A GPU-Based Implementation of the Firefly Algorithm for Variable Selection in Multivariate Calibration Problems.

PubMed

de Paula, Lauro C M; Soares, Anderson S; de Lima, Telma W; Delbem, Alexandre C B; Coelho, Clarimar J; Filho, Arlindo R G

2014-01-01

Several variable selection algorithms in multivariate calibration can be accelerated using Graphics Processing Units (GPU). Among these algorithms, the Firefly Algorithm (FA) is a recent proposed metaheuristic that may be used for variable selection. This paper presents a GPU-based FA (FA-MLR) with multiobjective formulation for variable selection in multivariate calibration problems and compares it with some traditional sequential algorithms in the literature. The advantage of the proposed implementation is demonstrated in an example involving a relatively large number of variables. The results showed that the FA-MLR, in comparison with the traditional algorithms is a more suitable choice and a relevant contribution for the variable selection problem. Additionally, the results also demonstrated that the FA-MLR performed in a GPU can be five times faster than its sequential implementation.

Canonical Measure of Correlation (CMC) and Canonical Measure of Distance (CMD) between sets of data. Part 3. Variable selection in classification.

PubMed

Ballabio, Davide; Consonni, Viviana; Mauri, Andrea; Todeschini, Roberto

2010-01-11

In multivariate regression and classification issues variable selection is an important procedure used to select an optimal subset of variables with the aim of producing more parsimonious and eventually more predictive models. Variable selection is often necessary when dealing with methodologies that produce thousands of variables, such as Quantitative Structure-Activity Relationships (QSARs) and highly dimensional analytical procedures. In this paper a novel method for variable selection for classification purposes is introduced. This method exploits the recently proposed Canonical Measure of Correlation between two sets of variables (CMC index). The CMC index is in this case calculated for two specific sets of variables, the former being comprised of the independent variables and the latter of the unfolded class matrix. The CMC values, calculated by considering one variable at a time, can be sorted and a ranking of the variables on the basis of their class discrimination capabilities results. Alternatively, CMC index can be calculated for all the possible combinations of variables and the variable subset with the maximal CMC can be selected, but this procedure is computationally more demanding and classification performance of the selected subset is not always the best one. The effectiveness of the CMC index in selecting variables with discriminative ability was compared with that of other well-known strategies for variable selection, such as the Wilks' Lambda, the VIP index based on the Partial Least Squares-Discriminant Analysis, and the selection provided by classification trees. A variable Forward Selection based on the CMC index was finally used in conjunction of Linear Discriminant Analysis. This approach was tested on several chemical data sets. Obtained results were encouraging.

Input variable selection for data-driven models of Coriolis flowmeters for two-phase flow measurement

NASA Astrophysics Data System (ADS)

Wang, Lijuan; Yan, Yong; Wang, Xue; Wang, Tao

2017-03-01

Input variable selection is an essential step in the development of data-driven models for environmental, biological and industrial applications. Through input variable selection to eliminate the irrelevant or redundant variables, a suitable subset of variables is identified as the input of a model. Meanwhile, through input variable selection the complexity of the model structure is simplified and the computational efficiency is improved. This paper describes the procedures of the input variable selection for the data-driven models for the measurement of liquid mass flowrate and gas volume fraction under two-phase flow conditions using Coriolis flowmeters. Three advanced input variable selection methods, including partial mutual information (PMI), genetic algorithm-artificial neural network (GA-ANN) and tree-based iterative input selection (IIS) are applied in this study. Typical data-driven models incorporating support vector machine (SVM) are established individually based on the input candidates resulting from the selection methods. The validity of the selection outcomes is assessed through an output performance comparison of the SVM based data-driven models and sensitivity analysis. The validation and analysis results suggest that the input variables selected from the PMI algorithm provide more effective information for the models to measure liquid mass flowrate while the IIS algorithm provides a fewer but more effective variables for the models to predict gas volume fraction.

Variable Selection in Logistic Regression.

DTIC Science & Technology

1987-06-01

23 %. AUTIOR(.) S. CONTRACT OR GRANT NUMBE Rf.i %Z. D. Bai, P. R. Krishnaiah and . C. Zhao F49620-85- C-0008 " PERFORMING ORGANIZATION NAME AND AOORESS...d I7 IOK-TK- d 7 -I0 7’ VARIABLE SELECTION IN LOGISTIC REGRESSION Z. D. Bai, P. R. Krishnaiah and L. C. Zhao Center for Multivariate Analysis...University of Pittsburgh Center for Multivariate Analysis University of Pittsburgh Y !I VARIABLE SELECTION IN LOGISTIC REGRESSION Z- 0. Bai, P. R. Krishnaiah

Charts Showing Relations Among Primary Aerodynamic Variables for Helicopter-performance Estimation

NASA Technical Reports Server (NTRS)

Talkin, Herbert W

1947-01-01

In order to facilitate solutions of the general problem of helicopter selection, the aerodynamic performance of rotors is presented in the form of charts showing relations between primary design and performance variables. By the use of conventional helicopter theory, certain variables are plotted and other variables are considered fixed. Charts constructed in such a manner show typical results, trends, and limits of helicopter performance. Performance conditions considered include hovering, horizontal flight, climb, and ceiling. Special problems discussed include vertical climb and the use of rotor-speed-reduction gears for hovering.

The Effects of Basic Gymnastics Training Integrated with Physical Education Courses on Selected Motor Performance Variables

ERIC Educational Resources Information Center

Alpkaya, Ufuk

2013-01-01

The purpose of this study is to determine the influence of gymnastics training integrated with physical education courses on selected motor performance variables in seven year old girls. Subjects were divided into two groups: (1) control group (N=15, X=7.56 plus or minus 0.46 year old); (2) gymnastics group (N=16, X=7.60 plus or minus 0.50 year…

Variable Selection for Nonparametric Quantile Regression via Smoothing Spline AN OVA

PubMed Central

Lin, Chen-Yen; Bondell, Howard; Zhang, Hao Helen; Zou, Hui

2014-01-01

Quantile regression provides a more thorough view of the effect of covariates on a response. Nonparametric quantile regression has become a viable alternative to avoid restrictive parametric assumption. The problem of variable selection for quantile regression is challenging, since important variables can influence various quantiles in different ways. We tackle the problem via regularization in the context of smoothing spline ANOVA models. The proposed sparse nonparametric quantile regression (SNQR) can identify important variables and provide flexible estimates for quantiles. Our numerical study suggests the promising performance of the new procedure in variable selection and function estimation. Supplementary materials for this article are available online. PMID:24554792

Comparing Selections of Environmental Variables for Ecological Studies: A Focus on Terrain Attributes.

PubMed

Lecours, Vincent; Brown, Craig J; Devillers, Rodolphe; Lucieer, Vanessa L; Edinger, Evan N

2016-01-01

Selecting appropriate environmental variables is a key step in ecology. Terrain attributes (e.g. slope, rugosity) are routinely used as abiotic surrogates of species distribution and to produce habitat maps that can be used in decision-making for conservation or management. Selecting appropriate terrain attributes for ecological studies may be a challenging process that can lead users to select a subjective, potentially sub-optimal combination of attributes for their applications. The objective of this paper is to assess the impacts of subjectively selecting terrain attributes for ecological applications by comparing the performance of different combinations of terrain attributes in the production of habitat maps and species distribution models. Seven different selections of terrain attributes, alone or in combination with other environmental variables, were used to map benthic habitats of German Bank (off Nova Scotia, Canada). 29 maps of potential habitats based on unsupervised classifications of biophysical characteristics of German Bank were produced, and 29 species distribution models of sea scallops were generated using MaxEnt. The performances of the 58 maps were quantified and compared to evaluate the effectiveness of the various combinations of environmental variables. One of the combinations of terrain attributes-recommended in a related study and that includes a measure of relative position, slope, two measures of orientation, topographic mean and a measure of rugosity-yielded better results than the other selections for both methodologies, confirming that they together best describe terrain properties. Important differences in performance (up to 47% in accuracy measurement) and spatial outputs (up to 58% in spatial distribution of habitats) highlighted the importance of carefully selecting variables for ecological applications. This paper demonstrates that making a subjective choice of variables may reduce map accuracy and produce maps that do not adequately represent habitats and species distributions, thus having important implications when these maps are used for decision-making.

An Ensemble Successive Project Algorithm for Liquor Detection Using Near Infrared Sensor.

PubMed

Qu, Fangfang; Ren, Dong; Wang, Jihua; Zhang, Zhong; Lu, Na; Meng, Lei

2016-01-11

Spectral analysis technique based on near infrared (NIR) sensor is a powerful tool for complex information processing and high precision recognition, and it has been widely applied to quality analysis and online inspection of agricultural products. This paper proposes a new method to address the instability of small sample sizes in the successive projections algorithm (SPA) as well as the lack of association between selected variables and the analyte. The proposed method is an evaluated bootstrap ensemble SPA method (EBSPA) based on a variable evaluation index (EI) for variable selection, and is applied to the quantitative prediction of alcohol concentrations in liquor using NIR sensor. In the experiment, the proposed EBSPA with three kinds of modeling methods are established to test their performance. In addition, the proposed EBSPA combined with partial least square is compared with other state-of-the-art variable selection methods. The results show that the proposed method can solve the defects of SPA and it has the best generalization performance and stability. Furthermore, the physical meaning of the selected variables from the near infrared sensor data is clear, which can effectively reduce the variables and improve their prediction accuracy.

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

EPA Science Inventory

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

Variable screening via quantile partial correlation

PubMed Central

Ma, Shujie; Tsai, Chih-Ling

2016-01-01

In quantile linear regression with ultra-high dimensional data, we propose an algorithm for screening all candidate variables and subsequently selecting relevant predictors. Specifically, we first employ quantile partial correlation for screening, and then we apply the extended Bayesian information criterion (EBIC) for best subset selection. Our proposed method can successfully select predictors when the variables are highly correlated, and it can also identify variables that make a contribution to the conditional quantiles but are marginally uncorrelated or weakly correlated with the response. Theoretical results show that the proposed algorithm can yield the sure screening set. By controlling the false selection rate, model selection consistency can be achieved theoretically. In practice, we proposed using EBIC for best subset selection so that the resulting model is screening consistent. Simulation studies demonstrate that the proposed algorithm performs well, and an empirical example is presented. PMID:28943683

Investigating systematic individual differences in sleep-deprived performance on a high-fidelity flight simulator.

PubMed

Van Dongen, Hans P A; Caldwell, John A; Caldwell, J Lynn

2006-05-01

Laboratory research has revealed considerable systematic variability in the degree to which individuals' alertness and performance are affected by sleep deprivation. However, little is known about whether or not different populations exhibit similar levels of individual variability. In the present study, we examined individual variability in performance impairment due to sleep loss in a highly select population of militaryjet pilots. Ten active-duty F-117 pilots were deprived of sleep for 38 h and studied repeatedly in a high-fidelity flight simulator. Data were analyzed with a mixed-model ANOVA to quantify individual variability. Statistically significant, systematic individual differences in the effects of sleep deprivation were observed, even when baseline differences were accounted for. The findings suggest that highly select populations may exhibit individual differences in vulnerability to performance impairment from sleep loss just as the general population does. Thus, the scientific and operational communities' reliance on group data as opposed to individual data may entail substantial misestimation of the impact of job-related stressors on safety and performance.

A critique of assumptions about selecting chemical-resistant gloves: a case for workplace evaluation of glove efficacy.

PubMed

Klingner, Thomas D; Boeniger, Mark F

2002-05-01

Wearing chemical-resistant gloves and clothing is the primary method used to prevent skin exposure to toxic chemicals in the workplace. The process for selecting gloves is usually based on manufacturers' laboratory-generated chemical permeation data. However, such data may not reflect conditions in the workplace where many variables are encountered (e.g., elevated temperature, flexing, pressure, and product variation between suppliers). Thus, the reliance on this selection process is questionable. Variables that may influence the performance of chemical-resistant gloves are identified and discussed. Passive dermal monitoring is recommended to evaluate glove performance under actual-use conditions and can bridge the gap between laboratory data and real-world performance.

Variable selection based near infrared spectroscopy quantitative and qualitative analysis on wheat wet gluten

NASA Astrophysics Data System (ADS)

Lü, Chengxu; Jiang, Xunpeng; Zhou, Xingfan; Zhang, Yinqiao; Zhang, Naiqian; Wei, Chongfeng; Mao, Wenhua

2017-10-01

Wet gluten is a useful quality indicator for wheat, and short wave near infrared spectroscopy (NIRS) is a high performance technique with the advantage of economic rapid and nondestructive test. To study the feasibility of short wave NIRS analyzing wet gluten directly from wheat seed, 54 representative wheat seed samples were collected and scanned by spectrometer. 8 spectral pretreatment method and genetic algorithm (GA) variable selection method were used to optimize analysis. Both quantitative and qualitative model of wet gluten were built by partial least squares regression and discriminate analysis. For quantitative analysis, normalization is the optimized pretreatment method, 17 wet gluten sensitive variables are selected by GA, and GA model performs a better result than that of all variable model, with R2V=0.88, and RMSEV=1.47. For qualitative analysis, automatic weighted least squares baseline is the optimized pretreatment method, all variable models perform better results than those of GA models. The correct classification rates of 3 class of <24%, 24-30%, >30% wet gluten content are 95.45, 84.52, and 90.00%, respectively. The short wave NIRS technique shows potential for both quantitative and qualitative analysis of wet gluten for wheat seed.

Variable selection with stepwise and best subset approaches

PubMed Central

2016-01-01

While purposeful selection is performed partly by software and partly by hand, the stepwise and best subset approaches are automatically performed by software. Two R functions stepAIC() and bestglm() are well designed for stepwise and best subset regression, respectively. The stepAIC() function begins with a full or null model, and methods for stepwise regression can be specified in the direction argument with character values “forward”, “backward” and “both”. The bestglm() function begins with a data frame containing explanatory variables and response variables. The response variable should be in the last column. Varieties of goodness-of-fit criteria can be specified in the IC argument. The Bayesian information criterion (BIC) usually results in more parsimonious model than the Akaike information criterion. PMID:27162786

Transferability of optimally-selected climate models in the quantification of climate change impacts on hydrology

NASA Astrophysics Data System (ADS)

Chen, Jie; Brissette, François P.; Lucas-Picher, Philippe

2016-11-01

Given the ever increasing number of climate change simulations being carried out, it has become impractical to use all of them to cover the uncertainty of climate change impacts. Various methods have been proposed to optimally select subsets of a large ensemble of climate simulations for impact studies. However, the behaviour of optimally-selected subsets of climate simulations for climate change impacts is unknown, since the transfer process from climate projections to the impact study world is usually highly non-linear. Consequently, this study investigates the transferability of optimally-selected subsets of climate simulations in the case of hydrological impacts. Two different methods were used for the optimal selection of subsets of climate scenarios, and both were found to be capable of adequately representing the spread of selected climate model variables contained in the original large ensemble. However, in both cases, the optimal subsets had limited transferability to hydrological impacts. To capture a similar variability in the impact model world, many more simulations have to be used than those that are needed to simply cover variability from the climate model variables' perspective. Overall, both optimal subset selection methods were better than random selection when small subsets were selected from a large ensemble for impact studies. However, as the number of selected simulations increased, random selection often performed better than the two optimal methods. To ensure adequate uncertainty coverage, the results of this study imply that selecting as many climate change simulations as possible is the best avenue. Where this was not possible, the two optimal methods were found to perform adequately.

Group Variable Selection Via Convex Log-Exp-Sum Penalty with Application to a Breast Cancer Survivor Study

PubMed Central

Geng, Zhigeng; Wang, Sijian; Yu, Menggang; Monahan, Patrick O.; Champion, Victoria; Wahba, Grace

2017-01-01

Summary In many scientific and engineering applications, covariates are naturally grouped. When the group structures are available among covariates, people are usually interested in identifying both important groups and important variables within the selected groups. Among existing successful group variable selection methods, some methods fail to conduct the within group selection. Some methods are able to conduct both group and within group selection, but the corresponding objective functions are non-convex. Such a non-convexity may require extra numerical effort. In this article, we propose a novel Log-Exp-Sum(LES) penalty for group variable selection. The LES penalty is strictly convex. It can identify important groups as well as select important variables within the group. We develop an efficient group-level coordinate descent algorithm to fit the model. We also derive non-asymptotic error bounds and asymptotic group selection consistency for our method in the high-dimensional setting where the number of covariates can be much larger than the sample size. Numerical results demonstrate the good performance of our method in both variable selection and prediction. We applied the proposed method to an American Cancer Society breast cancer survivor dataset. The findings are clinically meaningful and may help design intervention programs to improve the qualify of life for breast cancer survivors. PMID:25257196

A novel variable selection approach that iteratively optimizes variable space using weighted binary matrix sampling.

PubMed

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/.

[Application of characteristic NIR variables selection in portable detection of soluble solids content of apple by near infrared spectroscopy].

PubMed

Fan, Shu-Xiang; Huang, Wen-Qian; Li, Jiang-Bo; Guo, Zhi-Ming; Zhaq, Chun-Jiang

2014-10-01

In order to detect the soluble solids content(SSC)of apple conveniently and rapidly, a ring fiber probe and a portable spectrometer were applied to obtain the spectroscopy of apple. Different wavelength variable selection methods, including unin- formative variable elimination (UVE), competitive adaptive reweighted sampling (CARS) and genetic algorithm (GA) were pro- posed to select effective wavelength variables of the NIR spectroscopy of the SSC in apple based on PLS. The back interval LS- SVM (BiLS-SVM) and GA were used to select effective wavelength variables based on LS-SVM. Selected wavelength variables and full wavelength range were set as input variables of PLS model and LS-SVM model, respectively. The results indicated that PLS model built using GA-CARS on 50 characteristic variables selected from full-spectrum which had 1512 wavelengths achieved the optimal performance. The correlation coefficient (Rp) and root mean square error of prediction (RMSEP) for prediction sets were 0.962, 0.403°Brix respectively for SSC. The proposed method of GA-CARS could effectively simplify the portable detection model of SSC in apple based on near infrared spectroscopy and enhance the predictive precision. The study can provide a reference for the development of portable apple soluble solids content spectrometer.

A Time-Series Water Level Forecasting Model Based on Imputation and Variable Selection Method.

PubMed

Yang, Jun-He; Cheng, Ching-Hsue; Chan, Chia-Pan

2017-01-01

Reservoirs are important for households and impact the national economy. This paper proposed a time-series forecasting model based on estimating a missing value followed by variable selection to forecast the reservoir's water level. This study collected data from the Taiwan Shimen Reservoir as well as daily atmospheric data from 2008 to 2015. The two datasets are concatenated into an integrated dataset based on ordering of the data as a research dataset. The proposed time-series forecasting model summarily has three foci. First, this study uses five imputation methods to directly delete the missing value. Second, we identified the key variable via factor analysis and then deleted the unimportant variables sequentially via the variable selection method. Finally, the proposed model uses a Random Forest to build the forecasting model of the reservoir's water level. This was done to compare with the listing method under the forecasting error. These experimental results indicate that the Random Forest forecasting model when applied to variable selection with full variables has better forecasting performance than the listing model. In addition, this experiment shows that the proposed variable selection can help determine five forecast methods used here to improve the forecasting capability.

Theory and design of variable conductance heat pipes

NASA Technical Reports Server (NTRS)

Marcus, B. D.

1972-01-01

A comprehensive review and analysis of all aspects of heat pipe technology pertinent to the design of self-controlled, variable conductance devices for spacecraft thermal control is presented. Subjects considered include hydrostatics, hydrodynamics, heat transfer into and out of the pipe, fluid selection, materials compatibility and variable conductance control techniques. The report includes a selected bibliography of pertinent literature, analytical formulations of various models and theories describing variable conductance heat pipe behavior, and the results of numerous experiments on the steady state and transient performance of gas controlled variable conductance heat pipes. Also included is a discussion of VCHP design techniques.

Improved Sparse Multi-Class SVM and Its Application for Gene Selection in Cancer Classification

PubMed Central

Huang, Lingkang; Zhang, Hao Helen; Zeng, Zhao-Bang; Bushel, Pierre R.

2013-01-01

Background Microarray techniques provide promising tools for cancer diagnosis using gene expression profiles. However, molecular diagnosis based on high-throughput platforms presents great challenges due to the overwhelming number of variables versus the small sample size and the complex nature of multi-type tumors. Support vector machines (SVMs) have shown superior performance in cancer classification due to their ability to handle high dimensional low sample size data. The multi-class SVM algorithm of Crammer and Singer provides a natural framework for multi-class learning. Despite its effective performance, the procedure utilizes all variables without selection. In this paper, we propose to improve the procedure by imposing shrinkage penalties in learning to enforce solution sparsity. Results The original multi-class SVM of Crammer and Singer is effective for multi-class classification but does not conduct variable selection. We improved the method by introducing soft-thresholding type penalties to incorporate variable selection into multi-class classification for high dimensional data. The new methods were applied to simulated data and two cancer gene expression data sets. The results demonstrate that the new methods can select a small number of genes for building accurate multi-class classification rules. Furthermore, the important genes selected by the methods overlap significantly, suggesting general agreement among different variable selection schemes. Conclusions High accuracy and sparsity make the new methods attractive for cancer diagnostics with gene expression data and defining targets of therapeutic intervention. Availability: The source MATLAB code are available from http://math.arizona.edu/~hzhang/software.html. PMID:23966761

Assessing the accuracy and stability of variable selection ...

EPA Pesticide Factsheets

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

A Longitudinal Examination of the Relationship of Selected Institutional Variables to Community College Foundation Revenue

ERIC Educational Resources Information Center

Patterson, Van; Justice, Madeline; Scott, Joyce A.

2012-01-01

The primary purpose of this study was to examine the annual revenue received by United States public Community College Foundations from 2008-2009 in relation to selected variables associated in the literature with successful foundation performance. This study also collected longitudinal data by replicating and expanding upon a similar study…

A Longitudinal Examination of the Relationship of Selected Institutional Variables to Community College Foundation Revenue

ERIC Educational Resources Information Center

Patterson, Vandiver L.

2011-01-01

The primary purpose of this study was to examine the annual revenue received by United States public community college foundations from 2008-2009 in relation to selected variables associated in the literature with successful foundation performance. This study also attempted to collect longitudinal data by replicating and expanding upon a similar…

Stimulus Over-Selectivity and Extinction-Induced Recovery of Performance as a Product of Intellectual Impairment and Autism Severity

ERIC Educational Resources Information Center

Kelly, Michelle P.; Leader, Geraldine; Reed, Phil

2015-01-01

The current experiment investigated the extent to which three variables (autism severity, nonverbal intellectual functioning, and verbal intellectual functioning) are associated with over-selective responding in a group of children with Autism Spectrum Disorder. This paper also analyzed the association of these three variables with the recovery of…

Variable Effect during Polymerization

ERIC Educational Resources Information Center

Lunsford, S. K.

2005-01-01

An experiment performing the polymerization of 3-methylthiophene(P-3MT) onto the conditions for the selective electrode to determine the catechol by using cyclic voltammetry was performed. The P-3MT formed under optimized conditions improved electrochemical reversibility, selectivity and reproducibility for the detection of the catechol.

Cholinergic enhancement reduces functional connectivity and BOLD variability in visual extrastriate cortex during selective attention.

PubMed

Ricciardi, Emiliano; Handjaras, Giacomo; Bernardi, Giulio; Pietrini, Pietro; Furey, Maura L

2013-01-01

Enhancing cholinergic function improves performance on various cognitive tasks and alters neural responses in task specific brain regions. We have hypothesized that the changes in neural activity observed during increased cholinergic function reflect an increase in neural efficiency that leads to improved task performance. The current study tested this hypothesis by assessing neural efficiency based on cholinergically-mediated effects on regional brain connectivity and BOLD signal variability. Nine subjects participated in a double-blind, placebo-controlled crossover fMRI study. Following an infusion of physostigmine (1 mg/h) or placebo, echo-planar imaging (EPI) was conducted as participants performed a selective attention task. During the task, two images comprised of superimposed pictures of faces and houses were presented. Subjects were instructed periodically to shift their attention from one stimulus component to the other and to perform a matching task using hand held response buttons. A control condition included phase-scrambled images of superimposed faces and houses that were presented in the same temporal and spatial manner as the attention task; participants were instructed to perform a matching task. Cholinergic enhancement improved performance during the selective attention task, with no change during the control task. Functional connectivity analyses showed that the strength of connectivity between ventral visual processing areas and task-related occipital, parietal and prefrontal regions reduced significantly during cholinergic enhancement, exclusively during the selective attention task. Physostigmine administration also reduced BOLD signal temporal variability relative to placebo throughout temporal and occipital visual processing areas, again during the selective attention task only. Together with the observed behavioral improvement, the decreases in connectivity strength throughout task-relevant regions and BOLD variability within stimulus processing regions support the hypothesis that cholinergic augmentation results in enhanced neural efficiency. This article is part of a Special Issue entitled 'Cognitive Enhancers'. Copyright © 2012 Elsevier Ltd. All rights reserved.

Cholinergic enhancement reduces functional connectivity and BOLD variability in visual extrastriate cortex during selective attention

PubMed Central

Ricciardi, Emiliano; Handjaras, Giacomo; Bernardi, Giulio; Pietrini, Pietro; Furey, Maura L.

2012-01-01

Enhancing cholinergic function improves performance on various cognitive tasks and alters neural responses in task specific brain regions. Previous findings by our group strongly suggested that the changes in neural activity observed during increased cholinergic function may reflect an increase in neural efficiency that leads to improved task performance. The current study was designed to assess the effects of cholinergic enhancement on regional brain connectivity and BOLD signal variability. Nine subjects participated in a double-blind, placebo-controlled crossover functional magnetic resonance imaging (fMRI) study. Following an infusion of physostigmine (1mg/hr) or placebo, echo-planar imaging (EPI) was conducted as participants performed a selective attention task. During the task, two images comprised of superimposed pictures of faces and houses were presented. Subjects were instructed periodically to shift their attention from one stimulus component to the other and to perform a matching task using hand held response buttons. A control condition included phase-scrambled images of superimposed faces and houses that were presented in the same temporal and spatial manner as the attention task; participants were instructed to perform a matching task. Cholinergic enhancement improved performance during the selective attention task, with no change during the control task. Functional connectivity analyses showed that the strength of connectivity between ventral visual processing areas and task-related occipital, parietal and prefrontal regions was reduced significantly during cholinergic enhancement, exclusively during the selective attention task. Cholinergic enhancement also reduced BOLD signal temporal variability relative to placebo throughout temporal and occipital visual processing areas, again during the selective attention task only. Together with the observed behavioral improvement, the decreases in connectivity strength throughout task-relevant regions and BOLD variability within stimulus processing regions provide further support to the hypothesis that cholinergic augmentation results in enhanced neural efficiency. PMID:22906685

An Investigation of Selected Variables Related to Student Algebra I Performance in Mississippi

ERIC Educational Resources Information Center

Scott, Undray

2016-01-01

This research study attempted to determine if specific variables were related to student performance on the Algebra I subject-area test. This study also sought to determine in which of grades 8, 9, or 10 students performed better on the Algebra I Subject Area Test. This study also investigated the different criteria that are used to schedule…

Identification of spectral regions for the quantification of red wine tannins with fourier transform mid-infrared spectroscopy.

PubMed

Jensen, Jacob S; Egebo, Max; Meyer, Anne S

2008-05-28

Accomplishment of fast tannin measurements is receiving increased interest as tannins are important for the mouthfeel and color properties of red wines. Fourier transform mid-infrared spectroscopy allows fast measurement of different wine components, but quantification of tannins is difficult due to interferences from spectral responses of other wine components. Four different variable selection tools were investigated for the identification of the most important spectral regions which would allow quantification of tannins from the spectra using partial least-squares regression. The study included the development of a new variable selection tool, iterative backward elimination of changeable size intervals PLS. The spectral regions identified by the different variable selection methods were not identical, but all included two regions (1485-1425 and 1060-995 cm(-1)), which therefore were concluded to be particularly important for tannin quantification. The spectral regions identified from the variable selection methods were used to develop calibration models. All four variable selection methods identified regions that allowed an improved quantitative prediction of tannins (RMSEP = 69-79 mg of CE/L; r = 0.93-0.94) as compared to a calibration model developed using all variables (RMSEP = 115 mg of CE/L; r = 0.87). Only minor differences in the performance of the variable selection methods were observed.

Determinants of Students' Academic Performance in Four Selected Accounting Courses at University of Zimbabwe

ERIC Educational Resources Information Center

Nyikahadzoi, Loveness; Matamande, Wilson; Taderera, Ever; Mandimika, Elinah

2013-01-01

The study seeks to establish scientific evidence of the factors affecting academic performance for first year accounting students using four selected courses at the University of Zimbabwe. It uses Ordinary Least Squares method to analyse the influence of personal and family background on performance. The findings show that variables age gender,…

VARIABLE SELECTION FOR REGRESSION MODELS WITH MISSING DATA

PubMed Central

Garcia, Ramon I.; Ibrahim, Joseph G.; Zhu, Hongtu

2009-01-01

We consider the variable selection problem for a class of statistical models with missing data, including missing covariate and/or response data. We investigate the smoothly clipped absolute deviation penalty (SCAD) and adaptive LASSO and propose a unified model selection and estimation procedure for use in the presence of missing data. We develop a computationally attractive algorithm for simultaneously optimizing the penalized likelihood function and estimating the penalty parameters. Particularly, we propose to use a model selection criterion, called the ICQ statistic, for selecting the penalty parameters. We show that the variable selection procedure based on ICQ automatically and consistently selects the important covariates and leads to efficient estimates with oracle properties. The methodology is very general and can be applied to numerous situations involving missing data, from covariates missing at random in arbitrary regression models to nonignorably missing longitudinal responses and/or covariates. Simulations are given to demonstrate the methodology and examine the finite sample performance of the variable selection procedures. Melanoma data from a cancer clinical trial is presented to illustrate the proposed methodology. PMID:20336190

Econometric analysis on the impact of macroeconomic variables toward financial performance: A case of Malaysian public listed logistics companies

NASA Astrophysics Data System (ADS)

Zakariah, Sahidah; Pyeman, Jaafar; Ghazali, Rahmat; Rahman, Ibrahim A.; Rashid, Ahmad Husni Mohd; Shamsuddin, Sofian

2014-12-01

The primary concern of this study is to analyse the impact against macroeconomic variables upon the financial performance, particularly in the case of public listed logistics companies in Malaysia. This study incorporated five macroeconomic variables and four proxies of financial performance. The macroeconomic variables selected are gross domestic product (GDP), total trade (XM), foreign direct investment (FDI), inflation rate (INF), and interest rate (INT). This study is extended to the usage of ratio analysis to predict financial performance in relation to the changes upon macroeconomic variables. As such, this study selected four (4) ratios as proxies to financial performance, which is Operating Profit Margin (OPM), Net Profit Margin (NPM), Return on Asset (ROA), Return on Equity (ROE). The findings of this study may appear non-controversial to some, but it resulted in the following important consensus; (1) GDP is found to be highly impacting NPM and least of ROA, (2) XM has high positive impact on OPM and least on ROE, (3) FDI appear to have insignificant impact towards NPM, and (4) INF and INT show similar negative impact on financial performance, precisely highly negative on OPM and least on ROA. Such findings also conform to the local logistic industry settings, specifically in regards to public listed logistics companies in relation to its financial performance.

Improving the use of environmental diversity as a surrogate for species representation.

PubMed

Albuquerque, Fabio; Beier, Paul

2018-01-01

The continuous p-median approach to environmental diversity (ED) is a reliable way to identify sites that efficiently represent species. A recently developed maximum dispersion (maxdisp) approach to ED is computationally simpler, does not require the user to reduce environmental space to two dimensions, and performed better than continuous p-median for datasets of South African animals. We tested whether maxdisp performs as well as continuous p-median for 12 datasets that included plants and other continents, and whether particular types of environmental variables produced consistently better models of ED. We selected 12 species inventories and atlases to span a broad range of taxa (plants, birds, mammals, reptiles, and amphibians), spatial extents, and resolutions. For each dataset, we used continuous p-median ED and maxdisp ED in combination with five sets of environmental variables (five combinations of temperature, precipitation, insolation, NDVI, and topographic variables) to select environmentally diverse sites. We used the species accumulation index (SAI) to evaluate the efficiency of ED in representing species for each approach and set of environmental variables. Maxdisp ED represented species better than continuous p-median ED in five of 12 biodiversity datasets, and about the same for the other seven biodiversity datasets. Efficiency of ED also varied with type of variables used to define environmental space, but no particular combination of variables consistently performed best. We conclude that maxdisp ED performs at least as well as continuous p-median ED, and has the advantage of faster and simpler computation. Surprisingly, using all 38 environmental variables was not consistently better than using subsets of variables, nor did any subset emerge as consistently best or worst; further work is needed to identify the best variables to define environmental space. Results can help ecologists and conservationists select sites for species representation and assist in conservation planning.

Improved Variable Selection Algorithm Using a LASSO-Type Penalty, with an Application to Assessing Hepatitis B Infection Relevant Factors in Community Residents

PubMed Central

Guo, Pi; Zeng, Fangfang; Hu, Xiaomin; Zhang, Dingmei; Zhu, Shuming; Deng, Yu; Hao, Yuantao

2015-01-01

Objectives In epidemiological studies, it is important to identify independent associations between collective exposures and a health outcome. The current stepwise selection technique ignores stochastic errors and suffers from a lack of stability. The alternative LASSO-penalized regression model can be applied to detect significant predictors from a pool of candidate variables. However, this technique is prone to false positives and tends to create excessive biases. It remains challenging to develop robust variable selection methods and enhance predictability. Material and methods Two improved algorithms denoted the two-stage hybrid and bootstrap ranking procedures, both using a LASSO-type penalty, were developed for epidemiological association analysis. The performance of the proposed procedures and other methods including conventional LASSO, Bolasso, stepwise and stability selection models were evaluated using intensive simulation. In addition, methods were compared by using an empirical analysis based on large-scale survey data of hepatitis B infection-relevant factors among Guangdong residents. Results The proposed procedures produced comparable or less biased selection results when compared to conventional variable selection models. In total, the two newly proposed procedures were stable with respect to various scenarios of simulation, demonstrating a higher power and a lower false positive rate during variable selection than the compared methods. In empirical analysis, the proposed procedures yielding a sparse set of hepatitis B infection-relevant factors gave the best predictive performance and showed that the procedures were able to select a more stringent set of factors. The individual history of hepatitis B vaccination, family and individual history of hepatitis B infection were associated with hepatitis B infection in the studied residents according to the proposed procedures. Conclusions The newly proposed procedures improve the identification of significant variables and enable us to derive a new insight into epidemiological association analysis. PMID:26214802

Delay correlation analysis and representation for vital complaint VHDL models

DOEpatents

Rich, Marvin J.; Misra, Ashutosh

2004-11-09

A method and system unbind a rise/fall tuple of a VHDL generic variable and create rise time and fall time generics of each generic variable that are independent of each other. Then, according to a predetermined correlation policy, the method and system collect delay values in a VHDL standard delay file, sort the delay values, remove duplicate delay values, group the delay values into correlation sets, and output an analysis file. The correlation policy may include collecting all generic variables in a VHDL standard delay file, selecting each generic variable, and performing reductions on the set of delay values associated with each selected generic variable.

IRAS variables as galactic structure tracers - Classification of the bright variables

NASA Technical Reports Server (NTRS)

Allen, L. E.; Kleinmann, S. G.; Weinberg, M. D.

1993-01-01

The characteristics of the 'bright infrared variables' (BIRVs), a sample consisting of the 300 brightest stars in the IRAS Point Source Catalog with IRAS variability index VAR of 98 or greater, are investigated with the purpose of establishing which of IRAS variables are AGB stars (e.g., oxygen-rich Miras and carbon stars, as was assumed by Weinberg (1992)). Results of the analysis of optical, infrared, and microwave spectroscopy of these stars indicate that, out of 88 stars in the BIRV sample identified with cataloged variables, 86 can be classified as Miras. Results of a similar analysis performed for a color-selected sample of stars, using the color limits employed by Habing (1988) to select AGB stars, showed that, out of 52 percent of classified stars, 38 percent are non-AGB stars, including H II regions, planetary nebulae, supergiants, and young stellar objects, indicating that studies using color-selected samples are subject to misinterpretation.

Overcoming Objections to the Use of Temperament Variables in Selection.

ERIC Educational Resources Information Center

Hough, Leaetta M.

Much of the scientific community has believed that temperament variables could not be included in batteries of tests to predict job performance because no generalized principles could be discerned from the results. For Project A, a major Army project on the prediction of job performance, a temperament inventory was developed and implemented. This…

Choice of Control Variables in Variational Data Assimilation and Its Analysis and Forecast Impact

NASA Astrophysics Data System (ADS)

Xie, Yuanfu; Sun, Jenny; Fang, Wei-ting

2014-05-01

Choice of control variables directly impacts the analysis qualify of a variational data assimilation and its forecasts. A theory on selecting control variables for wind and moisture field is introduced for 3DVAR or 4DVAR. For a good control variable selection, Parseval's theory is applied to 3-4DVAR and the behavior of different control variables is illustrated in physical and Fourier space in terms of minimization condition, meteorological dynamic scales and practical implementation. The computational and meteorological benefits will be discussed. Numerical experiments have been performed using WRF-DA for wind control variables and CRTM for moisture control variables. It is evident of the WRF forecast improvement and faster convergence of CRTM satellite data assimilation.

Punishment induced behavioural and neurophysiological variability reveals dopamine-dependent selection of kinematic movement parameters

PubMed Central

Galea, Joseph M.; Ruge, Diane; Buijink, Arthur; Bestmann, Sven; Rothwell, John C.

2013-01-01

Action selection describes the high-level process which selects between competing movements. In animals, behavioural variability is critical for the motor exploration required to select the action which optimizes reward and minimizes cost/punishment, and is guided by dopamine (DA). The aim of this study was to test in humans whether low-level movement parameters are affected by punishment and reward in ways similar to high-level action selection. Moreover, we addressed the proposed dependence of behavioural and neurophysiological variability on DA, and whether this may underpin the exploration of kinematic parameters. Participants performed an out-and-back index finger movement and were instructed that monetary reward and punishment were based on its maximal acceleration (MA). In fact, the feedback was not contingent on the participant’s behaviour but pre-determined. Blocks highly-biased towards punishment were associated with increased MA variability relative to blocks with either reward or without feedback. This increase in behavioural variability was positively correlated with neurophysiological variability, as measured by changes in cortico-spinal excitability with transcranial magnetic stimulation over the primary motor cortex. Following the administration of a DA-antagonist, the variability associated with punishment diminished and the correlation between behavioural and neurophysiological variability no longer existed. Similar changes in variability were not observed when participants executed a pre-determined MA, nor did DA influence resting neurophysiological variability. Thus, under conditions of punishment, DA-dependent processes influence the selection of low-level movement parameters. We propose that the enhanced behavioural variability reflects the exploration of kinematic parameters for less punishing, or conversely more rewarding, outcomes. PMID:23447607

Environmental diversity as a surrogate for species representation.

PubMed

Beier, Paul; de Albuquerque, Fábio Suzart

2015-10-01

Because many species have not been described and most species ranges have not been mapped, conservation planners often use surrogates for conservation planning, but evidence for surrogate effectiveness is weak. Surrogates are well-mapped features such as soil types, landforms, occurrences of an easily observed taxon (discrete surrogates), and well-mapped environmental conditions (continuous surrogate). In the context of reserve selection, the idea is that a set of sites selected to span diversity in the surrogate will efficiently represent most species. Environmental diversity (ED) is a rarely used surrogate that selects sites to efficiently span multivariate ordination space. Because it selects across continuous environmental space, ED should perform better than discrete surrogates (which necessarily ignore within-bin and between-bin heterogeneity). Despite this theoretical advantage, ED appears to have performed poorly in previous tests of its ability to identify 50 × 50 km cells that represented vertebrates in Western Europe. Using an improved implementation of ED, we retested ED on Western European birds, mammals, reptiles, amphibians, and combined terrestrial vertebrates. We also tested ED on data sets for plants of Zimbabwe, birds of Spain, and birds of Arizona (United States). Sites selected using ED represented European mammals no better than randomly selected cells, but they represented species in the other 7 data sets with 20% to 84% effectiveness. This far exceeds the performance in previous tests of ED, and exceeds the performance of most discrete surrogates. We believe ED performed poorly in previous tests because those tests considered only a few candidate explanatory variables and used suboptimal forms of ED's selection algorithm. We suggest future work on ED focus on analyses at finer grain sizes more relevant to conservation decisions, explore the effect of selecting the explanatory variables most associated with species turnover, and investigate whether nonclimate abiotic variables can provide useful surrogates in an ED framework. © 2015 Society for Conservation Biology.

Predicting Bobsled Pushing Ability from Various Combine Testing Events.

PubMed

Tomasevicz, Curtis L; Ransone, Jack W; Bach, Christopher W

2018-03-12

The requisite combination of speed, power, and strength necessary for a bobsled push athlete coupled with the difficulty in directly measuring pushing ability makes selecting effective push crews challenging. Current practices by USA Bobsled and Skeleton (USABS) utilize field combine testing to assess and identify specifically selected performance variables in an attempt to best predict push performance abilities. Combine data consisting of 11 physical performance variables were collected from 75 subjects across two winter Olympic qualification years (2009 and 2013). These variables were sprints of 15-, 30-, and 60 m, a flying 30 m sprint, a standing broad jump, a shot toss, squat, power clean, body mass, and dry-land brake and side bobsled pushes. Discriminant Analysis (DA) in addition to Principle Component Analysis (PCA) was used to investigate two cases (Case 1: Olympians vs. non-Olympians; Case 2: National Team vs. non-National Team). Using these 11 variables, DA led to a classification rule that proved capable of identifying Olympians from non-Olympians and National Team members from non-National Team members with 9.33% and 14.67% misclassification rates, respectively. The PCA was used to find similar test variables within the combine that provided redundant or useless data. After eliminating the unnecessary variables, DA on the new combinations showed that 8 (Case 1) and 20 (Case 2) other combinations with fewer performance variables yielded misclassification rates as low as 6.67% and 13.33% respectively. Utilizing fewer performance variables can allow governing bodies in many other sports to create more appropriate combine testing that maximize accuracy, while minimizing irrelevant and redundant strategies.

Predictors of professional behaviour and academic outcomes in a UK medical school: A longitudinal cohort study.

PubMed

Adam, Jane; Bore, Miles; Childs, Roy; Dunn, Jason; Mckendree, Jean; Munro, Don; Powis, David

2015-01-01

Over the past 70 years, there has been a recurring debate in the literature and in the popular press about how best to select medical students. This implies that we are still not getting it right: either some students are unsuited to medicine or the graduating doctors are considered unsatisfactory, or both. To determine whether particular variables at the point of selection might distinguish those more likely to become satisfactory professional doctors, by following a complete intake cohort of students throughout medical school and analysing all the data used for the students' selection, their performance on a range of other potential selection tests, academic and clinical assessments throughout their studies, and records of professional behaviour covering the entire five years of the course. A longitudinal database captured the following anonymised information for every student (n = 146) admitted in 2007 to the Hull York Medical School (HYMS) in the UK: demographic data (age, sex, citizenship); performance in each component of the selection procedure; performance in some other possible selection instruments (cognitive and non-cognitive psychometric tests); professional behaviour in tutorials and in other clinical settings; academic performance, clinical and communication skills at summative assessments throughout; professional behaviour lapses monitored routinely as part of the fitness-to-practise procedures. Correlations were sought between predictor variables and criterion variables chosen to demonstrate the full range of course outcomes from failure to complete the course to graduation with honours, and to reveal clinical and professional strengths and weaknesses. Student demography was found to be an important predictor of outcomes, with females, younger students and British citizens performing better overall. The selection variable "HYMS academic score", based on prior academic performance, was a significant predictor of components of Year 4 written and Year 5 clinical examinations. Some cognitive subtest scores from the UK Clinical Aptitude Test (UKCAT) and the UKCAT total score were also significant predictors of the same components, and a unique predictor of the Year 5 written examination. A number of the non-cognitive tests were significant independent predictors of Years 4 and 5 clinical performance, and of lapses in professional behaviour. First- and second-year tutor ratings were significant predictors of all outcomes, both desirable and undesirable. Performance in Years 1 and 2 written exams did not predict performance in Year 4 but did generally predict Year 5 written and clinical performance. Measures of a range of relevant selection attributes and personal qualities can predict intermediate and end of course achievements in academic, clinical and professional behaviour domains. In this study HYMS academic score, some UKCAT subtest scores and the total UKCAT score, and some non-cognitive tests completed at the outset of studies, together predicted outcomes most comprehensively. Tutor evaluation of students early in the course also identified the more and less successful students in the three domains of academic, clinical and professional performance. These results may be helpful in informing the future development of selection tools.

Comparison of climate envelope models developed using expert-selected variables versus statistical selection

USGS Publications Warehouse

Brandt, Laura A.; Benscoter, Allison; Harvey, Rebecca G.; Speroterra, Carolina; Bucklin, David N.; Romañach, Stephanie; Watling, James I.; Mazzotti, Frank J.

2017-01-01

Climate envelope models are widely used to describe potential future distribution of species under different climate change scenarios. It is broadly recognized that there are both strengths and limitations to using climate envelope models and that outcomes are sensitive to initial assumptions, inputs, and modeling methods Selection of predictor variables, a central step in modeling, is one of the areas where different techniques can yield varying results. Selection of climate variables to use as predictors is often done using statistical approaches that develop correlations between occurrences and climate data. These approaches have received criticism in that they rely on the statistical properties of the data rather than directly incorporating biological information about species responses to temperature and precipitation. We evaluated and compared models and prediction maps for 15 threatened or endangered species in Florida based on two variable selection techniques: expert opinion and a statistical method. We compared model performance between these two approaches for contemporary predictions, and the spatial correlation, spatial overlap and area predicted for contemporary and future climate predictions. In general, experts identified more variables as being important than the statistical method and there was low overlap in the variable sets (<40%) between the two methods Despite these differences in variable sets (expert versus statistical), models had high performance metrics (>0.9 for area under the curve (AUC) and >0.7 for true skill statistic (TSS). Spatial overlap, which compares the spatial configuration between maps constructed using the different variable selection techniques, was only moderate overall (about 60%), with a great deal of variability across species. Difference in spatial overlap was even greater under future climate projections, indicating additional divergence of model outputs from different variable selection techniques. Our work is in agreement with other studies which have found that for broad-scale species distribution modeling, using statistical methods of variable selection is a useful first step, especially when there is a need to model a large number of species or expert knowledge of the species is limited. Expert input can then be used to refine models that seem unrealistic or for species that experts believe are particularly sensitive to change. It also emphasizes the importance of using multiple models to reduce uncertainty and improve map outputs for conservation planning. Where outputs overlap or show the same direction of change there is greater certainty in the predictions. Areas of disagreement can be used for learning by asking why the models do not agree, and may highlight areas where additional on-the-ground data collection could improve the models.

Correlations among Five Demographic Variables and the Performance of Selected Jamaican 11th-Graders on Some Numerical Problems on Energy

ERIC Educational Resources Information Center

Emepue, Nicholas; Soyibo, Kola

2009-01-01

This study was designed to assess whether the level of performance of selected Jamaican 11th-grade physics students on some numerical problems on the energy concept was satisfactory and if there were significant differences in their performance linked to their gender, socioeconomic background (SEB), school location, English language and…

Applied Music Teaching Behavior as a Function of Selected Personality Variables.

ERIC Educational Resources Information Center

Schmidt, Charles P.

1989-01-01

Investigates the relationships among applied music teaching behaviors and personality variables as measured by the Myers-Briggs Type Indicator (MBTI). Suggests that personality variables may be important factors underlying four applied music teaching behaviors: approvals, rate of reinforcement, teacher model/performance, and pace. (LS)

Selecting Populations for Non-Analogous Climate Conditions Using Universal Response Functions: The Case of Douglas-Fir in Central Europe.

PubMed

Chakraborty, Debojyoti; Wang, Tongli; Andre, Konrad; Konnert, Monika; Lexer, Manfred J; Matulla, Christoph; Schueler, Silvio

2015-01-01

Identifying populations within tree species potentially adapted to future climatic conditions is an important requirement for reforestation and assisted migration programmes. Such populations can be identified either by empirical response functions based on correlations of quantitative traits with climate variables or by climate envelope models that compare the climate of seed sources and potential growing areas. In the present study, we analyzed the intraspecific variation in climate growth response of Douglas-fir planted within the non-analogous climate conditions of Central and continental Europe. With data from 50 common garden trials, we developed Universal Response Functions (URF) for tree height and mean basal area and compared the growth performance of the selected best performing populations with that of populations identified through a climate envelope approach. Climate variables of the trial location were found to be stronger predictors of growth performance than climate variables of the population origin. Although the precipitation regime of the population sources varied strongly none of the precipitation related climate variables of population origin was found to be significant within the models. Overall, the URFs explained more than 88% of variation in growth performance. Populations identified by the URF models originate from western Cascades and coastal areas of Washington and Oregon and show significantly higher growth performance than populations identified by the climate envelope approach under both current and climate change scenarios. The URFs predict decreasing growth performance at low and middle elevations of the case study area, but increasing growth performance on high elevation sites. Our analysis suggests that population recommendations based on empirical approaches should be preferred and population selections by climate envelope models without considering climatic constrains of growth performance should be carefully appraised before transferring populations to planting locations with novel or dissimilar climate.

SVM-RFE based feature selection and Taguchi parameters optimization for multiclass SVM classifier.

PubMed

Huang, Mei-Ling; Hung, Yung-Hsiang; Lee, W M; Li, R K; Jiang, Bo-Ru

2014-01-01

Recently, support vector machine (SVM) has excellent performance on classification and prediction and is widely used on disease diagnosis or medical assistance. However, SVM only functions well on two-group classification problems. This study combines feature selection and SVM recursive feature elimination (SVM-RFE) to investigate the classification accuracy of multiclass problems for Dermatology and Zoo databases. Dermatology dataset contains 33 feature variables, 1 class variable, and 366 testing instances; and the Zoo dataset contains 16 feature variables, 1 class variable, and 101 testing instances. The feature variables in the two datasets were sorted in descending order by explanatory power, and different feature sets were selected by SVM-RFE to explore classification accuracy. Meanwhile, Taguchi method was jointly combined with SVM classifier in order to optimize parameters C and γ to increase classification accuracy for multiclass classification. The experimental results show that the classification accuracy can be more than 95% after SVM-RFE feature selection and Taguchi parameter optimization for Dermatology and Zoo databases.

SVM-RFE Based Feature Selection and Taguchi Parameters Optimization for Multiclass SVM Classifier

PubMed Central

Huang, Mei-Ling; Hung, Yung-Hsiang; Lee, W. M.; Li, R. K.; Jiang, Bo-Ru

2014-01-01

Recently, support vector machine (SVM) has excellent performance on classification and prediction and is widely used on disease diagnosis or medical assistance. However, SVM only functions well on two-group classification problems. This study combines feature selection and SVM recursive feature elimination (SVM-RFE) to investigate the classification accuracy of multiclass problems for Dermatology and Zoo databases. Dermatology dataset contains 33 feature variables, 1 class variable, and 366 testing instances; and the Zoo dataset contains 16 feature variables, 1 class variable, and 101 testing instances. The feature variables in the two datasets were sorted in descending order by explanatory power, and different feature sets were selected by SVM-RFE to explore classification accuracy. Meanwhile, Taguchi method was jointly combined with SVM classifier in order to optimize parameters C and γ to increase classification accuracy for multiclass classification. The experimental results show that the classification accuracy can be more than 95% after SVM-RFE feature selection and Taguchi parameter optimization for Dermatology and Zoo databases. PMID:25295306

Variable selection and model choice in geoadditive regression models.

PubMed

Kneib, Thomas; Hothorn, Torsten; Tutz, Gerhard

2009-06-01

Model choice and variable selection are issues of major concern in practical regression analyses, arising in many biometric applications such as habitat suitability analyses, where the aim is to identify the influence of potentially many environmental conditions on certain species. We describe regression models for breeding bird communities that facilitate both model choice and variable selection, by a boosting algorithm that works within a class of geoadditive regression models comprising spatial effects, nonparametric effects of continuous covariates, interaction surfaces, and varying coefficients. The major modeling components are penalized splines and their bivariate tensor product extensions. All smooth model terms are represented as the sum of a parametric component and a smooth component with one degree of freedom to obtain a fair comparison between the model terms. A generic representation of the geoadditive model allows us to devise a general boosting algorithm that automatically performs model choice and variable selection.

Evaluation of variable selection methods for random forests and omics data sets.

PubMed

Degenhardt, Frauke; Seifert, Stephan; Szymczak, Silke

2017-10-16

Machine learning methods and in particular random forests are promising approaches for prediction based on high dimensional omics data sets. They provide variable importance measures to rank predictors according to their predictive power. If building a prediction model is the main goal of a study, often a minimal set of variables with good prediction performance is selected. However, if the objective is the identification of involved variables to find active networks and pathways, approaches that aim to select all relevant variables should be preferred. We evaluated several variable selection procedures based on simulated data as well as publicly available experimental methylation and gene expression data. Our comparison included the Boruta algorithm, the Vita method, recurrent relative variable importance, a permutation approach and its parametric variant (Altmann) as well as recursive feature elimination (RFE). In our simulation studies, Boruta was the most powerful approach, followed closely by the Vita method. Both approaches demonstrated similar stability in variable selection, while Vita was the most robust approach under a pure null model without any predictor variables related to the outcome. In the analysis of the different experimental data sets, Vita demonstrated slightly better stability in variable selection and was less computationally intensive than Boruta.In conclusion, we recommend the Boruta and Vita approaches for the analysis of high-dimensional data sets. Vita is considerably faster than Boruta and thus more suitable for large data sets, but only Boruta can also be applied in low-dimensional settings. © The Author 2017. Published by Oxford University Press.

Supersonic variable-cycle engines

NASA Technical Reports Server (NTRS)

Willis, E. A.; Welliver, A. D.

1976-01-01

The evolution and current status of selected recent variable cycle engine (VCE) studies are reviewed, and how the results were influenced by airplane requirements is described. Promising VCE concepts are described, their designs are simplified and the potential benefits in terms of aircraft performance are identified. This includes range, noise, emissions, and the time and effort it may require to ensure technical readiness of sufficient depth to satisfy reasonable economic, performance, and environmental constraints. A brief overview of closely related, ongoing technology programs in acoustics and exhaust emissions is also presented. Realistic technology advancements in critical areas combined with well matched aircraft and selected VCE concepts can lead to significantly improved economic and environmental performance relative to first generation SST predictions.

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

PubMed

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

2017-07-01

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

Integrative Bayesian variable selection with gene-based informative priors for genome-wide association studies.

PubMed

Zhang, Xiaoshuai; Xue, Fuzhong; Liu, Hong; Zhu, Dianwen; Peng, Bin; Wiemels, Joseph L; Yang, Xiaowei

2014-12-10

Genome-wide Association Studies (GWAS) are typically designed to identify phenotype-associated single nucleotide polymorphisms (SNPs) individually using univariate analysis methods. Though providing valuable insights into genetic risks of common diseases, the genetic variants identified by GWAS generally account for only a small proportion of the total heritability for complex diseases. To solve this "missing heritability" problem, we implemented a strategy called integrative Bayesian Variable Selection (iBVS), which is based on a hierarchical model that incorporates an informative prior by considering the gene interrelationship as a network. It was applied here to both simulated and real data sets. Simulation studies indicated that the iBVS method was advantageous in its performance with highest AUC in both variable selection and outcome prediction, when compared to Stepwise and LASSO based strategies. In an analysis of a leprosy case-control study, iBVS selected 94 SNPs as predictors, while LASSO selected 100 SNPs. The Stepwise regression yielded a more parsimonious model with only 3 SNPs. The prediction results demonstrated that the iBVS method had comparable performance with that of LASSO, but better than Stepwise strategies. The proposed iBVS strategy is a novel and valid method for Genome-wide Association Studies, with the additional advantage in that it produces more interpretable posterior probabilities for each variable unlike LASSO and other penalized regression methods.

Distributed Space Mission Design for Earth Observation Using Model-Based Performance Evaluation

NASA Technical Reports Server (NTRS)

Nag, Sreeja; LeMoigne-Stewart, Jacqueline; Cervantes, Ben; DeWeck, Oliver

2015-01-01

Distributed Space Missions (DSMs) are gaining momentum in their application to earth observation missions owing to their unique ability to increase observation sampling in multiple dimensions. DSM design is a complex problem with many design variables, multiple objectives determining performance and cost and emergent, often unexpected, behaviors. There are very few open-access tools available to explore the tradespace of variables, minimize cost and maximize performance for pre-defined science goals, and therefore select the most optimal design. This paper presents a software tool that can multiple DSM architectures based on pre-defined design variable ranges and size those architectures in terms of predefined science and cost metrics. The tool will help a user select Pareto optimal DSM designs based on design of experiments techniques. The tool will be applied to some earth observation examples to demonstrate its applicability in making some key decisions between different performance metrics and cost metrics early in the design lifecycle.

Identification of Urinary Polyphenol Metabolite Patterns Associated with Polyphenol-Rich Food Intake in Adults from Four European Countries.

PubMed

Noh, Hwayoung; Freisling, Heinz; Assi, Nada; Zamora-Ros, Raul; Achaintre, David; Affret, Aurélie; Mancini, Francesca; Boutron-Ruault, Marie-Christine; Flögel, Anna; Boeing, Heiner; Kühn, Tilman; Schübel, Ruth; Trichopoulou, Antonia; Naska, Androniki; Kritikou, Maria; Palli, Domenico; Pala, Valeria; Tumino, Rosario; Ricceri, Fulvio; Santucci de Magistris, Maria; Cross, Amanda; Slimani, Nadia; Scalbert, Augustin; Ferrari, Pietro

2017-07-25

We identified urinary polyphenol metabolite patterns by a novel algorithm that combines dimension reduction and variable selection methods to explain polyphenol-rich food intake, and compared their respective performance with that of single biomarkers in the European Prospective Investigation into Cancer and Nutrition (EPIC) study. The study included 475 adults from four European countries (Germany, France, Italy, and Greece). Dietary intakes were assessed with 24-h dietary recalls (24-HDR) and dietary questionnaires (DQ). Thirty-four polyphenols were measured by ultra-performance liquid chromatography-electrospray ionization-tandem mass spectrometry (UPLC-ESI-MS-MS) in 24-h urine. Reduced rank regression-based variable importance in projection (RRR-VIP) and least absolute shrinkage and selection operator (LASSO) methods were used to select polyphenol metabolites. Reduced rank regression (RRR) was then used to identify patterns in these metabolites, maximizing the explained variability in intake of pre-selected polyphenol-rich foods. The performance of RRR models was evaluated using internal cross-validation to control for over-optimistic findings from over-fitting. High performance was observed for explaining recent intake (24-HDR) of red wine ( r = 0.65; AUC = 89.1%), coffee ( r = 0.51; AUC = 89.1%), and olives ( r = 0.35; AUC = 82.2%). These metabolite patterns performed better or equally well compared to single polyphenol biomarkers. Neither metabolite patterns nor single biomarkers performed well in explaining habitual intake (as reported in the DQ) of polyphenol-rich foods. This proposed strategy of biomarker pattern identification has the potential of expanding the currently still limited list of available dietary intake biomarkers.

[Measurement of Water COD Based on UV-Vis Spectroscopy Technology].

PubMed

Wang, Xiao-ming; Zhang, Hai-liang; Luo, Wei; Liu, Xue-mei

2016-01-01

Ultraviolet/visible (UV/Vis) spectroscopy technology was used to measure water COD. A total of 135 water samples were collected from Zhejiang province. Raw spectra with 3 different pretreatment methods (Multiplicative Scatter Correction (MSC), Standard Normal Variate (SNV) and 1st Derivatives were compared to determine the optimal pretreatment method for analysis. Spectral variable selection is an important strategy in spectrum modeling analysis, because it tends to parsimonious data representation and can lead to multivariate models with better performance. In order to simply calibration models, the preprocessed spectra were then used to select sensitive wavelengths by competitive adaptive reweighted sampling (CARS), Random frog and Successive Genetic Algorithm (GA) methods. Different numbers of sensitive wavelengths were selected by different variable selection methods with SNV preprocessing method. Partial least squares (PLS) was used to build models with the full spectra, and Extreme Learning Machine (ELM) was applied to build models with the selected wavelength variables. The overall results showed that ELM model performed better than PLS model, and the ELM model with the selected wavelengths based on CARS obtained the best results with the determination coefficient (R2), RMSEP and RPD were 0.82, 14.48 and 2.34 for prediction set. The results indicated that it was feasible to use UV/Vis with characteristic wavelengths which were obtained by CARS variable selection method, combined with ELM calibration could apply for the rapid and accurate determination of COD in aquaculture water. Moreover, this study laid the foundation for further implementation of online analysis of aquaculture water and rapid determination of other water quality parameters.

Movement variability in the golf swing.

PubMed

Langdown, Ben L; Bridge, Matt; Li, Francois-Xavier

2012-06-01

Traditionally, golf biomechanics has focused upon achieving consistency in swing kinematics and kinetics, whilst variability was considered to be noise and dysfunctional. There has been a growing argument that variability is an intrinsic aspect of skilled motor performance and plays a functional role. Two types of variability are described: 'strategic shot selection' and 'movement variability'. In 'strategic shot selection', the outcome remains consistent, but the swing kinematics/kinetics (resulting in the desired ball flight) are free to vary; 'movement variability' is the changes in swing kinematics and kinetics from trial to trial when the golfer attempts to hit the same shot. These changes will emerge due to constraints of the golfer's body, the environment, and the task. Biomechanical research has focused upon aspects of technique such as elite versus non-elite kinematics, kinetics, kinematic sequencing, peak angular velocities of body segments, wrist function, ground reaction forces, and electromyography, mainly in the search for greater distance and clubhead velocity. To date very little is known about the impact of variability on this complex motor skill, and it has yet to be fully researched to determine where the trade-off between functional and detrimental variability lies when in pursuit of enhanced performance outcomes.

Variable selection for marginal longitudinal generalized linear models.

PubMed

Cantoni, Eva; Flemming, Joanna Mills; Ronchetti, Elvezio

2005-06-01

Variable selection is an essential part of any statistical analysis and yet has been somewhat neglected in the context of longitudinal data analysis. In this article, we propose a generalized version of Mallows's C(p) (GC(p)) suitable for use with both parametric and nonparametric models. GC(p) provides an estimate of a measure of model's adequacy for prediction. We examine its performance with popular marginal longitudinal models (fitted using GEE) and contrast results with what is typically done in practice: variable selection based on Wald-type or score-type tests. An application to real data further demonstrates the merits of our approach while at the same time emphasizing some important robust features inherent to GC(p).

A Framework for Orbital Performance Evaluation in Distributed Space Missions for Earth Observation

NASA Technical Reports Server (NTRS)

Nag, Sreeja; LeMoigne-Stewart, Jacqueline; Miller, David W.; de Weck, Olivier

2015-01-01

Distributed Space Missions (DSMs) are gaining momentum in their application to earth science missions owing to their unique ability to increase observation sampling in spatial, spectral and temporal dimensions simultaneously. DSM architectures have a large number of design variables and since they are expected to increase mission flexibility, scalability, evolvability and robustness, their design is a complex problem with many variables and objectives affecting performance. There are very few open-access tools available to explore the tradespace of variables which allow performance assessment and are easy to plug into science goals, and therefore select the most optimal design. This paper presents a software tool developed on the MATLAB engine interfacing with STK, for DSM orbit design and selection. It is capable of generating thousands of homogeneous constellation or formation flight architectures based on pre-defined design variable ranges and sizing those architectures in terms of predefined performance metrics. The metrics can be input into observing system simulation experiments, as available from the science teams, allowing dynamic coupling of science and engineering designs. Design variables include but are not restricted to constellation type, formation flight type, FOV of instrument, altitude and inclination of chief orbits, differential orbital elements, leader satellites, latitudes or regions of interest, planes and satellite numbers. Intermediate performance metrics include angular coverage, number of accesses, revisit coverage, access deterioration over time at every point of the Earth's grid. The orbit design process can be streamlined and variables more bounded along the way, owing to the availability of low fidelity and low complexity models such as corrected HCW equations up to high precision STK models with J2 and drag. The tool can thus help any scientist or program manager select pre-Phase A, Pareto optimal DSM designs for a variety of science goals without having to delve into the details of the engineering design process.

Rapid Elemental Analysis and Provenance Study of Blumea balsamifera DC Using Laser-Induced Breakdown Spectroscopy

PubMed Central

Liu, Xiaona; Zhang, Qiao; Wu, Zhisheng; Shi, Xinyuan; Zhao, Na; Qiao, Yanjiang

2015-01-01

Laser-induced breakdown spectroscopy (LIBS) was applied to perform a rapid elemental analysis and provenance study of Blumea balsamifera DC. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were implemented to exploit the multivariate nature of the LIBS data. Scores and loadings of computed principal components visually illustrated the differing spectral data. The PLS-DA algorithm showed good classification performance. The PLS-DA model using complete spectra as input variables had similar discrimination performance to using selected spectral lines as input variables. The down-selection of spectral lines was specifically focused on the major elements of B. balsamifera samples. Results indicated that LIBS could be used to rapidly analyze elements and to perform provenance study of B. balsamifera. PMID:25558999

Prediction of Osteopathic Medical School Performance on the basis of MCAT score, GPA, sex, undergraduate major, and undergraduate institution.

PubMed

Dixon, Donna

2012-04-01

The relationships of students' preadmission academic variables, sex, undergraduate major, and undergraduate institution to academic performance in medical school have not been thoroughly examined. To determine the ability of students' preadmission academic variables to predict osteopathic medical school performance and whether students' sex, undergraduate major, or undergraduate institution influence osteopathic medical school performance. The study followed students who graduated from New York College of Osteopathic Medicine of New York Institute of Technology in Old Westbury between 2003 and 2006. Student preadmission data were Medical College Admission Test (MCAT) scores, undergraduate grade point averages (GPAs), sex, undergraduate major, and undergraduate institutional selectivity. Medical school performance variables were GPAs, clinical performance (ie, clinical subject examinations and clerkship evaluations), and scores on the Comprehensive Osteopathic Medical Licensing Examination-USA (COMLEX-USA) Level 1 and Level 2-Clinical Evaluation (CE). Data were analyzed with Pearson product moment correlation coefficients and multivariate linear regression analyses. Differences between student groups were compared with the independent-samples, 2-tailed t test. A total of 737 students were included. All preadmission academic variables, except nonscience undergraduate GPA, were statistically significant predictors of performance on COMLEX-USA Level 1, and all preadmission academic variables were statistically significant predictors of performance on COMLEX-USA Level 2-CE. The MCAT score for biological sciences had the highest correlation among all variables with COMLEX-USA Level 1 performance (Pearson r=0.304; P<.001) and Level 2-CE performance (Pearson r=0.272; P<.001). All preadmission variables were moderately correlated with the mean clinical subject examination scores. The mean clerkship evaluation score was moderately correlated with mean clinical examination results (Pearson r=0.267; P<.001) and COMLEX-USA Level 2-CE performance (Pearson r=0.301; P<.001). Clinical subject examination scores were highly correlated with COMLEX-USA Level 2-CE scores (Pearson r=0.817; P<.001). No statistically significant difference in medical school performance was found between students with science and nonscience undergraduate majors, nor was undergraduate institutional selectivity a factor influencing performance. Students' preadmission academic variables were predictive of osteopathic medical school performance, including GPAs, clinical performance, and COMLEX-USA Level 1 and Level 2-CE results. Clinical performance was predictive of COMLEX-USA Level 2-CE performance.

Manipulating measurement scales in medical statistical analysis and data mining: A review of methodologies

PubMed Central

Marateb, Hamid Reza; Mansourian, Marjan; Adibi, Peyman; Farina, Dario

2014-01-01

Background: selecting the correct statistical test and data mining method depends highly on the measurement scale of data, type of variables, and purpose of the analysis. Different measurement scales are studied in details and statistical comparison, modeling, and data mining methods are studied based upon using several medical examples. We have presented two ordinal–variables clustering examples, as more challenging variable in analysis, using Wisconsin Breast Cancer Data (WBCD). Ordinal-to-Interval scale conversion example: a breast cancer database of nine 10-level ordinal variables for 683 patients was analyzed by two ordinal-scale clustering methods. The performance of the clustering methods was assessed by comparison with the gold standard groups of malignant and benign cases that had been identified by clinical tests. Results: the sensitivity and accuracy of the two clustering methods were 98% and 96%, respectively. Their specificity was comparable. Conclusion: by using appropriate clustering algorithm based on the measurement scale of the variables in the study, high performance is granted. Moreover, descriptive and inferential statistics in addition to modeling approach must be selected based on the scale of the variables. PMID:24672565

Effect analysis of design variables on the disc in a double-eccentric butterfly valve.

PubMed

Kang, Sangmo; Kim, Da-Eun; Kim, Kuk-Kyeom; Kim, Jun-Oh

2014-01-01

We have performed a shape optimization of the disc in an industrial double-eccentric butterfly valve using the effect analysis of design variables to enhance the valve performance. For the optimization, we select three performance quantities such as pressure drop, maximum stress, and mass (weight) as the responses and three dimensions regarding the disc shape as the design variables. Subsequently, we compose a layout of orthogonal array (L16) by performing numerical simulations on the flow and structure using a commercial package, ANSYS v13.0, and then make an effect analysis of the design variables on the responses using the design of experiments. Finally, we formulate a multiobjective function consisting of the three responses and then propose an optimal combination of the design variables to maximize the valve performance. Simulation results show that the disc thickness makes the most significant effect on the performance and the optimal design provides better performance than the initial design.

Predicting Eight Grade Students' Equation Solving Performances via Concepts of Variable and Equality

ERIC Educational Resources Information Center

Ertekin, Erhan

2017-01-01

This study focused on how two algebraic concepts- equality and variable- predicted 8th grade students' equation solving performance. In this study, predictive design as a correlational research design was used. Randomly selected 407 eight-grade students who were from the central districts of a city in the central region of Turkey participated in…

The Acquisition of Selected Life-Science Concepts by Beginning Kindergarten Children From Three Different Community Settings.

ERIC Educational Resources Information Center

Riechard, Donald Edward

Physical, mental, and socio-cultural variables were associated with performance on the Life-Science Concept Acquisition Test (L-SCAT), developed by the investigator. The usefulness of the variables as predictors of performance on L-SCAT was also investigated. The L-SCAT was administered as a picture-stimulus structured interview to each of 51…

Selective Attention Deficits Associated with Mild Cognitive Impairment and Early Stage Alzheimer's Disease in Adults with Down Syndrome

ERIC Educational Resources Information Center

Krinsky-McHale, Sharon J.; Devenny, Darlynne A.; Kittler, Phyllis; Silverman, Wayne

2008-01-01

Adults with Down syndrome and early stage Alzheimer's disease showed decline in their ability to selectively attend to stimuli in a multitrial cancellation task. They also showed variability in their performance over the test trials, whereas healthy participants showed stability. These changes in performance were observed approximately 2 years…

Spectroscopic follow-up of variability-selected active galactic nuclei in the Chandra Deep Field South

NASA Astrophysics Data System (ADS)

Boutsia, K.; Leibundgut, B.; Trevese, D.; Vagnetti, F.

2009-04-01

Context: Supermassive black holes with masses of 10^5-109 M⊙ are believed to inhabit most, if not all, nuclear regions of galaxies, and both observational evidence and theoretical models suggest a scenario where galaxy and black hole evolution are tightly related. Luminous AGNs are usually selected by their non-stellar colours or their X-ray emission. Colour selection cannot be used to select low-luminosity AGNs, since their emission is dominated by the host galaxy. Objects with low X-ray to optical ratio escape even the deepest X-ray surveys performed so far. In a previous study we presented a sample of candidates selected through optical variability in the Chandra Deep Field South, where repeated optical observations were performed in the framework of the STRESS supernova survey. Aims: The analysis is devoted to breaking down the sample in AGNs, starburst galaxies, and low-ionisation narrow-emission line objects, to providing new information about the possible dependence of the emission mechanisms on nuclear luminosity and black-hole mass, and eventually studying the evolution in cosmic time of the different populations. Methods: We obtained new optical spectroscopy for a sample of variability selected candidates with the ESO NTT telescope. We analysed the new spectra, together with those existing in the literature and studied the distribution of the objects in U-B and B-V colours, optical and X-ray luminosity, and variability amplitude. Results: A large fraction (17/27) of the observed candidates are broad-line luminous AGNs, confirming the efficiency of variability in detecting quasars. We detect: i) extended objects which would have escaped the colour selection and ii) objects of very low X-ray to optical ratio, in a few cases without any X-ray detection at all. Several objects resulted to be narrow-emission line galaxies where variability indicates nuclear activity, while no emission lines were detected in others. Some of these galaxies have variability and X-ray to optical ratio close to active galactic nuclei, while others have much lower variability and X-ray to optical ratio. This result can be explained by the dilution of the nuclear light due to the host galaxy. Conclusions: Our results demonstrate the effectiveness of supernova search programmes to detect large samples of low-luminosity AGNs. A sizable fraction of the AGN in our variability sample had escaped X-ray detection (5/47) and/or colour selection (9/48). Spectroscopic follow-up to fainter flux limits is strongly encouraged. Based on observations collected at the European Southern Observatory, Chile, 080.B-0187(A).

Test-retest reliability of jump execution variables using mechanography: a comparison of jump protocols.

PubMed

Fitzgerald, John S; Johnson, LuAnn; Tomkinson, Grant; Stein, Jesse; Roemmich, James N

2018-05-01

Mechanography during the vertical jump may enhance screening and determining mechanistic causes underlying physical performance changes. Utility of jump mechanography for evaluation is limited by scant test-retest reliability data on force-time variables. This study examined the test-retest reliability of eight jump execution variables assessed from mechanography. Thirty-two women (mean±SD: age 20.8 ± 1.3 yr) and 16 men (age 22.1 ± 1.9 yr) attended a familiarization session and two testing sessions, all one week apart. Participants performed two variations of the squat jump with squat depth self-selected and controlled using a goniometer to 80º knee flexion. Test-retest reliability was quantified as the systematic error (using effect size between jumps), random error (using coefficients of variation), and test-retest correlations (using intra-class correlation coefficients). Overall, jump execution variables demonstrated acceptable reliability, evidenced by small systematic errors (mean±95%CI: 0.2 ± 0.07), moderate random errors (mean±95%CI: 17.8 ± 3.7%), and very strong test-retest correlations (range: 0.73-0.97). Differences in random errors between controlled and self-selected protocols were negligible (mean±95%CI: 1.3 ± 2.3%). Jump execution variables demonstrated acceptable reliability, with no meaningful differences between the controlled and self-selected jump protocols. To simplify testing, a self-selected jump protocol can be used to assess force-time variables with negligible impact on measurement error.

Optimization and design of ibuprofen-loaded nanostructured lipid carriers using a hybrid-design approach for ocular drug delivery

NASA Astrophysics Data System (ADS)

Rathod, Vishal

The objective of the present project was to develop the Ibuprofen-loaded Nanostructured Lipid Carrier (IBU-NLCs) for topical ocular delivery based on substantial pre-formulation screening of the components and understanding the interplay between the formulation and process variables. The BCS Class II drug: Ibuprofen was selected as the model drug for the current study. IBU-NLCs were prepared by melt emulsification and ultrasonication technique. Extensive pre-formulation studies were performed to screen the lipid components (solid and liquid) based on drug's solubility and affinity as well as components compatibility. The results from DSC & XRD assisted in selecting the most suitable ratio to be utilized for future studies. DynasanRTM 114 was selected as the solid lipid & MiglyolRTM 840 was selected as the liquid lipid based on preliminary lipid screening. The ratio of 6:4 was predicted to be the best based on its crystallinity index and the thermal events. As there are many variables involved for further optimization of the formulation, a single design approach is not always adequate. A hybrid-design approach was applied by employing the Plackett Burman design (PBD) for preliminary screening of 7 critical variables, followed by Box-Behnken design (BBD), a sub-type of response surface methodology (RSM) design using 2 relatively significant variables from the former design and incorporating Surfactant/Co-surfactant ratio as the third variable. Comparatively, KolliphorRTM HS15 demonstrated lower Mean Particle Size (PS) & Polydispersity Index (PDI) and KolliphorRTM P188 resulted in Zeta Potential (ZP) < -20 mV during the surfactant screening & stability studies. Hence, Surfactant/Cosurfactant ratio was employed as the third variable to understand its synergistic effect on the response variables. We selected PS, PDI, and ZP as critical response variables in the PBD since they significantly influence the stability & performance of NLCs. Formulations prepared using BBD were further characterized and evaluated concerning PS, PDI, ZP and Entrapment Efficiency (EE) to identify the multi-factor interactions between selected formulation variables. In vitro release studies were performed using Spectra/por dialysis membrane on Franz diffusion cell and Phosphate Saline buffer (7.4 pH) as the medium. Samples for assay, EE, Loading Capacity (LC), Solubility studies & in-vitro release were filtered using Amicon 50K and analyzed via UPLC system (Waters) at a detection wavelength of 220 nm. Significant variables were selected through PBD, and the third variable was incorporated based on surfactant screening & stability studies for the next design. Assay of the BBD based formulations was found to be within 95-104% of the theoretically calculated values. Further studies were investigated for PS, PDI, ZP & EE. PS was found to be in the range of 103-194 nm with PDI ranging from 0.118 to 0.265. The ZP and EE were observed to be in the range of -22.2 to -11 mV & 90 to 98.7 % respectively. Drug release of 30% was observed from the optimized formulation in the first 6 hr of in-vitro studies, and the drug release showed a sustained release of ibuprofen thereafter over several hours. These values also confirm that the production method, and all other selected variables, effectively promoted the incorporation of ibuprofen in NLC. Quality by Design (QbD) approach was successfully implemented in developing a robust ophthalmic formulation with superior physicochemical and morphometric properties. NLCs as the nanocarrier demonstrated promising perspective for topical delivery of poorly water-soluble drugs.

Natural image classification driven by human brain activity

NASA Astrophysics Data System (ADS)

Zhang, Dai; Peng, Hanyang; Wang, Jinqiao; Tang, Ming; Xue, Rong; Zuo, Zhentao

2016-03-01

Natural image classification has been a hot topic in computer vision and pattern recognition research field. Since the performance of an image classification system can be improved by feature selection, many image feature selection methods have been developed. However, the existing supervised feature selection methods are typically driven by the class label information that are identical for different samples from the same class, ignoring with-in class image variability and therefore degrading the feature selection performance. In this study, we propose a novel feature selection method, driven by human brain activity signals collected using fMRI technique when human subjects were viewing natural images of different categories. The fMRI signals associated with subjects viewing different images encode the human perception of natural images, and therefore may capture image variability within- and cross- categories. We then select image features with the guidance of fMRI signals from brain regions with active response to image viewing. Particularly, bag of words features based on GIST descriptor are extracted from natural images for classification, and a sparse regression base feature selection method is adapted to select image features that can best predict fMRI signals. Finally, a classification model is built on the select image features to classify images without fMRI signals. The validation experiments for classifying images from 4 categories of two subjects have demonstrated that our method could achieve much better classification performance than the classifiers built on image feature selected by traditional feature selection methods.

Incorporating abundance information and guiding variable selection for climate-based ensemble forecasting of species' distributional shifts.

PubMed

Tanner, Evan P; Papeş, Monica; Elmore, R Dwayne; Fuhlendorf, Samuel D; Davis, Craig A

2017-01-01

Ecological niche models (ENMs) have increasingly been used to estimate the potential effects of climate change on species' distributions worldwide. Recently, predictions of species abundance have also been obtained with such models, though knowledge about the climatic variables affecting species abundance is often lacking. To address this, we used a well-studied guild (temperate North American quail) and the Maxent modeling algorithm to compare model performance of three variable selection approaches: correlation/variable contribution (CVC), biological (i.e., variables known to affect species abundance), and random. We then applied the best approach to forecast potential distributions, under future climatic conditions, and analyze future potential distributions in light of available abundance data and presence-only occurrence data. To estimate species' distributional shifts we generated ensemble forecasts using four global circulation models, four representative concentration pathways, and two time periods (2050 and 2070). Furthermore, we present distributional shifts where 75%, 90%, and 100% of our ensemble models agreed. The CVC variable selection approach outperformed our biological approach for four of the six species. Model projections indicated species-specific effects of climate change on future distributions of temperate North American quail. The Gambel's quail (Callipepla gambelii) was the only species predicted to gain area in climatic suitability across all three scenarios of ensemble model agreement. Conversely, the scaled quail (Callipepla squamata) was the only species predicted to lose area in climatic suitability across all three scenarios of ensemble model agreement. Our models projected future loss of areas for the northern bobwhite (Colinus virginianus) and scaled quail in portions of their distributions which are currently areas of high abundance. Climatic variables that influence local abundance may not always scale up to influence species' distributions. Special attention should be given to selecting variables for ENMs, and tests of model performance should be used to validate the choice of variables.

The role of respiratory measures to assess mental load in pilot selection.

PubMed

Grassmann, Mariel; Vlemincx, Elke; von Leupoldt, Andreas; Van den Bergh, Omer

2016-06-01

While cardiovascular measures have a long tradition of being used to determine operator load, responsiveness of the respiratory system to mental load has rarely been investigated. In this study, we assessed basic and variability measures of respiration rate (RR), partial pressure of end-tidal carbon dioxide (petCO2) as well as performance measures in 63 male pilot candidates during completion of a complex cognitive task and subsequent recovery. Mental load was associated with an increase in RR and a decrease in respiratory variability. A significant decrease was also found for petCO2. RR and respiratory variability showed partial and complete effects of recovery, respectively, whereas petCO2 did not return to baseline level. Overall, a good performance was related to a stronger reactivity in RR. Our findings suggest that respiratory parameters would be a useful supplement to common measures for the assessment of mental load in pilot selection. Practitioner Summary: Respiratory measures are a promising yet poorly investigated approach to monitor operator load. For pilot selection, we assessed respiration in response to multitasking in 63 candidates. Task-related changes as well as covariation with performance strongly support the consideration of respiratory parameters when evaluating reactivity to mental load.

Variable selection in semiparametric cure models based on penalized likelihood, with application to breast cancer clinical trials.

PubMed

Liu, Xiang; Peng, Yingwei; Tu, Dongsheng; Liang, Hua

2012-10-30

Survival data with a sizable cure fraction are commonly encountered in cancer research. The semiparametric proportional hazards cure model has been recently used to analyze such data. As seen in the analysis of data from a breast cancer study, a variable selection approach is needed to identify important factors in predicting the cure status and risk of breast cancer recurrence. However, no specific variable selection method for the cure model is available. In this paper, we present a variable selection approach with penalized likelihood for the cure model. The estimation can be implemented easily by combining the computational methods for penalized logistic regression and the penalized Cox proportional hazards models with the expectation-maximization algorithm. We illustrate the proposed approach on data from a breast cancer study. We conducted Monte Carlo simulations to evaluate the performance of the proposed method. We used and compared different penalty functions in the simulation studies. Copyright © 2012 John Wiley & Sons, Ltd.

Relationship between balance performance in the elderly and some anthropometric variables.

PubMed

Fabunmi, A A; Gbiri, C A

2008-12-01

Ability to maintain either static or dynamic balance has been found to be influenced by many factors such as height and weight in the elderly. The relationship between other anthropometric variables and balance performance among elderly Nigerians has not been widely studied. The aim of this study was to investigate the relationship between these other anthropometric variables and balance performance among old individuals aged >60 years in Ibadan, Nigeria. The study used the ex-post facto design and involved two hundred and three apparently healthy (103 males and 100 females) elderly participants with ages between 60 years and 74 years, selected using multiple step-wise sampling techniques from churches, mosques and market place within Ibadan. They were without history of neurological problem, postural hypotension, orthopeadic conditions or injury to the back and/or upper and lower extremities within the past one year. Selected anthropometric variables were measured, Sharpened Romberg Test (SRT) and Functional Reach Test (FRT) was used to assess static balance and dynamic balance respectively. All data were summarized using range, mean and standard deviation. Pearson's product moment correlation coefficient was used to determine the relationship between the physical characteristics, anthropometric variables and performance on each of the two balance tests. The results showed that there were low but significant positive correlations between performance on FRT and each of height, weight, trunk length, foot length, shoulder girth and hip girth. (p<0.05). There was low significant and positive correlation between SRT with eyes closed and arm length, foot length and shoulder girth. (p<0.05) and there was low but significant positive correlation between SRT with eyes opened and shoulder girth and foot length (P<0.05). Anthropometric variables affect balance performances in apparently healthy elderly.

Design study and performance analysis of a high-speed multistage variable-geometry fan for a variable cycle engine

NASA Technical Reports Server (NTRS)

Sullivan, T. J.; Parker, D. E.

1979-01-01

A design technology study was performed to identify a high speed, multistage, variable geometry fan configuration capable of achieving wide flow modulation with near optimum efficiency at the important operating condition. A parametric screening study of the front and rear block fans was conducted in which the influence of major fan design features on weight and efficiency was determined. Key design parameters were varied systematically to determine the fan configuration most suited for a double bypass, variable cycle engine. Two and three stage fans were considered for the front block. A single stage, core driven fan was studied for the rear block. Variable geometry concepts were evaluated to provide near optimum off design performance. A detailed aerodynamic design and a preliminary mechanical design were carried out for the selected fan configuration. Performance predictions were made for the front and rear block fans.

Variable selection under multiple imputation using the bootstrap in a prognostic study

PubMed Central

Heymans, Martijn W; van Buuren, Stef; Knol, Dirk L; van Mechelen, Willem; de Vet, Henrica CW

2007-01-01

Background Missing data is a challenging problem in many prognostic studies. Multiple imputation (MI) accounts for imputation uncertainty that allows for adequate statistical testing. We developed and tested a methodology combining MI with bootstrapping techniques for studying prognostic variable selection. Method In our prospective cohort study we merged data from three different randomized controlled trials (RCTs) to assess prognostic variables for chronicity of low back pain. Among the outcome and prognostic variables data were missing in the range of 0 and 48.1%. We used four methods to investigate the influence of respectively sampling and imputation variation: MI only, bootstrap only, and two methods that combine MI and bootstrapping. Variables were selected based on the inclusion frequency of each prognostic variable, i.e. the proportion of times that the variable appeared in the model. The discriminative and calibrative abilities of prognostic models developed by the four methods were assessed at different inclusion levels. Results We found that the effect of imputation variation on the inclusion frequency was larger than the effect of sampling variation. When MI and bootstrapping were combined at the range of 0% (full model) to 90% of variable selection, bootstrap corrected c-index values of 0.70 to 0.71 and slope values of 0.64 to 0.86 were found. Conclusion We recommend to account for both imputation and sampling variation in sets of missing data. The new procedure of combining MI with bootstrapping for variable selection, results in multivariable prognostic models with good performance and is therefore attractive to apply on data sets with missing values. PMID:17629912

Is the technical performance of young soccer players influenced by hormonal status, sexual maturity, anthropometric profile, and physical performance?

PubMed

Moreira, Alexandre; Massa, Marcelo; Thiengo, Carlos R; Rodrigues Lopes, Rafael Alan; Lima, Marcelo R; Vaeyens, Roel; Barbosa, Wesley P; Aoki, Marcelo S

2017-12-01

The aim of this study was to examine the influence of hormonal status, anthropometric profile, sexual maturity level, and physical performance on the technical abilities of 40 young male soccer players during small-sided games (SSGs). Anthropometric profiling, saliva sampling, sexual maturity assessment (Tanner scale), and physical performance tests (Yo-Yo and vertical jumps) were conducted two weeks prior to the SSGs. Salivary testosterone was determined by the enzyme-linked immunosorbent assay method. Technical performance was determined by the frequency of actions during SSGs. Principal component analyses identified four technical actions of importance: total number of passes, effectiveness, goal attempts, and total tackles. A multivariate canonical correlation analysis was then employed to verify the prediction of a multiple dependent variables set (composed of four technical actions) from an independent set of variables, composed of testosterone concentration, stage of pubic hair and genitalia development, vertical jumps and Yo-Yo performance. A moderate-to-large relationship between the technical performance set and the independent set was observed. The canonical correlation was 0.75 with a canonical R 2 of 0.45. The highest structure coefficient in the technical performance set was observed for tackles (0.77), while testosterone presented the highest structure coefficient (0.75) for the variables of the independent set. The current data suggest that the selected independent set of variables might be useful in predicting SSG performance in young soccer players. Coaches should be aware that physical development plays a key role in technical performance to avoid decision-making mistakes during the selection of young players.

Performance Variability as a Predictor of Response to Aphasia Treatment.

PubMed

Duncan, E Susan; Schmah, Tanya; Small, Steven L

2016-10-01

Performance variability in individuals with aphasia is typically regarded as a nuisance factor complicating assessment and treatment. We present the alternative hypothesis that intraindividual variability represents a fundamental characteristic of an individual's functioning and an important biomarker for therapeutic selection and prognosis. A total of 19 individuals with chronic aphasia participated in a 6-week trial of imitation-based speech therapy. We assessed improvement both on overall language functioning and repetition ability. Furthermore, we determined which pretreatment variables best predicted improvement on the repetition test. Significant gains were made on the Western Aphasia Battery-Revised (WAB) Aphasia Quotient, Cortical Quotient, and 2 subtests as well as on a separate repetition test. Using stepwise regression, we found that pretreatment intraindividual variability was the only predictor of improvement in performance on the repetition test, with greater pretreatment variability predicting greater improvement. Furthermore, the degree of reduction in this variability over the course of treatment was positively correlated with the degree of improvement. Intraindividual variability may be indicative of potential for improvement on a given task, with more uniform performance suggesting functioning at or near peak potential. © The Author(s) 2016.

Selecting Populations for Non-Analogous Climate Conditions Using Universal Response Functions: The Case of Douglas-Fir in Central Europe

PubMed Central

Chakraborty, Debojyoti; Wang, Tongli; Andre, Konrad; Konnert, Monika; Lexer, Manfred J.; Matulla, Christoph; Schueler, Silvio

2015-01-01

Identifying populations within tree species potentially adapted to future climatic conditions is an important requirement for reforestation and assisted migration programmes. Such populations can be identified either by empirical response functions based on correlations of quantitative traits with climate variables or by climate envelope models that compare the climate of seed sources and potential growing areas. In the present study, we analyzed the intraspecific variation in climate growth response of Douglas-fir planted within the non-analogous climate conditions of Central and continental Europe. With data from 50 common garden trials, we developed Universal Response Functions (URF) for tree height and mean basal area and compared the growth performance of the selected best performing populations with that of populations identified through a climate envelope approach. Climate variables of the trial location were found to be stronger predictors of growth performance than climate variables of the population origin. Although the precipitation regime of the population sources varied strongly none of the precipitation related climate variables of population origin was found to be significant within the models. Overall, the URFs explained more than 88% of variation in growth performance. Populations identified by the URF models originate from western Cascades and coastal areas of Washington and Oregon and show significantly higher growth performance than populations identified by the climate envelope approach under both current and climate change scenarios. The URFs predict decreasing growth performance at low and middle elevations of the case study area, but increasing growth performance on high elevation sites. Our analysis suggests that population recommendations based on empirical approaches should be preferred and population selections by climate envelope models without considering climatic constrains of growth performance should be carefully appraised before transferring populations to planting locations with novel or dissimilar climate. PMID:26288363

Effects of spinal manipulation and myofascial techniques on heart rate variability: A systematic review.

PubMed

Amoroso Borges, Bruno Luis; Bortolazzo, Gustavo Luiz; Neto, Hugo Pasin

2018-01-01

The analysis of heart rate variability is important to the investigation of stimuli from the autonomic nervous system. Osteopathy is a form of treatment that can influence this system in healthy individuals as well as those with a disorder or disease. The aim of the present study was to perform a systematic review of the literature regarding the effect of spinal manipulation and myofascial techniques on heart rate variability. Searches were performed of the Pubmed, Scielo, Lilacs, PEDro, Ibesco, Cochrane and Scopus databases for relevant studies. The PEDro scale was used to assess the methodological quality of each study selected. A total of 505 articles were retrieved during the initial search. After an analysis of the abstracts, nine studies were selected for the present review. Based on the findings, osteopathy exerts an influence on the autonomic nervous system depending on the stimulation site and type. A greater parasympathetic response was found when stimulation was performed in the cervical and lumbar regions, whereas a greater sympathetic response was found when stimulation was performed in the thoracic region. Copyright © 2017 Elsevier Ltd. All rights reserved.

Using Decision Tree Analysis to Understand Foundation Science Student Performance. Insight Gained at One South African University

NASA Astrophysics Data System (ADS)

Kirby, Nicola Frances; Dempster, Edith Roslyn

2014-11-01

The Foundation Programme of the Centre for Science Access at the University of KwaZulu-Natal, South Africa provides access to tertiary science studies to educationally disadvantaged students who do not meet formal faculty entrance requirements. The low number of students proceeding from the programme into mainstream is of concern, particularly given the national imperative to increase participation and levels of performance in tertiary-level science. An attempt was made to understand foundation student performance in a campus of this university, with the view to identifying challenges and opportunities for remediation in the curriculum and processes of selection into the programme. A classification and regression tree analysis was used to identify which variables best described student performance. The explanatory variables included biographical and school-history data, performance in selection tests, and socio-economic data pertaining to their year in the programme. The results illustrate the prognostic reliability of the model used to select students, raise concerns about the inefficiency of school performance indicators as a measure of students' academic potential in the Foundation Programme, and highlight the importance of accommodation arrangements and financial support for student success in their access year.

Robust Variable Selection with Exponential Squared Loss.

PubMed

Wang, Xueqin; Jiang, Yunlu; Huang, Mian; Zhang, Heping

2013-04-01

Robust variable selection procedures through penalized regression have been gaining increased attention in the literature. They can be used to perform variable selection and are expected to yield robust estimates. However, to the best of our knowledge, the robustness of those penalized regression procedures has not been well characterized. In this paper, we propose a class of penalized robust regression estimators based on exponential squared loss. The motivation for this new procedure is that it enables us to characterize its robustness that has not been done for the existing procedures, while its performance is near optimal and superior to some recently developed methods. Specifically, under defined regularity conditions, our estimators are [Formula: see text] and possess the oracle property. Importantly, we show that our estimators can achieve the highest asymptotic breakdown point of 1/2 and that their influence functions are bounded with respect to the outliers in either the response or the covariate domain. We performed simulation studies to compare our proposed method with some recent methods, using the oracle method as the benchmark. We consider common sources of influential points. Our simulation studies reveal that our proposed method performs similarly to the oracle method in terms of the model error and the positive selection rate even in the presence of influential points. In contrast, other existing procedures have a much lower non-causal selection rate. Furthermore, we re-analyze the Boston Housing Price Dataset and the Plasma Beta-Carotene Level Dataset that are commonly used examples for regression diagnostics of influential points. Our analysis unravels the discrepancies of using our robust method versus the other penalized regression method, underscoring the importance of developing and applying robust penalized regression methods.

Robust Variable Selection with Exponential Squared Loss

PubMed Central

Wang, Xueqin; Jiang, Yunlu; Huang, Mian; Zhang, Heping

2013-01-01

Robust variable selection procedures through penalized regression have been gaining increased attention in the literature. They can be used to perform variable selection and are expected to yield robust estimates. However, to the best of our knowledge, the robustness of those penalized regression procedures has not been well characterized. In this paper, we propose a class of penalized robust regression estimators based on exponential squared loss. The motivation for this new procedure is that it enables us to characterize its robustness that has not been done for the existing procedures, while its performance is near optimal and superior to some recently developed methods. Specifically, under defined regularity conditions, our estimators are n-consistent and possess the oracle property. Importantly, we show that our estimators can achieve the highest asymptotic breakdown point of 1/2 and that their influence functions are bounded with respect to the outliers in either the response or the covariate domain. We performed simulation studies to compare our proposed method with some recent methods, using the oracle method as the benchmark. We consider common sources of influential points. Our simulation studies reveal that our proposed method performs similarly to the oracle method in terms of the model error and the positive selection rate even in the presence of influential points. In contrast, other existing procedures have a much lower non-causal selection rate. Furthermore, we re-analyze the Boston Housing Price Dataset and the Plasma Beta-Carotene Level Dataset that are commonly used examples for regression diagnostics of influential points. Our analysis unravels the discrepancies of using our robust method versus the other penalized regression method, underscoring the importance of developing and applying robust penalized regression methods. PMID:23913996

Intractable Ménière's disease. Modelling of the treatment by means of statistical analysis.

PubMed

Sanchez-Ferrandiz, Noelia; Fernandez-Gonzalez, Secundino; Guillen-Grima, Francisco; Perez-Fernandez, Nicolas

2010-08-01

To evaluate the value of different variables of the clinical history, auditory and vestibular tests and handicap measurements to define intractable or disabling Ménière's disease. This is a prospective study with 212 patients of which 155 were treated with intratympanic gentamicin and considered to be suffering a medically intractable Ménière's disease. Age and sex adjustments were performed with the 11 variables selected. Discriminant analysis was performed either using the aforementioned variables or following the stepwise method. Different variables needed to be sex and/or age adjusted and both data were included in the discriminant function. Two different mathematical formulas were obtained and four models were analyzed. With the model selected, diagnostic accuracy is 77.7%, sensitivity is 94.9% and specificity is 52.8%. After discriminant analysis we found that the most informative variables were the number of vertigo spells, the speech discrimination score, the time constant of the VOR and a measure of handicap, the "dizziness index". Copyright 2009 Elsevier Ireland Ltd. All rights reserved.

[Winter wheat yield gap between field blocks based on comparative performance analysis].

PubMed

Chen, Jian; Wang, Zhong-Yi; Li, Liang-Tao; Zhang, Ke-Feng; Yu, Zhen-Rong

2008-09-01

Based on a two-year household survey data, the yield gap of winter wheat in Quzhou County of Hebei Province, China in 2003-2004 was studied through comparative performance analysis (CPA). The results showed that there was a greater yield gap (from 4.2 to 7.9 t x hm(-2)) between field blocks, with a variation coefficient of 0.14. Through stepwise forward linear multiple regression, it was found that the yield model with 8 selected variables could explain 63% variability of winter wheat yield. Among the variables selected, soil salinity, soil fertility, and irrigation water quality were the most important limiting factors, accounting for 52% of the total yield gap. Crop variety was another important limiting factor, accounting for 14%; while planting date, fertilizer type, disease and pest, and water press accounted for 7%, 14%, 10%, and 3%, respectively. Therefore, besides soil and climate conditions, management practices occupied the majority of yield variability in Quzhou County, suggesting that the yield gap could be reduced significantly through optimum field management.

Variable-cycle engines for supersonic cruising aircraft

NASA Technical Reports Server (NTRS)

Willis, E. A.; Welliver, A. D.

1976-01-01

The paper reviews the evolution and current status of selected recent variable-cycle engine (VCE) studies and describes how the results are influenced by airplane requirements. The engine/airplane studies are intended to identify promising VCE concepts, simplify their designs and identify the potential benefits in terms of aircraft performance. This includes range, noise, emissions, and the time and effort it may require to ensure technical readiness of sufficient depth to satisfy reasonable economic, performance, and environmental constraints. A brief overview of closely-related, on-going technology programs in acoustics and exhaust emissions is presented. It is shown that realistic technology advancements in critical areas combined with well matched aircraft and selected VCE concepts can lead to significantly improved economic and environmental performance relative to first-generation SST predictions.

Analytical review based on statistics on good and poor financial performance of LPD in Bangli regency.

NASA Astrophysics Data System (ADS)

Yasa, I. B. A.; Parnata, I. K.; Susilawati, N. L. N. A. S.

2018-01-01

This study aims to apply analytical review model to analyze the influence of GCG, accounting conservatism, financial distress models and company size on good and poor financial performance of LPD in Bangli Regency. Ordinal regression analysis is used to perform analytical review, so that obtained the influence and relationship between variables to be considered further audit. Respondents in this study were LPDs in Bangli Regency, which amounted to 159 LPDs of that number 100 LPDs were determined as randomly selected samples. The test results found GCG and company size have a significant effect on both the good and poor financial performance, while the conservatism and financial distress model has no significant effect. The influence of the four variables on the overall financial performance of 58.8%, while the remaining 41.2% influenced by other variables. Size, FDM and accounting conservatism are variables, which are further recommended to be audited.

Variable selection based cotton bollworm odor spectroscopic detection

NASA Astrophysics Data System (ADS)

Lü, Chengxu; Gai, Shasha; Luo, Min; Zhao, Bo

2016-10-01

Aiming at rapid automatic pest detection based efficient and targeting pesticide application and shooting the trouble of reflectance spectral signal covered and attenuated by the solid plant, the possibility of near infrared spectroscopy (NIRS) detection on cotton bollworm odor is studied. Three cotton bollworm odor samples and 3 blank air gas samples were prepared. Different concentrations of cotton bollworm odor were prepared by mixing the above gas samples, resulting a calibration group of 62 samples and a validation group of 31 samples. Spectral collection system includes light source, optical fiber, sample chamber, spectrometer. Spectra were pretreated by baseline correction, modeled with partial least squares (PLS), and optimized by genetic algorithm (GA) and competitive adaptive reweighted sampling (CARS). Minor counts differences are found among spectra of different cotton bollworm odor concentrations. PLS model of all the variables was built presenting RMSEV of 14 and RV2 of 0.89, its theory basis is insect volatilizes specific odor, including pheromone and allelochemics, which are used for intra-specific and inter-specific communication and could be detected by NIR spectroscopy. 28 sensitive variables are selected by GA, presenting the model performance of RMSEV of 14 and RV2 of 0.90. Comparably, 8 sensitive variables are selected by CARS, presenting the model performance of RMSEV of 13 and RV2 of 0.92. CARS model employs only 1.5% variables presenting smaller error than that of all variable. Odor gas based NIR technique shows the potential for cotton bollworm detection.

Predicting performance of junior doctors: Association of workplace based assessment with demographic characteristics, emotional intelligence, selection scores, and undergraduate academic performance.

PubMed

Carr, Sandra E; Celenza, Antonio; Mercer, Annette M; Lake, Fiona; Puddey, Ian B

2018-01-21

Predicting workplace performance of junior doctors from before entry or during medical school is difficult and has limited available evidence. This study explored the association between selected predictor variables and workplace based performance in junior doctors during their first postgraduate year. Two cohorts of medical students (n = 200) from one university in Western Australia participated in the longitudinal study. Pearson correlation coefficients and multivariate analyses utilizing linear regression were used to assess the relationships between performance on the Junior Doctor Assessment Tool (JDAT) and its sub-components with demographic characteristics, selection scores for medical school entry, emotional intelligence, and undergraduate academic performance. Grade Point Average (GPA) at the completion of undergraduate studies had the most significant association with better performance on the overall JDAT and each subscale. Increased age was a negative predictor for junior doctor performance on the Clinical management subscale and understanding emotion was a predictor for the JDAT Communication subscale. Secondary school performance measured by Tertiary Entry Rank on entry to medical school score predicted GPA but not junior doctor performance. The GPA as a composite measure of ability and performance in medical school is associated with junior doctor assessment scores. Using this variable to identify students at risk of difficulty could assist planning for appropriate supervision, support, and training for medical graduates transitioning to the workplace.

Role of functionally dominant species in varying environmental regimes: evidence for the performance-enhancing effect of biodiversity.

PubMed

Langenheder, Silke; Bulling, Mark T; Prosser, James I; Solan, Martin

2012-07-30

Theory suggests that biodiversity can act as a buffer against disturbances and environmental variability via two major mechanisms: Firstly, a stabilising effect by decreasing the temporal variance in ecosystem functioning due to compensatory processes; and secondly, a performance enhancing effect by raising the level of community response through the selection of better performing species. Empirical evidence for the stabilizing effect of biodiversity is readily available, whereas experimental confirmation of the performance-enhancing effect of biodiversity is sparse. Here, we test the effect of different environmental regimes (constant versus fluctuating temperature) on bacterial biodiversity-ecosystem functioning relations. We show that positive effects of species richness on ecosystem functioning are enhanced by stronger temperature fluctuations due to the increased performance of individual species. Our results provide evidence for the performance enhancing effect and suggest that selection towards functionally dominant species is likely to benefit the maintenance of ecosystem functioning under more variable conditions.

Radiograph and passive data analysis using mixed variable optimization

DOEpatents

Temple, Brian A.; Armstrong, Jerawan C.; Buescher, Kevin L.; Favorite, Jeffrey A.

2015-06-02

Disclosed herein are representative embodiments of methods, apparatus, and systems for performing radiography analysis. For example, certain embodiments perform radiographic analysis using mixed variable computation techniques. One exemplary system comprises a radiation source, a two-dimensional detector for detecting radiation transmitted through a object between the radiation source and detector, and a computer. In this embodiment, the computer is configured to input the radiographic image data from the two-dimensional detector and to determine one or more materials that form the object by using an iterative analysis technique that selects the one or more materials from hierarchically arranged solution spaces of discrete material possibilities and selects the layer interfaces from the optimization of the continuous interface data.

Analysis of model development strategies: predicting ventral hernia recurrence.

PubMed

Holihan, Julie L; Li, Linda T; Askenasy, Erik P; Greenberg, Jacob A; Keith, Jerrod N; Martindale, Robert G; Roth, J Scott; Liang, Mike K

2016-11-01

There have been many attempts to identify variables associated with ventral hernia recurrence; however, it is unclear which statistical modeling approach results in models with greatest internal and external validity. We aim to assess the predictive accuracy of models developed using five common variable selection strategies to determine variables associated with hernia recurrence. Two multicenter ventral hernia databases were used. Database 1 was randomly split into "development" and "internal validation" cohorts. Database 2 was designated "external validation". The dependent variable for model development was hernia recurrence. Five variable selection strategies were used: (1) "clinical"-variables considered clinically relevant, (2) "selective stepwise"-all variables with a P value <0.20 were assessed in a step-backward model, (3) "liberal stepwise"-all variables were included and step-backward regression was performed, (4) "restrictive internal resampling," and (5) "liberal internal resampling." Variables were included with P < 0.05 for the Restrictive model and P < 0.10 for the Liberal model. A time-to-event analysis using Cox regression was performed using these strategies. The predictive accuracy of the developed models was tested on the internal and external validation cohorts using Harrell's C-statistic where C > 0.70 was considered "reasonable". The recurrence rate was 32.9% (n = 173/526; median/range follow-up, 20/1-58 mo) for the development cohort, 36.0% (n = 95/264, median/range follow-up 20/1-61 mo) for the internal validation cohort, and 12.7% (n = 155/1224, median/range follow-up 9/1-50 mo) for the external validation cohort. Internal validation demonstrated reasonable predictive accuracy (C-statistics = 0.772, 0.760, 0.767, 0.757, 0.763), while on external validation, predictive accuracy dipped precipitously (C-statistic = 0.561, 0.557, 0.562, 0.553, 0.560). Predictive accuracy was equally adequate on internal validation among models; however, on external validation, all five models failed to demonstrate utility. Future studies should report multiple variable selection techniques and demonstrate predictive accuracy on external data sets for model validation. Copyright © 2016 Elsevier Inc. All rights reserved.

The predictive validity of a situational judgement test, a clinical problem solving test and the core medical training selection methods for performance in specialty training .

PubMed

Patterson, Fiona; Lopes, Safiatu; Harding, Stephen; Vaux, Emma; Berkin, Liz; Black, David

2017-02-01

The aim of this study was to follow up a sample of physicians who began core medical training (CMT) in 2009. This paper examines the long-term validity of CMT and GP selection methods in predicting performance in the Membership of Royal College of Physicians (MRCP(UK)) examinations. We performed a longitudinal study, examining the extent to which the GP and CMT selection methods (T1) predict performance in the MRCP(UK) examinations (T2). A total of 2,569 applicants from 2008-09 who completed CMT and GP selection methods were included in the study. Looking at MRCP(UK) part 1, part 2 written and PACES scores, both CMT and GP selection methods show evidence of predictive validity for the outcome variables, and hierarchical regressions show the GP methods add significant value to the CMT selection process. CMT selection methods predict performance in important outcomes and have good evidence of validity; the GP methods may have an additional role alongside the CMT selection methods. © Royal College of Physicians 2017. All rights reserved.

Learning and Study Strategies Inventory subtests and factors as predictors of National Board of Chiropractic Examiners Part 1 examination performance.

PubMed

Schutz, Christine M; Dalton, Leanne; Tepe, Rodger E

2013-01-01

This study was designed to extend research on the relationship between chiropractic students' learning and study strategies and national board examination performance. Sixty-nine first trimester chiropractic students self-administered the Learning and Study Strategies Inventory (LASSI). Linear trends tests (for continuous variables) and Mantel-Haenszel trend tests (for categorical variables) were utilized to determine if the 10 LASSI subtests and 3 factors predicted low, medium and high levels of National Board of Chiropractic Examiners (NBCE) Part 1 scores. Multiple regression was performed to predict overall mean NBCE examination scores using the 3 LASSI factors as predictor variables. Four LASSI subtests (Anxiety, Concentration, Selecting Main Ideas, Test Strategies) and one factor (Goal Orientation) were significantly associated with NBCE examination levels. One factor (Goal Orientation) was a significant predictor of overall mean NBCE examination performance. Learning and study strategies are predictive of NBCE Part 1 examination performance in chiropractic students. The current study found LASSI subtests Anxiety, Concentration, Selecting Main Ideas, and Test Strategies, and the Goal-Orientation factor to be significant predictors of NBCE scores. The LASSI may be useful to educators in preparing students for academic success. Further research is warranted to explore the effects of learning and study strategies training on GPA and NBCE performance.

Mating tactics determine patterns of condition dependence in a dimorphic horned beetle.

PubMed

Knell, Robert J; Simmons, Leigh W

2010-08-07

The persistence of genetic variability in performance traits such as strength is surprising given the directional selection that such traits experience, which should cause the fixation of the best genetic variants. One possible explanation is 'genic capture' which is usually considered as a candidate mechanism for the maintenance of high genetic variability in sexual signalling traits. This states that if a trait is 'condition dependent', with expression being strongly influenced by the bearer's overall viability, then genetic variability can be maintained via mutation-selection balance. Using a species of dimorphic beetle with males that gain matings either by fighting or by 'sneaking', we tested the prediction of strong condition dependence for strength, walking speed and testes mass. Strength was strongly condition dependent only in those beetles that fight for access to females. Walking speed, with less of an obvious selective advantage, showed no condition dependence, and testes mass was more condition dependent in sneaks, which engage in higher levels of sperm competition. Within a species, therefore, condition dependent expression varies between morphs, and corresponds to the specific selection pressures experienced by that morph. These results support genic capture as a general explanation for the maintenance of genetic variability in traits under directional selection.

A regularized variable selection procedure in additive hazards model with stratified case-cohort design.

PubMed

Ni, Ai; Cai, Jianwen

2018-07-01

Case-cohort designs are commonly used in large epidemiological studies to reduce the cost associated with covariate measurement. In many such studies the number of covariates is very large. An efficient variable selection method is needed for case-cohort studies where the covariates are only observed in a subset of the sample. Current literature on this topic has been focused on the proportional hazards model. However, in many studies the additive hazards model is preferred over the proportional hazards model either because the proportional hazards assumption is violated or the additive hazards model provides more relevent information to the research question. Motivated by one such study, the Atherosclerosis Risk in Communities (ARIC) study, we investigate the properties of a regularized variable selection procedure in stratified case-cohort design under an additive hazards model with a diverging number of parameters. We establish the consistency and asymptotic normality of the penalized estimator and prove its oracle property. Simulation studies are conducted to assess the finite sample performance of the proposed method with a modified cross-validation tuning parameter selection methods. We apply the variable selection procedure to the ARIC study to demonstrate its practical use.

Effects of Achievement Tendencies and Competitive Outcomes on Performance.

ERIC Educational Resources Information Center

Grove, J. Robert; Pargman, David

A study examined cognitive and behavioral consequences of continuous success or failure in a competitive situation involving 40 undergraduate males. Three performance variables were selected for examination: expectancies for success, amount of self-motivated practice, and performance quality. Subjects were informed that they would be competing…

Variable selection based on clustering analysis for improvement of polyphenols prediction in green tea using synchronous fluorescence spectra

NASA Astrophysics Data System (ADS)

Shan, Jiajia; Wang, Xue; Zhou, Hao; Han, Shuqing; Riza, Dimas Firmanda Al; Kondo, Naoshi

2018-04-01

Synchronous fluorescence spectra, combined with multivariate analysis were used to predict flavonoids content in green tea rapidly and nondestructively. This paper presented a new and efficient spectral intervals selection method called clustering based partial least square (CL-PLS), which selected informative wavelengths by combining clustering concept and partial least square (PLS) methods to improve models’ performance by synchronous fluorescence spectra. The fluorescence spectra of tea samples were obtained and k-means and kohonen-self organizing map clustering algorithms were carried out to cluster full spectra into several clusters, and sub-PLS regression model was developed on each cluster. Finally, CL-PLS models consisting of gradually selected clusters were built. Correlation coefficient (R) was used to evaluate the effect on prediction performance of PLS models. In addition, variable influence on projection partial least square (VIP-PLS), selectivity ratio partial least square (SR-PLS), interval partial least square (iPLS) models and full spectra PLS model were investigated and the results were compared. The results showed that CL-PLS presented the best result for flavonoids prediction using synchronous fluorescence spectra.

Variable selection based on clustering analysis for improvement of polyphenols prediction in green tea using synchronous fluorescence spectra.

PubMed

Shan, Jiajia; Wang, Xue; Zhou, Hao; Han, Shuqing; Riza, Dimas Firmanda Al; Kondo, Naoshi

2018-03-13

Synchronous fluorescence spectra, combined with multivariate analysis were used to predict flavonoids content in green tea rapidly and nondestructively. This paper presented a new and efficient spectral intervals selection method called clustering based partial least square (CL-PLS), which selected informative wavelengths by combining clustering concept and partial least square (PLS) methods to improve models' performance by synchronous fluorescence spectra. The fluorescence spectra of tea samples were obtained and k-means and kohonen-self organizing map clustering algorithms were carried out to cluster full spectra into several clusters, and sub-PLS regression model was developed on each cluster. Finally, CL-PLS models consisting of gradually selected clusters were built. Correlation coefficient (R) was used to evaluate the effect on prediction performance of PLS models. In addition, variable influence on projection partial least square (VIP-PLS), selectivity ratio partial least square (SR-PLS), interval partial least square (iPLS) models and full spectra PLS model were investigated and the results were compared. The results showed that CL-PLS presented the best result for flavonoids prediction using synchronous fluorescence spectra.

Repeated-sprint ability and team selection in Australian football league players.

PubMed

Le Rossignol, Peter; Gabbett, Tim J; Comerford, Dan; Stanton, Warren R

2014-01-01

To investigate the relationship between selected physical capacities and repeated-sprint performance of Australian Football League (AFL) players and to determine which physical capacities contributed to being selected for the first competition game. Sum of skinfolds, 40-m sprint (with 10-, 20-, 30-, and 40-m splits), repeated-sprint ability (6 × 30-m sprints), and 3-km-run time were measured during the preseason in 20 AFL players. The physical qualities of players selected to play the first match of the season and those not selected were compared. Pearson correlation coefficients were used to determine the relationship among variables, and a regression analysis identified variables significantly related to repeated-sprint performance. In the regression analysis, maximum velocity was the best predictor of repeated-sprint time, with 3-km-run time also contributing significantly to the predictive model. Sum of skinfolds was significantly correlated with 10-m (r = .61, P < .01) and 30-m (r = .53, P < .05) sprint times. A 2.6% ± 2.1% difference in repeated-sprint time (P < .05, ES = 0.88 ± 0.72) was observed between those selected (25.26 ± 0.55 s) and not selected (25.82 ± 0.80 s) for the first game of the season. The findings indicate that maximum-velocity training using intervals of 30-40 m may contribute more to improving repeated-sprint performance in AFL players than short 10- to 20-m intervals from standing starts. Further research is warranted to establish the relative importance of endurance training for improving repeated-sprint performance in AFL football.

Variable selection in subdistribution hazard frailty models with competing risks data

PubMed Central

Do Ha, Il; Lee, Minjung; Oh, Seungyoung; Jeong, Jong-Hyeon; Sylvester, Richard; Lee, Youngjo

2014-01-01

The proportional subdistribution hazards model (i.e. Fine-Gray model) has been widely used for analyzing univariate competing risks data. Recently, this model has been extended to clustered competing risks data via frailty. To the best of our knowledge, however, there has been no literature on variable selection method for such competing risks frailty models. In this paper, we propose a simple but unified procedure via a penalized h-likelihood (HL) for variable selection of fixed effects in a general class of subdistribution hazard frailty models, in which random effects may be shared or correlated. We consider three penalty functions (LASSO, SCAD and HL) in our variable selection procedure. We show that the proposed method can be easily implemented using a slight modification to existing h-likelihood estimation approaches. Numerical studies demonstrate that the proposed procedure using the HL penalty performs well, providing a higher probability of choosing the true model than LASSO and SCAD methods without losing prediction accuracy. The usefulness of the new method is illustrated using two actual data sets from multi-center clinical trials. PMID:25042872

Comparison of Feature Selection Techniques in Machine Learning for Anatomical Brain MRI in Dementia.

PubMed

Tohka, Jussi; Moradi, Elaheh; Huttunen, Heikki

2016-07-01

We present a comparative split-half resampling analysis of various data driven feature selection and classification methods for the whole brain voxel-based classification analysis of anatomical magnetic resonance images. We compared support vector machines (SVMs), with or without filter based feature selection, several embedded feature selection methods and stability selection. While comparisons of the accuracy of various classification methods have been reported previously, the variability of the out-of-training sample classification accuracy and the set of selected features due to independent training and test sets have not been previously addressed in a brain imaging context. We studied two classification problems: 1) Alzheimer's disease (AD) vs. normal control (NC) and 2) mild cognitive impairment (MCI) vs. NC classification. In AD vs. NC classification, the variability in the test accuracy due to the subject sample did not vary between different methods and exceeded the variability due to different classifiers. In MCI vs. NC classification, particularly with a large training set, embedded feature selection methods outperformed SVM-based ones with the difference in the test accuracy exceeding the test accuracy variability due to the subject sample. The filter and embedded methods produced divergent feature patterns for MCI vs. NC classification that suggests the utility of the embedded feature selection for this problem when linked with the good generalization performance. The stability of the feature sets was strongly correlated with the number of features selected, weakly correlated with the stability of classification accuracy, and uncorrelated with the average classification accuracy.

A family of variable step-size affine projection adaptive filter algorithms using statistics of channel impulse response

NASA Astrophysics Data System (ADS)

Shams Esfand Abadi, Mohammad; AbbasZadeh Arani, Seyed Ali Asghar

2011-12-01

This paper extends the recently introduced variable step-size (VSS) approach to the family of adaptive filter algorithms. This method uses prior knowledge of the channel impulse response statistic. Accordingly, optimal step-size vector is obtained by minimizing the mean-square deviation (MSD). The presented algorithms are the VSS affine projection algorithm (VSS-APA), the VSS selective partial update NLMS (VSS-SPU-NLMS), the VSS-SPU-APA, and the VSS selective regressor APA (VSS-SR-APA). In VSS-SPU adaptive algorithms the filter coefficients are partially updated which reduce the computational complexity. In VSS-SR-APA, the optimal selection of input regressors is performed during the adaptation. The presented algorithms have good convergence speed, low steady state mean square error (MSE), and low computational complexity features. We demonstrate the good performance of the proposed algorithms through several simulations in system identification scenario.

Structure-function covariation with nonfeeding ecological variables influences evolution of feeding specialization in Carnivora

PubMed Central

Tseng, Z. Jack; Flynn, John J.

2018-01-01

Skull shape convergence is pervasive among vertebrates. Although this is frequently inferred to indicate similar functional underpinnings, neither the specific structure-function linkages nor the selective environments in which the supposed functional adaptations arose are commonly identified and tested. We demonstrate that nonfeeding factors relating to sexual maturity and precipitation-related arboreality also can generate structure-function relationships in the skulls of carnivorans (dogs, cats, seals, and relatives) through covariation with masticatory performance. We estimated measures of masticatory performance related to ecological variables that covary with cranial shape in the mammalian order Carnivora, integrating geometric morphometrics and finite element analyses. Even after accounting for phylogenetic autocorrelation, cranial shapes are significantly correlated to both feeding and nonfeeding ecological variables, and covariation with both variable types generated significant masticatory performance gradients. This suggests that mechanisms of obligate shape covariation with nonfeeding variables can produce performance changes resembling those arising from feeding adaptations in Carnivora. PMID:29441363

The Effects of Music Salience on the Gait Performance of Young Adults.

PubMed

de Bruin, Natalie; Kempster, Cody; Doucette, Angelica; Doan, Jon B; Hu, Bin; Brown, Lesley A

2015-01-01

The presence of a rhythmic beat in the form of a metronome tone or beat-accentuated original music can modulate gait performance; however, it has yet to be determined whether gait modulation can be achieved using commercially available music. The current study investigated the effects of commercially available music on the walking of healthy young adults. Specific aims were (a) to determine whether commercially available music can be used to influence gait (i.e., gait velocity, stride length, cadence, stride time variability), (b) to establish the effect of music salience on gait (i.e., gait velocity, stride length, cadence, stride time variability), and (c) to examine whether music tempi differentially effected gait (i.e., gait velocity, stride length, cadence, stride time variability). Twenty-five participants walked the length of an unobstructed walkway while listening to music. Music selections differed with respect to the salience or the tempo of the music. The genre of music and artists were self-selected by participants. Listening to music while walking was an enjoyable activity that influenced gait. Specifically, salient music selections increased measures of cadence, velocity, and stride length; in contrast, gait was unaltered by the presence of non-salient music. Music tempo did not differentially affect gait performance (gait velocity, stride length, cadence, stride time variability) in these participants. Gait performance was differentially influenced by music salience. These results have implications for clinicians considering the use of commercially available music as an alternative to the traditional rhythmic auditory cues used in rehabilitation programs. © the American Music Therapy Association 2015. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Continuous-variable measurement-device-independent quantum key distribution with virtual photon subtraction

NASA Astrophysics Data System (ADS)

Zhao, Yijia; Zhang, Yichen; Xu, Bingjie; Yu, Song; Guo, Hong

2018-04-01

The method of improving the performance of continuous-variable quantum key distribution protocols by postselection has been recently proposed and verified. In continuous-variable measurement-device-independent quantum key distribution (CV-MDI QKD) protocols, the measurement results are obtained from untrusted third party Charlie. There is still not an effective method of improving CV-MDI QKD by the postselection with untrusted measurement. We propose a method to improve the performance of coherent-state CV-MDI QKD protocol by virtual photon subtraction via non-Gaussian postselection. The non-Gaussian postselection of transmitted data is equivalent to an ideal photon subtraction on the two-mode squeezed vacuum state, which is favorable to enhance the performance of CV-MDI QKD. In CV-MDI QKD protocol with non-Gaussian postselection, two users select their own data independently. We demonstrate that the optimal performance of the renovated CV-MDI QKD protocol is obtained with the transmitted data only selected by Alice. By setting appropriate parameters of the virtual photon subtraction, the secret key rate and tolerable excess noise are both improved at long transmission distance. The method provides an effective optimization scheme for the application of CV-MDI QKD protocols.

Harmonize input selection for sediment transport prediction

NASA Astrophysics Data System (ADS)

Afan, Haitham Abdulmohsin; Keshtegar, Behrooz; Mohtar, Wan Hanna Melini Wan; El-Shafie, Ahmed

2017-09-01

In this paper, three modeling approaches using a Neural Network (NN), Response Surface Method (RSM) and response surface method basis Global Harmony Search (GHS) are applied to predict the daily time series suspended sediment load. Generally, the input variables for forecasting the suspended sediment load are manually selected based on the maximum correlations of input variables in the modeling approaches based on NN and RSM. The RSM is improved to select the input variables by using the errors terms of training data based on the GHS, namely as response surface method and global harmony search (RSM-GHS) modeling method. The second-order polynomial function with cross terms is applied to calibrate the time series suspended sediment load with three, four and five input variables in the proposed RSM-GHS. The linear, square and cross corrections of twenty input variables of antecedent values of suspended sediment load and water discharge are investigated to achieve the best predictions of the RSM based on the GHS method. The performances of the NN, RSM and proposed RSM-GHS including both accuracy and simplicity are compared through several comparative predicted and error statistics. The results illustrated that the proposed RSM-GHS is as uncomplicated as the RSM but performed better, where fewer errors and better correlation was observed (R = 0.95, MAE = 18.09 (ton/day), RMSE = 25.16 (ton/day)) compared to the ANN (R = 0.91, MAE = 20.17 (ton/day), RMSE = 33.09 (ton/day)) and RSM (R = 0.91, MAE = 20.06 (ton/day), RMSE = 31.92 (ton/day)) for all types of input variables.

Gait performance is not influenced by working memory when walking at a self-selected pace.

PubMed

Grubaugh, Jordan; Rhea, Christopher K

2014-02-01

Gait performance exhibits patterns within the stride-to-stride variability that can be indexed using detrended fluctuation analysis (DFA). Previous work employing DFA has shown that gait patterns can be influenced by constraints, such as natural aging or disease, and they are informative regarding a person's functional ability. Many activities of daily living require concurrent performance in the cognitive and gait domains; specifically working memory is commonly engaged while walking, which is considered dual-tasking. It is unknown if taxing working memory while walking influences gait performance as assessed by DFA. This study used a dual-tasking paradigm to determine if performance decrements are observed in gait or working memory when performed concurrently. Healthy young participants (N = 16) performed a working memory task (automated operation span task) and a gait task (walking at a self-selected speed on a treadmill) in single- and dual-task conditions. A second dual-task condition (reading while walking) was included to control for visual attention, but also introduced a task that taxed working memory over the long term. All trials involving gait lasted at least 10 min. Performance in the working memory task was indexed using five dependent variables (absolute score, partial score, speed error, accuracy error, and math error), while gait performance was indexed by quantifying the mean, standard deviation, and DFA α of the stride interval time series. Two multivariate analyses of variance (one for gait and one for working memory) were used to examine performance in the single- and dual-task conditions. No differences were observed in any of the gait or working memory dependent variables as a function of task condition. The results suggest the locomotor system is adaptive enough to complete a working memory task without compromising gait performance when walking at a self-selected pace.

Monthly streamflow forecasting using continuous wavelet and multi-gene genetic programming combination

NASA Astrophysics Data System (ADS)

Hadi, Sinan Jasim; Tombul, Mustafa

2018-06-01

Streamflow is an essential component of the hydrologic cycle in the regional and global scale and the main source of fresh water supply. It is highly associated with natural disasters, such as droughts and floods. Therefore, accurate streamflow forecasting is essential. Forecasting streamflow in general and monthly streamflow in particular is a complex process that cannot be handled by data-driven models (DDMs) only and requires pre-processing. Wavelet transformation is a pre-processing technique; however, application of continuous wavelet transformation (CWT) produces many scales that cause deterioration in the performance of any DDM because of the high number of redundant variables. This study proposes multigene genetic programming (MGGP) as a selection tool. After the CWT analysis, it selects important scales to be imposed into the artificial neural network (ANN). A basin located in the southeast of Turkey is selected as case study to prove the forecasting ability of the proposed model. One month ahead downstream flow is used as output, and downstream flow, upstream, rainfall, temperature, and potential evapotranspiration with associated lags are used as inputs. Before modeling, wavelet coherence transformation (WCT) analysis was conducted to analyze the relationship between variables in the time-frequency domain. Several combinations were developed to investigate the effect of the variables on streamflow forecasting. The results indicated a high localized correlation between the streamflow and other variables, especially the upstream. In the models of the standalone layout where the data were entered to ANN and MGGP without CWT, the performance is found poor. In the best-scale layout, where the best scale of the CWT identified as the highest correlated scale is chosen and enters to ANN and MGGP, the performance increased slightly. Using the proposed model, the performance improved dramatically particularly in forecasting the peak values because of the inclusion of several scales in which seasonality and irregularity can be captured. Using hydrological and meteorological variables also improved the ability to forecast the streamflow.

Preliminary Internal Performance Data for a Variable-Ejector Assembly on the XJ79-GE-1 Turbojet Engine. II; Afterburning Configurations

NASA Technical Reports Server (NTRS)

Bloomer, Harry E.; Groesbeck, Donald E.

1957-01-01

Internal performance of an XJ79-GE-1 variable ejector was experimentally determined with the primary nozzle in two representative after-burning positions. Jet-thrust and air-handling data were obtained in quiescent air for 4 selected ejector configurations over a wide range of secondary to primary airflow ratios and primary-nozzle pressure ratios. The experimental ejector data are presented in both graphical and tabulated form.

Comparison of Self-Efficacy and Self-Regulation between the Students with School Refusal Behavior (SRB) and the Students without (SRB), and the Relationships of These Variables to Academic Performance

ERIC Educational Resources Information Center

Khanehkeshi, Ali; Ahmedi, Farahnaz Azizi Tas

2013-01-01

The purpose of this study was to compare self-efficacy and self-regulation between the students with SRB and students with NSRB, and the relationship of these variables to academic performance. Using a random stratified sampling technique 60 girl students who had school refusal behavior (SRB) and 60 of students without SRB were selected from 8…

Evaluating Moisture Control of Variable-Capacity Heat Pumps in Mechanically Ventilated, Low-Load Homes in Climate Zone 2A

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

Martin, Eric; Withers, Chuck; McIlvaine, Janet

Low-load homes can present a challenge when selecting appropriate space-conditioning equipment. Conventional, fixed-capacity heating and cooling equipment is often oversized for small homes, causing increased first costs and operating costs. This report evaluates the performance of variable-capacity comfort systems, with a focus on inverter-driven, variable-capacity systems, as well as proposed system enhancements.

Quantifying interindividual variability and asymmetry of face-selective regions: a probabilistic functional atlas.

PubMed

Zhen, Zonglei; Yang, Zetian; Huang, Lijie; Kong, Xiang-Zhen; Wang, Xu; Dang, Xiaobin; Huang, Yangyue; Song, Yiying; Liu, Jia

2015-06-01

Face-selective regions (FSRs) are among the most widely studied functional regions in the human brain. However, individual variability of the FSRs has not been well quantified. Here we use functional magnetic resonance imaging (fMRI) to localize the FSRs and quantify their spatial and functional variabilities in 202 healthy adults. The occipital face area (OFA), posterior and anterior fusiform face areas (pFFA and aFFA), posterior continuation of the superior temporal sulcus (pcSTS), and posterior and anterior STS (pSTS and aSTS) were delineated for each individual with a semi-automated procedure. A probabilistic atlas was constructed to characterize their interindividual variability, revealing that the FSRs were highly variable in location and extent across subjects. The variability of FSRs was further quantified on both functional (i.e., face selectivity) and spatial (i.e., volume, location of peak activation, and anatomical location) features. Considerable interindividual variability and rightward asymmetry were found in all FSRs on these features. Taken together, our work presents the first effort to characterize comprehensively the variability of FSRs in a large sample of healthy subjects, and invites future work on the origin of the variability and its relation to individual differences in behavioral performance. Moreover, the probabilistic functional atlas will provide an adequate spatial reference for mapping the face network. Copyright © 2015 Elsevier Inc. All rights reserved.

Impact of auditory selective attention on verbal short-term memory and vocabulary development.

PubMed

Majerus, Steve; Heiligenstein, Lucie; Gautherot, Nathalie; Poncelet, Martine; Van der Linden, Martial

2009-05-01

This study investigated the role of auditory selective attention capacities as a possible mediator of the well-established association between verbal short-term memory (STM) and vocabulary development. A total of 47 6- and 7-year-olds were administered verbal immediate serial recall and auditory attention tasks. Both task types probed processing of item and serial order information because recent studies have shown this distinction to be critical when exploring relations between STM and lexical development. Multiple regression and variance partitioning analyses highlighted two variables as determinants of vocabulary development: (a) a serial order processing variable shared by STM order recall and a selective attention task for sequence information and (b) an attentional variable shared by selective attention measures targeting item or sequence information. The current study highlights the need for integrative STM models, accounting for conjoined influences of attentional capacities and serial order processing capacities on STM performance and the establishment of the lexical language network.

Non-destructive technique for determining the viability of soybean (Glycine max) seeds using FT-NIR spectroscopy.

PubMed

Kusumaningrum, Dewi; Lee, Hoonsoo; Lohumi, Santosh; Mo, Changyeun; Kim, Moon S; Cho, Byoung-Kwan

2018-03-01

The viability of seeds is important for determining their quality. A high-quality seed is one that has a high capability of germination that is necessary to ensure high productivity. Hence, developing technology for the detection of seed viability is a high priority in agriculture. Fourier transform near-infrared (FT-NIR) spectroscopy is one of the most popular devices among other vibrational spectroscopies. This study aims to use FT-NIR spectroscopy to determine the viability of soybean seeds. Viable and artificial ageing seeds as non-viable soybeans were used in this research. The FT-NIR spectra of soybean seeds were collected and analysed using a partial least-squares discriminant analysis (PLS-DA) to classify viable and non-viable soybean seeds. Moreover, the variable importance in projection (VIP) method for variable selection combined with the PLS-DA was employed. The most effective wavelengths were selected by the VIP method, which selected 146 optimal variables from the full set of 1557 variables. The results demonstrated that the FT-NIR spectral analysis with the PLS-DA method that uses all variables or the selected variables showed good performance based on the high value of prediction accuracy for soybean viability with an accuracy close to 100%. Hence, FT-NIR techniques with a chemometric analysis have the potential for rapidly measuring soybean seed viability. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

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

PubMed

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

2009-06-01

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

Artificial neural networks for the performance prediction of heat pump hot water heaters

NASA Astrophysics Data System (ADS)

Mathioulakis, E.; Panaras, G.; Belessiotis, V.

2018-02-01

The rapid progression in the use of heat pumps, due to the decrease in the equipment cost, together with the favourable economics of the consumed electrical energy, has been combined with the wide dissemination of air-to-water heat pumps (AWHPs) in the residential sector. The entrance of the respective systems in the commercial sector has made important the modelling of the processes. In this work, the suitability of artificial neural networks (ANN) in the modelling of AWHPs is investigated. The ambient air temperature in the evaporator inlet and the water temperature in the condenser inlet have been selected as the input variables; energy performance indices and quantities characterising the operation of the system have been selected as output variables. The results verify that the, easy-to-implement, trained ANN can represent an effective tool for the prediction of the AWHP performance in various operation conditions and the parametrical investigation of their behaviour.

Draft-camp predictors of subsequent career success in the Australian Football League.

PubMed

Burgess, Darren; Naughton, Geraldine; Hopkins, Will

2012-11-01

The National Draft Camp results are generally considered to be important for informing talent scouts about the physical performance capacities of talented young Australian Rules Football (AFL) players. The purpose of this project was to determine magnitude of associations between five year career success in the AFL and physical draft camp tests, final draft selection order and previous match physical performance. Physical testing data of 99 players from the National Under 18 (U 18) competition were retrospectively analysed across 2002 and 2003 National Draft Camps. Physical match data was collected on these players and links with subsequent early career success (AFL games played) were explored. TrakPerformance Software was used to quantify the movement of 92 players during competitive games of the National U 18 Championships. Linear modelling using results from draft camp data involving 95 U 18 players, along with final draft selection order, was used to predict five year career success in senior AFL. Multiple U 18 match variables demonstrated large associations (sprints/min=43% more games, % sprint=43% more games) with five year career success in AFL. Final draft order and single variable predictors had moderate associations with career success. Neither U 18 matches nor draft camp testing was predictive of injuries incurring over the five years. Variability in senior AFL career success had a large association with a combination of match physical variables and draft test results. The objective data available should be considered in the selection of prospective player success. Copyright © 2012 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.

Comparison of the sensitivity of the UKCAT and A Levels to sociodemographic characteristics: a national study

PubMed Central

2014-01-01

Background The UK Clinical Aptitude Test (UKCAT) was introduced to facilitate widening participation in medical and dental education in the UK by providing universities with a continuous variable to aid selection; one that might be less sensitive to the sociodemographic background of candidates compared to traditional measures of educational attainment. Initial research suggested that males, candidates from more advantaged socioeconomic backgrounds and those who attended independent or grammar schools performed better on the test. The introduction of the A* grade at A level permits more detailed analysis of the relationship between UKCAT scores, secondary educational attainment and sociodemographic variables. Thus, our aim was to further assess whether the UKCAT is likely to add incremental value over A level (predicted or actual) attainment in the selection process. Methods Data relating to UKCAT and A level performance from 8,180 candidates applying to medicine in 2009 who had complete information relating to six key sociodemographic variables were analysed. A series of regression analyses were conducted in order to evaluate the ability of sociodemographic status to predict performance on two outcome measures: A level ‘best of three’ tariff score; and the UKCAT scores. Results In this sample A level attainment was independently and positively predicted by four sociodemographic variables (independent/grammar schooling, White ethnicity, age and professional social class background). These variables also independently and positively predicted UKCAT scores. There was a suggestion that UKCAT scores were less sensitive to educational background compared to A level attainment. In contrast to A level attainment, UKCAT score was independently and positively predicted by having English as a first language and male sex. Conclusions Our findings are consistent with a previous report; most of the sociodemographic factors that predict A level attainment also predict UKCAT performance. However, compared to A levels, males and those speaking English as a first language perform better on UKCAT. Our findings suggest that UKCAT scores may be more influenced by sex and less sensitive to school type compared to A levels. These factors must be considered by institutions utilising the UKCAT as a component of the medical and dental school selection process. PMID:24400861

Exploring the Impact of Early Decisions in Variable Ordering for Constraint Satisfaction Problems.

PubMed

Ortiz-Bayliss, José Carlos; Amaya, Ivan; Conant-Pablos, Santiago Enrique; Terashima-Marín, Hugo

2018-01-01

When solving constraint satisfaction problems (CSPs), it is a common practice to rely on heuristics to decide which variable should be instantiated at each stage of the search. But, this ordering influences the search cost. Even so, and to the best of our knowledge, no earlier work has dealt with how first variable orderings affect the overall cost. In this paper, we explore the cost of finding high-quality orderings of variables within constraint satisfaction problems. We also study differences among the orderings produced by some commonly used heuristics and the way bad first decisions affect the search cost. One of the most important findings of this work confirms the paramount importance of first decisions. Another one is the evidence that many of the existing variable ordering heuristics fail to appropriately select the first variable to instantiate. Another one is the evidence that many of the existing variable ordering heuristics fail to appropriately select the first variable to instantiate. We propose a simple method to improve early decisions of heuristics. By using it, performance of heuristics increases.

Exploring the Impact of Early Decisions in Variable Ordering for Constraint Satisfaction Problems

PubMed Central

Amaya, Ivan

2018-01-01

When solving constraint satisfaction problems (CSPs), it is a common practice to rely on heuristics to decide which variable should be instantiated at each stage of the search. But, this ordering influences the search cost. Even so, and to the best of our knowledge, no earlier work has dealt with how first variable orderings affect the overall cost. In this paper, we explore the cost of finding high-quality orderings of variables within constraint satisfaction problems. We also study differences among the orderings produced by some commonly used heuristics and the way bad first decisions affect the search cost. One of the most important findings of this work confirms the paramount importance of first decisions. Another one is the evidence that many of the existing variable ordering heuristics fail to appropriately select the first variable to instantiate. Another one is the evidence that many of the existing variable ordering heuristics fail to appropriately select the first variable to instantiate. We propose a simple method to improve early decisions of heuristics. By using it, performance of heuristics increases. PMID:29681923

Integrating biological knowledge into variable selection: an empirical Bayes approach with an application in cancer biology

PubMed Central

2012-01-01

Background An important question in the analysis of biochemical data is that of identifying subsets of molecular variables that may jointly influence a biological response. Statistical variable selection methods have been widely used for this purpose. In many settings, it may be important to incorporate ancillary biological information concerning the variables of interest. Pathway and network maps are one example of a source of such information. However, although ancillary information is increasingly available, it is not always clear how it should be used nor how it should be weighted in relation to primary data. Results We put forward an approach in which biological knowledge is incorporated using informative prior distributions over variable subsets, with prior information selected and weighted in an automated, objective manner using an empirical Bayes formulation. We employ continuous, linear models with interaction terms and exploit biochemically-motivated sparsity constraints to permit exact inference. We show an example of priors for pathway- and network-based information and illustrate our proposed method on both synthetic response data and by an application to cancer drug response data. Comparisons are also made to alternative Bayesian and frequentist penalised-likelihood methods for incorporating network-based information. Conclusions The empirical Bayes method proposed here can aid prior elicitation for Bayesian variable selection studies and help to guard against mis-specification of priors. Empirical Bayes, together with the proposed pathway-based priors, results in an approach with a competitive variable selection performance. In addition, the overall procedure is fast, deterministic, and has very few user-set parameters, yet is capable of capturing interplay between molecular players. The approach presented is general and readily applicable in any setting with multiple sources of biological prior knowledge. PMID:22578440

Data re-arranging techniques leading to proper variable selections in high energy physics

NASA Astrophysics Data System (ADS)

Kůs, Václav; Bouř, Petr

2017-12-01

We introduce a new data based approach to homogeneity testing and variable selection carried out in high energy physics experiments, where one of the basic tasks is to test the homogeneity of weighted samples, mainly the Monte Carlo simulations (weighted) and real data measurements (unweighted). This technique is called ’data re-arranging’ and it enables variable selection performed by means of the classical statistical homogeneity tests such as Kolmogorov-Smirnov, Anderson-Darling, or Pearson’s chi-square divergence test. P-values of our variants of homogeneity tests are investigated and the empirical verification through 46 dimensional high energy particle physics data sets is accomplished under newly proposed (equiprobable) quantile binning. Particularly, the procedure of homogeneity testing is applied to re-arranged Monte Carlo samples and real DATA sets measured at the particle accelerator Tevatron in Fermilab at DØ experiment originating from top-antitop quark pair production in two decay channels (electron, muon) with 2, 3, or 4+ jets detected. Finally, the variable selections in the electron and muon channels induced by the re-arranging procedure for homogeneity testing are provided for Tevatron top-antitop quark data sets.

Both Handwriting Speed and Selective Attention Are Important to Lecture Note-Taking

ERIC Educational Resources Information Center

Peverly, Stephen T.; Garner, Joanna K.; Vekaria, Pooja C.

2014-01-01

The primary purpose of this investigation was to evaluate the relationship of handwriting speed, fine motor fluency, speed of verbal access, language comprehension, working memory, and attention (executive control; selective) to note-taking and all of the aforementioned variables to test performance (written recall). A second purpose was to…

Exploring uncertainty and model predictive performance concepts via a modular snowmelt-runoff modeling framework

Treesearch

Tyler Jon Smith; Lucy Amanda Marshall

2010-01-01

Model selection is an extremely important aspect of many hydrologic modeling studies because of the complexity, variability, and uncertainty that surrounds the current understanding of watershed-scale systems. However, development and implementation of a complete precipitation-runoff modeling framework, from model selection to calibration and uncertainty analysis, are...

Selecting an Informative/Discriminating Multivariate Response for Inverse Prediction

DOE PAGES

Thomas, Edward V.; Lewis, John R.; Anderson-Cook, Christine M.; ...

2017-11-21

nverse prediction is important in a wide variety of scientific and engineering contexts. One might use inverse prediction to predict fundamental properties/characteristics of an object using measurements obtained from it. This can be accomplished by “inverting” parameterized forward models that relate the measurements (responses) to the properties/characteristics of interest. Sometimes forward models are science based; but often, forward models are empirically based, using the results of experimentation. For empirically-based forward models, it is important that the experiments provide a sound basis to develop accurate forward models in terms of the properties/characteristics (factors). While nature dictates the causal relationship between factorsmore » and responses, experimenters can influence control of the type, accuracy, and precision of forward models that can be constructed via selection of factors, factor levels, and the set of trials that are performed. Whether the forward models are based on science, experiments or both, researchers can influence the ability to perform inverse prediction by selecting informative response variables. By using an errors-in-variables framework for inverse prediction, this paper shows via simple analysis and examples how the capability of a multivariate response (with respect to being informative and discriminating) can vary depending on how well the various responses complement one another over the range of the factor-space of interest. Insights derived from this analysis could be useful for selecting a set of response variables among candidates in cases where the number of response variables that can be acquired is limited by difficulty, expense, and/or availability of material.« less

Selecting an Informative/Discriminating Multivariate Response for Inverse Prediction

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

Thomas, Edward V.; Lewis, John R.; Anderson-Cook, Christine M.

nverse prediction is important in a wide variety of scientific and engineering contexts. One might use inverse prediction to predict fundamental properties/characteristics of an object using measurements obtained from it. This can be accomplished by “inverting” parameterized forward models that relate the measurements (responses) to the properties/characteristics of interest. Sometimes forward models are science based; but often, forward models are empirically based, using the results of experimentation. For empirically-based forward models, it is important that the experiments provide a sound basis to develop accurate forward models in terms of the properties/characteristics (factors). While nature dictates the causal relationship between factorsmore » and responses, experimenters can influence control of the type, accuracy, and precision of forward models that can be constructed via selection of factors, factor levels, and the set of trials that are performed. Whether the forward models are based on science, experiments or both, researchers can influence the ability to perform inverse prediction by selecting informative response variables. By using an errors-in-variables framework for inverse prediction, this paper shows via simple analysis and examples how the capability of a multivariate response (with respect to being informative and discriminating) can vary depending on how well the various responses complement one another over the range of the factor-space of interest. Insights derived from this analysis could be useful for selecting a set of response variables among candidates in cases where the number of response variables that can be acquired is limited by difficulty, expense, and/or availability of material.« less

Upper Limb Assessment in Tetraplegia: Clinical, Functional and Kinematic Correlations

ERIC Educational Resources Information Center

Cacho, Enio Walker Azevedo; de Oliveira, Roberta; Ortolan, Rodrigo L.; Varoto, Renato; Cliquet, Alberto

2011-01-01

The aim of this study was to correlate clinical and functional evaluations with kinematic variables of upper limp reach-to-grasp movement in patients with tetraplegia. Twenty chronic patients were selected to perform reach-to-grasp kinematic assessment using a target placed at a distance equal to the arm's length. Kinematic variables (hand peak…

Structural Modeling of Variables Related to Parental Support in Mexican Children's Perfomance on Reading and Writing

ERIC Educational Resources Information Center

Bazan-Ramirez, Aldo; Castellanos-Simons, Doris; Lopez-Valenzuela, Mercedes

2010-01-01

This paper aims at analysing the structural relationships among some latent and observed variables related to the assessment of written language performance in 139 fourth grade students of Elementary School selected from nine public schools of the northwest of Mexico. Questionnaires were also applied to the children's parents and teachers. The…

Application variables and their influence on forest herbicide efficacy and selectivity: gaining understanding and control

Treesearch

James H. Miller

1998-01-01

Available research is reviewed on the interactions of application variables, herbicides, and species. Objectives of this review are to gain insights into why variation occurs with herbicide performance, how current knowledge might be applied to enhance efficacy and consistency, and research pathways that should foster integration of application-efficacy models. A...

MHA admission criteria and program performance: do they predict career performance?

PubMed

Porter, J; Galfano, V J

1987-01-01

The purpose of this study was to determine to what extent admission criteria predict graduate school and career performance. The study also analyzed which objective and subjective criteria served as the best predictors. MHA graduates of the University of Minnesota from 1974 to 1977 were surveyed to assess career performance. Student files served as the data base on admission criteria and program performance. Career performance was measured by four variables: total compensation, satisfaction, fiscal responsibility, and level of authority. High levels of MHA program performance were associated with women who had high undergraduate GPAs from highly selective undergraduate colleges, were undergraduate business majors, and participated in extracurricular activities. High levels of compensation were associated with relatively low undergraduate GPAs, high levels of participation in undergraduate extracurricular activities, and being single at admission to graduate school. Admission to MHA programs should be based upon both objective and subjective criteria. Emphasis should be placed upon the selection process for MHA students since admission criteria are shown to explain 30 percent of the variability in graduate program performance, and as much as 65 percent of the variance in level of position authority.

Using exogenous variables in testing for monotonic trends in hydrologic time series

USGS Publications Warehouse

Alley, William M.

1988-01-01

One approach that has been used in performing a nonparametric test for monotonic trend in a hydrologic time series consists of a two-stage analysis. First, a regression equation is estimated for the variable being tested as a function of an exogenous variable. A nonparametric trend test such as the Kendall test is then performed on the residuals from the equation. By analogy to stagewise regression and through Monte Carlo experiments, it is demonstrated that this approach will tend to underestimate the magnitude of the trend and to result in some loss in power as a result of ignoring the interaction between the exogenous variable and time. An alternative approach, referred to as the adjusted variable Kendall test, is demonstrated to generally have increased statistical power and to provide more reliable estimates of the trend slope. In addition, the utility of including an exogenous variable in a trend test is examined under selected conditions.

Development of a neural-based forecasting tool to classify recreational water quality using fecal indicator organisms.

PubMed

Motamarri, Srinivas; Boccelli, Dominic L

2012-09-15

Users of recreational waters may be exposed to elevated pathogen levels through various point/non-point sources. Typical daily notifications rely on microbial analysis of indicator organisms (e.g., Escherichia coli) that require 18, or more, hours to provide an adequate response. Modeling approaches, such as multivariate linear regression (MLR) and artificial neural networks (ANN), have been utilized to provide quick predictions of microbial concentrations for classification purposes, but generally suffer from high false negative rates. This study introduces the use of learning vector quantization (LVQ)--a direct classification approach--for comparison with MLR and ANN approaches and integrates input selection for model development with respect to primary and secondary water quality standards within the Charles River Basin (Massachusetts, USA) using meteorologic, hydrologic, and microbial explanatory variables. Integrating input selection into model development showed that discharge variables were the most important explanatory variables while antecedent rainfall and time since previous events were also important. With respect to classification, all three models adequately represented the non-violated samples (>90%). The MLR approach had the highest false negative rates associated with classifying violated samples (41-62% vs 13-43% (ANN) and <16% (LVQ)) when using five or more explanatory variables. The ANN performance was more similar to LVQ when a larger number of explanatory variables were utilized, but the ANN performance degraded toward MLR performance as explanatory variables were removed. Overall, the use of LVQ as a direct classifier provided the best overall classification ability with respect to violated/non-violated samples for both standards. Copyright © 2012 Elsevier Ltd. All rights reserved.

Fine-scale habitat modeling of a top marine predator: do prey data improve predictive capacity?

PubMed

Torres, Leigh G; Read, Andrew J; Halpin, Patrick

2008-10-01

Predators and prey assort themselves relative to each other, the availability of resources and refuges, and the temporal and spatial scale of their interaction. Predictive models of predator distributions often rely on these relationships by incorporating data on environmental variability and prey availability to determine predator habitat selection patterns. This approach to predictive modeling holds true in marine systems where observations of predators are logistically difficult, emphasizing the need for accurate models. In this paper, we ask whether including prey distribution data in fine-scale predictive models of bottlenose dolphin (Tursiops truncatus) habitat selection in Florida Bay, Florida, U.S.A., improves predictive capacity. Environmental characteristics are often used as predictor variables in habitat models of top marine predators with the assumption that they act as proxies of prey distribution. We examine the validity of this assumption by comparing the response of dolphin distribution and fish catch rates to the same environmental variables. Next, the predictive capacities of four models, with and without prey distribution data, are tested to determine whether dolphin habitat selection can be predicted without recourse to describing the distribution of their prey. The final analysis determines the accuracy of predictive maps of dolphin distribution produced by modeling areas of high fish catch based on significant environmental characteristics. We use spatial analysis and independent data sets to train and test the models. Our results indicate that, due to high habitat heterogeneity and the spatial variability of prey patches, fine-scale models of dolphin habitat selection in coastal habitats will be more successful if environmental variables are used as predictor variables of predator distributions rather than relying on prey data as explanatory variables. However, predictive modeling of prey distribution as the response variable based on environmental variability did produce high predictive performance of dolphin habitat selection, particularly foraging habitat.

[Research on fast detecting tomato seedlings nitrogen content based on NIR characteristic spectrum selection].

PubMed

Wu, Jing-zhu; Wang, Feng-zhu; Wang, Li-li; Zhang, Xiao-chao; Mao, Wen-hua

2015-01-01

In order to improve the accuracy and robustness of detecting tomato seedlings nitrogen content based on near-infrared spectroscopy (NIR), 4 kinds of characteristic spectrum selecting methods were studied in the present paper, i. e. competitive adaptive reweighted sampling (CARS), Monte Carlo uninformative variables elimination (MCUVE), backward interval partial least squares (BiPLS) and synergy interval partial least squares (SiPLS). There were totally 60 tomato seedlings cultivated at 10 different nitrogen-treatment levels (urea concentration from 0 to 120 mg . L-1), with 6 samples at each nitrogen-treatment level. They are in different degrees of over nitrogen, moderate nitrogen, lack of nitrogen and no nitrogen status. Each sample leaves were collected to scan near-infrared spectroscopy from 12 500 to 3 600 cm-1. The quantitative models based on the above 4 methods were established. According to the experimental result, the calibration model based on CARS and MCUVE selecting methods show better performance than those based on BiPLS and SiPLS selecting methods, but their prediction ability is much lower than that of the latter. Among them, the model built by BiPLS has the best prediction performance. The correlation coefficient (r), root mean square error of prediction (RMSEP) and ratio of performance to standard derivate (RPD) is 0. 952 7, 0. 118 3 and 3. 291, respectively. Therefore, NIR technology combined with characteristic spectrum selecting methods can improve the model performance. But the characteristic spectrum selecting methods are not universal. For the built model based or single wavelength variables selection is more sensitive, it is more suitable for the uniform object. While the anti-interference ability of the model built based on wavelength interval selection is much stronger, it is more suitable for the uneven and poor reproducibility object. Therefore, the characteristic spectrum selection will only play a better role in building model, combined with the consideration of sample state and the model indexes.

Selective attention deficits in obsessive-compulsive disorder: the role of metacognitive processes.

PubMed

Koch, Julia; Exner, Cornelia

2015-02-28

While initial studies supported the hypothesis that cognitive characteristics that capture cognitive resources act as underlying mechanisms in memory deficits in obsessive-compulsive disorder (OCD), the influence of those characteristics on selective attention has not been studied, yet. In this study, we examined the influence of cognitive self-consciousness (CSC), rumination and worrying on performance in selective attention in OCD and compared the results to a depressive and a healthy control group. We found that 36 OCD and 36 depressive participants were impaired in selective attention in comparison to 36 healthy controls. In all groups, hierarchical regression analyses demonstrated that age, intelligence and years in school significantly predicted performance in selective attention. But only in OCD, the predictive power of the regression model was improved when CSC, rumination and worrying were implemented as predictor variables. In contrast, in none of the three groups the predictive power improved when indicators of severity of obsessive-compulsive (OC) and depressive symptoms and trait anxiety were introduced as predictor variables. Thus, our results support the assumption that mental characteristics that bind cognitive resources play an important role in the understanding of selective attention deficits in OCD and that this mechanism is especially relevant for OCD. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Socioeconomic, emotional, and physical execution variables as predictors of cognitive performance in a Spanish sample of middle-aged and older community-dwelling participants.

PubMed

González, Mari Feli; Facal, David; Juncos-Rabadán, Onésimo; Yanguas, Javier

2017-10-01

Cognitive performance is not easily predicted, since different variables play an important role in the manifestation of age-related declines. The objective of this study is to analyze the predictors of cognitive performance in a Spanish sample over 50 years from a multidimensional perspective, including socioeconomic, affective, and physical variables. Some of them are well-known predictors of cognition and others are emergent variables in the study of cognition. The total sample, drawn from the "Longitudinal Study Aging in Spain (ELES)" project, consisted of 832 individuals without signs of cognitive impairment. Cognitive function was measured with tests evaluating episodic and working memory, visuomotor speed, fluency, and naming. Thirteen independent variables were selected as predictors belonging to socioeconomic, emotional, and physical execution areas. Multiple linear regressions, following the enter method, were calculated for each age group in order to study the influence of these variables in cognitive performance. Education is the variable which best predicts cognitive performance in the 50-59, 60-69, and 70-79 years old groups. In the 80+ group, the best predictor is objective economic status and education does not enter in the model. Age-related decline can be modified by the influence of educational and socioeconomic variables. In this context, it is relevant to take into account how easy is to modify certain variables, compared to others which depend on each person's life course.

Reconfigurable pipelined processor

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

Saccardi, R.J.

1989-09-19

This patent describes a reconfigurable pipelined processor for processing data. It comprises: a plurality of memory devices for storing bits of data; a plurality of arithmetic units for performing arithmetic functions with the data; cross bar means for connecting the memory devices with the arithmetic units for transferring data therebetween; at least one counter connected with the cross bar means for providing a source of addresses to the memory devices; at least one variable tick delay device connected with each of the memory devices and arithmetic units; and means for providing control bits to the variable tick delay device formore » variably controlling the input and output operations thereof to selectively delay the memory devices and arithmetic units to align the data for processing in a selected sequence.« less

Model building strategy for logistic regression: purposeful selection.

PubMed

Zhang, Zhongheng

2016-03-01

Logistic regression is one of the most commonly used models to account for confounders in medical literature. The article introduces how to perform purposeful selection model building strategy with R. I stress on the use of likelihood ratio test to see whether deleting a variable will have significant impact on model fit. A deleted variable should also be checked for whether it is an important adjustment of remaining covariates. Interaction should be checked to disentangle complex relationship between covariates and their synergistic effect on response variable. Model should be checked for the goodness-of-fit (GOF). In other words, how the fitted model reflects the real data. Hosmer-Lemeshow GOF test is the most widely used for logistic regression model.

Correlations among Jamaican 12th-Graders' Five Variables and Performance in Genetics

ERIC Educational Resources Information Center

Bloomfield, Deen-Paul; Soyibo, Kola

2008-01-01

This study was aimed at finding out if the level of performance of selected Jamaican Grade 12 students on an achievement test on the concept of genetics was satisfactory; if there were statistically significant differences in their performance on the concept linked to their gender, self-esteem, cognitive abilities in biology, school-type and…

The role of multidimensional attentional abilities in academic skills of children with ADHD.

PubMed

Preston, Andrew S; Heaton, Shelley C; McCann, Sarah J; Watson, William D; Selke, Gregg

2009-01-01

Despite reports of academic difficulties in children with attention-deficit/hyperactivity disorder (ADHD), little is known about the relationship between performance on tests of academic achievement and measures of attention. The current study assessed intellectual ability, parent-reported inattention, academic achievement, and attention in 45 children (ages 7-15) diagnosed with ADHD. Hierarchical regressions were performed with selective, sustained, and attentional control/switching domains of the Test of Everyday Attention for Children as predictor variables and with performance on the Wechsler Individual Achievement Test-Second Edition as dependent variables. It was hypothesized that sustained attention and attentional control/switching would predict performance on achievement tests. Results demonstrate that attentional control/ switching accounted for a significant amount of variance in all academic areas (reading, math, and spelling), even after accounting for verbal IQ and parent-reported inattention. Sustained attention predicted variance only in math, whereas selective attention did not account for variance in any achievement domain. Therefore, attentional control/switching, which involves components of executive functions, plays an important role in academic performance.

Enhancement of hepatitis virus immunoassay outcome predictions in imbalanced routine pathology data by data balancing and feature selection before the application of support vector machines.

PubMed

Richardson, Alice M; Lidbury, Brett A

2017-08-14

Data mining techniques such as support vector machines (SVMs) have been successfully used to predict outcomes for complex problems, including for human health. Much health data is imbalanced, with many more controls than positive cases. The impact of three balancing methods and one feature selection method is explored, to assess the ability of SVMs to classify imbalanced diagnostic pathology data associated with the laboratory diagnosis of hepatitis B (HBV) and hepatitis C (HCV) infections. Random forests (RFs) for predictor variable selection, and data reshaping to overcome a large imbalance of negative to positive test results in relation to HBV and HCV immunoassay results, are examined. The methodology is illustrated using data from ACT Pathology (Canberra, Australia), consisting of laboratory test records from 18,625 individuals who underwent hepatitis virus testing over the decade from 1997 to 2007. Overall, the prediction of HCV test results by immunoassay was more accurate than for HBV immunoassay results associated with identical routine pathology predictor variable data. HBV and HCV negative results were vastly in excess of positive results, so three approaches to handling the negative/positive data imbalance were compared. Generating datasets by the Synthetic Minority Oversampling Technique (SMOTE) resulted in significantly more accurate prediction than single downsizing or multiple downsizing (MDS) of the dataset. For downsized data sets, applying a RF for predictor variable selection had a small effect on the performance, which varied depending on the virus. For SMOTE, a RF had a negative effect on performance. An analysis of variance of the performance across settings supports these findings. Finally, age and assay results for alanine aminotransferase (ALT), sodium for HBV and urea for HCV were found to have a significant impact upon laboratory diagnosis of HBV or HCV infection using an optimised SVM model. Laboratories looking to include machine learning via SVM as part of their decision support need to be aware that the balancing method, predictor variable selection and the virus type interact to affect the laboratory diagnosis of hepatitis virus infection with routine pathology laboratory variables in different ways depending on which combination is being studied. This awareness should lead to careful use of existing machine learning methods, thus improving the quality of laboratory diagnosis.

Individualised training to address variability of radiologists' performance

NASA Astrophysics Data System (ADS)

Sun, Shanghua; Taylor, Paul; Wilkinson, Louise; Khoo, Lisanne

2008-03-01

Computer-based tools are increasingly used for training and the continuing professional development of radiologists. We propose an adaptive training system to support individualised learning in mammography, based on a set of real cases, which are annotated with educational content by experienced breast radiologists. The system has knowledge of the strengths and weakness of each radiologist's performance: each radiologist is assessed to compute a profile showing how they perform on different sets of cases, classified by type of abnormality, breast density, and perceptual difficulty. We also assess variability in cognitive aspects of image perception, classifying errors made by radiologists as errors of search, recognition or decision. This is a novel element in our approach. The profile is used to select cases to present to the radiologist. The intelligent and flexible presentation of these cases distinguishes our system from existing training tools. The training cases are organised and indexed by an ontology we have developed for breast radiologist training, which is consistent with the radiologists' profile. Hence, the training system is able to select appropriate cases to compose an individualised training path, addressing the variability of the radiologists' performance. A substantial part of the system, the ontology has been evaluated on a large number of cases, and the training system is under implementation for further evaluation.

Variable Behavior and Repeated Learning in Two Mouse Strains: Developmental and Genetic Contributions.

PubMed

Arnold, Megan A; Newland, M Christopher

2018-06-16

Behavioral inflexibility is often assessed using reversal learning tasks, which require a relatively low degree of response variability. No studies have assessed sensitivity to reinforcement contingencies that specifically select highly variable response patterns in mice, let alone in models of neurodevelopmental disorders involving limited response variation. Operant variability and incremental repeated acquisition (IRA) were used to assess unique aspects of behavioral variability of two mouse strains: BALB/c, a model of some deficits in ASD, and C57Bl/6. On the operant variability task, BALB/c mice responded more repetitively during adolescence than C57Bl/6 mice when reinforcement did not require variability but responded more variably when reinforcement required variability. During IRA testing in adulthood, both strains acquired an unchanging, performance sequence equally well. Strain differences emerged, however, after novel learning sequences began alternating with the performance sequence: BALB/c mice substantially outperformed C57Bl/6 mice. Using litter-mate controls, it was found that adolescent experience with variability did not affect either learning or performance on the IRA task in adulthood. These findings constrain the use of BALB/c mice as a model of ASD, but once again reveal this strain is highly sensitive to reinforcement contingencies and they are fast and robust learners. Copyright © 2018. Published by Elsevier B.V.

Optimized tuner selection for engine performance estimation

NASA Technical Reports Server (NTRS)

Simon, Donald L. (Inventor); Garg, Sanjay (Inventor)

2013-01-01

A methodology for minimizing the error in on-line Kalman filter-based aircraft engine performance estimation applications is presented. This technique specifically addresses the underdetermined estimation problem, where there are more unknown parameters than available sensor measurements. A systematic approach is applied to produce a model tuning parameter vector of appropriate dimension to enable estimation by a Kalman filter, while minimizing the estimation error in the parameters of interest. Tuning parameter selection is performed using a multi-variable iterative search routine which seeks to minimize the theoretical mean-squared estimation error. Theoretical Kalman filter estimation error bias and variance values are derived at steady-state operating conditions, and the tuner selection routine is applied to minimize these values. The new methodology yields an improvement in on-line engine performance estimation accuracy.

Correlation study of resistance components in the selection of Capsicum genotypes resistant to the fungus Colletotrichum gloeosporioides.

PubMed

Maracahipes, A C; Correa, J W S; Teodoro, P E; Araújo, K L; Barelli, M A A; Neves, L G

2017-08-17

Anthracnose is among the major diseases of the Capsicum culture. It is caused by different species of the genus Colletotrichum, which may result in major damages to the cultivation of this genus. Studies aiming to search for cultivars resistant to diseases are essential to reduce financial and agricultural losses. The objective of this study was to evaluate the correlation between the variables analyzed to select Capsicum genotypes resistant to the fungus Colletotrichum gloeosporioides. The experimental design was completely randomized blocks with three replications, 88 treatments, four ripe fruits, and four unripe fruits per replication. Accessions of Capsicum from the Germplasm Active Bank of Universidade do Estado de Mato Grosso (UNEMAT) were evaluated as for resistance to the fungus. Fruits were collected from each plot and taken to the laboratory for disinfestation. A lesion was performed in the middle region of the fruit using a sterile needle, where a spore suspension drop, adjusted to 10 6 spores/mL, was deposited. An ultrapure water drop was deposited into control fruits. The fruits were placed in humid chambers, and the evaluation was performed by measuring the diameter and the length of lesions using a caliper for 11 days. After data were obtained, analyses of variance, correlation, and path analysis were performed using the GENES software and R. According to the likelihood-ratio test, the effects of genotypes (G), fruit stage (F), and its interaction (G x F) were significant (P < 0.05). There were differences between the magnitudes of genotype correlations according to fruit stage. Different variables must be taken into account for an indirect selection in this culture in function of fruit stage since the variable AUDPC is an important criterion for selecting resistant accessions. We found through the path analysis that the variables DULRD and DULRL exerted the greatest effects on AUDPC.

The Relationship of Teacher Evaluation Scores Generated by a Process-Product Evaluation Instrument to Selected Variables.

ERIC Educational Resources Information Center

Tadlock, James; Nesbit, Lamar

The Jackson Municipal Separate School District, Mississippi, has instituted a mixed-criteria reduction-in-force procedure emphasizing classroom performance to a greater degree than seniority, certification, and staff development participation. The district evaluation process--measuring classroom teaching performance--generated data for the present…

Physique and motor performance characteristics of US national rugby players.

PubMed

Carlson, B R; Carter, J E; Patterson, P; Petti, K; Orfanos, S M; Noffal, G J

1994-08-01

Anthropometric and performance data were collected on 65 US rugby players (mean age = 26.3 years) to make comparison on these characteristics by player position and performance level. Anthropometry included stature, body mass, nine skinfolds, two girths and two bone breadths. Skinfold patterns, estimated percent fat and Heath-Carter somatotypes were calculated from anthropometry. Motor performance measures included standing vertical jump, 40 yard dash, 110 yard dash, shuttle run, repeated jump in place, push-up, sit-up and squat thrust. Descriptive statistics were used for the total sample as well as selected sub-groups. Discriminant function analyses were employed to determine which combination of variables best discriminated between position and level of performance for the anthropometric and performance data. The results indicated that forwards were taller, heavier and had more subcutaneous adiposity than backs. Additionally, forwards and backs differed in somatotypes, with forwards being more endo-mesomorphic than backs and with a greater scatter about their mean. The anthropometric variables that best discriminated between backs and forwards were body mass, femur breadth and arm girth, with 88% correctly classified using these variables. The motor performance variables that best discriminated between backs and forwards were repeated jump in place, push-up and standing vertical jump, with 76% correct classification using these variables. Classification into three playing levels was unsatisfactory using either anthropometric or motor performance variables. These data can be used to assess present status and change in players, or potential national players, by position to locate strengths and weaknesses.

Evaluation of aircraft crash hazard at Los Alamos National Laboratory facilities

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

Selvage, R.D.

This report selects a method for use in calculating the frequency of an aircraft crash occurring at selected facilities at the Los Alamos National Laboratory (the Laboratory). The Solomon method was chosen to determine these probabilities. Each variable in the Solomon method is defined and a value for each variable is selected for fourteen facilities at the Laboratory. These values and calculated probabilities are to be used in all safety analysis reports and hazards analyses for the facilities addressed in this report. This report also gives detailed directions to perform aircraft-crash frequency calculations for other facilities. This will ensure thatmore » future aircraft-crash frequency calculations are consistent with calculations in this report.« less

Determinants of project success

NASA Technical Reports Server (NTRS)

Murphy, D. C.; Baker, B. N.; Fisher, D.

1974-01-01

The interactions of numerous project characteristics, with particular reference to project performance, were studied. Determinants of success are identified along with the accompanying implications for client organization, parent organization, project organization, and future research. Variables are selected which are found to have the greatest impact on project outcome, and the methodology and analytic techniques to be employed in identification of those variables are discussed.

Concave 1-norm group selection

PubMed Central

Jiang, Dingfeng; Huang, Jian

2015-01-01

Grouping structures arise naturally in many high-dimensional problems. Incorporation of such information can improve model fitting and variable selection. Existing group selection methods, such as the group Lasso, require correct membership. However, in practice it can be difficult to correctly specify group membership of all variables. Thus, it is important to develop group selection methods that are robust against group mis-specification. Also, it is desirable to select groups as well as individual variables in many applications. We propose a class of concave \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{upgreek} \\usepackage{mathrsfs} \\setlength{\\oddsidemargin}{-69pt} \\begin{document} }{}$1$\\end{document}-norm group penalties that is robust to grouping structure and can perform bi-level selection. A coordinate descent algorithm is developed to calculate solutions of the proposed group selection method. Theoretical convergence of the algorithm is proved under certain regularity conditions. Comparison with other methods suggests the proposed method is the most robust approach under membership mis-specification. Simulation studies and real data application indicate that the \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{upgreek} \\usepackage{mathrsfs} \\setlength{\\oddsidemargin}{-69pt} \\begin{document} }{}$1$\\end{document}-norm concave group selection approach achieves better control of false discovery rates. An R package grppenalty implementing the proposed method is available at CRAN. PMID:25417206

Non-targeted 1H NMR fingerprinting and multivariate statistical analyses for the characterisation of the geographical origin of Italian sweet cherries.

PubMed

Longobardi, F; Ventrella, A; Bianco, A; Catucci, L; Cafagna, I; Gallo, V; Mastrorilli, P; Agostiano, A

2013-12-01

In this study, non-targeted (1)H NMR fingerprinting was used in combination with multivariate statistical techniques for the classification of Italian sweet cherries based on their different geographical origins (Emilia Romagna and Puglia). As classification techniques, Soft Independent Modelling of Class Analogy (SIMCA), Partial Least Squares Discriminant Analysis (PLS-DA), and Linear Discriminant Analysis (LDA) were carried out and the results were compared. For LDA, before performing a refined selection of the number/combination of variables, two different strategies for a preliminary reduction of the variable number were tested. The best average recognition and CV prediction abilities (both 100.0%) were obtained for all the LDA models, although PLS-DA also showed remarkable performances (94.6%). All the statistical models were validated by observing the prediction abilities with respect to an external set of cherry samples. The best result (94.9%) was obtained with LDA by performing a best subset selection procedure on a set of 30 principal components previously selected by a stepwise decorrelation. The metabolites that mostly contributed to the classification performances of such LDA model, were found to be malate, glucose, fructose, glutamine and succinate. Copyright © 2013 Elsevier Ltd. All rights reserved.

Modulation Depth Estimation and Variable Selection in State-Space Models for Neural Interfaces

PubMed Central

Hochberg, Leigh R.; Donoghue, John P.; Brown, Emery N.

2015-01-01

Rapid developments in neural interface technology are making it possible to record increasingly large signal sets of neural activity. Various factors such as asymmetrical information distribution and across-channel redundancy may, however, limit the benefit of high-dimensional signal sets, and the increased computational complexity may not yield corresponding improvement in system performance. High-dimensional system models may also lead to overfitting and lack of generalizability. To address these issues, we present a generalized modulation depth measure using the state-space framework that quantifies the tuning of a neural signal channel to relevant behavioral covariates. For a dynamical system, we develop computationally efficient procedures for estimating modulation depth from multivariate data. We show that this measure can be used to rank neural signals and select an optimal channel subset for inclusion in the neural decoding algorithm. We present a scheme for choosing the optimal subset based on model order selection criteria. We apply this method to neuronal ensemble spike-rate decoding in neural interfaces, using our framework to relate motor cortical activity with intended movement kinematics. With offline analysis of intracortical motor imagery data obtained from individuals with tetraplegia using the BrainGate neural interface, we demonstrate that our variable selection scheme is useful for identifying and ranking the most information-rich neural signals. We demonstrate that our approach offers several orders of magnitude lower complexity but virtually identical decoding performance compared to greedy search and other selection schemes. Our statistical analysis shows that the modulation depth of human motor cortical single-unit signals is well characterized by the generalized Pareto distribution. Our variable selection scheme has wide applicability in problems involving multisensor signal modeling and estimation in biomedical engineering systems. PMID:25265627

Evolution of catalytic RNA in the laboratory

NASA Technical Reports Server (NTRS)

Joyce, Gerald F.

1992-01-01

We are interested in the biochemistry of existing RNA enzymes and in the development of RNA enzymes with novel catalytic function. The focal point of our research program has been the design and operation of a laboratory system for the controlled evolution of catalytic RNA. This system serves as working model of RNA-based life and can be used to explore the catalytic potential of RNA. Evolution requires the integration of three chemical processes: amplification, mutation, and selection. Amplification results in additional copies of the genetic material. Mutation operates at the level of genotype to introduce variability, this variability in turn being expressed as a range of phenotypes. Selection operates at the level of phenotype to reduce variability by excluding those individuals that do not conform to the prevailing fitness criteria. These three processes must be linked so that only the selected individuals are amplified, subject to mutational error, to produce a progeny distribution of mutant individuals. We devised techniques for the amplification, mutation, and selection of catalytic RNA, all of which can be performed rapidly in vitro within a single reaction vessel. We integrated these techniques in such a way that they can be performed iteratively and routinely. This allowed us to conduct evolution experiments in response to artificially-imposed selection constraints. Our objective was to develop novel RNA enzymes by altering the selection constraints in a controlled manner. In this way we were able to expand the catalytic repertoire of RNA. Our long-range objective is to develop an RNA enzyme with RNA replicase activity. If such an enzyme had the ability to produce additional copies of itself, then RNA evolution would operate autonomously and the origin of life will have been realized in the laboratory.

Identification of Coffee Varieties Using Laser-Induced Breakdown Spectroscopy and Chemometrics.

PubMed

Zhang, Chu; Shen, Tingting; Liu, Fei; He, Yong

2017-12-31

We linked coffee quality to its different varieties. This is of interest because the identification of coffee varieties should help coffee trading and consumption. Laser-induced breakdown spectroscopy (LIBS) combined with chemometric methods was used to identify coffee varieties. Wavelet transform (WT) was used to reduce LIBS spectra noise. Partial least squares-discriminant analysis (PLS-DA), radial basis function neural network (RBFNN), and support vector machine (SVM) were used to build classification models. Loadings of principal component analysis (PCA) were used to select the spectral variables contributing most to the identification of coffee varieties. Twenty wavelength variables corresponding to C I, Mg I, Mg II, Al II, CN, H, Ca II, Fe I, K I, Na I, N I, and O I were selected. PLS-DA, RBFNN, and SVM models on selected wavelength variables showed acceptable results. SVM and RBFNN models performed better with a classification accuracy of over 80% in the prediction set, for both full spectra and the selected variables. The overall results indicated that it was feasible to use LIBS and chemometric methods to identify coffee varieties. For further studies, more samples are needed to produce robust classification models, research should be conducted on which methods to use to select spectral peaks that correspond to the elements contributing most to identification, and the methods for acquiring stable spectra should also be studied.

Identification of Coffee Varieties Using Laser-Induced Breakdown Spectroscopy and Chemometrics

PubMed Central

Zhang, Chu; Shen, Tingting

2017-01-01

We linked coffee quality to its different varieties. This is of interest because the identification of coffee varieties should help coffee trading and consumption. Laser-induced breakdown spectroscopy (LIBS) combined with chemometric methods was used to identify coffee varieties. Wavelet transform (WT) was used to reduce LIBS spectra noise. Partial least squares-discriminant analysis (PLS-DA), radial basis function neural network (RBFNN), and support vector machine (SVM) were used to build classification models. Loadings of principal component analysis (PCA) were used to select the spectral variables contributing most to the identification of coffee varieties. Twenty wavelength variables corresponding to C I, Mg I, Mg II, Al II, CN, H, Ca II, Fe I, K I, Na I, N I, and O I were selected. PLS-DA, RBFNN, and SVM models on selected wavelength variables showed acceptable results. SVM and RBFNN models performed better with a classification accuracy of over 80% in the prediction set, for both full spectra and the selected variables. The overall results indicated that it was feasible to use LIBS and chemometric methods to identify coffee varieties. For further studies, more samples are needed to produce robust classification models, research should be conducted on which methods to use to select spectral peaks that correspond to the elements contributing most to identification, and the methods for acquiring stable spectra should also be studied. PMID:29301228

Hasse diagram as a green analytical metrics tool: ranking of methods for benzo[a]pyrene determination in sediments.

PubMed

Bigus, Paulina; Tsakovski, Stefan; Simeonov, Vasil; Namieśnik, Jacek; Tobiszewski, Marek

2016-05-01

This study presents an application of the Hasse diagram technique (HDT) as the assessment tool to select the most appropriate analytical procedures according to their greenness or the best analytical performance. The dataset consists of analytical procedures for benzo[a]pyrene determination in sediment samples, which were described by 11 variables concerning their greenness and analytical performance. Two analyses with the HDT were performed-the first one with metrological variables and the second one with "green" variables as input data. Both HDT analyses ranked different analytical procedures as the most valuable, suggesting that green analytical chemistry is not in accordance with metrology when benzo[a]pyrene in sediment samples is determined. The HDT can be used as a good decision support tool to choose the proper analytical procedure concerning green analytical chemistry principles and analytical performance merits.

Gas engine heat pump cycle analysis. Volume 1: Model description and generic analysis

NASA Astrophysics Data System (ADS)

Fischer, R. D.

1986-10-01

The task has prepared performance and cost information to assist in evaluating the selection of high voltage alternating current components, values for component design variables, and system configurations and operating strategy. A steady-state computer model for performance simulation of engine-driven and electrically driven heat pumps was prepared and effectively used for parametric and seasonal performance analyses. Parametric analysis showed the effect of variables associated with design of recuperators, brine coils, domestic hot water heat exchanger, compressor size, engine efficiency, insulation on exhaust and brine piping. Seasonal performance data were prepared for residential and commercial units in six cities with system configurations closely related to existing or contemplated hardware of the five GRI engine contractors. Similar data were prepared for an advanced variable-speed electric unit for comparison purposes. The effect of domestic hot water production on operating costs was determined. Four fan-operating strategies and two brine loop configurations were explored.

A decision tool for selecting trench cap designs

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

Paige, G.B.; Stone, J.J.; Lane, L.J.

1995-12-31

A computer based prototype decision support system (PDSS) is being developed to assist the risk manager in selecting an appropriate trench cap design for waste disposal sites. The selection of the {open_quote}best{close_quote} design among feasible alternatives requires consideration of multiple and often conflicting objectives. The methodology used in the selection process consists of: selecting and parameterizing decision variables using data, simulation models, or expert opinion; selecting feasible trench cap design alternatives; ordering the decision variables and ranking the design alternatives. The decision model is based on multi-objective decision theory and uses a unique approach to order the decision variables andmore » rank the design alternatives. Trench cap designs are evaluated based on federal regulations, hydrologic performance, cover stability and cost. Four trench cap designs, which were monitored for a four year period at Hill Air Force Base in Utah, are used to demonstrate the application of the PDSS and evaluate the results of the decision model. The results of the PDSS, using both data and simulations, illustrate the relative advantages of each of the cap designs and which cap is the {open_quotes}best{close_quotes} alternative for a given set of criteria and a particular importance order of those decision criteria.« less

Characterization and evaluation of an aeolian-photovoltaic system in operation

NASA Astrophysics Data System (ADS)

Bonfatti, F.; Calzolari, P. U.; Cardinali, G. C.; Vivanti, G.; Zani, A.

Data management, analysis techniques and results of performance monitoring of a prototype combined photovoltaic (PV)-wind turbine farm power plant in northern Italy are reported. Emphasis is placed on the PV I-V characteristics and irradiance and cell temperatures. Automated instrumentation monitors and records meteorological data and generator variables such as voltages, currents, output, battery electrolyte temperature, etc. Analysis proceeds by automated selection of I-V data for specific intervals of the year when other variables can be treated as constants. The technique permits characterization of generator performance, adjusting the power plant set points for optimal output, and tracking performance degradation over time.

The influence of construction measurement and structure storey on seismic performance of masonry structure

NASA Astrophysics Data System (ADS)

Sun, Baitao; Zhao, Hexian; Yan, Peilei

2017-08-01

The damage of masonry structures in earthquakes is generally more severe than other structures. Through the analysis of two typical earthquake damage buildings in the Wenchuan earthquake in Xuankou middle school, we found that the number of storeys and the construction measures had great influence on the seismic performance of masonry structures. This paper takes a teachers’ dormitory in Xuankou middle school as an example, selected the structure arrangement and storey number as two independent variables to design working conditions. Finally we researched on the seismic performance difference of masonry structure under two variables by finite element analysis method.

Advanced supersonic propulsion study, phases 3 and 4. [variable cycle engines

NASA Technical Reports Server (NTRS)

Allan, R. D.; Joy, W.

1977-01-01

An evaluation of various advanced propulsion concepts for supersonic cruise aircraft resulted in the identification of the double-bypass variable cycle engine as the most promising concept. This engine design utilizes special variable geometry components and an annular exhaust nozzle to provide high take-off thrust and low jet noise. The engine also provides good performance at both supersonic cruise and subsonic cruise. Emission characteristics are excellent. The advanced technology double-bypass variable cycle engine offers an improvement in aircraft range performance relative to earlier supersonic jet engine designs and yet at a lower level of engine noise. Research and technology programs required in certain design areas for this engine concept to realize its potential benefits include refined parametric analysis of selected variable cycle engines, screening of additional unconventional concepts, and engine preliminary design studies. Required critical technology programs are summarized.

Comparison between splines and fractional polynomials for multivariable model building with continuous covariates: a simulation study with continuous response.

PubMed

Binder, Harald; Sauerbrei, Willi; Royston, Patrick

2013-06-15

In observational studies, many continuous or categorical covariates may be related to an outcome. Various spline-based procedures or the multivariable fractional polynomial (MFP) procedure can be used to identify important variables and functional forms for continuous covariates. This is the main aim of an explanatory model, as opposed to a model only for prediction. The type of analysis often guides the complexity of the final model. Spline-based procedures and MFP have tuning parameters for choosing the required complexity. To compare model selection approaches, we perform a simulation study in the linear regression context based on a data structure intended to reflect realistic biomedical data. We vary the sample size, variance explained and complexity parameters for model selection. We consider 15 variables. A sample size of 200 (1000) and R(2)  = 0.2 (0.8) is the scenario with the smallest (largest) amount of information. For assessing performance, we consider prediction error, correct and incorrect inclusion of covariates, qualitative measures for judging selected functional forms and further novel criteria. From limited information, a suitable explanatory model cannot be obtained. Prediction performance from all types of models is similar. With a medium amount of information, MFP performs better than splines on several criteria. MFP better recovers simpler functions, whereas splines better recover more complex functions. For a large amount of information and no local structure, MFP and the spline procedures often select similar explanatory models. Copyright © 2012 John Wiley & Sons, Ltd.

Hybrid robust model based on an improved functional link neural network integrating with partial least square (IFLNN-PLS) and its application to predicting key process variables.

PubMed

He, Yan-Lin; Xu, Yuan; Geng, Zhi-Qiang; Zhu, Qun-Xiong

2016-03-01

In this paper, a hybrid robust model based on an improved functional link neural network integrating with partial least square (IFLNN-PLS) is proposed. Firstly, an improved functional link neural network with small norm of expanded weights and high input-output correlation (SNEWHIOC-FLNN) was proposed for enhancing the generalization performance of FLNN. Unlike the traditional FLNN, the expanded variables of the original inputs are not directly used as the inputs in the proposed SNEWHIOC-FLNN model. The original inputs are attached to some small norm of expanded weights. As a result, the correlation coefficient between some of the expanded variables and the outputs is enhanced. The larger the correlation coefficient is, the more relevant the expanded variables tend to be. In the end, the expanded variables with larger correlation coefficient are selected as the inputs to improve the performance of the traditional FLNN. In order to test the proposed SNEWHIOC-FLNN model, three UCI (University of California, Irvine) regression datasets named Housing, Concrete Compressive Strength (CCS), and Yacht Hydro Dynamics (YHD) are selected. Then a hybrid model based on the improved FLNN integrating with partial least square (IFLNN-PLS) was built. In IFLNN-PLS model, the connection weights are calculated using the partial least square method but not the error back propagation algorithm. Lastly, IFLNN-PLS was developed as an intelligent measurement model for accurately predicting the key variables in the Purified Terephthalic Acid (PTA) process and the High Density Polyethylene (HDPE) process. Simulation results illustrated that the IFLNN-PLS could significant improve the prediction performance. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Identifying Slow Molecular Motions in Complex Chemical Reactions.

PubMed

Piccini, GiovanniMaria; Polino, Daniela; Parrinello, Michele

2017-09-07

We have studied the cyclization reaction of deprotonated 4-chloro-1-butanethiol to tetrahydrothiophene by means of well-tempered metadynamics. To properly select the collective variables, we used the recently proposed variational approach to conformational dynamics within the framework of metadyanmics. This allowed us to select the appropriate linear combinations from a set of collective variables representing the slow degrees of freedom that best describe the slow modes of the reaction. We performed our calculations at three different temperatures, namely, 300, 350, and 400 K. We show that the choice of such collective variables allows one to easily interpret the complex free-energy surface of such a reaction by univocal identification of the conformers belonging to reactants and product states playing a fundamental role in the reaction mechanism.

Valorization of genetic variability for the qualitative improvement of autochthonous grape cultivars of Cirò's terroir through the self-fertilization.

PubMed

Meneghetti, Stefano; Gaiotti, Federica; Giust, Mirella; Belfiore, Nicola; Tomasi, Diego

2015-03-01

This study uses PCR-derived marker systems to investigate the genetic differences of 22 grapevine accessions obtained through a self-fertilization program using Gaglioppo and Magliocco dolce. The aim of the study was to improve some qualitative parameters, while preserving the adaptive characteristics of these two cultivars to the adverse environmental conditions of the Calabria region (southern Italy). These two Calabrian grapevines have been cultivated within a restricted area and have been placed under a strong anthropic pressure which has limited their phenotypical variability with no selection of higher performant biotypes. Therefore, to have accessions with improved qualitative traits, a program of genetic improvement based on the self-fertilization of Gaglioppo and Magliocco dolce cultivars was performed in 1998, producing 3,122 accessions. Selection cycles were performed in 14 years. A first selection cycle (1998-2000), based on visual inspection of vegetative traits, selected 1,320 accessions, planted in an experimental vineyard in 2000. A second selection cycle (2000-2008), based on phenotypic traits, sanitary aspects, and chemical composition of the grapes, selected 42 accessions, planted in a new experimental vineyard in 2008. A final selection cycle (2008-2012), produced 22 accessions (virus free), with the best agronomic, sanitary, and qualitative aspects: two accessions obtained from Gaglioppo have been selected by color characteristics (i.e., anthocyanin total content and stability); 20 genotypes obtained from Magliocco dolce had a better macro-composition of the grape (i.e., good sugar content with a balanced acidity). SSR analyses were performed to check the self-fertilization process. The study of genetic differences between accessions was performed by AFLPs, SAMPLs, and M-AFLPs. The application of the above-mentioned techniques allowed both to discriminate molecularly the 22 accessions grouped these accessions according to their genetic similarity. The self-fertilization approach has enabled improvement in the quality of the grapes, while preserving the high degree of adaptation to the environment of these two native Calabrian cultivars in southern Italy.

Empirical Investigation of Predictors of Success in an MBA Programme

ERIC Educational Resources Information Center

Gupta, Atul; Turek, Joseph

2015-01-01

Purpose: The twofold purpose of this study was to determine if selected variables were predictors of: student performance in the MBA programme; and student performance on the MBA MFT exam. Design/methodology/approach: This study focuses on MBA graduates at a US university who have successfully completed the entire programme requirements. Real…

A Structural Examination of Academic Integration, Perceived Stress, Academic Performance, and Goal Commitment from an Elaborated Model of Adult Student Persistence.

ERIC Educational Resources Information Center

Sandler, Martin E.

The effects of selected variables on the academic persistence of adult students were examined in a study of a random sample of 469 adult students aged 24 years or older enrolled in a four-year college. The survey questionnaire, the Adult Student Experiences Survey, collected data regarding 12 endogenous variables and 13 exogenous variables…

Variable Selection for Support Vector Machines in Moderately High Dimensions

PubMed Central

Zhang, Xiang; Wu, Yichao; Wang, Lan; Li, Runze

2015-01-01

Summary The support vector machine (SVM) is a powerful binary classification tool with high accuracy and great flexibility. It has achieved great success, but its performance can be seriously impaired if many redundant covariates are included. Some efforts have been devoted to studying variable selection for SVMs, but asymptotic properties, such as variable selection consistency, are largely unknown when the number of predictors diverges to infinity. In this work, we establish a unified theory for a general class of nonconvex penalized SVMs. We first prove that in ultra-high dimensions, there exists one local minimizer to the objective function of nonconvex penalized SVMs possessing the desired oracle property. We further address the problem of nonunique local minimizers by showing that the local linear approximation algorithm is guaranteed to converge to the oracle estimator even in the ultra-high dimensional setting if an appropriate initial estimator is available. This condition on initial estimator is verified to be automatically valid as long as the dimensions are moderately high. Numerical examples provide supportive evidence. PMID:26778916

Sparse partial least squares regression for simultaneous dimension reduction and variable selection

PubMed Central

Chun, Hyonho; Keleş, Sündüz

2010-01-01

Partial least squares regression has been an alternative to ordinary least squares for handling multicollinearity in several areas of scientific research since the 1960s. It has recently gained much attention in the analysis of high dimensional genomic data. We show that known asymptotic consistency of the partial least squares estimator for a univariate response does not hold with the very large p and small n paradigm. We derive a similar result for a multivariate response regression with partial least squares. We then propose a sparse partial least squares formulation which aims simultaneously to achieve good predictive performance and variable selection by producing sparse linear combinations of the original predictors. We provide an efficient implementation of sparse partial least squares regression and compare it with well-known variable selection and dimension reduction approaches via simulation experiments. We illustrate the practical utility of sparse partial least squares regression in a joint analysis of gene expression and genomewide binding data. PMID:20107611

A database of 629 English compound words: ratings of familiarity, lexeme meaning dominance, semantic transparency, age of acquisition, imageability, and sensory experience.

PubMed

Juhasz, Barbara J; Lai, Yun-Hsuan; Woodcock, Michelle L

2015-12-01

Since the work of Taft and Forster (1976), a growing literature has examined how English compound words are recognized and organized in the mental lexicon. Much of this research has focused on whether compound words are decomposed during recognition by manipulating the word frequencies of their lexemes. However, many variables may impact morphological processing, including relational semantic variables such as semantic transparency, as well as additional form-related and semantic variables. In the present study, ratings were collected on 629 English compound words for six variables [familiarity, age of acquisition (AoA), semantic transparency, lexeme meaning dominance (LMD), imageability, and sensory experience ratings (SER)]. All of the compound words selected for this study are contained within the English Lexicon Project (Balota et al., 2007), which made it possible to use a regression approach to examine the predictive power of these variables for lexical decision and word naming performance. Analyses indicated that familiarity, AoA, imageability, and SER were all significant predictors of both lexical decision and word naming performance when they were added separately to a model containing the length and frequency of the compounds, as well as the lexeme frequencies. In addition, rated semantic transparency also predicted lexical decision performance. The database of English compound words should be beneficial to word recognition researchers who are interested in selecting items for experiments on compound words, and it will also allow researchers to conduct further analyses using the available data combined with word recognition times included in the English Lexicon Project.

Effects of non-neuronal components for functional connectivity analysis from resting-state functional MRI toward automated diagnosis of schizophrenia

NASA Astrophysics Data System (ADS)

Kim, Junghoe; Lee, Jong-Hwan

2014-03-01

A functional connectivity (FC) analysis from resting-state functional MRI (rsfMRI) is gaining its popularity toward the clinical application such as diagnosis of neuropsychiatric disease. To delineate the brain networks from rsfMRI data, non-neuronal components including head motions and physiological artifacts mainly observed in cerebrospinal fluid (CSF), white matter (WM) along with a global brain signal have been regarded as nuisance variables in calculating the FC level. However, it is still unclear how the non-neuronal components can affect the performance toward diagnosis of neuropsychiatric disease. In this study, a systematic comparison of classification performance of schizophrenia patients was provided employing the partial correlation coefficients (CCs) as feature elements. Pair-wise partial CCs were calculated between brain regions, in which six combinatorial sets of nuisance variables were considered. The partial CCs were used as candidate feature elements followed by feature selection based on the statistical significance test between two groups in the training set. Once a linear support vector machine was trained using the selected features from the training set, the classification performance was evaluated using the features from the test set (i.e. leaveone- out cross validation scheme). From the results, the error rate using all non-neuronal components as nuisance variables (12.4%) was significantly lower than those using remaining combination of non-neuronal components as nuisance variables (13.8 ~ 20.0%). In conclusion, the non-neuronal components substantially degraded the automated diagnosis performance, which supports our hypothesis that the non-neuronal components are crucial in controlling the automated diagnosis performance of the neuropsychiatric disease using an fMRI modality.

Random forest feature selection approach for image segmentation

NASA Astrophysics Data System (ADS)

Lefkovits, László; Lefkovits, Szidónia; Emerich, Simina; Vaida, Mircea Florin

2017-03-01

In the field of image segmentation, discriminative models have shown promising performance. Generally, every such model begins with the extraction of numerous features from annotated images. Most authors create their discriminative model by using many features without using any selection criteria. A more reliable model can be built by using a framework that selects the important variables, from the point of view of the classification, and eliminates the unimportant once. In this article we present a framework for feature selection and data dimensionality reduction. The methodology is built around the random forest (RF) algorithm and its variable importance evaluation. In order to deal with datasets so large as to be practically unmanageable, we propose an algorithm based on RF that reduces the dimension of the database by eliminating irrelevant features. Furthermore, this framework is applied to optimize our discriminative model for brain tumor segmentation.

Nonparametric regression applied to quantitative structure-activity relationships

PubMed

Constans; Hirst

2000-03-01

Several nonparametric regressors have been applied to modeling quantitative structure-activity relationship (QSAR) data. The simplest regressor, the Nadaraya-Watson, was assessed in a genuine multivariate setting. Other regressors, the local linear and the shifted Nadaraya-Watson, were implemented within additive models--a computationally more expedient approach, better suited for low-density designs. Performances were benchmarked against the nonlinear method of smoothing splines. A linear reference point was provided by multilinear regression (MLR). Variable selection was explored using systematic combinations of different variables and combinations of principal components. For the data set examined, 47 inhibitors of dopamine beta-hydroxylase, the additive nonparametric regressors have greater predictive accuracy (as measured by the mean absolute error of the predictions or the Pearson correlation in cross-validation trails) than MLR. The use of principal components did not improve the performance of the nonparametric regressors over use of the original descriptors, since the original descriptors are not strongly correlated. It remains to be seen if the nonparametric regressors can be successfully coupled with better variable selection and dimensionality reduction in the context of high-dimensional QSARs.

Detection and characterisation of frauds in bovine meat in natura by non-meat ingredient additions using data fusion of chemical parameters and ATR-FTIR spectroscopy.

PubMed

Nunes, Karen M; Andrade, Marcus Vinícius O; Santos Filho, Antônio M P; Lasmar, Marcelo C; Sena, Marcelo M

2016-08-15

Concerns about meat authenticity are increasing recently, due to great fraud scandals. This paper analysed real samples (43 adulterated and 12 controls) originated from criminal networks dismantled by the Brazilian Police. This fraud consisted of injecting solutions of non-meat ingredients (NaCl, phosphates, carrageenan, maltodextrin) in bovine meat, aiming to increase its water holding capacity. Five physico-chemical variables were determined, protein, ash, chloride, sodium, phosphate. Additionally, infrared spectra were recorded. Supervised classification PLS-DA models were built with each data set individually, but the best model was obtained with data fusion, correctly detecting 91% of the adulterated samples. From this model, a variable selection based on the highest VIPscores was performed and a new data fusion model was built with only one chemical variable, providing slightly lower predictions, but a good cost/performance ratio. Finally, some of the selected infrared bands were specifically associated to the presence of adulterants NaCl, tripolyphosphate and carrageenan. Copyright © 2016 Elsevier Ltd. All rights reserved.

The variability of software scoring of the CDMAM phantom associated with a limited number of images

NASA Astrophysics Data System (ADS)

Yang, Chang-Ying J.; Van Metter, Richard

2007-03-01

Software scoring approaches provide an attractive alternative to human evaluation of CDMAM images from digital mammography systems, particularly for annual quality control testing as recommended by the European Protocol for the Quality Control of the Physical and Technical Aspects of Mammography Screening (EPQCM). Methods for correlating CDCOM-based results with human observer performance have been proposed. A common feature of all methods is the use of a small number (at most eight) of CDMAM images to evaluate the system. This study focuses on the potential variability in the estimated system performance that is associated with these methods. Sets of 36 CDMAM images were acquired under carefully controlled conditions from three different digital mammography systems. The threshold visibility thickness (TVT) for each disk diameter was determined using previously reported post-analysis methods from the CDCOM scorings for a randomly selected group of eight images for one measurement trial. This random selection process was repeated 3000 times to estimate the variability in the resulting TVT values for each disk diameter. The results from using different post-analysis methods, different random selection strategies and different digital systems were compared. Additional variability of the 0.1 mm disk diameter was explored by comparing the results from two different image data sets acquired under the same conditions from the same system. The magnitude and the type of error estimated for experimental data was explained through modeling. The modeled results also suggest a limitation in the current phantom design for the 0.1 mm diameter disks. Through modeling, it was also found that, because of the binomial statistic nature of the CDMAM test, the true variability of the test could be underestimated by the commonly used method of random re-sampling.

Multiple Off-Ice Performance Variables Predict On-Ice Skating Performance in Male and Female Division III Ice Hockey Players.

PubMed

Janot, Jeffrey M; Beltz, Nicholas M; Dalleck, Lance D

2015-09-01

The purpose of this study was to determine if off-ice performance variables could predict on-ice skating performance in Division III collegiate hockey players. Both men (n = 15) and women (n = 11) hockey players (age = 20.5 ± 1.4 years) participated in the study. The skating tests were agility cornering S-turn, 6.10 m acceleration, 44.80 m speed, modified repeat skate, and 15.20 m full speed. Off-ice variables assessed were years of playing experience, height, weight and percent body fat and off-ice performance variables included vertical jump (VJ), 40-yd dash (36.58m), 1-RM squat, pro-agility, Wingate peak power and peak power percentage drop (% drop), and 1.5 mile (2.4km) run. Results indicated that 40-yd dash (36.58m), VJ, 1.5 mile (2.4km) run, and % drop were significant predictors of skating performance for repeat skate (slowest, fastest, and average time) and 44.80 m speed time, respectively. Four predictive equations were derived from multiple regression analyses: 1) slowest repeat skate time = 2.362 + (1.68 x 40-yd dash time) + (0.005 x 1.5 mile run), 2) fastest repeat skate time = 9.762 - (0.089 x VJ) - (0.998 x 40-yd dash time), 3) average repeat skate time = 7.770 + (1.041 x 40-yd dash time) - (0.63 x VJ) + (0.003 x 1.5 mile time), and 4) 47.85 m speed test = 7.707 - (0.050 x VJ) - (0.01 x % drop). It was concluded that selected off-ice tests could be used to predict on-ice performance regarding speed and recovery ability in Division III male and female hockey players. Key pointsThe 40-yd dash (36.58m) and vertical jump tests are significant predictors of on-ice skating performance specific to speed.In addition to 40-yd dash and vertical jump, the 1.5 mile (2.4km) run for time and percent power drop from the Wingate anaerobic power test were also significant predictors of skating performance that incorporates the aspect of recovery from skating activity.Due to the specificity of selected off-ice variables as predictors of on-ice performance, coaches can elect to assess player performance off-ice and focus on other uses of valuable ice time for their individual teams.

Emergency cesarean section and the 30-minute rule: definitions.

PubMed

Schauberger, Charles W; Chauhan, Suneet P

2009-03-01

We explored the role that lack of a standard definition and heterogeneity in patient selection criteria in the literature might have on the apparent inability to routinely begin an emergency cesarean section in less than 30 minutes. A review of the literature on emergency cesarean delivery was performed. Although there are some similarities in definitions and the criteria used for patient selection in multiple studies, the variability in the definitions could be responsible for some of the apparent timeliness performance deficiency in the literature. A standard definition and directions for future research are suggested.

Annual variability of PAH concentrations in the Potomac River watershed

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

Maher, I.L.; Foster, G.D.

1995-12-31

Dynamics of organic contaminant transport in a large river system is influenced by annual variability in organic contaminant concentrations. Surface runoff and groundwater input control the flow of river waters. They are also the two major inputs of contaminants to river waters. The annual variability of contaminant concentrations in rivers may or may not represent similar trends to the flow changes of river waters. The purpose of the research is to define the annual variability in concentrations of polycyclic aromatic hydrocarbons (PAH) in riverine environment. To accomplish this, from March 1992 to March 1995 samples of Potomac River water weremore » collected monthly or bimonthly downstream of the Chesapeake Bay fall line (Chain Bridge) during base flow and main storm flow hydrologic conditions. Concentrations of selected PAHs were measured in the dissolved phase and the particulate phase via GC/MS. The study of the annual variability of PAH concentrations will be performed through comparisons of PAH concentrations seasonally, annually, and through study of PAH concentration river discharge dependency and rainfall dependency. For selected PAHs monthly and annual loadings will be estimated based on their measured concentrations and average daily river discharge. The monthly loadings of selected PAHs will be compared by seasons and annually.« less

The GRE Subject Test Performance of U.S. and Non-U.S. Examinees, 1982-84: A Comparative Analysis.

ERIC Educational Resources Information Center

Wilson, Kenneth M.

The Graduate Record Examination (GRE) subject-matter achievement tests are offered in 17 fields. This study investigated the performance of U.S. and non-U.S. citizens on the Subject Tests, and the relationship of selected English-proficiency-related background variables to the test performance of non-U.S. citizens. It was also concerned with…

Selected questions on biomechanical exposures for surveillance of upper-limb work-related musculoskeletal disorders

PubMed Central

Descatha, Alexis; Roquelaure, Yves; Evanoff, Bradley; Niedhammer, Isabelle; Chastang, Jean François; Mariot, Camille; Ha, Catherine; Imbernon, Ellen; Goldberg, Marcel; Leclerc, Annette

2007-01-01

Objective Questionnaires for assessment of biomechanical exposure are frequently used in surveillance programs, though few studies have evaluated which key questions are needed. We sought to reduce the number of variables on a surveillance questionnaire by identifying which variables best summarized biomechanical exposure in a survey of the French working population. Methods We used data from the 2002–2003 French experimental network of Upper-limb work-related musculoskeletal disorders (UWMSD), performed on 2685 subjects in which 37 variables assessing biomechanical exposures were available (divided into four ordinal categories, according to the task frequency or duration). Principal Component Analysis (PCA) with orthogonal rotation was performed on these variables. Variables closely associated with factors issued from PCA were retained, except those highly correlated to another variable (rho>0.70). In order to study the relevance of the final list of variables, correlations between a score based on retained variables (PCA score) and the exposure score suggested by the SALTSA group were calculated. The associations between the PCA score and the prevalence of UWMSD were also studied. In a final step, we added back to the list a few variables not retained by PCA, because of their established recognition as risk factors. Results According to the results of the PCA, seven interpretable factors were identified: posture exposures, repetitiveness, handling of heavy loads, distal biomechanical exposures, computer use, forklift operator specific task, and recovery time. Twenty variables strongly correlated with the factors obtained from PCA were retained. The PCA score was strongly correlated both with the SALTSA score and with UWMSD prevalence (p<0.0001). In the final step, six variables were reintegrated. Conclusion Twenty-six variables out of 37 were efficiently selected according to their ability to summarize major biomechanical constraints in a working population, with an approach combining statistical analyses and existing knowledge. PMID:17476519

An Integrated Optimization Design Method Based on Surrogate Modeling Applied to Diverging Duct Design

NASA Astrophysics Data System (ADS)

Hanan, Lu; Qiushi, Li; Shaobin, Li

2016-12-01

This paper presents an integrated optimization design method in which uniform design, response surface methodology and genetic algorithm are used in combination. In detail, uniform design is used to select the experimental sampling points in the experimental domain and the system performance is evaluated by means of computational fluid dynamics to construct a database. After that, response surface methodology is employed to generate a surrogate mathematical model relating the optimization objective and the design variables. Subsequently, genetic algorithm is adopted and applied to the surrogate model to acquire the optimal solution in the case of satisfying some constraints. The method has been applied to the optimization design of an axisymmetric diverging duct, dealing with three design variables including one qualitative variable and two quantitative variables. The method of modeling and optimization design performs well in improving the duct aerodynamic performance and can be also applied to wider fields of mechanical design and seen as a useful tool for engineering designers, by reducing the design time and computation consumption.

The effect of artificial selection on phenotypic plasticity in maize.

PubMed

Gage, Joseph L; Jarquin, Diego; Romay, Cinta; Lorenz, Aaron; Buckler, Edward S; Kaeppler, Shawn; Alkhalifah, Naser; Bohn, Martin; Campbell, Darwin A; Edwards, Jode; Ertl, David; Flint-Garcia, Sherry; Gardiner, Jack; Good, Byron; Hirsch, Candice N; Holland, Jim; Hooker, David C; Knoll, Joseph; Kolkman, Judith; Kruger, Greg; Lauter, Nick; Lawrence-Dill, Carolyn J; Lee, Elizabeth; Lynch, Jonathan; Murray, Seth C; Nelson, Rebecca; Petzoldt, Jane; Rocheford, Torbert; Schnable, James; Schnable, Patrick S; Scully, Brian; Smith, Margaret; Springer, Nathan M; Srinivasan, Srikant; Walton, Renee; Weldekidan, Teclemariam; Wisser, Randall J; Xu, Wenwei; Yu, Jianming; de Leon, Natalia

2017-11-07

Remarkable productivity has been achieved in crop species through artificial selection and adaptation to modern agronomic practices. Whether intensive selection has changed the ability of improved cultivars to maintain high productivity across variable environments is unknown. Understanding the genetic control of phenotypic plasticity and genotype by environment (G × E) interaction will enhance crop performance predictions across diverse environments. Here we use data generated from the Genomes to Fields (G2F) Maize G × E project to assess the effect of selection on G × E variation and characterize polymorphisms associated with plasticity. Genomic regions putatively selected during modern temperate maize breeding explain less variability for yield G × E than unselected regions, indicating that improvement by breeding may have reduced G × E of modern temperate cultivars. Trends in genomic position of variants associated with stability reveal fewer genic associations and enrichment of variants 0-5000 base pairs upstream of genes, hypothetically due to control of plasticity by short-range regulatory elements.

Game Indicators Determining Sports Performance in the NBA

PubMed Central

Mikołajec, Kazimierz; Maszczyk, Adam; Zając, Tomasz

The main goal of the present study was to identify basketball game performance indicators which best determine sports level in the National Basketball Association (NBA) league. The research material consisted of all NBA game statistics at the turn of eight seasons (2003–11) and included 52 performance variables. Through detailed analysis the variables with high influence on game effectiveness were selected for final procedures. It has been proven that a limited number of factors, mostly offensive, determines sports performance in the NBA. The most critical indicators are: Win%, Offensive EFF, 3rd Quarter PPG, Win% CG, Avg Fauls and Avg Steals. In practical applications these results connected with top teams and elite players may help coaches to design better training programs. PMID:24146715

Game Indicators Determining Sports Performance in the NBA.

PubMed

Mikołajec, Kazimierz; Maszczyk, Adam; Zając, Tomasz

2013-01-01

The main goal of the present study was to identify basketball game performance indicators which best determine sports level in the National Basketball Association (NBA) league. The research material consisted of all NBA game statistics at the turn of eight seasons (2003-11) and included 52 performance variables. Through detailed analysis the variables with high influence on game effectiveness were selected for final procedures. It has been proven that a limited number of factors, mostly offensive, determines sports performance in the NBA. The most critical indicators are: Win%, Offensive EFF, 3rd Quarter PPG, Win% CG, Avg Fauls and Avg Steals. In practical applications these results connected with top teams and elite players may help coaches to design better training programs.

Do Residency Selection Factors Predict Radiology Resident Performance?

PubMed

Agarwal, Vikas; Bump, Gregory M; Heller, Matthew T; Chen, Ling-Wan; Branstetter, Barton F; Amesur, Nikhil B; Hughes, Marion A

2018-03-01

The purpose of our study is to determine what information in medical student residency applications predicts radiology residency success as defined by objective clinical performance data. We performed a retrospective cohort study of residents who entered our institution's residency program through the National Resident Matching Program as postgraduate year 2 residents and completed the program over the past 2 years. Medical school grades, selection to Alpha Omega Alpha (AOA) Honor Society, United States Medical Licensing Examination (USMLE) scores, publication in peer-reviewed journals, and whether the applicant was from a peer institution were the variables examined. Clinical performance was determined by calculating each resident's cumulative major discordance rate for on-call cases the resident read and gave a preliminary interpretation. A major discordance was defined as a difference between the preliminary resident and the final attending interpretations that could immediately impact the care of the patient. A multivariate logistic regression was performed to determine significant variables. Twenty-seven residents provided preliminary reports on call for 67,145 studies. The mean major discordance rate was 1.08% (range 0.34%-2.54%). Higher USMLE Step 1 scores, publication before residency, and election to AOA Honor Society were all statistically significant predictors of lower major discordance rates (P values 0.01, 0.01,  and <0.001, respectively). Overall resident performance was excellent. There are predictors that help select the better performing residents, namely higher USMLE Step 1 scores, one to two publications during medical school, and election to AOA in the junior year of medical school. Copyright © 2018 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

WEATHERABILITY OF ENHANCED DEGRADABLE PLASTICS

EPA Science Inventory

The main objective of this study was to assess the performance and the asociated variability of several selected enhanced degradable plastic materials under a variety of different exposure conditions. Other objectives were to identify the major products formed during degradation ...

THESEUS: maximum likelihood superpositioning and analysis of macromolecular structures.

PubMed

Theobald, Douglas L; Wuttke, Deborah S

2006-09-01

THESEUS is a command line program for performing maximum likelihood (ML) superpositions and analysis of macromolecular structures. While conventional superpositioning methods use ordinary least-squares (LS) as the optimization criterion, ML superpositions provide substantially improved accuracy by down-weighting variable structural regions and by correcting for correlations among atoms. ML superpositioning is robust and insensitive to the specific atoms included in the analysis, and thus it does not require subjective pruning of selected variable atomic coordinates. Output includes both likelihood-based and frequentist statistics for accurate evaluation of the adequacy of a superposition and for reliable analysis of structural similarities and differences. THESEUS performs principal components analysis for analyzing the complex correlations found among atoms within a structural ensemble. ANSI C source code and selected binaries for various computing platforms are available under the GNU open source license from http://monkshood.colorado.edu/theseus/ or http://www.theseus3d.org.

Empirical Performance of Cross-Validation With Oracle Methods in a Genomics Context.

PubMed

Martinez, Josue G; Carroll, Raymond J; Müller, Samuel; Sampson, Joshua N; Chatterjee, Nilanjan

2011-11-01

When employing model selection methods with oracle properties such as the smoothly clipped absolute deviation (SCAD) and the Adaptive Lasso, it is typical to estimate the smoothing parameter by m-fold cross-validation, for example, m = 10. In problems where the true regression function is sparse and the signals large, such cross-validation typically works well. However, in regression modeling of genomic studies involving Single Nucleotide Polymorphisms (SNP), the true regression functions, while thought to be sparse, do not have large signals. We demonstrate empirically that in such problems, the number of selected variables using SCAD and the Adaptive Lasso, with 10-fold cross-validation, is a random variable that has considerable and surprising variation. Similar remarks apply to non-oracle methods such as the Lasso. Our study strongly questions the suitability of performing only a single run of m-fold cross-validation with any oracle method, and not just the SCAD and Adaptive Lasso.

An improved partial least-squares regression method for Raman spectroscopy

NASA Astrophysics Data System (ADS)

Momenpour Tehran Monfared, Ali; Anis, Hanan

2017-10-01

It is known that the performance of partial least-squares (PLS) regression analysis can be improved using the backward variable selection method (BVSPLS). In this paper, we further improve the BVSPLS based on a novel selection mechanism. The proposed method is based on sorting the weighted regression coefficients, and then the importance of each variable of the sorted list is evaluated using root mean square errors of prediction (RMSEP) criterion in each iteration step. Our Improved BVSPLS (IBVSPLS) method has been applied to leukemia and heparin data sets and led to an improvement in limit of detection of Raman biosensing ranged from 10% to 43% compared to PLS. Our IBVSPLS was also compared to the jack-knifing (simpler) and Genetic Algorithm (more complex) methods. Our method was consistently better than the jack-knifing method and showed either a similar or a better performance compared to the genetic algorithm.

Variability-aware compact modeling and statistical circuit validation on SRAM test array

NASA Astrophysics Data System (ADS)

Qiao, Ying; Spanos, Costas J.

2016-03-01

Variability modeling at the compact transistor model level can enable statistically optimized designs in view of limitations imposed by the fabrication technology. In this work we propose a variability-aware compact model characterization methodology based on stepwise parameter selection. Transistor I-V measurements are obtained from bit transistor accessible SRAM test array fabricated using a collaborating foundry's 28nm FDSOI technology. Our in-house customized Monte Carlo simulation bench can incorporate these statistical compact models; and simulation results on SRAM writability performance are very close to measurements in distribution estimation. Our proposed statistical compact model parameter extraction methodology also has the potential of predicting non-Gaussian behavior in statistical circuit performances through mixtures of Gaussian distributions.

Influence of BMI and dietary restraint on self-selected portions of prepared meals in US women.

PubMed

Labbe, David; Rytz, Andréas; Brunstrom, Jeffrey M; Forde, Ciarán G; Martin, Nathalie

2017-04-01

The rise of obesity prevalence has been attributed in part to an increase in food and beverage portion sizes selected and consumed among overweight and obese consumers. Nevertheless, evidence from observations of adults is mixed and contradictory findings might reflect the use of small or unrepresentative samples. The objective of this study was i) to determine the extent to which BMI and dietary restraint predict self-selected portion sizes for a range of commercially available prepared savoury meals and ii) to consider the importance of these variables relative to two previously established predictors of portion selection, expected satiation and expected liking. A representative sample of female consumers (N = 300, range 18-55 years) evaluated 15 frozen savoury prepared meals. For each meal, participants rated their expected satiation and expected liking, and selected their ideal portion using a previously validated computer-based task. Dietary restraint was quantified using the Dutch Eating Behaviour Questionnaire (DEBQ-R). Hierarchical multiple regression was performed on self-selected portions with age, hunger level, and meal familiarity entered as control variables in the first step of the model, expected satiation and expected liking as predictor variables in the second step, and DEBQ-R and BMI as exploratory predictor variables in the third step. The second and third steps significantly explained variance in portion size selection (18% and 4%, respectively). Larger portion selections were significantly associated with lower dietary restraint and with lower expected satiation. There was a positive relationship between BMI and portion size selection (p = 0.06) and between expected liking and portion size selection (p = 0.06). Our discussion considers future research directions, the limited variance explained by our model, and the potential for portion size underreporting by overweight participants. Copyright © 2016 Nestec S.A. Published by Elsevier Ltd.. All rights reserved.

Sparse representation based biomarker selection for schizophrenia with integrated analysis of fMRI and SNPs.

PubMed

Cao, Hongbao; Duan, Junbo; Lin, Dongdong; Shugart, Yin Yao; Calhoun, Vince; Wang, Yu-Ping

2014-11-15

Integrative analysis of multiple data types can take advantage of their complementary information and therefore may provide higher power to identify potential biomarkers that would be missed using individual data analysis. Due to different natures of diverse data modality, data integration is challenging. Here we address the data integration problem by developing a generalized sparse model (GSM) using weighting factors to integrate multi-modality data for biomarker selection. As an example, we applied the GSM model to a joint analysis of two types of schizophrenia data sets: 759,075 SNPs and 153,594 functional magnetic resonance imaging (fMRI) voxels in 208 subjects (92 cases/116 controls). To solve this small-sample-large-variable problem, we developed a novel sparse representation based variable selection (SRVS) algorithm, with the primary aim to identify biomarkers associated with schizophrenia. To validate the effectiveness of the selected variables, we performed multivariate classification followed by a ten-fold cross validation. We compared our proposed SRVS algorithm with an earlier sparse model based variable selection algorithm for integrated analysis. In addition, we compared with the traditional statistics method for uni-variant data analysis (Chi-squared test for SNP data and ANOVA for fMRI data). Results showed that our proposed SRVS method can identify novel biomarkers that show stronger capability in distinguishing schizophrenia patients from healthy controls. Moreover, better classification ratios were achieved using biomarkers from both types of data, suggesting the importance of integrative analysis. Copyright © 2014 Elsevier Inc. All rights reserved.

Implementation of MCA Method for Identification of Factors for Conceptual Cost Estimation of Residential Buildings

NASA Astrophysics Data System (ADS)

Juszczyk, Michał; Leśniak, Agnieszka; Zima, Krzysztof

2013-06-01

Conceptual cost estimation is important for construction projects. Either underestimation or overestimation of building raising cost may lead to failure of a project. In the paper authors present application of a multicriteria comparative analysis (MCA) in order to select factors influencing residential building raising cost. The aim of the analysis is to indicate key factors useful in conceptual cost estimation in the early design stage. Key factors are being investigated on basis of the elementary information about the function, form and structure of the building, and primary assumptions of technological and organizational solutions applied in construction process. The mentioned factors are considered as variables of the model which aim is to make possible conceptual cost estimation fast and with satisfying accuracy. The whole analysis included three steps: preliminary research, choice of a set of potential variables and reduction of this set to select the final set of variables. Multicriteria comparative analysis is applied in problem solution. Performed analysis allowed to select group of factors, defined well enough at the conceptual stage of the design process, to be used as a describing variables of the model.

Resampling procedures to identify important SNPs using a consensus approach.

PubMed

Pardy, Christopher; Motyer, Allan; Wilson, Susan

2011-11-29

Our goal is to identify common single-nucleotide polymorphisms (SNPs) (minor allele frequency > 1%) that add predictive accuracy above that gained by knowledge of easily measured clinical variables. We take an algorithmic approach to predict each phenotypic variable using a combination of phenotypic and genotypic predictors. We perform our procedure on the first simulated replicate and then validate against the others. Our procedure performs well when predicting Q1 but is less successful for the other outcomes. We use resampling procedures where possible to guard against false positives and to improve generalizability. The approach is based on finding a consensus regarding important SNPs by applying random forests and the least absolute shrinkage and selection operator (LASSO) on multiple subsamples. Random forests are used first to discard unimportant predictors, narrowing our focus to roughly 100 important SNPs. A cross-validation LASSO is then used to further select variables. We combine these procedures to guarantee that cross-validation can be used to choose a shrinkage parameter for the LASSO. If the clinical variables were unavailable, this prefiltering step would be essential. We perform the SNP-based analyses simultaneously rather than one at a time to estimate SNP effects in the presence of other causal variants. We analyzed the first simulated replicate of Genetic Analysis Workshop 17 without knowledge of the true model. Post-conference knowledge of the simulation parameters allowed us to investigate the limitations of our approach. We found that many of the false positives we identified were substantially correlated with genuine causal SNPs.

Combining cow and bull reference populations to increase accuracy of genomic prediction and genome-wide association studies.

PubMed

Calus, M P L; de Haas, Y; Veerkamp, R F

2013-10-01

Genomic selection holds the promise to be particularly beneficial for traits that are difficult or expensive to measure, such that access to phenotypes on large daughter groups of bulls is limited. Instead, cow reference populations can be generated, potentially supplemented with existing information from the same or (highly) correlated traits available on bull reference populations. The objective of this study, therefore, was to develop a model to perform genomic predictions and genome-wide association studies based on a combined cow and bull reference data set, with the accuracy of the phenotypes differing between the cow and bull genomic selection reference populations. The developed bivariate Bayesian stochastic search variable selection model allowed for an unbalanced design by imputing residuals in the residual updating scheme for all missing records. The performance of this model is demonstrated on a real data example, where the analyzed trait, being milk fat or protein yield, was either measured only on a cow or a bull reference population, or recorded on both. Our results were that the developed bivariate Bayesian stochastic search variable selection model was able to analyze 2 traits, even though animals had measurements on only 1 of 2 traits. The Bayesian stochastic search variable selection model yielded consistently higher accuracy for fat yield compared with a model without variable selection, both for the univariate and bivariate analyses, whereas the accuracy of both models was very similar for protein yield. The bivariate model identified several additional quantitative trait loci peaks compared with the single-trait models on either trait. In addition, the bivariate models showed a marginal increase in accuracy of genomic predictions for the cow traits (0.01-0.05), although a greater increase in accuracy is expected as the size of the bull population increases. Our results emphasize that the chosen value of priors in Bayesian genomic prediction models are especially important in small data sets. Copyright © 2013 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Electromagnetic fields from mobile phone base station - variability analysis.

PubMed

Bienkowski, Pawel; Zubrzak, Bartlomiej

2015-09-01

The article describes the character of electromagnetic field (EMF) in mobile phone base station (BS) surroundings and its variability in time with an emphasis on the measurement difficulties related to its pulse and multi-frequency nature. Work also presents long-term monitoring measurements performed recently in different locations in Poland - small city with dispersed building development and in major polish city - dense urban area. Authors tried to determine the trends in changing of EMF spectrum analyzing daily changes of measured EMF levels in those locations. Research was performed using selective electromagnetic meters and also EMF meter with spectrum analysis.

Sensitivity of solar-cell performance to atmospheric variables. 2: Dissimilar cells at several locations

NASA Technical Reports Server (NTRS)

Klucher, T. M.; Hart, R. E.

1976-01-01

Several solar cells having dissimilar spectral response curves and cell construction were measured at various locations in the United States to determine sensitivity of cell performance to atmospheric water vapor and turbidity. The locations selected represent a broad range of summer atmospheric conditions, from clear and dry to turbid and humid. Cell short circuit current under direct normal incidence sunlight, the intensity, water vapor and turbidity were measured. Regression equations were developed from the limited data base in order to provide a single method of prediction of cell current sensitivity to the atmospheric variables.

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

PubMed

Zhao, Yize; Long, Qi

2017-01-01

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

Probabilistic Analysis of Solid Oxide Fuel Cell Based Hybrid Gas Turbine System

NASA Technical Reports Server (NTRS)

Gorla, Rama S. R.; Pai, Shantaram S.; Rusick, Jeffrey J.

2003-01-01

The emergence of fuel cell systems and hybrid fuel cell systems requires the evolution of analysis strategies for evaluating thermodynamic performance. A gas turbine thermodynamic cycle integrated with a fuel cell was computationally simulated and probabilistically evaluated in view of the several uncertainties in the thermodynamic performance parameters. Cumulative distribution functions and sensitivity factors were computed for the overall thermal efficiency and net specific power output due to the uncertainties in the thermodynamic random variables. These results can be used to quickly identify the most critical design variables in order to optimize the design and make it cost effective. The analysis leads to the selection of criteria for gas turbine performance.

Autonomous Fault Detection for Performance Bugs in Component Based Robotic Systems

DTIC Science & Technology

2016-12-01

platform performs a modified version of the restaurant task from the RoboCup@Home competition 2015 [20]. Here, an operator first guides the robot around a...Control. Berlin: Springer, 2008. DOI: 10.1007/ 978-3-540-76304-8. [18] H. Zou and T. Hastie, “Regularization and variable selection via the elastic net

A STUDY OF CERTAIN FACTORS AFFECTING CHILDRENS' SCHOOL PERFORMANCE.

ERIC Educational Resources Information Center

SPENCE, JANET T.

AS PART OF THE RESEARCH ON THE INFLUENCE OF RESPONSE-CONTINGENT REINFORCERS ON THE LEARNING AND PROBLEM-SOLVING BEHAVIOR OF CHILDREN, THE EFFECTS OF A LIMITED NUMBER OF VARIABLES ON THE PERFORMANCE OF CHILDREN OF THREE AGE LEVELS (4-5, 7-8, AND 10-11), SELECTED EQUALLY FROM MIDDLE- AND LOWER-CLASS BACKGROUNDS, WERE INVESTIGATED. THE EXPERIMENTAL…

Development of custom measurement system for biomechanical evaluation of independent wheelchair transfers.

PubMed

Koontz, Alicia M; Lin, Yen-Sheng; Kankipati, Padmaja; Boninger, Michael L; Cooper, Rory A

2011-01-01

This study describes a new custom measurement system designed to investigate the biomechanics of sitting-pivot wheelchair transfers and assesses the reliability of selected biomechanical variables. Variables assessed include horizontal and vertical reaction forces underneath both hands and three-dimensional trunk, shoulder, and elbow range of motion. We examined the reliability of these measures between 5 consecutive transfer trials for 5 subjects with spinal cord injury and 12 nondisabled subjects while they performed a self-selected sitting pivot transfer from a wheelchair to a level bench. A majority of the biomechanical variables demonstrated moderate to excellent reliability (r > 0.6). The transfer measurement system recorded reliable and valid biomechanical data for future studies of sitting-pivot wheelchair transfers.We recommend a minimum of five transfer trials to obtain a reliable measure of transfer technique for future studies.

Robust nonlinear variable selective control for networked systems

NASA Astrophysics Data System (ADS)

Rahmani, Behrooz

2016-10-01

This paper is concerned with the networked control of a class of uncertain nonlinear systems. In this way, Takagi-Sugeno (T-S) fuzzy modelling is used to extend the previously proposed variable selective control (VSC) methodology to nonlinear systems. This extension is based upon the decomposition of the nonlinear system to a set of fuzzy-blended locally linearised subsystems and further application of the VSC methodology to each subsystem. To increase the applicability of the T-S approach for uncertain nonlinear networked control systems, this study considers the asynchronous premise variables in the plant and the controller, and then introduces a robust stability analysis and control synthesis. The resulting optimal switching-fuzzy controller provides a minimum guaranteed cost on an H2 performance index. Simulation studies on three nonlinear benchmark problems demonstrate the effectiveness of the proposed method.

Economic evaluation of genomic selection in small ruminants: a sheep meat breeding program.

PubMed

Shumbusho, F; Raoul, J; Astruc, J M; Palhiere, I; Lemarié, S; Fugeray-Scarbel, A; Elsen, J M

2016-06-01

Recent genomic evaluation studies using real data and predicting genetic gain by modeling breeding programs have reported moderate expected benefits from the replacement of classic selection schemes by genomic selection (GS) in small ruminants. The objectives of this study were to compare the cost, monetary genetic gain and economic efficiency of classic selection and GS schemes in the meat sheep industry. Deterministic methods were used to model selection based on multi-trait indices from a sheep meat breeding program. Decisional variables related to male selection candidates and progeny testing were optimized to maximize the annual monetary genetic gain (AMGG), that is, a weighted sum of meat and maternal traits annual genetic gains. For GS, a reference population of 2000 individuals was assumed and genomic information was available for evaluation of male candidates only. In the classic selection scheme, males breeding values were estimated from own and offspring phenotypes. In GS, different scenarios were considered, differing by the information used to select males (genomic only, genomic+own performance, genomic+offspring phenotypes). The results showed that all GS scenarios were associated with higher total variable costs than classic selection (if the cost of genotyping was 123 euros/animal). In terms of AMGG and economic returns, GS scenarios were found to be superior to classic selection only if genomic information was combined with their own meat phenotypes (GS-Pheno) or with their progeny test information. The predicted economic efficiency, defined as returns (proportional to number of expressions of AMGG in the nucleus and commercial flocks) minus total variable costs, showed that the best GS scenario (GS-Pheno) was up to 15% more efficient than classic selection. For all selection scenarios, optimization increased the overall AMGG, returns and economic efficiency. As a conclusion, our study shows that some forms of GS strategies are more advantageous than classic selection, provided that GS is already initiated (i.e. the initial reference population is available). Optimizing decisional variables of the classic selection scheme could be of greater benefit than including genomic information in optimized designs.

Natural selection on thermal performance in a novel thermal environment

PubMed Central

Logan, Michael L.; Cox, Robert M.; Calsbeek, Ryan

2014-01-01

Tropical ectotherms are thought to be especially vulnerable to climate change because they are adapted to relatively stable temperature regimes, such that even small increases in environmental temperature may lead to large decreases in physiological performance. One way in which tropical organisms may mitigate the detrimental effects of warming is through evolutionary change in thermal physiology. The speed and magnitude of this response depend, in part, on the strength of climate-driven selection. However, many ectotherms use behavioral adjustments to maintain preferred body temperatures in the face of environmental variation. These behaviors may shelter individuals from natural selection, preventing evolutionary adaptation to changing conditions. Here, we mimic the effects of climate change by experimentally transplanting a population of Anolis sagrei lizards to a novel thermal environment. Transplanted lizards experienced warmer and more thermally variable conditions, which resulted in strong directional selection on thermal performance traits. These same traits were not under selection in a reference population studied in a less thermally stressful environment. Our results indicate that climate change can exert strong natural selection on tropical ectotherms, despite their ability to thermoregulate behaviorally. To the extent that thermal performance traits are heritable, populations may be capable of rapid adaptation to anthropogenic warming. PMID:25225361

Natural selection on thermal performance in a novel thermal environment.

PubMed

Logan, Michael L; Cox, Robert M; Calsbeek, Ryan

2014-09-30

Tropical ectotherms are thought to be especially vulnerable to climate change because they are adapted to relatively stable temperature regimes, such that even small increases in environmental temperature may lead to large decreases in physiological performance. One way in which tropical organisms may mitigate the detrimental effects of warming is through evolutionary change in thermal physiology. The speed and magnitude of this response depend, in part, on the strength of climate-driven selection. However, many ectotherms use behavioral adjustments to maintain preferred body temperatures in the face of environmental variation. These behaviors may shelter individuals from natural selection, preventing evolutionary adaptation to changing conditions. Here, we mimic the effects of climate change by experimentally transplanting a population of Anolis sagrei lizards to a novel thermal environment. Transplanted lizards experienced warmer and more thermally variable conditions, which resulted in strong directional selection on thermal performance traits. These same traits were not under selection in a reference population studied in a less thermally stressful environment. Our results indicate that climate change can exert strong natural selection on tropical ectotherms, despite their ability to thermoregulate behaviorally. To the extent that thermal performance traits are heritable, populations may be capable of rapid adaptation to anthropogenic warming.

Intelligent scanning: automated standard plane selection and biometric measurement of early gestational sac in routine ultrasound examination.

PubMed

Zhang, Ling; Chen, Siping; Chin, Chien Ting; Wang, Tianfu; Li, Shengli

2012-08-01

To assist radiologists and decrease interobserver variability when using 2D ultrasonography (US) to locate the standardized plane of early gestational sac (SPGS) and to perform gestational sac (GS) biometric measurements. In this paper, the authors report the design of the first automatic solution, called "intelligent scanning" (IS), for selecting SPGS and performing biometric measurements using real-time 2D US. First, the GS is efficiently and precisely located in each ultrasound frame by exploiting a coarse to fine detection scheme based on the training of two cascade AdaBoost classifiers. Next, the SPGS are automatically selected by eliminating false positives. This is accomplished using local context information based on the relative position of anatomies in the image sequence. Finally, a database-guided multiscale normalized cuts algorithm is proposed to generate the initial contour of the GS, based on which the GS is automatically segmented for measurement by a modified snake model. This system was validated on 31 ultrasound videos involving 31 pregnant volunteers. The differences between system performance and radiologist performance with respect to SPGS selection and length and depth (diameter) measurements are 7.5% ± 5.0%, 5.5% ± 5.2%, and 6.5% ± 4.6%, respectively. Additional validations prove that the IS precision is in the range of interobserver variability. Our system can display the SPGS along with biometric measurements in approximately three seconds after the video ends, when using a 1.9 GHz dual-core computer. IS of the GS from 2D real-time US is a practical, reproducible, and reliable approach.

Interaction of attention and acoustic factors in dichotic listening for fused words.

PubMed

McCulloch, Katie; Lachner Bass, Natascha; Dial, Heather; Hiscock, Merrill; Jansen, Ben

2017-07-01

Two dichotic listening experiments examined the degree to which the right-ear advantage (REA) for linguistic stimuli is altered by a "top-down" variable (i.e., directed attention) in conjunction with selected "bottom-up" (acoustic) variables. Halwes fused dichotic words were administered to 99 right-handed adults with instructions to attend to the left or right ear, or to divide attention equally. Stimuli in Experiment 1 were presented without noise or mixed with noise that was high-pass or low-pass filtered, or unfiltered. The stimuli themselves in Experiment 2 were high-pass or low-pass filtered, or unfiltered. The initial consonants of each dichotic pair were categorized according to voice onset time (VOT) and place of articulation (PoA). White noise extinguished both the REA and selective attention, and filtered noise nullified selective attention without extinguishing the REA. Frequency filtering of the words themselves did not alter performance. VOT effects were inconsistent across experiments but PoA analyses indicated that paired velar consonants (/k/ and /g/) yield a left-ear advantage and paradoxical selective-attention results. The findings show that ear asymmetry and the effectiveness of directed attention can be altered by bottom-up variables.

A fully synthetic human Fab antibody library based on fixed VH/VL framework pairings with favorable biophysical properties

PubMed Central

Tiller, Thomas; Schuster, Ingrid; Deppe, Dorothée; Siegers, Katja; Strohner, Ralf; Herrmann, Tanja; Berenguer, Marion; Poujol, Dominique; Stehle, Jennifer; Stark, Yvonne; Heßling, Martin; Daubert, Daniela; Felderer, Karin; Kaden, Stefan; Kölln, Johanna; Enzelberger, Markus; Urlinger, Stefanie

2013-01-01

This report describes the design, generation and testing of Ylanthia, a fully synthetic human Fab antibody library with 1.3E+11 clones. Ylanthia comprises 36 fixed immunoglobulin (Ig) variable heavy (VH)/variable light (VL) chain pairs, which cover a broad range of canonical complementarity-determining region (CDR) structures. The variable Ig heavy and Ig light (VH/VL) chain pairs were selected for biophysical characteristics favorable to manufacturing and development. The selection process included multiple parameters, e.g., assessment of protein expression yield, thermal stability and aggregation propensity in fragment antigen binding (Fab) and IgG1 formats, and relative Fab display rate on phage. The framework regions are fixed and the diversified CDRs were designed based on a systematic analysis of a large set of rearranged human antibody sequences. Care was taken to minimize the occurrence of potential posttranslational modification sites within the CDRs. Phage selection was performed against various antigens and unique antibodies with excellent biophysical properties were isolated. Our results confirm that quality can be built into an antibody library by prudent selection of unmodified, fully human VH/VL pairs as scaffolds. PMID:23571156

Which Are the Determinants of Online Students' Efficiency in Higher Education?

NASA Astrophysics Data System (ADS)

Castillo-Merino, David; Serradell-Lopez, Enric; González-González, Inés

International literature shows that the positive effect on students performance from the adoption of innovations in the technology of teaching and learning do not affect all teaching methods and learning styles equally, as it depends on university strategy and policy towards Information and Communication Technologies (ICT) adoption, students abilities, technology uses in the educational process by teachers and students, or the selection of a methodology that matches with digital uses. This paper provides empirical answers to these questions with data from online students at the Open University of Catalonia (UOC). An empirical model based on structural equations has been defined to explain complex relationships between variables. Our results show that motivation is the main variable affecting online students' performance. It appears as a latent variable influenced by students' perception of efficiency, a driver for indirect positive and significant effect on students' performance from students' ability in ICT uses.

The Effect of Biological Movement Variability on the Performance of the Golf Swing in High- and Low-Handicapped Players

ERIC Educational Resources Information Center

Bradshaw, Elizabeth J.; Keogh, Justin W. L.; Hume, Patria A.; Maulder, Peter S.; Nortje, Jacques; Marnewick, Michel

2009-01-01

The purpose of this study was to examine the role of neuromotor noise on golf swing performance in high- and low-handicap players. Selected two-dimensional kinematic measures of 20 male golfers (n = 10 per high- or low-handicap group) performing 10 golf swings with a 5-iron club was obtained through video analysis. Neuromotor noise was calculated…

Early Brain Damage and the Development of Motor Behavior in Children: Clues for Therapeutic Intervention?

PubMed Central

Hadders-Algra, Mijna

2001-01-01

The Neuronal Group Selection Theory (NGST) could offer new insights into the mechanisms directing motor disorders, such as cerebral palsy and developmental coordination disorder. According to NGST, normal motor development is characterized by two phases of variability. Variation is not at random but determined by criteria set by genetic information. Development starts with the phase of primary variability,during which variation in motor behavior is not geared to external conditions. At function-specific ages secondary variability starts, during which motor performance can be adapted to specific situations. In both forms, of variability, selection on the basis of afferent information plays a significant role. From the NGST point of view, children with pre- or perinatally acquired brain damage, such as children with cerebral palsy and part of the children with developmental coordination disorder, suffer from stereotyped motor behavior, produced by a limited repertoire or primary (sub)cortical neuronal networks. These children also have roblems in selecting the most efficient neuronal activity, due to deficits in the processing of sensory information. Therefore, NGST suggests that intervention in these children at early age should aim at an enlargement of the primary neuronal networks. With increasing age, the emphasis of intervention could shift to the provision of ample opportunities for active practice, which might form a compensation for the impaired selection. PMID:11530887

The influence of anthropometric, kinematic and energetic variables and gender on swimming performance in youth athletes.

PubMed

Morais, Jorge E; Garrido, Nuno D; Marques, Mário C; Silva, António J; Marinho, Daniel A; Barbosa, Tiago M

2013-12-18

(i) gender; (ii) performance and; (iii) gender versus performance interactions in young swimmers' anthropometric, kinematic and energetic variables. One hundred and thirty six young swimmers (62 boys: 12.76 ± 0.72 years old at Tanner stages 1-2 by self-evaluation; and 64 girls: 11.89 ± 0.93 years old at Tanner stages 1-2 by self-evaluation) were evaluated. Performance, anthropometrics, kinematics and energetic variables were selected. There was a non-significant gender effect on performance, body mass, height, arm span, trunk transverse surface area, stroke length, speed fluctuation, swimming velocity, propulsive efficiency, stroke index and critical velocity. A significant gender effect was found for foot surface area, hand surface area and stroke frequency. A significant sports level effect was verified for all variables, except for stroke frequency, speed fluctuation and propulsive efficiency. Overall, swimmers in quartile 1 (the ones with highest sports level) had higher anthropometric dimensions, better stroke mechanics and energetics. These traits decrease consistently throughout following quartiles up to the fourth one (i.e. swimmers with the lowest sports level). There was a non-significant interaction between gender and sports level for all variables. Our main conclusions were as follows: (i) there are non-significant differences in performance, anthropometrics, kinematics and energetics between boys and girls; (ii) swimmers with best performance are taller, have higher surface areas and better stroke mechanics; (iii) there are non-significant interactions between sports level and gender for anthropometrics, kinematics and energetics.

Variable selection in near-infrared spectroscopy: benchmarking of feature selection methods on biodiesel data.

PubMed

Balabin, Roman M; Smirnov, Sergey V

2011-04-29

During the past several years, near-infrared (near-IR/NIR) spectroscopy has increasingly been adopted as an analytical tool in various fields from petroleum to biomedical sectors. The NIR spectrum (above 4000 cm(-1)) of a sample is typically measured by modern instruments at a few hundred of wavelengths. Recently, considerable effort has been directed towards developing procedures to identify variables (wavelengths) that contribute useful information. Variable selection (VS) or feature selection, also called frequency selection or wavelength selection, is a critical step in data analysis for vibrational spectroscopy (infrared, Raman, or NIRS). In this paper, we compare the performance of 16 different feature selection methods for the prediction of properties of biodiesel fuel, including density, viscosity, methanol content, and water concentration. The feature selection algorithms tested include stepwise multiple linear regression (MLR-step), interval partial least squares regression (iPLS), backward iPLS (BiPLS), forward iPLS (FiPLS), moving window partial least squares regression (MWPLS), (modified) changeable size moving window partial least squares (CSMWPLS/MCSMWPLSR), searching combination moving window partial least squares (SCMWPLS), successive projections algorithm (SPA), uninformative variable elimination (UVE, including UVE-SPA), simulated annealing (SA), back-propagation artificial neural networks (BP-ANN), Kohonen artificial neural network (K-ANN), and genetic algorithms (GAs, including GA-iPLS). Two linear techniques for calibration model building, namely multiple linear regression (MLR) and partial least squares regression/projection to latent structures (PLS/PLSR), are used for the evaluation of biofuel properties. A comparison with a non-linear calibration model, artificial neural networks (ANN-MLP), is also provided. Discussion of gasoline, ethanol-gasoline (bioethanol), and diesel fuel data is presented. The results of other spectroscopic techniques application, such as Raman, ultraviolet-visible (UV-vis), or nuclear magnetic resonance (NMR) spectroscopies, can be greatly improved by an appropriate feature selection choice. Copyright © 2011 Elsevier B.V. All rights reserved.

Using Seasonal Forecasts for medium-term Electricity Demand Forecasting on Italy

NASA Astrophysics Data System (ADS)

De Felice, M.; Alessandri, A.; Ruti, P.

2012-12-01

Electricity demand forecast is an essential tool for energy management and operation scheduling for electric utilities. In power engineering, medium-term forecasting is defined as the prediction up to 12 months ahead, and commonly is performed considering weather climatology and not actual forecasts. This work aims to analyze the predictability of electricity demand on seasonal time scale, considering seasonal samples, i.e. average on three months. Electricity demand data has been provided by Italian Transmission System Operator for eight different geographical areas, in Fig. 1 for each area is shown the average yearly demand anomaly for each season. This work uses data for each summer during 1990-2010 and all the datasets have been pre-processed to remove trends and reduce the influence of calendar and economic effects. The choice of focusing this research on the summer period is due to the critical peaks of demand that power grid is subject during hot days. Weather data have been included considering observations provided by ECMWF ERA-INTERIM reanalyses. Primitive variables (2-metres temperature, pressure, etc) and derived variables (cooling and heating degree days) have been averaged for summer months. A particular attention has been given to the influence of persistence of positive temperature anomaly and a derived variable which count the number of consecutive days of extreme-days has been used. Electricity demand forecast has been performed using linear and nonlinear regression methods and stepwise model selection procedures have been used to perform a variable selection with respect to performance measures. Significance tests on multiple linear regression showed the importance of cooling degree days during summer in the North-East and South of Italy with an increase of statistical significance after 2003, a result consistent with the diffusion of air condition and ventilation equipment in the last decade. Finally, using seasonal climate forecasts we evaluate the performances of electricity demand forecast performed with predicted variables on Italian regions with encouraging results on the South of Italy. This work gives an initial assessment on the predictability of electricity demand on seasonal time scale, evaluating the relevance of climate information provided by seasonal forecasts for electricity management during high-demand periods.;

Efficient Variable Selection Method for Exposure Variables on Binary Data

NASA Astrophysics Data System (ADS)

Ohno, Manabu; Tarumi, Tomoyuki

In this paper, we propose a new variable selection method for "robust" exposure variables. We define "robust" as property that the same variable can select among original data and perturbed data. There are few studies of effective for the selection method. The problem that selects exposure variables is almost the same as a problem that extracts correlation rules without robustness. [Brin 97] is suggested that correlation rules are possible to extract efficiently using chi-squared statistic of contingency table having monotone property on binary data. But the chi-squared value does not have monotone property, so it's is easy to judge the method to be not independent with an increase in the dimension though the variable set is completely independent, and the method is not usable in variable selection for robust exposure variables. We assume anti-monotone property for independent variables to select robust independent variables and use the apriori algorithm for it. The apriori algorithm is one of the algorithms which find association rules from the market basket data. The algorithm use anti-monotone property on the support which is defined by association rules. But independent property does not completely have anti-monotone property on the AIC of independent probability model, but the tendency to have anti-monotone property is strong. Therefore, selected variables with anti-monotone property on the AIC have robustness. Our method judges whether a certain variable is exposure variable for the independent variable using previous comparison of the AIC. Our numerical experiments show that our method can select robust exposure variables efficiently and precisely.

Variability in Floral Scent in Rewarding and Deceptive Orchids: The Signature of Pollinator-imposed Selection?

PubMed Central

Salzmann, Charlotte C.; Nardella, Antonio M.; Cozzolino, Salvatore; Schiestl, Florian P.

2007-01-01

Background and Aims A comparative investigation was made of floral scent variation in the closely related, food-rewarding Anacamptis coriophora and the food-deceptive Anacamptis morio in order to identify patterns of variability of odour compounds in the two species and their role in pollinator attraction/avoidance learning. Methods Scent was collected from plants in natural populations and samples were analysed via quantitative gas chromatography and mass spectrometry. Combined gas chromatography and electroantennographic detection was used to identify compounds that are detected by the pollinators. Experimental reduction of scent variability was performed in the field with plots of A. morio plants supplemented with a uniform amount of anisaldehyde. Key Results Both orchid species emitted complex odour bouquets. In A. coriophora the two main benzenoid compounds, hydroquinone dimethyl ether (1,4-dimethoxybenzene) and anisaldehyde (methoxybenzaldehyde), triggered electrophysiological responses in olfactory neurons of honey-bee and bumble-bee workers. The scent of A. morio, however, was too weak to elicit any electrophysiological responses. The overall variation in scent was significantly lower in the rewarding A. coriophora than in the deceptive A. morio, suggesting pollinator avoidance-learning selecting for high variation in the deceptive species. A. morio flowers supplemented with non-variable scent in plot experiments, however, did not show significantly reduced pollination success. Conclusions Whereas in the rewarding A. coriophora stabilizing selection imposed by floral constancy of the pollinators may reduce scent variability, in the deceptive A. morio the emitted scent seems to be too weak to be detected by pollinators and thus its high variability may result from relaxed selection on this floral trait. PMID:17684024

[Optimization of resistance training using elastic bands].

PubMed

Guex, K

2015-07-15

Resistance training using elastic bands allows to perform a large variety of exercises for upper and lower body. It can be considered as a real alternative to the use of fitness equipment or free weight. After having determined the goal of the resistance training (i.e., maximal strength, hypertrophy, power or local muscular endurance), the acute program variables (i.e., muscle action, loading, volume, exercise selection and order, rest periods and repetition velocity) must be selected regarding the recommendations for strength training. The load is the most important variable in resistance program design. To determine it in an accurate way, when using elastic bands, it is recommended to use the Multiple RM test.

Physical attraction to reliable, low variability nervous systems: Reaction time variability predicts attractiveness.

PubMed

Butler, Emily E; Saville, Christopher W N; Ward, Robert; Ramsey, Richard

2017-01-01

The human face cues a range of important fitness information, which guides mate selection towards desirable others. Given humans' high investment in the central nervous system (CNS), cues to CNS function should be especially important in social selection. We tested if facial attractiveness preferences are sensitive to the reliability of human nervous system function. Several decades of research suggest an operational measure for CNS reliability is reaction time variability, which is measured by standard deviation of reaction times across trials. Across two experiments, we show that low reaction time variability is associated with facial attractiveness. Moreover, variability in performance made a unique contribution to attractiveness judgements above and beyond both physical health and sex-typicality judgements, which have previously been associated with perceptions of attractiveness. In a third experiment, we empirically estimated the distribution of attractiveness preferences expected by chance and show that the size and direction of our results in Experiments 1 and 2 are statistically unlikely without reference to reaction time variability. We conclude that an operating characteristic of the human nervous system, reliability of information processing, is signalled to others through facial appearance. Copyright © 2016 Elsevier B.V. All rights reserved.

Molecular descriptor subset selection in theoretical peptide quantitative structure-retention relationship model development using nature-inspired optimization algorithms.

PubMed

Žuvela, Petar; Liu, J Jay; Macur, Katarzyna; Bączek, Tomasz

2015-10-06

In this work, performance of five nature-inspired optimization algorithms, genetic algorithm (GA), particle swarm optimization (PSO), artificial bee colony (ABC), firefly algorithm (FA), and flower pollination algorithm (FPA), was compared in molecular descriptor selection for development of quantitative structure-retention relationship (QSRR) models for 83 peptides that originate from eight model proteins. The matrix with 423 descriptors was used as input, and QSRR models based on selected descriptors were built using partial least squares (PLS), whereas root mean square error of prediction (RMSEP) was used as a fitness function for their selection. Three performance criteria, prediction accuracy, computational cost, and the number of selected descriptors, were used to evaluate the developed QSRR models. The results show that all five variable selection methods outperform interval PLS (iPLS), sparse PLS (sPLS), and the full PLS model, whereas GA is superior because of its lowest computational cost and higher accuracy (RMSEP of 5.534%) with a smaller number of variables (nine descriptors). The GA-QSRR model was validated initially through Y-randomization. In addition, it was successfully validated with an external testing set out of 102 peptides originating from Bacillus subtilis proteomes (RMSEP of 22.030%). Its applicability domain was defined, from which it was evident that the developed GA-QSRR exhibited strong robustness. All the sources of the model's error were identified, thus allowing for further application of the developed methodology in proteomics.

Data on polymorphisms in CYP2A6 associated to risk and predispose to smoking related variables.

PubMed

López-Flores, Luis A; Pérez-Rubio, Gloria; Ramírez-Venegas, Alejandra; Ambrocio-Ortiz, Enrique; Sansores, Raúl H; Falfán-Valencia, Ramcés

2017-12-01

This article contains data on the single nucleotide polymorphisms (SNPs) rs1137115, rs1801272 and rs28399433 rs4105144 in CYP2A6 associated to smoking related variables in Mexican Mestizo smokers (Pérez-Rubio et al., 2017) [1]. These SNPs were selected due to previous associations with other populations. Mexican Mestizo smokers were classified according their smoking pattern. A genetic association test was performed.

Performance and anthropometric characteristics of prospective elite junior Australian footballers: a case study in one junior team.

PubMed

Veale, James P; Pearce, Alan J; Koehn, Stefan; Carlson, John S

2008-04-01

The aim of the study was to compare anthropometric and physical performance data of players who were selected for a Victorian elite junior U18 Australian rules football squad. Prior to the selection of the final training squad, 54 players were assessed using a battery of standard anthropometric and physical performance tests. Multivariate analysis (MANOVA) showed significant (p<0.05) differences between selected and non-selected players when height, mass, 20-m sprint, agility and vertical jump height were considered collectively. Univariate analysis revealed that the vertical jump was the only significant (p<0.05) individual test and a near significant trend (p=0.07) for height differentiating between selected and non-selected players with medium effect sizes for all other tests except endurance. In this elite junior football squad, physical characteristics can be observed that discriminate between players selected and non-selected, and demonstrates the value of physical fitness testing within the talent identification process of junior (16-18 years) players for squad and/or team selection. Based on MANOVA results, the findings from this study suggest team selection appeared to be related to a generally higher performance across the range of tests. Further, age was not a confounding variable as players selected tended to be younger than those non-selected. These findings reflect the general consensus that, in state-based junior competition, there is evidence of promoting overall player development, selecting those who are generally able to fulfil a range of positions and selecting players on their potential.

A Commentary on the Use of Cut-Scores to Increase the Emphasis of Non-Cognitive Variables in Medical School Admissions

ERIC Educational Resources Information Center

Kreiter, Clarence D.

2007-01-01

The academic performance consequences of relying solely on non-cognitive factors for selecting applicants above a GPA and MCAT threshold have not been fully considered in the literature. This commentary considers the impact of using a "threshold approach" on academic performance as assessed with the USMLE Step 1.

An Investigation of NCLEX-PN Performance and Student Perceptions among Practical Nursing Graduates

ERIC Educational Resources Information Center

Abston-Coleman, Sharon L.; Levy, Dessie R.

2010-01-01

Students in practical nursing programs require 32 weeks of coursework (1 academic year) and completion of a national licensing exam (NCLEX-PN) to secure employment. The purpose of this study was to identify selected academic variables that were related to NCLEX-PN performance for first-time test takers of two types of practical nursing programs at…

Application of neural networks and sensitivity analysis to improved prediction of trauma survival.

PubMed

Hunter, A; Kennedy, L; Henry, J; Ferguson, I

2000-05-01

The performance of trauma departments is widely audited by applying predictive models that assess probability of survival, and examining the rate of unexpected survivals and deaths. Although the TRISS methodology, a logistic regression modelling technique, is still the de facto standard, it is known that neural network models perform better. A key issue when applying neural network models is the selection of input variables. This paper proposes a novel form of sensitivity analysis, which is simpler to apply than existing techniques, and can be used for both numeric and nominal input variables. The technique is applied to the audit survival problem, and used to analyse the TRISS variables. The conclusions discuss the implications for the design of further improved scoring schemes and predictive models.

Identifying overarching excipient properties towards an in-depth understanding of process and product performance for continuous twin-screw wet granulation.

PubMed

Willecke, N; Szepes, A; Wunderlich, M; Remon, J P; Vervaet, C; De Beer, T

2017-04-30

The overall objective of this work is to understand how excipient characteristics influence the process and product performance for a continuous twin-screw wet granulation process. The knowledge gained through this study is intended to be used for a Quality by Design (QbD)-based formulation design approach and formulation optimization. A total of 9 preferred fillers and 9 preferred binders were selected for this study. The selected fillers and binders were extensively characterized regarding their physico-chemical and solid state properties using 21 material characterization techniques. Subsequently, principal component analysis (PCA) was performed on the data sets of filler and binder characteristics in order to reduce the variety of single characteristics to a limited number of overarching properties. Four principal components (PC) explained 98.4% of the overall variability in the fillers data set, while three principal components explained 93.4% of the overall variability in the data set of binders. Both PCA models allowed in-depth evaluation of similarities and differences in the excipient properties. Copyright © 2017. Published by Elsevier B.V.

Advanced colorectal neoplasia risk stratification by penalized logistic regression.

PubMed

Lin, Yunzhi; Yu, Menggang; Wang, Sijian; Chappell, Richard; Imperiale, Thomas F

2016-08-01

Colorectal cancer is the second leading cause of death from cancer in the United States. To facilitate the efficiency of colorectal cancer screening, there is a need to stratify risk for colorectal cancer among the 90% of US residents who are considered "average risk." In this article, we investigate such risk stratification rules for advanced colorectal neoplasia (colorectal cancer and advanced, precancerous polyps). We use a recently completed large cohort study of subjects who underwent a first screening colonoscopy. Logistic regression models have been used in the literature to estimate the risk of advanced colorectal neoplasia based on quantifiable risk factors. However, logistic regression may be prone to overfitting and instability in variable selection. Since most of the risk factors in our study have several categories, it was tempting to collapse these categories into fewer risk groups. We propose a penalized logistic regression method that automatically and simultaneously selects variables, groups categories, and estimates their coefficients by penalizing the [Formula: see text]-norm of both the coefficients and their differences. Hence, it encourages sparsity in the categories, i.e. grouping of the categories, and sparsity in the variables, i.e. variable selection. We apply the penalized logistic regression method to our data. The important variables are selected, with close categories simultaneously grouped, by penalized regression models with and without the interactions terms. The models are validated with 10-fold cross-validation. The receiver operating characteristic curves of the penalized regression models dominate the receiver operating characteristic curve of naive logistic regressions, indicating a superior discriminative performance. © The Author(s) 2013.

Soft sensor for real-time cement fineness estimation.

PubMed

Stanišić, Darko; Jorgovanović, Nikola; Popov, Nikola; Čongradac, Velimir

2015-03-01

This paper describes the design and implementation of soft sensors to estimate cement fineness. Soft sensors are mathematical models that use available data to provide real-time information on process variables when the information, for whatever reason, is not available by direct measurement. In this application, soft sensors are used to provide information on process variable normally provided by off-line laboratory tests performed at large time intervals. Cement fineness is one of the crucial parameters that define the quality of produced cement. Providing real-time information on cement fineness using soft sensors can overcome limitations and problems that originate from a lack of information between two laboratory tests. The model inputs were selected from candidate process variables using an information theoretic approach. Models based on multi-layer perceptrons were developed, and their ability to estimate cement fineness of laboratory samples was analyzed. Models that had the best performance, and capacity to adopt changes in the cement grinding circuit were selected to implement soft sensors. Soft sensors were tested using data from a continuous cement production to demonstrate their use in real-time fineness estimation. Their performance was highly satisfactory, and the sensors proved to be capable of providing valuable information on cement grinding circuit performance. After successful off-line tests, soft sensors were implemented and installed in the control room of a cement factory. Results on the site confirm results obtained by tests conducted during soft sensor development. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Efficient robust doubly adaptive regularized regression with applications.

PubMed

Karunamuni, Rohana J; Kong, Linglong; Tu, Wei

2018-01-01

We consider the problem of estimation and variable selection for general linear regression models. Regularized regression procedures have been widely used for variable selection, but most existing methods perform poorly in the presence of outliers. We construct a new penalized procedure that simultaneously attains full efficiency and maximum robustness. Furthermore, the proposed procedure satisfies the oracle properties. The new procedure is designed to achieve sparse and robust solutions by imposing adaptive weights on both the decision loss and the penalty function. The proposed method of estimation and variable selection attains full efficiency when the model is correct and, at the same time, achieves maximum robustness when outliers are present. We examine the robustness properties using the finite-sample breakdown point and an influence function. We show that the proposed estimator attains the maximum breakdown point. Furthermore, there is no loss in efficiency when there are no outliers or the error distribution is normal. For practical implementation of the proposed method, we present a computational algorithm. We examine the finite-sample and robustness properties using Monte Carlo studies. Two datasets are also analyzed.

A review of selection-based tests of abiotic surrogates for species representation.

PubMed

Beier, Paul; Sutcliffe, Patricia; Hjort, Jan; Faith, Daniel P; Pressey, Robert L; Albuquerque, Fabio

2015-06-01

Because conservation planners typically lack data on where species occur, environmental surrogates--including geophysical settings and climate types--have been used to prioritize sites within a planning area. We reviewed 622 evaluations of the effectiveness of abiotic surrogates in representing species in 19 study areas. Sites selected using abiotic surrogates represented more species than an equal number of randomly selected sites in 43% of tests (55% for plants) and on average improved on random selection of sites by about 8% (21% for plants). Environmental diversity (ED) (42% median improvement on random selection) and biotically informed clusters showed promising results and merit additional testing. We suggest 4 ways to improve performance of abiotic surrogates. First, analysts should consider a broad spectrum of candidate variables to define surrogates, including rarely used variables related to geographic separation, distance from coast, hydrology, and within-site abiotic diversity. Second, abiotic surrogates should be defined at fine thematic resolution. Third, sites (the landscape units prioritized within a planning area) should be small enough to ensure that surrogates reflect species' environments and to produce prioritizations that match the spatial resolution of conservation decisions. Fourth, if species inventories are available for some planning units, planners should define surrogates based on the abiotic variables that most influence species turnover in the planning area. Although species inventories increase the cost of using abiotic surrogates, a modest number of inventories could provide the data needed to select variables and evaluate surrogates. Additional tests of nonclimate abiotic surrogates are needed to evaluate the utility of conserving nature's stage as a strategy for conservation planning in the face of climate change. © 2015 Society for Conservation Biology.

A Sensory Material Approach for Reducing Variability in Additively Manufactured Metal Parts.

PubMed

Franco, B E; Ma, J; Loveall, B; Tapia, G A; Karayagiz, K; Liu, J; Elwany, A; Arroyave, R; Karaman, I

2017-06-15

Despite the recent growth in interest for metal additive manufacturing (AM) in the biomedical and aerospace industries, variability in the performance, composition, and microstructure of AM parts remains a major impediment to its widespread adoption. The underlying physical mechanisms, which cause variability, as well as the scale and nature of variability are not well understood, and current methods are ineffective at capturing these details. Here, a Nickel-Titanium alloy is used as a sensory material in order to quantitatively, and rather rapidly, observe compositional and/or microstructural variability in selective laser melting manufactured parts; thereby providing a means to evaluate the role of process parameters on the variability. We perform detailed microstructural investigations using transmission electron microscopy at various locations to reveal the origins of microstructural variability in this sensory material. This approach helped reveal how reducing the distance between adjacent laser scans below a critical value greatly reduces both the in-sample and sample-to-sample variability. Microstructural investigations revealed that when the laser scan distance is wide, there is an inhomogeneity in subgrain size, precipitate distribution, and dislocation density in the microstructure, responsible for the observed variability. These results provide an important first step towards understanding the nature of variability in additively manufactured parts.

Scoping review of the literature on shoulder impairments and disability after neck dissection.

PubMed

Goldstein, David P; Ringash, Jolie; Bissada, Eric; Jaquet, Yves; Irish, Jonathan; Chepeha, Douglas; Davis, Aileen M

2014-02-01

The purpose of this article was to provide a review of the literature on shoulder disability after neck dissection. A literature review was performed using Ovid Medline and Embase databases. A total of 306 abstracts and 78 full-text articles were reviewed. Forty-two articles were eligible for inclusion. Patients undergoing nerve-sacrifice neck dissections have greater disability and lower quality of life scores than those undergoing neck dissections with the least manipulation (ie, selective neck dissections). Shoulder impairments can still occur in patients undergoing selective neck dissections. Disability typically improves over time in patients undergoing nerve-sparing neck dissections. There was significant variability in the literature in terms of the prevalence and recovery of shoulder morbidity after neck dissection. This variability may not just be related to surgical technique or rehabilitation, but also to study design, definitions, and the variability in disability questionnaires used. Copyright © 2013 Wiley Periodicals, Inc.

Development of custom measurement system for biomechanical evaluation of independent wheelchair transfers

PubMed Central

Koontz, Alicia M.; Lin, Yen-Sheng; Kankipati, Padmaja; Boninger, Michael L.; Cooper, Rory A.

2017-01-01

This study describes a new custom measurement system designed to investigate the biomechanics of sitting-pivot wheelchair transfers and assesses the reliability of selected biomechanical variables. Variables assessed include horizontal and vertical reaction forces underneath both hands and three-dimensional trunk, shoulder, and elbow range of motion. We examined the reliability of these measures between 5 consecutive transfer trials for 5 subjects with spinal cord injury and 12 non-disabled subjects while they performed a self-selected sitting pivot transfer from a wheelchair to a level bench. A majority of the biomechanical variables demonstrated moderate to excellent reliability (r > 0.6). The transfer measurement system recorded reliable and valid biomechanical data for future studies of sitting-pivot wheelchair transfers. We recommend a minimum of five transfer trials to obtain a reliable measure of transfer technique for future studies. PMID:22068376

Functional assessment using Constant's Shoulder Scale after modified radical and selective neck dissection.

PubMed

Chepeha, Douglas B; Taylor, Rodney J; Chepeha, Judith C; Teknos, Theodoros N; Bradford, Carol R; Sharma, Pramod K; Terrell, Jeffrey E; Wolf, Gregory T

2002-05-01

Constant's Shoulder Scale is a validated and widely applied instrument for assessment of shoulder function. We used this instrument to assess which treatment and demographic variables contribute to shoulder dysfunction after neck dissection in head and neck cancer patients. A convenience sample of 54 patients with 64 neck dissections and minimum follow-up of 11 months were evaluated. Thirty-two accessory nerve-sparing modified radical (MRND) and 32 selective neck (SND) dissections were performed. Multivariable regression analysis was used to determine the variables that were predictive for shoulder dysfunction. Clinical variables included age, time from surgery, handedness, weight, radiation therapy, neck dissection type, tumor stage, and site. Patients receiving MRND had significantly worse shoulder function than patients with SND (p =.0007). Radiation therapy contributed negatively, whereas weight contributed positively (p =.0001). The critical factors contributing to shoulder dysfunction after neck dissection were weight, radiation therapy, and neck dissection type. Copyright 2002 Wiley Periodicals, Inc.

Enhanced CAH dechlorination in a low permeability, variably-saturated medium

USGS Publications Warehouse

Martin, J.P.; Sorenson, K.S.; Peterson, L.N.; Brennan, R.A.; Werth, C.J.; Sanford, R.A.; Bures, G.H.; Taylor, C.J.; ,

2002-01-01

An innovative pilot-scale field test was performed to enhance the anaerobic reductive dechlorination (ARD) of chlorinated aliphatic hydrocarbons (CAHs) in a low permeability, variably-saturated formation. The selected technology combines the use of a hydraulic fracturing (fracking) technique with enhanced bioremediation through the creation of highly-permeable sand- and electron donor-filled fractures in the low permeability matrix. Chitin was selected as the electron donor because of its unique properties as a polymeric organic material and based on the results of lab studies that indicated its ability to support ARD. The distribution and impact of chitin- and sand-filled fractures to the system was evaluated using hydrologic, geophysical, and geochemical parameters. The results indicate that, where distributed, chitin favorably impacted redox conditions and supported enhanced ARD of CAHs. These results indicate that this technology may be a viable and cost-effective approach for remediation of low-permeability, variably saturated systems.

A Discriminant Distance Based Composite Vector Selection Method for Odor Classification

PubMed Central

Choi, Sang-Il; Jeong, Gu-Min

2014-01-01

We present a composite vector selection method for an effective electronic nose system that performs well even in noisy environments. Each composite vector generated from a electronic nose data sample is evaluated by computing the discriminant distance. By quantitatively measuring the amount of discriminative information in each composite vector, composite vectors containing informative variables can be distinguished and the final composite features for odor classification are extracted using the selected composite vectors. Using the only informative composite vectors can be also helpful to extract better composite features instead of using all the generated composite vectors. Experimental results with different volatile organic compound data show that the proposed system has good classification performance even in a noisy environment compared to other methods. PMID:24747735

A Monte Carlo investigation of thrust imbalance of solid rocket motor pairs

NASA Technical Reports Server (NTRS)

Sforzini, R. H.; Foster, W. A., Jr.; Johnson, J. S., Jr.

1974-01-01

A technique is described for theoretical, statistical evaluation of the thrust imbalance of pairs of solid-propellant rocket motors (SRMs) firing in parallel. Sets of the significant variables, determined as a part of the research, are selected using a random sampling technique and the imbalance calculated for a large number of motor pairs. The performance model is upgraded to include the effects of statistical variations in the ovality and alignment of the motor case and mandrel. Effects of cross-correlations of variables are minimized by selecting for the most part completely independent input variables, over forty in number. The imbalance is evaluated in terms of six time - varying parameters as well as eleven single valued ones which themselves are subject to statistical analysis. A sample study of the thrust imbalance of 50 pairs of 146 in. dia. SRMs of the type to be used on the space shuttle is presented. The FORTRAN IV computer program of the analysis and complete instructions for its use are included. Performance computation time for one pair of SRMs is approximately 35 seconds on the IBM 370/155 using the FORTRAN H compiler.

Diagnostic Value of Selected Echocardiographic Variables to Identify Pulmonary Hypertension in Dogs with Myxomatous Mitral Valve Disease.

PubMed

Tidholm, A; Höglund, K; Häggström, J; Ljungvall, I

2015-01-01

Pulmonary hypertension (PH) is commonly associated with myxomatous mitral valve disease (MMVD). Because dogs with PH present without measureable tricuspid regurgitation (TR), it would be useful to investigate echocardiographic variables that can identify PH. To investigate associations between estimated systolic TR pressure gradient (TRPG) and dog characteristics and selected echocardiographic variables. 156 privately owned dogs. Prospective observational study comparing the estimations of TRPG with dog characteristics and selected echocardiographic variables in dogs with MMVD and measureable TR. Tricuspid regurgitation pressure gradient was significantly (P < .05) associated with body weight corrected right (RVIDDn) and left (LVIDDn) ventricular end-diastolic and systolic (LVIDSn) internal diameters, pulmonary arterial (PA) acceleration to deceleration time ratio (AT/DT), heart rate, left atrial to aortic root ratio (LA/Ao), and the presence of congestive heart failure. Four variables remained significant in the multiple regression analysis with TRPG as a dependent variable: modeled as linear variables LA/Ao (P < .0001) and RVIDDn (P = .041), modeled as second order polynomial variables: AT/DT (P = .0039) and LVIDDn (P < .0001) The adjusted R(2) -value for the final model was 0.45 and receiver operating characteristic curve analysis suggested the model's performance to predict PH, defined as 36, 45, and 55 mmHg as fair (area under the curve [AUC] = 0.80), good (AUC = 0.86), and excellent (AUC = 0.92), respectively. In dogs with MMVD, the presence of PH might be suspected with the combination of decreased PA AT/DT, increased RVIDDn and LA/Ao, and a small or great LVIDDn. Copyright © 2015 The Authors Journal of Veterinary Internal Medicine published by Wiley Periodicals, Inc. on behalf of the American College of Veterinary Internal Medicine.

Principal components analysis in clinical studies.

PubMed

Zhang, Zhongheng; Castelló, Adela

2017-09-01

In multivariate analysis, independent variables are usually correlated to each other which can introduce multicollinearity in the regression models. One approach to solve this problem is to apply principal components analysis (PCA) over these variables. This method uses orthogonal transformation to represent sets of potentially correlated variables with principal components (PC) that are linearly uncorrelated. PCs are ordered so that the first PC has the largest possible variance and only some components are selected to represent the correlated variables. As a result, the dimension of the variable space is reduced. This tutorial illustrates how to perform PCA in R environment, the example is a simulated dataset in which two PCs are responsible for the majority of the variance in the data. Furthermore, the visualization of PCA is highlighted.

Multiple Off-Ice Performance Variables Predict On-Ice Skating Performance in Male and Female Division III Ice Hockey Players

PubMed Central

Janot, Jeffrey M.; Beltz, Nicholas M.; Dalleck, Lance D.

2015-01-01

The purpose of this study was to determine if off-ice performance variables could predict on-ice skating performance in Division III collegiate hockey players. Both men (n = 15) and women (n = 11) hockey players (age = 20.5 ± 1.4 years) participated in the study. The skating tests were agility cornering S-turn, 6.10 m acceleration, 44.80 m speed, modified repeat skate, and 15.20 m full speed. Off-ice variables assessed were years of playing experience, height, weight and percent body fat and off-ice performance variables included vertical jump (VJ), 40-yd dash (36.58m), 1-RM squat, pro-agility, Wingate peak power and peak power percentage drop (% drop), and 1.5 mile (2.4km) run. Results indicated that 40-yd dash (36.58m), VJ, 1.5 mile (2.4km) run, and % drop were significant predictors of skating performance for repeat skate (slowest, fastest, and average time) and 44.80 m speed time, respectively. Four predictive equations were derived from multiple regression analyses: 1) slowest repeat skate time = 2.362 + (1.68 x 40-yd dash time) + (0.005 x 1.5 mile run), 2) fastest repeat skate time = 9.762 - (0.089 x VJ) - (0.998 x 40-yd dash time), 3) average repeat skate time = 7.770 + (1.041 x 40-yd dash time) - (0.63 x VJ) + (0.003 x 1.5 mile time), and 4) 47.85 m speed test = 7.707 - (0.050 x VJ) - (0.01 x % drop). It was concluded that selected off-ice tests could be used to predict on-ice performance regarding speed and recovery ability in Division III male and female hockey players. Key points The 40-yd dash (36.58m) and vertical jump tests are significant predictors of on-ice skating performance specific to speed. In addition to 40-yd dash and vertical jump, the 1.5 mile (2.4km) run for time and percent power drop from the Wingate anaerobic power test were also significant predictors of skating performance that incorporates the aspect of recovery from skating activity. Due to the specificity of selected off-ice variables as predictors of on-ice performance, coaches can elect to assess player performance off-ice and focus on other uses of valuable ice time for their individual teams. PMID:26336338

Seedling performance covaries with dormancy thresholds: maintaining cryptic seed heteromorphism in a fire-prone system.

PubMed

Liyanage, Ganesha S; Ayre, David J; Ooi, Mark K J

2016-11-01

The production of morphologically different seeds or fruits by the same individual plant is known as seed heteromorphism. Such variation is expected to be selected for in disturbance-prone environments to allow germination into inherently variable regeneration niches. However, there are few demonstrations that heteromorphic seed characteristics should be favored by selection or how they may be maintained. In fire-prone ecosystems, seed heteromorphism is found in the temperatures needed to break physical dormancy, with seeds responding to high or low temperatures, ensuring emergence under variable fire-regime-related soil heating. Because of the relationship between dormancy-breaking temperature thresholds and fire severity, we hypothesize that different post-fire resource conditions have selected for covarying seedling traits, which contribute to maintenance of such heteromorphism. Seeds with low thresholds emerge into competitive conditions, either after low-severity fire or in vegetation gaps, and are therefore likely to experience selection for seedling characteristics that make them good competitors. On the other hand, high-temperature-threshold seeds would emerge into less competitive environments, indicative of stand-clearing high-severity fires, and would not experience the same selective forces. We identified high and low-threshold seed morphs via dormancy-breaking heat treatments and germination trials for two study species and compared seed mass and other morphological characteristics between morphs. We then grew seedlings from the two different morphs, with and without competition, and measured growth and biomass allocation as indicators of seedling performance. Seedlings from low-threshold seeds of both species performed better than their high-threshold counterparts, growing more quickly under competitive conditions, confirming that different performance can result from this seed characteristic. Seed mass or appearance did not differ between morphs, indicating that dormancy-breaking temperature threshold variation is a form of cryptic heteromorphism. The potential shown for the selective influence of different post-fire environmental conditions on seedling performance provides evidence of a mechanism for the maintenance of heteromorphic variation in dormancy-breaking temperature thresholds. © 2016 The Authors. Ecology, published by Wiley Periodicals, Inc., on behalf of the Ecological Society of America.

A new model of selection in women's handball.

PubMed

Srhoj, Vatromir; Rogulj, Nenad; Zagorac, Nebojsa; Katić, Ratko

2006-09-01

The aim of the study was to assess the basic motor abilities that determine top performance in women's handball, and to identify test panel for primary selection at handball school. The study included 155 female attendants of the Split Handball School, mean age 12.5 years. Differences in the basic motor abilities between the subjects that developed into elite handball players after 7-year training process and those that abandoned handball for being unable to meet the competition criteria were evaluated by use of discriminative analysis. The former were found to have also been superior initially in all variables analyzed, and in arm coordination, overall body coordination, throw and jump explosive strength, arm movement frequency and repetitive trunk strength in particular. Motor superiority based on the abilities of coordination, explosive strength and speed determines performance in women's handball, qualifying these abilities as reliable selection criteria. Based on this study results, a new model of selection in women's handball, with fine arm coordination as the major limiting factor of performance, has been proposed.

Optimal Tuner Selection for Kalman Filter-Based Aircraft Engine Performance Estimation

NASA Technical Reports Server (NTRS)

Simon, Donald L.; Garg, Sanjay

2010-01-01

A linear point design methodology for minimizing the error in on-line Kalman filter-based aircraft engine performance estimation applications is presented. This technique specifically addresses the underdetermined estimation problem, where there are more unknown parameters than available sensor measurements. A systematic approach is applied to produce a model tuning parameter vector of appropriate dimension to enable estimation by a Kalman filter, while minimizing the estimation error in the parameters of interest. Tuning parameter selection is performed using a multi-variable iterative search routine which seeks to minimize the theoretical mean-squared estimation error. This paper derives theoretical Kalman filter estimation error bias and variance values at steady-state operating conditions, and presents the tuner selection routine applied to minimize these values. Results from the application of the technique to an aircraft engine simulation are presented and compared to the conventional approach of tuner selection. Experimental simulation results are found to be in agreement with theoretical predictions. The new methodology is shown to yield a significant improvement in on-line engine performance estimation accuracy

A Study of Factors Related to Success in Nursing Chemistry

ERIC Educational Resources Information Center

Mamantov, C. B.; Wyatt, W. W.

1978-01-01

Examines the relationship between selected variables in the student's background and success in nursing chemistry and the relationship between the student's performance on the American Chemical Society's Cooperative Examination and the Chemistry Achievement Examination of the National League for Nursing. (CP)

Physiological Changes in Elite Male Distance Runners Training for Olympic Competition.

ERIC Educational Resources Information Center

Martin, D. E.; And Others

1986-01-01

Nine elite male distance runners were evaluated by comprehensive periodic monitoring of selected blood chemistry variables, percent body fat and lean body mass, and cardiopulmonary performance as they prepared for the 1984 Olympic Summer Games. Results are discussed. (MT)

Fear of falling and gait variability in older adults: a systematic review and meta-analysis.

PubMed

Ayoubi, Farah; Launay, Cyrille P; Annweiler, Cédric; Beauchet, Olivier

2015-01-01

Fear of falling (FOF) and increased gait variability are both independent markers of gait instability. There is a complex interplay between both entities. The purposes of this study were (1) to perform a qualitative analysis of all published studies on FOF-related changes in gait variability through a systematic review, and (2) to quantitatively synthesize FOF-related changes in gait variability. A systematic Medline literature search was conducted in May 2014 using the Medical Subject Heading (MeSH) terms "Fear" OR "fear of falling" combined with "Accidental Falls" AND "Gait" OR "Gait Apraxia" OR "Gait Ataxia" OR "Gait disorders, Neurologic" OR "Gait assessment" OR "Functional gait assessment" AND "Self efficacy" OR "Self confidence" AND "Aged" OR "Aged, 80 and over." Systematic review and fixed-effects meta-analysis using an inverse-variance method were performed. Of the 2184 selected studies, 10 observational studies (including 5 cross-sectional studies, 4 prospective cohort studies, and 1 case-control study) met the selection criteria. All were of good quality. The number of participants ranged from 52 to 1307 older community-dwellers (26.2%-85.0% women). The meta-analysis was performed on 10 studies with a total of 999 cases and 4502 controls. In one study, the higher limits of the effect size's confidence interval (CI) were lower than zero. In the remaining studies, the higher limits of the CI were positive. The summary random effect size of 0.29 (95% CI 0.13-0.45) was significant albeit of small magnitude, and indicated that gait variability was overall 0.29 SD higher in FOF cases compared with controls. Our findings show that FOF is associated with a statistically significant, albeit of small magnitude, increase in gait variability. Copyright © 2015 AMDA – The Society for Post-Acute and Long-Term Care Medicine. Published by Elsevier Inc. All rights reserved.

Global Association of Air Pollution and Cardiorespiratory Diseases: A Systematic Review, Meta-Analysis, and Investigation of Modifier Variables

PubMed Central

Adams, Matthew D.; Arain, Altaf; Papatheodorou, Stefania; Koutrakis, Petros; Mahmoud, Moataz

2018-01-01

Background. Little is known about the health risks of air pollution and cardiorespiratory diseases, globally, across regions and populations, which may differ because of external factors. Objectives. We systematically reviewed the evidence on the association between air pollution and cardiorespiratory diseases (hospital admissions and mortality), including variability by energy, transportation, socioeconomic status, and air quality. Search Methods. We conducted a literature search (PubMed and Web of Science) for studies published between 2006 and May 11, 2016. Selection Criteria. We included studies if they met all of the following criteria: (1) considered at least 1 of these air pollutants: carbon monoxide, sulfur dioxide, nitrogen dioxide, ozone, or particulate matter (PM2.5 or PM10); (2) reported risk for hospital admissions, mortality, or both; (3) presented individual results for respiratory diseases, cardiovascular diseases, or both; (4) considered the age groups younger than 5 years, older than 65 years, or all ages; and (5) did not segregate the analysis by gender. Data Collection and Analysis. We extracted data from each study, including location, health outcome, and risk estimates. We performed a meta-analysis to estimate the overall effect and to account for both within- and between-study heterogeneity. Then, we applied a model selection (least absolute shrinkage and selection operator) to assess the modifier variables, and, lastly, we performed meta-regression analyses to evaluate the modifier variables contributing to heterogeneity among studies. Main Results. We assessed 2183 studies, of which we selected 529 for in-depth review, and 70 articles fulfilled our study inclusion criteria. The 70 studies selected for meta-analysis encompass more than 30 million events across 28 countries. We found positive associations between cardiorespiratory diseases and different air pollutants. For example, when we considered only the association between PM2.5 and respiratory diseases (Figure 1, we observed a risk equal to 2.7% (95% confidence interval = 0.9%, 7.7%). Our results showed statistical significance in the test of moderators for all pollutants, suggesting that the modifier variables influence the average cardiorespiratory disease risk and may explain the varying effects of air pollution. Conclusions. Variables related to aspects of energy, transportation, and socioeconomic status may explain the varying effect size of the association between air pollution and cardiorespiratory diseases. Public Health Implications. Our study provides a transferable model to estimate the health effects of air pollutants to support the creation of environmental health public policies for national and international intervention. PMID:29072932

Investigating the Effect of Recruitment Variability on Length-Based Recruitment Indices for Antarctic Krill Using an Individual-Based Population Dynamics Model

PubMed Central

Thanassekos, Stéphane; Cox, Martin J.; Reid, Keith

2014-01-01

Antarctic krill (Euphausia superba; herein krill) is monitored as part of an on-going fisheries observer program that collects length-frequency data. A krill feedback management programme is currently being developed, and as part of this development, the utility of data-derived indices describing population level processes is being assessed. To date, however, little work has been carried out on the selection of optimum recruitment indices and it has not been possible to assess the performance of length-based recruitment indices across a range of recruitment variability. Neither has there been an assessment of uncertainty in the relationship between an index and the actual level of recruitment. Thus, until now, it has not been possible to take into account recruitment index uncertainty in krill stock management or when investigating relationships between recruitment and environmental drivers. Using length-frequency samples from a simulated population – where recruitment is known – the performance of six potential length-based recruitment indices is assessed, by exploring the index-to-recruitment relationship under increasing levels of recruitment variability (from ±10% to ±100% around a mean annual recruitment). The annual minimum of the proportion of individuals smaller than 40 mm (F40 min, %) was selected because it had the most robust index-to-recruitment relationship across differing levels of recruitment variability. The relationship was curvilinear and best described by a power law. Model uncertainty was described using the 95% prediction intervals, which were used to calculate coverage probabilities and assess model performance. Despite being the optimum recruitment index, the performance of F40 min degraded under high (>50%) recruitment variability. Due to the persistence of cohorts in the population over several years, the inclusion of F40 min values from preceding years in the relationship used to estimate recruitment in a given year improved its accuracy (mean bias reduction of 8.3% when including three F40 min values under a recruitment variability of 60%). PMID:25470296

A discriminant function model for admission at undergraduate university level

NASA Astrophysics Data System (ADS)

Ali, Hamdi F.; Charbaji, Abdulrazzak; Hajj, Nada Kassim

1992-09-01

The study is aimed at predicting objective criteria based on a statistically tested model for admitting undergraduate students to Beirut University College. The University is faced with a dual problem of having to select only a fraction of an increasing number of applicants, and of trying to minimize the number of students placed on academic probation (currently 36 percent of new admissions). Out of 659 new students, a sample of 272 students (45 percent) were selected; these were all the students on the Dean's list and on academic probation. With academic performance as the dependent variable, the model included ten independent variables and their interactions. These variables included the type of high school, the language of instruction in high school, recommendations, sex, academic average in high school, score on the English Entrance Examination, the major in high school, and whether the major was originally applied for by the student. Discriminant analysis was used to evaluate the relative weight of the independent variables, and from the analysis three equations were developed, one for each academic division in the College. The predictive power of these equations was tested by using them to classify students not in the selected sample into successful and unsuccessful ones. Applicability of the model to other institutions of higher learning is discussed.

Relationship of nutrition and physical activity behaviors and fitness measures to academic performance for sixth graders in a midwest city school district.

PubMed

Edwards, Jane U; Mauch, Lois; Winkelman, Mark R

2011-02-01

To support curriculum and policy, a midwest city school district assessed the association of selected categories of nutrition and physical activity (NUTR/PA) behaviors, fitness measures, and body mass index (BMI) with academic performance (AP) for 800 sixth graders. Students completed an adapted Youth Risk Behavior Surveillance Survey (NUTR/PA behaviors), fitness assessments (mile run, curl-ups, push-ups, height, and weight) with results matched to standardized scores (Measures of Academic Progress [MAP]), meal price status, and gender. Differences in mean MAP scores (math and reading) were compared by selected categories of each variable utilizing 1-way analysis of variance. Associations were determined by stepwise multiple regression utilizing mean MAP scores (for math and for reading) as the dependent variable and NUTR/PA behaviors, fitness, and BMI categories as independent variables. Significance was set at α = 0.05. Higher MAP math scores were associated with NUTR (more milk and breakfast; less 100% fruit juice and sweetened beverages [SB]) and PA (increased vigorous PA and sports teams; reduced television), and fitness (higher mile run performance). Higher MAP reading scores were associated with NUTR (fewer SB) and PA (increased vigorous PA, reduced television). Regression analysis indicated about 11.1% of the variation in the mean MAP math scores and 6.7% of the mean MAP reading scores could be accounted for by selected NUTR/PA behaviors, fitness, meal price status, and gender. Many positive NUTR/PA behaviors and fitness measures were associated with higher MAP scores supporting the school district focus on healthy lifestyles. Additional factors, including meal price status and gender, contribute to AP. © 2011, Fargo Public School.

Covariate selection with group lasso and doubly robust estimation of causal effects

PubMed Central

Koch, Brandon; Vock, David M.; Wolfson, Julian

2017-01-01

Summary The efficiency of doubly robust estimators of the average causal effect (ACE) of a treatment can be improved by including in the treatment and outcome models only those covariates which are related to both treatment and outcome (i.e., confounders) or related only to the outcome. However, it is often challenging to identify such covariates among the large number that may be measured in a given study. In this paper, we propose GLiDeR (Group Lasso and Doubly Robust Estimation), a novel variable selection technique for identifying confounders and predictors of outcome using an adaptive group lasso approach that simultaneously performs coefficient selection, regularization, and estimation across the treatment and outcome models. The selected variables and corresponding coefficient estimates are used in a standard doubly robust ACE estimator. We provide asymptotic results showing that, for a broad class of data generating mechanisms, GLiDeR yields a consistent estimator of the ACE when either the outcome or treatment model is correctly specified. A comprehensive simulation study shows that GLiDeR is more efficient than doubly robust methods using standard variable selection techniques and has substantial computational advantages over a recently proposed doubly robust Bayesian model averaging method. We illustrate our method by estimating the causal treatment effect of bilateral versus single-lung transplant on forced expiratory volume in one year after transplant using an observational registry. PMID:28636276

Covariate selection with group lasso and doubly robust estimation of causal effects.

PubMed

Koch, Brandon; Vock, David M; Wolfson, Julian

2018-03-01

The efficiency of doubly robust estimators of the average causal effect (ACE) of a treatment can be improved by including in the treatment and outcome models only those covariates which are related to both treatment and outcome (i.e., confounders) or related only to the outcome. However, it is often challenging to identify such covariates among the large number that may be measured in a given study. In this article, we propose GLiDeR (Group Lasso and Doubly Robust Estimation), a novel variable selection technique for identifying confounders and predictors of outcome using an adaptive group lasso approach that simultaneously performs coefficient selection, regularization, and estimation across the treatment and outcome models. The selected variables and corresponding coefficient estimates are used in a standard doubly robust ACE estimator. We provide asymptotic results showing that, for a broad class of data generating mechanisms, GLiDeR yields a consistent estimator of the ACE when either the outcome or treatment model is correctly specified. A comprehensive simulation study shows that GLiDeR is more efficient than doubly robust methods using standard variable selection techniques and has substantial computational advantages over a recently proposed doubly robust Bayesian model averaging method. We illustrate our method by estimating the causal treatment effect of bilateral versus single-lung transplant on forced expiratory volume in one year after transplant using an observational registry. © 2017, The International Biometric Society.

Production of Recombinant Human scFv Against Tetanus Toxin Heavy Chain by Phage Display Technology.

PubMed

Khalili, Ehsan; Lakzaei, Mostafa; Rasaee, Mohhamad Javad; Aminian, Mahdi

2015-10-01

Tetanus, as a major cause of death in developing countries, is caused by tetanus neurotoxin. Recombinant antibodies against tetanus neurotoxin can be useful in tetanus management. Phage display of antibody fragments from immune human antibody libraries with single chain constructs combining the variable fragments (scFv) has been one of the most prominent technologies in antibody engineering. The aim of this study was the generation of a single chain fragment of variable region (scFv) library and selection of specific antibodies with high affinity against tetanus toxin. Immune human single chain fragment variable (HuscFv) antibody phagemid library was displayed on pIII of filamentous bacteriophage. Selection of scFv clones was performed against tetanus toxin antigens after three rounds of panning. The selected scFv clones were analyzed for inhibition of tetanus toxin binding to ganglioside GT1b. After the third round of panning, over 35 HuscFv phages specific for tetanus toxin were isolated from this library of which 15 clones were found to bind specifically to tetanus toxin. The selected HuscFv phages expressed as a soluble HuscFv peptide and some clones showed positive signals against tetanus toxin. We found that six HuscFv clones inhibit toxin binding to ganglioside GT1b. These selected antibodies can be used in the management of tetanus.

Estimating and mapping ecological processes influencing microbial community assembly

PubMed Central

Stegen, James C.; Lin, Xueju; Fredrickson, Jim K.; Konopka, Allan E.

2015-01-01

Ecological community assembly is governed by a combination of (i) selection resulting from among-taxa differences in performance; (ii) dispersal resulting from organismal movement; and (iii) ecological drift resulting from stochastic changes in population sizes. The relative importance and nature of these processes can vary across environments. Selection can be homogeneous or variable, and while dispersal is a rate, we conceptualize extreme dispersal rates as two categories; dispersal limitation results from limited exchange of organisms among communities, and homogenizing dispersal results from high levels of organism exchange. To estimate the influence and spatial variation of each process we extend a recently developed statistical framework, use a simulation model to evaluate the accuracy of the extended framework, and use the framework to examine subsurface microbial communities over two geologic formations. For each subsurface community we estimate the degree to which it is influenced by homogeneous selection, variable selection, dispersal limitation, and homogenizing dispersal. Our analyses revealed that the relative influences of these ecological processes vary substantially across communities even within a geologic formation. We further identify environmental and spatial features associated with each ecological process, which allowed mapping of spatial variation in ecological-process-influences. The resulting maps provide a new lens through which ecological systems can be understood; in the subsurface system investigated here they revealed that the influence of variable selection was associated with the rate at which redox conditions change with subsurface depth. PMID:25983725

Selecting minimum dataset soil variables using PLSR as a regressive multivariate method

NASA Astrophysics Data System (ADS)

Stellacci, Anna Maria; Armenise, Elena; Castellini, Mirko; Rossi, Roberta; Vitti, Carolina; Leogrande, Rita; De Benedetto, Daniela; Ferrara, Rossana M.; Vivaldi, Gaetano A.

2017-04-01

Long-term field experiments and science-based tools that characterize soil status (namely the soil quality indices, SQIs) assume a strategic role in assessing the effect of agronomic techniques and thus in improving soil management especially in marginal environments. Selecting key soil variables able to best represent soil status is a critical step for the calculation of SQIs. Current studies show the effectiveness of statistical methods for variable selection to extract relevant information deriving from multivariate datasets. Principal component analysis (PCA) has been mainly used, however supervised multivariate methods and regressive techniques are progressively being evaluated (Armenise et al., 2013; de Paul Obade et al., 2016; Pulido Moncada et al., 2014). The present study explores the effectiveness of partial least square regression (PLSR) in selecting critical soil variables, using a dataset comparing conventional tillage and sod-seeding on durum wheat. The results were compared to those obtained using PCA and stepwise discriminant analysis (SDA). The soil data derived from a long-term field experiment in Southern Italy. On samples collected in April 2015, the following set of variables was quantified: (i) chemical: total organic carbon and nitrogen (TOC and TN), alkali-extractable C (TEC and humic substances - HA-FA), water extractable N and organic C (WEN and WEOC), Olsen extractable P, exchangeable cations, pH and EC; (ii) physical: texture, dry bulk density (BD), macroporosity (Pmac), air capacity (AC), and relative field capacity (RFC); (iii) biological: carbon of the microbial biomass quantified with the fumigation-extraction method. PCA and SDA were previously applied to the multivariate dataset (Stellacci et al., 2016). PLSR was carried out on mean centered and variance scaled data of predictors (soil variables) and response (wheat yield) variables using the PLS procedure of SAS/STAT. In addition, variable importance for projection (VIP) statistics was used to quantitatively assess the predictors most relevant for response variable estimation and then for variable selection (Andersen and Bro, 2010). PCA and SDA returned TOC and RFC as influential variables both on the set of chemical and physical data analyzed separately as well as on the whole dataset (Stellacci et al., 2016). Highly weighted variables in PCA were also TEC, followed by K, and AC, followed by Pmac and BD, in the first PC (41.2% of total variance); Olsen P and HA-FA in the second PC (12.6%), Ca in the third (10.6%) component. Variables enabling maximum discrimination among treatments for SDA were WEOC, on the whole dataset, humic substances, followed by Olsen P, EC and clay, in the separate data analyses. The highest PLS-VIP statistics were recorded for Olsen P and Pmac, followed by TOC, TEC, pH and Mg for chemical variables and clay, RFC and AC for the physical variables. Results show that different methods may provide different ranking of the selected variables and the presence of a response variable, in regressive techniques, may affect variable selection. Further investigation with different response variables and with multi-year datasets would allow to better define advantages and limits of single or combined approaches. Acknowledgment The work was supported by the projects "BIOTILLAGE, approcci innovative per il miglioramento delle performances ambientali e produttive dei sistemi cerealicoli no-tillage", financed by PSR-Basilicata 2007-2013, and "DESERT, Low-cost water desalination and sensor technology compact module" financed by ERANET-WATERWORKS 2014. References Andersen C.M. and Bro R., 2010. Variable selection in regression - a tutorial. Journal of Chemometrics, 24 728-737. Armenise et al., 2013. Developing a soil quality index to compare soil fitness for agricultural use under different managements in the mediterranean environment. Soil and Tillage Research, 130:91-98. de Paul Obade et al., 2016. A standardized soil quality index for diverse field conditions. Sci. Total Env. 541:424-434. Pulido Moncada et al., 2014. Data-driven analysis of soil quality indicators using limited data. Geoderma, 235:271-278. Stellacci et al., 2016. Comparison of different multivariate methods to select key soil variables for soil quality indices computation. XLV Congress of the Italian Society of Agronomy (SIA), Sassari, 20-22 September 2016.

Consequences of plant-chemical diversity for domestic goat food preference in Mediterranean forests

NASA Astrophysics Data System (ADS)

Baraza, Elena; Hódar, José A.; Zamora, Regino

2009-01-01

The domestic goat, a major herbivore in the Mediterranean basin, has demonstrated a strong ability to adapt its feeding behaviour to the chemical characteristics of food, selecting plants according to their nutritive quality. In this study, we determine some chemical characteristics related to plant nutritional quality and its variability among and within five tree species, these being the main components of the mountain forests of SE Spain, with the aim of determining their influence on food selection by this generalist herbivore. We analyse nitrogen, total phenols, condensed tannins and fibre concentration as an indicator of the nutritive value of the different trees. To determine the preference by the domestic goat, we performed two types of feeding-choice assays, where goats had to select between different species or between branches of the same species but from trees of different nutritional quality. The analysis of the plant nutritional quality showed significant differences in the chemical characteristics between species, and a high variability within species. However, when faced with different tree species, the domestic goat selected some of them but showed striking individual differences between goats. When selecting between trees of the same species, the goats showed no differential selection. This limited effect of chemical plant characteristics, together with the variability in foraging behaviour, resulted in a widespread consumption of diverse plant species, which can potentially modulate the effect of the goat on vegetation composition, and open the way for the conservation of traditional livestock grazing on natural protected areas.

Equilibrium Noise in Ion Selective Field Effect Transistors.

DTIC Science & Technology

1982-07-21

face. These parameters have been evaluated for several ion-selective membranes. DD I JAN ") 1473 EDITION or I Mov 09SIS OSSOLETE ONi 0102-LF-0146601...the "integrated circuit" noise on the processing parameters which were different for the two laboratories. This variability in the "integrated circuit...systems and is useful in the identification of the parameters limiting the performance of -11- these systems. In thermodynamic equilibrium, every

Convergent validity and sex differences in healthy elderly adults for performance on 3D virtual reality navigation learning and 2D hidden maze tasks.

PubMed

Tippett, William J; Lee, Jang-Han; Mraz, Richard; Zakzanis, Konstantine K; Snyder, Peter J; Black, Sandra E; Graham, Simon J

2009-04-01

This study assessed the convergent validity of a virtual environment (VE) navigation learning task, the Groton Maze Learning Test (GMLT), and selected traditional neuropsychological tests performed in a group of healthy elderly adults (n = 24). The cohort was divided equally between males and females to explore performance variability due to sex differences, which were subsequently characterized and reported as part of the analysis. To facilitate performance comparisons, specific "efficiency" scores were created for both the VE navigation task and the GMLT. Men reached peak performance more rapidly than women during VE navigation and on the GMLT and significantly outperformed women on the first learning trial in the VE. Results suggest reasonable convergent validity across the VE task, GMLT, and selected neuropsychological tests for assessment of spatial memory.

How players exploit variability and regularity of game actions in female volleyball teams.

PubMed

Ramos, Ana; Coutinho, Patrícia; Silva, Pedro; Davids, Keith; Mesquita, Isabel

2017-05-01

Variability analysis has been used to understand how competitive constraints shape different behaviours in team sports. In this study, we analysed and compared variability of tactical performance indices in players within complex I at two different competitive levels in volleyball. We also examined whether variability was influenced by set type and period. Eight matches from the 2012 Olympics competition and from the Portuguese national league in the 2014-2015 season were analysed (1496 rallies). Variability of setting conditions, attack zone, attack tempo and block opposition was assessed using Shannon entropy measures. Magnitude-based inferences were used to analyse the practical significance of compared values of selected variables. Results showed differences between elite and national teams for all variables, which were co-adapted to the competitive constraints of set type and set periods. Elite teams exploited system stability in setting conditions and block opposition, but greater unpredictability in zone and tempo of attack. These findings suggest that uncertainty in attacking actions was a key factor that could only be achieved with greater performance stability in other game actions. Data suggested how coaches could help setters develop the capacity to play at faster tempos, diversifying attack zones, especially at critical moments in competition.

Multimodal information Management: Evaluation of Auditory and Haptic Cues for NextGen Communication Displays

NASA Technical Reports Server (NTRS)

Begault, Durand R.; Bittner, Rachel M.; Anderson, Mark R.

2012-01-01

Auditory communication displays within the NextGen data link system may use multiple synthetic speech messages replacing traditional ATC and company communications. The design of an interface for selecting amongst multiple incoming messages can impact both performance (time to select, audit and release a message) and preference. Two design factors were evaluated: physical pressure-sensitive switches versus flat panel "virtual switches", and the presence or absence of auditory feedback from switch contact. Performance with stimuli using physical switches was 1.2 s faster than virtual switches (2.0 s vs. 3.2 s); auditory feedback provided a 0.54 s performance advantage (2.33 s vs. 2.87 s). There was no interaction between these variables. Preference data were highly correlated with performance.

Considerations for performance evaluation of solar heating and cooling systems

NASA Technical Reports Server (NTRS)

Littles, J. W.; Cody, J. C.

1975-01-01

One of the many factors which must be considered in performance evaluation of solar energy systems is the relative merit of a given solar energy system when compared to a standard conventional system. Although initial and operational costs will be dominant factors in the comparison of the two types of systems and will be given prime consideration in system selection, sufficient data are not yet available for a definitive treatment of these variables. It is possible, however, to formulate relationships between the nonsolar energy requirements of the solar energy systems and the energy requirements of a conventional system in terms of the primary performance parameters of the systems. Derivations of such relationships, some parametric data for selected ranges of the performance parameters, and data with respect to limiting conditions are presented.

Computational methods in the development of a knowledge-based system for the prediction of solid catalyst performance.

PubMed

Procelewska, Joanna; Galilea, Javier Llamas; Clerc, Frederic; Farrusseng, David; Schüth, Ferdi

2007-01-01

The objective of this work is the construction of a correlation between characteristics of heterogeneous catalysts, encoded in a descriptor vector, and their experimentally measured performances in the propene oxidation reaction. In this paper the key issue in the modeling process, namely the selection of adequate input variables, is explored. Several data-driven feature selection strategies were applied in order to obtain an estimate of the differences in variance and information content of various attributes, furthermore to compare their relative importance. Quantitative property activity relationship techniques using probabilistic neural networks have been used for the creation of various semi-empirical models. Finally, a robust classification model, assigning selected attributes of solid compounds as input to an appropriate performance class in the model reaction was obtained. It has been evident that the mathematical support for the primary attributes set proposed by chemists can be highly desirable.

Evaluation of Maryland abutment scour equation through selected threshold velocity methods

USGS Publications Warehouse

Benedict, S.T.

2010-01-01

The U.S. Geological Survey, in cooperation with the Maryland State Highway Administration, used field measurements of scour to evaluate the sensitivity of the Maryland abutment scour equation to the critical (or threshold) velocity variable. Four selected methods for estimating threshold velocity were applied to the Maryland abutment scour equation, and the predicted scour to the field measurements were compared. Results indicated that performance of the Maryland abutment scour equation was sensitive to the threshold velocity with some threshold velocity methods producing better estimates of predicted scour than did others. In addition, results indicated that regional stream characteristics can affect the performance of the Maryland abutment scour equation with moderate-gradient streams performing differently from low-gradient streams. On the basis of the findings of the investigation, guidance for selecting threshold velocity methods for application to the Maryland abutment scour equation are provided, and limitations are noted.

Use of the Performance Diagnostic Checklist to Select an Intervention Designed to Increase the Offering of Promotional Stamps at Two Sites of a Restaurant Franchise

ERIC Educational Resources Information Center

Rodriguez, Manuel; Wilder, David A.; Therrien, Kelly; Wine, Byron; Miranti, Reylissa; Daratany, Kenneth; Salume, Gloria; Baranovsky, Greg; Rodriquez, Matias

2006-01-01

The performance diagnostic checklist (PDC) was administered to examine the variables influencing the offering of promotional stamps by employees at two sites of a restaurant franchise. PDC results suggested that a lack of appropriate antecedents, equipment and processes, and consequences were responsible for the deficits. Based on these results,…

Multi-objective robust design of energy-absorbing components using coupled process-performance simulations

NASA Astrophysics Data System (ADS)

Najafi, Ali; Acar, Erdem; Rais-Rohani, Masoud

2014-02-01

The stochastic uncertainties associated with the material, process and product are represented and propagated to process and performance responses. A finite element-based sequential coupled process-performance framework is used to simulate the forming and energy absorption responses of a thin-walled tube in a manner that both material properties and component geometry can evolve from one stage to the next for better prediction of the structural performance measures. Metamodelling techniques are used to develop surrogate models for manufacturing and performance responses. One set of metamodels relates the responses to the random variables whereas the other relates the mean and standard deviation of the responses to the selected design variables. A multi-objective robust design optimization problem is formulated and solved to illustrate the methodology and the influence of uncertainties on manufacturability and energy absorption of a metallic double-hat tube. The results are compared with those of deterministic and augmented robust optimization problems.

Probabilistic Analysis of Gas Turbine Field Performance

NASA Technical Reports Server (NTRS)

Gorla, Rama S. R.; Pai, Shantaram S.; Rusick, Jeffrey J.

2002-01-01

A gas turbine thermodynamic cycle was computationally simulated and probabilistically evaluated in view of the several uncertainties in the performance parameters, which are indices of gas turbine health. Cumulative distribution functions and sensitivity factors were computed for the overall thermal efficiency and net specific power output due to the thermodynamic random variables. These results can be used to quickly identify the most critical design variables in order to optimize the design, enhance performance, increase system availability and make it cost effective. The analysis leads to the selection of the appropriate measurements to be used in the gas turbine health determination and to the identification of both the most critical measurements and parameters. Probabilistic analysis aims at unifying and improving the control and health monitoring of gas turbine aero-engines by increasing the quality and quantity of information available about the engine's health and performance.

LPTA versus Tradeoff: Analysis of Contract Source Selection Strategies and Performance Outcomes

DTIC Science & Technology

2016-06-15

methodologies contracting professionals employ to acquire what the DOD needs. Contracting professionals may use lowest price technically acceptable...17 1. 2. Lowest Price Technically Acceptable...Acquisition Streamlining Act Government Accountability Office highest technically rated offeror independent variable lowest price technically

Empirical Performance of Cross-Validation With Oracle Methods in a Genomics Context

PubMed Central

Martinez, Josue G.; Carroll, Raymond J.; Müller, Samuel; Sampson, Joshua N.; Chatterjee, Nilanjan

2012-01-01

When employing model selection methods with oracle properties such as the smoothly clipped absolute deviation (SCAD) and the Adaptive Lasso, it is typical to estimate the smoothing parameter by m-fold cross-validation, for example, m = 10. In problems where the true regression function is sparse and the signals large, such cross-validation typically works well. However, in regression modeling of genomic studies involving Single Nucleotide Polymorphisms (SNP), the true regression functions, while thought to be sparse, do not have large signals. We demonstrate empirically that in such problems, the number of selected variables using SCAD and the Adaptive Lasso, with 10-fold cross-validation, is a random variable that has considerable and surprising variation. Similar remarks apply to non-oracle methods such as the Lasso. Our study strongly questions the suitability of performing only a single run of m-fold cross-validation with any oracle method, and not just the SCAD and Adaptive Lasso. PMID:22347720

Regularization Paths for Conditional Logistic Regression: The clogitL1 Package.

PubMed

Reid, Stephen; Tibshirani, Rob

2014-07-01

We apply the cyclic coordinate descent algorithm of Friedman, Hastie, and Tibshirani (2010) to the fitting of a conditional logistic regression model with lasso [Formula: see text] and elastic net penalties. The sequential strong rules of Tibshirani, Bien, Hastie, Friedman, Taylor, Simon, and Tibshirani (2012) are also used in the algorithm and it is shown that these offer a considerable speed up over the standard coordinate descent algorithm with warm starts. Once implemented, the algorithm is used in simulation studies to compare the variable selection and prediction performance of the conditional logistic regression model against that of its unconditional (standard) counterpart. We find that the conditional model performs admirably on datasets drawn from a suitable conditional distribution, outperforming its unconditional counterpart at variable selection. The conditional model is also fit to a small real world dataset, demonstrating how we obtain regularization paths for the parameters of the model and how we apply cross validation for this method where natural unconditional prediction rules are hard to come by.

Regularization Paths for Conditional Logistic Regression: The clogitL1 Package

PubMed Central

Reid, Stephen; Tibshirani, Rob

2014-01-01

We apply the cyclic coordinate descent algorithm of Friedman, Hastie, and Tibshirani (2010) to the fitting of a conditional logistic regression model with lasso (ℓ1) and elastic net penalties. The sequential strong rules of Tibshirani, Bien, Hastie, Friedman, Taylor, Simon, and Tibshirani (2012) are also used in the algorithm and it is shown that these offer a considerable speed up over the standard coordinate descent algorithm with warm starts. Once implemented, the algorithm is used in simulation studies to compare the variable selection and prediction performance of the conditional logistic regression model against that of its unconditional (standard) counterpart. We find that the conditional model performs admirably on datasets drawn from a suitable conditional distribution, outperforming its unconditional counterpart at variable selection. The conditional model is also fit to a small real world dataset, demonstrating how we obtain regularization paths for the parameters of the model and how we apply cross validation for this method where natural unconditional prediction rules are hard to come by. PMID:26257587

Making stronger causal inferences: Accounting for selection bias in associations between high performance work systems, leadership, and employee and customer satisfaction.

PubMed

Schmidt, Joseph A; Pohler, Dionne M

2018-05-17

We develop competing hypotheses about the relationship between high performance work systems (HPWS) with employee and customer satisfaction. Drawing on 8 years of employee and customer survey data from a financial services firm, we used a recently developed empirical technique-covariate balanced propensity score (CBPS) weighting-to examine if the proposed relationships between HPWS and satisfaction outcomes can be explained by reverse causality, selection effects, or commonly omitted variables such as leadership behavior. The results provide support for leader behaviors as a primary driver of customer satisfaction, rather than HPWS, and also suggest that the problem of reverse causality requires additional attention in future human resource (HR) systems research. Model comparisons suggest that the estimates and conclusions vary across CBPS, meta-analytic, cross-sectional, and time-lagged models (with and without a lagged dependent variable as a control). We highlight the theoretical and methodological implications of the findings for HR systems research. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Firefly algorithm versus genetic algorithm as powerful variable selection tools and their effect on different multivariate calibration models in spectroscopy: A comparative study

NASA Astrophysics Data System (ADS)

Attia, Khalid A. M.; Nassar, Mohammed W. I.; El-Zeiny, Mohamed B.; Serag, Ahmed

2017-01-01

For the first time, a new variable selection method based on swarm intelligence namely firefly algorithm is coupled with three different multivariate calibration models namely, concentration residual augmented classical least squares, artificial neural network and support vector regression in UV spectral data. A comparative study between the firefly algorithm and the well-known genetic algorithm was developed. The discussion revealed the superiority of using this new powerful algorithm over the well-known genetic algorithm. Moreover, different statistical tests were performed and no significant differences were found between all the models regarding their predictabilities. This ensures that simpler and faster models were obtained without any deterioration of the quality of the calibration.

Recursive feature selection with significant variables of support vectors.

PubMed

Tsai, Chen-An; Huang, Chien-Hsun; Chang, Ching-Wei; Chen, Chun-Houh

2012-01-01

The development of DNA microarray makes researchers screen thousands of genes simultaneously and it also helps determine high- and low-expression level genes in normal and disease tissues. Selecting relevant genes for cancer classification is an important issue. Most of the gene selection methods use univariate ranking criteria and arbitrarily choose a threshold to choose genes. However, the parameter setting may not be compatible to the selected classification algorithms. In this paper, we propose a new gene selection method (SVM-t) based on the use of t-statistics embedded in support vector machine. We compared the performance to two similar SVM-based methods: SVM recursive feature elimination (SVMRFE) and recursive support vector machine (RSVM). The three methods were compared based on extensive simulation experiments and analyses of two published microarray datasets. In the simulation experiments, we found that the proposed method is more robust in selecting informative genes than SVMRFE and RSVM and capable to attain good classification performance when the variations of informative and noninformative genes are different. In the analysis of two microarray datasets, the proposed method yields better performance in identifying fewer genes with good prediction accuracy, compared to SVMRFE and RSVM.

Quantifying Variability of Avian Colours: Are Signalling Traits More Variable?

PubMed Central

Delhey, Kaspar; Peters, Anne

2008-01-01

Background Increased variability in sexually selected ornaments, a key assumption of evolutionary theory, is thought to be maintained through condition-dependence. Condition-dependent handicap models of sexual selection predict that (a) sexually selected traits show amplified variability compared to equivalent non-sexually selected traits, and since males are usually the sexually selected sex, that (b) males are more variable than females, and (c) sexually dimorphic traits more variable than monomorphic ones. So far these predictions have only been tested for metric traits. Surprisingly, they have not been examined for bright coloration, one of the most prominent sexual traits. This omission stems from computational difficulties: different types of colours are quantified on different scales precluding the use of coefficients of variation. Methodology/Principal Findings Based on physiological models of avian colour vision we develop an index to quantify the degree of discriminable colour variation as it can be perceived by conspecifics. A comparison of variability in ornamental and non-ornamental colours in six bird species confirmed (a) that those coloured patches that are sexually selected or act as indicators of quality show increased chromatic variability. However, we found no support for (b) that males generally show higher levels of variability than females, or (c) that sexual dichromatism per se is associated with increased variability. Conclusions/Significance We show that it is currently possible to realistically estimate variability of animal colours as perceived by them, something difficult to achieve with other traits. Increased variability of known sexually-selected/quality-indicating colours in the studied species, provides support to the predictions borne from sexual selection theory but the lack of increased overall variability in males or dimorphic colours in general indicates that sexual differences might not always be shaped by similar selective forces. PMID:18301766

Anthropometric and physical characteristics allow differentiation of young female volleyball players according to playing position and level of expertise

PubMed Central

Milić, M; Grgantov, Z; Chamari, K; Bianco, A; Padulo, J

2016-01-01

The aim of our study was to determine the differences in some anthropometric and physical performance variables of young Croatian female volleyball players (aged 13 to 15) in relation to playing position (i.e., independent variable) and performance level within each position (i.e., independent variable). Players were categorized according to playing position (i.e., role) as middle blockers (n=28), opposite hitters (n=41), passer-hitters (n=54), setters (n=30), and liberos (n=28). Within each position, players were divided into a more successful group and a less successful group according to team ranking in the latest regional championship and player quality within the team. Height and body mass, somatotype by the Heath-Carter method, and four tests of lower body power, speed, agility and upper body power (i.e., dependent variables) were assessed. Players in different positions differed significantly in height and all three somatotype components, but no significant differences were found in body mass, body mass index or measured physical performance variables. Players of different performance level differed significantly in both anthropometric and physical performance variables. Generally, middle blockers were taller, more ectomorphic, less mesomorphic and endomorphic, whereas liberos were shorter, less ectomorphic, more mesomorphic and endomorphic than players in other positions. More successful players in all positions had a lower body mass index, were less mesomorphic and endomorphic, and more ectomorphic than less successful players. Furthermore, more successful players showed better lower body power, speed, agility and upper body power. The results of this study can potentially provide coaches with useful indications about the use of somatotype selection and physical performance assessment for talent identification and development. PMID:28416892

On the period determination of ASAS eclipsing binaries

NASA Astrophysics Data System (ADS)

Mayangsari, L.; Priyatikanto, R.; Putra, M.

2014-03-01

Variable stars, or particularly eclipsing binaries, are very essential astronomical occurrence. Surveys are the backbone of astronomy, and many discoveries of variable stars are the results of surveys. All-Sky Automated Survey (ASAS) is one of the observing projects whose ultimate goal is photometric monitoring of variable stars. Since its first light in 1997, ASAS has collected 50,099 variable stars, with 11,076 eclipsing binaries among them. In the present work we focus on the period determination of the eclipsing binaries. Since the number of data points in each ASAS eclipsing binary light curve is sparse, period determination of any system is a not straightforward process. For 30 samples of such systems we compare the implementation of Lomb-Scargle algorithm which is an Fast Fourier Transform (FFT) basis and Phase Dispersion Minimization (PDM) method which is non-FFT basis to determine their period. It is demonstrated that PDM gives better performance at handling eclipsing detached (ED) systems whose variability are non-sinusoidal. More over, using semi-automatic recipes, we get better period solution and satisfactorily improve 53% of the selected object's light curves, but failed against another 7% of selected objects. In addition, we also highlight 4 interesting objects for further investigation.

Emotion rendering in music: range and characteristic values of seven musical variables.

PubMed

Bresin, Roberto; Friberg, Anders

2011-10-01

Many studies on the synthesis of emotional expression in music performance have focused on the effect of individual performance variables on perceived emotional quality by making a systematical variation of variables. However, most of the studies have used a predetermined small number of levels for each variable, and the selection of these levels has often been done arbitrarily. The main aim of this research work is to improve upon existing methodologies by taking a synthesis approach. In a production experiment, 20 performers were asked to manipulate values of 7 musical variables simultaneously (tempo, sound level, articulation, phrasing, register, timbre, and attack speed) for communicating 5 different emotional expressions (neutral, happy, scary, peaceful, sad) for each of 4 scores. The scores were compositions communicating four different emotions (happiness, sadness, fear, calmness). Emotional expressions and music scores were presented in combination and in random order for each performer for a total of 5 × 4 stimuli. The experiment allowed for a systematic investigation of the interaction between emotion of each score and intended expressed emotions by performers. A two-way analysis of variance (ANOVA), repeated measures, with factors emotion and score was conducted on the participants' values separately for each of the seven musical factors. There are two main results. The first one is that musical variables were manipulated in the same direction as reported in previous research on emotional expressive music performance. The second one is the identification for each of the five emotions the mean values and ranges of the five musical variables tempo, sound level, articulation, register, and instrument. These values resulted to be independent from the particular score and its emotion. The results presented in this study therefore allow for both the design and control of emotionally expressive computerized musical stimuli that are more ecologically valid than stimuli without performance variations. Copyright © 2011 Elsevier Srl. All rights reserved.

Bayesian dynamic modeling of time series of dengue disease case counts.

PubMed

Martínez-Bello, Daniel Adyro; López-Quílez, Antonio; Torres-Prieto, Alexander

2017-07-01

The aim of this study is to model the association between weekly time series of dengue case counts and meteorological variables, in a high-incidence city of Colombia, applying Bayesian hierarchical dynamic generalized linear models over the period January 2008 to August 2015. Additionally, we evaluate the model's short-term performance for predicting dengue cases. The methodology shows dynamic Poisson log link models including constant or time-varying coefficients for the meteorological variables. Calendar effects were modeled using constant or first- or second-order random walk time-varying coefficients. The meteorological variables were modeled using constant coefficients and first-order random walk time-varying coefficients. We applied Markov Chain Monte Carlo simulations for parameter estimation, and deviance information criterion statistic (DIC) for model selection. We assessed the short-term predictive performance of the selected final model, at several time points within the study period using the mean absolute percentage error. The results showed the best model including first-order random walk time-varying coefficients for calendar trend and first-order random walk time-varying coefficients for the meteorological variables. Besides the computational challenges, interpreting the results implies a complete analysis of the time series of dengue with respect to the parameter estimates of the meteorological effects. We found small values of the mean absolute percentage errors at one or two weeks out-of-sample predictions for most prediction points, associated with low volatility periods in the dengue counts. We discuss the advantages and limitations of the dynamic Poisson models for studying the association between time series of dengue disease and meteorological variables. The key conclusion of the study is that dynamic Poisson models account for the dynamic nature of the variables involved in the modeling of time series of dengue disease, producing useful models for decision-making in public health.

Identification of cognitive and non-cognitive predictive variables related to attrition in baccalaureate nursing education programs in Mississippi

NASA Astrophysics Data System (ADS)

Hayes, Catherine

2005-07-01

This study sought to identify a variable or variables predictive of attrition among baccalaureate nursing students. The study was quantitative in design and multivariate correlational statistics and discriminant statistical analysis were used to identify a model for prediction of attrition. The analysis then weighted variables according to their predictive value to determine the most parsimonious model with the greatest predictive value. Three public university nursing education programs in Mississippi offering a Bachelors Degree in Nursing were selected for the study. The population consisted of students accepted and enrolled in these three programs for the years 2001 and 2002 and graduating in the years 2003 and 2004 (N = 195). The categorical dependent variable was attrition (includes academic failure or withdrawal) from the program of nursing education. The ten independent variables selected for the study and considered to have possible predictive value were: Grade Point Average for Pre-requisite Course Work; ACT Composite Score, ACT Reading Subscore, and ACT Mathematics Subscore; Letter Grades in the Courses: Anatomy & Physiology and Lab I, Algebra I, English I (101), Chemistry & Lab I, and Microbiology & Lab I; and Number of Institutions Attended (Universities, Colleges, Junior Colleges or Community Colleges). Descriptive analysis was performed and the means of each of the ten independent variables was compared for students who attrited and those who were retained in the population. The discriminant statistical analysis performed created a matrix using the ten variable model that was able to correctly predicted attrition in the study's population in 77.6% of the cases. Variables were then combined and recombined to produce the most efficient and parsimonious model for prediction. A six variable model resulted which weighted each variable according to predictive value: GPA for Prerequisite Coursework, ACT Composite, English I, Chemistry & Lab I, Microbiology & Lab I, and Number of Institutions Attended. Results of the study indicate that it is possible to predict attrition among students enrolled in baccalaureate nursing education programs and that additional investigation on the subject is warranted.

A comparison of regression methods for model selection in individual-based landscape genetic analysis.

PubMed

Shirk, Andrew J; Landguth, Erin L; Cushman, Samuel A

2018-01-01

Anthropogenic migration barriers fragment many populations and limit the ability of species to respond to climate-induced biome shifts. Conservation actions designed to conserve habitat connectivity and mitigate barriers are needed to unite fragmented populations into larger, more viable metapopulations, and to allow species to track their climate envelope over time. Landscape genetic analysis provides an empirical means to infer landscape factors influencing gene flow and thereby inform such conservation actions. However, there are currently many methods available for model selection in landscape genetics, and considerable uncertainty as to which provide the greatest accuracy in identifying the true landscape model influencing gene flow among competing alternative hypotheses. In this study, we used population genetic simulations to evaluate the performance of seven regression-based model selection methods on a broad array of landscapes that varied by the number and type of variables contributing to resistance, the magnitude and cohesion of resistance, as well as the functional relationship between variables and resistance. We also assessed the effect of transformations designed to linearize the relationship between genetic and landscape distances. We found that linear mixed effects models had the highest accuracy in every way we evaluated model performance; however, other methods also performed well in many circumstances, particularly when landscape resistance was high and the correlation among competing hypotheses was limited. Our results provide guidance for which regression-based model selection methods provide the most accurate inferences in landscape genetic analysis and thereby best inform connectivity conservation actions. Published 2017. This article is a U.S. Government work and is in the public domain in the USA.

Selection of molecular descriptors with artificial intelligence for the understanding of HIV-1 protease peptidomimetic inhibitors-activity.

PubMed

Sirois, S; Tsoukas, C M; Chou, Kuo-Chen; Wei, Dongqing; Boucher, C; Hatzakis, G E

2005-03-01

Quantitative Structure Activity Relationship (QSAR) techniques are used routinely by computational chemists in drug discovery and development to analyze datasets of compounds. Quantitative numerical methods like Partial Least Squares (PLS) and Artificial Neural Networks (ANN) have been used on QSAR to establish correlations between molecular properties and bioactivity. However, ANN may be advantageous over PLS because it considers the interrelations of the modeled variables. This study focused on the HIV-1 Protease (HIV-1 Pr) inhibitors belonging to the peptidomimetic class of compounds. The main objective was to select molecular descriptors with the best predictive value for antiviral potency (Ki). PLS and ANN were used to predict Ki activity of HIV-1 Pr inhibitors and the results were compared. To address the issue of dimensionality reduction, Genetic Algorithms (GA) were used for variable selection and their performance was compared against that of ANN. Finally, the structure of the optimum ANN achieving the highest Pearson's-R coefficient was determined. On the basis of Pearson's-R, PLS and ANN were compared to determine which exhibits maximum performance. Training and validation of models was performed on 15 random split sets of the master dataset consisted of 231 compounds. For each compound 192 molecular descriptors were considered. The molecular structure and constant of inhibition (Ki) were selected from the NIAID database. Study findings suggested that non-covalent interactions such as hydrophobicity, shape and hydrogen bonding describe well the antiviral activity of the HIV-1 Pr compounds. The significance of lipophilicity and relationship to HIV-1 associated hyperlipidemia and lipodystrophy syndrome warrant further investigation.

Ontogenetic shifts in thermal tolerance, selected body temperature and thermal dependence of food assimilation and locomotor performance in a lacertid lizard, Eremias brenchleyi.

PubMed

Xu, Xue-Feng; Ji, Xiang

2006-01-01

We used Eremias brenchleyi as a model animal to examine differences in thermal tolerance, selected body temperature, and the thermal dependence of food assimilation and locomotor performance between juvenile and adult lizards. Adults selected higher body temperatures (33.5 vs. 31.7 degrees C) and were able to tolerate a wider range of body temperatures (3.4-43.6 vs. 5.1-40.8 degrees C) than juveniles. Within the body temperature range of 26-38 degrees C, adults overall ate more than juveniles, and food passage rate was faster in adults than juveniles. Apparent digestive coefficient (ADC) and assimilation efficiency (AE) varied among temperature treatments but no clear temperature associated patterns could be discerned for these two variables. At each test temperature ADC and AE were both higher in adults than in juveniles. Sprint speed increased with increase in body temperature at lower body temperatures, but decreased at higher body temperatures. At each test temperature adults ran faster than did juveniles, and the range of body temperatures where lizards maintained 90% of maximum speed differed between adults (27-34 degrees C) and juveniles (29-37 degrees C). Optimal temperatures and thermal sensitivities differed between food assimilation and sprint speed. Our results not only show strong patterns of ontogenetic variation in thermal tolerance, selected body temperature and thermal dependence of food assimilation and locomotor performance in E. brenchleyi, but also add support for the multiple optima hypothesis for the thermal dependence of behavioral and physiological variables in reptiles.

Resolving the Conflict Between Associative Overdominance and Background Selection

PubMed Central

Zhao, Lei; Charlesworth, Brian

2016-01-01

In small populations, genetic linkage between a polymorphic neutral locus and loci subject to selection, either against partially recessive mutations or in favor of heterozygotes, may result in an apparent selective advantage to heterozygotes at the neutral locus (associative overdominance) and a retardation of the rate of loss of variability by genetic drift at this locus. In large populations, selection against deleterious mutations has previously been shown to reduce variability at linked neutral loci (background selection). We describe analytical, numerical, and simulation studies that shed light on the conditions under which retardation vs. acceleration of loss of variability occurs at a neutral locus linked to a locus under selection. We consider a finite, randomly mating population initiated from an infinite population in equilibrium at a locus under selection. With mutation and selection, retardation occurs only when S, the product of twice the effective population size and the selection coefficient, is of order 1. With S >> 1, background selection always causes an acceleration of loss of variability. Apparent heterozygote advantage at the neutral locus is, however, always observed when mutations are partially recessive, even if there is an accelerated rate of loss of variability. With heterozygote advantage at the selected locus, loss of variability is nearly always retarded. The results shed light on experiments on the loss of variability at marker loci in laboratory populations and on the results of computer simulations of the effects of multiple selected loci on neutral variability. PMID:27182952

Rethinking Dental School Admission Criteria: Correlation Between Pre-Admission Variables and First-Year Performance for Six Classes at One Dental School.

PubMed

Rowland, Kevin C; Rieken, Susan

2018-04-01

Admissions committees in dental schools are charged with the responsibility of selecting candidates who will succeed in school and become successful members of the profession. Identifying students who will have academic difficulty is challenging. The aim of this study was to determine the predictive value of pre-admission variables for the first-year performance of six classes at one U.S. dental school. The authors hypothesized that the variables undergraduate grade point average (GPA), undergraduate science GPA (biology, chemistry, and physics), and Dental Admission Test (DAT) scores would predict the level of performance achieved in the first year of dental school, measured by year-end GPA. Data were collected in 2015 from school records for all 297 students in the six cohorts who completed the first year (Classes of 2007 through 2013). In the results, statistically significant correlations existed between all pre-admission variables and first-year GPA, but the associations were only weak to moderate. Lower performing students at the end of the first year (lowest 10% of GPA) had, on average, lower pre-admission variables than the other students, but the differences were small (≤10.8% in all categories). When all the pre-admission variables were considered together in a multiple regression analysis, a significant association was found between pre-admission variables and first-year GPA, but the association was weak (adjusted R 2 =0.238). This weak association suggests that these students' first-year dental school GPAs were mostly determined by factors other than the pre-admission variables studied and has resulted in the school's placing greater emphasis on other factors for admission decisions.

Fertilization Is Not a New Beginning: The Relationship between Sperm Longevity and Offspring Performance

PubMed Central

Crean, Angela J.; Dwyer, John M.; Marshall, Dustin J.

2012-01-01

Sperm are the most diverse cell type known: varying not only among- and within- species, but also among- and within-ejaculates of a single male. Recently, the causes and consequences of variability in sperm phenotypes have received much attention, but the importance of within-ejaculate variability remains largely unknown. Correlative evidence suggests that reduced within-ejaculate variation in sperm phenotype increases a male’s fertilization success in competitive conditions; but the transgenerational consequences of within-ejaculate variation in sperm phenotype remain relatively unexplored. Here we examine the relationship between sperm longevity and offspring performance in a marine invertebrate with external fertilization, Styela plicata. Offspring sired by longer-lived sperm had higher performance compared to offspring sired by freshly-extracted sperm of the same ejaculate, both in the laboratory and the field. This indicates that within-ejaculate differences in sperm longevity can influence offspring fitness – a source of variability in offspring phenotypes that has not previously been considered. Links between sperm phenotype and offspring performance may constrain responses to selection on either sperm or offspring traits, with broad ecological and evolutionary implications. PMID:23155458

Factor Analysis of Various Anaerobic Power Tests.

ERIC Educational Resources Information Center

Manning, James M.; And Others

A study investigated the relationship between selected anthropometric variables and of numerous anaerobic power tests with measures obtained on an isokinetic dynamometer. Thirty-one male college students performed several anaerobic power tests, including: the vertical jump using the Lewis formula; the Margaria-Kalamen stair climb test; the Wingate…

A Selectionist Perspective on Systemic and Behavioral Change in Organizations

ERIC Educational Resources Information Center

Sandaker, Ingunn

2009-01-01

This article provides a discussion of how different dynamics in production processes and communication structures in the organization serve as different environmental contingencies favoring different behavioral patterns and variability of performance in organizations. Finally, an elaboration on a systems perspective on the selection of corporate…

A Robust Adaptive Autonomous Approach to Optimal Experimental Design

NASA Astrophysics Data System (ADS)

Gu, Hairong

Experimentation is the fundamental tool of scientific inquiries to understand the laws governing the nature and human behaviors. Many complex real-world experimental scenarios, particularly in quest of prediction accuracy, often encounter difficulties to conduct experiments using an existing experimental procedure for the following two reasons. First, the existing experimental procedures require a parametric model to serve as the proxy of the latent data structure or data-generating mechanism at the beginning of an experiment. However, for those experimental scenarios of concern, a sound model is often unavailable before an experiment. Second, those experimental scenarios usually contain a large number of design variables, which potentially leads to a lengthy and costly data collection cycle. Incompetently, the existing experimental procedures are unable to optimize large-scale experiments so as to minimize the experimental length and cost. Facing the two challenges in those experimental scenarios, the aim of the present study is to develop a new experimental procedure that allows an experiment to be conducted without the assumption of a parametric model while still achieving satisfactory prediction, and performs optimization of experimental designs to improve the efficiency of an experiment. The new experimental procedure developed in the present study is named robust adaptive autonomous system (RAAS). RAAS is a procedure for sequential experiments composed of multiple experimental trials, which performs function estimation, variable selection, reverse prediction and design optimization on each trial. Directly addressing the challenges in those experimental scenarios of concern, function estimation and variable selection are performed by data-driven modeling methods to generate a predictive model from data collected during the course of an experiment, thus exempting the requirement of a parametric model at the beginning of an experiment; design optimization is performed to select experimental designs on the fly of an experiment based on their usefulness so that fewest designs are needed to reach useful inferential conclusions. Technically, function estimation is realized by Bayesian P-splines, variable selection is realized by Bayesian spike-and-slab prior, reverse prediction is realized by grid-search and design optimization is realized by the concepts of active learning. The present study demonstrated that RAAS achieves statistical robustness by making accurate predictions without the assumption of a parametric model serving as the proxy of latent data structure while the existing procedures can draw poor statistical inferences if a misspecified model is assumed; RAAS also achieves inferential efficiency by taking fewer designs to acquire useful statistical inferences than non-optimal procedures. Thus, RAAS is expected to be a principled solution to real-world experimental scenarios pursuing robust prediction and efficient experimentation.

Paper spray mass spectrometry and PLS-DA improved by variable selection for the forensic discrimination of beers.

PubMed

Pereira, Hebert Vinicius; Amador, Victória Silva; Sena, Marcelo Martins; Augusti, Rodinei; Piccin, Evandro

2016-10-12

Paper spray mass spectrometry (PS-MS) combined with partial least squares discriminant analysis (PLS-DA) was applied for the first time in a forensic context to a fast and effective differentiation of beers. Eight different brands of American standard lager beers produced by four different breweries (141 samples from 55 batches) were studied with the aim at performing a differentiation according to their market prices. The three leader brands in the Brazilian beer market, which have been subject to fraud, were modeled as the higher-price class, while the five brands most used for counterfeiting were modeled as the lower-price class. Parameters affecting the paper spray ionization were examined and optimized. The best MS signal stability and intensity was obtained while using the positive ion mode, with PS(+) mass spectra characterized by intense pairs of signals corresponding to sodium and potassium adducts of malto-oligosaccharides. Discrimination was not apparent neither by using visual inspection nor principal component analysis (PCA). However, supervised classification models provided high rates of sensitivity and specificity. A PLS-DA model using full scan mass spectra were improved by variable selection with ordered predictors selection (OPS), providing 100% of reliability rate and reducing the number of variables from 1701 to 60. This model was interpreted by detecting fifteen variables as the most significant VIP (variable importance in projection) scores, which were therefore considered diagnostic ions for this type of beer counterfeit. Copyright © 2016 Elsevier B.V. All rights reserved.

Evaluating choices by single neurons in the frontal lobe: outcome value encoded across multiple decision variables

PubMed Central

Kennerley, Steven W.; Wallis, Jonathan D.

2009-01-01

Damage to the frontal lobe can cause severe decision-making impairments. A mechanism that may underlie this is that neurons in the frontal cortex encode many variables that contribute to the valuation of a choice, such as its costs, benefits and probability of success. However, optimal decision-making requires that one considers these variables, not only when faced with the choice, but also when evaluating the outcome of the choice, in order to adapt future behaviour appropriately. To examine the role of the frontal cortex in encoding the value of different choice outcomes, we simultaneously recorded the activity of multiple single neurons in the anterior cingulate cortex (ACC), orbitofrontal cortex (OFC) and lateral prefrontal cortex (LPFC) while subjects evaluated the outcome of choices involving manipulations of probability, payoff and cost. Frontal neurons encoded many of the parameters that enabled the calculation of the value of these variables, including the onset and offset of reward and the amount of work performed, and often encoded the value of outcomes across multiple decision variables. In addition, many neurons encoded both the predicted outcome during the choice phase of the task as well as the experienced outcome in the outcome phase of the task. These patterns of selectivity were more prevalent in ACC relative to OFC and LPFC. These results support a role for the frontal cortex, principally ACC, in selecting between choice alternatives and evaluating the outcome of that selection thereby ensuring that choices are optimal and adaptive. PMID:19453638

Firefly as a novel swarm intelligence variable selection method in spectroscopy.

PubMed

Goodarzi, Mohammad; dos Santos Coelho, Leandro

2014-12-10

A critical step in multivariate calibration is wavelength selection, which is used to build models with better prediction performance when applied to spectral data. Up to now, many feature selection techniques have been developed. Among all different types of feature selection techniques, those based on swarm intelligence optimization methodologies are more interesting since they are usually simulated based on animal and insect life behavior to, e.g., find the shortest path between a food source and their nests. This decision is made by a crowd, leading to a more robust model with less falling in local minima during the optimization cycle. This paper represents a novel feature selection approach to the selection of spectroscopic data, leading to more robust calibration models. The performance of the firefly algorithm, a swarm intelligence paradigm, was evaluated and compared with genetic algorithm and particle swarm optimization. All three techniques were coupled with partial least squares (PLS) and applied to three spectroscopic data sets. They demonstrate improved prediction results in comparison to when only a PLS model was built using all wavelengths. Results show that firefly algorithm as a novel swarm paradigm leads to a lower number of selected wavelengths while the prediction performance of built PLS stays the same. Copyright © 2014. Published by Elsevier B.V.

Full-Length Characterization of Hepatitis C Virus Subtype 3a Reveals Novel Hypervariable Regions under Positive Selection during Acute Infection▿

PubMed Central

Humphreys, Isla; Fleming, Vicki; Fabris, Paolo; Parker, Joe; Schulenberg, Bodo; Brown, Anthony; Demetriou, Charis; Gaudieri, Silvana; Pfafferott, Katja; Lucas, Michaela; Collier, Jane; Huang, Kuan-Hsiang Gary; Pybus, Oliver G.; Klenerman, Paul; Barnes, Eleanor

2009-01-01

Hepatitis C virus subtype 3a is a highly prevalent and globally distributed strain that is often associated with infection via injection drug use. This subtype exhibits particular phenotypic characteristics. In spite of this, detailed genetic analysis of this subtype has rarely been performed. We performed full-length viral sequence analysis in 18 patients with chronic HCV subtype 3a infection and assessed genomic viral variability in comparison to other HCV subtypes. Two novel regions of intragenotypic hypervariability within the envelope protein E2, of HCV genotype 3a, were identified. We named these regions HVR495 and HVR575. They consisted of flanking conserved hydrophobic amino acids and central variable residues. A 5-amino-acid insertion found only in genotype 3a and a putative glycosylation site is contained within HVR575. Evolutionary analysis of E2 showed that positively selected sites within genotype 3a infection were largely restricted to HVR1, HVR495, and HVR575. Further analysis of clonal viral populations within single hosts showed that viral variation within HVR495 and HVR575 were subject to intrahost positive selecting forces. Longitudinal analysis of four patients with acute HCV subtype 3a infection sampled at multiple time points showed that positively selected mutations within HVR495 and HVR575 arose early during primary infection. HVR495 and HVR575 were not present in HCV subtypes 1a, 1b, 2a, or 6a. Some variability that was not subject to positive selection was present in subtype 4a HVR575. Further defining the functional significance of these regions may have important implications for genotype 3a E2 virus-receptor interactions and for vaccine studies that aim to induce cross-reactive anti-E2 antibodies. PMID:19740991

Exploratory Spectroscopy of Magnetic Cataclysmic Variables Candidates and Other Variable Objects

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

Oliveira, A. S.; Palhares, M. S.; Rodrigues, C. V.

2017-04-01

The increasing number of synoptic surveys made by small robotic telescopes, such as the photometric Catalina Real-Time Transient Survey (CRTS), provides a unique opportunity to discover variable sources and improves the statistical samples of such classes of objects. Our goal is the discovery of magnetic Cataclysmic Variables (mCVs). These are rare objects that probe interesting accretion scenarios controlled by the white-dwarf magnetic field. In particular, improved statistics of mCVs would help to address open questions on their formation and evolution. We performed an optical spectroscopy survey to search for signatures of magnetic accretion in 45 variable objects selected mostly from themore » CRTS. In this sample, we found 32 CVs, 22 being mCV candidates, 13 of which were previously unreported as such. If the proposed classifications are confirmed, it would represent an increase of 4% in the number of known polars and 12% in the number of known IPs. A fraction of our initial sample was classified as extragalactic sources or other types of variable stars by the inspection of the identification spectra. Despite the inherent complexity in identifying a source as an mCV, variability-based selection, followed by spectroscopic snapshot observations, has proved to be an efficient strategy for their discoveries, being a relatively inexpensive approach in terms of telescope time.« less

Effects of Three Sets of Instructional Strategies and Three Demographic Variables on the Food and Nutrition Test Performance of Some Jamaican Tenth-Graders

ERIC Educational Resources Information Center

Kelly, Dennis; Soyibo, Kola

2005-01-01

This study was designed to find out if students taught food and nutrition concepts using the lecture method and practical work would perform significantly better than their counterparts taught with the lecture and teacher demonstrations and the lecture method only. The sample comprised 114 Jamaican 10th-graders (56 boys, 58 girls) selected from…

Optimization on the impeller of a low-specific-speed centrifugal pump for hydraulic performance improvement

NASA Astrophysics Data System (ADS)

Pei, Ji; Wang, Wenjie; Yuan, Shouqi; Zhang, Jinfeng

2016-09-01

In order to widen the high-efficiency operating range of a low-specific-speed centrifugal pump, an optimization process for considering efficiencies under 1.0 Q d and 1.4 Q d is proposed. Three parameters, namely, the blade outlet width b 2, blade outlet angle β 2, and blade wrap angle φ, are selected as design variables. Impellers are generated using the optimal Latin hypercube sampling method. The pump efficiencies are calculated using the software CFX 14.5 at two operating points selected as objectives. Surrogate models are also constructed to analyze the relationship between the objectives and the design variables. Finally, the particle swarm optimization algorithm is applied to calculate the surrogate model to determine the best combination of the impeller parameters. The results show that the performance curve predicted by numerical simulation has a good agreement with the experimental results. Compared with the efficiencies of the original impeller, the hydraulic efficiencies of the optimized impeller are increased by 4.18% and 0.62% under 1.0 Q d and 1.4Qd, respectively. The comparison of inner flow between the original pump and optimized one illustrates the improvement of performance. The optimization process can provide a useful reference on performance improvement of other pumps, even on reduction of pressure fluctuations.

Is the Web as good as the lab? Comparable performance from Web and lab in cognitive/perceptual experiments.

PubMed

Germine, Laura; Nakayama, Ken; Duchaine, Bradley C; Chabris, Christopher F; Chatterjee, Garga; Wilmer, Jeremy B

2012-10-01

With the increasing sophistication and ubiquity of the Internet, behavioral research is on the cusp of a revolution that will do for population sampling what the computer did for stimulus control and measurement. It remains a common assumption, however, that data from self-selected Web samples must involve a trade-off between participant numbers and data quality. Concerns about data quality are heightened for performance-based cognitive and perceptual measures, particularly those that are timed or that involve complex stimuli. In experiments run with uncompensated, anonymous participants whose motivation for participation is unknown, reduced conscientiousness or lack of focus could produce results that would be difficult to interpret due to decreased overall performance, increased variability of performance, or increased measurement noise. Here, we addressed the question of data quality across a range of cognitive and perceptual tests. For three key performance metrics-mean performance, performance variance, and internal reliability-the results from self-selected Web samples did not differ systematically from those obtained from traditionally recruited and/or lab-tested samples. These findings demonstrate that collecting data from uncompensated, anonymous, unsupervised, self-selected participants need not reduce data quality, even for demanding cognitive and perceptual experiments.

Advancing Forward-Looking Metrics: A Linear Program Optimization and Robust Variable Selection for Change in Stock Levels as a Result of Recurring MICAP Parts

DTIC Science & Technology

2013-06-01

Kobu, 2007) Gunasekaran and Kobu also presented six observations as they relate to these key performance indicators ( KPI ), as follows: 1...Internal business process (50% of the KPI ) and customers (50% of the KPI ) play a significant role in SC environments. This implies that internal business...process PMs have significant impact on the operational performance. 2. The most widely used PM is financial performance (38% of the KPI ). This

DECIDE: a software for computer-assisted evaluation of diagnostic test performance.

PubMed

Chiecchio, A; Bo, A; Manzone, P; Giglioli, F

1993-05-01

The evaluation of the performance of clinical tests is a complex problem involving different steps and many statistical tools, not always structured in an organic and rational system. This paper presents a software which provides an organic system of statistical tools helping evaluation of clinical test performance. The program allows (a) the building and the organization of a working database, (b) the selection of the minimal set of tests with the maximum information content, (c) the search of the model best fitting the distribution of the test values, (d) the selection of optimal diagnostic cut-off value of the test for every positive/negative situation, (e) the evaluation of performance of the combinations of correlated and uncorrelated tests. The uncertainty associated with all the variables involved is evaluated. The program works in a MS-DOS environment with EGA or higher performing graphic card.

Improving permafrost distribution modelling using feature selection algorithms

NASA Astrophysics Data System (ADS)

Deluigi, Nicola; Lambiel, Christophe; Kanevski, Mikhail

2016-04-01

The availability of an increasing number of spatial data on the occurrence of mountain permafrost allows the employment of machine learning (ML) classification algorithms for modelling the distribution of the phenomenon. One of the major problems when dealing with high-dimensional dataset is the number of input features (variables) involved. Application of ML classification algorithms to this large number of variables leads to the risk of overfitting, with the consequence of a poor generalization/prediction. For this reason, applying feature selection (FS) techniques helps simplifying the amount of factors required and improves the knowledge on adopted features and their relation with the studied phenomenon. Moreover, taking away irrelevant or redundant variables from the dataset effectively improves the quality of the ML prediction. This research deals with a comparative analysis of permafrost distribution models supported by FS variable importance assessment. The input dataset (dimension = 20-25, 10 m spatial resolution) was constructed using landcover maps, climate data and DEM derived variables (altitude, aspect, slope, terrain curvature, solar radiation, etc.). It was completed with permafrost evidences (geophysical and thermal data and rock glacier inventories) that serve as training permafrost data. Used FS algorithms informed about variables that appeared less statistically important for permafrost presence/absence. Three different algorithms were compared: Information Gain (IG), Correlation-based Feature Selection (CFS) and Random Forest (RF). IG is a filter technique that evaluates the worth of a predictor by measuring the information gain with respect to the permafrost presence/absence. Conversely, CFS is a wrapper technique that evaluates the worth of a subset of predictors by considering the individual predictive ability of each variable along with the degree of redundancy between them. Finally, RF is a ML algorithm that performs FS as part of its overall operation. It operates by constructing a large collection of decorrelated classification trees, and then predicts the permafrost occurrence through a majority vote. With the so-called out-of-bag (OOB) error estimate, the classification of permafrost data can be validated as well as the contribution of each predictor can be assessed. The performances of compared permafrost distribution models (computed on independent testing sets) increased with the application of FS algorithms on the original dataset and irrelevant or redundant variables were removed. As a consequence, the process provided faster and more cost-effective predictors and a better understanding of the underlying structures residing in permafrost data. Our work demonstrates the usefulness of a feature selection step prior to applying a machine learning algorithm. In fact, permafrost predictors could be ranked not only based on their heuristic and subjective importance (expert knowledge), but also based on their statistical relevance in relation of the permafrost distribution.

Independence screening for high dimensional nonlinear additive ODE models with applications to dynamic gene regulatory networks.

PubMed

Xue, Hongqi; Wu, Shuang; Wu, Yichao; Ramirez Idarraga, Juan C; Wu, Hulin

2018-05-02

Mechanism-driven low-dimensional ordinary differential equation (ODE) models are often used to model viral dynamics at cellular levels and epidemics of infectious diseases. However, low-dimensional mechanism-based ODE models are limited for modeling infectious diseases at molecular levels such as transcriptomic or proteomic levels, which is critical to understand pathogenesis of diseases. Although linear ODE models have been proposed for gene regulatory networks (GRNs), nonlinear regulations are common in GRNs. The reconstruction of large-scale nonlinear networks from time-course gene expression data remains an unresolved issue. Here, we use high-dimensional nonlinear additive ODEs to model GRNs and propose a 4-step procedure to efficiently perform variable selection for nonlinear ODEs. To tackle the challenge of high dimensionality, we couple the 2-stage smoothing-based estimation method for ODEs and a nonlinear independence screening method to perform variable selection for the nonlinear ODE models. We have shown that our method possesses the sure screening property and it can handle problems with non-polynomial dimensionality. Numerical performance of the proposed method is illustrated with simulated data and a real data example for identifying the dynamic GRN of Saccharomyces cerevisiae. Copyright © 2018 John Wiley & Sons, Ltd.

Plasticity models of material variability based on uncertainty quantification techniques

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

Jones, Reese E.; Rizzi, Francesco; Boyce, Brad

The advent of fabrication techniques like additive manufacturing has focused attention on the considerable variability of material response due to defects and other micro-structural aspects. This variability motivates the development of an enhanced design methodology that incorporates inherent material variability to provide robust predictions of performance. In this work, we develop plasticity models capable of representing the distribution of mechanical responses observed in experiments using traditional plasticity models of the mean response and recently developed uncertainty quantification (UQ) techniques. Lastly, we demonstrate that the new method provides predictive realizations that are superior to more traditional ones, and how these UQmore » techniques can be used in model selection and assessing the quality of calibrated physical parameters.« less

EFFECTS OF PH, SOLID/SOLUTION RATIO, IONIC STRENGTH, AND ORGANIC ACIDS ON PB AND CD SOPRTION ON KAOLINITE

EPA Science Inventory

Potentiometric and ion-selective electrode titrations together with batch sorption/desorption experiments, were performed to explain the aqueous and surface complexation reactions between kaolinite, Pb, Cd and three organic acids. Variables included pH, ionic strength, metal conc...

Selective Attention, Anxiety, Depressive Symptomatology and Academic Performance in Adolescents

ERIC Educational Resources Information Center

Fernandez-Castillo, Antonio; Gutierrez-Rojas, Maria Esperanza

2009-01-01

Introduction: In this cross-sectional, descriptive research we studied the relation between three psychological variables (anxiety, depression and attention) in order to analyze their possible association with and predictive power for academic achievement (as expressed in school grades) in a sample of secondary students. Method: For this purpose…

Do We Need Incentives for PhD Supervisors?

ERIC Educational Resources Information Center

Sadowski, Dieter; Schneider, Peter; Thaller, Nicole

2008-01-01

This article presents empirical results of explorative case studies that examine whether the New Public Management mechanisms have improved the academic performance of PhD education in selected German and European economics departments. Our data rely on document analyses of organisational variables and in-depth semi-structured interviews with…

Forecasting of cyanobacterial density in Torrão reservoir using artificial neural networks.

PubMed

Torres, Rita; Pereira, Elisa; Vasconcelos, Vítor; Teles, Luís Oliva

2011-06-01

The ability of general regression neural networks (GRNN) to forecast the density of cyanobacteria in the Torrão reservoir (Tâmega river, Portugal), in a period of 15 days, based on three years of collected physical and chemical data, was assessed. Several models were developed and 176 were selected based on their correlation values for the verification series. A time lag of 11 was used, equivalent to one sample (periods of 15 days in the summer and 30 days in the winter). Several combinations of the series were used. Input and output data collected from three depths of the reservoir were applied (surface, euphotic zone limit and bottom). The model that presented a higher average correlation value presented the correlations 0.991; 0.843; 0.978 for training, verification and test series. This model had the three series independent in time: first test series, then verification series and, finally, training series. Only six input variables were considered significant to the performance of this model: ammonia, phosphates, dissolved oxygen, water temperature, pH and water evaporation, physical and chemical parameters referring to the three depths of the reservoir. These variables are common to the next four best models produced and, although these included other input variables, their performance was not better than the selected best model.

Determinants of financial performance of home-visit nursing agencies in Japan.

PubMed

Fukui, Sakiko; Yoshiuchi, Kazuhiro; Fujita, Junko; Ikezaki, Sumie

2014-01-09

Japan has the highest aging population in the world and promotion of home health services is an urgent policy issue. As home-visit nursing plays a major role in home health services, the Japanese government began promotion of this activity in 1994. However, the scale of home-visit nursing agencies has remained small (the average numbers of nursing staff and other staff were 4.2 and 1.7, respectively, in 2011) and financial performance (profitability) is a concern in such small agencies. Additionally, the factors related to profitability in home-visit nursing agencies in Japan have not been examined multilaterally and in detail. Therefore, the purpose of the study was to examine the determinants of financial performance of home-visit nursing agencies. We performed a nationwide survey of 2,912 randomly selected home-visit nursing agencies in Japan. Multinomial logistic regression was used to clarify the determinants of profitability of the agency (profitable, stable or unprofitable) based on variables related to management of the agency (operating structure, management by a nurse manager, employment, patient utilization, quality control, regional cooperation, and financial condition). Among the selected home-visit nursing agencies, responses suitable for analysis were obtained from 1,340 (effective response rate, 46.0%). Multinomial logistic regression analysis showed that both profitability and unprofitability were related to multiple variables in management of the agency when compared to agencies with stable financial performance. These variables included the number of nursing staff/rehabilitation staff/patients, being owned by a hospital, the number of cooperative hospitals, home-death rate among terminal patients, controlling staff objectives by nurse managers, and income going to compensation. The results suggest that many variables in management of a home-visit nursing agency, including the operating structure of the agency, regional cooperation, staff employment, patient utilization, and quality control of care, have an influence in both profitable and unprofitable agencies. These findings indicate the importance of consideration of management issues in achieving stable financial performance in home-visit nursing agencies in Japan. The findings may also be useful in other countries with growing aging populations.

Determinants of financial performance of home-visit nursing agencies in Japan

PubMed Central

2014-01-01

Background Japan has the highest aging population in the world and promotion of home health services is an urgent policy issue. As home-visit nursing plays a major role in home health services, the Japanese government began promotion of this activity in 1994. However, the scale of home-visit nursing agencies has remained small (the average numbers of nursing staff and other staff were 4.2 and 1.7, respectively, in 2011) and financial performance (profitability) is a concern in such small agencies. Additionally, the factors related to profitability in home-visit nursing agencies in Japan have not been examined multilaterally and in detail. Therefore, the purpose of the study was to examine the determinants of financial performance of home-visit nursing agencies. Methods We performed a nationwide survey of 2,912 randomly selected home-visit nursing agencies in Japan. Multinomial logistic regression was used to clarify the determinants of profitability of the agency (profitable, stable or unprofitable) based on variables related to management of the agency (operating structure, management by a nurse manager, employment, patient utilization, quality control, regional cooperation, and financial condition). Results Among the selected home-visit nursing agencies, responses suitable for analysis were obtained from 1,340 (effective response rate, 46.0%). Multinomial logistic regression analysis showed that both profitability and unprofitability were related to multiple variables in management of the agency when compared to agencies with stable financial performance. These variables included the number of nursing staff/rehabilitation staff/patients, being owned by a hospital, the number of cooperative hospitals, home-death rate among terminal patients, controlling staff objectives by nurse managers, and income going to compensation. Conclusions The results suggest that many variables in management of a home-visit nursing agency, including the operating structure of the agency, regional cooperation, staff employment, patient utilization, and quality control of care, have an influence in both profitable and unprofitable agencies. These findings indicate the importance of consideration of management issues in achieving stable financial performance in home-visit nursing agencies in Japan. The findings may also be useful in other countries with growing aging populations. PMID:24400964

Estimating and mapping ecological processes influencing microbial community assembly

DOE PAGES

Stegen, James C.; Lin, Xueju; Fredrickson, Jim K.; ...

2015-05-01

Ecological community assembly is governed by a combination of (i) selection resulting from among-taxa differences in performance; (ii) dispersal resulting from organismal movement; and (iii) ecological drift resulting from stochastic changes in population sizes. The relative importance and nature of these processes can vary across environments. Selection can be homogeneous or variable, and while dispersal is a rate, we conceptualize extreme dispersal rates as two categories; dispersal limitation results from limited exchange of organisms among communities, and homogenizing dispersal results from high levels of organism exchange. To estimate the influence and spatial variation of each process we extend a recentlymore » developed statistical framework, use a simulation model to evaluate the accuracy of the extended framework, and use the framework to examine subsurface microbial communities over two geologic formations. For each subsurface community we estimate the degree to which it is influenced by homogeneous selection, variable selection, dispersal limitation, and homogenizing dispersal. Our analyses revealed that the relative influences of these ecological processes vary substantially across communities even within a geologic formation. We further identify environmental and spatial features associated with each ecological process, which allowed mapping of spatial variation in ecological-process-influences. The resulting maps provide a new lens through which ecological systems can be understood; in the subsurface system investigated here they revealed that the influence of variable selection was associated with the rate at which redox conditions change with subsurface depth.« less

Human embryo culture media comparisons.

PubMed

Pool, Thomas B; Schoolfield, John; Han, David

2012-01-01

Every program of assisted reproduction strives to maximize pregnancy outcomes from in vitro fertilization and selecting an embryo culture medium, or medium pair, consistent with high success rates is key to this process. The common approach is to replace an existing medium with a new one of interest in the overall culture system and then perform enough cycles of IVF to see if a difference is noted both in laboratory measures of embryo quality and in pregnancy. This approach may allow a laboratory to select one medium over another but the outcomes are only relevant to that program, given that there are well over 200 other variables that may influence the results in an IVF cycle. A study design that will allow for a more global application of IVF results, ones due to culture medium composition as the single variable, is suggested. To perform a study of this design, the center must have a patient caseload appropriate to meet study entrance criteria, success rates high enough to reveal a difference if one exists and a strong program of quality assurance and control in both the laboratory and clinic. Sibling oocytes are randomized to two study arms and embryos are evaluated on day 3 for quality grades. Inter and intra-observer variability are evaluated by kappa statistics and statistical power and study size estimates are performed to bring discriminatory capability to the study. Finally, the complications associated with extending such a study to include blastocyst production on day 5 or 6 are enumerated.

Boosting multi-state models.

PubMed

Reulen, Holger; Kneib, Thomas

2016-04-01

One important goal in multi-state modelling is to explore information about conditional transition-type-specific hazard rate functions by estimating influencing effects of explanatory variables. This may be performed using single transition-type-specific models if these covariate effects are assumed to be different across transition-types. To investigate whether this assumption holds or whether one of the effects is equal across several transition-types (cross-transition-type effect), a combined model has to be applied, for instance with the use of a stratified partial likelihood formulation. Here, prior knowledge about the underlying covariate effect mechanisms is often sparse, especially about ineffectivenesses of transition-type-specific or cross-transition-type effects. As a consequence, data-driven variable selection is an important task: a large number of estimable effects has to be taken into account if joint modelling of all transition-types is performed. A related but subsequent task is model choice: is an effect satisfactory estimated assuming linearity, or is the true underlying nature strongly deviating from linearity? This article introduces component-wise Functional Gradient Descent Boosting (short boosting) for multi-state models, an approach performing unsupervised variable selection and model choice simultaneously within a single estimation run. We demonstrate that features and advantages in the application of boosting introduced and illustrated in classical regression scenarios remain present in the transfer to multi-state models. As a consequence, boosting provides an effective means to answer questions about ineffectiveness and non-linearity of single transition-type-specific or cross-transition-type effects.

Impact of multicollinearity on small sample hydrologic regression models

NASA Astrophysics Data System (ADS)

Kroll, Charles N.; Song, Peter

2013-06-01

Often hydrologic regression models are developed with ordinary least squares (OLS) procedures. The use of OLS with highly correlated explanatory variables produces multicollinearity, which creates highly sensitive parameter estimators with inflated variances and improper model selection. It is not clear how to best address multicollinearity in hydrologic regression models. Here a Monte Carlo simulation is developed to compare four techniques to address multicollinearity: OLS, OLS with variance inflation factor screening (VIF), principal component regression (PCR), and partial least squares regression (PLS). The performance of these four techniques was observed for varying sample sizes, correlation coefficients between the explanatory variables, and model error variances consistent with hydrologic regional regression models. The negative effects of multicollinearity are magnified at smaller sample sizes, higher correlations between the variables, and larger model error variances (smaller R2). The Monte Carlo simulation indicates that if the true model is known, multicollinearity is present, and the estimation and statistical testing of regression parameters are of interest, then PCR or PLS should be employed. If the model is unknown, or if the interest is solely on model predictions, is it recommended that OLS be employed since using more complicated techniques did not produce any improvement in model performance. A leave-one-out cross-validation case study was also performed using low-streamflow data sets from the eastern United States. Results indicate that OLS with stepwise selection generally produces models across study regions with varying levels of multicollinearity that are as good as biased regression techniques such as PCR and PLS.

Early-onset neonatal sepsis is associated with a high heart rate during automatically selected stationary periods.

PubMed

Nguyen, Nga; Vandenbroucke, Laurent; Hernández, Alfredo; Pham, Tu; Beuchée, Alain; Pladys, Patrick

2017-05-01

This study examined the heart rate variability characteristics associated with early-onset neonatal sepsis in a prospective, observational controlled study. Eligible patients were full-term neonates hospitalised with clinical signs that suggested early-onset sepsis and a C-reactive protein of >10 mg/L. Sepsis was considered proven in cases of symptomatic septicaemia, meningitis, pneumonia or enterocolitis. Heart rate variability parameters (n = 16) were assessed from five-, 15- and 30-minute stationary sequences automatically selected from electrocardiographic recordings performed at admission and compared with a control group using the U-test with post hoc Benjamini-Yekutieli correction. Stationary sequences corresponded to the periods with the lowest changes of heart rate variability over time. A total of 40 full-term infants were enrolled, including 14 with proven sepsis. The mean duration of the cardiac cycle length was lower in the proven sepsis group than in the control group (n = 11), without other significant changes in heart rate variability parameters. These durations, measured in five-minute stationary periods, were 406 (367-433) ms in proven sepsis group versus 507 (463-522) ms in the control group (p < 0.05). Early-onset neonatal sepsis was associated with a high mean heart rate measured during automatically selected stationary periods. ©2017 Foundation Acta Paediatrica. Published by John Wiley & Sons Ltd.

Variable Selection through Correlation Sifting

NASA Astrophysics Data System (ADS)

Huang, Jim C.; Jojic, Nebojsa

Many applications of computational biology require a variable selection procedure to sift through a large number of input variables and select some smaller number that influence a target variable of interest. For example, in virology, only some small number of viral protein fragments influence the nature of the immune response during viral infection. Due to the large number of variables to be considered, a brute-force search for the subset of variables is in general intractable. To approximate this, methods based on ℓ1-regularized linear regression have been proposed and have been found to be particularly successful. It is well understood however that such methods fail to choose the correct subset of variables if these are highly correlated with other "decoy" variables. We present a method for sifting through sets of highly correlated variables which leads to higher accuracy in selecting the correct variables. The main innovation is a filtering step that reduces correlations among variables to be selected, making the ℓ1-regularization effective for datasets on which many methods for variable selection fail. The filtering step changes both the values of the predictor variables and output values by projections onto components obtained through a computationally-inexpensive principal components analysis. In this paper we demonstrate the usefulness of our method on synthetic datasets and on novel applications in virology. These include HIV viral load analysis based on patients' HIV sequences and immune types, as well as the analysis of seasonal variation in influenza death rates based on the regions of the influenza genome that undergo diversifying selection in the previous season.

A Study on the Characteristics of Design Variables for IRSS Diffuser

NASA Astrophysics Data System (ADS)

Cho, Yong-Jin; Ko, Dae-Eun

2017-11-01

In modern naval ships, infrared signature suppression systems (IRSS) are installed to decrease the temperature of waste gas generated in propulsion engine and the metallic surface temperature of heated exhaust pipes. Generally, IRSS is composed of eductor, mixing tube, and diffuser. Diffuser serves to reduce the temperature by creating an air film using the pressure difference between internal gas and external air. In this study, design variables were selected by analyzing the diffuser and the characteristics of design variables that affect the performance of diffuser were examined using Taguchi experiment method. For the diffuser performance analysis, a heat flow analysis technique established in previous research was used. The IRSS performance evaluation was carried out based on the average area value of the metal surface temperature and the temperature of the exhaust gas at the outlet of the diffuser, which are variables directly related to the intensity of infrared signature in naval ships. It was verified that the exhaust gas temperature is greatly affected by changes in the diameter of the diffuser outlet, and the metal surface temperature of diffuser is greatly affected by changes in the number of diffuser rings.

A review of covariate selection for non-experimental comparative effectiveness research.

PubMed

Sauer, Brian C; Brookhart, M Alan; Roy, Jason; VanderWeele, Tyler

2013-11-01

This paper addresses strategies for selecting variables for adjustment in non-experimental comparative effectiveness research and uses causal graphs to illustrate the causal network that relates treatment to outcome. Variables in the causal network take on multiple structural forms. Adjustment for a common cause pathway between treatment and outcome can remove confounding, whereas adjustment for other structural types may increase bias. For this reason, variable selection would ideally be based on an understanding of the causal network; however, the true causal network is rarely known. Therefore, we describe more practical variable selection approaches based on background knowledge when the causal structure is only partially known. These approaches include adjustment for all observed pretreatment variables thought to have some connection to the outcome, all known risk factors for the outcome, and all direct causes of the treatment or the outcome. Empirical approaches, such as forward and backward selection and automatic high-dimensional proxy adjustment, are also discussed. As there is a continuum between knowing and not knowing the causal, structural relations of variables, we recommend addressing variable selection in a practical way that involves a combination of background knowledge and empirical selection and that uses high-dimensional approaches. This empirical approach can be used to select from a set of a priori variables based on the researcher's knowledge to be included in the final analysis or to identify additional variables for consideration. This more limited use of empirically derived variables may reduce confounding while simultaneously reducing the risk of including variables that may increase bias. Copyright © 2013 John Wiley & Sons, Ltd.

A Review of Covariate Selection for Nonexperimental Comparative Effectiveness Research

PubMed Central

Sauer, Brian C.; Brookhart, Alan; Roy, Jason; Vanderweele, Tyler

2014-01-01

This paper addresses strategies for selecting variables for adjustment in non-experimental comparative effectiveness research (CER), and uses causal graphs to illustrate the causal network that relates treatment to outcome. Variables in the causal network take on multiple structural forms. Adjustment for on a common cause pathway between treatment and outcome can remove confounding, while adjustment for other structural types may increase bias. For this reason variable selection would ideally be based on an understanding of the causal network; however, the true causal network is rarely know. Therefore, we describe more practical variable selection approaches based on background knowledge when the causal structure is only partially known. These approaches include adjustment for all observed pretreatment variables thought to have some connection to the outcome, all known risk factors for the outcome, and all direct causes of the treatment or the outcome. Empirical approaches, such as forward and backward selection and automatic high-dimensional proxy adjustment, are also discussed. As there is a continuum between knowing and not knowing the causal, structural relations of variables, we recommend addressing variable selection in a practical way that involves a combination of background knowledge and empirical selection and that uses the high-dimensional approaches. This empirical approach can be used to select from a set of a priori variables based on the researcher’s knowledge to be included in the final analysis or to identify additional variables for consideration. This more limited use of empirically-derived variables may reduce confounding while simultaneously reducing the risk of including variables that may increase bias. PMID:24006330

Ecological prediction with nonlinear multivariate time-frequency functional data models

USGS Publications Warehouse

Yang, Wen-Hsi; Wikle, Christopher K.; Holan, Scott H.; Wildhaber, Mark L.

2013-01-01

Time-frequency analysis has become a fundamental component of many scientific inquiries. Due to improvements in technology, the amount of high-frequency signals that are collected for ecological and other scientific processes is increasing at a dramatic rate. In order to facilitate the use of these data in ecological prediction, we introduce a class of nonlinear multivariate time-frequency functional models that can identify important features of each signal as well as the interaction of signals corresponding to the response variable of interest. Our methodology is of independent interest and utilizes stochastic search variable selection to improve model selection and performs model averaging to enhance prediction. We illustrate the effectiveness of our approach through simulation and by application to predicting spawning success of shovelnose sturgeon in the Lower Missouri River.

Design Optimization of a Centrifugal Fan with Splitter Blades

NASA Astrophysics Data System (ADS)

Heo, Man-Woong; Kim, Jin-Hyuk; Kim, Kwang-Yong

2015-05-01

Multi-objective optimization of a centrifugal fan with additionally installed splitter blades was performed to simultaneously maximize the efficiency and pressure rise using three-dimensional Reynolds-averaged Navier-Stokes equations and hybrid multi-objective evolutionary algorithm. Two design variables defining the location of splitter, and the height ratio between inlet and outlet of impeller were selected for the optimization. In addition, the aerodynamic characteristics of the centrifugal fan were investigated with the variation of design variables in the design space. Latin hypercube sampling was used to select the training points, and response surface approximation models were constructed as surrogate models of the objective functions. With the optimization, both the efficiency and pressure rise of the centrifugal fan with splitter blades were improved considerably compared to the reference model.

Firefly algorithm versus genetic algorithm as powerful variable selection tools and their effect on different multivariate calibration models in spectroscopy: A comparative study.

PubMed

Attia, Khalid A M; Nassar, Mohammed W I; El-Zeiny, Mohamed B; Serag, Ahmed

2017-01-05

For the first time, a new variable selection method based on swarm intelligence namely firefly algorithm is coupled with three different multivariate calibration models namely, concentration residual augmented classical least squares, artificial neural network and support vector regression in UV spectral data. A comparative study between the firefly algorithm and the well-known genetic algorithm was developed. The discussion revealed the superiority of using this new powerful algorithm over the well-known genetic algorithm. Moreover, different statistical tests were performed and no significant differences were found between all the models regarding their predictabilities. This ensures that simpler and faster models were obtained without any deterioration of the quality of the calibration. Copyright © 2016 Elsevier B.V. All rights reserved.

Quantitative structure-activity relationships studies of CCR5 inhibitors and toxicity of aromatic compounds using gene expression programming.

PubMed

Shi, Weimin; Zhang, Xiaoya; Shen, Qi

2010-01-01

Quantitative structure-activity relationship (QSAR) study of chemokine receptor 5 (CCR5) binding affinity of substituted 1-(3,3-diphenylpropyl)-piperidinyl amides and ureas and toxicity of aromatic compounds have been performed. The gene expression programming (GEP) was used to select variables and produce nonlinear QSAR models simultaneously using the selected variables. In our GEP implementation, a simple and convenient method was proposed to infer the K-expression from the number of arguments of the function in a gene, without building the expression tree. The results were compared to those obtained by artificial neural network (ANN) and support vector machine (SVM). It has been demonstrated that the GEP is a useful tool for QSAR modeling. Copyright 2009 Elsevier Masson SAS. All rights reserved.

Choosing a Surgeon: An Exploratory Study of Factors Influencing Selection of a Gender Affirmation Surgeon.

PubMed

Ettner, Randi; Ettner, Frederic; White, Tonya

2016-01-01

Purpose: Selecting a healthcare provider is often a complicated process. Many factors appear to govern the decision as to how to select the provider in the patient-provider relationship. While the possibility of changing primary care physicians or specialists exists, decisions regarding surgeons are immutable once surgery has been performed. This study is an attempt to assess the importance attached to various factors involved in selecting a surgeon to perform gender affirmation surgery (GAS). It was hypothesized that owing to the intimate nature of the surgery, the expense typically involved, the emotional meaning attached to the surgery, and other variables, decisions regarding choice of surgeon for this procedure would involve factors other than those that inform more typical healthcare provider selection or surgeon selection for other plastic/reconstructive procedures. Methods: Questionnaires were distributed to individuals who had undergone GAS and individuals who had undergone elective plastic surgery to assess decision-making. Results: The results generally confirm previous findings regarding how patients select providers. Conclusion: Choosing a surgeon to perform gender-affirming surgery is a challenging process, but patients are quite rational in their decision-making. Unlike prior studies, we did not find a preference for gender-concordant surgeons, even though the surgery involves the genital area. Providing strategies and resources for surgical selection can improve patient satisfaction.

Monosomy 3 by FISH in uveal melanoma: variability in techniques and results.

PubMed

Aronow, Mary; Sun, Yang; Saunthararajah, Yogen; Biscotti, Charles; Tubbs, Raymond; Triozzi, Pierre; Singh, Arun D

2012-09-01

Tumor monosomy 3 confers a poor prognosis in patients with uveal melanoma. We critically review the techniques used for fluorescence in situ hybridization (FISH) detection of monosomy 3 in order to assess variability in practice patterns and to explain differences in results. Significant variability that has likely affected reported results was found in tissue sampling methods, selection of FISH probes, number of cells counted, and the cut-off point used to determine monosomy 3 status. Clinical parameters and specific techniques employed to report FISH results should be specified so as to allow meta-analysis of published studies. FISH-based detection of monosomy 3 in uveal melanoma has not been performed in a standardized manner, which limits conclusions regarding its clinical utility. FISH is a widely available, versatile technology, and when performed optimally has the potential to be a valuable tool for determining the prognosis of uveal melanoma. Copyright © 2012 Elsevier Inc. All rights reserved.

Improvement of two-way continuous-variable quantum key distribution with virtual photon subtraction

NASA Astrophysics Data System (ADS)

Zhao, Yijia; Zhang, Yichen; Li, Zhengyu; Yu, Song; Guo, Hong

2017-08-01

We propose a method to improve the performance of two-way continuous-variable quantum key distribution protocol by virtual photon subtraction. The virtual photon subtraction implemented via non-Gaussian post-selection not only enhances the entanglement of two-mode squeezed vacuum state but also has advantages in simplifying physical operation and promoting efficiency. In two-way protocol, virtual photon subtraction could be applied on two sources independently. Numerical simulations show that the optimal performance of renovated two-way protocol is obtained with photon subtraction only used by Alice. The transmission distance and tolerable excess noise are improved by using the virtual photon subtraction with appropriate parameters. Moreover, the tolerable excess noise maintains a high value with the increase in distance so that the robustness of two-way continuous-variable quantum key distribution system is significantly improved, especially at long transmission distance.

Creating a non-linear total sediment load formula using polynomial best subset regression model

NASA Astrophysics Data System (ADS)

Okcu, Davut; Pektas, Ali Osman; Uyumaz, Ali

2016-08-01

The aim of this study is to derive a new total sediment load formula which is more accurate and which has less application constraints than the well-known formulae of the literature. 5 most known stream power concept sediment formulae which are approved by ASCE are used for benchmarking on a wide range of datasets that includes both field and flume (lab) observations. The dimensionless parameters of these widely used formulae are used as inputs in a new regression approach. The new approach is called Polynomial Best subset regression (PBSR) analysis. The aim of the PBRS analysis is fitting and testing all possible combinations of the input variables and selecting the best subset. Whole the input variables with their second and third powers are included in the regression to test the possible relation between the explanatory variables and the dependent variable. While selecting the best subset a multistep approach is used that depends on significance values and also the multicollinearity degrees of inputs. The new formula is compared to others in a holdout dataset and detailed performance investigations are conducted for field and lab datasets within this holdout data. Different goodness of fit statistics are used as they represent different perspectives of the model accuracy. After the detailed comparisons are carried out we figured out the most accurate equation that is also applicable on both flume and river data. Especially, on field dataset the prediction performance of the proposed formula outperformed the benchmark formulations.

Association Between Pre-season Training and Performance in Elite Australian Football.

PubMed

McCaskie, Callum J; Young, Warren B; Fahrner, Brendan B; Sim, Marc

2018-06-12

To examine the association between pre-season training variables and subsequent in-season performance in an elite Australian football team. Data from forty-one elite male Australian footballers (mean±SD: age=23.4±3.1y; height=188.4±7.1cm; mass=86.7±7.9kg) was collected from one Australian Football League (AFL) club. Pre-season training data (external load, internal load, fitness testing and session participation) were collected across the 17-week pre-season phase (6-weeks pre-Christmas, 11-weeks post-Christmas). Champion Data© Player Rank (CDPR), coaches' ratings (CR) and round one selection were used as in-season performance measures. CDPR and CR were examined over the entire season, first half of the season and the first four games. Both Pearson and partial (controlling for AFL age) correlations were calculated to assess if any associations existed between pre-season training variables and in-season performance measures. A median-split was also employed to differentiate between higher and lower performing players for each performance measure. Pre-season training activities appeared to have almost no association with performance measured across the entire season and the first half of the season. However, many pre-season training variables were significantly linked with performance measured across the first four games. Pre-season training variables that were measured post-Christmas were the most strongly associated with in-season performance measures. Specifically, Total on-field session rating of perceived exertion (sRPE) post-Xmas, a measurement of internal load, displayed the greatest association with performance. Late pre-season training (especially on-field match specific training) is associated with better performance in the early season.

Prediction of municipal solid waste generation using nonlinear autoregressive network.

PubMed

Younes, Mohammad K; Nopiah, Z M; Basri, N E Ahmad; Basri, H; Abushammala, Mohammed F M; Maulud, K N A

2015-12-01

Most of the developing countries have solid waste management problems. Solid waste strategic planning requires accurate prediction of the quality and quantity of the generated waste. In developing countries, such as Malaysia, the solid waste generation rate is increasing rapidly, due to population growth and new consumption trends that characterize society. This paper proposes an artificial neural network (ANN) approach using feedforward nonlinear autoregressive network with exogenous inputs (NARX) to predict annual solid waste generation in relation to demographic and economic variables like population number, gross domestic product, electricity demand per capita and employment and unemployment numbers. In addition, variable selection procedures are also developed to select a significant explanatory variable. The model evaluation was performed using coefficient of determination (R(2)) and mean square error (MSE). The optimum model that produced the lowest testing MSE (2.46) and the highest R(2) (0.97) had three inputs (gross domestic product, population and employment), eight neurons and one lag in the hidden layer, and used Fletcher-Powell's conjugate gradient as the training algorithm.

K-Means Algorithm Performance Analysis With Determining The Value Of Starting Centroid With Random And KD-Tree Method

NASA Astrophysics Data System (ADS)

Sirait, Kamson; Tulus; Budhiarti Nababan, Erna

2017-12-01

Clustering methods that have high accuracy and time efficiency are necessary for the filtering process. One method that has been known and applied in clustering is K-Means Clustering. In its application, the determination of the begining value of the cluster center greatly affects the results of the K-Means algorithm. This research discusses the results of K-Means Clustering with starting centroid determination with a random and KD-Tree method. The initial determination of random centroid on the data set of 1000 student academic data to classify the potentially dropout has a sse value of 952972 for the quality variable and 232.48 for the GPA, whereas the initial centroid determination by KD-Tree has a sse value of 504302 for the quality variable and 214,37 for the GPA variable. The smaller sse values indicate that the result of K-Means Clustering with initial KD-Tree centroid selection have better accuracy than K-Means Clustering method with random initial centorid selection.

Individual Difference Variables and the Occurrence and Effectiveness of Faking Behavior in Interviews

PubMed Central

Buehl, Anne-Kathrin; Melchers, Klaus G.

2017-01-01

There is widespread fear that applicants can fake during selection interviews and that this impairs the quality of selection decisions. Several theories assume that faking occurrence is influenced by personality and attitudes, which together influence applicants’ motivation to show faking behavior. However, for faking behavior to be effective, interviewees also need certain skills and abilities. To investigate the impact of several relevant individual difference variables on faking behavior and interview success, we conducted two studies. In Study 1, we surveyed 222 individuals to assess different personality variables, attitude toward faking, cognitive ability, self-reported faking behavior, and success in previous interviews, and in Study 2, we assessed cognitive ability, social skills, faking behavior, and interview performance in an interview simulation with 108 participants. Taken together, personality, as well as attitude toward faking, influenced who showed faking behavior in an interview, but there was no evidence for the assumed moderating effect of cognitive ability or social skills on interview success. PMID:28539895

Sex steroids, sexual behavior, and selection attention for erotic stimuli in women using oral contraceptives.

PubMed

Alexander, G M; Sherwin, B B

1993-01-01

The relationship between sex steroids and sexual behavior was examined in 19 oral contraceptive users. Retrospective assessment of sexual attitudes were obtained and women completed daily ratings of sexual behavior and well-being for 28 days. Plasma levels of free testosterone (T), estradiol, and progesterone were measured at weekly intervals. In addition, women performed a novel selective attention task designed to measure the strength of the tendency to be distracted by sexual stimuli. Multiple regression analyses using average sexual behavior variables as dependent variables, and hormone levels sexual attitudes and well-being as predictor variables, showed that free T was strongly and positively associated with sexual desire, sexual thoughts, and anticipation of sexual activity. A role for T in attention to sexual stimuli was also supported by the positive correlation between free T and the bias for sexual stimuli in a subgroup of women. These results are consistent with the hypothesis that T may enhance cognitive aspects of women's sexual behavior.

Normalised subband adaptive filtering with extended adaptiveness on degree of subband filters

NASA Astrophysics Data System (ADS)

Samuyelu, Bommu; Rajesh Kumar, Pullakura

2017-12-01

This paper proposes an adaptive normalised subband adaptive filtering (NSAF) to accomplish the betterment of NSAF performance. In the proposed NSAF, an extended adaptiveness is introduced from its variants in two ways. In the first way, the step-size is set adaptive, and in the second way, the selection of subbands is set adaptive. Hence, the proposed NSAF is termed here as variable step-size-based NSAF with selected subbands (VS-SNSAF). Experimental investigations are carried out to demonstrate the performance (in terms of convergence) of the VS-SNSAF against the conventional NSAF and its state-of-the-art adaptive variants. The results report the superior performance of VS-SNSAF over the traditional NSAF and its variants. It is also proved for its stability, robustness against noise and substantial computing complexity.

Shade selection performed by novice dental professionals and colorimeter.

PubMed

Klemetti, E; Matela, A-M; Haag, P; Kononen, M

2006-01-01

The objective of this study was to test inter-observer variability in shade selection for porcelain restorations, using three different shade guides: Vita Lumin Vacuum, Vita 3D-Master and Procera. Nineteen young dental professionals acted as observers. The results were also compared with those of a digital colorimeter (Shade Eye Ex; Shofu, Japan). Regarding repeatability, no significant differences were found between the three shade guides, although repeatability was relatively low (33-43%). Agreement with the colorimetric results was also low (8-34%). In conclusion, shade selection shows moderate to great inter-observer variation. In teaching and standardizing the shade selection procedure, a digital colorimeter may be a useful educational tool.

Potential effectiveness of visible and near infrared spectroscopy coupled with wavelength selection for real time grapevine leaf water status measurement.

PubMed

Giovenzana, Valentina; Beghi, Roberto; Parisi, Simone; Brancadoro, Lucio; Guidetti, Riccardo

2018-03-01

Increasing attention is being paid to non-destructive methods for water status real time monitoring as a potential solution to replace the tedious conventional techniques which are time consuming and not easy to perform directly in the field. The objective of this study was to test the potential effectiveness of two portable optical devices (visible/near infrared (vis/NIR) and near infrared (NIR) spectrophotometers) for the rapid and non-destructive evaluation of the water status of grapevine leaves. Moreover, a variable selection methodology was proposed to determine a set of candidate variables for the prediction of water potential (Ψ, MPa) related to leaf water status in view of a simplified optical device. The statistics of the partial least square (PLS) models showed in validation R 2 between 0.67 and 0.77 for models arising from vis/NIR spectra, and R 2 ranged from 0.77 to 0.85 for the NIR region. The overall performance of the multiple linear regression (MLR) models from selected wavelengths was slightly worse than that of the PLS models. Regarding the NIR range, acceptable MLR models were obtained only using 14 effective variables (R 2 range 0.63-0.69). To address the market demand for portable optical devices and heading towards the trend of miniaturization and low cost of the devices, individual wavelengths could be useful for the design of a simplified and low-cost handheld system providing useful information for better irrigation scheduling. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

Hyperspectral imaging for predicting the allicin and soluble solid content of garlic with variable selection algorithms and chemometric models.

PubMed

Rahman, Anisur; Faqeerzada, Mohammad A; Cho, Byoung-Kwan

2018-03-14

Allicin and soluble solid content (SSC) in garlic is the responsible for its pungent flavor and odor. However, current conventional methods such as the use of high-pressure liquid chromatography and a refractometer have critical drawbacks in that they are time-consuming, labor-intensive and destructive procedures. The present study aimed to predict allicin and SSC in garlic using hyperspectral imaging in combination with variable selection algorithms and calibration models. Hyperspectral images of 100 garlic cloves were acquired that covered two spectral ranges, from which the mean spectra of each clove were extracted. The calibration models included partial least squares (PLS) and least squares-support vector machine (LS-SVM) regression, as well as different spectral pre-processing techniques, from which the highest performing spectral preprocessing technique and spectral range were selected. Then, variable selection methods, such as regression coefficients, variable importance in projection (VIP) and the successive projections algorithm (SPA), were evaluated for the selection of effective wavelengths (EWs). Furthermore, PLS and LS-SVM regression methods were applied to quantitatively predict the quality attributes of garlic using the selected EWs. Of the established models, the SPA-LS-SVM model obtained an Rpred2 of 0.90 and standard error of prediction (SEP) of 1.01% for SSC prediction, whereas the VIP-LS-SVM model produced the best result with an Rpred2 of 0.83 and SEP of 0.19 mg g -1 for allicin prediction in the range 1000-1700 nm. Furthermore, chemical images of garlic were developed using the best predictive model to facilitate visualization of the spatial distributions of allicin and SSC. The present study clearly demonstrates that hyperspectral imaging combined with an appropriate chemometrics method can potentially be employed as a fast, non-invasive method to predict the allicin and SSC in garlic. © 2018 Society of Chemical Industry. © 2018 Society of Chemical Industry.

Physical Characteristics and Performance of Japanese Top-Level American Football Players.

PubMed

Yamashita, Daichi; Asakura, Masaki; Ito, Yoshihiko; Yamada, Shinzo; Yamada, Yosuke

2017-09-01

Yamashita, D, Asakura, M, Ito, Y, Yamada, S, and Yamada, Y. Physical characteristics and performance of Japanese top-level American football players. J Strength Cond Res 31(9): 2455-2461, 2017-This study aimed to compare the physical characteristics and performance between top-level nonprofessional football players in Japan and National Football League (NFL) Combine invited players and between top-level and middle-level players in Japan to determine the factors that enhance performance in international and national competitions. A total of 168 American football players (>20 years) in Japan participated in an anthropometric (height and weight) and physical (vertical jump, long jump, 40-yard dash, pro-agility shuttle, 3-cone drill, and bench press repetition test) measurement program based on the NFL Combine program to compete in the selection of candidates for the Senior World Championship. All players were categorized into 1 of the 3 position groups based on playing position: skill players, big skill players, and linemen. Japanese players were additionally categorized into selected and nonselected players for the second tryout. The NFL Combine candidates had significantly better performance than selected Japanese players on all variables except on performance related to quickness among the 3 position groups. Compared with nonselected players, selected Japanese skill players had better performance in the 40-yard dash and bench press test and big skill players had better performance in the vertical jump, broad jump, and 40-yard dash. Selected and nonselected Japanese linemen were not different in any measurements. These results showed the challenges in American football in Japan, which include not only improving physical performance of top-level players, but also increasing the number of football players with good physical performance.

Physical Characteristics and Performance of Japanese Top-Level American Football Players

PubMed Central

Asakura, Masaki; Ito, Yoshihiko; Yamada, Shinzo; Yamada, Yosuke

2017-01-01

Abstract Yamashita, D, Asakura, M, Ito, Y, Yamada, S, and Yamada, Y. Physical characteristics and performance of Japanese top-level American football players. J Strength Cond Res 31(9): 2455–2461, 2017—This study aimed to compare the physical characteristics and performance between top-level nonprofessional football players in Japan and National Football League (NFL) Combine invited players and between top-level and middle-level players in Japan to determine the factors that enhance performance in international and national competitions. A total of 168 American football players (>20 years) in Japan participated in an anthropometric (height and weight) and physical (vertical jump, long jump, 40-yard dash, pro-agility shuttle, 3-cone drill, and bench press repetition test) measurement program based on the NFL Combine program to compete in the selection of candidates for the Senior World Championship. All players were categorized into 1 of the 3 position groups based on playing position: skill players, big skill players, and linemen. Japanese players were additionally categorized into selected and nonselected players for the second tryout. The NFL Combine candidates had significantly better performance than selected Japanese players on all variables except on performance related to quickness among the 3 position groups. Compared with nonselected players, selected Japanese skill players had better performance in the 40-yard dash and bench press test and big skill players had better performance in the vertical jump, broad jump, and 40-yard dash. Selected and nonselected Japanese linemen were not different in any measurements. These results showed the challenges in American football in Japan, which include not only improving physical performance of top-level players, but also increasing the number of football players with good physical performance. PMID:28052052

Rejecting salient distractors: Generalization from experience.

PubMed

Vatterott, Daniel B; Mozer, Michael C; Vecera, Shaun P

2018-02-01

Distraction impairs performance of many important, everyday tasks. Attentional control limits distraction by preferentially selecting important items for limited-capacity cognitive operations. Research in attentional control has typically investigated the degree to which selection of items is stimulus-driven versus goal-driven. Recent work finds that when observers initially learn a task, the selection is based on stimulus-driven factors, but through experience, goal-driven factors have an increasing influence. The modulation of selection by goals has been studied within the paradigm of learned distractor rejection, in which experience over a sequence of trials enables individuals eventually to ignore a perceptually salient distractor. The experiments presented examine whether observers can generalize learned distractor rejection to novel distractors. Observers searched for a target and ignored a salient color-singleton distractor that appeared in half of the trials. In Experiment 1, observers who learned distractor rejection in a variable environment rejected a novel distractor more effectively than observers who learned distractor rejection in a less variable, homogeneous environment, demonstrating that variable, heterogeneous stimulus environments encourage generalizable learned distractor rejection. Experiments 2 and 3 investigated the time course of learned distractor rejection across the experiment and found that after experiencing four color-singleton distractors in different blocks, observers could effectively reject subsequent novel color-singleton distractors. These results suggest that the optimization of attentional control to the task environment can be interpreted as a form of learning, demonstrating experience's critical role in attentional control.

Morphological selection and the evaluation of potential tradeoffs between escape from predators and the climbing of waterfalls in the Hawaiian stream goby Sicyopterus stimpsoni.

PubMed

Blob, Richard W; Kawano, Sandy M; Moody, Kristine N; Bridges, William C; Maie, Takashi; Ptacek, Margaret B; Julius, Matthew L; Schoenfuss, Heiko L

2010-12-01

Environmental pressures may vary over the geographic range of a species, exposing subpopulations to divergent functional demands. How does exposure to competing demands shape the morphology of species and influence the divergence of populations? We explored these questions by performing selection experiments on juveniles of the Hawaiian goby Sicyopterus stimpsoni, an amphidromous fish that exhibits morphological differences across portions of its geographic range where different environmental pressures predominate. Juvenile S. stimpsoni face two primary and potentially opposing selective pressures on body shape as they return from the ocean to freshwater streams on islands: (1) avoiding predators in the lower reaches of a stream; and (2) climbing waterfalls to reach the habitats occupied by adults. These pressures differ in importance across the Hawaiian Islands. On the youngest island, Hawai'i, waterfalls are close to shore, thereby minimizing exposure to predators and placing a premium on climbing performance. In contrast, on the oldest major island, Kaua'i, waterfalls have eroded further inland, lengthening the exposure of juveniles to predators before migrating juveniles begin climbing. Both juvenile and adult fish show differences in body shape between these islands that would be predicted to improve evasion of predators by fish from Kaua'i (e.g., taller bodies that improve thrust) and climbing performance for fish from Hawai'i (e.g., narrower bodies that reduce drag), matching the prevailing environmental demand on each island. To evaluate how competing selection pressures and functional tradeoffs contribute to the divergence in body shape observed in S. stimpsoni, we compared selection imposed on juvenile body shape by (1) predation by the native fish Eleotris sandwicensis versus (2) climbing an artificial waterfall (∼100 body lengths). Some variables showed opposing patterns of selection that matched predictions: for example, survivors of predation had lower fineness ratios than did control fish (i.e., greater body depth for a given length), whereas successful climbers had higher fineness ratios (reducing drag) than did fish that failed. However, most morphological variables showed significant selection in only one treatment rather than opposing selection across both. Thus, functional tradeoffs between evasion of predators and minimizing drag during climbing might influence divergence in body shape across subpopulations, but even when selection is an important contributing mechanism, directly opposite patterns of selection across environmental demands are not required to generate morphological divergence.

Leisure activity, health, and medical correlates of neurocognitive performance among monozygotic twins: the Older Australian Twins Study.

PubMed

Lee, Teresa; Lipnicki, Darren M; Crawford, John D; Henry, Julie D; Trollor, Julian N; Ames, David; Wright, Margaret J; Sachdev, Perminder S

2014-07-01

We aimed to examine associations between each of three leisure activities (Cognitive, Physical, and Social) and performance in selected cognitive domains (Speed, Memory, Verbal ability, and Executive functions) and global cognition. We also aimed to explore associations between medical and health factors and late-life cognition. Our sample comprised 119 pairs of monozygotic twins from the Older Australian Twins Study. Their mean age was 71 years and 66% were women. We used a discordant co-twin design, with cognitive performance measures as dependent variables and leisure activities as independent variables. Multiple regression analyses were performed, adjusting for potentially relevant medical and health factors. Discordance in Cognitive Activity and Social Activity participation was positively associated with discordance in performance on some cognitive domains. There were no associations between Physical Activity participation and cognition. Discordance in several cardiovascular, frailty, and sensory variables was associated with discordance in cognitive performance measures. This study identified lifestyle and health-related influences on late-life cognition. Our findings not only help in understanding the neurobiological mechanisms, they also have practical implications for interventions to prevent or slow age-related cognitive decline. © The Author 2013. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

A Cash-back Rebate Program for Healthy Food Purchases in South Africa: Selection and Program Effects in Self-reported Diet Patterns.

PubMed

An, Ruopeng; Sturm, Roland

2017-03-01

A South African insurer launched a rebate program for healthy food purchases for its members, but only available in program-designated supermarkets. To eliminate selection bias in program enrollment, we estimated the impact of subsidies in nudging the population towards a healthier diet using an instrumental variable approach. Data came from a health behavior questionnaire administered among members in the health promotion program. Individual and supermarket addresses were geocoded and differential distances from home to program-designated supermarkets versus competing supermarkets were calculated. Bivariate probit and linear instrumental variable models were performed to control for likely unobserved selection biases, employing differential distances as a predictor of program enrollment. For regular fast-food, processed meat, and salty food consumption, approximately two-thirds of the difference between participants and nonparticipants was attributable to the intervention and one-third to selection effects. For fruit/ vegetable and fried food consumption, merely one-eighth of the difference was selection. The rebate reduced regular consumption of fast food by 15% and foods high in salt/sugar and fried foods by 22%- 26%, and increased fruit/vegetable consumption by 21% (0.66 serving/day). Large population interventions are an essential complement to laboratory experiments, but selection biases require explicit attention in evaluation studies conducted in naturalistic settings.

Off-design performance loss model for radial turbines with pivoting, variable-area stators

NASA Technical Reports Server (NTRS)

Meitner, P. L.; Glassman, A. J.

1980-01-01

An off-design performance loss model was developed for variable stator (pivoted vane), radial turbines through analytical modeling and experimental data analysis. Stator loss is determined by a viscous loss model; stator vane end-clearance leakage effects are determined by a clearance flow model. Rotor loss coefficient were obtained by analyzing the experimental data from a turbine rotor previously tested with six stators having throat areas from 20 to 144 percent of design area and were correlated with stator-to-rotor throat area ratio. An incidence loss model was selected to obtain best agreement with experimental results. Predicted turbine performance is compared with experimental results for the design rotor as well as with results for extended and cutback versions of the rotor. Sample calculations were made to show the effects of stator vane end-clearance leakage.

Validity analysis on merged and averaged data using within and between analysis: focus on effect of qualitative social capital on self-rated health.

PubMed

Shin, Sang Soo; Shin, Young-Jeon

2016-01-01

With an increasing number of studies highlighting regional social capital (SC) as a determinant of health, many studies are using multi-level analysis with merged and averaged scores of community residents' survey responses calculated from community SC data. Sufficient examination is required to validate if the merged and averaged data can represent the community. Therefore, this study analyzes the validity of the selected indicators and their applicability in multi-level analysis. Within and between analysis (WABA) was performed after creating community variables using merged and averaged data of community residents' responses from the 2013 Community Health Survey in Korea, using subjective self-rated health assessment as a dependent variable. Further analysis was performed following the model suggested by WABA result. Both E-test results (1) and WABA results (2) revealed that single-level analysis needs to be performed using qualitative SC variable with cluster mean centering. Through single-level multivariate regression analysis, qualitative SC with cluster mean centering showed positive effect on self-rated health (0.054, p<0.001), although there was no substantial difference in comparison to analysis using SC variables without cluster mean centering or multi-level analysis. As modification in qualitative SC was larger within the community than between communities, we validate that relational analysis of individual self-rated health can be performed within the group, using cluster mean centering. Other tests besides the WABA can be performed in the future to confirm the validity of using community variables and their applicability in multi-level analysis.

New insights into time series analysis. II - Non-correlated observations

NASA Astrophysics Data System (ADS)

Ferreira Lopes, C. E.; Cross, N. J. G.

2017-08-01

Context. Statistical parameters are used to draw conclusions in a vast number of fields such as finance, weather, industrial, and science. These parameters are also used to identify variability patterns on photometric data to select non-stochastic variations that are indicative of astrophysical effects. New, more efficient, selection methods are mandatory to analyze the huge amount of astronomical data. Aims: We seek to improve the current methods used to select non-stochastic variations on non-correlated data. Methods: We used standard and new data-mining parameters to analyze non-correlated data to find the best way to discriminate between stochastic and non-stochastic variations. A new approach that includes a modified Strateva function was performed to select non-stochastic variations. Monte Carlo simulations and public time-domain data were used to estimate its accuracy and performance. Results: We introduce 16 modified statistical parameters covering different features of statistical distribution such as average, dispersion, and shape parameters. Many dispersion and shape parameters are unbound parameters, I.e. equations that do not require the calculation of average. Unbound parameters are computed with single loop and hence decreasing running time. Moreover, the majority of these parameters have lower errors than previous parameters, which is mainly observed for distributions with few measurements. A set of non-correlated variability indices, sample size corrections, and a new noise model along with tests of different apertures and cut-offs on the data (BAS approach) are introduced. The number of mis-selections are reduced by about 520% using a single waveband and 1200% combining all wavebands. On the other hand, the even-mean also improves the correlated indices introduced in Paper I. The mis-selection rate is reduced by about 18% if the even-mean is used instead of the mean to compute the correlated indices in the WFCAM database. Even-statistics allows us to improve the effectiveness of both correlated and non-correlated indices. Conclusions: The selection of non-stochastic variations is improved by non-correlated indices. The even-averages provide a better estimation of mean and median for almost all statistical distributions analyzed. The correlated variability indices, which are proposed in the first paper of this series, are also improved if the even-mean is used. The even-parameters will also be useful for classifying light curves in the last step of this project. We consider that the first step of this project, where we set new techniques and methods that provide a huge improvement on the efficiency of selection of variable stars, is now complete. Many of these techniques may be useful for a large number of fields. Next, we will commence a new step of this project regarding the analysis of period search methods.

[How medical students perform academically by admission types?].

PubMed

Kim, Se-Hoon; Lee, Keumho; Hur, Yera; Kim, Ji-Ha

2013-09-01

Despite the importance of selecting students whom are capable for medical education and to become a good doctor, not enough studies have been done in the category. This study focused on analysing the medical students' academic performance (grade point average, GPA) differences, flunk and dropout rates by admission types. From 2004 to 2010, we gathered 369 Konyang University College of Medicine's students admission data and analyzed the differences between admission method and academic achievement, differences in failure and dropout rates. Analysis of variance (ANOVA), ordinary least square, and logistic regression were used. The rolling students showed higher academic achievement from year 1 to 3 than regular students (p < 0.01). Using admission type variable as control variable in multiple regression model similar results were shown. But unlike the results of ANOVA, GPA differences by admission types were shown not only in lower academic years but also in year 6 (p < 0.01). From the regression analysis of flunk and dropout rate by admission types, regular admission type students showed higher drop out rate than the rolling ones which demonstrates admission types gives significant effect on flunk or dropout rates in medical students (p < 0.01). The rolling admissions type students tend to show lower flunk rate and dropout rates and perform better academically. This implies selecting students primarily by Korean College Scholastic Ability Test does not guarantee their academic success in medical education. Thus we suggest a more in-depth comprehensive method of selecting students that are appropriate to individual medical school's educational goal.

RTJ-303: Variable geometry, oblique wing supersonic aircraft

NASA Technical Reports Server (NTRS)

Antaran, Albert; Belete, Hailu; Dryzmkowski, Mark; Higgins, James; Klenk, Alan; Rienecker, Lisa

1992-01-01

This document is a preliminary design of a High Speed Civil Transport (HSCT) named the RTJ-303. It is a 300 passenger, Mach 1.6 transport with a range of 5000 nautical miles. It features four mixed-flow turbofan engines, variable geometry oblique wing, with conventional tail-aft control surfaces. The preliminary cost analysis for a production of 300 aircraft shows that flyaway cost would be 183 million dollars (1992) per aircraft. The aircraft uses standard jet fuel and requires no special materials to handle aerodynamic heating in flight because the stagnation temperatures are approximately 130 degrees Fahrenheit in the supersonic cruise condition. It should be stressed that this aircraft could be built with today's technology and does not rely on vague and uncertain assumptions of technology advances. Included in this report are sections discussing the details of the preliminary design sequence including the mission to be performed, operational and performance constraints, the aircraft configuration and the tradeoffs of the final choice, wing design, a detailed fuselage design, empennage design, sizing of tail geometry, and selection of control surfaces, a discussion on propulsion system/inlet choice and their position on the aircraft, landing gear design including a look at tire selection, tip-over criterion, pavement loading, and retraction kinematics, structures design including load determination, and materials selection, aircraft performance, a look at stability and handling qualities, systems layout including location of key components, operations requirements maintenance characteristics, a preliminary cost analysis, and conclusions made regarding the design, and recommendations for further study.

Clustering and variable selection in the presence of mixed variable types and missing data.

PubMed

Storlie, C B; Myers, S M; Katusic, S K; Weaver, A L; Voigt, R G; Croarkin, P E; Stoeckel, R E; Port, J D

2018-05-17

We consider the problem of model-based clustering in the presence of many correlated, mixed continuous, and discrete variables, some of which may have missing values. Discrete variables are treated with a latent continuous variable approach, and the Dirichlet process is used to construct a mixture model with an unknown number of components. Variable selection is also performed to identify the variables that are most influential for determining cluster membership. The work is motivated by the need to cluster patients thought to potentially have autism spectrum disorder on the basis of many cognitive and/or behavioral test scores. There are a modest number of patients (486) in the data set along with many (55) test score variables (many of which are discrete valued and/or missing). The goal of the work is to (1) cluster these patients into similar groups to help identify those with similar clinical presentation and (2) identify a sparse subset of tests that inform the clusters in order to eliminate unnecessary testing. The proposed approach compares very favorably with other methods via simulation of problems of this type. The results of the autism spectrum disorder analysis suggested 3 clusters to be most likely, while only 4 test scores had high (>0.5) posterior probability of being informative. This will result in much more efficient and informative testing. The need to cluster observations on the basis of many correlated, continuous/discrete variables with missing values is a common problem in the health sciences as well as in many other disciplines. Copyright © 2018 John Wiley & Sons, Ltd.

Species distribution model transferability and model grain size - finer may not always be better.

PubMed

Manzoor, Syed Amir; Griffiths, Geoffrey; Lukac, Martin

2018-05-08

Species distribution models have been used to predict the distribution of invasive species for conservation planning. Understanding spatial transferability of niche predictions is critical to promote species-habitat conservation and forecasting areas vulnerable to invasion. Grain size of predictor variables is an important factor affecting the accuracy and transferability of species distribution models. Choice of grain size is often dependent on the type of predictor variables used and the selection of predictors sometimes rely on data availability. This study employed the MAXENT species distribution model to investigate the effect of the grain size on model transferability for an invasive plant species. We modelled the distribution of Rhododendron ponticum in Wales, U.K. and tested model performance and transferability by varying grain size (50 m, 300 m, and 1 km). MAXENT-based models are sensitive to grain size and selection of variables. We found that over-reliance on the commonly used bioclimatic variables may lead to less accurate models as it often compromises the finer grain size of biophysical variables which may be more important determinants of species distribution at small spatial scales. Model accuracy is likely to increase with decreasing grain size. However, successful model transferability may require optimization of model grain size.

Effects and detection of raw material variability on the performance of near-infrared calibration models for pharmaceutical products.

PubMed

Igne, Benoit; Shi, Zhenqi; Drennen, James K; Anderson, Carl A

2014-02-01

The impact of raw material variability on the prediction ability of a near-infrared calibration model was studied. Calibrations, developed from a quaternary mixture design comprising theophylline anhydrous, lactose monohydrate, microcrystalline cellulose, and soluble starch, were challenged by intentional variation of raw material properties. A design with two theophylline physical forms, three lactose particle sizes, and two starch manufacturers was created to test model robustness. Further challenges to the models were accomplished through environmental conditions. Along with full-spectrum partial least squares (PLS) modeling, variable selection by dynamic backward PLS and genetic algorithms was utilized in an effort to mitigate the effects of raw material variability. In addition to evaluating models based on their prediction statistics, prediction residuals were analyzed by analyses of variance and model diagnostics (Hotelling's T(2) and Q residuals). Full-spectrum models were significantly affected by lactose particle size. Models developed by selecting variables gave lower prediction errors and proved to be a good approach to limit the effect of changing raw material characteristics. Hotelling's T(2) and Q residuals provided valuable information that was not detectable when studying only prediction trends. Diagnostic statistics were demonstrated to be critical in the appropriate interpretation of the prediction of quality parameters. © 2013 Wiley Periodicals, Inc. and the American Pharmacists Association.

Fast Solution in Sparse LDA for Binary Classification

NASA Technical Reports Server (NTRS)

Moghaddam, Baback

2010-01-01

An algorithm that performs sparse linear discriminant analysis (Sparse-LDA) finds near-optimal solutions in far less time than the prior art when specialized to binary classification (of 2 classes). Sparse-LDA is a type of feature- or variable- selection problem with numerous applications in statistics, machine learning, computer vision, computational finance, operations research, and bio-informatics. Because of its combinatorial nature, feature- or variable-selection problems are NP-hard or computationally intractable in cases involving more than 30 variables or features. Therefore, one typically seeks approximate solutions by means of greedy search algorithms. The prior Sparse-LDA algorithm was a greedy algorithm that considered the best variable or feature to add/ delete to/ from its subsets in order to maximally discriminate between multiple classes of data. The present algorithm is designed for the special but prevalent case of 2-class or binary classification (e.g. 1 vs. 0, functioning vs. malfunctioning, or change versus no change). The present algorithm provides near-optimal solutions on large real-world datasets having hundreds or even thousands of variables or features (e.g. selecting the fewest wavelength bands in a hyperspectral sensor to do terrain classification) and does so in typical computation times of minutes as compared to days or weeks as taken by the prior art. Sparse LDA requires solving generalized eigenvalue problems for a large number of variable subsets (represented by the submatrices of the input within-class and between-class covariance matrices). In the general (fullrank) case, the amount of computation scales at least cubically with the number of variables and thus the size of the problems that can be solved is limited accordingly. However, in binary classification, the principal eigenvalues can be found using a special analytic formula, without resorting to costly iterative techniques. The present algorithm exploits this analytic form along with the inherent sequential nature of greedy search itself. Together this enables the use of highly-efficient partitioned-matrix-inverse techniques that result in large speedups of computation in both the forward-selection and backward-elimination stages of greedy algorithms in general.

Exploring the repetition bias in voluntary task switching.

PubMed

Mittelstädt, Victor; Dignath, David; Schmidt-Ott, Magdalena; Kiesel, Andrea

2018-01-01

In the voluntary task-switching paradigm, participants are required to randomly select tasks. We reasoned that the consistent finding of a repetition bias (i.e., participants repeat tasks more often than expected by chance) reflects reasonable adaptive task selection behavior to balance the goal of random task selection with the goals to minimize the time and effort for task performance. We conducted two experiments in which participants were provided with variable amount of preview for the non-chosen task stimuli (i.e., potential switch stimuli). We assumed that switch stimuli would initiate some pre-processing resulting in improved performance in switch trials. Results showed that reduced switch costs due to extra-preview in advance of each trial were accompanied by more task switches. This finding is in line with the characteristics of rational adaptive behavior. However, participants were not biased to switch tasks more often than chance despite large switch benefits. We suggest that participants might avoid effortful additional control processes that modulate the effects of preview on task performance and task choice.

Variable Neighborhood Search Heuristics for Selecting a Subset of Variables in Principal Component Analysis

ERIC Educational Resources Information Center

Brusco, Michael J.; Singh, Renu; Steinley, Douglas

2009-01-01

The selection of a subset of variables from a pool of candidates is an important problem in several areas of multivariate statistics. Within the context of principal component analysis (PCA), a number of authors have argued that subset selection is crucial for identifying those variables that are required for correct interpretation of the…

Effects of a cognitive dual task on variability and local dynamic stability in sustained repetitive arm movements using principal component analysis: a pilot study.

PubMed

Longo, Alessia; Federolf, Peter; Haid, Thomas; Meulenbroek, Ruud

2018-06-01

In many daily jobs, repetitive arm movements are performed for extended periods of time under continuous cognitive demands. Even highly monotonous tasks exhibit an inherent motor variability and subtle fluctuations in movement stability. Variability and stability are different aspects of system dynamics, whose magnitude may be further affected by a cognitive load. Thus, the aim of the study was to explore and compare the effects of a cognitive dual task on the variability and local dynamic stability in a repetitive bimanual task. Thirteen healthy volunteers performed the repetitive motor task with and without a concurrent cognitive task of counting aloud backwards in multiples of three. Upper-body 3D kinematics were collected and postural reconfigurations-the variability related to the volunteer's postural change-were determined through a principal component analysis-based procedure. Subsequently, the most salient component was selected for the analysis of (1) cycle-to-cycle spatial and temporal variability, and (2) local dynamic stability as reflected by the largest Lyapunov exponent. Finally, end-point variability was evaluated as a control measure. The dual cognitive task proved to increase the temporal variability and reduce the local dynamic stability, marginally decrease endpoint variability, and substantially lower the incidence of postural reconfigurations. Particularly, the latter effect is considered to be relevant for the prevention of work-related musculoskeletal disorders since reduced variability in sustained repetitive tasks might increase the risk of overuse injuries.

Artificial neural network model for ozone concentration estimation and Monte Carlo analysis

NASA Astrophysics Data System (ADS)

Gao, Meng; Yin, Liting; Ning, Jicai

2018-07-01

Air pollution in urban atmosphere directly affects public-health; therefore, it is very essential to predict air pollutant concentrations. Air quality is a complex function of emissions, meteorology and topography, and artificial neural networks (ANNs) provide a sound framework for relating these variables. In this study, we investigated the feasibility of using ANN model with meteorological parameters as input variables to predict ozone concentration in the urban area of Jinan, a metropolis in Northern China. We firstly found that the architecture of network of neurons had little effect on the predicting capability of ANN model. A parsimonious ANN model with 6 routinely monitored meteorological parameters and one temporal covariate (the category of day, i.e. working day, legal holiday and regular weekend) as input variables was identified, where the 7 input variables were selected following the forward selection procedure. Compared with the benchmarking ANN model with 9 meteorological and photochemical parameters as input variables, the predicting capability of the parsimonious ANN model was acceptable. Its predicting capability was also verified in term of warming success ratio during the pollution episodes. Finally, uncertainty and sensitivity analysis were also performed based on Monte Carlo simulations (MCS). It was concluded that the ANN could properly predict the ambient ozone level. Maximum temperature, atmospheric pressure, sunshine duration and maximum wind speed were identified as the predominate input variables significantly influencing the prediction of ambient ozone concentrations.

Applying causal mediation analysis to personality disorder research.

PubMed

Walters, Glenn D

2018-01-01

This article is designed to address fundamental issues in the application of causal mediation analysis to research on personality disorders. Causal mediation analysis is used to identify mechanisms of effect by testing variables as putative links between the independent and dependent variables. As such, it would appear to have relevance to personality disorder research. It is argued that proper implementation of causal mediation analysis requires that investigators take several factors into account. These factors are discussed under 5 headings: variable selection, model specification, significance evaluation, effect size estimation, and sensitivity testing. First, care must be taken when selecting the independent, dependent, mediator, and control variables for a mediation analysis. Some variables make better mediators than others and all variables should be based on reasonably reliable indicators. Second, the mediation model needs to be properly specified. This requires that the data for the analysis be prospectively or historically ordered and possess proper causal direction. Third, it is imperative that the significance of the identified pathways be established, preferably with a nonparametric bootstrap resampling approach. Fourth, effect size estimates should be computed or competing pathways compared. Finally, investigators employing the mediation method are advised to perform a sensitivity analysis. Additional topics covered in this article include parallel and serial multiple mediation designs, moderation, and the relationship between mediation and moderation. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

An exploratory study of the association of acute posttraumatic stress, depression, and pain to cognitive functioning in mild traumatic brain injury.

PubMed

Massey, Jessica S; Meares, Susanne; Batchelor, Jennifer; Bryant, Richard A

2015-07-01

Few studies have examined whether psychological distress and pain affect cognitive functioning in the acute to subacute phase (up to 30 days postinjury) following mild traumatic brain injury (mTBI). The current study explored whether acute posttraumatic stress, depression, and pain were associated with performance on a task of selective and sustained attention completed under conditions of increasing cognitive demands (standard, auditory distraction, and dual-task), and on tests of working memory, memory, processing speed, reaction time (RT), and verbal fluency. At a mean of 2.87 days (SD = 2.32) postinjury, 50 adult mTBI participants, consecutive admissions to a Level 1 trauma hospital, completed neuropsychological tests and self-report measures of acute posttraumatic stress, depression, and pain. A series of canonical correlation analyses was used to explore the relationships of a common set of psychological variables to various sets of neuropsychological variables. Significant results were found on the task of selective and sustained attention. Strong relationships were found between psychological variables and speed (r(c) = .56, p = .02) and psychological variables and accuracy (r(c) = .68, p = .002). Pain and acute posttraumatic stress were associated with higher speed scores (reflecting more correctly marked targets) under standard conditions. Acute posttraumatic stress was associated with lower accuracy scores across all task conditions. Moderate but nonsignificant associations were found between psychological variables and most cognitive tasks. Acute posttraumatic stress and pain show strong associations with selective and sustained attention following mTBI. (c) 2015 APA, all rights reserved).

Therapeutic Effects of Exercise Training On Selected Cardio-Pulmonary Parameters and Body Composition of Nigerians with Chronic Heart Failure (A Preliminary Study).

PubMed

Ajiboye, O A; Anigbogu, C N; Ajuluchukwu, J N; Jaja, S I

2013-01-01

The use of exercise training in the management of individuals with chronic heart failure has not been widely accepted by health care providers especially in Sub-Saharan Africa because of the possibility that the failing hearts may have a negative response to the increased workload and stress of exercise. The study aimed to evaluate the effects of exercise training (ET) on selected cardio-respiratory and body composition variables of Nigerians with chronic heart failure (CHF). Thirty two Nigerians with CHF (male - 17), aged 30 to 71 years, mean age 54.2 ± 1.9 years and New York Heart Association Functional Class (NYHA) II-III recruited from Cardiology Clinic of Lagos University Teaching Hospital Nigeria participated in the study. They were randomized into exercise (EG) and control groups (CG). Exercise group performed 12-weeks of aerobic and resistance training for 60 minutes, three sessions per week. Selected cardio-respiratory and body composition variables were measured pre and post intervention in both groups. Data was analyzed using SPSS-17 package. Level of significance was set at 0.05. There was no significant difference in the measured variables between the groups at baseline (p > 0.05). Significant improvement was seen in EG in all the measured variables except the systolic (p = 0.29) and diastolic blood pressure (p = 0.45). No adverse effect was observed during the exercise training. No significant improvement was observed in the control group (p > 0.05). Exercise training may improve cardiorespiratory and body composition variables in patients with chronic heart failure.

Model selection bias and Freedman's paradox

USGS Publications Warehouse

Lukacs, P.M.; Burnham, K.P.; Anderson, D.R.

2010-01-01

In situations where limited knowledge of a system exists and the ratio of data points to variables is small, variable selection methods can often be misleading. Freedman (Am Stat 37:152-155, 1983) demonstrated how common it is to select completely unrelated variables as highly "significant" when the number of data points is similar in magnitude to the number of variables. A new type of model averaging estimator based on model selection with Akaike's AIC is used with linear regression to investigate the problems of likely inclusion of spurious effects and model selection bias, the bias introduced while using the data to select a single seemingly "best" model from a (often large) set of models employing many predictor variables. The new model averaging estimator helps reduce these problems and provides confidence interval coverage at the nominal level while traditional stepwise selection has poor inferential properties. ?? The Institute of Statistical Mathematics, Tokyo 2009.

Mode Selection Techniques in Variable Mass Flexible Body Modeling

NASA Technical Reports Server (NTRS)

Quiocho, Leslie J.; Ghosh, Tushar K.; Frenkel, David; Huynh, An

2010-01-01

In developing a flexible body spacecraft simulation for the Launch Abort System of the Orion vehicle, when a rapid mass depletion takes place, the dynamics problem with time varying eigenmodes had to be addressed. Three different techniques were implemented, with different trade-offs made between performance and fidelity. A number of technical issues had to be solved in the process. This paper covers the background of the variable mass flexibility problem, the three approaches to simulating it, and the technical issues that were solved in formulating and implementing them.

Transceivers and receivers for quantum key distribution and methods pertaining thereto

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

DeRose, Christopher; Sarovar, Mohan; Soh, Daniel B.S.

Various technologies for performing continuous-variable (CV) and discrete-variable (DV) quantum key distribution (QKD) with integrated electro-optical circuits are described herein. An integrated DV-QKD system uses Mach-Zehnder modulators to modulate a polarization of photons at a transmitter and select a photon polarization measurement basis at a receiver. An integrated CV-QKD system uses wavelength division multiplexing to send and receive amplitude-modulated and phase-modulated optical signals with a local oscillator signal while maintaining phase coherence between the modulated signals and the local oscillator signal.

Aquatic Habitat Studies on the Lower Mississippi River, River Mile 480 to 530. Report 7. Management of Ecological Data in Large River Ecosystems.

DTIC Science & Technology

1982-02-01

operational. Many times at the startup of a project, variable selection and research data formats are often tentative because of the unknown biological...previously. This flow of data is shown in Figure 1. ELPROGI also made quality control decisions; when a variable for a given observation failed a...a series MENT OF ECOLOGICAL DATA IN LARGE RIVER ECOSYSTEMS s. PERFORMING ONG. REPORT NUMBER 7. AUTNOlt.s) S. CONTRACT O GRANT N UNMSE-) Michael P

Future of the Pacific: Inspiring the Next Generation of Scientists and Engineers Through Place-Based Problem-Solving Using Innovative STEM Curriculum and Technology Tools

DTIC Science & Technology

2016-03-30

lesson 8.4, " Wind Turbine Design Inquiry." 13 The goal of her project was to combine a1t and science in project-based learning. Although pmt of an...challenged to design, test, and redesign wind turbine blades, defining variables and measuring performance. Their goal was to optimize perfonnance through...hydroelectric. In each model there are more than one variable. For example, the wind farm activity enables the user to select number of turbines

Local hidden-variable model for a recent experimental test of quantum nonlocality and local contextuality

NASA Astrophysics Data System (ADS)

La Cour, Brian R.

2017-07-01

An experiment has recently been performed to demonstrate quantum nonlocality by establishing contextuality in one of a pair of photons encoding four qubits; however, low detection efficiencies and use of the fair-sampling hypothesis leave these results open to possible criticism due to the detection loophole. In this Letter, a physically motivated local hidden-variable model is considered as a possible mechanism for explaining the experimentally observed results. The model, though not intrinsically contextual, acquires this quality upon post-selection of coincident detections.

Contrasting determinants for the introduction and establishment success of exotic birds in Taiwan using decision trees models.

PubMed

Liang, Shih-Hsiung; Walther, Bruno Andreas; Shieh, Bao-Sen

2017-01-01

Biological invasions have become a major threat to biodiversity, and identifying determinants underlying success at different stages of the invasion process is essential for both prevention management and testing ecological theories. To investigate variables associated with different stages of the invasion process in a local region such as Taiwan, potential problems using traditional parametric analyses include too many variables of different data types (nominal, ordinal, and interval) and a relatively small data set with too many missing values. We therefore used five decision tree models instead and compared their performance. Our dataset contains 283 exotic bird species which were transported to Taiwan; of these 283 species, 95 species escaped to the field successfully (introduction success); of these 95 introduced species, 36 species reproduced in the field of Taiwan successfully (establishment success). For each species, we collected 22 variables associated with human selectivity and species traits which may determine success during the introduction stage and establishment stage. For each decision tree model, we performed three variable treatments: (I) including all 22 variables, (II) excluding nominal variables, and (III) excluding nominal variables and replacing ordinal values with binary ones. Five performance measures were used to compare models, namely, area under the receiver operating characteristic curve (AUROC), specificity, precision, recall, and accuracy. The gradient boosting models performed best overall among the five decision tree models for both introduction and establishment success and across variable treatments. The most important variables for predicting introduction success were the bird family, the number of invaded countries, and variables associated with environmental adaptation, whereas the most important variables for predicting establishment success were the number of invaded countries and variables associated with reproduction. Our final optimal models achieved relatively high performance values, and we discuss differences in performance with regard to sample size and variable treatments. Our results showed that, for both the establishment model and introduction model, the number of invaded countries was the most important or second most important determinant, respectively. Therefore, we suggest that future success for introduction and establishment of exotic birds may be gauged by simply looking at previous success in invading other countries. Finally, we found that species traits related to reproduction were more important in establishment models than in introduction models; importantly, these determinants were not averaged but either minimum or maximum values of species traits. Therefore, we suggest that in addition to averaged values, reproductive potential represented by minimum and maximum values of species traits should be considered in invasion studies.

Contrasting determinants for the introduction and establishment success of exotic birds in Taiwan using decision trees models

PubMed Central

Liang, Shih-Hsiung; Walther, Bruno Andreas

2017-01-01

Background Biological invasions have become a major threat to biodiversity, and identifying determinants underlying success at different stages of the invasion process is essential for both prevention management and testing ecological theories. To investigate variables associated with different stages of the invasion process in a local region such as Taiwan, potential problems using traditional parametric analyses include too many variables of different data types (nominal, ordinal, and interval) and a relatively small data set with too many missing values. Methods We therefore used five decision tree models instead and compared their performance. Our dataset contains 283 exotic bird species which were transported to Taiwan; of these 283 species, 95 species escaped to the field successfully (introduction success); of these 95 introduced species, 36 species reproduced in the field of Taiwan successfully (establishment success). For each species, we collected 22 variables associated with human selectivity and species traits which may determine success during the introduction stage and establishment stage. For each decision tree model, we performed three variable treatments: (I) including all 22 variables, (II) excluding nominal variables, and (III) excluding nominal variables and replacing ordinal values with binary ones. Five performance measures were used to compare models, namely, area under the receiver operating characteristic curve (AUROC), specificity, precision, recall, and accuracy. Results The gradient boosting models performed best overall among the five decision tree models for both introduction and establishment success and across variable treatments. The most important variables for predicting introduction success were the bird family, the number of invaded countries, and variables associated with environmental adaptation, whereas the most important variables for predicting establishment success were the number of invaded countries and variables associated with reproduction. Discussion Our final optimal models achieved relatively high performance values, and we discuss differences in performance with regard to sample size and variable treatments. Our results showed that, for both the establishment model and introduction model, the number of invaded countries was the most important or second most important determinant, respectively. Therefore, we suggest that future success for introduction and establishment of exotic birds may be gauged by simply looking at previous success in invading other countries. Finally, we found that species traits related to reproduction were more important in establishment models than in introduction models; importantly, these determinants were not averaged but either minimum or maximum values of species traits. Therefore, we suggest that in addition to averaged values, reproductive potential represented by minimum and maximum values of species traits should be considered in invasion studies. PMID:28316893

THESEUS: maximum likelihood superpositioning and analysis of macromolecular structures

PubMed Central

Theobald, Douglas L.; Wuttke, Deborah S.

2008-01-01

Summary THESEUS is a command line program for performing maximum likelihood (ML) superpositions and analysis of macromolecular structures. While conventional superpositioning methods use ordinary least-squares (LS) as the optimization criterion, ML superpositions provide substantially improved accuracy by down-weighting variable structural regions and by correcting for correlations among atoms. ML superpositioning is robust and insensitive to the specific atoms included in the analysis, and thus it does not require subjective pruning of selected variable atomic coordinates. Output includes both likelihood-based and frequentist statistics for accurate evaluation of the adequacy of a superposition and for reliable analysis of structural similarities and differences. THESEUS performs principal components analysis for analyzing the complex correlations found among atoms within a structural ensemble. PMID:16777907

Variables selection methods in near-infrared spectroscopy.

PubMed

Xiaobo, Zou; Jiewen, Zhao; Povey, Malcolm J W; Holmes, Mel; Hanpin, Mao

2010-05-14

Near-infrared (NIR) spectroscopy has increasingly been adopted as an analytical tool in various fields, such as the petrochemical, pharmaceutical, environmental, clinical, agricultural, food and biomedical sectors during the past 15 years. A NIR spectrum of a sample is typically measured by modern scanning instruments at hundreds of equally spaced wavelengths. The large number of spectral variables in most data sets encountered in NIR spectral chemometrics often renders the prediction of a dependent variable unreliable. Recently, considerable effort has been directed towards developing and evaluating different procedures that objectively identify variables which contribute useful information and/or eliminate variables containing mostly noise. This review focuses on the variable selection methods in NIR spectroscopy. Selection methods include some classical approaches, such as manual approach (knowledge based selection), "Univariate" and "Sequential" selection methods; sophisticated methods such as successive projections algorithm (SPA) and uninformative variable elimination (UVE), elaborate search-based strategies such as simulated annealing (SA), artificial neural networks (ANN) and genetic algorithms (GAs) and interval base algorithms such as interval partial least squares (iPLS), windows PLS and iterative PLS. Wavelength selection with B-spline, Kalman filtering, Fisher's weights and Bayesian are also mentioned. Finally, the websites of some variable selection software and toolboxes for non-commercial use are given. Copyright 2010 Elsevier B.V. All rights reserved.

Effect of Age, Country, and Gender on Music Listening Preferences.

ERIC Educational Resources Information Center

LeBlanc, Albert; Jin, Young Chang; Stamou, Lelouda; McCrary, Jan

1999-01-01

Examines the music listening preferences of 2,042 students from Greece, South Korea, and the United States using a survey that listed selections from art music, traditional jazz, and rock music. Finds that age, gender, and country all exerted influence, but the variables did not perform the same way in each country. (CMK)

Differential Analysis of Selected Prompts and Neurological Variables in Motor Assessment of Moderately Mentally Retarded Children.

ERIC Educational Resources Information Center

Ilmer, Steven; Drews, Judith

1980-01-01

The relative effectiveness of multisensory-, physical-, modeling-, and verbal-prompting assessment strategies upon the gross motor performance of 40 moderately retarded children (ages 5 to 15 years) was investigated, taking into account the impact of the Ss' levels of reflexive maturation and orthopedic functioning. (Author/DLS)

Predicting habits of vegetable parenting practices to facilitate the design of change programmes

USDA-ARS?s Scientific Manuscript database

Habit has been defined as the automatic performance of a usual behaviour. The present paper reports the relationships of variables from a Model of Goal Directed Behavior to four scales in regard to parents' habits when feeding their children: habit of (i) actively involving child in selection of veg...

Multiple Logistic Regression Analysis of Cigarette Use among High School Students

ERIC Educational Resources Information Center

Adwere-Boamah, Joseph

2011-01-01

A binary logistic regression analysis was performed to predict high school students' cigarette smoking behavior from selected predictors from 2009 CDC Youth Risk Behavior Surveillance Survey. The specific target student behavior of interest was frequent cigarette use. Five predictor variables included in the model were: a) race, b) frequency of…

Calibrating SALT: a sampling scheme to improve estimates of suspended sediment yield

Treesearch

Robert B. Thomas

1986-01-01

Abstract - SALT (Selection At List Time) is a variable probability sampling scheme that provides unbiased estimates of suspended sediment yield and its variance. SALT performs better than standard schemes which are estimate variance. Sampling probabilities are based on a sediment rating function which promotes greater sampling intensity during periods of high...

Preparation for implementation of the mechanistic-empirical pavement design guide in Michigan : part 2 - evaluation of rehabilitation fixes (part 1).

DOT National Transportation Integrated Search

2013-08-01

The main objectives of Task 2 of the project were to determine the impact of various input variables on the predicted pavement performance for the selected rehabilitation design alternatives in the MEPDG/DARWin-ME, and to verify the pavement performa...

Hyperspectral remote sensing of plant biochemistry using Bayesian model averaging with variable and band selection

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

Zhao, Kaiguang; Valle, Denis; Popescu, Sorin

2013-05-15

Model specification remains challenging in spectroscopy of plant biochemistry, as exemplified by the availability of various spectral indices or band combinations for estimating the same biochemical. This lack of consensus in model choice across applications argues for a paradigm shift in hyperspectral methods to address model uncertainty and misspecification. We demonstrated one such method using Bayesian model averaging (BMA), which performs variable/band selection and quantifies the relative merits of many candidate models to synthesize a weighted average model with improved predictive performances. The utility of BMA was examined using a portfolio of 27 foliage spectral–chemical datasets representing over 80 speciesmore » across the globe to estimate multiple biochemical properties, including nitrogen, hydrogen, carbon, cellulose, lignin, chlorophyll (a or b), carotenoid, polar and nonpolar extractives, leaf mass per area, and equivalent water thickness. We also compared BMA with partial least squares (PLS) and stepwise multiple regression (SMR). Results showed that all the biochemicals except carotenoid were accurately estimated from hyerspectral data with R2 values > 0.80.« less

Effects of unsteady conditions on propulsion generated by the hand's motion in swimming: a systematic review.

PubMed

Gomes, Lara Elena; Loss, Jefferson Fagundes

2015-01-01

The understanding of swimming propulsion is a key factor in the improvement of performance in this sport. Propulsive forces have been quantified under steady conditions since the 1970s, but actual swimming involves unsteady conditions. Thus, the purpose of the present article was to review the effects of unsteady conditions on swimming propulsion based on studies that have compared steady and unsteady conditions while exploring their methods, their limitations and their results, as well as encouraging new studies based on the findings of this systematic review. A multiple database search was performed, and only those studies that met all eligibility criteria were included. Six studies that compared steady and unsteady conditions using physical experiments or numerical simulations were selected. The selected studies verified the effects of one or more factors that characterise a condition as unsteady on the propulsive forces. Consequently, much research is necessary to understand the effect of each individual variable that characterises a condition as unsteady on swimming propulsion, as well as the effects of these variables as a whole on swimming propulsion.

Evolutionary algorithm for vehicle driving cycle generation.

PubMed

Perhinschi, Mario G; Marlowe, Christopher; Tamayo, Sergio; Tu, Jun; Wayne, W Scott

2011-09-01

Modeling transit bus emissions and fuel economy requires a large amount of experimental data over wide ranges of operational conditions. Chassis dynamometer tests are typically performed using representative driving cycles defined based on vehicle instantaneous speed as sequences of "microtrips", which are intervals between consecutive vehicle stops. Overall significant parameters of the driving cycle, such as average speed, stops per mile, kinetic intensity, and others, are used as independent variables in the modeling process. Performing tests at all the necessary combinations of parameters is expensive and time consuming. In this paper, a methodology is proposed for building driving cycles at prescribed independent variable values using experimental data through the concatenation of "microtrips" isolated from a limited number of standard chassis dynamometer test cycles. The selection of the adequate "microtrips" is achieved through a customized evolutionary algorithm. The genetic representation uses microtrip definitions as genes. Specific mutation, crossover, and karyotype alteration operators have been defined. The Roulette-Wheel selection technique with elitist strategy drives the optimization process, which consists of minimizing the errors to desired overall cycle parameters. This utility is part of the Integrated Bus Information System developed at West Virginia University.

Effects of Music Interventions on Emotional States and Running Performance

PubMed Central

Lane, Andrew M.; Davis, Paul A.; Devonport, Tracey J.

2011-01-01

The present study compared the effects of two different music interventions on changes in emotional states before and during running, and also explored effects of music interventions upon performance outcome. Volunteer participants (n = 65) who regularly listened to music when running registered online to participate in a three-stage study. Participants attempted to attain a personally important running goal to establish baseline performance. Thereafter, participants were randomly assigned to either a self-selected music group or an Audiofuel music group. Audiofuel produce pieces of music designed to assist synchronous running. The self-selected music group followed guidelines for selecting motivating playlists. In both experimental groups, participants used the Brunel Music Rating Inventory-2 (BMRI-2) to facilitate selection of motivational music. Participants again completed the BMRI-2 post- intervention to assess the motivational qualities of Audiofuel music or the music they selected for use during the study. Results revealed no significant differences between self-selected music and Audiofuel music on all variables analyzed. Participants in both music groups reported increased pleasant emotions and decreased unpleasant emotions following intervention. Significant performance improvements were demonstrated post-intervention with participants reporting a belief that emotional states related to performance. Further analysis indicated that enhanced performance was significantly greater among participants reporting music to be motivational as indicated by high scores on the BMRI-2. Findings suggest that both individual athletes and practitioners should consider using the BMRI-2 when selecting music for running. Key points Listening to music with a high motivational quotient as indicated by scores on the BMRI-2 was associated with enhanced running performance and meta-emotional beliefs that emotions experienced during running helped performance. Beliefs on the effectiveness of music intended to alter emotions were associated with high scores on the BMRI-2. Runners seeking to use music as an emotion regulating strategy should consider using the BMRI-2 as an effective means by which to identify potentially motivating tracks. PMID:24149889

Digital high speed programmable convolver

NASA Astrophysics Data System (ADS)

Rearick, T. C.

1984-12-01

A circuit module for rapidly calculating a discrete numerical convolution is described. A convolution such as finding the sum of the products of a 16 bit constant and a 16 bit variable is performed by a module which is programmable so that the constant may be changed for a new problem. In addition, the module may be programmed to find the sum of the products of 4 and 8 bit constants and variables. RAM (Random Access Memories) are loaded with partial products of the selected constant and all possible variables. Then, when the actual variable is loaded, it acts as an address to find the correct partial product in the particular RAM. The partial products from all of the RAMs are shifted to the appropriate numerical power position (if necessary) and then added in adder elements.

Selection Practices of Group Leaders: A National Survey.

ERIC Educational Resources Information Center

Riva, Maria T.; Lippert, Laurel; Tackett, M. Jan

2000-01-01

Study surveys the selection practices of group leaders. Explores methods of selection, variables used to make selection decisions, and the types of selection errors that leaders have experienced. Results suggest that group leaders use clinical judgment to make selection decisions and endorse using some specific variables in selection. (Contains 22…

The validity of ACT-PEP test scores for predicting academic performance of registered nurses in BSN programs.

PubMed

Yang, J C; Noble, J

1990-01-01

This study investigated the validity of three American College Testing-Proficiency Examination Program (ACT-PEP) tests (Maternal and Child Nursing, Psychiatric/Mental Health Nursing, Adult Nursing) for predicting the academic performance of registered nurses (RNs) enrolled in bachelor's degree BSN programs nationwide. This study also examined RN students' performance on the ACT-PEP tests by their demographic characteristics: student's age, sex, race, student status (full- or part-time), and employment status (full- or part-time). The total sample for the three tests comprised 2,600 students from eight institutions nationwide. The median correlation coefficients between the three ACT-PEP tests and the semester grade point averages ranged from .36 to .56. Median correlation coefficients increased over time, supporting the stability of ACT-PEP test scores for predicting academic performance over time. The relative importance of selected independent variables for predicting academic performance was also examined; the most important variable for predicting academic performance was typically the ACT-PEP test score. Across the institutions, student demographic characteristics did not contribute significantly to explaining academic performance, over and above ACT-PEP scores.

Automatized Photometric Monitoring of Active Galactic Nuclei with the 46cm Telescope of the Wise Observatory

NASA Astrophysics Data System (ADS)

Pozo Nuñez, Francisco; Chelouche, Doron; Kaspi, Shai; Niv, Saar

2017-09-01

We present the first results of an ongoing variability monitoring program of active galactic nuclei (AGNs) using the 46 cm telescope of the Wise Observatory in Israel. The telescope has a field of view of 1.25^\\circ × 0.84^\\circ and is specially equipped with five narrowband filters at 4300, 5200, 5700, 6200, and 7000 Å to perform photometric reverberation mapping studies of the central engine of AGNs. The program aims to observe a sample of 27 AGNs (V < 17 mag) selected according to tentative continuum and line time delay measurements obtained in previous works. We describe the autonomous operation of the telescope together with the fully automatic pipeline used to achieve high-performance unassisted observations, data reduction, and light curves extraction using different photometric methods. The science verification data presented here demonstrates the performance of the monitoring program in particular for efficiently photometric reverberation mapping of AGNs with additional capabilities to carry out complementary studies of other transient and variable phenomena such as variable stars studies.

The Relationship of Previous Training and Experience of Journal Peer Reviewers to Subsequent Review Quality

PubMed Central

Callaham, Michael L; Tercier, John

2007-01-01

Background Peer review is considered crucial to the selection and publication of quality science, but very little is known about the previous experiences and training that might identify high-quality peer reviewers. The reviewer selection processes of most journals, and thus the qualifications of their reviewers, are ill defined. More objective selection of peer reviewers might improve the journal peer review process and thus the quality of published science. Methods and Findings 306 experienced reviewers (71% of all those associated with a specialty journal) completed a survey of past training and experiences postulated to improve peer review skills. Reviewers performed 2,856 reviews of 1,484 separate manuscripts during a four-year study period, all prospectively rated on a standardized quality scale by editors. Multivariable analysis revealed that most variables, including academic rank, formal training in critical appraisal or statistics, or status as principal investigator of a grant, failed to predict performance of higher-quality reviews. The only significant predictors of quality were working in a university-operated hospital versus other teaching environment and relative youth (under ten years of experience after finishing training). Being on an editorial board and doing formal grant (study section) review were each predictors for only one of our two comparisons. However, the predictive power of all variables was weak. Conclusions Our study confirms that there are no easily identifiable types of formal training or experience that predict reviewer performance. Skill in scientific peer review may be as ill defined and hard to impart as is “common sense.” Without a better understanding of those skills, it seems unlikely journals and editors will be successful in systematically improving their selection of reviewers. This inability to predict performance makes it imperative that all but the smallest journals implement routine review ratings systems to routinely monitor the quality of their reviews (and thus the quality of the science they publish). PMID:17411314

Comparison of statistical methods for detection of serum lipid biomarkers for mesothelioma and asbestos exposure.

PubMed

Xu, Rengyi; Mesaros, Clementina; Weng, Liwei; Snyder, Nathaniel W; Vachani, Anil; Blair, Ian A; Hwang, Wei-Ting

2017-07-01

We compared three statistical methods in selecting a panel of serum lipid biomarkers for mesothelioma and asbestos exposure. Serum samples from mesothelioma, asbestos-exposed subjects and controls (40 per group) were analyzed. Three variable selection methods were considered: top-ranked predictors from univariate model, stepwise and least absolute shrinkage and selection operator. Crossed-validated area under the receiver operating characteristic curve was used to compare the prediction performance. Lipids with high crossed-validated area under the curve were identified. Lipid with mass-to-charge ratio of 372.31 was selected by all three methods comparing mesothelioma versus control. Lipids with mass-to-charge ratio of 1464.80 and 329.21 were selected by two models for asbestos exposure versus control. Different methods selected a similar set of serum lipids. Combining candidate biomarkers can improve prediction.

Comparison of statistical tests for association between rare variants and binary traits.

PubMed

Bacanu, Silviu-Alin; Nelson, Matthew R; Whittaker, John C

2012-01-01

Genome-wide association studies have found thousands of common genetic variants associated with a wide variety of diseases and other complex traits. However, a large portion of the predicted genetic contribution to many traits remains unknown. One plausible explanation is that some of the missing variation is due to the effects of rare variants. Nonetheless, the statistical analysis of rare variants is challenging. A commonly used method is to contrast, within the same region (gene), the frequency of minor alleles at rare variants between cases and controls. However, this strategy is most useful under the assumption that the tested variants have similar effects. We previously proposed a method that can accommodate heterogeneous effects in the analysis of quantitative traits. Here we extend this method to include binary traits that can accommodate covariates. We use simulations for a variety of causal and covariate impact scenarios to compare the performance of the proposed method to standard logistic regression, C-alpha, SKAT, and EREC. We found that i) logistic regression methods perform well when the heterogeneity of the effects is not extreme and ii) SKAT and EREC have good performance under all tested scenarios but they can be computationally intensive. Consequently, it would be more computationally desirable to use a two-step strategy by (i) selecting promising genes by faster methods and ii) analyzing selected genes using SKAT/EREC. To select promising genes one can use (1) regression methods when effect heterogeneity is assumed to be low and the covariates explain a non-negligible part of trait variability, (2) C-alpha when heterogeneity is assumed to be large and covariates explain a small fraction of trait's variability and (3) the proposed trend and heterogeneity test when the heterogeneity is assumed to be non-trivial and the covariates explain a large fraction of trait variability.

Effect of Methodological and Ecological Approaches on Heterogeneity of Nest-Site Selection of a Long-Lived Vulture

PubMed Central

Moreno-Opo, Rubén; Fernández-Olalla, Mariana; Margalida, Antoni; Arredondo, Ángel; Guil, Francisco

2012-01-01

The application of scientific-based conservation measures requires that sampling methodologies in studies modelling similar ecological aspects produce comparable results making easier their interpretation. We aimed to show how the choice of different methodological and ecological approaches can affect conclusions in nest-site selection studies along different Palearctic meta-populations of an indicator species. First, a multivariate analysis of the variables affecting nest-site selection in a breeding colony of cinereous vulture (Aegypius monachus) in central Spain was performed. Then, a meta-analysis was applied to establish how methodological and habitat-type factors determine differences and similarities in the results obtained by previous studies that have modelled the forest breeding habitat of the species. Our results revealed patterns in nesting-habitat modelling by the cinereous vulture throughout its whole range: steep and south-facing slopes, great cover of large trees and distance to human activities were generally selected. The ratio and situation of the studied plots (nests/random), the use of plots vs. polygons as sampling units and the number of years of data set determined the variability explained by the model. Moreover, a greater size of the breeding colony implied that ecological and geomorphological variables at landscape level were more influential. Additionally, human activities affected in greater proportion to colonies situated in Mediterranean forests. For the first time, a meta-analysis regarding the factors determining nest-site selection heterogeneity for a single species at broad scale was achieved. It is essential to homogenize and coordinate experimental design in modelling the selection of species' ecological requirements in order to avoid that differences in results among studies would be due to methodological heterogeneity. This would optimize best conservation and management practices for habitats and species in a global context. PMID:22413023

[Selective attention and schizophrenia before the administration of neuroleptics].

PubMed

Lussier, I; Stip, E

1999-01-01

In recent years, the presence of attention deficits has been recognized as a key feature of schizophrenia. Past studies reveal that selective attention, or the ability to select relevant information while ignoring simultaneously irrelevant information, is disturbed in schizophrenic patients. According to Treisman feature-integration theory of selective attention, visual search for conjunctive targets (e.g., shape and color) requires controlled processes, that necessitate attention and operate in a serial manner. Reaction times (RTs) are therefore function of the number of stimuli in the display. When subjects are asked to detect the presence or absence of a target in an array of a variable number of stimuli, different performance patterns are expected for positive (present target) and negative trials (absent target). For positive trials, a self-terminating search is triggered, that is, the search is ended when the target is encountered. For negative trials, an exhaustive search strategy is displayed, where each stimulus is examined before the search can end; the RT slope pattern is thus double that of the positive trials. To assess the integrity of these processes, thirteen drug naive schizophrenic patients were compared to twenty normal control subjects. Neuroleptic naive patients were chosen as subjects to avoid the potential influence of medication and chronicity-related factors on performance. The subjects had to specify as fast as possible the presence or absence of the target in an array of a variable number of stimuli presented in a circular display, and comprising or not the target. Results showed that the patients can use self-terminating search strategies as well as normal control subjects. However, their ability to trigger exhaustive search strategies is impaired. Not only were patients slower than controls, but their pattern of RT results was different. These results argue in favor of an early impairment in selective attention capacities in schizophrenia, which appears before the introduction of neuroleptics. The attention performance was also shown to present some association to clinical symptoms.

Procrastinating Behavior in Computer-Based Learning Environments to Predict Performance: A Case Study in Moodle

PubMed Central

Cerezo, Rebeca; Esteban, María; Sánchez-Santillán, Miguel; Núñez, José C.

2017-01-01

Introduction: Research about student performance has traditionally considered academic procrastination as a behavior that has negative effects on academic achievement. Although there is much evidence for this in class-based environments, there is a lack of research on Computer-Based Learning Environments (CBLEs). Therefore, the purpose of this study is to evaluate student behavior in a blended learning program and specifically procrastination behavior in relation to performance through Data Mining techniques. Materials and Methods: A sample of 140 undergraduate students participated in a blended learning experience implemented in a Moodle (Modular Object Oriented Developmental Learning Environment) Management System. Relevant interaction variables were selected for the study, taking into account student achievement and analyzing data by means of association rules, a mining technique. The association rules were arrived at and filtered through two selection criteria: 1, rules must have an accuracy over 0.8 and 2, they must be present in both sub-samples. Results: The findings of our study highlight the influence of time management in online learning environments, particularly on academic achievement, as there is an association between procrastination variables and student performance. Conclusion: Negative impact of procrastination in learning outcomes has been observed again but in virtual learning environments where practical implications, prevention of, and intervention in, are different from class-based learning. These aspects are discussed to help resolve student difficulties at various ages. PMID:28883801

Procrastinating Behavior in Computer-Based Learning Environments to Predict Performance: A Case Study in Moodle.

PubMed

Cerezo, Rebeca; Esteban, María; Sánchez-Santillán, Miguel; Núñez, José C

2017-01-01

Introduction: Research about student performance has traditionally considered academic procrastination as a behavior that has negative effects on academic achievement. Although there is much evidence for this in class-based environments, there is a lack of research on Computer-Based Learning Environments (CBLEs) . Therefore, the purpose of this study is to evaluate student behavior in a blended learning program and specifically procrastination behavior in relation to performance through Data Mining techniques. Materials and Methods: A sample of 140 undergraduate students participated in a blended learning experience implemented in a Moodle (Modular Object Oriented Developmental Learning Environment) Management System. Relevant interaction variables were selected for the study, taking into account student achievement and analyzing data by means of association rules, a mining technique. The association rules were arrived at and filtered through two selection criteria: 1, rules must have an accuracy over 0.8 and 2, they must be present in both sub-samples. Results: The findings of our study highlight the influence of time management in online learning environments, particularly on academic achievement, as there is an association between procrastination variables and student performance. Conclusion: Negative impact of procrastination in learning outcomes has been observed again but in virtual learning environments where practical implications, prevention of, and intervention in, are different from class-based learning. These aspects are discussed to help resolve student difficulties at various ages.

Plasticity of muscle function in a thermoregulating ectotherm (Crocodylus porosus): biomechanics and metabolism.

PubMed

Seebacher, Frank; James, Rob S

2008-03-01

Thermoregulation and thermal sensitivity of performance are thought to have coevolved so that performance is optimized within the selected body temperature range. However, locomotor performance in thermoregulating crocodiles (Crocodylus porosus) is plastic and maxima shift to different selected body temperatures in different thermal environments. Here we test the hypothesis that muscle metabolic and biomechanical parameters are optimized at the body temperatures selected in different thermal environments. Hence, we related indices of anaerobic (lactate dehydrogenase) and aerobic (cytochrome c oxidase) metabolic capacities and myofibrillar ATPase activity to the biomechanics of isometric and work loop caudofemoralis muscle function. Maximal isometric stress (force per muscle cross-sectional area) did not change with thermal acclimation, but muscle work loop power output increased with cold acclimation as a result of shorter activation and relaxation times. The thermal sensitivity of myofibrillar ATPase activity decreased with cold acclimation in caudofemoralis muscle. Neither aerobic nor anaerobic metabolic capacities were directly linked to changes in muscle performance during thermal acclimation, although there was a negative relationship between anaerobic capacity and isometric twitch stress in cold-acclimated animals. We conclude that by combining thermoregulation with plasticity in biomechanical function, crocodiles maximize performance in environments with highly variable thermal properties.

Structural equation model analysis for the evaluation of overall driving performance: A driving simulator study focusing on driver distraction.

PubMed

Papantoniou, Panagiotis

2018-04-03

The present research relies on 2 main objectives. The first is to investigate whether latent model analysis through a structural equation model can be implemented on driving simulator data in order to define an unobserved driving performance variable. Subsequently, the second objective is to investigate and quantify the effect of several risk factors including distraction sources, driver characteristics, and road and traffic environment on the overall driving performance and not in independent driving performance measures. For the scope of the present research, 95 participants from all age groups were asked to drive under different types of distraction (conversation with passenger, cell phone use) in urban and rural road environments with low and high traffic volume in a driving simulator experiment. Then, in the framework of the statistical analysis, a correlation table is presented investigating any of a broad class of statistical relationships between driving simulator measures and a structural equation model is developed in which overall driving performance is estimated as a latent variable based on several individual driving simulator measures. Results confirm the suitability of the structural equation model and indicate that the selection of the specific performance measures that define overall performance should be guided by a rule of representativeness between the selected variables. Moreover, results indicate that conversation with the passenger was not found to have a statistically significant effect, indicating that drivers do not change their performance while conversing with a passenger compared to undistracted driving. On the other hand, results support the hypothesis that cell phone use has a negative effect on driving performance. Furthermore, regarding driver characteristics, age, gender, and experience all have a significant effect on driving performance, indicating that driver-related characteristics play the most crucial role in overall driving performance. The findings of this study allow a new approach to the investigation of driving behavior in driving simulator experiments and in general. By the successful implementation of the structural equation model, driving behavior can be assessed in terms of overall performance and not through individual performance measures, which allows an important scientific step forward from piecemeal analyses to a sound combined analysis of the interrelationship between several risk factors and overall driving performance.

Mental health courts and their selection processes: modeling variation for consistency.

PubMed

Wolff, Nancy; Fabrikant, Nicole; Belenko, Steven

2011-10-01

Admission into mental health courts is based on a complicated and often variable decision-making process that involves multiple parties representing different expertise and interests. To the extent that eligibility criteria of mental health courts are more suggestive than deterministic, selection bias can be expected. Very little research has focused on the selection processes underpinning problem-solving courts even though such processes may dominate the performance of these interventions. This article describes a qualitative study designed to deconstruct the selection and admission processes of mental health courts. In this article, we describe a multi-stage, complex process for screening and admitting clients into mental health courts. The selection filtering model that is described has three eligibility screening stages: initial, assessment, and evaluation. The results of this study suggest that clients selected by mental health courts are shaped by the formal and informal selection criteria, as well as by the local treatment system.

Cooled variable nozzle radial turbine for rotor craft applications

NASA Technical Reports Server (NTRS)

Rogo, C.

1981-01-01

An advanced, small 2.27 kb/sec (5 lbs/sec), high temperature, variable area radial turbine was studied for a rotor craft application. Variable capacity cycles including single-shaft and free-turbine engine configurations were analyzed to define an optimum engine design configuration. Parametric optimizations were made on cooled and uncooled rotor configurations. A detailed structural and heat transfer analysis was conducted to provide a 4000-hour life HP turbine with material properties of the 1988 time frame. A pivoted vane and a moveable sidewall geometry were analyzed. Cooling and variable geometry penalties were included in the cycle analysis. A variable geometry free-turbine engine configuration with a design 1477K (2200 F) inlet temperature and a compressor pressure ratio of 16:1 was selected. An uncooled HP radial turbine rotor with a moveable sidewall nozzle showed the highest performance potential for a time weighted duty cycle.

Optimizing the models for rapid determination of chlorogenic acid, scopoletin and rutin in plant samples by near-infrared diffuse reflectance spectroscopy

NASA Astrophysics Data System (ADS)

Mao, Zhiyi; Shan, Ruifeng; Wang, Jiajun; Cai, Wensheng; Shao, Xueguang

2014-07-01

Polyphenols in plant samples have been extensively studied because phenolic compounds are ubiquitous in plants and can be used as antioxidants in promoting human health. A method for rapid determination of three phenolic compounds (chlorogenic acid, scopoletin and rutin) in plant samples using near-infrared diffuse reflectance spectroscopy (NIRDRS) is studied in this work. Partial least squares (PLS) regression was used for building the calibration models, and the effects of spectral preprocessing and variable selection on the models are investigated for optimization of the models. The results show that individual spectral preprocessing and variable selection has no or slight influence on the models, but the combination of the techniques can significantly improve the models. The combination of continuous wavelet transform (CWT) for removing the variant background, multiplicative scatter correction (MSC) for correcting the scattering effect and randomization test (RT) for selecting the informative variables was found to be the best way for building the optimal models. For validation of the models, the polyphenol contents in an independent sample set were predicted. The correlation coefficients between the predicted values and the contents determined by high performance liquid chromatography (HPLC) analysis are as high as 0.964, 0.948 and 0.934 for chlorogenic acid, scopoletin and rutin, respectively.

Regularization Methods for High-Dimensional Instrumental Variables Regression With an Application to Genetical Genomics

PubMed Central

Lin, Wei; Feng, Rui; Li, Hongzhe

2014-01-01

In genetical genomics studies, it is important to jointly analyze gene expression data and genetic variants in exploring their associations with complex traits, where the dimensionality of gene expressions and genetic variants can both be much larger than the sample size. Motivated by such modern applications, we consider the problem of variable selection and estimation in high-dimensional sparse instrumental variables models. To overcome the difficulty of high dimensionality and unknown optimal instruments, we propose a two-stage regularization framework for identifying and estimating important covariate effects while selecting and estimating optimal instruments. The methodology extends the classical two-stage least squares estimator to high dimensions by exploiting sparsity using sparsity-inducing penalty functions in both stages. The resulting procedure is efficiently implemented by coordinate descent optimization. For the representative L1 regularization and a class of concave regularization methods, we establish estimation, prediction, and model selection properties of the two-stage regularized estimators in the high-dimensional setting where the dimensionality of co-variates and instruments are both allowed to grow exponentially with the sample size. The practical performance of the proposed method is evaluated by simulation studies and its usefulness is illustrated by an analysis of mouse obesity data. Supplementary materials for this article are available online. PMID:26392642

Mediators of methylphenidate effects on math performance in children with attention-deficit hyperactivity disorder.

PubMed

Froehlich, Tanya E; Antonini, Tanya N; Brinkman, William B; Langberg, Joshua M; Simon, John O; Adams, Ryan; Fredstrom, Bridget; Narad, Megan E; Kingery, Kathleen M; Altaye, Mekibib; Matheson, Heather; Tamm, Leanne; Epstein, Jeffery N

2014-01-01

Stimulant medications, such as methylphenidate (MPH), improve the academic performance of children with attention-deficit hyperactivity disorder (ADHD). However, the mechanism by which MPH exerts an effect on academic performance is unclear. We examined MPH effects on math performance and investigated possible mediation of MPH effects by changes in time on-task, inhibitory control, selective attention, and reaction time variability. Children with ADHD aged 7 to 11 years (N = 93) completed a timed math worksheet (with problems tailored to each individual's level of proficiency) and 2 neuropsychological tasks (Go/No-Go and Child Attention Network Test) at baseline, then participated in a 4-week, randomized, controlled, titration trial of MPH. Children were then randomly assigned to their optimal MPH dose or placebo for 1 week (administered double-blind) and repeated the math and neuropsychological tasks (posttest). Baseline and posttest videorecordings of children performing the math task were coded to assess time on-task. Children taking MPH completed 23 more math problems at posttest compared to baseline, whereas the placebo group completed 24 fewer problems on posttest versus baseline, but the effects on math accuracy (percent correct) did not differ. Path analyses revealed that only change in time on-task was a significant mediator of MPH's improvements in math productivity. MPH-derived math productivity improvements may be explained in part by increased time spent on-task, rather than improvements in neurocognitive parameters, such as inhibitory control, selective attention, or reaction time variability.

Blanket design and optimization demonstrations of the first wall/blanket/shield design and optimization system (BSDOS).

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

Gohar, Y.; Nuclear Engineering Division

2005-05-01

In fusion reactors, the blanket design and its characteristics have a major impact on the reactor performance, size, and economics. The selection and arrangement of the blanket materials, dimensions of the different blanket zones, and different requirements of the selected materials for a satisfactory performance are the main parameters, which define the blanket performance. These parameters translate to a large number of variables and design constraints, which need to be simultaneously considered in the blanket design process. This represents a major design challenge because of the lack of a comprehensive design tool capable of considering all these variables to definemore » the optimum blanket design and satisfying all the design constraints for the adopted figure of merit and the blanket design criteria. The blanket design capabilities of the First Wall/Blanket/Shield Design and Optimization System (BSDOS) have been developed to overcome this difficulty and to provide the state-of-the-art research and design tool for performing blanket design analyses. This paper describes some of the BSDOS capabilities and demonstrates its use. In addition, the use of the optimization capability of the BSDOS can result in a significant blanket performance enhancement and cost saving for the reactor design under consideration. In this paper, examples are presented, which utilize an earlier version of the ITER solid breeder blanket design and a high power density self-cooled lithium blanket design for demonstrating some of the BSDOS blanket design capabilities.« less

Blanket Design and Optimization Demonstrations of the First Wall/Blanket/Shield Design and Optimization System (BSDOS)

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

Gohar, Yousry

2005-05-15

In fusion reactors, the blanket design and its characteristics have a major impact on the reactor performance, size, and economics. The selection and arrangement of the blanket materials, dimensions of the different blanket zones, and different requirements of the selected materials for a satisfactory performance are the main parameters, which define the blanket performance. These parameters translate to a large number of variables and design constraints, which need to be simultaneously considered in the blanket design process. This represents a major design challenge because of the lack of a comprehensive design tool capable of considering all these variables to definemore » the optimum blanket design and satisfying all the design constraints for the adopted figure of merit and the blanket design criteria. The blanket design capabilities of the First Wall/Blanket/Shield Design and Optimization System (BSDOS) have been developed to overcome this difficulty and to provide the state-of-the-art research and design tool for performing blanket design analyses. This paper describes some of the BSDOS capabilities and demonstrates its use. In addition, the use of the optimization capability of the BSDOS can result in a significant blanket performance enhancement and cost saving for the reactor design under consideration. In this paper, examples are presented, which utilize an earlier version of the ITER solid breeder blanket design and a high power density self-cooled lithium blanket design for demonstrating some of the BSDOS blanket design capabilities.« less

Individual 3D measurements of end users to personalize work wear clothing

NASA Astrophysics Data System (ADS)

Mielicka, E.; Napieralska, L.; Jasińska, I.; Jarzyna, V.

2017-10-01

Body silhouette 3D measurements need to be performed separately in each country due to significant ethnic differences in body silhouette which preclude the transfer of European data to particular countries. Systematic research allows to update information on the population body silhouette and body proportions as well as select the size changes possible to implement in clothing construction modifications. The diversity in body silhouettes and sizes as well as the issue of clothing fitting encourage clothing producers to provide work wear clothing based on individual measurements of the end users’ bodies. In the framework of the carried research, the group of construction workers was selected as the target group of the analysed work wear clothing users. 42 construction workers, men only, were measured. The body silhouette measurement process was non-contact and was carried out with the use of 3D body scanner. The collected data on the body silhouette allowed to select sizes used to construct the work wear clothing and identify the clothing size. The selected measurement points of the body silhouette underwent statistical analysis to determine the distribution of random variables, here body sizes. The variables distribution characteristics were calculated. On that basis, the fitting appraisal of work wear clothing with respect to the size of the finished product corresponding to the adequate size of individually measured worker/end user was performed. The size overview of the work wear clothing used by the specific professional group and the appraisal of the size fitting to the body silhouette took into consideration the work wear clothing ergonomic functionality with respect to the body posture when performing the tasks, design and clothing construction. The analysis based on the currently gathered end users’ remarks and objections concerning the clothing fitting and performed body silhouette measurements allows to modify the existing work wear clothing for the selected group of end users. The research was aimed at the improvement of the work wear clothing fitting thanks to personalization based on individual body measurements at the stage of construction design.

Peptide-based protein capture agents with high affinity, selectivity, and stability as antibody replacements in biodetection assays

NASA Astrophysics Data System (ADS)

Coppock, Matthew B.; Farrow, Blake; Warner, Candice; Finch, Amethist S.; Lai, Bert; Sarkes, Deborah A.; Heath, James R.; Stratis-Cullum, Dimitra

2014-05-01

Current biodetection assays that employ monoclonal antibodies as primary capture agents exhibit limited fieldability, shelf life, and performance due to batch-to-batch production variability and restricted thermal stability. In order to improve upon the detection of biological threats in fieldable assays and systems for the Army, we are investigating protein catalyzed capture (PCC) agents as drop-in replacements for the existing antibody technology through iterative in situ click chemistry. The PCC agent oligopeptides are developed against known protein epitopes and can be mass produced using robotic methods. In this work, a PCC agent under development will be discussed. The performance, including affinity, selectivity, and stability of the capture agent technology, is analyzed by immunoprecipitation, western blotting, and ELISA experiments. The oligopeptide demonstrates superb selectivity coupled with high affinity through multi-ligand design, and improved thermal, chemical, and biochemical stability due to non-natural amino acid PCC agent design.

Hybrid intelligent monironing systems for thermal power plant trips

NASA Astrophysics Data System (ADS)

Barsoum, Nader; Ismail, Firas Basim

2012-11-01

Steam boiler is one of the main equipment in thermal power plants. If the steam boiler trips it may lead to entire shutdown of the plant, which is economically burdensome. Early boiler trips monitoring is crucial to maintain normal and safe operational conditions. In the present work two artificial intelligent monitoring systems specialized in boiler trips have been proposed and coded within the MATLAB environment. The training and validation of the two systems has been performed using real operational data captured from the plant control system of selected power plant. An integrated plant data preparation framework for seven boiler trips with related operational variables has been proposed for IMSs data analysis. The first IMS represents the use of pure Artificial Neural Network system for boiler trip detection. All seven boiler trips under consideration have been detected by IMSs before or at the same time of the plant control system. The second IMS represents the use of Genetic Algorithms and Artificial Neural Networks as a hybrid intelligent system. A slightly lower root mean square error was observed in the second system which reveals that the hybrid intelligent system performed better than the pure neural network system. Also, the optimal selection of the most influencing variables performed successfully by the hybrid intelligent system.

Numerical analysis of radiation propagation in innovative volumetric receivers based on selective laser melting techniques

NASA Astrophysics Data System (ADS)

Alberti, Fabrizio; Santiago, Sergio; Roccabruna, Mattia; Luque, Salvador; Gonzalez-Aguilar, Jose; Crema, Luigi; Romero, Manuel

2016-05-01

Volumetric absorbers constitute one of the key elements in order to achieve high thermal conversion efficiencies in concentrating solar power plants. Regardless of the working fluid or thermodynamic cycle employed, design trends towards higher absorber output temperatures are widespread, which lead to the general need of components of high solar absorptance, high conduction within the receiver material, high internal convection, low radiative and convective heat losses and high mechanical durability. In this context, the use of advanced manufacturing techniques, such as selective laser melting, has allowed for the fabrication of intricate geometries that are capable of fulfilling the previous requirements. This paper presents a parametric design and analysis of the optical performance of volumetric absorbers of variable porosity conducted by means of detailed numerical ray tracing simulations. Sections of variable macroscopic porosity along the absorber depth were constructed by the fractal growth of single-cell structures. Measures of performance analyzed include optical reflection losses from the absorber front and rear faces, penetration of radiation inside the absorber volume, and radiation absorption as a function of absorber depth. The effects of engineering design parameters such as absorber length and wall thickness, material reflectance and porosity distribution on the optical performance of absorbers are discussed, and general design guidelines are given.

Variable is better than invariable: sparse VSS-NLMS algorithms with application to adaptive MIMO channel estimation.

PubMed

Gui, Guan; Chen, Zhang-xin; Xu, Li; Wan, Qun; Huang, Jiyan; Adachi, Fumiyuki

2014-01-01

Channel estimation problem is one of the key technical issues in sparse frequency-selective fading multiple-input multiple-output (MIMO) communication systems using orthogonal frequency division multiplexing (OFDM) scheme. To estimate sparse MIMO channels, sparse invariable step-size normalized least mean square (ISS-NLMS) algorithms were applied to adaptive sparse channel estimation (ACSE). It is well known that step-size is a critical parameter which controls three aspects: algorithm stability, estimation performance, and computational cost. However, traditional methods are vulnerable to cause estimation performance loss because ISS cannot balance the three aspects simultaneously. In this paper, we propose two stable sparse variable step-size NLMS (VSS-NLMS) algorithms to improve the accuracy of MIMO channel estimators. First, ASCE is formulated in MIMO-OFDM systems. Second, different sparse penalties are introduced to VSS-NLMS algorithm for ASCE. In addition, difference between sparse ISS-NLMS algorithms and sparse VSS-NLMS ones is explained and their lower bounds are also derived. At last, to verify the effectiveness of the proposed algorithms for ASCE, several selected simulation results are shown to prove that the proposed sparse VSS-NLMS algorithms can achieve better estimation performance than the conventional methods via mean square error (MSE) and bit error rate (BER) metrics.

Variable Is Better Than Invariable: Sparse VSS-NLMS Algorithms with Application to Adaptive MIMO Channel Estimation

PubMed Central

Gui, Guan; Chen, Zhang-xin; Xu, Li; Wan, Qun; Huang, Jiyan; Adachi, Fumiyuki

2014-01-01

Channel estimation problem is one of the key technical issues in sparse frequency-selective fading multiple-input multiple-output (MIMO) communication systems using orthogonal frequency division multiplexing (OFDM) scheme. To estimate sparse MIMO channels, sparse invariable step-size normalized least mean square (ISS-NLMS) algorithms were applied to adaptive sparse channel estimation (ACSE). It is well known that step-size is a critical parameter which controls three aspects: algorithm stability, estimation performance, and computational cost. However, traditional methods are vulnerable to cause estimation performance loss because ISS cannot balance the three aspects simultaneously. In this paper, we propose two stable sparse variable step-size NLMS (VSS-NLMS) algorithms to improve the accuracy of MIMO channel estimators. First, ASCE is formulated in MIMO-OFDM systems. Second, different sparse penalties are introduced to VSS-NLMS algorithm for ASCE. In addition, difference between sparse ISS-NLMS algorithms and sparse VSS-NLMS ones is explained and their lower bounds are also derived. At last, to verify the effectiveness of the proposed algorithms for ASCE, several selected simulation results are shown to prove that the proposed sparse VSS-NLMS algorithms can achieve better estimation performance than the conventional methods via mean square error (MSE) and bit error rate (BER) metrics. PMID:25089286

The Performance of Short-Term Heart Rate Variability in the Detection of Congestive Heart Failure

PubMed Central

Barros, Allan Kardec; Ohnishi, Noboru

2016-01-01

Congestive heart failure (CHF) is a cardiac disease associated with the decreasing capacity of the cardiac output. It has been shown that the CHF is the main cause of the cardiac death around the world. Some works proposed to discriminate CHF subjects from healthy subjects using either electrocardiogram (ECG) or heart rate variability (HRV) from long-term recordings. In this work, we propose an alternative framework to discriminate CHF from healthy subjects by using HRV short-term intervals based on 256 RR continuous samples. Our framework uses a matching pursuit algorithm based on Gabor functions. From the selected Gabor functions, we derived a set of features that are inputted into a hybrid framework which uses a genetic algorithm and k-nearest neighbour classifier to select a subset of features that has the best classification performance. The performance of the framework is analyzed using both Fantasia and CHF database from Physionet archives which are, respectively, composed of 40 healthy volunteers and 29 subjects. From a set of nonstandard 16 features, the proposed framework reaches an overall accuracy of 100% with five features. Our results suggest that the application of hybrid frameworks whose classifier algorithms are based on genetic algorithms has outperformed well-known classifier methods. PMID:27891509

A machine learned classifier for RR Lyrae in the VVV survey

NASA Astrophysics Data System (ADS)

Elorrieta, Felipe; Eyheramendy, Susana; Jordán, Andrés; Dékány, István; Catelan, Márcio; Angeloni, Rodolfo; Alonso-García, Javier; Contreras-Ramos, Rodrigo; Gran, Felipe; Hajdu, Gergely; Espinoza, Néstor; Saito, Roberto K.; Minniti, Dante

2016-11-01

Variable stars of RR Lyrae type are a prime tool with which to obtain distances to old stellar populations in the Milky Way. One of the main aims of the Vista Variables in the Via Lactea (VVV) near-infrared survey is to use them to map the structure of the Galactic Bulge. Owing to the large number of expected sources, this requires an automated mechanism for selecting RR Lyrae, and particularly those of the more easily recognized type ab (I.e., fundamental-mode pulsators), from the 106-107 variables expected in the VVV survey area. In this work we describe a supervised machine-learned classifier constructed for assigning a score to a Ks-band VVV light curve that indicates its likelihood of being ab-type RR Lyrae. We describe the key steps in the construction of the classifier, which were the choice of features, training set, selection of aperture, and family of classifiers. We find that the AdaBoost family of classifiers give consistently the best performance for our problem, and obtain a classifier based on the AdaBoost algorithm that achieves a harmonic mean between false positives and false negatives of ≈7% for typical VVV light-curve sets. This performance is estimated using cross-validation and through the comparison to two independent datasets that were classified by human experts.

An optimized index of human cardiovascular adaptation to simulated weightlessness

NASA Technical Reports Server (NTRS)

Wang, M.; Hassebrook, L.; Evans, J.; Varghese, T.; Knapp, C.

1996-01-01

Prolonged exposure to weightlessness is known to produce a variety of cardiovascular changes, some of which may influence the astronaut's performance during a mission. In order to find a reliable indicator of cardiovascular adaptation to weightlessness, we analyzed data from nine male subjects after a 24-hour period of normal activity and after a period of simulated weightlessness produced by two hours in a launch position followed by 20 hours of 6 degrees head-down tilt plus pharmacologically induced diuresis (furosemide). Heart rate, arterial pressure, thoracic fluid index, and radial flow were analyzed. Autoregressive spectral estimation and decomposition were used to obtain the spectral components of each variable from the subjects in the supine position during pre- and post-simulated weightlessness. We found a significant decrease in heart rate power and an increase in thoracic fluid index power in the high frequency region (0.2-0.45 Hz) and significant increases in radial flow and arterial pressure powers in the low frequency region (<0.2 Hz) in response to simulated weightlessness. However, due to the variability among subjects, any single variable appeared limited as a dependable index of cardiovascular adaptation to weightlessness. The backward elimination algorithm was then used to select the best discriminatory features from these spectral components. Fisher's linear discriminant and Bayes' quadratic discriminant were used to combine the selected features to obtain an optimal index of adaptation to simulated weightlessness. Results showed that both techniques provided improved discriminant performance over any single variable and thus have the potential for use as an index to track adaptation and prescribe countermeasures to the effects of weightlessness.

Stabilometric parameters are affected by anthropometry and foot placement.

PubMed

Chiari, Lorenzo; Rocchi, Laura; Cappello, Angelo

2002-01-01

To recognize and quantify the influence of biomechanical factors, namely anthropometry and foot placement, on the more common measures of stabilometric performance, including new-generation stochastic parameters. Fifty normal-bodied young adults were selected in order to cover a sufficiently wide range of anthropometric properties. They were allowed to choose their preferred side-by-side foot position and their quiet stance was recorded with eyes open and closed by a force platform. biomechanical factors are known to influence postural stability but their impact on stabilometric parameters has not been extensively explored yet. Principal component analysis was used for feature selection among several biomechanical factors. A collection of 55 stabilometric parameters from the literature was estimated from the center-of-pressure time series. Linear relations between stabilometric parameters and selected biomechanical factors were investigated by robust regression techniques. The feature selection process returned height, weight, maximum foot width, base-of-support area, and foot opening angle as the relevant biomechanical variables. Only eleven out of the 55 stabilometric parameters were completely immune from a linear dependence on these variables. The remaining parameters showed a moderate to high dependence that was strengthened upon eye closure. For these parameters, a normalization procedure was proposed, to remove what can well be considered, in clinical investigations, a spurious source of between-subject variability. Care should be taken when quantifying postural sway through stabilometric parameters. It is suggested as a good practice to include some anthropometric measurements in the experimental protocol, and to standardize or trace foot position. Although the role of anthropometry and foot placement has been investigated in specific studies, there are no studies in the literature that systematically explore the relationship between such BF and stabilometric parameters. This knowledge may contribute to better defining the experimental protocol and improving the functional evaluation of postural sway for clinical purposes, e.g. by removing through normalization the spurious effects of body properties and foot position on postural performance.

Multi-omics facilitated variable selection in Cox-regression model for cancer prognosis prediction.

PubMed

Liu, Cong; Wang, Xujun; Genchev, Georgi Z; Lu, Hui

2017-07-15

New developments in high-throughput genomic technologies have enabled the measurement of diverse types of omics biomarkers in a cost-efficient and clinically-feasible manner. Developing computational methods and tools for analysis and translation of such genomic data into clinically-relevant information is an ongoing and active area of investigation. For example, several studies have utilized an unsupervised learning framework to cluster patients by integrating omics data. Despite such recent advances, predicting cancer prognosis using integrated omics biomarkers remains a challenge. There is also a shortage of computational tools for predicting cancer prognosis by using supervised learning methods. The current standard approach is to fit a Cox regression model by concatenating the different types of omics data in a linear manner, while penalty could be added for feature selection. A more powerful approach, however, would be to incorporate data by considering relationships among omics datatypes. Here we developed two methods: a SKI-Cox method and a wLASSO-Cox method to incorporate the association among different types of omics data. Both methods fit the Cox proportional hazards model and predict a risk score based on mRNA expression profiles. SKI-Cox borrows the information generated by these additional types of omics data to guide variable selection, while wLASSO-Cox incorporates this information as a penalty factor during model fitting. We show that SKI-Cox and wLASSO-Cox models select more true variables than a LASSO-Cox model in simulation studies. We assess the performance of SKI-Cox and wLASSO-Cox using TCGA glioblastoma multiforme and lung adenocarcinoma data. In each case, mRNA expression, methylation, and copy number variation data are integrated to predict the overall survival time of cancer patients. Our methods achieve better performance in predicting patients' survival in glioblastoma and lung adenocarcinoma. Copyright © 2017. Published by Elsevier Inc.

Prediction of Baseflow Index of Catchments using Machine Learning Algorithms

NASA Astrophysics Data System (ADS)

Yadav, B.; Hatfield, K.

2017-12-01

We present the results of eight machine learning techniques for predicting the baseflow index (BFI) of ungauged basins using a surrogate of catchment scale climate and physiographic data. The tested algorithms include ordinary least squares, ridge regression, least absolute shrinkage and selection operator (lasso), elasticnet, support vector machine, gradient boosted regression trees, random forests, and extremely randomized trees. Our work seeks to identify the dominant controls of BFI that can be readily obtained from ancillary geospatial databases and remote sensing measurements, such that the developed techniques can be extended to ungauged catchments. More than 800 gauged catchments spanning the continental United States were selected to develop the general methodology. The BFI calculation was based on the baseflow separated from daily streamflow hydrograph using HYSEP filter. The surrogate catchment attributes were compiled from multiple sources including digital elevation model, soil, landuse, climate data, other publicly available ancillary and geospatial data. 80% catchments were used to train the ML algorithms, and the remaining 20% of the catchments were used as an independent test set to measure the generalization performance of fitted models. A k-fold cross-validation using exhaustive grid search was used to fit the hyperparameters of each model. Initial model development was based on 19 independent variables, but after variable selection and feature ranking, we generated revised sparse models of BFI prediction that are based on only six catchment attributes. These key predictive variables selected after the careful evaluation of bias-variance tradeoff include average catchment elevation, slope, fraction of sand, permeability, temperature, and precipitation. The most promising algorithms exceeding an accuracy score (r-square) of 0.7 on test data include support vector machine, gradient boosted regression trees, random forests, and extremely randomized trees. Considering both the accuracy and the computational complexity of these algorithms, we identify the extremely randomized trees as the best performing algorithm for BFI prediction in ungauged basins.

Adolescent sleep disturbance and school performance: the confounding variable of socioeconomics.

PubMed

Pagel, James F; Forister, Natalie; Kwiatkowki, Carol

2007-02-15

To assess how selected socioeconomic variables known to affect school performance alter the association between reported sleep disturbance and poor school performance in a contiguous middle school/high school population. A school district/college IRB approved questionnaire was distributed in science and health classes in middle school and high school. This questionnaire included a frequency scaled pediatric sleep disturbance questionnaire for completion by students and a permission and demographic questionnaire for completion by parents (completed questionnaires n = 238 with 69.3% including GPA). Sleep complaints occur at high frequency in this sample (sleep onset insomnia 60% > 1 x /wk.; 21.2% every night; sleepiness during the day (45.7% > 1 x /wk.; 15.2 % every night), and difficulty concentrating (54.6% > 1 x /wk.; 12.9% always). Students with lower grade point averages (GPAs) were more likely to have restless/aching legs when trying to fall asleep, difficulty concentrating during the day, snoring every night, difficulty waking in the morning, sleepiness during the day, and falling asleep in class. Lower reported GPAs were significantly associated with lower household incomes. After statistically controlling for income, restless legs, sleepiness during the day, and difficulty with concentration continued to significantly affect school performance. This study provides additional evidence indicating that sleep disturbances occur at high frequencies in adolescents and significantly affect daytime performance, as measured by GPA. The socioeconomic variable of household income also significantly affects GPA. After statistically controlling for age and household income, the number and type of sleep variables noted to significantly affect GPA are altered but persistent in demonstrating significant effects on school performance.

Bayesian dynamic modeling of time series of dengue disease case counts

PubMed Central

López-Quílez, Antonio; Torres-Prieto, Alexander

2017-01-01

The aim of this study is to model the association between weekly time series of dengue case counts and meteorological variables, in a high-incidence city of Colombia, applying Bayesian hierarchical dynamic generalized linear models over the period January 2008 to August 2015. Additionally, we evaluate the model’s short-term performance for predicting dengue cases. The methodology shows dynamic Poisson log link models including constant or time-varying coefficients for the meteorological variables. Calendar effects were modeled using constant or first- or second-order random walk time-varying coefficients. The meteorological variables were modeled using constant coefficients and first-order random walk time-varying coefficients. We applied Markov Chain Monte Carlo simulations for parameter estimation, and deviance information criterion statistic (DIC) for model selection. We assessed the short-term predictive performance of the selected final model, at several time points within the study period using the mean absolute percentage error. The results showed the best model including first-order random walk time-varying coefficients for calendar trend and first-order random walk time-varying coefficients for the meteorological variables. Besides the computational challenges, interpreting the results implies a complete analysis of the time series of dengue with respect to the parameter estimates of the meteorological effects. We found small values of the mean absolute percentage errors at one or two weeks out-of-sample predictions for most prediction points, associated with low volatility periods in the dengue counts. We discuss the advantages and limitations of the dynamic Poisson models for studying the association between time series of dengue disease and meteorological variables. The key conclusion of the study is that dynamic Poisson models account for the dynamic nature of the variables involved in the modeling of time series of dengue disease, producing useful models for decision-making in public health. PMID:28671941

Robust portfolio selection based on asymmetric measures of variability of stock returns

NASA Astrophysics Data System (ADS)

Chen, Wei; Tan, Shaohua

2009-10-01

This paper addresses a new uncertainty set--interval random uncertainty set for robust optimization. The form of interval random uncertainty set makes it suitable for capturing the downside and upside deviations of real-world data. These deviation measures capture distributional asymmetry and lead to better optimization results. We also apply our interval random chance-constrained programming to robust mean-variance portfolio selection under interval random uncertainty sets in the elements of mean vector and covariance matrix. Numerical experiments with real market data indicate that our approach results in better portfolio performance.

Infrastructure features outperform environmental variables explaining rabbit abundance around motorways.

PubMed

Planillo, Aimara; Malo, Juan E

2018-01-01

Human disturbance is widespread across landscapes in the form of roads that alter wildlife populations. Knowing which road features are responsible for the species response and their relevance in comparison with environmental variables will provide useful information for effective conservation measures. We sampled relative abundance of European rabbits, a very widespread species, in motorway verges at regional scale, in an area with large variability in environmental and infrastructure conditions. Environmental variables included vegetation structure, plant productivity, distance to water sources, and altitude. Infrastructure characteristics were the type of vegetation in verges, verge width, traffic volume, and the presence of embankments. We performed a variance partitioning analysis to determine the relative importance of two sets of variables on rabbit abundance. Additionally, we identified the most important variables and their effects model averaging after model selection by AICc on hypothesis-based models. As a group, infrastructure features explained four times more variability in rabbit abundance than environmental variables, being the effects of the former critical in motorway stretches located in altered landscapes with no available habitat for rabbits, such as agricultural fields. Model selection and Akaike weights showed that verge width and traffic volume are the most important variables explaining rabbit abundance index, with positive and negative effects, respectively. In the light of these results, the response of species to the infrastructure can be modulated through the modification of motorway features, being some of them manageable in the design phase. The identification of such features leads to suggestions for improvement through low-cost corrective measures and conservation plans. As a general indication, keeping motorway verges less than 10 m wide will prevent high densities of rabbits and avoid the unwanted effects that rabbit populations can generate in some areas.

ASTRAL-R score predicts non-recanalisation after intravenous thrombolysis in acute ischaemic stroke.

PubMed

Vanacker, Peter; Heldner, Mirjam R; Seiffge, David; Mueller, Hubertus; Eskandari, Ashraf; Traenka, Christopher; Ntaios, George; Mosimann, Pascal J; Sztajzel, Roman; Mendes Pereira, Vitor; Cras, Patrick; Engelter, Stefan; Lyrer, Philippe; Fischer, Urs; Lambrou, Dimitris; Arnold, Marcel; Michel, Patrik

2015-05-01

Intravenous thrombolysis (IVT) as treatment in acute ischaemic strokes may be insufficient to achieve recanalisation in certain patients. Predicting probability of non-recanalisation after IVT may have the potential to influence patient selection to more aggressive management strategies. We aimed at deriving and internally validating a predictive score for post-thrombolytic non-recanalisation, using clinical and radiological variables. In thrombolysis registries from four Swiss academic stroke centres (Lausanne, Bern, Basel and Geneva), patients were selected with large arterial occlusion on acute imaging and with repeated arterial assessment at 24 hours. Based on a logistic regression analysis, an integer-based score for each covariate of the fitted multivariate model was generated. Performance of integer-based predictive model was assessed by bootstrapping available data and cross validation (delete-d method). In 599 thrombolysed strokes, five variables were identified as independent predictors of absence of recanalisation: Acute glucose > 7 mmol/l (A), significant extracranial vessel STenosis (ST), decreased Range of visual fields (R), large Arterial occlusion (A) and decreased Level of consciousness (L). All variables were weighted 1, except for (L) which obtained 2 points based on β-coefficients on the logistic scale. ASTRAL-R scores 0, 3 and 6 corresponded to non-recanalisation probabilities of 18, 44 and 74 % respectively. Predictive ability showed AUC of 0.66 (95 %CI, 0.61-0.70) when using bootstrap and 0.66 (0.63-0.68) when using delete-d cross validation. In conclusion, the 5-item ASTRAL-R score moderately predicts non-recanalisation at 24 hours in thrombolysed ischaemic strokes. If its performance can be confirmed by external validation and its clinical usefulness can be proven, the score may influence patient selection for more aggressive revascularisation strategies in routine clinical practice.

Robust check loss-based variable selection of high-dimensional single-index varying-coefficient model

NASA Astrophysics Data System (ADS)

Song, Yunquan; Lin, Lu; Jian, Ling

2016-07-01

Single-index varying-coefficient model is an important mathematical modeling method to model nonlinear phenomena in science and engineering. In this paper, we develop a variable selection method for high-dimensional single-index varying-coefficient models using a shrinkage idea. The proposed procedure can simultaneously select significant nonparametric components and parametric components. Under defined regularity conditions, with appropriate selection of tuning parameters, the consistency of the variable selection procedure and the oracle property of the estimators are established. Moreover, due to the robustness of the check loss function to outliers in the finite samples, our proposed variable selection method is more robust than the ones based on the least squares criterion. Finally, the method is illustrated with numerical simulations.

The Educational Impact of Online Learning: How Do University Students Perform in Subsequent Courses?

ERIC Educational Resources Information Center

Krieg, John M.; Henson, Steven E.

2016-01-01

Using a large student-level dataset from a medium-sized regional comprehensive university, we measure the impact of taking an online prerequisite course on follow-up course grades. To control for self-selection into online courses, we utilize student, instructor, course, and time fixed effects augmented with an instrumental variable approach. We…

Water quality parameter measurement using spectral signatures

NASA Technical Reports Server (NTRS)

White, P. E.

1973-01-01

Regression analysis is applied to the problem of measuring water quality parameters from remote sensing spectral signature data. The equations necessary to perform regression analysis are presented and methods of testing the strength and reliability of a regression are described. An efficient algorithm for selecting an optimal subset of the independent variables available for a regression is also presented.

Selected Influences on Solo and Small-Ensemble Festival Ratings: Replication and Extension

ERIC Educational Resources Information Center

Bergee, Martin J.; McWhirter, Jamila L.

2005-01-01

Festival performance is no trivial endeavor. At one midwestern state festival alone, 10,938 events received a rating over a 3-year period (2001-2003). Such an extensive level of participation justifies sustained study. To learn more about variables that may underlie success at solo and small ensemble evaluative festivals, Bergee and Platt (2003)…

Poverty and Algebra Performance: A Comparative Spatial Analysis of a Border South State

ERIC Educational Resources Information Center

Tate, William F.; Hogrebe, Mark C.

2015-01-01

This research uses two measures of poverty, as well as mobility and selected education variables to study how their relationships vary across 543 Missouri high school districts. Using Missouri and U.S. Census American Community Survey (ACS) data, local R[superscript 2]'s from geographically weighted regressions are spatially mapped to demonstrate…

The use of disease severity variables in predicting efficacy of FOV4 resistance selection

USDA-ARS?s Scientific Manuscript database

In 2015, 85 Upland (Gossypium hirsutum L.) accessions from the USDA-ARS Cotton Collection and 126 F6 Pima-S6 x Pima-S7 (G. barbadense L.) recombinant inbred lines were evaluated for disease performance under pressure of Fusarium oxysporum f. sp. vasinfectum race 4 (FOV4) in a replicated field trial ...

The Relationship between Music Participation and Mathematics Achievement in Middle School Students

ERIC Educational Resources Information Center

Boyd, Joshua Robert

2013-01-01

A comparative analysis was used to study the results from a descriptive survey of selected middle school students in Grades 6, 7, and 8. Student responses to the survey tool was used to compare multiple variables of music participation and duration of various musical activities, such as singing and performing on instruments, to the mathematics…

Incorporating biological information in sparse principal component analysis with application to genomic data.

PubMed

Li, Ziyi; Safo, Sandra E; Long, Qi

2017-07-11

Sparse principal component analysis (PCA) is a popular tool for dimensionality reduction, pattern recognition, and visualization of high dimensional data. It has been recognized that complex biological mechanisms occur through concerted relationships of multiple genes working in networks that are often represented by graphs. Recent work has shown that incorporating such biological information improves feature selection and prediction performance in regression analysis, but there has been limited work on extending this approach to PCA. In this article, we propose two new sparse PCA methods called Fused and Grouped sparse PCA that enable incorporation of prior biological information in variable selection. Our simulation studies suggest that, compared to existing sparse PCA methods, the proposed methods achieve higher sensitivity and specificity when the graph structure is correctly specified, and are fairly robust to misspecified graph structures. Application to a glioblastoma gene expression dataset identified pathways that are suggested in the literature to be related with glioblastoma. The proposed sparse PCA methods Fused and Grouped sparse PCA can effectively incorporate prior biological information in variable selection, leading to improved feature selection and more interpretable principal component loadings and potentially providing insights on molecular underpinnings of complex diseases.

A Lifetime Maximization Relay Selection Scheme in Wireless Body Area Networks.

PubMed

Zhang, Yu; Zhang, Bing; Zhang, Shi

2017-06-02

Network Lifetime is one of the most important metrics in Wireless Body Area Networks (WBANs). In this paper, a relay selection scheme is proposed under the topology constrains specified in the IEEE 802.15.6 standard to maximize the lifetime of WBANs through formulating and solving an optimization problem where relay selection of each node acts as optimization variable. Considering the diversity of the sensor nodes in WBANs, the optimization problem takes not only energy consumption rate but also energy difference among sensor nodes into account to improve the network lifetime performance. Since it is Non-deterministic Polynomial-hard (NP-hard) and intractable, a heuristic solution is then designed to rapidly address the optimization. The simulation results indicate that the proposed relay selection scheme has better performance in network lifetime compared with existing algorithms and that the heuristic solution has low time complexity with only a negligible performance degradation gap from optimal value. Furthermore, we also conduct simulations based on a general WBAN model to comprehensively illustrate the advantages of the proposed algorithm. At the end of the evaluation, we validate the feasibility of our proposed scheme via an implementation discussion.

Cognitive factors contributing to spelling performance in children with prenatal alcohol exposure.

PubMed

Glass, Leila; Graham, Diana M; Akshoomoff, Natacha; Mattson, Sarah N

2015-11-01

Heavy prenatal alcohol exposure is associated with impaired school functioning. Spelling performance has not been comprehensively evaluated. We examined whether children with heavy prenatal alcohol exposure demonstrate deficits in spelling and related abilities, including reading, and tested whether there are unique underlying mechanisms for observed deficits in this population. Ninety-six school-age children made up 2 groups: children with heavy prenatal alcohol exposure (AE, n = 49) and control children (CON, n = 47). Children completed select subtests from the Wechsler Individual Achievement Test-Second Edition and the NEPSY-II. Group differences and relations between spelling and theoretically related cognitive variables were evaluated using multivariate analysis of variance and Pearson correlations. Hierarchical regression analyses were used to assess contributions of group membership and cognitive variables to spelling performance. The specificity of these deficits and underlying mechanisms was tested by examining the relations between reading ability, group membership, and cognitive variables. Groups differed significantly on all variables. Group membership and phonological processing significantly contributed to spelling performance, whereas for reading, group membership and all cognitive variables contributed significantly. For both reading and spelling, group × working memory interactions revealed that working memory contributed independently only for alcohol-exposed children. Alcohol-exposed children demonstrated a unique pattern of spelling deficits. The relation of working memory to spelling and reading was specific to the AE group, suggesting that if prenatal alcohol exposure is known or suspected, working memory ability should be considered in the development and implementation of explicit instruction. (c) 2015 APA, all rights reserved).

Cognitive Factors Contributing to Spelling Performance in Children with Prenatal Alcohol Exposure

PubMed Central

Glass, Leila; Graham, Diana M.; Akshoomoff, Natacha; Mattson, Sarah N.

2015-01-01

Objective Heavy prenatal alcohol exposure is associated with impaired school functioning. Spelling performance has not been comprehensively evaluated. We examined whether children with heavy prenatal alcohol exposure demonstrate deficits in spelling and related abilities, including reading, and tested whether there are unique underlying mechanisms for observed deficits in this population. Method Ninety-six school-age children comprised two groups: children with heavy prenatal alcohol exposure (AE, n=49) and control children (CON, n=47). Children completed select subtests from the WIAT-II and NEPSY-II. Group differences and relations between spelling and theoretically-related cognitive variables were evaluated using MANOVA and Pearson correlations. Hierarchical regression analyses were utilized to assess contributions of group membership and cognitive variables to spelling performance. The specificity of these deficits and underlying mechanisms was tested by examining the relations between reading ability, group membership, and cognitive variables. Results Groups differed significantly on all variables. Group membership and phonological processing significantly contributed to spelling performance. In addition, a significant group*working memory interaction revealed that working memory independently contributed significantly to spelling only for the AE group. All cognitive variables contributed to reading across groups and a group*working memory interaction revealed that working memory contributed independently to reading only for alcohol-exposed children. Conclusion Alcohol-exposed children demonstrated a unique pattern of spelling deficits. The relation of working memory to spelling and reading was specific to the AE group, suggesting that if prenatal alcohol exposure is known or suspected, working memory ability should be considered in the development and implementation of explicit instruction. PMID:25643217

Measurement error in performance studies of health information technology: lessons from the management literature.

PubMed

Litwin, A S; Avgar, A C; Pronovost, P J

2012-01-01

Just as researchers and clinicians struggle to pin down the benefits attendant to health information technology (IT), management scholars have long labored to identify the performance effects arising from new technologies and from other organizational innovations, namely the reorganization of work and the devolution of decision-making authority. This paper applies lessons from that literature to theorize the likely sources of measurement error that yield the weak statistical relationship between measures of health IT and various performance outcomes. In so doing, it complements the evaluation literature's more conceptual examination of health IT's limited performance impact. The paper focuses on seven issues, in particular, that likely bias downward the estimated performance effects of health IT. They are 1.) negative self-selection, 2.) omitted or unobserved variables, 3.) mis-measured contextual variables, 4.) mismeasured health IT variables, 5.) lack of attention to the specific stage of the adoption-to-use continuum being examined, 6.) too short of a time horizon, and 7.) inappropriate units-of-analysis. The authors offer ways to counter these challenges. Looking forward more broadly, they suggest that researchers take an organizationally-grounded approach that privileges internal validity over generalizability. This focus on statistical and empirical issues in health IT-performance studies should be complemented by a focus on theoretical issues, in particular, the ways that health IT creates value and apportions it to various stakeholders.

Can You Hear Me Now? Musical Training Shapes Functional Brain Networks for Selective Auditory Attention and Hearing Speech in Noise

PubMed Central

Strait, Dana L.; Kraus, Nina

2011-01-01

Even in the quietest of rooms, our senses are perpetually inundated by a barrage of sounds, requiring the auditory system to adapt to a variety of listening conditions in order to extract signals of interest (e.g., one speaker's voice amidst others). Brain networks that promote selective attention are thought to sharpen the neural encoding of a target signal, suppressing competing sounds and enhancing perceptual performance. Here, we ask: does musical training benefit cortical mechanisms that underlie selective attention to speech? To answer this question, we assessed the impact of selective auditory attention on cortical auditory-evoked response variability in musicians and non-musicians. Outcomes indicate strengthened brain networks for selective auditory attention in musicians in that musicians but not non-musicians demonstrate decreased prefrontal response variability with auditory attention. Results are interpreted in the context of previous work documenting perceptual and subcortical advantages in musicians for the hearing and neural encoding of speech in background noise. Musicians’ neural proficiency for selectively engaging and sustaining auditory attention to language indicates a potential benefit of music for auditory training. Given the importance of auditory attention for the development and maintenance of language-related skills, musical training may aid in the prevention, habilitation, and remediation of individuals with a wide range of attention-based language, listening and learning impairments. PMID:21716636

Deriving estimates of individual variability in genetic potentials of performance traits for 3 dairy breeds, using a model of lifetime nutrient partitioning.

PubMed

Phuong, H N; Martin, O; de Boer, I J M; Ingvartsen, K L; Schmidely, Ph; Friggens, N C

2015-01-01

This study explored the ability of an existing lifetime nutrient partitioning model for simulating individual variability in genetic potentials of dairy cows. Generally, the model assumes a universal trajectory of dynamic partitioning of priority between life functions and genetic scaling parameters are then incorporated to simulate individual difference in performance. Data of 102 cows including 180 lactations of 3 breeds: Danish Red, Danish Holstein, and Jersey, which were completely independent from those used previously for model development, were used. Individual cow performance records through sequential lactations were used to derive genetic scaling parameters for each animal by calibrating the model to achieve best fit, cow by cow. The model was able to fit individual curves of body weight, and milk fat, milk protein, and milk lactose concentrations with a high degree of accuracy. Daily milk yield and dry matter intake were satisfactorily predicted in early and mid lactation, but underpredictions were found in late lactation. Breeds and parities did not significantly affect the prediction accuracy. The means of genetic scaling parameters between Danish Red and Danish Holstein were similar but significantly different from those of Jersey. The extent of correlations between the genetic scaling parameters was consistent with that reported in the literature. In conclusion, this model is of value as a tool to derive estimates of genetic potentials of milk yield, milk composition, body reserve usage, and growth for different genotypes of cow. Moreover, it can be used to separate genetic variability in performance between individual cows from environmental noise. The model enables simulation of the effects of a genetic selection strategy on lifetime efficiency of individual cows, which has a main advantage of including the rearing costs, and thus, can be used to explore the impact of future selection on animal performance and efficiency. Copyright © 2015 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

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

PubMed

Duan, Ran; Fu, Haoda

2015-08-30

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

A survey of variable selection methods in two Chinese epidemiology journals

PubMed Central

2010-01-01

Background Although much has been written on developing better procedures for variable selection, there is little research on how it is practiced in actual studies. This review surveys the variable selection methods reported in two high-ranking Chinese epidemiology journals. Methods Articles published in 2004, 2006, and 2008 in the Chinese Journal of Epidemiology and the Chinese Journal of Preventive Medicine were reviewed. Five categories of methods were identified whereby variables were selected using: A - bivariate analyses; B - multivariable analysis; e.g. stepwise or individual significance testing of model coefficients; C - first bivariate analyses, followed by multivariable analysis; D - bivariate analyses or multivariable analysis; and E - other criteria like prior knowledge or personal judgment. Results Among the 287 articles that reported using variable selection methods, 6%, 26%, 30%, 21%, and 17% were in categories A through E, respectively. One hundred sixty-three studies selected variables using bivariate analyses, 80% (130/163) via multiple significance testing at the 5% alpha-level. Of the 219 multivariable analyses, 97 (44%) used stepwise procedures, 89 (41%) tested individual regression coefficients, but 33 (15%) did not mention how variables were selected. Sixty percent (58/97) of the stepwise routines also did not specify the algorithm and/or significance levels. Conclusions The variable selection methods reported in the two journals were limited in variety, and details were often missing. Many studies still relied on problematic techniques like stepwise procedures and/or multiple testing of bivariate associations at the 0.05 alpha-level. These deficiencies should be rectified to safeguard the scientific validity of articles published in Chinese epidemiology journals. PMID:20920252

Methodological development for selection of significant predictors explaining fatal road accidents.

PubMed

Dadashova, Bahar; Arenas-Ramírez, Blanca; Mira-McWilliams, José; Aparicio-Izquierdo, Francisco

2016-05-01

Identification of the most relevant factors for explaining road accident occurrence is an important issue in road safety research, particularly for future decision-making processes in transport policy. However model selection for this particular purpose is still an ongoing research. In this paper we propose a methodological development for model selection which addresses both explanatory variable and adequate model selection issues. A variable selection procedure, TIM (two-input model) method is carried out by combining neural network design and statistical approaches. The error structure of the fitted model is assumed to follow an autoregressive process. All models are estimated using Markov Chain Monte Carlo method where the model parameters are assigned non-informative prior distributions. The final model is built using the results of the variable selection. For the application of the proposed methodology the number of fatal accidents in Spain during 2000-2011 was used. This indicator has experienced the maximum reduction internationally during the indicated years thus making it an interesting time series from a road safety policy perspective. Hence the identification of the variables that have affected this reduction is of particular interest for future decision making. The results of the variable selection process show that the selected variables are main subjects of road safety policy measures. Published by Elsevier Ltd.

A Comparative Propulsion System Analysis for the High-Speed Civil Transport

NASA Technical Reports Server (NTRS)

Berton, Jeffrey J.; Haller, William J.; Senick, Paul F.; Jones, Scott M.; Seidel, Jonathan A.

2005-01-01

Six of the candidate propulsion systems for the High-Speed Civil Transport are the turbojet, turbine bypass engine, mixed flow turbofan, variable cycle engine, Flade engine, and the inverting flow valve engine. A comparison of these propulsion systems by NASA's Glenn Research Center, paralleling studies within the aircraft industry, is presented. This report describes the Glenn Aeropropulsion Analysis Office's contribution to the High-Speed Research Program's 1993 and 1994 propulsion system selections. A parametric investigation of each propulsion cycle's primary design variables is analytically performed. Performance, weight, and geometric data are calculated for each engine. The resulting engines are then evaluated on two airframer-derived supersonic commercial aircraft for a 5000 nautical mile, Mach 2.4 cruise design mission. The effects of takeoff noise, cruise emissions, and cycle design rules are examined.

Commonality between Reduced Gravity and Microgravity Habitats for Long Duration Missions

NASA Technical Reports Server (NTRS)

Howard, Robert

2014-01-01

Many conceptual studies for long duration missions beyond Earth orbit have assumed unique habitat designs for each destination and for transit habitation. This may not be the most effective approach. A variable gravity habitat, one designed for use in microgravity, lunar, Martian, and terrestrial environments may provide savings that offset the loss of environment-specific optimization. However, a brief analysis of selected flown spacecraft and Constellation-era conceptual habitat designs suggests that one cannot simply lift a habitat from one environment and place it in another that it was not designed for without incurring significant human performance compromises. By comparison, a conceptual habitat based on the Skylab II framework but designed specifically to accommodate variable gravity environments can be shown to yield significant advantages while incurring only minimal human performance compromises.

Calibration of ultrasonic power output in water, ethanol and sodium polytungstate

NASA Astrophysics Data System (ADS)

Mentler, Axel; Schomakers, Jasmin; Kloss, Stefanie; Zechmeister-Boltenstern, Sophie; Schuller, Reinhard; Mayer, Herwig

2017-10-01

Ultrasonic power is the main variable that forms the basis for many soil disaggregation experiments. Thus, a procedure for the rapid determination of this variable has been developed and is described in this article. Calorimetric experiments serve to measure specific heat capacity and ultrasonic power. Ultrasonic power is determined experimentally for deionised water, 30% ethanol and sodium polytungstate with a density of 1.6 g cm-3 and 1.8 g cm-3. All experiments are performed with a pre-selected ultrasonic probe vibration amplitude. Under these conditions, it was found that the emitted ultrasonic power was comparable in the four fluids. It is suggested, however, to perform calibration experiments prior to dispersion experiments, since the used fluid, as well as the employed ultrasonic equipment, may influence the power output.

Variable optical attenuator and dynamic mode group equalizer for few mode fibers.

PubMed

Blau, Miri; Weiss, Israel; Gerufi, Jonathan; Sinefeld, David; Bin-Nun, Moran; Lingle, Robert; Grüner-Nielsen, Lars; Marom, Dan M

2014-12-15

Variable optical attenuation (VOA) for three-mode fiber is experimentally presented, utilizing an amplitude spatial light modulator (SLM), achieving up to -28dB uniform attenuation for all modes. Using the ability to spatially vary the attenuation distribution with the SLM, we also achieve up to 10dB differential attenuation between the fiber's two supported mode group (LP₀₁ and LP₁₁). The spatially selective attenuation serves as the basis of a dynamic mode-group equalizer (DME), potentially gain-balancing mode dependent optical amplification. We extend the experimental three mode DME functionality with a performance analysis of a fiber supporting 6 spatial modes in four mode groups. The spatial modes' distribution and overlap limit the available dynamic range and performance of the DME in the higher mode count case.

Ecological and personal predictors of science achievement in an urban center

NASA Astrophysics Data System (ADS)

Guidubaldi, John Michael

This study sought to examine selected personal and environmental factors that predict urban students' achievement test scores on the science subject area of the Ohio standardized test. Variables examined were in the general categories of teacher/classroom, student, and parent/home. It assumed that these clusters might add independent variance to a best predictor model, and that discovering relative strength of different predictors might lead to better selection of intervention strategies to improve student performance. This study was conducted in an urban school district and was comprised of teachers and students enrolled in ninth grade science in three of this district's high schools. Consenting teachers (9), students (196), and parents (196) received written surveys with questions designed to examine the predictive power of each variable cluster. Regression analyses were used to determine which factors best correlate with student scores and classroom science grades. Selected factors were then compiled into a best predictive model, predicting success on standardized science tests. Students t tests of gender and racial subgroups confirmed that there were racial differences in OPT scores, and both gender and racial differences in science grades. Additional examinations were therefore conducted for all 12 variables to determine whether gender and race had an impact on the strength of individual variable predictions and on the final best predictor model. Of the 15 original OPT and cluster variable hypotheses, eight showed significant positive relationships that occurred in the expected direction. However, when more broadly based end-of-the-year science class grade was used as a criterion, 13 of the 15 hypotheses showed significant relationships in the expected direction. With both criteria, significant gender and racial differences were observed in the strength of individual predictors and in the composition of best predictor models.

Identification of phreatophytic groundwater dependent ecosystems using geospatial technologies

NASA Astrophysics Data System (ADS)

Perez Hoyos, Isabel Cristina

The protection of groundwater dependent ecosystems (GDEs) is increasingly being recognized as an essential aspect for the sustainable management and allocation of water resources. Ecosystem services are crucial for human well-being and for a variety of flora and fauna. However, the conservation of GDEs is only possible if knowledge about their location and extent is available. Several studies have focused on the identification of GDEs at specific locations using ground-based measurements. However, recent progress in technologies such as remote sensing and their integration with geographic information systems (GIS) has provided alternative ways to map GDEs at much larger spatial extents. This study is concerned with the discovery of patterns in geospatial data sets using data mining techniques for mapping phreatophytic GDEs in the United States at 1 km spatial resolution. A methodology to identify the probability of an ecosystem to be groundwater dependent is developed. Probabilities are obtained by modeling the relationship between the known locations of GDEs and main factors influencing groundwater dependency, namely water table depth (WTD) and aridity index (AI). A methodology is proposed to predict WTD at 1 km spatial resolution using relevant geospatial data sets calibrated with WTD observations. An ensemble learning algorithm called random forest (RF) is used in order to model the distribution of groundwater in three study areas: Nevada, California, and Washington, as well as in the entire United States. RF regression performance is compared with a single regression tree (RT). The comparison is based on contrasting training error, true prediction error, and variable importance estimates of both methods. Additionally, remote sensing variables are omitted from the process of fitting the RF model to the data to evaluate the deterioration in the model performance when these variables are not used as an input. Research results suggest that although the prediction accuracy of a single RT is reduced in comparison with RFs, single trees can still be used to understand the interactions that might be taking place between predictor variables and the response variable. Regarding RF, there is a great potential in using the power of an ensemble of trees for prediction of WTD. The superior capability of RF to accurately map water table position in Nevada, California, and Washington demonstrate that this technique can be applied at scales larger than regional levels. It is also shown that the removal of remote sensing variables from the RF training process degrades the performance of the model. Using the predicted WTD, the probability of an ecosystem to be groundwater dependent (GDE probability) is estimated at 1 km spatial resolution. The modeling technique is evaluated in the state of Nevada, USA to develop a systematic approach for the identification of GDEs and it is then applied in the United States. The modeling approach selected for the development of the GDE probability map results from a comparison of the performance of classification trees (CT) and classification forests (CF). Predictive performance evaluation for the selection of the most accurate model is achieved using a threshold independent technique, and the prediction accuracy of both models is assessed in greater detail using threshold-dependent measures. The resulting GDE probability map can potentially be used for the definition of conservation areas since it can be translated into a binary classification map with two classes: GDE and NON-GDE. These maps are created by selecting a probability threshold. It is demonstrated that the choice of this threshold has dramatic effects on deterministic model performance measures.

Inward Leakage Variability between Respirator Fit Test Panels – Part I. Deterministic Approach

PubMed Central

Zhuang, Ziqing; Liu, Yuewei; Coffey, Christopher C.; Miller, Colleen; Szalajda, Jonathan

2015-01-01

Inter-panel variability has never been investigated. The objective of this study was to determine the variability between different anthropometric panels used to determine the inward leakage (IL) of N95 filtering facepiece respirators (FFRs) and elastomeric half-mask respirators (EHRs). A total of 144 subjects, who were both experienced and non-experienced N95 FFR users, were recruited. Five N95 FFRs and five N95 EHRs were randomly selected from among those models tested previously in our laboratory. The PortaCount Pro+ (without N95-Companion) was used to measure IL of the ambient particles with a detectable size range of 0.02 to 1 μm. The Occupational Safety and Health Administration standard fit test exercises were used for this study. IL test were performed for each subject using each of the 10 respirators. Each respirator/subject combination was tested in duplicate, resulting in a total 20 IL tests for each subject. Three 35-member panels were randomly selected without replacement from the 144 study subjects stratified by the National Institute for Occupational Safety and Health bivariate panel cell for conducting statistical analyses. The geometric mean (GM) IL values for all 10 studied respirators were not significantly different among the three randomly selected 35-member panels. Passing rate was not significantly different among the three panels for all respirators combined or by each model. This was true for all IL pass/fail levels of 1%, 2%, and 5%. Using 26 or more subjects to pass the IL test, all three panels had consistent passing/failing results for pass/fail levels of 1% and 5%. Some disagreement was observed for the 2% pass/fail level. Inter-panel variability exists, but it is small relative to the other sources of variation in fit testing data. The concern about inter-panel variability and other types of variability can be alleviated by properly selecting: pass/fail level (IL 1–5%); panel size (e.g., 25 or 35); and minimum number of subjects required to pass (e.g., 26 of 35 or 23 of 35). PMID:26011282

Inter-rater reliability of select physical examination procedures in patients with neck pain.

PubMed

Hanney, William J; George, Steven Z; Kolber, Morey J; Young, Ian; Salamh, Paul A; Cleland, Joshua A

2014-07-01

This study evaluated the inter-rater reliability of select examination procedures in patients with neck pain (NP) conducted over a 24- to 48-h period. Twenty-two patients with mechanical NP participated in a standardized examination. One examiner performed standardized examination procedures and a second blinded examiner repeated the procedures 24-48 h later with no treatment administered between examinations. Inter-rater reliability was calculated with the Cohen Kappa and weighted Kappa for ordinal data while continuous level data were calculated using an intraclass correlation coefficient model 2,1 (ICC2,1). Coefficients for categorical variables ranged from poor to moderate agreement (-0.22 to 0.70 Kappa) and coefficients for continuous data ranged from slight to moderate (ICC2,1 0.28-0.74). The standard error of measurement for cervical range of motion ranged from 5.3° to 9.9° while the minimal detectable change ranged from 12.5° to 23.1°. This study is the first to report inter-rater reliability values for select components of the cervical examination in those patients with NP performed 24-48 h after the initial examination. There was considerably less reliability when compared to previous studies, thus clinicians should consider how the passage of time may influence variability in examination findings over a 24- to 48-h period.

Hyperspectral Imaging for Predicting the Internal Quality of Kiwifruits Based on Variable Selection Algorithms and Chemometric Models.

PubMed

Zhu, Hongyan; Chu, Bingquan; Fan, Yangyang; Tao, Xiaoya; Yin, Wenxin; He, Yong

2017-08-10

We investigated the feasibility and potentiality of determining firmness, soluble solids content (SSC), and pH in kiwifruits using hyperspectral imaging, combined with variable selection methods and calibration models. The images were acquired by a push-broom hyperspectral reflectance imaging system covering two spectral ranges. Weighted regression coefficients (BW), successive projections algorithm (SPA) and genetic algorithm-partial least square (GAPLS) were compared and evaluated for the selection of effective wavelengths. Moreover, multiple linear regression (MLR), partial least squares regression and least squares support vector machine (LS-SVM) were developed to predict quality attributes quantitatively using effective wavelengths. The established models, particularly SPA-MLR, SPA-LS-SVM and GAPLS-LS-SVM, performed well. The SPA-MLR models for firmness (R pre  = 0.9812, RPD = 5.17) and SSC (R pre  = 0.9523, RPD = 3.26) at 380-1023 nm showed excellent performance, whereas GAPLS-LS-SVM was the optimal model at 874-1734 nm for predicting pH (R pre  = 0.9070, RPD = 2.60). Image processing algorithms were developed to transfer the predictive model in every pixel to generate prediction maps that visualize the spatial distribution of firmness and SSC. Hence, the results clearly demonstrated that hyperspectral imaging has the potential as a fast and non-invasive method to predict the quality attributes of kiwifruits.

Successful selection of rainbow trout (Oncorhynchus mykiss) on their ability to grow with a diet completely devoid of fishmeal and fish oil, and correlated changes in nutritional traits.

PubMed

Callet, Thérèse; Médale, Françoise; Larroquet, Laurence; Surget, Anne; Aguirre, Pierre; Kerneis, Thierry; Labbé, Laurent; Quillet, Edwige; Geurden, Inge; Skiba-Cassy, Sandrine; Dupont-Nivet, Mathilde

2017-01-01

In the context of limited marine resources, the exponential growth of aquaculture requires the substitution of fish oil and fishmeal, the traditional components of fish feeds by terrestrial plant ingredients. High levels of such substitution are known to negatively impact fish performance such as growth and survival in rainbow trout (Oncorhynchus mykiss) as in other salmonids. In this respect, genetic selection is a key enabler for improving those performances and hence for the further sustainable development of aquaculture. We selected a rainbow trout line over three generations for its ability to survive and grow on a 100% plant-based diet devoid of both fish oil and fishmeal (V diet) from the very first meal. In the present study, we compared the control line and the selected line after 3 generations of selection, both fed either the V diet or a marine resources-based diet (M diet). The objective of the study was to assess the efficiency of selection and the consequences on various correlated nutritional traits: feed intake, feed efficiency, digestibility, composition of whole fish, nutrient retention and fatty acid (FA) profile. We demonstrated that the genetic variability present in our rainbow trout population can be selected to improve survival and growth. The major result of the study is that after only three generations of selection, selected fish fed the V diet grew at the same rate as the control line fed the M diet, whilst the relative reduction of body weight was 36.8% before the selection. This enhanced performance on the V diet seems to be mostly linked to a higher feed intake for the selected fish.

Successful selection of rainbow trout (Oncorhynchus mykiss) on their ability to grow with a diet completely devoid of fishmeal and fish oil, and correlated changes in nutritional traits

PubMed Central

Médale, Françoise; Larroquet, Laurence; Surget, Anne; Aguirre, Pierre; Kerneis, Thierry; Labbé, Laurent; Quillet, Edwige; Geurden, Inge; Skiba-Cassy, Sandrine; Dupont-Nivet, Mathilde

2017-01-01

In the context of limited marine resources, the exponential growth of aquaculture requires the substitution of fish oil and fishmeal, the traditional components of fish feeds by terrestrial plant ingredients. High levels of such substitution are known to negatively impact fish performance such as growth and survival in rainbow trout (Oncorhynchus mykiss) as in other salmonids. In this respect, genetic selection is a key enabler for improving those performances and hence for the further sustainable development of aquaculture. We selected a rainbow trout line over three generations for its ability to survive and grow on a 100% plant-based diet devoid of both fish oil and fishmeal (V diet) from the very first meal. In the present study, we compared the control line and the selected line after 3 generations of selection, both fed either the V diet or a marine resources-based diet (M diet). The objective of the study was to assess the efficiency of selection and the consequences on various correlated nutritional traits: feed intake, feed efficiency, digestibility, composition of whole fish, nutrient retention and fatty acid (FA) profile. We demonstrated that the genetic variability present in our rainbow trout population can be selected to improve survival and growth. The major result of the study is that after only three generations of selection, selected fish fed the V diet grew at the same rate as the control line fed the M diet, whilst the relative reduction of body weight was 36.8% before the selection. This enhanced performance on the V diet seems to be mostly linked to a higher feed intake for the selected fish. PMID:29059226

Performance Indicators of the Top Basketball Players: Relations with Several Variables.

PubMed

Sindik, Josko

2015-09-01

The aim of this study was to determine the differences in performance indicators for top senior male basketball players, with respect to several independent variables: position in the team, total situation-related efficiency, age, playing experience and the time spent on the court within the game and during championship season. The final sample of participants was selected from all teams in A-1 Croatian men's basketball league. Significant differences have been found according to the players': position in the team, total situation-related efficiency, and in interactions of the position in the team / total situation-related efficiency and minutes spent on the court in a game / playing experience. The differences in the situation-related efficiency between players have not been found according to the players' age and the number of games played. Further research can be directed towards deeper analysis of the influence of more complex differentiated variables playing experience and time spent on the court in a game on situation-related efficiency in basketball.

Effects of Talker Variability on Perceptual Learning of Dialects

PubMed Central

Clopper, Cynthia G.; Pisoni, David B.

2012-01-01

Two groups of listeners learned to categorize a set of unfamiliar talkers by dialect region using sentences selected from the TIMIT speech corpus. One group learned to categorize a single talker from each of six American English dialect regions. A second group learned to categorize three talkers from each dialect region. Following training, both groups were asked to categorize new talkers using the same categorization task. While the single-talker group was more accurate during initial training and test phases when familiar talkers produced the sentences, the three-talker group performed better on the generalization task with unfamiliar talkers. This cross-over effect in dialect categorization suggests that while talker variation during initial perceptual learning leads to more difficult learning of specific exemplars, exposure to intertalker variability facilitates robust perceptual learning and promotes better categorization performance of unfamiliar talkers. The results suggest that listeners encode and use acoustic-phonetic variability in speech to reliably perceive the dialect of unfamiliar talkers. PMID:15697151

A diagnostic model for chronic hypersensitivity pneumonitis

PubMed Central

Johannson, Kerri A; Elicker, Brett M; Vittinghoff, Eric; Assayag, Deborah; de Boer, Kaïssa; Golden, Jeffrey A; Jones, Kirk D; King, Talmadge E; Koth, Laura L; Lee, Joyce S; Ley, Brett; Wolters, Paul J; Collard, Harold R

2017-01-01

The objective of this study was to develop a diagnostic model that allows for a highly specific diagnosis of chronic hypersensitivity pneumonitis using clinical and radiological variables alone. Chronic hypersensitivity pneumonitis and other interstitial lung disease cases were retrospectively identified from a longitudinal database. High-resolution CT scans were blindly scored for radiographic features (eg, ground-glass opacity, mosaic perfusion) as well as the radiologist’s diagnostic impression. Candidate models were developed then evaluated using clinical and radiographic variables and assessed by the cross-validated C-statistic. Forty-four chronic hypersensitivity pneumonitis and eighty other interstitial lung disease cases were identified. Two models were selected based on their statistical performance, clinical applicability and face validity. Key model variables included age, down feather and/or bird exposure, radiographic presence of ground-glass opacity and mosaic perfusion and moderate or high confidence in the radiographic impression of chronic hypersensitivity pneumonitis. Models were internally validated with good performance, and cut-off values were established that resulted in high specificity for a diagnosis of chronic hypersensitivity pneumonitis. PMID:27245779

How motivation affects academic performance: a structural equation modelling analysis.

PubMed

Kusurkar, R A; Ten Cate, Th J; Vos, C M P; Westers, P; Croiset, G

2013-03-01

Few studies in medical education have studied effect of quality of motivation on performance. Self-Determination Theory based on quality of motivation differentiates between Autonomous Motivation (AM) that originates within an individual and Controlled Motivation (CM) that originates from external sources. To determine whether Relative Autonomous Motivation (RAM, a measure of the balance between AM and CM) affects academic performance through good study strategy and higher study effort and compare this model between subgroups: males and females; students selected via two different systems namely qualitative and weighted lottery selection. Data on motivation, study strategy and effort was collected from 383 medical students of VU University Medical Center Amsterdam and their academic performance results were obtained from the student administration. Structural Equation Modelling analysis technique was used to test a hypothesized model in which high RAM would positively affect Good Study Strategy (GSS) and study effort, which in turn would positively affect academic performance in the form of grade point averages. This model fit well with the data, Chi square = 1.095, df = 3, p = 0.778, RMSEA model fit = 0.000. This model also fitted well for all tested subgroups of students. Differences were found in the strength of relationships between the variables for the different subgroups as expected. In conclusion, RAM positively correlated with academic performance through deep strategy towards study and higher study effort. This model seems valid in medical education in subgroups such as males, females, students selected by qualitative and weighted lottery selection.

Sexual differences in telomere selection in the wild.

PubMed

Olsson, Mats; Pauliny, Angela; Wapstra, Erik; Uller, Tobias; Schwartz, Tonia; Miller, Emily; Blomqvist, Donald

2011-05-01

Telomere length is restored primarily through the action of the reverse transcriptase telomerase, which may contribute to a prolonged lifespan in some but not all species and may result in longer telomeres in one sex than the other. To what extent this is an effect of proximate mechanisms (e.g. higher stress in males, higher oestradiol/oestrogen levels in females), or is an evolved adaptation (stronger selection for telomere length in one sex), usually remains unknown. Sand lizard (Lacerta agilis) females have longer telomeres than males and better maintain telomere length through life than males do. We also show that telomere length more strongly contributes to life span and lifetime reproductive success in females than males and that telomere length is under sexually diversifying selection in the wild. Finally, we performed a selection analysis with number of recruited offspring into the adult population as a response variable with telomere length, life span and body size as predictor variables. This showed significant differences in selection pressures between the sexes with strong ongoing selection in females, with these three predictors explaining 63% of the variation in recruitment. Thus, the sexually dimorphic telomere dynamics with longer telomeres in females is a result of past and ongoing selection in sand lizards. Finally, we compared the results from our selection analyses based on Telometric-derived data to the results based on data generated by the software ImageJ. ImageJ resulted in shorter average telomere length, but this difference had virtually no qualitative effect on the patterns of ongoing selection. © 2011 Blackwell Publishing Ltd.

Application of the Trend Filtering Algorithm for Photometric Time Series Data

NASA Astrophysics Data System (ADS)

Gopalan, Giri; Plavchan, Peter; van Eyken, Julian; Ciardi, David; von Braun, Kaspar; Kane, Stephen R.

2016-08-01

Detecting transient light curves (e.g., transiting planets) requires high-precision data, and thus it is important to effectively filter systematic trends affecting ground-based wide-field surveys. We apply an implementation of the Trend Filtering Algorithm (TFA) to the 2MASS calibration catalog and select Palomar Transient Factory (PTF) photometric time series data. TFA is successful at reducing the overall dispersion of light curves, however, it may over-filter intrinsic variables and increase “instantaneous” dispersion when a template set is not judiciously chosen. In an attempt to rectify these issues we modify the original TFA from the literature by including measurement uncertainties in its computation, including ancillary data correlated with noise, and algorithmically selecting a template set using clustering algorithms as suggested by various authors. This approach may be particularly useful for appropriately accounting for variable photometric precision surveys and/or combined data sets. In summary, our contributions are to provide a MATLAB software implementation of TFA and a number of modifications tested on synthetics and real data, summarize the performance of TFA and various modifications on real ground-based data sets (2MASS and PTF), and assess the efficacy of TFA and modifications using synthetic light curve tests consisting of transiting and sinusoidal variables. While the transiting variables test indicates that these modifications confer no advantage to transit detection, the sinusoidal variables test indicates potential improvements in detection accuracy.

Decision tree modeling using R.

PubMed

Zhang, Zhongheng

2016-08-01

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

A non-linear data mining parameter selection algorithm for continuous variables

PubMed Central

Razavi, Marianne; Brady, Sean

2017-01-01

In this article, we propose a new data mining algorithm, by which one can both capture the non-linearity in data and also find the best subset model. To produce an enhanced subset of the original variables, a preferred selection method should have the potential of adding a supplementary level of regression analysis that would capture complex relationships in the data via mathematical transformation of the predictors and exploration of synergistic effects of combined variables. The method that we present here has the potential to produce an optimal subset of variables, rendering the overall process of model selection more efficient. This algorithm introduces interpretable parameters by transforming the original inputs and also a faithful fit to the data. The core objective of this paper is to introduce a new estimation technique for the classical least square regression framework. This new automatic variable transformation and model selection method could offer an optimal and stable model that minimizes the mean square error and variability, while combining all possible subset selection methodology with the inclusion variable transformations and interactions. Moreover, this method controls multicollinearity, leading to an optimal set of explanatory variables. PMID:29131829

Fast classification of hazelnut cultivars through portable infrared spectroscopy and chemometrics

NASA Astrophysics Data System (ADS)

Manfredi, Marcello; Robotti, Elisa; Quasso, Fabio; Mazzucco, Eleonora; Calabrese, Giorgio; Marengo, Emilio

2018-01-01

The authentication and traceability of hazelnuts is very important for both the consumer and the food industry, to safeguard the protected varieties and the food quality. This study investigates the use of a portable FTIR spectrometer coupled to multivariate statistical analysis for the classification of raw hazelnuts. The method discriminates hazelnuts from different origins/cultivars based on differences of the signal intensities of their IR spectra. The multivariate classification methods, namely principal component analysis (PCA) followed by linear discriminant analysis (LDA) and partial least square discriminant analysis (PLS-DA), with or without variable selection, allowed a very good discrimination among the groups, with PLS-DA coupled to variable selection providing the best results. Due to the fast analysis, high sensitivity, simplicity and no sample preparation, the proposed analytical methodology could be successfully used to verify the cultivar of hazelnuts, and the analysis can be performed quickly and directly on site.

Selective heart rate variability analysis to account for uterine activity during labor and improve classification of fetal distress.

PubMed

Warmerdam, G J J; Vullings, R; Van Laar, J O E H; Van der Hout-Van der Jagt, M B; Bergmans, J W M; Schmitt, L; Oei, S G

2016-08-01

Cardiotocography (CTG) is currently the most often used technique for detection of fetal distress. Unfortunately, CTG has a poor specificity. Recent studies suggest that, in addition to CTG, information on fetal distress can be obtained from analysis of fetal heart rate variability (HRV). However, uterine contractions can strongly influence fetal HRV. The aim of this study is therefore to investigate whether HRV analysis for detection of fetal distress can be improved by distinguishing contractions from rest periods. Our results from feature selection indicate that HRV features calculated separately during contractions or during rest periods are more informative on fetal distress than HRV features that are calculated over the entire fetal heart rate. Furthermore, classification performance improved from a geometric mean of 69.0% to 79.6% when including the contraction-dependent HRV features, in addition to HRV features calculated over the entire fetal heart rate.

Sparse Zero-Sum Games as Stable Functional Feature Selection

PubMed Central

Sokolovska, Nataliya; Teytaud, Olivier; Rizkalla, Salwa; Clément, Karine; Zucker, Jean-Daniel

2015-01-01

In large-scale systems biology applications, features are structured in hidden functional categories whose predictive power is identical. Feature selection, therefore, can lead not only to a problem with a reduced dimensionality, but also reveal some knowledge on functional classes of variables. In this contribution, we propose a framework based on a sparse zero-sum game which performs a stable functional feature selection. In particular, the approach is based on feature subsets ranking by a thresholding stochastic bandit. We provide a theoretical analysis of the introduced algorithm. We illustrate by experiments on both synthetic and real complex data that the proposed method is competitive from the predictive and stability viewpoints. PMID:26325268

Determinants of safety outcomes and performance: A systematic literature review of research in four high-risk industries.

PubMed

Cornelissen, Pieter A; Van Hoof, Joris J; De Jong, Menno D T

2017-09-01

In spite of increasing governmental and organizational efforts, organizations still struggle to improve the safety of their employees as evidenced by the yearly 2.3 million work-related deaths worldwide. Occupational safety research is scattered and inaccessible, especially for practitioners. Through systematically reviewing the safety literature, this study aims to provide a comprehensive overview of behavioral and circumstantial factors that endanger or support employee safety. A broad search on occupational safety literature using four online bibliographical databases yielded 27.527 articles. Through a systematic reviewing process 176 online articles were identified that met the inclusion criteria (e.g., original peer-reviewed research; conducted in selected high-risk industries; published between 1980-2016). Variables and the nature of their interrelationships (i.e., positive, negative, or nonsignificant) were extracted, and then grouped and classified through a process of bottom-up coding. The results indicate that safety outcomes and performance prevail as dependent research areas, dependent on variables related to management & colleagues, work(place) characteristics & circumstances, employee demographics, climate & culture, and external factors. Consensus was found for five variables related to safety outcomes and seven variables related to performance, while there is debate about 31 other relationships. Last, 21 variables related to safety outcomes and performance appear understudied. The majority of safety research has focused on addressing negative safety outcomes and performance through variables related to others within the organization, the work(place) itself, employee demographics, and-to a lesser extent-climate & culture and external factors. This systematic literature review provides both scientists and safety practitioners an overview of the (under)studied behavioral and circumstantial factors related to occupational safety behavior. Scientists could use this overview to study gaps, and validate or falsify relationships. Safety practitioners could use the insights to evaluate organizational safety policies, and to further development of safety interventions. Copyright © 2017 National Safety Council and Elsevier Ltd. All rights reserved.

The Successive Projections Algorithm for interval selection in trilinear partial least-squares with residual bilinearization.

PubMed

Gomes, Adriano de Araújo; Alcaraz, Mirta Raquel; Goicoechea, Hector C; Araújo, Mario Cesar U

2014-02-06

In this work the Successive Projection Algorithm is presented for intervals selection in N-PLS for three-way data modeling. The proposed algorithm combines noise-reduction properties of PLS with the possibility of discarding uninformative variables in SPA. In addition, second-order advantage can be achieved by the residual bilinearization (RBL) procedure when an unexpected constituent is present in a test sample. For this purpose, SPA was modified in order to select intervals for use in trilinear PLS. The ability of the proposed algorithm, namely iSPA-N-PLS, was evaluated on one simulated and two experimental data sets, comparing the results to those obtained by N-PLS. In the simulated system, two analytes were quantitated in two test sets, with and without unexpected constituent. In the first experimental system, the determination of the four fluorophores (l-phenylalanine; l-3,4-dihydroxyphenylalanine; 1,4-dihydroxybenzene and l-tryptophan) was conducted with excitation-emission data matrices. In the second experimental system, quantitation of ofloxacin was performed in water samples containing two other uncalibrated quinolones (ciprofloxacin and danofloxacin) by high performance liquid chromatography with UV-vis diode array detector. For comparison purpose, a GA algorithm coupled with N-PLS/RBL was also used in this work. In most of the studied cases iSPA-N-PLS proved to be a promising tool for selection of variables in second-order calibration, generating models with smaller RMSEP, when compared to both the global model using all of the sensors in two dimensions and GA-NPLS/RBL. Copyright © 2013 Elsevier B.V. All rights reserved.

The effect of pacing strategy on physiological, kinetic and performance variables during simulated rowing ergometer.

PubMed

Dimakopoulou, Eleni; Zacharogiannis, Elias; Chairopoulou, Chrysoula; Kaloupsis, Socratis; Platanou, Theodoros

2017-02-21

This study compared the effects of self selected (SSP), negative (NPS) and even (EPS) pacing strategy on performance time, kinetic and physiological variables in overall 2km rowing and in first and second 1km. Fifteen male rowers (15.37 ± 1.34 yrs) realized four tests: an incremental test on a rowing ergometer to determine their VO2peak and three experimental 2 km rowing race, where first 1km was manipulated. From SSP a negative pacing strategy, 4% slower than the mean velocity of SSP, and an even pacing strategy (EPS) with mean velocity of SSP, were developed. High stroke rate and better performance time was observed in SSP. Fstr and Fpeak decreased, whereas performance time increased, in SSP and EPS from first to second 1km.Unlike, performance time, stroke rate and Pst in NPS presented better values (p=0.001) with the exception of decreased stroke length (p=0.03). There was an increase in physiological responses in all pacing strategies from first to second 1km (p=0.001). Performance time, stroke rate and Fstr were better in SSP and EPS compared to NPS in first 1km (p=0.001). VE, VE/VO2, VCO2 were better in SSP and EPS compared to NPS (p=0.001) in both first and second 1km. Stroke length was smaller in SSP compared to NPS and EPS in second 1km (p=0.001). Self selected pacing (parabolic-shaped profile) allowed rowers to cover the 2 km distance in higher stroke rate and in shorter performance time compared to negative and even pacing strategies presenting same physiological responses.

The influence of pre-admission tracks on students' academic performance in a medical programme: Universiti Sains Malaysia.

PubMed

Arzuman, H; Ja'afar, R; Fakri, N M R M

2012-11-01

An aim of medical schools is to select the most suitable candidates who are more likely to become good doctors, fulfilling societal expectations. It is imperative to better understand the influence of 'selection' variables on students' academic performance. We conducted a retrospective record review (3R) to examine the predictive power of pre-admission tracks on academic performance in the medical programme at the Universiti Sains Malaysia. Data were collected on medical graduates' of the university for the years 2003 through 2007. This represented 805 graduates after exclusion of 42 for incomplete and inconsistent data related to the analysis. A total of 95% of the graduates were included in this analysis; 67% were female. Of the 805 graduates, 75% were from the Matriculation course track, 22% from the High School Certificate (HSC) course and 1% from other pre-admission tracks. There was 2% missing information. The majority (79%) were Biology majors and 13% were Physics majors. Graduates from the HSC course and with a Biology background demonstrated a strong correlation with positive academic performance (P < 0.05) compared with other groups. The HSC track and Biology background may be helpful for the medical school in selecting future students.

Analyze the Impact of Habitat Patches on Wildlife Road-Kill

NASA Astrophysics Data System (ADS)

Seok, S.; Lee, J.

2015-10-01

The ecosystem fragmentation due to transportation infrastructure causes a road-kill phenomenon. When making policies for mitigating road-kill it is important to select target-species in order to enhance its efficiency. However, many wildlife crossing structures have been questioned regarding their effectiveness due to lack of considerations such as target-species selection, site selection, management, etc. The purpose of this study is to analyse the impact of habitat patches on wildlife road-kill and to suggest that spatial location of habitat patches should be considered as one of the important factors when making policies for mitigating road-kill. Habitat patches were presumed from habitat variables and a suitability index on target-species that was chosen by literature review. The road-kill hotspot was calculated using Getis-Ord Gi*. After that, we performed a correlation analysis between Gi Z-score and the distance from habitat patches to the roads. As a result, there is a low negative correlation between two variables and it increases the Gi Z-score if the habitat patches and the roads become closer.

Chemical spoilage extent traceability of two kinds of processed pork meats using one multispectral system developed by hyperspectral imaging combined with effective variable selection methods.

PubMed

Cheng, Weiwei; Sun, Da-Wen; Pu, Hongbin; Wei, Qingyi

2017-04-15

The feasibility of hyperspectral imaging (HSI) (400-1000nm) for tracing the chemical spoilage extent of the raw meat used for two kinds of processed meats was investigated. Calibration models established separately for salted and cooked meats using full wavebands showed good results with the determination coefficient in prediction (R 2 P ) of 0.887 and 0.832, respectively. For simplifying the calibration models, two variable selection methods were used and compared. The results showed that genetic algorithm-partial least squares (GA-PLS) with as much continuous wavebands selected as possible always had better performance. The potential of HSI to develop one multispectral system for simultaneously tracing the chemical spoilage extent of the two kinds of processed meats was also studied. Good result with an R 2 P of 0.854 was obtained using GA-PLS as the dimension reduction method, which was thus used to visualize total volatile base nitrogen (TVB-N) contents corresponding to each pixel of the image. Copyright © 2016 Elsevier Ltd. All rights reserved.

A randomized evaluation of a computer-based physician's workstation: design considerations and baseline results.

PubMed Central

Rotman, B. L.; Sullivan, A. N.; McDonald, T.; DeSmedt, P.; Goodnature, D.; Higgins, M.; Suermondt, H. J.; Young, C. Y.; Owens, D. K.

1995-01-01

We are performing a randomized, controlled trial of a Physician's Workstation (PWS), an ambulatory care information system, developed for use in the General Medical Clinic (GMC) of the Palo Alto VA. Goals for the project include selecting appropriate outcome variables and developing a statistically powerful experimental design with a limited number of subjects. As PWS provides real-time drug-ordering advice, we retrospectively examined drug costs and drug-drug interactions in order to select outcome variables sensitive to our short-term intervention as well as to estimate the statistical efficiency of alternative design possibilities. Drug cost data revealed the mean daily cost per physician per patient was 99.3 cents +/- 13.4 cents, with a range from 0.77 cent to 1.37 cents. The rate of major interactions per prescription for each physician was 2.9% +/- 1%, with a range from 1.5% to 4.8%. Based on these baseline analyses, we selected a two-period parallel design for the evaluation, which maximized statistical power while minimizing sources of bias. PMID:8563376

The Simultaneous Medicina-Planck Experiment: data acquisition, reduction and first results

NASA Astrophysics Data System (ADS)

Procopio, P.; Massardi, M.; Righini, S.; Zanichelli, A.; Ricciardi, S.; Libardi, P.; Burigana, C.; Cuttaia, F.; Mack, K.-H.; Terenzi, L.; Villa, F.; Bonavera, L.; Morgante, G.; Trigilio, C.; Trombetti, T.; Umana, G.

2011-10-01

The Simultaneous Medicina-Planck Experiment (SiMPlE) is aimed at observing a selected sample of 263 extragalactic and Galactic sources with the Medicina 32-m single-dish radio telescope in the same epoch as the Planck satellite observations. The data, acquired with a frequency coverage down to 5 GHz and combined with Planck at frequencies above 30 GHz, will constitute a useful reference catalogue of bright sources over the whole Northern hemisphere. Furthermore, source observations performed in different epochs and comparisons with other catalogues will allow the investigation of source variabilities on different time-scales. In this work, we describe the sample selection, the ongoing data acquisition campaign, the data reduction procedures, the developed tools and the comparison with other data sets. We present 5 and 8.3 GHz data for the SiMPlE Northern sample, consisting of 79 sources with δ≥ 45° selected from our catalogue and observed during the first 6 months of the project. A first analysis of their spectral behaviour and long-term variability is also presented.

Comparisons of Selected Student and Teacher Variables in All-Girls and Coeducational Physical Education Environments

ERIC Educational Resources Information Center

Derry, Julie A.; Phillips, D. Allen

2004-01-01

The purpose of this study was to investigate selected student and teacher variables and compare the differences between these variables for female students and female teachers in coeducation and single-sex physical education classes. Eighteen female teachers and intact classes were selected; 9 teachers from coeducation and 9 teachers from…

Petroleomics by electrospray ionization FT-ICR mass spectrometry coupled to partial least squares with variable selection methods: prediction of the total acid number of crude oils.

PubMed

Terra, Luciana A; Filgueiras, Paulo R; Tose, Lílian V; Romão, Wanderson; de Souza, Douglas D; de Castro, Eustáquio V R; de Oliveira, Mirela S L; Dias, Júlio C M; Poppi, Ronei J

2014-10-07

Negative-ion mode electrospray ionization, ESI(-), with Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) was coupled to a Partial Least Squares (PLS) regression and variable selection methods to estimate the total acid number (TAN) of Brazilian crude oil samples. Generally, ESI(-)-FT-ICR mass spectra present a power of resolution of ca. 500,000 and a mass accuracy less than 1 ppm, producing a data matrix containing over 5700 variables per sample. These variables correspond to heteroatom-containing species detected as deprotonated molecules, [M - H](-) ions, which are identified primarily as naphthenic acids, phenols and carbazole analog species. The TAN values for all samples ranged from 0.06 to 3.61 mg of KOH g(-1). To facilitate the spectral interpretation, three methods of variable selection were studied: variable importance in the projection (VIP), interval partial least squares (iPLS) and elimination of uninformative variables (UVE). The UVE method seems to be more appropriate for selecting important variables, reducing the dimension of the variables to 183 and producing a root mean square error of prediction of 0.32 mg of KOH g(-1). By reducing the size of the data, it was possible to relate the selected variables with their corresponding molecular formulas, thus identifying the main chemical species responsible for the TAN values.

A Selective Overview of Variable Selection in High Dimensional Feature Space

PubMed Central

Fan, Jianqing

2010-01-01

High dimensional statistical problems arise from diverse fields of scientific research and technological development. Variable selection plays a pivotal role in contemporary statistical learning and scientific discoveries. The traditional idea of best subset selection methods, which can be regarded as a specific form of penalized likelihood, is computationally too expensive for many modern statistical applications. Other forms of penalized likelihood methods have been successfully developed over the last decade to cope with high dimensionality. They have been widely applied for simultaneously selecting important variables and estimating their effects in high dimensional statistical inference. In this article, we present a brief account of the recent developments of theory, methods, and implementations for high dimensional variable selection. What limits of the dimensionality such methods can handle, what the role of penalty functions is, and what the statistical properties are rapidly drive the advances of the field. The properties of non-concave penalized likelihood and its roles in high dimensional statistical modeling are emphasized. We also review some recent advances in ultra-high dimensional variable selection, with emphasis on independence screening and two-scale methods. PMID:21572976

Effect of trotting speed on kinematic variables measured by use of extremity-mounted inertial measurement units in nonlame horses performing controlled treadmill exercise.

PubMed

Cruz, Antonio M; Vidondo, Beatriz; Ramseyer, Alessandra A; Maninchedda, Ugo E

2018-02-01

OBJECTIVE To assess effects of speed on kinematic variables measured by use of extremity-mounted inertial measurement units (IMUs) in nonlame horses performing controlled exercise on a treadmill. ANIMALS 10 nonlame horses. PROCEDURES 6 IMUs were attached at predetermined locations on 10 nonlame Franches Montagnes horses. Data were collected in triplicate during trotting at 3.33 and 3.88 m/s on a high-speed treadmill. Thirty-three selected kinematic variables were analyzed. Repeated-measures ANOVA was used to assess the effect of speed. RESULTS Significant differences between the 2 speeds were detected for most temporal (11/14) and spatial (12/19) variables. The observed spatial and temporal changes would translate into a gait for the higher speed characterized by increased stride length, protraction and retraction, flexion and extension, mediolateral movement of the tibia, and symmetry, but with similar temporal variables and a reduction in stride duration. However, even though the tibia coronal range of motion was significantly different between speeds, the high degree of variability raised concerns about whether these changes were clinically relevant. For some variables, the lower trotting speed apparently was associated with more variability than was the higher trotting speed. CONCLUSIONS AND CLINICAL RELEVANCE At a higher trotting speed, horses moved in the same manner (eg, the temporal events investigated occurred at the same relative time within the stride). However, from a spatial perspective, horses moved with greater action of the segments evaluated. The detected changes in kinematic variables indicated that trotting speed should be controlled or kept constant during gait evaluation.

Quantifying human disturbance in watersheds: Variable selection and performance of a GIS-based disturbance index for predicting the biological condition of perennial streams

USGS Publications Warehouse

Falcone, James A.; Carlisle, Daren M.; Weber, Lisa C.

2010-01-01

Characterizing the relative severity of human disturbance in watersheds is often part of stream assessments and is frequently done with the aid of Geographic Information System (GIS)-derived data. However, the choice of variables and how they are used to quantify disturbance are often subjective. In this study, we developed a number of disturbance indices by testing sets of variables, scoring methods, and weightings of 33 potential disturbance factors derived from readily available GIS data. The indices were calibrated using 770 watersheds located in the western United States for which the severity of disturbance had previously been classified from detailed local data by the United States Environmental Protection Agency (USEPA) Environmental Monitoring and Assessment Program (EMAP). The indices were calibrated by determining which variable or variable combinations and aggregation method best differentiated between least- and most-disturbed sites. Indices composed of several variables performed better than any individual variable, and best results came from a threshold method of scoring using six uncorrelated variables: housing unit density, road density, pesticide application, dam storage, land cover along a mainstem buffer, and distance to nearest canal/pipeline. The final index was validated with 192 withheld watersheds and correctly classified about two-thirds (68%) of least- and most-disturbed sites. These results provide information about the potential for using a disturbance index as a screening tool for a priori ranking of watersheds at a regional/national scale, and which landscape variables and methods of combination may be most helpful in doing so.

The Influence of Carbohydrate Mouth Rinse on Self-Selected Intermittent Running Performance.

PubMed

Rollo, Ian; Homewood, George; Williams, Clyde; Carter, James; Goosey-Tolfrey, Vicky L

2015-12-01

This study investigated the influence of mouth rinsing a carbohydrate solution on self-selected intermittent variable-speed running performance. Eleven male amateur soccer players completed a modified version of the Loughborough Intermittent Shuttle Test (LIST) on 2 occasions separated by 1 wk. The modified LIST allowed the self-selection of running speeds during Block 6 of the protocol (75-90 min). Players rinsed and expectorated 25 ml of noncaloric placebo (PLA) or 10% maltodextrin solution (CHO) for 10 s, routinely during Block 6 of the LIST. Self-selected speeds during the walk and cruise phases of the LIST were similar between trials. Jogging speed was significantly faster during the CHO (11.3 ± 0.7 km · h(-1)) than during the PLA trial (10.5 ± 1.3 km · h(-1)) (p = .010); 15-m sprint speeds were not different between trials (PLA: 2.69 ± 0.18 s: CHO: 2.65 ± 0.13 s) (F(2, 10), p = .157), but significant benefits were observed for sprint distance covered (p = .024). The threshold for the smallest worthwhile change in sprint performance was set at 0.2 s. Inferential statistical analysis showed the chance that CHO mouth rinse was beneficial, negligible, or detrimental to repeated sprint performance was 86%, 10%, and 4%, respectively. In conclusion, mouth rinsing and expectorating a 10% maltodextrin solution was associated with a significant increase in self-selected jogging speed. Repeated 15-m sprint performance was also 86% likely to benefit from routinely mouth rinsing a carbohydrate solution in comparison with a taste-matched placebo.

Optimum allocation of test resources and comparison of breeding strategies for hybrid wheat.

PubMed

Longin, C Friedrich H; Mi, Xuefei; Melchinger, Albrecht E; Reif, Jochen C; Würschum, Tobias

2014-10-01

The use of a breeding strategy combining the evaluation of line per se with testcross performance maximizes annual selection gain for hybrid wheat breeding. Recent experimental studies confirmed a high commercial potential for hybrid wheat requiring the design of optimum breeding strategies. Our objectives were to (1) determine the optimum allocation of the type and number of testers, the number of test locations and the number of doubled haploid lines for different breeding strategies, (2) identify the best breeding strategy and (3) elaborate key parameters for an efficient hybrid wheat breeding program. We performed model calculations using the selection gain for grain yield as target variable to optimize the number of lines, testers and test locations in four different breeding strategies. A breeding strategy (BS2) combining the evaluation of line per se performance and general combining ability (GCA) had a far larger annual selection gain across all considered scenarios than a breeding strategy (BS1) focusing only on GCA. In the combined strategy, the production of testcross seed conducted in parallel with the first yield trial for line per se performance (BS2rapid) resulted in a further increase of the annual selection gain. For the current situation in hybrid wheat, this relative superiority of the strategy BS2rapid amounted to 67 % in annual selection gain compared to BS1. Varying a large number of parameters, we identified the high costs for hybrid seed production and the low variance of GCA in hybrid wheat breeding as key parameters limiting selection gain in BS2rapid.

Evaluation of redundancy analysis to identify signatures of local adaptation.

PubMed

Capblancq, Thibaut; Luu, Keurcien; Blum, Michael G B; Bazin, Eric

2018-05-26

Ordination is a common tool in ecology that aims at representing complex biological information in a reduced space. In landscape genetics, ordination methods such as principal component analysis (PCA) have been used to detect adaptive variation based on genomic data. Taking advantage of environmental data in addition to genotype data, redundancy analysis (RDA) is another ordination approach that is useful to detect adaptive variation. This paper aims at proposing a test statistic based on RDA to search for loci under selection. We compare redundancy analysis to pcadapt, which is a nonconstrained ordination method, and to a latent factor mixed model (LFMM), which is a univariate genotype-environment association method. Individual-based simulations identify evolutionary scenarios where RDA genome scans have a greater statistical power than genome scans based on PCA. By constraining the analysis with environmental variables, RDA performs better than PCA in identifying adaptive variation when selection gradients are weakly correlated with population structure. Additionally, we show that if RDA and LFMM have a similar power to identify genetic markers associated with environmental variables, the RDA-based procedure has the advantage to identify the main selective gradients as a combination of environmental variables. To give a concrete illustration of RDA in population genomics, we apply this method to the detection of outliers and selective gradients on an SNP data set of Populus trichocarpa (Geraldes et al., 2013). The RDA-based approach identifies the main selective gradient contrasting southern and coastal populations to northern and continental populations in the northwestern American coast. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Diversity in livestock resources in pastoral systems in Africa.

PubMed

Kaufmann, B A; Lelea, M A; Hulsebusch, C G

2016-11-01

Pastoral systems are important producers and repositories of livestock diversity. Pastoralists use variability in their livestock resources to manage high levels of environmental variability in economically advantageous ways. In pastoral systems, human-animal-environment interactions are the basis of production and the key to higher productivity and efficiency. In other words, pastoralists manage a production system that exploits variability and keeps production costs low. When differentiating, characterising and evaluating pastoral breeds, this context-specific, functional dimension of diversity in livestock resources needs to be considered. The interaction of animals with their environment is determined not only by morphological and physiological traits but also by experience and socially learned behaviour. This high proportion of non-genetic components determining the performance of livestock means that current models for analysing livestock diversity and performance, which are based on genetic inheritance, have limited ability to describe pastoral performance. There is a need for methodological innovations to evaluate pastoral breeds and animals, since comparisons based on performance 'under optimal conditions' are irrelevant within this production system. Such innovations must acknowledge that livestock or breed performance is governed by complex human-animal-environment interactions, and varies through time and space due to the mobile and seasonal nature of the pastoral system. Pastoralists' breeding concepts and selection strategies seem to be geared towards improving their animals' capability to exploit variability, by - among other things - enhancing within-breed diversity. In-depth studies of these concepts and strategies could contribute considerably towards developing methodological innovations for the characterisation and evaluation of pastoral livestock resources.

Gene features selection for three-class disease classification via multiple orthogonal partial least square discriminant analysis and S-plot using microarray data.

PubMed

Yang, Mingxing; Li, Xiumin; Li, Zhibin; Ou, Zhimin; Liu, Ming; Liu, Suhuan; Li, Xuejun; Yang, Shuyu

2013-01-01

DNA microarray analysis is characterized by obtaining a large number of gene variables from a small number of observations. Cluster analysis is widely used to analyze DNA microarray data to make classification and diagnosis of disease. Because there are so many irrelevant and insignificant genes in a dataset, a feature selection approach must be employed in data analysis. The performance of cluster analysis of this high-throughput data depends on whether the feature selection approach chooses the most relevant genes associated with disease classes. Here we proposed a new method using multiple Orthogonal Partial Least Squares-Discriminant Analysis (mOPLS-DA) models and S-plots to select the most relevant genes to conduct three-class disease classification and prediction. We tested our method using Golub's leukemia microarray data. For three classes with subtypes, we proposed hierarchical orthogonal partial least squares-discriminant analysis (OPLS-DA) models and S-plots to select features for two main classes and their subtypes. For three classes in parallel, we employed three OPLS-DA models and S-plots to choose marker genes for each class. The power of feature selection to classify and predict three-class disease was evaluated using cluster analysis. Further, the general performance of our method was tested using four public datasets and compared with those of four other feature selection methods. The results revealed that our method effectively selected the most relevant features for disease classification and prediction, and its performance was better than that of the other methods.

The performance of diphoton primary vertex reconstruction methods in H → γγ+Met channel of ATLAS experiment

NASA Astrophysics Data System (ADS)

Tomiwa, K. G.

2017-09-01

The search for new physics in the H → γγ+met relies on how well the missing transverse energy is reconstructed. The Met algorithm used by the ATLAS experiment in turns uses input variables like photon and jets which depend on the reconstruction of the primary vertex. This document presents the performance of di-photon vertex reconstruction algorithms (hardest vertex method and Neural Network method). Comparing the performance of these algorithms for the nominal Standard Model sample and the Beyond Standard Model sample, we see the overall performance of the Neural Network method of primary vertex selection performed better than the Hardest vertex method.

Reducing I/O variability using dynamic I/O path characterization in petascale storage systems

DOE PAGES

Son, Seung Woo; Sehrish, Saba; Liao, Wei-keng; ...

2016-11-01

In petascale systems with a million CPU cores, scalable and consistent I/O performance is becoming increasingly difficult to sustain mainly because of I/O variability. Furthermore, the I/O variability is caused by concurrently running processes/jobs competing for I/O or a RAID rebuild when a disk drive fails. We present a mechanism that stripes across a selected subset of I/O nodes with the lightest workload at runtime to achieve the highest I/O bandwidth available in the system. In this paper, we propose a probing mechanism to enable application-level dynamic file striping to mitigate I/O variability. We also implement the proposed mechanism inmore » the high-level I/O library that enables memory-to-file data layout transformation and allows transparent file partitioning using subfiling. Subfiling is a technique that partitions data into a set of files of smaller size and manages file access to them, making data to be treated as a single, normal file to users. Here, we demonstrate that our bandwidth probing mechanism can successfully identify temporally slower I/O nodes without noticeable runtime overhead. Experimental results on NERSC’s systems also show that our approach isolates I/O variability effectively on shared systems and improves overall collective I/O performance with less variation.« less

The Effects of Aural versus Notated Instructional Materials on Achievement and Self-Efficacy in Jazz Improvisation

ERIC Educational Resources Information Center

Watson, Kevin E.

2010-01-01

The purpose of the present study was to investigate the effects of aural versus notated pedagogical materials on achievement and self-efficacy in instrumental jazz improvisation performance. A secondary purpose of this study was to investigate how achievement and self-efficacy may be related to selected experience variables. The sample for the…

Predicting Performance on the Tennessee Comprehensive Assessment for Third Grade Reading Students Using Reading Curriculum Based Measures

ERIC Educational Resources Information Center

Kirkham, Scott; Lampley, James H.

2014-01-01

The purpose of this study was to investigate the relationship between three predictor variables (Fall R-CBM, Winter R-CBM, and Spring R-CBM) and the Tennessee Comprehensive Assessment Program third grade reading and language arts assessment. The population selected for this study included all third grade students from an East Tennessee school…

Evaluation of a School Systems Plan to Utilize Teachers' Perceptions of Principal Leadership to Improve Student Achievement

ERIC Educational Resources Information Center

Williams, Ed

2009-01-01

This study examines fourth grade student achievement in relation to teacher perceptions of principal leadership and other selected variables in a large urban school district in Georgia. Student achievement was measured by performance on the Georgia Criterion-Referenced Competency Tests (CRCT) during the 2004-05 and 2005-06 school years. The…

Practical Comptrollership Course

DTIC Science & Technology

1987-03-01

variables that drive selection of "significant" areas. " Multilocation audits investigate specific functions. In 1985, for example, the NAS performed... audits of health care functions, and of supply departments, focusing on spare parts. These two audits were part of DoD-wide multilocation audit ...should have included more practical examples. Evaluator A questioned the inclusion of Chapter IIIE, addressing auditing , in the comptrollership course

A comparison of the performance of threshold criteria for binary classification in terms of predicted prevalence and Kappa

Treesearch

Elizabeth A. Freeman; Gretchen G. Moisen

2008-01-01

Modelling techniques used in binary classification problems often result in a predicted probability surface, which is then translated into a presence - absence classification map. However, this translation requires a (possibly subjective) choice of threshold above which the variable of interest is predicted to be present. The selection of this threshold value can have...

Selective attrition and intraindividual variability in response time moderate cognitive change.

PubMed

Yao, Christie; Stawski, Robert S; Hultsch, David F; MacDonald, Stuart W S

2016-01-01

Selection of a developmental time metric is useful for understanding causal processes that underlie aging-related cognitive change and for the identification of potential moderators of cognitive decline. Building on research suggesting that time to attrition is a metric sensitive to non-normative influences of aging (e.g., subclinical health conditions), we examined reason for attrition and intraindividual variability (IIV) in reaction time as predictors of cognitive performance. Three hundred and four community dwelling older adults (64-92 years) completed annual assessments in a longitudinal study. IIV was calculated from baseline performance on reaction time tasks. Multilevel models were fit to examine patterns and predictors of cognitive change. We show that time to attrition was associated with cognitive decline. Greater IIV was associated with declines on executive functioning and episodic memory measures. Attrition due to personal health reasons was also associated with decreased executive functioning compared to that of individuals who remained in the study. These findings suggest that time to attrition is a useful metric for representing cognitive change, and reason for attrition and IIV are predictive of non-normative influences that may underlie instances of cognitive loss in older adults.