the completion of drug abuse treatment. The results support the notion there are early indicators of treatment completion that may allow for modification of approaches more tailored to fitting the needs of individuals and potentially provide more successful treatment engagement and improved outcomes. PMID:25134038

Semiparametric methods for estimation of a nonlinear exposure‐outcome relationship using instrumental variables with application to Mendelian randomization

PubMed Central

Staley, James R.

2017-01-01

ABSTRACT Mendelian randomization, the use of genetic variants as instrumental variables (IV), can test for and estimate the causal effect of an exposure on an outcome. Most IV methods assume that the function relating the exposure to the expected value of the outcome (the exposure‐outcome relationship) is linear. However, in practice, this assumption may not hold. Indeed, often the primary question of interest is to assess the shape of this relationship. We present two novel IV methods for investigating the shape of the exposure‐outcome relationship: a fractional polynomial method and a piecewise linear method. We divide the population into strata using the exposure distribution, and estimate a causal effect, referred to as a localized average causal effect (LACE), in each stratum of population. The fractional polynomial method performs metaregression on these LACE estimates. The piecewise linear method estimates a continuous piecewise linear function, the gradient of which is the LACE estimate in each stratum. Both methods were demonstrated in a simulation study to estimate the true exposure‐outcome relationship well, particularly when the relationship was a fractional polynomial (for the fractional polynomial method) or was piecewise linear (for the piecewise linear method). The methods were used to investigate the shape of relationship of body mass index with systolic blood pressure and diastolic blood pressure. PMID:28317167

Design and implementation of a dental caries prevention trial in remote Canadian Aboriginal communities.

PubMed

Harrison, Rosamund; Veronneau, Jacques; Leroux, Brian

2010-05-13

The goal of this cluster randomized trial is to test the effectiveness of a counseling approach, Motivational Interviewing, to control dental caries in young Aboriginal children. Motivational Interviewing, a client-centred, directive counseling style, has not yet been evaluated as an approach for promotion of behaviour change in indigenous communities in remote settings. Aboriginal women were hired from the 9 communities to recruit expectant and new mothers to the trial, administer questionnaires and deliver the counseling to mothers in the test communities. The goal is for mothers to receive the intervention during pregnancy and at their child's immunization visits. Data on children's dental health status and family dental health practices will be collected when children are 30-months of age. The communities were randomly allocated to test or control group by a random "draw" over community radio. Sample size and power were determined based on an anticipated 20% reduction in caries prevalence. Randomization checks were conducted between groups. In the 5 test and 4 control communities, 272 of the original target sample size of 309 mothers have been recruited over a two-and-a-half year period. A power calculation using the actual attained sample size showed power to be 79% to detect a treatment effect. If an attrition fraction of 4% per year is maintained, power will remain at 80%. Power will still be > 90% to detect a 25% reduction in caries prevalence. The distribution of most baseline variables was similar for the two randomized groups of mothers. However, despite the random assignment of communities to treatment conditions, group differences exist for stage of pregnancy and prior tooth extractions in the family. Because of the group imbalances on certain variables, control of baseline variables will be done in the analyses of treatment effects. This paper explains the challenges of conducting randomized trials in remote settings, the importance of thorough community collaboration, and also illustrates the likelihood that some baseline variables that may be clinically important will be unevenly split in group-randomized trials when the number of groups is small. This trial is registered as ISRCTN41467632.

Design and implementation of a dental caries prevention trial in remote Canadian Aboriginal communities

PubMed Central

2010-01-01

Background The goal of this cluster randomized trial is to test the effectiveness of a counseling approach, Motivational Interviewing, to control dental caries in young Aboriginal children. Motivational Interviewing, a client-centred, directive counseling style, has not yet been evaluated as an approach for promotion of behaviour change in indigenous communities in remote settings. Methods/design Aboriginal women were hired from the 9 communities to recruit expectant and new mothers to the trial, administer questionnaires and deliver the counseling to mothers in the test communities. The goal is for mothers to receive the intervention during pregnancy and at their child's immunization visits. Data on children's dental health status and family dental health practices will be collected when children are 30-months of age. The communities were randomly allocated to test or control group by a random "draw" over community radio. Sample size and power were determined based on an anticipated 20% reduction in caries prevalence. Randomization checks were conducted between groups. Discussion In the 5 test and 4 control communities, 272 of the original target sample size of 309 mothers have been recruited over a two-and-a-half year period. A power calculation using the actual attained sample size showed power to be 79% to detect a treatment effect. If an attrition fraction of 4% per year is maintained, power will remain at 80%. Power will still be > 90% to detect a 25% reduction in caries prevalence. The distribution of most baseline variables was similar for the two randomized groups of mothers. However, despite the random assignment of communities to treatment conditions, group differences exist for stage of pregnancy and prior tooth extractions in the family. Because of the group imbalances on certain variables, control of baseline variables will be done in the analyses of treatment effects. This paper explains the challenges of conducting randomized trials in remote settings, the importance of thorough community collaboration, and also illustrates the likelihood that some baseline variables that may be clinically important will be unevenly split in group-randomized trials when the number of groups is small. Trial registration This trial is registered as ISRCTN41467632. PMID:20465831

Random trinomial tree models and vanilla options

NASA Astrophysics Data System (ADS)

Ganikhodjaev, Nasir; Bayram, Kamola

2013-09-01

In this paper we introduce and study random trinomial model. The usual trinomial model is prescribed by triple of numbers (u, d, m). We call the triple (u, d, m) an environment of the trinomial model. A triple (Un, Dn, Mn), where {Un}, {Dn} and {Mn} are the sequences of independent, identically distributed random variables with 0 < Dn < 1 < Un and Mn = 1 for all n, is called a random environment and trinomial tree model with random environment is called random trinomial model. The random trinomial model is considered to produce more accurate results than the random binomial model or usual trinomial model.

Quantifying Uncertainties in N2O Emission Due to N Fertilizer Application in Cultivated Areas

PubMed Central

Philibert, Aurore; Loyce, Chantal; Makowski, David

2012-01-01

Nitrous oxide (N2O) is a greenhouse gas with a global warming potential approximately 298 times greater than that of CO2. In 2006, the Intergovernmental Panel on Climate Change (IPCC) estimated N2O emission due to synthetic and organic nitrogen (N) fertilization at 1% of applied N. We investigated the uncertainty on this estimated value, by fitting 13 different models to a published dataset including 985 N2O measurements. These models were characterized by (i) the presence or absence of the explanatory variable “applied N”, (ii) the function relating N2O emission to applied N (exponential or linear function), (iii) fixed or random background (i.e. in the absence of N application) N2O emission and (iv) fixed or random applied N effect. We calculated ranges of uncertainty on N2O emissions from a subset of these models, and compared them with the uncertainty ranges currently used in the IPCC-Tier 1 method. The exponential models outperformed the linear models, and models including one or two random effects outperformed those including fixed effects only. The use of an exponential function rather than a linear function has an important practical consequence: the emission factor is not constant and increases as a function of applied N. Emission factors estimated using the exponential function were lower than 1% when the amount of N applied was below 160 kg N ha−1. Our uncertainty analysis shows that the uncertainty range currently used by the IPCC-Tier 1 method could be reduced. PMID:23226430

Effect of climate data on simulated carbon and nitrogen balances for Europe

NASA Astrophysics Data System (ADS)

Blanke, Jan Hendrik; Lindeskog, Mats; Lindström, Johan; Lehsten, Veiko

2016-05-01

In this study, we systematically assess the spatial variability in carbon and nitrogen balance simulations related to the choice of global circulation models (GCMs), representative concentration pathways (RCPs), spatial resolutions, and the downscaling methods used as calculated with LPJ-GUESS. We employed a complete factorial design and performed 24 simulations for Europe with different climate input data sets and different combinations of these four factors. Our results reveal that the variability in simulated output in Europe is moderate with 35.6%-93.5% of the total variability being common among all combinations of factors. The spatial resolution is the most important factor among the examined factors, explaining 1.5%-10.7% of the total variability followed by GCMs (0.3%-7.6%), RCPs (0%-6.3%), and downscaling methods (0.1%-4.6%). The higher-order interactions effect that captures nonlinear relations between the factors and random effects is pronounced and accounts for 1.6%-45.8% to the total variability. The most distinct hot spots of variability include the mountain ranges in North Scandinavia and the Alps, and the Iberian Peninsula. Based on our findings, we advise to conduct the application of models such as LPJ-GUESS at a reasonably high spatial resolution which is supported by the model structure. There is no notable gain in simulations of ecosystem carbon and nitrogen stocks and fluxes from using regionally downscaled climate in preference to bias-corrected, bilinearly interpolated CMIP5 projections.

Genetic variability of Brazilian isolates of Alternaria alternata detected by AFLP and RAPD techniques

PubMed Central

Dini-Andreote, Francisco; Pietrobon, Vivian Cristina; Andreote, Fernando Dini; Romão, Aline Silva; Spósito, Marcel Bellato; Araújo, Welington Luiz

2009-01-01

The Alternaria brown spot (ABS) is a disease caused in tangerine plants and its hybrids by the fungus Alternaria alternata f. sp. citri which has been found in Brazil since 2001. Due to the recent occurrence in Brazilian orchards, the epidemiology and genetic variability of this pathogen is still an issue to be addressed. Here it is presented a survey about the genetic variability of this fungus by the characterization of twenty four pathogenic isolates of A. alternata f. sp. citri from citrus plants and four endophytic isolates from mango (one Alternaria tenuissima and three Alternaria arborescens). The application of two molecular markers Random Amplified Polymorphic DNA (RAPD) and Amplified Fragment Length Polymorphism (AFLP) had revealed the isolates clustering in distinct groups when fingerprintings were analyzed by Principal Components Analysis (PCA). Despite the better assessment of the genetic variability through the AFLP, significant modifications in clusters components were not observed, and only slight shifts in the positioning of isolates LRS 39/3 and 25M were observed in PCA plots. Furthermore, in both analyses, only the isolates from lemon plants revealed to be clustered, differently from the absence of clustering for other hosts or plant tissues. Summarizing, both RAPD and AFLP analyses were both efficient to detect the genetic variability within the population of the pathogenic fungus Alternaria spp., supplying information on the genetic variability of this species as a basis for further studies aiming the disease control. PMID:24031413

Relevance of anisotropy and spatial variability of gas diffusivity for soil-gas transport

NASA Astrophysics Data System (ADS)

Schack-Kirchner, Helmer; Kühne, Anke; Lang, Friederike

2017-04-01

Models of soil gas transport generally do not consider neither direction dependence of gas diffusivity, nor its small-scale variability. However, in a recent study, we could provide evidence for anisotropy favouring vertical gas diffusion in natural soils. We hypothesize that gas transport models based on gas diffusion data measured with soil rings are strongly influenced by both, anisotropy and spatial variability and the use of averaged diffusivities could be misleading. To test this we used a 2-dimensional model of soil gas transport to under compacted wheel tracks to model the soil-air oxygen distribution in the soil. The model was parametrized with data obtained from soil-ring measurements with its central tendency and variability. The model includes vertical parameter variability as well as variation perpendicular to the elongated wheel track. Different parametrization types have been tested: [i)]Averaged values for wheel track and undisturbed. em [ii)]Random distribution of soil cells with normally distributed variability within the strata. em [iii)]Random distributed soil cells with uniformly distributed variability within the strata. All three types of small-scale variability has been tested for [j)] isotropic gas diffusivity and em [jj)]reduced horizontal gas diffusivity (constant factor), yielding in total six models. As expected the different parametrizations had an important influence to the aeration state under wheel tracks with the strongest oxygen depletion in case of uniformly distributed variability and anisotropy towards higher vertical diffusivity. The simple simulation approach clearly showed the relevance of anisotropy and spatial variability in case of identical central tendency measures of gas diffusivity. However, until now it did not consider spatial dependency of variability, that could even aggravate effects. To consider anisotropy and spatial variability in gas transport models we recommend a) to measure soil-gas transport parameters spatially explicit including different directions and b) to use random-field stochastic models to assess the possible effects for gas-exchange models.

Minimization for conditional simulation: Relationship to optimal transport

NASA Astrophysics Data System (ADS)

Oliver, Dean S.

2014-05-01

In this paper, we consider the problem of generating independent samples from a conditional distribution when independent samples from the prior distribution are available. Although there are exact methods for sampling from the posterior (e.g. Markov chain Monte Carlo or acceptance/rejection), these methods tend to be computationally demanding when evaluation of the likelihood function is expensive, as it is for most geoscience applications. As an alternative, in this paper we discuss deterministic mappings of variables distributed according to the prior to variables distributed according to the posterior. Although any deterministic mappings might be equally useful, we will focus our discussion on a class of algorithms that obtain implicit mappings by minimization of a cost function that includes measures of data mismatch and model variable mismatch. Algorithms of this type include quasi-linear estimation, randomized maximum likelihood, perturbed observation ensemble Kalman filter, and ensemble of perturbed analyses (4D-Var). When the prior pdf is Gaussian and the observation operators are linear, we show that these minimization-based simulation methods solve an optimal transport problem with a nonstandard cost function. When the observation operators are nonlinear, however, the mapping of variables from the prior to the posterior obtained from those methods is only approximate. Errors arise from neglect of the Jacobian determinant of the transformation and from the possibility of discontinuous mappings.

Modelling Geomechanical Heterogeneity of Rock Masses Using Direct and Indirect Geostatistical Conditional Simulation Methods

NASA Astrophysics Data System (ADS)

Eivazy, Hesameddin; Esmaieli, Kamran; Jean, Raynald

2017-12-01

An accurate characterization and modelling of rock mass geomechanical heterogeneity can lead to more efficient mine planning and design. Using deterministic approaches and random field methods for modelling rock mass heterogeneity is known to be limited in simulating the spatial variation and spatial pattern of the geomechanical properties. Although the applications of geostatistical techniques have demonstrated improvements in modelling the heterogeneity of geomechanical properties, geostatistical estimation methods such as Kriging result in estimates of geomechanical variables that are not fully representative of field observations. This paper reports on the development of 3D models for spatial variability of rock mass geomechanical properties using geostatistical conditional simulation method based on sequential Gaussian simulation. A methodology to simulate the heterogeneity of rock mass quality based on the rock mass rating is proposed and applied to a large open-pit mine in Canada. Using geomechanical core logging data collected from the mine site, a direct and an indirect approach were used to model the spatial variability of rock mass quality. The results of the two modelling approaches were validated against collected field data. The study aims to quantify the risks of pit slope failure and provides a measure of uncertainties in spatial variability of rock mass properties in different areas of the pit.

A Gaussian Mixture Model Representation of Endmember Variability in Hyperspectral Unmixing

NASA Astrophysics Data System (ADS)

Zhou, Yuan; Rangarajan, Anand; Gader, Paul D.

2018-05-01

Hyperspectral unmixing while considering endmember variability is usually performed by the normal compositional model (NCM), where the endmembers for each pixel are assumed to be sampled from unimodal Gaussian distributions. However, in real applications, the distribution of a material is often not Gaussian. In this paper, we use Gaussian mixture models (GMM) to represent the endmember variability. We show, given the GMM starting premise, that the distribution of the mixed pixel (under the linear mixing model) is also a GMM (and this is shown from two perspectives). The first perspective originates from the random variable transformation and gives a conditional density function of the pixels given the abundances and GMM parameters. With proper smoothness and sparsity prior constraints on the abundances, the conditional density function leads to a standard maximum a posteriori (MAP) problem which can be solved using generalized expectation maximization. The second perspective originates from marginalizing over the endmembers in the GMM, which provides us with a foundation to solve for the endmembers at each pixel. Hence, our model can not only estimate the abundances and distribution parameters, but also the distinct endmember set for each pixel. We tested the proposed GMM on several synthetic and real datasets, and showed its potential by comparing it to current popular methods.

Branching random walk with step size coming from a power law

NASA Astrophysics Data System (ADS)

Bhattacharya, Ayan; Subhra Hazra, Rajat; Roy, Parthanil

2015-09-01

In their seminal work, Brunet and Derrida made predictions on the random point configurations associated with branching random walks. We shall discuss the limiting behavior of such point configurations when the displacement random variables come from a power law. In particular, we establish that two prediction of remains valid in this setup and investigate various other issues mentioned in their paper.

Application of nanoparticle of rock phosphate and biofertilizer in increasing some soil chemical characteristics of variable charge soil

NASA Astrophysics Data System (ADS)

Devnita, Rina; Joy, Benny; Arifin, Mahfud; Hudaya, Ridha; Oktaviani, Nurul

2018-02-01

Soils in Indonesia are dominated by variable charge soils where the technology like fertilization did not give the same result as the soils with permanent charge. The objectives of this research is to increase some chemical characteristic of variable charge soils by using the high negative charge ameliorations like rock phosphate in nanoparticle combined with biofertilizer. The research used a complete randomized experimental design in factorial with two factors. The first factor was nanoparticle of rock phosphate consists of four doses on soil weight percentage (0%, 2.5%, 5.0% and 7.5%). The second factor was biofertilizer consisted of two doses (without biofertilizer and 1 g.kg-1 soil biofertilizer). The combination treatments replicated three times. Variable charge soil used was Andisol. Andisol and the treatments were incubated for 4 months. Soil samples were taken after one and four months during incubation period to be analyzed for P-retention, available P and potential P. The result showed that all combinations of rock phosphate and biofertilizer decreased the P-retention to 75-77% after one month. Independently, application of 7.5% of rock phosphate decreased P-retention to 87.22% after four months, increased available P (245.37 and 19.12 mg.kg-1) and potential P (1354.78 and 3000.99 mg/100) after one and four months. Independently, biofertilizer increased the P-retention to 91.66% after four months, decreased available P to 121.55 mg.kg-1 after one month but increased to 12.55 mg.kg-1 after four months, decreased potential P to 635.30 after one month but increased to 1810.40 mg.100 g-1 after four months.

A Literature Survey and Experimental Evaluation of the State-of-the-Art in Uplift Modeling: A Stepping Stone Toward the Development of Prescriptive Analytics.

PubMed

Devriendt, Floris; Moldovan, Darie; Verbeke, Wouter

2018-03-01

Prescriptive analytics extends on predictive analytics by allowing to estimate an outcome in function of control variables, allowing as such to establish the required level of control variables for realizing a desired outcome. Uplift modeling is at the heart of prescriptive analytics and aims at estimating the net difference in an outcome resulting from a specific action or treatment that is applied. In this article, a structured and detailed literature survey on uplift modeling is provided by identifying and contrasting various groups of approaches. In addition, evaluation metrics for assessing the performance of uplift models are reviewed. An experimental evaluation on four real-world data sets provides further insight into their use. Uplift random forests are found to be consistently among the best performing techniques in terms of the Qini and Gini measures, although considerable variability in performance across the various data sets of the experiments is observed. In addition, uplift models are frequently observed to be unstable and display a strong variability in terms of performance across different folds in the cross-validation experimental setup. This potentially threatens their actual use for business applications. Moreover, it is found that the available evaluation metrics do not provide an intuitively understandable indication of the actual use and performance of a model. Specifically, existing evaluation metrics do not facilitate a comparison of uplift models and predictive models and evaluate performance either at an arbitrary cutoff or over the full spectrum of potential cutoffs. In conclusion, we highlight the instability of uplift models and the need for an application-oriented approach to assess uplift models as prime topics for further research.

Effect of study design on the reported effect of cardiac resynchronization therapy (CRT) on quantitative physiological measures: stratified meta-analysis in narrow-QRS heart failure and implications for planning future studies.

PubMed

Jabbour, Richard J; Shun-Shin, Matthew J; Finegold, Judith A; Afzal Sohaib, S M; Cook, Christopher; Nijjer, Sukhjinder S; Whinnett, Zachary I; Manisty, Charlotte H; Brugada, Josep; Francis, Darrel P

2015-01-06

Biventricular pacing (CRT) shows clear benefits in heart failure with wide QRS, but results in narrow QRS have appeared conflicting. We tested the hypothesis that study design might have influenced findings. We identified all reports of CRT-P/D therapy in subjects with narrow QRS reporting effects on continuous physiological variables. Twelve studies (2074 patients) met these criteria. Studies were stratified by presence of bias-resistance steps: the presence of a randomized control arm over a single arm, and blinded outcome measurement. Change in each endpoint was quantified using a standardized effect size (Cohen's d). We conducted separate meta-analyses for each variable in turn, stratified by trial quality. In non-randomized, non-blinded studies, the majority of variables (10 of 12, 83%) showed significant improvement, ranging from a standardized mean effect size of +1.57 (95%CI +0.43 to +2.7) for ejection fraction to +2.87 (+1.78 to +3.95) for NYHA class. In the randomized, non-blinded study, only 3 out of 6 variables (50%) showed improvement. For the randomized blinded studies, 0 out of 9 variables (0%) showed benefit, ranging from -0.04 (-0.31 to +0.22) for ejection fraction to -0.1 (-0.73 to +0.53) for 6-minute walk test. Differences in degrees of resistance to bias, rather than choice of endpoint, explain the variation between studies of CRT in narrow-QRS heart failure addressing physiological variables. When bias-resistance features are implemented, it becomes clear that these patients do not improve in any tested physiological variable. Guidance from studies without careful planning to resist bias may be far less useful than commonly perceived. © 2015 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley Blackwell.

Bayesian estimation of the discrete coefficient of determination.

PubMed

Chen, Ting; Braga-Neto, Ulisses M

2016-12-01

The discrete coefficient of determination (CoD) measures the nonlinear interaction between discrete predictor and target variables and has had far-reaching applications in Genomic Signal Processing. Previous work has addressed the inference of the discrete CoD using classical parametric and nonparametric approaches. In this paper, we introduce a Bayesian framework for the inference of the discrete CoD. We derive analytically the optimal minimum mean-square error (MMSE) CoD estimator, as well as a CoD estimator based on the Optimal Bayesian Predictor (OBP). For the latter estimator, exact expressions for its bias, variance, and root-mean-square (RMS) are given. The accuracy of both Bayesian CoD estimators with non-informative and informative priors, under fixed or random parameters, is studied via analytical and numerical approaches. We also demonstrate the application of the proposed Bayesian approach in the inference of gene regulatory networks, using gene-expression data from a previously published study on metastatic melanoma.

Synthetic Sediments and Stochastic Groundwater Hydrology

NASA Astrophysics Data System (ADS)

Wilson, J. L.

2002-12-01

For over twenty years the groundwater community has pursued the somewhat elusive goal of describing the effects of aquifer heterogeneity on subsurface flow and chemical transport. While small perturbation stochastic moment methods have significantly advanced theoretical understanding, why is it that stochastic applications use instead simulations of flow and transport through multiple realizations of synthetic geology? Allan Gutjahr was a principle proponent of the Fast Fourier Transform method for the synthetic generation of aquifer properties and recently explored new, more geologically sound, synthetic methods based on multi-scale Markov random fields. Focusing on sedimentary aquifers, how has the state-of-the-art of synthetic generation changed and what new developments can be expected, for example, to deal with issues like conceptual model uncertainty, the differences between measurement and modeling scales, and subgrid scale variability? What will it take to get stochastic methods, whether based on moments, multiple realizations, or some other approach, into widespread application?

A multilevel model for cardiovascular disease prevalence in the US and its application to micro area prevalence estimates.

PubMed

Congdon, Peter

2009-01-30

Estimates of disease prevalence for small areas are increasingly required for the allocation of health funds according to local need. Both individual level and geographic risk factors are likely to be relevant to explaining prevalence variations, and in turn relevant to the procedure for small area prevalence estimation. Prevalence estimates are of particular importance for major chronic illnesses such as cardiovascular disease. A multilevel prevalence model for cardiovascular outcomes is proposed that incorporates both survey information on patient risk factors and the effects of geographic location. The model is applied to derive micro area prevalence estimates, specifically estimates of cardiovascular disease for Zip Code Tabulation Areas in the USA. The model incorporates prevalence differentials by age, sex, ethnicity and educational attainment from the 2005 Behavioral Risk Factor Surveillance System survey. Influences of geographic context are modelled at both county and state level, with the county effects relating to poverty and urbanity. State level influences are modelled using a random effects approach that allows both for spatial correlation and spatial isolates. To assess the importance of geographic variables, three types of model are compared: a model with person level variables only; a model with geographic effects that do not interact with person attributes; and a full model, allowing for state level random effects that differ by ethnicity. There is clear evidence that geographic effects improve statistical fit. Geographic variations in disease prevalence partly reflect the demographic composition of area populations. However, prevalence variations may also show distinct geographic 'contextual' effects. The present study demonstrates by formal modelling methods that improved explanation is obtained by allowing for distinct geographic effects (for counties and states) and for interaction between geographic and person variables. Thus an appropriate methodology to estimate prevalence at small area level should include geographic effects as well as person level demographic variables.

A multilevel model for cardiovascular disease prevalence in the US and its application to micro area prevalence estimates

PubMed Central

Congdon, Peter

2009-01-01

Background Estimates of disease prevalence for small areas are increasingly required for the allocation of health funds according to local need. Both individual level and geographic risk factors are likely to be relevant to explaining prevalence variations, and in turn relevant to the procedure for small area prevalence estimation. Prevalence estimates are of particular importance for major chronic illnesses such as cardiovascular disease. Methods A multilevel prevalence model for cardiovascular outcomes is proposed that incorporates both survey information on patient risk factors and the effects of geographic location. The model is applied to derive micro area prevalence estimates, specifically estimates of cardiovascular disease for Zip Code Tabulation Areas in the USA. The model incorporates prevalence differentials by age, sex, ethnicity and educational attainment from the 2005 Behavioral Risk Factor Surveillance System survey. Influences of geographic context are modelled at both county and state level, with the county effects relating to poverty and urbanity. State level influences are modelled using a random effects approach that allows both for spatial correlation and spatial isolates. Results To assess the importance of geographic variables, three types of model are compared: a model with person level variables only; a model with geographic effects that do not interact with person attributes; and a full model, allowing for state level random effects that differ by ethnicity. There is clear evidence that geographic effects improve statistical fit. Conclusion Geographic variations in disease prevalence partly reflect the demographic composition of area populations. However, prevalence variations may also show distinct geographic 'contextual' effects. The present study demonstrates by formal modelling methods that improved explanation is obtained by allowing for distinct geographic effects (for counties and states) and for interaction between geographic and person variables. Thus an appropriate methodology to estimate prevalence at small area level should include geographic effects as well as person level demographic variables. PMID:19183458

Use of allele scores as instrumental variables for Mendelian randomization

PubMed Central

Burgess, Stephen; Thompson, Simon G

2013-01-01

Background An allele score is a single variable summarizing multiple genetic variants associated with a risk factor. It is calculated as the total number of risk factor-increasing alleles for an individual (unweighted score), or the sum of weights for each allele corresponding to estimated genetic effect sizes (weighted score). An allele score can be used in a Mendelian randomization analysis to estimate the causal effect of the risk factor on an outcome. Methods Data were simulated to investigate the use of allele scores in Mendelian randomization where conventional instrumental variable techniques using multiple genetic variants demonstrate ‘weak instrument’ bias. The robustness of estimates using the allele score to misspecification (for example non-linearity, effect modification) and to violations of the instrumental variable assumptions was assessed. Results Causal estimates using a correctly specified allele score were unbiased with appropriate coverage levels. The estimates were generally robust to misspecification of the allele score, but not to instrumental variable violations, even if the majority of variants in the allele score were valid instruments. Using a weighted rather than an unweighted allele score increased power, but the increase was small when genetic variants had similar effect sizes. Naive use of the data under analysis to choose which variants to include in an allele score, or for deriving weights, resulted in substantial biases. Conclusions Allele scores enable valid causal estimates with large numbers of genetic variants. The stringency of criteria for genetic variants in Mendelian randomization should be maintained for all variants in an allele score. PMID:24062299

Random Assignment of Schools to Groups in the Drug Resistance Strategies Rural Project: Some New Methodological Twists

PubMed Central

Pettigrew, Jonathan; Miller-Day, Michelle; Krieger, Janice L.; Zhou, Jiangxiu; Hecht, Michael L.

2014-01-01

Random assignment to groups is the foundation for scientifically rigorous clinical trials. But assignment is challenging in group randomized trials when only a few units (schools) are assigned to each condition. In the DRSR project, we assigned 39 rural Pennsylvania and Ohio schools to three conditions (rural, classic, control). But even with 13 schools per condition, achieving pretest equivalence on important variables is not guaranteed. We collected data on six important school-level variables: rurality, number of grades in the school, enrollment per grade, percent white, percent receiving free/assisted lunch, and test scores. Key to our procedure was the inclusion of school-level drug use data, available for a subset of the schools. Also, key was that we handled the partial data with modern missing data techniques. We chose to create one composite stratifying variable based on the seven school-level variables available. Principal components analysis with the seven variables yielded two factors, which were averaged to form the composite inflate-suppress (CIS) score which was the basis of stratification. The CIS score was broken into three strata within each state; schools were assigned at random to the three program conditions from within each stratum, within each state. Results showed that program group membership was unrelated to the CIS score, the two factors making up the CIS score, and the seven items making up the factors. Program group membership was not significantly related to pretest measures of drug use (alcohol, cigarettes, marijuana, chewing tobacco; smallest p>.15), thus verifying that pretest equivalence was achieved. PMID:23722619

Eliminating Survivor Bias in Two-stage Instrumental Variable Estimators.

PubMed

Vansteelandt, Stijn; Walter, Stefan; Tchetgen Tchetgen, Eric

2018-07-01

Mendelian randomization studies commonly focus on elderly populations. This makes the instrumental variables analysis of such studies sensitive to survivor bias, a type of selection bias. A particular concern is that the instrumental variable conditions, even when valid for the source population, may be violated for the selective population of individuals who survive the onset of the study. This is potentially very damaging because Mendelian randomization studies are known to be sensitive to bias due to even minor violations of the instrumental variable conditions. Interestingly, the instrumental variable conditions continue to hold within certain risk sets of individuals who are still alive at a given age when the instrument and unmeasured confounders exert additive effects on the exposure, and moreover, the exposure and unmeasured confounders exert additive effects on the hazard of death. In this article, we will exploit this property to derive a two-stage instrumental variable estimator for the effect of exposure on mortality, which is insulated against the above described selection bias under these additivity assumptions.

Estimating degradation in real time and accelerated stability tests with random lot-to-lot variation: a simulation study.

PubMed

Magari, Robert T

2002-03-01

The effect of different lot-to-lot variability levels on the prediction of stability are studied based on two statistical models for estimating degradation in real time and accelerated stability tests. Lot-to-lot variability is considered as random in both models, and is attributed to two sources-variability at time zero, and variability of degradation rate. Real-time stability tests are modeled as a function of time while accelerated stability tests as a function of time and temperatures. Several data sets were simulated, and a maximum likelihood approach was used for estimation. The 95% confidence intervals for the degradation rate depend on the amount of lot-to-lot variability. When lot-to-lot degradation rate variability is relatively large (CV > or = 8%) the estimated confidence intervals do not represent the trend for individual lots. In such cases it is recommended to analyze each lot individually. Copyright 2002 Wiley-Liss, Inc. and the American Pharmaceutical Association J Pharm Sci 91: 893-899, 2002

Unsupervised classification of multivariate geostatistical data: Two algorithms

NASA Astrophysics Data System (ADS)

Romary, Thomas; Ors, Fabien; Rivoirard, Jacques; Deraisme, Jacques

2015-12-01

With the increasing development of remote sensing platforms and the evolution of sampling facilities in mining and oil industry, spatial datasets are becoming increasingly large, inform a growing number of variables and cover wider and wider areas. Therefore, it is often necessary to split the domain of study to account for radically different behaviors of the natural phenomenon over the domain and to simplify the subsequent modeling step. The definition of these areas can be seen as a problem of unsupervised classification, or clustering, where we try to divide the domain into homogeneous domains with respect to the values taken by the variables in hand. The application of classical clustering methods, designed for independent observations, does not ensure the spatial coherence of the resulting classes. Image segmentation methods, based on e.g. Markov random fields, are not adapted to irregularly sampled data. Other existing approaches, based on mixtures of Gaussian random functions estimated via the expectation-maximization algorithm, are limited to reasonable sample sizes and a small number of variables. In this work, we propose two algorithms based on adaptations of classical algorithms to multivariate geostatistical data. Both algorithms are model free and can handle large volumes of multivariate, irregularly spaced data. The first one proceeds by agglomerative hierarchical clustering. The spatial coherence is ensured by a proximity condition imposed for two clusters to merge. This proximity condition relies on a graph organizing the data in the coordinates space. The hierarchical algorithm can then be seen as a graph-partitioning algorithm. Following this interpretation, a spatial version of the spectral clustering algorithm is also proposed. The performances of both algorithms are assessed on toy examples and a mining dataset.

[Effect of Sijunzi Decoction and enteral nutrition on T-cell subsets and nutritional status in patients with gastric cancer after operation: a randomized controlled trial].

PubMed

Cai, Jun; Wang, Hua; Zhou, Sheng; Wu, Bin; Song, Hua-Rong; Xuan, Zheng-Rong

2008-01-01

To observe the effect of perioperative application of Sijunzi Decoction and enteral nutrition on T-cell subsets and nutritional status in patients with gastric cancer after operation. In this prospective, single-blinded, controlled clinical trial, fifty-nine patients with gastric cancer were randomly divided into three groups: control group (n=20) and two study groups (group A, n=21; group B, n=18). Sjunzi Decoction (100 ml) was administered via nasogastric tube to the patients in the study group B from the second postoperation day to the 9th postoperation day. Patients in the two study groups were given an isocaloric and isonitrogonous enteral diet, which was started on the second day after operation, and continued for eight days. Patients in the control group were given an isocaloric and isonitrogonous parenteral diet for 9 days. All variables of nutritional status such as serum albumin (ALB), prealbumin (PA), transferrin (TRF) and T-cell subsets were measured one day before operation, and one day and 10 days after operation. All the nutritional variables and the levels of CD3(+), CD4(+), CD4(+)/CD8(+) were decreased significantly after operation. Ten days after operation, T-cell subsets and nutritional variables in the two study groups were increased as compare with the control group. The levels of ALB, TRF and T-cell subsets in the study group B were increased significantly as compared with the study group A (P<0.05). Enteral nutrition assisted with Sijunzi Decoction can positively improve and optimize cellular immune function and nutritional status in the patients with gastric cancer after operation.

Predictors for Physical Activity in Adolescent Girls Using Statistical Shrinkage Techniques for Hierarchical Longitudinal Mixed Effects Models

PubMed Central

Grant, Edward M.; Young, Deborah Rohm; Wu, Tong Tong

2015-01-01

We examined associations among longitudinal, multilevel variables and girls’ physical activity to determine the important predictors for physical activity change at different adolescent ages. The Trial of Activity for Adolescent Girls 2 study (Maryland) contributed participants from 8th (2009) to 11th grade (2011) (n=561). Questionnaires were used to obtain demographic, and psychosocial information (individual- and social-level variables); height, weight, and triceps skinfold to assess body composition; interviews and surveys for school-level data; and self-report for neighborhood-level variables. Moderate to vigorous physical activity minutes were assessed from accelerometers. A doubly regularized linear mixed effects model was used for the longitudinal multilevel data to identify the most important covariates for physical activity. Three fixed effects at the individual level and one random effect at the school level were chosen from an initial total of 66 variables, consisting of 47 fixed effects and 19 random effects variables, in additional to the time effect. Self-management strategies, perceived barriers, and social support from friends were the three selected fixed effects, and whether intramural or interscholastic programs were offered in middle school was the selected random effect. Psychosocial factors and friend support, plus a school’s physical activity environment, affect adolescent girl’s moderate to vigorous physical activity longitudinally. PMID:25928064

Night-to-Night Sleep Variability in Older Adults With Chronic Insomnia: Mediators and Moderators in a Randomized Controlled Trial of Brief Behavioral Therapy (BBT-I)

PubMed Central

Chan, Wai Sze; Williams, Jacob; Dautovich, Natalie D.; McNamara, Joseph P.H.; Stripling, Ashley; Dzierzewski, Joseph M.; Berry, Richard B.; McCoy, Karin J.M.; McCrae, Christina S.

2017-01-01

Study Objectives: Sleep variability is a clinically significant variable in understanding and treating insomnia in older adults. The current study examined changes in sleep variability in the course of brief behavioral therapy for insomnia (BBT-I) in older adults who had chronic insomnia. Additionally, the current study examined the mediating mechanisms underlying reductions of sleep variability and the moderating effects of baseline sleep variability on treatment responsiveness. Methods: Sixty-two elderly participants were randomly assigned to either BBT-I or self-monitoring and attention control (SMAC). Sleep was assessed by sleep diaries and actigraphy from baseline to posttreatment and at 3-month follow-up. Mixed models were used to examine changes in sleep variability (within-person standard deviations of weekly sleep parameters) and the hypothesized mediation and moderation effects. Results: Variabilities in sleep diary-assessed sleep onset latency (SOL) and actigraphy-assessed total sleep time (TST) significantly decreased in BBT-I compared to SMAC (Pseudo R2 = .12, .27; P = .018, .008). These effects were mediated by reductions in bedtime and wake time variability and time in bed. Significant time × group × baseline sleep variability interactions on sleep outcomes indicated that participants who had higher baseline sleep variability were more responsive to BBT-I; their actigraphy-assessed TST, SOL, and sleep efficiency improved to a greater degree (Pseudo R2 = .15 to .66; P < .001 to .044). Conclusions: BBT-I is effective in reducing sleep variability in older adults who have chronic insomnia. Increased consistency in bedtime and wake time and decreased time in bed mediate reductions of sleep variability. Baseline sleep variability may serve as a marker of high treatment responsiveness to BBT-I. Clinical Trial Registration: ClinicalTrials.gov, Identifier: NCT02967185 Citation: Chan WS, Williams J, Dautovich ND, McNamara JP, Stripling A, Dzierzewski JM, Berry RB, McCoy KJ, McCrae CS. Night-to-night sleep variability in older adults with chronic insomnia: mediators and moderators in a randomized controlled trial of brief behavioral therapy (BBT-I). J Clin Sleep Med. 2017;13(11):1243–1254. PMID:28992829

Pigeons' Choices between Fixed-Interval and Random-Interval Schedules: Utility of Variability?

ERIC Educational Resources Information Center

Andrzejewski, Matthew E.; Cardinal, Claudia D.; Field, Douglas P.; Flannery, Barbara A.; Johnson, Michael; Bailey, Kathleen; Hineline, Philip N.

2005-01-01

Pigeons' choosing between fixed-interval and random-interval schedules of reinforcement was investigated in three experiments using a discrete-trial procedure. In all three experiments, the random-interval schedule was generated by sampling a probability distribution at an interval (and in multiples of the interval) equal to that of the…

An Alternative Method for Computing Mean and Covariance Matrix of Some Multivariate Distributions

ERIC Educational Resources Information Center

Radhakrishnan, R.; Choudhury, Askar

2009-01-01

Computing the mean and covariance matrix of some multivariate distributions, in particular, multivariate normal distribution and Wishart distribution are considered in this article. It involves a matrix transformation of the normal random vector into a random vector whose components are independent normal random variables, and then integrating…

Causal Inference and Omitted Variable Bias in Financial Aid Research: Assessing Solutions

ERIC Educational Resources Information Center

Riegg, Stephanie K.

2008-01-01

This article highlights the problem of omitted variable bias in research on the causal effect of financial aid on college-going. I first describe the problem of self-selection and the resulting bias from omitted variables. I then assess and explore the strengths and weaknesses of random assignment, multivariate regression, proxy variables, fixed…

A Dynamic Bayesian Network Model for the Production and Inventory Control

NASA Astrophysics Data System (ADS)

Shin, Ji-Sun; Takazaki, Noriyuki; Lee, Tae-Hong; Kim, Jin-Il; Lee, Hee-Hyol

In general, the production quantities and delivered goods are changed randomly and then the total stock is also changed randomly. This paper deals with the production and inventory control using the Dynamic Bayesian Network. Bayesian Network is a probabilistic model which represents the qualitative dependence between two or more random variables by the graph structure, and indicates the quantitative relations between individual variables by the conditional probability. The probabilistic distribution of the total stock is calculated through the propagation of the probability on the network. Moreover, an adjusting rule of the production quantities to maintain the probability of a lower limit and a ceiling of the total stock to certain values is shown.

Low-contrast lesion detection in tomosynthetic breast imaging using a realistic breast phantom

NASA Astrophysics Data System (ADS)

Zhou, Lili; Oldan, Jorge; Fisher, Paul; Gindi, Gene

2006-03-01

Tomosynthesis mammography is a potentially valuable technique for detection of breast cancer. In this simulation study, we investigate the efficacy of three different tomographic reconstruction methods, EM, SART and Backprojection, in the context of an especially difficult mammographic detection task. The task is the detection of a very low-contrast mass embedded in very dense fibro-glandular tissue - a clinically useful task for which tomosynthesis may be well suited. The project uses an anatomically realistic 3D digital breast phantom whose normal anatomic variability limits lesion conspicuity. In order to capture anatomical object variability, we generate an ensemble of phantoms, each of which comprises random instances of various breast structures. We construct medium-sized 3D breast phantoms which model random instances of ductal structures, fibrous connective tissue, Cooper's ligaments and power law structural noise for small scale object variability. Random instances of 7-8 mm irregular masses are generated by a 3D random walk algorithm and placed in very dense fibro-glandular tissue. Several other components of the breast phantom are held fixed, i.e. not randomly generated. These include the fixed breast shape and size, nipple structure, fixed lesion location, and a pectoralis muscle. We collect low-dose data using an isocentric tomosynthetic geometry at 11 angles over 50 degrees and add Poisson noise. The data is reconstructed using the three algorithms. Reconstructed slices through the center of the lesion are presented to human observers in a 2AFC (two-alternative-forced-choice) test that measures detectability by computing AUC (area under the ROC curve). The data collected in each simulation includes two sources of variability, that due to the anatomical variability of the phantom and that due to the Poisson data noise. We found that for this difficult task that the AUC value for EM (0.89) was greater than that for SART (0.83) and Backprojection (0.66).

A comparison of confidence interval methods for the intraclass correlation coefficient in community-based cluster randomization trials with a binary outcome.

PubMed

Braschel, Melissa C; Svec, Ivana; Darlington, Gerarda A; Donner, Allan

2016-04-01

Many investigators rely on previously published point estimates of the intraclass correlation coefficient rather than on their associated confidence intervals to determine the required size of a newly planned cluster randomized trial. Although confidence interval methods for the intraclass correlation coefficient that can be applied to community-based trials have been developed for a continuous outcome variable, fewer methods exist for a binary outcome variable. The aim of this study is to evaluate confidence interval methods for the intraclass correlation coefficient applied to binary outcomes in community intervention trials enrolling a small number of large clusters. Existing methods for confidence interval construction are examined and compared to a new ad hoc approach based on dividing clusters into a large number of smaller sub-clusters and subsequently applying existing methods to the resulting data. Monte Carlo simulation is used to assess the width and coverage of confidence intervals for the intraclass correlation coefficient based on Smith's large sample approximation of the standard error of the one-way analysis of variance estimator, an inverted modified Wald test for the Fleiss-Cuzick estimator, and intervals constructed using a bootstrap-t applied to a variance-stabilizing transformation of the intraclass correlation coefficient estimate. In addition, a new approach is applied in which clusters are randomly divided into a large number of smaller sub-clusters with the same methods applied to these data (with the exception of the bootstrap-t interval, which assumes large cluster sizes). These methods are also applied to a cluster randomized trial on adolescent tobacco use for illustration. When applied to a binary outcome variable in a small number of large clusters, existing confidence interval methods for the intraclass correlation coefficient provide poor coverage. However, confidence intervals constructed using the new approach combined with Smith's method provide nominal or close to nominal coverage when the intraclass correlation coefficient is small (<0.05), as is the case in most community intervention trials. This study concludes that when a binary outcome variable is measured in a small number of large clusters, confidence intervals for the intraclass correlation coefficient may be constructed by dividing existing clusters into sub-clusters (e.g. groups of 5) and using Smith's method. The resulting confidence intervals provide nominal or close to nominal coverage across a wide range of parameters when the intraclass correlation coefficient is small (<0.05). Application of this method should provide investigators with a better understanding of the uncertainty associated with a point estimator of the intraclass correlation coefficient used for determining the sample size needed for a newly designed community-based trial. © The Author(s) 2015.

Estimating overall exposure effects for the clustered and censored outcome using random effect Tobit regression models.

PubMed

Wang, Wei; Griswold, Michael E

2016-11-30

The random effect Tobit model is a regression model that accommodates both left- and/or right-censoring and within-cluster dependence of the outcome variable. Regression coefficients of random effect Tobit models have conditional interpretations on a constructed latent dependent variable and do not provide inference of overall exposure effects on the original outcome scale. Marginalized random effects model (MREM) permits likelihood-based estimation of marginal mean parameters for the clustered data. For random effect Tobit models, we extend the MREM to marginalize over both the random effects and the normal space and boundary components of the censored response to estimate overall exposure effects at population level. We also extend the 'Average Predicted Value' method to estimate the model-predicted marginal means for each person under different exposure status in a designated reference group by integrating over the random effects and then use the calculated difference to assess the overall exposure effect. The maximum likelihood estimation is proposed utilizing a quasi-Newton optimization algorithm with Gauss-Hermite quadrature to approximate the integration of the random effects. We use these methods to carefully analyze two real datasets. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Development of a Probabilistic Dynamic Synthesis Method for the Analysis of Nondeterministic Structures

NASA Technical Reports Server (NTRS)

Brown, A. M.

1998-01-01

Accounting for the statistical geometric and material variability of structures in analysis has been a topic of considerable research for the last 30 years. The determination of quantifiable measures of statistical probability of a desired response variable, such as natural frequency, maximum displacement, or stress, to replace experience-based "safety factors" has been a primary goal of these studies. There are, however, several problems associated with their satisfactory application to realistic structures, such as bladed disks in turbomachinery. These include the accurate definition of the input random variables (rv's), the large size of the finite element models frequently used to simulate these structures, which makes even a single deterministic analysis expensive, and accurate generation of the cumulative distribution function (CDF) necessary to obtain the probability of the desired response variables. The research presented here applies a methodology called probabilistic dynamic synthesis (PDS) to solve these problems. The PDS method uses dynamic characteristics of substructures measured from modal test as the input rv's, rather than "primitive" rv's such as material or geometric uncertainties. These dynamic characteristics, which are the free-free eigenvalues, eigenvectors, and residual flexibility (RF), are readily measured and for many substructures, a reasonable sample set of these measurements can be obtained. The statistics for these rv's accurately account for the entire random character of the substructure. Using the RF method of component mode synthesis, these dynamic characteristics are used to generate reduced-size sample models of the substructures, which are then coupled to form system models. These sample models are used to obtain the CDF of the response variable by either applying Monte Carlo simulation or by generating data points for use in the response surface reliability method, which can perform the probabilistic analysis with an order of magnitude less computational effort. Both free- and forced-response analyses have been performed, and the results indicate that, while there is considerable room for improvement, the method produces usable and more representative solutions for the design of realistic structures with a substantial savings in computer time.

Effects of ion channel noise on neural circuits: an application to the respiratory pattern generator to investigate breathing variability.

PubMed

Yu, Haitao; Dhingra, Rishi R; Dick, Thomas E; Galán, Roberto F

2017-01-01

Neural activity generally displays irregular firing patterns even in circuits with apparently regular outputs, such as motor pattern generators, in which the output frequency fluctuates randomly around a mean value. This "circuit noise" is inherited from the random firing of single neurons, which emerges from stochastic ion channel gating (channel noise), spontaneous neurotransmitter release, and its diffusion and binding to synaptic receptors. Here we demonstrate how to expand conductance-based network models that are originally deterministic to include realistic, physiological noise, focusing on stochastic ion channel gating. We illustrate this procedure with a well-established conductance-based model of the respiratory pattern generator, which allows us to investigate how channel noise affects neural dynamics at the circuit level and, in particular, to understand the relationship between the respiratory pattern and its breath-to-breath variability. We show that as the channel number increases, the duration of inspiration and expiration varies, and so does the coefficient of variation of the breath-to-breath interval, which attains a minimum when the mean duration of expiration slightly exceeds that of inspiration. For small channel numbers, the variability of the expiratory phase dominates over that of the inspiratory phase, and vice versa for large channel numbers. Among the four different cell types in the respiratory pattern generator, pacemaker cells exhibit the highest sensitivity to channel noise. The model shows that suppressing input from the pons leads to longer inspiratory phases, a reduction in breathing frequency, and larger breath-to-breath variability, whereas enhanced input from the raphe nucleus increases breathing frequency without changing its pattern. A major source of noise in neuronal circuits is the "flickering" of ion currents passing through the neurons' membranes (channel noise), which cannot be suppressed experimentally. Computational simulations are therefore the best way to investigate the effects of this physiological noise by manipulating its level at will. We investigate the role of noise in the respiratory pattern generator and show that endogenous, breath-to-breath variability is tightly linked to the respiratory pattern. Copyright © 2017 the American Physiological Society.

Models of multidimensional discrete distribution of probabilities of random variables in information systems

NASA Astrophysics Data System (ADS)

Gromov, Yu Yu; Minin, Yu V.; Ivanova, O. G.; Morozova, O. N.

2018-03-01

Multidimensional discrete distributions of probabilities of independent random values were received. Their one-dimensional distribution is widely used in probability theory. Producing functions of those multidimensional distributions were also received.

Effect of sexual intercourse on the absorption of levonorgestrel after vaginal administration of 0.75 mg in Carraguard® gel: a randomized, cross-over, pharmacokinetic study☆

PubMed Central

Brache, Vivian; Croxatto, Horacio; Kumar, Narender; Sitruk-Ware, Regine; Cochón, Leila; Schiappacasse, Veronica; Sivin, Irving; Muñoz, Carla; Maguire, Robin; Faundes, Anibal

2010-01-01

Background The Population Council studied a pre-coital contraceptive microbicide vaginal product containing levonorgestrel (LNG) as active component and Carraguard® gel as a vehicle (Carra/LNG gel) for couples who engage in occasional unplanned intercourse. The objective of this study was to evaluate the effect of sexual intercourse after vaginal application of Carra/LNG gel on serum levels of LNG in women and to assess LNG absorption by the male partner. Study Design This was a randomized, cross-over, pharmacokinetic study including an abstinence arm and an arm in which couples engaged in sexual intercourse between 2 and 4 h after gel application. In each study arm, each woman received a single application of Carra/LNG gel (0.75 mg in 4 mL gel) followed by serial blood samples taken at 0, 1, 2, 4, 8, 24 and 48 h after gel application for LNG measurements. In the intercourse arm, LNG was measured in blood samples taken from the male partner before intercourse and at 4, 8 and 24 h after gel application in the female partner. Results Time concentration curves for serum LNG levels showed a mean Cmax of 7.8±5.5 and 8.3±5.7 nmol/L, a mean Tmax of 6.2±5.9 and 7.5±5.7, and comparable area under the curve for the intercourse and abstinence arm, respectively. Pharmacokinetic parameters presented large variability between subjects, but excellent reproducibility within each subject. LNG was undetectable in 10 out of 12 male partners. Conclusion Sexual intercourse does not appear to interfere with vaginal absorption of LNG after application of a Carra/LNG gel. A vaginal pre-coital contraceptive gel is feasible. PMID:19135574

Effectiveness of olive oil for the prevention of pressure ulcers caused in immobilized patients within the scope of primary health care: study protocol for a randomized controlled trial

PubMed Central

2013-01-01

Background Pressure ulcers are considered an important issue, mainly affecting immobilized older patients. These pressure ulcers increase the care burden for the professional health service staff as well as pharmaceutical expenditure. There are a number of studies on the effectiveness of different products used for the prevention of pressure ulcers; however, most of these studies were carried out at a hospital level, basically using hyperoxygenated fatty acids (HOFA). There are no studies focused specifically on the use of olive-oil-based products and therefore this research is intended to find the most cost-effective treatment and achieve an alternative treatment. Methods/design The main objective is to assess the effectiveness of olive oil, comparing it with HOFA, to treat immobilized patients at home who are at risk of pressure ulcers. As a secondary objective, the cost-effectiveness balance of this new application with regard to the HOFA will be assessed. The study is designed as a noninferiority, triple-blinded, parallel, multi-center, randomized clinical trial. The scope of the study is the population attending primary health centers in Andalucía (Spain) in the regional areas of Malaga, Granada, Seville, and Cadiz. Immobilized patients at risk of pressure ulcers will be targeted. The target group will be treated by application of an olive-oil-based formula whereas the control group will be treated by application of HOFA to the control group. The follow-up period will be 16 weeks. The main variable will be the presence of pressure ulcers in the patient. Secondary variables include sociodemographic and clinical information, caregiver information, and whether technical support exists. Statistical analysis will include the Kolmogorov-Smirnov test, symmetry and kurtosis analysis, bivariate analysis using the Student’s t and chi-squared tests as well as the Wilcoxon and the Man-Whitney U tests, ANOVA and multivariate logistic regression analysis. Discussion The regular use of olive-oil-based formulas should be effective in preventing pressure ulcers in immobilized patients, thus leading to a more cost-effective product and an alternative treatment. Trial registration Clinicaltrials.gov identifier: NCT01595347. PMID:24152576

Effectiveness of olive oil for the prevention of pressure ulcers caused in immobilized patients within the scope of primary health care: study protocol for a randomized controlled trial.

PubMed

Lupiáñez-Pérez, Inmaculada; Morilla-Herrera, Juan Carlos; Ginel-Mendoza, Leovigildo; Martín-Santos, Francisco Javier; Navarro-Moya, Francisco Javier; Sepúlveda-Guerra, Rafaela Pilar; Vázquez-Cerdeiros, Rosa; Cuevas-Fernández-Gallego, Magdalena; Benítez-Serrano, Isabel María; Lupiáñez-Pérez, Yolanda; Morales-Asencio, José Miguel

2013-10-23

Pressure ulcers are considered an important issue, mainly affecting immobilized older patients. These pressure ulcers increase the care burden for the professional health service staff as well as pharmaceutical expenditure. There are a number of studies on the effectiveness of different products used for the prevention of pressure ulcers; however, most of these studies were carried out at a hospital level, basically using hyperoxygenated fatty acids (HOFA). There are no studies focused specifically on the use of olive-oil-based products and therefore this research is intended to find the most cost-effective treatment and achieve an alternative treatment. The main objective is to assess the effectiveness of olive oil, comparing it with HOFA, to treat immobilized patients at home who are at risk of pressure ulcers. As a secondary objective, the cost-effectiveness balance of this new application with regard to the HOFA will be assessed. The study is designed as a noninferiority, triple-blinded, parallel, multi-center, randomized clinical trial. The scope of the study is the population attending primary health centers in Andalucía (Spain) in the regional areas of Malaga, Granada, Seville, and Cadiz. Immobilized patients at risk of pressure ulcers will be targeted. The target group will be treated by application of an olive-oil-based formula whereas the control group will be treated by application of HOFA to the control group. The follow-up period will be 16 weeks. The main variable will be the presence of pressure ulcers in the patient. Secondary variables include sociodemographic and clinical information, caregiver information, and whether technical support exists. Statistical analysis will include the Kolmogorov-Smirnov test, symmetry and kurtosis analysis, bivariate analysis using the Student's t and chi-squared tests as well as the Wilcoxon and the Man-Whitney U tests, ANOVA and multivariate logistic regression analysis. The regular use of olive-oil-based formulas should be effective in preventing pressure ulcers in immobilized patients, thus leading to a more cost-effective product and an alternative treatment. Clinicaltrials.gov identifier: NCT01595347.

CMOS-compatible spintronic devices: a review

NASA Astrophysics Data System (ADS)

Makarov, Alexander; Windbacher, Thomas; Sverdlov, Viktor; Selberherr, Siegfried

2016-11-01

For many decades CMOS devices have been successfully scaled down to achieve higher speed and increased performance of integrated circuits at lower cost. Today’s charge-based CMOS electronics encounters two major challenges: power dissipation and variability. Spintronics is a rapidly evolving research and development field, which offers a potential solution to these issues by introducing novel ‘more than Moore’ devices. Spin-based magnetoresistive random-access memory (MRAM) is already recognized as one of the most promising candidates for future universal memory. Magnetic tunnel junctions, the main elements of MRAM cells, can also be used to build logic-in-memory circuits with non-volatile storage elements on top of CMOS logic circuits, as well as versatile compact on-chip oscillators with low power consumption. We give an overview of CMOS-compatible spintronics applications. First, we present a brief introduction to the physical background considering such effects as magnetoresistance, spin-transfer torque (STT), spin Hall effect, and magnetoelectric effects. We continue with a comprehensive review of the state-of-the-art spintronic devices for memory applications (STT-MRAM, domain wall-motion MRAM, and spin-orbit torque MRAM), oscillators (spin torque oscillators and spin Hall nano-oscillators), logic (logic-in-memory, all-spin logic, and buffered magnetic logic gate grid), sensors, and random number generators. Devices with different types of resistivity switching are analyzed and compared, with their advantages highlighted and challenges revealed. CMOS-compatible spintronic devices are demonstrated beginning with predictive simulations, proceeding to their experimental confirmation and realization, and finalized by the current status of application in modern integrated systems and circuits. We conclude the review with an outlook, where we share our vision on the future applications of the prospective devices in the area.

Evaluating the Bias of Alternative Cost Progress Models: Tests Using Aerospace Industry Acquisition Programs

DTIC Science & Technology

1992-12-01

suspect :mat, -n2 extent predict:.on cas jas ccsiziveiv crrei:=e amonc e v:arious models, :he fandom *.;aik, learn ha r ur e, i;<ea- variable and Bemis...Functions, Production Rate Adjustment Model, Learning Curve Model. Random Walk Model. Bemis Model. Evaluating Model Bias, Cost Prediction Bias. Cost...of four cost progress models--a random walk model, the tradiuonai learning curve model, a production rate model Ifixed-variable model). and a model

Multi-scale assessment of human-induced changes to ...

EPA Pesticide Factsheets

Context: Land use change and forest degradation have myriad effects on tropical ecosystems. Yet their consequences for low-order streams remain very poorly understood, including in the world´s largest freshwater basin, the Amazon.Objectives: Determine the degree to which physical and chemical characteristics of the instream habitat of low-order Amazonian streams change in response to past local- and catchment-level anthropogenic disturbances. Methods: To do so, we collected field instream habitat (i.e., physical habitat and water quality) and landscape data from 99 stream sites in two eastern Brazilian Amazon regions. We used random forest regression trees to assess the relative importance of different predictor variables in determining changes in instream habitat response variables. Adaptations the USEPA’s National Aquatic Resource Survey (NARS) designs, field methods, and approaches for assessing ecological condition have been applied in state and basin stream surveys throughout the U.S., and also in countries outside of the U.S. These applications not only provide valuable tests of the NARS approaches, but generate new understandings of natural and anthropogenic controls on biota and physical habitat in streams. Results from applications in Brazil, for example, not only aid interpretation of the condition of Brazilian streams, but also refine approaches for interpreting aquatic resource surveys in the U.S. and elsewhere. In this article, the authors des

Modified Whole Effluent Toxicity Test to Assess and Decouple Wastewater Effects from Environmental Gradients

PubMed Central

Sauco, Sebastián; Gómez, Julio; Barboza, Francisco R.; Lercari, Diego; Defeo, Omar

2013-01-01

Environmental gradients and wastewater discharges produce aggregated effects on marine populations, obscuring the detection of human impact. Classical assessment methods do not include environmental effects in toxicity tests designs, which could lead to incorrect conclusions. We proposed a modified Whole Effluent Toxicity test (mWET) that includes environmental gradients in addition to effluent dilutions, together with the application of Generalized Linear Mixed Models (GLMM) to assess and decouple those effects. We tested this approach, analyzing the lethal effects of wastewater on a marine sandy beach bivalve affected by an artificial canal freshwater discharge used for rice crops irrigation. To this end, we compared bivalve mortality between canal water dilutions (CWd) and salinity controls (SC: without canal water). CWd were prepared by diluting the water effluent (sampled during the pesticide application period) with artificial marine water. The salinity gradient was included in the design by achieving the same final salinities in both CWd and SC, allowing us to account for the effects of salinity by including this variable as a random factor in the GLMM. Our approach detected significantly higher mortalities in CWd, indicating potential toxic effects of the effluent discharge. mWET represents an improvement over the internationally standardized WET tests, since it considers environmental variability and uses appropriate statistical analyses. PMID:23755304

Therapeutic Magnets Do Not Affect Tissue Temperatures

PubMed Central

Sweeney, Kathleen B.; Ingersoll, Christopher D.; Swez, John A.

2001-01-01

Objective: Manufacturers of commercially available “therapeutic” magnets claim that these magnets cause physiologic thermal effects that promote tissue healing. We conducted this study to determine if skin or intramuscular temperatures differed among magnet, sham, and control treatments during 60 minutes of application to the quadriceps muscle. Design and Setting: A 3 × 3 mixed-model, factorial design with repeated measures on both independent variables was used. The first independent variable, application duration, had 3 random levels (20, 40, and 60 minutes). The second independent variable, treatment, had 3 fixed levels (magnet, sham, and control). The dependent variable was tissue temperature (°C). Measurement depth served as a control variable, with 2 levels: skin and 1 cm below the fat layer. Data were collected in a thermoneutral laboratory setting and analyzed using a repeated-measures analysis of variance. Subjects: The study included 13 healthy student volunteers (8 men, 5 women; age, 20.5 ± 0.9 years; height, 176.8 ± 10.4 cm; weight, 73.8 ± 11.8 kg; anterior thigh skinfold thickness, 16.9 ± 6.5 mm). Measurements: Temperatures were measured at 30-second intervals using surface and implantable thermocouples. Temperature data at 20, 40, and 60 minutes were used for analysis. Each subject received all 3 treatments on different days. Results: Neither skin nor intramuscular temperatures were different across the 3 treatments at any time. For both skin and intramuscular temperatures, a statistically significant but not clinically meaningful temperature increase (less than 1°C), was observed over time within treatments, but this increase was similar in all treatment groups. Conclusions: No meaningful thermal effect was observed with any treatment over time, and treatments did not differ from each other. We conclude that flexible therapeutic magnets were not effective for increasing skin or deep temperatures, contradicting one of the fundamental claims made by magnet distributors. PMID:12937511

Lower limb ice application alters ground reaction force during gait initiation.

PubMed

Muniz, Thiago B; Moraes, Renato; Guirro, Rinaldo R J

2015-01-01

Cryotherapy is a widely used technique in physical therapy clinics and sports. However, the effects of cryotherapy on dynamic neuromuscular control are incompletely explained. To evaluate the effects of cryotherapy applied to the calf, ankle and sole of the foot in healthy young adults on ground reaction forces during gait initiation. This study evaluated the gait initiation forces, maximum propulsion, braking forces and impulses of 21 women volunteers through a force platform, which provided maximum and minimum ground reaction force values. To assess the effects of cooling, the task--gait initiation--was performed before ice application, immediately after and 30 minutes after removal of the ice pack. Ice was randomly applied on separate days to the calf, ankle and sole of the foot of the participants. It was demonstrated that ice application for 30 minutes to the sole of the foot and calf resulted in significant changes in the vertical force variables, which returned to their pre-application values 30 minutes after the removal of the ice pack. Ice application to the ankle only reduced propulsion impulse. These results suggest that although caution is necessary when performing activities that require good gait control, the application of ice to the ankle, sole of the foot or calf in 30-minute intervals may be safe even preceding such activities.

Variable complexity online sequential extreme learning machine, with applications to streamflow prediction

NASA Astrophysics Data System (ADS)

Lima, Aranildo R.; Hsieh, William W.; Cannon, Alex J.

2017-12-01

In situations where new data arrive continually, online learning algorithms are computationally much less costly than batch learning ones in maintaining the model up-to-date. The extreme learning machine (ELM), a single hidden layer artificial neural network with random weights in the hidden layer, is solved by linear least squares, and has an online learning version, the online sequential ELM (OSELM). As more data become available during online learning, information on the longer time scale becomes available, so ideally the model complexity should be allowed to change, but the number of hidden nodes (HN) remains fixed in OSELM. A variable complexity VC-OSELM algorithm is proposed to dynamically add or remove HN in the OSELM, allowing the model complexity to vary automatically as online learning proceeds. The performance of VC-OSELM was compared with OSELM in daily streamflow predictions at two hydrological stations in British Columbia, Canada, with VC-OSELM significantly outperforming OSELM in mean absolute error, root mean squared error and Nash-Sutcliffe efficiency at both stations.

Optimizing Multi-Product Multi-Constraint Inventory Control Systems with Stochastic Replenishments

NASA Astrophysics Data System (ADS)

Allah Taleizadeh, Ata; Aryanezhad, Mir-Bahador; Niaki, Seyed Taghi Akhavan

Multi-periodic inventory control problems are mainly studied employing two assumptions. The first is the continuous review, where depending on the inventory level orders can happen at any time and the other is the periodic review, where orders can only happen at the beginning of each period. In this study, we relax these assumptions and assume that the periodic replenishments are stochastic in nature. Furthermore, we assume that the periods between two replenishments are independent and identically random variables. For the problem at hand, the decision variables are of integer-type and there are two kinds of space and service level constraints for each product. We develop a model of the problem in which a combination of back-order and lost-sales are considered for the shortages. Then, we show that the model is of an integer-nonlinear-programming type and in order to solve it, a search algorithm can be utilized. We employ a simulated annealing approach and provide a numerical example to demonstrate the applicability of the proposed methodology.

Reliability analysis of composite laminates with load sharing

NASA Technical Reports Server (NTRS)

Wetherhold, Robert C.; Thomas, David J.

1991-01-01

By viewing a composite lamina as a homogeneous solid whose directional strengths are random variables, lamina reliability under multiaxial stresses may be determined using either an interactive or a noninteractive criterion. From the reliability values for the individual laminae comprising a given laminate, Thomas and Wetherhold (1991) have proposed a method for determining bounds for the overall laminate reliability. In this paper, simple physically plausible phenomenological rules are proposed for redistribution of load after a lamina has failed within the confines of a laminate. These rules are illustrated by application to (0/ +/-15)s and (90/ +/-45/0)s graphite/epoxy laminates, and the results are compared to the previously proposed bounds.

Semiparametric estimation of treatment effect in a pretest-posttest study.

PubMed

Leon, Selene; Tsiatis, Anastasios A; Davidian, Marie

2003-12-01

Inference on treatment effects in a pretest-posttest study is a routine objective in medicine, public health, and other fields. A number of approaches have been advocated. We take a semiparametric perspective, making no assumptions about the distributions of baseline and posttest responses. By representing the situation in terms of counterfactual random variables, we exploit recent developments in the literature on missing data and causal inference, to derive the class of all consistent treatment effect estimators, identify the most efficient such estimator, and outline strategies for implementation of estimators that may improve on popular methods. We demonstrate the methods and their properties via simulation and by application to a data set from an HIV clinical trial.

Bayesian Modeling for Identification and Estimation of the Learning Effects of Pointing Tasks

NASA Astrophysics Data System (ADS)

Kyo, Koki

Recently, in the field of human-computer interaction, a model containing the systematic factor and human factor has been proposed to evaluate the performance of the input devices of a computer. This is called the SH-model. In this paper, in order to extend the range of application of the SH-model, we propose some new models based on the Box-Cox transformation and apply a Bayesian modeling method for identification and estimation of the learning effects of pointing tasks. We consider the parameters describing the learning effect as random variables and introduce smoothness priors for them. Illustrative results show that the newly-proposed models work well.

Variations and heredity in bacterial colonies

PubMed Central

Čepl, Jaroslav; Blahůšková, Anna; Neubauer, Zdeněk; Markoš, Anton

2016-01-01

ABSTRACT Spontaneous variation in appearance was studied in bacterial colonies of Serratia marcescens F morphotype1: (i) A defined array of non-heritable phenotype variations does appear repeatedly; (ii) The presence of colonies of different bacterial species will narrow the variability toward the typical F appearance, as if such an added environmental factor curtailed the capacity of colony morphospace; (iii) Similarly the morphospace becomes reduced by random mutations leading to new, heritable morphotypes—at the same time opening a new array of variations typical for the mutant but not accessible directly from the original F morphospace. Results are discussed in context with biphasic model of early morphogenesis applicable to all multicellular bodies. PMID:28042382

Dimensional Reduction for the General Markov Model on Phylogenetic Trees.

PubMed

Sumner, Jeremy G

2017-03-01

We present a method of dimensional reduction for the general Markov model of sequence evolution on a phylogenetic tree. We show that taking certain linear combinations of the associated random variables (site pattern counts) reduces the dimensionality of the model from exponential in the number of extant taxa, to quadratic in the number of taxa, while retaining the ability to statistically identify phylogenetic divergence events. A key feature is the identification of an invariant subspace which depends only bilinearly on the model parameters, in contrast to the usual multi-linear dependence in the full space. We discuss potential applications including the computation of split (edge) weights on phylogenetic trees from observed sequence data.

Profit intensity and cases of non-compliance with the law of demand/supply

NASA Astrophysics Data System (ADS)

Makowski, Marcin; Piotrowski, Edward W.; Sładkowski, Jan; Syska, Jacek

2017-05-01

We consider properties of the measurement intensity ρ of a random variable for which the probability density function represented by the corresponding Wigner function attains negative values on a part of the domain. We consider a simple economic interpretation of this problem. This model is used to present the applicability of the method to the analysis of the negative probability on markets where there are anomalies in the law of supply and demand (e.g. Giffen's goods). It turns out that the new conditions to optimize the intensity ρ require a new strategy. We propose a strategy (so-called à rebours strategy) based on the fixed point method and explore its effectiveness.

Randomized controlled dissemination study of community-to-clinic navigation to promote CRC screening: Study design and implications.

PubMed

Larkey, Linda; Szalacha, Laura; Herman, Patricia; Gonzalez, Julie; Menon, Usha

2017-02-01

Regular screening facilitates early diagnosis of colorectal cancer (CRC) and reduction of CRC morbidity and mortality. Screening rates for minorities and low-income populations remain suboptimal. Provider referral for CRC screening is one of the strongest predictors of adherence, but referrals are unlikely among those who have no clinic home (common among poor and minority populations). This group randomized controlled study will test the effectiveness of an evidence based tailored messaging intervention in a community-to-clinic navigation context compared to no navigation. Multicultural, underinsured individuals from community sites will be randomized (by site) to receive CRC screening education only, or education plus navigation. In Phase I, those randomized to education plus navigation will be guided to make a clinic appointment to receive a provider referral for CRC screening. Patients attending clinic appointments will continue to receive navigation until screened (Phase II) regardless of initial arm assignment. We hypothesize that those receiving education plus navigation will be more likely to attend clinic appointments (H1) and show higher rates of screening (H2) compared to those receiving education only. Phase I group assignment will be used as a control variable in analysis of screening follow-through in Phase II. Costs per screening achieved will be evaluated for each condition and the RE-AIM framework will be used to examine dissemination results. The novelty of our study design is the translational dissemination model that will allow us to assess the real-world application of an efficacious intervention previously tested in a randomized controlled trial. Copyright © 2016. Published by Elsevier Inc.

Interpretation of sucrose gradient sedimentation pattern of deoxyribonucleic acid fragments resulting from random breaks.

PubMed

Litwin, S; Shahn, E; Kozinski, A W

1969-07-01

Mass distribution in a sucrose gradient of deoxyribonucleic acid (DNA) fragments arising as a result of random breaks is predicted by analytical means from which computer evaluations are plotted. The analytical results are compared with the results of verifying experiments: (i) a Monte Carlo computer experiment in which simulated molecules of DNA were individuals of unit length subjected to random "breaks" applied by a random number generator, and (ii) an in vitro experiment in which molecules of T4 DNA, highly labeled with (32)P, were stored in liquid nitrogen for variable periods of time during which a precisely known number of (32)P atoms decayed, causing single-stranded breaks. The distribution of sizes of the resulting fragments was measured in an alkaline sucrose gradient. The profiles obtained in this fashion were compared with the mathematical predictions. Both experiments agree with the analytical approach and thus permit the use of the graphs obtained from the latter as a means of determining the average number of random breaks in DNA from distributions obtained experimentally in a sucrose gradient. An example of the application of this procedure to a previously unresolved problem is provided in the case of DNA from ultraviolet-irradiated phage which undergoes a dose-dependent intracellular breakdown. The relationship between the number of lethal hits and the number of single-stranded breaks was not previously established. A comparison of the calculated number of nicks per strand of DNA with the known dose in phage-lethal hits reveals a relationship closely approximating one lethal hit to one single-stranded break.

Network Mendelian randomization: using genetic variants as instrumental variables to investigate mediation in causal pathways

PubMed Central

Burgess, Stephen; Daniel, Rhian M; Butterworth, Adam S; Thompson, Simon G

2015-01-01

Background: Mendelian randomization uses genetic variants, assumed to be instrumental variables for a particular exposure, to estimate the causal effect of that exposure on an outcome. If the instrumental variable criteria are satisfied, the resulting estimator is consistent even in the presence of unmeasured confounding and reverse causation. Methods: We extend the Mendelian randomization paradigm to investigate more complex networks of relationships between variables, in particular where some of the effect of an exposure on the outcome may operate through an intermediate variable (a mediator). If instrumental variables for the exposure and mediator are available, direct and indirect effects of the exposure on the outcome can be estimated, for example using either a regression-based method or structural equation models. The direction of effect between the exposure and a possible mediator can also be assessed. Methods are illustrated in an applied example considering causal relationships between body mass index, C-reactive protein and uric acid. Results: These estimators are consistent in the presence of unmeasured confounding if, in addition to the instrumental variable assumptions, the effects of both the exposure on the mediator and the mediator on the outcome are homogeneous across individuals and linear without interactions. Nevertheless, a simulation study demonstrates that even considerable heterogeneity in these effects does not lead to bias in the estimates. Conclusions: These methods can be used to estimate direct and indirect causal effects in a mediation setting, and have potential for the investigation of more complex networks between multiple interrelated exposures and disease outcomes. PMID:25150977

A geospatial model of ambient sound pressure levels in the contiguous United States.

PubMed

Mennitt, Daniel; Sherrill, Kirk; Fristrup, Kurt

2014-05-01

This paper presents a model that predicts measured sound pressure levels using geospatial features such as topography, climate, hydrology, and anthropogenic activity. The model utilizes random forest, a tree-based machine learning algorithm, which does not incorporate a priori knowledge of source characteristics or propagation mechanics. The response data encompasses 270 000 h of acoustical measurements from 190 sites located in National Parks across the contiguous United States. The explanatory variables were derived from national geospatial data layers and cross validation procedures were used to evaluate model performance and identify variables with predictive power. Using the model, the effects of individual explanatory variables on sound pressure level were isolated and quantified to reveal systematic trends across environmental gradients. Model performance varies by the acoustical metric of interest; the seasonal L50 can be predicted with a median absolute deviation of approximately 3 dB. The primary application for this model is to generalize point measurements to maps expressing spatial variation in ambient sound levels. An example of this mapping capability is presented for Zion National Park and Cedar Breaks National Monument in southwestern Utah.

Inability of the entropy vector method to certify nonclassicality in linelike causal structures

NASA Astrophysics Data System (ADS)

Weilenmann, Mirjam; Colbeck, Roger

2016-10-01

Bell's theorem shows that our intuitive understanding of causation must be overturned in light of quantum correlations. Nevertheless, quantum mechanics does not permit signaling and hence a notion of cause remains. Understanding this notion is not only important at a fundamental level, but also for technological applications such as key distribution and randomness expansion. It has recently been shown that a useful way to decide which classical causal structures could give rise to a given set of correlations is to use entropy vectors. These are vectors whose components are the entropies of all subsets of the observed variables in the causal structure. The entropy vector method employs causal relationships among the variables to restrict the set of possible entropy vectors. Here, we consider whether the same approach can lead to useful certificates of nonclassicality within a given causal structure. Surprisingly, we find that for a family of causal structures that includes the usual bipartite Bell structure they do not. For all members of this family, no function of the entropies of the observed variables gives such a certificate, in spite of the existence of nonclassical correlations. It is therefore necessary to look beyond entropy vectors to understand cause from a quantum perspective.