randomly selected samples: Topics by WorldWideScience.org

Sample records for randomly selected samples

40 CFR 761.308 - Sample selection by random number generation on any two-dimensional square grid.

Science.gov (United States)

2010-07-01

... 40 Protection of Environment 30 2010-07-01 2010-07-01 false Sample selection by random number... Â§ 761.79(b)(3) § 761.308 Sample selection by random number generation on any two-dimensional square... area created in accordance with paragraph (a) of this section, select two random numbers: one each for...
Random selection of items. Selection of n1 samples among N items composing a stratum

International Nuclear Information System (INIS)

Jaech, J.L.; Lemaire, R.J.

1987-02-01

STR-224 provides generalized procedures to determine required sample sizes, for instance in the course of a Physical Inventory Verification at Bulk Handling Facilities. The present report describes procedures to generate random numbers and select groups of items to be verified in a given stratum through each of the measurement methods involved in the verification. (author). 3 refs
40 CFR 761.306 - Sampling 1 meter square surfaces by random selection of halves.

Science.gov (United States)

2010-07-01

... 40 Protection of Environment 30 2010-07-01 2010-07-01 false Sampling 1 meter square surfaces by...(b)(3) § 761.306 Sampling 1 meter square surfaces by random selection of halves. (a) Divide each 1 meter square portion where it is necessary to collect a surface wipe test sample into two equal (or as...
Acceptance sampling using judgmental and randomly selected samples

Energy Technology Data Exchange (ETDEWEB)

Sego, Landon H.; Shulman, Stanley A.; Anderson, Kevin K.; Wilson, John E.; Pulsipher, Brent A.; Sieber, W. Karl

2010-09-01

We present a Bayesian model for acceptance sampling where the population consists of two groups, each with different levels of risk of containing unacceptable items. Expert opinion, or judgment, may be required to distinguish between the high and low-risk groups. Hence, high-risk items are likely to be identifed (and sampled) using expert judgment, while the remaining low-risk items are sampled randomly. We focus on the situation where all observed samples must be acceptable. Consequently, the objective of the statistical inference is to quantify the probability that a large percentage of the unsampled items in the population are also acceptable. We demonstrate that traditional (frequentist) acceptance sampling and simpler Bayesian formulations of the problem are essentially special cases of the proposed model. We explore the properties of the model in detail, and discuss the conditions necessary to ensure that required samples sizes are non-decreasing function of the population size. The method is applicable to a variety of acceptance sampling problems, and, in particular, to environmental sampling where the objective is to demonstrate the safety of reoccupying a remediated facility that has been contaminated with a lethal agent.
Field-based random sampling without a sampling frame: control selection for a case-control study in rural Africa.

Science.gov (United States)

Crampin, A C; Mwinuka, V; Malema, S S; Glynn, J R; Fine, P E

2001-01-01

Selection bias, particularly of controls, is common in case-control studies and may materially affect the results. Methods of control selection should be tailored both for the risk factors and disease under investigation and for the population being studied. We present here a control selection method devised for a case-control study of tuberculosis in rural Africa (Karonga, northern Malawi) that selects an age/sex frequency-matched random sample of the population, with a geographical distribution in proportion to the population density. We also present an audit of the selection process, and discuss the potential of this method in other settings.
Sample size estimation and sampling techniques for selecting a representative sample

Directory of Open Access Journals (Sweden)

Aamir Omair

2014-01-01

Full Text Available Introduction: The purpose of this article is to provide a general understanding of the concepts of sampling as applied to health-related research. Sample Size Estimation: It is important to select a representative sample in quantitative research in order to be able to generalize the results to the target population. The sample should be of the required sample size and must be selected using an appropriate probability sampling technique. There are many hidden biases which can adversely affect the outcome of the study. Important factors to consider for estimating the sample size include the size of the study population, confidence level, expected proportion of the outcome variable (for categorical variables/standard deviation of the outcome variable (for numerical variables, and the required precision (margin of accuracy from the study. The more the precision required, the greater is the required sample size. Sampling Techniques: The probability sampling techniques applied for health related research include simple random sampling, systematic random sampling, stratified random sampling, cluster sampling, and multistage sampling. These are more recommended than the nonprobability sampling techniques, because the results of the study can be generalized to the target population.
Blocked Randomization with Randomly Selected Block Sizes

Directory of Open Access Journals (Sweden)

Jimmy Efird

2010-12-01

Full Text Available When planning a randomized clinical trial, careful consideration must be given to how participants are selected for various arms of a study. Selection and accidental bias may occur when participants are not assigned to study groups with equal probability. A simple random allocation scheme is a process by which each participant has equal likelihood of being assigned to treatment versus referent groups. However, by chance an unequal number of individuals may be assigned to each arm of the study and thus decrease the power to detect statistically significant differences between groups. Block randomization is a commonly used technique in clinical trial design to reduce bias and achieve balance in the allocation of participants to treatment arms, especially when the sample size is small. This method increases the probability that each arm will contain an equal number of individuals by sequencing participant assignments by block. Yet still, the allocation process may be predictable, for example, when the investigator is not blind and the block size is fixed. This paper provides an overview of blocked randomization and illustrates how to avoid selection bias by using random block sizes.
Risk Attitudes, Sample Selection and Attrition in a Longitudinal Field Experiment

DEFF Research Database (Denmark)

Harrison, Glenn W.; Lau, Morten Igel

with respect to risk attitudes. Our design builds in explicit randomization on the incentives for participation. We show that there are significant sample selection effects on inferences about the extent of risk aversion, but that the effects of subsequent sample attrition are minimal. Ignoring sample...... selection leads to inferences that subjects in the population are more risk averse than they actually are. Correcting for sample selection and attrition affects utility curvature, but does not affect inferences about probability weighting. Properly accounting for sample selection and attrition effects leads...... to findings of temporal stability in overall risk aversion. However, that stability is around different levels of risk aversion than one might naively infer without the controls for sample selection and attrition we are able to implement. This evidence of “randomization bias” from sample selection...
The genealogy of samples in models with selection.

Science.gov (United States)

Neuhauser, C; Krone, S M

1997-02-01

We introduce the genealogy of a random sample of genes taken from a large haploid population that evolves according to random reproduction with selection and mutation. Without selection, the genealogy is described by Kingman's well-known coalescent process. In the selective case, the genealogy of the sample is embedded in a graph with a coalescing and branching structure. We describe this graph, called the ancestral selection graph, and point out differences and similarities with Kingman's coalescent. We present simulations for a two-allele model with symmetric mutation in which one of the alleles has a selective advantage over the other. We find that when the allele frequencies in the population are already in equilibrium, then the genealogy does not differ much from the neutral case. This is supported by rigorous results. Furthermore, we describe the ancestral selection graph for other selective models with finitely many selection classes, such as the K-allele models, infinitely-many-alleles models. DNA sequence models, and infinitely-many-sites models, and briefly discuss the diploid case.
BWIP-RANDOM-SAMPLING, Random Sample Generation for Nuclear Waste Disposal

International Nuclear Information System (INIS)

Sagar, B.

1989-01-01

1 - Description of program or function: Random samples for different distribution types are generated. Distribution types as required for performance assessment modeling of geologic nuclear waste disposal are provided. These are: - Uniform, - Log-uniform (base 10 or natural), - Normal, - Lognormal (base 10 or natural), - Exponential, - Bernoulli, - User defined continuous distribution. 2 - Method of solution: A linear congruential generator is used for uniform random numbers. A set of functions is used to transform the uniform distribution to the other distributions. Stratified, rather than random, sampling can be chosen. Truncated limits can be specified on many distributions, whose usual definition has an infinite support. 3 - Restrictions on the complexity of the problem: Generation of correlated random variables is not included
Sampling large random knots in a confined space

International Nuclear Information System (INIS)

Arsuaga, J; Blackstone, T; Diao, Y; Hinson, K; Karadayi, E; Saito, M

2007-01-01

DNA knots formed under extreme conditions of condensation, as in bacteriophage P4, are difficult to analyze experimentally and theoretically. In this paper, we propose to use the uniform random polygon model as a supplementary method to the existing methods for generating random knots in confinement. The uniform random polygon model allows us to sample knots with large crossing numbers and also to generate large diagrammatically prime knot diagrams. We show numerically that uniform random polygons sample knots with large minimum crossing numbers and certain complicated knot invariants (as those observed experimentally). We do this in terms of the knot determinants or colorings. Our numerical results suggest that the average determinant of a uniform random polygon of n vertices grows faster than O(e n 2 )). We also investigate the complexity of prime knot diagrams. We show rigorously that the probability that a randomly selected 2D uniform random polygon of n vertices is almost diagrammatically prime goes to 1 as n goes to infinity. Furthermore, the average number of crossings in such a diagram is at the order of O(n 2 ). Therefore, the two-dimensional uniform random polygons offer an effective way in sampling large (prime) knots, which can be useful in various applications
Sampling large random knots in a confined space

Science.gov (United States)

Arsuaga, J.; Blackstone, T.; Diao, Y.; Hinson, K.; Karadayi, E.; Saito, M.

2007-09-01

DNA knots formed under extreme conditions of condensation, as in bacteriophage P4, are difficult to analyze experimentally and theoretically. In this paper, we propose to use the uniform random polygon model as a supplementary method to the existing methods for generating random knots in confinement. The uniform random polygon model allows us to sample knots with large crossing numbers and also to generate large diagrammatically prime knot diagrams. We show numerically that uniform random polygons sample knots with large minimum crossing numbers and certain complicated knot invariants (as those observed experimentally). We do this in terms of the knot determinants or colorings. Our numerical results suggest that the average determinant of a uniform random polygon of n vertices grows faster than O(e^{n^2}) . We also investigate the complexity of prime knot diagrams. We show rigorously that the probability that a randomly selected 2D uniform random polygon of n vertices is almost diagrammatically prime goes to 1 as n goes to infinity. Furthermore, the average number of crossings in such a diagram is at the order of O(n2). Therefore, the two-dimensional uniform random polygons offer an effective way in sampling large (prime) knots, which can be useful in various applications.
Sampling large random knots in a confined space

Energy Technology Data Exchange (ETDEWEB)

Arsuaga, J [Department of Mathematics, San Francisco State University, 1600 Holloway Ave, San Francisco, CA 94132 (United States); Blackstone, T [Department of Computer Science, San Francisco State University, 1600 Holloway Ave., San Francisco, CA 94132 (United States); Diao, Y [Department of Mathematics and Statistics, University of North Carolina at Charlotte, Charlotte, NC 28223 (United States); Hinson, K [Department of Mathematics and Statistics, University of North Carolina at Charlotte, Charlotte, NC 28223 (United States); Karadayi, E [Department of Mathematics, University of South Florida, 4202 E Fowler Avenue, Tampa, FL 33620 (United States); Saito, M [Department of Mathematics, University of South Florida, 4202 E Fowler Avenue, Tampa, FL 33620 (United States)

2007-09-28

DNA knots formed under extreme conditions of condensation, as in bacteriophage P4, are difficult to analyze experimentally and theoretically. In this paper, we propose to use the uniform random polygon model as a supplementary method to the existing methods for generating random knots in confinement. The uniform random polygon model allows us to sample knots with large crossing numbers and also to generate large diagrammatically prime knot diagrams. We show numerically that uniform random polygons sample knots with large minimum crossing numbers and certain complicated knot invariants (as those observed experimentally). We do this in terms of the knot determinants or colorings. Our numerical results suggest that the average determinant of a uniform random polygon of n vertices grows faster than O(e{sup n{sup 2}}). We also investigate the complexity of prime knot diagrams. We show rigorously that the probability that a randomly selected 2D uniform random polygon of n vertices is almost diagrammatically prime goes to 1 as n goes to infinity. Furthermore, the average number of crossings in such a diagram is at the order of O(n{sup 2}). Therefore, the two-dimensional uniform random polygons offer an effective way in sampling large (prime) knots, which can be useful in various applications.
Application of random effects to the study of resource selection by animals.

Science.gov (United States)

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
Local randomization in neighbor selection improves PRM roadmap quality

KAUST Repository

McMahon, Troy; Jacobs, Sam; Boyd, Bryan; Tapia, Lydia; Amato, Nancy M.

2012-01-01

Probabilistic Roadmap Methods (PRMs) are one of the most used classes of motion planning methods. These sampling-based methods generate robot configurations (nodes) and then connect them to form a graph (roadmap) containing representative feasible pathways. A key step in PRM roadmap construction involves identifying a set of candidate neighbors for each node. Traditionally, these candidates are chosen to be the k-closest nodes based on a given distance metric. In this paper, we propose a new neighbor selection policy called LocalRand(k,K'), that first computes the K' closest nodes to a specified node and then selects k of those nodes at random. Intuitively, LocalRand attempts to benefit from random sampling while maintaining the higher levels of local planner success inherent to selecting more local neighbors. We provide a methodology for selecting the parameters k and K'. We perform an experimental comparison which shows that for both rigid and articulated robots, LocalRand results in roadmaps that are better connected than the traditional k-closest policy or a purely random neighbor selection policy. The cost required to achieve these results is shown to be comparable to k-closest. © 2012 IEEE.
Local randomization in neighbor selection improves PRM roadmap quality

KAUST Repository

McMahon, Troy

2012-10-01

Probabilistic Roadmap Methods (PRMs) are one of the most used classes of motion planning methods. These sampling-based methods generate robot configurations (nodes) and then connect them to form a graph (roadmap) containing representative feasible pathways. A key step in PRM roadmap construction involves identifying a set of candidate neighbors for each node. Traditionally, these candidates are chosen to be the k-closest nodes based on a given distance metric. In this paper, we propose a new neighbor selection policy called LocalRand(k,K\\'), that first computes the K\\' closest nodes to a specified node and then selects k of those nodes at random. Intuitively, LocalRand attempts to benefit from random sampling while maintaining the higher levels of local planner success inherent to selecting more local neighbors. We provide a methodology for selecting the parameters k and K\\'. We perform an experimental comparison which shows that for both rigid and articulated robots, LocalRand results in roadmaps that are better connected than the traditional k-closest policy or a purely random neighbor selection policy. The cost required to achieve these results is shown to be comparable to k-closest. © 2012 IEEE.
Using maximum entropy modeling for optimal selection of sampling sites for monitoring networks

Science.gov (United States)

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.
A Bayesian Justification for Random Sampling in Sample Survey

Directory of Open Access Journals (Sweden)

Glen Meeden

2012-07-01

Full Text Available In the usual Bayesian approach to survey sampling the sampling design, plays a minimal role, at best. Although a close relationship between exchangeable prior distributions and simple random sampling has been noted; how to formally integrate simple random sampling into the Bayesian paradigm is not clear. Recently it has been argued that the sampling design can be thought of as part of a Bayesian's prior distribution. We will show here that under this scenario simple random sample can be given a Bayesian justification in survey sampling.
Robust inference in sample selection models

KAUST Repository

Zhelonkin, Mikhail; Genton, Marc G.; Ronchetti, Elvezio

2015-01-01

The problem of non-random sample selectivity often occurs in practice in many fields. The classical estimators introduced by Heckman are the backbone of the standard statistical analysis of these models. However, these estimators are very sensitive to small deviations from the distributional assumptions which are often not satisfied in practice. We develop a general framework to study the robustness properties of estimators and tests in sample selection models. We derive the influence function and the change-of-variance function of Heckman's two-stage estimator, and we demonstrate the non-robustness of this estimator and its estimated variance to small deviations from the model assumed. We propose a procedure for robustifying the estimator, prove its asymptotic normality and give its asymptotic variance. Both cases with and without an exclusion restriction are covered. This allows us to construct a simple robust alternative to the sample selection bias test. We illustrate the use of our new methodology in an analysis of ambulatory expenditures and we compare the performance of the classical and robust methods in a Monte Carlo simulation study.
Robust inference in sample selection models

KAUST Repository

Zhelonkin, Mikhail

2015-11-20

The problem of non-random sample selectivity often occurs in practice in many fields. The classical estimators introduced by Heckman are the backbone of the standard statistical analysis of these models. However, these estimators are very sensitive to small deviations from the distributional assumptions which are often not satisfied in practice. We develop a general framework to study the robustness properties of estimators and tests in sample selection models. We derive the influence function and the change-of-variance function of Heckman\\'s two-stage estimator, and we demonstrate the non-robustness of this estimator and its estimated variance to small deviations from the model assumed. We propose a procedure for robustifying the estimator, prove its asymptotic normality and give its asymptotic variance. Both cases with and without an exclusion restriction are covered. This allows us to construct a simple robust alternative to the sample selection bias test. We illustrate the use of our new methodology in an analysis of ambulatory expenditures and we compare the performance of the classical and robust methods in a Monte Carlo simulation study.

An unbiased estimator of the variance of simple random sampling using mixed random-systematic sampling

OpenAIRE

Padilla, Alberto

2009-01-01

Systematic sampling is a commonly used technique due to its simplicity and ease of implementation. The drawback of this simplicity is that it is not possible to estimate the design variance without bias. There are several ways to circumvent this problem. One method is to suppose that the variable of interest has a random order in the population, so the sample variance of simple random sampling without replacement is used. By means of a mixed random - systematic sample, an unbiased estimator o...
40 CFR 205.57-2 - Test vehicle sample selection.

Science.gov (United States)

2010-07-01

... pursuant to a test request in accordance with this subpart will be selected in the manner specified in the... then using a table of random numbers to select the number of vehicles as specified in paragraph (c) of... with the desig-nated AQL are contained in Appendix I, -Table II. (c) The appropriate batch sample size...
Selection of representative calibration sample sets for near-infrared reflectance spectroscopy to predict nitrogen concentration in grasses

DEFF Research Database (Denmark)

Shetty, Nisha; Rinnan, Åsmund; Gislum, René

2012-01-01

) algorithm were used and compared. Both Puchwein and CADEX methods provide a calibration set equally distributed in space, and both methods require a minimum prior of knowledge. The samples were also selected randomly using complete random, cultivar random (year fixed), year random (cultivar fixed......) and interaction (cultivar × year fixed) random procedures to see the influence of different factors on sample selection. Puchwein's method performed best with lowest RMSEP followed by CADEX, interaction random, year random, cultivar random and complete random. Out of 118 samples of the complete calibration set...... effectively enhance the cost-effectiveness of NIR spectral analysis by reducing the number of analyzed samples in the calibration set by more than 80%, which substantially reduces the effort of laboratory analyses with no significant loss in prediction accuracy....
Random sampling of elementary flux modes in large-scale metabolic networks.

Science.gov (United States)

Machado, Daniel; Soons, Zita; Patil, Kiran Raosaheb; Ferreira, Eugénio C; Rocha, Isabel

2012-09-15

The description of a metabolic network in terms of elementary (flux) modes (EMs) provides an important framework for metabolic pathway analysis. However, their application to large networks has been hampered by the combinatorial explosion in the number of modes. In this work, we develop a method for generating random samples of EMs without computing the whole set. Our algorithm is an adaptation of the canonical basis approach, where we add an additional filtering step which, at each iteration, selects a random subset of the new combinations of modes. In order to obtain an unbiased sample, all candidates are assigned the same probability of getting selected. This approach avoids the exponential growth of the number of modes during computation, thus generating a random sample of the complete set of EMs within reasonable time. We generated samples of different sizes for a metabolic network of Escherichia coli, and observed that they preserve several properties of the full EM set. It is also shown that EM sampling can be used for rational strain design. A well distributed sample, that is representative of the complete set of EMs, should be suitable to most EM-based methods for analysis and optimization of metabolic networks. Source code for a cross-platform implementation in Python is freely available at http://code.google.com/p/emsampler. dmachado@deb.uminho.pt Supplementary data are available at Bioinformatics online.
Systematic versus random sampling in stereological studies.

Science.gov (United States)

West, Mark J

2012-12-01

The sampling that takes place at all levels of an experimental design must be random if the estimate is to be unbiased in a statistical sense. There are two fundamental ways by which one can make a random sample of the sections and positions to be probed on the sections. Using a card-sampling analogy, one can pick any card at all out of a deck of cards. This is referred to as independent random sampling because the sampling of any one card is made without reference to the position of the other cards. The other approach to obtaining a random sample would be to pick a card within a set number of cards and others at equal intervals within the deck. Systematic sampling along one axis of many biological structures is more efficient than random sampling, because most biological structures are not randomly organized. This article discusses the merits of systematic versus random sampling in stereological studies.
k-Means: Random Sampling Procedure

Indian Academy of Sciences (India)

First page Back Continue Last page Overview Graphics. k-Means: Random Sampling Procedure. Optimal 1-Mean is. Approximation of Centroid (Inaba et al). S = random sample of size O(1/ ); Centroid of S is a (1+ )-approx centroid of P with constant probability.
Generation and Analysis of Constrained Random Sampling Patterns

DEFF Research Database (Denmark)

Pierzchlewski, Jacek; Arildsen, Thomas

2016-01-01

Random sampling is a technique for signal acquisition which is gaining popularity in practical signal processing systems. Nowadays, event-driven analog-to-digital converters make random sampling feasible in practical applications. A process of random sampling is defined by a sampling pattern, which...... indicates signal sampling points in time. Practical random sampling patterns are constrained by ADC characteristics and application requirements. In this paper, we introduce statistical methods which evaluate random sampling pattern generators with emphasis on practical applications. Furthermore, we propose...... algorithm generates random sampling patterns dedicated for event-driven-ADCs better than existed sampling pattern generators. Finally, implementation issues of random sampling patterns are discussed....
Failure Probability Estimation Using Asymptotic Sampling and Its Dependence upon the Selected Sampling Scheme

Directory of Open Access Journals (Sweden)

Martinásková Magdalena

2017-12-01

Full Text Available The article examines the use of Asymptotic Sampling (AS for the estimation of failure probability. The AS algorithm requires samples of multidimensional Gaussian random vectors, which may be obtained by many alternative means that influence the performance of the AS method. Several reliability problems (test functions have been selected in order to test AS with various sampling schemes: (i Monte Carlo designs; (ii LHS designs optimized using the Periodic Audze-Eglājs (PAE criterion; (iii designs prepared using Sobol’ sequences. All results are compared with the exact failure probability value.
Sampling problems for randomly broken sticks

Energy Technology Data Exchange (ETDEWEB)

Huillet, Thierry [Laboratoire de Physique Theorique et Modelisation, CNRS-UMR 8089 et Universite de Cergy-Pontoise, 5 mail Gay-Lussac, 95031, Neuville sur Oise (France)

2003-04-11

Consider the random partitioning model of a population (represented by a stick of length 1) into n species (fragments) with identically distributed random weights (sizes). Upon ranking the fragments' weights according to ascending sizes, let S{sub m:n} be the size of the mth smallest fragment. Assume that some observer is sampling such populations as follows: drop at random k points (the sample size) onto this stick and record the corresponding numbers of visited fragments. We shall investigate the following sampling problems: (1) what is the sample size if the sampling is carried out until the first visit of the smallest fragment (size S{sub 1:n})? (2) For a given sample size, have all the fragments of the stick been visited at least once or not? This question is related to Feller's random coupon collector problem. (3) In what order are new fragments being discovered and what is the random number of samples separating the discovery of consecutive new fragments until exhaustion of the list? For this problem, the distribution of the size-biased permutation of the species' weights, as the sequence of their weights in their order of appearance is needed and studied.
Does self-selection affect samples' representativeness in online surveys? An investigation in online video game research.

Science.gov (United States)

Khazaal, Yasser; van Singer, Mathias; Chatton, Anne; Achab, Sophia; Zullino, Daniele; Rothen, Stephane; Khan, Riaz; Billieux, Joel; Thorens, Gabriel

2014-07-07

The number of medical studies performed through online surveys has increased dramatically in recent years. Despite their numerous advantages (eg, sample size, facilitated access to individuals presenting stigmatizing issues), selection bias may exist in online surveys. However, evidence on the representativeness of self-selected samples in online studies is patchy. Our objective was to explore the representativeness of a self-selected sample of online gamers using online players' virtual characters (avatars). All avatars belonged to individuals playing World of Warcraft (WoW), currently the most widely used online game. Avatars' characteristics were defined using various games' scores, reported on the WoW's official website, and two self-selected samples from previous studies were compared with a randomly selected sample of avatars. We used scores linked to 1240 avatars (762 from the self-selected samples and 478 from the random sample). The two self-selected samples of avatars had higher scores on most of the assessed variables (except for guild membership and exploration). Furthermore, some guilds were overrepresented in the self-selected samples. Our results suggest that more proficient players or players more involved in the game may be more likely to participate in online surveys. Caution is needed in the interpretation of studies based on online surveys that used a self-selection recruitment procedure. Epidemiological evidence on the reduced representativeness of sample of online surveys is warranted.
47 CFR 1.1602 - Designation for random selection.

Science.gov (United States)

2010-10-01

... 47 Telecommunication 1 2010-10-01 2010-10-01 false Designation for random selection. 1.1602 Section 1.1602 Telecommunication FEDERAL COMMUNICATIONS COMMISSION GENERAL PRACTICE AND PROCEDURE Random Selection Procedures for Mass Media Services General Procedures § 1.1602 Designation for random selection...
47 CFR 1.1603 - Conduct of random selection.

Science.gov (United States)

2010-10-01

... 47 Telecommunication 1 2010-10-01 2010-10-01 false Conduct of random selection. 1.1603 Section 1.1603 Telecommunication FEDERAL COMMUNICATIONS COMMISSION GENERAL PRACTICE AND PROCEDURE Random Selection Procedures for Mass Media Services General Procedures § 1.1603 Conduct of random selection. The...
Independent random sampling methods

CERN Document Server

Martino, Luca; Míguez, Joaquín

2018-01-01

This book systematically addresses the design and analysis of efficient techniques for independent random sampling. Both general-purpose approaches, which can be used to generate samples from arbitrary probability distributions, and tailored techniques, designed to efficiently address common real-world practical problems, are introduced and discussed in detail. In turn, the monograph presents fundamental results and methodologies in the field, elaborating and developing them into the latest techniques. The theory and methods are illustrated with a varied collection of examples, which are discussed in detail in the text and supplemented with ready-to-run computer code. The main problem addressed in the book is how to generate independent random samples from an arbitrary probability distribution with the weakest possible constraints or assumptions in a form suitable for practical implementation. The authors review the fundamental results and methods in the field, address the latest methods, and emphasize the li...
Systematic random sampling of the comet assay.

Science.gov (United States)

McArt, Darragh G; Wasson, Gillian R; McKerr, George; Saetzler, Kurt; Reed, Matt; Howard, C Vyvyan

2009-07-01

The comet assay is a technique used to quantify DNA damage and repair at a cellular level. In the assay, cells are embedded in agarose and the cellular content is stripped away leaving only the DNA trapped in an agarose cavity which can then be electrophoresed. The damaged DNA can enter the agarose and migrate while the undamaged DNA cannot and is retained. DNA damage is measured as the proportion of the migratory 'tail' DNA compared to the total DNA in the cell. The fundamental basis of these arbitrary values is obtained in the comet acquisition phase using fluorescence microscopy with a stoichiometric stain in tandem with image analysis software. Current methods deployed in such an acquisition are expected to be both objectively and randomly obtained. In this paper we examine the 'randomness' of the acquisition phase and suggest an alternative method that offers both objective and unbiased comet selection. In order to achieve this, we have adopted a survey sampling approach widely used in stereology, which offers a method of systematic random sampling (SRS). This is desirable as it offers an impartial and reproducible method of comet analysis that can be used both manually or automated. By making use of an unbiased sampling frame and using microscope verniers, we are able to increase the precision of estimates of DNA damage. Results obtained from a multiple-user pooled variation experiment showed that the SRS technique attained a lower variability than that of the traditional approach. The analysis of a single user with repetition experiment showed greater individual variances while not being detrimental to overall averages. This would suggest that the SRS method offers a better reflection of DNA damage for a given slide and also offers better user reproducibility.
Randomized branch sampling to estimatefruit production in Pecan trees cv. ‘Barton’

Directory of Open Access Journals (Sweden)

Filemom Manoel Mokochinski

Full Text Available ABSTRACT: Sampling techniques to quantify the production of fruits are still very scarce and create a gap in crop development research. This study was conducted in a rural property in the county of Cachoeira do Sul - RS to estimate the efficiency of randomized branch sampling (RBS in quantifying the production of pecan fruit at three different ages (5,7 and 10 years. Two selection techniques were tested: the probability proportional to the diameter (PPD and the uniform probability (UP techniques, which were performed on nine trees, three from each age and randomly chosen. The RBS underestimated fruit production for all ages, and its main drawback was the high sampling error (125.17% - PPD and 111.04% - UP. The UP was regarded as more efficient than the PPD, though both techniques estimated similar production and similar experimental errors. In conclusion, we reported that branch sampling was inaccurate for this case study, requiring new studies to produce estimates with smaller sampling error.
An efficient method of randomly sampling the coherent angular scatter distribution

International Nuclear Information System (INIS)

Williamson, J.F.; Morin, R.L.

1983-01-01

Monte Carlo simulations of photon transport phenomena require random selection of an interaction process at each collision site along the photon track. Possible choices are usually limited to photoelectric absorption and incoherent scatter as approximated by the Klein-Nishina distribution. A technique is described for sampling the coherent angular scatter distribution, for the benefit of workers in medical physics. (U.K.)
Robust online tracking via adaptive samples selection with saliency detection

Science.gov (United States)

Yan, Jia; Chen, Xi; Zhu, QiuPing

2013-12-01

Online tracking has shown to be successful in tracking of previously unknown objects. However, there are two important factors which lead to drift problem of online tracking, the one is how to select the exact labeled samples even when the target locations are inaccurate, and the other is how to handle the confusors which have similar features with the target. In this article, we propose a robust online tracking algorithm with adaptive samples selection based on saliency detection to overcome the drift problem. To deal with the problem of degrading the classifiers using mis-aligned samples, we introduce the saliency detection method to our tracking problem. Saliency maps and the strong classifiers are combined to extract the most correct positive samples. Our approach employs a simple yet saliency detection algorithm based on image spectral residual analysis. Furthermore, instead of using the random patches as the negative samples, we propose a reasonable selection criterion, in which both the saliency confidence and similarity are considered with the benefits that confusors in the surrounding background are incorporated into the classifiers update process before the drift occurs. The tracking task is formulated as a binary classification via online boosting framework. Experiment results in several challenging video sequences demonstrate the accuracy and stability of our tracker.
The concentration of heavy metals: zinc, cadmium, lead, copper, mercury, iron and calcium in head hair of a randomly selected sample of Kenyan people

International Nuclear Information System (INIS)

Wandiga, S.O.; Jumba, I.O.

1982-01-01

An intercomparative analysis of the concentration of heavy metals:zinc, cadmium, lead, copper, mercury, iron and calcium in head hair of a randomly selected sample of Kenyan people using the techniques of atomic absorption spectrophotometry (AAS) and differential pulse anodic stripping voltammetry (DPAS) has been undertaken. The percent relative standard deviation for each sample analysed using either of the techniques show good sensitivity and correlation between the techniques. The DPAS was found to be slightly sensitive than the AAs instrument used. The recalculated body burden rations of Cd to Zn, Pb to Fe reveal no unusual health impairement symptoms and suggest a relatively clean environment in Kenya.(author)
Frequency-Selective Signal Sensing with Sub-Nyquist Uniform Sampling Scheme

DEFF Research Database (Denmark)

Pierzchlewski, Jacek; Arildsen, Thomas

2015-01-01

In this paper the authors discuss a problem of acquisition and reconstruction of a signal polluted by adjacent- channel interference. The authors propose a method to find a sub-Nyquist uniform sampling pattern which allows for correct reconstruction of selected frequencies. The method is inspired...... by the Restricted Isometry Property, which is known from the field of compressed sensing. Then, compressed sensing is used to successfully reconstruct a wanted signal even if some of the uniform samples were randomly lost, e. g. due to ADC saturation. An experiment which tests the proposed method in practice...
Selection for altruism through random drift in variable size populations

Directory of Open Access Journals (Sweden)

Houchmandzadeh Bahram

2012-05-01

Full Text Available Abstract Background Altruistic behavior is defined as helping others at a cost to oneself and a lowered fitness. The lower fitness implies that altruists should be selected against, which is in contradiction with their widespread presence is nature. Present models of selection for altruism (kin or multilevel show that altruistic behaviors can have ‘hidden’ advantages if the ‘common good’ produced by altruists is restricted to some related or unrelated groups. These models are mostly deterministic, or assume a frequency dependent fitness. Results Evolutionary dynamics is a competition between deterministic selection pressure and stochastic events due to random sampling from one generation to the next. We show here that an altruistic allele extending the carrying capacity of the habitat can win by increasing the random drift of “selfish” alleles. In other terms, the fixation probability of altruistic genes can be higher than those of a selfish ones, even though altruists have a smaller fitness. Moreover when populations are geographically structured, the altruists advantage can be highly amplified and the fixation probability of selfish genes can tend toward zero. The above results are obtained both by numerical and analytical calculations. Analytical results are obtained in the limit of large populations. Conclusions The theory we present does not involve kin or multilevel selection, but is based on the existence of random drift in variable size populations. The model is a generalization of the original Fisher-Wright and Moran models where the carrying capacity depends on the number of altruists.

Efficient sampling of complex network with modified random walk strategies

Science.gov (United States)

Xie, Yunya; Chang, Shuhua; Zhang, Zhipeng; Zhang, Mi; Yang, Lei

2018-02-01

We present two novel random walk strategies, choosing seed node (CSN) random walk and no-retracing (NR) random walk. Different from the classical random walk sampling, the CSN and NR strategies focus on the influences of the seed node choice and path overlap, respectively. Three random walk samplings are applied in the Erdös-Rényi (ER), Barabási-Albert (BA), Watts-Strogatz (WS), and the weighted USAir networks, respectively. Then, the major properties of sampled subnets, such as sampling efficiency, degree distributions, average degree and average clustering coefficient, are studied. The similar conclusions can be reached with these three random walk strategies. Firstly, the networks with small scales and simple structures are conducive to the sampling. Secondly, the average degree and the average clustering coefficient of the sampled subnet tend to the corresponding values of original networks with limited steps. And thirdly, all the degree distributions of the subnets are slightly biased to the high degree side. However, the NR strategy performs better for the average clustering coefficient of the subnet. In the real weighted USAir networks, some obvious characters like the larger clustering coefficient and the fluctuation of degree distribution are reproduced well by these random walk strategies.
Are most samples of animals systematically biased? Consistent individual trait differences bias samples despite random sampling.

Science.gov (United States)

Biro, Peter A

2013-02-01

Sampling animals from the wild for study is something nearly every biologist has done, but despite our best efforts to obtain random samples of animals, 'hidden' trait biases may still exist. For example, consistent behavioral traits can affect trappability/catchability, independent of obvious factors such as size and gender, and these traits are often correlated with other repeatable physiological and/or life history traits. If so, systematic sampling bias may exist for any of these traits. The extent to which this is a problem, of course, depends on the magnitude of bias, which is presently unknown because the underlying trait distributions in populations are usually unknown, or unknowable. Indeed, our present knowledge about sampling bias comes from samples (not complete population censuses), which can possess bias to begin with. I had the unique opportunity to create naturalized populations of fish by seeding each of four small fishless lakes with equal densities of slow-, intermediate-, and fast-growing fish. Using sampling methods that are not size-selective, I observed that fast-growing fish were up to two-times more likely to be sampled than slower-growing fish. This indicates substantial and systematic bias with respect to an important life history trait (growth rate). If correlations between behavioral, physiological and life-history traits are as widespread as the literature suggests, then many animal samples may be systematically biased with respect to these traits (e.g., when collecting animals for laboratory use), and affect our inferences about population structure and abundance. I conclude with a discussion on ways to minimize sampling bias for particular physiological/behavioral/life-history types within animal populations.
Selectivity and sparseness in randomly connected balanced networks.

Directory of Open Access Journals (Sweden)

Cengiz Pehlevan

Full Text Available Neurons in sensory cortex show stimulus selectivity and sparse population response, even in cases where no strong functionally specific structure in connectivity can be detected. This raises the question whether selectivity and sparseness can be generated and maintained in randomly connected networks. We consider a recurrent network of excitatory and inhibitory spiking neurons with random connectivity, driven by random projections from an input layer of stimulus selective neurons. In this architecture, the stimulus-to-stimulus and neuron-to-neuron modulation of total synaptic input is weak compared to the mean input. Surprisingly, we show that in the balanced state the network can still support high stimulus selectivity and sparse population response. In the balanced state, strong synapses amplify the variation in synaptic input and recurrent inhibition cancels the mean. Functional specificity in connectivity emerges due to the inhomogeneity caused by the generative statistical rule used to build the network. We further elucidate the mechanism behind and evaluate the effects of model parameters on population sparseness and stimulus selectivity. Network response to mixtures of stimuli is investigated. It is shown that a balanced state with unselective inhibition can be achieved with densely connected input to inhibitory population. Balanced networks exhibit the "paradoxical" effect: an increase in excitatory drive to inhibition leads to decreased inhibitory population firing rate. We compare and contrast selectivity and sparseness generated by the balanced network to randomly connected unbalanced networks. Finally, we discuss our results in light of experiments.
RandomSpot: A web-based tool for systematic random sampling of virtual slides.

Science.gov (United States)

Wright, Alexander I; Grabsch, Heike I; Treanor, Darren E

2015-01-01

This paper describes work presented at the Nordic Symposium on Digital Pathology 2014, Linköping, Sweden. Systematic random sampling (SRS) is a stereological tool, which provides a framework to quickly build an accurate estimation of the distribution of objects or classes within an image, whilst minimizing the number of observations required. RandomSpot is a web-based tool for SRS in stereology, which systematically places equidistant points within a given region of interest on a virtual slide. Each point can then be visually inspected by a pathologist in order to generate an unbiased sample of the distribution of classes within the tissue. Further measurements can then be derived from the distribution, such as the ratio of tumor to stroma. RandomSpot replicates the fundamental principle of traditional light microscope grid-shaped graticules, with the added benefits associated with virtual slides, such as facilitated collaboration and automated navigation between points. Once the sample points have been added to the region(s) of interest, users can download the annotations and view them locally using their virtual slide viewing software. Since its introduction, RandomSpot has been used extensively for international collaborative projects, clinical trials and independent research projects. So far, the system has been used to generate over 21,000 sample sets, and has been used to generate data for use in multiple publications, identifying significant new prognostic markers in colorectal, upper gastro-intestinal and breast cancer. Data generated using RandomSpot also has significant value for training image analysis algorithms using sample point coordinates and pathologist classifications.
The signature of positive selection at randomly chosen loci.

Science.gov (United States)

Przeworski, Molly

2002-03-01

In Drosophila and humans, there are accumulating examples of loci with a significant excess of high-frequency-derived alleles or high levels of linkage disequilibrium, relative to a neutral model of a random-mating population of constant size. These are features expected after a recent selective sweep. Their prevalence suggests that positive directional selection may be widespread in both species. However, as I show here, these features do not persist long after the sweep ends: The high-frequency alleles drift to fixation and no longer contribute to polymorphism, while linkage disequilibrium is broken down by recombination. As a result, loci chosen without independent evidence of recent selection are not expected to exhibit either of these features, even if they have been affected by numerous sweeps in their genealogical history. How then can we explain the patterns in the data? One possibility is population structure, with unequal sampling from different subpopulations. Alternatively, positive selection may not operate as is commonly modeled. In particular, the rate of fixation of advantageous mutations may have increased in the recent past.
Optimal Subset Selection of Time-Series MODIS Images and Sample Data Transfer with Random Forests for Supervised Classification Modelling.

Science.gov (United States)

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.
Testing, Selection, and Implementation of Random Number Generators

National Research Council Canada - National Science Library

Collins, Joseph C

2008-01-01

An exhaustive evaluation of state-of-the-art random number generators with several well-known suites of tests provides the basis for selection of suitable random number generators for use in stochastic simulations...
40 CFR 89.507 - Sample selection.

Science.gov (United States)

2010-07-01

... Auditing § 89.507 Sample selection. (a) Engines comprising a test sample will be selected at the location...). However, once the manufacturer ships any test engine, it relinquishes the prerogative to conduct retests...
Correcting Classifiers for Sample Selection Bias in Two-Phase Case-Control Studies

Science.gov (United States)

Theis, Fabian J.

2017-01-01

Epidemiological studies often utilize stratified data in which rare outcomes or exposures are artificially enriched. This design can increase precision in association tests but distorts predictions when applying classifiers on nonstratified data. Several methods correct for this so-called sample selection bias, but their performance remains unclear especially for machine learning classifiers. With an emphasis on two-phase case-control studies, we aim to assess which corrections to perform in which setting and to obtain methods suitable for machine learning techniques, especially the random forest. We propose two new resampling-based methods to resemble the original data and covariance structure: stochastic inverse-probability oversampling and parametric inverse-probability bagging. We compare all techniques for the random forest and other classifiers, both theoretically and on simulated and real data. Empirical results show that the random forest profits from only the parametric inverse-probability bagging proposed by us. For other classifiers, correction is mostly advantageous, and methods perform uniformly. We discuss consequences of inappropriate distribution assumptions and reason for different behaviors between the random forest and other classifiers. In conclusion, we provide guidance for choosing correction methods when training classifiers on biased samples. For random forests, our method outperforms state-of-the-art procedures if distribution assumptions are roughly fulfilled. We provide our implementation in the R package sambia. PMID:29312464
40 CFR 90.507 - Sample selection.

Science.gov (United States)

2010-07-01

... Auditing § 90.507 Sample selection. (a) Engines comprising a test sample will be selected at the location... manufacturer ships any test engine, it relinquishes the prerogative to conduct retests as provided in § 90.508...
A random spatial sampling method in a rural developing nation

Science.gov (United States)

Michelle C. Kondo; Kent D.W. Bream; Frances K. Barg; Charles C. Branas

2014-01-01

Nonrandom sampling of populations in developing nations has limitations and can inaccurately estimate health phenomena, especially among hard-to-reach populations such as rural residents. However, random sampling of rural populations in developing nations can be challenged by incomplete enumeration of the base population. We describe a stratified random sampling method...
Randomization of grab-sampling strategies for estimating the annual exposure of U miners to Rn daughters.

Science.gov (United States)

Borak, T B

1986-04-01

Periodic grab sampling in combination with time-of-occupancy surveys has been the accepted procedure for estimating the annual exposure of underground U miners to Rn daughters. Temporal variations in the concentration of potential alpha energy in the mine generate uncertainties in this process. A system to randomize the selection of locations for measurement is described which can reduce uncertainties and eliminate systematic biases in the data. In general, a sample frequency of 50 measurements per year is sufficient to satisfy the criteria that the annual exposure be determined in working level months to within +/- 50% of the true value with a 95% level of confidence. Suggestions for implementing this randomization scheme are presented.
Using ArcMap, Google Earth, and Global Positioning Systems to select and locate random households in rural Haiti.

Science.gov (United States)

Wampler, Peter J; Rediske, Richard R; Molla, Azizur R

2013-01-18

A remote sensing technique was developed which combines a Geographic Information System (GIS); Google Earth, and Microsoft Excel to identify home locations for a random sample of households in rural Haiti. The method was used to select homes for ethnographic and water quality research in a region of rural Haiti located within 9 km of a local hospital and source of health education in Deschapelles, Haiti. The technique does not require access to governmental records or ground based surveys to collect household location data and can be performed in a rapid, cost-effective manner. The random selection of households and the location of these households during field surveys were accomplished using GIS, Google Earth, Microsoft Excel, and handheld Garmin GPSmap 76CSx GPS units. Homes were identified and mapped in Google Earth, exported to ArcMap 10.0, and a random list of homes was generated using Microsoft Excel which was then loaded onto handheld GPS units for field location. The development and use of a remote sensing method was essential to the selection and location of random households. A total of 537 homes initially were mapped and a randomized subset of 96 was identified as potential survey locations. Over 96% of the homes mapped using Google Earth imagery were correctly identified as occupied dwellings. Only 3.6% of the occupants of mapped homes visited declined to be interviewed. 16.4% of the homes visited were not occupied at the time of the visit due to work away from the home or market days. A total of 55 households were located using this method during the 10 days of fieldwork in May and June of 2012. The method used to generate and field locate random homes for surveys and water sampling was an effective means of selecting random households in a rural environment lacking geolocation infrastructure. The success rate for locating households using a handheld GPS was excellent and only rarely was local knowledge required to identify and locate households. This
Using ArcMap, Google Earth, and Global Positioning Systems to select and locate random households in rural Haiti

Directory of Open Access Journals (Sweden)

Wampler Peter J

2013-01-01

Full Text Available Abstract Background A remote sensing technique was developed which combines a Geographic Information System (GIS; Google Earth, and Microsoft Excel to identify home locations for a random sample of households in rural Haiti. The method was used to select homes for ethnographic and water quality research in a region of rural Haiti located within 9 km of a local hospital and source of health education in Deschapelles, Haiti. The technique does not require access to governmental records or ground based surveys to collect household location data and can be performed in a rapid, cost-effective manner. Methods The random selection of households and the location of these households during field surveys were accomplished using GIS, Google Earth, Microsoft Excel, and handheld Garmin GPSmap 76CSx GPS units. Homes were identified and mapped in Google Earth, exported to ArcMap 10.0, and a random list of homes was generated using Microsoft Excel which was then loaded onto handheld GPS units for field location. The development and use of a remote sensing method was essential to the selection and location of random households. Results A total of 537 homes initially were mapped and a randomized subset of 96 was identified as potential survey locations. Over 96% of the homes mapped using Google Earth imagery were correctly identified as occupied dwellings. Only 3.6% of the occupants of mapped homes visited declined to be interviewed. 16.4% of the homes visited were not occupied at the time of the visit due to work away from the home or market days. A total of 55 households were located using this method during the 10 days of fieldwork in May and June of 2012. Conclusions The method used to generate and field locate random homes for surveys and water sampling was an effective means of selecting random households in a rural environment lacking geolocation infrastructure. The success rate for locating households using a handheld GPS was excellent and only
Sample Selection for Training Cascade Detectors.

Science.gov (United States)

Vállez, Noelia; Deniz, Oscar; Bueno, Gloria

2015-01-01

Automatic detection systems usually require large and representative training datasets in order to obtain good detection and false positive rates. Training datasets are such that the positive set has few samples and/or the negative set should represent anything except the object of interest. In this respect, the negative set typically contains orders of magnitude more images than the positive set. However, imbalanced training databases lead to biased classifiers. In this paper, we focus our attention on a negative sample selection method to properly balance the training data for cascade detectors. The method is based on the selection of the most informative false positive samples generated in one stage to feed the next stage. The results show that the proposed cascade detector with sample selection obtains on average better partial AUC and smaller standard deviation than the other compared cascade detectors.
A Bayesian random effects discrete-choice model for resource selection: Population-level selection inference

Science.gov (United States)

Thomas, D.L.; Johnson, D.; Griffith, B.

2006-01-01

Modeling the probability of use of land units characterized by discrete and continuous measures, we present a Bayesian random-effects model to assess resource selection. This model provides simultaneous estimation of both individual- and population-level selection. Deviance information criterion (DIC), a Bayesian alternative to AIC that is sample-size specific, is used for model selection. Aerial radiolocation data from 76 adult female caribou (Rangifer tarandus) and calf pairs during 1 year on an Arctic coastal plain calving ground were used to illustrate models and assess population-level selection of landscape attributes, as well as individual heterogeneity of selection. Landscape attributes included elevation, NDVI (a measure of forage greenness), and land cover-type classification. Results from the first of a 2-stage model-selection procedure indicated that there is substantial heterogeneity among cow-calf pairs with respect to selection of the landscape attributes. In the second stage, selection of models with heterogeneity included indicated that at the population-level, NDVI and land cover class were significant attributes for selection of different landscapes by pairs on the calving ground. Population-level selection coefficients indicate that the pairs generally select landscapes with higher levels of NDVI, but the relationship is quadratic. The highest rate of selection occurs at values of NDVI less than the maximum observed. Results for land cover-class selections coefficients indicate that wet sedge, moist sedge, herbaceous tussock tundra, and shrub tussock tundra are selected at approximately the same rate, while alpine and sparsely vegetated landscapes are selected at a lower rate. Furthermore, the variability in selection by individual caribou for moist sedge and sparsely vegetated landscapes is large relative to the variability in selection of other land cover types. The example analysis illustrates that, while sometimes computationally intense, a
Application of a stratified random sampling technique to the estimation and minimization of respirable quartz exposure to underground miners

International Nuclear Information System (INIS)

Makepeace, C.E.; Horvath, F.J.; Stocker, H.

1981-11-01

The aim of a stratified random sampling plan is to provide the best estimate (in the absence of full-shift personal gravimetric sampling) of personal exposure to respirable quartz among underground miners. One also gains information of the exposure distribution of all the miners at the same time. Three variables (or strata) are considered in the present scheme: locations, occupations and times of sampling. Random sampling within each stratum ensures that each location, occupation and time of sampling has equal opportunity of being selected without bias. Following implementation of the plan and analysis of collected data, one can determine the individual exposures and the mean. This information can then be used to identify those groups whose exposure contributes significantly to the collective exposure. In turn, this identification, along with other considerations, allows the mine operator to carry out a cost-benefit optimization and eventual implementation of engineering controls for these groups. This optimization and engineering control procedure, together with the random sampling plan, can then be used in an iterative manner to minimize the mean value of the distribution and collective exposures
Determination of Initial Conditions for the Safety Analysis by Random Sampling of Operating Parameters

International Nuclear Information System (INIS)

Jeong, Hae-Yong; Park, Moon-Ghu

2015-01-01

In most existing evaluation methodologies, which follow a conservative approach, the most conservative initial conditions are searched for each transient scenario through tremendous assessment for wide operating windows or limiting conditions for operation (LCO) allowed by the operating guidelines. In this procedure, a user effect could be involved and a remarkable time and human resources are consumed. In the present study, we investigated a more effective statistical method for the selection of the most conservative initial condition by the use of random sampling of operating parameters affecting the initial conditions. A method for the determination of initial conditions based on random sampling of plant design parameters is proposed. This method is expected to be applied for the selection of the most conservative initial plant conditions in the safety analysis using a conservative evaluation methodology. In the method, it is suggested that the initial conditions of reactor coolant flow rate, pressurizer level, pressurizer pressure, and SG level are adjusted by controlling the pump rated flow, setpoints of PLCS, PPCS, and FWCS, respectively. The proposed technique is expected to contribute to eliminate the human factors introduced in the conventional safety analysis procedure and also to reduce the human resources invested in the safety evaluation of nuclear power plants
Sample Selection for Training Cascade Detectors.

Directory of Open Access Journals (Sweden)

Noelia Vállez

Full Text Available Automatic detection systems usually require large and representative training datasets in order to obtain good detection and false positive rates. Training datasets are such that the positive set has few samples and/or the negative set should represent anything except the object of interest. In this respect, the negative set typically contains orders of magnitude more images than the positive set. However, imbalanced training databases lead to biased classifiers. In this paper, we focus our attention on a negative sample selection method to properly balance the training data for cascade detectors. The method is based on the selection of the most informative false positive samples generated in one stage to feed the next stage. The results show that the proposed cascade detector with sample selection obtains on average better partial AUC and smaller standard deviation than the other compared cascade detectors.
SOME SYSTEMATIC SAMPLING STRATEGIES USING MULTIPLE RANDOM STARTS

OpenAIRE

Sampath Sundaram; Ammani Sivaraman

2010-01-01

In this paper an attempt is made to extend linear systematic sampling using multiple random starts due to Gautschi(1957)for various types of systematic sampling schemes available in literature, namely(i) Balanced Systematic Sampling (BSS) of Sethi (1965) and (ii) Modified Systematic Sampling (MSS) of Singh, Jindal, and Garg (1968). Further, the proposed methods were compared with Yates corrected estimator developed with reference to Gautschi’s Linear systematic samplin...

UNLABELED SELECTED SAMPLES IN FEATURE EXTRACTION FOR CLASSIFICATION OF HYPERSPECTRAL IMAGES WITH LIMITED TRAINING SAMPLES

Directory of Open Access Journals (Sweden)

A. Kianisarkaleh

2015-12-01

Full Text Available Feature extraction plays a key role in hyperspectral images classification. Using unlabeled samples, often unlimitedly available, unsupervised and semisupervised feature extraction methods show better performance when limited number of training samples exists. This paper illustrates the importance of selecting appropriate unlabeled samples that used in feature extraction methods. Also proposes a new method for unlabeled samples selection using spectral and spatial information. The proposed method has four parts including: PCA, prior classification, posterior classification and sample selection. As hyperspectral image passes these parts, selected unlabeled samples can be used in arbitrary feature extraction methods. The effectiveness of the proposed unlabeled selected samples in unsupervised and semisupervised feature extraction is demonstrated using two real hyperspectral datasets. Results show that through selecting appropriate unlabeled samples, the proposed method can improve the performance of feature extraction methods and increase classification accuracy.
RANdom SAmple Consensus (RANSAC) algorithm for material-informatics: application to photovoltaic solar cells.

Science.gov (United States)

Kaspi, Omer; Yosipof, Abraham; Senderowitz, Hanoch

2017-06-06

An important aspect of chemoinformatics and material-informatics is the usage of machine learning algorithms to build Quantitative Structure Activity Relationship (QSAR) models. The RANdom SAmple Consensus (RANSAC) algorithm is a predictive modeling tool widely used in the image processing field for cleaning datasets from noise. RANSAC could be used as a "one stop shop" algorithm for developing and validating QSAR models, performing outlier removal, descriptors selection, model development and predictions for test set samples using applicability domain. For "future" predictions (i.e., for samples not included in the original test set) RANSAC provides a statistical estimate for the probability of obtaining reliable predictions, i.e., predictions within a pre-defined number of standard deviations from the true values. In this work we describe the first application of RNASAC in material informatics, focusing on the analysis of solar cells. We demonstrate that for three datasets representing different metal oxide (MO) based solar cell libraries RANSAC-derived models select descriptors previously shown to correlate with key photovoltaic properties and lead to good predictive statistics for these properties. These models were subsequently used to predict the properties of virtual solar cells libraries highlighting interesting dependencies of PV properties on MO compositions.
Health plan auditing: 100-percent-of-claims vs. random-sample audits.

Science.gov (United States)

Sillup, George P; Klimberg, Ronald K

2011-01-01

The objective of this study was to examine the relative efficacy of two different methodologies for auditing self-funded medical claim expenses: 100-percent-of-claims auditing versus random-sampling auditing. Multiple data sets of claim errors or 'exceptions' from two Fortune-100 corporations were analysed and compared to 100 simulated audits of 300- and 400-claim random samples. Random-sample simulations failed to identify a significant number and amount of the errors that ranged from $200,000 to $750,000. These results suggest that health plan expenses of corporations could be significantly reduced if they audited 100% of claims and embraced a zero-defect approach.
Methodology Series Module 5: Sampling Strategies.

Science.gov (United States)

Setia, Maninder Singh

2016-01-01

Once the research question and the research design have been finalised, it is important to select the appropriate sample for the study. The method by which the researcher selects the sample is the ' Sampling Method'. There are essentially two types of sampling methods: 1) probability sampling - based on chance events (such as random numbers, flipping a coin etc.); and 2) non-probability sampling - based on researcher's choice, population that accessible & available. Some of the non-probability sampling methods are: purposive sampling, convenience sampling, or quota sampling. Random sampling method (such as simple random sample or stratified random sample) is a form of probability sampling. It is important to understand the different sampling methods used in clinical studies and mention this method clearly in the manuscript. The researcher should not misrepresent the sampling method in the manuscript (such as using the term ' random sample' when the researcher has used convenience sample). The sampling method will depend on the research question. For instance, the researcher may want to understand an issue in greater detail for one particular population rather than worry about the ' generalizability' of these results. In such a scenario, the researcher may want to use ' purposive sampling' for the study.
Methodology series module 5: Sampling strategies

Directory of Open Access Journals (Sweden)

Maninder Singh Setia

2016-01-01

Full Text Available Once the research question and the research design have been finalised, it is important to select the appropriate sample for the study. The method by which the researcher selects the sample is the 'Sampling Method'. There are essentially two types of sampling methods: 1 probability sampling – based on chance events (such as random numbers, flipping a coin etc.; and 2 non-probability sampling – based on researcher's choice, population that accessible & available. Some of the non-probability sampling methods are: purposive sampling, convenience sampling, or quota sampling. Random sampling method (such as simple random sample or stratified random sample is a form of probability sampling. It is important to understand the different sampling methods used in clinical studies and mention this method clearly in the manuscript. The researcher should not misrepresent the sampling method in the manuscript (such as using the term 'random sample' when the researcher has used convenience sample. The sampling method will depend on the research question. For instance, the researcher may want to understand an issue in greater detail for one particular population rather than worry about the 'generalizability' of these results. In such a scenario, the researcher may want to use 'purposive sampling' for the study.
Sampling Large Graphs for Anticipatory Analytics

Science.gov (United States)

2015-05-15

low. C. Random Area Sampling Random area sampling [8] is a “ snowball ” sampling method in which a set of random seed vertices are selected and areas... Sampling Large Graphs for Anticipatory Analytics Lauren Edwards, Luke Johnson, Maja Milosavljevic, Vijay Gadepally, Benjamin A. Miller Lincoln...systems, greater human-in-the-loop involvement, or through complex algorithms. We are investigating the use of sampling to mitigate these challenges
Methodology Series Module 5: Sampling Strategies

Science.gov (United States)

Setia, Maninder Singh

2016-01-01

Once the research question and the research design have been finalised, it is important to select the appropriate sample for the study. The method by which the researcher selects the sample is the ‘ Sampling Method’. There are essentially two types of sampling methods: 1) probability sampling – based on chance events (such as random numbers, flipping a coin etc.); and 2) non-probability sampling – based on researcher's choice, population that accessible & available. Some of the non-probability sampling methods are: purposive sampling, convenience sampling, or quota sampling. Random sampling method (such as simple random sample or stratified random sample) is a form of probability sampling. It is important to understand the different sampling methods used in clinical studies and mention this method clearly in the manuscript. The researcher should not misrepresent the sampling method in the manuscript (such as using the term ‘ random sample’ when the researcher has used convenience sample). The sampling method will depend on the research question. For instance, the researcher may want to understand an issue in greater detail for one particular population rather than worry about the ‘ generalizability’ of these results. In such a scenario, the researcher may want to use ‘ purposive sampling’ for the study. PMID:27688438
Evaluation of a Class of Simple and Effective Uncertainty Methods for Sparse Samples of Random Variables and Functions

Energy Technology Data Exchange (ETDEWEB)

Romero, Vicente [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Bonney, Matthew [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Schroeder, Benjamin [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Weirs, V. Gregory [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

2017-11-01

When very few samples of a random quantity are available from a source distribution of unknown shape, it is usually not possible to accurately infer the exact distribution from which the data samples come. Under-estimation of important quantities such as response variance and failure probabilities can result. For many engineering purposes, including design and risk analysis, we attempt to avoid under-estimation with a strategy to conservatively estimate (bound) these types of quantities -- without being overly conservative -- when only a few samples of a random quantity are available from model predictions or replicate experiments. This report examines a class of related sparse-data uncertainty representation and inference approaches that are relatively simple, inexpensive, and effective. Tradeoffs between the methods' conservatism, reliability, and risk versus number of data samples (cost) are quantified with multi-attribute metrics use d to assess method performance for conservative estimation of two representative quantities: central 95% of response; and 10^-4 probability of exceeding a response threshold in a tail of the distribution. Each method's performance is characterized with 10,000 random trials on a large number of diverse and challenging distributions. The best method and number of samples to use in a given circumstance depends on the uncertainty quantity to be estimated, the PDF character, and the desired reliability of bounding the true value. On the basis of this large data base and study, a strategy is proposed for selecting the method and number of samples for attaining reasonable credibility levels in bounding these types of quantities when sparse samples of random variables or functions are available from experiments or simulations.
Estimates of Inequality Indices Based on Simple Random, Ranked Set, and Systematic Sampling

OpenAIRE

Bansal, Pooja; Arora, Sangeeta; Mahajan, Kalpana K.

2013-01-01

Gini index, Bonferroni index, and Absolute Lorenz index are some popular indices of inequality showing different features of inequality measurement. In general simple random sampling procedure is commonly used to estimate the inequality indices and their related inference. The key condition that the samples must be drawn via simple random sampling procedure though makes calculations much simpler but this assumption is often violated in practice as the data does not always yield simple random ...
Coordination of Conditional Poisson Samples

Directory of Open Access Journals (Sweden)

Grafström Anton

2015-12-01

Full Text Available Sample coordination seeks to maximize or to minimize the overlap of two or more samples. The former is known as positive coordination, and the latter as negative coordination. Positive coordination is mainly used for estimation purposes and to reduce data collection costs. Negative coordination is mainly performed to diminish the response burden of the sampled units. Poisson sampling design with permanent random numbers provides an optimum coordination degree of two or more samples. The size of a Poisson sample is, however, random. Conditional Poisson (CP sampling is a modification of the classical Poisson sampling that produces a fixed-size πps sample. We introduce two methods to coordinate Conditional Poisson samples over time or simultaneously. The first one uses permanent random numbers and the list-sequential implementation of CP sampling. The second method uses a CP sample in the first selection and provides an approximate one in the second selection because the prescribed inclusion probabilities are not respected exactly. The methods are evaluated using the size of the expected sample overlap, and are compared with their competitors using Monte Carlo simulation. The new methods provide a good coordination degree of two samples, close to the performance of Poisson sampling with permanent random numbers.
An alternative procedure for estimating the population mean in simple random sampling

Directory of Open Access Journals (Sweden)

Housila P. Singh

2012-03-01

Full Text Available This paper deals with the problem of estimating the finite population mean using auxiliary information in simple random sampling. Firstly we have suggested a correction to the mean squared error of the estimator proposed by Gupta and Shabbir [On improvement in estimating the population mean in simple random sampling. Jour. Appl. Statist. 35(5 (2008, pp. 559-566]. Later we have proposed a ratio type estimator and its properties are studied in simple random sampling. Numerically we have shown that the proposed class of estimators is more efficient than different known estimators including Gupta and Shabbir (2008 estimator.
Correlated random sampling for multivariate normal and log-normal distributions

International Nuclear Information System (INIS)

Žerovnik, Gašper; Trkov, Andrej; Kodeli, Ivan A.

2012-01-01

A method for correlated random sampling is presented. Representative samples for multivariate normal or log-normal distribution can be produced. Furthermore, any combination of normally and log-normally distributed correlated variables may be sampled to any requested accuracy. Possible applications of the method include sampling of resonance parameters which are used for reactor calculations.
Systematic sampling of discrete and continuous populations: sample selection and the choice of estimator

Science.gov (United States)

Harry T. Valentine; David L. R. Affleck; Timothy G. Gregoire

2009-01-01

Systematic sampling is easy, efficient, and widely used, though it is not generally recognized that a systematic sample may be drawn from the population of interest with or without restrictions on randomization. The restrictions or the lack of them determine which estimators are unbiased, when using the sampling design as the basis for inference. We describe the...
Progressive sampling-based Bayesian optimization for efficient and automatic machine learning model selection.

Science.gov (United States)

Zeng, Xueqiang; Luo, Gang

2017-12-01

Machine learning is broadly used for clinical data analysis. Before training a model, a machine learning algorithm must be selected. Also, the values of one or more model parameters termed hyper-parameters must be set. Selecting algorithms and hyper-parameter values requires advanced machine learning knowledge and many labor-intensive manual iterations. To lower the bar to machine learning, miscellaneous automatic selection methods for algorithms and/or hyper-parameter values have been proposed. Existing automatic selection methods are inefficient on large data sets. This poses a challenge for using machine learning in the clinical big data era. To address the challenge, this paper presents progressive sampling-based Bayesian optimization, an efficient and automatic selection method for both algorithms and hyper-parameter values. We report an implementation of the method. We show that compared to a state of the art automatic selection method, our method can significantly reduce search time, classification error rate, and standard deviation of error rate due to randomization. This is major progress towards enabling fast turnaround in identifying high-quality solutions required by many machine learning-based clinical data analysis tasks.
Analysis and applications of a frequency selective surface via a random distribution method

International Nuclear Information System (INIS)

Xie Shao-Yi; Huang Jing-Jian; Yuan Nai-Chang; Liu Li-Guo

2014-01-01

A novel frequency selective surface (FSS) for reducing radar cross section (RCS) is proposed in this paper. This FSS is based on the random distribution method, so it can be called random surface. In this paper, the stacked patches serving as periodic elements are employed for RCS reduction. Previous work has demonstrated the efficiency by utilizing the microstrip patches, especially for the reflectarray. First, the relevant theory of the method is described. Then a sample of a three-layer variable-sized stacked patch random surface with a dimension of 260 mm×260 mm is simulated, fabricated, and measured in order to demonstrate the validity of the proposed design. For the normal incidence, the 8-dB RCS reduction can be achieved both by the simulation and the measurement in 8 GHz–13 GHz. The oblique incidence of 30° is also investigated, in which the 7-dB RCS reduction can be obtained in a frequency range of 8 GHz–14 GHz. (condensed matter: electronic structure, electrical, magnetic, and optical properties)
40 CFR 205.171-3 - Test motorcycle sample selection.

Science.gov (United States)

2010-07-01

... 40 Protection of Environment 24 2010-07-01 2010-07-01 false Test motorcycle sample selection. 205... ABATEMENT PROGRAMS TRANSPORTATION EQUIPMENT NOISE EMISSION CONTROLS Motorcycle Exhaust Systems § 205.171-3 Test motorcycle sample selection. A test motorcycle to be used for selective enforcement audit testing...
42 CFR 431.814 - Sampling plan and procedures.

Science.gov (United States)

2010-10-01

... reliability of the reduced sample. (4) The sample selection procedure. Systematic random sampling is... sampling, and yield estimates with the same or better precision than achieved in systematic random sampling... 42 Public Health 4 2010-10-01 2010-10-01 false Sampling plan and procedures. 431.814 Section 431...
Random sampling of evolution time space and Fourier transform processing

International Nuclear Information System (INIS)

Kazimierczuk, Krzysztof; Zawadzka, Anna; Kozminski, Wiktor; Zhukov, Igor

2006-01-01

Application of Fourier Transform for processing 3D NMR spectra with random sampling of evolution time space is presented. The 2D FT is calculated for pairs of frequencies, instead of conventional sequence of one-dimensional transforms. Signal to noise ratios and linewidths for different random distributions were investigated by simulations and experiments. The experimental examples include 3D HNCA, HNCACB and 15 N-edited NOESY-HSQC spectra of 13 C 15 N labeled ubiquitin sample. Obtained results revealed general applicability of proposed method and the significant improvement of resolution in comparison with conventional spectra recorded in the same time
Treatment selection in a randomized clinical trial via covariate-specific treatment effect curves.

Science.gov (United States)

Ma, Yunbei; Zhou, Xiao-Hua

2017-02-01

For time-to-event data in a randomized clinical trial, we proposed two new methods for selecting an optimal treatment for a patient based on the covariate-specific treatment effect curve, which is used to represent the clinical utility of a predictive biomarker. To select an optimal treatment for a patient with a specific biomarker value, we proposed pointwise confidence intervals for each covariate-specific treatment effect curve and the difference between covariate-specific treatment effect curves of two treatments. Furthermore, to select an optimal treatment for a future biomarker-defined subpopulation of patients, we proposed confidence bands for each covariate-specific treatment effect curve and the difference between each pair of covariate-specific treatment effect curve over a fixed interval of biomarker values. We constructed the confidence bands based on a resampling technique. We also conducted simulation studies to evaluate finite-sample properties of the proposed estimation methods. Finally, we illustrated the application of the proposed method in a real-world data set.
A random sampling procedure for anisotropic distributions

International Nuclear Information System (INIS)

Nagrajan, P.S.; Sethulakshmi, P.; Raghavendran, C.P.; Bhatia, D.P.

1975-01-01

A procedure is described for sampling the scattering angle of neutrons as per specified angular distribution data. The cosine of the scattering angle is written as a double Legendre expansion in the incident neutron energy and a random number. The coefficients of the expansion are given for C, N, O, Si, Ca, Fe and Pb and these elements are of interest in dosimetry and shielding. (author)

MUP, CEC-DES, STRADE. Codes for uncertainty propagation, experimental design and stratified random sampling techniques

International Nuclear Information System (INIS)

Amendola, A.; Astolfi, M.; Lisanti, B.

1983-01-01

The report describes the how-to-use of the codes: MUP (Monte Carlo Uncertainty Propagation) for uncertainty analysis by Monte Carlo simulation, including correlation analysis, extreme value identification and study of selected ranges of the variable space; CEC-DES (Central Composite Design) for building experimental matrices according to the requirements of Central Composite and Factorial Experimental Designs; and, STRADE (Stratified Random Design) for experimental designs based on the Latin Hypercube Sampling Techniques. Application fields, of the codes are probabilistic risk assessment, experimental design, sensitivity analysis and system identification problems
Applications of random forest feature selection for fine-scale genetic population assignment.

Science.gov (United States)

Sylvester, Emma V A; Bentzen, Paul; Bradbury, Ian R; Clément, Marie; Pearce, Jon; Horne, John; Beiko, Robert G

2018-02-01

Genetic population assignment used to inform wildlife management and conservation efforts requires panels of highly informative genetic markers and sensitive assignment tests. We explored the utility of machine-learning algorithms (random forest, regularized random forest and guided regularized random forest) compared with F ST ranking for selection of single nucleotide polymorphisms (SNP) for fine-scale population assignment. We applied these methods to an unpublished SNP data set for Atlantic salmon ( Salmo salar ) and a published SNP data set for Alaskan Chinook salmon ( Oncorhynchus tshawytscha ). In each species, we identified the minimum panel size required to obtain a self-assignment accuracy of at least 90% using each method to create panels of 50-700 markers Panels of SNPs identified using random forest-based methods performed up to 7.8 and 11.2 percentage points better than F ST -selected panels of similar size for the Atlantic salmon and Chinook salmon data, respectively. Self-assignment accuracy ≥90% was obtained with panels of 670 and 384 SNPs for each data set, respectively, a level of accuracy never reached for these species using F ST -selected panels. Our results demonstrate a role for machine-learning approaches in marker selection across large genomic data sets to improve assignment for management and conservation of exploited populations.
Fast egg collection method greatly improves randomness of egg sampling in Drosophila melanogaster

DEFF Research Database (Denmark)

Schou, Mads Fristrup

2013-01-01

When obtaining samples for population genetic studies, it is essential that the sampling is random. For Drosophila, one of the crucial steps in sampling experimental flies is the collection of eggs. Here an egg collection method is presented, which randomizes the eggs in a water column...... and diminishes environmental variance. This method was compared with a traditional egg collection method where eggs are collected directly from the medium. Within each method the observed and expected standard deviations of egg-to-adult viability were compared, whereby the difference in the randomness...... and to obtain a representative collection of genotypes, the method presented here is strongly recommended when collecting eggs from Drosophila....
Interference-aware random beam selection for spectrum sharing systems

KAUST Repository

Abdallah, Mohamed M.

2012-09-01

Spectrum sharing systems have been introduced to alleviate the problem of spectrum scarcity by allowing secondary unlicensed networks to share the spectrum with primary licensed networks under acceptable interference levels to the primary users. In this paper, we develop interference-aware random beam selection schemes that provide enhanced throughput for the secondary link under the condition that the interference observed at the primary link is within a predetermined acceptable value. For a secondary transmitter equipped with multiple antennas, our schemes select a random beam, among a set of power- optimized orthogonal random beams, that maximizes the capacity of the secondary link while satisfying the interference constraint at the primary receiver for different levels of feedback information describing the interference level at the primary receiver. For the proposed schemes, we develop a statistical analysis for the signal-to-noise and interference ratio (SINR) statistics as well as the capacity of the secondary link. Finally, we present numerical results that study the effect of system parameters including number of beams and the maximum transmission power on the capacity of the secondary link attained using the proposed schemes. © 2012 IEEE.
Interference-aware random beam selection for spectrum sharing systems

KAUST Repository

Abdallah, Mohamed M.; Sayed, Mostafa M.; Alouini, Mohamed-Slim; Qaraqe, Khalid A.

2012-01-01

. In this paper, we develop interference-aware random beam selection schemes that provide enhanced throughput for the secondary link under the condition that the interference observed at the primary link is within a predetermined acceptable value. For a secondary
Choosing a suitable sample size in descriptive sampling

International Nuclear Information System (INIS)

Lee, Yong Kyun; Choi, Dong Hoon; Cha, Kyung Joon

2010-01-01

Descriptive sampling (DS) is an alternative to crude Monte Carlo sampling (CMCS) in finding solutions to structural reliability problems. It is known to be an effective sampling method in approximating the distribution of a random variable because it uses the deterministic selection of sample values and their random permutation,. However, because this method is difficult to apply to complex simulations, the sample size is occasionally determined without thorough consideration. Input sample variability may cause the sample size to change between runs, leading to poor simulation results. This paper proposes a numerical method for choosing a suitable sample size for use in DS. Using this method, one can estimate a more accurate probability of failure in a reliability problem while running a minimal number of simulations. The method is then applied to several examples and compared with CMCS and conventional DS to validate its usefulness and efficiency
STATISTICAL LANDMARKS AND PRACTICAL ISSUES REGARDING THE USE OF SIMPLE RANDOM SAMPLING IN MARKET RESEARCHES

Directory of Open Access Journals (Sweden)

CODRUŢA DURA

2010-01-01

Full Text Available The sample represents a particular segment of the statistical populationchosen to represent it as a whole. The representativeness of the sample determines the accuracyfor estimations made on the basis of calculating the research indicators and the inferentialstatistics. The method of random sampling is part of probabilistic methods which can be usedwithin marketing research and it is characterized by the fact that it imposes the requirementthat each unit belonging to the statistical population should have an equal chance of beingselected for the sampling process. When the simple random sampling is meant to be rigorouslyput into practice, it is recommended to use the technique of random number tables in order toconfigure the sample which will provide information that the marketer needs. The paper alsodetails the practical procedure implemented in order to create a sample for a marketingresearch by generating random numbers using the facilities offered by Microsoft Excel.
CONSISTENCY UNDER SAMPLING OF EXPONENTIAL RANDOM GRAPH MODELS.

Science.gov (United States)

Shalizi, Cosma Rohilla; Rinaldo, Alessandro

2013-04-01

The growing availability of network data and of scientific interest in distributed systems has led to the rapid development of statistical models of network structure. Typically, however, these are models for the entire network, while the data consists only of a sampled sub-network. Parameters for the whole network, which is what is of interest, are estimated by applying the model to the sub-network. This assumes that the model is consistent under sampling , or, in terms of the theory of stochastic processes, that it defines a projective family. Focusing on the popular class of exponential random graph models (ERGMs), we show that this apparently trivial condition is in fact violated by many popular and scientifically appealing models, and that satisfying it drastically limits ERGM's expressive power. These results are actually special cases of more general results about exponential families of dependent random variables, which we also prove. Using such results, we offer easily checked conditions for the consistency of maximum likelihood estimation in ERGMs, and discuss some possible constructive responses.
The signature of positive selection at randomly chosen loci.

OpenAIRE

Przeworski, Molly

2002-01-01

In Drosophila and humans, there are accumulating examples of loci with a significant excess of high-frequency-derived alleles or high levels of linkage disequilibrium, relative to a neutral model of a random-mating population of constant size. These are features expected after a recent selective sweep. Their prevalence suggests that positive directional selection may be widespread in both species. However, as I show here, these features do not persist long after the sweep ends: The high-frequ...
Simulated Performance Evaluation of a Selective Tracker Through Random Scenario Generation

DEFF Research Database (Denmark)

Hussain, Dil Muhammad Akbar

2006-01-01

performance assessment. Therefore, a random target motion scenario is adopted. Its implementation in particular for testing the proposed selective track splitting algorithm using Kalman filters is investigated through a number of performance parameters which gives the activity profile of the tracking scenario...... The paper presents a simulation study on the performance of a target tracker using selective track splitting filter algorithm through a random scenario implemented on a digital signal processor. In a typical track splitting filter all the observation which fall inside a likelihood ellipse...... are used for update, however, in our proposed selective track splitting filter less number of observations are used for track update. Much of the previous performance work [1] has been done on specific (deterministic) scenarios. One of the reasons for considering the specific scenarios, which were...
Minimization over randomly selected lines

Directory of Open Access Journals (Sweden)

Ismet Sahin

2013-07-01

Full Text Available This paper presents a population-based evolutionary optimization method for minimizing a given cost function. The mutation operator of this method selects randomly oriented lines in the cost function domain, constructs quadratic functions interpolating the cost function at three different points over each line, and uses extrema of the quadratics as mutated points. The crossover operator modifies each mutated point based on components of two points in population, instead of one point as is usually performed in other evolutionary algorithms. The stopping criterion of this method depends on the number of almost degenerate quadratics. We demonstrate that the proposed method with these mutation and crossover operations achieves faster and more robust convergence than the well-known Differential Evolution and Particle Swarm algorithms.
Characterization of electron microscopes with binary pseudo-random multilayer test samples

Science.gov (United States)

Yashchuk, Valeriy V.; Conley, Raymond; Anderson, Erik H.; Barber, Samuel K.; Bouet, Nathalie; McKinney, Wayne R.; Takacs, Peter Z.; Voronov, Dmitriy L.

2011-09-01

Verification of the reliability of metrology data from high quality X-ray optics requires that adequate methods for test and calibration of the instruments be developed. For such verification for optical surface profilometers in the spatial frequency domain, a modulation transfer function (MTF) calibration method based on binary pseudo-random (BPR) gratings and arrays has been suggested [1,2] and proven to be an effective calibration method for a number of interferometric microscopes, a phase shifting Fizeau interferometer, and a scatterometer [5]. Here we describe the details of development of binary pseudo-random multilayer (BPRML) test samples suitable for characterization of scanning (SEM) and transmission (TEM) electron microscopes. We discuss the results of TEM measurements with the BPRML test samples fabricated from a WiSi 2/Si multilayer coating with pseudo-randomly distributed layers. In particular, we demonstrate that significant information about the metrological reliability of the TEM measurements can be extracted even when the fundamental frequency of the BPRML sample is smaller than the Nyquist frequency of the measurements. The measurements demonstrate a number of problems related to the interpretation of the SEM and TEM data. Note that similar BPRML test samples can be used to characterize X-ray microscopes. Corresponding work with X-ray microscopes is in progress.
Characterization of electron microscopes with binary pseudo-random multilayer test samples

International Nuclear Information System (INIS)

Yashchuk, Valeriy V.; Conley, Raymond; Anderson, Erik H.; Barber, Samuel K.; Bouet, Nathalie; McKinney, Wayne R.; Takacs, Peter Z.; Voronov, Dmitriy L.

2011-01-01

Verification of the reliability of metrology data from high quality X-ray optics requires that adequate methods for test and calibration of the instruments be developed. For such verification for optical surface profilometers in the spatial frequency domain, a modulation transfer function (MTF) calibration method based on binary pseudo-random (BPR) gratings and arrays has been suggested and proven to be an effective calibration method for a number of interferometric microscopes, a phase shifting Fizeau interferometer, and a scatterometer [5]. Here we describe the details of development of binary pseudo-random multilayer (BPRML) test samples suitable for characterization of scanning (SEM) and transmission (TEM) electron microscopes. We discuss the results of TEM measurements with the BPRML test samples fabricated from a WiSi 2 /Si multilayer coating with pseudo-randomly distributed layers. In particular, we demonstrate that significant information about the metrological reliability of the TEM measurements can be extracted even when the fundamental frequency of the BPRML sample is smaller than the Nyquist frequency of the measurements. The measurements demonstrate a number of problems related to the interpretation of the SEM and TEM data. Note that similar BPRML test samples can be used to characterize X-ray microscopes. Corresponding work with X-ray microscopes is in progress.
LOD score exclusion analyses for candidate genes using random population samples.

Science.gov (United States)

Deng, H W; Li, J; Recker, R R

2001-05-01

While extensive analyses have been conducted to test for, no formal analyses have been conducted to test against, the importance of candidate genes with random population samples. We develop a LOD score approach for exclusion analyses of candidate genes with random population samples. Under this approach, specific genetic effects and inheritance models at candidate genes can be analysed and if a LOD score is < or = - 2.0, the locus can be excluded from having an effect larger than that specified. Computer simulations show that, with sample sizes often employed in association studies, this approach has high power to exclude a gene from having moderate genetic effects. In contrast to regular association analyses, population admixture will not affect the robustness of our analyses; in fact, it renders our analyses more conservative and thus any significant exclusion result is robust. Our exclusion analysis complements association analysis for candidate genes in random population samples and is parallel to the exclusion mapping analyses that may be conducted in linkage analyses with pedigrees or relative pairs. The usefulness of the approach is demonstrated by an application to test the importance of vitamin D receptor and estrogen receptor genes underlying the differential risk to osteoporotic fractures.
Statistical sampling strategies

International Nuclear Information System (INIS)

Andres, T.H.

1987-01-01

Systems assessment codes use mathematical models to simulate natural and engineered systems. Probabilistic systems assessment codes carry out multiple simulations to reveal the uncertainty in values of output variables due to uncertainty in the values of the model parameters. In this paper, methods are described for sampling sets of parameter values to be used in a probabilistic systems assessment code. Three Monte Carlo parameter selection methods are discussed: simple random sampling, Latin hypercube sampling, and sampling using two-level orthogonal arrays. Three post-selection transformations are also described: truncation, importance transformation, and discretization. Advantages and disadvantages of each method are summarized
40 CFR 205.160-2 - Test sample selection and preparation.

Science.gov (United States)

2010-07-01

... 40 Protection of Environment 24 2010-07-01 2010-07-01 false Test sample selection and preparation... sample selection and preparation. (a) Vehicles comprising the sample which are required to be tested... maintained in any manner unless such preparation, tests, modifications, adjustments or maintenance are part...
Generating Random Samples of a Given Size Using Social Security Numbers.

Science.gov (United States)

Erickson, Richard C.; Brauchle, Paul E.

1984-01-01

The purposes of this article are (1) to present a method by which social security numbers may be used to draw cluster samples of a predetermined size and (2) to describe procedures used to validate this method of drawing random samples. (JOW)
Random On-Board Pixel Sampling (ROPS) X-Ray Camera

Energy Technology Data Exchange (ETDEWEB)

Wang, Zhehui [Los Alamos; Iaroshenko, O. [Los Alamos; Li, S. [Los Alamos; Liu, T. [Fermilab; Parab, N. [Argonne (main); Chen, W. W. [Purdue U.; Chu, P. [Los Alamos; Kenyon, G. [Los Alamos; Lipton, R. [Fermilab; Sun, K.-X. [Nevada U., Las Vegas

2017-09-25

Recent advances in compressed sensing theory and algorithms offer new possibilities for high-speed X-ray camera design. In many CMOS cameras, each pixel has an independent on-board circuit that includes an amplifier, noise rejection, signal shaper, an analog-to-digital converter (ADC), and optional in-pixel storage. When X-ray images are sparse, i.e., when one of the following cases is true: (a.) The number of pixels with true X-ray hits is much smaller than the total number of pixels; (b.) The X-ray information is redundant; or (c.) Some prior knowledge about the X-ray images exists, sparse sampling may be allowed. Here we first illustrate the feasibility of random on-board pixel sampling (ROPS) using an existing set of X-ray images, followed by a discussion about signal to noise as a function of pixel size. Next, we describe a possible circuit architecture to achieve random pixel access and in-pixel storage. The combination of a multilayer architecture, sparse on-chip sampling, and computational image techniques, is expected to facilitate the development and applications of high-speed X-ray camera technology.
Selection bias and subject refusal in a cluster-randomized controlled trial

Directory of Open Access Journals (Sweden)

Rochelle Yang

2017-07-01

Full Text Available Abstract Background Selection bias and non-participation bias are major methodological concerns which impact external validity. Cluster-randomized controlled trials are especially prone to selection bias as it is impractical to blind clusters to their allocation into intervention or control. This study assessed the impact of selection bias in a large cluster-randomized controlled trial. Methods The Improved Cardiovascular Risk Reduction to Enhance Rural Primary Care (ICARE study examined the impact of a remote pharmacist-led intervention in twelve medical offices. To assess eligibility, a standardized form containing patient demographics and medical information was completed for each screened patient. Eligible patients were approached by the study coordinator for recruitment. Both the study coordinator and the patient were aware of the site’s allocation prior to consent. Patients who consented or declined to participate were compared across control and intervention arms for differing characteristics. Statistical significance was determined using a two-tailed, equal variance t-test and a chi-square test with adjusted Bonferroni p-values. Results were adjusted for random cluster variation. Results There were 2749 completed screening forms returned to research staff with 461 subjects who had either consented or declined participation. Patients with poorly controlled diabetes were found to be significantly more likely to decline participation in intervention sites compared to those in control sites. A higher mean diastolic blood pressure was seen in patients with uncontrolled hypertension who declined in the control sites compared to those who declined in the intervention sites. However, these findings were no longer significant after adjustment for random variation among the sites. After this adjustment, females were now found to be significantly more likely to consent than males (odds ratio = 1.41; 95% confidence interval = 1.03, 1
Misrepresenting random sampling? A systematic review of research papers in the Journal of Advanced Nursing.

Science.gov (United States)

Williamson, Graham R

2003-11-01

This paper discusses the theoretical limitations of the use of random sampling and probability theory in the production of a significance level (or P-value) in nursing research. Potential alternatives, in the form of randomization tests, are proposed. Research papers in nursing, medicine and psychology frequently misrepresent their statistical findings, as the P-values reported assume random sampling. In this systematic review of studies published between January 1995 and June 2002 in the Journal of Advanced Nursing, 89 (68%) studies broke this assumption because they used convenience samples or entire populations. As a result, some of the findings may be questionable. The key ideas of random sampling and probability theory for statistical testing (for generating a P-value) are outlined. The result of a systematic review of research papers published in the Journal of Advanced Nursing is then presented, showing how frequently random sampling appears to have been misrepresented. Useful alternative techniques that might overcome these limitations are then discussed. REVIEW LIMITATIONS: This review is limited in scope because it is applied to one journal, and so the findings cannot be generalized to other nursing journals or to nursing research in general. However, it is possible that other nursing journals are also publishing research articles based on the misrepresentation of random sampling. The review is also limited because in several of the articles the sampling method was not completely clearly stated, and in this circumstance a judgment has been made as to the sampling method employed, based on the indications given by author(s). Quantitative researchers in nursing should be very careful that the statistical techniques they use are appropriate for the design and sampling methods of their studies. If the techniques they employ are not appropriate, they run the risk of misinterpreting findings by using inappropriate, unrepresentative and biased samples.

Methodology Series Module 5: Sampling Strategies

OpenAIRE

Setia, Maninder Singh

2016-01-01

Once the research question and the research design have been finalised, it is important to select the appropriate sample for the study. The method by which the researcher selects the sample is the ? Sampling Method?. There are essentially two types of sampling methods: 1) probability sampling ? based on chance events (such as random numbers, flipping a coin etc.); and 2) non-probability sampling ? based on researcher's choice, population that accessible & available. Some of the non-probabilit...
Smoothing the redshift distributions of random samples for the baryon acoustic oscillations: applications to the SDSS-III BOSS DR12 and QPM mock samples

Science.gov (United States)

Wang, Shao-Jiang; Guo, Qi; Cai, Rong-Gen

2017-12-01

We investigate the impact of different redshift distributions of random samples on the baryon acoustic oscillations (BAO) measurements of D_V(z)r_d^fid/r_d from the two-point correlation functions of galaxies in the Data Release 12 of the Baryon Oscillation Spectroscopic Survey (BOSS). Big surveys, such as BOSS, usually assign redshifts to the random samples by randomly drawing values from the measured redshift distributions of the data, which would necessarily introduce fiducial signals of fluctuations into the random samples, weakening the signals of BAO, if the cosmic variance cannot be ignored. We propose a smooth function of redshift distribution that fits the data well to populate the random galaxy samples. The resulting cosmological parameters match the input parameters of the mock catalogue very well. The significance of BAO signals has been improved by 0.33σ for a low-redshift sample and by 0.03σ for a constant-stellar-mass sample, though the absolute values do not change significantly. Given the precision of the measurements of current cosmological parameters, it would be appreciated for the future improvements on the measurements of galaxy clustering.
Interference-aware random beam selection schemes for spectrum sharing systems

KAUST Repository

Abdallah, Mohamed; Qaraqe, Khalid; Alouini, Mohamed-Slim

2012-01-01

users. In this work, we develop interference-aware random beam selection schemes that provide enhanced performance for the secondary network under the condition that the interference observed by the receivers of the primary network is below a
The quasar luminosity function from a variability-selected sample

Science.gov (United States)

Hawkins, M. R. S.; Veron, P.

1993-01-01

A sample of quasars is selected from a 10-yr sequence of 30 UK Schmidt plates. Luminosity functions are derived in several redshift intervals, which in each case show a featureless power-law rise towards low luminosities. There is no sign of the 'break' found in the recent UVX sample of Boyle et al. It is suggested that reasons for the disagreement are connected with biases in the selection of the UVX sample. The question of the nature of quasar evolution appears to be still unresolved.
On a Robust MaxEnt Process Regression Model with Sample-Selection

Directory of Open Access Journals (Sweden)

Hea-Jung Kim

2018-04-01

Full Text Available In a regression analysis, a sample-selection bias arises when a dependent variable is partially observed as a result of the sample selection. This study introduces a Maximum Entropy (MaxEnt process regression model that assumes a MaxEnt prior distribution for its nonparametric regression function and finds that the MaxEnt process regression model includes the well-known Gaussian process regression (GPR model as a special case. Then, this special MaxEnt process regression model, i.e., the GPR model, is generalized to obtain a robust sample-selection Gaussian process regression (RSGPR model that deals with non-normal data in the sample selection. Various properties of the RSGPR model are established, including the stochastic representation, distributional hierarchy, and magnitude of the sample-selection bias. These properties are used in the paper to develop a hierarchical Bayesian methodology to estimate the model. This involves a simple and computationally feasible Markov chain Monte Carlo algorithm that avoids analytical or numerical derivatives of the log-likelihood function of the model. The performance of the RSGPR model in terms of the sample-selection bias correction, robustness to non-normality, and prediction, is demonstrated through results in simulations that attest to its good finite-sample performance.
Continuous-Time Mean-Variance Portfolio Selection with Random Horizon

International Nuclear Information System (INIS)

Yu, Zhiyong

2013-01-01

This paper examines the continuous-time mean-variance optimal portfolio selection problem with random market parameters and random time horizon. Treating this problem as a linearly constrained stochastic linear-quadratic optimal control problem, I explicitly derive the efficient portfolios and efficient frontier in closed forms based on the solutions of two backward stochastic differential equations. Some related issues such as a minimum variance portfolio and a mutual fund theorem are also addressed. All the results are markedly different from those in the problem with deterministic exit time. A key part of my analysis involves proving the global solvability of a stochastic Riccati equation, which is interesting in its own right
Continuous-Time Mean-Variance Portfolio Selection with Random Horizon

Energy Technology Data Exchange (ETDEWEB)

Yu, Zhiyong, E-mail: yuzhiyong@sdu.edu.cn [Shandong University, School of Mathematics (China)

2013-12-15

This paper examines the continuous-time mean-variance optimal portfolio selection problem with random market parameters and random time horizon. Treating this problem as a linearly constrained stochastic linear-quadratic optimal control problem, I explicitly derive the efficient portfolios and efficient frontier in closed forms based on the solutions of two backward stochastic differential equations. Some related issues such as a minimum variance portfolio and a mutual fund theorem are also addressed. All the results are markedly different from those in the problem with deterministic exit time. A key part of my analysis involves proving the global solvability of a stochastic Riccati equation, which is interesting in its own right.
Sampling in epidemiological research: issues, hazards and pitfalls

Science.gov (United States)

Tyrer, Stephen; Heyman, Bob

2016-01-01

Surveys of people's opinions are fraught with difficulties. It is easier to obtain information from those who respond to text messages or to emails than to attempt to obtain a representative sample. Samples of the population that are selected non-randomly in this way are termed convenience samples as they are easy to recruit. This introduces a sampling bias. Such non-probability samples have merit in many situations, but an epidemiological enquiry is of little value unless a random sample is obtained. If a sufficient number of those selected actually complete a survey, the results are likely to be representative of the population. This editorial describes probability and non-probability sampling methods and illustrates the difficulties and suggested solutions in performing accurate epidemiological research. PMID:27087985
TEHRAN AIR POLLUTANTS PREDICTION BASED ON RANDOM FOREST FEATURE SELECTION METHOD

Directory of Open Access Journals (Sweden)

A. Shamsoddini

2017-09-01

Full Text Available Air pollution as one of the most serious forms of environmental pollutions poses huge threat to human life. Air pollution leads to environmental instability, and has harmful and undesirable effects on the environment. Modern prediction methods of the pollutant concentration are able to improve decision making and provide appropriate solutions. This study examines the performance of the Random Forest feature selection in combination with multiple-linear regression and Multilayer Perceptron Artificial Neural Networks methods, in order to achieve an efficient model to estimate carbon monoxide and nitrogen dioxide, sulfur dioxide and PM2.5 contents in the air. The results indicated that Artificial Neural Networks fed by the attributes selected by Random Forest feature selection method performed more accurate than other models for the modeling of all pollutants. The estimation accuracy of sulfur dioxide emissions was lower than the other air contaminants whereas the nitrogen dioxide was predicted more accurate than the other pollutants.
Tehran Air Pollutants Prediction Based on Random Forest Feature Selection Method

Science.gov (United States)

Shamsoddini, A.; Aboodi, M. R.; Karami, J.

2017-09-01

Air pollution as one of the most serious forms of environmental pollutions poses huge threat to human life. Air pollution leads to environmental instability, and has harmful and undesirable effects on the environment. Modern prediction methods of the pollutant concentration are able to improve decision making and provide appropriate solutions. This study examines the performance of the Random Forest feature selection in combination with multiple-linear regression and Multilayer Perceptron Artificial Neural Networks methods, in order to achieve an efficient model to estimate carbon monoxide and nitrogen dioxide, sulfur dioxide and PM2.5 contents in the air. The results indicated that Artificial Neural Networks fed by the attributes selected by Random Forest feature selection method performed more accurate than other models for the modeling of all pollutants. The estimation accuracy of sulfur dioxide emissions was lower than the other air contaminants whereas the nitrogen dioxide was predicted more accurate than the other pollutants.
Assessment of fracture risk: value of random population-based samples--the Geelong Osteoporosis Study.

Science.gov (United States)

Henry, M J; Pasco, J A; Seeman, E; Nicholson, G C; Sanders, K M; Kotowicz, M A

2001-01-01

Fracture risk is determined by bone mineral density (BMD). The T-score, a measure of fracture risk, is the position of an individual's BMD in relation to a reference range. The aim of this study was to determine the magnitude of change in the T-score when different sampling techniques were used to produce the reference range. Reference ranges were derived from three samples, drawn from the same region: (1) an age-stratified population-based random sample, (2) unselected volunteers, and (3) a selected healthy subset of the population-based sample with no diseases or drugs known to affect bone. T-scores were calculated using the three reference ranges for a cohort of women who had sustained a fracture and as a group had a low mean BMD (ages 35-72 yr; n = 484). For most comparisons, the T-scores for the fracture cohort were more negative using the population reference range. The difference in T-scores reached 1.0 SD. The proportion of the fracture cohort classified as having osteoporosis at the spine was 26, 14, and 23% when the population, volunteer, and healthy reference ranges were applied, respectively. The use of inappropriate reference ranges results in substantial changes to T-scores and may lead to inappropriate management.
Hebbian Learning in a Random Network Captures Selectivity Properties of the Prefrontal Cortex

Science.gov (United States)

Lindsay, Grace W.

2017-01-01

Complex cognitive behaviors, such as context-switching and rule-following, are thought to be supported by the prefrontal cortex (PFC). Neural activity in the PFC must thus be specialized to specific tasks while retaining flexibility. Nonlinear “mixed” selectivity is an important neurophysiological trait for enabling complex and context-dependent behaviors. Here we investigate (1) the extent to which the PFC exhibits computationally relevant properties, such as mixed selectivity, and (2) how such properties could arise via circuit mechanisms. We show that PFC cells recorded from male and female rhesus macaques during a complex task show a moderate level of specialization and structure that is not replicated by a model wherein cells receive random feedforward inputs. While random connectivity can be effective at generating mixed selectivity, the data show significantly more mixed selectivity than predicted by a model with otherwise matched parameters. A simple Hebbian learning rule applied to the random connectivity, however, increases mixed selectivity and enables the model to match the data more accurately. To explain how learning achieves this, we provide analysis along with a clear geometric interpretation of the impact of learning on selectivity. After learning, the model also matches the data on measures of noise, response density, clustering, and the distribution of selectivities. Of two styles of Hebbian learning tested, the simpler and more biologically plausible option better matches the data. These modeling results provide clues about how neural properties important for cognition can arise in a circuit and make clear experimental predictions regarding how various measures of selectivity would evolve during animal training. SIGNIFICANCE STATEMENT The prefrontal cortex is a brain region believed to support the ability of animals to engage in complex behavior. How neurons in this area respond to stimuli—and in particular, to combinations of stimuli (�
Performance Evaluation of User Selection Protocols in Random Networks with Energy Harvesting and Hardware Impairments

Directory of Open Access Journals (Sweden)

Tan Nhat Nguyen

2016-01-01

Full Text Available In this paper, we evaluate performances of various user selection protocols under impact of hardware impairments. In the considered protocols, a Base Station (BS selects one of available Users (US to serve, while the remaining USs harvest the energy from the Radio Frequency (RF transmitted by the BS. We assume that all of the US randomly appear around the BS. In the Random Selection Protocol (RAN, the BS randomly selects a US to transmit the data. In the second proposed protocol, named Minimum Distance Protocol (MIND, the US that is nearest to the BS will be chosen. In the Optimal Selection Protocol (OPT, the US providing the highest channel gain between itself and the BS will be served. For performance evaluation, we derive exact and asymptotic closed-form expressions of average Outage Probability (OP over Rayleigh fading channels. We also consider average harvested energy per a US. Finally, Monte-Carlo simulations are then performed to verify the theoretical results.
Monitoring oil persistence on beaches : SCAT versus stratified random sampling designs

International Nuclear Information System (INIS)

Short, J.W.; Lindeberg, M.R.; Harris, P.M.; Maselko, J.M.; Pella, J.J.; Rice, S.D.

2003-01-01

In the event of a coastal oil spill, shoreline clean-up assessment teams (SCAT) commonly rely on visual inspection of the entire affected area to monitor the persistence of the oil on beaches. Occasionally, pits are excavated to evaluate the persistence of subsurface oil. This approach is practical for directing clean-up efforts directly following a spill. However, sampling of the 1989 Exxon Valdez oil spill in Prince William Sound 12 years later has shown that visual inspection combined with pit excavation does not offer estimates of contaminated beach area of stranded oil volumes. This information is needed to statistically evaluate the significance of change with time. Assumptions regarding the correlation of visually-evident surface oil and cryptic subsurface oil are usually not evaluated as part of the SCAT mandate. Stratified random sampling can avoid such problems and could produce precise estimates of oiled area and volume that allow for statistical assessment of major temporal trends and the extent of the impact. The 2001 sampling of the shoreline of Prince William Sound showed that 15 per cent of surface oil occurrences were associated with subsurface oil. This study demonstrates the usefulness of the stratified random sampling method and shows how sampling design parameters impact statistical outcome. Power analysis based on the study results, indicate that optimum power is derived when unnecessary stratification is avoided. It was emphasized that sampling effort should be balanced between choosing sufficient beaches for sampling and the intensity of sampling
Characteristics of men with substance use disorder consequent to illicit drug use: comparison of a random sample and volunteers.

Science.gov (United States)

Reynolds, Maureen D; Tarter, Ralph E; Kirisci, Levent

2004-09-06

Men qualifying for substance use disorder (SUD) consequent to consumption of an illicit drug were compared according to recruitment method. It was hypothesized that volunteers would be more self-disclosing and exhibit more severe disturbances compared to randomly recruited subjects. Personal, demographic, family, social, substance use, psychiatric, and SUD characteristics of volunteers (N = 146) were compared to randomly recruited (N = 102) subjects. Volunteers had lower socioceconomic status, were more likely to be African American, and had lower IQ than randomly recruited subjects. Volunteers also evidenced greater social and family maladjustment and more frequently had received treatment for substance abuse. In addition, lower social desirability response bias was observed in the volunteers. SUD was not more severe in the volunteers; however, they reported a higher lifetime rate of opiate, diet, depressant, and analgesic drug use. Volunteers and randomly recruited subjects qualifying for SUD consequent to illicit drug use are similar in SUD severity but differ in terms of severity of psychosocial disturbance and history of drug involvement. The factors discriminating volunteers and randomly recruited subjects are well known to impact on outcome, hence they need to be considered in research design, especially when selecting a sampling strategy in treatment research.
HOT-DUST-POOR QUASARS IN MID-INFRARED AND OPTICALLY SELECTED SAMPLES

International Nuclear Information System (INIS)

Hao Heng; Elvis, Martin; Civano, Francesca; Lawrence, Andy

2011-01-01

We show that the hot-dust-poor (HDP) quasars, originally found in the X-ray-selected XMM-COSMOS type 1 active galactic nucleus (AGN) sample, are just as common in two samples selected at optical/infrared wavelengths: the Richards et al. Spitzer/SDSS sample (8.7% ± 2.2%) and the Palomar-Green-quasar-dominated sample of Elvis et al. (9.5% ± 5.0%). The properties of the HDP quasars in these two samples are consistent with the XMM-COSMOS sample, except that, at the 99% (∼ 2.5σ) significance, a larger proportion of the HDP quasars in the Spitzer/SDSS sample have weak host galaxy contributions, probably due to the selection criteria used. Either the host dust is destroyed (dynamically or by radiation) or is offset from the central black hole due to recoiling. Alternatively, the universality of HDP quasars in samples with different selection methods and the continuous distribution of dust covering factor in type 1 AGNs suggest that the range of spectral energy distributions could be related to the range of tilts in warped fueling disks, as in the model of Lawrence and Elvis, with HDP quasars having relatively small warps.
Genome-wide association data classification and SNPs selection using two-stage quality-based Random Forests.

Science.gov (United States)

Nguyen, Thanh-Tung; Huang, Joshua; Wu, Qingyao; Nguyen, Thuy; Li, Mark

2015-01-01

Single-nucleotide polymorphisms (SNPs) selection and identification are the most important tasks in Genome-wide association data analysis. The problem is difficult because genome-wide association data is very high dimensional and a large portion of SNPs in the data is irrelevant to the disease. Advanced machine learning methods have been successfully used in Genome-wide association studies (GWAS) for identification of genetic variants that have relatively big effects in some common, complex diseases. Among them, the most successful one is Random Forests (RF). Despite of performing well in terms of prediction accuracy in some data sets with moderate size, RF still suffers from working in GWAS for selecting informative SNPs and building accurate prediction models. In this paper, we propose to use a new two-stage quality-based sampling method in random forests, named ts-RF, for SNP subspace selection for GWAS. The method first applies p-value assessment to find a cut-off point that separates informative and irrelevant SNPs in two groups. The informative SNPs group is further divided into two sub-groups: highly informative and weak informative SNPs. When sampling the SNP subspace for building trees for the forest, only those SNPs from the two sub-groups are taken into account. The feature subspaces always contain highly informative SNPs when used to split a node at a tree. This approach enables one to generate more accurate trees with a lower prediction error, meanwhile possibly avoiding overfitting. It allows one to detect interactions of multiple SNPs with the diseases, and to reduce the dimensionality and the amount of Genome-wide association data needed for learning the RF model. Extensive experiments on two genome-wide SNP data sets (Parkinson case-control data comprised of 408,803 SNPs and Alzheimer case-control data comprised of 380,157 SNPs) and 10 gene data sets have demonstrated that the proposed model significantly reduced prediction errors and outperformed
The reliability of randomly selected final year pharmacy students in ...

African Journals Online (AJOL)

Employing ANOVA, factorial experimental analysis, and the theory of error, reliability studies were conducted on the assessment of the drug product chloroquine phosphate tablets. The G–Study employed equal numbers of the factors for uniform control, and involved three analysts (randomly selected final year Pharmacy ...
A simple and efficient alternative to implementing systematic random sampling in stereological designs without a motorized microscope stage.

Science.gov (United States)

Melvin, Neal R; Poda, Daniel; Sutherland, Robert J

2007-10-01

When properly applied, stereology is a very robust and efficient method to quantify a variety of parameters from biological material. A common sampling strategy in stereology is systematic random sampling, which involves choosing a random sampling [corrected] start point outside the structure of interest, and sampling relevant objects at [corrected] sites that are placed at pre-determined, equidistant intervals. This has proven to be a very efficient sampling strategy, and is used widely in stereological designs. At the microscopic level, this is most often achieved through the use of a motorized stage that facilitates the systematic random stepping across the structure of interest. Here, we report a simple, precise and cost-effective software-based alternative to accomplishing systematic random sampling under the microscope. We believe that this approach will facilitate the use of stereological designs that employ systematic random sampling in laboratories that lack the resources to acquire costly, fully automated systems.
Accounting for Sampling Error in Genetic Eigenvalues Using Random Matrix Theory.

Science.gov (United States)

Sztepanacz, Jacqueline L; Blows, Mark W

2017-07-01

The distribution of genetic variance in multivariate phenotypes is characterized by the empirical spectral distribution of the eigenvalues of the genetic covariance matrix. Empirical estimates of genetic eigenvalues from random effects linear models are known to be overdispersed by sampling error, where large eigenvalues are biased upward, and small eigenvalues are biased downward. The overdispersion of the leading eigenvalues of sample covariance matrices have been demonstrated to conform to the Tracy-Widom (TW) distribution. Here we show that genetic eigenvalues estimated using restricted maximum likelihood (REML) in a multivariate random effects model with an unconstrained genetic covariance structure will also conform to the TW distribution after empirical scaling and centering. However, where estimation procedures using either REML or MCMC impose boundary constraints, the resulting genetic eigenvalues tend not be TW distributed. We show how using confidence intervals from sampling distributions of genetic eigenvalues without reference to the TW distribution is insufficient protection against mistaking sampling error as genetic variance, particularly when eigenvalues are small. By scaling such sampling distributions to the appropriate TW distribution, the critical value of the TW statistic can be used to determine if the magnitude of a genetic eigenvalue exceeds the sampling error for each eigenvalue in the spectral distribution of a given genetic covariance matrix. Copyright © 2017 by the Genetics Society of America.

Sampling Methods in Cardiovascular Nursing Research: An Overview.

Science.gov (United States)

Kandola, Damanpreet; Banner, Davina; O'Keefe-McCarthy, Sheila; Jassal, Debbie

2014-01-01

Cardiovascular nursing research covers a wide array of topics from health services to psychosocial patient experiences. The selection of specific participant samples is an important part of the research design and process. The sampling strategy employed is of utmost importance to ensure that a representative sample of participants is chosen. There are two main categories of sampling methods: probability and non-probability. Probability sampling is the random selection of elements from the population, where each element of the population has an equal and independent chance of being included in the sample. There are five main types of probability sampling including simple random sampling, systematic sampling, stratified sampling, cluster sampling, and multi-stage sampling. Non-probability sampling methods are those in which elements are chosen through non-random methods for inclusion into the research study and include convenience sampling, purposive sampling, and snowball sampling. Each approach offers distinct advantages and disadvantages and must be considered critically. In this research column, we provide an introduction to these key sampling techniques and draw on examples from the cardiovascular research. Understanding the differences in sampling techniques may aid nurses in effective appraisal of research literature and provide a reference pointfor nurses who engage in cardiovascular research.
Concentration of ions in selected bottled water samples sold in Malaysia

Science.gov (United States)

Aris, Ahmad Zaharin; Kam, Ryan Chuan Yang; Lim, Ai Phing; Praveena, Sarva Mangala

2013-03-01

Many consumers around the world, including Malaysians, have turned to bottled water as their main source of drinking water. The aim of this study is to determine the physical and chemical properties of bottled water samples sold in Selangor, Malaysia. A total of 20 bottled water brands consisting of `natural mineral (NM)' and `packaged drinking (PD)' types were randomly collected and analyzed for their physical-chemical characteristics: hydrogen ion concentration (pH), electrical conductivity (EC) and total dissolved solids (TDS), selected major ions: calcium (Ca), potassium (K), magnesium (Mg) and sodium (Na), and minor trace constituents: copper (Cu) and zinc (Zn) to ascertain their suitability for human consumption. The results obtained were compared with guideline values recommended by World Health Organization (WHO) and Malaysian Ministry of Health (MMOH), respectively. It was found that all bottled water samples were in accordance with the guidelines set by WHO and MMOH except for one sample (D3) which was below the pH limit of 6.5. Both NM and PD bottled water were dominated by Na + K > Ca > Mg. Low values for EC and TDS in the bottled water samples showed that water was deficient in essential elements, likely an indication that these were removed by water treatment. Minerals like major ions were present in very low concentrations which could pose a risk to individuals who consume this water on a regular basis. Generally, the overall quality of the supplied bottled water was in accordance to standards and guidelines set by WHO and MMOH and safe for consumption.
A Heckman Selection- t Model

KAUST Repository

Marchenko, Yulia V.

2012-03-01

Sample selection arises often in practice as a result of the partial observability of the outcome of interest in a study. In the presence of sample selection, the observed data do not represent a random sample from the population, even after controlling for explanatory variables. That is, data are missing not at random. Thus, standard analysis using only complete cases will lead to biased results. Heckman introduced a sample selection model to analyze such data and proposed a full maximum likelihood estimation method under the assumption of normality. The method was criticized in the literature because of its sensitivity to the normality assumption. In practice, data, such as income or expenditure data, often violate the normality assumption because of heavier tails. We first establish a new link between sample selection models and recently studied families of extended skew-elliptical distributions. Then, this allows us to introduce a selection-t (SLt) model, which models the error distribution using a Student\\'s t distribution. We study its properties and investigate the finite-sample performance of the maximum likelihood estimators for this model. We compare the performance of the SLt model to the conventional Heckman selection-normal (SLN) model and apply it to analyze ambulatory expenditures. Unlike the SLNmodel, our analysis using the SLt model provides statistical evidence for the existence of sample selection bias in these data. We also investigate the performance of the test for sample selection bias based on the SLt model and compare it with the performances of several tests used with the SLN model. Our findings indicate that the latter tests can be misleading in the presence of heavy-tailed data. © 2012 American Statistical Association.
A novel heterogeneous training sample selection method on space-time adaptive processing

Science.gov (United States)

Wang, Qiang; Zhang, Yongshun; Guo, Yiduo

2018-04-01

The performance of ground target detection about space-time adaptive processing (STAP) decreases when non-homogeneity of clutter power is caused because of training samples contaminated by target-like signals. In order to solve this problem, a novel nonhomogeneous training sample selection method based on sample similarity is proposed, which converts the training sample selection into a convex optimization problem. Firstly, the existing deficiencies on the sample selection using generalized inner product (GIP) are analyzed. Secondly, the similarities of different training samples are obtained by calculating mean-hausdorff distance so as to reject the contaminated training samples. Thirdly, cell under test (CUT) and the residual training samples are projected into the orthogonal subspace of the target in the CUT, and mean-hausdorff distances between the projected CUT and training samples are calculated. Fourthly, the distances are sorted in order of value and the training samples which have the bigger value are selective preference to realize the reduced-dimension. Finally, simulation results with Mountain-Top data verify the effectiveness of the proposed method.
A random sampling approach for robust estimation of tissue-to-plasma ratio from extremely sparse data.

Science.gov (United States)

Chu, Hui-May; Ette, Ene I

2005-09-02

his study was performed to develop a new nonparametric approach for the estimation of robust tissue-to-plasma ratio from extremely sparsely sampled paired data (ie, one sample each from plasma and tissue per subject). Tissue-to-plasma ratio was estimated from paired/unpaired experimental data using independent time points approach, area under the curve (AUC) values calculated with the naïve data averaging approach, and AUC values calculated using sampling based approaches (eg, the pseudoprofile-based bootstrap [PpbB] approach and the random sampling approach [our proposed approach]). The random sampling approach involves the use of a 2-phase algorithm. The convergence of the sampling/resampling approaches was investigated, as well as the robustness of the estimates produced by different approaches. To evaluate the latter, new data sets were generated by introducing outlier(s) into the real data set. One to 2 concentration values were inflated by 10% to 40% from their original values to produce the outliers. Tissue-to-plasma ratios computed using the independent time points approach varied between 0 and 50 across time points. The ratio obtained from AUC values acquired using the naive data averaging approach was not associated with any measure of uncertainty or variability. Calculating the ratio without regard to pairing yielded poorer estimates. The random sampling and pseudoprofile-based bootstrap approaches yielded tissue-to-plasma ratios with uncertainty and variability. However, the random sampling approach, because of the 2-phase nature of its algorithm, yielded more robust estimates and required fewer replications. Therefore, a 2-phase random sampling approach is proposed for the robust estimation of tissue-to-plasma ratio from extremely sparsely sampled data.
6. Label-free selective plane illumination microscopy of tissue samples

Directory of Open Access Journals (Sweden)

Muteb Alharbi

2017-10-01

Conclusion: Overall this method meets the demands of the current needs for 3D imaging tissue samples in a label-free manner. Label-free Selective Plane Microscopy directly provides excellent information about the structure of the tissue samples. This work has highlighted the superiority of Label-free Selective Plane Microscopy to current approaches to label-free 3D imaging of tissue.
The mathematics of random mutation and natural selection for multiple simultaneous selection pressures and the evolution of antimicrobial drug resistance.

Science.gov (United States)

Kleinman, Alan

2016-12-20

The random mutation and natural selection phenomenon act in a mathematically predictable behavior, which when understood leads to approaches to reduce and prevent the failure of the use of these selection pressures when treating infections and cancers. The underlying principle to impair the random mutation and natural selection phenomenon is to use combination therapy, which forces the population to evolve to multiple selection pressures simultaneously that invoke the multiplication rule of probabilities simultaneously as well. Recently, it has been seen that combination therapy for the treatment of malaria has failed to prevent the emergence of drug-resistant variants. Using this empirical example and the principles of probability theory, the derivation of the equations describing this treatment failure is carried out. These equations give guidance as to how to use combination therapy for the treatment of cancers and infectious diseases and prevent the emergence of drug resistance. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.
Sample size methodology

CERN Document Server

Desu, M M

2012-01-01

One of the most important problems in designing an experiment or a survey is sample size determination and this book presents the currently available methodology. It includes both random sampling from standard probability distributions and from finite populations. Also discussed is sample size determination for estimating parameters in a Bayesian setting by considering the posterior distribution of the parameter and specifying the necessary requirements. The determination of the sample size is considered for ranking and selection problems as well as for the design of clinical trials. Appropria
Random Sampling of Correlated Parameters – a Consistent Solution for Unfavourable Conditions

Energy Technology Data Exchange (ETDEWEB)

Žerovnik, G., E-mail: gasper.zerovnik@ijs.si [Jožef Stefan Institute, Jamova cesta 39, SI-1000 Ljubljana (Slovenia); Trkov, A. [Jožef Stefan Institute, Jamova cesta 39, SI-1000 Ljubljana (Slovenia); International Atomic Energy Agency, PO Box 100, A-1400 Vienna (Austria); Kodeli, I.A. [Jožef Stefan Institute, Jamova cesta 39, SI-1000 Ljubljana (Slovenia); Capote, R. [International Atomic Energy Agency, PO Box 100, A-1400 Vienna (Austria); Smith, D.L. [Argonne National Laboratory, 1710 Avenida del Mundo, Coronado, CA 92118-3073 (United States)

2015-01-15

Two methods for random sampling according to a multivariate lognormal distribution – the correlated sampling method and the method of transformation of correlation coefficients – are briefly presented. The methods are mathematically exact and enable consistent sampling of correlated inherently positive parameters with given information on the first two distribution moments. Furthermore, a weighted sampling method to accelerate the convergence of parameters with extremely large relative uncertainties is described. However, the method is efficient only for a limited number of correlated parameters.
Coupling methods for multistage sampling

OpenAIRE

Chauvet, Guillaume

2015-01-01

Multistage sampling is commonly used for household surveys when there exists no sampling frame, or when the population is scattered over a wide area. Multistage sampling usually introduces a complex dependence in the selection of the final units, which makes asymptotic results quite difficult to prove. In this work, we consider multistage sampling with simple random without replacement sampling at the first stage, and with an arbitrary sampling design for further stages. We consider coupling ...
The Bootstrap, the Jackknife, and the Randomization Test: A Sampling Taxonomy.

Science.gov (United States)

Rodgers, J L

1999-10-01

A simple sampling taxonomy is defined that shows the differences between and relationships among the bootstrap, the jackknife, and the randomization test. Each method has as its goal the creation of an empirical sampling distribution that can be used to test statistical hypotheses, estimate standard errors, and/or create confidence intervals. Distinctions between the methods can be made based on the sampling approach (with replacement versus without replacement) and the sample size (replacing the whole original sample versus replacing a subset of the original sample). The taxonomy is useful for teaching the goals and purposes of resampling schemes. An extension of the taxonomy implies other possible resampling approaches that have not previously been considered. Univariate and multivariate examples are presented.
Survey research with a random digit dial national mobile phone sample in Ghana: Methods and sample quality

Science.gov (United States)

Sefa, Eunice; Adimazoya, Edward Akolgo; Yartey, Emmanuel; Lenzi, Rachel; Tarpo, Cindy; Heward-Mills, Nii Lante; Lew, Katherine; Ampeh, Yvonne

2018-01-01

Introduction Generating a nationally representative sample in low and middle income countries typically requires resource-intensive household level sampling with door-to-door data collection. High mobile phone penetration rates in developing countries provide new opportunities for alternative sampling and data collection methods, but there is limited information about response rates and sample biases in coverage and nonresponse using these methods. We utilized data from an interactive voice response, random-digit dial, national mobile phone survey in Ghana to calculate standardized response rates and assess representativeness of the obtained sample. Materials and methods The survey methodology was piloted in two rounds of data collection. The final survey included 18 demographic, media exposure, and health behavior questions. Call outcomes and response rates were calculated according to the American Association of Public Opinion Research guidelines. Sample characteristics, productivity, and costs per interview were calculated. Representativeness was assessed by comparing data to the Ghana Demographic and Health Survey and the National Population and Housing Census. Results The survey was fielded during a 27-day period in February-March 2017. There were 9,469 completed interviews and 3,547 partial interviews. Response, cooperation, refusal, and contact rates were 31%, 81%, 7%, and 39% respectively. Twenty-three calls were dialed to produce an eligible contact: nonresponse was substantial due to the automated calling system and dialing of many unassigned or non-working numbers. Younger, urban, better educated, and male respondents were overrepresented in the sample. Conclusions The innovative mobile phone data collection methodology yielded a large sample in a relatively short period. Response rates were comparable to other surveys, although substantial coverage bias resulted from fewer women, rural, and older residents completing the mobile phone survey in
Survey research with a random digit dial national mobile phone sample in Ghana: Methods and sample quality.

Science.gov (United States)

L'Engle, Kelly; Sefa, Eunice; Adimazoya, Edward Akolgo; Yartey, Emmanuel; Lenzi, Rachel; Tarpo, Cindy; Heward-Mills, Nii Lante; Lew, Katherine; Ampeh, Yvonne

2018-01-01

Generating a nationally representative sample in low and middle income countries typically requires resource-intensive household level sampling with door-to-door data collection. High mobile phone penetration rates in developing countries provide new opportunities for alternative sampling and data collection methods, but there is limited information about response rates and sample biases in coverage and nonresponse using these methods. We utilized data from an interactive voice response, random-digit dial, national mobile phone survey in Ghana to calculate standardized response rates and assess representativeness of the obtained sample. The survey methodology was piloted in two rounds of data collection. The final survey included 18 demographic, media exposure, and health behavior questions. Call outcomes and response rates were calculated according to the American Association of Public Opinion Research guidelines. Sample characteristics, productivity, and costs per interview were calculated. Representativeness was assessed by comparing data to the Ghana Demographic and Health Survey and the National Population and Housing Census. The survey was fielded during a 27-day period in February-March 2017. There were 9,469 completed interviews and 3,547 partial interviews. Response, cooperation, refusal, and contact rates were 31%, 81%, 7%, and 39% respectively. Twenty-three calls were dialed to produce an eligible contact: nonresponse was substantial due to the automated calling system and dialing of many unassigned or non-working numbers. Younger, urban, better educated, and male respondents were overrepresented in the sample. The innovative mobile phone data collection methodology yielded a large sample in a relatively short period. Response rates were comparable to other surveys, although substantial coverage bias resulted from fewer women, rural, and older residents completing the mobile phone survey in comparison to household surveys. Random digit dialing of mobile
Survey research with a random digit dial national mobile phone sample in Ghana: Methods and sample quality.

Directory of Open Access Journals (Sweden)

Kelly L'Engle

Full Text Available Generating a nationally representative sample in low and middle income countries typically requires resource-intensive household level sampling with door-to-door data collection. High mobile phone penetration rates in developing countries provide new opportunities for alternative sampling and data collection methods, but there is limited information about response rates and sample biases in coverage and nonresponse using these methods. We utilized data from an interactive voice response, random-digit dial, national mobile phone survey in Ghana to calculate standardized response rates and assess representativeness of the obtained sample.The survey methodology was piloted in two rounds of data collection. The final survey included 18 demographic, media exposure, and health behavior questions. Call outcomes and response rates were calculated according to the American Association of Public Opinion Research guidelines. Sample characteristics, productivity, and costs per interview were calculated. Representativeness was assessed by comparing data to the Ghana Demographic and Health Survey and the National Population and Housing Census.The survey was fielded during a 27-day period in February-March 2017. There were 9,469 completed interviews and 3,547 partial interviews. Response, cooperation, refusal, and contact rates were 31%, 81%, 7%, and 39% respectively. Twenty-three calls were dialed to produce an eligible contact: nonresponse was substantial due to the automated calling system and dialing of many unassigned or non-working numbers. Younger, urban, better educated, and male respondents were overrepresented in the sample.The innovative mobile phone data collection methodology yielded a large sample in a relatively short period. Response rates were comparable to other surveys, although substantial coverage bias resulted from fewer women, rural, and older residents completing the mobile phone survey in comparison to household surveys. Random digit
Patch-based visual tracking with online representative sample selection

Science.gov (United States)

Ou, Weihua; Yuan, Di; Li, Donghao; Liu, Bin; Xia, Daoxun; Zeng, Wu

2017-05-01

Occlusion is one of the most challenging problems in visual object tracking. Recently, a lot of discriminative methods have been proposed to deal with this problem. For the discriminative methods, it is difficult to select the representative samples for the target template updating. In general, the holistic bounding boxes that contain tracked results are selected as the positive samples. However, when the objects are occluded, this simple strategy easily introduces the noises into the training data set and the target template and then leads the tracker to drift away from the target seriously. To address this problem, we propose a robust patch-based visual tracker with online representative sample selection. Different from previous works, we divide the object and the candidates into several patches uniformly and propose a score function to calculate the score of each patch independently. Then, the average score is adopted to determine the optimal candidate. Finally, we utilize the non-negative least square method to find the representative samples, which are used to update the target template. The experimental results on the object tracking benchmark 2013 and on the 13 challenging sequences show that the proposed method is robust to the occlusion and achieves promising results.
Occupational position and its relation to mental distress in a random sample of Danish residents

DEFF Research Database (Denmark)

Rugulies, Reiner Ernst; Madsen, Ida E H; Nielsen, Maj Britt D

2010-01-01

PURPOSE: To analyze the distribution of depressive, anxiety, and somatization symptoms across different occupational positions in a random sample of Danish residents. METHODS: The study sample consisted of 591 Danish residents (50% women), aged 20-65, drawn from an age- and gender-stratified random...... sample of the Danish population. Participants filled out a survey that included the 92 item version of the Hopkins Symptom Checklist (SCL-92). We categorized occupational position into seven groups: high- and low-grade non-manual workers, skilled and unskilled manual workers, high- and low-grade self...
Polycyclic aromatic hydrocarbons in air samples of meat smokehouses

DEFF Research Database (Denmark)

Hansen, Åse Marie; Olsen, I L; Poulsen, O M

1992-01-01

In a screening programme nine Danish meat smokehouses were randomly selected for measurements on concentration of airborne polycyclic aromatic hydrocarbons (PAH). A total of 23 stationary air samples were collected during the entire working period of the kiln either above the kiln doors or approx......In a screening programme nine Danish meat smokehouses were randomly selected for measurements on concentration of airborne polycyclic aromatic hydrocarbons (PAH). A total of 23 stationary air samples were collected during the entire working period of the kiln either above the kiln doors...
Estimation of Sensitive Proportion by Randomized Response Data in Successive Sampling

Directory of Open Access Journals (Sweden)

Bo Yu

2015-01-01

Full Text Available This paper considers the problem of estimation for binomial proportions of sensitive or stigmatizing attributes in the population of interest. Randomized response techniques are suggested for protecting the privacy of respondents and reducing the response bias while eliciting information on sensitive attributes. In many sensitive question surveys, the same population is often sampled repeatedly on each occasion. In this paper, we apply successive sampling scheme to improve the estimation of the sensitive proportion on current occasion.
Differential privacy-based evaporative cooling feature selection and classification with relief-F and random forests.

Science.gov (United States)

Le, Trang T; Simmons, W Kyle; Misaki, Masaya; Bodurka, Jerzy; White, Bill C; Savitz, Jonathan; McKinney, Brett A

2017-09-15

Classification of individuals into disease or clinical categories from high-dimensional biological data with low prediction error is an important challenge of statistical learning in bioinformatics. Feature selection can improve classification accuracy but must be incorporated carefully into cross-validation to avoid overfitting. Recently, feature selection methods based on differential privacy, such as differentially private random forests and reusable holdout sets, have been proposed. However, for domains such as bioinformatics, where the number of features is much larger than the number of observations p≫n , these differential privacy methods are susceptible to overfitting. We introduce private Evaporative Cooling, a stochastic privacy-preserving machine learning algorithm that uses Relief-F for feature selection and random forest for privacy preserving classification that also prevents overfitting. We relate the privacy-preserving threshold mechanism to a thermodynamic Maxwell-Boltzmann distribution, where the temperature represents the privacy threshold. We use the thermal statistical physics concept of Evaporative Cooling of atomic gases to perform backward stepwise privacy-preserving feature selection. On simulated data with main effects and statistical interactions, we compare accuracies on holdout and validation sets for three privacy-preserving methods: the reusable holdout, reusable holdout with random forest, and private Evaporative Cooling, which uses Relief-F feature selection and random forest classification. In simulations where interactions exist between attributes, private Evaporative Cooling provides higher classification accuracy without overfitting based on an independent validation set. In simulations without interactions, thresholdout with random forest and private Evaporative Cooling give comparable accuracies. We also apply these privacy methods to human brain resting-state fMRI data from a study of major depressive disorder. Code
Data Quality Objectives For Selecting Waste Samples For Bench-Scale Reformer Treatability Studies

International Nuclear Information System (INIS)

Banning, D.L.

2011-01-01

This document describes the data quality objectives to select archived samples located at the 222-S Laboratory for Bench-Scale Reforming testing. The type, quantity, and quality of the data required to select the samples for Fluid Bed Steam Reformer testing are discussed. In order to maximize the efficiency and minimize the time to treat Hanford tank waste in the Waste Treatment and Immobilization Plant, additional treatment processes may be required. One of the potential treatment processes is the fluidized bed steam reformer. A determination of the adequacy of the fluidized bed steam reformer process to treat Hanford tank waste is required. The initial step in determining the adequacy of the fluidized bed steam reformer process is to select archived waste samples from the 222-S Laboratory that will be used in a bench scale tests. Analyses of the selected samples will be required to confirm the samples meet the shipping requirements and for comparison to the bench scale reformer (BSR) test sample selection requirements.

Adaptive importance sampling of random walks on continuous state spaces

International Nuclear Information System (INIS)

Baggerly, K.; Cox, D.; Picard, R.

1998-01-01

The authors consider adaptive importance sampling for a random walk with scoring in a general state space. Conditions under which exponential convergence occurs to the zero-variance solution are reviewed. These results generalize previous work for finite, discrete state spaces in Kollman (1993) and in Kollman, Baggerly, Cox, and Picard (1996). This paper is intended for nonstatisticians and includes considerable explanatory material
Precision of systematic and random sampling in clustered populations: habitat patches and aggregating organisms.

Science.gov (United States)

McGarvey, Richard; Burch, Paul; Matthews, Janet M

2016-01-01

Natural populations of plants and animals spatially cluster because (1) suitable habitat is patchy, and (2) within suitable habitat, individuals aggregate further into clusters of higher density. We compare the precision of random and systematic field sampling survey designs under these two processes of species clustering. Second, we evaluate the performance of 13 estimators for the variance of the sample mean from a systematic survey. Replicated simulated surveys, as counts from 100 transects, allocated either randomly or systematically within the study region, were used to estimate population density in six spatial point populations including habitat patches and Matérn circular clustered aggregations of organisms, together and in combination. The standard one-start aligned systematic survey design, a uniform 10 x 10 grid of transects, was much more precise. Variances of the 10 000 replicated systematic survey mean densities were one-third to one-fifth of those from randomly allocated transects, implying transect sample sizes giving equivalent precision by random survey would need to be three to five times larger. Organisms being restricted to patches of habitat was alone sufficient to yield this precision advantage for the systematic design. But this improved precision for systematic sampling in clustered populations is underestimated by standard variance estimators used to compute confidence intervals. True variance for the survey sample mean was computed from the variance of 10 000 simulated survey mean estimates. Testing 10 published and three newly proposed variance estimators, the two variance estimators (v) that corrected for inter-transect correlation (ν₈ and ν(W)) were the most accurate and also the most precise in clustered populations. These greatly outperformed the two "post-stratification" variance estimators (ν₂ and ν₃) that are now more commonly applied in systematic surveys. Similar variance estimator performance rankings were found with
Interference-aware random beam selection schemes for spectrum sharing systems

KAUST Repository

Abdallah, Mohamed

2012-10-19

Spectrum sharing systems have been recently introduced to alleviate the problem of spectrum scarcity by allowing secondary unlicensed networks to share the spectrum with primary licensed networks under acceptable interference levels to the primary users. In this work, we develop interference-aware random beam selection schemes that provide enhanced performance for the secondary network under the condition that the interference observed by the receivers of the primary network is below a predetermined/acceptable value. We consider a secondary link composed of a transmitter equipped with multiple antennas and a single-antenna receiver sharing the same spectrum with a primary link composed of a single-antenna transmitter and a single-antenna receiver. The proposed schemes select a beam, among a set of power-optimized random beams, that maximizes the signal-to-interference-plus-noise ratio (SINR) of the secondary link while satisfying the primary interference constraint for different levels of feedback information describing the interference level at the primary receiver. For the proposed schemes, we develop a statistical analysis for the SINR statistics as well as the capacity and bit error rate (BER) of the secondary link.
Convergence analysis for Latin-hypercube lattice-sample selection strategies for 3D correlated random hydraulic-conductivity fields

OpenAIRE

Simuta-Champo, R.; Herrera-Zamarrón, G. S.

2010-01-01

The Monte Carlo technique provides a natural method for evaluating uncertainties. The uncertainty is represented by a probability distribution or by related quantities such as statistical moments. When the groundwater flow and transport governing equations are solved and the hydraulic conductivity field is treated as a random spatial function, the hydraulic head, velocities and concentrations also become random spatial functions. When that is the case, for the stochastic simulation of groundw...
Selection of the Sample for Data-Driven $Z \\to \

CERN Document Server

Krauss, Martin

2009-01-01

The topic of this study was to improve the selection of the sample for data-driven Z → ν ν background estimation, which is a major contribution in supersymmetric searches in ̄ a no-lepton search mode. The data is based on Z → + − samples using data created with ATLAS simulation software. This method works if two leptons are reconstructed, but using cuts that are typical for SUSY searches reconstruction efficiency for electrons and muons is rather low. For this reason it was tried to enhance the data sample. Therefore events were considered, where only one electron was reconstructed. In this case the invariant mass for the electron and each jet was computed to select the jet with the best match for the Z boson mass as not reconstructed electron. This way the sample can be extended but significantly looses purity because of also reconstructed background events. To improve this method other variables have to be considered which were not available for this study. Applying a similar method to muons using ...
Topology-selective jamming of fully-connected, code-division random-access networks

Science.gov (United States)

Polydoros, Andreas; Cheng, Unjeng

1990-01-01

The purpose is to introduce certain models of topology selective stochastic jamming and examine its impact on a class of fully-connected, spread-spectrum, slotted ALOHA-type random access networks. The theory covers dedicated as well as half-duplex units. The dominant role of the spatial duty factor is established, and connections with the dual concept of time selective jamming are discussed. The optimal choices of coding rate and link access parameters (from the users' side) and the jamming spatial fraction are numerically established for DS and FH spreading.
Handling missing data in ranked set sampling

CERN Document Server

Bouza-Herrera, Carlos N

2013-01-01

The existence of missing observations is a very important aspect to be considered in the application of survey sampling, for example. In human populations they may be caused by a refusal of some interviewees to give the true value for the variable of interest. Traditionally, simple random sampling is used to select samples. Most statistical models are supported by the use of samples selected by means of this design. In recent decades, an alternative design has started being used, which, in many cases, shows an improvement in terms of accuracy compared with traditional sampling. It is called R
A Table-Based Random Sampling Simulation for Bioluminescence Tomography

Directory of Open Access Journals (Sweden)

Xiaomeng Zhang

2006-01-01

Full Text Available As a popular simulation of photon propagation in turbid media, the main problem of Monte Carlo (MC method is its cumbersome computation. In this work a table-based random sampling simulation (TBRS is proposed. The key idea of TBRS is to simplify multisteps of scattering to a single-step process, through randomly table querying, thus greatly reducing the computing complexity of the conventional MC algorithm and expediting the computation. The TBRS simulation is a fast algorithm of the conventional MC simulation of photon propagation. It retained the merits of flexibility and accuracy of conventional MC method and adapted well to complex geometric media and various source shapes. Both MC simulations were conducted in a homogeneous medium in our work. Also, we present a reconstructing approach to estimate the position of the fluorescent source based on the trial-and-error theory as a validation of the TBRS algorithm. Good agreement is found between the conventional MC simulation and the TBRS simulation.
A Nationwide Random Sampling Survey of Potential Complicated Grief in Japan

Science.gov (United States)

Mizuno, Yasunao; Kishimoto, Junji; Asukai, Nozomu

2012-01-01

To investigate the prevalence of significant loss, potential complicated grief (CG), and its contributing factors, we conducted a nationwide random sampling survey of Japanese adults aged 18 or older (N = 1,343) using a self-rating Japanese-language version of the Complicated Grief Brief Screen. Among them, 37.0% experienced their most significant…
Reinforcing Sampling Distributions through a Randomization-Based Activity for Introducing ANOVA

Science.gov (United States)

Taylor, Laura; Doehler, Kirsten

2015-01-01

This paper examines the use of a randomization-based activity to introduce the ANOVA F-test to students. The two main goals of this activity are to successfully teach students to comprehend ANOVA F-tests and to increase student comprehension of sampling distributions. Four sections of students in an advanced introductory statistics course…
Peculiarities of the statistics of spectrally selected fluorescence radiation in laser-pumped dye-doped random media

Science.gov (United States)

Yuvchenko, S. A.; Ushakova, E. V.; Pavlova, M. V.; Alonova, M. V.; Zimnyakov, D. A.

2018-04-01

We consider the practical realization of a new optical probe method of the random media which is defined as the reference-free path length interferometry with the intensity moments analysis. A peculiarity in the statistics of the spectrally selected fluorescence radiation in laser-pumped dye-doped random medium is discussed. Previously established correlations between the second- and the third-order moments of the intensity fluctuations in the random interference patterns, the coherence function of the probe radiation, and the path difference probability density for the interfering partial waves in the medium are confirmed. The correlations were verified using the statistical analysis of the spectrally selected fluorescence radiation emitted by a laser-pumped dye-doped random medium. Water solution of Rhodamine 6G was applied as the doping fluorescent agent for the ensembles of the densely packed silica grains, which were pumped by the 532 nm radiation of a solid state laser. The spectrum of the mean path length for a random medium was reconstructed.
Randomization tests

CERN Document Server

Edgington, Eugene

2007-01-01

Statistical Tests That Do Not Require Random Sampling Randomization Tests Numerical Examples Randomization Tests and Nonrandom Samples The Prevalence of Nonrandom Samples in Experiments The Irrelevance of Random Samples for the Typical Experiment Generalizing from Nonrandom Samples Intelligibility Respect for the Validity of Randomization Tests Versatility Practicality Precursors of Randomization Tests Other Applications of Permutation Tests Questions and Exercises Notes References Randomized Experiments Unique Benefits of Experiments Experimentation without Mani
40 CFR 205.171-2 - Test exhaust system sample selection and preparation.

Science.gov (United States)

2010-07-01

... Systems § 205.171-2 Test exhaust system sample selection and preparation. (a)(1) Exhaust systems... 40 Protection of Environment 24 2010-07-01 2010-07-01 false Test exhaust system sample selection and preparation. 205.171-2 Section 205.171-2 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY...
Selective information sampling

Directory of Open Access Journals (Sweden)

Peter A. F. Fraser-Mackenzie

2009-06-01

Full Text Available This study investigates the amount and valence of information selected during single item evaluation. One hundred and thirty-five participants evaluated a cell phone by reading hypothetical customers reports. Some participants were first asked to provide a preliminary rating based on a picture of the phone and some technical specifications. The participants who were given the customer reports only after they made a preliminary rating exhibited valence bias in their selection of customers reports. In contrast, the participants that did not make an initial rating sought subsequent information in a more balanced, albeit still selective, manner. The preliminary raters used the least amount of information in their final decision, resulting in faster decision times. The study appears to support the notion that selective exposure is utilized in order to develop cognitive coherence.
LOD score exclusion analyses for candidate QTLs using random population samples.

Science.gov (United States)

Deng, Hong-Wen

2003-11-01

While extensive analyses have been conducted to test for, no formal analyses have been conducted to test against, the importance of candidate genes as putative QTLs using random population samples. Previously, we developed an LOD score exclusion mapping approach for candidate genes for complex diseases. Here, we extend this LOD score approach for exclusion analyses of candidate genes for quantitative traits. Under this approach, specific genetic effects (as reflected by heritability) and inheritance models at candidate QTLs can be analyzed and if an LOD score is < or = -2.0, the locus can be excluded from having a heritability larger than that specified. Simulations show that this approach has high power to exclude a candidate gene from having moderate genetic effects if it is not a QTL and is robust to population admixture. Our exclusion analysis complements association analysis for candidate genes as putative QTLs in random population samples. The approach is applied to test the importance of Vitamin D receptor (VDR) gene as a potential QTL underlying the variation of bone mass, an important determinant of osteoporosis.
The study of combining Latin Hypercube Sampling method and LU decomposition method (LULHS method) for constructing spatial random field

Science.gov (United States)

WANG, P. T.

2015-12-01

Groundwater modeling requires to assign hydrogeological properties to every numerical grid. Due to the lack of detailed information and the inherent spatial heterogeneity, geological properties can be treated as random variables. Hydrogeological property is assumed to be a multivariate distribution with spatial correlations. By sampling random numbers from a given statistical distribution and assigning a value to each grid, a random field for modeling can be completed. Therefore, statistics sampling plays an important role in the efficiency of modeling procedure. Latin Hypercube Sampling (LHS) is a stratified random sampling procedure that provides an efficient way to sample variables from their multivariate distributions. This study combines the the stratified random procedure from LHS and the simulation by using LU decomposition to form LULHS. Both conditional and unconditional simulations of LULHS were develpoed. The simulation efficiency and spatial correlation of LULHS are compared to the other three different simulation methods. The results show that for the conditional simulation and unconditional simulation, LULHS method is more efficient in terms of computational effort. Less realizations are required to achieve the required statistical accuracy and spatial correlation.
The Dirichet-Multinomial model for multivariate randomized response data and small samples

NARCIS (Netherlands)

Avetisyan, Marianna; Fox, Gerardus J.A.

2012-01-01

In survey sampling the randomized response (RR) technique can be used to obtain truthful answers to sensitive questions. Although the individual answers are masked due to the RR technique, individual (sensitive) response rates can be estimated when observing multivariate response data. The
Contributions to sampling statistics

CERN Document Server

Conti, Pier; Ranalli, Maria

2014-01-01

This book contains a selection of the papers presented at the ITACOSM 2013 Conference, held in Milan in June 2013. ITACOSM is the bi-annual meeting of the Survey Sampling Group S2G of the Italian Statistical Society, intended as an international forum of scientific discussion on the developments of theory and application of survey sampling methodologies and applications in human and natural sciences. The book gathers research papers carefully selected from both invited and contributed sessions of the conference. The whole book appears to be a relevant contribution to various key aspects of sampling methodology and techniques; it deals with some hot topics in sampling theory, such as calibration, quantile-regression and multiple frame surveys, and with innovative methodologies in important topics of both sampling theory and applications. Contributions cut across current sampling methodologies such as interval estimation for complex samples, randomized responses, bootstrap, weighting, modeling, imputati...
An improved selective sampling method

International Nuclear Information System (INIS)

Miyahara, Hiroshi; Iida, Nobuyuki; Watanabe, Tamaki

1986-01-01

The coincidence methods which are currently used for the accurate activity standardisation of radio-nuclides, require dead time and resolving time corrections which tend to become increasingly uncertain as countrates exceed about 10 K. To reduce the dependence on such corrections, Muller, in 1981, proposed the selective sampling method using a fast multichannel analyser (50 ns ch -1 ) for measuring the countrates. It is, in many ways, more convenient and possibly potentially more reliable to replace the MCA with scalers and a circuit is described employing five scalers; two of them serving to measure the background correction. Results of comparisons using our new method and the coincidence method for measuring the activity of 60 Co sources yielded agree-ment within statistical uncertainties. (author)
Additive non-uniform random sampling in superimposed fiber Bragg grating strain gauge

Science.gov (United States)

Ma, Y. C.; Liu, H. Y.; Yan, S. B.; Yang, Y. H.; Yang, M. W.; Li, J. M.; Tang, J.

2013-05-01

This paper demonstrates an additive non-uniform random sampling and interrogation method for dynamic and/or static strain gauge using a reflection spectrum from two superimposed fiber Bragg gratings (FBGs). The superimposed FBGs are designed to generate non-equidistant space of a sensing pulse train in the time domain during dynamic strain gauge. By combining centroid finding with smooth filtering methods, both the interrogation speed and accuracy are improved. A 1.9 kHz dynamic strain is measured by generating an additive non-uniform randomly distributed 2 kHz optical sensing pulse train from a mean 500 Hz triangular periodically changing scanning frequency.

Path integral methods for primordial density perturbations - sampling of constrained Gaussian random fields

International Nuclear Information System (INIS)

Bertschinger, E.

1987-01-01

Path integrals may be used to describe the statistical properties of a random field such as the primordial density perturbation field. In this framework the probability distribution is given for a Gaussian random field subjected to constraints such as the presence of a protovoid or supercluster at a specific location in the initial conditions. An algorithm has been constructed for generating samples of a constrained Gaussian random field on a lattice using Monte Carlo techniques. The method makes possible a systematic study of the density field around peaks or other constrained regions in the biased galaxy formation scenario, and it is effective for generating initial conditions for N-body simulations with rare objects in the computational volume. 21 references
Mineral Composition of Selected Serbian Propolis Samples

Directory of Open Access Journals (Sweden)

Tosic Snezana

2017-06-01

Full Text Available The aim of this work was to determine the content of 22 macro- and microelements in ten raw Serbian propolis samples which differ in geographical and botanical origin as well as in polluted agent contents by atomic emission spectrometry with inductively coupled plasma (ICP-OES. The macroelements were more common and present Ca content was the highest while Na content the lowest. Among the studied essential trace elements Fe was the most common element. The levels of toxic elements (Pb, Cd, As and Hg were also analyzed, since they were possible environmental contaminants that could be transferred into propolis products for human consumption. As and Hg were not detected in any of the analyzed samples but a high level of Pb (2.0-9.7 mg/kg was detected and only selected portions of raw propolis could be used to produce natural medicines and dietary supplements for humans. Obtained results were statistically analyzed, and the examined samples showed a wide range of element content.
A simple highly sensitive and selective aptamer-based colorimetric sensor for environmental toxins microcystin-LR in water samples.

Science.gov (United States)

Li, Xiuyan; Cheng, Ruojie; Shi, Huijie; Tang, Bo; Xiao, Hanshuang; Zhao, Guohua

2016-03-05

A simple and highly sensitive aptamer-based colorimetric sensor was developed for selective detection of Microcystin-LR (MC-LR). The aptamer (ABA) was employed as recognition element which could bind MC-LR with high-affinity, while gold nanoparticles (AuNPs) worked as sensing materials whose plasma resonance absorption peaks red shifted upon binding of the targets at a high concentration of sodium chloride. With the addition of MC-LR, the random coil aptamer adsorbed on Au NPs altered into regulated structure to form MC-LR-aptamer complexes and broke away from the surface of Au NPs, leading to the aggregation of AuNPs, and the color converted from red to blue due to the interparticle plasmon coupling. Results showed that our aptamer-based colorimetric sensor exhibited rapid and sensitive detection performance for MC-LR with linear range from 0.5 nM to 7.5 μM and the detection limit reached 0.37 nM. Meanwhile, the pollutants usually coexisting with MC-LR in pollutant water samples had not demonstrated disturbance for detecting of MC-LR. The mechanism was also proposed suggesting that high affinity interaction between aptamer and MC-LR significantly enhanced the sensitivity and selectivity for MC-LR detection. Besides, the established method was utilized in analyzing real water samples and splendid sensitivity and selectivity were obtained as well. Copyright © 2015 Elsevier B.V. All rights reserved.
Random vs. systematic sampling from administrative databases involving human subjects.

Science.gov (United States)

Hagino, C; Lo, R J

1998-09-01

Two sampling techniques, simple random sampling (SRS) and systematic sampling (SS), were compared to determine whether they yield similar and accurate distributions for the following four factors: age, gender, geographic location and years in practice. Any point estimate within 7 yr or 7 percentage points of its reference standard (SRS or the entire data set, i.e., the target population) was considered "acceptably similar" to the reference standard. The sampling frame was from the entire membership database of the Canadian Chiropractic Association. The two sampling methods were tested using eight different sample sizes of n (50, 100, 150, 200, 250, 300, 500, 800). From the profile/characteristics, summaries of four known factors [gender, average age, number (%) of chiropractors in each province and years in practice], between- and within-methods chi 2 tests and unpaired t tests were performed to determine whether any of the differences [descriptively greater than 7% or 7 yr] were also statistically significant. The strengths of the agreements between the provincial distributions were quantified by calculating the percent agreements for each (provincial pairwise-comparison methods). Any percent agreement less than 70% was judged to be unacceptable. Our assessments of the two sampling methods (SRS and SS) for the different sample sizes tested suggest that SRS and SS yielded acceptably similar results. Both methods started to yield "correct" sample profiles at approximately the same sample size (n > 200). SS is not only convenient, it can be recommended for sampling from large databases in which the data are listed without any inherent order biases other than alphabetical listing by surname.
Blind Measurement Selection: A Random Matrix Theory Approach

KAUST Repository

Elkhalil, Khalil

2016-12-14

This paper considers the problem of selecting a set of $k$ measurements from $n$ available sensor observations. The selected measurements should minimize a certain error function assessing the error in estimating a certain $m$ dimensional parameter vector. The exhaustive search inspecting each of the $n\\\\choose k$ possible choices would require a very high computational complexity and as such is not practical for large $n$ and $k$. Alternative methods with low complexity have recently been investigated but their main drawbacks are that 1) they require perfect knowledge of the measurement matrix and 2) they need to be applied at the pace of change of the measurement matrix. To overcome these issues, we consider the asymptotic regime in which $k$, $n$ and $m$ grow large at the same pace. Tools from random matrix theory are then used to approximate in closed-form the most important error measures that are commonly used. The asymptotic approximations are then leveraged to select properly $k$ measurements exhibiting low values for the asymptotic error measures. Two heuristic algorithms are proposed: the first one merely consists in applying the convex optimization artifice to the asymptotic error measure. The second algorithm is a low-complexity greedy algorithm that attempts to look for a sufficiently good solution for the original minimization problem. The greedy algorithm can be applied to both the exact and the asymptotic error measures and can be thus implemented in blind and channel-aware fashions. We present two potential applications where the proposed algorithms can be used, namely antenna selection for uplink transmissions in large scale multi-user systems and sensor selection for wireless sensor networks. Numerical results are also presented and sustain the efficiency of the proposed blind methods in reaching the performances of channel-aware algorithms.
Estimation of Finite Population Mean in Multivariate Stratified Sampling under Cost Function Using Goal Programming

Directory of Open Access Journals (Sweden)

Atta Ullah

2014-01-01

Full Text Available In practical utilization of stratified random sampling scheme, the investigator meets a problem to select a sample that maximizes the precision of a finite population mean under cost constraint. An allocation of sample size becomes complicated when more than one characteristic is observed from each selected unit in a sample. In many real life situations, a linear cost function of a sample size nh is not a good approximation to actual cost of sample survey when traveling cost between selected units in a stratum is significant. In this paper, sample allocation problem in multivariate stratified random sampling with proposed cost function is formulated in integer nonlinear multiobjective mathematical programming. A solution procedure is proposed using extended lexicographic goal programming approach. A numerical example is presented to illustrate the computational details and to compare the efficiency of proposed compromise allocation.
Additive non-uniform random sampling in superimposed fiber Bragg grating strain gauge

International Nuclear Information System (INIS)

Ma, Y C; Liu, H Y; Yan, S B; Li, J M; Tang, J; Yang, Y H; Yang, M W

2013-01-01

This paper demonstrates an additive non-uniform random sampling and interrogation method for dynamic and/or static strain gauge using a reflection spectrum from two superimposed fiber Bragg gratings (FBGs). The superimposed FBGs are designed to generate non-equidistant space of a sensing pulse train in the time domain during dynamic strain gauge. By combining centroid finding with smooth filtering methods, both the interrogation speed and accuracy are improved. A 1.9 kHz dynamic strain is measured by generating an additive non-uniform randomly distributed 2 kHz optical sensing pulse train from a mean 500 Hz triangular periodically changing scanning frequency. (paper)
The Dirichlet-Multinomial Model for Multivariate Randomized Response Data and Small Samples

Science.gov (United States)

Avetisyan, Marianna; Fox, Jean-Paul

2012-01-01

In survey sampling the randomized response (RR) technique can be used to obtain truthful answers to sensitive questions. Although the individual answers are masked due to the RR technique, individual (sensitive) response rates can be estimated when observing multivariate response data. The beta-binomial model for binary RR data will be generalized…
40 CFR 761.247 - Sample site selection for pipe segment removal.

Science.gov (United States)

2010-07-01

... end of the pipe segment. (3) If the pipe segment is cut with a saw or other mechanical device, take..., take samples from a total of seven segments. (A) Sample the first and last segments removed. (B) Select... total length for purposes of disposal, take samples of each segment that is 1/2 mile distant from the...
Optimizing Event Selection with the Random Grid Search

Energy Technology Data Exchange (ETDEWEB)

Bhat, Pushpalatha C. [Fermilab; Prosper, Harrison B. [Florida State U.; Sekmen, Sezen [Kyungpook Natl. U.; Stewart, Chip [Broad Inst., Cambridge

2017-06-29

The random grid search (RGS) is a simple, but efficient, stochastic algorithm to find optimal cuts that was developed in the context of the search for the top quark at Fermilab in the mid-1990s. The algorithm, and associated code, have been enhanced recently with the introduction of two new cut types, one of which has been successfully used in searches for supersymmetry at the Large Hadron Collider. The RGS optimization algorithm is described along with the recent developments, which are illustrated with two examples from particle physics. One explores the optimization of the selection of vector boson fusion events in the four-lepton decay mode of the Higgs boson and the other optimizes SUSY searches using boosted objects and the razor variables.
Why sample selection matters in exploratory factor analysis: implications for the 12-item World Health Organization Disability Assessment Schedule 2.0.

Science.gov (United States)

Gaskin, Cadeyrn J; Lambert, Sylvie D; Bowe, Steven J; Orellana, Liliana

2017-03-11

Sample selection can substantially affect the solutions generated using exploratory factor analysis. Validation studies of the 12-item World Health Organization (WHO) Disability Assessment Schedule 2.0 (WHODAS 2.0) have generally involved samples in which substantial proportions of people had no, or minimal, disability. With the WHODAS 2.0 oriented towards measuring disability across six life domains (cognition, mobility, self-care, getting along, life activities, and participation in society), performing factor analysis with samples of people with disability may be more appropriate. We determined the influence of the sampling strategy on (a) the number of factors extracted and (b) the factor structure of the WHODAS 2.0. Using data from adults aged 50+ from the six countries in Wave 1 of the WHO's longitudinal Study on global AGEing and adult health (SAGE), we repeatedly selected samples (n = 750) using two strategies: (1) simple random sampling that reproduced nationally representative distributions of WHODAS 2.0 summary scores for each country (i.e., positively skewed distributions with many zero scores indicating the absence of disability), and (2) stratified random sampling with weights designed to obtain approximately symmetric distributions of summary scores for each country (i.e. predominantly including people with varying degrees of disability). Samples with skewed distributions typically produced one-factor solutions, except for the two countries with the lowest percentages of zero scores, in which the majority of samples produced two factors. Samples with approximately symmetric distributions, generally produced two- or three-factor solutions. In the two-factor solutions, the getting along domain items loaded on one factor (commonly with a cognition domain item), with remaining items loading on a second factor. In the three-factor solutions, the getting along and self-care domain items loaded separately on two factors and three other domains
Non-random mating for selection with restricted rates of inbreeding and overlapping generations

NARCIS (Netherlands)

Sonesson, A.K.; Meuwissen, T.H.E.

2002-01-01

Minimum coancestry mating with a maximum of one offspring per mating pair (MC1) is compared with random mating schemes for populations with overlapping generations. Optimum contribution selection is used, whereby $\\\\\\\\Delta F$ is restricted. For schemes with $\\\\\\\\Delta F$ restricted to 0.25% per
Reliability of impingement sampling designs: An example from the Indian Point station

International Nuclear Information System (INIS)

Mattson, M.T.; Waxman, J.B.; Watson, D.A.

1988-01-01

A 4-year data base (1976-1979) of daily fish impingement counts at the Indian Point electric power station on the Hudson River was used to compare the precision and reliability of three random-sampling designs: (1) simple random, (2) seasonally stratified, and (3) empirically stratified. The precision of daily impingement estimates improved logarithmically for each design as more days in the year were sampled. Simple random sampling was the least, and empirically stratified sampling was the most precise design, and the difference in precision between the two stratified designs was small. Computer-simulated sampling was used to estimate the reliability of the two stratified-random-sampling designs. A seasonally stratified sampling design was selected as the most appropriate reduced-sampling program for Indian Point station because: (1) reasonably precise and reliable impingement estimates were obtained using this design for all species combined and for eight common Hudson River fish by sampling only 30% of the days in a year (110 d); and (2) seasonal strata may be more precise and reliable than empirical strata if future changes in annual impingement patterns occur. The seasonally stratified design applied to the 1976-1983 Indian Point impingement data showed that selection of sampling dates based on daily species-specific impingement variability gave results that were more precise, but not more consistently reliable, than sampling allocations based on the variability of all fish species combined. 14 refs., 1 fig., 6 tabs
Application of random coherence order selection in gradient-enhanced multidimensional NMR

International Nuclear Information System (INIS)

Bostock, Mark J.; Nietlispach, Daniel

2016-01-01

Development of multidimensional NMR is essential to many applications, for example in high resolution structural studies of biomolecules. Multidimensional techniques enable separation of NMR signals over several dimensions, improving signal resolution, whilst also allowing identification of new connectivities. However, these advantages come at a significant cost. The Fourier transform theorem requires acquisition of a grid of regularly spaced points to satisfy the Nyquist criterion, while frequency discrimination and acquisition of a pure phase spectrum require acquisition of both quadrature components for each time point in every indirect (non-acquisition) dimension, adding a factor of 2 N -1 to the number of free- induction decays which must be acquired, where N is the number of dimensions. Compressed sensing (CS) ℓ 1 -norm minimisation in combination with non-uniform sampling (NUS) has been shown to be extremely successful in overcoming the Nyquist criterion. Previously, maximum entropy reconstruction has also been used to overcome the limitation of frequency discrimination, processing data acquired with only one quadrature component at a given time interval, known as random phase detection (RPD), allowing a factor of two reduction in the number of points for each indirect dimension (Maciejewski et al. 2011 PNAS 108 16640). However, whilst this approach can be easily applied in situations where the quadrature components are acquired as amplitude modulated data, the same principle is not easily extended to phase modulated (P-/N-type) experiments where data is acquired in the form exp (iωt) or exp (-iωt), and which make up many of the multidimensional experiments used in modern NMR. Here we demonstrate a modification of the CS ℓ 1 -norm approach to allow random coherence order selection (RCS) for phase modulated experiments; we generalise the nomenclature for RCS and RPD as random quadrature detection (RQD). With this method, the power of RQD can be extended
Hybrid nested sampling algorithm for Bayesian model selection applied to inverse subsurface flow problems

International Nuclear Information System (INIS)

Elsheikh, Ahmed H.; Wheeler, Mary F.; Hoteit, Ibrahim

2014-01-01

A Hybrid Nested Sampling (HNS) algorithm is proposed for efficient Bayesian model calibration and prior model selection. The proposed algorithm combines, Nested Sampling (NS) algorithm, Hybrid Monte Carlo (HMC) sampling and gradient estimation using Stochastic Ensemble Method (SEM). NS is an efficient sampling algorithm that can be used for Bayesian calibration and estimating the Bayesian evidence for prior model selection. Nested sampling has the advantage of computational feasibility. Within the nested sampling algorithm, a constrained sampling step is performed. For this step, we utilize HMC to reduce the correlation between successive sampled states. HMC relies on the gradient of the logarithm of the posterior distribution, which we estimate using a stochastic ensemble method based on an ensemble of directional derivatives. SEM only requires forward model runs and the simulator is then used as a black box and no adjoint code is needed. The developed HNS algorithm is successfully applied for Bayesian calibration and prior model selection of several nonlinear subsurface flow problems
Hybrid nested sampling algorithm for Bayesian model selection applied to inverse subsurface flow problems

Energy Technology Data Exchange (ETDEWEB)

Elsheikh, Ahmed H., E-mail: aelsheikh@ices.utexas.edu [Institute for Computational Engineering and Sciences (ICES), University of Texas at Austin, TX (United States); Institute of Petroleum Engineering, Heriot-Watt University, Edinburgh EH14 4AS (United Kingdom); Wheeler, Mary F. [Institute for Computational Engineering and Sciences (ICES), University of Texas at Austin, TX (United States); Hoteit, Ibrahim [Department of Earth Sciences and Engineering, King Abdullah University of Science and Technology (KAUST), Thuwal (Saudi Arabia)

2014-02-01

A Hybrid Nested Sampling (HNS) algorithm is proposed for efficient Bayesian model calibration and prior model selection. The proposed algorithm combines, Nested Sampling (NS) algorithm, Hybrid Monte Carlo (HMC) sampling and gradient estimation using Stochastic Ensemble Method (SEM). NS is an efficient sampling algorithm that can be used for Bayesian calibration and estimating the Bayesian evidence for prior model selection. Nested sampling has the advantage of computational feasibility. Within the nested sampling algorithm, a constrained sampling step is performed. For this step, we utilize HMC to reduce the correlation between successive sampled states. HMC relies on the gradient of the logarithm of the posterior distribution, which we estimate using a stochastic ensemble method based on an ensemble of directional derivatives. SEM only requires forward model runs and the simulator is then used as a black box and no adjoint code is needed. The developed HNS algorithm is successfully applied for Bayesian calibration and prior model selection of several nonlinear subsurface flow problems.
Hybrid nested sampling algorithm for Bayesian model selection applied to inverse subsurface flow problems

KAUST Repository

Elsheikh, Ahmed H.

2014-02-01

A Hybrid Nested Sampling (HNS) algorithm is proposed for efficient Bayesian model calibration and prior model selection. The proposed algorithm combines, Nested Sampling (NS) algorithm, Hybrid Monte Carlo (HMC) sampling and gradient estimation using Stochastic Ensemble Method (SEM). NS is an efficient sampling algorithm that can be used for Bayesian calibration and estimating the Bayesian evidence for prior model selection. Nested sampling has the advantage of computational feasibility. Within the nested sampling algorithm, a constrained sampling step is performed. For this step, we utilize HMC to reduce the correlation between successive sampled states. HMC relies on the gradient of the logarithm of the posterior distribution, which we estimate using a stochastic ensemble method based on an ensemble of directional derivatives. SEM only requires forward model runs and the simulator is then used as a black box and no adjoint code is needed. The developed HNS algorithm is successfully applied for Bayesian calibration and prior model selection of several nonlinear subsurface flow problems. © 2013 Elsevier Inc.
Landslide Susceptibility Assessment Using Frequency Ratio Technique with Iterative Random Sampling

Directory of Open Access Journals (Sweden)

Hyun-Joo Oh

2017-01-01

Full Text Available This paper assesses the performance of the landslide susceptibility analysis using frequency ratio (FR with an iterative random sampling. A pair of before-and-after digital aerial photographs with 50 cm spatial resolution was used to detect landslide occurrences in Yongin area, Korea. Iterative random sampling was run ten times in total and each time it was applied to the training and validation datasets. Thirteen landslide causative factors were derived from the topographic, soil, forest, and geological maps. The FR scores were calculated from the causative factors and training occurrences repeatedly ten times. The ten landslide susceptibility maps were obtained from the integration of causative factors that assigned FR scores. The landslide susceptibility maps were validated by using each validation dataset. The FR method achieved susceptibility accuracies from 89.48% to 93.21%. And the landslide susceptibility accuracy of the FR method is higher than 89%. Moreover, the ten times iterative FR modeling may contribute to a better understanding of a regularized relationship between the causative factors and landslide susceptibility. This makes it possible to incorporate knowledge-driven considerations of the causative factors into the landslide susceptibility analysis and also be extensively used to other areas.
Comparative Evaluations of Randomly Selected Four Point-of-Care Glucometer Devices in Addis Ababa, Ethiopia.

Science.gov (United States)

Wolde, Mistire; Tarekegn, Getahun; Kebede, Tedla

2018-05-01

Point-of-care glucometer (PoCG) devices play a significant role in self-monitoring of the blood sugar level, particularly in the follow-up of high blood sugar therapeutic response. The aim of this study was to evaluate blood glucose test results performed with four randomly selected glucometers on diabetes and control subjects versus standard wet chemistry (hexokinase) methods in Addis Ababa, Ethiopia. A prospective cross-sectional study was conducted on randomly selected 200 study participants (100 participants with diabetes and 100 healthy controls). Four randomly selected PoCG devices (CareSens N, DIAVUE Prudential, On Call Extra, i-QARE DS-W) were evaluated against hexokinase method and ISO 15197:2003 and ISO 15197:2013 standards. The minimum and maximum blood sugar values were recorded by CareSens N (21 mg/dl) and hexokinase method (498.8 mg/dl), respectively. The mean sugar values of all PoCG devices except On Call Extra showed significant differences compared with the reference hexokinase method. Meanwhile, all four PoCG devices had strong positive relationship (>80%) with the reference method (hexokinase). On the other hand, none of the four PoCG devices fulfilled the minimum accuracy measurement set by ISO 15197:2003 and ISO 15197:2013 standards. In addition, the linear regression analysis revealed that all four selected PoCG overestimated the glucose concentrations. The overall evaluation of the selected four PoCG measurements were poorly correlated with standard reference method. Therefore, before introducing PoCG devices to the market, there should be a standardized evaluation platform for validation. Further similar large-scale studies on other PoCG devices also need to be undertaken.
Geography and genography: prediction of continental origin using randomly selected single nucleotide polymorphisms

Directory of Open Access Journals (Sweden)

Ramoni Marco F

2007-03-01

Full Text Available Abstract Background Recent studies have shown that when individuals are grouped on the basis of genetic similarity, group membership corresponds closely to continental origin. There has been considerable debate about the implications of these findings in the context of larger debates about race and the extent of genetic variation between groups. Some have argued that clustering according to continental origin demonstrates the existence of significant genetic differences between groups and that these differences may have important implications for differences in health and disease. Others argue that clustering according to continental origin requires the use of large amounts of genetic data or specifically chosen markers and is indicative only of very subtle genetic differences that are unlikely to have biomedical significance. Results We used small numbers of randomly selected single nucleotide polymorphisms (SNPs from the International HapMap Project to train naïve Bayes classifiers for prediction of ancestral continent of origin. Predictive accuracy was tested on two independent data sets. Genetically similar groups should be difficult to distinguish, especially if only a small number of genetic markers are used. The genetic differences between continentally defined groups are sufficiently large that one can accurately predict ancestral continent of origin using only a minute, randomly selected fraction of the genetic variation present in the human genome. Genotype data from only 50 random SNPs was sufficient to predict ancestral continent of origin in our primary test data set with an average accuracy of 95%. Genetic variations informative about ancestry were common and widely distributed throughout the genome. Conclusion Accurate characterization of ancestry is possible using small numbers of randomly selected SNPs. The results presented here show how investigators conducting genetic association studies can use small numbers of arbitrarily

Measurement of radioactivity in the environment - Soil - Part 2: Guidance for the selection of the sampling strategy, sampling and pre-treatment of samples

International Nuclear Information System (INIS)

2007-01-01

This part of ISO 18589 specifies the general requirements, based on ISO 11074 and ISO/IEC 17025, for all steps in the planning (desk study and area reconnaissance) of the sampling and the preparation of samples for testing. It includes the selection of the sampling strategy, the outline of the sampling plan, the presentation of general sampling methods and equipment, as well as the methodology of the pre-treatment of samples adapted to the measurements of the activity of radionuclides in soil. This part of ISO 18589 is addressed to the people responsible for determining the radioactivity present in soil for the purpose of radiation protection. It is applicable to soil from gardens, farmland, urban or industrial sites, as well as soil not affected by human activities. This part of ISO 18589 is applicable to all laboratories regardless of the number of personnel or the range of the testing performed. When a laboratory does not undertake one or more of the activities covered by this part of ISO 18589, such as planning, sampling or testing, the corresponding requirements do not apply. Information is provided on scope, normative references, terms and definitions and symbols, principle, sampling strategy, sampling plan, sampling process, pre-treatment of samples and recorded information. Five annexes inform about selection of the sampling strategy according to the objectives and the radiological characterization of the site and sampling areas, diagram of the evolution of the sample characteristics from the sampling site to the laboratory, example of sampling plan for a site divided in three sampling areas, example of a sampling record for a single/composite sample and example for a sample record for a soil profile with soil description. A bibliography is provided
[A comparison of convenience sampling and purposive sampling].

Science.gov (United States)

Suen, Lee-Jen Wu; Huang, Hui-Man; Lee, Hao-Hsien

2014-06-01

Convenience sampling and purposive sampling are two different sampling methods. This article first explains sampling terms such as target population, accessible population, simple random sampling, intended sample, actual sample, and statistical power analysis. These terms are then used to explain the difference between "convenience sampling" and purposive sampling." Convenience sampling is a non-probabilistic sampling technique applicable to qualitative or quantitative studies, although it is most frequently used in quantitative studies. In convenience samples, subjects more readily accessible to the researcher are more likely to be included. Thus, in quantitative studies, opportunity to participate is not equal for all qualified individuals in the target population and study results are not necessarily generalizable to this population. As in all quantitative studies, increasing the sample size increases the statistical power of the convenience sample. In contrast, purposive sampling is typically used in qualitative studies. Researchers who use this technique carefully select subjects based on study purpose with the expectation that each participant will provide unique and rich information of value to the study. As a result, members of the accessible population are not interchangeable and sample size is determined by data saturation not by statistical power analysis.
Proposal for selecting an ore sample from mining shaft under Kvanefjeld

International Nuclear Information System (INIS)

Lund Clausen, F.

1979-02-01

Uranium ore recovered from the tunnel under Kvanefjeld (Greenland) will be processed in a pilot plant. Selection of a fully representative ore sample for both the whole area and single local sites is discussed. A FORTRAN program for ore distribution is presented, in order to enable correct sampling. (EG)
Fuzzy Random λ-Mean SAD Portfolio Selection Problem: An Ant Colony Optimization Approach

Science.gov (United States)

Thakur, Gour Sundar Mitra; Bhattacharyya, Rupak; Mitra, Swapan Kumar

2010-10-01

To reach the investment goal, one has to select a combination of securities among different portfolios containing large number of securities. Only the past records of each security do not guarantee the future return. As there are many uncertain factors which directly or indirectly influence the stock market and there are also some newer stock markets which do not have enough historical data, experts' expectation and experience must be combined with the past records to generate an effective portfolio selection model. In this paper the return of security is assumed to be Fuzzy Random Variable Set (FRVS), where returns are set of random numbers which are in turn fuzzy numbers. A new λ-Mean Semi Absolute Deviation (λ-MSAD) portfolio selection model is developed. The subjective opinions of the investors to the rate of returns of each security are taken into consideration by introducing a pessimistic-optimistic parameter vector λ. λ-Mean Semi Absolute Deviation (λ-MSAD) model is preferred as it follows absolute deviation of the rate of returns of a portfolio instead of the variance as the measure of the risk. As this model can be reduced to Linear Programming Problem (LPP) it can be solved much faster than quadratic programming problems. Ant Colony Optimization (ACO) is used for solving the portfolio selection problem. ACO is a paradigm for designing meta-heuristic algorithms for combinatorial optimization problem. Data from BSE is used for illustration.
A simple sample size formula for analysis of covariance in cluster randomized trials.

NARCIS (Netherlands)

Teerenstra, S.; Eldridge, S.; Graff, M.J.; Hoop, E. de; Borm, G.F.

2012-01-01

For cluster randomized trials with a continuous outcome, the sample size is often calculated as if an analysis of the outcomes at the end of the treatment period (follow-up scores) would be performed. However, often a baseline measurement of the outcome is available or feasible to obtain. An
Accounting for animal movement in estimation of resource selection functions: sampling and data analysis.

Science.gov (United States)

Forester, James D; Im, Hae Kyung; Rathouz, Paul J

2009-12-01

Patterns of resource selection by animal populations emerge as a result of the behavior of many individuals. Statistical models that describe these population-level patterns of habitat use can miss important interactions between individual animals and characteristics of their local environment; however, identifying these interactions is difficult. One approach to this problem is to incorporate models of individual movement into resource selection models. To do this, we propose a model for step selection functions (SSF) that is composed of a resource-independent movement kernel and a resource selection function (RSF). We show that standard case-control logistic regression may be used to fit the SSF; however, the sampling scheme used to generate control points (i.e., the definition of availability) must be accommodated. We used three sampling schemes to analyze simulated movement data and found that ignoring sampling and the resource-independent movement kernel yielded biased estimates of selection. The level of bias depended on the method used to generate control locations, the strength of selection, and the spatial scale of the resource map. Using empirical or parametric methods to sample control locations produced biased estimates under stronger selection; however, we show that the addition of a distance function to the analysis substantially reduced that bias. Assuming a uniform availability within a fixed buffer yielded strongly biased selection estimates that could be corrected by including the distance function but remained inefficient relative to the empirical and parametric sampling methods. As a case study, we used location data collected from elk in Yellowstone National Park, USA, to show that selection and bias may be temporally variable. Because under constant selection the amount of bias depends on the scale at which a resource is distributed in the landscape, we suggest that distance always be included as a covariate in SSF analyses. This approach to
Pediatric selective mutism therapy: a randomized controlled trial.

Science.gov (United States)

Esposito, Maria; Gimigliano, Francesca; Barillari, Maria R; Precenzano, Francesco; Ruberto, Maria; Sepe, Joseph; Barillari, Umberto; Gimigliano, Raffaele; Militerni, Roberto; Messina, Giovanni; Carotenuto, Marco

2017-10-01

Selective mutism (SM) is a rare disease in children coded by DSM-5 as an anxiety disorder. Despite the disabling nature of the disease, there is still no specific treatment. The aims of this study were to verify the efficacy of six-month standard psychomotor treatment and the positive changes in lifestyle, in a population of children affected by SM. Randomized controlled trial registered in the European Clinical Trials Registry (EuDract 2015-001161-36). University third level Centre (Child and Adolescent Neuropsychiatry Clinic). Study population was composed by 67 children in group A (psychomotricity treatment) (35 M, mean age 7.84±1.15) and 71 children in group B (behavioral and educational counseling) (37 M, mean age 7.75±1.36). Psychomotor treatment was administered by trained child therapists in residential settings three times per week. Each child was treated for the whole period by the same therapist and all the therapists shared the same protocol. The standard psychomotor session length is of 45 minutes. At T0 and after 6 months (T1) of treatments, patients underwent a behavioral and SM severity assessment. To verify the effects of the psychomotor management, the Child Behavior Checklist questionnaire (CBCL) and Selective Mutism Questionnaire (SMQ) were administered to the parents. After 6 months of psychomotor treatment SM children showed a significant reduction among CBCL scores such as in social relations, anxious/depressed, social problems and total problems (Pselective mutism, even if further studies are needed. The present study identifies in psychomotricity a safe and efficacy therapy for pediatric selective mutism.
Event-triggered synchronization for reaction-diffusion complex networks via random sampling

Science.gov (United States)

Dong, Tao; Wang, Aijuan; Zhu, Huiyun; Liao, Xiaofeng

2018-04-01

In this paper, the synchronization problem of the reaction-diffusion complex networks (RDCNs) with Dirichlet boundary conditions is considered, where the data is sampled randomly. An event-triggered controller based on the sampled data is proposed, which can reduce the number of controller and the communication load. Under this strategy, the synchronization problem of the diffusion complex network is equivalently converted to the stability of a of reaction-diffusion complex dynamical systems with time delay. By using the matrix inequality technique and Lyapunov method, the synchronization conditions of the RDCNs are derived, which are dependent on the diffusion term. Moreover, it is found the proposed control strategy can get rid of the Zeno behavior naturally. Finally, a numerical example is given to verify the obtained results.
Evaluation of pump pulsation in respirable size-selective sampling: part II. Changes in sampling efficiency.

Science.gov (United States)

Lee, Eun Gyung; Lee, Taekhee; Kim, Seung Won; Lee, Larry; Flemmer, Michael M; Harper, Martin

2014-01-01

This second, and concluding, part of this study evaluated changes in sampling efficiency of respirable size-selective samplers due to air pulsations generated by the selected personal sampling pumps characterized in Part I (Lee E, Lee L, Möhlmann C et al. Evaluation of pump pulsation in respirable size-selective sampling: Part I. Pulsation measurements. Ann Occup Hyg 2013). Nine particle sizes of monodisperse ammonium fluorescein (from 1 to 9 μm mass median aerodynamic diameter) were generated individually by a vibrating orifice aerosol generator from dilute solutions of fluorescein in aqueous ammonia and then injected into an environmental chamber. To collect these particles, 10-mm nylon cyclones, also known as Dorr-Oliver (DO) cyclones, were used with five medium volumetric flow rate pumps. Those were the Apex IS, HFS513, GilAir5, Elite5, and Basic5 pumps, which were found in Part I to generate pulsations of 5% (the lowest), 25%, 30%, 56%, and 70% (the highest), respectively. GK2.69 cyclones were used with the Legacy [pump pulsation (PP) = 15%] and Elite12 (PP = 41%) pumps for collection at high flows. The DO cyclone was also used to evaluate changes in sampling efficiency due to pulse shape. The HFS513 pump, which generates a more complex pulse shape, was compared to a single sine wave fluctuation generated by a piston. The luminescent intensity of the fluorescein extracted from each sample was measured with a luminescence spectrometer. Sampling efficiencies were obtained by dividing the intensity of the fluorescein extracted from the filter placed in a cyclone with the intensity obtained from the filter used with a sharp-edged reference sampler. Then, sampling efficiency curves were generated using a sigmoid function with three parameters and each sampling efficiency curve was compared to that of the reference cyclone by constructing bias maps. In general, no change in sampling efficiency (bias under ±10%) was observed until pulsations exceeded 25% for the
A quick method based on SIMPLISMA-KPLS for simultaneously selecting outlier samples and informative samples for model standardization in near infrared spectroscopy

Science.gov (United States)

Li, Li-Na; Ma, Chang-Ming; Chang, Ming; Zhang, Ren-Cheng

2017-12-01

A novel method based on SIMPLe-to-use Interactive Self-modeling Mixture Analysis (SIMPLISMA) and Kernel Partial Least Square (KPLS), named as SIMPLISMA-KPLS, is proposed in this paper for selection of outlier samples and informative samples simultaneously. It is a quick algorithm used to model standardization (or named as model transfer) in near infrared (NIR) spectroscopy. The NIR experiment data of the corn for analysis of the protein content is introduced to evaluate the proposed method. Piecewise direct standardization (PDS) is employed in model transfer. And the comparison of SIMPLISMA-PDS-KPLS and KS-PDS-KPLS is given in this research by discussion of the prediction accuracy of protein content and calculation speed of each algorithm. The conclusions include that SIMPLISMA-KPLS can be utilized as an alternative sample selection method for model transfer. Although it has similar accuracy to Kennard-Stone (KS), it is different from KS as it employs concentration information in selection program. This means that it ensures analyte information is involved in analysis, and the spectra (X) of the selected samples is interrelated with concentration (y). And it can be used for outlier sample elimination simultaneously by validation of calibration. According to the statistical data results of running time, it is clear that the sample selection process is more rapid when using KPLS. The quick algorithm of SIMPLISMA-KPLS is beneficial to improve the speed of online measurement using NIR spectroscopy.
Unwilling or Unable to Cheat? Evidence from a Randomized Tax Audit Experiment in Denmark

OpenAIRE

Henrik J. Kleven; Martin B. Knudsen; Claus T. Kreiner; Søren Pedersen; Emmanuel Saez

2010-01-01

This paper analyzes a randomized tax enforcement experiment in Denmark. In the base year, a stratified and representative sample of over 40,000 individual income tax filers was selected for the experiment. Half of the tax filers were randomly selected to be thoroughly audited, while the rest were deliberately not audited. The following year, "threat-of-audit" letters were randomly assigned and sent to tax filers in both groups. Using comprehensive administrative tax data, we present four main...
Estimating HIES Data through Ratio and Regression Methods for Different Sampling Designs

Directory of Open Access Journals (Sweden)

Faqir Muhammad

2007-01-01

Full Text Available In this study, comparison has been made for different sampling designs, using the HIES data of North West Frontier Province (NWFP for 2001-02 and 1998-99 collected from the Federal Bureau of Statistics, Statistical Division, Government of Pakistan, Islamabad. The performance of the estimators has also been considered using bootstrap and Jacknife. A two-stage stratified random sample design is adopted by HIES. In the first stage, enumeration blocks and villages are treated as the first stage Primary Sampling Units (PSU. The sample PSU’s are selected with probability proportional to size. Secondary Sampling Units (SSU i.e., households are selected by systematic sampling with a random start. They have used a single study variable. We have compared the HIES technique with some other designs, which are: Stratified Simple Random Sampling. Stratified Systematic Sampling. Stratified Ranked Set Sampling. Stratified Two Phase Sampling. Ratio and Regression methods were applied with two study variables, which are: Income (y and Household sizes (x. Jacknife and Bootstrap are used for variance replication. Simple Random Sampling with sample size (462 to 561 gave moderate variances both by Jacknife and Bootstrap. By applying Systematic Sampling, we received moderate variance with sample size (467. In Jacknife with Systematic Sampling, we obtained variance of regression estimator greater than that of ratio estimator for a sample size (467 to 631. At a sample size (952 variance of ratio estimator gets greater than that of regression estimator. The most efficient design comes out to be Ranked set sampling compared with other designs. The Ranked set sampling with jackknife and bootstrap, gives minimum variance even with the smallest sample size (467. Two Phase sampling gave poor performance. Multi-stage sampling applied by HIES gave large variances especially if used with a single study variable.
Primitive polynomials selection method for pseudo-random number generator

Science.gov (United States)

Anikin, I. V.; Alnajjar, Kh

2018-01-01

In this paper we suggested the method for primitive polynomials selection of special type. This kind of polynomials can be efficiently used as a characteristic polynomials for linear feedback shift registers in pseudo-random number generators. The proposed method consists of two basic steps: finding minimum-cost irreducible polynomials of the desired degree and applying primitivity tests to get the primitive ones. Finally two primitive polynomials, which was found by the proposed method, used in pseudorandom number generator based on fuzzy logic (FRNG) which had been suggested before by the authors. The sequences generated by new version of FRNG have low correlation magnitude, high linear complexity, less power consumption, is more balanced and have better statistical properties.
Selection Component Analysis of Natural Polymorphisms using Population Samples Including Mother-Offspring Combinations, II

DEFF Research Database (Denmark)

Jarmer, Hanne Østergaard; Christiansen, Freddy Bugge

1981-01-01

Population samples including mother-offspring combinations provide information on the selection components: zygotic selection, sexual selection, gametic seletion and fecundity selection, on the mating pattern, and on the deviation from linkage equilibrium among the loci studied. The theory...
Privacy problems in the small sample selection

Directory of Open Access Journals (Sweden)

Loredana Cerbara

2013-05-01

Full Text Available The side of social research that uses small samples for the production of micro data, today finds some operating difficulties due to the privacy law. The privacy code is a really important and necessary law because it guarantees the Italian citizen’s rights, as already happens in other Countries of the world. However it does not seem appropriate to limit once more the possibilities of the data production of the national centres of research. That possibilities are already moreover compromised due to insufficient founds is a common problem becoming more and more frequent in the research field. It would be necessary, therefore, to include in the law the possibility to use telephonic lists to select samples useful for activities directly of interest and importance to the citizen, such as the collection of the data carried out on the basis of opinion polls by the centres of research of the Italian CNR and some universities.
Data Quality Objectives For Selecting Waste Samples To Test The Fluid Bed Steam Reformer Test

International Nuclear Information System (INIS)

Banning, D.L.

2010-01-01

This document describes the data quality objectives to select archived samples located at the 222-S Laboratory for Fluid Bed Steam Reformer testing. The type, quantity and quality of the data required to select the samples for Fluid Bed Steam Reformer testing are discussed. In order to maximize the efficiency and minimize the time to treat Hanford tank waste in the Waste Treatment and Immobilization Plant, additional treatment processes may be required. One of the potential treatment processes is the fluid bed steam reformer (FBSR). A determination of the adequacy of the FBSR process to treat Hanford tank waste is required. The initial step in determining the adequacy of the FBSR process is to select archived waste samples from the 222-S Laboratory that will be used to test the FBSR process. Analyses of the selected samples will be required to confirm the samples meet the testing criteria.
Approaches to sampling and case selection in qualitative research: examples in the geography of health.

Science.gov (United States)

Curtis, S; Gesler, W; Smith, G; Washburn, S

2000-04-01

This paper focuses on the question of sampling (or selection of cases) in qualitative research. Although the literature includes some very useful discussions of qualitative sampling strategies, the question of sampling often seems to receive less attention in methodological discussion than questions of how data is collected or is analysed. Decisions about sampling are likely to be important in many qualitative studies (although it may not be an issue in some research). There are varying accounts of the principles applicable to sampling or case selection. Those who espouse 'theoretical sampling', based on a 'grounded theory' approach, are in some ways opposed to those who promote forms of 'purposive sampling' suitable for research informed by an existing body of social theory. Diversity also results from the many different methods for drawing purposive samples which are applicable to qualitative research. We explore the value of a framework suggested by Miles and Huberman [Miles, M., Huberman,, A., 1994. Qualitative Data Analysis, Sage, London.], to evaluate the sampling strategies employed in three examples of research by the authors. Our examples comprise three studies which respectively involve selection of: 'healing places'; rural places which incorporated national anti-malarial policies; young male interviewees, identified as either chronically ill or disabled. The examples are used to show how in these three studies the (sometimes conflicting) requirements of the different criteria were resolved, as well as the potential and constraints placed on the research by the selection decisions which were made. We also consider how far the criteria Miles and Huberman suggest seem helpful for planning 'sample' selection in qualitative research.
Investigating causal associations between use of nicotine, alcohol, caffeine and cannabis: a two-sample bidirectional Mendelian randomization study.

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Verweij, Karin J H; Treur, Jorien L; Vink, Jacqueline M

2018-07-01

Epidemiological studies consistently show co-occurrence of use of different addictive substances. Whether these associations are causal or due to overlapping underlying influences remains an important question in addiction research. Methodological advances have made it possible to use published genetic associations to infer causal relationships between phenotypes. In this exploratory study, we used Mendelian randomization (MR) to examine the causality of well-established associations between nicotine, alcohol, caffeine and cannabis use. Two-sample MR was employed to estimate bidirectional causal effects between four addictive substances: nicotine (smoking initiation and cigarettes smoked per day), caffeine (cups of coffee per day), alcohol (units per week) and cannabis (initiation). Based on existing genome-wide association results we selected genetic variants associated with the exposure measure as an instrument to estimate causal effects. Where possible we applied sensitivity analyses (MR-Egger and weighted median) more robust to horizontal pleiotropy. Most MR tests did not reveal causal associations. There was some weak evidence for a causal positive effect of genetically instrumented alcohol use on smoking initiation and of cigarettes per day on caffeine use, but these were not supported by the sensitivity analyses. There was also some suggestive evidence for a positive effect of alcohol use on caffeine use (only with MR-Egger) and smoking initiation on cannabis initiation (only with weighted median). None of the suggestive causal associations survived corrections for multiple testing. Two-sample Mendelian randomization analyses found little evidence for causal relationships between nicotine, alcohol, caffeine and cannabis use. © 2018 Society for the Study of Addiction.
Random Walks on Directed Networks: Inference and Respondent-Driven Sampling

Directory of Open Access Journals (Sweden)

Malmros Jens

2016-06-01

Full Text Available Respondent-driven sampling (RDS is often used to estimate population properties (e.g., sexual risk behavior in hard-to-reach populations. In RDS, already sampled individuals recruit population members to the sample from their social contacts in an efficient snowball-like sampling procedure. By assuming a Markov model for the recruitment of individuals, asymptotically unbiased estimates of population characteristics can be obtained. Current RDS estimation methodology assumes that the social network is undirected, that is, all edges are reciprocal. However, empirical social networks in general also include a substantial number of nonreciprocal edges. In this article, we develop an estimation method for RDS in populations connected by social networks that include reciprocal and nonreciprocal edges. We derive estimators of the selection probabilities of individuals as a function of the number of outgoing edges of sampled individuals. The proposed estimators are evaluated on artificial and empirical networks and are shown to generally perform better than existing estimators. This is the case in particular when the fraction of directed edges in the network is large.
Randomizing Roaches: Exploring the "Bugs" of Randomization in Experimental Design

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Wagler, Amy; Wagler, Ron

2014-01-01

Understanding the roles of random selection and random assignment in experimental design is a central learning objective in most introductory statistics courses. This article describes an activity, appropriate for a high school or introductory statistics course, designed to teach the concepts, values and pitfalls of random selection and assignment…

New sorbent materials for selective extraction of cocaine and benzoylecgonine from human urine samples.

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Bujak, Renata; Gadzała-Kopciuch, Renata; Nowaczyk, Alicja; Raczak-Gutknecht, Joanna; Kordalewska, Marta; Struck-Lewicka, Wiktoria; Waszczuk-Jankowska, Małgorzata; Tomczak, Ewa; Kaliszan, Michał; Buszewski, Bogusław; Markuszewski, Michał J

2016-02-20

An increase in cocaine consumption has been observed in Europe during the last decade. Benzoylecgonine, as a main urinary metabolite of cocaine in human, is so far the most reliable marker of cocaine consumption. Determination of cocaine and its metabolite in complex biological samples as urine or blood, requires efficient and selective sample pretreatment. In this preliminary study, the newly synthesized sorbent materials were proposed for selective extraction of cocaine and benzoylecgonine from urine samples. Application of these sorbent media allowed to determine cocaine and benzoylecgonine in urine samples at the concentration level of 100ng/ml with good recovery values as 81.7%±6.6 and 73.8%±4.2, respectively. The newly synthesized materials provided efficient, inexpensive and selective extraction of both cocaine and benzoylecgonine from urine samples, which can consequently lead to an increase of the sensitivity of the current available screening diagnostic tests. Copyright © 2015 Elsevier B.V. All rights reserved.
Comparison of address-based sampling and random-digit dialing methods for recruiting young men as controls in a case-control study of testicular cancer susceptibility.

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Clagett, Bartholt; Nathanson, Katherine L; Ciosek, Stephanie L; McDermoth, Monique; Vaughn, David J; Mitra, Nandita; Weiss, Andrew; Martonik, Rachel; Kanetsky, Peter A

2013-12-01

Random-digit dialing (RDD) using landline telephone numbers is the historical gold standard for control recruitment in population-based epidemiologic research. However, increasing cell-phone usage and diminishing response rates suggest that the effectiveness of RDD in recruiting a random sample of the general population, particularly for younger target populations, is decreasing. In this study, we compared landline RDD with alternative methods of control recruitment, including RDD using cell-phone numbers and address-based sampling (ABS), to recruit primarily white men aged 18-55 years into a study of testicular cancer susceptibility conducted in the Philadelphia, Pennsylvania, metropolitan area between 2009 and 2012. With few exceptions, eligible and enrolled controls recruited by means of RDD and ABS were similar with regard to characteristics for which data were collected on the screening survey. While we find ABS to be a comparably effective method of recruiting young males compared with landline RDD, we acknowledge the potential impact that selection bias may have had on our results because of poor overall response rates, which ranged from 11.4% for landline RDD to 1.7% for ABS.
Observed Characteristics and Teacher Quality: Impacts of Sample Selection on a Value Added Model

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Winters, Marcus A.; Dixon, Bruce L.; Greene, Jay P.

2012-01-01

We measure the impact of observed teacher characteristics on student math and reading proficiency using a rich dataset from Florida. We expand upon prior work by accounting directly for nonrandom attrition of teachers from the classroom in a sample selection framework. We find evidence that sample selection is present in the estimation of the…
Materials selection for oxide-based resistive random access memories

International Nuclear Information System (INIS)

Guo, Yuzheng; Robertson, John

2014-01-01

The energies of atomic processes in resistive random access memories (RRAMs) are calculated for four typical oxides, HfO 2 , TiO 2 , Ta 2 O 5 , and Al 2 O 3 , to define a materials selection process. O vacancies have the lowest defect formation energy in the O-poor limit and dominate the processes. A band diagram defines the operating Fermi energy and O chemical potential range. It is shown how the scavenger metal can be used to vary the O vacancy formation energy, via controlling the O chemical potential, and the mean Fermi energy. The high endurance of Ta 2 O 5 RRAM is related to its more stable amorphous phase and the adaptive lattice rearrangements of its O vacancy
Nested sampling algorithm for subsurface flow model selection, uncertainty quantification, and nonlinear calibration

KAUST Repository

Elsheikh, A. H.

2013-12-01

Calibration of subsurface flow models is an essential step for managing ground water aquifers, designing of contaminant remediation plans, and maximizing recovery from hydrocarbon reservoirs. We investigate an efficient sampling algorithm known as nested sampling (NS), which can simultaneously sample the posterior distribution for uncertainty quantification, and estimate the Bayesian evidence for model selection. Model selection statistics, such as the Bayesian evidence, are needed to choose or assign different weights to different models of different levels of complexities. In this work, we report the first successful application of nested sampling for calibration of several nonlinear subsurface flow problems. The estimated Bayesian evidence by the NS algorithm is used to weight different parameterizations of the subsurface flow models (prior model selection). The results of the numerical evaluation implicitly enforced Occam\\'s razor where simpler models with fewer number of parameters are favored over complex models. The proper level of model complexity was automatically determined based on the information content of the calibration data and the data mismatch of the calibrated model.
The Impact of Selection, Gene Conversion, and Biased Sampling on the Assessment of Microbial Demography.

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Lapierre, Marguerite; Blin, Camille; Lambert, Amaury; Achaz, Guillaume; Rocha, Eduardo P C

2016-07-01

Recent studies have linked demographic changes and epidemiological patterns in bacterial populations using coalescent-based approaches. We identified 26 studies using skyline plots and found that 21 inferred overall population expansion. This surprising result led us to analyze the impact of natural selection, recombination (gene conversion), and sampling biases on demographic inference using skyline plots and site frequency spectra (SFS). Forward simulations based on biologically relevant parameters from Escherichia coli populations showed that theoretical arguments on the detrimental impact of recombination and especially natural selection on the reconstructed genealogies cannot be ignored in practice. In fact, both processes systematically lead to spurious interpretations of population expansion in skyline plots (and in SFS for selection). Weak purifying selection, and especially positive selection, had important effects on skyline plots, showing patterns akin to those of population expansions. State-of-the-art techniques to remove recombination further amplified these biases. We simulated three common sampling biases in microbiological research: uniform, clustered, and mixed sampling. Alone, or together with recombination and selection, they further mislead demographic inferences producing almost any possible skyline shape or SFS. Interestingly, sampling sub-populations also affected skyline plots and SFS, because the coalescent rates of populations and their sub-populations had different distributions. This study suggests that extreme caution is needed to infer demographic changes solely based on reconstructed genealogies. We suggest that the development of novel sampling strategies and the joint analyzes of diverse population genetic methods are strictly necessary to estimate demographic changes in populations where selection, recombination, and biased sampling are present. © The Author 2016. Published by Oxford University Press on behalf of the Society for
A systematic examination of a random sampling strategy for source apportionment calculations.

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Andersson, August

2011-12-15

Estimating the relative contributions from multiple potential sources of a specific component in a mixed environmental matrix is a general challenge in diverse fields such as atmospheric, environmental and earth sciences. Perhaps the most common strategy for tackling such problems is by setting up a system of linear equations for the fractional influence of different sources. Even though an algebraic solution of this approach is possible for the common situation with N+1 sources and N source markers, such methodology introduces a bias, since it is implicitly assumed that the calculated fractions and the corresponding uncertainties are independent of the variability of the source distributions. Here, a random sampling (RS) strategy for accounting for such statistical bias is examined by investigating rationally designed synthetic data sets. This random sampling methodology is found to be robust and accurate with respect to reproducibility and predictability. This method is also compared to a numerical integration solution for a two-source situation where source variability also is included. A general observation from this examination is that the variability of the source profiles not only affects the calculated precision but also the mean/median source contributions. Copyright © 2011 Elsevier B.V. All rights reserved.
Sampling point selection for energy estimation in the quasicontinuum method

NARCIS (Netherlands)

Beex, L.A.A.; Peerlings, R.H.J.; Geers, M.G.D.

2010-01-01

The quasicontinuum (QC) method reduces computational costs of atomistic calculations by using interpolation between a small number of so-called repatoms to represent the displacements of the complete lattice and by selecting a small number of sampling atoms to estimate the total potential energy of
Emergence of multilevel selection in the prisoner's dilemma game on coevolving random networks

International Nuclear Information System (INIS)

Szolnoki, Attila; Perc, Matjaz

2009-01-01

We study the evolution of cooperation in the prisoner's dilemma game, whereby a coevolutionary rule is introduced that molds the random topology of the interaction network in two ways. First, existing links are deleted whenever a player adopts a new strategy or its degree exceeds a threshold value; second, new links are added randomly after a given number of game iterations. These coevolutionary processes correspond to the generic formation of new links and deletion of existing links that, especially in human societies, appear frequently as a consequence of ongoing socialization, change of lifestyle or death. Due to the counteraction of deletions and additions of links the initial heterogeneity of the interaction network is qualitatively preserved, and thus cannot be held responsible for the observed promotion of cooperation. Indeed, the coevolutionary rule evokes the spontaneous emergence of a powerful multilevel selection mechanism, which despite the sustained random topology of the evolving network, maintains cooperation across the whole span of defection temptation values.
A Systematic Review of Surgical Randomized Controlled Trials: Part 2. Funding Source, Conflict of Interest, and Sample Size in Plastic Surgery.

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Voineskos, Sophocles H; Coroneos, Christopher J; Ziolkowski, Natalia I; Kaur, Manraj N; Banfield, Laura; Meade, Maureen O; Chung, Kevin C; Thoma, Achilleas; Bhandari, Mohit

2016-02-01

The authors examined industry support, conflict of interest, and sample size in plastic surgery randomized controlled trials that compared surgical interventions. They hypothesized that industry-funded trials demonstrate statistically significant outcomes more often, and randomized controlled trials with small sample sizes report statistically significant results more frequently. An electronic search identified randomized controlled trials published between 2000 and 2013. Independent reviewers assessed manuscripts and performed data extraction. Funding source, conflict of interest, primary outcome direction, and sample size were examined. Chi-squared and independent-samples t tests were used in the analysis. The search identified 173 randomized controlled trials, of which 100 (58 percent) did not acknowledge funding status. A relationship between funding source and trial outcome direction was not observed. Both funding status and conflict of interest reporting improved over time. Only 24 percent (six of 25) of industry-funded randomized controlled trials reported authors to have independent control of data and manuscript contents. The mean number of patients randomized was 73 per trial (median, 43, minimum, 3, maximum, 936). Small trials were not found to be positive more often than large trials (p = 0.87). Randomized controlled trials with small sample size were common; however, this provides great opportunity for the field to engage in further collaboration and produce larger, more definitive trials. Reporting of trial funding and conflict of interest is historically poor, but it greatly improved over the study period. Underreporting at author and journal levels remains a limitation when assessing the relationship between funding source and trial outcomes. Improved reporting and manuscript control should be goals that both authors and journals can actively achieve.
Assessing the accuracy and stability of variable selection methods for random forest modeling in ecology

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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...
Distributed fiber sparse-wideband vibration sensing by sub-Nyquist additive random sampling

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Zhang, Jingdong; Zheng, Hua; Zhu, Tao; Yin, Guolu; Liu, Min; Bai, Yongzhong; Qu, Dingrong; Qiu, Feng; Huang, Xianbing

2018-05-01

The round trip time of the light pulse limits the maximum detectable vibration frequency response range of phase-sensitive optical time domain reflectometry ({\\phi}-OTDR). Unlike the uniform laser pulse interval in conventional {\\phi}-OTDR, we randomly modulate the pulse interval, so that an equivalent sub-Nyquist additive random sampling (sNARS) is realized for every sensing point of the long interrogation fiber. For an {\\phi}-OTDR system with 10 km sensing length, the sNARS method is optimized by theoretical analysis and Monte Carlo simulation, and the experimental results verify that a wide-band spars signal can be identified and reconstructed. Such a method can broaden the vibration frequency response range of {\\phi}-OTDR, which is of great significance in sparse-wideband-frequency vibration signal detection, such as rail track monitoring and metal defect detection.
Optimization of the Dutch Matrix Test by Random Selection of Sentences From a Preselected Subset

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Rolph Houben

2015-04-01

Full Text Available Matrix tests are available for speech recognition testing in many languages. For an accurate measurement, a steep psychometric function of the speech materials is required. For existing tests, it would be beneficial if it were possible to further optimize the available materials by increasing the function’s steepness. The objective is to show if the steepness of the psychometric function of an existing matrix test can be increased by selecting a homogeneous subset of recordings with the steepest sentence-based psychometric functions. We took data from a previous multicenter evaluation of the Dutch matrix test (45 normal-hearing listeners. Based on half of the data set, first the sentences (140 out of 311 with a similar speech reception threshold and with the steepest psychometric function (≥9.7%/dB were selected. Subsequently, the steepness of the psychometric function for this selection was calculated from the remaining (unused second half of the data set. The calculation showed that the slope increased from 10.2%/dB to 13.7%/dB. The resulting subset did not allow the construction of enough balanced test lists. Therefore, the measurement procedure was changed to randomly select the sentences during testing. Random selection may interfere with a representative occurrence of phonemes. However, in our material, the median phonemic occurrence remained close to that of the original test. This finding indicates that phonemic occurrence is not a critical factor. The work highlights the possibility that existing speech tests might be improved by selecting sentences with a steep psychometric function.
A large sample of Kohonen selected E+A (post-starburst) galaxies from the Sloan Digital Sky Survey

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Meusinger, H.; Brünecke, J.; Schalldach, P.; in der Au, A.

2017-01-01

Context. The galaxy population in the contemporary Universe is characterised by a clear bimodality, blue galaxies with significant ongoing star formation and red galaxies with only a little. The migration between the blue and the red cloud of galaxies is an issue of active research. Post starburst (PSB) galaxies are thought to be observed in the short-lived transition phase. Aims: We aim to create a large sample of local PSB galaxies from the Sloan Digital Sky Survey (SDSS) to study their characteristic properties, particularly morphological features indicative of gravitational distortions and indications for active galactic nuclei (AGNs). Another aim is to present a tool set for an efficient search in a large database of SDSS spectra based on Kohonen self-organising maps (SOMs). Methods: We computed a huge Kohonen SOM for ∼106 spectra from SDSS data release 7. The SOM is made fully available, in combination with an interactive user interface, for the astronomical community. We selected a large sample of PSB galaxies taking advantage of the clustering behaviour of the SOM. The morphologies of both PSB galaxies and randomly selected galaxies from a comparison sample in SDSS Stripe 82 (S82) were inspected on deep co-added SDSS images to search for indications of gravitational distortions. We used the Portsmouth galaxy property computations to study the evolutionary stage of the PSB galaxies and archival multi-wavelength data to search for hidden AGNs. Results: We compiled a catalogue of 2665 PSB galaxies with redshifts z 3 Å and z cloud, in agreement with the idea that PSB galaxies represent the transitioning phase between actively and passively evolving galaxies. The relative frequency of distorted PSB galaxies is at least 57% for EW(Hδ) > 5 Å, significantly higher than in the comparison sample. The search for AGNs based on conventional selection criteria in the radio and MIR results in a low AGN fraction of ∼2-3%. We confirm an MIR excess in the mean SED of
Effective traffic features selection algorithm for cyber-attacks samples

Science.gov (United States)

Li, Yihong; Liu, Fangzheng; Du, Zhenyu

2018-05-01

By studying the defense scheme of Network attacks, this paper propose an effective traffic features selection algorithm based on k-means++ clustering to deal with the problem of high dimensionality of traffic features which extracted from cyber-attacks samples. Firstly, this algorithm divide the original feature set into attack traffic feature set and background traffic feature set by the clustering. Then, we calculates the variation of clustering performance after removing a certain feature. Finally, evaluating the degree of distinctiveness of the feature vector according to the result. Among them, the effective feature vector is whose degree of distinctiveness exceeds the set threshold. The purpose of this paper is to select out the effective features from the extracted original feature set. In this way, it can reduce the dimensionality of the features so as to reduce the space-time overhead of subsequent detection. The experimental results show that the proposed algorithm is feasible and it has some advantages over other selection algorithms.
Use of Matrix Sampling Procedures to Assess Achievement in Solving Open Addition and Subtraction Sentences.

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Montague, Margariete A.

This study investigated the feasibility of concurrently and randomly sampling examinees and items in order to estimate group achievement. Seven 32-item tests reflecting a 640-item universe of simple open sentences were used such that item selection (random, systematic) and assignment (random, systematic) of items (four, eight, sixteen) to forms…
Does Self-Selection Affect Samples’ Representativeness in Online Surveys? An Investigation in Online Video Game Research

Science.gov (United States)

van Singer, Mathias; Chatton, Anne; Achab, Sophia; Zullino, Daniele; Rothen, Stephane; Khan, Riaz; Billieux, Joel; Thorens, Gabriel

2014-01-01

Background The number of medical studies performed through online surveys has increased dramatically in recent years. Despite their numerous advantages (eg, sample size, facilitated access to individuals presenting stigmatizing issues), selection bias may exist in online surveys. However, evidence on the representativeness of self-selected samples in online studies is patchy. Objective Our objective was to explore the representativeness of a self-selected sample of online gamers using online players’ virtual characters (avatars). Methods All avatars belonged to individuals playing World of Warcraft (WoW), currently the most widely used online game. Avatars’ characteristics were defined using various games’ scores, reported on the WoW’s official website, and two self-selected samples from previous studies were compared with a randomly selected sample of avatars. Results We used scores linked to 1240 avatars (762 from the self-selected samples and 478 from the random sample). The two self-selected samples of avatars had higher scores on most of the assessed variables (except for guild membership and exploration). Furthermore, some guilds were overrepresented in the self-selected samples. Conclusions Our results suggest that more proficient players or players more involved in the game may be more likely to participate in online surveys. Caution is needed in the interpretation of studies based on online surveys that used a self-selection recruitment procedure. Epidemiological evidence on the reduced representativeness of sample of online surveys is warranted. PMID:25001007
Hybrid nested sampling algorithm for Bayesian model selection applied to inverse subsurface flow problems

KAUST Repository

Elsheikh, Ahmed H.; Wheeler, Mary Fanett; Hoteit, Ibrahim

2014-01-01

A Hybrid Nested Sampling (HNS) algorithm is proposed for efficient Bayesian model calibration and prior model selection. The proposed algorithm combines, Nested Sampling (NS) algorithm, Hybrid Monte Carlo (HMC) sampling and gradient estimation using
Random Evolutionary Dynamics Driven by Fitness and House-of-Cards Mutations: Sampling Formulae

Science.gov (United States)

Huillet, Thierry E.

2017-07-01

We first revisit the multi-allelic mutation-fitness balance problem, especially when mutations obey a house of cards condition, where the discrete-time deterministic evolutionary dynamics of the allelic frequencies derives from a Shahshahani potential. We then consider multi-allelic Wright-Fisher stochastic models whose deviation to neutrality is from the Shahshahani mutation/selection potential. We next focus on the weak selection, weak mutation cases and, making use of a Gamma calculus, we compute the normalizing partition functions of the invariant probability densities appearing in their Wright-Fisher diffusive approximations. Using these results, generalized Ewens sampling formulae (ESF) from the equilibrium distributions are derived. We start treating the ESF in the mixed mutation/selection potential case and then we restrict ourselves to the ESF in the simpler house-of-cards mutations only situation. We also address some issues concerning sampling problems from infinitely-many alleles weak limits.
Random drift versus selection in academic vocabulary: an evolutionary analysis of published keywords.

Science.gov (United States)

Bentley, R Alexander

2008-08-27

The evolution of vocabulary in academic publishing is characterized via keyword frequencies recorded in the ISI Web of Science citations database. In four distinct case-studies, evolutionary analysis of keyword frequency change through time is compared to a model of random copying used as the null hypothesis, such that selection may be identified against it. The case studies from the physical sciences indicate greater selection in keyword choice than in the social sciences. Similar evolutionary analyses can be applied to a wide range of phenomena; wherever the popularity of multiple items through time has been recorded, as with web searches, or sales of popular music and books, for example.

Materials selection for oxide-based resistive random access memories

Energy Technology Data Exchange (ETDEWEB)

Guo, Yuzheng; Robertson, John [Engineering Department, Cambridge University, Cambridge CB2 1PZ (United Kingdom)

2014-12-01

The energies of atomic processes in resistive random access memories (RRAMs) are calculated for four typical oxides, HfO{sub 2}, TiO{sub 2}, Ta{sub 2}O{sub 5}, and Al{sub 2}O{sub 3}, to define a materials selection process. O vacancies have the lowest defect formation energy in the O-poor limit and dominate the processes. A band diagram defines the operating Fermi energy and O chemical potential range. It is shown how the scavenger metal can be used to vary the O vacancy formation energy, via controlling the O chemical potential, and the mean Fermi energy. The high endurance of Ta{sub 2}O{sub 5} RRAM is related to its more stable amorphous phase and the adaptive lattice rearrangements of its O vacancy.
Honest Importance Sampling with Multiple Markov Chains.

Science.gov (United States)

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 bias in population-based cancer case-control studies due to incomplete sampling frame coverage.

Science.gov (United States)

Walsh, Matthew C; Trentham-Dietz, Amy; Gangnon, Ronald E; Nieto, F Javier; Newcomb, Polly A; Palta, Mari

2012-06-01

Increasing numbers of individuals are choosing to opt out of population-based sampling frames due to privacy concerns. This is especially a problem in the selection of controls for case-control studies, as the cases often arise from relatively complete population-based registries, whereas control selection requires a sampling frame. If opt out is also related to risk factors, bias can arise. We linked breast cancer cases who reported having a valid driver's license from the 2004-2008 Wisconsin women's health study (N = 2,988) with a master list of licensed drivers from the Wisconsin Department of Transportation (WDOT). This master list excludes Wisconsin drivers that requested their information not be sold by the state. Multivariate-adjusted selection probability ratios (SPR) were calculated to estimate potential bias when using this driver's license sampling frame to select controls. A total of 962 cases (32%) had opted out of the WDOT sampling frame. Cases age <40 (SPR = 0.90), income either unreported (SPR = 0.89) or greater than $50,000 (SPR = 0.94), lower parity (SPR = 0.96 per one-child decrease), and hormone use (SPR = 0.93) were significantly less likely to be covered by the WDOT sampling frame (α = 0.05 level). Our results indicate the potential for selection bias due to differential opt out between various demographic and behavioral subgroups of controls. As selection bias may differ by exposure and study base, the assessment of potential bias needs to be ongoing. SPRs can be used to predict the direction of bias when cases and controls stem from different sampling frames in population-based case-control studies.
Tobacco smoking surveillance: is quota sampling an efficient tool for monitoring national trends? A comparison with a random cross-sectional survey.

Directory of Open Access Journals (Sweden)

Romain Guignard

Full Text Available OBJECTIVES: It is crucial for policy makers to monitor the evolution of tobacco smoking prevalence. In France, this monitoring is based on a series of cross-sectional general population surveys, the Health Barometers, conducted every five years and based on random samples. A methodological study has been carried out to assess the reliability of a monitoring system based on regular quota sampling surveys for smoking prevalence. DESIGN / OUTCOME MEASURES: In 2010, current and daily tobacco smoking prevalences obtained in a quota survey on 8,018 people were compared with those of the 2010 Health Barometer carried out on 27,653 people. Prevalences were assessed separately according to the telephone equipment of the interviewee (landline phone owner vs "mobile-only", and logistic regressions were conducted in the pooled database to assess the impact of the telephone equipment and of the survey mode on the prevalences found. Finally, logistic regressions adjusted for sociodemographic characteristics were conducted in the random sample in order to determine the impact of the needed number of calls to interwiew "hard-to-reach" people on the prevalence found. RESULTS: Current and daily prevalences were higher in the random sample (respectively 33.9% and 27.5% in 15-75 years-old than in the quota sample (respectively 30.2% and 25.3%. In both surveys, current and daily prevalences were lower among landline phone owners (respectively 31.8% and 25.5% in the random sample and 28.9% and 24.0% in the quota survey. The required number of calls was slightly related to the smoking status after adjustment for sociodemographic characteristics. CONCLUSION: Random sampling appears to be more effective than quota sampling, mainly by making it possible to interview hard-to-reach populations.
Selection of 3013 Containers for Field Surveillance

International Nuclear Information System (INIS)

Larry Peppers; Elizabeth Kelly; James McClard; Gary Friday; Theodore Venetz; Jerry Stakebade

2007-01-01

This report revises and combines three earlier reports dealing with the binning, statistical sampling, and sample selection of 3013 containers for field surveillance. It includes changes to the binning specification resulting from completion of the Savannah River Site packaging campaign and new information from the shelf-life program and field surveillance activities. The revised bin assignments result in changes to the random sample specification. These changes are necessary to meet the statistical requirements of the surveillance program. This report will be reviewed regularly and revised as needed. Section 1 of this report summarizes the results of an extensive effort to assign all of the current and projected 3013 containers in the Department of Energy (DOE) inventory to one of three bins (Innocuous, Pressure and Corrosion, or Pressure) based on potential failure mechanisms. Grouping containers into bins provides a framework to make a statistical selection of individual containers from the entire population for destructive and nondestructive field surveillance. The binning process consisted of three main steps. First, the packaged containers were binned using information in the Integrated Surveillance Program database and a decision tree. The second task was to assign those containers that could not be binned using the decision tree to a specific bin using container-by-container engineering review. The final task was to evaluate containers not yet packaged and assign them to bins using process knowledge. The technical basis for the decisions made during the binning process is included in Section 1. A composite decision tree and a summary table show all of the containers projected to be in the DOE inventory at the conclusion of packaging at all sites. Decision trees that provide an overview of the binning process and logic are included for each site. Section 2 of this report describes the approach to the statistical selection of containers for surveillance and
Exploring pseudo- and chaotic random Monte Carlo simulations

Science.gov (United States)

Blais, J. A. Rod; Zhang, Zhan

2011-07-01

Computer simulations are an increasingly important area of geoscience research and development. At the core of stochastic or Monte Carlo simulations are the random number sequences that are assumed to be distributed with specific characteristics. Computer-generated random numbers, uniformly distributed on (0, 1), can be very different depending on the selection of pseudo-random number (PRN) or chaotic random number (CRN) generators. In the evaluation of some definite integrals, the resulting error variances can even be of different orders of magnitude. Furthermore, practical techniques for variance reduction such as importance sampling and stratified sampling can be applied in most Monte Carlo simulations and significantly improve the results. A comparative analysis of these strategies has been carried out for computational applications in planar and spatial contexts. Based on these experiments, and on some practical examples of geodetic direct and inverse problems, conclusions and recommendations concerning their performance and general applicability are included.
Iterative algorithm of discrete Fourier transform for processing randomly sampled NMR data sets

International Nuclear Information System (INIS)

Stanek, Jan; Kozminski, Wiktor

2010-01-01

Spectra obtained by application of multidimensional Fourier Transformation (MFT) to sparsely sampled nD NMR signals are usually corrupted due to missing data. In the present paper this phenomenon is investigated on simulations and experiments. An effective iterative algorithm for artifact suppression for sparse on-grid NMR data sets is discussed in detail. It includes automated peak recognition based on statistical methods. The results enable one to study NMR spectra of high dynamic range of peak intensities preserving benefits of random sampling, namely the superior resolution in indirectly measured dimensions. Experimental examples include 3D 15 N- and 13 C-edited NOESY-HSQC spectra of human ubiquitin.
Adult health study reference papers. Selection of the sample. Characteristics of the sample

Energy Technology Data Exchange (ETDEWEB)

Beebe, G W; Fujisawa, Hideo; Yamasaki, Mitsuru

1960-12-14

The characteristics and selection of the clinical sample have been described in some detail to provide information on the comparability of the exposure groups with respect to factors excluded from the matching criteria and to provide basic descriptive information potentially relevant to individual studies that may be done within the framework of the Adult Health Study. The characteristics under review here are age, sex, many different aspects of residence, marital status, occupation and industry, details of location and shielding ATB, acute radiation signs and symptoms, and prior ABCC medical or pathology examinations. 5 references, 57 tables.
From Protocols to Publications: A Study in Selective Reporting of Outcomes in Randomized Trials in Oncology

Science.gov (United States)

Raghav, Kanwal Pratap Singh; Mahajan, Sminil; Yao, James C.; Hobbs, Brian P.; Berry, Donald A.; Pentz, Rebecca D.; Tam, Alda; Hong, Waun K.; Ellis, Lee M.; Abbruzzese, James; Overman, Michael J.

2015-01-01

Purpose The decision by journals to append protocols to published reports of randomized trials was a landmark event in clinical trial reporting. However, limited information is available on how this initiative effected transparency and selective reporting of clinical trial data. Methods We analyzed 74 oncology-based randomized trials published in Journal of Clinical Oncology, the New England Journal of Medicine, and The Lancet in 2012. To ascertain integrity of reporting, we compared published reports with their respective appended protocols with regard to primary end points, nonprimary end points, unplanned end points, and unplanned analyses. Results A total of 86 primary end points were reported in 74 randomized trials; nine trials had greater than one primary end point. Nine trials (12.2%) had some discrepancy between their planned and published primary end points. A total of 579 nonprimary end points (median, seven per trial) were planned, of which 373 (64.4%; median, five per trial) were reported. A significant positive correlation was found between the number of planned and nonreported nonprimary end points (Spearman r = 0.66; P < .001). Twenty-eight studies (37.8%) reported a total of 65 unplanned end points; 52 (80.0%) of which were not identified as unplanned. Thirty-one (41.9%) and 19 (25.7%) of 74 trials reported a total of 52 unplanned analyses involving primary end points and 33 unplanned analyses involving nonprimary end points, respectively. Studies reported positive unplanned end points and unplanned analyses more frequently than negative outcomes in abstracts (unplanned end points odds ratio, 6.8; P = .002; unplanned analyses odd ratio, 8.4; P = .007). Conclusion Despite public and reviewer access to protocols, selective outcome reporting persists and is a major concern in the reporting of randomized clinical trials. To foster credible evidence-based medicine, additional initiatives are needed to minimize selective reporting. PMID:26304898
[Comparison study on sampling methods of Oncomelania hupensis snail survey in marshland schistosomiasis epidemic areas in China].

Science.gov (United States)

An, Zhao; Wen-Xin, Zhang; Zhong, Yao; Yu-Kuan, Ma; Qing, Liu; Hou-Lang, Duan; Yi-di, Shang

2016-06-29

To optimize and simplify the survey method of Oncomelania hupensis snail in marshland endemic region of schistosomiasis and increase the precision, efficiency and economy of the snail survey. A quadrate experimental field was selected as the subject of 50 m×50 m size in Chayegang marshland near Henghu farm in the Poyang Lake region and a whole-covered method was adopted to survey the snails. The simple random sampling, systematic sampling and stratified random sampling methods were applied to calculate the minimum sample size, relative sampling error and absolute sampling error. The minimum sample sizes of the simple random sampling, systematic sampling and stratified random sampling methods were 300, 300 and 225, respectively. The relative sampling errors of three methods were all less than 15%. The absolute sampling errors were 0.221 7, 0.302 4 and 0.047 8, respectively. The spatial stratified sampling with altitude as the stratum variable is an efficient approach of lower cost and higher precision for the snail survey.
Joint random beam and spectrum selection for spectrum sharing systems with partial channel state information

KAUST Repository

Abdallah, Mohamed M.

2013-11-01

In this work, we develop joint interference-aware random beam and spectrum selection scheme that provide enhanced performance for the secondary network under the condition that the interference observed at the primary receiver is below a predetermined acceptable value. We consider a secondary link composed of a transmitter equipped with multiple antennas and a single-antenna receiver sharing the same spectrum with a set of primary links composed of a single-antenna transmitter and a single-antenna receiver. The proposed schemes jointly select a beam, among a set of power-optimized random beams, as well as the primary spectrum that maximizes the signal-to-interference-plus-noise ratio (SINR) of the secondary link while satisfying the primary interference constraint. In particular, we consider the case where the interference level is described by a q-bit description of its magnitude, whereby we propose a technique to find the optimal quantizer thresholds in a mean square error (MSE) sense. © 2013 IEEE.
Joint random beam and spectrum selection for spectrum sharing systems with partial channel state information

KAUST Repository

Abdallah, Mohamed M.; Sayed, Mostafa M.; Alouini, Mohamed-Slim; Qaraqe, Khalid A.

2013-01-01

In this work, we develop joint interference-aware random beam and spectrum selection scheme that provide enhanced performance for the secondary network under the condition that the interference observed at the primary receiver is below a predetermined acceptable value. We consider a secondary link composed of a transmitter equipped with multiple antennas and a single-antenna receiver sharing the same spectrum with a set of primary links composed of a single-antenna transmitter and a single-antenna receiver. The proposed schemes jointly select a beam, among a set of power-optimized random beams, as well as the primary spectrum that maximizes the signal-to-interference-plus-noise ratio (SINR) of the secondary link while satisfying the primary interference constraint. In particular, we consider the case where the interference level is described by a q-bit description of its magnitude, whereby we propose a technique to find the optimal quantizer thresholds in a mean square error (MSE) sense. © 2013 IEEE.
The RANDOM computer program: A linear congruential random number generator

Science.gov (United States)

Miles, R. F., Jr.

1986-01-01

The RANDOM Computer Program is a FORTRAN program for generating random number sequences and testing linear congruential random number generators (LCGs). The linear congruential form of random number generator is discussed, and the selection of parameters of an LCG for a microcomputer described. This document describes the following: (1) The RANDOM Computer Program; (2) RANDOM.MOD, the computer code needed to implement an LCG in a FORTRAN program; and (3) The RANCYCLE and the ARITH Computer Programs that provide computational assistance in the selection of parameters for an LCG. The RANDOM, RANCYCLE, and ARITH Computer Programs are written in Microsoft FORTRAN for the IBM PC microcomputer and its compatibles. With only minor modifications, the RANDOM Computer Program and its LCG can be run on most micromputers or mainframe computers.
Accelerating Markov chain Monte Carlo simulation by differential evolution with self-adaptive randomized subspace sampling

Energy Technology Data Exchange (ETDEWEB)

Vrugt, Jasper A [Los Alamos National Laboratory; Hyman, James M [Los Alamos National Laboratory; Robinson, Bruce A [Los Alamos National Laboratory; Higdon, Dave [Los Alamos National Laboratory; Ter Braak, Cajo J F [NETHERLANDS; Diks, Cees G H [UNIV OF AMSTERDAM

2008-01-01

Markov chain Monte Carlo (MCMC) methods have found widespread use in many fields of study to estimate the average properties of complex systems, and for posterior inference in a Bayesian framework. Existing theory and experiments prove convergence of well constructed MCMC schemes to the appropriate limiting distribution under a variety of different conditions. In practice, however this convergence is often observed to be disturbingly slow. This is frequently caused by an inappropriate selection of the proposal distribution used to generate trial moves in the Markov Chain. Here we show that significant improvements to the efficiency of MCMC simulation can be made by using a self-adaptive Differential Evolution learning strategy within a population-based evolutionary framework. This scheme, entitled DiffeRential Evolution Adaptive Metropolis or DREAM, runs multiple different chains simultaneously for global exploration, and automatically tunes the scale and orientation of the proposal distribution in randomized subspaces during the search. Ergodicity of the algorithm is proved, and various examples involving nonlinearity, high-dimensionality, and multimodality show that DREAM is generally superior to other adaptive MCMC sampling approaches. The DREAM scheme significantly enhances the applicability of MCMC simulation to complex, multi-modal search problems.
Random drift versus selection in academic vocabulary: an evolutionary analysis of published keywords.

Directory of Open Access Journals (Sweden)

R Alexander Bentley

Full Text Available The evolution of vocabulary in academic publishing is characterized via keyword frequencies recorded in the ISI Web of Science citations database. In four distinct case-studies, evolutionary analysis of keyword frequency change through time is compared to a model of random copying used as the null hypothesis, such that selection may be identified against it. The case studies from the physical sciences indicate greater selection in keyword choice than in the social sciences. Similar evolutionary analyses can be applied to a wide range of phenomena; wherever the popularity of multiple items through time has been recorded, as with web searches, or sales of popular music and books, for example.
On theoretical models of gene expression evolution with random genetic drift and natural selection.

Directory of Open Access Journals (Sweden)

Osamu Ogasawara

2009-11-01

Full Text Available The relative contributions of natural selection and random genetic drift are a major source of debate in the study of gene expression evolution, which is hypothesized to serve as a bridge from molecular to phenotypic evolution. It has been suggested that the conflict between views is caused by the lack of a definite model of the neutral hypothesis, which can describe the long-run behavior of evolutionary change in mRNA abundance. Therefore previous studies have used inadequate analogies with the neutral prediction of other phenomena, such as amino acid or nucleotide sequence evolution, as the null hypothesis of their statistical inference.In this study, we introduced two novel theoretical models, one based on neutral drift and the other assuming natural selection, by focusing on a common property of the distribution of mRNA abundance among a variety of eukaryotic cells, which reflects the result of long-term evolution. Our results demonstrated that (1 our models can reproduce two independently found phenomena simultaneously: the time development of gene expression divergence and Zipf's law of the transcriptome; (2 cytological constraints can be explicitly formulated to describe long-term evolution; (3 the model assuming that natural selection optimized relative mRNA abundance was more consistent with previously published observations than the model of optimized absolute mRNA abundances.The models introduced in this study give a formulation of evolutionary change in the mRNA abundance of each gene as a stochastic process, on the basis of previously published observations. This model provides a foundation for interpreting observed data in studies of gene expression evolution, including identifying an adequate time scale for discriminating the effect of natural selection from that of random genetic drift of selectively neutral variations.
Malaria parasitemia amongst pregnant women attending selected ...

African Journals Online (AJOL)

A cross-sectional study to determine malaria parasitemia amongst 300 randomly selected pregnant women attending government and private healthcare facilities in Rivers State was carried out. Blood samples were obtained through venous procedure and the presence or absence of Plasmodium was determined ...
From Protocols to Publications: A Study in Selective Reporting of Outcomes in Randomized Trials in Oncology.

Science.gov (United States)

Raghav, Kanwal Pratap Singh; Mahajan, Sminil; Yao, James C; Hobbs, Brian P; Berry, Donald A; Pentz, Rebecca D; Tam, Alda; Hong, Waun K; Ellis, Lee M; Abbruzzese, James; Overman, Michael J

2015-11-01

The decision by journals to append protocols to published reports of randomized trials was a landmark event in clinical trial reporting. However, limited information is available on how this initiative effected transparency and selective reporting of clinical trial data. We analyzed 74 oncology-based randomized trials published in Journal of Clinical Oncology, the New England Journal of Medicine, and The Lancet in 2012. To ascertain integrity of reporting, we compared published reports with their respective appended protocols with regard to primary end points, nonprimary end points, unplanned end points, and unplanned analyses. A total of 86 primary end points were reported in 74 randomized trials; nine trials had greater than one primary end point. Nine trials (12.2%) had some discrepancy between their planned and published primary end points. A total of 579 nonprimary end points (median, seven per trial) were planned, of which 373 (64.4%; median, five per trial) were reported. A significant positive correlation was found between the number of planned and nonreported nonprimary end points (Spearman r = 0.66; P medicine, additional initiatives are needed to minimize selective reporting. © 2015 by American Society of Clinical Oncology.
Adoption of selected innovations in rice production and their effect ...

African Journals Online (AJOL)

Adoption of selected innovations in rice production and their effect on farmers living standard in Bauchi local government area, Bauchi state, Nigeria. ... International Journal of Natural and Applied Sciences ... Simple random sampling technique was used for the selection of 82 rice growers from these villages. The data ...
Computer code ENDSAM for random sampling and validation of the resonance parameters covariance matrices of some major nuclear data libraries

International Nuclear Information System (INIS)

Plevnik, Lucijan; Žerovnik, Gašper

2016-01-01

Highlights: • Methods for random sampling of correlated parameters. • Link to open-source code for sampling of resonance parameters in ENDF-6 format. • Validation of the code on realistic and artificial data. • Validation of covariances in three major contemporary nuclear data libraries. - Abstract: Methods for random sampling of correlated parameters are presented. The methods are implemented for sampling of resonance parameters in ENDF-6 format and a link to the open-source code ENDSAM is given. The code has been validated on realistic data. Additionally, consistency of covariances of resonance parameters of three major contemporary nuclear data libraries (JEFF-3.2, ENDF/B-VII.1 and JENDL-4.0u2) has been checked.

Random sampling of quantum states: a survey of methods and some issues regarding the Overparametrized Method

International Nuclear Information System (INIS)

Maziero, Jonas

2015-01-01

The numerical generation of random quantum states (RQS) is an important procedure for investigations in quantum information science. Here, we review some methods that may be used for performing that task. We start by presenting a simple procedure for generating random state vectors, for which the main tool is the random sampling of unbiased discrete probability distributions (DPD). Afterwards, the creation of random density matrices is addressed. In this context, we first present the standard method, which consists in using the spectral decomposition of a quantum state for getting RQS from random DPDs and random unitary matrices. In the sequence, the Bloch vector parametrization method is described. This approach, despite being useful in several instances, is not in general convenient for RQS generation. In the last part of the article, we regard the overparametrized method (OPM) and the related Ginibre and Bures techniques. The OPM can be used to create random positive semidefinite matrices with unit trace from randomly produced general complex matrices in a simple way that is friendly for numerical implementations. We consider a physically relevant issue related to the possible domains that may be used for the real and imaginary parts of the elements of such general complex matrices. Subsequently, a too fast concentration of measure in the quantum state space that appears in this parametrization is noticed. (author)
Improving ambulatory saliva-sampling compliance in pregnant women: a randomized controlled study.

Directory of Open Access Journals (Sweden)

Julian Moeller

Full Text Available OBJECTIVE: Noncompliance with scheduled ambulatory saliva sampling is common and has been associated with biased cortisol estimates in nonpregnant subjects. This study is the first to investigate in pregnant women strategies to improve ambulatory saliva-sampling compliance, and the association between sampling noncompliance and saliva cortisol estimates. METHODS: We instructed 64 pregnant women to collect eight scheduled saliva samples on two consecutive days each. Objective compliance with scheduled sampling times was assessed with a Medication Event Monitoring System and self-reported compliance with a paper-and-pencil diary. In a randomized controlled study, we estimated whether a disclosure intervention (informing women about objective compliance monitoring and a reminder intervention (use of acoustical reminders improved compliance. A mixed model analysis was used to estimate associations between women's objective compliance and their diurnal cortisol profiles, and between deviation from scheduled sampling and the cortisol concentration measured in the related sample. RESULTS: Self-reported compliance with a saliva-sampling protocol was 91%, and objective compliance was 70%. The disclosure intervention was associated with improved objective compliance (informed: 81%, noninformed: 60%, F(1,60 = 17.64, p<0.001, but not the reminder intervention (reminders: 68%, without reminders: 72%, F(1,60 = 0.78, p = 0.379. Furthermore, a woman's increased objective compliance was associated with a higher diurnal cortisol profile, F(2,64 = 8.22, p<0.001. Altered cortisol levels were observed in less objective compliant samples, F(1,705 = 7.38, p = 0.007, with delayed sampling associated with lower cortisol levels. CONCLUSIONS: The results suggest that in pregnant women, objective noncompliance with scheduled ambulatory saliva sampling is common and is associated with biased cortisol estimates. To improve sampling compliance, results suggest
High Entropy Random Selection Protocols

NARCIS (Netherlands)

H. Buhrman (Harry); M. Christandl (Matthias); M. Koucky (Michal); Z. Lotker (Zvi); B. Patt-Shamir; M. Charikar; K. Jansen; O. Reingold; J. Rolim

2007-01-01

textabstractIn this paper, we construct protocols for two parties that do not trust each other, to generate random variables with high Shannon entropy. We improve known bounds for the trade off between the number of rounds, length of communication and the entropy of the outcome.
Comparison of sampling methods for hard-to-reach francophone populations: yield and adequacy of advertisement and respondent-driven sampling.

Science.gov (United States)

Ngwakongnwi, Emmanuel; King-Shier, Kathryn M; Hemmelgarn, Brenda R; Musto, Richard; Quan, Hude

2014-01-01

Francophones who live outside the primarily French-speaking province of Quebec, Canada, risk being excluded from research by lack of a sampling frame. We examined the adequacy of random sampling, advertising, and respondent-driven sampling for recruitment of francophones for survey research. We recruited francophones residing in the city of Calgary, Alberta, through advertising and respondentdriven sampling. These 2 samples were then compared with a random subsample of Calgary francophones derived from the 2006 Canadian Community Health Survey (CCHS). We assessed the effectiveness of advertising and respondent-driven sampling in relation to the CCHS sample by comparing demographic characteristics and selected items from the CCHS (specifically self-reported general health status, perceived weight, and having a family doctor). We recruited 120 francophones through advertising and 145 through respondent-driven sampling; the random sample from the CCHS consisted of 259 records. The samples derived from advertising and respondentdriven sampling differed from the CCHS in terms of age (mean ages 41.0, 37.6, and 42.5 years, respectively), sex (proportion of males 26.1%, 40.6%, and 56.6%, respectively), education (college or higher 86.7% , 77.9% , and 59.1%, respectively), place of birth (immigrants accounting for 45.8%, 55.2%, and 3.7%, respectively), and not having a regular medical doctor (16.7%, 34.5%, and 16.6%, respectively). Differences were not tested statistically because of limitations on the analysis of CCHS data imposed by Statistics Canada. The samples generated exclusively through advertising and respondent-driven sampling were not representative of the gold standard sample from the CCHS. Use of such biased samples for research studies could generate misleading results.
Population genetics inference for longitudinally-sampled mutants under strong selection.

Science.gov (United States)

Lacerda, Miguel; Seoighe, Cathal

2014-11-01

Longitudinal allele frequency data are becoming increasingly prevalent. Such samples permit statistical inference of the population genetics parameters that influence the fate of mutant variants. To infer these parameters by maximum likelihood, the mutant frequency is often assumed to evolve according to the Wright-Fisher model. For computational reasons, this discrete model is commonly approximated by a diffusion process that requires the assumption that the forces of natural selection and mutation are weak. This assumption is not always appropriate. For example, mutations that impart drug resistance in pathogens may evolve under strong selective pressure. Here, we present an alternative approximation to the mutant-frequency distribution that does not make any assumptions about the magnitude of selection or mutation and is much more computationally efficient than the standard diffusion approximation. Simulation studies are used to compare the performance of our method to that of the Wright-Fisher and Gaussian diffusion approximations. For large populations, our method is found to provide a much better approximation to the mutant-frequency distribution when selection is strong, while all three methods perform comparably when selection is weak. Importantly, maximum-likelihood estimates of the selection coefficient are severely attenuated when selection is strong under the two diffusion models, but not when our method is used. This is further demonstrated with an application to mutant-frequency data from an experimental study of bacteriophage evolution. We therefore recommend our method for estimating the selection coefficient when the effective population size is too large to utilize the discrete Wright-Fisher model. Copyright © 2014 by the Genetics Society of America.
A census-weighted, spatially-stratified household sampling strategy for urban malaria epidemiology

Directory of Open Access Journals (Sweden)

Slutsker Laurence

2008-02-01

Full Text Available Abstract Background Urban malaria is likely to become increasingly important as a consequence of the growing proportion of Africans living in cities. A novel sampling strategy was developed for urban areas to generate a sample simultaneously representative of population and inhabited environments. Such a strategy should facilitate analysis of important epidemiological relationships in this ecological context. Methods Census maps and summary data for Kisumu, Kenya, were used to create a pseudo-sampling frame using the geographic coordinates of census-sampled structures. For every enumeration area (EA designated as urban by the census (n = 535, a sample of structures equal to one-tenth the number of households was selected. In EAs designated as rural (n = 32, a geographically random sample totalling one-tenth the number of households was selected from a grid of points at 100 m intervals. The selected samples were cross-referenced to a geographic information system, and coordinates transferred to handheld global positioning units. Interviewers found the closest eligible household to the sampling point and interviewed the caregiver of a child aged Results 4,336 interviews were completed in 473 of the 567 study area EAs from June 2002 through February 2003. EAs without completed interviews were randomly distributed, and non-response was approximately 2%. Mean distance from the assigned sampling point to the completed interview was 74.6 m, and was significantly less in urban than rural EAs, even when controlling for number of households. The selected sample had significantly more children and females of childbearing age than the general population, and fewer older individuals. Conclusion This method selected a sample that was simultaneously population-representative and inclusive of important environmental variation. The use of a pseudo-sampling frame and pre-programmed handheld GPS units is more efficient and may yield a more complete sample than
Gender Wage Gap : A Semi-Parametric Approach With Sample Selection Correction

NARCIS (Netherlands)

Picchio, M.; Mussida, C.

2010-01-01

Sizeable gender differences in employment rates are observed in many countries. Sample selection into the workforce might therefore be a relevant issue when estimating gender wage gaps. This paper proposes a new semi-parametric estimator of densities in the presence of covariates which incorporates
Integrated Behavior Therapy for Selective Mutism: a randomized controlled pilot study.

Science.gov (United States)

Bergman, R Lindsey; Gonzalez, Araceli; Piacentini, John; Keller, Melody L

2013-10-01

To evaluate the feasibility, acceptability, and preliminary efficacy of a novel behavioral intervention for reducing symptoms of selective mutism and increasing functional speech. A total of 21 children ages 4 to 8 with primary selective mutism were randomized to 24 weeks of Integrated Behavior Therapy for Selective Mutism (IBTSM) or a 12-week Waitlist control. Clinical outcomes were assessed using blind independent evaluators, parent-, and teacher-report, and an objective behavioral measure. Treatment recipients completed a three-month follow-up to assess durability of treatment gains. Data indicated increased functional speaking behavior post-treatment as rated by parents and teachers, with a high rate of treatment responders as rated by blind independent evaluators (75%). Conversely, children in the Waitlist comparison group did not experience significant improvements in speaking behaviors. Children who received IBTSM also demonstrated significant improvements in number of words spoken at school compared to baseline, however, significant group differences did not emerge. Treatment recipients also experienced significant reductions in social anxiety per parent, but not teacher, report. Clinical gains were maintained over 3 month follow-up. IBTSM appears to be a promising new intervention that is efficacious in increasing functional speaking behaviors, feasible, and acceptable to parents and teachers. Copyright © 2013 Elsevier Ltd. All rights reserved.
Statistical auditing and randomness test of lotto k/N-type games

Science.gov (United States)

Coronel-Brizio, H. F.; Hernández-Montoya, A. R.; Rapallo, F.; Scalas, E.

2008-11-01

One of the most popular lottery games worldwide is the so-called “lotto k/N”. It considers N numbers 1,2,…,N from which k are drawn randomly, without replacement. A player selects k or more numbers and the first prize is shared amongst those players whose selected numbers match all of the k randomly drawn. Exact rules may vary in different countries. In this paper, mean values and covariances for the random variables representing the numbers drawn from this kind of game are presented, with the aim of using them to audit statistically the consistency of a given sample of historical results with theoretical values coming from a hypergeometric statistical model. The method can be adapted to test pseudorandom number generators.
Bayesian dose selection design for a binary outcome using restricted response adaptive randomization.

Science.gov (United States)

Meinzer, Caitlyn; Martin, Renee; Suarez, Jose I

2017-09-08

In phase II trials, the most efficacious dose is usually not known. Moreover, given limited resources, it is difficult to robustly identify a dose while also testing for a signal of efficacy that would support a phase III trial. Recent designs have sought to be more efficient by exploring multiple doses through the use of adaptive strategies. However, the added flexibility may potentially increase the risk of making incorrect assumptions and reduce the total amount of information available across the dose range as a function of imbalanced sample size. To balance these challenges, a novel placebo-controlled design is presented in which a restricted Bayesian response adaptive randomization (RAR) is used to allocate a majority of subjects to the optimal dose of active drug, defined as the dose with the lowest probability of poor outcome. However, the allocation between subjects who receive active drug or placebo is held constant to retain the maximum possible power for a hypothesis test of overall efficacy comparing the optimal dose to placebo. The design properties and optimization of the design are presented in the context of a phase II trial for subarachnoid hemorrhage. For a fixed total sample size, a trade-off exists between the ability to select the optimal dose and the probability of rejecting the null hypothesis. This relationship is modified by the allocation ratio between active and control subjects, the choice of RAR algorithm, and the number of subjects allocated to an initial fixed allocation period. While a responsive RAR algorithm improves the ability to select the correct dose, there is an increased risk of assigning more subjects to a worse arm as a function of ephemeral trends in the data. A subarachnoid treatment trial is used to illustrate how this design can be customized for specific objectives and available data. Bayesian adaptive designs are a flexible approach to addressing multiple questions surrounding the optimal dose for treatment efficacy
Evaluation of Stress Loaded Steel Samples Using Selected Electromagnetic Methods

International Nuclear Information System (INIS)

Chady, T.

2004-01-01

In this paper the magnetic leakage flux and eddy current method were used to evaluate changes of materials' properties caused by stress. Seven samples made of ferromagnetic material with different level of applied stress were prepared. First, the leakage magnetic fields were measured by scanning the surface of the specimens with GMR gradiometer. Next, the same samples were evaluated using an eddy current sensor. A comparison between results obtained from both methods was carried out. Finally, selected parameters of the measured signal were calculated and utilized to evaluate level of the applied stress. A strong coincidence between amount of the applied stress and the maximum amplitude of the derivative was confirmed
Towards a pro-health food-selection model for gatekeepers in ...

African Journals Online (AJOL)

The purpose of this study was to develop a pro-health food selection model for gatekeepers of Bulawayo high-density suburbs in Zimbabwe. Gatekeepers in five suburbs constituted the study population from which a sample of 250 subjects was randomly selected. Of the total respondents (N= 182), 167 had their own ...
Multistage Selection and the Financing of New Ventures

OpenAIRE

Jonathan T. Eckhardt; Scott Shane; Frédéric Delmar

2006-01-01

Using a random sample of 221 new Swedish ventures initiated in 1998, we examine why some new ventures are more likely than others to successfully be awarded capital from external sources. We examine venture financing as a staged selection process in which two sequential selection events systematically winnow the population of ventures and influence which ventures receive financing. For a venture to receive external financing its founders must first select it as a candidate for external fundin...
Magnetically separable polymer (Mag-MIP) for selective analysis of biotin in food samples.

Science.gov (United States)

Uzuriaga-Sánchez, Rosario Josefina; Khan, Sabir; Wong, Ademar; Picasso, Gino; Pividori, Maria Isabel; Sotomayor, Maria Del Pilar Taboada

2016-01-01

This work presents an efficient method for the preparation of magnetic nanoparticles modified with molecularly imprinted polymers (Mag-MIP) through core-shell method for the determination of biotin in milk food samples. The functional monomer acrylic acid was selected from molecular modeling, EGDMA was used as cross-linking monomer and AIBN as radical initiator. The Mag-MIP and Mag-NIP were characterized by FTIR, magnetic hysteresis, XRD, SEM and N2-sorption measurements. The capacity of Mag-MIP for biotin adsorption, its kinetics and selectivity were studied in detail. The adsorption data was well described by Freundlich isotherm model with adsorption equilibrium constant (KF) of 1.46 mL g(-1). The selectivity experiments revealed that prepared Mag-MIP had higher selectivity toward biotin compared to other molecules with different chemical structure. The material was successfully applied for the determination of biotin in diverse milk samples using HPLC for quantification of the analyte, obtaining the mean value of 87.4% recovery. Copyright © 2015 Elsevier Ltd. All rights reserved.
Selection of Sampling Pumps Used for Groundwater Monitoring at the Hanford Site

Energy Technology Data Exchange (ETDEWEB)

Schalla, Ronald; Webber, William D.; Smith, Ronald M.

2001-11-05

The variable frequency drive centrifugal submersible pump, Redi-Flo2a made by Grundfosa, was selected for universal application for Hanford Site groundwater monitoring. Specifications for the selected pump and five other pumps were evaluated against current and future Hanford groundwater monitoring performance requirements, and the Redi-Flo2 was selected as the most versatile and applicable for the range of monitoring conditions. The Redi-Flo2 pump distinguished itself from the other pumps considered because of its wide range in output flow rate and its comparatively moderate maintenance and low capital costs. The Redi-Flo2 pump is able to purge a well at a high flow rate and then supply water for sampling at a low flow rate. Groundwater sampling using a low-volume-purging technique (e.g., low flow, minimal purge, no purge, or micropurgea) is planned in the future, eliminating the need for the pump to supply a high-output flow rate. Under those conditions, the Well Wizard bladder pump, manufactured by QED Environmental Systems, Inc., may be the preferred pump because of the lower capital cost.
Passive sampling of selected endocrine disrupting compounds using polar organic chemical integrative samplers

International Nuclear Information System (INIS)

Arditsoglou, Anastasia; Voutsa, Dimitra

2008-01-01

Two types of polar organic chemical integrative samplers (pharmaceutical POCIS and pesticide POCIS) were examined for their sampling efficiency of selected endocrine disrupting compounds (EDCs). Laboratory-based calibration of POCISs was conducted by exposing them at high and low concentrations of 14 EDCs (4-alkyl-phenols, their ethoxylate oligomers, bisphenol A, selected estrogens and synthetic steroids) for different time periods. The kinetic studies showed an integrative uptake up to 28 days. The sampling rates for the individual compounds were obtained. The use of POCISs could result in an integrative approach to the quality status of the aquatic systems especially in the case of high variation of water concentrations of EDCs. The sampling efficiency of POCISs under various field conditions was assessed after their deployment in different aquatic environments. - Calibration and field performance of polar organic integrative samplers for monitoring EDCs in aquatic environments
Don't spin the pen: two alternative methods for second-stage sampling in urban cluster surveys

Directory of Open Access Journals (Sweden)

Rose Angela MC

2007-06-01

Full Text Available Abstract In two-stage cluster surveys, the traditional method used in second-stage sampling (in which the first household in a cluster is selected is time-consuming and may result in biased estimates of the indicator of interest. Firstly, a random direction from the center of the cluster is selected, usually by spinning a pen. The houses along that direction are then counted out to the boundary of the cluster, and one is then selected at random to be the first household surveyed. This process favors households towards the center of the cluster, but it could easily be improved. During a recent meningitis vaccination coverage survey in Maradi, Niger, we compared this method of first household selection to two alternatives in urban zones: 1 using a superimposed grid on the map of the cluster area and randomly selecting an intersection; and 2 drawing the perimeter of the cluster area using a Global Positioning System (GPS and randomly selecting one point within the perimeter. Although we only compared a limited number of clusters using each method, we found the sampling grid method to be the fastest and easiest for field survey teams, although it does require a map of the area. Selecting a random GPS point was also found to be a good method, once adequate training can be provided. Spinning the pen and counting households to the boundary was the most complicated and time-consuming. The two methods tested here represent simpler, quicker and potentially more robust alternatives to spinning the pen for cluster surveys in urban areas. However, in rural areas, these alternatives would favor initial household selection from lower density (or even potentially empty areas. Bearing in mind these limitations, as well as available resources and feasibility, investigators should choose the most appropriate method for their particular survey context.
Modern survey sampling

CERN Document Server

Chaudhuri, Arijit

2014-01-01

Exposure to SamplingAbstract Introduction Concepts of Population, Sample, and SamplingInitial RamificationsAbstract Introduction Sampling Design, Sampling SchemeRandom Numbers and Their Uses in Simple RandomSampling (SRS)Drawing Simple Random Samples with and withoutReplacementEstimation of Mean, Total, Ratio of Totals/Means:Variance and Variance EstimationDetermination of Sample SizesA.2 Appendix to Chapter 2 A.More on Equal Probability Sampling A.Horvitz-Thompson EstimatorA.SufficiencyA.LikelihoodA.Non-Existence Theorem More Intricacies Abstract Introduction Unequal Probability Sampling StrategiesPPS Sampling Exploring Improved WaysAbstract Introduction Stratified Sampling Cluster SamplingMulti-Stage SamplingMulti-Phase Sampling: Ratio and RegressionEstimationviiviii ContentsControlled SamplingModeling Introduction Super-Population ModelingPrediction Approach Model-Assisted Approach Bayesian Methods Spatial SmoothingSampling on Successive Occasions: Panel Rotation Non-Response and Not-at-Homes Weighting Adj...
Size selective isocyanate aerosols personal air sampling using porous plastic foams

International Nuclear Information System (INIS)

Cong Khanh Huynh; Trinh Vu Duc

2009-01-01

As part of a European project (SMT4-CT96-2137), various European institutions specialized in occupational hygiene (BGIA, HSL, IOM, INRS, IST, Ambiente e Lavoro) have established a program of scientific collaboration to develop one or more prototypes of European personal samplers for the collection of simultaneous three dust fractions: inhalable, thoracic and respirable. These samplers based on existing sampling heads (IOM, GSP and cassettes) use Polyurethane Plastic Foam (PUF) according to their porosity to support sampling and separator size of the particles. In this study, the authors present an original application of size selective personal air sampling using chemical impregnated PUF to perform isocyanate aerosols capturing and derivatizing in industrial spray-painting shops.
Using a Calendar and Explanatory Instructions to Aid Within-Household Selection in Mail Surveys

Science.gov (United States)

Stange, Mathew; Smyth, Jolene D.; Olson, Kristen

2016-01-01

Although researchers can easily select probability samples of addresses using the U.S. Postal Service's Delivery Sequence File, randomly selecting respondents within households for surveys remains challenging. Researchers often place within-household selection instructions, such as the next or last birthday methods, in survey cover letters to…

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

Directory of Open Access Journals (Sweden)

Bochaton Audrey

2007-06-01

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

Science.gov (United States)

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

2007-06-01

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. 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. 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. This paper describes the conceptual reasoning behind the construction of the survey sample and shows that it can be
Sample selection based on kernel-subclustering for the signal reconstruction of multifunctional sensors

International Nuclear Information System (INIS)

Wang, Xin; Wei, Guo; Sun, Jinwei

2013-01-01

The signal reconstruction methods based on inverse modeling for the signal reconstruction of multifunctional sensors have been widely studied in recent years. To improve the accuracy, the reconstruction methods have become more and more complicated because of the increase in the model parameters and sample points. However, there is another factor that affects the reconstruction accuracy, the position of the sample points, which has not been studied. A reasonable selection of the sample points could improve the signal reconstruction quality in at least two ways: improved accuracy with the same number of sample points or the same accuracy obtained with a smaller number of sample points. Both ways are valuable for improving the accuracy and decreasing the workload, especially for large batches of multifunctional sensors. In this paper, we propose a sample selection method based on kernel-subclustering distill groupings of the sample data and produce the representation of the data set for inverse modeling. The method calculates the distance between two data points based on the kernel-induced distance instead of the conventional distance. The kernel function is a generalization of the distance metric by mapping the data that are non-separable in the original space into homogeneous groups in the high-dimensional space. The method obtained the best results compared with the other three methods in the simulation. (paper)
Revisiting random walk based sampling in networks: evasion of burn-in period and frequent regenerations.

Science.gov (United States)

Avrachenkov, Konstantin; Borkar, Vivek S; Kadavankandy, Arun; Sreedharan, Jithin K

2018-01-01

In the framework of network sampling, random walk (RW) based estimation techniques provide many pragmatic solutions while uncovering the unknown network as little as possible. Despite several theoretical advances in this area, RW based sampling techniques usually make a strong assumption that the samples are in stationary regime, and hence are impelled to leave out the samples collected during the burn-in period. This work proposes two sampling schemes without burn-in time constraint to estimate the average of an arbitrary function defined on the network nodes, for example, the average age of users in a social network. The central idea of the algorithms lies in exploiting regeneration of RWs at revisits to an aggregated super-node or to a set of nodes, and in strategies to enhance the frequency of such regenerations either by contracting the graph or by making the hitting set larger. Our first algorithm, which is based on reinforcement learning (RL), uses stochastic approximation to derive an estimator. This method can be seen as intermediate between purely stochastic Markov chain Monte Carlo iterations and deterministic relative value iterations. The second algorithm, which we call the Ratio with Tours (RT)-estimator, is a modified form of respondent-driven sampling (RDS) that accommodates the idea of regeneration. We study the methods via simulations on real networks. We observe that the trajectories of RL-estimator are much more stable than those of standard random walk based estimation procedures, and its error performance is comparable to that of respondent-driven sampling (RDS) which has a smaller asymptotic variance than many other estimators. Simulation studies also show that the mean squared error of RT-estimator decays much faster than that of RDS with time. The newly developed RW based estimators (RL- and RT-estimators) allow to avoid burn-in period, provide better control of stability along the sample path, and overall reduce the estimation time. Our
Selective solid-phase extraction of Ni(II) by an ion-imprinted polymer from water samples

International Nuclear Information System (INIS)

Saraji, Mohammad; Yousefi, Hamideh

2009-01-01

A new ion-imprinted polymer (IIP) material was synthesized by copolymerization of 4-vinylpyridine as monomer, ethyleneglycoldimethacrylate as crosslinking agent and 2,2'-azobis-sobutyronitrile as initiator in the presence of Ni-dithizone complex. The IIP was used as sorbent in a solid-phase extraction column. The effects of sampling volume, elution conditions, sample pH and sample flow rate on the extraction of Ni ions form water samples were studied. The maximum adsorption capacity and the relative selectivity coefficients of imprinted polymer for Ni(II)/Co(II), Ni(II)/Cu(II) and Ni(II)/Cd(II) were calculated. Compared with non-imprinted polymer particles, the IIP had higher selectivity for Ni(II). The relative selectivity factor (α r ) values of Ni(II)/Co(II), Ni(II)/Cu(II) and Ni(II)/Cd(II) were 21.6, 54.3, and 22.7, respectively, which are greater than 1. The relative standard deviation of the five replicate determinations of Ni(II) was 3.4%. The detection limit for 150 mL of sample was 1.6 μg L -1 using flame atomic absorption spectrometry. The developed method was successfully applied to the determination of trace nickel in water samples with satisfactory results.
An Improved Nested Sampling Algorithm for Model Selection and Assessment

Science.gov (United States)

Zeng, X.; Ye, M.; Wu, J.; WANG, D.

2017-12-01

Multimodel strategy is a general approach for treating model structure uncertainty in recent researches. The unknown groundwater system is represented by several plausible conceptual models. Each alternative conceptual model is attached with a weight which represents the possibility of this model. In Bayesian framework, the posterior model weight is computed as the product of model prior weight and marginal likelihood (or termed as model evidence). As a result, estimating marginal likelihoods is crucial for reliable model selection and assessment in multimodel analysis. Nested sampling estimator (NSE) is a new proposed algorithm for marginal likelihood estimation. The implementation of NSE comprises searching the parameters' space from low likelihood area to high likelihood area gradually, and this evolution is finished iteratively via local sampling procedure. Thus, the efficiency of NSE is dominated by the strength of local sampling procedure. Currently, Metropolis-Hasting (M-H) algorithm and its variants are often used for local sampling in NSE. However, M-H is not an efficient sampling algorithm for high-dimensional or complex likelihood function. For improving the performance of NSE, it could be feasible to integrate more efficient and elaborated sampling algorithm - DREAMzs into the local sampling. In addition, in order to overcome the computation burden problem of large quantity of repeating model executions in marginal likelihood estimation, an adaptive sparse grid stochastic collocation method is used to build the surrogates for original groundwater model.
Selective decontamination in pediatric liver transplants. A randomized prospective study.

Science.gov (United States)

Smith, S D; Jackson, R J; Hannakan, C J; Wadowsky, R M; Tzakis, A G; Rowe, M I

1993-06-01

Although it has been suggested that selective decontamination of the digestive tract (SDD) decreases postoperative aerobic Gram-negative and fungal infections in orthotopic liver transplantation (OLT), no controlled trials exist in pediatric patients. This prospective, randomized controlled study of 36 pediatric OLT patients examines the effect of short-term SDD on postoperative infection and digestive tract flora. Patients were randomized into two groups. The control group received perioperative parenteral antibiotics only. The SDD group received in addition polymyxin E, tobramycin, and amphotericin B enterally and by oropharyngeal swab postoperatively until oral intake was tolerated (6 +/- 4 days). Indications for operation, preoperative status, age, and intensive care unit and hospital length of stay were no different in SDD (n = 18) and control (n = 18) groups. A total of 14 Gram-negative infections (intraabdominal abscess 7, septicemia 5, pneumonia 1, urinary tract 1) developed in the 36 patients studied. Mortality was not significantly different in the two groups. However, there were significantly fewer patients with Gram-negative infections in the SDD group: 3/18 patients (11%) vs. 11/18 patients (50%) in the control group, P < 0.001. There was also significant reduction in aerobic Gram-negative flora in the stool and pharynx in patients receiving SDD. Gram-positive and anaerobic organisms were unaffected. We conclude that short-term postoperative SDD significantly reduces Gram-negative infections in pediatric OLT patients.
Principal Stratification in sample selection problems with non normal error terms

DEFF Research Database (Denmark)

Rocci, Roberto; Mellace, Giovanni

The aim of the paper is to relax distributional assumptions on the error terms, often imposed in parametric sample selection models to estimate causal effects, when plausible exclusion restrictions are not available. Within the principal stratification framework, we approximate the true distribut...... an application to the Job Corps training program....
Fixed-location hydroacoustic monitoring designs for estimating fish passage using stratified random and systematic sampling

International Nuclear Information System (INIS)

Skalski, J.R.; Hoffman, A.; Ransom, B.H.; Steig, T.W.

1993-01-01

Five alternate sampling designs are compared using 15 d of 24-h continuous hydroacoustic data to identify the most favorable approach to fixed-location hydroacoustic monitoring of salmonid outmigrants. Four alternative aproaches to systematic sampling are compared among themselves and with stratified random sampling (STRS). Stratifying systematic sampling (STSYS) on a daily basis is found to reduce sampling error in multiday monitoring studies. Although sampling precision was predictable with varying levels of effort in STRS, neither magnitude nor direction of change in precision was predictable when effort was varied in systematic sampling (SYS). Furthermore, modifying systematic sampling to include replicated (e.g., nested) sampling (RSYS) is further shown to provide unbiased point and variance estimates as does STRS. Numerous short sampling intervals (e.g., 12 samples of 1-min duration per hour) must be monitored hourly using RSYS to provide efficient, unbiased point and interval estimates. For equal levels of effort, STRS outperformed all variations of SYS examined. Parametric approaches to confidence interval estimates are found to be superior to nonparametric interval estimates (i.e., bootstrap and jackknife) in estimating total fish passage. 10 refs., 1 fig., 8 tabs
Sampling Strategies for Evaluating the Rate of Adventitious Transgene Presence in Non-Genetically Modified Crop Fields.

Science.gov (United States)

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.
Experimental and Sampling Design for the INL-2 Sample Collection Operational Test

Energy Technology Data Exchange (ETDEWEB)

Piepel, Gregory F.; Amidan, Brett G.; Matzke, Brett D.

2009-02-16

, sample extraction, and analytical methods to be used in the INL-2 study. For each of the five test events, the specified floor of the INL building will be contaminated with BG using a point-release device located in the room specified in the experimental design. Then quality control (QC), reference material coupon (RMC), judgmental, and probabilistic samples will be collected according to the sampling plan for each test event. Judgmental samples will be selected based on professional judgment and prior information. Probabilistic samples were selected with a random aspect and in sufficient numbers to provide desired confidence for detecting contamination or clearing uncontaminated (or decontaminated) areas. Following sample collection for a given test event, the INL building will be decontaminated. For possibly contaminated areas, the numbers of probabilistic samples were chosen to provide 95% confidence of detecting contaminated areas of specified sizes. For rooms that may be uncontaminated following a contamination event, or for whole floors after decontamination, the numbers of judgmental and probabilistic samples were chosen using the CJR approach. The numbers of samples were chosen to support making X%/Y% clearance statements with X = 95% or 99% and Y = 96% or 97%. The experimental and sampling design also provides for making X%/Y% clearance statements using only probabilistic samples. For each test event, the numbers of characterization and clearance samples were selected within limits based on operational considerations while still maintaining high confidence for detection and clearance aspects. The sampling design for all five test events contains 2085 samples, with 1142 after contamination and 943 after decontamination. These numbers include QC, RMC, judgmental, and probabilistic samples. The experimental and sampling design specified in this report provides a good statistical foundation for achieving the objectives of the INL-2 study.
Day-ahead load forecast using random forest and expert input selection

International Nuclear Information System (INIS)

Lahouar, A.; Ben Hadj Slama, J.

2015-01-01

Highlights: • A model based on random forests for short term load forecast is proposed. • An expert feature selection is added to refine inputs. • Special attention is paid to customers behavior, load profile and special holidays. • The model is flexible and able to handle complex load signal. • A technical comparison is performed to assess the forecast accuracy. - Abstract: The electrical load forecast is getting more and more important in recent years due to the electricity market deregulation and integration of renewable resources. To overcome the incoming challenges and ensure accurate power prediction for different time horizons, sophisticated intelligent methods are elaborated. Utilization of intelligent forecast algorithms is among main characteristics of smart grids, and is an efficient tool to face uncertainty. Several crucial tasks of power operators such as load dispatch rely on the short term forecast, thus it should be as accurate as possible. To this end, this paper proposes a short term load predictor, able to forecast the next 24 h of load. Using random forest, characterized by immunity to parameter variations and internal cross validation, the model is constructed following an online learning process. The inputs are refined by expert feature selection using a set of if–then rules, in order to include the own user specifications about the country weather or market, and to generalize the forecast ability. The proposed approach is tested through a real historical set from the Tunisian Power Company, and the simulation shows accurate and satisfactory results for one day in advance, with an average error exceeding rarely 2.3%. The model is validated for regular working days and weekends, and special attention is paid to moving holidays, following non Gregorian calendar
At convenience and systematic random sampling: effects on the prognostic value of nuclear area assessments in breast cancer patients.

Science.gov (United States)

Jannink, I; Bennen, J N; Blaauw, J; van Diest, P J; Baak, J P

1995-01-01

This study compares the influence of two different nuclear sampling methods on the prognostic value of assessments of mean and standard deviation of nuclear area (MNA, SDNA) in 191 consecutive invasive breast cancer patients with long term follow up. The first sampling method used was 'at convenience' sampling (ACS); the second, systematic random sampling (SRS). Both sampling methods were tested with a sample size of 50 nuclei (ACS-50 and SRS-50). To determine whether, besides the sampling methods, sample size had impact on prognostic value as well, the SRS method was also tested using a sample size of 100 nuclei (SRS-100). SDNA values were systematically lower for ACS, obviously due to (unconsciously) not including small and large nuclei. Testing prognostic value of a series of cut off points, MNA and SDNA values assessed by the SRS method were prognostically significantly stronger than the values obtained by the ACS method. This was confirmed in Cox regression analysis. For the MNA, the Mantel-Cox p-values from SRS-50 and SRS-100 measurements were not significantly different. However, for the SDNA, SRS-100 yielded significantly lower p-values than SRS-50. In conclusion, compared with the 'at convenience' nuclear sampling method, systematic random sampling of nuclei is not only superior with respect to reproducibility of results, but also provides a better prognostic value in patients with invasive breast cancer.
Obscured AGN at z ~ 1 from the zCOSMOS-Bright Survey. I. Selection and optical properties of a [Ne v]-selected sample

Science.gov (United States)

Mignoli, M.; Vignali, C.; Gilli, R.; Comastri, A.; Zamorani, G.; Bolzonella, M.; Bongiorno, A.; Lamareille, F.; Nair, P.; Pozzetti, L.; Lilly, S. J.; Carollo, C. M.; Contini, T.; Kneib, J.-P.; Le Fèvre, O.; Mainieri, V.; Renzini, A.; Scodeggio, M.; Bardelli, S.; Caputi, K.; Cucciati, O.; de la Torre, S.; de Ravel, L.; Franzetti, P.; Garilli, B.; Iovino, A.; Kampczyk, P.; Knobel, C.; Kovač, K.; Le Borgne, J.-F.; Le Brun, V.; Maier, C.; Pellò, R.; Peng, Y.; Perez Montero, E.; Presotto, V.; Silverman, J. D.; Tanaka, M.; Tasca, L.; Tresse, L.; Vergani, D.; Zucca, E.; Bordoloi, R.; Cappi, A.; Cimatti, A.; Koekemoer, A. M.; McCracken, H. J.; Moresco, M.; Welikala, N.

2013-08-01

Aims: The application of multi-wavelength selection techniques is essential for obtaining a complete and unbiased census of active galactic nuclei (AGN). We present here a method for selecting z ~ 1 obscured AGN from optical spectroscopic surveys. Methods: A sample of 94 narrow-line AGN with 0.65 advantage of the large amount of data available in the COSMOS field, the properties of the [Ne v]-selected type 2 AGN were investigated, focusing on their host galaxies, X-ray emission, and optical line-flux ratios. Finally, a previously developed diagnostic, based on the X-ray-to-[Ne v] luminosity ratio, was exploited to search for the more heavily obscured AGN. Results: We found that [Ne v]-selected narrow-line AGN have Seyfert 2-like optical spectra, although their emission line ratios are diluted by a star-forming component. The ACS morphologies and stellar component in the optical spectra indicate a preference for our type 2 AGN to be hosted in early-type spirals with stellar masses greater than 109.5 - 10 M⊙, on average higher than those of the galaxy parent sample. The fraction of galaxies hosting [Ne v]-selected obscured AGN increases with the stellar mass, reaching a maximum of about 3% at ≈2 × 1011 M⊙. A comparison with other selection techniques at z ~ 1, namely the line-ratio diagnostics and X-ray detections, shows that the detection of the [Ne v] λ3426 line is an effective method for selecting AGN in the optical band, in particular the most heavily obscured ones, but cannot provide a complete census of type 2 AGN by itself. Finally, the high fraction of [Ne v]-selected type 2 AGN not detected in medium-deep (≈100-200 ks) Chandra observations (67%) is suggestive of the inclusion of Compton-thick (i.e., with NH > 1024 cm-2) sources in our sample. The presence of a population of heavily obscured AGN is corroborated by the X-ray-to-[Ne v] ratio; we estimated, by means of an X-ray stacking technique and simulations, that the Compton-thick fraction in our
Selective parathyroid venous sampling in primary hyperparathyroidism: A systematic review and meta-analysis.

Science.gov (United States)

Ibraheem, Kareem; Toraih, Eman A; Haddad, Antoine B; Farag, Mahmoud; Randolph, Gregory W; Kandil, Emad

2018-05-14

Minimally invasive parathyroidectomy requires accurate preoperative localization techniques. There is considerable controversy about the effectiveness of selective parathyroid venous sampling (sPVS) in primary hyperparathyroidism (PHPT) patients. The aim of this meta-analysis is to examine the diagnostic accuracy of sPVS as a preoperative localization modality in PHPT. Studies evaluating the diagnostic accuracy of sPVS for PHPT were electronically searched in the PubMed, EMBASE, Web of Science, and Cochrane Controlled Trials Register databases. Two independent authors reviewed the studies, and revised quality assessment of diagnostic accuracy study tool was used for the quality assessment. Study heterogeneity and pooled estimates were calculated. Two hundred and two unique studies were identified. Of those, 12 studies were included in the meta-analysis. Pooled sensitivity, specificity, and positive likelihood ratio (PLR) of sPVS were 74%, 41%, and 1.55, respectively. The area-under-the-receiver operating characteristic curve was 0.684, indicating an average discriminatory ability of sPVS. On comparison between sPVS and noninvasive imaging modalities, sensitivity, PLR, and positive posttest probability were significantly higher in sPVS compared to noninvasive imaging modalities. Interestingly, super-selective venous sampling had the highest sensitivity, accuracy, and positive posttest probability compared to other parathyroid venous sampling techniques. This is the first meta-analysis to examine the accuracy of sPVS in PHPT. sPVS had higher pooled sensitivity when compared to noninvasive modalities in revision parathyroid surgery. However, the invasiveness of this technique does not favor its routine use for preoperative localization. Super-selective venous sampling was the most accurate among all other parathyroid venous sampling techniques. Laryngoscope, 2018. © 2018 The American Laryngological, Rhinological and Otological Society, Inc.
Detailed RIF decomposition with selection : the gender pay gap in Italy

OpenAIRE

Töpfer, Marina

2017-01-01

In this paper, we estimate the gender pay gap along the wage distribution using a detailed decomposition approach based on unconditional quantile regressions. Non-randomness of the sample leads to biased and inconsistent estimates of the wage equation as well as of the components of the wage gap. Therefore, the method is extended to account for sample selection problems. The decomposition is conducted by using Italian microdata. Accounting for labor market selection may be particularly rele...
Students' level of skillfulness and use of the internet in selected ...

African Journals Online (AJOL)

The study examined level of skillfulness and the use of the Internet for learning among secondary school students in Lagos State, Nigeria. The descriptive survey research method was adopted for the study. A sample of 450 students was randomly selected from the three secondary schools. One intact arm was selected from ...
Distribution of orientation selectivity in recurrent networks of spiking neurons with different random topologies.

Science.gov (United States)

Sadeh, Sadra; Rotter, Stefan

2014-01-01

Neurons in the primary visual cortex are more or less selective for the orientation of a light bar used for stimulation. A broad distribution of individual grades of orientation selectivity has in fact been reported in all species. A possible reason for emergence of broad distributions is the recurrent network within which the stimulus is being processed. Here we compute the distribution of orientation selectivity in randomly connected model networks that are equipped with different spatial patterns of connectivity. We show that, for a wide variety of connectivity patterns, a linear theory based on firing rates accurately approximates the outcome of direct numerical simulations of networks of spiking neurons. Distance dependent connectivity in networks with a more biologically realistic structure does not compromise our linear analysis, as long as the linearized dynamics, and hence the uniform asynchronous irregular activity state, remain stable. We conclude that linear mechanisms of stimulus processing are indeed responsible for the emergence of orientation selectivity and its distribution in recurrent networks with functionally heterogeneous synaptic connectivity.
Randomized comparison of vaginal self-sampling by standard vs. dry swabs for Human papillomavirus testing

International Nuclear Information System (INIS)

Eperon, Isabelle; Vassilakos, Pierre; Navarria, Isabelle; Menoud, Pierre-Alain; Gauthier, Aude; Pache, Jean-Claude; Boulvain, Michel; Untiet, Sarah; Petignat, Patrick

2013-01-01

To evaluate if human papillomavirus (HPV) self-sampling (Self-HPV) using a dry vaginal swab is a valid alternative for HPV testing. Women attending colposcopy clinic were recruited to collect two consecutive Self-HPV samples: a Self-HPV using a dry swab (S-DRY) and a Self-HPV using a standard wet transport medium (S-WET). These samples were analyzed for HPV using real time PCR (Roche Cobas). Participants were randomized to determine the order of the tests. Questionnaires assessing preferences and acceptability for both tests were conducted. Subsequently, women were invited for colposcopic examination; a physician collected a cervical sample (physician-sampling) with a broom-type device and placed it into a liquid-based cytology medium. Specimens were then processed for the production of cytology slides and a Hybrid Capture HPV DNA test (Qiagen) was performed from the residual liquid. Biopsies were performed if indicated. Unweighted kappa statistics (κ) and McNemar tests were used to measure the agreement among the sampling methods. A total of 120 women were randomized. Overall HPV prevalence was 68.7% (95% Confidence Interval (CI) 59.3–77.2) by S-WET, 54.4% (95% CI 44.8–63.9) by S-DRY and 53.8% (95% CI 43.8–63.7) by HC. Among paired samples (S-WET and S-DRY), the overall agreement was good (85.7%; 95% CI 77.8–91.6) and the κ was substantial (0.70; 95% CI 0.57-0.70). The proportion of positive type-specific HPV agreement was also good (77.3%; 95% CI 68.2-84.9). No differences in sensitivity for cervical intraepithelial neoplasia grade one (CIN1) or worse between the two Self-HPV tests were observed. Women reported the two Self-HPV tests as highly acceptable. Self-HPV using dry swab transfer does not appear to compromise specimen integrity. Further study in a large screening population is needed. ClinicalTrials.gov: http://clinicaltrials.gov/show/NCT01316120
Transfer function design based on user selected samples for intuitive multivariate volume exploration

KAUST Repository

Zhou, Liang

2013-02-01

Multivariate volumetric datasets are important to both science and medicine. We propose a transfer function (TF) design approach based on user selected samples in the spatial domain to make multivariate volumetric data visualization more accessible for domain users. Specifically, the user starts the visualization by probing features of interest on slices and the data values are instantly queried by user selection. The queried sample values are then used to automatically and robustly generate high dimensional transfer functions (HDTFs) via kernel density estimation (KDE). Alternatively, 2D Gaussian TFs can be automatically generated in the dimensionality reduced space using these samples. With the extracted features rendered in the volume rendering view, the user can further refine these features using segmentation brushes. Interactivity is achieved in our system and different views are tightly linked. Use cases show that our system has been successfully applied for simulation and complicated seismic data sets. © 2013 IEEE.

Transfer function design based on user selected samples for intuitive multivariate volume exploration

KAUST Repository

Zhou, Liang; Hansen, Charles

2013-01-01

Multivariate volumetric datasets are important to both science and medicine. We propose a transfer function (TF) design approach based on user selected samples in the spatial domain to make multivariate volumetric data visualization more accessible for domain users. Specifically, the user starts the visualization by probing features of interest on slices and the data values are instantly queried by user selection. The queried sample values are then used to automatically and robustly generate high dimensional transfer functions (HDTFs) via kernel density estimation (KDE). Alternatively, 2D Gaussian TFs can be automatically generated in the dimensionality reduced space using these samples. With the extracted features rendered in the volume rendering view, the user can further refine these features using segmentation brushes. Interactivity is achieved in our system and different views are tightly linked. Use cases show that our system has been successfully applied for simulation and complicated seismic data sets. © 2013 IEEE.
Rural Women\\'s Response To Selected Crop Production ...

African Journals Online (AJOL)

The study centered on rural women's response to selected crop production technologies in Imo State with a view to making policy recommendations. Structured questionnaire and interview schedule were administered through the assistance of extension agents to 258 randomly sampled rural women farmers from the three ...
A Combined Weighting Method Based on Hybrid of Interval Evidence Fusion and Random Sampling

Directory of Open Access Journals (Sweden)

Ying Yan

2017-01-01

Full Text Available Due to the complexity of system and lack of expertise, epistemic uncertainties may present in the experts’ judgment on the importance of certain indices during group decision-making. A novel combination weighting method is proposed to solve the index weighting problem when various uncertainties are present in expert comments. Based on the idea of evidence theory, various types of uncertain evaluation information are uniformly expressed through interval evidence structures. Similarity matrix between interval evidences is constructed, and expert’s information is fused. Comment grades are quantified using the interval number, and cumulative probability function for evaluating the importance of indices is constructed based on the fused information. Finally, index weights are obtained by Monte Carlo random sampling. The method can process expert’s information with varying degrees of uncertainties, which possesses good compatibility. Difficulty in effectively fusing high-conflict group decision-making information and large information loss after fusion is avertible. Original expert judgments are retained rather objectively throughout the processing procedure. Cumulative probability function constructing and random sampling processes do not require any human intervention or judgment. It can be implemented by computer programs easily, thus having an apparent advantage in evaluation practices of fairly huge index systems.
An algorithm to improve sampling efficiency for uncertainty propagation using sampling based method

International Nuclear Information System (INIS)

Campolina, Daniel; Lima, Paulo Rubens I.; Pereira, Claubia; Veloso, Maria Auxiliadora F.

2015-01-01

Sample size and computational uncertainty were varied in order to investigate sample efficiency and convergence of the sampling based method for uncertainty propagation. Transport code MCNPX was used to simulate a LWR model and allow the mapping, from uncertain inputs of the benchmark experiment, to uncertain outputs. Random sampling efficiency was improved through the use of an algorithm for selecting distributions. Mean range, standard deviation range and skewness were verified in order to obtain a better representation of uncertainty figures. Standard deviation of 5 pcm in the propagated uncertainties for 10 n-samples replicates was adopted as convergence criterion to the method. Estimation of 75 pcm uncertainty on reactor k eff was accomplished by using sample of size 93 and computational uncertainty of 28 pcm to propagate 1σ uncertainty of burnable poison radius. For a fixed computational time, in order to reduce the variance of the uncertainty propagated, it was found, for the example under investigation, it is preferable double the sample size than double the amount of particles followed by Monte Carlo process in MCNPX code. (author)
Fine mapping quantitative trait loci under selective phenotyping strategies based on linkage and linkage disequilibrium criteria

DEFF Research Database (Denmark)

Ansari-Mahyari, S; Berg, P; Lund, M S

2009-01-01

disequilibrium-based sampling criteria (LDC) for selecting individuals to phenotype are compared to random phenotyping in a quantitative trait loci (QTL) verification experiment using stochastic simulation. Several strategies based on LAC and LDC for selecting the most informative 30%, 40% or 50% of individuals...... for phenotyping to extract maximum power and precision in a QTL fine mapping experiment were developed and assessed. Linkage analyses for the mapping was performed for individuals sampled on LAC within families and combined linkage disequilibrium and linkage analyses was performed for individuals sampled across...... the whole population based on LDC. The results showed that selecting individuals with similar haplotypes to the paternal haplotypes (minimum recombination criterion) using LAC compared to random phenotyping gave at least the same power to detect a QTL but decreased the accuracy of the QTL position. However...
Selective removal of phosphate for analysis of organic acids in complex samples.

Science.gov (United States)

Deshmukh, Sandeep; Frolov, Andrej; Marcillo, Andrea; Birkemeyer, Claudia

2015-04-03

Accurate quantitation of compounds in samples of biological origin is often hampered by matrix interferences one of which occurs in GC-MS analysis from the presence of highly abundant phosphate. Consequently, high concentrations of phosphate need to be removed before sample analysis. Within this context, we screened 17 anion exchange solid-phase extraction (SPE) materials for selective phosphate removal using different protocols to meet the challenge of simultaneous recovery of six common organic acids in aqueous samples prior to derivatization for GC-MS analysis. Up to 75% recovery was achieved for the most organic acids, only the low pKa tartaric and citric acids were badly recovered. Compared to the traditional approach of phosphate removal by precipitation, SPE had a broader compatibility with common detection methods and performed more selectively among the organic acids under investigation. Based on the results of this study, it is recommended that phosphate removal strategies during the analysis of biologically relevant small molecular weight organic acids consider the respective pKa of the anticipated analytes and the detection method of choice. Copyright © 2015 Elsevier B.V. All rights reserved.
THE zCOSMOS-SINFONI PROJECT. I. SAMPLE SELECTION AND NATURAL-SEEING OBSERVATIONS

Energy Technology Data Exchange (ETDEWEB)

Mancini, C.; Renzini, A. [INAF-OAPD, Osservatorio Astronomico di Padova, Vicolo Osservatorio 5, I-35122 Padova (Italy); Foerster Schreiber, N. M.; Hicks, E. K. S.; Genzel, R.; Tacconi, L.; Davies, R. [Max-Planck-Institut fuer Extraterrestrische Physik, Giessenbachstrasse, D-85748 Garching (Germany); Cresci, G. [Osservatorio Astrofisico di Arcetri (OAF), INAF-Firenze, Largo E. Fermi 5, I-50125 Firenze (Italy); Peng, Y.; Lilly, S.; Carollo, M.; Oesch, P. [Institute of Astronomy, Department of Physics, Eidgenossische Technische Hochschule, ETH Zurich CH-8093 (Switzerland); Vergani, D.; Pozzetti, L.; Zamorani, G. [INAF-Bologna, Via Ranzani, I-40127 Bologna (Italy); Daddi, E. [CEA-Saclay, DSM/DAPNIA/Service d' Astrophysique, F-91191 Gif-Sur Yvette Cedex (France); Maraston, C. [Institute of Cosmology and Gravitation, University of Portsmouth, Dennis Sciama Building, Burnaby Road, PO1 3HE Portsmouth (United Kingdom); McCracken, H. J. [IAP, 98bis bd Arago, F-75014 Paris (France); Bouche, N. [Department of Physics, University of California, Santa Barbara, CA 93106 (United States); Shapiro, K. [Aerospace Research Laboratories, Northrop Grumman Aerospace Systems, Redondo Beach, CA 90278 (United States); and others

2011-12-10

The zCOSMOS-SINFONI project is aimed at studying the physical and kinematical properties of a sample of massive z {approx} 1.4-2.5 star-forming galaxies, through SINFONI near-infrared integral field spectroscopy (IFS), combined with the multiwavelength information from the zCOSMOS (COSMOS) survey. The project is based on one hour of natural-seeing observations per target, and adaptive optics (AO) follow-up for a major part of the sample, which includes 30 galaxies selected from the zCOSMOS/VIMOS spectroscopic survey. This first paper presents the sample selection, and the global physical characterization of the target galaxies from multicolor photometry, i.e., star formation rate (SFR), stellar mass, age, etc. The H{alpha} integrated properties, such as, flux, velocity dispersion, and size, are derived from the natural-seeing observations, while the follow-up AO observations will be presented in the next paper of this series. Our sample appears to be well representative of star-forming galaxies at z {approx} 2, covering a wide range in mass and SFR. The H{alpha} integrated properties of the 25 H{alpha} detected galaxies are similar to those of other IFS samples at the same redshifts. Good agreement is found among the SFRs derived from H{alpha} luminosity and other diagnostic methods, provided the extinction affecting the H{alpha} luminosity is about twice that affecting the continuum. A preliminary kinematic analysis, based on the maximum observed velocity difference across the source and on the integrated velocity dispersion, indicates that the sample splits nearly 50-50 into rotation-dominated and velocity-dispersion-dominated galaxies, in good agreement with previous surveys.
Calibration model maintenance in melamine resin production: Integrating drift detection, smart sample selection and model adaptation.

Science.gov (United States)

Nikzad-Langerodi, Ramin; Lughofer, Edwin; Cernuda, Carlos; Reischer, Thomas; Kantner, Wolfgang; Pawliczek, Marcin; Brandstetter, Markus

2018-07-12

selection of samples by active learning (AL) used for subsequent model adaptation is advantageous compared to passive (random) selection in case that a drift leads to persistent prediction bias allowing more rapid adaptation at lower reference measurement rates. Fully unsupervised adaptation using FLEXFIS-PLS could improve predictive accuracy significantly for light drifts but was not able to fully compensate for prediction bias in case of significant lack of fit w.r.t. the latent variable space. Copyright © 2018 Elsevier B.V. All rights reserved.
40 CFR Appendix A to Subpart G of... - Sampling Plans for Selective Enforcement Auditing of Marine Engines

Science.gov (United States)

2010-07-01

... Enforcement Auditing of Marine Engines A Appendix A to Subpart G of Part 91 Protection of Environment...-IGNITION ENGINES Selective Enforcement Auditing Regulations Pt. 91, Subpt. G, App. A Appendix A to Subpart G of Part 91—Sampling Plans for Selective Enforcement Auditing of Marine Engines Table 1—Sampling...
Self-reference and random sampling approach for label-free identification of DNA composition using plasmonic nanomaterials.

Science.gov (United States)

Freeman, Lindsay M; Pang, Lin; Fainman, Yeshaiahu

2018-05-09

The analysis of DNA has led to revolutionary advancements in the fields of medical diagnostics, genomics, prenatal screening, and forensic science, with the global DNA testing market expected to reach revenues of USD 10.04 billion per year by 2020. However, the current methods for DNA analysis remain dependent on the necessity for fluorophores or conjugated proteins, leading to high costs associated with consumable materials and manual labor. Here, we demonstrate a potential label-free DNA composition detection method using surface-enhanced Raman spectroscopy (SERS) in which we identify the composition of cytosine and adenine within single strands of DNA. This approach depends on the fact that there is one phosphate backbone per nucleotide, which we use as a reference to compensate for systematic measurement variations. We utilize plasmonic nanomaterials with random Raman sampling to perform label-free detection of the nucleotide composition within DNA strands, generating a calibration curve from standard samples of DNA and demonstrating the capability of resolving the nucleotide composition. The work represents an innovative way for detection of the DNA composition within DNA strands without the necessity of attached labels, offering a highly sensitive and reproducible method that factors in random sampling to minimize error.
40 CFR Appendix A to Subpart F of... - Sampling Plans for Selective Enforcement Auditing of Nonroad Engines

Science.gov (United States)

2010-07-01

... Enforcement Auditing of Nonroad Engines A Appendix A to Subpart F of Part 89 Protection of Environment... NONROAD COMPRESSION-IGNITION ENGINES Selective Enforcement Auditing Pt. 89, Subpt. F, App. A Appendix A to Subpart F of Part 89—Sampling Plans for Selective Enforcement Auditing of Nonroad Engines Table 1—Sampling...
Comparing cluster-level dynamic treatment regimens using sequential, multiple assignment, randomized trials: Regression estimation and sample size considerations.

Science.gov (United States)

NeCamp, Timothy; Kilbourne, Amy; Almirall, Daniel

2017-08-01

Cluster-level dynamic treatment regimens can be used to guide sequential treatment decision-making at the cluster level in order to improve outcomes at the individual or patient-level. In a cluster-level dynamic treatment regimen, the treatment is potentially adapted and re-adapted over time based on changes in the cluster that could be impacted by prior intervention, including aggregate measures of the individuals or patients that compose it. Cluster-randomized sequential multiple assignment randomized trials can be used to answer multiple open questions preventing scientists from developing high-quality cluster-level dynamic treatment regimens. In a cluster-randomized sequential multiple assignment randomized trial, sequential randomizations occur at the cluster level and outcomes are observed at the individual level. This manuscript makes two contributions to the design and analysis of cluster-randomized sequential multiple assignment randomized trials. First, a weighted least squares regression approach is proposed for comparing the mean of a patient-level outcome between the cluster-level dynamic treatment regimens embedded in a sequential multiple assignment randomized trial. The regression approach facilitates the use of baseline covariates which is often critical in the analysis of cluster-level trials. Second, sample size calculators are derived for two common cluster-randomized sequential multiple assignment randomized trial designs for use when the primary aim is a between-dynamic treatment regimen comparison of the mean of a continuous patient-level outcome. The methods are motivated by the Adaptive Implementation of Effective Programs Trial which is, to our knowledge, the first-ever cluster-randomized sequential multiple assignment randomized trial in psychiatry.
Electromembrane extraction as a rapid and selective miniaturized sample preparation technique for biological fluids

DEFF Research Database (Denmark)

Gjelstad, Astrid; Pedersen-Bjergaard, Stig; Seip, Knut Fredrik

2015-01-01

This special report discusses the sample preparation method electromembrane extraction, which was introduced in 2006 as a rapid and selective miniaturized extraction method. The extraction principle is based on isolation of charged analytes extracted from an aqueous sample, across a thin film....... Technical aspects of electromembrane extraction, important extraction parameters as well as a handful of examples of applications from different biological samples and bioanalytical areas are discussed in the paper....
A genetic algorithm-based framework for wavelength selection on sample categorization.

Science.gov (United States)

Anzanello, Michel J; Yamashita, Gabrielli; Marcelo, Marcelo; Fogliatto, Flávio S; Ortiz, Rafael S; Mariotti, Kristiane; Ferrão, Marco F

2017-08-01

In forensic and pharmaceutical scenarios, the application of chemometrics and optimization techniques has unveiled common and peculiar features of seized medicine and drug samples, helping investigative forces to track illegal operations. This paper proposes a novel framework aimed at identifying relevant subsets of attenuated total reflectance Fourier transform infrared (ATR-FTIR) wavelengths for classifying samples into two classes, for example authentic or forged categories in case of medicines, or salt or base form in cocaine analysis. In the first step of the framework, the ATR-FTIR spectra were partitioned into equidistant intervals and the k-nearest neighbour (KNN) classification technique was applied to each interval to insert samples into proper classes. In the next step, selected intervals were refined through the genetic algorithm (GA) by identifying a limited number of wavelengths from the intervals previously selected aimed at maximizing classification accuracy. When applied to Cialis®, Viagra®, and cocaine ATR-FTIR datasets, the proposed method substantially decreased the number of wavelengths needed to categorize, and increased the classification accuracy. From a practical perspective, the proposed method provides investigative forces with valuable information towards monitoring illegal production of drugs and medicines. In addition, focusing on a reduced subset of wavelengths allows the development of portable devices capable of testing the authenticity of samples during police checking events, avoiding the need for later laboratorial analyses and reducing equipment expenses. Theoretically, the proposed GA-based approach yields more refined solutions than the current methods relying on interval approaches, which tend to insert irrelevant wavelengths in the retained intervals. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.
Gray bootstrap method for estimating frequency-varying random vibration signals with small samples

Directory of Open Access Journals (Sweden)

Wang Yanqing

2014-04-01

Full Text Available During environment testing, the estimation of random vibration signals (RVS is an important technique for the airborne platform safety and reliability. However, the available methods including extreme value envelope method (EVEM, statistical tolerances method (STM and improved statistical tolerance method (ISTM require large samples and typical probability distribution. Moreover, the frequency-varying characteristic of RVS is usually not taken into account. Gray bootstrap method (GBM is proposed to solve the problem of estimating frequency-varying RVS with small samples. Firstly, the estimated indexes are obtained including the estimated interval, the estimated uncertainty, the estimated value, the estimated error and estimated reliability. In addition, GBM is applied to estimating the single flight testing of certain aircraft. At last, in order to evaluate the estimated performance, GBM is compared with bootstrap method (BM and gray method (GM in testing analysis. The result shows that GBM has superiority for estimating dynamic signals with small samples and estimated reliability is proved to be 100% at the given confidence level.
Comparing the performance of cluster random sampling and integrated threshold mapping for targeting trachoma control, using computer simulation.

Directory of Open Access Journals (Sweden)

Jennifer L Smith

Full Text Available Implementation of trachoma control strategies requires reliable district-level estimates of trachomatous inflammation-follicular (TF, generally collected using the recommended gold-standard cluster randomized surveys (CRS. Integrated Threshold Mapping (ITM has been proposed as an integrated and cost-effective means of rapidly surveying trachoma in order to classify districts according to treatment thresholds. ITM differs from CRS in a number of important ways, including the use of a school-based sampling platform for children aged 1-9 and a different age distribution of participants. This study uses computerised sampling simulations to compare the performance of these survey designs and evaluate the impact of varying key parameters.Realistic pseudo gold standard data for 100 districts were generated that maintained the relative risk of disease between important sub-groups and incorporated empirical estimates of disease clustering at the household, village and district level. To simulate the different sampling approaches, 20 clusters were selected from each district, with individuals sampled according to the protocol for ITM and CRS. Results showed that ITM generally under-estimated the true prevalence of TF over a range of epidemiological settings and introduced more district misclassification according to treatment thresholds than did CRS. However, the extent of underestimation and resulting misclassification was found to be dependent on three main factors: (i the district prevalence of TF; (ii the relative risk of TF between enrolled and non-enrolled children within clusters; and (iii the enrollment rate in schools.Although in some contexts the two methodologies may be equivalent, ITM can introduce a bias-dependent shift as prevalence of TF increases, resulting in a greater risk of misclassification around treatment thresholds. In addition to strengthening the evidence base around choice of trachoma survey methodologies, this study illustrates
An active learning representative subset selection method using net analyte signal

Science.gov (United States)

He, Zhonghai; Ma, Zhenhe; Luan, Jingmin; Cai, Xi

2018-05-01

To guarantee accurate predictions, representative samples are needed when building a calibration model for spectroscopic measurements. However, in general, it is not known whether a sample is representative prior to measuring its concentration, which is both time-consuming and expensive. In this paper, a method to determine whether a sample should be selected into a calibration set is presented. The selection is based on the difference of Euclidean norm of net analyte signal (NAS) vector between the candidate and existing samples. First, the concentrations and spectra of a group of samples are used to compute the projection matrix, NAS vector, and scalar values. Next, the NAS vectors of candidate samples are computed by multiplying projection matrix with spectra of samples. Scalar value of NAS is obtained by norm computation. The distance between the candidate set and the selected set is computed, and samples with the largest distance are added to selected set sequentially. Last, the concentration of the analyte is measured such that the sample can be used as a calibration sample. Using a validation test, it is shown that the presented method is more efficient than random selection. As a result, the amount of time and money spent on reference measurements is greatly reduced.
Acute stress symptoms during the second Lebanon war in a random sample of Israeli citizens.

Science.gov (United States)

Cohen, Miri; Yahav, Rivka

2008-02-01

The aims of this study were to assess prevalence of acute stress disorder (ASD) and acute stress symptoms (ASS) in Israel during the second Lebanon war. A telephone survey was conducted in July 2006 of a random sample of 235 residents of northern Israel, who were subjected to missile attacks, and of central Israel, who were not subjected to missile attacks. Results indicate that ASS scores were higher in the northern respondents; 6.8% of the northern sample and 3.9% of the central sample met ASD criteria. Appearance of each symptom ranged from 15.4% for dissociative to 88.4% for reexperiencing, with significant differences between northern and central respondents only for reexperiencing and arousal. A low ASD rate and a moderate difference between areas subjected and not subjected to attack were found.
Subrandom methods for multidimensional nonuniform sampling.

Science.gov (United States)

Worley, Bradley

2016-08-01

Methods of nonuniform sampling that utilize pseudorandom number sequences to select points from a weighted Nyquist grid are commonplace in biomolecular NMR studies, due to the beneficial incoherence introduced by pseudorandom sampling. However, these methods require the specification of a non-arbitrary seed number in order to initialize a pseudorandom number generator. Because the performance of pseudorandom sampling schedules can substantially vary based on seed number, this can complicate the task of routine data collection. Approaches such as jittered sampling and stochastic gap sampling are effective at reducing random seed dependence of nonuniform sampling schedules, but still require the specification of a seed number. This work formalizes the use of subrandom number sequences in nonuniform sampling as a means of seed-independent sampling, and compares the performance of three subrandom methods to their pseudorandom counterparts using commonly applied schedule performance metrics. Reconstruction results using experimental datasets are also provided to validate claims made using these performance metrics. Copyright © 2016 Elsevier Inc. All rights reserved.
Prevalence and correlates of problematic smartphone use in a large random sample of Chinese undergraduates.

Science.gov (United States)

Long, Jiang; Liu, Tie-Qiao; Liao, Yan-Hui; Qi, Chang; He, Hao-Yu; Chen, Shu-Bao; Billieux, Joël

2016-11-17

Smartphones are becoming a daily necessity for most undergraduates in Mainland China. Because the present scenario of problematic smartphone use (PSU) is largely unexplored, in the current study we aimed to estimate the prevalence of PSU and to screen suitable predictors for PSU among Chinese undergraduates in the framework of the stress-coping theory. A sample of 1062 undergraduate smartphone users was recruited by means of the stratified cluster random sampling strategy between April and May 2015. The Problematic Cellular Phone Use Questionnaire was used to identify PSU. We evaluated five candidate risk factors for PSU by using logistic regression analysis while controlling for demographic characteristics and specific features of smartphone use. The prevalence of PSU among Chinese undergraduates was estimated to be 21.3%. The risk factors for PSU were majoring in the humanities, high monthly income from the family (≥1500 RMB), serious emotional symptoms, high perceived stress, and perfectionism-related factors (high doubts about actions, high parental expectations). PSU among undergraduates appears to be ubiquitous and thus constitutes a public health issue in Mainland China. Although further longitudinal studies are required to test whether PSU is a transient phenomenon or a chronic and progressive condition, our study successfully identified socio-demographic and psychological risk factors for PSU. These results, obtained from a random and thus representative sample of undergraduates, opens up new avenues in terms of prevention and regulation policies.

Randomized Comparison of Two Vaginal Self-Sampling Methods for Human Papillomavirus Detection: Dry Swab versus FTA Cartridge

OpenAIRE

Catarino, Rosa; Vassilakos, Pierre; Bilancioni, Aline; Vanden Eynde, Mathieu; Meyer-Hamme, Ulrike; Menoud, Pierre-Alain; Guerry, Fr?d?ric; Petignat, Patrick

2015-01-01

Background Human papillomavirus (HPV) self-sampling (self-HPV) is valuable in cervical cancer screening. HPV testing is usually performed on physician-collected cervical smears stored in liquid-based medium. Dry filters and swabs are an alternative. We evaluated the adequacy of self-HPV using two dry storage and transport devices, the FTA cartridge and swab. Methods A total of 130 women performed two consecutive self-HPV samples. Randomization determined which of the two tests was performed f...
Random Sampling with Interspike-Intervals of the Exponential Integrate and Fire Neuron: A Computational Interpretation of UP-States.

Directory of Open Access Journals (Sweden)

Andreas Steimer

Full Text Available Oscillations between high and low values of the membrane potential (UP and DOWN states respectively are an ubiquitous feature of cortical neurons during slow wave sleep and anesthesia. Nevertheless, a surprisingly small number of quantitative studies have been conducted only that deal with this phenomenon's implications for computation. Here we present a novel theory that explains on a detailed mathematical level the computational benefits of UP states. The theory is based on random sampling by means of interspike intervals (ISIs of the exponential integrate and fire (EIF model neuron, such that each spike is considered a sample, whose analog value corresponds to the spike's preceding ISI. As we show, the EIF's exponential sodium current, that kicks in when balancing a noisy membrane potential around values close to the firing threshold, leads to a particularly simple, approximative relationship between the neuron's ISI distribution and input current. Approximation quality depends on the frequency spectrum of the current and is improved upon increasing the voltage baseline towards threshold. Thus, the conceptually simpler leaky integrate and fire neuron that is missing such an additional current boost performs consistently worse than the EIF and does not improve when voltage baseline is increased. For the EIF in contrast, the presented mechanism is particularly effective in the high-conductance regime, which is a hallmark feature of UP-states. Our theoretical results are confirmed by accompanying simulations, which were conducted for input currents of varying spectral composition. Moreover, we provide analytical estimations of the range of ISI distributions the EIF neuron can sample from at a given approximation level. Such samples may be considered by any algorithmic procedure that is based on random sampling, such as Markov Chain Monte Carlo or message-passing methods. Finally, we explain how spike-based random sampling relates to existing
Random Sampling with Interspike-Intervals of the Exponential Integrate and Fire Neuron: A Computational Interpretation of UP-States.

Science.gov (United States)

Steimer, Andreas; Schindler, Kaspar

2015-01-01

Oscillations between high and low values of the membrane potential (UP and DOWN states respectively) are an ubiquitous feature of cortical neurons during slow wave sleep and anesthesia. Nevertheless, a surprisingly small number of quantitative studies have been conducted only that deal with this phenomenon's implications for computation. Here we present a novel theory that explains on a detailed mathematical level the computational benefits of UP states. The theory is based on random sampling by means of interspike intervals (ISIs) of the exponential integrate and fire (EIF) model neuron, such that each spike is considered a sample, whose analog value corresponds to the spike's preceding ISI. As we show, the EIF's exponential sodium current, that kicks in when balancing a noisy membrane potential around values close to the firing threshold, leads to a particularly simple, approximative relationship between the neuron's ISI distribution and input current. Approximation quality depends on the frequency spectrum of the current and is improved upon increasing the voltage baseline towards threshold. Thus, the conceptually simpler leaky integrate and fire neuron that is missing such an additional current boost performs consistently worse than the EIF and does not improve when voltage baseline is increased. For the EIF in contrast, the presented mechanism is particularly effective in the high-conductance regime, which is a hallmark feature of UP-states. Our theoretical results are confirmed by accompanying simulations, which were conducted for input currents of varying spectral composition. Moreover, we provide analytical estimations of the range of ISI distributions the EIF neuron can sample from at a given approximation level. Such samples may be considered by any algorithmic procedure that is based on random sampling, such as Markov Chain Monte Carlo or message-passing methods. Finally, we explain how spike-based random sampling relates to existing computational
Presence of psychoactive substances in oral fluid from randomly selected drivers in Denmark

DEFF Research Database (Denmark)

Simonsen, K. Wiese; Steentoft, A.; Hels, Tove

2012-01-01

. The percentage of drivers positive for medicinal drugs above the Danish legal concentration limit was 0.4%; while, 0.3% of the drivers tested positive for one or more illicit drug at concentrations exceeding the Danish legal limit. Tetrahydrocannabinol, cocaine, and amphetamine were the most frequent illicit......This roadside study is the Danish part of the EU-project DRUID (Driving under the Influence of Drugs, Alcohol, and Medicines) and included three representative regions in Denmark. Oral fluid samples (n = 3002) were collected randomly from drivers using a sampling scheme stratified by time, season......, and road type. The oral fluid samples were screened for 29 illegal and legal psychoactive substances and metabolites as well as ethanol. Fourteen (0.5%) drivers were positive for ethanol alone or in combination with drugs) at concentrations above 0.53 g/l (0.5 mg/g), which is the Danish legal limit...
Errors in causal inference: an organizational schema for systematic error and random error.

Science.gov (United States)

Suzuki, Etsuji; Tsuda, Toshihide; Mitsuhashi, Toshiharu; Mansournia, Mohammad Ali; Yamamoto, Eiji

2016-11-01

To provide an organizational schema for systematic error and random error in estimating causal measures, aimed at clarifying the concept of errors from the perspective of causal inference. We propose to divide systematic error into structural error and analytic error. With regard to random error, our schema shows its four major sources: nondeterministic counterfactuals, sampling variability, a mechanism that generates exposure events and measurement variability. Structural error is defined from the perspective of counterfactual reasoning and divided into nonexchangeability bias (which comprises confounding bias and selection bias) and measurement bias. Directed acyclic graphs are useful to illustrate this kind of error. Nonexchangeability bias implies a lack of "exchangeability" between the selected exposed and unexposed groups. A lack of exchangeability is not a primary concern of measurement bias, justifying its separation from confounding bias and selection bias. Many forms of analytic errors result from the small-sample properties of the estimator used and vanish asymptotically. Analytic error also results from wrong (misspecified) statistical models and inappropriate statistical methods. Our organizational schema is helpful for understanding the relationship between systematic error and random error from a previously less investigated aspect, enabling us to better understand the relationship between accuracy, validity, and precision. Copyright © 2016 Elsevier Inc. All rights reserved.
Decomposing the Gender Wage Gap in the Netherlands with Sample Selection Adjustments

NARCIS (Netherlands)

Albrecht, James; Vuuren, van Aico; Vroman, Susan

2004-01-01

In this paper, we use quantile regression decomposition methods to analyzethe gender gap between men and women who work full time in the Nether-lands. Because the fraction of women working full time in the Netherlands isquite low, sample selection is a serious issue. In addition to shedding light
Early prevention of antisocial personality: long-term follow-up of two randomized controlled trials comparing indicated and selective approaches.

Science.gov (United States)

Scott, Stephen; Briskman, Jackie; O'Connor, Thomas G

2014-06-01

Antisocial personality is a common adult problem that imposes a major public health burden, but for which there is no effective treatment. Affected individuals exhibit persistent antisocial behavior and pervasive antisocial character traits, such as irritability, manipulativeness, and lack of remorse. Prevention of antisocial personality in childhood has been advocated, but evidence for effective interventions is lacking. The authors conducted two follow-up studies of randomized trials of group parent training. One involved 120 clinic-referred 3- to 7-year-olds with severe antisocial behavior for whom treatment was indicated, 93 of whom were reassessed between ages 10 and 17. The other involved 109 high-risk 4- to 6-year-olds with elevated antisocial behavior who were selectively screened from the community, 90 of whom were reassessed between ages 9 and 13. The primary psychiatric outcome measures were the two elements of antisocial personality, namely, antisocial behavior (assessed by a diagnostic interview) and antisocial character traits (assessed by a questionnaire). Also assessed were reading achievement (an important domain of youth functioning at work) and parent-adolescent relationship quality. In the indicated sample, both elements of antisocial personality were improved in the early intervention group at long-term follow-up compared with the control group (antisocial behavior: odds ratio of oppositional defiant disorder=0.20, 95% CI=0.06, 0.69; antisocial character traits: B=-4.41, 95% CI=-1.12, -8.64). Additionally, reading ability improved (B=9.18, 95% CI=0.58, 18.0). Parental expressed emotion was warmer (B=0.86, 95% CI=0.20, 1.41) and supervision was closer (B=-0.43, 95% CI=-0.11, -0.75), but direct observation of parenting showed no differences. Teacher-rated and self-rated antisocial behavior were unchanged. In contrast, in the selective high-risk sample, early intervention was not associated with improved long-term outcomes. Early intervention with
The relationship between blood viscosity and blood pressure in a random sample of the population aged 55 to 74 years.

Science.gov (United States)

Fowkes, F G; Lowe, G D; Rumley, A; Lennie, S E; Smith, F B; Donnan, P T

1993-05-01

Blood viscosity is elevated in hypertensive subjects, but the association of viscosity with arterial blood pressure in the general population, and the influence of social, lifestyle and disease characteristics on this association, are not established. In the Edinburgh Artery Study, 1592 men and women aged 55-74 years selected randomly from the general population attended a university clinic. A fasting blood sample was taken for the measurement of blood viscosity and its major determinants (haematocrit, plasma viscosity and fibrinogen). Systolic pressure was related univariately to blood viscosity (P viscosity (P index. Diastolic pressure was related univariately to blood viscosity (P viscosity (P viscosity and systolic pressure was confined to males. Blood viscosity was associated equally with systolic and diastolic pressures in males, and remained independently related on multivariate analysis adjusting for age, sex, body mass index, social class, smoking, alcohol intake, exercise, angina, HDL and non-HDL cholesterol, diabetes mellitus, plasma viscosity, fibrinogen, and haematocrit.
Semiparametric efficient and robust estimation of an unknown symmetric population under arbitrary sample selection bias

KAUST Repository

Ma, Yanyuan

2013-09-01

We propose semiparametric methods to estimate the center and shape of a symmetric population when a representative sample of the population is unavailable due to selection bias. We allow an arbitrary sample selection mechanism determined by the data collection procedure, and we do not impose any parametric form on the population distribution. Under this general framework, we construct a family of consistent estimators of the center that is robust to population model misspecification, and we identify the efficient member that reaches the minimum possible estimation variance. The asymptotic properties and finite sample performance of the estimation and inference procedures are illustrated through theoretical analysis and simulations. A data example is also provided to illustrate the usefulness of the methods in practice. © 2013 American Statistical Association.
Discriminative motif discovery via simulated evolution and random under-sampling.

Directory of Open Access Journals (Sweden)

Tao Song

Full Text Available Conserved motifs in biological sequences are closely related to their structure and functions. Recently, discriminative motif discovery methods have attracted more and more attention. However, little attention has been devoted to the data imbalance problem, which is one of the main reasons affecting the performance of the discriminative models. In this article, a simulated evolution method is applied to solve the multi-class imbalance problem at the stage of data preprocessing, and at the stage of Hidden Markov Models (HMMs training, a random under-sampling method is introduced for the imbalance between the positive and negative datasets. It is shown that, in the task of discovering targeting motifs of nine subcellular compartments, the motifs found by our method are more conserved than the methods without considering data imbalance problem and recover the most known targeting motifs from Minimotif Miner and InterPro. Meanwhile, we use the found motifs to predict protein subcellular localization and achieve higher prediction precision and recall for the minority classes.
Discriminative motif discovery via simulated evolution and random under-sampling.

Science.gov (United States)

Song, Tao; Gu, Hong

2014-01-01

Conserved motifs in biological sequences are closely related to their structure and functions. Recently, discriminative motif discovery methods have attracted more and more attention. However, little attention has been devoted to the data imbalance problem, which is one of the main reasons affecting the performance of the discriminative models. In this article, a simulated evolution method is applied to solve the multi-class imbalance problem at the stage of data preprocessing, and at the stage of Hidden Markov Models (HMMs) training, a random under-sampling method is introduced for the imbalance between the positive and negative datasets. It is shown that, in the task of discovering targeting motifs of nine subcellular compartments, the motifs found by our method are more conserved than the methods without considering data imbalance problem and recover the most known targeting motifs from Minimotif Miner and InterPro. Meanwhile, we use the found motifs to predict protein subcellular localization and achieve higher prediction precision and recall for the minority classes.
Using Random Forests to Select Optimal Input Variables for Short-Term Wind Speed Forecasting Models

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Hui Wang

2017-10-01

Full Text Available Achieving relatively high-accuracy short-term wind speed forecasting estimates is a precondition for the construction and grid-connected operation of wind power forecasting systems for wind farms. Currently, most research is focused on the structure of forecasting models and does not consider the selection of input variables, which can have significant impacts on forecasting performance. This paper presents an input variable selection method for wind speed forecasting models. The candidate input variables for various leading periods are selected and random forests (RF is employed to evaluate the importance of all variable as features. The feature subset with the best evaluation performance is selected as the optimal feature set. Then, kernel-based extreme learning machine is constructed to evaluate the performance of input variables selection based on RF. The results of the case study show that by removing the uncorrelated and redundant features, RF effectively extracts the most strongly correlated set of features from the candidate input variables. By finding the optimal feature combination to represent the original information, RF simplifies the structure of the wind speed forecasting model, shortens the training time required, and substantially improves the model’s accuracy and generalization ability, demonstrating that the input variables selected by RF are effective.
Application of the Sampling Selection Technique in Approaching Financial Audit

Directory of Open Access Journals (Sweden)

Victor Munteanu

2018-03-01

Full Text Available In his professional approach, the financial auditor has a wide range of working techniques, including selection techniques. They are applied depending on the nature of the information available to the financial auditor, the manner in which they are presented - paper or electronic format, and, last but not least, the time available. Several techniques are applied, successively or in parallel, to increase the safety of the expressed opinion and to provide the audit report with a solid basis of information. Sampling is used in the phase of control or clarification of the identified error. The main purpose is to corroborate or measure the degree of risk detected following a pertinent analysis. Since the auditor does not have time or means to thoroughly rebuild the information, the sampling technique can provide an effective response to the need for valorization.
Correlations fo Sc, rare earths and other elements in selected rock samples from Arrua-i

Energy Technology Data Exchange (ETDEWEB)

Facetti, J F; Prats, M [Asuncion Nacional Univ. (Paraguay). Inst. de Ciencias

1972-01-01

The Sc and Eu contents in selected rocks samples from the stock of Arrua-i have been determined and correlations established with other elements and with the relative amount of some rare earths. These correlations suggest metasomatic phenomena for the formation of the rock samples.
Correlations fo Sc, rare earths and other elements in selected rock samples from Arrua-i

International Nuclear Information System (INIS)

Facetti, J.F.; Prats, M.

1972-01-01

The Sc and Eu contents in selected rocks samples from the stock of Arrua-i have been determined and correlations established with other elements and with the relative amount of some rare earths. These correlations suggest metasomatic phenomena for the formation of the rock samples
Assessing sample representativeness in randomized controlled trials: application to the National Institute of Drug Abuse Clinical Trials Network.

Science.gov (United States)

Susukida, Ryoko; Crum, Rosa M; Stuart, Elizabeth A; Ebnesajjad, Cyrus; Mojtabai, Ramin

2016-07-01

To compare the characteristics of individuals participating in randomized controlled trials (RCTs) of treatments of substance use disorder (SUD) with individuals receiving treatment in usual care settings, and to provide a summary quantitative measure of differences between characteristics of these two groups of individuals using propensity score methods. Design Analyses using data from RCT samples from the National Institute of Drug Abuse Clinical Trials Network (CTN) and target populations of patients drawn from the Treatment Episodes Data Set-Admissions (TEDS-A). Settings Multiple clinical trial sites and nation-wide usual SUD treatment settings in the United States. A total of 3592 individuals from 10 CTN samples and 1 602 226 individuals selected from TEDS-A between 2001 and 2009. Measurements The propensity scores for enrolling in the RCTs were computed based on the following nine observable characteristics: sex, race/ethnicity, age, education, employment status, marital status, admission to treatment through criminal justice, intravenous drug use and the number of prior treatments. Findings The proportion of those with ≥ 12 years of education and the proportion of those who had full-time jobs were significantly higher among RCT samples than among target populations (in seven and nine trials, respectively, at P difference in the mean propensity scores between the RCTs and the target population was 1.54 standard deviations and was statistically significant at P different from individuals receiving treatment in usual care settings. Notably, RCT participants tend to have more years of education and a greater likelihood of full-time work compared with people receiving care in usual care settings. © 2016 Society for the Study of Addiction.
Optimal Selection of the Sampling Interval for Estimation of Modal Parameters by an ARMA- Model

DEFF Research Database (Denmark)

Kirkegaard, Poul Henning

1993-01-01

Optimal selection of the sampling interval for estimation of the modal parameters by an ARMA-model for a white noise loaded structure modelled as a single degree of- freedom linear mechanical system is considered. An analytical solution for an optimal uniform sampling interval, which is optimal...
Accounting for randomness in measurement and sampling in studying cancer cell population dynamics.

Science.gov (United States)

Ghavami, Siavash; Wolkenhauer, Olaf; Lahouti, Farshad; Ullah, Mukhtar; Linnebacher, Michael

2014-10-01

Knowing the expected temporal evolution of the proportion of different cell types in sample tissues gives an indication about the progression of the disease and its possible response to drugs. Such systems have been modelled using Markov processes. We here consider an experimentally realistic scenario in which transition probabilities are estimated from noisy cell population size measurements. Using aggregated data of FACS measurements, we develop MMSE and ML estimators and formulate two problems to find the minimum number of required samples and measurements to guarantee the accuracy of predicted population sizes. Our numerical results show that the convergence mechanism of transition probabilities and steady states differ widely from the real values if one uses the standard deterministic approach for noisy measurements. This provides support for our argument that for the analysis of FACS data one should consider the observed state as a random variable. The second problem we address is about the consequences of estimating the probability of a cell being in a particular state from measurements of small population of cells. We show how the uncertainty arising from small sample sizes can be captured by a distribution for the state probability.
Relationship between accuracy and number of samples on statistical quantity and contour map of environmental gamma-ray dose rate. Example of random sampling

International Nuclear Information System (INIS)

Matsuda, Hideharu; Minato, Susumu

2002-01-01

The accuracy of statistical quantity like the mean value and contour map obtained by measurement of the environmental gamma-ray dose rate was evaluated by random sampling of 5 different model distribution maps made by the mean slope, -1.3, of power spectra calculated from the actually measured values. The values were derived from 58 natural gamma dose rate data reported worldwide ranging in the means of 10-100 Gy/h rates and 10 -3 -10 7 km 2 areas. The accuracy of the mean value was found around ±7% even for 60 or 80 samplings (the most frequent number) and the standard deviation had the accuracy less than 1/4-1/3 of the means. The correlation coefficient of the frequency distribution was found 0.860 or more for 200-400 samplings (the most frequent number) but of the contour map, 0.502-0.770. (K.H.)
40 CFR 1051.310 - How must I select vehicles or engines for production-line testing?

Science.gov (United States)

2010-07-01

... engines, randomly select and test one more engine. Then, calculate the required sample size for the model... distribution. σ = Test sample standard deviation (see paragraph (c)(2) of this section). x = Mean of emission... sample size for the model year as described in paragraph (c) of this section. (3) In the first test...

Bias in random forest variable importance measures: Illustrations, sources and a solution

Directory of Open Access Journals (Sweden)

Hothorn Torsten

2007-01-01

Full Text Available Abstract Background Variable importance measures for random forests have been receiving increased attention as a means of variable selection in many classification tasks in bioinformatics and related scientific fields, for instance to select a subset of genetic markers relevant for the prediction of a certain disease. We show that random forest variable importance measures are a sensible means for variable selection in many applications, but are not reliable in situations where potential predictor variables vary in their scale of measurement or their number of categories. This is particularly important in genomics and computational biology, where predictors often include variables of different types, for example when predictors include both sequence data and continuous variables such as folding energy, or when amino acid sequence data show different numbers of categories. Results Simulation studies are presented illustrating that, when random forest variable importance measures are used with data of varying types, the results are misleading because suboptimal predictor variables may be artificially preferred in variable selection. The two mechanisms underlying this deficiency are biased variable selection in the individual classification trees used to build the random forest on one hand, and effects induced by bootstrap sampling with replacement on the other hand. Conclusion We propose to employ an alternative implementation of random forests, that provides unbiased variable selection in the individual classification trees. When this method is applied using subsampling without replacement, the resulting variable importance measures can be used reliably for variable selection even in situations where the potential predictor variables vary in their scale of measurement or their number of categories. The usage of both random forest algorithms and their variable importance measures in the R system for statistical computing is illustrated and
Estimating the residential demand function for natural gas in Seoul with correction for sample selection bias

International Nuclear Information System (INIS)

Yoo, Seung-Hoon; Lim, Hea-Jin; Kwak, Seung-Jun

2009-01-01

Over the last twenty years, the consumption of natural gas in Korea has increased dramatically. This increase has mainly resulted from the rise of consumption in the residential sector. The main objective of the study is to estimate households' demand function for natural gas by applying a sample selection model using data from a survey of households in Seoul. The results show that there exists a selection bias in the sample and that failure to correct for sample selection bias distorts the mean estimate, of the demand for natural gas, downward by 48.1%. In addition, according to the estimation results, the size of the house, the dummy variable for dwelling in an apartment, the dummy variable for having a bed in an inner room, and the household's income all have positive relationships with the demand for natural gas. On the other hand, the size of the family and the price of gas negatively contribute to the demand for natural gas. (author)
Acrylamide exposure among Turkish toddlers from selected cereal-based baby food samples.

Science.gov (United States)

Cengiz, Mehmet Fatih; Gündüz, Cennet Pelin Boyacı

2013-10-01

In this study, acrylamide exposure from selected cereal-based baby food samples was investigated among toddlers aged 1-3 years in Turkey. The study contained three steps. The first step was collecting food consumption data and toddlers' physical properties, such as gender, age and body weight, using a questionnaire given to parents by a trained interviewer between January and March 2012. The second step was determining the acrylamide levels in food samples that were reported on by the parents in the questionnaire, using a gas chromatography-mass spectrometry (GC-MS) method. The last step was combining the determined acrylamide levels in selected food samples with individual food consumption and body weight data using a deterministic approach to estimate the acrylamide exposure levels. The mean acrylamide levels of baby biscuits, breads, baby bread-rusks, crackers, biscuits, breakfast cereals and powdered cereal-based baby foods were 153, 225, 121, 604, 495, 290 and 36 μg/kg, respectively. The minimum, mean and maximum acrylamide exposures were estimated to be 0.06, 1.43 and 6.41 μg/kg BW per day, respectively. The foods that contributed to acrylamide exposure were aligned from high to low as bread, crackers, biscuits, baby biscuits, powdered cereal-based baby foods, baby bread-rusks and breakfast cereals. Copyright © 2013 Elsevier Ltd. All rights reserved.
A bayesian hierarchical model for classification with selection of functional predictors.

Science.gov (United States)

Zhu, Hongxiao; Vannucci, Marina; Cox, Dennis D

2010-06-01

In functional data classification, functional observations are often contaminated by various systematic effects, such as random batch effects caused by device artifacts, or fixed effects caused by sample-related factors. These effects may lead to classification bias and thus should not be neglected. Another issue of concern is the selection of functions when predictors consist of multiple functions, some of which may be redundant. The above issues arise in a real data application where we use fluorescence spectroscopy to detect cervical precancer. In this article, we propose a Bayesian hierarchical model that takes into account random batch effects and selects effective functions among multiple functional predictors. Fixed effects or predictors in nonfunctional form are also included in the model. The dimension of the functional data is reduced through orthonormal basis expansion or functional principal components. For posterior sampling, we use a hybrid Metropolis-Hastings/Gibbs sampler, which suffers slow mixing. An evolutionary Monte Carlo algorithm is applied to improve the mixing. Simulation and real data application show that the proposed model provides accurate selection of functional predictors as well as good classification.
HICOSMO - cosmology with a complete sample of galaxy clusters - I. Data analysis, sample selection and luminosity-mass scaling relation

Science.gov (United States)

Schellenberger, G.; Reiprich, T. H.

2017-08-01

The X-ray regime, where the most massive visible component of galaxy clusters, the intracluster medium, is visible, offers directly measured quantities, like the luminosity, and derived quantities, like the total mass, to characterize these objects. The aim of this project is to analyse a complete sample of galaxy clusters in detail and constrain cosmological parameters, like the matter density, Ωm, or the amplitude of initial density fluctuations, σ8. The purely X-ray flux-limited sample (HIFLUGCS) consists of the 64 X-ray brightest galaxy clusters, which are excellent targets to study the systematic effects, that can bias results. We analysed in total 196 Chandra observations of the 64 HIFLUGCS clusters, with a total exposure time of 7.7 Ms. Here, we present our data analysis procedure (including an automated substructure detection and an energy band optimization for surface brightness profile analysis) that gives individually determined, robust total mass estimates. These masses are tested against dynamical and Planck Sunyaev-Zeldovich (SZ) derived masses of the same clusters, where good overall agreement is found with the dynamical masses. The Planck SZ masses seem to show a mass-dependent bias to our hydrostatic masses; possible biases in this mass-mass comparison are discussed including the Planck selection function. Furthermore, we show the results for the (0.1-2.4) keV luminosity versus mass scaling relation. The overall slope of the sample (1.34) is in agreement with expectations and values from literature. Splitting the sample into galaxy groups and clusters reveals, even after a selection bias correction, that galaxy groups exhibit a significantly steeper slope (1.88) compared to clusters (1.06).
A Uniformly Selected Sample of Low-mass Black Holes in Seyfert 1 Galaxies. II. The SDSS DR7 Sample

Science.gov (United States)

Liu, He-Yang; Yuan, Weimin; Dong, Xiao-Bo; Zhou, Hongyan; Liu, Wen-Juan

2018-04-01

A new sample of 204 low-mass black holes (LMBHs) in active galactic nuclei (AGNs) is presented with black hole masses in the range of (1–20) × 105 M ⊙. The AGNs are selected through a systematic search among galaxies in the Seventh Data Release (DR7) of the Sloan Digital Sky Survey (SDSS), and careful analyses of their optical spectra and precise measurement of spectral parameters. Combining them with our previous sample selected from SDSS DR4 makes it the largest LMBH sample so far, totaling over 500 objects. Some of the statistical properties of the combined LMBH AGN sample are briefly discussed in the context of exploring the low-mass end of the AGN population. Their X-ray luminosities follow the extension of the previously known correlation with the [O III] luminosity. The effective optical-to-X-ray spectral indices α OX, albeit with a large scatter, are broadly consistent with the extension of the relation with the near-UV luminosity L 2500 Å. Interestingly, a correlation of α OX with black hole mass is also found, with α OX being statistically flatter (stronger X-ray relative to optical) for lower black hole masses. Only 26 objects, mostly radio loud, were detected in radio at 20 cm in the FIRST survey, giving a radio-loud fraction of 4%. The host galaxies of LMBHs have stellar masses in the range of 108.8–1012.4 M ⊙ and optical colors typical of Sbc spirals. They are dominated by young stellar populations that seem to have undergone continuous star formation history.
75 FR 17943 - Notice of Proposed Information Collection for Public Comment: Section 8 Random Digit Dialing Fair...

Science.gov (United States)

2010-04-08

... Management and Property Disposition programs; payment standards for the Rental Voucher program; and any other... select statistically random samples of telephone numbers to locate certain types of rental housing units...
Obscured AGN at z similar to 1 from the zCOSMOS-Bright Survey : I. Selection and optical properties of a [Ne v]-selected sample

NARCIS (Netherlands)

Mignoli, M.; Vignali, C.; Gilli, R.; Comastri, A.; Zamorani, G.; Bolzonella, M.; Bongiorno, A.; Lamareille, F.; Nair, P.; Pozzetti, L.; Lilly, S. J.; Carollo, C. M.; Contini, T.; Kneib, J. -P.; Le Fevre, O.; Mainieri, V.; Renzini, A.; Scodeggio, M.; Bardelli, S.; Caputi, K.; Cucciati, O.; de la Torre, S.; de Ravel, L.; Franzetti, P.; Garilli, B.; Iovino, A.; Kampczyk, P.; Knobel, C.; Kovac, K.; Le Borgne, J. -F.; Le Brun, V.; Maier, C.; Pello, R.; Peng, Y.; Montero, E. Perez; Presotto, V.; Silverman, J. D.; Tanaka, M.; Tasca, L.; Tresse, L.; Vergani, D.; Zucca, E.; Bordoloi, R.; Cappi, A.; Cimatti, A.; Koekemoer, A. M.; McCracken, H. J.; Moresco, M.; Welikala, N.

Aims. The application of multi-wavelength selection techniques is essential for obtaining a complete and unbiased census of active galactic nuclei (AGN). We present here a method for selecting z similar to 1 obscured AGN from optical spectroscopic surveys. Methods. A sample of 94 narrow-line AGN
Extending cluster lot quality assurance sampling designs for surveillance programs.

Science.gov (United States)

Hund, Lauren; Pagano, Marcello

2014-07-20

Lot quality assurance sampling (LQAS) has a long history of applications in industrial quality control. LQAS is frequently used for rapid surveillance in global health settings, with areas classified as poor or acceptable performance on the basis of the binary classification of an indicator. Historically, LQAS surveys have relied on simple random samples from the population; however, implementing two-stage cluster designs for surveillance sampling is often more cost-effective than simple random sampling. By applying survey sampling results to the binary classification procedure, we develop a simple and flexible nonparametric procedure to incorporate clustering effects into the LQAS sample design to appropriately inflate the sample size, accommodating finite numbers of clusters in the population when relevant. We use this framework to then discuss principled selection of survey design parameters in longitudinal surveillance programs. We apply this framework to design surveys to detect rises in malnutrition prevalence in nutrition surveillance programs in Kenya and South Sudan, accounting for clustering within villages. By combining historical information with data from previous surveys, we design surveys to detect spikes in the childhood malnutrition rate. Copyright © 2014 John Wiley & Sons, Ltd.
Sampling pig farms at the abattoir in a cross-sectional study - Evaluation of a sampling method.

Science.gov (United States)

Birkegård, Anna Camilla; Halasa, Tariq; Toft, Nils

2017-09-15

procedure was reproducible with results comparable to the collected sample. However, the sampling procedure favoured sampling of large farms. Furthermore, both under-sampled and over-sampled areas were found using scan statistics. In conclusion, sampling conducted at abattoirs can provide a spatially representative sample. Hence it is a possible cost-effective alternative to simple random sampling. However, it is important to assess the properties of the resulting sample so that any potential selection bias can be addressed when reporting the findings. Copyright © 2017 Elsevier B.V. All rights reserved.
Randomized clinical trials in dentistry: Risks of bias, risks of random errors, reporting quality, and methodologic quality over the years 1955-2013.

Directory of Open Access Journals (Sweden)

Humam Saltaji

Full Text Available To examine the risks of bias, risks of random errors, reporting quality, and methodological quality of randomized clinical trials of oral health interventions and the development of these aspects over time.We included 540 randomized clinical trials from 64 selected systematic reviews. We extracted, in duplicate, details from each of the selected randomized clinical trials with respect to publication and trial characteristics, reporting and methodologic characteristics, and Cochrane risk of bias domains. We analyzed data using logistic regression and Chi-square statistics.Sequence generation was assessed to be inadequate (at unclear or high risk of bias in 68% (n = 367 of the trials, while allocation concealment was inadequate in the majority of trials (n = 464; 85.9%. Blinding of participants and blinding of the outcome assessment were judged to be inadequate in 28.5% (n = 154 and 40.5% (n = 219 of the trials, respectively. A sample size calculation before the initiation of the study was not performed/reported in 79.1% (n = 427 of the trials, while the sample size was assessed as adequate in only 17.6% (n = 95 of the trials. Two thirds of the trials were not described as double blinded (n = 358; 66.3%, while the method of blinding was appropriate in 53% (n = 286 of the trials. We identified a significant decrease over time (1955-2013 in the proportion of trials assessed as having inadequately addressed methodological quality items (P < 0.05 in 30 out of the 40 quality criteria, or as being inadequate (at high or unclear risk of bias in five domains of the Cochrane risk of bias tool: sequence generation, allocation concealment, incomplete outcome data, other sources of bias, and overall risk of bias.The risks of bias, risks of random errors, reporting quality, and methodological quality of randomized clinical trials of oral health interventions have improved over time; however, further efforts that contribute to the development of more stringent
Impact of selective genotyping in the training population on accuracy and bias of genomic selection.

Science.gov (United States)

Zhao, Yusheng; Gowda, Manje; Longin, Friedrich H; Würschum, Tobias; Ranc, Nicolas; Reif, Jochen C

2012-08-01

Estimating marker effects based on routinely generated phenotypic data of breeding programs is a cost-effective strategy to implement genomic selection. Truncation selection in breeding populations, however, could have a strong impact on the accuracy to predict genomic breeding values. The main objective of our study was to investigate the influence of phenotypic selection on the accuracy and bias of genomic selection. We used experimental data of 788 testcross progenies from an elite maize breeding program. The testcross progenies were evaluated in unreplicated field trials in ten environments and fingerprinted with 857 SNP markers. Random regression best linear unbiased prediction method was used in combination with fivefold cross-validation based on genotypic sampling. We observed a substantial loss in the accuracy to predict genomic breeding values in unidirectional selected populations. In contrast, estimating marker effects based on bidirectional selected populations led to only a marginal decrease in the prediction accuracy of genomic breeding values. We concluded that bidirectional selection is a valuable approach to efficiently implement genomic selection in applied plant breeding programs.
Implications of structural genomics target selection strategies: Pfam5000, whole genome, and random approaches

Energy Technology Data Exchange (ETDEWEB)

Chandonia, John-Marc; Brenner, Steven E.

2004-07-14

The structural genomics project is an international effort to determine the three-dimensional shapes of all important biological macromolecules, with a primary focus on proteins. Target proteins should be selected according to a strategy which is medically and biologically relevant, of good value, and tractable. As an option to consider, we present the Pfam5000 strategy, which involves selecting the 5000 most important families from the Pfam database as sources for targets. We compare the Pfam5000 strategy to several other proposed strategies that would require similar numbers of targets. These include including complete solution of several small to moderately sized bacterial proteomes, partial coverage of the human proteome, and random selection of approximately 5000 targets from sequenced genomes. We measure the impact that successful implementation of these strategies would have upon structural interpretation of the proteins in Swiss-Prot, TrEMBL, and 131 complete proteomes (including 10 of eukaryotes) from the Proteome Analysis database at EBI. Solving the structures of proteins from the 5000 largest Pfam families would allow accurate fold assignment for approximately 68 percent of all prokaryotic proteins (covering 59 percent of residues) and 61 percent of eukaryotic proteins (40 percent of residues). More fine-grained coverage which would allow accurate modeling of these proteins would require an order of magnitude more targets. The Pfam5000 strategy may be modified in several ways, for example to focus on larger families, bacterial sequences, or eukaryotic sequences; as long as secondary consideration is given to large families within Pfam, coverage results vary only slightly. In contrast, focusing structural genomics on a single tractable genome would have only a limited impact in structural knowledge of other proteomes: a significant fraction (about 30-40 percent of the proteins, and 40-60 percent of the residues) of each proteome is classified in small
Location and multi-depot vehicle routing for emergency vehicles using tour coverage and random sampling

Directory of Open Access Journals (Sweden)

Alireza Goli

2015-09-01

Full Text Available Distribution and optimum allocation of emergency resources are the most important tasks, which need to be accomplished during crisis. When a natural disaster such as earthquake, flood, etc. takes place, it is necessary to deliver rescue efforts as quickly as possible. Therefore, it is important to find optimum location and distribution of emergency relief resources. When a natural disaster occurs, it is not possible to reach some damaged areas. In this paper, location and multi-depot vehicle routing for emergency vehicles using tour coverage and random sampling is investigated. In this study, there is no need to visit all the places and some demand points receive their needs from the nearest possible location. The proposed study is implemented for some randomly generated numbers in different sizes. The preliminary results indicate that the proposed method was capable of reaching desirable solutions in reasonable amount of time.
Prevalence of Dental Fear and Anxiety amongst Patients in Selected Dental Clinics in Ghana

Science.gov (United States)

Ofori, Marian A.; Adu-Ababio, F.; Nyako, E. A.; Ndanu, Tom A.

2009-01-01

Objective: To find out the prevalence of dental anxiety and fear amongst patients in various selected dental clinics in Accra, Ghana. Study design: Dental patients (n = 279) who had either been exposed to dental treatments or had no prior dental exposure, attending four selected dental clinics in Accra were randomly sampled. They were interviewed…
Development of ion imprinted polymers for the selective extraction of lanthanides from environmental samples

International Nuclear Information System (INIS)

Moussa, Manel

2016-01-01

The analysis of the lanthanide ions present at trace level in complex environmental matrices requires often a purification and preconcentration step. The solid phase extraction (SPE) is the most used sample preparation technique. To improve the selectivity of this step, Ion Imprinted Polymers (IIPs) can be used as SPE solid supports. The aim of this work was the development of IIPs for the selective extraction of lanthanide ions from environmental samples. In a first part, IIPs were prepared according to the trapping approach using 5,7-dichloroquinoline-8-ol as non-vinylated ligand. For the first time, the loss of the trapped ligand during template ion removal and sedimentation steps was demonstrated by HPLC-UV. Moreover, this loss was not repeatable, which led to a lack of repeatability of the SPE profiles. It was then demonstrated that the trapping approach is not appropriate for the IIPs synthesis. In a second part, IIPs were synthesized by chemical immobilization of methacrylic acid as vinylated monomer. The repeatability of the synthesis and the SPE protocol were confirmed. A good selectivity of the IIPs for all the lanthanide ions was obtained. IIPs were successfully used to selectively extract lanthanide ions from tap and river water. Finally, IIPs were synthesized by chemical immobilization of methacrylic acid and 4-vinylpyridine as functional monomers and either a light (Nd 3+ ) or a heavy (Er 3+ ) lanthanide ion as template. Both kinds of IIPs led to a similar selectivity for all lanthanide ions. Nevertheless, this selectivity can be modified by changing the nature and the pH of the washing solution used in the SPE protocol. (author)
The diagnostic value of CT scan and selective venous sampling in Cushing's syndrome

International Nuclear Information System (INIS)

Negoro, Makoto; Kuwayama, Akio; Yamamoto, Naoto; Nakane, Toshichi; Yokoe, Toshio; Kageyama, Naoki; Ichihara, Kaoru; Ishiguchi, Tsuneo; Sakuma, Sadayuki

1986-01-01

We studied 24 patients with Cushing's syndrome in order to find the best way to confirm the pituitary adenoma preoperatively. At first, the sellar content was studied by means of a high-resolution CT scan in each patient. Second, by selective catheterization in the bilateral internal jugular vein and the inferior petrosal sinus, venous samples (c) were obtained for ACTH assay. Simultaneously, peripheral blood sampling (P) was made at the anterior cubital vein for the same purpose, and the C/P ratio was carefully calculated in each patient. If the C/P ratio exceeded 2, it was highly suggestive of the presence of pituitary adenoma. Even by an advanced high-resolution CT scan with a thickness of 2 mm, pituitary adenomas were detected in only 32 % of the patients studied. The result of image diagnosis in Cushing disease was discouraging. As for the chemical diagnosis, the results were as follows. At the early stage of this study, the catheterization was terminated in the jugular veins of nine patients. Among these, in five patients the presence of pituitary adenoma was predicted correctly in the preoperative stage. Later, by means of inferior petrosal sinus samplings, pituitary microadenomas were detected in ten patients among the twelve. Selective venous sampling for ACTH in the inferior petrosal sinus or jugular vein proved to be useful for the differential diagnosis of Cushing's syndrome when other diagnostic measures such as CT scan were inconclusive. (author)
Participant-selected music and physical activity in older adults following cardiac rehabilitation: a randomized controlled trial.

Science.gov (United States)

Clark, Imogen N; Baker, Felicity A; Peiris, Casey L; Shoebridge, Georgie; Taylor, Nicholas F

2017-03-01

To evaluate effects of participant-selected music on older adults' achievement of activity levels recommended in the physical activity guidelines following cardiac rehabilitation. A parallel group randomized controlled trial with measurements at Weeks 0, 6 and 26. A multisite outpatient rehabilitation programme of a publicly funded metropolitan health service. Adults aged 60 years and older who had completed a cardiac rehabilitation programme. Experimental participants selected music to support walking with guidance from a music therapist. Control participants received usual care only. The primary outcome was the proportion of participants achieving activity levels recommended in physical activity guidelines. Secondary outcomes compared amounts of physical activity, exercise capacity, cardiac risk factors, and exercise self-efficacy. A total of 56 participants, mean age 68.2 years (SD = 6.5), were randomized to the experimental ( n = 28) and control groups ( n = 28). There were no differences between groups in proportions of participants achieving activity recommended in physical activity guidelines at Week 6 or 26. Secondary outcomes demonstrated between-group differences in male waist circumference at both measurements (Week 6 difference -2.0 cm, 95% CI -4.0 to 0; Week 26 difference -2.8 cm, 95% CI -5.4 to -0.1), and observed effect sizes favoured the experimental group for amounts of physical activity (d = 0.30), exercise capacity (d = 0.48), and blood pressure (d = -0.32). Participant-selected music did not increase the proportion of participants achieving recommended amounts of physical activity, but may have contributed to exercise-related benefits.
Diagnostic-test evaluation of immunoassays for anti-Toxoplasma gondii IgG antibodies in a random sample of Mexican population.

Science.gov (United States)

Caballero-Ortega, Heriberto; Castillo-Cruz, Rocío; Murieta, Sandra; Ortíz-Alegría, Luz Belinda; Calderón-Segura, Esther; Conde-Glez, Carlos J; Cañedo-Solares, Irma; Correa, Dolores

2014-05-14

There are few articles on evaluation of Toxoplasma gondii serological tests. Besides, commercially available tests are not always useful and are expensive for studies in open population. The aim of this study was to evaluate in-house ELISA and western blot for IgG antibodies in a representative sample of people living in Mexico. Three hundred and five serum samples were randomly selected from two national seroepidemiological survey banks; they were taken from men and women of all ages and from all areas of the country. ELISA cut-off was established using the mean plus three standard deviations of negative samples. Western blots were analysed by two experienced technicians and positivity was established according to the presence of at least three diagnostic bands. A commercial ELISA kit was used as a third test. Two reference standards were built up: one using concordant results of two assays leaving the evaluated test out and the other in which the evaluated test was included (IN) with at least two concordant results to define diagnosis. the lowest values of diagnostic parameters were obtained with the OUT reference standards: in-house ELISA had 96.9% sensitivity, 62.1% specificity, 49.6% PPV, 98.1% NPV and 71.8% accuracy, while western blot presented 81.8%, 89.7%, 84.0%, 88.2% and 86.6% values and the best kappa coefficient (0.72-0.82). The in-house ELISA is useful for screening people of Mexico, due to its high sensitivity, while western blot may be used to confirm diagnosis. These techniques might prove useful in other Latin American countries.
r2VIM: A new variable selection method for random forests in genome-wide association studies.

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Szymczak, Silke; Holzinger, Emily; Dasgupta, Abhijit; Malley, James D; Molloy, Anne M; Mills, James L; Brody, Lawrence C; Stambolian, Dwight; Bailey-Wilson, Joan E

2016-01-01

Machine learning methods and in particular random forests (RFs) are a promising alternative to standard single SNP analyses in genome-wide association studies (GWAS). RFs provide variable importance measures (VIMs) to rank SNPs according to their predictive power. However, in contrast to the established genome-wide significance threshold, no clear criteria exist to determine how many SNPs should be selected for downstream analyses. We propose a new variable selection approach, recurrent relative variable importance measure (r2VIM). Importance values are calculated relative to an observed minimal importance score for several runs of RF and only SNPs with large relative VIMs in all of the runs are selected as important. Evaluations on simulated GWAS data show that the new method controls the number of false-positives under the null hypothesis. Under a simple alternative hypothesis with several independent main effects it is only slightly less powerful than logistic regression. In an experimental GWAS data set, the same strong signal is identified while the approach selects none of the SNPs in an underpowered GWAS. The novel variable selection method r2VIM is a promising extension to standard RF for objectively selecting relevant SNPs in GWAS while controlling the number of false-positive results.

Selecting Sample Preparation Workflows for Mass Spectrometry-Based Proteomic and Phosphoproteomic Analysis of Patient Samples with Acute Myeloid Leukemia.

Science.gov (United States)

Hernandez-Valladares, Maria; Aasebø, Elise; Selheim, Frode; Berven, Frode S; Bruserud, Øystein

2016-08-22

Global mass spectrometry (MS)-based proteomic and phosphoproteomic studies of acute myeloid leukemia (AML) biomarkers represent a powerful strategy to identify and confirm proteins and their phosphorylated modifications that could be applied in diagnosis and prognosis, as a support for individual treatment regimens and selection of patients for bone marrow transplant. MS-based studies require optimal and reproducible workflows that allow a satisfactory coverage of the proteome and its modifications. Preparation of samples for global MS analysis is a crucial step and it usually requires method testing, tuning and optimization. Different proteomic workflows that have been used to prepare AML patient samples for global MS analysis usually include a standard protein in-solution digestion procedure with a urea-based lysis buffer. The enrichment of phosphopeptides from AML patient samples has previously been carried out either with immobilized metal affinity chromatography (IMAC) or metal oxide affinity chromatography (MOAC). We have recently tested several methods of sample preparation for MS analysis of the AML proteome and phosphoproteome and introduced filter-aided sample preparation (FASP) as a superior methodology for the sensitive and reproducible generation of peptides from patient samples. FASP-prepared peptides can be further fractionated or IMAC-enriched for proteome or phosphoproteome analyses. Herein, we will review both in-solution and FASP-based sample preparation workflows and encourage the use of the latter for the highest protein and phosphorylation coverage and reproducibility.
Molecularly imprinted membrane extraction combined with high-performance liquid chromatography for selective analysis of cloxacillin from shrimp samples.

Science.gov (United States)

Du, Wei; Sun, Min; Guo, Pengqi; Chang, Chun; Fu, Qiang

2018-09-01

Nowadays, the abuse of antibiotics in aquaculture has generated considerable problems for food safety. Therefore, it is imperative to develop a simple and selective method for monitoring illegal use of antibiotics in aquatic products. In this study, a method combined molecularly imprinted membranes (MIMs) extraction and liquid chromatography was developed for the selective analysis of cloxacillin from shrimp samples. The MIMs was synthesized by UV photopolymerization, and characterized by scanning electron microscope, Fourier transform infrared spectra, thermo-gravimetric analysis and swelling test. The results showed that the MIMs exhibited excellent permselectivity, high adsorption capacity and fast adsorption rate for cloxacillin. Finally, the method was utilized to determine cloxacillin from shrimp samples, with good accuracies and acceptable relative standard deviation values for precision. The proposed method was a promising alternative for selective analysis of cloxacillin in shrimp samples, due to the easy-operation and excellent selectivity. Copyright © 2018. Published by Elsevier Ltd.
Effect of mirtazapine versus selective serotonin reuptake inhibitors on benzodiazepine use in patients with major depressive disorder: a pragmatic, multicenter, open-label, randomized, active-controlled, 24-week trial.

Science.gov (United States)

Hashimoto, Tasuku; Shiina, Akihiro; Hasegawa, Tadashi; Kimura, Hiroshi; Oda, Yasunori; Niitsu, Tomihisa; Ishikawa, Masatomo; Tachibana, Masumi; Muneoka, Katsumasa; Matsuki, Satoshi; Nakazato, Michiko; Iyo, Masaomi

2016-01-01

This study aimed to evaluate whether selecting mirtazapine as the first choice for current depressive episode instead of selective serotonin reuptake inhibitors (SSRIs) reduces benzodiazepine use in patients with major depressive disorder (MDD). We concurrently examined the relationship between clinical responses and serum mature brain-derived neurotrophic factor (BDNF) and its precursor, proBDNF. We conducted an open-label randomized trial in routine psychiatric practice settings. Seventy-seven MDD outpatients were randomly assigned to the mirtazapine or predetermined SSRIs groups, and investigators arbitrarily selected sertraline or paroxetine. The primary outcome was the proportion of benzodiazepine users at weeks 6, 12, and 24 between the groups. We defined patients showing a ≥50 % reduction in Hamilton depression rating scale (HDRS) scores from baseline as responders. Blood samples were collected at baseline, weeks 6, 12, and 24. Sixty-five patients prescribed benzodiazepines from prescription day 1 were analyzed for the primary outcome. The percentage of benzodiazepine users was significantly lower in the mirtazapine than in the SSRIs group at weeks 6, 12, and 24 (21.4 vs. 81.8 %; 11.1 vs. 85.7 %, both P depressive episodes may reduce benzodiazepine use in patients with MDD. Trial registration UMIN000004144. Registered 2nd September 2010. The date of enrolment of the first participant to the trial was 24th August 2010. This study was retrospectively registered 9 days after the first participant was enrolled.
Parameters, test criteria and fault assessment in random sampling of waste barrels from non-qualified processes

International Nuclear Information System (INIS)

Martens, B.R.

1989-01-01

In the context of random sampling tests, parameters are checked on the waste barrels and criteria are given on which these tests are based. Also, it is shown how faulty data on the properties of the waste or faulty waste barrels should be treated. To decide the extent of testing, the properties of the waste relevant to final storage are determined based on the conditioning process used. (DG) [de
Re-Emergence of Under-Selected Stimuli, after the Extinction of Over-Selected Stimuli in an Automated Match to Samples Procedure

Science.gov (United States)

Broomfield, Laura; McHugh, Louise; Reed, Phil

2008-01-01

Stimulus over-selectivity occurs when one of potentially many aspects of the environment comes to control behaviour. In two experiments, adults with no developmental disabilities, were trained and tested in an automated match to samples (MTS) paradigm. In Experiment 1, participants completed two conditions, in one of which the over-selected…
An econometric method for estimating population parameters from non-random samples: An application to clinical case finding.

Science.gov (United States)

Burger, Rulof P; McLaren, Zoë M

2017-09-01

The problem of sample selection complicates the process of drawing inference about populations. Selective sampling arises in many real world situations when agents such as doctors and customs officials search for targets with high values of a characteristic. We propose a new method for estimating population characteristics from these types of selected samples. We develop a model that captures key features of the agent's sampling decision. We use a generalized method of moments with instrumental variables and maximum likelihood to estimate the population prevalence of the characteristic of interest and the agents' accuracy in identifying targets. We apply this method to tuberculosis (TB), which is the leading infectious disease cause of death worldwide. We use a national database of TB test data from South Africa to examine testing for multidrug resistant TB (MDR-TB). Approximately one quarter of MDR-TB cases was undiagnosed between 2004 and 2010. The official estimate of 2.5% is therefore too low, and MDR-TB prevalence is as high as 3.5%. Signal-to-noise ratios are estimated to be between 0.5 and 1. Our approach is widely applicable because of the availability of routinely collected data and abundance of potential instruments. Using routinely collected data to monitor population prevalence can guide evidence-based policy making. Copyright © 2017 John Wiley & Sons, Ltd.
Discrimination of handlebar grip samples by fourier transform infrared microspectroscopy analysis and statistics

Directory of Open Access Journals (Sweden)

Zeyu Lin

2017-01-01

Full Text Available In this paper, the authors presented a study on the discrimination of handlebar grip samples, to provide effective forensic science service for hit and run traffic cases. 50 bicycle handlebar grip samples, 49 electric bike handlebar grip samples, and 96 motorcycle handlebar grip samples have been randomly collected by the local police in Beijing (China. Fourier transform infrared microspectroscopy (FTIR was utilized as analytical technology. Then, target absorption selection, data pretreatment, and discrimination of linked samples and unlinked samples were chosen as three steps to improve the discrimination of FTIR spectrums collected from different handlebar grip samples. Principal component analysis and receiver operating characteristic curve were utilized to evaluate different data selection methods and different data pretreatment methods, respectively. It is possible to explore the evidential value of handlebar grip residue evidence through instrumental analysis and statistical treatments. It will provide a universal discrimination method for other forensic science samples as well.
Effects of Video Game Training on Measures of Selective Attention and Working Memory in Older Adults: Results from a Randomized Controlled Trial

Science.gov (United States)

Ballesteros, Soledad; Mayas, Julia; Prieto, Antonio; Ruiz-Marquez, Eloísa; Toril, Pilar; Reales, José M.

2017-01-01

Video game training with older adults potentially enhances aspects of cognition that decline with aging and could therefore offer a promising training approach. Although, previous published studies suggest that training can produce transfer, many of them have certain shortcomings. This randomized controlled trial (RCT; Clinicaltrials.gov ID: NCT02796508) tried to overcome some of these limitations by incorporating an active control group and the assessment of motivation and expectations. Seventy-five older volunteers were randomly assigned to the experimental group trained for 16 sessions with non-action video games from Lumosity, a commercial platform (http://www.lumosity.com/) or to an active control group trained for the same number of sessions with simulation strategy games. The final sample included 55 older adults (30 in the experimental group and 25 in the active control group). Participants were tested individually before and after training to assess working memory (WM) and selective attention and also reported their perceived improvement, motivation and engagement. The results showed improved performance across the training sessions. The main results were: (1) the experimental group did not show greater improvements in measures of selective attention and working memory than the active control group (the opposite occurred in the oddball task); (2) a marginal training effect was observed for the N-back task, but not for the Stroop task while both groups improved in the Corsi Blocks task. Based on these results, one can conclude that training with non-action games provide modest benefits for untrained tasks. The effect is not specific for that kind of training as a similar effect was observed for strategy video games. Groups did not differ in motivation, engagement or expectations. PMID:29163136
Effects of Video Game Training on Measures of Selective Attention and Working Memory in Older Adults: Results from a Randomized Controlled Trial

Directory of Open Access Journals (Sweden)

Soledad Ballesteros

2017-11-01

Full Text Available Video game training with older adults potentially enhances aspects of cognition that decline with aging and could therefore offer a promising training approach. Although, previous published studies suggest that training can produce transfer, many of them have certain shortcomings. This randomized controlled trial (RCT; Clinicaltrials.gov ID: NCT02796508 tried to overcome some of these limitations by incorporating an active control group and the assessment of motivation and expectations. Seventy-five older volunteers were randomly assigned to the experimental group trained for 16 sessions with non-action video games from Lumosity, a commercial platform (http://www.lumosity.com/ or to an active control group trained for the same number of sessions with simulation strategy games. The final sample included 55 older adults (30 in the experimental group and 25 in the active control group. Participants were tested individually before and after training to assess working memory (WM and selective attention and also reported their perceived improvement, motivation and engagement. The results showed improved performance across the training sessions. The main results were: (1 the experimental group did not show greater improvements in measures of selective attention and working memory than the active control group (the opposite occurred in the oddball task; (2 a marginal training effect was observed for the N-back task, but not for the Stroop task while both groups improved in the Corsi Blocks task. Based on these results, one can conclude that training with non-action games provide modest benefits for untrained tasks. The effect is not specific for that kind of training as a similar effect was observed for strategy video games. Groups did not differ in motivation, engagement or expectations.
Effects of Video Game Training on Measures of Selective Attention and Working Memory in Older Adults: Results from a Randomized Controlled Trial.

Science.gov (United States)

Ballesteros, Soledad; Mayas, Julia; Prieto, Antonio; Ruiz-Marquez, Eloísa; Toril, Pilar; Reales, José M

2017-01-01

Video game training with older adults potentially enhances aspects of cognition that decline with aging and could therefore offer a promising training approach. Although, previous published studies suggest that training can produce transfer, many of them have certain shortcomings. This randomized controlled trial (RCT; Clinicaltrials.gov ID: NCT02796508) tried to overcome some of these limitations by incorporating an active control group and the assessment of motivation and expectations. Seventy-five older volunteers were randomly assigned to the experimental group trained for 16 sessions with non-action video games from Lumosity , a commercial platform (http://www.lumosity.com/) or to an active control group trained for the same number of sessions with simulation strategy games. The final sample included 55 older adults (30 in the experimental group and 25 in the active control group). Participants were tested individually before and after training to assess working memory (WM) and selective attention and also reported their perceived improvement, motivation and engagement. The results showed improved performance across the training sessions. The main results were: (1) the experimental group did not show greater improvements in measures of selective attention and working memory than the active control group (the opposite occurred in the oddball task); (2) a marginal training effect was observed for the N -back task, but not for the Stroop task while both groups improved in the Corsi Blocks task. Based on these results, one can conclude that training with non-action games provide modest benefits for untrained tasks. The effect is not specific for that kind of training as a similar effect was observed for strategy video games. Groups did not differ in motivation, engagement or expectations.
Economic Intervention and Parenting: A Randomized Experiment of Statewide Child Development Accounts

Science.gov (United States)

Nam, Yunju; Wikoff, Nora; Sherraden, Michael

2016-01-01

Objective: We examine the effects of Child Development Accounts (CDAs) on parenting stress and practices. Methods: We use data from the SEED for Oklahoma Kids (SEED OK) experiment. SEED OK selected caregivers of infants from Oklahoma birth certificates using a probability sampling method, randomly assigned caregivers to the treatment (n = 1,132)…
Attitudes towards biomedical use of tissue sample collections, consent, and biobanks among Finns

DEFF Research Database (Denmark)

Tupasela, Aaro Mikael; Sihvo, Sinikka; Snell, Karolna

2010-01-01

To ascertain the attitudes towards the use of existing diagnostic and research samples, the setting up of a national biobank, and different types of informed consent among Finns. Method: A population survey of 2,400 randomly selected Finns aged 24-65 was conducted at the beginning of 2007....
A large sample of Kohonen-selected SDSS quasars with weak emission lines: selection effects and statistical properties

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Meusinger, H.; Balafkan, N.

2014-08-01

Aims: A tiny fraction of the quasar population shows remarkably weak emission lines. Several hypotheses have been developed, but the weak line quasar (WLQ) phenomenon still remains puzzling. The aim of this study was to create a sizeable sample of WLQs and WLQ-like objects and to evaluate various properties of this sample. Methods: We performed a search for WLQs in the spectroscopic data from the Sloan Digital Sky Survey Data Release 7 based on Kohonen self-organising maps for nearly 105 quasar spectra. The final sample consists of 365 quasars in the redshift range z = 0.6 - 4.2 (z¯ = 1.50 ± 0.45) and includes in particular a subsample of 46 WLQs with equivalent widths WMg iiattention was paid to selection effects. Results: The WLQs have, on average, significantly higher luminosities, Eddington ratios, and accretion rates. About half of the excess comes from a selection bias, but an intrinsic excess remains probably caused primarily by higher accretion rates. The spectral energy distribution shows a bluer continuum at rest-frame wavelengths ≳1500 Å. The variability in the optical and UV is relatively low, even taking the variability-luminosity anti-correlation into account. The percentage of radio detected quasars and of core-dominant radio sources is significantly higher than for the control sample, whereas the mean radio-loudness is lower. Conclusions: The properties of our WLQ sample can be consistently understood assuming that it consists of a mix of quasars at the beginning of a stage of increased accretion activity and of beamed radio-quiet quasars. The higher luminosities and Eddington ratios in combination with a bluer spectral energy distribution can be explained by hotter continua, i.e. higher accretion rates. If quasar activity consists of subphases with different accretion rates, a change towards a higher rate is probably accompanied by an only slow development of the broad line region. The composite WLQ spectrum can be reasonably matched by the
Randomized Comparison of Two Vaginal Self-Sampling Methods for Human Papillomavirus Detection: Dry Swab versus FTA Cartridge.

Science.gov (United States)

Catarino, Rosa; Vassilakos, Pierre; Bilancioni, Aline; Vanden Eynde, Mathieu; Meyer-Hamme, Ulrike; Menoud, Pierre-Alain; Guerry, Frédéric; Petignat, Patrick

2015-01-01

Human papillomavirus (HPV) self-sampling (self-HPV) is valuable in cervical cancer screening. HPV testing is usually performed on physician-collected cervical smears stored in liquid-based medium. Dry filters and swabs are an alternative. We evaluated the adequacy of self-HPV using two dry storage and transport devices, the FTA cartridge and swab. A total of 130 women performed two consecutive self-HPV samples. Randomization determined which of the two tests was performed first: self-HPV using dry swabs (s-DRY) or vaginal specimen collection using a cytobrush applied to an FTA cartridge (s-FTA). After self-HPV, a physician collected a cervical sample using liquid-based medium (Dr-WET). HPV types were identified by real-time PCR. Agreement between collection methods was measured using the kappa statistic. HPV prevalence for high-risk types was 62.3% (95%CI: 53.7-70.2) detected by s-DRY, 56.2% (95%CI: 47.6-64.4) by Dr-WET, and 54.6% (95%CI: 46.1-62.9) by s-FTA. There was overall agreement of 70.8% between s-FTA and s-DRY samples (kappa = 0.34), and of 82.3% between self-HPV and Dr-WET samples (kappa = 0.56). Detection sensitivities for low-grade squamous intraepithelial lesion or worse (LSIL+) were: 64.0% (95%CI: 44.5-79.8) for s-FTA, 84.6% (95%CI: 66.5-93.9) for s-DRY, and 76.9% (95%CI: 58.0-89.0) for Dr-WET. The preferred self-collection method among patients was s-DRY (40.8% vs. 15.4%). Regarding costs, FTA card was five times more expensive than the swab (~5 US dollars (USD)/per card vs. ~1 USD/per swab). Self-HPV using dry swabs is sensitive for detecting LSIL+ and less expensive than s-FTA. International Standard Randomized Controlled Trial Number (ISRCTN): 43310942.
Antibiotic content of selective culture media for isolation of Capnocytophaga species from oral polymicrobial samples.

Science.gov (United States)

Ehrmann, E; Jolivet-Gougeon, A; Bonnaure-Mallet, M; Fosse, T

2013-10-01

In oral microbiome, because of the abundance of commensal competitive flora, selective media with antibiotics are necessary for the recovery of fastidious Capnocytophaga species. The performances of six culture media (blood agar, chocolate blood agar, VCAT medium, CAPE medium, bacitracin chocolate blood agar and VK medium) were compared with literature data concerning five other media (FAA, LB, TSBV, CapR and TBBP media). To understand variable growth on selective media, the MICs of each antimicrobial agent contained in this different media (colistin, kanamycin, trimethoprim, trimethoprim-sulfamethoxazole, vancomycin, aztreonam and bacitracin) were determined for all Capnocytophaga species. Overall, VCAT medium (Columbia, 10% cooked horse blood, polyvitaminic supplement, 3·75 mg l(-1) of colistin, 1·5 mg l(-1) of trimethoprim, 1 mg l(-1) of vancomycin and 0·5 mg l(-1) of amphotericin B, Oxoid, France) was the more efficient selective medium, with regard to the detection of Capnocytophaga species from oral samples (P culture, a simple blood agar allowed the growth of all Capnocytophaga species. Nonetheless, in oral samples, because of the abundance of commensal competitive flora, selective media with antibiotics are necessary for the recovery of Capnocytophaga species. The demonstrated superiority of VCAT medium made its use essential for the optimal detection of this bacterial genus. This work showed that extreme caution should be exercised when reporting the isolation of Capnocytophaga species from oral polymicrobial samples, because the culture medium is a determining factor. © 2013 The Society for Applied Microbiology.
Unwilling or Unable to Cheat? Evidence from a Randomized Tax Audit Experiment in Denmark

DEFF Research Database (Denmark)

Kleven, Henrik Jacobsen; Knudsen, Martin B.; Kreiner, Claus Thustrup

2010-01-01

This paper analyzes a randomized tax enforcement experiment in Denmark. In the base year, a stratified and representative sample of over 40,000 individual income tax filers was selected for the experiment. Half of the tax filers were randomly selected to be thoroughly audited, while the rest were...... deliberately not audited. The following year, "threat-of-audit" letters were randomly assigned and sent to tax filers in both groups. Using comprehensive administrative tax data, we present four main findings. First, we find that the tax evasion rate is very small (0.3%) for income subject to third...... impact on tax evasion, but that this effect is small in comparison to avoidance responses. Third, we find that prior audits substantially increase self-reported income, implying that individuals update their beliefs about detection probability based on experiencing an audit. Fourth, threat-of-audit...
Investigating the Randomness of Numbers

Science.gov (United States)

Pendleton, Kenn L.

2009-01-01

The use of random numbers is pervasive in today's world. Random numbers have practical applications in such far-flung arenas as computer simulations, cryptography, gambling, the legal system, statistical sampling, and even the war on terrorism. Evaluating the randomness of extremely large samples is a complex, intricate process. However, the…
Two-year Randomized Clinical Trial of Self-etching Adhesives and Selective Enamel Etching.

Science.gov (United States)

Pena, C E; Rodrigues, J A; Ely, C; Giannini, M; Reis, A F

2016-01-01

The aim of this randomized, controlled prospective clinical trial was to evaluate the clinical effectiveness of restoring noncarious cervical lesions with two self-etching adhesive systems applied with or without selective enamel etching. A one-step self-etching adhesive (Xeno V(+)) and a two-step self-etching system (Clearfil SE Bond) were used. The effectiveness of phosphoric acid selective etching of enamel margins was also evaluated. Fifty-six cavities were restored with each adhesive system and divided into two subgroups (n=28; etch and non-etch). All 112 cavities were restored with the nanohybrid composite Esthet.X HD. The clinical effectiveness of restorations was recorded in terms of retention, marginal integrity, marginal staining, caries recurrence, and postoperative sensitivity after 3, 6, 12, 18, and 24 months (modified United States Public Health Service). The Friedman test detected significant differences only after 18 months for marginal staining in the groups Clearfil SE non-etch (p=0.009) and Xeno V(+) etch (p=0.004). One restoration was lost during the trial (Xeno V(+) etch; p>0.05). Although an increase in marginal staining was recorded for groups Clearfil SE non-etch and Xeno V(+) etch, the clinical effectiveness of restorations was considered acceptable for the single-step and two-step self-etching systems with or without selective enamel etching in this 24-month clinical trial.
A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data

Directory of Open Access Journals (Sweden)

Himmelreich Uwe

2009-07-01

Full Text Available Abstract Background Regularized regression methods such as principal component or partial least squares regression perform well in learning tasks on high dimensional spectral data, but cannot explicitly eliminate irrelevant features. The random forest classifier with its associated Gini feature importance, on the other hand, allows for an explicit feature elimination, but may not be optimally adapted to spectral data due to the topology of its constituent classification trees which are based on orthogonal splits in feature space. Results We propose to combine the best of both approaches, and evaluated the joint use of a feature selection based on a recursive feature elimination using the Gini importance of random forests' together with regularized classification methods on spectral data sets from medical diagnostics, chemotaxonomy, biomedical analytics, food science, and synthetically modified spectral data. Here, a feature selection using the Gini feature importance with a regularized classification by discriminant partial least squares regression performed as well as or better than a filtering according to different univariate statistical tests, or using regression coefficients in a backward feature elimination. It outperformed the direct application of the random forest classifier, or the direct application of the regularized classifiers on the full set of features. Conclusion The Gini importance of the random forest provided superior means for measuring feature relevance on spectral data, but – on an optimal subset of features – the regularized classifiers might be preferable over the random forest classifier, in spite of their limitation to model linear dependencies only. A feature selection based on Gini importance, however, may precede a regularized linear classification to identify this optimal subset of features, and to earn a double benefit of both dimensionality reduction and the elimination of noise from the classification task.
Random effect selection in generalised linear models

DEFF Research Database (Denmark)

Denwood, Matt; Houe, Hans; Forkman, Björn

We analysed abattoir recordings of meat inspection codes with possible relevance to onfarm animal welfare in cattle. Random effects logistic regression models were used to describe individual-level data obtained from 461,406 cattle slaughtered in Denmark. Our results demonstrate that the largest...

Theory of sampling and its application in tissue based diagnosis

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Kayser Gian

2009-02-01

Full Text Available Abstract Background A general theory of sampling and its application in tissue based diagnosis is presented. Sampling is defined as extraction of information from certain limited spaces and its transformation into a statement or measure that is valid for the entire (reference space. The procedure should be reproducible in time and space, i.e. give the same results when applied under similar circumstances. Sampling includes two different aspects, the procedure of sample selection and the efficiency of its performance. The practical performance of sample selection focuses on search for localization of specific compartments within the basic space, and search for presence of specific compartments. Methods When a sampling procedure is applied in diagnostic processes two different procedures can be distinguished: I the evaluation of a diagnostic significance of a certain object, which is the probability that the object can be grouped into a certain diagnosis, and II the probability to detect these basic units. Sampling can be performed without or with external knowledge, such as size of searched objects, neighbourhood conditions, spatial distribution of objects, etc. If the sample size is much larger than the object size, the application of a translation invariant transformation results in Kriege's formula, which is widely used in search for ores. Usually, sampling is performed in a series of area (space selections of identical size. The size can be defined in relation to the reference space or according to interspatial relationship. The first method is called random sampling, the second stratified sampling. Results Random sampling does not require knowledge about the reference space, and is used to estimate the number and size of objects. Estimated features include area (volume fraction, numerical, boundary and surface densities. Stratified sampling requires the knowledge of objects (and their features and evaluates spatial features in relation to
Selecting Sample Preparation Workflows for Mass Spectrometry-Based Proteomic and Phosphoproteomic Analysis of Patient Samples with Acute Myeloid Leukemia

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Maria Hernandez-Valladares

2016-08-01

Full Text Available Global mass spectrometry (MS-based proteomic and phosphoproteomic studies of acute myeloid leukemia (AML biomarkers represent a powerful strategy to identify and confirm proteins and their phosphorylated modifications that could be applied in diagnosis and prognosis, as a support for individual treatment regimens and selection of patients for bone marrow transplant. MS-based studies require optimal and reproducible workflows that allow a satisfactory coverage of the proteome and its modifications. Preparation of samples for global MS analysis is a crucial step and it usually requires method testing, tuning and optimization. Different proteomic workflows that have been used to prepare AML patient samples for global MS analysis usually include a standard protein in-solution digestion procedure with a urea-based lysis buffer. The enrichment of phosphopeptides from AML patient samples has previously been carried out either with immobilized metal affinity chromatography (IMAC or metal oxide affinity chromatography (MOAC. We have recently tested several methods of sample preparation for MS analysis of the AML proteome and phosphoproteome and introduced filter-aided sample preparation (FASP as a superior methodology for the sensitive and reproducible generation of peptides from patient samples. FASP-prepared peptides can be further fractionated or IMAC-enriched for proteome or phosphoproteome analyses. Herein, we will review both in-solution and FASP-based sample preparation workflows and encourage the use of the latter for the highest protein and phosphorylation coverage and reproducibility.
Nitrates and bone turnover (NABT) - trial to select the best nitrate preparation: study protocol for a randomized controlled trial.

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Bucur, Roxana C; Reid, Lauren S; Hamilton, Celeste J; Cummings, Steven R; Jamal, Sophie A

2013-09-08

Organic nitrates uncouple bone turnover, improve bone mineral density, and improve trabecular and cortical components of bone. These changes in turnover, strength and geometry may translate into an important reduction in fractures. However, before proceeding with a large fracture trial, there is a need to identify the nitrate formulation that has both the greatest efficacy (with regards to bone turnover markers) and gives the fewest headaches. Ascertaining which nitrate formulation this may be is the purpose of the current study. This will be an open-label randomized, controlled trial conducted at Women's College Hospital comparing five formulations of nitrates for their effects on bone turnover markers and headache. We will recruit postmenopausal women age 50 years or older with no contraindications to nitroglycerin. Our trial will consist of a run-in phase and a treatment phase. We will enroll 420 women in the run-in phase, each to receive all of the 5 potential treatments in random order for 2 days, each with a 2-day washout period between treatments. Those who tolerate all formulations will enter the 12-week treatment phase and be randomly assigned to one of five groups: 0.3 mg sublingual nitroglycerin tablet, 0.6 mg of the sublingual tablet, a 20 mg tablet of isosorbide mononitrate, a 160 mg nitroglycerin transdermal patch (used for 8 h), and 15 mg of nitroglycerin ointment as used in a previous trial by our group. We will continue enrolment until we have randomized 210 women or 35 women per group. Concentrations of bone formation (bone-specific alkaline phosphatase and procollagen type I N-terminal propeptide) and bone resorption (C-telopeptides of collagen crosslinks and N-terminal crosslinks of collagen) agents will be measured in samples taken at study entry (the start of the run in phase) and 12 weeks. Subjects will record the frequency and severity of headaches daily during the run-in phase and then monthly after that. We will use the 'multiple
Selective extraction of dimethoate from cucumber samples by use of molecularly imprinted microspheres

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Jiao-Jiao Du

2015-06-01

Full Text Available Molecularly imprinted polymers for dimethoate recognition were synthesized by the precipitation polymerization technique using methyl methacrylate (MMA as the functional monomer and ethylene glycol dimethacrylate (EGDMA as the cross-linker. The morphology, adsorption and recognition properties were investigated by scanning electron microscopy (SEM, static adsorption test, and competitive adsorption test. To obtain the best selectivity and binding performance, the synthesis and adsorption conditions of MIPs were optimized through single factor experiments. Under the optimized conditions, the resultant polymers exhibited uniform size, satisfactory binding capacity and significant selectivity. Furthermore, the imprinted polymers were successfully applied as a specific solid-phase extractants combined with high performance liquid chromatography (HPLC for determination of dimethoate residues in the cucumber samples. The average recoveries of three spiked samples ranged from 78.5% to 87.9% with the relative standard deviations (RSDs less than 4.4% and the limit of detection (LOD obtained for dimethoate as low as 2.3Â Î¼g/mL. Keywords: Molecularly imprinted polymer, Precipitation polymerization, Dimethoate, Cucumber, HPLC
X-Ray Temperatures, Luminosities, and Masses from XMM-Newton Follow-up of the First Shear-selected Galaxy Cluster Sample

Energy Technology Data Exchange (ETDEWEB)

Deshpande, Amruta J.; Hughes, John P. [Department of Physics and Astronomy, Rutgers the State University of New Jersey, 136 Frelinghuysen Road, Piscataway, NJ 08854 (United States); Wittman, David, E-mail: amrejd@physics.rutgers.edu, E-mail: jph@physics.rutgers.edu, E-mail: dwittman@physics.ucdavis.edu [Department of Physics, University of California, Davis, One Shields Avenue, Davis, CA 95616 (United States)

2017-04-20

We continue the study of the first sample of shear-selected clusters from the initial 8.6 square degrees of the Deep Lens Survey (DLS); a sample with well-defined selection criteria corresponding to the highest ranked shear peaks in the survey area. We aim to characterize the weak lensing selection by examining the sample’s X-ray properties. There are multiple X-ray clusters associated with nearly all the shear peaks: 14 X-ray clusters corresponding to seven DLS shear peaks. An additional three X-ray clusters cannot be definitively associated with shear peaks, mainly due to large positional offsets between the X-ray centroid and the shear peak. Here we report on the XMM-Newton properties of the 17 X-ray clusters. The X-ray clusters display a wide range of luminosities and temperatures; the L {sub X} − T {sub X} relation we determine for the shear-associated X-ray clusters is consistent with X-ray cluster samples selected without regard to dynamical state, while it is inconsistent with self-similarity. For a subset of the sample, we measure X-ray masses using temperature as a proxy, and compare to weak lensing masses determined by the DLS team. The resulting mass comparison is consistent with equality. The X-ray and weak lensing masses show considerable intrinsic scatter (∼48%), which is consistent with X-ray selected samples when their X-ray and weak lensing masses are independently determined.
Judgment sampling: a health care improvement perspective.

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Perla, Rocco J; Provost, Lloyd P

2012-01-01

Sampling plays a major role in quality improvement work. Random sampling (assumed by most traditional statistical methods) is the exception in improvement situations. In most cases, some type of "judgment sample" is used to collect data from a system. Unfortunately, judgment sampling is not well understood. Judgment sampling relies upon those with process and subject matter knowledge to select useful samples for learning about process performance and the impact of changes over time. It many cases, where the goal is to learn about or improve a specific process or system, judgment samples are not merely the most convenient and economical approach, they are technically and conceptually the most appropriate approach. This is because improvement work is done in the real world in complex situations involving specific areas of concern and focus; in these situations, the assumptions of classical measurement theory neither can be met nor should an attempt be made to meet them. The purpose of this article is to describe judgment sampling and its importance in quality improvement work and studies with a focus on health care settings.
Generalizability of causal inference in observational studies under retrospective convenience sampling.

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Hu, Zonghui; Qin, Jing

2018-05-20

Many observational studies adopt what we call retrospective convenience sampling (RCS). With the sample size in each arm prespecified, RCS randomly selects subjects from the treatment-inclined subpopulation into the treatment arm and those from the control-inclined into the control arm. Samples in each arm are representative of the respective subpopulation, but the proportion of the 2 subpopulations is usually not preserved in the sample data. We show in this work that, under RCS, existing causal effect estimators actually estimate the treatment effect over the sample population instead of the underlying study population. We investigate how to correct existing methods for consistent estimation of the treatment effect over the underlying population. Although RCS is adopted in medical studies for ethical and cost-effective purposes, it also has a big advantage for statistical inference: When the tendency to receive treatment is low in a study population, treatment effect estimators under RCS, with proper correction, are more efficient than their parallels under random sampling. These properties are investigated both theoretically and through numerical demonstration. Published 2018. This article is a U.S. Government work and is in the public domain in the USA.
Sample Size Calculation: Inaccurate A Priori Assumptions for Nuisance Parameters Can Greatly Affect the Power of a Randomized Controlled Trial.

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Elsa Tavernier

Full Text Available We aimed to examine the extent to which inaccurate assumptions for nuisance parameters used to calculate sample size can affect the power of a randomized controlled trial (RCT. In a simulation study, we separately considered an RCT with continuous, dichotomous or time-to-event outcomes, with associated nuisance parameters of standard deviation, success rate in the control group and survival rate in the control group at some time point, respectively. For each type of outcome, we calculated a required sample size N for a hypothesized treatment effect, an assumed nuisance parameter and a nominal power of 80%. We then assumed a nuisance parameter associated with a relative error at the design stage. For each type of outcome, we randomly drew 10,000 relative errors of the associated nuisance parameter (from empirical distributions derived from a previously published review. Then, retro-fitting the sample size formula, we derived, for the pre-calculated sample size N, the real power of the RCT, taking into account the relative error for the nuisance parameter. In total, 23%, 0% and 18% of RCTs with continuous, binary and time-to-event outcomes, respectively, were underpowered (i.e., the real power was 90%. Even with proper calculation of sample size, a substantial number of trials are underpowered or overpowered because of imprecise knowledge of nuisance parameters. Such findings raise questions about how sample size for RCTs should be determined.
Phytochemical analysis and biological evaluation of selected African propolis samples from Cameroon and Congo

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Papachroni, D.; Graikou, K.; Kosalec, I.; Damianakos, H.; Ingram, V.J.; Chinou, I.

2015-01-01

The objective of this study was the chemical analysis of four selected samples of African propolis (Congo and Cameroon) and their biological evaluation. Twenty-one secondary metabolites belonging to four different chemical groups were isolated from the 70% ethanolic extracts of propolis and their
Climate Change and Agricultural Productivity in Sub-Saharan Africa: A Spatial Sample Selection Model

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Ward, P.S.; Florax, R.J.G.M.; Flores-Lagunes, A.

2014-01-01

Using spatially explicit data, we estimate a cereal yield response function using a recently developed estimator for spatial error models when endogenous sample selection is of concern. Our results suggest that yields across Sub-Saharan Africa will decline with projected climatic changes, and that
Maximizing the Diversity of Ensemble Random Forests for Tree Genera Classification Using High Density LiDAR Data

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Connie Ko

2016-08-01

Full Text Available Recent research into improving the effectiveness of forest inventory management using airborne LiDAR data has focused on developing advanced theories in data analytics. Furthermore, supervised learning as a predictive model for classifying tree genera (and species, where possible has been gaining popularity in order to minimize this labor-intensive task. However, bottlenecks remain that hinder the immediate adoption of supervised learning methods. With supervised classification, training samples are required for learning the parameters that govern the performance of a classifier, yet the selection of training data is often subjective and the quality of such samples is critically important. For LiDAR scanning in forest environments, the quantification of data quality is somewhat abstract, normally referring to some metric related to the completeness of individual tree crowns; however, this is not an issue that has received much attention in the literature. Intuitively the choice of training samples having varying quality will affect classification accuracy. In this paper a Diversity Index (DI is proposed that characterizes the diversity of data quality (Qi among selected training samples required for constructing a classification model of tree genera. The training sample is diversified in terms of data quality as opposed to the number of samples per class. The diversified training sample allows the classifier to better learn the positive and negative instances and; therefore; has a higher classification accuracy in discriminating the “unknown” class samples from the “known” samples. Our algorithm is implemented within the Random Forests base classifiers with six derived geometric features from LiDAR data. The training sample contains three tree genera (pine; poplar; and maple and the validation samples contains four labels (pine; poplar; maple; and “unknown”. Classification accuracy improved from 72.8%; when training samples were
Random forest variable selection in spatial malaria transmission modelling in Mpumalanga Province, South Africa

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Thandi Kapwata

2016-11-01

Full Text Available Malaria is an environmentally driven disease. In order to quantify the spatial variability of malaria transmission, it is imperative to understand the interactions between environmental variables and malaria epidemiology at a micro-geographic level using a novel statistical approach. The random forest (RF statistical learning method, a relatively new variable-importance ranking method, measures the variable importance of potentially influential parameters through the percent increase of the mean squared error. As this value increases, so does the relative importance of the associated variable. The principal aim of this study was to create predictive malaria maps generated using the selected variables based on the RF algorithm in the Ehlanzeni District of Mpumalanga Province, South Africa. From the seven environmental variables used [temperature, lag temperature, rainfall, lag rainfall, humidity, altitude, and the normalized difference vegetation index (NDVI], altitude was identified as the most influential predictor variable due its high selection frequency. It was selected as the top predictor for 4 out of 12 months of the year, followed by NDVI, temperature and lag rainfall, which were each selected twice. The combination of climatic variables that produced the highest prediction accuracy was altitude, NDVI, and temperature. This suggests that these three variables have high predictive capabilities in relation to malaria transmission. Furthermore, it is anticipated that the predictive maps generated from predictions made by the RF algorithm could be used to monitor the progression of malaria and assist in intervention and prevention efforts with respect to malaria.
Selecting Optimal Parameters of Random Linear Network Coding for Wireless Sensor Networks

DEFF Research Database (Denmark)

Heide, J; Zhang, Qi; Fitzek, F H P

2013-01-01

This work studies how to select optimal code parameters of Random Linear Network Coding (RLNC) in Wireless Sensor Networks (WSNs). With Rateless Deluge [1] the authors proposed to apply Network Coding (NC) for Over-the-Air Programming (OAP) in WSNs, and demonstrated that with NC a significant...... reduction in the number of transmitted packets can be achieved. However, NC introduces additional computations and potentially a non-negligible transmission overhead, both of which depend on the chosen coding parameters. Therefore it is necessary to consider the trade-off that these coding parameters...... present in order to obtain the lowest energy consumption per transmitted bit. This problem is analyzed and suitable coding parameters are determined for the popular Tmote Sky platform. Compared to the use of traditional RLNC, these parameters enable a reduction in the energy spent per bit which grows...
Model selection with multiple regression on distance matrices leads to incorrect inferences.

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Ryan P Franckowiak

Full Text Available In landscape genetics, model selection procedures based on Information Theoretic and Bayesian principles have been used with multiple regression on distance matrices (MRM to test the relationship between multiple vectors of pairwise genetic, geographic, and environmental distance. Using Monte Carlo simulations, we examined the ability of model selection criteria based on Akaike's information criterion (AIC, its small-sample correction (AICc, and the Bayesian information criterion (BIC to reliably rank candidate models when applied with MRM while varying the sample size. The results showed a serious problem: all three criteria exhibit a systematic bias toward selecting unnecessarily complex models containing spurious random variables and erroneously suggest a high level of support for the incorrectly ranked best model. These problems effectively increased with increasing sample size. The failure of AIC, AICc, and BIC was likely driven by the inflated sample size and different sum-of-squares partitioned by MRM, and the resulting effect on delta values. Based on these findings, we strongly discourage the continued application of AIC, AICc, and BIC for model selection with MRM.
A sequential logic circuit for coincidences randomly distributed in 'time' and 'duration', with selection and total sampling

International Nuclear Information System (INIS)

Carnet, Bernard; Delhumeau, Michel

1971-06-01

The principles of binary analysis applied to the investigation of sequential circuits were used to design a two way coincidence circuit whose input may be, random or periodic variables of constant or variable duration. The output signal strictly reproduces the characteristics of the input signal triggering the coincidence. A coincidence between input signals does not produce any output signal if one of the signals has already triggered the output signal. The characteristics of the output signal in relation to those of the input signal are: minimum time jitter, excellent duration reproducibility and maximum efficiency. Some rules are given for achieving these results. The symmetry, transitivity and non-transitivity characteristics of the edges on the primitive graph are analyzed and lead to some rules for positioning the states on a secondary graph. It is from this graph that the equations of the circuits can be calculated. The development of the circuit and its dynamic testing are discussed. For this testing, the functioning of the circuit is simulated by feeding into the input randomly generated signals
Fixation probability in a two-locus intersexual selection model.

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Durand, Guillermo; Lessard, Sabin

2016-06-01

We study a two-locus model of intersexual selection in a finite haploid population reproducing according to a discrete-time Moran model with a trait locus expressed in males and a preference locus expressed in females. We show that the probability of ultimate fixation of a single mutant allele for a male ornament introduced at random at the trait locus given any initial frequency state at the preference locus is increased by weak intersexual selection and recombination, weak or strong. Moreover, this probability exceeds the initial frequency of the mutant allele even in the case of a costly male ornament if intersexual selection is not too weak. On the other hand, the probability of ultimate fixation of a single mutant allele for a female preference towards a male ornament introduced at random at the preference locus is increased by weak intersexual selection and weak recombination if the female preference is not costly, and is strong enough in the case of a costly male ornament. The analysis relies on an extension of the ancestral recombination-selection graph for samples of haplotypes to take into account events of intersexual selection, while the symbolic calculation of the fixation probabilities is made possible in a reasonable time by an optimizing algorithm. Copyright © 2016 Elsevier Inc. All rights reserved.
Active Learning Not Associated with Student Learning in a Random Sample of College Biology Courses

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Andrews, T. M.; Leonard, M. J.; Colgrove, C. A.; Kalinowski, S. T.

2011-01-01

Previous research has suggested that adding active learning to traditional college science lectures substantially improves student learning. However, this research predominantly studied courses taught by science education researchers, who are likely to have exceptional teaching expertise. The present study investigated introductory biology courses randomly selected from a list of prominent colleges and universities to include instructors representing a broader population. We examined the relationship between active learning and student learning in the subject area of natural selection. We found no association between student learning gains and the use of active-learning instruction. Although active learning has the potential to substantially improve student learning, this research suggests that active learning, as used by typical college biology instructors, is not associated with greater learning gains. We contend that most instructors lack the rich and nuanced understanding of teaching and learning that science education researchers have developed. Therefore, active learning as designed and implemented by typical college biology instructors may superficially resemble active learning used by education researchers, but lacks the constructivist elements necessary for improving learning. PMID:22135373
Detection of Salmonella spp. in veterinary samples by combining selective enrichment and real-time PCR.

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Goodman, Laura B; McDonough, Patrick L; Anderson, Renee R; Franklin-Guild, Rebecca J; Ryan, James R; Perkins, Gillian A; Thachil, Anil J; Glaser, Amy L; Thompson, Belinda S

2017-11-01

Rapid screening for enteric bacterial pathogens in clinical environments is essential for biosecurity. Salmonella found in veterinary hospitals, particularly Salmonella enterica serovar Dublin, can pose unique challenges for culture and testing because of its poor growth. Multiple Salmonella serovars including Dublin are emerging threats to public health given increasing prevalence and antimicrobial resistance. We adapted an automated food testing method to veterinary samples and evaluated the performance of the method in a variety of matrices including environmental samples ( n = 81), tissues ( n = 52), feces ( n = 148), and feed ( n = 29). A commercial kit was chosen as the basis for this approach in view of extensive performance characterizations published by multiple independent organizations. A workflow was established for efficiently and accurately testing veterinary matrices and environmental samples by use of real-time PCR after selective enrichment in Rappaport-Vassiliadis soya (RVS) medium. Using this method, the detection limit for S. Dublin improved by 100-fold over subculture on selective agars (eosin-methylene blue, brilliant green, and xylose-lysine-deoxycholate). Overall, the procedure was effective in detecting Salmonella spp. and provided next-day results.
Evaluation of sampling strategies to estimate crown biomass

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Krishna P Poudel

2015-01-01

Full Text Available Background Depending on tree and site characteristics crown biomass accounts for a significant portion of the total aboveground biomass in the tree. Crown biomass estimation is useful for different purposes including evaluating the economic feasibility of crown utilization for energy production or forest products, fuel load assessments and fire management strategies, and wildfire modeling. However, crown biomass is difficult to predict because of the variability within and among species and sites. Thus the allometric equations used for predicting crown biomass should be based on data collected with precise and unbiased sampling strategies. In this study, we evaluate the performance different sampling strategies to estimate crown biomass and to evaluate the effect of sample size in estimating crown biomass. Methods Using data collected from 20 destructively sampled trees, we evaluated 11 different sampling strategies using six evaluation statistics: bias, relative bias, root mean square error (RMSE, relative RMSE, amount of biomass sampled, and relative biomass sampled. We also evaluated the performance of the selected sampling strategies when different numbers of branches (3, 6, 9, and 12 are selected from each tree. Tree specific log linear model with branch diameter and branch length as covariates was used to obtain individual branch biomass. Results Compared to all other methods stratified sampling with probability proportional to size estimation technique produced better results when three or six branches per tree were sampled. However, the systematic sampling with ratio estimation technique was the best when at least nine branches per tree were sampled. Under the stratified sampling strategy, selecting unequal number of branches per stratum produced approximately similar results to simple random sampling, but it further decreased RMSE when information on branch diameter is used in the design and estimation phases. Conclusions Use of
Sample selection via angular distance in the space of the arguments of an artificial neural network

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Fernández Jaramillo, J. M.; Mayerle, R.

2018-05-01

In the construction of an artificial neural network (ANN) a proper data splitting of the available samples plays a major role in the training process. This selection of subsets for training, testing and validation affects the generalization ability of the neural network. Also the number of samples has an impact in the time required for the design of the ANN and the training. This paper introduces an efficient and simple method for reducing the set of samples used for training a neural network. The method reduces the required time to calculate the network coefficients, while keeping the diversity and avoiding overtraining the ANN due the presence of similar samples. The proposed method is based on the calculation of the angle between two vectors, each one representing one input of the neural network. When the angle formed among samples is smaller than a defined threshold only one input is accepted for the training. The accepted inputs are scattered throughout the sample space. Tidal records are used to demonstrate the proposed method. The results of a cross-validation show that with few inputs the quality of the outputs is not accurate and depends on the selection of the first sample, but as the number of inputs increases the accuracy is improved and differences among the scenarios with a different starting sample have and important reduction. A comparison with the K-means clustering algorithm shows that for this application the proposed method with a smaller number of samples is producing a more accurate network.

On Random Numbers and Design

Science.gov (United States)

Ben-Ari, Morechai

2004-01-01

The term "random" is frequently used in discussion of the theory of evolution, even though the mathematical concept of randomness is problematic and of little relevance in the theory. Therefore, since the core concept of the theory of evolution is the non-random process of natural selection, the term random should not be used in teaching the…
Territory and nest site selection patterns by Grasshopper Sparrows in southeastern Arizona

Science.gov (United States)

Ruth, Janet M.; Skagen, Susan K.

2017-01-01

Grassland bird populations are showing some of the greatest rates of decline of any North American birds, prompting measures to protect and improve important habitat. We assessed how vegetation structure and composition, habitat features often targeted for management, affected territory and nest site selection by Grasshopper Sparrows (Ammodramus savannarum ammolegus) in southeastern Arizona. To identify features important to males establishing territories, we compared vegetation characteristics of known territories and random samples on 2 sites over 5 years. We examined habitat selection patterns of females by comparing characteristics of nest sites with territories over 3 years. Males selected territories in areas of sparser vegetation structure and more tall shrubs (>2 m) than random plots on the site with low shrub densities. Males did not select territories based on the proportion of exotic grasses. Females generally located nest sites in areas with lower small shrub (1–2 m tall) densities than territories overall when possible and preferentially selected native grasses for nest construction. Whether habitat selection was apparent depended upon the range of vegetation structure that was available. We identified an upper threshold above which grass structure seemed to be too high and dense for Grasshopper Sparrows. Our results suggest that some management that reduces vegetative structure may benefit this species in desert grasslands at the nest and territory scale. However, we did not assess initial male habitat selection at a broader landscape scale where their selection patterns may be different and could be influenced by vegetation density and structure outside the range of values sampled in this study.
A GMM-Based Test for Normal Disturbances of the Heckman Sample Selection Model

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Michael Pfaffermayr

2014-10-01

Full Text Available The Heckman sample selection model relies on the assumption of normal and homoskedastic disturbances. However, before considering more general, alternative semiparametric models that do not need the normality assumption, it seems useful to test this assumption. Following Meijer and Wansbeek (2007, the present contribution derives a GMM-based pseudo-score LM test on whether the third and fourth moments of the disturbances of the outcome equation of the Heckman model conform to those implied by the truncated normal distribution. The test is easy to calculate and in Monte Carlo simulations it shows good performance for sample sizes of 1000 or larger.
Selecting for Fast Protein-Protein Association As Demonstrated on a Random TEM1 Yeast Library Binding BLIP.

Science.gov (United States)

Cohen-Khait, Ruth; Schreiber, Gideon

2018-04-27

Protein-protein interactions mediate the vast majority of cellular processes. Though protein interactions obey basic chemical principles also within the cell, the in vivo physiological environment may not allow for equilibrium to be reached. Thus, in vitro measured thermodynamic affinity may not provide a complete picture of protein interactions in the biological context. Binding kinetics composed of the association and dissociation rate constants are relevant and important in the cell. Therefore, changes in protein-protein interaction kinetics have a significant impact on the in vivo activity of the proteins. The common protocol for the selection of tighter binders from a mutant library selects for protein complexes with slower dissociation rate constants. Here we describe a method to specifically select for variants with faster association rate constants by using pre-equilibrium selection, starting from a large random library. Toward this end, we refine the selection conditions of a TEM1-β-lactamase library against its natural nanomolar affinity binder β-lactamase inhibitor protein (BLIP). The optimal selection conditions depend on the ligand concentration and on the incubation time. In addition, we show that a second sort of the library helps to separate signal from noise, resulting in a higher percent of faster binders in the selected library. Fast associating protein variants are of particular interest for drug development and other biotechnological applications.
Domain Adaptation for Machine Translation with Instance Selection

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Biçici Ergun

2015-04-01

Full Text Available Domain adaptation for machine translation (MT can be achieved by selecting training instances close to the test set from a larger set of instances. We consider 7 different domain adaptation strategies and answer 7 research questions, which give us a recipe for domain adaptation in MT. We perform English to German statistical MT (SMT experiments in a setting where test and training sentences can come from different corpora and one of our goals is to learn the parameters of the sampling process. Domain adaptation with training instance selection can obtain 22% increase in target 2-gram recall and can gain up to 3:55 BLEU points compared with random selection. Domain adaptation with feature decay algorithm (FDA not only achieves the highest target 2-gram recall and BLEU performance but also perfectly learns the test sample distribution parameter with correlation 0:99. Moses SMT systems built with FDA selected 10K training sentences is able to obtain F1 results as good as the baselines that use up to 2M sentences. Moses SMT systems built with FDA selected 50K training sentences is able to obtain F1 point better results than the baselines.
Fast randomized point location without preprocessing in two- and three-dimensional Delaunay triangulations

Energy Technology Data Exchange (ETDEWEB)

Muecke, E.P.; Saias, I.; Zhu, B.

1996-05-01

This paper studies the point location problem in Delaunay triangulations without preprocessing and additional storage. The proposed procedure finds the query point simply by walking through the triangulation, after selecting a good starting point by random sampling. The analysis generalizes and extends a recent result of d = 2 dimensions by proving this procedure to take expected time close to O(n{sup 1/(d+1)}) for point location in Delaunay triangulations of n random points in d = 3 dimensions. Empirical results in both two and three dimensions show that this procedure is efficient in practice.
Forecasting Urban Air Quality via a Back-Propagation Neural Network and a Selection Sample Rule

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Yonghong Liu

2015-07-01

Full Text Available In this paper, based on a sample selection rule and a Back Propagation (BP neural network, a new model of forecasting daily SO2, NO2, and PM10 concentration in seven sites of Guangzhou was developed using data from January 2006 to April 2012. A meteorological similarity principle was applied in the development of the sample selection rule. The key meteorological factors influencing SO2, NO2, and PM10 daily concentrations as well as weight matrices and threshold matrices were determined. A basic model was then developed based on the improved BP neural network. Improving the basic model, identification of the factor variation consistency was added in the rule, and seven sets of sensitivity experiments in one of the seven sites were conducted to obtain the selected model. A comparison of the basic model from May 2011 to April 2012 in one site showed that the selected model for PM10 displayed better forecasting performance, with Mean Absolute Percentage Error (MAPE values decreasing by 4% and R2 values increasing from 0.53 to 0.68. Evaluations conducted at the six other sites revealed a similar performance. On the whole, the analysis showed that the models presented here could provide local authorities with reliable and precise predictions and alarms about air quality if used at an operational scale.
Importance sampling of heavy-tailed iterated random functions

NARCIS (Netherlands)

B. Chen (Bohan); C.H. Rhee (Chang-Han); A.P. Zwart (Bert)

2016-01-01

textabstractWe consider a stochastic recurrence equation of the form $Z_{n+1} = A_{n+1} Z_n+B_{n+1}$, where $\\mathbb{E}[\\log A_1]<0$, $\\mathbb{E}[\\log^+ B_1]<\\infty$ and $\\{(A_n,B_n)\\}_{n\\in\\mathbb{N}}$ is an i.i.d. sequence of positive random vectors. The stationary distribution of this Markov
Randomized Comparison of Two Vaginal Self-Sampling Methods for Human Papillomavirus Detection: Dry Swab versus FTA Cartridge.

Directory of Open Access Journals (Sweden)

Rosa Catarino

Full Text Available Human papillomavirus (HPV self-sampling (self-HPV is valuable in cervical cancer screening. HPV testing is usually performed on physician-collected cervical smears stored in liquid-based medium. Dry filters and swabs are an alternative. We evaluated the adequacy of self-HPV using two dry storage and transport devices, the FTA cartridge and swab.A total of 130 women performed two consecutive self-HPV samples. Randomization determined which of the two tests was performed first: self-HPV using dry swabs (s-DRY or vaginal specimen collection using a cytobrush applied to an FTA cartridge (s-FTA. After self-HPV, a physician collected a cervical sample using liquid-based medium (Dr-WET. HPV types were identified by real-time PCR. Agreement between collection methods was measured using the kappa statistic.HPV prevalence for high-risk types was 62.3% (95%CI: 53.7-70.2 detected by s-DRY, 56.2% (95%CI: 47.6-64.4 by Dr-WET, and 54.6% (95%CI: 46.1-62.9 by s-FTA. There was overall agreement of 70.8% between s-FTA and s-DRY samples (kappa = 0.34, and of 82.3% between self-HPV and Dr-WET samples (kappa = 0.56. Detection sensitivities for low-grade squamous intraepithelial lesion or worse (LSIL+ were: 64.0% (95%CI: 44.5-79.8 for s-FTA, 84.6% (95%CI: 66.5-93.9 for s-DRY, and 76.9% (95%CI: 58.0-89.0 for Dr-WET. The preferred self-collection method among patients was s-DRY (40.8% vs. 15.4%. Regarding costs, FTA card was five times more expensive than the swab (~5 US dollars (USD/per card vs. ~1 USD/per swab.Self-HPV using dry swabs is sensitive for detecting LSIL+ and less expensive than s-FTA.International Standard Randomized Controlled Trial Number (ISRCTN: 43310942.
A Permutation Importance-Based Feature Selection Method for Short-Term Electricity Load Forecasting Using Random Forest

Directory of Open Access Journals (Sweden)

Nantian Huang

2016-09-01

Full Text Available The prediction accuracy of short-term load forecast (STLF depends on prediction model choice and feature selection result. In this paper, a novel random forest (RF-based feature selection method for STLF is proposed. First, 243 related features were extracted from historical load data and the time information of prediction points to form the original feature set. Subsequently, the original feature set was used to train an RF as the original model. After the training process, the prediction error of the original model on the test set was recorded and the permutation importance (PI value of each feature was obtained. Then, an improved sequential backward search method was used to select the optimal forecasting feature subset based on the PI value of each feature. Finally, the optimal forecasting feature subset was used to train a new RF model as the final prediction model. Experiments showed that the prediction accuracy of RF trained by the optimal forecasting feature subset was higher than that of the original model and comparative models based on support vector regression and artificial neural network.
Relative efficiency and sample size for cluster randomized trials with variable cluster sizes.

Science.gov (United States)

You, Zhiying; Williams, O Dale; Aban, Inmaculada; Kabagambe, Edmond Kato; Tiwari, Hemant K; Cutter, Gary

2011-02-01

The statistical power of cluster randomized trials depends on two sample size components, the number of clusters per group and the numbers of individuals within clusters (cluster size). Variable cluster sizes are common and this variation alone may have significant impact on study power. Previous approaches have taken this into account by either adjusting total sample size using a designated design effect or adjusting the number of clusters according to an assessment of the relative efficiency of unequal versus equal cluster sizes. This article defines a relative efficiency of unequal versus equal cluster sizes using noncentrality parameters, investigates properties of this measure, and proposes an approach for adjusting the required sample size accordingly. We focus on comparing two groups with normally distributed outcomes using t-test, and use the noncentrality parameter to define the relative efficiency of unequal versus equal cluster sizes and show that statistical power depends only on this parameter for a given number of clusters. We calculate the sample size required for an unequal cluster sizes trial to have the same power as one with equal cluster sizes. Relative efficiency based on the noncentrality parameter is straightforward to calculate and easy to interpret. It connects the required mean cluster size directly to the required sample size with equal cluster sizes. Consequently, our approach first determines the sample size requirements with equal cluster sizes for a pre-specified study power and then calculates the required mean cluster size while keeping the number of clusters unchanged. Our approach allows adjustment in mean cluster size alone or simultaneous adjustment in mean cluster size and number of clusters, and is a flexible alternative to and a useful complement to existing methods. Comparison indicated that we have defined a relative efficiency that is greater than the relative efficiency in the literature under some conditions. Our measure
The Complete Local Volume Groups Sample - I. Sample selection and X-ray properties of the high-richness subsample

Science.gov (United States)

O'Sullivan, Ewan; Ponman, Trevor J.; Kolokythas, Konstantinos; Raychaudhury, Somak; Babul, Arif; Vrtilek, Jan M.; David, Laurence P.; Giacintucci, Simona; Gitti, Myriam; Haines, Chris P.

2017-12-01

We present the Complete Local-Volume Groups Sample (CLoGS), a statistically complete optically selected sample of 53 groups within 80 Mpc. Our goal is to combine X-ray, radio and optical data to investigate the relationship between member galaxies, their active nuclei and the hot intra-group medium (IGM). We describe sample selection, define a 26-group high-richness subsample of groups containing at least four optically bright (log LB ≥ 10.2 LB⊙) galaxies, and report the results of XMM-Newton and Chandra observations of these systems. We find that 14 of the 26 groups are X-ray bright, possessing a group-scale IGM extending at least 65 kpc and with luminosity >1041 erg s-1, while a further three groups host smaller galaxy-scale gas haloes. The X-ray bright groups have masses in the range M500 ≃ 0.5-5 × 1013 M⊙, based on system temperatures of 0.4-1.4 keV, and X-ray luminosities in the range 2-200 × 1041 erg s-1. We find that ∼53-65 per cent of the X-ray bright groups have cool cores, a somewhat lower fraction than found by previous archival surveys. Approximately 30 per cent of the X-ray bright groups show evidence of recent dynamical interactions (mergers or sloshing), and ∼35 per cent of their dominant early-type galaxies host active galactic nuclei with radio jets. We find no groups with unusually high central entropies, as predicted by some simulations, and confirm that CLoGS is in principle capable of detecting such systems. We identify three previously unrecognized groups, and find that they are either faint (LX, R500 < 1042 erg s-1) with no concentrated cool core, or highly disturbed. This leads us to suggest that ∼20 per cent of X-ray bright groups in the local universe may still be unidentified.
A novel variable selection approach that iteratively optimizes variable space using weighted binary matrix sampling.

Science.gov (United States)

Deng, Bai-chuan; Yun, Yong-huan; Liang, Yi-zeng; Yi, Lun-zhao

2014-10-07

In this study, a new optimization algorithm called the Variable Iterative Space Shrinkage Approach (VISSA) that is based on the idea of model population analysis (MPA) is proposed for variable selection. Unlike most of the existing optimization methods for variable selection, VISSA statistically evaluates the performance of variable space in each step of optimization. Weighted binary matrix sampling (WBMS) is proposed to generate sub-models that span the variable subspace. Two rules are highlighted during the optimization procedure. First, the variable space shrinks in each step. Second, the new variable space outperforms the previous one. The second rule, which is rarely satisfied in most of the existing methods, is the core of the VISSA strategy. Compared with some promising variable selection methods such as competitive adaptive reweighted sampling (CARS), Monte Carlo uninformative variable elimination (MCUVE) and iteratively retaining informative variables (IRIV), VISSA showed better prediction ability for the calibration of NIR data. In addition, VISSA is user-friendly; only a few insensitive parameters are needed, and the program terminates automatically without any additional conditions. The Matlab codes for implementing VISSA are freely available on the website: https://sourceforge.net/projects/multivariateanalysis/files/VISSA/.
Direct random insertion mutagenesis of Helicobacter pylori

NARCIS (Netherlands)

de Jonge, Ramon; Bakker, Dennis; van Vliet, Arnoud H. M.; Kuipers, Ernst J.; Vandenbroucke-Grauls, Christina M. J. E.; Kusters, Johannes G.

2003-01-01

Random insertion mutagenesis is a widely used technique for the identification of bacterial virulence genes. Most strategies for random mutagenesis involve cloning in Escherichia coli for passage of plasmids or for phenotypic selection. This can result in biased selection due to restriction or
Direct random insertion mutagenesis of Helicobacter pylori.

NARCIS (Netherlands)

Jonge, de R.; Bakker, D.; Vliet, van AH; Kuipers, E.J.; Vandenbroucke-Grauls, C.M.J.E.; Kusters, J.G.

2003-01-01

Random insertion mutagenesis is a widely used technique for the identification of bacterial virulence genes. Most strategies for random mutagenesis involve cloning in Escherichia coli for passage of plasmids or for phenotypic selection. This can result in biased selection due to restriction or
New complete sample of identified radio sources. Part 2. Statistical study

International Nuclear Information System (INIS)

Soltan, A.

1978-01-01

Complete sample of radio sources with known redshifts selected in Paper I is studied. Source counts in the sample and the luminosity - volume test show that both quasars and galaxies are subject to the evolution. Luminosity functions for different ranges of redshifts are obtained. Due to many uncertainties only simplified models of the evolution are tested. Exponential decline of the liminosity with time of all the bright sources is in a good agreement both with the luminosity- volume test and N(S) realtion in the entire range of observed flux densities. It is shown that sources in the sample are randomly distributed in scales greater than about 17 Mpc. (author)
Internet use by students in selected departments of the faculty of ...

African Journals Online (AJOL)

The study investigated the use to which the internet is put by students of the faculty of Agriculture, University of Benin, Benin City, Nigeria. A simple random sampling technique was used in selecting four departments from six departments in the faculty. Data were gathered with the use of a structured questionnaire from a ...
40 CFR Appendix Xi to Part 86 - Sampling Plans for Selective Enforcement Auditing of Light-Duty Vehicles

Science.gov (United States)

2010-07-01

... 40 Protection of Environment 19 2010-07-01 2010-07-01 false Sampling Plans for Selective Enforcement Auditing of Light-Duty Vehicles XI Appendix XI to Part 86 Protection of Environment ENVIRONMENTAL... Enforcement Auditing of Light-Duty Vehicles 40% AQL Table 1—Sampling Plan Code Letter Annual sales of...
Variation in the annual unsatisfactory rates of selected pathogens and indicators in ready-to-eat food sampled from the point of sale or service in Wales, United Kingdom.

Science.gov (United States)

Meldrum, R J; Garside, J; Mannion, P; Charles, D; Ellis, P

2012-12-01

The Welsh Food Microbiological Forum "shopping basket" survey is a long running, structured surveillance program examining ready-to-eat food randomly sampled from the point of sale or service in Wales, United Kingdom. The annual unsatisfactory rates for selected indicators and pathogens for 1998 through 2008 were examined. All the annual unsatisfactory rates for the selected pathogens were <0.5%, and no pattern with the annual rate was observed. There was also no discernible trend observed for the annual rates of Listeria spp. (not moncytogenes), with all rates <0.5%. However, there was a trend observed for Esherichia coli, with a decrease in rate between 1998 and 2003, rapid in the first few years, and then a gradual increase in rate up to 2008. It was concluded that there was no discernible pattern to the annual unsatisfactory rates for Listeria spp. (not monocytogenes), L. monocytogenes, Staphylococcus aureus, and Bacillus cereus, but that a definite trend had been observed for E. coli.
Power Spectrum Estimation of Randomly Sampled Signals

DEFF Research Database (Denmark)

Velte, C. M.; Buchhave, P.; K. George, W.

algorithms; sample and-hold and the direct spectral estimator without residence time weighting. The computer generated signal is a Poisson process with a sample rate proportional to velocity magnitude that consist of well-defined frequency content, which makes bias easy to spot. The idea...

A sero-survey of rinderpest in nomadic pastoral systems in central and southern Somalia from 2002 to 2003, using a spatially integrated random sampling approach.

Science.gov (United States)

Tempia, S; Salman, M D; Keefe, T; Morley, P; Freier, J E; DeMartini, J C; Wamwayi, H M; Njeumi, F; Soumaré, B; Abdi, A M

2010-12-01

A cross-sectional sero-survey, using a two-stage cluster sampling design, was conducted between 2002 and 2003 in ten administrative regions of central and southern Somalia, to estimate the seroprevalence and geographic distribution of rinderpest (RP) in the study area, as well as to identify potential risk factors for the observed seroprevalence distribution. The study was also used to test the feasibility of the spatially integrated investigation technique in nomadic and semi-nomadic pastoral systems. In the absence of a systematic list of livestock holdings, the primary sampling units were selected by generating random map coordinates. A total of 9,216 serum samples were collected from cattle aged 12 to 36 months at 562 sampling sites. Two apparent clusters of RP seroprevalence were detected. Four potential risk factors associated with the observed seroprevalence were identified: the mobility of cattle herds, the cattle population density, the proximity of cattle herds to cattle trade routes and cattle herd size. Risk maps were then generated to assist in designing more targeted surveillance strategies. The observed seroprevalence in these areas declined over time. In subsequent years, similar seroprevalence studies in neighbouring areas of Kenya and Ethiopia also showed a very low seroprevalence of RP or the absence of antibodies against RP. The progressive decline in RP antibody prevalence is consistent with virus extinction. Verification of freedom from RP infection in the Somali ecosystem is currently in progress.
40 CFR Appendix X to Part 86 - Sampling Plans for Selective Enforcement Auditing of Heavy-Duty Engines and Light-Duty Trucks

Science.gov (United States)

2010-07-01

... 40 Protection of Environment 19 2010-07-01 2010-07-01 false Sampling Plans for Selective Enforcement Auditing of Heavy-Duty Engines and Light-Duty Trucks X Appendix X to Part 86 Protection of... Plans for Selective Enforcement Auditing of Heavy-Duty Engines and Light-Duty Trucks Table 1—Sampling...
Selective oropharyngeal decontamination versus selective digestive decontamination in critically ill patients: a meta-analysis of randomized controlled trials

Directory of Open Access Journals (Sweden)

Zhao D

2015-07-01

Full Text Available Di Zhao,1,* Jian Song,2,* Xuan Gao,3 Fei Gao,4 Yupeng Wu,2 Yingying Lu,5 Kai Hou1 1Department of Neurosurgery, The First Hospital of Hebei Medical University, 2Department of Neurosurgery, 3Department of Neurology, The Second Hospital of Hebei Medical University, 4Hebei Provincial Procurement Centers for Medical Drugs and Devices, 5Department of Neurosurgery, The Second Hospital of Hebei Medical University, Shijiazhuang People’s Republic of China *These authors contributed equally to this work Background: Selective digestive decontamination (SDD and selective oropharyngeal decontamination (SOD are associated with reduced mortality and infection rates among patients in intensive care units (ICUs; however, whether SOD has a superior effect than SDD remains uncertain. Hence, we conducted a meta-analysis of randomized controlled trials (RCTs to compare SOD with SDD in terms of clinical outcomes and antimicrobial resistance rates in patients who were critically ill. Methods: RCTs published in PubMed, Embase, and Web of Science were systematically reviewed to compare the effects of SOD and SDD in patients who were critically ill. Outcomes included day-28 mortality, length of ICU stay, length of hospital stay, duration of mechanical ventilation, ICU-acquired bacteremia, and prevalence of antibiotic-resistant Gram-negative bacteria. Results were expressed as risk ratio (RR with 95% confidence intervals (CIs, and weighted mean differences (WMDs with 95% CIs. Pooled estimates were performed using a fixed-effects model or random-effects model, depending on the heterogeneity among studies. Results: A total of four RCTs involving 23,822 patients met the inclusion criteria and were included in this meta-analysis. Among patients whose admitting specialty was surgery, cardiothoracic surgery (57.3% and neurosurgery (29.7% were the two main types of surgery being performed. Pooled results showed that SOD had similar effects as SDD in day-28 mortality (RR =1
Marginal bone level in two Danish cross-sectional population samples in 1997-1998 and 2007-2008.

Science.gov (United States)

Bahrami, Golnosh; Vaeth, Michael; Wenzel, Ann; Isidor, Flemming

2018-04-12

The aim of this study was to compare the marginal bone level of two randomly selected population samples from 1997/1998 and 2007/2008, with special emphasis on the role of smoking habits and gender. Two cross-sectional randomly selected population samples [1997/1998 (N = 616) and 2007/2008 (N = 396)] were analysed with respect to the marginal bone level. The marginal bone level was measured in full-mouth intraoral radiographs. Information on smoking was gathered using questionnaires. Multiple regression analysis was used in order to adjust for correlating factors (gender, age, smoking habits and number of teeth). After adjusting for confounding factors, the population sample from 2007/2008 had on average a slightly, but statistically significantly, more reduced average marginal bone level (0.15 mm) than the population sample from 1997/1998. Men had more reduced marginal bone level than women (0.12 mm). Smokers in both population samples had more reduced marginal bone level than non-smokers (0.39 mm and 0.12 mm for 1997/1998; 0.65 mm and 0.16 mm for 2007/2008). In these populations, sampled 10 years apart, the 2007/2008 population sample had a slightly more reduced marginal bone level than the 1997/1998 population sample. Men had more reduced marginal bone level than women, and smoking is considered a major risk factor for a reduced marginal bone level.
Strategyproof Peer Selection using Randomization, Partitioning, and Apportionment

OpenAIRE

Aziz, Haris; Lev, Omer; Mattei, Nicholas; Rosenschein, Jeffrey S.; Walsh, Toby

2016-01-01

Peer review, evaluation, and selection is a fundamental aspect of modern science. Funding bodies the world over employ experts to review and select the best proposals of those submitted for funding. The problem of peer selection, however, is much more general: a professional society may want to give a subset of its members awards based on the opinions of all members; an instructor for a MOOC or online course may want to crowdsource grading; or a marketing company may select ideas from group b...
Selective whole genome amplification for resequencing target microbial species from complex natural samples.

Science.gov (United States)

Leichty, Aaron R; Brisson, Dustin

2014-10-01

Population genomic analyses have demonstrated power to address major questions in evolutionary and molecular microbiology. Collecting populations of genomes is hindered in many microbial species by the absence of a cost effective and practical method to collect ample quantities of sufficiently pure genomic DNA for next-generation sequencing. Here we present a simple method to amplify genomes of a target microbial species present in a complex, natural sample. The selective whole genome amplification (SWGA) technique amplifies target genomes using nucleotide sequence motifs that are common in the target microbe genome, but rare in the background genomes, to prime the highly processive phi29 polymerase. SWGA thus selectively amplifies the target genome from samples in which it originally represented a minor fraction of the total DNA. The post-SWGA samples are enriched in target genomic DNA, which are ideal for population resequencing. We demonstrate the efficacy of SWGA using both laboratory-prepared mixtures of cultured microbes as well as a natural host-microbe association. Targeted amplification of Borrelia burgdorferi mixed with Escherichia coli at genome ratios of 1:2000 resulted in >10(5)-fold amplification of the target genomes with genomic extracts from Wolbachia pipientis-infected Drosophila melanogaster resulted in up to 70% of high-throughput resequencing reads mapping to the W. pipientis genome. By contrast, 2-9% of sequencing reads were derived from W. pipientis without prior amplification. The SWGA technique results in high sequencing coverage at a fraction of the sequencing effort, thus allowing population genomic studies at affordable costs. Copyright © 2014 by the Genetics Society of America.
Concurrent and Longitudinal Associations Among Temperament, Parental Feeding Styles, and Selective Eating in a Preschool Sample.

Science.gov (United States)

Kidwell, Katherine M; Kozikowski, Chelsea; Roth, Taylor; Lundahl, Alyssa; Nelson, Timothy D

2018-06-01

To examine the associations among negative/reactive temperament, feeding styles, and selective eating in a sample of preschoolers because preschool eating behaviors likely have lasting implications for children's health. A community sample of preschoolers aged 3-5 years (M = 4.49 years, 49.5% female, 75.7% European American) in the Midwest of the United States was recruited to participate in the study (N = 297). Parents completed measures of temperament and feeding styles at two time points 6 months apart. A series of regressions indicated that children who had temperaments high in negative affectivity were significantly more likely to experience instrumental and emotional feeding styles. They were also significantly more likely to be selective eaters. These associations were present when examined both concurrently and after 6 months. This study provides a novel investigation of child temperament and eating behaviors, allowing for a better understanding of how negative affectivity is associated with instrumental feeding, emotional feeding, and selective eating. These results inform interventions to improve child health.
Sampling Strategies and Processing of Biobank Tissue Samples from Porcine Biomedical Models.

Science.gov (United States)

Blutke, Andreas; Wanke, Rüdiger

2018-03-06

In translational medical research, porcine models have steadily become more popular. Considering the high value of individual animals, particularly of genetically modified pig models, and the often-limited number of available animals of these models, establishment of (biobank) collections of adequately processed tissue samples suited for a broad spectrum of subsequent analyses methods, including analyses not specified at the time point of sampling, represent meaningful approaches to take full advantage of the translational value of the model. With respect to the peculiarities of porcine anatomy, comprehensive guidelines have recently been established for standardized generation of representative, high-quality samples from different porcine organs and tissues. These guidelines are essential prerequisites for the reproducibility of results and their comparability between different studies and investigators. The recording of basic data, such as organ weights and volumes, the determination of the sampling locations and of the numbers of tissue samples to be generated, as well as their orientation, size, processing and trimming directions, are relevant factors determining the generalizability and usability of the specimen for molecular, qualitative, and quantitative morphological analyses. Here, an illustrative, practical, step-by-step demonstration of the most important techniques for generation of representative, multi-purpose biobank specimen from porcine tissues is presented. The methods described here include determination of organ/tissue volumes and densities, the application of a volume-weighted systematic random sampling procedure for parenchymal organs by point-counting, determination of the extent of tissue shrinkage related to histological embedding of samples, and generation of randomly oriented samples for quantitative stereological analyses, such as isotropic uniform random (IUR) sections generated by the "Orientator" and "Isector" methods, and vertical
Assessing differences in groups randomized by recruitment chain in a respondent-driven sample of Seattle-area injection drug users.

Science.gov (United States)

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.
Different hunting strategies select for different weights in red deer

Science.gov (United States)

Martínez, María; Rodríguez-Vigal, Carlos; Jones, Owen R; Coulson, Tim; Miguel, Alfonso San

2005-01-01

Much insight can be derived from records of shot animals. Most researchers using such data assume that their data represents a random sample of a particular demographic class. However, hunters typically select a non-random subset of the population and hunting is, therefore, not a random process. Here, with red deer (Cervus elaphus) hunting data from a ranch in Toledo, Spain, we demonstrate that data collection methods have a significant influence upon the apparent relationship between age and weight. We argue that a failure to correct for such methodological bias may have significant consequences for the interpretation of analyses involving weight or correlated traits such as breeding success, and urge researchers to explore methods to identify and correct for such bias in their data. PMID:17148205
Correlations Between Life-Detection Techniques and Implications for Sampling Site Selection in Planetary Analog Missions

Science.gov (United States)

Gentry, Diana M.; Amador, Elena S.; Cable, Morgan L.; Chaudry, Nosheen; Cullen, Thomas; Jacobsen, Malene B.; Murukesan, Gayathri; Schwieterman, Edward W.; Stevens, Adam H.; Stockton, Amanda; Tan, George; Yin, Chang; Cullen, David C.; Geppert, Wolf

2017-10-01

We conducted an analog sampling expedition under simulated mission constraints to areas dominated by basaltic tephra of the Eldfell and Fimmvörðuháls lava fields (Iceland). Sites were selected to be "homogeneous" at a coarse remote sensing resolution (10-100 m) in apparent color, morphology, moisture, and grain size, with best-effort realism in numbers of locations and replicates. Three different biomarker assays (counting of nucleic-acid-stained cells via fluorescent microscopy, a luciferin/luciferase assay for adenosine triphosphate, and quantitative polymerase chain reaction (qPCR) to detect DNA associated with bacteria, archaea, and fungi) were characterized at four nested spatial scales (1 m, 10 m, 100 m, and >1 km) by using five common metrics for sample site representativeness (sample mean variance, group F tests, pairwise t tests, and the distribution-free rank sum H and u tests). Correlations between all assays were characterized with Spearman's rank test. The bioluminescence assay showed the most variance across the sites, followed by qPCR for bacterial and archaeal DNA; these results could not be considered representative at the finest resolution tested (1 m). Cell concentration and fungal DNA also had significant local variation, but they were homogeneous over scales of >1 km. These results show that the selection of life detection assays and the number, distribution, and location of sampling sites in a low biomass environment with limited a priori characterization can yield both contrasting and complementary results, and that their interdependence must be given due consideration to maximize science return in future biomarker sampling expeditions.
Effects of soil water saturation on sampling equilibrium and kinetics of selected polycyclic aromatic hydrocarbons.

Science.gov (United States)

Kim, Pil-Gon; Roh, Ji-Yeon; Hong, Yongseok; Kwon, Jung-Hwan

2017-10-01

Passive sampling can be applied for measuring the freely dissolved concentration of hydrophobic organic chemicals (HOCs) in soil pore water. When using passive samplers under field conditions, however, there are factors that might affect passive sampling equilibrium and kinetics, such as soil water saturation. To determine the effects of soil water saturation on passive sampling, the equilibrium and kinetics of passive sampling were evaluated by observing changes in the distribution coefficient between sampler and soil (K sampler/soil ) and the uptake rate constant (k u ) at various soil water saturations. Polydimethylsiloxane (PDMS) passive samplers were deployed into artificial soils spiked with seven selected polycyclic aromatic hydrocarbons (PAHs). In dry soil (0% water saturation), both K sampler/soil and k u values were much lower than those in wet soils likely due to the contribution of adsorption of PAHs onto soil mineral surfaces and the conformational changes in soil organic matter. For high molecular weight PAHs (chrysene, benzo[a]pyrene, and dibenzo[a,h]anthracene), both K sampler/soil and k u values increased with increasing soil water saturation, whereas they decreased with increasing soil water saturation for low molecular weight PAHs (phenanthrene, anthracene, fluoranthene, and pyrene). Changes in the sorption capacity of soil organic matter with soil water content would be the main cause of the changes in passive sampling equilibrium. Henry's law constant could explain the different behaviors in uptake kinetics of the selected PAHs. The results of this study would be helpful when passive samplers are deployed under various soil water saturations. Copyright © 2017 Elsevier Ltd. All rights reserved.
A random cluster survey and a convenience sample give comparable estimates of immunity to vaccine preventable diseases in children of school age in Victoria, Australia.

Science.gov (United States)

Kelly, Heath; Riddell, Michaela A; Gidding, Heather F; Nolan, Terry; Gilbert, Gwendolyn L

2002-08-19

We compared estimates of the age-specific population immunity to measles, mumps, rubella, hepatitis B and varicella zoster viruses in Victorian school children obtained by a national sero-survey, using a convenience sample of residual sera from diagnostic laboratories throughout Australia, with those from a three-stage random cluster survey. When grouped according to school age (primary or secondary school) there was no significant difference in the estimates of immunity to measles, mumps, hepatitis B or varicella. Compared with the convenience sample, the random cluster survey estimated higher immunity to rubella in samples from both primary (98.7% versus 93.6%, P = 0.002) and secondary school students (98.4% versus 93.2%, P = 0.03). Despite some limitations, this study suggests that the collection of a convenience sample of sera from diagnostic laboratories is an appropriate sampling strategy to provide population immunity data that will inform Australia's current and future immunisation policies. Copyright 2002 Elsevier Science Ltd.
The contribution of simple random sampling to observed variations in faecal egg counts.

Science.gov (United States)

Torgerson, Paul R; Paul, Michaela; Lewis, Fraser I

2012-09-10

It has been over 100 years since the classical paper published by Gosset in 1907, under the pseudonym "Student", demonstrated that yeast cells suspended in a fluid and measured by a haemocytometer conformed to a Poisson process. Similarly parasite eggs in a faecal suspension also conform to a Poisson process. Despite this there are common misconceptions how to analyse or interpret observations from the McMaster or similar quantitative parasitic diagnostic techniques, widely used for evaluating parasite eggs in faeces. The McMaster technique can easily be shown from a theoretical perspective to give variable results that inevitably arise from the random distribution of parasite eggs in a well mixed faecal sample. The Poisson processes that lead to this variability are described and illustrative examples of the potentially large confidence intervals that can arise from observed faecal eggs counts that are calculated from the observations on a McMaster slide. Attempts to modify the McMaster technique, or indeed other quantitative techniques, to ensure uniform egg counts are doomed to failure and belie ignorance of Poisson processes. A simple method to immediately identify excess variation/poor sampling from replicate counts is provided. Copyright © 2012 Elsevier B.V. All rights reserved.
Study on the effects of sample selection on spectral reflectance reconstruction based on the algorithm of compressive sensing

International Nuclear Information System (INIS)

Zhang, Leihong; Liang, Dong

2016-01-01

In order to solve the problem that reconstruction efficiency and precision is not high, in this paper different samples are selected to reconstruct spectral reflectance, and a new kind of spectral reflectance reconstruction method based on the algorithm of compressive sensing is provided. Four different color numbers of matte color cards such as the ColorChecker Color Rendition Chart and Color Checker SG, the copperplate paper spot color card of Panton, and the Munsell colors card are chosen as training samples, the spectral image is reconstructed respectively by the algorithm of compressive sensing and pseudo-inverse and Wiener, and the results are compared. These methods of spectral reconstruction are evaluated by root mean square error and color difference accuracy. The experiments show that the cumulative contribution rate and color difference of the Munsell colors card are better than those of the other three numbers of color cards in the same conditions of reconstruction, and the accuracy of the spectral reconstruction will be affected by the training sample of different numbers of color cards. The key technology of reconstruction means that the uniformity and representation of the training sample selection has important significance upon reconstruction. In this paper, the influence of the sample selection on the spectral image reconstruction is studied. The precision of the spectral reconstruction based on the algorithm of compressive sensing is higher than that of the traditional algorithm of spectral reconstruction. By the MATLAB simulation results, it can be seen that the spectral reconstruction precision and efficiency are affected by the different color numbers of the training sample. (paper)
Interval sampling methods and measurement error: a computer simulation.

Science.gov (United States)

Wirth, Oliver; Slaven, James; Taylor, Matthew A

2014-01-01

A simulation study was conducted to provide a more thorough account of measurement error associated with interval sampling methods. A computer program simulated the application of momentary time sampling, partial-interval recording, and whole-interval recording methods on target events randomly distributed across an observation period. The simulation yielded measures of error for multiple combinations of observation period, interval duration, event duration, and cumulative event duration. The simulations were conducted up to 100 times to yield measures of error variability. Although the present simulation confirmed some previously reported characteristics of interval sampling methods, it also revealed many new findings that pertain to each method's inherent strengths and weaknesses. The analysis and resulting error tables can help guide the selection of the most appropriate sampling method for observation-based behavioral assessments. © Society for the Experimental Analysis of Behavior.
Sampling strategy for a large scale indoor radiation survey - a pilot project

International Nuclear Information System (INIS)

Strand, T.; Stranden, E.

1986-01-01

Optimisation of a stratified random sampling strategy for large scale indoor radiation surveys is discussed. It is based on the results from a small scale pilot project where variances in dose rates within different categories of houses were assessed. By selecting a predetermined precision level for the mean dose rate in a given region, the number of measurements needed can be optimised. The results of a pilot project in Norway are presented together with the development of the final sampling strategy for a planned large scale survey. (author)
Randomized Oversampling for Generalized Multiscale Finite Element Methods

KAUST Repository

Calo, Victor M.

2016-03-23

In this paper, we develop efficient multiscale methods for flows in heterogeneous media. We use the generalized multiscale finite element (GMsFEM) framework. GMsFEM approximates the solution space locally using a few multiscale basis functions. This approximation selects an appropriate snapshot space and a local spectral decomposition, e.g., the use of oversampled regions, in order to achieve an efficient model reduction. However, the successful construction of snapshot spaces may be costly if too many local problems need to be solved in order to obtain these spaces. We use a moderate quantity of local solutions (or snapshot vectors) with random boundary conditions on oversampled regions with zero forcing to deliver an efficient methodology. Motivated by the randomized algorithm presented in [P. G. Martinsson, V. Rokhlin, and M. Tygert, A Randomized Algorithm for the approximation of Matrices, YALEU/DCS/TR-1361, Yale University, 2006], we consider a snapshot space which consists of harmonic extensions of random boundary conditions defined in a domain larger than the target region. Furthermore, we perform an eigenvalue decomposition in this small space. We study the application of randomized sampling for GMsFEM in conjunction with adaptivity, where local multiscale spaces are adaptively enriched. Convergence analysis is provided. We present representative numerical results to validate the method proposed.
A quantitative method to detect explosives and selected semivolatiles in soil samples by Fourier transform infrared spectroscopy

International Nuclear Information System (INIS)

Clapper-Gowdy, M.; Dermirgian, J.; Robitaille, G.

1995-01-01

This paper describes a novel Fourier transform infrared (FTIR) spectroscopic method that can be used to rapidly screen soil samples from potentially hazardous waste sites. Samples are heated in a thermal desorption unit and the resultant vapors are collected and analyzed in a long-path gas cell mounted in a FTIR. Laboratory analysis of a soil sample by FTIR takes approximately 10 minutes. This method has been developed to identify and quantify microgram concentrations of explosives in soil samples and is directly applicable to the detection of selected volatile organics, semivolatile organics, and pesticides
47 CFR 1.1604 - Post-selection hearings.

Science.gov (United States)

2010-10-01

... 47 Telecommunication 1 2010-10-01 2010-10-01 false Post-selection hearings. 1.1604 Section 1.1604 Telecommunication FEDERAL COMMUNICATIONS COMMISSION GENERAL PRACTICE AND PROCEDURE Random Selection Procedures for Mass Media Services General Procedures § 1.1604 Post-selection hearings. (a) Following the random...

The Atacama Cosmology Telescope: Physical Properties and Purity of a Galaxy Cluster Sample Selected Via the Sunyaev-Zel'Dovich Effect

Science.gov (United States)

Menanteau, Felipe; Gonzalez, Jorge; Juin, Jean-Baptiste; Marriage, Tobias; Reese, Erik D.; Acquaviva, Viviana; Aguirre, Paula; Appel, John Willam; Baker, Andrew J.; Barrientos, L. Felipe;

2010-01-01

We present optical and X-ray properties for the first confirmed galaxy cluster sample selected by the Sunyaev-Zel'dovich Effect from 148 GHz maps over 455 square degrees of sky made with the Atacama Cosmology Telescope. These maps. coupled with multi-band imaging on 4-meter-class optical telescopes, have yielded a sample of 23 galaxy clusters with redshifts between 0.118 and 1.066. Of these 23 clusters, 10 are newly discovered. The selection of this sample is approximately mass limited and essentially independent of redshift. We provide optical positions, images, redshifts and X-ray fluxes and luminosities for the full sample, and X-ray temperatures of an important subset. The mass limit of the full sample is around 8.0 x 10(exp 14) Stellar Mass. with a number distribution that peaks around a redshift of 0.4. For the 10 highest significance SZE-selected cluster candidates, all of which are optically confirmed, the mass threshold is 1 x 10(exp 15) Stellar Mass and the redshift range is 0.167 to 1.066. Archival observations from Chandra, XMM-Newton. and ROSAT provide X-ray luminosities and temperatures that are broadly consistent with this mass threshold. Our optical follow-up procedure also allowed us to assess the purity of the ACT cluster sample. Eighty (one hundred) percent of the 148 GHz candidates with signal-to-noise ratios greater than 5.1 (5.7) are confirmed as massive clusters. The reported sample represents one of the largest SZE-selected sample of massive clusters over all redshifts within a cosmologically-significant survey volume, which will enable cosmological studies as well as future studies on the evolution, morphology, and stellar populations in the most massive clusters in the Universe.

Random genetic drift, natural selection, and noise in human cranial evolution.

Science.gov (United States)

Roseman, Charles C

2016-08-01

This study assesses the extent to which relationships among groups complicate comparative studies of adaptation in recent human cranial variation and the extent to which departures from neutral additive models of evolution hinder the reconstruction of population relationships among groups using cranial morphology. Using a maximum likelihood evolutionary model fitting approach and a mixed population genomic and cranial data set, I evaluate the relative fits of several widely used models of human cranial evolution. Moreover, I compare the goodness of fit of models of cranial evolution constrained by genomic variation to test hypotheses about population specific departures from neutrality. Models from population genomics are much better fits to cranial variation than are traditional models from comparative human biology. There is not enough evolutionary information in the cranium to reconstruct much of recent human evolution but the influence of population history on cranial variation is strong enough to cause comparative studies of adaptation serious difficulties. Deviations from a model of random genetic drift along a tree-like population history show the importance of environmental effects, gene flow, and/or natural selection on human cranial variation. Moreover, there is a strong signal of the effect of natural selection or an environmental factor on a group of humans from Siberia. The evolution of the human cranium is complex and no one evolutionary process has prevailed at the expense of all others. A holistic unification of phenome, genome, and environmental context, gives us a strong point of purchase on these problems, which is unavailable to any one traditional approach alone. Am J Phys Anthropol 160:582-592, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.
Automated sample plan selection for OPC modeling

Science.gov (United States)

Casati, Nathalie; Gabrani, Maria; Viswanathan, Ramya; Bayraktar, Zikri; Jaiswal, Om; DeMaris, David; Abdo, Amr Y.; Oberschmidt, James; Krause, Andreas

2014-03-01

It is desired to reduce the time required to produce metrology data for calibration of Optical Proximity Correction (OPC) models and also maintain or improve the quality of the data collected with regard to how well that data represents the types of patterns that occur in real circuit designs. Previous work based on clustering in geometry and/or image parameter space has shown some benefit over strictly manual or intuitive selection, but leads to arbitrary pattern exclusion or selection which may not be the best representation of the product. Forming the pattern selection as an optimization problem, which co-optimizes a number of objective functions reflecting modelers' insight and expertise, has shown to produce models with equivalent quality to the traditional plan of record (POR) set but in a less time.
Random and systematic sampling error when hooking fish to monitor skin fluke (Benedenia seriolae) and gill fluke (Zeuxapta seriolae) burden in Australian farmed yellowtail kingfish (Seriola lalandi).

Science.gov (United States)

Fensham, J R; Bubner, E; D'Antignana, T; Landos, M; Caraguel, C G B

2018-05-01

The Australian farmed yellowtail kingfish (Seriola lalandi, YTK) industry monitor skin fluke (Benedenia seriolae) and gill fluke (Zeuxapta seriolae) burden by pooling the fluke count of 10 hooked YTK. The random and systematic error of this sampling strategy was evaluated to assess potential impact on treatment decisions. Fluke abundance (fluke count per fish) in a study cage (estimated 30,502 fish) was assessed five times using the current sampling protocol and its repeatability was estimated the repeatability coefficient (CR) and the coefficient of variation (CV). Individual body weight, fork length, fluke abundance, prevalence, intensity (fluke count per infested fish) and density (fluke count per Kg of fish) were compared between 100 hooked and 100 seined YTK (assumed representative of the entire population) to estimate potential selection bias. Depending on the fluke species and age category, CR (expected difference in parasite count between 2 sampling iterations) ranged from 0.78 to 114 flukes per fish. Capturing YTK by hooking increased the selection of fish of a weight and length in the lowest 5th percentile of the cage (RR = 5.75, 95% CI: 2.06-16.03, P-value = 0.0001). These lower end YTK had on average an extra 31 juveniles and 6 adults Z. seriolae per Kg of fish and an extra 3 juvenile and 0.4 adult B. seriolae per Kg of fish, compared to the rest of the cage population (P-value sampling towards the smallest and most heavily infested fish in the population, resulting in poor repeatability (more variability amongst sampled fish) and an overestimation of parasite burden in the population. In this particular commercial situation these finding supported that health management program, where the finding of an underestimation of parasite burden could provide a production impact on the study population. In instances where fish populations and parasite burdens are more homogenous, sampling error may be less severe. Sampling error when capturing fish
Sampling for Patient Exit Interviews: Assessment of Methods Using Mathematical Derivation and Computer Simulations.

Science.gov (United States)

Geldsetzer, Pascal; Fink, Günther; Vaikath, Maria; Bärnighausen, Till

2018-02-01

(1) To evaluate the operational efficiency of various sampling methods for patient exit interviews; (2) to discuss under what circumstances each method yields an unbiased sample; and (3) to propose a new, operationally efficient, and unbiased sampling method. Literature review, mathematical derivation, and Monte Carlo simulations. Our simulations show that in patient exit interviews it is most operationally efficient if the interviewer, after completing an interview, selects the next patient exiting the clinical consultation. We demonstrate mathematically that this method yields a biased sample: patients who spend a longer time with the clinician are overrepresented. This bias can be removed by selecting the next patient who enters, rather than exits, the consultation room. We show that this sampling method is operationally more efficient than alternative methods (systematic and simple random sampling) in most primary health care settings. Under the assumption that the order in which patients enter the consultation room is unrelated to the length of time spent with the clinician and the interviewer, selecting the next patient entering the consultation room tends to be the operationally most efficient unbiased sampling method for patient exit interviews. © 2016 The Authors. Health Services Research published by Wiley Periodicals, Inc. on behalf of Health Research and Educational Trust.
Studying hardness, workability and minimum bending radius in selectively laser-sintered Ti–6Al–4V alloy samples

Science.gov (United States)

Galkina, N. V.; Nosova, Y. A.; Balyakin, A. V.

2018-03-01

This research is relevant as it tries to improve the mechanical and service performance of the Ti–6Al–4V titanium alloy obtained by selective laser sintering. For that purpose, sintered samples were annealed at 750 and 850°C for an hour. Sintered and annealed samples were tested for hardness, workability and microstructure. It was found that incomplete annealing of selectively laser-sintered Ti–6Al–4V samples results in an insignificant reduction in hardness and ductility. Sintered and incompletely annealed samples had a hardness of 32..33 HRC, which is lower than the value of annealed parts specified in standards. Complete annealing at temperature 850°C reduces the hardness to 25 HRC and ductility by 15...20%. Incomplete annealing lowers the ductility factor from 0.08 to 0.06. Complete annealing lowers that value to 0.025. Complete annealing probably results in the embrittlement of sintered samples, perhaps due to their oxidation and hydrogenation in the air. Optical metallography showed lateral fractures in both sintered and annealed samples, which might be the reason why they had lower hardness and ductility.
Stability of selected volatile breath constituents in Tedlar, Kynar and Flexfilm sampling bags

Science.gov (United States)

Mochalski, Paweł; King, Julian; Unterkofler, Karl; Amann, Anton

2016-01-01

The stability of 41 selected breath constituents in three types of polymer sampling bags, Tedlar, Kynar, and Flexfilm, was investigated using solid phase microextraction and gas chromatography mass spectrometry. The tested molecular species belong to different chemical classes (hydrocarbons, ketones, aldehydes, aromatics, sulphurs, esters, terpenes, etc.) and exhibit close-to-breath low ppb levels (3–12 ppb) with the exception of isoprene, acetone and acetonitrile (106 ppb, 760 ppb, 42 ppb respectively). Stability tests comprised the background emission of contaminants, recovery from dry samples, recovery from humid samples (RH 80% at 37 °C), influence of the bag’s filling degree, and reusability. Findings yield evidence of the superiority of Tedlar bags over remaining polymers in terms of background emission, species stability (up to 7 days for dry samples), and reusability. Recoveries of species under study suffered from the presence of high amounts of water (losses up to 10%). However, only heavier volatiles, with molecular masses higher than 90, exhibited more pronounced losses (20–40%). The sample size (the degree of bag filling) was found to be one of the most important factors affecting the sample integrity. To sum up, it is recommended to store breath samples in pre-conditioned Tedlar bags up to 6 hours at the maximum possible filling volume. Among the remaining films, Kynar can be considered as an alternative to Tedlar; however, higher losses of compounds should be expected even within the first hours of storage. Due to the high background emission Flexfilm is not suitable for sampling and storage of samples for analyses aiming at volatiles at a low ppb level. PMID:23323261
Semi-selective medium for Fusarium graminearum detection in seed samples

Directory of Open Access Journals (Sweden)

Marivane Segalin

2010-12-01

Full Text Available Fungi of the genus Fusarium cause a variety of difficult to control diseases in different crops, including winter cereals and maize. Among the species of this genus Fusarium graminearum deserves attention. The aim of this work was to develop a semi-selective medium to study this fungus. In several experiments, substrates for fungal growth were tested, including fungicides and antibiotics such as iprodiona, nystatin and triadimenol, and the antibacterial agents streptomycin and neomycin sulfate. Five seed samples of wheat, barley, oat, black beans and soybeans for F. graminearum detection by using the media Nash and Snyder agar (NSA, Segalin & Reis agar (SRA and one-quarter dextrose agar (1/4PDA; potato 50g; dextrose 5g and agar 20g, either unsupplemented or supplemented with various concentrations of the antimicrobial agents cited above. The selected components and concentrations (g.L-1 of the proposed medium, Segalin & Reis agar (SRA-FG, were: iprodiona 0.05; nystatin 0,025; triadimenol 0.015; neomycin sulfate 0.05; and streptomycin sulfate, 0.3 added of ¼ potato sucrose agar. In the isolation from seeds of cited plant species, the sensitivity of this medium was similar to that of NSA but with de advantage of maintaining the colony morphological aspects similar to those observed in potato-dextrose-agar medium.
Sperm head's birefringence: a new criterion for sperm selection.

Science.gov (United States)

Gianaroli, Luca; Magli, M Cristina; Collodel, Giulia; Moretti, Elena; Ferraretti, Anna P; Baccetti, Baccio

2008-07-01

To investigate the characteristics of birefringence in human sperm heads and apply polarization microscopy for sperm selection at intracytoplasmic sperm injection (ICSI). Prospective randomized study. Reproductive Medicine Unit, Società Italiana Studi Medicina della Riproduzione, Bologna, Italy. A total of 112 male patients had birefringent sperm selected for ICSI (study group). The clinical outcome was compared with that obtained in 119 couples who underwent a conventional ICSI cycle (control group). The proportion of birefringent spermatozoa was evaluated before and after treatment in relation to the sperm sample quality. Embryo development and clinical outcome in the study group were compared with those in the controls. Proportion of birefringent sperm heads, rates of fertilization, cleavage, pregnancy, implantation, and ongoing implantation. The proportion of birefringent spermatozoa was significantly higher in normospermic samples when compared with oligoasthenoteratospermic samples with no progressive motility and testicular sperm extraction samples. Although fertilization and cleavage rates did not differ between the study and control groups, in the most severe male factor condition (oligoasthenoteratospermic with no progressive motility and testicular sperm extraction), the rates of clinical pregnancy, ongoing pregnancy, and implantation were significantly higher in the study group versus the controls. The analysis of birefringence in the sperm head could represent both a diagnostic tool and a novel method for sperm selection.
Prediction of soil CO2 flux in sugarcane management systems using the Random Forest approach

Directory of Open Access Journals (Sweden)

Rose Luiza Moraes Tavares

Full Text Available ABSTRACT: The Random Forest algorithm is a data mining technique used for classifying attributes in order of importance to explain the variation in an attribute-target, as soil CO2 flux. This study aimed to identify prediction of soil CO2 flux variables in management systems of sugarcane through the machine-learning algorithm called Random Forest. Two different management areas of sugarcane in the state of São Paulo, Brazil, were selected: burned and green. In each area, we assembled a sampling grid with 81 georeferenced points to assess soil CO2 flux through automated portable soil gas chamber with measuring spectroscopy in the infrared during the dry season of 2011 and the rainy season of 2012. In addition, we sampled the soil to evaluate physical, chemical, and microbiological attributes. For data interpretation, we used the Random Forest algorithm, based on the combination of predicted decision trees (machine learning algorithms in which every tree depends on the values of a random vector sampled independently with the same distribution to all the trees of the forest. The results indicated that clay content in the soil was the most important attribute to explain the CO2 flux in the areas studied during the evaluated period. The use of the Random Forest algorithm originated a model with a good fit (R2 = 0.80 for predicted and observed values.
Biological sample collector

Science.gov (United States)

Murphy, Gloria A [French Camp, CA

2010-09-07

A biological sample collector is adapted to a collect several biological samples in a plurality of filter wells. A biological sample collector may comprise a manifold plate for mounting a filter plate thereon, the filter plate having a plurality of filter wells therein; a hollow slider for engaging and positioning a tube that slides therethrough; and a slide case within which the hollow slider travels to allow the tube to be aligned with a selected filter well of the plurality of filter wells, wherein when the tube is aligned with the selected filter well, the tube is pushed through the hollow slider and into the selected filter well to sealingly engage the selected filter well and to allow the tube to deposit a biological sample onto a filter in the bottom of the selected filter well. The biological sample collector may be portable.
Effects of prey abundance, distribution, visual contrast and morphology on selection by a pelagic piscivore

Science.gov (United States)

Hansen, Adam G.; Beauchamp, David A.

2014-01-01

Most predators eat only a subset of possible prey. However, studies evaluating diet selection rarely measure prey availability in a manner that accounts for temporal–spatial overlap with predators, the sensory mechanisms employed to detect prey, and constraints on prey capture.We evaluated the diet selection of cutthroat trout (Oncorhynchus clarkii) feeding on a diverse planktivore assemblage in Lake Washington to test the hypothesis that the diet selection of piscivores would reflect random (opportunistic) as opposed to non-random (targeted) feeding, after accounting for predator–prey overlap, visual detection and capture constraints.Diets of cutthroat trout were sampled in autumn 2005, when the abundance of transparent, age-0 longfin smelt (Spirinchus thaleichthys) was low, and 2006, when the abundance of smelt was nearly seven times higher. Diet selection was evaluated separately using depth-integrated and depth-specific (accounted for predator–prey overlap) prey abundance. The abundance of different prey was then adjusted for differences in detectability and vulnerability to predation to see whether these factors could explain diet selection.In 2005, cutthroat trout fed non-randomly by selecting against the smaller, transparent age-0 longfin smelt, but for the larger age-1 longfin smelt. After adjusting prey abundance for visual detection and capture, cutthroat trout fed randomly. In 2006, depth-integrated and depth-specific abundance explained the diets of cutthroat trout well, indicating random feeding. Feeding became non-random after adjusting for visual detection and capture. Cutthroat trout selected strongly for age-0 longfin smelt, but against similar sized threespine stickleback (Gasterosteus aculeatus) and larger age-1 longfin smelt in 2006. Overlap with juvenile sockeye salmon (O. nerka) was minimal in both years, and sockeye salmon were rare in the diets of cutthroat trout.The direction of the shift between random and non-random selection
Hybrid Feature Selection Approach Based on GRASP for Cancer Microarray Data

Directory of Open Access Journals (Sweden)

Arpita Nagpal

2017-01-01

Full Text Available Microarray data usually contain a large number of genes, but a small number of samples. Feature subset selection for microarray data aims at reducing the number of genes so that useful information can be extracted from the samples. Reducing the dimension of data sets further helps in improving the computational efficiency of the learning model. In this paper, we propose a modified algorithm based on the tabu search as local search procedures to a Greedy Randomized Adaptive Search Procedure (GRASP for high dimensional microarray data sets. The proposed Tabu based Greedy Randomized Adaptive Search Procedure algorithm is named as TGRASP. In TGRASP, a new parameter has been introduced named as Tabu Tenure and the existing parameters, NumIter and size have been modified. We observed that different parameter settings affect the quality of the optimum. The second proposed algorithm known as FFGRASP (Firefly Greedy Randomized Adaptive Search Procedure uses a firefly optimization algorithm in the local search optimzation phase of the greedy randomized adaptive search procedure (GRASP. Firefly algorithm is one of the powerful algorithms for optimization of multimodal applications. Experimental results show that the proposed TGRASP and FFGRASP algorithms are much better than existing algorithm with respect to three performance parameters viz. accuracy, run time, number of a selected subset of features. We have also compared both the approaches with a unified metric (Extended Adjusted Ratio of Ratios which has shown that TGRASP approach outperforms existing approach for six out of nine cancer microarray datasets and FFGRASP performs better on seven out of nine datasets.
Assessing the accuracy and stability of variable selection methods for random forest modeling in ecology.

Science.gov (United States)

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
Testing the normality assumption in the sample selection model with an application to travel demand

NARCIS (Netherlands)

van der Klaauw, B.; Koning, R.H.

2003-01-01

In this article we introduce a test for the normality assumption in the sample selection model. The test is based on a flexible parametric specification of the density function of the error terms in the model. This specification follows a Hermite series with bivariate normality as a special case.
Testing the normality assumption in the sample selection model with an application to travel demand

NARCIS (Netherlands)

van der Klauw, B.; Koning, R.H.

In this article we introduce a test for the normality assumption in the sample selection model. The test is based on a flexible parametric specification of the density function of the error terms in the model. This specification follows a Hermite series with bivariate normality as a special case.
Stratified random sampling plans designed to assist in the determination of radon and radon daughter concentrations in underground uranium mine atmosphere

International Nuclear Information System (INIS)

Makepeace, C.E.

1981-01-01

Sampling strategies for the monitoring of deleterious agents present in uranium mine air in underground and surface mining areas are described. These methods are designed to prevent overexposure of the lining of the respiratory system of uranium miners to ionizing radiation from radon and radon daughters, and whole body overexposure to external gamma radiation. A detailed description is provided of stratified random sampling monitoring methodology for obtaining baseline data to be used as a reference for subsequent compliance assessment
The lack of selection bias in a snowball sampled case-control study on drug abuse.

Science.gov (United States)

Lopes, C S; Rodrigues, L C; Sichieri, R

1996-12-01

Friend controls in matched case-control studies can be a potential source of bias based on the assumption that friends are more likely to share exposure factors. This study evaluates the role of selection bias in a case-control study that used the snowball sampling method based on friendship for the selection of cases and controls. The cases selected fro the study were drug abusers located in the community. Exposure was defined by the presence of at least one psychiatric diagnosis. Psychiatric and drug abuse/dependence diagnoses were made according to the Diagnostic and Statistical Manual of Mental Disorders (DSM-III-R) criteria. Cases and controls were matched on sex, age and friendship. The measurement of selection bias was made through the comparison of the proportion of exposed controls selected by exposed cases (p1) with the proportion of exposed controls selected by unexposed cases (p2). If p1 = p2 then, selection bias should not occur. The observed distribution of the 185 matched pairs having at least one psychiatric disorder showed a p1 value of 0.52 and a p2 value of 0.51, indicating no selection bias in this study. Our findings support the idea that the use of friend controls can produce a valid basis for a case-control study.
Heterogeneous Causal Effects and Sample Selection Bias

DEFF Research Database (Denmark)

Breen, Richard; Choi, Seongsoo; Holm, Anders

2015-01-01

The role of education in the process of socioeconomic attainment is a topic of long standing interest to sociologists and economists. Recently there has been growing interest not only in estimating the average causal effect of education on outcomes such as earnings, but also in estimating how...... causal effects might vary over individuals or groups. In this paper we point out one of the under-appreciated hazards of seeking to estimate heterogeneous causal effects: conventional selection bias (that is, selection on baseline differences) can easily be mistaken for heterogeneity of causal effects....... This might lead us to find heterogeneous effects when the true effect is homogenous, or to wrongly estimate not only the magnitude but also the sign of heterogeneous effects. We apply a test for the robustness of heterogeneous causal effects in the face of varying degrees and patterns of selection bias...
40 CFR Appendix A to Subpart F of... - Sampling Plans for Selective Enforcement Auditing of Small Nonroad Engines

Science.gov (United States)

2010-07-01

... Enforcement Auditing of Small Nonroad Engines A Appendix A to Subpart F of Part 90 Protection of Environment...-IGNITION ENGINES AT OR BELOW 19 KILOWATTS Selective Enforcement Auditing Pt. 90, Subpt. F, App. A Appendix A to Subpart F of Part 90—Sampling Plans for Selective Enforcement Auditing of Small Nonroad Engines...

Influence of Support Configurations on the Characteristics of Selective Laser-Melted Inconel 718

Science.gov (United States)

Nadammal, Naresh; Kromm, Arne; Saliwan-Neumann, Romeo; Farahbod, Lena; Haberland, Christoph; Portella, Pedro Dolabella

2018-03-01

Samples fabricated using two different support configurations by following identical scan strategies during selective laser melting of superalloy Inconel 718 were characterized in this study. Characterization methods included optical microscopy, electron back-scattered diffraction and x-ray diffraction residual stress measurement. For the scan strategy considered, microstructure and residual stress development in the samples were influenced by the support structures. However, crystallographic texture intensity and the texture components formed within the core part of the samples were almost independent of the support. The formation of finer grains closer to the support as well as within the columnar grain boundaries resulted in randomization and texture intensity reduction by nearly half for the sample built on a lattice support. Heat transfer rates dictated by the support configurations in addition to the scan strategy influenced the microstructure and residual stress development in selective laser-melted Inconel 718 samples.
The quality of the reported sample size calculations in randomized controlled trials indexed in PubMed.

Science.gov (United States)

Lee, Paul H; Tse, Andy C Y

2017-05-01

There are limited data on the quality of reporting of information essential for replication of the calculation as well as the accuracy of the sample size calculation. We examine the current quality of reporting of the sample size calculation in randomized controlled trials (RCTs) published in PubMed and to examine the variation in reporting across study design, study characteristics, and journal impact factor. We also reviewed the targeted sample size reported in trial registries. We reviewed and analyzed all RCTs published in December 2014 with journals indexed in PubMed. The 2014 Impact Factors for the journals were used as proxies for their quality. Of the 451 analyzed papers, 58.1% reported an a priori sample size calculation. Nearly all papers provided the level of significance (97.7%) and desired power (96.6%), and most of the papers reported the minimum clinically important effect size (73.3%). The median (inter-quartile range) of the percentage difference of the reported and calculated sample size calculation was 0.0% (IQR -4.6%;3.0%). The accuracy of the reported sample size was better for studies published in journals that endorsed the CONSORT statement and journals with an impact factor. A total of 98 papers had provided targeted sample size on trial registries and about two-third of these papers (n=62) reported sample size calculation, but only 25 (40.3%) had no discrepancy with the reported number in the trial registries. The reporting of the sample size calculation in RCTs published in PubMed-indexed journals and trial registries were poor. The CONSORT statement should be more widely endorsed. Copyright © 2016 European Federation of Internal Medicine. Published by Elsevier B.V. All rights reserved.
Training set optimization under population structure in genomic selection.

Science.gov (United States)

Isidro, Julio; Jannink, Jean-Luc; Akdemir, Deniz; Poland, Jesse; Heslot, Nicolas; Sorrells, Mark E

2015-01-01

Population structure must be evaluated before optimization of the training set population. Maximizing the phenotypic variance captured by the training set is important for optimal performance. The optimization of the training set (TRS) in genomic selection has received much interest in both animal and plant breeding, because it is critical to the accuracy of the prediction models. In this study, five different TRS sampling algorithms, stratified sampling, mean of the coefficient of determination (CDmean), mean of predictor error variance (PEVmean), stratified CDmean (StratCDmean) and random sampling, were evaluated for prediction accuracy in the presence of different levels of population structure. In the presence of population structure, the most phenotypic variation captured by a sampling method in the TRS is desirable. The wheat dataset showed mild population structure, and CDmean and stratified CDmean methods showed the highest accuracies for all the traits except for test weight and heading date. The rice dataset had strong population structure and the approach based on stratified sampling showed the highest accuracies for all traits. In general, CDmean minimized the relationship between genotypes in the TRS, maximizing the relationship between TRS and the test set. This makes it suitable as an optimization criterion for long-term selection. Our results indicated that the best selection criterion used to optimize the TRS seems to depend on the interaction of trait architecture and population structure.
Randomized controlled trial of attention bias modification in a racially diverse, socially anxious, alcohol dependent sample.

Science.gov (United States)

Clerkin, Elise M; Magee, Joshua C; Wells, Tony T; Beard, Courtney; Barnett, Nancy P

2016-12-01

Attention biases may be an important treatment target for both alcohol dependence and social anxiety. This is the first ABM trial to investigate two (vs. one) targets of attention bias within a sample with co-occurring symptoms of social anxiety and alcohol dependence. Additionally, we used trial-level bias scores (TL-BS) to capture the phenomena of attention bias in a more ecologically valid, dynamic way compared to traditional attention bias scores. Adult participants (N = 86; 41% Female; 52% African American; 40% White) with elevated social anxiety symptoms and alcohol dependence were randomly assigned to an 8-session training condition in this 2 (Social Anxiety ABM vs. Social Anxiety Control) by 2 (Alcohol ABM vs. Alcohol Control) design. Symptoms of social anxiety, alcohol dependence, and attention bias were assessed across time. Multilevel models estimated the trajectories for each measure within individuals, and tested whether these trajectories differed according to the randomized training conditions. Across time, there were significant or trending decreases in all attention TL-BS parameters (but not traditional attention bias scores) and most symptom measures. However, there were not significant differences in the trajectories of change between any ABM and control conditions for any symptom measures. These findings add to previous evidence questioning the robustness of ABM and point to the need to extend the effects of ABM to samples that are racially diverse and/or have co-occurring psychopathology. The results also illustrate the potential importance of calculating trial-level attention bias scores rather than only including traditional bias scores. Copyright Â© 2016 Elsevier Ltd. All rights reserved.
Randomized Controlled Trial of Attention Bias Modification in a Racially Diverse, Socially Anxious, Alcohol Dependent Sample

Science.gov (United States)

Clerkin, Elise M.; Magee, Joshua C.; Wells, Tony T.; Beard, Courtney; Barnett, Nancy P.

2016-01-01

Objective Attention biases may be an important treatment target for both alcohol dependence and social anxiety. This is the first ABM trial to investigate two (vs. one) targets of attention bias within a sample with co-occurring symptoms of social anxiety and alcohol dependence. Additionally, we used trial-level bias scores (TL-BS) to capture the phenomena of attention bias in a more ecologically valid, dynamic way compared to traditional attention bias scores. Method Adult participants (N=86; 41% Female; 52% African American; 40% White) with elevated social anxiety symptoms and alcohol dependence were randomly assigned to an 8-session training condition in this 2 (Social Anxiety ABM vs. Social Anxiety Control) by 2 (Alcohol ABM vs. Alcohol Control) design. Symptoms of social anxiety, alcohol dependence, and attention bias were assessed across time. Results Multilevel models estimated the trajectories for each measure within individuals, and tested whether these trajectories differed according to the randomized training conditions. Across time, there were significant or trending decreases in all attention TL-BS parameters (but not traditional attention bias scores) and most symptom measures. However, there were not significant differences in the trajectories of change between any ABM and control conditions for any symptom measures. Conclusions These findings add to previous evidence questioning the robustness of ABM and point to the need to extend the effects of ABM to samples that are racially diverse and/or have co-occurring psychopathology. The results also illustrate the potential importance of calculating trial-level attention bias scores rather than only including traditional bias scores. PMID:27591918
Moderate Physical Activity and Its Relationship to Select Measures of a Healthy Diet

Science.gov (United States)

Blakely, Frank; Dunnagan, Tim; Haynes, George; Moore, Sylvia; Pelican, Suzanne

2004-01-01

In rural communities, physical activity may influence and predict nutritional behaviors. The purpose of this investigation was to determine if an individual's stage of participation in moderate physical activity was related to select measures of a healthy diet. Data were collected using a mail-in survey from a random sample conducted in the…
Applied Math & Science Levels Utilized in Selected Trade & Industrial Vocational Education. Final Report.

Science.gov (United States)

Gray, James R.

Research identified and evaluated the level of applied mathematics and science used in selected trade and industrial (T&I) subjects taught in the Kentucky Vocational Education System. The random sample was composed of 52 programs: 21 carpentry, 20 electricity/electronics, and 11 machine shop. The 96 math content items that were identified as…
The Amnesiac Lookback Option: Selectively Monitored Lookback Options and Cryptocurrencies

Directory of Open Access Journals (Sweden)

Ho-Chun Herbert Chang

2018-05-01

Full Text Available This study proposes a strategy to make the lookback option cheaper and more practical, and suggests the use of its properties to reduce risk exposure in cryptocurrency markets through blockchain enforced smart contracts and correct for informational inefficiencies surrounding prices and volatility. This paper generalizes partial, discretely-monitored lookback options that dilute premiums by selecting a subset of specified periods to determine payoff, which we call amnesiac lookback options. Prior literature on discretely-monitored lookback options considers the number of periods and assumes equidistant lookback periods in pricing partial lookback options. This study by contrast considers random sampling of lookback periods and compares resulting payoff of the call, put and spread options under floating and fixed strikes. Amnesiac lookbacks are priced with Monte Carlo simulations of Gaussian random walks under equidistant and random periods. Results are compared to analytic and binomial pricing models for the same derivatives. Simulations show diminishing marginal increases to the fair price as the number of selected periods is increased. The returns correspond to a Hill curve whose parameters are set by interest rate and volatility. We demonstrate over-pricing under equidistant monitoring assumptions with error increasing as the lookback periods decrease. An example of a direct implication for event trading is when shock is forecasted but its timing uncertain, equidistant sampling produces a lower error on the true maximum than random choice. We conclude that the instrument provides an ideal space for investors to balance their risk, and as a prime candidate to hedge extreme volatility. We discuss the application of the amnesiac lookback option and path-dependent options to cryptocurrencies and blockchain commodities in the context of smart contracts.
Imaging a Large Sample with Selective Plane Illumination Microscopy Based on Multiple Fluorescent Microsphere Tracking

Science.gov (United States)

Ryu, Inkeon; Kim, Daekeun

2018-04-01

A typical selective plane illumination microscopy (SPIM) image size is basically limited by the field of view, which is a characteristic of the objective lens. If an image larger than the imaging area of the sample is to be obtained, image stitching, which combines step-scanned images into a single panoramic image, is required. However, accurately registering the step-scanned images is very difficult because the SPIM system uses a customized sample mount where uncertainties for the translational and the rotational motions exist. In this paper, an image registration technique based on multiple fluorescent microsphere tracking is proposed in the view of quantifying the constellations and measuring the distances between at least two fluorescent microspheres embedded in the sample. Image stitching results are demonstrated for optically cleared large tissue with various staining methods. Compensation for the effect of the sample rotation that occurs during the translational motion in the sample mount is also discussed.
Estimation of reference intervals from small samples: an example using canine plasma creatinine.

Science.gov (United States)

Geffré, A; Braun, J P; Trumel, C; Concordet, D

2009-12-01

According to international recommendations, reference intervals should be determined from at least 120 reference individuals, which often are impossible to achieve in veterinary clinical pathology, especially for wild animals. When only a small number of reference subjects is available, the possible bias cannot be known and the normality of the distribution cannot be evaluated. A comparison of reference intervals estimated by different methods could be helpful. The purpose of this study was to compare reference limits determined from a large set of canine plasma creatinine reference values, and large subsets of this data, with estimates obtained from small samples selected randomly. Twenty sets each of 120 and 27 samples were randomly selected from a set of 1439 plasma creatinine results obtained from healthy dogs in another study. Reference intervals for the whole sample and for the large samples were determined by a nonparametric method. The estimated reference limits for the small samples were minimum and maximum, mean +/- 2 SD of native and Box-Cox-transformed values, 2.5th and 97.5th percentiles by a robust method on native and Box-Cox-transformed values, and estimates from diagrams of cumulative distribution functions. The whole sample had a heavily skewed distribution, which approached Gaussian after Box-Cox transformation. The reference limits estimated from small samples were highly variable. The closest estimates to the 1439-result reference interval for 27-result subsamples were obtained by both parametric and robust methods after Box-Cox transformation but were grossly erroneous in some cases. For small samples, it is recommended that all values be reported graphically in a dot plot or histogram and that estimates of the reference limits be compared using different methods.
Why choose Random Forest to predict rare species distribution with few samples in large undersampled areas? Three Asian crane species models provide supporting evidence

Directory of Open Access Journals (Sweden)

Chunrong Mi

2017-01-01

Full Text Available Species distribution models (SDMs have become an essential tool in ecology, biogeography, evolution and, more recently, in conservation biology. How to generalize species distributions in large undersampled areas, especially with few samples, is a fundamental issue of SDMs. In order to explore this issue, we used the best available presence records for the Hooded Crane (Grus monacha, n = 33, White-naped Crane (Grus vipio, n = 40, and Black-necked Crane (Grus nigricollis, n = 75 in China as three case studies, employing four powerful and commonly used machine learning algorithms to map the breeding distributions of the three species: TreeNet (Stochastic Gradient Boosting, Boosted Regression Tree Model, Random Forest, CART (Classification and Regression Tree and Maxent (Maximum Entropy Models. In addition, we developed an ensemble forecast by averaging predicted probability of the above four models results. Commonly used model performance metrics (Area under ROC (AUC and true skill statistic (TSS were employed to evaluate model accuracy. The latest satellite tracking data and compiled literature data were used as two independent testing datasets to confront model predictions. We found Random Forest demonstrated the best performance for the most assessment method, provided a better model fit to the testing data, and achieved better species range maps for each crane species in undersampled areas. Random Forest has been generally available for more than 20 years and has been known to perform extremely well in ecological predictions. However, while increasingly on the rise, its potential is still widely underused in conservation, (spatial ecological applications and for inference. Our results show that it informs ecological and biogeographical theories as well as being suitable for conservation applications, specifically when the study area is undersampled. This method helps to save model-selection time and effort, and allows robust and rapid
Why choose Random Forest to predict rare species distribution with few samples in large undersampled areas? Three Asian crane species models provide supporting evidence.

Science.gov (United States)

Mi, Chunrong; Huettmann, Falk; Guo, Yumin; Han, Xuesong; Wen, Lijia

2017-01-01

Species distribution models (SDMs) have become an essential tool in ecology, biogeography, evolution and, more recently, in conservation biology. How to generalize species distributions in large undersampled areas, especially with few samples, is a fundamental issue of SDMs. In order to explore this issue, we used the best available presence records for the Hooded Crane ( Grus monacha , n = 33), White-naped Crane ( Grus vipio , n = 40), and Black-necked Crane ( Grus nigricollis , n = 75) in China as three case studies, employing four powerful and commonly used machine learning algorithms to map the breeding distributions of the three species: TreeNet (Stochastic Gradient Boosting, Boosted Regression Tree Model), Random Forest, CART (Classification and Regression Tree) and Maxent (Maximum Entropy Models). In addition, we developed an ensemble forecast by averaging predicted probability of the above four models results. Commonly used model performance metrics (Area under ROC (AUC) and true skill statistic (TSS)) were employed to evaluate model accuracy. The latest satellite tracking data and compiled literature data were used as two independent testing datasets to confront model predictions. We found Random Forest demonstrated the best performance for the most assessment method, provided a better model fit to the testing data, and achieved better species range maps for each crane species in undersampled areas. Random Forest has been generally available for more than 20 years and has been known to perform extremely well in ecological predictions. However, while increasingly on the rise, its potential is still widely underused in conservation, (spatial) ecological applications and for inference. Our results show that it informs ecological and biogeographical theories as well as being suitable for conservation applications, specifically when the study area is undersampled. This method helps to save model-selection time and effort, and allows robust and rapid
ACTIVE LEARNING TO OVERCOME SAMPLE SELECTION BIAS: APPLICATION TO PHOTOMETRIC VARIABLE STAR CLASSIFICATION

Energy Technology Data Exchange (ETDEWEB)

Richards, Joseph W.; Starr, Dan L.; Miller, Adam A.; Bloom, Joshua S.; Butler, Nathaniel R.; Berian James, J. [Astronomy Department, University of California, Berkeley, CA 94720-7450 (United States); Brink, Henrik [Dark Cosmology Centre, Juliane Maries Vej 30, 2100 Copenhagen O (Denmark); Long, James P.; Rice, John, E-mail: jwrichar@stat.berkeley.edu [Statistics Department, University of California, Berkeley, CA 94720-7450 (United States)

2012-01-10

Despite the great promise of machine-learning algorithms to classify and predict astrophysical parameters for the vast numbers of astrophysical sources and transients observed in large-scale surveys, the peculiarities of the training data often manifest as strongly biased predictions on the data of interest. Typically, training sets are derived from historical surveys of brighter, more nearby objects than those from more extensive, deeper surveys (testing data). This sample selection bias can cause catastrophic errors in predictions on the testing data because (1) standard assumptions for machine-learned model selection procedures break down and (2) dense regions of testing space might be completely devoid of training data. We explore possible remedies to sample selection bias, including importance weighting, co-training, and active learning (AL). We argue that AL-where the data whose inclusion in the training set would most improve predictions on the testing set are queried for manual follow-up-is an effective approach and is appropriate for many astronomical applications. For a variable star classification problem on a well-studied set of stars from Hipparcos and Optical Gravitational Lensing Experiment, AL is the optimal method in terms of error rate on the testing data, beating the off-the-shelf classifier by 3.4% and the other proposed methods by at least 3.0%. To aid with manual labeling of variable stars, we developed a Web interface which allows for easy light curve visualization and querying of external databases. Finally, we apply AL to classify variable stars in the All Sky Automated Survey, finding dramatic improvement in our agreement with the ASAS Catalog of Variable Stars, from 65.5% to 79.5%, and a significant increase in the classifier's average confidence for the testing set, from 14.6% to 42.9%, after a few AL iterations.
ACTIVE LEARNING TO OVERCOME SAMPLE SELECTION BIAS: APPLICATION TO PHOTOMETRIC VARIABLE STAR CLASSIFICATION

International Nuclear Information System (INIS)

Richards, Joseph W.; Starr, Dan L.; Miller, Adam A.; Bloom, Joshua S.; Butler, Nathaniel R.; Berian James, J.; Brink, Henrik; Long, James P.; Rice, John

2012-01-01

Despite the great promise of machine-learning algorithms to classify and predict astrophysical parameters for the vast numbers of astrophysical sources and transients observed in large-scale surveys, the peculiarities of the training data often manifest as strongly biased predictions on the data of interest. Typically, training sets are derived from historical surveys of brighter, more nearby objects than those from more extensive, deeper surveys (testing data). This sample selection bias can cause catastrophic errors in predictions on the testing data because (1) standard assumptions for machine-learned model selection procedures break down and (2) dense regions of testing space might be completely devoid of training data. We explore possible remedies to sample selection bias, including importance weighting, co-training, and active learning (AL). We argue that AL—where the data whose inclusion in the training set would most improve predictions on the testing set are queried for manual follow-up—is an effective approach and is appropriate for many astronomical applications. For a variable star classification problem on a well-studied set of stars from Hipparcos and Optical Gravitational Lensing Experiment, AL is the optimal method in terms of error rate on the testing data, beating the off-the-shelf classifier by 3.4% and the other proposed methods by at least 3.0%. To aid with manual labeling of variable stars, we developed a Web interface which allows for easy light curve visualization and querying of external databases. Finally, we apply AL to classify variable stars in the All Sky Automated Survey, finding dramatic improvement in our agreement with the ASAS Catalog of Variable Stars, from 65.5% to 79.5%, and a significant increase in the classifier's average confidence for the testing set, from 14.6% to 42.9%, after a few AL iterations.
Active Learning to Overcome Sample Selection Bias: Application to Photometric Variable Star Classification

Science.gov (United States)

Richards, Joseph W.; Starr, Dan L.; Brink, Henrik; Miller, Adam A.; Bloom, Joshua S.; Butler, Nathaniel R.; James, J. Berian; Long, James P.; Rice, John

2012-01-01

Despite the great promise of machine-learning algorithms to classify and predict astrophysical parameters for the vast numbers of astrophysical sources and transients observed in large-scale surveys, the peculiarities of the training data often manifest as strongly biased predictions on the data of interest. Typically, training sets are derived from historical surveys of brighter, more nearby objects than those from more extensive, deeper surveys (testing data). This sample selection bias can cause catastrophic errors in predictions on the testing data because (1) standard assumptions for machine-learned model selection procedures break down and (2) dense regions of testing space might be completely devoid of training data. We explore possible remedies to sample selection bias, including importance weighting, co-training, and active learning (AL). We argue that AL—where the data whose inclusion in the training set would most improve predictions on the testing set are queried for manual follow-up—is an effective approach and is appropriate for many astronomical applications. For a variable star classification problem on a well-studied set of stars from Hipparcos and Optical Gravitational Lensing Experiment, AL is the optimal method in terms of error rate on the testing data, beating the off-the-shelf classifier by 3.4% and the other proposed methods by at least 3.0%. To aid with manual labeling of variable stars, we developed a Web interface which allows for easy light curve visualization and querying of external databases. Finally, we apply AL to classify variable stars in the All Sky Automated Survey, finding dramatic improvement in our agreement with the ASAS Catalog of Variable Stars, from 65.5% to 79.5%, and a significant increase in the classifier's average confidence for the testing set, from 14.6% to 42.9%, after a few AL iterations.
Reliability of sampling strategies for measuring dairy cattle welfare on commercial farms.

Science.gov (United States)

Van Os, Jennifer M C; Winckler, Christoph; Trieb, Julia; Matarazzo, Soraia V; Lehenbauer, Terry W; Champagne, John D; Tucker, Cassandra B

2018-02-01

Our objective was to evaluate how the proportion of high-producing lactating cows sampled on each farm and the selection method affect prevalence estimates for animal-based measures. We assessed the entire high-producing pen (days in milk size calculations from the Welfare Quality Protocol; and (4) selecting the first, middle, or final third of cows exiting the milking parlor. Estimates were compared with true values using regression analysis and were considered accurate if they met 3 criteria: the coefficient of determination was ≥0.9 and the slope and intercept did not differ significantly from 1 and 0, respectively. All estimates met the slope and intercept criteria, whereas the coefficient of determination increased when more cows were sampled. All estimates were accurate for neck alterations, ocular discharge (22.2 ± 27.4%), and carpal joint hair loss (14.1 ± 17.4%). Selecting a third of the milking order or using the Welfare Quality sample size calculations failed to accurately estimate all measures simultaneously. However, all estimates were accurate when selecting at least 2 of every 3 cows locked at the feed bunk. Using restraint position at the feed bunk did not differ systematically from computer-selecting the same proportion of cows randomly, and the former may be a simpler approach for welfare assessments. Copyright © 2018 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.
A Study to Assess the Knowledge on Risk for Fall among Geriatric People Above 60 Years at Selected Oldage Home at Karaikal

OpenAIRE

R.G. Padmapriya; E. Kalaivani

2017-01-01

A study to assess the risk for fall among geriatric people above 60 years of age in selected old age home at karaikal. The research approach was adopted for this study was quantitative approach. The research design used for this study is descriptive design. A simple random sampling technique adopted to select the desired sample. Sample size is 50. Donna conley fall risk assessment scale was used to assess the risk for fall among geriatric people above 60 years of age . The collected data was...
Effect of tetracycline dose and treatment-mode on selection of resistant coliform bacteria in nursery pigs

DEFF Research Database (Denmark)

Græsbøll, Kaare; Damborg, Peter; Mellerup, Anders

2017-01-01

This study describes results of a randomized clinical trial investigating the effect of oxytetracycline treatment dose and mode of administration on selection of antibiotic resistant coliform bacteria in fecal samples from nursery pigs. Nursery pigs (pigs of 4-7 weeks of age) were treated...... with oxytetracycline against Lawsonia intracellularis induced diarrhea in five pig herds. Each group was randomly allocated to one of five treatment groups: oral flock treatment with (i) high (20 mg/kg), (ii) medium (10 mg/kg) and (iii) low (5 mg/kg) dosage, (iv) oral-pen-wise (small group) treatment (10 mg...... significant changes in number or proportion of tetracycline resistant coliforms. Selection for tetracycline-resistant coliforms was significantly correlated to selection for ampicillin- and sulfonamide-resistant, but not to cefotaxime-resistant strains. In conclusion, difference in dose of oxytetracycline...
Composition of Trace Metals in Dust Samples Collected from Selected High Schools in Pretoria, South Africa

Directory of Open Access Journals (Sweden)

J. O. Olowoyo

2016-01-01

Full Text Available Potential health risks associated with trace metal pollution have necessitated the importance of monitoring their levels in the environment. The present study investigated the concentrations and compositions of trace metals in dust samples collected from classrooms and playing ground from the selected high schools In Pretoria. Schools were selected from Pretoria based on factors such as proximity to high traffic ways, industrial areas, and residential areas. Thirty-two dust samples were collected from inside and outside the classrooms, where learners often stay during recess period. The dust samples were analysed for trace metal concentrations using Inductively Coupled Plasma-Mass Spectrometry (ICP-MS. The composition of the elements showed that the concentrations of Zn were more than all other elements except from one of the schools. There were significant differences in the concentrations of trace metals from the schools (p<0.05. Regular cleaning, proximity to busy road, and well maintained gardens seem to have positive effects on the concentrations of trace metals recorded from the classrooms dust. The result further revealed a positive correlation for elements such as Pb, Cu, Zn, Mn, and Sb, indicating that the dust might have a common source.
Vibration and acoustic frequency spectra for industrial process modeling using selective fusion multi-condition samples and multi-source features

Science.gov (United States)

Tang, Jian; Qiao, Junfei; Wu, ZhiWei; Chai, Tianyou; Zhang, Jian; Yu, Wen

2018-01-01

Frequency spectral data of mechanical vibration and acoustic signals relate to difficult-to-measure production quality and quantity parameters of complex industrial processes. A selective ensemble (SEN) algorithm can be used to build a soft sensor model of these process parameters by fusing valued information selectively from different perspectives. However, a combination of several optimized ensemble sub-models with SEN cannot guarantee the best prediction model. In this study, we use several techniques to construct mechanical vibration and acoustic frequency spectra of a data-driven industrial process parameter model based on selective fusion multi-condition samples and multi-source features. Multi-layer SEN (MLSEN) strategy is used to simulate the domain expert cognitive process. Genetic algorithm and kernel partial least squares are used to construct the inside-layer SEN sub-model based on each mechanical vibration and acoustic frequency spectral feature subset. Branch-and-bound and adaptive weighted fusion algorithms are integrated to select and combine outputs of the inside-layer SEN sub-models. Then, the outside-layer SEN is constructed. Thus, "sub-sampling training examples"-based and "manipulating input features"-based ensemble construction methods are integrated, thereby realizing the selective information fusion process based on multi-condition history samples and multi-source input features. This novel approach is applied to a laboratory-scale ball mill grinding process. A comparison with other methods indicates that the proposed MLSEN approach effectively models mechanical vibration and acoustic signals.

Auditory detection of an increment in the rate of a random process

International Nuclear Information System (INIS)

Brown, W.S.; Emmerich, D.S.

1994-01-01

Recent experiments have presented listeners with complex tonal stimuli consisting of components with values (i.e., intensities or frequencies) randomly sampled from probability distributions [e.g., R. A. Lutfi, J. Acoust. Soc. Am. 86, 934--944 (1989)]. In the present experiment, brief tones were presented at intervals corresponding to the intensity of a random process. Specifically, the intervals between tones were randomly selected from exponential probability functions. Listeners were asked to decide whether tones presented during a defined observation interval represented a ''noise'' process alone or the ''noise'' with a ''signal'' process added to it. The number of tones occurring in any observation interval is a Poisson variable; receiver operating characteristics (ROCs) arising from Poisson processes have been considered by Egan [Signal Detection Theory and ROC Analysis (Academic, New York, 1975)]. Several sets of noise and signal intensities and observation interval durations were selected which were expected to yield equivalent performance. Rating ROCs were generated based on subjects' responses in a single-interval, yes--no task. The performance levels achieved by listeners and the effects of intensity and duration are compared to those predicted for an ideal observer
Blind Measurement Selection: A Random Matrix Theory Approach

KAUST Repository

Elkhalil, Khalil; Kammoun, Abla; Al-Naffouri, Tareq Y.; Alouini, Mohamed-Slim

2016-01-01

-aware fashions. We present two potential applications where the proposed algorithms can be used, namely antenna selection for uplink transmissions in large scale multi-user systems and sensor selection for wireless sensor networks. Numerical results are also
A novel knot selection method for the error-bounded B-spline curve fitting of sampling points in the measuring process

International Nuclear Information System (INIS)

Liang, Fusheng; Zhao, Ji; Ji, Shijun; Zhang, Bing; Fan, Cheng

2017-01-01

The B-spline curve has been widely used in the reconstruction of measurement data. The error-bounded sampling points reconstruction can be achieved by the knot addition method (KAM) based B-spline curve fitting. In KAM, the selection pattern of initial knot vector has been associated with the ultimate necessary number of knots. This paper provides a novel initial knots selection method to condense the knot vector required for the error-bounded B-spline curve fitting. The initial knots are determined by the distribution of features which include the chord length (arc length) and bending degree (curvature) contained in the discrete sampling points. Firstly, the sampling points are fitted into an approximate B-spline curve Gs with intensively uniform knot vector to substitute the description of the feature of the sampling points. The feature integral of Gs is built as a monotone increasing function in an analytic form. Then, the initial knots are selected according to the constant increment of the feature integral. After that, an iterative knot insertion (IKI) process starting from the initial knots is introduced to improve the fitting precision, and the ultimate knot vector for the error-bounded B-spline curve fitting is achieved. Lastly, two simulations and the measurement experiment are provided, and the results indicate that the proposed knot selection method can reduce the number of ultimate knots available. (paper)
Determination of specific activity of americium and plutonium in selected environmental samples

International Nuclear Information System (INIS)

Trebunova, T.

1999-01-01

The aim of this work was development of method for determination of americium and plutonium in environmental samples. Developed method was evaluated on soil samples and after they was applied on selected samples of fishes (smoked mackerel, herring and fillet from Alaska hake). The method for separation of americium is based on liquid separation with Aliquate-336, precipitation with oxalic acid and using of chromatographic material TRU-Spec TM .The intervals of radiochemical yields were from 13.0% to 80.9% for plutonium-236 and from 10.5% to 100% for americium-241. Determined specific activities of plutonium-239,240 were from (2.3 ± 1.4) mBq/kg to (82 ± 29) mBq/kg, the specific activities of plutonium-238 were from (14.2 ± 3.7) mBq/kg to (708 ± 86) mBq/kg. The specific activities of americium-241 were from (1.4 ± 0.9) mBq/kg to (3360 ± 210) mBq/kg. The fishes from Baltic Sea as well as from North Sea show highest specific activities then fresh-water fishes from Slovakia. Therefore the monitoring of alpha radionuclides in foods imported from territories with nuclear testing is recommended
Revisiting sample size: are big trials the answer?

Science.gov (United States)

Lurati Buse, Giovanna A L; Botto, Fernando; Devereaux, P J

2012-07-18

The superiority of the evidence generated in randomized controlled trials over observational data is not only conditional to randomization. Randomized controlled trials require proper design and implementation to provide a reliable effect estimate. Adequate random sequence generation, allocation implementation, analyses based on the intention-to-treat principle, and sufficient power are crucial to the quality of a randomized controlled trial. Power, or the probability of the trial to detect a difference when a real difference between treatments exists, strongly depends on sample size. The quality of orthopaedic randomized controlled trials is frequently threatened by a limited sample size. This paper reviews basic concepts and pitfalls in sample-size estimation and focuses on the importance of large trials in the generation of valid evidence.
Effects of choice architecture and chef-enhanced meals on the selection and consumption of healthier school foods: a randomized clinical trial.

Science.gov (United States)

Cohen, Juliana F W; Richardson, Scott A; Cluggish, Sarah A; Parker, Ellen; Catalano, Paul J; Rimm, Eric B

2015-05-01

Little is known about the long-term effect of a chef-enhanced menu on healthier food selection and consumption in school lunchrooms. In addition, it remains unclear if extended exposure to other strategies to promote healthier foods (eg, choice architecture) also improves food selection or consumption. To evaluate the short- and long-term effects of chef-enhanced meals and extended exposure to choice architecture on healthier school food selection and consumption. A school-based randomized clinical trial was conducted during the 2011-2012 school year among 14 elementary and middle schools in 2 urban, low-income school districts (intent-to-treat analysis). Included in the study were 2638 students in grades 3 through 8 attending participating schools (38.4% of eligible participants). Schools were first randomized to receive a professional chef to improve school meal palatability (chef schools) or to a delayed intervention (control group). To assess the effect of choice architecture (smart café), all schools after 3 months were then randomized to the smart café intervention or to the control group. School food selection was recorded, and consumption was measured using plate waste methods. After 3 months, vegetable selection increased in chef vs control schools (odds ratio [OR], 1.75; 95% CI, 1.36-2.24), but there was no effect on the selection of other components or on meal consumption. After long-term or extended exposure to the chef or smart café intervention, fruit selection increased in the chef (OR, 3.08; 95% CI, 2.23-4.25), smart café (OR, 1.45; 95% CI, 1.13-1.87), and chef plus smart café (OR, 3.10; 95% CI, 2.26-4.25) schools compared with the control schools, and consumption increased in the chef schools (OR, 0.17; 95% CI, 0.03-0.30 cups/d). Vegetable selection increased in the chef (OR, 2.54; 95% CI, 1.83-3.54), smart café (OR, 1.91; 95% CI, 1.46-2.50), and chef plus smart café schools (OR, 7.38, 95% CI, 5.26-10.35) compared with the control schools
Distribution of peak expiratory flow variability by age, gender and smoking habits in a random population sample aged 20-70 yrs

NARCIS (Netherlands)

Boezen, H M; Schouten, J. P.; Postma, D S; Rijcken, B

1994-01-01

Peak expiratory flow (PEF) variability can be considered as an index of bronchial lability. Population studies on PEF variability are few. The purpose of the current paper is to describe the distribution of PEF variability in a random population sample of adults with a wide age range (20-70 yrs),
Modified random hinge transport mechanics and multiple scattering step-size selection in EGS5

International Nuclear Information System (INIS)

Wilderman, S.J.; Bielajew, A.F.

2005-01-01

The new transport mechanics in EGS5 allows for significantly longer electron transport step sizes and hence shorter computation times than required for identical problems in EGS4. But as with all Monte Carlo electron transport algorithms, certain classes of problems exhibit step-size dependencies even when operating within recommended ranges, sometimes making selection of step-sizes a daunting task for novice users. Further contributing to this problem, because of the decoupling of multiple scattering and continuous energy loss in the dual random hinge transport mechanics of EGS5, there are two independent step sizes in EGS5, one for multiple scattering and one for continuous energy loss, each of which influences speed and accuracy in a different manner. Further, whereas EGS4 used a single value of fractional energy loss (ESTEPE) to determine step sizes at all energies, to increase performance by decreasing the amount of effort expended simulating lower energy particles, EGS5 permits the fractional energy loss values which are used to determine both the multiple scattering and continuous energy loss step sizes to vary with energy. This results in requiring the user to specify four fractional energy loss values when optimizing computations for speed. Thus, in order to simplify step-size selection and to mitigate step-size dependencies, a method has been devised to automatically optimize step-size selection based on a single material dependent input related to the size of problem tally region. In this paper we discuss the new transport mechanics in EGS5 and describe the automatic step-size optimization algorithm. (author)
Polymer platforms for selective detection of cocaine in street samples adulterated with levamisole.

Science.gov (United States)

Florea, Anca; Cowen, Todd; Piletsky, Sergey; De Wael, Karolien

2018-08-15

Accurate drug detection is of utmost importance for fighting against drug abuse. With a high number of cutting agents and adulterants being added to cut or mask drugs in street powders the number of false results is increasing. We demonstrate for the first time the usefulness of employing polymers readily synthesized by electrodeposition to selectively detect cocaine in the presence of the commonly used adulterant levamisole. The polymers were selected by computational modelling to exhibit high binding affinity towards cocaine and deposited directly on the surface of graphene-modified electrodes via electropolymerization. The resulting platforms allowed a distinct electrochemical signal for cocaine, which is otherwise suppressed by levamisole. Square wave voltammetry was used to quantify cocaine alone and in the presence of levamisole. The usefulness of the platforms was demonstrated in the screening of real street samples. Copyright © 2018 Elsevier B.V. All rights reserved.
Selection of 3013 containers for field surveillance: LA-14310, Revision 1

International Nuclear Information System (INIS)

Peppers, Larry; Kelly, Elizabeth; McClard, James; Friday, Gary; Venetz, Theodore; Stakebake, Jerry

2009-01-01

This document is the fifth in a series of reports that document the binning, statistical sampling, and sample selection of 3013 containers for field surveillance. 1,2,3,39 Revisions to binning assignments documented in this report are primarily a result of new prompt gamma data. This report also documents changes to the random sample specification resulting from these binning changes and identifies and provides the rationale for the engineering judgment sample items for Fiscal Year (FY) 2008 and 2009. This revision also updates the changes to the previous surveillance sample resulting from changes to the order that specific containers undergo surveillance. This report will continue to be reviewed regularly and revised as needed to meet the requirements of the surveillance program.
APTIMA assay on SurePath liquid-based cervical samples compared to endocervical swab samples facilitated by a real time database

Directory of Open Access Journals (Sweden)

Khader Samer

2010-01-01

Full Text Available Background: Liquid-based cytology (LBC cervical samples are increasingly being used to test for pathogens, including: HPV, Chlamydia trachomatis (CT and Neisseria gonorrhoeae (GC using nucleic acid amplification tests. Several reports have shown the accuracy of such testing on ThinPrep (TP LBC samples. Fewer studies have evaluated SurePath (SP LBC samples, which utilize a different specimen preservative. This study was undertaken to assess the performance of the Aptima Combo 2 Assay (AC2 for CT and GC on SP versus endocervical swab samples in our laboratory. Materials and Methods: The live pathology database of Montefiore Medical Center was searched for patients with AC2 endocervical swab specimens and SP Paps taken the same day. SP samples from CT- and/or GC-positive endocervical swab patients and randomly selected negative patients were studied. In each case, 1.5 ml of the residual SP vial sample, which was in SP preservative and stored at room temperature, was transferred within seven days of collection to APTIMA specimen transfer tubes without any sample or patient identifiers. Blind testing with the AC2 assay was performed on the Tigris DTS System (Gen-probe, San Diego, CA. Finalized SP results were compared with the previously reported endocervical swab results for the entire group and separately for patients 25 years and younger and patients over 25 years. Results: SP specimens from 300 patients were tested. This included 181 swab CT-positive, 12 swab GC-positive, 7 CT and GC positive and 100 randomly selected swab CT and GC negative patients. Using the endocervical swab results as the patient′s infection status, AC2 assay of the SP samples showed: CT sensitivity 89.3%, CT specificity 100.0%; GC sensitivity and specificity 100.0%. CT sensitivity for patients 25 years or younger was 93.1%, versus 80.7% for patients over 25 years, a statistically significant difference (P = 0.02. Conclusions: Our results show that AC2 assay of 1.5 ml SP
Soft X-Ray Observations of a Complete Sample of X-Ray--selected BL Lacertae Objects

Science.gov (United States)

Perlman, Eric S.; Stocke, John T.; Wang, Q. Daniel; Morris, Simon L.

1996-01-01

We present the results of ROSAT PSPC observations of the X-ray selected BL Lacertae objects (XBLs) in the complete Einstein Extended Medium Sensitivity Survey (EM MS) sample. None of the objects is resolved in their respective PSPC images, but all are easily detected. All BL Lac objects in this sample are well-fitted by single power laws. Their X-ray spectra exhibit a variety of spectral slopes, with best-fit energy power-law spectral indices between α = 0.5-2.3. The PSPC spectra of this sample are slightly steeper than those typical of flat ratio-spectrum quasars. Because almost all of the individual PSPC spectral indices are equal to or slightly steeper than the overall optical to X-ray spectral indices for these same objects, we infer that BL Lac soft X-ray continua are dominated by steep-spectrum synchrotron radiation from a broad X-ray jet, rather than flat-spectrum inverse Compton radiation linked to the narrower radio/millimeter jet. The softness of the X-ray spectra of these XBLs revives the possibility proposed by Guilbert, Fabian, & McCray (1983) that BL Lac objects are lineless because the circumnuclear gas cannot be heated sufficiently to permit two stable gas phases, the cooler of which would comprise the broad emission-line clouds. Because unified schemes predict that hard self-Compton radiation is beamed only into a small solid angle in BL Lac objects, the steep-spectrum synchrotron tail controls the temperature of the circumnuclear gas at r ≤ 1018 cm and prevents broad-line cloud formation. We use these new ROSAT data to recalculate the X-ray luminosity function and cosmological evolution of the complete EMSS sample by determining accurate K-corrections for the sample and estimating the effects of variability and the possibility of incompleteness in the sample. Our analysis confirms that XBLs are evolving "negatively," opposite in sense to quasars, with Ve/Va = 0.331±0.060. The statistically significant difference between the values for X
40 CFR 86.607-84 - Sample selection.

Science.gov (United States)

2010-07-01

... Auditing of New Light-Duty Vehicles, Light-Duty Trucks, and Heavy-Duty Vehicles § 86.607-84 Sample..., once a manufacturer ships any vehicle from the test sample, it relinquishes the prerogative to conduct...
Evolving artificial metalloenzymes via random mutagenesis

Science.gov (United States)

Yang, Hao; Swartz, Alan M.; Park, Hyun June; Srivastava, Poonam; Ellis-Guardiola, Ken; Upp, David M.; Lee, Gihoon; Belsare, Ketaki; Gu, Yifan; Zhang, Chen; Moellering, Raymond E.; Lewis, Jared C.

2018-03-01

Random mutagenesis has the potential to optimize the efficiency and selectivity of protein catalysts without requiring detailed knowledge of protein structure; however, introducing synthetic metal cofactors complicates the expression and screening of enzyme libraries, and activity arising from free cofactor must be eliminated. Here we report an efficient platform to create and screen libraries of artificial metalloenzymes (ArMs) via random mutagenesis, which we use to evolve highly selective dirhodium cyclopropanases. Error-prone PCR and combinatorial codon mutagenesis enabled multiplexed analysis of random mutations, including at sites distal to the putative ArM active site that are difficult to identify using targeted mutagenesis approaches. Variants that exhibited significantly improved selectivity for each of the cyclopropane product enantiomers were identified, and higher activity than previously reported ArM cyclopropanases obtained via targeted mutagenesis was also observed. This improved selectivity carried over to other dirhodium-catalysed transformations, including N-H, S-H and Si-H insertion, demonstrating that ArMs evolved for one reaction can serve as starting points to evolve catalysts for others.
A stratified two-stage sampling design for digital soil mapping in a Mediterranean basin

Science.gov (United States)

Blaschek, Michael; Duttmann, Rainer

2015-04-01

The quality of environmental modelling results often depends on reliable soil information. In order to obtain soil data in an efficient manner, several sampling strategies are at hand depending on the level of prior knowledge and the overall objective of the planned survey. This study focuses on the collection of soil samples considering available continuous secondary information in an undulating, 16 km²-sized river catchment near Ussana in southern Sardinia (Italy). A design-based, stratified, two-stage sampling design has been applied aiming at the spatial prediction of soil property values at individual locations. The stratification based on quantiles from density functions of two land-surface parameters - topographic wetness index and potential incoming solar radiation - derived from a digital elevation model. Combined with four main geological units, the applied procedure led to 30 different classes in the given test site. Up to six polygons of each available class were selected randomly excluding those areas smaller than 1ha to avoid incorrect location of the points in the field. Further exclusion rules were applied before polygon selection masking out roads and buildings using a 20m buffer. The selection procedure was repeated ten times and the set of polygons with the best geographical spread were chosen. Finally, exact point locations were selected randomly from inside the chosen polygon features. A second selection based on the same stratification and following the same methodology (selecting one polygon instead of six) was made in order to create an appropriate validation set. Supplementary samples were obtained during a second survey focusing on polygons that have either not been considered during the first phase at all or were not adequately represented with respect to feature size. In total, both field campaigns produced an interpolation set of 156 samples and a validation set of 41 points. The selection of sample point locations has been done using
Dynamical implications of sample shape for avalanches in 2-dimensional random-field Ising model with saw-tooth domain wall

Science.gov (United States)

Tadić, Bosiljka

2018-03-01

We study dynamics of a built-in domain wall (DW) in 2-dimensional disordered ferromagnets with different sample shapes using random-field Ising model on a square lattice rotated by 45 degrees. The saw-tooth DW of the length Lx is created along one side and swept through the sample by slow ramping of the external field until the complete magnetisation reversal and the wall annihilation at the open top boundary at a distance Ly. By fixing the number of spins N =Lx ×Ly = 106 and the random-field distribution at a value above the critical disorder, we vary the ratio of the DW length to the annihilation distance in the range Lx /Ly ∈ [ 1 / 16 , 16 ] . The periodic boundary conditions are applied in the y-direction so that these ratios comprise different samples, i.e., surfaces of cylinders with the changing perimeter Lx and height Ly. We analyse the avalanches of the DW slips between following field updates, and the multifractal structure of the magnetisation fluctuation time series. Our main findings are that the domain-wall lengths materialised in different sample shapes have an impact on the dynamics at all scales. Moreover, the domain-wall motion at the beginning of the hysteresis loop (HLB) probes the disorder effects resulting in the fluctuations that are significantly different from the large avalanches in the central part of the loop (HLC), where the strong fields dominate. Specifically, the fluctuations in HLB exhibit a wide multi-fractal spectrum, which shifts towards higher values of the exponents when the DW length is reduced. The distributions of the avalanches in this segments of the loops obey power-law decay and the exponential cutoffs with the exponents firmly in the mean-field universality class for long DW. In contrast, the avalanches in the HLC obey Tsallis density distribution with the power-law tails which indicate the new categories of the scale invariant behaviour for different ratios Lx /Ly. The large fluctuations in the HLC, on the other
Simultaneous escaping of explicit and hidden free energy barriers: application of the orthogonal space random walk strategy in generalized ensemble based conformational sampling.

Science.gov (United States)

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.
Compressive sampling of polynomial chaos expansions: Convergence analysis and sampling strategies

International Nuclear Information System (INIS)

Hampton, Jerrad; Doostan, Alireza

2015-01-01

Sampling orthogonal polynomial bases via Monte Carlo is of interest for uncertainty quantification of models with random inputs, using Polynomial Chaos (PC) expansions. It is known that bounding a probabilistic parameter, referred to as coherence, yields a bound on the number of samples necessary to identify coefficients in a sparse PC expansion via solution to an ℓ 1 -minimization problem. Utilizing results for orthogonal polynomials, we bound the coherence parameter for polynomials of Hermite and Legendre type under their respective natural sampling distribution. In both polynomial bases we identify an importance sampling distribution which yields a bound with weaker dependence on the order of the approximation. For more general orthonormal bases, we propose the coherence-optimal sampling: a Markov Chain Monte Carlo sampling, which directly uses the basis functions under consideration to achieve a statistical optimality among all sampling schemes with identical support. We demonstrate these different sampling strategies numerically in both high-order and high-dimensional, manufactured PC expansions. In addition, the quality of each sampling method is compared in the identification of solutions to two differential equations, one with a high-dimensional random input and the other with a high-order PC expansion. In both cases, the coherence-optimal sampling scheme leads to similar or considerably improved accuracy
Compressive sampling of polynomial chaos expansions: Convergence analysis and sampling strategies

Science.gov (United States)

Hampton, Jerrad; Doostan, Alireza

2015-01-01

Sampling orthogonal polynomial bases via Monte Carlo is of interest for uncertainty quantification of models with random inputs, using Polynomial Chaos (PC) expansions. It is known that bounding a probabilistic parameter, referred to as coherence, yields a bound on the number of samples necessary to identify coefficients in a sparse PC expansion via solution to an ℓ1-minimization problem. Utilizing results for orthogonal polynomials, we bound the coherence parameter for polynomials of Hermite and Legendre type under their respective natural sampling distribution. In both polynomial bases we identify an importance sampling distribution which yields a bound with weaker dependence on the order of the approximation. For more general orthonormal bases, we propose the coherence-optimal sampling: a Markov Chain Monte Carlo sampling, which directly uses the basis functions under consideration to achieve a statistical optimality among all sampling schemes with identical support. We demonstrate these different sampling strategies numerically in both high-order and high-dimensional, manufactured PC expansions. In addition, the quality of each sampling method is compared in the identification of solutions to two differential equations, one with a high-dimensional random input and the other with a high-order PC expansion. In both cases, the coherence-optimal sampling scheme leads to similar or considerably improved accuracy.
Soil map disaggregation improved by soil-landscape relationships, area-proportional sampling and random forest implementation

DEFF Research Database (Denmark)

Møller, Anders Bjørn; Malone, Brendan P.; Odgers, Nathan

implementation generally improved the algorithm’s ability to predict the correct soil class. The implementation of soil-landscape relationships and area-proportional sampling generally increased the calculation time, while the random forest implementation reduced the calculation time. In the most successful......Detailed soil information is often needed to support agricultural practices, environmental protection and policy decisions. Several digital approaches can be used to map soil properties based on field observations. When soil observations are sparse or missing, an alternative approach...... is to disaggregate existing conventional soil maps. At present, the DSMART algorithm represents the most sophisticated approach for disaggregating conventional soil maps (Odgers et al., 2014). The algorithm relies on classification trees trained from resampled points, which are assigned classes according...

Pierre Gy's sampling theory and sampling practice heterogeneity, sampling correctness, and statistical process control

CERN Document Server

Pitard, Francis F

1993-01-01

Pierre Gy's Sampling Theory and Sampling Practice, Second Edition is a concise, step-by-step guide for process variability management and methods. Updated and expanded, this new edition provides a comprehensive study of heterogeneity, covering the basic principles of sampling theory and its various applications. It presents many practical examples to allow readers to select appropriate sampling protocols and assess the validity of sampling protocols from others. The variability of dynamic process streams using variography is discussed to help bridge sampling theory with statistical process control. Many descriptions of good sampling devices, as well as descriptions of poor ones, are featured to educate readers on what to look for when purchasing sampling systems. The book uses its accessible, tutorial style to focus on professional selection and use of methods. The book will be a valuable guide for mineral processing engineers; metallurgists; geologists; miners; chemists; environmental scientists; and practit...
Comparison of Address-based Sampling and Random-digit Dialing Methods for Recruiting Young Men as Controls in a Case-Control Study of Testicular Cancer Susceptibility

OpenAIRE

Clagett, Bartholt; Nathanson, Katherine L.; Ciosek, Stephanie L.; McDermoth, Monique; Vaughn, David J.; Mitra, Nandita; Weiss, Andrew; Martonik, Rachel; Kanetsky, Peter A.

2013-01-01

Random-digit dialing (RDD) using landline telephone numbers is the historical gold standard for control recruitment in population-based epidemiologic research. However, increasing cell-phone usage and diminishing response rates suggest that the effectiveness of RDD in recruiting a random sample of the general population, particularly for younger target populations, is decreasing. In this study, we compared landline RDD with alternative methods of control recruitment, including RDD using cell-...
Two-dimensional random arrays for real time volumetric imaging

DEFF Research Database (Denmark)

Davidsen, Richard E.; Jensen, Jørgen Arendt; Smith, Stephen W.

1994-01-01

real time volumetric imaging system, which employs a wide transmit beam and receive mode parallel processing to increase image frame rate. Depth-of-field comparisons were made from simulated on-axis and off-axis beamplots at ranges from 30 to 160 mm for both coaxial and offset transmit and receive......Two-dimensional arrays are necessary for a variety of ultrasonic imaging techniques, including elevation focusing, 2-D phase aberration correction, and real time volumetric imaging. In order to reduce system cost and complexity, sparse 2-D arrays have been considered with element geometries...... selected ad hoc, by algorithm, or by random process. Two random sparse array geometries and a sparse array with a Mills cross receive pattern were simulated and compared to a fully sampled aperture with the same overall dimensions. The sparse arrays were designed to the constraints of the Duke University...
Impacts of Sample Design for Validation Data on the Accuracy of Feedforward Neural Network Classification

Directory of Open Access Journals (Sweden)

Giles M. Foody

2017-08-01

Full Text Available Validation data are often used to evaluate the performance of a trained neural network and used in the selection of a network deemed optimal for the task at-hand. Optimality is commonly assessed with a measure, such as overall classification accuracy. The latter is often calculated directly from a confusion matrix showing the counts of cases in the validation set with particular labelling properties. The sample design used to form the validation set can, however, influence the estimated magnitude of the accuracy. Commonly, the validation set is formed with a stratified sample to give balanced classes, but also via random sampling, which reflects class abundance. It is suggested that if the ultimate aim is to accurately classify a dataset in which the classes do vary in abundance, a validation set formed via random, rather than stratified, sampling is preferred. This is illustrated with the classification of simulated and remotely-sensed datasets. With both datasets, statistically significant differences in the accuracy with which the data could be classified arose from the use of validation sets formed via random and stratified sampling (z = 2.7 and 1.9 for the simulated and real datasets respectively, for both p < 0.05%. The accuracy of the classifications that used a stratified sample in validation were smaller, a result of cases of an abundant class being commissioned into a rarer class. Simple means to address the issue are suggested.
Effect of Genital Sampling Site on the Detection and Quantification of Ureaplasma Species with Quantitative Polymerase Chain Reaction during Pregnancy

OpenAIRE

Faron, Gilles; Vancutsem, Ellen; Naessens, Anne; Buyl, Ronald; Gucciardo, Leonardo; Foulon, Walter

2017-01-01

Objective. This study aimed to compare the qualitative and quantitative reproducibility of quantitative PCR (qPCR) for Ureaplasma species (Ureaplasma spp.) throughout pregnancy and according to the genital sampling site. Study Design. Between 5 and 14 weeks of gestation (T1), vaginal, fornix, and two cervical samples were taken. Sampling was repeated during the 2nd (T2) and 3rd (T3) trimester in randomly selected T1 positive and negative women. Qualitative and quantitative reproducibility wer...
Antimicrobial and antibiofilm effects of selected food preservatives against Salmonella spp. isolated from chicken samples.

Science.gov (United States)

Er, Buket; Demirhan, Burak; Onurdag, Fatma Kaynak; Ozgacar, Selda Özgen; Oktem, Aysel Bayhan

2014-03-01

Salmonella spp. are widespread foodborne pathogens that contaminate egg and poultry meats. Attachment, colonization, as well as biofilm formation capacity of Salmonella spp. on food and contact surfaces of food may cause continuous contamination. Biofilm may play a crucial role in the survival of salmonellae under unfavorable environmental conditions, such as in animal slaughterhouses and processing plants. This could serve as a reservoir compromising food safety and human health. Addition of antimicrobial preservatives extends shelf lives of food products, but even when products are supplemented with adequate amounts of preservatives, it is not always possible to inhibit the microorganisms in a biofilm community. In this study, our aims were i) to determine the minimum inhibitory concentrations (MIC) and minimum biofilm inhibitory concentrations (MBIC) of selected preservatives against planktonic and biofilm forms of Salmonella spp. isolated from chicken samples and Salmonella Typhimurium SL1344 standard strain, ii) to show the differences in the susceptibility patterns of same strains versus the planktonic and biofilm forms to the same preservative agent, and iii) to determine and compare antimicrobial and antibiofilm effects of selected food preservatives against Salmonella spp. For this purpose, Salmonella Typhimurium SL1344 standard strain and 4 Salmonella spp. strains isolated from chicken samples were used. Investigation of antimicrobial and antibiofilm effects of selected food preservatives against Salmonella spp. was done according to Clinical and Laboratory Standards Institute M100-S18 guidelines and BioTimer assay, respectively. As preservative agents, pure ciprofloxacin, sodium nitrite, potassium sorbate, sodium benzoate, methyl paraben, and propyl paraben were selected. As a result, it was determined that MBIC values are greater than the MIC values of the preservatives. This result verified the resistance seen in a biofilm community to food
The effect of local heat on term neonates pain intensity during heel-blood sampling

Directory of Open Access Journals (Sweden)

R. GHobadi Mohebi

2017-04-01

Full Text Available Aims: Newborns are more sensitive to pain than adults and are more susceptible to the long-term complications of pain. So, it is necessary to use procedures for reducing pain in newborns. The aim of this study was to determine the effect of local heat on the pain intensity of heel-blood sampling in the term newborns. Material & Methods: In this randomized controlled clinical trial study, in 2012, 63 healthy 3 to 5-day newborns who were referred to Shahid Delkhah Health Center in Ferdows were selected by random sampling method and randomly divided into 3 groups (21 people in each group: test (heat, placebo (sound and control. The pain intensity of newborns before, during and after heel-blood sampling was evaluated. The data collection tools were demographic questionnaire and Neonatal Infant Pain Scale (NIPS. Data were analyzed by SPSS 14.5 software and chi-square test, one-way ANOVA, Tukey's post hoc test, and ANOVA with repeated observations. Finding: The mean pain intensity in the three groups was not significantly different before intervention (p=0.86, but the mean pain intensity was lower in the test group than in the other two groups (p=0.006. After heel-blood sampling, the mean pain intensity was the least in the test group and was the most in the control group (p<0.001. Conclusion: Local heat during and after heel blood sampling decreases pain intensity in the term newborns.
Progressive sample processing of band selection for hyperspectral imagery

Science.gov (United States)

Liu, Keng-Hao; Chien, Hung-Chang; Chen, Shih-Yu

2017-10-01

Band selection (BS) is one of the most important topics in hyperspectral image (HSI) processing. The objective of BS is to find a set of representative bands that can represent the whole image with lower inter-band redundancy. Many types of BS algorithms were proposed in the past. However, most of them can be carried on in an off-line manner. It means that they can only be implemented on the pre-collected data. Those off-line based methods are sometime useless for those applications that are timeliness, particular in disaster prevention and target detection. To tackle this issue, a new concept, called progressive sample processing (PSP), was proposed recently. The PSP is an "on-line" framework where the specific type of algorithm can process the currently collected data during the data transmission under band-interleavedby-sample/pixel (BIS/BIP) protocol. This paper proposes an online BS method that integrates a sparse-based BS into PSP framework, called PSP-BS. In PSP-BS, the BS can be carried out by updating BS result recursively pixel by pixel in the same way that a Kalman filter does for updating data information in a recursive fashion. The sparse regression is solved by orthogonal matching pursuit (OMP) algorithm, and the recursive equations of PSP-BS are derived by using matrix decomposition. The experiments conducted on a real hyperspectral image show that the PSP-BS can progressively output the BS status with very low computing time. The convergence of BS results during the transmission can be quickly achieved by using a rearranged pixel transmission sequence. This significant advantage allows BS to be implemented in a real time manner when the HSI data is transmitted pixel by pixel.
Cold Spray Deposition of Freestanding Inconel Samples and Comparative Analysis with Selective Laser Melting

Science.gov (United States)

Bagherifard, Sara; Roscioli, Gianluca; Zuccoli, Maria Vittoria; Hadi, Mehdi; D'Elia, Gaetano; Demir, Ali Gökhan; Previtali, Barbara; Kondás, Ján; Guagliano, Mario

2017-10-01

Cold spray offers the possibility of obtaining almost zero-porosity buildups with no theoretical limit to the thickness. Moreover, cold spray can eliminate particle melting, evaporation, crystallization, grain growth, unwanted oxidation, undesirable phases and thermally induced tensile residual stresses. Such characteristics can boost its potential to be used as an additive manufacturing technique. Indeed, deposition via cold spray is recently finding its path toward fabrication of freeform components since it can address the common challenges of powder-bed additive manufacturing techniques including major size constraints, deposition rate limitations and high process temperature. Herein, we prepared nickel-based superalloy Inconel 718 samples with cold spray technique and compared them with similar samples fabricated by selective laser melting method. The samples fabricated using both methods were characterized in terms of mechanical strength, microstructural and porosity characteristics, Vickers microhardness and residual stresses distribution. Different heat treatment cycles were applied to the cold-sprayed samples in order to enhance their mechanical characteristics. The obtained data confirm that cold spray technique can be used as a complementary additive manufacturing method for fabrication of high-quality freestanding components where higher deposition rate, larger final size and lower fabrication temperatures are desired.
Assessment of selected contaminants in streambed- and suspended-sediment samples collected in Bexar County, Texas, 2007-09

Science.gov (United States)

Wilson, Jennifer T.

2011-01-01

Elevated concentrations of sediment-associated contaminants are typically associated with urban areas such as San Antonio, Texas, in Bexar County, the seventh most populous city in the United States. This report describes an assessment of selected sediment-associated contaminants in samples collected in Bexar County from sites on the following streams: Medio Creek, Medina River, Elm Creek, Martinez Creek, Chupaderas Creek, Leon Creek, Salado Creek, and San Antonio River. During 2007-09, the U.S. Geological Survey periodically collected surficial streambed-sediment samples during base flow and suspended-sediment (large-volume suspended-sediment) samples from selected streams during stormwater runoff. All sediment samples were analyzed for major and trace elements and for organic compounds including halogenated organic compounds and polycyclic aromatic hydrocarbons (PAHs). Selected contaminants in streambed and suspended sediments in watersheds of the eight major streams in Bexar County were assessed by using a variety of methods—observations of occurrence and distribution, comparison to sediment-quality guidelines and data from previous studies, statistical analyses, and source indicators. Trace elements concentrations were low compared to the consensus-based sediment-quality guidelines threshold effect concentration (TEC) and probable effect concentration (PEC). Trace element concentrations were greater than the TEC in 28 percent of the samples and greater than the PEC in 1.5 percent of the samples. Chromium concentrations exceeded sediment-quality guidelines more frequently than concentrations of any other constituents analyzed in this study (greater than the TEC in 69 percent of samples and greater than the PEC in 8 percent of samples). Mean trace element concentrations generally are lower in Bexar County samples compared to concentrations in samples collected during previous studies in the Austin and Fort Worth, Texas, areas, but considering the relatively
Plasma as alternatively sample to quantify tetanus antitoxin

Directory of Open Access Journals (Sweden)

Ariel Menéndez-Barrios

2015-08-01

Full Text Available Tetanus antitoxin is quantified in Cuba at blood banks, from the serum of immunized donors, to produce aspecific human gamma globulin. A heterogeneous indirect immunoenzymatic assay is used, using the serum as analytical sample. The possible use of plasma obtained from plasmapheresis as alternative sample was evaluated in this research, to minimize the volume of total blood extracted to the donors. One hundred plasma donors who came to donate between October and November 2013 were selected by simple random sampling. Serum sample was obtained for extraction of 5 mL of blood, deposited in dry glass tube. While the other sample took 1.5 mL of plasma in a plastic tube with cover, at the end of the donation directly of the unit of plasma collected. Comparison of the difference between the means of both groups was done using SPSS for Windows. It was found that the values obtained in serum were bigger than those obtained in plasma. Difference between the means of both groups was statistically significant (p 0.00. It is not advisable to use the obtained plasma of the plasmapheresis as analytic sample in this assay.
The Quasar Fraction in Low-Frequency Selected Complete Samples and Implications for Unified Schemes

Science.gov (United States)

Willott, Chris J.; Rawlings, Steve; Blundell, Katherine M.; Lacy, Mark

2000-01-01

Low-frequency radio surveys are ideal for selecting orientation-independent samples of extragalactic sources because the sample members are selected by virtue of their isotropic steep-spectrum extended emission. We use the new 7C Redshift Survey along with the brighter 3CRR and 6C samples to investigate the fraction of objects with observed broad emission lines - the 'quasar fraction' - as a function of redshift and of radio and narrow emission line luminosity. We find that the quasar fraction is more strongly dependent upon luminosity (both narrow line and radio) than it is on redshift. Above a narrow [OII] emission line luminosity of log(base 10) (L(sub [OII])/W) approximately > 35 [or radio luminosity log(base 10) (L(sub 151)/ W/Hz.sr) approximately > 26.5], the quasar fraction is virtually independent of redshift and luminosity; this is consistent with a simple unified scheme with an obscuring torus with a half-opening angle theta(sub trans) approximately equal 53 deg. For objects with less luminous narrow lines, the quasar fraction is lower. We show that this is not due to the difficulty of detecting lower-luminosity broad emission lines in a less luminous, but otherwise similar, quasar population. We discuss evidence which supports at least two probable physical causes for the drop in quasar fraction at low luminosity: (i) a gradual decrease in theta(sub trans) and/or a gradual increase in the fraction of lightly-reddened (0 approximately quasar luminosity; and (ii) the emergence of a distinct second population of low luminosity radio sources which, like M8T, lack a well-fed quasar nucleus and may well lack a thick obscuring torus.
Autonomous site selection and instrument positioning for sample acquisition

Science.gov (United States)

Shaw, A.; Barnes, D.; Pugh, S.

The European Space Agency Aurora Exploration Program aims to establish a European long-term programme for the exploration of Space, culminating in a human mission to space in the 2030 timeframe. Two flagship missions, namely Mars Sample Return and ExoMars, have been proposed as recognised steps along the way. The Exomars Rover is the first of these flagship missions and includes a rover carrying the Pasteur Payload, a mobile exobiology instrumentation package, and the Beagle 2 arm. The primary objective is the search for evidence of past or present life on mars, but the payload will also study the evolution of the planet and the atmosphere, look for evidence of seismological activity and survey the environment in preparation for future missions. The operation of rovers in unknown environments is complicated, and requires large resources not only on the planet but also in ground based operations. Currently, this can be very labour intensive, and costly, if large teams of scientists and engineers are required to assess mission progress, plan mission scenarios, and construct a sequence of events or goals for uplink. Furthermore, the constraints in communication imposed by the time delay involved over such large distances, and line-of-sight required, make autonomy paramount to mission success, affording the ability to operate in the event of communications outages and be opportunistic with respect to scientific discovery. As part of this drive to reduce mission costs and increase autonomy the Space Robotics group at the University of Wales, Aberystwyth is researching methods of autonomous site selection and instrument positioning, directly applicable to the ExoMars mission. The site selection technique used builds on the geometric reasoning algorithms used previously for localisation and navigation [Shaw 03]. It is proposed that a digital elevation model (DEM) of the local surface, generated during traverse and without interaction from ground based operators, can be
Evaluating effectiveness of down-sampling for stratified designs and unbalanced prevalence in Random Forest models of tree species distributions in Nevada

Science.gov (United States)

Elizabeth A. Freeman; Gretchen G. Moisen; Tracy S. Frescino

2012-01-01

Random Forests is frequently used to model species distributions over large geographic areas. Complications arise when data used to train the models have been collected in stratified designs that involve different sampling intensity per stratum. The modeling process is further complicated if some of the target species are relatively rare on the landscape leading to an...
Wavelength Selection Method Based on Differential Evolution for Precise Quantitative Analysis Using Terahertz Time-Domain Spectroscopy.

Science.gov (United States)

Li, Zhi; Chen, Weidong; Lian, Feiyu; Ge, Hongyi; Guan, Aihong

2017-12-01

Quantitative analysis of component mixtures is an important application of terahertz time-domain spectroscopy (THz-TDS) and has attracted broad interest in recent research. Although the accuracy of quantitative analysis using THz-TDS is affected by a host of factors, wavelength selection from the sample's THz absorption spectrum is the most crucial component. The raw spectrum consists of signals from the sample and scattering and other random disturbances that can critically influence the quantitative accuracy. For precise quantitative analysis using THz-TDS, the signal from the sample needs to be retained while the scattering and other noise sources are eliminated. In this paper, a novel wavelength selection method based on differential evolution (DE) is investigated. By performing quantitative experiments on a series of binary amino acid mixtures using THz-TDS, we demonstrate the efficacy of the DE-based wavelength selection method, which yields an error rate below 5%.
Random versus Deterministic Descent in RNA Energy Landscape Analysis

Directory of Open Access Journals (Sweden)

Luke Day

2016-01-01

Full Text Available Identifying sets of metastable conformations is a major research topic in RNA energy landscape analysis, and recently several methods have been proposed for finding local minima in landscapes spawned by RNA secondary structures. An important and time-critical component of such methods is steepest, or gradient, descent in attraction basins of local minima. We analyse the speed-up achievable by randomised descent in attraction basins in the context of large sample sets where the size has an order of magnitude in the region of ~106. While the gain for each individual sample might be marginal, the overall run-time improvement can be significant. Moreover, for the two nongradient methods we analysed for partial energy landscapes induced by ten different RNA sequences, we obtained that the number of observed local minima is on average larger by 7.3% and 3.5%, respectively. The run-time improvement is approximately 16.6% and 6.8% on average over the ten partial energy landscapes. For the large sample size we selected for descent procedures, the coverage of local minima is very high up to energy values of the region where the samples were randomly selected from the partial energy landscapes; that is, the difference to the total set of local minima is mainly due to the upper area of the energy landscapes.
40 CFR 91.506 - Engine sample selection.

Science.gov (United States)

2010-07-01

... paragraph (b)(2) of this section. It defines one-tail, 95 percent confidence intervals. σ=actual test sample... individual engine x=mean of emission test results of the actual sample FEL=Family Emission Limit n=The actual... carry-over engine families: After one engine is tested, the manufacturer will combine the test with the...
Importance of participation rate in sampling of data in population based studies, with special reference to bone mass in Sweden.

OpenAIRE

Düppe, H; Gärdsell, P; Hanson, B S; Johnell, O; Nilsson, B E

1996-01-01

OBJECTIVE: To study the effects of participation rate in sampling on "normative" bone mass data. DESIGN: This was a comparison between two randomly selected samples from the same population. The participation rates in the two samples were 61.9% and 83.6%. Measurements were made of bone mass at different skeletal sites and of muscle strength, as well as an assessment of physical activity. SETTING: Malmö, Sweden. SUBJECTS: There were 230 subjects (117 men, 113 women), aged 21 to 42 years. RESUL...
Indirect estimation of signal-dependent noise with nonadaptive heterogeneous samples.

Science.gov (United States)

Azzari, Lucio; Foi, Alessandro

2014-08-01

We consider the estimation of signal-dependent noise from a single image. Unlike conventional algorithms that build a scatterplot of local mean-variance pairs from either small or adaptively selected homogeneous data samples, our proposed approach relies on arbitrarily large patches of heterogeneous data extracted at random from the image. We demonstrate the feasibility of our approach through an extensive theoretical analysis based on mixture of Gaussian distributions. A prototype algorithm is also developed in order to validate the approach on simulated data as well as on real camera raw images.
Comparison of confirmed inactive and randomly selected compounds as negative training examples in support vector machine-based virtual screening.

Science.gov (United States)

Heikamp, Kathrin; Bajorath, Jürgen

2013-07-22

The choice of negative training data for machine learning is a little explored issue in chemoinformatics. In this study, the influence of alternative sets of negative training data and different background databases on support vector machine (SVM) modeling and virtual screening has been investigated. Target-directed SVM models have been derived on the basis of differently composed training sets containing confirmed inactive molecules or randomly selected database compounds as negative training instances. These models were then applied to search background databases consisting of biological screening data or randomly assembled compounds for available hits. Negative training data were found to systematically influence compound recall in virtual screening. In addition, different background databases had a strong influence on the search results. Our findings also indicated that typical benchmark settings lead to an overestimation of SVM-based virtual screening performance compared to search conditions that are more relevant for practical applications.

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