Detecting most influencing courses on students grades using block PCA

NASA Astrophysics Data System (ADS)

Othman, Osama H.; Gebril, Rami Salah

2014-12-01

One of the modern solutions adopted in dealing with the problem of large number of variables in statistical analyses is the Block Principal Component Analysis (Block PCA). This modified technique can be used to reduce the vertical dimension (variables) of the data matrix Xn×p by selecting a smaller number of variables, (say m) containing most of the statistical information. These selected variables can then be employed in further investigations and analyses. Block PCA is an adapted multistage technique of the original PCA. It involves the application of Cluster Analysis (CA) and variable selection throughout sub principal components scores (PC's). The application of Block PCA in this paper is a modified version of the original work of Liu et al (2002). The main objective was to apply PCA on each group of variables, (established using cluster analysis), instead of involving the whole large pack of variables which was proved to be unreliable. In this work, the Block PCA is used to reduce the size of a huge data matrix ((n = 41) × (p = 251)) consisting of Grade Point Average (GPA) of the students in 251 courses (variables) in the faculty of science in Benghazi University. In other words, we are constructing a smaller analytical data matrix of the GPA's of the students with less variables containing most variation (statistical information) in the original database. By applying the Block PCA, (12) courses were found to `absorb' most of the variation or influence from the original data matrix, and hence worth to be keep for future statistical exploring and analytical studies. In addition, the course Independent Study (Math.) was found to be the most influencing course on students GPA among the 12 selected courses.

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

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

Do bioclimate variables improve performance of climate envelope models?

USGS Publications Warehouse

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

2012-01-01

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

A general procedure to generate models for urban environmental-noise pollution using feature selection and machine learning methods.

PubMed

Torija, Antonio J; Ruiz, Diego P

2015-02-01

The prediction of environmental noise in urban environments requires the solution of a complex and non-linear problem, since there are complex relationships among the multitude of variables involved in the characterization and modelling of environmental noise and environmental-noise magnitudes. Moreover, the inclusion of the great spatial heterogeneity characteristic of urban environments seems to be essential in order to achieve an accurate environmental-noise prediction in cities. This problem is addressed in this paper, where a procedure based on feature-selection techniques and machine-learning regression methods is proposed and applied to this environmental problem. Three machine-learning regression methods, which are considered very robust in solving non-linear problems, are used to estimate the energy-equivalent sound-pressure level descriptor (LAeq). These three methods are: (i) multilayer perceptron (MLP), (ii) sequential minimal optimisation (SMO), and (iii) Gaussian processes for regression (GPR). In addition, because of the high number of input variables involved in environmental-noise modelling and estimation in urban environments, which make LAeq prediction models quite complex and costly in terms of time and resources for application to real situations, three different techniques are used to approach feature selection or data reduction. The feature-selection techniques used are: (i) correlation-based feature-subset selection (CFS), (ii) wrapper for feature-subset selection (WFS), and the data reduction technique is principal-component analysis (PCA). The subsequent analysis leads to a proposal of different schemes, depending on the needs regarding data collection and accuracy. The use of WFS as the feature-selection technique with the implementation of SMO or GPR as regression algorithm provides the best LAeq estimation (R(2)=0.94 and mean absolute error (MAE)=1.14-1.16 dB(A)). Copyright © 2014 Elsevier B.V. All rights reserved.

Navigation bronchoscopy for diagnosis and small nodule location

PubMed Central

Muñoz-Largacha, Juan A.; Litle, Virginia R.

2017-01-01

Lung cancer continues to be the most common cause of cancer death. Screening programs for high risk patients with the use of low-dose computed tomography (CT) has led to the identification of small lung lesions that were difficult to identify using previous imaging modalities. Electromagnetic navigational bronchoscopy (ENB) is a novel technique that has shown to be of great utility during the evaluation of small, peripheral lesions, that would otherwise be challenging to evaluate with conventional bronchoscopy. The diagnostic yield of navigational bronchoscopy however is highly variable, with reports ranging from 59% to 94%. This variability suggests that well-defined selection criteria and standardized protocols for the use of ENB are lacking. Despite this variability, we believe that this technique is a useful tool evaluating small peripheral lung lesions when patients are properly selected. PMID:28446971

A comparison of small-area estimation techniques to estimate selected stand attributes using LiDAR-derived auxiliary variables

Treesearch

Michael E. Goerndt; Vicente J. Monleon; Hailemariam Temesgen

2011-01-01

One of the challenges often faced in forestry is the estimation of forest attributes for smaller areas of interest within a larger population. Small-area estimation (SAE) is a set of techniques well suited to estimation of forest attributes for small areas in which the existing sample size is small and auxiliary information is available. Selected SAE methods were...

Selecting predictors for discriminant analysis of species performance: an example from an amphibious softwater plant.

PubMed

Vanderhaeghe, F; Smolders, A J P; Roelofs, J G M; Hoffmann, M

2012-03-01

Selecting an appropriate variable subset in linear multivariate methods is an important methodological issue for ecologists. Interest often exists in obtaining general predictive capacity or in finding causal inferences from predictor variables. Because of a lack of solid knowledge on a studied phenomenon, scientists explore predictor variables in order to find the most meaningful (i.e. discriminating) ones. As an example, we modelled the response of the amphibious softwater plant Eleocharis multicaulis using canonical discriminant function analysis. We asked how variables can be selected through comparison of several methods: univariate Pearson chi-square screening, principal components analysis (PCA) and step-wise analysis, as well as combinations of some methods. We expected PCA to perform best. The selected methods were evaluated through fit and stability of the resulting discriminant functions and through correlations between these functions and the predictor variables. The chi-square subset, at P < 0.05, followed by a step-wise sub-selection, gave the best results. In contrast to expectations, PCA performed poorly, as so did step-wise analysis. The different chi-square subset methods all yielded ecologically meaningful variables, while probable noise variables were also selected by PCA and step-wise analysis. We advise against the simple use of PCA or step-wise discriminant analysis to obtain an ecologically meaningful variable subset; the former because it does not take into account the response variable, the latter because noise variables are likely to be selected. We suggest that univariate screening techniques are a worthwhile alternative for variable selection in ecology. © 2011 German Botanical Society and The Royal Botanical Society of the Netherlands.

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.

Covariate Selection for Multilevel Models with Missing Data

PubMed Central

Marino, Miguel; Buxton, Orfeu M.; Li, Yi

2017-01-01

Missing covariate data hampers variable selection in multilevel regression settings. Current variable selection techniques for multiply-imputed data commonly address missingness in the predictors through list-wise deletion and stepwise-selection methods which are problematic. Moreover, most variable selection methods are developed for independent linear regression models and do not accommodate multilevel mixed effects regression models with incomplete covariate data. We develop a novel methodology that is able to perform covariate selection across multiply-imputed data for multilevel random effects models when missing data is present. Specifically, we propose to stack the multiply-imputed data sets from a multiple imputation procedure and to apply a group variable selection procedure through group lasso regularization to assess the overall impact of each predictor on the outcome across the imputed data sets. Simulations confirm the advantageous performance of the proposed method compared with the competing methods. We applied the method to reanalyze the Healthy Directions-Small Business cancer prevention study, which evaluated a behavioral intervention program targeting multiple risk-related behaviors in a working-class, multi-ethnic population. PMID:28239457

Cider fermentation process monitoring by Vis-NIR sensor system and chemometrics.

PubMed

Villar, Alberto; Vadillo, Julen; Santos, Jose I; Gorritxategi, Eneko; Mabe, Jon; Arnaiz, Aitor; Fernández, Luis A

2017-04-15

Optimization of a multivariate calibration process has been undertaken for a Visible-Near Infrared (400-1100nm) sensor system, applied in the monitoring of the fermentation process of the cider produced in the Basque Country (Spain). The main parameters that were monitored included alcoholic proof, l-lactic acid content, glucose+fructose and acetic acid content. The multivariate calibration was carried out using a combination of different variable selection techniques and the most suitable pre-processing strategies were selected based on the spectra characteristics obtained by the sensor system. The variable selection techniques studied in this work include Martens Uncertainty test, interval Partial Least Square Regression (iPLS) and Genetic Algorithm (GA). This procedure arises from the need to improve the calibration models prediction ability for cider monitoring. Copyright © 2016 Elsevier Ltd. All rights reserved.

Neuropsychological Test Selection for Cognitive Impairment Classification: A Machine Learning Approach

PubMed Central

Williams, Jennifer A.; Schmitter-Edgecombe, Maureen; Cook, Diane J.

2016-01-01

Introduction Reducing the amount of testing required to accurately detect cognitive impairment is clinically relevant. The aim of this research was to determine the fewest number of clinical measures required to accurately classify participants as healthy older adult, mild cognitive impairment (MCI) or dementia using a suite of classification techniques. Methods Two variable selection machine learning models (i.e., naive Bayes, decision tree), a logistic regression, and two participant datasets (i.e., clinical diagnosis, clinical dementia rating; CDR) were explored. Participants classified using clinical diagnosis criteria included 52 individuals with dementia, 97 with MCI, and 161 cognitively healthy older adults. Participants classified using CDR included 154 individuals CDR = 0, 93 individuals with CDR = 0.5, and 25 individuals with CDR = 1.0+. Twenty-seven demographic, psychological, and neuropsychological variables were available for variable selection. Results No significant difference was observed between naive Bayes, decision tree, and logistic regression models for classification of both clinical diagnosis and CDR datasets. Participant classification (70.0 – 99.1%), geometric mean (60.9 – 98.1%), sensitivity (44.2 – 100%), and specificity (52.7 – 100%) were generally satisfactory. Unsurprisingly, the MCI/CDR = 0.5 participant group was the most challenging to classify. Through variable selection only 2 – 9 variables were required for classification and varied between datasets in a clinically meaningful way. Conclusions The current study results reveal that machine learning techniques can accurately classifying cognitive impairment and reduce the number of measures required for diagnosis. PMID:26332171

Approximate techniques of structural reanalysis

NASA Technical Reports Server (NTRS)

Noor, A. K.; Lowder, H. E.

1974-01-01

A study is made of two approximate techniques for structural reanalysis. These include Taylor series expansions for response variables in terms of design variables and the reduced-basis method. In addition, modifications to these techniques are proposed to overcome some of their major drawbacks. The modifications include a rational approach to the selection of the reduced-basis vectors and the use of Taylor series approximation in an iterative process. For the reduced basis a normalized set of vectors is chosen which consists of the original analyzed design and the first-order sensitivity analysis vectors. The use of the Taylor series approximation as a first (initial) estimate in an iterative process, can lead to significant improvements in accuracy, even with one iteration cycle. Therefore, the range of applicability of the reanalysis technique can be extended. Numerical examples are presented which demonstrate the gain in accuracy obtained by using the proposed modification techniques, for a wide range of variations in the design variables.

Limitations to mapping habitat-use areas in changing landscapes using the Mahalanobis distance statistic

USGS Publications Warehouse

Knick, Steven T.; Rotenberry, J.T.

1998-01-01

We tested the potential of a GIS mapping technique, using a resource selection model developed for black-tailed jackrabbits (Lepus californicus) and based on the Mahalanobis distance statistic, to track changes in shrubsteppe habitats in southwestern Idaho. If successful, the technique could be used to predict animal use areas, or those undergoing change, in different regions from the same selection function and variables without additional sampling. We determined the multivariate mean vector of 7 GIS variables that described habitats used by jackrabbits. We then ranked the similarity of all cells in the GIS coverage from their Mahalanobis distance to the mean habitat vector. The resulting map accurately depicted areas where we sighted jackrabbits on verification surveys. We then simulated an increase in shrublands (which are important habitats). Contrary to expectation, the new configurations were classified as lower similarity relative to the original mean habitat vector. Because the selection function is based on a unimodal mean, any deviation, even if biologically positive, creates larger Malanobis distances and lower similarity values. We recommend the Mahalanobis distance technique for mapping animal use areas when animals are distributed optimally, the landscape is well-sampled to determine the mean habitat vector, and distributions of the habitat variables does not change.

Modeling Hurricane Katrina's merchantable timber and wood damage in south Mississippi using remotely sensed and field-measured data

NASA Astrophysics Data System (ADS)

Collins, Curtis Andrew

Ordinary and weighted least squares multiple linear regression techniques were used to derive 720 models predicting Katrina-induced storm damage in cubic foot volume (outside bark) and green weight tons (outside bark). The large number of models was dictated by the use of three damage classes, three product types, and four forest type model strata. These 36 models were then fit and reported across 10 variable sets and variable set combinations for volume and ton units. Along with large model counts, potential independent variables were created using power transforms and interactions. The basis of these variables was field measured plot data, satellite (Landsat TM and ETM+) imagery, and NOAA HWIND wind data variable types. As part of the modeling process, lone variable types as well as two-type and three-type combinations were examined. By deriving models with these varying inputs, model utility is flexible as all independent variable data are not needed in future applications. The large number of potential variables led to the use of forward, sequential, and exhaustive independent variable selection techniques. After variable selection, weighted least squares techniques were often employed using weights of one over the square root of the pre-storm volume or weight of interest. This was generally successful in improving residual variance homogeneity. Finished model fits, as represented by coefficient of determination (R2), surpassed 0.5 in numerous models with values over 0.6 noted in a few cases. Given these models, an analyst is provided with a toolset to aid in risk assessment and disaster recovery should Katrina-like weather events reoccur.

An investigation of dynamic-analysis methods for variable-geometry structures

NASA Technical Reports Server (NTRS)

Austin, F.

1980-01-01

Selected space structure configurations were reviewed in order to define dynamic analysis problems associated with variable geometry. The dynamics of a beam being constructed from a flexible base and the relocation of the completed beam by rotating the remote manipulator system about the shoulder joint were selected. Equations of motion were formulated in physical coordinates for both of these problems, and FORTRAN programs were developed to generate solutions by numerically integrating the equations. These solutions served as a standard of comparison to gauge the accuracy of approximate solution techniques that were developed and studied. Good control was achieved in both problems. Unstable control system coupling with the system flexibility did not occur. An approximate method was developed for each problem to enable the analyst to investigate variable geometry effects during a short time span using standard fixed geometry programs such as NASTRAN. The average angle and average length techniques are discussed.

Estimating forest attribute parameters for small areas using nearest neighbors techniques

Treesearch

Ronald E. McRoberts

2012-01-01

Nearest neighbors techniques have become extremely popular, particularly for use with forest inventory data. With these techniques, a population unit prediction is calculated as a linear combination of observations for a selected number of population units in a sample that are most similar, or nearest, in a space of ancillary variables to the population unit requiring...

Nuclear Technology. Course 32: Nondestructive Examination (NDE) Techniques II. Module 32-4, Operation of Magnetic Particle Test Equipment.

ERIC Educational Resources Information Center

Groseclose, Richard

This fourth in a series of six modules for a course titled Nondestructive Examination (NDE) Techniques II describes the specific technique variables and options which are available to the test technician, provides instructions for selecting and operating the appropriate test equipment, describes physical criteria for detectable discontinuities,…

An efficient swarm intelligence approach to feature selection based on invasive weed optimization: Application to multivariate calibration and classification using spectroscopic data

NASA Astrophysics Data System (ADS)

Sheykhizadeh, Saheleh; Naseri, Abdolhossein

2018-04-01

Variable selection plays a key role in classification and multivariate calibration. Variable selection methods are aimed at choosing a set of variables, from a large pool of available predictors, relevant to the analyte concentrations estimation, or to achieve better classification results. Many variable selection techniques have now been introduced among which, those which are based on the methodologies of swarm intelligence optimization have been more respected during a few last decades since they are mainly inspired by nature. In this work, a simple and new variable selection algorithm is proposed according to the invasive weed optimization (IWO) concept. IWO is considered a bio-inspired metaheuristic mimicking the weeds ecological behavior in colonizing as well as finding an appropriate place for growth and reproduction; it has been shown to be very adaptive and powerful to environmental changes. In this paper, the first application of IWO, as a very simple and powerful method, to variable selection is reported using different experimental datasets including FTIR and NIR data, so as to undertake classification and multivariate calibration tasks. Accordingly, invasive weed optimization - linear discrimination analysis (IWO-LDA) and invasive weed optimization- partial least squares (IWO-PLS) are introduced for multivariate classification and calibration, respectively.

An efficient swarm intelligence approach to feature selection based on invasive weed optimization: Application to multivariate calibration and classification using spectroscopic data.

PubMed

Sheykhizadeh, Saheleh; Naseri, Abdolhossein

2018-04-05

Variable selection plays a key role in classification and multivariate calibration. Variable selection methods are aimed at choosing a set of variables, from a large pool of available predictors, relevant to the analyte concentrations estimation, or to achieve better classification results. Many variable selection techniques have now been introduced among which, those which are based on the methodologies of swarm intelligence optimization have been more respected during a few last decades since they are mainly inspired by nature. In this work, a simple and new variable selection algorithm is proposed according to the invasive weed optimization (IWO) concept. IWO is considered a bio-inspired metaheuristic mimicking the weeds ecological behavior in colonizing as well as finding an appropriate place for growth and reproduction; it has been shown to be very adaptive and powerful to environmental changes. In this paper, the first application of IWO, as a very simple and powerful method, to variable selection is reported using different experimental datasets including FTIR and NIR data, so as to undertake classification and multivariate calibration tasks. Accordingly, invasive weed optimization - linear discrimination analysis (IWO-LDA) and invasive weed optimization- partial least squares (IWO-PLS) are introduced for multivariate classification and calibration, respectively. Copyright © 2018 Elsevier B.V. All rights reserved.

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

Identification of Genes Involved in Breast Cancer Metastasis by Integrating Protein-Protein Interaction Information with Expression Data.

PubMed

Tian, Xin; Xin, Mingyuan; Luo, Jian; Liu, Mingyao; Jiang, Zhenran

2017-02-01

The selection of relevant genes for breast cancer metastasis is critical for the treatment and prognosis of cancer patients. Although much effort has been devoted to the gene selection procedures by use of different statistical analysis methods or computational techniques, the interpretation of the variables in the resulting survival models has been limited so far. This article proposes a new Random Forest (RF)-based algorithm to identify important variables highly related with breast cancer metastasis, which is based on the important scores of two variable selection algorithms, including the mean decrease Gini (MDG) criteria of Random Forest and the GeneRank algorithm with protein-protein interaction (PPI) information. The new gene selection algorithm can be called PPIRF. The improved prediction accuracy fully illustrated the reliability and high interpretability of gene list selected by the PPIRF approach.

Machine learning search for variable stars

NASA Astrophysics Data System (ADS)

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

2018-04-01

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

FIRE: an SPSS program for variable selection in multiple linear regression analysis via the relative importance of predictors.

PubMed

Lorenzo-Seva, Urbano; Ferrando, Pere J

2011-03-01

We provide an SPSS program that implements currently recommended techniques and recent developments for selecting variables in multiple linear regression analysis via the relative importance of predictors. The approach consists of: (1) optimally splitting the data for cross-validation, (2) selecting the final set of predictors to be retained in the equation regression, and (3) assessing the behavior of the chosen model using standard indices and procedures. The SPSS syntax, a short manual, and data files related to this article are available as supplemental materials from brm.psychonomic-journals.org/content/supplemental.

Variable mechanical ventilation

PubMed Central

Fontela, Paula Caitano; Prestes, Renata Bernardy; Forgiarini Jr., Luiz Alberto; Friedman, Gilberto

2017-01-01

Objective To review the literature on the use of variable mechanical ventilation and the main outcomes of this technique. Methods Search, selection, and analysis of all original articles on variable ventilation, without restriction on the period of publication and language, available in the electronic databases LILACS, MEDLINE®, and PubMed, by searching the terms "variable ventilation" OR "noisy ventilation" OR "biologically variable ventilation". Results A total of 36 studies were selected. Of these, 24 were original studies, including 21 experimental studies and three clinical studies. Conclusion Several experimental studies reported the beneficial effects of distinct variable ventilation strategies on lung function using different models of lung injury and healthy lungs. Variable ventilation seems to be a viable strategy for improving gas exchange and respiratory mechanics and preventing lung injury associated with mechanical ventilation. However, further clinical studies are necessary to assess the potential of variable ventilation strategies for the clinical improvement of patients undergoing mechanical ventilation. PMID:28444076

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.

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.

Interpretation of tropospheric ozone variability in data with different vertical and temporal resolution

NASA Astrophysics Data System (ADS)

Petropavlovskikh, I. V.; Disterhoft, P.; Johnson, B. J.; Rieder, H. E.; Manney, G. L.; Daffer, W.

2012-12-01

This work attributes tropospheric ozone variability derived from the ground-based Dobson and Brewer Umkehr measurements and from ozone sonde data to local sources and transport. It assesses capability and limitations in both types of measurements that are often used to analyze long- and short-term variability in tropospheric ozone time series. We will address the natural and instrument-related contribution to the variability found in both Umkehr and sonde data. Validation of Umkehr methods is often done by intercomparisons against independent ozone measuring techniques such as ozone sounding. We will use ozone-sounding in its original and AK-smoothed vertical profiles for assessment of ozone inter-annual variability over Boulder, CO. We will discuss possible reasons for differences between different ozone measuring techniques and its effects on the derived ozone trends. Next to standard evaluation techniques we utilize a STL-decomposition method to address temporal variability and trends in the Boulder Umkehr data. Further, we apply a statistical modeling approach to the ozone data set to attribute ozone variability to individual driving forces associated with natural and anthropogenic causes. To this aim we follow earlier work applying a backward selection method (i.e., a stepwise elimination procedure out of a set of total 44 explanatory variables) to determine those explanatory variables which contribute most significantly to the observed variability. We will present also some results associated with completeness (sampling rate) of the existing data sets. We will also use MERRA (Modern-Era Retrospective analysis for Research and Applications) re-analysis results selected for Boulder location as a transfer function in understanding of the effects that the temporal sampling and vertical resolution bring into trend and ozone variability analysis. Analyzing intra-annual variability in ozone measurements over Boulder, CO, in relation to the upper tropospheric subtropical and polar jets, we will address the stratospheric and tropospheric intrusions in the middle latitude troposphere ozone field.

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.

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.

TIMSS 2011 Student and Teacher Predictors for Mathematics Achievement Explored and Identified via Elastic Net.

PubMed

Yoo, Jin Eun

2018-01-01

A substantial body of research has been conducted on variables relating to students' mathematics achievement with TIMSS. However, most studies have employed conventional statistical methods, and have focused on selected few indicators instead of utilizing hundreds of variables TIMSS provides. This study aimed to find a prediction model for students' mathematics achievement using as many TIMSS student and teacher variables as possible. Elastic net, the selected machine learning technique in this study, takes advantage of both LASSO and ridge in terms of variable selection and multicollinearity, respectively. A logistic regression model was also employed to predict TIMSS 2011 Korean 4th graders' mathematics achievement. Ten-fold cross-validation with mean squared error was employed to determine the elastic net regularization parameter. Among 162 TIMSS variables explored, 12 student and 5 teacher variables were selected in the elastic net model, and the prediction accuracy, sensitivity, and specificity were 76.06, 70.23, and 80.34%, respectively. This study showed that the elastic net method can be successfully applied to educational large-scale data by selecting a subset of variables with reasonable prediction accuracy and finding new variables to predict students' mathematics achievement. Newly found variables via machine learning can shed light on the existing theories from a totally different perspective, which in turn propagates creation of a new theory or complement of existing ones. This study also examined the current scale development convention from a machine learning perspective.

TIMSS 2011 Student and Teacher Predictors for Mathematics Achievement Explored and Identified via Elastic Net

PubMed Central

Yoo, Jin Eun

2018-01-01

A substantial body of research has been conducted on variables relating to students' mathematics achievement with TIMSS. However, most studies have employed conventional statistical methods, and have focused on selected few indicators instead of utilizing hundreds of variables TIMSS provides. This study aimed to find a prediction model for students' mathematics achievement using as many TIMSS student and teacher variables as possible. Elastic net, the selected machine learning technique in this study, takes advantage of both LASSO and ridge in terms of variable selection and multicollinearity, respectively. A logistic regression model was also employed to predict TIMSS 2011 Korean 4th graders' mathematics achievement. Ten-fold cross-validation with mean squared error was employed to determine the elastic net regularization parameter. Among 162 TIMSS variables explored, 12 student and 5 teacher variables were selected in the elastic net model, and the prediction accuracy, sensitivity, and specificity were 76.06, 70.23, and 80.34%, respectively. This study showed that the elastic net method can be successfully applied to educational large-scale data by selecting a subset of variables with reasonable prediction accuracy and finding new variables to predict students' mathematics achievement. Newly found variables via machine learning can shed light on the existing theories from a totally different perspective, which in turn propagates creation of a new theory or complement of existing ones. This study also examined the current scale development convention from a machine learning perspective. PMID:29599736

Conjoint Analysis: A Study of the Effects of Using Person Variables.

ERIC Educational Resources Information Center

Fraas, John W.; Newman, Isadore

Three statistical techniques--conjoint analysis, a multiple linear regression model, and a multiple linear regression model with a surrogate person variable--were used to estimate the relative importance of five university attributes for students in the process of selecting a college. The five attributes include: availability and variety of…

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.

The relation between learning mathematics and students' competencies in undesrtanding texts

NASA Astrophysics Data System (ADS)

Hapipi, Azmi, Syahrul; Sripatmi, Amrullah

2017-08-01

This study was a descriptive study that aimed to gain an overview on the relation between learning mathematics and students' competencies in understanding texts. This research was classified as an ex post facto study due in part to the variable studied is the variable that was already happening. While the technique of taking the sample using stratified proportional sampling techniques. These techniques have been selected for the condition of the population, in the context of learning mathematics, diverse and also tiered. The results of this study indicate that there is a relationship between learning mathematics and students' competencies in understanding texts.

Quantifying Spatial Variability of Selected Soil Trace Elements and Their Scaling Relationships Using Multifractal Techniques

PubMed Central

Zhang, Fasheng; Yin, Guanghua; Wang, Zhenying; McLaughlin, Neil; Geng, Xiaoyuan; Liu, Zuoxin

2013-01-01

Multifractal techniques were utilized to quantify the spatial variability of selected soil trace elements and their scaling relationships in a 10.24-ha agricultural field in northeast China. 1024 soil samples were collected from the field and available Fe, Mn, Cu and Zn were measured in each sample. Descriptive results showed that Mn deficiencies were widespread throughout the field while Fe and Zn deficiencies tended to occur in patches. By estimating single multifractal spectra, we found that available Fe, Cu and Zn in the study soils exhibited high spatial variability and the existence of anomalies ([α(q)max−α(q)min]≥0.54), whereas available Mn had a relatively uniform distribution ([α(q)max−α(q)min]≈0.10). The joint multifractal spectra revealed that the strong positive relationships (r≥0.86, P<0.001) among available Fe, Cu and Zn were all valid across a wider range of scales and over the full range of data values, whereas available Mn was weakly related to available Fe and Zn (r≥0.18, P<0.01) but not related to available Cu (r = −0.03, P = 0.40). These results show that the variability and singularities of selected soil trace elements as well as their scaling relationships can be characterized by single and joint multifractal parameters. The findings presented in this study could be extended to predict selected soil trace elements at larger regional scales with the aid of geographic information systems. PMID:23874944

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.

Applying additive modeling and gradient boosting to assess the effects of watershed and reach characteristics on riverine assemblages

USGS Publications Warehouse

Maloney, Kelly O.; Schmid, Matthias; Weller, Donald E.

2012-01-01

Issues with ecological data (e.g. non-normality of errors, nonlinear relationships and autocorrelation of variables) and modelling (e.g. overfitting, variable selection and prediction) complicate regression analyses in ecology. Flexible models, such as generalized additive models (GAMs), can address data issues, and machine learning techniques (e.g. gradient boosting) can help resolve modelling issues. Gradient boosted GAMs do both. Here, we illustrate the advantages of this technique using data on benthic macroinvertebrates and fish from 1573 small streams in Maryland, USA.

Multiple-input multiple-output causal strategies for gene selection.

PubMed

Bontempi, Gianluca; Haibe-Kains, Benjamin; Desmedt, Christine; Sotiriou, Christos; Quackenbush, John

2011-11-25

Traditional strategies for selecting variables in high dimensional classification problems aim to find sets of maximally relevant variables able to explain the target variations. If these techniques may be effective in generalization accuracy they often do not reveal direct causes. The latter is essentially related to the fact that high correlation (or relevance) does not imply causation. In this study, we show how to efficiently incorporate causal information into gene selection by moving from a single-input single-output to a multiple-input multiple-output setting. We show in synthetic case study that a better prioritization of causal variables can be obtained by considering a relevance score which incorporates a causal term. In addition we show, in a meta-analysis study of six publicly available breast cancer microarray datasets, that the improvement occurs also in terms of accuracy. The biological interpretation of the results confirms the potential of a causal approach to gene selection. Integrating causal information into gene selection algorithms is effective both in terms of prediction accuracy and biological interpretation.

Methods of rapid, early selection of poplar clones for maximum yield potential: a manual of procedures.

Treesearch

USDA FS

1982-01-01

Instructions, illustrated with examples and experimental results, are given for the controlled-environment propagation and selection of poplar clones. Greenhouse and growth-room culture of poplar stock plants and scions are described, and statistical techniques for discriminating among clones on the basis of growth variables are emphasized.

Selection of key ambient particulate variables for epidemiological studies - applying cluster and heatmap analyses as tools for data reduction.

PubMed

Gu, Jianwei; Pitz, Mike; Breitner, Susanne; Birmili, Wolfram; von Klot, Stephanie; Schneider, Alexandra; Soentgen, Jens; Reller, Armin; Peters, Annette; Cyrys, Josef

2012-10-01

The success of epidemiological studies depends on the use of appropriate exposure variables. The purpose of this study is to extract a relatively small selection of variables characterizing ambient particulate matter from a large measurement data set. The original data set comprised a total of 96 particulate matter variables that have been continuously measured since 2004 at an urban background aerosol monitoring site in the city of Augsburg, Germany. Many of the original variables were derived from measured particle size distribution (PSD) across the particle diameter range 3 nm to 10 μm, including size-segregated particle number concentration, particle length concentration, particle surface concentration and particle mass concentration. The data set was complemented by integral aerosol variables. These variables were measured by independent instruments, including black carbon, sulfate, particle active surface concentration and particle length concentration. It is obvious that such a large number of measured variables cannot be used in health effect analyses simultaneously. The aim of this study is a pre-screening and a selection of the key variables that will be used as input in forthcoming epidemiological studies. In this study, we present two methods of parameter selection and apply them to data from a two-year period from 2007 to 2008. We used the agglomerative hierarchical cluster method to find groups of similar variables. In total, we selected 15 key variables from 9 clusters which are recommended for epidemiological analyses. We also applied a two-dimensional visualization technique called "heatmap" analysis to the Spearman correlation matrix. 12 key variables were selected using this method. Moreover, the positive matrix factorization (PMF) method was applied to the PSD data to characterize the possible particle sources. Correlations between the variables and PMF factors were used to interpret the meaning of the cluster and the heatmap analyses. Copyright © 2012 Elsevier B.V. All rights reserved.

Lecture Hall and Learning Design: A Survey of Variables, Parameters, Criteria and Interrelationships for Audio-Visual Presentation Systems and Audience Reception.

ERIC Educational Resources Information Center

Justin, J. Karl

Variables and parameters affecting architectural planning and audiovisual systems selection for lecture halls and other learning spaces are surveyed. Interrelationships of factors are discussed, including--(1) design requirements for modern educational techniques as differentiated from cinema, theater or auditorium design, (2) general hall…

Automatic differentiation evaluated as a tool for rotorcraft design and optimization

NASA Technical Reports Server (NTRS)

Walsh, Joanne L.; Young, Katherine C.

1995-01-01

This paper investigates the use of automatic differentiation (AD) as a means for generating sensitivity analyses in rotorcraft design and optimization. This technique transforms an existing computer program into a new program that performs sensitivity analysis in addition to the original analysis. The original FORTRAN program calculates a set of dependent (output) variables from a set of independent (input) variables, the new FORTRAN program calculates the partial derivatives of the dependent variables with respect to the independent variables. The AD technique is a systematic implementation of the chain rule of differentiation, this method produces derivatives to machine accuracy at a cost that is comparable with that of finite-differencing methods. For this study, an analysis code that consists of the Langley-developed hover analysis HOVT, the comprehensive rotor analysis CAMRAD/JA, and associated preprocessors is processed through the AD preprocessor ADIFOR 2.0. The resulting derivatives are compared with derivatives obtained from finite-differencing techniques. The derivatives obtained with ADIFOR 2.0 are exact within machine accuracy and do not depend on the selection of step-size, as are the derivatives obtained with finite-differencing techniques.

Adaptive Elastic Net for Generalized Methods of Moments.

PubMed

Caner, Mehmet; Zhang, Hao Helen

2014-01-30

Model selection and estimation are crucial parts of econometrics. This paper introduces a new technique that can simultaneously estimate and select the model in generalized method of moments (GMM) context. The GMM is particularly powerful for analyzing complex data sets such as longitudinal and panel data, and it has wide applications in econometrics. This paper extends the least squares based adaptive elastic net estimator of Zou and Zhang (2009) to nonlinear equation systems with endogenous variables. The extension is not trivial and involves a new proof technique due to estimators lack of closed form solutions. Compared to Bridge-GMM of Caner (2009), we allow for the number of parameters to diverge to infinity as well as collinearity among a large number of variables, also the redundant parameters set to zero via a data dependent technique. This method has the oracle property, meaning that we can estimate nonzero parameters with their standard limit and the redundant parameters are dropped from the equations simultaneously. Numerical examples are used to illustrate the performance of the new method.

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

Use of data mining techniques to classify soil CO2 emission induced by crop management in sugarcane field.

PubMed

Farhate, Camila Viana Vieira; Souza, Zigomar Menezes de; Oliveira, Stanley Robson de Medeiros; Tavares, Rose Luiza Moraes; Carvalho, João Luís Nunes

2018-01-01

Soil CO2 emissions are regarded as one of the largest flows of the global carbon cycle and small changes in their magnitude can have a large effect on the CO2 concentration in the atmosphere. Thus, a better understanding of this attribute would enable the identification of promoters and the development of strategies to mitigate the risks of climate change. Therefore, our study aimed at using data mining techniques to predict the soil CO2 emission induced by crop management in sugarcane areas in Brazil. To do so, we used different variable selection methods (correlation, chi-square, wrapper) and classification (Decision tree, Bayesian models, neural networks, support vector machine, bagging with logistic regression), and finally we tested the efficiency of different approaches through the Receiver Operating Characteristic (ROC) curve. The original dataset consisted of 19 variables (18 independent variables and one dependent (or response) variable). The association between cover crop and minimum tillage are effective strategies to promote the mitigation of soil CO2 emissions, in which the average CO2 emissions are 63 kg ha-1 day-1. The variables soil moisture, soil temperature (Ts), rainfall, pH, and organic carbon were most frequently selected for soil CO2 emission classification using different methods for attribute selection. According to the results of the ROC curve, the best approaches for soil CO2 emission classification were the following: (I)-the Multilayer Perceptron classifier with attribute selection through the wrapper method, that presented rate of false positive of 13,50%, true positive of 94,20% area under the curve (AUC) of 89,90% (II)-the Bagging classifier with logistic regression with attribute selection through the Chi-square method, that presented rate of false positive of 13,50%, true positive of 94,20% AUC of 89,90%. However, the (I) approach stands out in relation to (II) for its higher positive class accuracy (high CO2 emission) and lower computational cost.

Use of data mining techniques to classify soil CO2 emission induced by crop management in sugarcane field

PubMed Central

de Souza, Zigomar Menezes; Oliveira, Stanley Robson de Medeiros; Tavares, Rose Luiza Moraes; Carvalho, João Luís Nunes

2018-01-01

Soil CO2 emissions are regarded as one of the largest flows of the global carbon cycle and small changes in their magnitude can have a large effect on the CO2 concentration in the atmosphere. Thus, a better understanding of this attribute would enable the identification of promoters and the development of strategies to mitigate the risks of climate change. Therefore, our study aimed at using data mining techniques to predict the soil CO2 emission induced by crop management in sugarcane areas in Brazil. To do so, we used different variable selection methods (correlation, chi-square, wrapper) and classification (Decision tree, Bayesian models, neural networks, support vector machine, bagging with logistic regression), and finally we tested the efficiency of different approaches through the Receiver Operating Characteristic (ROC) curve. The original dataset consisted of 19 variables (18 independent variables and one dependent (or response) variable). The association between cover crop and minimum tillage are effective strategies to promote the mitigation of soil CO2 emissions, in which the average CO2 emissions are 63 kg ha-1 day-1. The variables soil moisture, soil temperature (Ts), rainfall, pH, and organic carbon were most frequently selected for soil CO2 emission classification using different methods for attribute selection. According to the results of the ROC curve, the best approaches for soil CO2 emission classification were the following: (I)–the Multilayer Perceptron classifier with attribute selection through the wrapper method, that presented rate of false positive of 13,50%, true positive of 94,20% area under the curve (AUC) of 89,90% (II)–the Bagging classifier with logistic regression with attribute selection through the Chi-square method, that presented rate of false positive of 13,50%, true positive of 94,20% AUC of 89,90%. However, the (I) approach stands out in relation to (II) for its higher positive class accuracy (high CO2 emission) and lower computational cost. PMID:29513765

Linear data mining the Wichita clinical matrix suggests sleep and allostatic load involvement in chronic fatigue syndrome.

PubMed

Gurbaxani, Brian M; Jones, James F; Goertzel, Benjamin N; Maloney, Elizabeth M

2006-04-01

To provide a mathematical introduction to the Wichita (KS, USA) clinical dataset, which is all of the nongenetic data (no microarray or single nucleotide polymorphism data) from the 2-day clinical evaluation, and show the preliminary findings and limitations, of popular, matrix algebra-based data mining techniques. An initial matrix of 440 variables by 227 human subjects was reduced to 183 variables by 164 subjects. Variables were excluded that strongly correlated with chronic fatigue syndrome (CFS) case classification by design (for example, the multidimensional fatigue inventory [MFI] data), that were otherwise self reporting in nature and also tended to correlate strongly with CFS classification, or were sparse or nonvarying between case and control. Subjects were excluded if they did not clearly fall into well-defined CFS classifications, had comorbid depression with melancholic features, or other medical or psychiatric exclusions. The popular data mining techniques, principle components analysis (PCA) and linear discriminant analysis (LDA), were used to determine how well the data separated into groups. Two different feature selection methods helped identify the most discriminating parameters. Although purely biological features (variables) were found to separate CFS cases from controls, including many allostatic load and sleep-related variables, most parameters were not statistically significant individually. However, biological correlates of CFS, such as heart rate and heart rate variability, require further investigation. Feature selection of a limited number of variables from the purely biological dataset produced better separation between groups than a PCA of the entire dataset. Feature selection highlighted the importance of many of the allostatic load variables studied in more detail by Maloney and colleagues in this issue [1] , as well as some sleep-related variables. Nonetheless, matrix linear algebra-based data mining approaches appeared to be of limited utility when compared with more sophisticated nonlinear analyses on richer data types, such as those found in Maloney and colleagues [1] and Goertzel and colleagues [2] in this issue.

Optimizing the sequence of diameter distributions and selection harvests for uneven-aged stand management

Treesearch

Robert G. Haight; J. Douglas Brodie; Darius M. Adams

1985-01-01

The determination of an optimal sequence of diameter distributions and selection harvests for uneven-aged stand management is formulated as a discrete-time optimal-control problem with bounded control variables and free-terminal point. An efficient programming technique utilizing gradients provides solutions that are stable and interpretable on the basis of economic...

Generating a Simulated Fluid Flow over a Surface Using Anisotropic Diffusion

NASA Technical Reports Server (NTRS)

Rodriguez, David L. (Inventor); Sturdza, Peter (Inventor)

2016-01-01

A fluid-flow simulation over a computer-generated surface is generated using a diffusion technique. The surface is comprised of a surface mesh of polygons. A boundary-layer fluid property is obtained for a subset of the polygons of the surface mesh. A gradient vector is determined for a selected polygon, the selected polygon belonging to the surface mesh but not one of the subset of polygons. A maximum and minimum diffusion rate is determined along directions determined using the gradient vector corresponding to the selected polygon. A diffusion-path vector is defined between a point in the selected polygon and a neighboring point in a neighboring polygon. An updated fluid property is determined for the selected polygon using a variable diffusion rate, the variable diffusion rate based on the minimum diffusion rate, maximum diffusion rate, and the gradient vector.

Variable Selection Strategies for Small-area Estimation Using FIA Plots and Remotely Sensed Data

Treesearch

Andrew Lister; Rachel Riemann; James Westfall; Mike Hoppus

2005-01-01

The USDA Forest Service's Forest Inventory and Analysis (FIA) unit maintains a network of tens of thousands of georeferenced forest inventory plots distributed across the United States. Data collected on these plots include direct measurements of tree diameter and height and other variables. We present a technique by which FIA plot data and coregistered...

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.

Essential Oil Variability and Biological Activities of Tetraclinis articulata (Vahl) Mast. Wood According to the Extraction Time.

PubMed

Djouahri, Abderrahmane; Saka, Boualem; Boudarene, Lynda; Baaliouamer, Aoumeur

2016-12-01

In the present work, the hydrodistillation (HD) and microwave-assisted hydrodistillation (MAHD) kinetics of essential oil (EO) extracted from Tetraclinis articulata (Vahl) Mast. wood was conducted, in order to assess the impact of extraction time and technique on chemical composition and biological activities. Gas chromatography (GC) and GC/mass spectrometry analyses showed significant differences between the extracted EOs, where each family class or component presents a specific kinetic according to extraction time, technique and especially for the major components: camphene, linalool, cedrol, carvacrol and α-acorenol. Furthermore, our findings showed a high variability for both antioxidant and anti-inflammatory activities, where each activity has a specific effect according to extraction time and technique. The highlighted variability reflects the high impact of extraction time and technique on chemical composition and biological activities, which led to conclude that we should select EOs to be investigated carefully depending on extraction time and technique, in order to isolate the bioactive components or to have the best quality of EO in terms of biological activities and preventive effects in food. © 2016 Wiley-VHCA AG, Zurich, Switzerland.

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.

Study of process variables associated with manufacturing hermetically-sealed nickel-cadmium cells

NASA Technical Reports Server (NTRS)

Miller, L.; Doan, D. J.; Carr, E. S.

1971-01-01

A program to determine and study the critical process variables associated with the manufacture of aerospace, hermetically-sealed, nickel-cadmium cells is described. The determination and study of the process variables associated with the positive and negative plaque impregnation/polarization process are emphasized. The experimental data resulting from the implementation of fractional factorial design experiments are analyzed by means of a linear multiple regression analysis technique. This analysis permits the selection of preferred levels for certain process variables to achieve desirable impregnated plaque characteristics.

Multivariate class modeling techniques applied to multielement analysis for the verification of the geographical origin of chili pepper.

PubMed

Naccarato, Attilio; Furia, Emilia; Sindona, Giovanni; Tagarelli, Antonio

2016-09-01

Four class-modeling techniques (soft independent modeling of class analogy (SIMCA), unequal dispersed classes (UNEQ), potential functions (PF), and multivariate range modeling (MRM)) were applied to multielement distribution to build chemometric models able to authenticate chili pepper samples grown in Calabria respect to those grown outside of Calabria. The multivariate techniques were applied by considering both all the variables (32 elements, Al, As, Ba, Ca, Cd, Ce, Co, Cr, Cs, Cu, Dy, Fe, Ga, La, Li, Mg, Mn, Na, Nd, Ni, Pb, Pr, Rb, Sc, Se, Sr, Tl, Tm, V, Y, Yb, Zn) and variables selected by means of stepwise linear discriminant analysis (S-LDA). In the first case, satisfactory and comparable results in terms of CV efficiency are obtained with the use of SIMCA and MRM (82.3 and 83.2% respectively), whereas MRM performs better than SIMCA in terms of forced model efficiency (96.5%). The selection of variables by S-LDA permitted to build models characterized, in general, by a higher efficiency. MRM provided again the best results for CV efficiency (87.7% with an effective balance of sensitivity and specificity) as well as forced model efficiency (96.5%). Copyright © 2016 Elsevier Ltd. All rights reserved.

Factors Affecting the Adoption of R&D Project Selection Techniques at the Air Force Wright Aeronautical Laboratories

DTIC Science & Technology

1988-09-01

tested. To measure 42 the adequacy of the sample, the Kaiser - Meyer - Olkin measure of sampling adequacy was used. This technique is described in Factor...40 4- 0 - 7 0 0 07 -58d the relatively large number of variables, there was concern about the adequacy of the sample size. A Kaiser - Meyer - Olkin

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.

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.

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.

Variable conductance heat pipe technology

NASA Technical Reports Server (NTRS)

Marcus, B. D.; Edwards, D. K.; Anderson, W. T.

1973-01-01

Research and development programs in variable conductance heat pipe technology were conducted. The treatment has been comprehensive, involving theoretical and/or experimental studies in hydrostatics, hydrodynamics, heat transfer into and out of the pipe, fluid selection, and materials compatibility, in addition to the principal subject of variable conductance control techniques. Efforts were not limited to analytical work and laboratory experimentation, but extended to the development, fabrication and test of spacecraft hardware, culminating in the successful flight of the Ames Heat Pipe Experiment on the OAO-C spacecraft.

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.

Generating a Simulated Fluid Flow Over an Aircraft Surface Using Anisotropic Diffusion

NASA Technical Reports Server (NTRS)

Rodriguez, David L. (Inventor); Sturdza, Peter (Inventor)

2013-01-01

A fluid-flow simulation over a computer-generated aircraft surface is generated using a diffusion technique. The surface is comprised of a surface mesh of polygons. A boundary-layer fluid property is obtained for a subset of the polygons of the surface mesh. A pressure-gradient vector is determined for a selected polygon, the selected polygon belonging to the surface mesh but not one of the subset of polygons. A maximum and minimum diffusion rate is determined along directions determined using a pressure gradient vector corresponding to the selected polygon. A diffusion-path vector is defined between a point in the selected polygon and a neighboring point in a neighboring polygon. An updated fluid property is determined for the selected polygon using a variable diffusion rate, the variable diffusion rate based on the minimum diffusion rate, maximum diffusion rate, and angular difference between the diffusion-path vector and the pressure-gradient vector.

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.

VizieR Online Data Catalog: Variability-selected AGN in Chandra DFS (Trevese+, 2008)

NASA Astrophysics Data System (ADS)

Trevese, D.; Boutsia, K.; Vagnetti, F.; Cappellaro, E.; Puccetti, S.

2008-11-01

Variability is a property shared by virtually all active galactic nuclei (AGNs), and was adopted as a criterion for their selection using data from multi epoch surveys. Low Luminosity AGNs (LLAGNs) are contaminated by the light of their host galaxies, and cannot therefore be detected by the usual colour techniques. For this reason, their evolution in cosmic time is poorly known. Consistency with the evolution derived from X-ray detected samples has not been clearly established so far, also because the low luminosity population consists of a mixture of different object types. LLAGNs can be detected by the nuclear optical variability of extended objects. Several variability surveys have been, or are being, conducted for the detection of supernovae (SNe). We propose to re-analyse these SNe data using a variability criterion optimised for AGN detection, to select a new AGN sample and study its properties. We analysed images acquired with the wide field imager at the 2.2m ESO/MPI telescope, in the framework of the STRESS supernova survey. We selected the AXAF field centred on the Chandra Deep Field South where, besides the deep X-ray survey, various optical data exist, originating in the EIS and COMBO-17 photometric surveys and the spectroscopic database of GOODS. (1 data file).

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.

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

Bayesian classification for the selection of in vitro human embryos using morphological and clinical data.

PubMed

Morales, Dinora Araceli; Bengoetxea, Endika; Larrañaga, Pedro; García, Miguel; Franco, Yosu; Fresnada, Mónica; Merino, Marisa

2008-05-01

In vitro fertilization (IVF) is a medically assisted reproduction technique that enables infertile couples to achieve successful pregnancy. Given the uncertainty of the treatment, we propose an intelligent decision support system based on supervised classification by Bayesian classifiers to aid to the selection of the most promising embryos that will form the batch to be transferred to the woman's uterus. The aim of the supervised classification system is to improve overall success rate of each IVF treatment in which a batch of embryos is transferred each time, where the success is achieved when implantation (i.e. pregnancy) is obtained. Due to ethical reasons, different legislative restrictions apply in every country on this technique. In Spain, legislation allows a maximum of three embryos to form each transfer batch. As a result, clinicians prefer to select the embryos by non-invasive embryo examination based on simple methods and observation focused on morphology and dynamics of embryo development after fertilization. This paper proposes the application of Bayesian classifiers to this embryo selection problem in order to provide a decision support system that allows a more accurate selection than with the actual procedures which fully rely on the expertise and experience of embryologists. For this, we propose to take into consideration a reduced subset of feature variables related to embryo morphology and clinical data of patients, and from this data to induce Bayesian classification models. Results obtained applying a filter technique to choose the subset of variables, and the performance of Bayesian classifiers using them, are presented.

Free variable selection QSPR study to predict 19F chemical shifts of some fluorinated organic compounds using Random Forest and RBF-PLS methods

NASA Astrophysics Data System (ADS)

Goudarzi, Nasser

2016-04-01

In this work, two new and powerful chemometrics methods are applied for the modeling and prediction of the 19F chemical shift values of some fluorinated organic compounds. The radial basis function-partial least square (RBF-PLS) and random forest (RF) are employed to construct the models to predict the 19F chemical shifts. In this study, we didn't used from any variable selection method and RF method can be used as variable selection and modeling technique. Effects of the important parameters affecting the ability of the RF prediction power such as the number of trees (nt) and the number of randomly selected variables to split each node (m) were investigated. The root-mean-square errors of prediction (RMSEP) for the training set and the prediction set for the RBF-PLS and RF models were 44.70, 23.86, 29.77, and 23.69, respectively. Also, the correlation coefficients of the prediction set for the RBF-PLS and RF models were 0.8684 and 0.9313, respectively. The results obtained reveal that the RF model can be used as a powerful chemometrics tool for the quantitative structure-property relationship (QSPR) studies.

Techniques for detecting effects of urban and rural land-use practices on stream-water chemistry in selected watersheds in Texas, Minnesota,and Illinois

USGS Publications Warehouse

Walker, J.F.

1993-01-01

Selected statistical techniques were applied to three urban watersheds in Texas and Minnesota and three rural watersheds in Illinois. For the urban watersheds, single- and paired-site data-collection strategies were considered. The paired-site strategy was much more effective than the singlesite strategy for detecting changes. Analysis of storm load regression residuals demonstrated the potential utility of regressions for variability reduction. For the rural watersheds, none of the selected techniques were effective at identifying changes, primarily due to a small degree of management-practice implementation, potential errors introduced through the estimation of storm load, and small sample sizes. A Monte Carlo sensitivity analysis was used to determine the percent change in water chemistry that could be detected for each watershed. In most instances, the use of regressions improved the ability to detect changes.

Phenotypic variability and selection of lipid-producing microalgae in a microfluidic centrifuge

NASA Astrophysics Data System (ADS)

Estévez-Torres, André.; Mestler, Troy; Austin, Robert H.

2010-03-01

Isogenic cells are known to display various expression levels that may result in different phenotypes within a population. Here we focus on the phenotypic variability of a species of unicellular algae that produce neutral lipids. Lipid-producing algae are one of the most promising sources of biofuel. We have implemented a simple microfluidic method to assess lipid-production variability in a population of algae that relays on density differences. We will discuss the reasons of this variability and address the promising avenues of this technique for directing the evolution of algae towards high lipid productivity.

A variational conformational dynamics approach to the selection of collective variables in metadynamics.

PubMed

McCarty, James; Parrinello, Michele

2017-11-28

In this paper, we combine two powerful computational techniques, well-tempered metadynamics and time-lagged independent component analysis. The aim is to develop a new tool for studying rare events and exploring complex free energy landscapes. Metadynamics is a well-established and widely used enhanced sampling method whose efficiency depends on an appropriate choice of collective variables. Often the initial choice is not optimal leading to slow convergence. However by analyzing the dynamics generated in one such run with a time-lagged independent component analysis and the techniques recently developed in the area of conformational dynamics, we obtain much more efficient collective variables that are also better capable of illuminating the physics of the system. We demonstrate the power of this approach in two paradigmatic examples.

A variational conformational dynamics approach to the selection of collective variables in metadynamics

NASA Astrophysics Data System (ADS)

McCarty, James; Parrinello, Michele

2017-11-01

In this paper, we combine two powerful computational techniques, well-tempered metadynamics and time-lagged independent component analysis. The aim is to develop a new tool for studying rare events and exploring complex free energy landscapes. Metadynamics is a well-established and widely used enhanced sampling method whose efficiency depends on an appropriate choice of collective variables. Often the initial choice is not optimal leading to slow convergence. However by analyzing the dynamics generated in one such run with a time-lagged independent component analysis and the techniques recently developed in the area of conformational dynamics, we obtain much more efficient collective variables that are also better capable of illuminating the physics of the system. We demonstrate the power of this approach in two paradigmatic examples.

Annual Research Review: Embracing Not Erasing Contextual Variability in Children's Behavior--Theory and Utility in the Selection and Use of Methods and Informants in Developmental Psychopathology

ERIC Educational Resources Information Center

Dirks, Melanie A.; De Los Reyes, Andres; Briggs-Gowan, Margaret; Cella, David; Wakschlag, Lauren S.

2012-01-01

This paper examines the selection and use of multiple methods and informants for the assessment of disruptive behavior syndromes and attention deficit/hyperactivity disorder, providing a critical discussion of (a) the bidirectional linkages between theoretical models of childhood psychopathology and current assessment techniques; and (b) current…

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.

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.

VARIABLE SELECTION FOR QUALITATIVE INTERACTIONS IN PERSONALIZED MEDICINE WHILE CONTROLLING THE FAMILY-WISE ERROR RATE

PubMed Central

Gunter, Lacey; Zhu, Ji; Murphy, Susan

2012-01-01

For many years, subset analysis has been a popular topic for the biostatistics and clinical trials literature. In more recent years, the discussion has focused on finding subsets of genomes which play a role in the effect of treatment, often referred to as stratified or personalized medicine. Though highly sought after, methods for detecting subsets with altering treatment effects are limited and lacking in power. In this article we discuss variable selection for qualitative interactions with the aim to discover these critical patient subsets. We propose a new technique designed specifically to find these interaction variables among a large set of variables while still controlling for the number of false discoveries. We compare this new method against standard qualitative interaction tests using simulations and give an example of its use on data from a randomized controlled trial for the treatment of depression. PMID:22023676

Dry-plasma-free chemical etch technique for variability reduction in multi-patterning (Conference Presentation)

NASA Astrophysics Data System (ADS)

Kal, Subhadeep; Mohanty, Nihar; Farrell, Richard A.; Franke, Elliott; Raley, Angelique; Thibaut, Sophie; Pereira, Cheryl; Pillai, Karthik; Ko, Akiteru; Mosden, Aelan; Biolsi, Peter

2017-04-01

Scaling beyond the 7nm technology node demands significant control over the variability down to a few angstroms, in order to achieve reasonable yield. For example, to meet the current scaling targets it is highly desirable to achieve sub 30nm pitch line/space features at back-end of the line (BEOL) or front end of line (FEOL); uniform and precise contact/hole patterning at middle of line (MOL). One of the quintessential requirements for such precise and possibly self-aligned patterning strategies is superior etch selectivity between the target films while other masks/films are exposed. The need to achieve high etch selectivity becomes more evident for unit process development at MOL and BEOL, as a result of low density films choices (compared to FEOL film choices) due to lower temperature budget. Low etch selectivity with conventional plasma and wet chemical etch techniques, causes significant gouging (un-intended etching of etch stop layer, as shown in Fig 1), high line edge roughness (LER)/line width roughness (LWR), non-uniformity, etc. In certain circumstances this may lead to added downstream process stochastics. Furthermore, conventional plasma etches may also have the added disadvantage of plasma VUV damage and corner rounding (Fig. 1). Finally, the above mentioned factors can potentially compromise edge placement error (EPE) and/or yield. Therefore a process flow enabled with extremely high selective etches inherent to film properties and/or etch chemistries is a significant advantage. To improve this etch selectivity for certain etch steps during a process flow, we have to implement alternate highly selective, plasma free techniques in conjunction with conventional plasma etches (Fig 2.). In this article, we will present our plasma free, chemical gas phase etch technique using chemistries that have high selectivity towards a spectrum of films owing to the reaction mechanism ( as shown Fig 1). Gas phase etches also help eliminate plasma damage to the features during the etch process. Herein we will also demonstrate a test case on how a combination or plasma assisted and plasma free etch techniques has the potential to improve process performance of a 193nm immersion based self aligned quandruple patterning (SAQP) for BEOL compliant films (an example shown in Fig 2). In addition, we will also present on the application of gas etches for (1) profile improvement, (2) selective mandrel pull (3) critical dimension trim of mandrels, with an analysis of advantages over conventional techniques in terms of LER and EPE.

Optimization techniques for integrating spatial data

USGS Publications Warehouse

Herzfeld, U.C.; Merriam, D.F.

1995-01-01

Two optimization techniques ta predict a spatial variable from any number of related spatial variables are presented. The applicability of the two different methods for petroleum-resource assessment is tested in a mature oil province of the Midcontinent (USA). The information on petroleum productivity, usually not directly accessible, is related indirectly to geological, geophysical, petrographical, and other observable data. This paper presents two approaches based on construction of a multivariate spatial model from the available data to determine a relationship for prediction. In the first approach, the variables are combined into a spatial model by an algebraic map-comparison/integration technique. Optimal weights for the map comparison function are determined by the Nelder-Mead downhill simplex algorithm in multidimensions. Geologic knowledge is necessary to provide a first guess of weights to start the automatization, because the solution is not unique. In the second approach, active set optimization for linear prediction of the target under positivity constraints is applied. Here, the procedure seems to select one variable from each data type (structure, isopachous, and petrophysical) eliminating data redundancy. Automating the determination of optimum combinations of different variables by applying optimization techniques is a valuable extension of the algebraic map-comparison/integration approach to analyzing spatial data. Because of the capability of handling multivariate data sets and partial retention of geographical information, the approaches can be useful in mineral-resource exploration. ?? 1995 International Association for Mathematical Geology.

Use of principal-component, correlation, and stepwise multiple-regression analyses to investigate selected physical and hydraulic properties of carbonate-rock aquifers

USGS Publications Warehouse

Brown, C. Erwin

1993-01-01

Correlation analysis in conjunction with principal-component and multiple-regression analyses were applied to laboratory chemical and petrographic data to assess the usefulness of these techniques in evaluating selected physical and hydraulic properties of carbonate-rock aquifers in central Pennsylvania. Correlation and principal-component analyses were used to establish relations and associations among variables, to determine dimensions of property variation of samples, and to filter the variables containing similar information. Principal-component and correlation analyses showed that porosity is related to other measured variables and that permeability is most related to porosity and grain size. Four principal components are found to be significant in explaining the variance of data. Stepwise multiple-regression analysis was used to see how well the measured variables could predict porosity and (or) permeability for this suite of rocks. The variation in permeability and porosity is not totally predicted by the other variables, but the regression is significant at the 5% significance level. ?? 1993.

Automated retrieval of forest structure variables based on multi-scale texture analysis of VHR satellite imagery

NASA Astrophysics Data System (ADS)

Beguet, Benoit; Guyon, Dominique; Boukir, Samia; Chehata, Nesrine

2014-10-01

The main goal of this study is to design a method to describe the structure of forest stands from Very High Resolution satellite imagery, relying on some typical variables such as crown diameter, tree height, trunk diameter, tree density and tree spacing. The emphasis is placed on the automatization of the process of identification of the most relevant image features for the forest structure retrieval task, exploiting both spectral and spatial information. Our approach is based on linear regressions between the forest structure variables to be estimated and various spectral and Haralick's texture features. The main drawback of this well-known texture representation is the underlying parameters which are extremely difficult to set due to the spatial complexity of the forest structure. To tackle this major issue, an automated feature selection process is proposed which is based on statistical modeling, exploring a wide range of parameter values. It provides texture measures of diverse spatial parameters hence implicitly inducing a multi-scale texture analysis. A new feature selection technique, we called Random PRiF, is proposed. It relies on random sampling in feature space, carefully addresses the multicollinearity issue in multiple-linear regression while ensuring accurate prediction of forest variables. Our automated forest variable estimation scheme was tested on Quickbird and Pléiades panchromatic and multispectral images, acquired at different periods on the maritime pine stands of two sites in South-Western France. It outperforms two well-established variable subset selection techniques. It has been successfully applied to identify the best texture features in modeling the five considered forest structure variables. The RMSE of all predicted forest variables is improved by combining multispectral and panchromatic texture features, with various parameterizations, highlighting the potential of a multi-resolution approach for retrieving forest structure variables from VHR satellite images. Thus an average prediction error of ˜ 1.1 m is expected on crown diameter, ˜ 0.9 m on tree spacing, ˜ 3 m on height and ˜ 0.06 m on diameter at breast height.

Vis-NIR spectrometric determination of Brix and sucrose in sugar production samples using kernel partial least squares with interval selection based on the successive projections algorithm.

PubMed

de Almeida, Valber Elias; de Araújo Gomes, Adriano; de Sousa Fernandes, David Douglas; Goicoechea, Héctor Casimiro; Galvão, Roberto Kawakami Harrop; Araújo, Mario Cesar Ugulino

2018-05-01

This paper proposes a new variable selection method for nonlinear multivariate calibration, combining the Successive Projections Algorithm for interval selection (iSPA) with the Kernel Partial Least Squares (Kernel-PLS) modelling technique. The proposed iSPA-Kernel-PLS algorithm is employed in a case study involving a Vis-NIR spectrometric dataset with complex nonlinear features. The analytical problem consists of determining Brix and sucrose content in samples from a sugar production system, on the basis of transflectance spectra. As compared to full-spectrum Kernel-PLS, the iSPA-Kernel-PLS models involve a smaller number of variables and display statistically significant superiority in terms of accuracy and/or bias in the predictions. Published by Elsevier B.V.

Determine new design and construction techniques for transportation of ethanol and ethanol/gasoline blends in new pipelines.

DOT National Transportation Integrated Search

2013-02-15

The technical tasks in this study included activities to characterize the impact of selected : metallurgical processing and fabrication variables on ethanol stress corrosion cracking (ethanol : SCC) of new pipeline steels, develop a better understand...

A comparative study of conventional and supercritical fluid extraction methods for the recovery of secondary metabolites from Syzygium campanulatum Korth.

PubMed

Memon, Abdul Hakeem; Hamil, Mohammad Shahrul Ridzuan; Laghari, Madeeha; Rithwan, Fahim; Zhari, Salman; Saeed, Mohammed Ali Ahmed; Ismail, Zhari; Majid, Amin Malik Shah Abdul

2016-09-01

Syzygium campanulatum Korth is a plant, which is a rich source of secondary metabolites (especially flavanones, chalcone, and triterpenoids). In our present study, three conventional solvent extraction (CSE) techniques and supercritical fluid extraction (SFE) techniques were performed to achieve a maximum recovery of two flavanones, chalcone, and two triterpenoids from S. campanulatum leaves. Furthermore, a Box-Behnken design was constructed for the SFE technique using pressure, temperature, and particle size as independent variables, and yields of crude extract, individual and total secondary metabolites as the dependent variables. In the CSE procedure, twenty extracts were produced using ten different solvents and three techniques (maceration, soxhletion, and reflux). An enriched extract of five secondary metabolites was collected using n-hexane:methanol (1:1) soxhletion. Using food-grade ethanol as a modifier, the SFE methods produced a higher recovery (25.5%‒84.9%) of selected secondary metabolites as compared to the CSE techniques (0.92%‒66.00%).

A comparative study of conventional and supercritical fluid extraction methods for the recovery of secondary metabolites from Syzygium campanulatum Korth#

PubMed Central

Memon, Abdul Hakeem; Hamil, Mohammad Shahrul Ridzuan; Laghari, Madeeha; Rithwan, Fahim; Zhari, Salman; Saeed, Mohammed Ali Ahmed; Ismail, Zhari; Majid, Amin Malik Shah Abdul

2016-01-01

Syzygium campanulatum Korth is a plant, which is a rich source of secondary metabolites (especially flavanones, chalcone, and triterpenoids). In our present study, three conventional solvent extraction (CSE) techniques and supercritical fluid extraction (SFE) techniques were performed to achieve a maximum recovery of two flavanones, chalcone, and two triterpenoids from S. campanulatum leaves. Furthermore, a Box-Behnken design was constructed for the SFE technique using pressure, temperature, and particle size as independent variables, and yields of crude extract, individual and total secondary metabolites as the dependent variables. In the CSE procedure, twenty extracts were produced using ten different solvents and three techniques (maceration, soxhletion, and reflux). An enriched extract of five secondary metabolites was collected using n-hexane:methanol (1:1) soxhletion. Using food-grade ethanol as a modifier, the SFE methods produced a higher recovery (25.5%‒84.9%) of selected secondary metabolites as compared to the CSE techniques (0.92%‒66.00%). PMID:27604860

Less or more hemodynamic monitoring in critically ill patients.

PubMed

Jozwiak, Mathieu; Monnet, Xavier; Teboul, Jean-Louis

2018-06-07

Hemodynamic investigations are required in patients with shock to identify the type of shock, to select the most appropriate treatments and to assess the patient's response to the selected therapy. We discuss how to select the most appropriate hemodynamic monitoring techniques in patients with shock as well as the future of hemodynamic monitoring. Over the last decades, the hemodynamic monitoring techniques have evolved from intermittent toward continuous and real-time measurements and from invasive toward less-invasive approaches. In patients with shock, current guidelines recommend the echocardiography as the preferred modality for the initial hemodynamic evaluation. In patients with shock nonresponsive to initial therapy and/or in the most complex patients, it is recommended to monitor the cardiac output and to use advanced hemodynamic monitoring techniques. They also provide other useful variables that are useful for managing the most complex cases. Uncalibrated and noninvasive cardiac output monitors are not reliable enough in the intensive care setting. The use of echocardiography should be initially encouraged in patients with shock to identify the type of shock and to select the most appropriate therapy. The use of more invasive hemodynamic monitoring techniques should be discussed on an individualized basis.

Use of Gene Expression Programming in regionalization of flow duration curve

NASA Astrophysics Data System (ADS)

Hashmi, Muhammad Z.; Shamseldin, Asaad Y.

2014-06-01

In this paper, a recently introduced artificial intelligence technique known as Gene Expression Programming (GEP) has been employed to perform symbolic regression for developing a parametric scheme of flow duration curve (FDC) regionalization, to relate selected FDC characteristics to catchment characteristics. Stream flow records of selected catchments located in the Auckland Region of New Zealand were used. FDCs of the selected catchments were normalised by dividing the ordinates by their median value. Input for the symbolic regression analysis using GEP was (a) selected characteristics of normalised FDCs; and (b) 26 catchment characteristics related to climate, morphology, soil properties and land cover properties obtained using the observed data and GIS analysis. Our study showed that application of this artificial intelligence technique expedites the selection of a set of the most relevant independent variables out of a large set, because these are automatically selected through the GEP process. Values of the FDC characteristics obtained from the developed relationships have high correlations with the observed values.

Quantitative structure-activity relationship study of P2X7 receptor inhibitors using combination of principal component analysis and artificial intelligence methods.

PubMed

Ahmadi, Mehdi; Shahlaei, Mohsen

2015-01-01

P2X7 antagonist activity for a set of 49 molecules of the P2X7 receptor antagonists, derivatives of purine, was modeled with the aid of chemometric and artificial intelligence techniques. The activity of these compounds was estimated by means of combination of principal component analysis (PCA), as a well-known data reduction method, genetic algorithm (GA), as a variable selection technique, and artificial neural network (ANN), as a non-linear modeling method. First, a linear regression, combined with PCA, (principal component regression) was operated to model the structure-activity relationships, and afterwards a combination of PCA and ANN algorithm was employed to accurately predict the biological activity of the P2X7 antagonist. PCA preserves as much of the information as possible contained in the original data set. Seven most important PC's to the studied activity were selected as the inputs of ANN box by an efficient variable selection method, GA. The best computational neural network model was a fully-connected, feed-forward model with 7-7-1 architecture. The developed ANN model was fully evaluated by different validation techniques, including internal and external validation, and chemical applicability domain. All validations showed that the constructed quantitative structure-activity relationship model suggested is robust and satisfactory.

Quantitative structure–activity relationship study of P2X7 receptor inhibitors using combination of principal component analysis and artificial intelligence methods

PubMed Central

Ahmadi, Mehdi; Shahlaei, Mohsen

2015-01-01

P2X7 antagonist activity for a set of 49 molecules of the P2X7 receptor antagonists, derivatives of purine, was modeled with the aid of chemometric and artificial intelligence techniques. The activity of these compounds was estimated by means of combination of principal component analysis (PCA), as a well-known data reduction method, genetic algorithm (GA), as a variable selection technique, and artificial neural network (ANN), as a non-linear modeling method. First, a linear regression, combined with PCA, (principal component regression) was operated to model the structure–activity relationships, and afterwards a combination of PCA and ANN algorithm was employed to accurately predict the biological activity of the P2X7 antagonist. PCA preserves as much of the information as possible contained in the original data set. Seven most important PC's to the studied activity were selected as the inputs of ANN box by an efficient variable selection method, GA. The best computational neural network model was a fully-connected, feed-forward model with 7−7−1 architecture. The developed ANN model was fully evaluated by different validation techniques, including internal and external validation, and chemical applicability domain. All validations showed that the constructed quantitative structure–activity relationship model suggested is robust and satisfactory. PMID:26600858

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.

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.

Validation of a biotelemetric technique, using ambulatory miniature black globe thermometers, to quantify thermoregulatory behaviour in ungulates.

PubMed

Hetem, Robyn S; Maloney, Shane K; Fuller, Andrea; Meyer, Leith C R; Mitchell, Duncan

2007-06-01

Behavioural thermoregulation is an animal's primary defence against changes in the thermal environment. We aimed to validate a remote technique to quantify the thermal environment behaviourally selected by free-ranging ungulates. First, we demonstrated that the temperature of miniature, 30 mm diameter, black globes (miniglobes) could be converted to standard, 150 mm diameter, black globe temperatures. Miniglobe temperature sensors subsequently were fitted to collars on three free-ranging ungulates, namely blue wildebeest (Connochaetes taurinus), impala (Aepyceros melampus) and horse (Equus caballus). Behavioural observations were reflected in animal miniglobe temperatures which differed from those recorded by an identical miniglobe on a nearby exposed weather station. The wildebeest often selected sites protected from the wind, whereas the impala and the horse sheltered from the sun. Nested analysis of variances revealed that the impala and horse selected significantly less variable environments than those recorded at the weather station (P<0.001) over a 20-min time interval, whereas, the microclimates selected by wildebeest tended to be more variable (P=0.08). Correlation of animal miniglobe against weather station miniglobe temperature resulted in regression slopes significantly less than one (P<0.001) for all species studied, implying that, overall, the animals selected cooler microclimates at high environmental heat loads and/or warmer microclimates at low environmental heat loads. We, therefore, have developed an ambulatory device, which can be attached to free-ranging animals, to remotely quantify thermoregulatory behaviour and selected microclimates. (c) 2007 Wiley-Liss, Inc.

An examination of predictive variables toward graduation of minority students in science at a selected urban university

NASA Astrophysics Data System (ADS)

Hunter, Evelyn M. Irving

1998-12-01

The purpose of this study was to examine the relationship and predictive power of the variables gender, high school GPA, class rank, SAT scores, ACT scores, and socioeconomic status on the graduation rates of minority college students majoring in the sciences at a selected urban university. Data was examined on these variables as they related to minority students majoring in science. The population consisted of 101 minority college students who had majored in the sciences from 1986 to 1996 at an urban university in the southwestern region of Texas. A non-probability sampling procedure was used in this study. The non-probability sampling procedure in this investigation was incidental sampling technique. A profile sheet was developed to record the information regarding the variables. The composite scores from SAT and ACT testing were used in the study. The dichotomous variables gender and socioeconomic status were dummy coded for analysis. For the gender variable, zero (0) indicated male, and one (1) indicated female. Additionally, zero (0) indicated high SES, and one (1) indicated low SES. Two parametric procedures were used to analyze the data in this investigation. They were the multiple correlation and multiple regression procedures. Multiple correlation is a statistical technique that indicates the relationship between one variable and a combination of two other variables. The variables socioeconomic status and GPA were found to contribute significantly to the graduation rates of minority students majoring in all sciences when combined with chemistry (Hypotheses Two and Four). These variables accounted for 7% and 15% of the respective variance in the graduation rates of minority students in the sciences and in chemistry. Hypotheses One and Three, the predictor variables gender, high school GPA, SAT Total Scores, class rank, and socioeconomic status did not contribute significantly to the graduation rates of minority students in biology and pharmacy.

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.

A probabilistic multi-criteria decision making technique for conceptual and preliminary aerospace systems design

NASA Astrophysics Data System (ADS)

Bandte, Oliver

It has always been the intention of systems engineering to invent or produce the best product possible. Many design techniques have been introduced over the course of decades that try to fulfill this intention. Unfortunately, no technique has succeeded in combining multi-criteria decision making with probabilistic design. The design technique developed in this thesis, the Joint Probabilistic Decision Making (JPDM) technique, successfully overcomes this deficiency by generating a multivariate probability distribution that serves in conjunction with a criterion value range of interest as a universally applicable objective function for multi-criteria optimization and product selection. This new objective function constitutes a meaningful Xnetric, called Probability of Success (POS), that allows the customer or designer to make a decision based on the chance of satisfying the customer's goals. In order to incorporate a joint probabilistic formulation into the systems design process, two algorithms are created that allow for an easy implementation into a numerical design framework: the (multivariate) Empirical Distribution Function and the Joint Probability Model. The Empirical Distribution Function estimates the probability that an event occurred by counting how many times it occurred in a given sample. The Joint Probability Model on the other hand is an analytical parametric model for the multivariate joint probability. It is comprised of the product of the univariate criterion distributions, generated by the traditional probabilistic design process, multiplied with a correlation function that is based on available correlation information between pairs of random variables. JPDM is an excellent tool for multi-objective optimization and product selection, because of its ability to transform disparate objectives into a single figure of merit, the likelihood of successfully meeting all goals or POS. The advantage of JPDM over other multi-criteria decision making techniques is that POS constitutes a single optimizable function or metric that enables a comparison of all alternative solutions on an equal basis. Hence, POS allows for the use of any standard single-objective optimization technique available and simplifies a complex multi-criteria selection problem into a simple ordering problem, where the solution with the highest POS is best. By distinguishing between controllable and uncontrollable variables in the design process, JPDM can account for the uncertain values of the uncontrollable variables that are inherent to the design problem, while facilitating an easy adjustment of the controllable ones to achieve the highest possible POS. Finally, JPDM's superiority over current multi-criteria decision making techniques is demonstrated with an optimization of a supersonic transport concept and ten contrived equations as well as a product selection example, determining an airline's best choice among Boeing's B-747, B-777, Airbus' A340, and a Supersonic Transport. The optimization examples demonstrate JPDM's ability to produce a better solution with a higher POS than an Overall Evaluation Criterion or Goal Programming approach. Similarly, the product selection example demonstrates JPDM's ability to produce a better solution with a higher POS and different ranking than the Overall Evaluation Criterion or Technique for Order Preferences by Similarity to the Ideal Solution (TOPSIS) approach.

UNCERTAINTY ANALYSIS IN WATER QUALITY MODELING USING QUAL2E

EPA Science Inventory

A strategy for incorporating uncertainty analysis techniques (sensitivity analysis, first order error analysis, and Monte Carlo simulation) into the mathematical water quality model QUAL2E is described. The model, named QUAL2E-UNCAS, automatically selects the input variables or p...

Isolation and characterization of anti ROR1 single chain fragment variable antibodies using phage display technique.

PubMed

Aghebati-Maleki, Leili; Younesi, Vahid; Jadidi-Niaragh, Farhad; Baradaran, Behzad; Majidi, Jafar; Yousefi, Mehdi

2017-01-01

Receptor tyrosine kinase-like orphan receptor (ROR1) belongs to one of the families of receptor tyrosine kinases (RTKs). RTKs are involved in the various physiologic cellular functions including proliferation, migration, survival, signaling and differentiation. Several RTKs are deregulated in various cancers implying the targeting potential of these molecules in cancer therapy. ROR1 has recently been shown to be expressed in various types of cancer cells but not in normal adult cells. Hence a molecular inhibitor of extracellular domain of ROR1 that inhibits ROR1-cell surface interaction is of great therapeutic importance. In an attempt to develop molecular inhibitors of ROR1, we screened single chain variable fragment (scFv) phage display libraries, Tomlinson I + J, against one specific synthetic oligopeptide from extracellular domain of ROR1 and selected scFvs were characterized using various immunological techniques. Several ROR1 specific scFvs were selected following five rounds of panning procedure. The scFvs showed specific binding to ROR1 using immunological techniques. Our results demonstrate successful isolation and characterization of specific ROR1 scFvs that may have great therapeutic potential in cancer immunotherapy.

Artificial nose, NIR and UV-visible spectroscopy for the characterisation of the PDO Chianti Classico olive oil.

PubMed

Forina, M; Oliveri, P; Bagnasco, L; Simonetti, R; Casolino, M C; Nizzi Grifi, F; Casale, M

2015-11-01

An authentication study of the Italian PDO (Protected Designation of Origin) olive oil Chianti Classico, based on artificial nose, near-infrared and UV-visible spectroscopy, with a set of samples representative of the whole Chianti Classico production area and a considerable number of samples from other Italian PDO regions was performed. The signals provided by the three analytical techniques were used both individually and jointly, after fusion of the respective variables, in order to build a model for the Chianti Classico PDO olive oil. Different signal pre-treatments were performed in order to investigate their importance and their effects in enhancing and extracting information from experimental data, correcting backgrounds or removing baseline variations. Stepwise-Linear Discriminant Analysis (STEP-LDA) was used as a feature selection technique and, afterward, Linear Discriminant Analysis (LDA) and the class-modelling technique Quadratic Discriminant Analysis-UNEQual dispersed classes (QDA-UNEQ) were applied to sub-sets of selected variables, in order to obtain efficient models capable of characterising the extra virgin olive oils produced in the Chianti Classico PDO area. Copyright © 2015 Elsevier B.V. All rights reserved.

Towards the methodological optimization of the moss bag technique in terms of contaminants concentrations and replicability values

NASA Astrophysics Data System (ADS)

Ares, A.; Fernández, J. A.; Carballeira, A.; Aboal, J. R.

2014-09-01

The moss bag technique is a simple and economical environmental monitoring tool used to monitor air quality. However, routine use of the method is not possible because the protocols involved have not yet been standardized. Some of the most variable methodological aspects include (i) selection of moss species, (ii) ratio of moss weight to surface area of the bag, (iii) duration of exposure, and (iv) height of exposure. In the present study, the best option for each of these aspects was selected on the basis of the mean concentrations and data replicability of Cd, Cu, Hg, Pb and Zn measured during at least two exposure periods in environments affected by different degrees of contamination. The optimal choices for the studied aspects were the following: (i) Sphagnum denticulatum, (ii) 5.68 mg of moss tissue for each cm-2 of bag surface, (iii) 8 weeks of exposure, and (iv) 4 m height of exposure. Duration of exposure and height of exposure accounted for most of the variability in the data. The aim of this methodological study was to provide data to help establish a standardized protocol that will enable use of the moss bag technique by public authorities.

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.

Normalization Approaches for Removing Systematic Biases Associated with Mass Spectrometry and Label-Free Proteomics

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

Callister, Stephen J.; Barry, Richard C.; Adkins, Joshua N.

2006-02-01

Central tendency, linear regression, locally weighted regression, and quantile techniques were investigated for normalization of peptide abundance measurements obtained from high-throughput liquid chromatography-Fourier transform ion cyclotron resonance mass spectrometry (LC-FTICR MS). Arbitrary abundances of peptides were obtained from three sample sets, including a standard protein sample, two Deinococcus radiodurans samples taken from different growth phases, and two mouse striatum samples from control and methamphetamine-stressed mice (strain C57BL/6). The selected normalization techniques were evaluated in both the absence and presence of biological variability by estimating extraneous variability prior to and following normalization. Prior to normalization, replicate runs from each sample setmore » were observed to be statistically different, while following normalization replicate runs were no longer statistically different. Although all techniques reduced systematic bias, assigned ranks among the techniques revealed significant trends. For most LC-FTICR MS analyses, linear regression normalization ranked either first or second among the four techniques, suggesting that this technique was more generally suitable for reducing systematic biases.« less

A New Catalog of Contact Binary Stars from ROTSE-I Sky Patrols

NASA Astrophysics Data System (ADS)

Gettel, S. J.; McKay, T. A.; Geske, M. T.

2005-05-01

Over 65,000 variable stars have been detected in the data from the ROTSE-I Sky Patrols. Using period-color and light curve selection techniques, about 5000 objects have been identified as contact binaries. This selection is tested for completeness against EW objects in the GCVS. By utilizing infrared color data from 2MASS, we fit a period-color-luminosity relation to these stars and estimate their distances.

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…

Initial proposition of kinematics model for selected karate actions analysis

NASA Astrophysics Data System (ADS)

Hachaj, Tomasz; Koptyra, Katarzyna; Ogiela, Marek R.

2017-03-01

The motivation for this paper is to initially propose and evaluate two new kinematics models that were developed to describe motion capture (MoCap) data of karate techniques. We decided to develop this novel proposition to create the model that is capable to handle actions description both from multimedia and professional MoCap hardware. For the evaluation purpose we have used 25-joints data with karate techniques recordings acquired with Kinect version 2. It is consisted of MoCap recordings of two professional sport (black belt) instructors and masters of Oyama Karate. We have selected following actions for initial analysis: left-handed furi-uchi punch, right leg hiza-geri kick, right leg yoko-geri kick and left-handed jodan-uke block. Basing on evaluation we made we can conclude that both proposed kinematics models seems to be convenient method for karate actions description. From two proposed variables models it seems that global might be more useful for further usage. We think that because in case of considered punches variables seems to be less correlated and they might also be easier to interpret because of single reference coordinate system. Also principal components analysis proved to be reliable way to examine the quality of kinematics models and with the plot of the variable in principal components space we can nicely present the dependences between variables.

The no-show patient in the model family practice unit.

PubMed

Dervin, J V; Stone, D L; Beck, C H

1978-12-01

Appointment breaking by patients causes problems for the physician's office. Patients who neither keep nor cancel their appointments are often referred to as "no shows." Twenty variables were identified as potential predictors of no-show behavior. These predictors were applied to 291 Family Practice Center patients during a one-month study in April 1977. A discriminant function and multiple regression procedure were utilized ascertain the predictability of the selected variables. Predictive accuracy of the variables was 67.4 percent compared to the presently utilized constant predictor technique, which is 73 percent accurate. Modification of appointment schedules based upon utilization of the variables studies as predictors of show/no-show behavior does not appear to be an effective strategy in the Family Practice Center of the Community Hospital of Sonoma County, Santa Rosa, due to the high proportion of patients who do, in fact, show. In clinics with lower show rates, the technique may prove to be an effective strategy.

The management of abdominal wall hernias – in search of consensus

PubMed Central

Bury, Kamil; Śmietański, Maciej

2015-01-01

Introduction Laparoscopic repair is becoming an increasingly popular alternative in the treatment of abdominal wall hernias. In spite of numerous studies evaluating this technique, indications for laparoscopic surgery have not been established. Similarly, implant selection and fixation techniques have not been unified and are the subject of scientific discussion. Aim To assess whether there is a consensus on the management of the most common ventral abdominal wall hernias among recognised experts. Material and methods Fourteen specialists representing the boards of European surgical societies were surveyed to determine their choice of surgical technique for nine typical primary ventral and incisional hernias. The access method, type of operation, mesh prosthesis and fixation method were evaluated. In addition to the laparoscopic procedures, the number of tackers and their arrangement were assessed. Results In none of the cases presented was a consensus of experts obtained. Laparoscopic and open techniques were used equally often. Especially in the group of large hernias, decisions on repair methods were characterised by high variability. The technique of laparoscopic mesh fixation was a subject of great variability in terms of both method selection and the numbers of tackers and sutures used. Conclusions Recognised experts have not reached a consensus on the management of abdominal wall hernias. Our survey results indicate the need for further research and the inclusion of large cohorts of patients in the dedicated registries to evaluate the results of different surgical methods, which would help in the development of treatment algorithms for surgical education in the future. PMID:25960793

Image Processing for Binarization Enhancement via Fuzzy Reasoning

NASA Technical Reports Server (NTRS)

Dominguez, Jesus A. (Inventor)

2009-01-01

A technique for enhancing a gray-scale image to improve conversions of the image to binary employs fuzzy reasoning. In the technique, pixels in the image are analyzed by comparing the pixel's gray scale value, which is indicative of its relative brightness, to the values of pixels immediately surrounding the selected pixel. The degree to which each pixel in the image differs in value from the values of surrounding pixels is employed as the variable in a fuzzy reasoning-based analysis that determines an appropriate amount by which the selected pixel's value should be adjusted to reduce vagueness and ambiguity in the image and improve retention of information during binarization of the enhanced gray-scale image.

A technique for pole-zero placement for dual-input control systems. [computer simulation of CH-47 helicopter longitudinal dynamics

NASA Technical Reports Server (NTRS)

Reid, G. F.

1976-01-01

A technique is presented for determining state variable feedback gains that will place both the poles and zeros of a selected transfer function of a dual-input control system at pre-determined locations in the s-plane. Leverrier's algorithm is used to determine the numerator and denominator coefficients of the closed-loop transfer function as functions of the feedback gains. The values of gain that match these coefficients to those of a pre-selected model are found by solving two systems of linear simultaneous equations. The algorithm has been used in a computer simulation of the CH-47 helicopter to control longitudinal dynamics.

Genetic characterization of fig tree mutants with molecular markers.

PubMed

Rodrigues, M G F; Martins, A B G; Desidério, J A; Bertoni, B W; Alves, M C

2012-08-06

The fig (Ficus carica L.) is a fruit tree of great world importance and, therefore, the genetic improvement becomes an important field of research for better crops, being necessary to gather information on this species, mainly regarding its genetic variability so that appropriate propagation projects and management are made. The improvement programs of fig trees using conventional procedures in order to obtain new cultivars are rare in many countries, such as Brazil, especially due to the little genetic variability and to the difficulties in obtaining plants from gamete fusion once the wasp Blastophaga psenes, responsible for the natural pollinating, is not found in Brazil. In this way, the mutagenic genetic improvement becomes a solution of it. For this reason, in an experiment conducted earlier, fig plants formed by cuttings treated with gamma ray were selected based on their agronomic characteristics of interest. We determined the genetic variability in these fig tree selections, using RAPD and AFLP molecular markers, comparing them to each other and to the Roxo-de-Valinhos, used as the standard. For the reactions of DNA amplification, 140 RAPD primers and 12 primer combinations for AFLP analysis were used. The selections did not differ genetically between themselves and between them and the Roxo-de-Valinhos cultivar. Techniques that can detect polymorphism between treatments, such as DNA sequencing, must be tested. The phenotypic variation of plants may be due to epigenetic variation, necessitating the use of techniques with methylation-sensitive restriction enzymes.

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

Honorio, J.; Goldstein, R.; Honorio, J.

We propose a simple, well grounded classification technique which is suited for group classification on brain fMRI data sets that have high dimensionality, small number of subjects, high noise level, high subject variability, imperfect registration and capture subtle cognitive effects. We propose threshold-split region as a new feature selection method and majority voteas the classification technique. Our method does not require a predefined set of regions of interest. We use average acros ssessions, only one feature perexperimental condition, feature independence assumption, and simple classifiers. The seeming counter-intuitive approach of using a simple design is supported by signal processing and statisticalmore » theory. Experimental results in two block design data sets that capture brain function under distinct monetary rewards for cocaine addicted and control subjects, show that our method exhibits increased generalization accuracy compared to commonly used feature selection and classification techniques.« less

Techniques for estimating flood-peak discharges from urban basins in Missouri

USGS Publications Warehouse

Becker, L.D.

1986-01-01

Techniques are defined for estimating the magnitude and frequency of future flood peak discharges of rainfall-induced runoff from small urban basins in Missouri. These techniques were developed from an initial analysis of flood records of 96 gaged sites in Missouri and adjacent states. Final regression equations are based on a balanced, representative sampling of 37 gaged sites in Missouri. This sample included 9 statewide urban study sites, 18 urban sites in St. Louis County, and 10 predominantly rural sites statewide. Short-term records were extended on the basis of long-term climatic records and use of a rainfall-runoff model. Linear least-squares regression analyses were used with log-transformed variables to relate flood magnitudes of selected recurrence intervals (dependent variables) to selected drainage basin indexes (independent variables). For gaged urban study sites within the State, the flood peak estimates are from the frequency curves defined from the synthesized long-term discharge records. Flood frequency estimates are made for ungaged sites by using regression equations that require determination of the drainage basin size and either the percentage of impervious area or a basin development factor. Alternative sets of equations are given for the 2-, 5-, 10-, 25-, 50-, and 100-yr recurrence interval floods. The average standard errors of estimate range from about 33% for the 2-yr flood to 26% for the 100-yr flood. The techniques for estimation are applicable to flood flows that are not significantly affected by storage caused by manmade activities. Flood peak discharge estimating equations are considered applicable for sites on basins draining approximately 0.25 to 40 sq mi. (Author 's abstract)

Numerical modeling of eastern connecticut's visual resources

Treesearch

Daniel L. Civco

1979-01-01

A numerical model capable of accurately predicting the preference for landscape photographs of selected points in eastern Connecticut is presented. A function of the social attitudes expressed toward thirty-two salient visual landscape features serves as the independent variable in predicting preferences. A technique for objectively assigning adjectives to landscape...

16 CFR 1107.21 - Periodic testing.

Code of Federal Regulations, 2012 CFR

2012-01-01

... samples selected for testing pass the test, there is a high degree of assurance that the other untested... determining the testing interval include, but are not limited to, the following: (i) High variability in test... process management techniques and tests provide a high degree of assurance of compliance if they are not...

16 CFR § 1107.21 - Periodic testing.

Code of Federal Regulations, 2013 CFR

2013-01-01

... samples selected for testing pass the test, there is a high degree of assurance that the other untested... determining the testing interval include, but are not limited to, the following: (i) High variability in test... process management techniques and tests provide a high degree of assurance of compliance if they are not...

16 CFR 1107.21 - Periodic testing.

Code of Federal Regulations, 2014 CFR

2014-01-01

... samples selected for testing pass the test, there is a high degree of assurance that the other untested... determining the testing interval include, but are not limited to, the following: (i) High variability in test... process management techniques and tests provide a high degree of assurance of compliance if they are not...

Variability-selected active galactic nuclei from supernova search in the Chandra deep field south

NASA Astrophysics Data System (ADS)

Trevese, D.; Boutsia, K.; Vagnetti, F.; Cappellaro, E.; Puccetti, S.

2008-09-01

Context: Variability is a property shared by virtually all active galactic nuclei (AGNs), and was adopted as a criterion for their selection using data from multi epoch surveys. Low Luminosity AGNs (LLAGNs) are contaminated by the light of their host galaxies, and cannot therefore be detected by the usual colour techniques. For this reason, their evolution in cosmic time is poorly known. Consistency with the evolution derived from X-ray detected samples has not been clearly established so far, also because the low luminosity population consists of a mixture of different object types. LLAGNs can be detected by the nuclear optical variability of extended objects. Aims: Several variability surveys have been, or are being, conducted for the detection of supernovae (SNe). We propose to re-analyse these SNe data using a variability criterion optimised for AGN detection, to select a new AGN sample and study its properties. Methods: We analysed images acquired with the wide field imager at the 2.2 m ESO/MPI telescope, in the framework of the STRESS supernova survey. We selected the AXAF field centred on the Chandra Deep Field South where, besides the deep X-ray survey, various optical data exist, originating in the EIS and COMBO-17 photometric surveys and the spectroscopic database of GOODS. Results: We obtained a catalogue of 132 variable AGN candidates. Several of the candidates are X-ray sources. We compare our results with an HST variability study of X-ray and IR detected AGNs, finding consistent results. The relatively high fraction of confirmed AGNs in our sample (60%) allowed us to extract a list of reliable AGN candidates for spectroscopic follow-up observations. Table [see full text] is only available in electronic form at http://www.aanda.org

An evaluation of Bayesian techniques for controlling model complexity and selecting inputs in a neural network for short-term load forecasting.

PubMed

Hippert, Henrique S; Taylor, James W

2010-04-01

Artificial neural networks have frequently been proposed for electricity load forecasting because of their capabilities for the nonlinear modelling of large multivariate data sets. Modelling with neural networks is not an easy task though; two of the main challenges are defining the appropriate level of model complexity, and choosing the input variables. This paper evaluates techniques for automatic neural network modelling within a Bayesian framework, as applied to six samples containing daily load and weather data for four different countries. We analyse input selection as carried out by the Bayesian 'automatic relevance determination', and the usefulness of the Bayesian 'evidence' for the selection of the best structure (in terms of number of neurones), as compared to methods based on cross-validation. Copyright 2009 Elsevier Ltd. All rights reserved.

Taguchi's technique: an effective method for improving X-ray medical radiographic screen performance.

PubMed

Vlachogiannis, J G

2003-01-01

Taguchi's technique is a helpful tool to achieve experimental optimization of a large number of decision variables with a small number of off-line experiments. The technique appears to be an ideal tool for improving the performance of X-ray medical radiographic screens under a noise source. Currently there are very many guides available for improving the efficiency of X-ray medical radiographic screens. These guides can be refined using a second-stage parameter optimization. based on Taguchi's technique, selecting the optimum levels of controllable X-ray radiographic screen factors. A real example of the proposed technique is presented giving certain performance criteria. The present research proposes the reinforcement of X-ray radiography by Taguchi's technique as a novel hardware mechanism.

Data mining and statistical inference in selective laser melting

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

Kamath, Chandrika

Selective laser melting (SLM) is an additive manufacturing process that builds a complex three-dimensional part, layer-by-layer, using a laser beam to fuse fine metal powder together. The design freedom afforded by SLM comes associated with complexity. As the physical phenomena occur over a broad range of length and time scales, the computational cost of modeling the process is high. At the same time, the large number of parameters that control the quality of a part make experiments expensive. In this paper, we describe ways in which we can use data mining and statistical inference techniques to intelligently combine simulations andmore » experiments to build parts with desired properties. We start with a brief summary of prior work in finding process parameters for high-density parts. We then expand on this work to show how we can improve the approach by using feature selection techniques to identify important variables, data-driven surrogate models to reduce computational costs, improved sampling techniques to cover the design space adequately, and uncertainty analysis for statistical inference. Here, our results indicate that techniques from data mining and statistics can complement those from physical modeling to provide greater insight into complex processes such as selective laser melting.« less

Data mining and statistical inference in selective laser melting

DOE PAGES

Kamath, Chandrika

2016-01-11

Selective laser melting (SLM) is an additive manufacturing process that builds a complex three-dimensional part, layer-by-layer, using a laser beam to fuse fine metal powder together. The design freedom afforded by SLM comes associated with complexity. As the physical phenomena occur over a broad range of length and time scales, the computational cost of modeling the process is high. At the same time, the large number of parameters that control the quality of a part make experiments expensive. In this paper, we describe ways in which we can use data mining and statistical inference techniques to intelligently combine simulations andmore » experiments to build parts with desired properties. We start with a brief summary of prior work in finding process parameters for high-density parts. We then expand on this work to show how we can improve the approach by using feature selection techniques to identify important variables, data-driven surrogate models to reduce computational costs, improved sampling techniques to cover the design space adequately, and uncertainty analysis for statistical inference. Here, our results indicate that techniques from data mining and statistics can complement those from physical modeling to provide greater insight into complex processes such as selective laser melting.« less

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

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.

Identification of boosted, hadronically decaying W bosons and comparisons with ATLAS data taken at √s = 8 TeV

DOE PAGES

Aad, G.; Abbott, B.; Abdallah, J.; ...

2016-03-17

This paper reports a detailed study of techniques for identifying boosted, hadronically decaying W bosons using 20.3 fb –1 of proton–proton collision data collected by the ATLAS detector at the LHC at a centre-of-mass energy √s = 8 TeV. A range of techniques for optimising the signal jet mass resolution are combined with various jet substructure variables. The results of these studies in Monte Carlo simulations show that a simple pairwise combination of groomed jet mass and one substructure variable can provide a 50 % efficiency for identifying W bosons with transverse momenta larger than 200 GeV while maintaining multijetmore » background efficiencies of 2–4 % for jets with the same transverse momentum. As a result, these signal and background efficiencies are confirmed in data for a selection of tagging techniques.« less

Identification of boosted, hadronically decaying W bosons and comparisons with ATLAS data taken at [Formula: see text] TeV.

PubMed

Aad, G; Abbott, B; Abdallah, J; Abdinov, O; Aben, R; Abolins, M; AbouZeid, O S; Abramowicz, H; Abreu, H; Abreu, R; Abulaiti, Y; Acharya, B S; Adamczyk, L; Adams, D L; Adelman, J; Adomeit, S; Adye, T; Affolder, A A; Agatonovic-Jovin, T; Agricola, J; Aguilar-Saavedra, J A; Ahlen, S P; Ahmadov, F; Aielli, G; Akerstedt, H; Åkesson, T P A; Akimov, A V; Alberghi, G L; Albert, J; Albrand, S; Alconada Verzini, M J; Aleksa, M; Aleksandrov, I N; Alexa, C; Alexander, G; Alexopoulos, T; Alhroob, M; Alimonti, G; Alio, L; Alison, J; Alkire, S P; Allbrooke, B M M; Allport, P P; Aloisio, A; Alonso, A; Alonso, F; Alpigiani, C; Altheimer, A; Alvarez Gonzalez, B; Álvarez Piqueras, D; Alviggi, M G; Amadio, B T; Amako, K; Amaral Coutinho, Y; Amelung, C; Amidei, D; Amor Dos Santos, S P; Amorim, A; Amoroso, S; Amram, N; Amundsen, G; Anastopoulos, C; Ancu, L S; Andari, N; Andeen, T; Anders, C F; Anders, G; Anders, J K; Anderson, K J; Andreazza, A; Andrei, V; Angelidakis, S; Angelozzi, I; Anger, P; Angerami, A; Anghinolfi, F; Anisenkov, A V; Anjos, N; Annovi, A; Antonelli, M; Antonov, A; Antos, J; Anulli, F; Aoki, M; Aperio Bella, L; Arabidze, G; Arai, Y; Araque, J P; Arce, A T H; Arduh, F A; Arguin, J-F; Argyropoulos, S; Arik, M; Armbruster, A J; Arnaez, O; Arnold, H; Arratia, M; Arslan, O; Artamonov, A; Artoni, G; Asai, S; Asbah, N; Ashkenazi, A; Åsman, B; Asquith, L; Assamagan, K; Astalos, R; Atkinson, M; Atlay, N B; Augsten, K; Aurousseau, M; Avolio, G; Axen, B; Ayoub, M K; Azuelos, G; Baak, M A; Baas, A E; Baca, M J; Bacci, C; Bachacou, H; Bachas, K; Backes, M; Backhaus, M; Bagiacchi, P; Bagnaia, P; Bai, Y; Bain, T; Baines, J T; Baker, O K; Baldin, E M; Balek, P; Balestri, T; Balli, F; Balunas, W K; Banas, E; Banerjee, Sw; Bannoura, A A E; Barak, L; Barberio, E L; Barberis, D; Barbero, M; Barillari, T; Barisonzi, M; Barklow, T; Barlow, N; Barnes, S L; Barnett, B M; Barnett, R M; Barnovska, Z; Baroncelli, A; Barone, G; Barr, A J; Barreiro, F; Barreiro Guimarães da Costa, J; Bartoldus, R; Barton, A E; Bartos, P; Basalaev, A; Bassalat, A; Basye, A; Bates, R L; Batista, S J; Batley, J R; Battaglia, M; Bauce, M; Bauer, F; Bawa, H S; Beacham, J B; Beattie, M D; Beau, T; Beauchemin, P H; Beccherle, R; Bechtle, P; Beck, H P; Becker, K; Becker, M; Beckingham, M; Becot, C; Beddall, A J; Beddall, A; Bednyakov, V A; Bee, C P; Beemster, L J; Beermann, T A; Begel, M; Behr, J K; Belanger-Champagne, C; Bell, W H; Bella, G; Bellagamba, L; Bellerive, A; Bellomo, M; Belotskiy, K; Beltramello, O; Benary, O; Benchekroun, D; Bender, M; Bendtz, K; Benekos, N; Benhammou, Y; Benhar Noccioli, E; Benitez Garcia, J A; Benjamin, D P; Bensinger, J R; Bentvelsen, S; Beresford, L; Beretta, M; Berge, D; Bergeaas Kuutmann, E; Berger, N; Berghaus, F; Beringer, J; Bernard, C; Bernard, N R; Bernius, C; Bernlochner, F U; Berry, T; Berta, P; Bertella, C; Bertoli, G; Bertolucci, F; Bertsche, C; Bertsche, D; Besana, M I; Besjes, G J; Bessidskaia Bylund, O; Bessner, M; Besson, N; Betancourt, C; Bethke, S; Bevan, A J; Bhimji, W; Bianchi, R M; Bianchini, L; Bianco, M; Biebel, O; Biedermann, D; Bieniek, S P; Biesuz, N V; Biglietti, M; Bilbao De Mendizabal, J; Bilokon, H; Bindi, M; Binet, S; Bingul, A; Bini, C; Biondi, S; Bjergaard, D M; Black, C W; Black, J E; Black, K M; Blackburn, D; Blair, R E; Blanchard, J-B; Blanco, J E; Blazek, T; Bloch, I; Blocker, C; Blum, W; Blumenschein, U; Blunier, S; Bobbink, G J; Bobrovnikov, V S; Bocchetta, S S; Bocci, A; Bock, C; Boehler, M; Bogaerts, J A; Bogavac, D; Bogdanchikov, A G; Bohm, C; Boisvert, V; Bokan, P; Bold, T; Boldea, V; Boldyrev, A S; Bomben, M; Bona, M; Boonekamp, M; Borisov, A; Borissov, G; Borroni, S; Bortfeldt, J; Bortolotto, V; Bos, K; Boscherini, D; Bosman, M; Boudreau, J; Bouffard, J; Bouhova-Thacker, E V; Boumediene, D; Bourdarios, C; Bousson, N; Boutle, S K; Boveia, A; Boyd, J; Boyko, I R; Bozic, I; Bracinik, J; Brandt, A; Brandt, G; Brandt, O; Bratzler, U; Brau, B; Brau, J E; Braun, H M; Breaden Madden, W D; Brendlinger, K; Brennan, A J; Brenner, L; Brenner, R; Bressler, S; Bristow, K; Bristow, T M; Britton, D; Britzger, D; Brochu, F M; Brock, I; Brock, R; Bronner, J; Brooijmans, G; Brooks, T; Brooks, W K; Brosamer, J; Brost, E; Bruckman de Renstrom, P A; Bruncko, D; Bruneliere, R; Bruni, A; Bruni, G; Bruschi, M; Bruscino, N; Bryngemark, L; Buanes, T; Buat, Q; Buchholz, P; Buckley, A G; Buda, S I; Budagov, I A; Buehrer, F; Bugge, L; Bugge, M K; Bulekov, O; Bullock, D; Burckhart, H; Burdin, S; Burgard, C D; Burghgrave, B; Burke, S; Burmeister, I; Busato, E; Büscher, D; Büscher, V; Bussey, P; Butler, J M; Butt, A I; Buttar, C M; Butterworth, J M; Butti, P; Buttinger, W; Buzatu, A; Buzykaev, A R; Cabrera Urbán, S; Caforio, D; Cairo, V M; Cakir, O; Calace, N; Calafiura, P; Calandri, A; Calderini, G; Calfayan, P; Caloba, L P; Calvet, D; Calvet, S; Camacho Toro, R; Camarda, S; Camarri, P; Cameron, D; Caminal Armadans, R; Campana, S; Campanelli, M; Campoverde, A; Canale, V; Canepa, A; Cano Bret, M; Cantero, J; Cantrill, R; Cao, T; Capeans Garrido, M D M; Caprini, I; Caprini, M; Capua, M; Caputo, R; Carbone, R M; Cardarelli, R; Cardillo, F; Carli, T; Carlino, G; Carminati, L; Caron, S; Carquin, E; Carrillo-Montoya, G D; Carter, J R; Carvalho, J; Casadei, D; Casado, M P; Casolino, M; Castaneda-Miranda, E; Castelli, A; Castillo Gimenez, V; Castro, N F; Catastini, P; Catinaccio, A; Catmore, J R; Cattai, A; Caudron, J; Cavaliere, V; Cavalli, D; Cavalli-Sforza, M; Cavasinni, V; Ceradini, F; Cerio, B C; Cerny, K; Cerqueira, A S; Cerri, A; Cerrito, L; Cerutti, F; Cerv, M; Cervelli, A; Cetin, S A; Chafaq, A; Chakraborty, D; Chalupkova, I; Chang, P; Chapman, J D; Charlton, D G; Chau, C C; Chavez Barajas, C A; Cheatham, S; Chegwidden, A; Chekanov, S; Chekulaev, S V; Chelkov, G A; Chelstowska, M A; Chen, C; Chen, H; Chen, K; Chen, L; Chen, S; Chen, S; Chen, X; Chen, Y; Cheng, H C; Cheng, Y; Cheplakov, A; Cheremushkina, E; Cherkaoui El Moursli, R; Chernyatin, V; Cheu, E; Chevalier, L; Chiarella, V; Chiarelli, G; Chiodini, G; Chisholm, A S; Chislett, R T; 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This paper reports a detailed study of techniques for identifying boosted, hadronically decaying W bosons using 20.3 fb[Formula: see text] of proton-proton collision data collected by the ATLAS detector at the LHC at a centre-of-mass energy [Formula: see text]. A range of techniques for optimising the signal jet mass resolution are combined with various jet substructure variables. The results of these studies in Monte Carlo simulations show that a simple pairwise combination of groomed jet mass and one substructure variable can provide a 50 % efficiency for identifying W bosons with transverse momenta larger than 200 GeV while maintaining multijet background efficiencies of 2-4 % for jets with the same transverse momentum. These signal and background efficiencies are confirmed in data for a selection of tagging techniques.

Strength and Power Qualities Are Highly Associated With Punching Impact in Elite Amateur Boxers.

PubMed

Loturco, Irineu; Nakamura, Fabio Y; Artioli, Guilherme G; Kobal, Ronaldo; Kitamura, Katia; Cal Abad, Cesar C; Cruz, Igor F; Romano, Felipe; Pereira, Lucas A; Franchini, Emerson

2016-01-01

This study investigated the relationship between punching impact and selected strength and power variables in 15 amateur boxers from the Brazilian National Team (9 men and 6 women). Punching impact was assessed in the following conditions: 3 jabs starting from the standardized position, 3 crosses starting from the standardized position, 3 jabs starting from a self-selected position, and 3 crosses starting from a self-selected position. For punching tests, a force platform (1.02 × 0.76 m) covered by a body shield was mounted on the wall at a height of 1 m, perpendicular to the floor. The selected strength and power variables were vertical jump height (in squat jump and countermovement jump), mean propulsive power in the jump squat, bench press (BP), and bench throw, maximum isometric force in squat and BP, and rate of force development in the squat and BP. Sex and position main effects were observed, with higher impact for males compared with females (p ≤ 0.05) and the self-selected distance resulting in higher impact in the jab technique compared with the fixed distance (p ≤ 0.05). Finally, the correlations between strength/power variables and punching impact indices ranged between 0.67 and 0.85. Because of the strong associations between punching impact and strength/power variables (e.g., lower limb muscle power), this study provides important information for coaches to specifically design better training strategies to improve punching impact.

Technique for ship/wake detection

DOEpatents

Roskovensky, John K [Albuquerque, NM

2012-05-01

An automated ship detection technique includes accessing data associated with an image of a portion of Earth. The data includes reflectance values. A first portion of pixels within the image are masked with a cloud and land mask based on spectral flatness of the reflectance values associated with the pixels. A given pixel selected from the first portion of pixels is unmasked when a threshold number of localized pixels surrounding the given pixel are not masked by the cloud and land mask. A spatial variability image is generated based on spatial derivatives of the reflectance values of the pixels which remain unmasked by the cloud and land mask. The spatial variability image is thresholded to identify one or more regions within the image as possible ship detection regions.

Single-step fabrication of thin-film linear variable bandpass filters based on metal-insulator-metal geometry.

PubMed

Williams, Calum; Rughoobur, Girish; Flewitt, Andrew J; Wilkinson, Timothy D

2016-11-10

A single-step fabrication method is presented for ultra-thin, linearly variable optical bandpass filters (LVBFs) based on a metal-insulator-metal arrangement using modified evaporation deposition techniques. This alternate process methodology offers reduced complexity and cost in comparison to conventional techniques for fabricating LVBFs. We are able to achieve linear variation of insulator thickness across a sample, by adjusting the geometrical parameters of a typical physical vapor deposition process. We demonstrate LVBFs with spectral selectivity from 400 to 850 nm based on Ag (25 nm) and MgF₂ (75-250 nm). Maximum spectral transmittance is measured at ∼70% with a Q-factor of ∼20.

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.

Feature selection through validation and un-censoring of endovascular repair survival data for predicting the risk of re-intervention.

PubMed

Attallah, Omneya; Karthikesalingam, Alan; Holt, Peter J E; Thompson, Matthew M; Sayers, Rob; Bown, Matthew J; Choke, Eddie C; Ma, Xianghong

2017-08-03

Feature selection (FS) process is essential in the medical area as it reduces the effort and time needed for physicians to measure unnecessary features. Choosing useful variables is a difficult task with the presence of censoring which is the unique characteristic in survival analysis. Most survival FS methods depend on Cox's proportional hazard model; however, machine learning techniques (MLT) are preferred but not commonly used due to censoring. Techniques that have been proposed to adopt MLT to perform FS with survival data cannot be used with the high level of censoring. The researcher's previous publications proposed a technique to deal with the high level of censoring. It also used existing FS techniques to reduce dataset dimension. However, in this paper a new FS technique was proposed and combined with feature transformation and the proposed uncensoring approaches to select a reduced set of features and produce a stable predictive model. In this paper, a FS technique based on artificial neural network (ANN) MLT is proposed to deal with highly censored Endovascular Aortic Repair (EVAR). Survival data EVAR datasets were collected during 2004 to 2010 from two vascular centers in order to produce a final stable model. They contain almost 91% of censored patients. The proposed approach used a wrapper FS method with ANN to select a reduced subset of features that predict the risk of EVAR re-intervention after 5 years to patients from two different centers located in the United Kingdom, to allow it to be potentially applied to cross-centers predictions. The proposed model is compared with the two popular FS techniques; Akaike and Bayesian information criteria (AIC, BIC) that are used with Cox's model. The final model outperforms other methods in distinguishing the high and low risk groups; as they both have concordance index and estimated AUC better than the Cox's model based on AIC, BIC, Lasso, and SCAD approaches. These models have p-values lower than 0.05, meaning that patients with different risk groups can be separated significantly and those who would need re-intervention can be correctly predicted. The proposed approach will save time and effort made by physicians to collect unnecessary variables. The final reduced model was able to predict the long-term risk of aortic complications after EVAR. This predictive model can help clinicians decide patients' future observation plan.

Comparison of partial least squares and lasso regression techniques as applied to laser-induced breakdown spectroscopy of geological samples

NASA Astrophysics Data System (ADS)

Dyar, M. D.; Carmosino, M. L.; Breves, E. A.; Ozanne, M. V.; Clegg, S. M.; Wiens, R. C.

2012-04-01

A remote laser-induced breakdown spectrometer (LIBS) designed to simulate the ChemCam instrument on the Mars Science Laboratory Rover Curiosity was used to probe 100 geologic samples at a 9-m standoff distance. ChemCam consists of an integrated remote LIBS instrument that will probe samples up to 7 m from the mast of the rover and a remote micro-imager (RMI) that will record context images. The elemental compositions of 100 igneous and highly-metamorphosed rocks are determined with LIBS using three variations of multivariate analysis, with a goal of improving the analytical accuracy. Two forms of partial least squares (PLS) regression are employed with finely-tuned parameters: PLS-1 regresses a single response variable (elemental concentration) against the observation variables (spectra, or intensity at each of 6144 spectrometer channels), while PLS-2 simultaneously regresses multiple response variables (concentrations of the ten major elements in rocks) against the observation predictor variables, taking advantage of natural correlations between elements. Those results are contrasted with those from the multivariate regression technique of the least absolute shrinkage and selection operator (lasso), which is a penalized shrunken regression method that selects the specific channels for each element that explain the most variance in the concentration of that element. To make this comparison, we use results of cross-validation and of held-out testing, and employ unscaled and uncentered spectral intensity data because all of the input variables are already in the same units. Results demonstrate that the lasso, PLS-1, and PLS-2 all yield comparable results in terms of accuracy for this dataset. However, the interpretability of these methods differs greatly in terms of fundamental understanding of LIBS emissions. PLS techniques generate principal components, linear combinations of intensities at any number of spectrometer channels, which explain as much variance in the response variables as possible while avoiding multicollinearity between principal components. When the selected number of principal components is projected back into the original feature space of the spectra, 6144 correlation coefficients are generated, a small fraction of which are mathematically significant to the regression. In contrast, the lasso models require only a small number (< 24) of non-zero correlation coefficients (β values) to determine the concentration of each of the ten major elements. Causality between the positively-correlated emission lines chosen by the lasso and the elemental concentration was examined. In general, the higher the lasso coefficient (β), the greater the likelihood that the selected line results from an emission of that element. Emission lines with negative β values should arise from elements that are anti-correlated with the element being predicted. For elements except Fe, Al, Ti, and P, the lasso-selected wavelength with the highest β value corresponds to the element being predicted, e.g. 559.8 nm for neutral Ca. However, the specific lines chosen by the lasso with positive β values are not always those from the element being predicted. Other wavelengths and the elements that most strongly correlate with them to predict concentration are obviously related to known geochemical correlations or close overlap of emission lines, while others must result from matrix effects. Use of the lasso technique thus directly informs our understanding of the underlying physical processes that give rise to LIBS emissions by determining which lines can best represent concentration, and which lines from other elements are causing matrix effects.

Thin layer activation techniques at the U-120 cyclotron of Bucharest

NASA Astrophysics Data System (ADS)

Constantinescu, B.; Ivanov, E. A.; Pascovici, G.; Popa-Simil, L.; Racolta, P. M.

1994-05-01

The Thin Layer Activation (TLA) technique is a nuclear method especially used for different types of wear (or corrosion) investigations. Experimental results for selection criteria of nuclear reactions for various tribological studies, using the IPNE U-120 classical variable energy Cyclotron are presented. Measuring methods for the main types of wear phenomena and home made instrumentations dedicated for TLA industrial applications are also reported. Some typical TLA tribological applications, a nuclear scanning method to obtain wear profile of piston-rings are presented as well.

The discovery of indicator variables for QSAR using inductive logic programming

NASA Astrophysics Data System (ADS)

King, Ross D.; Srinivasan, Ashwin

1997-11-01

A central problem in forming accurate regression equations in QSAR studies isthe selection of appropriate descriptors for the compounds under study. Wedescribe a novel procedure for using inductive logic programming (ILP) todiscover new indicator variables (attributes) for QSAR problems, and show thatthese improve the accuracy of the derived regression equations. ILP techniqueshave previously been shown to work well on drug design problems where thereis a large structural component or where clear comprehensible rules arerequired. However, ILP techniques have had the disadvantage of only being ableto make qualitative predictions (e.g. active, inactive) and not to predictreal numbers (regression). We unify ILP and linear regression techniques togive a QSAR method that has the strength of ILP at describing stericstructure, with the familiarity and power of linear regression. We evaluatedthe utility of this new QSAR technique by examining the prediction ofbiological activity with and without the addition of new structural indicatorvariables formed by ILP. In three out of five datasets examined the additionof ILP variables produced statistically better results (P < 0.01) over theoriginal description. The new ILP variables did not increase the overallcomplexity of the derived QSAR equations and added insight into possiblemechanisms of action. We conclude that ILP can aid in the process of drugdesign.

A Rapid Approach to Modeling Species-Habitat Relationships

NASA Technical Reports Server (NTRS)

Carter, Geoffrey M.; Breinger, David R.; Stolen, Eric D.

2005-01-01

A growing number of species require conservation or management efforts. Success of these activities requires knowledge of the species' occurrence pattern. Species-habitat models developed from GIS data sources are commonly used to predict species occurrence but commonly used data sources are often developed for purposes other than predicting species occurrence and are of inappropriate scale and the techniques used to extract predictor variables are often time consuming and cannot be repeated easily and thus cannot efficiently reflect changing conditions. We used digital orthophotographs and a grid cell classification scheme to develop an efficient technique to extract predictor variables. We combined our classification scheme with a priori hypothesis development using expert knowledge and a previously published habitat suitability index and used an objective model selection procedure to choose candidate models. We were able to classify a large area (57,000 ha) in a fraction of the time that would be required to map vegetation and were able to test models at varying scales using a windowing process. Interpretation of the selected models confirmed existing knowledge of factors important to Florida scrub-jay habitat occupancy. The potential uses and advantages of using a grid cell classification scheme in conjunction with expert knowledge or an habitat suitability index (HSI) and an objective model selection procedure are discussed.

Selection of Representative Models for Decision Analysis Under Uncertainty

NASA Astrophysics Data System (ADS)

Meira, Luis A. A.; Coelho, Guilherme P.; Santos, Antonio Alberto S.; Schiozer, Denis J.

2016-03-01

The decision-making process in oil fields includes a step of risk analysis associated with the uncertainties present in the variables of the problem. Such uncertainties lead to hundreds, even thousands, of possible scenarios that are supposed to be analyzed so an effective production strategy can be selected. Given this high number of scenarios, a technique to reduce this set to a smaller, feasible subset of representative scenarios is imperative. The selected scenarios must be representative of the original set and also free of optimistic and pessimistic bias. This paper is devoted to propose an assisted methodology to identify representative models in oil fields. To do so, first a mathematical function was developed to model the representativeness of a subset of models with respect to the full set that characterizes the problem. Then, an optimization tool was implemented to identify the representative models of any problem, considering not only the cross-plots of the main output variables, but also the risk curves and the probability distribution of the attribute-levels of the problem. The proposed technique was applied to two benchmark cases and the results, evaluated by experts in the field, indicate that the obtained solutions are richer than those identified by previously adopted manual approaches. The program bytecode is available under request.

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.

Hyper-temporal remote sensing for digital soil mapping: Characterizing soil-vegetation response to climatic variability

USDA-ARS?s Scientific Manuscript database

Indices derived from remotely-sensed imagery are commonly used to predict soil properties with digital soil mapping (DSM) techniques. The use of images from single dates or a small number of dates is most common for DSM; however, selection of the appropriate images is complicated by temporal variabi...

PeerShield: determining control and resilience criticality of collaborative cyber assets in networks

NASA Astrophysics Data System (ADS)

Cam, Hasan

2012-06-01

As attackers get more coordinated and advanced in cyber attacks, cyber assets are required to have much more resilience, control effectiveness, and collaboration in networks. Such a requirement makes it essential to take a comprehensive and objective approach for measuring the individual and relative performances of cyber security assets in network nodes. To this end, this paper presents four techniques as to how the relative importance of cyber assets can be measured more comprehensively and objectively by considering together the main variables of risk assessment (e.g., threats, vulnerabilities), multiple attributes (e.g., resilience, control, and influence), network connectivity and controllability among collaborative cyber assets in networks. In the first technique, a Bayesian network is used to include the random variables for control, recovery, and resilience attributes of nodes, in addition to the random variables of threats, vulnerabilities, and risk. The second technique shows how graph matching and coloring can be utilized to form collaborative pairs of nodes to shield together against threats and vulnerabilities. The third technique ranks the security assets of nodes by incorporating multiple weights and thresholds of attributes into a decision-making algorithm. In the fourth technique, the hierarchically well-separated tree is enhanced to first identify critical nodes of a network with respect to their attributes and network connectivity, and then selecting some nodes as driver nodes for network controllability.

Artist Material BRDF Database for Computer Graphics Rendering

NASA Astrophysics Data System (ADS)

Ashbaugh, Justin C.

The primary goal of this thesis was to create a physical library of artist material samples. This collection provides necessary data for the development of a gonio-imaging system for use in museums to more accurately document their collections. A sample set was produced consisting of 25 panels and containing nearly 600 unique samples. Selected materials are representative of those commonly used by artists both past and present. These take into account the variability in visual appearance resulting from the materials and application techniques used. Five attributes of variability were identified including medium, color, substrate, application technique and overcoat. Combinations of these attributes were selected based on those commonly observed in museum collections and suggested by surveying experts in the field. For each sample material, image data is collected and used to measure an average bi-directional reflectance distribution function (BRDF). The results are available as a public-domain image and optical database of artist materials at art-si.org. Additionally, the database includes specifications for each sample along with other information useful for computer graphics rendering such as the rectified sample images and normal maps.

Variable anodic thermal control coating on aluminum

NASA Technical Reports Server (NTRS)

Duckett, R. J.; Gilliland, C. S.

1983-01-01

A variable thermal control coating (modified chromic acid anodizing) has been developed to meet the needs for the thermal control of spacecraft. This coating, with controlled variable ranges of 0.10 to 0.72 thermal emittance and 0.2 to 0.4 solar absorptance, allows the user to select any value of thermal emittance and solar absorptance within the range specified and obtain both values within + or - 0.02. Preliminary solar stability has shown less than 15 percent degradation over 2000 hours of vacuum solar exposure. The technique has been determined to be sensitive to the parameters of voltage, rate of voltage application, time, temperature, acid concentration, and material pretreatment.

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.

Patients classification on weaning trials using neural networks and wavelet transform.

PubMed

Arizmendi, Carlos; Viviescas, Juan; González, Hernando; Giraldo, Beatriz

2014-01-01

The determination of the optimal time of the patients in weaning trial process from mechanical ventilation, between patients capable of maintaining spontaneous breathing and patients that fail to maintain spontaneous breathing, is a very important task in intensive care unit. Wavelet Transform (WT) and Neural Networks (NN) techniques were applied in order to develop a classifier for the study of patients on weaning trial process. The respiratory pattern of each patient was characterized through different time series. Genetic Algorithms (GA) and Forward Selection were used as feature selection techniques. A classification performance of 77.00±0.06% of well classified patients, was obtained using a NN and GA combination, with only 6 variables of the 14 initials.

Chemical Variability and Biological Activities of Brassica rapa var. rapifera Parts Essential Oils Depending on Geographic Variation and Extraction Technique.

PubMed

Saka, Boualem; Djouahri, Abderrahmane; Djerrad, Zineb; Terfi, Souhila; Aberrane, Sihem; Sabaou, Nasserdine; Baaliouamer, Aoumeur; Boudarene, Lynda

2017-06-01

In the present work, the Brassica rapa var. rapifera parts essential oils and their antioxidant and antimicrobial activities were investigated for the first time depending on geographic origin and extraction technique. Gas-chromatography (GC) and GC/mass spectrometry (MS) analyses showed several constituents, including alcohols, aldehydes, esters, ketones, norisoprenoids, terpenic, nitrogen and sulphur compounds, totalizing 38 and 41 compounds in leaves and root essential oils, respectively. Nitrogen compounds were the main volatiles in leaves essential oils and sulphur compounds were the main volatiles in root essential oils. Qualitative and quantitative differences were found among B. rapa var. rapifera parts essential oils collected from different locations and extracted by hydrodistillation and microwave-assisted hydrodistillation techniques. Furthermore, our findings showed a high variability for both antioxidant and antimicrobial activities. The highlighted variability reflects the high impact of plant part, geographic variation and extraction technique on chemical composition and biological activities, which led to conclude that we should select essential oils to be investigated carefully depending on these factors, in order to isolate the bioactive components or to have the best quality of essential oil in terms of biological activities and preventive effects in food. © 2017 Wiley-VHCA AG, Zurich, Switzerland.

Feasibility of automated dropsize distributions from holographic data using digital image processing techniques. [particle diameter measurement technique

NASA Technical Reports Server (NTRS)

Feinstein, S. P.; Girard, M. A.

1979-01-01

An automated technique for measuring particle diameters and their spatial coordinates from holographic reconstructions is being developed. Preliminary tests on actual cold-flow holograms of impinging jets indicate that a suitable discriminant algorithm consists of a Fourier-Gaussian noise filter and a contour thresholding technique. This process identifies circular as well as noncircular objects. The desired objects (in this case, circular or possibly ellipsoidal) are then selected automatically from the above set and stored with their parametric representations. From this data, dropsize distributions as a function of spatial coordinates can be generated and combustion effects due to hardware and/or physical variables studied.

Photometry Using Kepler "Superstamps" of Open Clusters NGC 6791 & NGC 6819

NASA Astrophysics Data System (ADS)

Kuehn, Charles A.; Drury, Jason A.; Bellamy, Beau R.; Stello, Dennis; Bedding, Timothy R.; Reed, Mike; Quick, Breanna

2015-09-01

The Kepler space telescope has proven to be a gold mine for the study of variable stars. Usually, Kepler only reads out a handful of pixels around each pre-selected target star, omitting a large number of stars in the Kepler field. Fortunately, for the open clusters NGC 6791 and NGC 6819, Kepler also read out larger "superstamps" which contained complete images of the central region of each cluster. These cluster images can be used to study additional stars in the open clusters that were not originally on Kepler's target list. We discuss our work on using two photometric techniques to analyze these superstamps and present sample results from this project to demonstrate the value of this technique for a wide variety of variable stars.

Hubby and Lewontin on Protein Variation in Natural Populations: When Molecular Genetics Came to the Rescue of Population Genetics.

PubMed

Charlesworth, Brian; Charlesworth, Deborah; Coyne, Jerry A; Langley, Charles H

2016-08-01

The 1966 GENETICS papers by John Hubby and Richard Lewontin were a landmark in the study of genome-wide levels of variability. They used the technique of gel electrophoresis of enzymes and proteins to study variation in natural populations of Drosophila pseudoobscura, at a set of loci that had been chosen purely for technical convenience, without prior knowledge of their levels of variability. Together with the independent study of human populations by Harry Harris, this seminal study provided the first relatively unbiased picture of the extent of genetic variability in protein sequences within populations, revealing that many genes had surprisingly high levels of diversity. These papers stimulated a large research program that found similarly high electrophoretic variability in many different species and led to statistical tools for interpreting the data in terms of population genetics processes such as genetic drift, balancing and purifying selection, and the effects of selection on linked variants. The current use of whole-genome sequences in studies of variation is the direct descendant of this pioneering work. Copyright © 2016 by the Genetics Society of America.

Retrograde renal hilar dissection and segmental arterial clamping: a simple modification to achieve super-selective robotic partial nephrectomy.

PubMed

Greene, Richard N; Sutherland, Douglas E; Tausch, Timothy J; Perez, Deo S

2014-03-01

Super-selective vascular control prior to robotic partial nephrectomy (also known as 'zero-ischemia') is a novel surgical technique that promises to reduce warm ischemia time. The technique has been shown to be feasible but adds substantial technical complexity and cost to the procedure. We present a simplified retrograde dissection of the renal hilum to achieve selective vascular control during robotic partial nephrectomy. Consecutive patients with stage 1 solid and complex cystic renal masses underwent robotic partial nephrectomies with selective vascular control using a modification to previously described super-selective robotic partial nephrectomy. In each case, the renal arterial branch supplying the mass and surrounding parenchyma was dissected in a retrograde fashion from the tumor. Intra-renal dissection of the interlobular artery was not performed. Intra-operative immunofluorescence was not utilized as assessment of parenchymal ischemia was documented before partial nephrectomy. Data was prospectively collected in an IRB-approved partial nephrectomy database. Operative variables between patients undergoing super-selective versus standard robotic partial nephrectomy were compared. Super-selective partial nephrectomy with retrograde hilar dissection was successfully completed in five consecutive patients. There were no complications or conversions to traditional partial nephrectomy. All were diagnosed with renal cell carcinoma and surgical margins were all negative. Estimated blood loss, warm ischemia time, operative time and length of stay were all comparable between patients undergoing super-selective and standard robotic partial nephrectomy. Retrograde hilar dissection appears to be a feasible and safe approach to super-selective partial nephrectomy without adding complex renovascular surgical techniques or cost to the procedure.

Combining techniques for screening and evaluating interaction terms on high-dimensional time-to-event data.

PubMed

Sariyar, Murat; Hoffmann, Isabell; Binder, Harald

2014-02-26

Molecular data, e.g. arising from microarray technology, is often used for predicting survival probabilities of patients. For multivariate risk prediction models on such high-dimensional data, there are established techniques that combine parameter estimation and variable selection. One big challenge is to incorporate interactions into such prediction models. In this feasibility study, we present building blocks for evaluating and incorporating interactions terms in high-dimensional time-to-event settings, especially for settings in which it is computationally too expensive to check all possible interactions. We use a boosting technique for estimation of effects and the following building blocks for pre-selecting interactions: (1) resampling, (2) random forests and (3) orthogonalization as a data pre-processing step. In a simulation study, the strategy that uses all building blocks is able to detect true main effects and interactions with high sensitivity in different kinds of scenarios. The main challenge are interactions composed of variables that do not represent main effects, but our findings are also promising in this regard. Results on real world data illustrate that effect sizes of interactions frequently may not be large enough to improve prediction performance, even though the interactions are potentially of biological relevance. Screening interactions through random forests is feasible and useful, when one is interested in finding relevant two-way interactions. The other building blocks also contribute considerably to an enhanced pre-selection of interactions. We determined the limits of interaction detection in terms of necessary effect sizes. Our study emphasizes the importance of making full use of existing methods in addition to establishing new ones.

Uniting statistical and individual-based approaches for animal movement modelling.

PubMed

Latombe, Guillaume; Parrott, Lael; Basille, Mathieu; Fortin, Daniel

2014-01-01

The dynamic nature of their internal states and the environment directly shape animals' spatial behaviours and give rise to emergent properties at broader scales in natural systems. However, integrating these dynamic features into habitat selection studies remains challenging, due to practically impossible field work to access internal states and the inability of current statistical models to produce dynamic outputs. To address these issues, we developed a robust method, which combines statistical and individual-based modelling. Using a statistical technique for forward modelling of the IBM has the advantage of being faster for parameterization than a pure inverse modelling technique and allows for robust selection of parameters. Using GPS locations from caribou monitored in Québec, caribou movements were modelled based on generative mechanisms accounting for dynamic variables at a low level of emergence. These variables were accessed by replicating real individuals' movements in parallel sub-models, and movement parameters were then empirically parameterized using Step Selection Functions. The final IBM model was validated using both k-fold cross-validation and emergent patterns validation and was tested for two different scenarios, with varying hardwood encroachment. Our results highlighted a functional response in habitat selection, which suggests that our method was able to capture the complexity of the natural system, and adequately provided projections on future possible states of the system in response to different management plans. This is especially relevant for testing the long-term impact of scenarios corresponding to environmental configurations that have yet to be observed in real systems.

Uniting Statistical and Individual-Based Approaches for Animal Movement Modelling

PubMed Central

Latombe, Guillaume; Parrott, Lael; Basille, Mathieu; Fortin, Daniel

2014-01-01

The dynamic nature of their internal states and the environment directly shape animals' spatial behaviours and give rise to emergent properties at broader scales in natural systems. However, integrating these dynamic features into habitat selection studies remains challenging, due to practically impossible field work to access internal states and the inability of current statistical models to produce dynamic outputs. To address these issues, we developed a robust method, which combines statistical and individual-based modelling. Using a statistical technique for forward modelling of the IBM has the advantage of being faster for parameterization than a pure inverse modelling technique and allows for robust selection of parameters. Using GPS locations from caribou monitored in Québec, caribou movements were modelled based on generative mechanisms accounting for dynamic variables at a low level of emergence. These variables were accessed by replicating real individuals' movements in parallel sub-models, and movement parameters were then empirically parameterized using Step Selection Functions. The final IBM model was validated using both k-fold cross-validation and emergent patterns validation and was tested for two different scenarios, with varying hardwood encroachment. Our results highlighted a functional response in habitat selection, which suggests that our method was able to capture the complexity of the natural system, and adequately provided projections on future possible states of the system in response to different management plans. This is especially relevant for testing the long-term impact of scenarios corresponding to environmental configurations that have yet to be observed in real systems. PMID:24979047

Directional selection in temporally replicated studies is remarkably consistent.

PubMed

Morrissey, Michael B; Hadfield, Jarrod D

2012-02-01

Temporal variation in selection is a fundamental determinant of evolutionary outcomes. A recent paper presented a synthetic analysis of temporal variation in selection in natural populations. The authors concluded that there is substantial variation in the strength and direction of selection over time, but acknowledged that sampling error would result in estimates of selection that were more variable than the true values. We reanalyze their dataset using techniques that account for the necessary effect of sampling error to inflate apparent levels of variation and show that directional selection is remarkably constant over time, both in magnitude and direction. Thus we cannot claim that the available data support the existence of substantial temporal heterogeneity in selection. Nonetheless, we conject that temporal variation in selection could be important, but that there are good reasons why it may not appear in the available data. These new analyses highlight the importance of applying techniques that estimate parameters of the distribution of selection, rather than parameters of the distribution of estimated selection (which will reflect both sampling error and "real" variation in selection); indeed, despite availability of methods for the former, focus on the latter has been common in synthetic reviews of the aspects of selection in nature, and can lead to serious misinterpretations. © 2011 The Author(s). Evolution© 2011 The Society for the Study of Evolution.

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.

Selection of stand variables in southern Maine for making volume estimates from aerial photos

Treesearch

Earl J. Rogers; Gene Avery; Roy A. Chapman

1959-01-01

Aerial photographs are used widely in forest inventories. But there is a continuing need for improving the techniques of photo interpretation and making more efficient use of photographs. When the number or intensity of sample ground plots is controlled by airphoto classifications, a reliable stratification of the timber area is a must.

An entropy-variables-based formulation of residual distribution schemes for non-equilibrium flows

NASA Astrophysics Data System (ADS)

Garicano-Mena, Jesús; Lani, Andrea; Degrez, Gérard

2018-06-01

In this paper we present an extension of Residual Distribution techniques for the simulation of compressible flows in non-equilibrium conditions. The latter are modeled by means of a state-of-the-art multi-species and two-temperature model. An entropy-based variable transformation that symmetrizes the projected advective Jacobian for such a thermophysical model is introduced. Moreover, the transformed advection Jacobian matrix presents a block diagonal structure, with mass-species and electronic-vibrational energy being completely decoupled from the momentum and total energy sub-system. The advantageous structure of the transformed advective Jacobian can be exploited by contour-integration-based Residual Distribution techniques: established schemes that operate on dense matrices can be substituted by the same scheme operating on the momentum-energy subsystem matrix and repeated application of scalar scheme to the mass-species and electronic-vibrational energy terms. Finally, the performance gain of the symmetrizing-variables formulation is quantified on a selection of representative testcases, ranging from subsonic to hypersonic, in inviscid or viscous conditions.

Variable beam dose rate and DMLC IMRT to moving body anatomy

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

Papiez, Lech; Abolfath, Ramin M.

2008-11-15

Derivation of formulas relating leaf speeds and beam dose rates for delivering planned intensity profiles to static and moving targets in dynamic multileaf collimator (DMLC) intensity modulated radiation therapy (IMRT) is presented. The analysis of equations determining algorithms for DMLC IMRT delivery under a variable beam dose rate reveals a multitude of possible delivery strategies for a given intensity map and for any given target motion patterns. From among all equivalent delivery strategies for DMLC IMRT treatments specific subclasses of strategies can be selected to provide deliveries that are particularly suitable for clinical applications providing existing delivery devices are used.more » Special attention is devoted to the subclass of beam dose rate variable DMLC delivery strategies to moving body anatomy that generalize existing techniques of such deliveries in Varian DMLC irradiation methodology to static body anatomy. Few examples of deliveries from this subclass of DMLC IMRT irradiations are investigated to illustrate the principle and show practical benefits of proposed techniques.« less

Learning planar Ising models

DOE PAGES

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

2016-12-01

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

Learning planar Ising models

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

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

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

Selection of a Geostatistical Method to Interpolate Soil Properties of the State Crop Testing Fields using Attributes of a Digital Terrain Model

NASA Astrophysics Data System (ADS)

Sahabiev, I. A.; Ryazanov, S. S.; Kolcova, T. G.; Grigoryan, B. R.

2018-03-01

The three most common techniques to interpolate soil properties at a field scale—ordinary kriging (OK), regression kriging with multiple linear regression drift model (RK + MLR), and regression kriging with principal component regression drift model (RK + PCR)—were examined. The results of the performed study were compiled into an algorithm of choosing the most appropriate soil mapping technique. Relief attributes were used as the auxiliary variables. When spatial dependence of a target variable was strong, the OK method showed more accurate interpolation results, and the inclusion of the auxiliary data resulted in an insignificant improvement in prediction accuracy. According to the algorithm, the RK + PCR method effectively eliminates multicollinearity of explanatory variables. However, if the number of predictors is less than ten, the probability of multicollinearity is reduced, and application of the PCR becomes irrational. In that case, the multiple linear regression should be used instead.

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

PubMed

Thoemmes, Felix; Rose, Norman

2014-01-01

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

Message Variability and Heterogeneity: A Core Challenge for Communication Research

PubMed Central

Slater, Michael D.; Peter, Jochen; Valkenberg, Patti

2015-01-01

Messages are central to human social experience, and pose key conceptual and methodological challenges in the study of communication. In response to these challenges, we outline a systematic approach to conceptualizing, operationalizing, and analyzing messages. At the conceptual level, we distinguish between two core aspects of messages: message variability (the defined and operationalized features of messages) and message heterogeneity (the undefined and unmeasured features of messages), and suggest preferred approaches to defining message variables. At the operational level, we identify message sampling, selection, and research design strategies responsive to issues of message variability and heterogeneity in experimental and survey research. At the analytical level, we highlight effective techniques to deal with message variability and heterogeneity. We conclude with seven recommendations to increase rigor in the study of communication through appropriately addressing the challenges presented by messages. PMID:26681816

Combating Unmeasured Confounding in Cross-Sectional Studies: Evaluating Instrumental-Variable and Heckman Selection Models

PubMed Central

DeMaris, Alfred

2014-01-01

Unmeasured confounding is the principal threat to unbiased estimation of treatment “effects” (i.e., regression parameters for binary regressors) in nonexperimental research. It refers to unmeasured characteristics of individuals that lead them both to be in a particular “treatment” category and to register higher or lower values than others on a response variable. In this article, I introduce readers to 2 econometric techniques designed to control the problem, with a particular emphasis on the Heckman selection model (HSM). Both techniques can be used with only cross-sectional data. Using a Monte Carlo experiment, I compare the performance of instrumental-variable regression (IVR) and HSM to that of ordinary least squares (OLS) under conditions with treatment and unmeasured confounding both present and absent. I find HSM generally to outperform IVR with respect to mean-square-error of treatment estimates, as well as power for detecting either a treatment effect or unobserved confounding. However, both HSM and IVR require a large sample to be fully effective. The use of HSM and IVR in tandem with OLS to untangle unobserved confounding bias in cross-sectional data is further demonstrated with an empirical application. Using data from the 2006–2010 General Social Survey (National Opinion Research Center, 2014), I examine the association between being married and subjective well-being. PMID:25110904

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.

Left Atrial Appendage Closure for Stroke Prevention: Devices, Techniques, and Efficacy.

PubMed

Iskandar, Sandia; Vacek, James; Lavu, Madhav; Lakkireddy, Dhanunjaya

2016-05-01

Left atrial appendage closure can be performed either surgically or percutaneously. Surgical approaches include direct suture, excision and suture, stapling, and clipping. Percutaneous approaches include endocardial, epicardial, and hybrid endocardial-epicardial techniques. Left atrial appendage anatomy is highly variable and complex; therefore, preprocedural imaging is crucial to determine device selection and sizing, which contribute to procedural success and reduction of complications. Currently, the WATCHMAN is the only device that is approved for left atrial appendage closure in the United States. Copyright © 2016 Elsevier Inc. All rights reserved.

Fabrication of Extremely Short Length Fiber Bragg Gratings for Sensor Applications

NASA Technical Reports Server (NTRS)

Wu, Meng-Chou; Rogowski, Robert S.; Tedjojuwono, Ken K.

2002-01-01

A new technique and a physical model for writing extremely short length Bragg gratings in optical fibers have been developed. The model describes the effects of diffraction on the spatial spectra and therefore, the wavelength spectra of the Bragg gratings. Using an interferometric technique and a variable aperture, short gratings of various lengths and center wavelengths were written in optical fibers. By selecting the related parameters, the Bragg gratings with typical length of several hundred microns and bandwidth of several nanometers can be obtained. These short gratings can be apodized with selected diffraction patterns and hence their broadband spectra have a well-defined bell shape. They are suitable for use as miniaturized distributed strain sensors, which have broad applications to aerospace research and industry as well.

2J, 3J-HMBC: A New Long-Range Heteronuclear Shift Correlation Technique Capable of Differentiating 2JCH from 3JCH Correlations to Protonated Carbons

NASA Astrophysics Data System (ADS)

Krishnamurthy, V. V.; Russell, David J.; Hadden, Chad E.; Martin, Gary E.

2000-09-01

The development of a series of new, accordion-optimized long-range heteronuclear shift correlation techniques has been reported. A further derivative of the constant time variable delay introduced in the IMPEACH-MBC experiment, a STAR (Selectively Tailored Accordion F1 Refocusing) operator is described in the present report. Incorporation of the STAR operator with the capability of user-selected homonuclear modulation scaling as in the CIGAR-HMBC experiment, into a long-range heteronuclear shift correlation pulse sequence, 2J,3J-HMBC, affords for the first time in a proton-detected experiment the means of unequivocally differentiating two-bond (2JCH) from three-bond (3JCH) long-range correlations to protonated carbons.

A 100 electrode intracortical array: structural variability.

PubMed

Campbell, P K; Jones, K E; Normann, R A

1990-01-01

A technique has been developed for fabricating three dimensional "hair brush" electrode arrays from monocrystalline silicon blocks. Arrays consist of a square pattern of 100 penetrating electrodes, with 400 microns interelectrode spacing. Each electrode is 1.5mm in length and tapers from about 100 microns at its base to a sharp point at the tip. The tips of each electrode are coated with platinum and the entire structure, with the exception of the tips, is insulated with polyimide. Electrical connection to selected electrodes is made by wire bonding polyimide insulated 25 microns diameter gold lead wires to bonding pads on the rear surface of the array. As the geometrical characteristics of the electrodes in such an aray will influence their electrical properties (such as impedance, capacitance, spreading resistance in an electrolyte, etc.) it is desirable that such an array have minimal variability in geometry from electrode to electrode. A study was performed to determine the geometrical variability resulting from our micromachining techniques. Measurements of the diameter of each of the 100 electrodes were made at various planes above the silicon substrate of the array. For the array that was measured, the standard deviation of the diameters was approximately 9% of the mean diameter near the tip, 8% near the middle, and 6% near the base. We describe fabrication techniques which should further reduce these variabilities.

Technique and cue selection for graphical presentation of generic hyperdimensional data

NASA Astrophysics Data System (ADS)

Howard, Lee M.; Burton, Robert P.

2013-12-01

Several presentation techniques have been created for visualization of data with more than three variables. Packages have been written, each of which implements a subset of these techniques. However, these packages generally fail to provide all the features needed by the user during the visualization process. Further, packages generally limit support for presentation techniques to a few techniques. A new package called Petrichor accommodates all necessary and useful features together in one system. Any presentation technique may be added easily through an extensible plugin system. Features are supported by a user interface that allows easy interaction with data. Annotations allow users to mark up visualizations and share information with others. By providing a hyperdimensional graphics package that easily accommodates presentation techniques and includes a complete set of features, including those that are rarely or never supported elsewhere, the user is provided with a tool that facilitates improved interaction with multivariate data to extract and disseminate information.

[Study of near infrared spectral preprocessing and wavelength selection methods for endometrial cancer tissue].

PubMed

Zhao, Li-Ting; Xiang, Yu-Hong; Dai, Yin-Mei; Zhang, Zhuo-Yong

2010-04-01

Near infrared spectroscopy was applied to measure the tissue slice of endometrial tissues for collecting the spectra. A total of 154 spectra were obtained from 154 samples. The number of normal, hyperplasia, and malignant samples was 36, 60, and 58, respectively. Original near infrared spectra are composed of many variables, for example, interference information including instrument errors and physical effects such as particle size and light scatter. In order to reduce these influences, original spectra data should be performed with different spectral preprocessing methods to compress variables and extract useful information. So the methods of spectral preprocessing and wavelength selection have played an important role in near infrared spectroscopy technique. In the present paper the raw spectra were processed using various preprocessing methods including first derivative, multiplication scatter correction, Savitzky-Golay first derivative algorithm, standard normal variate, smoothing, and moving-window median. Standard deviation was used to select the optimal spectral region of 4 000-6 000 cm(-1). Then principal component analysis was used for classification. Principal component analysis results showed that three types of samples could be discriminated completely and the accuracy almost achieved 100%. This study demonstrated that near infrared spectroscopy technology and chemometrics method could be a fast, efficient, and novel means to diagnose cancer. The proposed methods would be a promising and significant diagnosis technique of early stage cancer.

High-speed peak matching algorithm for retention time alignment of gas chromatographic data for chemometric analysis

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

Johnson, Kevin J.; Wright, Bob W.; Jarman, Kristin H.

2003-05-09

A rapid retention time alignment algorithm was developed as a preprocessing utility to be used prior to chemometric analysis of large datasets of diesel fuel gas chromatographic profiles. Retention time variation from chromatogram-to-chromatogram has been a significant impediment against the use of chemometric techniques in the analysis of chromatographic data due to the inability of current multivariate techniques to correctly model information that shifts from variable to variable within a dataset. The algorithm developed is shown to increase the efficacy of pattern recognition methods applied to a set of diesel fuel chromatograms by retaining chemical selectivity while reducing chromatogram-to-chromatogram retentionmore » time variations and to do so on a time scale that makes analysis of large sets of chromatographic data practical.« less

Time estimation as a secondary task to measure workload: Summary of research

NASA Technical Reports Server (NTRS)

Hart, S. G.; Mcpherson, D.; Loomis, L. L.

1978-01-01

Actively produced intervals of time were found to increase in length and variability, whereas retrospectively produced intervals decreased in length although they also increased in variability with the addition of a variety of flight-related tasks. If pilots counted aloud while making a production, however, the impact of concurrent activity was minimized, at least for the moderately demanding primary tasks that were selected. The effects of feedback on estimation accuracy and consistency were greatly enhanced if a counting or tapping production technique was used. This compares with the minimal effect that feedback had when no overt timekeeping technique was used. Actively made verbal estimates of sessions filled with different activities performed during the interval were increased. Retrospectively made verbal estimates, however, increased in length as the amount and complexity of activities performed during the interval were increased.

Ensemble classification of individual Pinus crowns from multispectral satellite imagery and airborne LiDAR

NASA Astrophysics Data System (ADS)

Kukunda, Collins B.; Duque-Lazo, Joaquín; González-Ferreiro, Eduardo; Thaden, Hauke; Kleinn, Christoph

2018-03-01

Distinguishing tree species is relevant in many contexts of remote sensing assisted forest inventory. Accurate tree species maps support management and conservation planning, pest and disease control and biomass estimation. This study evaluated the performance of applying ensemble techniques with the goal of automatically distinguishing Pinus sylvestris L. and Pinus uncinata Mill. Ex Mirb within a 1.3 km2 mountainous area in Barcelonnette (France). Three modelling schemes were examined, based on: (1) high-density LiDAR data (160 returns m-2), (2) Worldview-2 multispectral imagery, and (3) Worldview-2 and LiDAR in combination. Variables related to the crown structure and height of individual trees were extracted from the normalized LiDAR point cloud at individual-tree level, after performing individual tree crown (ITC) delineation. Vegetation indices and the Haralick texture indices were derived from Worldview-2 images and served as independent spectral variables. Selection of the best predictor subset was done after a comparison of three variable selection procedures: (1) Random Forests with cross validation (AUCRFcv), (2) Akaike Information Criterion (AIC) and (3) Bayesian Information Criterion (BIC). To classify the species, 9 regression techniques were combined using ensemble models. Predictions were evaluated using cross validation and an independent dataset. Integration of datasets and models improved individual tree species classification (True Skills Statistic, TSS; from 0.67 to 0.81) over individual techniques and maintained strong predictive power (Relative Operating Characteristic, ROC = 0.91). Assemblage of regression models and integration of the datasets provided more reliable species distribution maps and associated tree-scale mapping uncertainties. Our study highlights the potential of model and data assemblage at improving species classifications needed in present-day forest planning and management.

Factors affecting plant species composition of hedgerows: relative importance and hierarchy

NASA Astrophysics Data System (ADS)

Deckers, Bart; Hermy, Martin; Muys, Bart

2004-07-01

Although there has been a clear quantitative and qualitative decline in traditional hedgerow network landscapes during last century, hedgerows are crucial for the conservation of rural biodiversity, functioning as an important habitat, refuge and corridor for numerous species. To safeguard this conservation function, insight in the basic organizing principles of hedgerow plant communities is needed. The vegetation composition of 511 individual hedgerows situated within an ancient hedgerow network landscape in Flanders, Belgium was recorded, in combination with a wide range of explanatory variables, including a selection of spatial variables. Non-parametric statistics in combination with multivariate data analysis techniques were used to study the effect of individual explanatory variables. Next, variables were grouped in five distinct subsets and the relative importance of these variable groups was assessed by two related variation partitioning techniques, partial regression and partial canonical correspondence analysis, taking into account explicitly the existence of intercorrelations between variables of different factor groups. Most explanatory variables affected significantly hedgerow species richness and composition. Multivariate analysis showed that, besides adjacent land use, hedgerow management, soil conditions, hedgerow type and origin, the role of other factors such as hedge dimensions, intactness, etc., could certainly not be neglected. Furthermore, both methods revealed the same overall ranking of the five distinct factor groups. Besides a predominant impact of abiotic environmental conditions, it was found that management variables and structural aspects have a relatively larger influence on the distribution of plant species in hedgerows than their historical background or spatial configuration.

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.

Fusing Data Mining, Machine Learning and Traditional Statistics to Detect Biomarkers Associated with Depression

PubMed Central

Dipnall, Joanna F.

2016-01-01

Background Atheoretical large-scale data mining techniques using machine learning algorithms have promise in the analysis of large epidemiological datasets. This study illustrates the use of a hybrid methodology for variable selection that took account of missing data and complex survey design to identify key biomarkers associated with depression from a large epidemiological study. Methods The study used a three-step methodology amalgamating multiple imputation, a machine learning boosted regression algorithm and logistic regression, to identify key biomarkers associated with depression in the National Health and Nutrition Examination Study (2009–2010). Depression was measured using the Patient Health Questionnaire-9 and 67 biomarkers were analysed. Covariates in this study included gender, age, race, smoking, food security, Poverty Income Ratio, Body Mass Index, physical activity, alcohol use, medical conditions and medications. The final imputed weighted multiple logistic regression model included possible confounders and moderators. Results After the creation of 20 imputation data sets from multiple chained regression sequences, machine learning boosted regression initially identified 21 biomarkers associated with depression. Using traditional logistic regression methods, including controlling for possible confounders and moderators, a final set of three biomarkers were selected. The final three biomarkers from the novel hybrid variable selection methodology were red cell distribution width (OR 1.15; 95% CI 1.01, 1.30), serum glucose (OR 1.01; 95% CI 1.00, 1.01) and total bilirubin (OR 0.12; 95% CI 0.05, 0.28). Significant interactions were found between total bilirubin with Mexican American/Hispanic group (p = 0.016), and current smokers (p<0.001). Conclusion The systematic use of a hybrid methodology for variable selection, fusing data mining techniques using a machine learning algorithm with traditional statistical modelling, accounted for missing data and complex survey sampling methodology and was demonstrated to be a useful tool for detecting three biomarkers associated with depression for future hypothesis generation: red cell distribution width, serum glucose and total bilirubin. PMID:26848571

Fusing Data Mining, Machine Learning and Traditional Statistics to Detect Biomarkers Associated with Depression.

PubMed

Dipnall, Joanna F; Pasco, Julie A; Berk, Michael; Williams, Lana J; Dodd, Seetal; Jacka, Felice N; Meyer, Denny

2016-01-01

Atheoretical large-scale data mining techniques using machine learning algorithms have promise in the analysis of large epidemiological datasets. This study illustrates the use of a hybrid methodology for variable selection that took account of missing data and complex survey design to identify key biomarkers associated with depression from a large epidemiological study. The study used a three-step methodology amalgamating multiple imputation, a machine learning boosted regression algorithm and logistic regression, to identify key biomarkers associated with depression in the National Health and Nutrition Examination Study (2009-2010). Depression was measured using the Patient Health Questionnaire-9 and 67 biomarkers were analysed. Covariates in this study included gender, age, race, smoking, food security, Poverty Income Ratio, Body Mass Index, physical activity, alcohol use, medical conditions and medications. The final imputed weighted multiple logistic regression model included possible confounders and moderators. After the creation of 20 imputation data sets from multiple chained regression sequences, machine learning boosted regression initially identified 21 biomarkers associated with depression. Using traditional logistic regression methods, including controlling for possible confounders and moderators, a final set of three biomarkers were selected. The final three biomarkers from the novel hybrid variable selection methodology were red cell distribution width (OR 1.15; 95% CI 1.01, 1.30), serum glucose (OR 1.01; 95% CI 1.00, 1.01) and total bilirubin (OR 0.12; 95% CI 0.05, 0.28). Significant interactions were found between total bilirubin with Mexican American/Hispanic group (p = 0.016), and current smokers (p<0.001). The systematic use of a hybrid methodology for variable selection, fusing data mining techniques using a machine learning algorithm with traditional statistical modelling, accounted for missing data and complex survey sampling methodology and was demonstrated to be a useful tool for detecting three biomarkers associated with depression for future hypothesis generation: red cell distribution width, serum glucose and total bilirubin.

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.

Focused Feedback With Video Tape As An Aid In Counseling Underachieving College Freshmen. Final Report.

ERIC Educational Resources Information Center

Valine, Warren J.

This study examines the relative effectiveness of 3 group counseling techniques and a control group in counseling with underachieving college freshmen. The effectiveness of each method was determined through comparison of grade point averages (GPA) as well as by pre- and post-test scores on selected self concept variables of the Tennessee Self…

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...

NLSCIDNT user's guide maximum likehood parameter identification computer program with nonlinear rotorcraft model

NASA Technical Reports Server (NTRS)

1979-01-01

A nonlinear, maximum likelihood, parameter identification computer program (NLSCIDNT) is described which evaluates rotorcraft stability and control coefficients from flight test data. The optimal estimates of the parameters (stability and control coefficients) are determined (identified) by minimizing the negative log likelihood cost function. The minimization technique is the Levenberg-Marquardt method, which behaves like the steepest descent method when it is far from the minimum and behaves like the modified Newton-Raphson method when it is nearer the minimum. Twenty-one states and 40 measurement variables are modeled, and any subset may be selected. States which are not integrated may be fixed at an input value, or time history data may be substituted for the state in the equations of motion. Any aerodynamic coefficient may be expressed as a nonlinear polynomial function of selected 'expansion variables'.

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.

Intra- and interoperator variability of lobar pulmonary volumes and emphysema scores in patients with chronic obstructive pulmonary disease and emphysema: comparison of manual and semi-automated segmentation techniques.

PubMed

Molinari, Francesco; Pirronti, Tommaso; Sverzellati, Nicola; Diciotti, Stefano; Amato, Michele; Paolantonio, Guglielmo; Gentile, Luigia; Parapatt, George K; D'Argento, Francesco; Kuhnigk, Jan-Martin

2013-01-01

We aimed to compare the intra- and interoperator variability of lobar volumetry and emphysema scores obtained by semi-automated and manual segmentation techniques in lung emphysema patients. In two sessions held three months apart, two operators performed lobar volumetry of unenhanced chest computed tomography examinations of 47 consecutive patients with chronic obstructive pulmonary disease and lung emphysema. Both operators used the manual and semi-automated segmentation techniques. The intra- and interoperator variability of the volumes and emphysema scores obtained by semi-automated segmentation was compared with the variability obtained by manual segmentation of the five pulmonary lobes. The intra- and interoperator variability of the lobar volumes decreased when using semi-automated lobe segmentation (coefficients of repeatability for the first operator: right upper lobe, 147 vs. 96.3; right middle lobe, 137.7 vs. 73.4; right lower lobe, 89.2 vs. 42.4; left upper lobe, 262.2 vs. 54.8; and left lower lobe, 260.5 vs. 56.5; coefficients of repeatability for the second operator: right upper lobe, 61.4 vs. 48.1; right middle lobe, 56 vs. 46.4; right lower lobe, 26.9 vs. 16.7; left upper lobe, 61.4 vs. 27; and left lower lobe, 63.6 vs. 27.5; coefficients of reproducibility in the interoperator analysis: right upper lobe, 191.3 vs. 102.9; right middle lobe, 219.8 vs. 126.5; right lower lobe, 122.6 vs. 90.1; left upper lobe, 166.9 vs. 68.7; and left lower lobe, 168.7 vs. 71.6). The coefficients of repeatability and reproducibility of emphysema scores also decreased when using semi-automated segmentation and had ranges that varied depending on the target lobe and selected threshold of emphysema. Semi-automated segmentation reduces the intra- and interoperator variability of lobar volumetry and provides a more objective tool than manual technique for quantifying lung volumes and severity of emphysema.

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

Logistic-based patient grouping for multi-disciplinary treatment.

PubMed

Maruşter, Laura; Weijters, Ton; de Vries, Geerhard; van den Bosch, Antal; Daelemans, Walter

2002-01-01

Present-day healthcare witnesses a growing demand for coordination of patient care. Coordination is needed especially in those cases in which hospitals have structured healthcare into specialty-oriented units, while a substantial portion of patient care is not limited to single units. From a logistic point of view, this multi-disciplinary patient care creates a tension between controlling the hospital's units, and the need for a control of the patient flow between units. A possible solution is the creation of new units in which different specialties work together for specific groups of patients. A first step in this solution is to identify the salient patient groups in need of multi-disciplinary care. Grouping techniques seem to offer a solution. However, most grouping approaches in medicine are driven by a search for pathophysiological homogeneity. In this paper, we present an alternative logistic-driven grouping approach. The starting point of our approach is a database with medical cases for 3,603 patients with peripheral arterial vascular (PAV) diseases. For these medical cases, six basic logistic variables (such as the number of visits to different specialist) are selected. Using these logistic variables, clustering techniques are used to group the medical cases in logistically homogeneous groups. In our approach, the quality of the resulting grouping is not measured by statistical significance, but by (i) the usefulness of the grouping for the creation of new multi-disciplinary units; (ii) how well patients can be selected for treatment in the new units. Given a priori knowledge of a patient (e.g. age, diagnosis), machine learning techniques are employed to induce rules that can be used for the selection of the patients eligible for treatment in the new units. In the paper, we describe the results of the above-proposed methodology for patients with PAV diseases. Two groupings and the accompanied classification rule sets are presented. One grouping is based on all the logistic variables, and another grouping is based on two latent factors found by applying factor analysis. On the basis of the experimental results, we can conclude that it is possible to search for medical logistic homogenous groups (i) that can be characterized by rules based on the aggregated logistic variables; (ii) for which we can formulate rules to predict to which cluster new patients belong.

Detection of target-probe oligonucleotide hybridization using synthetic nanopore resistive pulse sensing.

PubMed

Booth, Marsilea Adela; Vogel, Robert; Curran, James M; Harbison, SallyAnn; Travas-Sejdic, Jadranka

2013-07-15

Despite the plethora of DNA sensor platforms available, a portable, sensitive, selective and economic sensor able to rival current fluorescence-based techniques would find use in many applications. In this research, probe oligonucleotide-grafted particles are used to detect target DNA in solution through a resistive pulse nanopore detection technique. Using carbodiimide chemistry, functionalized probe DNA strands are attached to carboxylated dextran-based magnetic particles. Subsequent incubation with complementary target DNA yields a change in surface properties as the two DNA strands hybridize. Particle-by-particle analysis with resistive pulse sensing is performed to detect these changes. A variable pressure method allows identification of changes in the surface charge of particles. As proof-of-principle, we demonstrate that target hybridization is selectively detected at micromolar concentrations (nanomoles of target) using resistive pulse sensing, confirmed by fluorescence and phase analysis light scattering as complementary techniques. The advantages, feasibility and limitations of using resistive pulse sensing for sample analysis are discussed. Copyright © 2013 Elsevier B.V. All rights reserved.

Selection of Optical Glasses Using Buchdahl's Chromatic Coordinate

NASA Technical Reports Server (NTRS)

Griffin, DeVon W.

1999-01-01

This investigation attempted to extend the method of reducing the size of glass catalogs to a global glass selection technique with the hope of guiding glass catalog offerings. Buchdahl's development of optical aberration coefficients included a transformation of the variable in the dispersion equation from wavelength to a chromatic coordinate omega defined as omega = (lambda - lambda(sub 0))/ 1 + 2.5(lambda - lambda(sub 0)) where lambda is the wavelength at which the wavelength is calculated and lambda(sub 0) is a base wavelength about which the expansion is performed. The advantage of this approach is that the dispersion equation may be written in terms of a simple power series and permits direct calculation of dispersion coefficients. While several promising examples were given, a systematic application of the technique to an entire glass catalog and analysis of the subsequent predictions was not performed. The goal of this work was to apply the technique in a systematic fashion to glasses in the Schoft catalog and assess the quality of the predictions.

Multistage variable probability forest volume inventory. [the Defiance Unit of the Navajo Nation

NASA Technical Reports Server (NTRS)

Anderson, J. E. (Principal Investigator)

1979-01-01

An inventory scheme based on the use of computer processed LANDSAT MSS data was developed. Output from the inventory scheme provides an estimate of the standing net saw timber volume of a major timber species on a selected forested area of the Navajo Nation. Such estimates are based on the values of parameters currently used for scaled sawlog conversion to mill output. The multistage variable probability sampling appears capable of producing estimates which compare favorably with those produced using conventional techniques. In addition, the reduction in time, manpower, and overall costs lend it to numerous applications.

Traveling wave and soliton solutions of coupled nonlinear Schrödinger equations with harmonic potential and variable coefficients.

PubMed

Zhong, Wei-Ping; Belić, Milivoj

2010-10-01

Exact traveling wave and soliton solutions, including the bright-bright and dark-dark soliton pairs, are found for the system of two coupled nonlinear Schrödinger equations with harmonic potential and variable coefficients, by employing the homogeneous balance principle and the F-expansion technique. A kind of shape-changing soliton collision is identified in the system. The collision is essentially elastic between the two solitons with opposite velocities. Our results demonstrate that the dynamics of solitons can be controlled by selecting the diffraction, nonlinearity, and gain coefficients.

The application of remote sensing techniques to selected inter and intra urban data acquisition problems

NASA Technical Reports Server (NTRS)

Horton, F. E.

1970-01-01

The utility of remote sensing techniques to urban data acquisition problems in several distinct areas was identified. This endeavor included a comparison of remote sensing systems for urban data collection, the extraction of housing quality data from aerial photography, utilization of photographic sensors in urban transportation studies, urban change detection, space photography utilization, and an application of remote sensing techniques to the acquisition of data concerning intra-urban commercial centers. The systematic evaluation of variable extraction for urban modeling and planning at several different scales, and the model derivation for identifying and predicting economic growth and change within a regional system of cities are also studied.

Formation and characterization of mullite fibers produced by inviscid melt-spinning

NASA Astrophysics Data System (ADS)

Xiao, Zhijun

IMS is a technique used to form fibers from low viscosity melts by means of stream stabilization in a reactant gas, in this case propane. Mullite (3Alsb2Osb3*2SiOsb2) was selected as the material to be fiberized. A stable mullite melt was obtained at 2000sp°C. Some short fibers and shot were formed in the fiber forming experiments. Crucible material selection is a prerequisite for proper application of the IMS technique. The effect of two crucible materials-graphite and boron nitride were studied. A carbothermal reaction occurred between the mullite melt and the graphite crucible. Boron nitride was selected as the crucible material because a relatively stable melt could be obtained. Operating environment is another factor that affects IMS mullite fiber formation. The effects of vacuum, nitrogen and argon on mullite melting behavior were studied. Argon gas was selected as the operating environment. A 2sp3 factorial design was developed to study the effect of such variables as temperature, holding time at the temperature, and heating rate on mullite melting behavior. The effects of the variables and interactions were calculated. Temperature has the biggest positive effect, holding time is the second, heating rate just has a very small negative effect. A detailed investigation of the mullite decomposition mechanism and kinetics was conducted in this work. A solid reaction mechanism was proposed. The kinetic results and IR analysis support the proposed mechanism. The carbon source inside the furnace led to the decomposition of mullite. A feasible experimental technique was developed to prevent the decomposition of mullite. The experiments with this design completely controlled the mullite decomposition. The short fibers, shot and some side products formed in the fiber forming experiments were characterized using XRD, XRF and SEM-EDS. The composition of the short fiber and shot was in the range of mullite composition. XRD showed that the diffraction pattern of shot is that of mullite.

The Effect of Selected Cleaning Techniques on Berkshire Lee Marble: A Scientific Study at Philadelphia City Hall

USGS Publications Warehouse

Mossotti, Victor G.; Eldeeb, A. Raouf; Fries, Terry L.; Coombs, Mary Jane; Naude, Virginia N.; Soderberg, Lisa; Wheeler, George S.

2002-01-01

This report describes a scientific investigation of the effects of eight different cleaning techniques on the Berkshire Lee marble component of the facade of the East Center Pavilion at Philadelphia City Hall; the study was commissioned by the city of Philadelphia. The eight cleaning techniques evaluated in this study were power wash (proprietary gel detergent followed by water rinse under pressure), misting (treatment with potable, nebulized water for 24-36 hours), gommage (proprietary Thomann-Hanry low-pressure, air-driven, small-particle, dry abrasion), combination (gommage followed by misting), Armax (sodium bicarbonate delivered under pressure in a water wash), JOS (dolomite powder delivered in a low-pressure, rotary-vortex water wash), laser (thermal ablation), and dry ice (powdered-dry-ice abrasion delivered under pressure). In our study approximately 160 cores were removed from the building for laboratory analysis. We developed a computer program to analyze scanning-electron-micrograph images for the microscale surface roughness and other morphologic parameters of the stone surface, including the near-surface fracture density of the stone. An analysis of more than 1,100 samples cut from the cores provided a statistical basis for crafting the essential elements of a reduced-form, mixed-kinetics conceptual model that represents the deterioration of calcareous stone in terms of self-organized soiling and erosion patterns. This model, in turn, provided a basis for identifying the variables that are affected by the cleaning techniques and for evaluating the extent to which such variables influence the stability of the stone. The model recognizes three classes of variables that may influence the soiling load on the stone, including such exogenous environmental variables as airborne moisture, pollutant concentrations, and local aerodynamics, and such endogenous stone variables as surface chemistry and microstructure (fracturing, roughness, and so on). This study showed that morphologic variables on the mesoscale to macroscale are not generally affected by the choice of a cleaning technique. The long-term soiling pattern on the building is independent of the cleaning technique applied. This study also showed that soluble salts do not play a significant role in the deterioration of Berkshire Lee marble. Although salts were evident in cracks and fissures of the heavily soiled stone, such salts did not penetrate the surface to a depth of more than a few hundred micrometers. The criteria used to differentiate the cleaning techniques were ultimately based on the ability of each technique to remove soiling without altering the texture of the stone surface. This study identified both the gommage and JOS techniques as appropriate for cleaning ashlar surfaces and the combination technique as appropriate for cleaning highly carved surfaces at the entablatures, cornices, and column capitals.

Predictors for Physical Activity in Adolescent Girls Using Statistical Shrinkage Techniques for Hierarchical Longitudinal Mixed Effects Models

PubMed Central

Grant, Edward M.; Young, Deborah Rohm; Wu, Tong Tong

2015-01-01

We examined associations among longitudinal, multilevel variables and girls’ physical activity to determine the important predictors for physical activity change at different adolescent ages. The Trial of Activity for Adolescent Girls 2 study (Maryland) contributed participants from 8th (2009) to 11th grade (2011) (n=561). Questionnaires were used to obtain demographic, and psychosocial information (individual- and social-level variables); height, weight, and triceps skinfold to assess body composition; interviews and surveys for school-level data; and self-report for neighborhood-level variables. Moderate to vigorous physical activity minutes were assessed from accelerometers. A doubly regularized linear mixed effects model was used for the longitudinal multilevel data to identify the most important covariates for physical activity. Three fixed effects at the individual level and one random effect at the school level were chosen from an initial total of 66 variables, consisting of 47 fixed effects and 19 random effects variables, in additional to the time effect. Self-management strategies, perceived barriers, and social support from friends were the three selected fixed effects, and whether intramural or interscholastic programs were offered in middle school was the selected random effect. Psychosocial factors and friend support, plus a school’s physical activity environment, affect adolescent girl’s moderate to vigorous physical activity longitudinally. PMID:25928064

Variability-selected active galactic nuclei in the VST-SUDARE/VOICE survey of the COSMOS field

NASA Astrophysics Data System (ADS)

De Cicco, D.; Paolillo, M.; Covone, G.; Falocco, S.; Longo, G.; Grado, A.; Limatola, L.; Botticella, M. T.; Pignata, G.; Cappellaro, E.; Vaccari, M.; Trevese, D.; Vagnetti, F.; Salvato, M.; Radovich, M.; Brandt, W. N.; Capaccioli, M.; Napolitano, N. R.; Schipani, P.

2015-02-01

Context. Active galaxies are characterized by variability at every wavelength, with timescales from hours to years depending on the observing window. Optical variability has proven to be an effective way of detecting AGNs in imaging surveys, lasting from weeks to years. Aims: In the present work we test the use of optical variability as a tool to identify active galactic nuclei in the VST multiepoch survey of the COSMOS field, originally tailored to detect supernova events. Methods: We make use of the multiwavelength data provided by other COSMOS surveys to discuss the reliability of the method and the nature of our AGN candidates. Results: The selection on the basis of optical variability returns a sample of 83 AGN candidates; based on a number of diagnostics, we conclude that 67 of them are confirmed AGNs (81% purity), 12 are classified as supernovae, while the nature of the remaining 4 is unknown. For the subsample of AGNs with some spectroscopic classification, we find that Type 1 are prevalent (89%) compared to Type 2 AGNs (11%). Overall, our approach is able to retrieve on average 15% of all AGNs in the field identified by means of spectroscopic or X-ray classification, with a strong dependence on the source apparent magnitude (completeness ranging from 26% to 5%). In particular, the completeness for Type 1 AGNs is 25%, while it drops to 6% for Type 2 AGNs. The rest of the X-ray selected AGN population presents on average a larger rms variability than the bulk of non-variable sources, indicating that variability detection for at least some of these objects is prevented only by the photometric accuracy of the data. The low completeness is in part due to the short observing span: we show that increasing the temporal baseline results in larger samples as expected for sources with a red-noise power spectrum. Our results allow us to assess the usefulness of this AGN selection technique in view of future wide-field surveys. Observations were provided by the ESO programs 088.D-0370 and 088.D-4013 (PI G. Pignata).Table 3 is available in electronic form at http://www.aanda.org

Financial Management and Control for Decision Making in Urban Local Bodies in India Using Statistical Techniques

NASA Astrophysics Data System (ADS)

Bhattacharyya, Sidhakam; Bandyopadhyay, Gautam

2010-10-01

The council of most of the Urban Local Bodies (ULBs) has a limited scope for decision making in the absence of appropriate financial control mechanism. The information about expected amount of own fund during a particular period is of great importance for decision making. Therefore, in this paper, efforts are being made to present set of findings and to establish a model of estimating receipts of own sources and payments thereof using multiple regression analysis. Data for sixty months from a reputed ULB in West Bengal have been considered for ascertaining the regression models. This can be used as a part of financial management and control procedure by the council to estimate the effect on own fund. In our study we have considered two models using multiple regression analysis. "Model I" comprises of total adjusted receipt as the dependent variable and selected individual receipts as the independent variables. Similarly "Model II" consists of total adjusted payments as the dependent variable and selected individual payments as independent variables. The resultant of Model I and Model II is the surplus or deficit effecting own fund. This may be applied for decision making purpose by the council.

Low-sensitivity, frequency-selective amplifier circuits for hybrid and bipolar fabrication.

NASA Technical Reports Server (NTRS)

Pi, C.; Dunn, W. R., Jr.

1972-01-01

A network is described which is suitable for realizing a low-sensitivity high-Q second-order frequency-selective amplifier for high-frequency operation. Circuits are obtained from this network which are well suited for realizing monolithic integrated circuits and which do not require any process steps more critical than those used for conventional monolithic operational and video amplifiers. A single chip version using compatible thin-film techniques for the frequency determination elements is then feasible. Center frequency and bandwidth can be set independently by trimming two resistors. The frequency selective circuits have a low sensitivity to the process variables, and the sensitivity of the center frequency and bandwidth to changes in temperature is very low.

Correlations of Handgrip Strength with Selected Hand-Arm-Anthropometric Variables in Indian Inter-university Female Volleyball Players

PubMed Central

Koley, Shyamal; Pal Kaur, Satinder

2011-01-01

Purpose The purpose of this study was to estimate the dominant handgrip strength and its correlations with some hand and arm anthropometric variables in 101 randomly selected Indian inter-university female volleyball players aged 18-25 years (mean age 20.52±1.40) from six Indian universities. Methods Three anthropometric variables, i.e. height, weight, BMI, two hand anthropometric variables, viz. right and left hand width and length, four arm anthropometric variables, i.e. upper arm length, lower arm length, upper extremity length, upper arm circumference and dominant right and non-dominant handgrip strength were measured among Indian inter-university female volleyball players by standard anthropometric techniques. Results The findings of the present study indicated that Indian female volleyball players had higher mean values in eleven variables and lesser mean values in two variables than their control counterparts, showing significant differences (P<0.032-0.001) in height (t=2.63), weight (t=8.66), left hand width (t=2.10), left and right hand length (t=9.99 and 10.40 respectively), right upper arm length (t=8.48), right forearm length (t=5.41), dominant (right) and non-dominant (left) handgrip strength (t=9.37 and 6.76 respectively). In female volleyball players, dominant handgrip strength had significantly positive correlations (P=0.01) with all the variables studied. Conclusion It may be concluded that dominant handgrip strength had strong positive correlations with all the variables studied in Indian inter-university female volleyball players. PMID:22375242

Effectiveness of touch and feel (TAF) technique on first aid measures for visually challenged.

PubMed

Mary, Helen; Sasikalaz, D; Venkatesan, Latha

2013-01-01

There is a common perception that a blind person cannot even help his own self. In order to challenge that view, a workshop for visually-impaired people to develop the skills to be independent and productive members of society was conceived. An experimental study was conducted at National Institute of Visually Handicapped, Chennai with the objective to assess the effectiveness of Touch and Feel (TAF) technique on first aid measures for the visually challenged. Total 25 visually challenged people were selected by non-probability purposive sampling technique and data was collected using demographic variable and structured knowledge questionnaire. The score obtained was categorised into three levels: inadequate (0-8), moderately adequate (8 - 17), adequate (17 -25). The study revealed that most of the visually challenged (40%) had inadequate knowledge, and 56 percent had moderately adequate and only few (4%) had adequate knowledge in the pre-test, whereas most (68%) of them had adequate knowledge in the post-test which is statistically significant at p < 0.000 with t-value 6.779. This proves that TAF technique was effective for the visually challenged. There was no association between the demographic variables and their level of knowledge regarding first aid.

Technical Note: The Initial Stages of Statistical Data Analysis

PubMed Central

Tandy, Richard D.

1998-01-01

Objective: To provide an overview of several important data-related considerations in the design stage of a research project and to review the levels of measurement and their relationship to the statistical technique chosen for the data analysis. Background: When planning a study, the researcher must clearly define the research problem and narrow it down to specific, testable questions. The next steps are to identify the variables in the study, decide how to group and treat subjects, and determine how to measure, and the underlying level of measurement of, the dependent variables. Then the appropriate statistical technique can be selected for data analysis. Description: The four levels of measurement in increasing complexity are nominal, ordinal, interval, and ratio. Nominal data are categorical or “count” data, and the numbers are treated as labels. Ordinal data can be ranked in a meaningful order by magnitude. Interval data possess the characteristics of ordinal data and also have equal distances between levels. Ratio data have a natural zero point. Nominal and ordinal data are analyzed with nonparametric statistical techniques and interval and ratio data with parametric statistical techniques. Advantages: Understanding the four levels of measurement and when it is appropriate to use each is important in determining which statistical technique to use when analyzing data. PMID:16558489

Overall uncertainty study of the hydrological impacts of climate change for a Canadian watershed

NASA Astrophysics Data System (ADS)

Chen, Jie; Brissette, FrançOis P.; Poulin, Annie; Leconte, Robert

2011-12-01

General circulation models (GCMs) and greenhouse gas emissions scenarios (GGES) are generally considered to be the two major sources of uncertainty in quantifying the climate change impacts on hydrology. Other sources of uncertainty have been given less attention. This study considers overall uncertainty by combining results from an ensemble of two GGES, six GCMs, five GCM initial conditions, four downscaling techniques, three hydrological model structures, and 10 sets of hydrological model parameters. Each climate projection is equally weighted to predict the hydrology on a Canadian watershed for the 2081-2100 horizon. The results show that the choice of GCM is consistently a major contributor to uncertainty. However, other sources of uncertainty, such as the choice of a downscaling method and the GCM initial conditions, also have a comparable or even larger uncertainty for some hydrological variables. Uncertainties linked to GGES and the hydrological model structure are somewhat less than those related to GCMs and downscaling techniques. Uncertainty due to the hydrological model parameter selection has the least important contribution among all the variables considered. Overall, this research underlines the importance of adequately covering all sources of uncertainty. A failure to do so may result in moderately to severely biased climate change impact studies. Results further indicate that the major contributors to uncertainty vary depending on the hydrological variables selected, and that the methodology presented in this paper is successful at identifying the key sources of uncertainty to consider for a climate change impact study.

Adaptive distributed source coding.

PubMed

Varodayan, David; Lin, Yao-Chung; Girod, Bernd

2012-05-01

We consider distributed source coding in the presence of hidden variables that parameterize the statistical dependence among sources. We derive the Slepian-Wolf bound and devise coding algorithms for a block-candidate model of this problem. The encoder sends, in addition to syndrome bits, a portion of the source to the decoder uncoded as doping bits. The decoder uses the sum-product algorithm to simultaneously recover the source symbols and the hidden statistical dependence variables. We also develop novel techniques based on density evolution (DE) to analyze the coding algorithms. We experimentally confirm that our DE analysis closely approximates practical performance. This result allows us to efficiently optimize parameters of the algorithms. In particular, we show that the system performs close to the Slepian-Wolf bound when an appropriate doping rate is selected. We then apply our coding and analysis techniques to a reduced-reference video quality monitoring system and show a bit rate saving of about 75% compared with fixed-length coding.

The use of C-near edge X-ray absorption fine structure spectroscopy for the elaboration of chemistry in lignocellulosics

Treesearch

Lucian A. Lucia; Hiroki Nanko; Alan W. Rudie; Doug G. Mancosky; Sue Wirick

2006-01-01

The research presented elucidates the oxidation chemistry occurring in hydrogen peroxide bleached kraft pulp fibers by employing carbon near edge x-ray absorption fine structure spectroscopy (C-NEXAFS). C-NEXAFS is a soft x-ray technique that selectively interrogates atomic moieties using photoelectrons (Xrays) of variable energies. The X1A beam line at the National...

A Study on Attitude and Opinion towards Using Computer Technology in Teaching among B.Ed. Trainees in Tiruchirappalli District

ERIC Educational Resources Information Center

Kumar, C. Ashok

2015-01-01

The main aim of the study was to find out whether there was a significant difference in the attitude and opinion towards using Computer Technology in teaching among B.Ed., trainees in terms of select independent variables. Normative survey was the technique employed. Opinion towards Computer Usage and Attitude towards Computer Technology inventory…

Predicting the graft survival for heart-lung transplantation patients: an integrated data mining methodology.

PubMed

Oztekin, Asil; Delen, Dursun; Kong, Zhenyu James

2009-12-01

Predicting the survival of heart-lung transplant patients has the potential to play a critical role in understanding and improving the matching procedure between the recipient and graft. Although voluminous data related to the transplantation procedures is being collected and stored, only a small subset of the predictive factors has been used in modeling heart-lung transplantation outcomes. The previous studies have mainly focused on applying statistical techniques to a small set of factors selected by the domain-experts in order to reveal the simple linear relationships between the factors and survival. The collection of methods known as 'data mining' offers significant advantages over conventional statistical techniques in dealing with the latter's limitations such as normality assumption of observations, independence of observations from each other, and linearity of the relationship between the observations and the output measure(s). There are statistical methods that overcome these limitations. Yet, they are computationally more expensive and do not provide fast and flexible solutions as do data mining techniques in large datasets. The main objective of this study is to improve the prediction of outcomes following combined heart-lung transplantation by proposing an integrated data-mining methodology. A large and feature-rich dataset (16,604 cases with 283 variables) is used to (1) develop machine learning based predictive models and (2) extract the most important predictive factors. Then, using three different variable selection methods, namely, (i) machine learning methods driven variables-using decision trees, neural networks, logistic regression, (ii) the literature review-based expert-defined variables, and (iii) common sense-based interaction variables, a consolidated set of factors is generated and used to develop Cox regression models for heart-lung graft survival. The predictive models' performance in terms of 10-fold cross-validation accuracy rates for two multi-imputed datasets ranged from 79% to 86% for neural networks, from 78% to 86% for logistic regression, and from 71% to 79% for decision trees. The results indicate that the proposed integrated data mining methodology using Cox hazard models better predicted the graft survival with different variables than the conventional approaches commonly used in the literature. This result is validated by the comparison of the corresponding Gains charts for our proposed methodology and the literature review based Cox results, and by the comparison of Akaike information criteria (AIC) values received from each. Data mining-based methodology proposed in this study reveals that there are undiscovered relationships (i.e. interactions of the existing variables) among the survival-related variables, which helps better predict the survival of the heart-lung transplants. It also brings a different set of variables into the scene to be evaluated by the domain-experts and be considered prior to the organ transplantation.

Deterministic quantum teleportation of photonic quantum bits by a hybrid technique.

PubMed

Takeda, Shuntaro; Mizuta, Takahiro; Fuwa, Maria; van Loock, Peter; Furusawa, Akira

2013-08-15

Quantum teleportation allows for the transfer of arbitrary unknown quantum states from a sender to a spatially distant receiver, provided that the two parties share an entangled state and can communicate classically. It is the essence of many sophisticated protocols for quantum communication and computation. Photons are an optimal choice for carrying information in the form of 'flying qubits', but the teleportation of photonic quantum bits (qubits) has been limited by experimental inefficiencies and restrictions. Main disadvantages include the fundamentally probabilistic nature of linear-optics Bell measurements, as well as the need either to destroy the teleported qubit or attenuate the input qubit when the detectors do not resolve photon numbers. Here we experimentally realize fully deterministic quantum teleportation of photonic qubits without post-selection. The key step is to make use of a hybrid technique involving continuous-variable teleportation of a discrete-variable, photonic qubit. When the receiver's feedforward gain is optimally tuned, the continuous-variable teleporter acts as a pure loss channel, and the input dual-rail-encoded qubit, based on a single photon, represents a quantum error detection code against photon loss and hence remains completely intact for most teleportation events. This allows for a faithful qubit transfer even with imperfect continuous-variable entangled states: for four qubits the overall transfer fidelities range from 0.79 to 0.82 and all of them exceed the classical limit of teleportation. Furthermore, even for a relatively low level of the entanglement, qubits are teleported much more efficiently than in previous experiments, albeit post-selectively (taking into account only the qubit subspaces), and with a fidelity comparable to the previously reported values.

A users' manual for MCPRAM (Monte Carlo PReprocessor for AMEER) and for the fuze options in AMEER (Aero Mechanical Equation Evaluation Routines)

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

LaFarge, R.A.

1990-05-01

MCPRAM (Monte Carlo PReprocessor for AMEER), a computer program that uses Monte Carlo techniques to create an input file for the AMEER trajectory code, has been developed for the Sandia National Laboratories VAX and Cray computers. Users can select the number of trajectories to compute, which AMEER variables to investigate, and the type of probability distribution for each variable. Any legal AMEER input variable can be investigated anywhere in the input run stream with either a normal, uniform, or Rayleigh distribution. Users also have the option to use covariance matrices for the investigation of certain correlated variables such as boostermore » pre-reentry errors and wind, axial force, and atmospheric models. In conjunction with MCPRAM, AMEER was modified to include the variables introduced by the covariance matrices and to include provisions for six types of fuze models. The new fuze models and the new AMEER variables are described in this report.« less

Periodicity and stability for variable-time impulsive neural networks.

PubMed

Li, Hongfei; Li, Chuandong; Huang, Tingwen

2017-10-01

The paper considers a general neural networks model with variable-time impulses. It is shown that each solution of the system intersects with every discontinuous surface exactly once via several new well-proposed assumptions. Moreover, based on the comparison principle, this paper shows that neural networks with variable-time impulse can be reduced to the corresponding neural network with fixed-time impulses under well-selected conditions. Meanwhile, the fixed-time impulsive systems can be regarded as the comparison system of the variable-time impulsive neural networks. Furthermore, a series of sufficient criteria are derived to ensure the existence and global exponential stability of periodic solution of variable-time impulsive neural networks, and to illustrate the same stability properties between variable-time impulsive neural networks and the fixed-time ones. The new criteria are established by applying Schaefer's fixed point theorem combined with the use of inequality technique. Finally, a numerical example is presented to show the effectiveness of the proposed results. Copyright © 2017 Elsevier Ltd. All rights reserved.

An improved survivability prognosis of breast cancer by using sampling and feature selection technique to solve imbalanced patient classification data.

PubMed

Wang, Kung-Jeng; Makond, Bunjira; Wang, Kung-Min

2013-11-09

Breast cancer is one of the most critical cancers and is a major cause of cancer death among women. It is essential to know the survivability of the patients in order to ease the decision making process regarding medical treatment and financial preparation. Recently, the breast cancer data sets have been imbalanced (i.e., the number of survival patients outnumbers the number of non-survival patients) whereas the standard classifiers are not applicable for the imbalanced data sets. The methods to improve survivability prognosis of breast cancer need for study. Two well-known five-year prognosis models/classifiers [i.e., logistic regression (LR) and decision tree (DT)] are constructed by combining synthetic minority over-sampling technique (SMOTE), cost-sensitive classifier technique (CSC), under-sampling, bagging, and boosting. The feature selection method is used to select relevant variables, while the pruning technique is applied to obtain low information-burden models. These methods are applied on data obtained from the Surveillance, Epidemiology, and End Results database. The improvements of survivability prognosis of breast cancer are investigated based on the experimental results. Experimental results confirm that the DT and LR models combined with SMOTE, CSC, and under-sampling generate higher predictive performance consecutively than the original ones. Most of the time, DT and LR models combined with SMOTE and CSC use less informative burden/features when a feature selection method and a pruning technique are applied. LR is found to have better statistical power than DT in predicting five-year survivability. CSC is superior to SMOTE, under-sampling, bagging, and boosting to improve the prognostic performance of DT and LR.

An improved survivability prognosis of breast cancer by using sampling and feature selection technique to solve imbalanced patient classification data

PubMed Central

2013-01-01

Background Breast cancer is one of the most critical cancers and is a major cause of cancer death among women. It is essential to know the survivability of the patients in order to ease the decision making process regarding medical treatment and financial preparation. Recently, the breast cancer data sets have been imbalanced (i.e., the number of survival patients outnumbers the number of non-survival patients) whereas the standard classifiers are not applicable for the imbalanced data sets. The methods to improve survivability prognosis of breast cancer need for study. Methods Two well-known five-year prognosis models/classifiers [i.e., logistic regression (LR) and decision tree (DT)] are constructed by combining synthetic minority over-sampling technique (SMOTE) ,cost-sensitive classifier technique (CSC), under-sampling, bagging, and boosting. The feature selection method is used to select relevant variables, while the pruning technique is applied to obtain low information-burden models. These methods are applied on data obtained from the Surveillance, Epidemiology, and End Results database. The improvements of survivability prognosis of breast cancer are investigated based on the experimental results. Results Experimental results confirm that the DT and LR models combined with SMOTE, CSC, and under-sampling generate higher predictive performance consecutively than the original ones. Most of the time, DT and LR models combined with SMOTE and CSC use less informative burden/features when a feature selection method and a pruning technique are applied. Conclusions LR is found to have better statistical power than DT in predicting five-year survivability. CSC is superior to SMOTE, under-sampling, bagging, and boosting to improve the prognostic performance of DT and LR. PMID:24207108

Comparison of stream invertebrate response models for bioassessment metric

USGS Publications Warehouse

Waite, Ian R.; Kennen, Jonathan G.; May, Jason T.; Brown, Larry R.; Cuffney, Thomas F.; Jones, Kimberly A.; Orlando, James L.

2012-01-01

We aggregated invertebrate data from various sources to assemble data for modeling in two ecoregions in Oregon and one in California. Our goal was to compare the performance of models developed using multiple linear regression (MLR) techniques with models developed using three relatively new techniques: classification and regression trees (CART), random forest (RF), and boosted regression trees (BRT). We used tolerance of taxa based on richness (RICHTOL) and ratio of observed to expected taxa (O/E) as response variables and land use/land cover as explanatory variables. Responses were generally linear; therefore, there was little improvement to the MLR models when compared to models using CART and RF. In general, the four modeling techniques (MLR, CART, RF, and BRT) consistently selected the same primary explanatory variables for each region. However, results from the BRT models showed significant improvement over the MLR models for each region; increases in R2 from 0.09 to 0.20. The O/E metric that was derived from models specifically calibrated for Oregon consistently had lower R2 values than RICHTOL for the two regions tested. Modeled O/E R2 values were between 0.06 and 0.10 lower for each of the four modeling methods applied in the Willamette Valley and were between 0.19 and 0.36 points lower for the Blue Mountains. As a result, BRT models may indeed represent a good alternative to MLR for modeling species distribution relative to environmental variables.

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.

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.

Radar-raingauge data combination techniques: a revision and analysis of their suitability for urban hydrology.

PubMed

Wang, Li-Pen; Ochoa-Rodríguez, Susana; Simões, Nuno Eduardo; Onof, Christian; Maksimović, Cedo

2013-01-01

The applicability of the operational radar and raingauge networks for urban hydrology is insufficient. Radar rainfall estimates provide a good description of the spatiotemporal variability of rainfall; however, their accuracy is in general insufficient. It is therefore necessary to adjust radar measurements using raingauge data, which provide accurate point rainfall information. Several gauge-based radar rainfall adjustment techniques have been developed and mainly applied at coarser spatial and temporal scales; however, their suitability for small-scale urban hydrology is seldom explored. In this paper a review of gauge-based adjustment techniques is first provided. After that, two techniques, respectively based upon the ideas of mean bias reduction and error variance minimisation, were selected and tested using as case study an urban catchment (∼8.65 km(2)) in North-East London. The radar rainfall estimates of four historical events (2010-2012) were adjusted using in situ raingauge estimates and the adjusted rainfall fields were applied to the hydraulic model of the study area. The results show that both techniques can effectively reduce mean bias; however, the technique based upon error variance minimisation can in general better reproduce the spatial and temporal variability of rainfall, which proved to have a significant impact on the subsequent hydraulic outputs. This suggests that error variance minimisation based methods may be more appropriate for urban-scale hydrological applications.

Selectivity in the Reinnervation of the Lateral Gastrocnemius Muscle after Nerve Repair with Ethyl Cyanoacrylate in the Rat

PubMed Central

Landegren, Thomas; Risling, Mårten; Hammarberg, Henrik; Persson, Jonas K. E.

2011-01-01

There is a need for complementary surgical techniques that enable rapid and reliable primary repair of transected nerves. Previous studies after peripheral nerve transection and repair with synthetic adhesives have demonstrated regeneration to an extent comparable to that of conventional techniques. The aim of this study was to compare two different repair techniques on the selectivity of muscle reinnervation after repair and completed regeneration. We used the cholera toxin B technique of retrograde axonal tracing to evaluate the morphology, the number, and the three-dimensional location of α-motoneurons innervating the lateral gastrocnemius muscle and compared the results after repair with either ethyl cyanoacrylate (ECA) or epineural sutures of the transected parent sciatic nerve. In addition, we recorded the wet weight of the muscle. Six months after transection and repair of the sciatic nerve, the redistribution of the motoneuron pool was markedly disorganized, the motoneurons had apparently increased in number, and they were scattered throughout a larger volume of the spinal cord gray matter with a decrease in the synaptic coverage compared to controls. A reduction in muscle weight was observed as well. No difference in morphometric variables or muscle weight between the two repair methods could be detected. We conclude that the selectivity of motor reinnervation following sciatic nerve transection and subsequent repair with ECA is comparable to that following conventional micro suturing. PMID:21577248

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.

Monte Carlo investigation of thrust imbalance of solid rocket motor pairs

NASA Technical Reports Server (NTRS)

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

1976-01-01

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

Estimating the efficacy of Alcoholics Anonymous without self-selection bias: An instrumental variables re-analysis of randomized clinical trials

PubMed Central

Humphreys, Keith; Blodgett, Janet C.; Wagner, Todd H.

2014-01-01

Background Observational studies of Alcoholics Anonymous’ (AA) effectiveness are vulnerable to self-selection bias because individuals choose whether or not to attend AA. The present study therefore employed an innovative statistical technique to derive a selection bias-free estimate of AA’s impact. Methods Six datasets from 5 National Institutes of Health-funded randomized trials (one with two independent parallel arms) of AA facilitation interventions were analyzed using instrumental variables models. Alcohol dependent individuals in one of the datasets (n = 774) were analyzed separately from the rest of sample (n = 1582 individuals pooled from 5 datasets) because of heterogeneity in sample parameters. Randomization itself was used as the instrumental variable. Results Randomization was a good instrument in both samples, effectively predicting increased AA attendance that could not be attributed to self-selection. In five of the six data sets, which were pooled for analysis, increased AA attendance that was attributable to randomization (i.e., free of self-selection bias) was effective at increasing days of abstinence at 3-month (B = .38, p = .001) and 15-month (B = 0.42, p = .04) follow-up. However, in the remaining dataset, in which pre-existing AA attendance was much higher, further increases in AA involvement caused by the randomly assigned facilitation intervention did not affect drinking outcome. Conclusions For most individuals seeking help for alcohol problems, increasing AA attendance leads to short and long term decreases in alcohol consumption that cannot be attributed to self-selection. However, for populations with high pre-existing AA involvement, further increases in AA attendance may have little impact. PMID:25421504

Estimating the efficacy of Alcoholics Anonymous without self-selection bias: an instrumental variables re-analysis of randomized clinical trials.

PubMed

Humphreys, Keith; Blodgett, Janet C; Wagner, Todd H

2014-11-01

Observational studies of Alcoholics Anonymous' (AA) effectiveness are vulnerable to self-selection bias because individuals choose whether or not to attend AA. The present study, therefore, employed an innovative statistical technique to derive a selection bias-free estimate of AA's impact. Six data sets from 5 National Institutes of Health-funded randomized trials (1 with 2 independent parallel arms) of AA facilitation interventions were analyzed using instrumental variables models. Alcohol-dependent individuals in one of the data sets (n = 774) were analyzed separately from the rest of sample (n = 1,582 individuals pooled from 5 data sets) because of heterogeneity in sample parameters. Randomization itself was used as the instrumental variable. Randomization was a good instrument in both samples, effectively predicting increased AA attendance that could not be attributed to self-selection. In 5 of the 6 data sets, which were pooled for analysis, increased AA attendance that was attributable to randomization (i.e., free of self-selection bias) was effective at increasing days of abstinence at 3-month (B = 0.38, p = 0.001) and 15-month (B = 0.42, p = 0.04) follow-up. However, in the remaining data set, in which preexisting AA attendance was much higher, further increases in AA involvement caused by the randomly assigned facilitation intervention did not affect drinking outcome. For most individuals seeking help for alcohol problems, increasing AA attendance leads to short- and long-term decreases in alcohol consumption that cannot be attributed to self-selection. However, for populations with high preexisting AA involvement, further increases in AA attendance may have little impact. Copyright © 2014 by the Research Society on Alcoholism.

An improved switching converter model. Ph.D. Thesis. Final Report

NASA Technical Reports Server (NTRS)

Shortt, D. J.

1982-01-01

The nonlinear modeling and analysis of dc-dc converters in the continuous mode and discontinuous mode was done by averaging and discrete sampling techniques. A model was developed by combining these two techniques. This model, the discrete average model, accurately predicts the envelope of the output voltage and is easy to implement in circuit and state variable forms. The proposed model is shown to be dependent on the type of duty cycle control. The proper selection of the power stage model, between average and discrete average, is largely a function of the error processor in the feedback loop. The accuracy of the measurement data taken by a conventional technique is affected by the conditions at which the data is collected.

ARM-based control system for terry rapier loom

NASA Astrophysics Data System (ADS)

Shi, Weimin; Gu, Yeqing; Wu, Zhenyu; Wang, Fan

2007-12-01

In this paper, a novel ARM-based mechatronics control technique applied in terry rapier loom was presented. Electronic weft selection, electronic fluff, electronic let-off and take-up motions system, which consists of position and speedcontrolled servomechanisms, were studied. The control system configuration, operation principle, and mathematical models of electronic drives system were analyzed. The synchronism among all mechanical motions and an improved intelligent control algorithm for the warp let-off tension control was discussed. The result indict that, by applying electronic and embedded control techniques and the individual servomechanisms, the electronic weft selection, electronic let-off device and electronic take-up device in HGA732T terry rapier loom have greatly simplified the initial complicated mechanism, kept the warp tension constant from full to empty beam, set the variable weft density, eliminated the start mark effectively, promoted its flexibility, reliability and properties, and improved the fabric quality.

Enhancement and Analysis of Human Antiaflatoxin B1 (AFB1) scFv Antibody-Ligand Interaction Using Chain Shuffling.

PubMed

Rangnoi, Kuntalee; Choowongkomon, Kiattawee; O'Kennedy, Richard; Rüker, Florian; Yamabhai, Montarop

2018-06-06

A human antiaflatoxin B1 (AFB1) scFv antibody (yAFB1-c3), selected from a naı̈ve human phage-displayed scFv library, was used as a template for improving and analysis of antibody-ligand interactions using the chain-shuffling technique. The variable-heavy and variable-light (VH/VL)-shuffled library was constructed from the VH of 25 preselected clones recombined with the VL of yAFB1-c3 and vice versa. Affinity selection from these libraries demonstrated that the VH domain played an important role in the binding of scFv to free AFB1. Therefore, in the next step, VH-shuffled scFv library was constructed from variable-heavy (VH) chain repertoires, amplified from the naı̈ve library, recombined with the variable-light (VL) chain of the clone yAFB1-c3. This library was then used to select a specific scFv antibody against soluble AFB1 by a standard biopanning method. Three clones that showed improved binding properties were isolated. Amino acid sequence analysis indicated that the improved clones have amino acid mutations in framework 1 (FR1) and the complementarity determining region (CDR1) of the VH chain. One clone, designated sAFH-3e3, showed 7.5-fold improvement in sensitivity over the original scFv clone and was selected for molecular binding studies with AFB1. Homology modeling and molecular docking were used to compare the binding of this and the original clones. The results confirmed that VH is more important than VL for AFB1 binding.

Path analysis and multi-criteria decision making: an approach for multivariate model selection and analysis in health.

PubMed

Vasconcelos, A G; Almeida, R M; Nobre, F F

2001-08-01

This paper introduces an approach that includes non-quantitative factors for the selection and assessment of multivariate complex models in health. A goodness-of-fit based methodology combined with fuzzy multi-criteria decision-making approach is proposed for model selection. Models were obtained using the Path Analysis (PA) methodology in order to explain the interrelationship between health determinants and the post-neonatal component of infant mortality in 59 municipalities of Brazil in the year 1991. Socioeconomic and demographic factors were used as exogenous variables, and environmental, health service and agglomeration as endogenous variables. Five PA models were developed and accepted by statistical criteria of goodness-of fit. These models were then submitted to a group of experts, seeking to characterize their preferences, according to predefined criteria that tried to evaluate model relevance and plausibility. Fuzzy set techniques were used to rank the alternative models according to the number of times a model was superior to ("dominated") the others. The best-ranked model explained above 90% of the endogenous variables variation, and showed the favorable influences of income and education levels on post-neonatal mortality. It also showed the unfavorable effect on mortality of fast population growth, through precarious dwelling conditions and decreased access to sanitation. It was possible to aggregate expert opinions in model evaluation. The proposed procedure for model selection allowed the inclusion of subjective information in a clear and systematic manner.

Metric Selection for Evaluation of Human Supervisory Control Systems

DTIC Science & Technology

2009-12-01

finding a significant effect when there is none becomes more likely. The inflation of type I error due to multiple dependent variables can be handled...with multivariate analysis techniques, such as Multivariate Analysis of Variance (MANOVA) (Johnson & Wichern, 2002). However, it should be noted that...the few significant differences among many insignificant ones. The best way to avoid failure to identify significant differences is to design an

Novel visualization studies of lignocellulosic oxidation chemistry by application of C-near edge X-ray absorption fine structure spectroscopy

Treesearch

Douglas G. Mancosky; Lucian A. Lucia; Hiroki Nanko; Sue Wirick; Alan W. Rudie; Robert Braun

2005-01-01

The research presented herein is the first attempt to probe the chemical nature of lignocellulosic samples by the application of carbon near edge X-ray absorption fine structure spectroscopy (C-NEXAFS). C-NEXAFS is a soft X-ray technique that principally provides selective interrogation of discrete atomic moieties using photoelectrons of variable energies. The X1A beam...

Early Formulation of Training Programs for Cost Effectiveness Analysis

DTIC Science & Technology

1978-07-01

training approaches. viii Although the method and media variables aid training program selection de- cisions, a technique is also required to monitor...fact that personnel must still be taught certain prerequisite skills and knowledges before they can begin to use the actual equipment, this approach...often difficult to identify causal relations. Good summaries have been produced, e.g., Meister, 1976,4 however, and are a great aid in pull- ing

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.

Focus of attention in an activity-based scheduler

NASA Technical Reports Server (NTRS)

Sadeh, Norman; Fox, Mark S.

1989-01-01

Earlier research in job shop scheduling has demonstrated the advantages of opportunistically combining order-based and resource-based scheduling techniques. An even more flexible approach is investigated where each activity is considered a decision point by itself. Heuristics to opportunistically select the next decision point on which to focus attention (i.e., variable ordering heuristics) and the next decision to be tried at this point (i.e., value ordering heuristics) are described that probabilistically account for both activity precedence and resource requirement interactions. Preliminary experimental results indicate that the variable ordering heuristic greatly increases search efficiency. While least constraining value ordering heuristics have been advocated in the literature, the experimental results suggest that other value ordering heuristics combined with our variable-ordering heuristic can produce much better schedules without significantly increasing search.

Combinatorial Methodology for Screening Selectivity in Polymeric Pervaporation Membranes.

PubMed

Godbole, Rutvik V; Ma, Lan; Doerfert, Michael D; Williams, Porsche; Hedden, Ronald C

2015-11-09

Combinatorial methodology is described for rapid screening of selectivity in polymeric pervaporation membrane materials for alcohol-water separations. The screening technique is demonstrated for ethanol-water separation using a model polyacrylate system. The materials studied are cross-linked random copolymers of a hydrophobic comonomer (n-butyl acrylate, B) and a hydrophilic comonomer (2-hydroxyethyl acrylate, H). A matrix of materials is prepared that has orthogonal variations in two key variables, H:B ratio and cross-linker concentration. For mixtures of ethanol and water, equilibrium selectivities and distribution coefficients are obtained by combining swelling measurements with high-throughput HPLC analysis. Based on the screening results, two copolymers are selected for further study as pervaporation membranes to quantify permeability selectivity and the flux of ethanol. The screening methodology described has good potential to accelerate the search for new membrane materials, as it is adaptable to a broad range of polymer chemistries.

Analyses of the most influential factors for vibration monitoring of planetary power transmissions in pellet mills by adaptive neuro-fuzzy technique

NASA Astrophysics Data System (ADS)

Milovančević, Miloš; Nikolić, Vlastimir; Anđelković, Boban

2017-01-01

Vibration-based structural health monitoring is widely recognized as an attractive strategy for early damage detection in civil structures. Vibration monitoring and prediction is important for any system since it can save many unpredictable behaviors of the system. If the vibration monitoring is properly managed, that can ensure economic and safe operations. Potentials for further improvement of vibration monitoring lie in the improvement of current control strategies. One of the options is the introduction of model predictive control. Multistep ahead predictive models of vibration are a starting point for creating a successful model predictive strategy. For the purpose of this article, predictive models of are created for vibration monitoring of planetary power transmissions in pellet mills. The models were developed using the novel method based on ANFIS (adaptive neuro fuzzy inference system). The aim of this study is to investigate the potential of ANFIS for selecting the most relevant variables for predictive models of vibration monitoring of pellet mills power transmission. The vibration data are collected by PIC (Programmable Interface Controller) microcontrollers. The goal of the predictive vibration monitoring of planetary power transmissions in pellet mills is to indicate deterioration in the vibration of the power transmissions before the actual failure occurs. The ANFIS process for variable selection was implemented in order to detect the predominant variables affecting the prediction of vibration monitoring. It was also used to select the minimal input subset of variables from the initial set of input variables - current and lagged variables (up to 11 steps) of vibration. The obtained results could be used for simplification of predictive methods so as to avoid multiple input variables. It was preferable to used models with less inputs because of overfitting between training and testing data. While the obtained results are promising, further work is required in order to get results that could be directly applied in practice.

Peering beneath the surface: novel imaging techniques to noninvasively select gametes and embryos for ART.

PubMed

Jasensky, Joshua; Swain, Jason E

2013-10-01

Embryo imaging has long been a critical tool for in vitro fertilization laboratories, aiding in morphological assessment of embryos, which remains the primary tool for embryo selection. With the recent emergence of clinically applicable real-time imaging systems to assess embryo morphokinetics, a renewed interest has emerged regarding noninvasive methods to assess gamete and embryo development as a means of inferring quality. Several studies exist that utilize novel imaging techniques to visualize or quantify intracellular components of gametes and embryos with the intent of correlating localization of organelles or molecular constitution with quality or outcome. However, the safety of these approaches varies due to the potential detrimental impact of light exposure or other variables. Along with complexity of equipment and cost, these drawbacks currently limit clinical application of these novel microscopes and imaging techniques. However, as evidenced by clinical incorporation of some real-time imaging devices as well as use of polarized microscopy, some of these imaging approaches may prove to be useful. This review summarizes the existing literature on novel imaging approaches utilized to examine gametes and embryos. Refinement of some of these imaging systems may permit clinical application and serve as a means to offer new, noninvasive selection tools to improve outcomes for various assisted reproductive technology procedures.

Analysis of Sediment Transport for Rivers in South Korea based on Data Mining technique

NASA Astrophysics Data System (ADS)

Jang, Eun-kyung; Ji, Un; Yeo, Woonkwang

2017-04-01

The purpose of this study is to calculate of sediment discharge assessment using data mining in South Korea. The Model Tree was selected for this study which is the most suitable technique to explicitly analyze the relationship between input and output variables in various and diverse databases among the Data Mining. In order to derive the sediment discharge equation using the Model Tree of Data Mining used the dimensionless variables used in Engelund and Hansen, Ackers and White, Brownlie and van Rijn equations as the analytical condition. In addition, total of 14 analytical conditions were set considering the conditions dimensional variables and the combination conditions of the dimensionless variables and the dimensional variables according to the relationship between the flow and the sediment transport. For each case, the analysis results were analyzed by mean of discrepancy ratio, root mean square error, mean absolute percent error, correlation coefficient. The results showed that the best fit was obtained by using five dimensional variables such as velocity, depth, slope, width and Median Diameter. And closest approximation to the best goodness-of-fit was estimated from the depth, slope, width, main grain size of bed material and dimensionless tractive force and except for the slope in the single variable. In addition, the three types of Model Tree that are most appropriate are compared with the Ackers and White equation which is the best fit among the existing equations, the mean discrepancy ration and the correlation coefficient of the Model Tree are improved compared to the Ackers and White equation.

Relationships among selected physical science misconceptions held by preservice elementary teachers and four variables: Formal reasoning ability, working memory capacity, verbal intelligence, and field dependence/independence

NASA Astrophysics Data System (ADS)

Griffin, Leslie Little

The purpose of this study was to determine the relationship of selected cognitive abilities and physical science misconceptions held by preservice elementary teachers. The cognitive abilities under investigation were: formal reasoning ability as measured by the Lawson Classroom Test of Formal Reasoning (Lawson, 1978); working memory capacity as measured by the Figural Intersection Test (Burtis & Pascual-Leone, 1974); verbal intelligence as measured by the Acorn National Academic Aptitude Test: Verbal Intelligence (Kobal, Wrightstone, & Kunze, 1944); and field dependence/independence as measured by the Group Embedded Figures Test (Witkin, Oltman, & Raskin, 1971). The number of physical science misconceptions held by preservice elementary teachers was measured by the Misconceptions in Science Questionnaire (Franklin, 1992). The data utilized in this investigation were obtained from 36 preservice elementary teachers enrolled in two sections of a science methods course at a small regional university in the southeastern United States. Multiple regression techniques were used to analyze the collected data. The following conclusions were reached following an analysis of the data. The variables of formal reasoning ability and verbal intelligence were identified as having significant relationships, both individually and in combination, to the dependent variable of selected physical science misconceptions. Though the correlations were not high enough to yield strong predictors of physical science misconceptions or strong relationships, they were of sufficient magnitude to warrant further investigation. It is recommended that further investigation be conducted replicating this study with a larger sample size. In addition, experimental research should be implemented to explore the relationships suggested in this study between the cognitive variables of formal reasoning ability and verbal intelligence and the dependent variable of selected physical science misconceptions. Further research should also focus on the detection of a broad range of science misconceptions among preservice elementary teachers.

Magnetization dynamics of weak stripe domains in Fe-N thin films: a multi-technique complementary approach.

PubMed

Camara, Ibrahima; Tacchi, Silvia; Garnier, Louis-Charles; Eddrief, Mahmoud; Fortuna, Franck; Carlotti, Giovanni; Marangolo, Massimiliano

2017-09-26

The resonant eigenmodes of a nitrogen-implanted iron α'-FeN characterized by weak stripe domains are investigated by Brillouin light scattering and broadband ferromagnetic resonance experiments, assisted by micromagnetic simulations. The spectrum of the dynamic eigenmodes in the presence of the weak stripes is very rich and two different families of modes can be selectively detected using different techniques or different experimental configurations. Attention is paid to the evolution of the mode frequencies and spatial profiles under the application of an external magnetic field, of variable intensity, in the direction parallel or transverse to the stripes. The different evolution of the modes with the external magnetic field is accompanied by a distinctive spatial localization in specific regions, such as the closure domains at the surface of the stripes and the bulk domains localized in the inner part of the stripes. The complementarity of BLS and FMR techniques, based on different selection rules, is found to be a fruitful tool for the study of the wealth of localized mag-netic excitations generally found in nanostructures. © 2017 IOP Publishing Ltd.

Magnetization dynamics of weak stripe domains in Fe-N thin films: a multi-technique complementary approach

NASA Astrophysics Data System (ADS)

Camara, I. S.; Tacchi, S.; Garnier, L.-C.; Eddrief, M.; Fortuna, F.; Carlotti, G.; Marangolo, M.

2017-11-01

The resonant eigenmodes of an α‧-FeN thin film characterized by weak stripe domains are investigated by Brillouin light scattering and broadband ferromagnetic resonance experiments, assisted by micromagnetic simulations. The spectrum of the dynamic eigenmodes in the presence of the weak stripes is very rich and two different families of modes can be selectively detected using different techniques or different experimental configurations. Attention is paid to the evolution of the mode frequencies and spatial profiles under the application of an external magnetic field, of variable intensity, in the direction parallel or transverse to the stripes. The different evolution of the modes with the external magnetic field is accompanied by a distinctive spatial localization in specific regions, such as the closure domains at the surface of the stripes and the bulk domains localized in the inner part of the stripes. The complementarity of BLS and FMR techniques, based on different selection rules, is found to be a fruitful tool for the study of the wealth of localized magnetic excitations generally found in nanostructures.

Structural reliability assessment of the Oman India Pipeline

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

Al-Sharif, A.M.; Preston, R.

1996-12-31

Reliability techniques are increasingly finding application in design. The special design conditions for the deep water sections of the Oman India Pipeline dictate their use since the experience basis for application of standard deterministic techniques is inadequate. The paper discusses the reliability analysis as applied to the Oman India Pipeline, including selection of a collapse model, characterization of the variability in the parameters that affect pipe resistance to collapse, and implementation of first and second order reliability analyses to assess the probability of pipe failure. The reliability analysis results are used as the basis for establishing the pipe wall thicknessmore » requirements for the pipeline.« less

Pedunculopontine Nucleus Region Deep Brain Stimulation in Parkinson Disease: Surgical Techniques, Side Effects, and Postoperative Imaging

PubMed Central

Hamani, Clement; Lozano, Andres M.; Mazzone, Paolo A.M.; Moro, Elena; Hutchison, William; Silburn, Peter A.; Zrinzo, Ludvic; Alam, Mesbah; Goetz, Laurent; Pereira, Erlick; Rughani, Anand; Thevathasan, Wesley; Aziz, Tipu; Bloem, Bastiaan R.; Brown, Peter; Chabardes, Stephan; Coyne, Terry; Foote, Kelly; Garcia-Rill, Edgar; Hirsch, Etienne C.; Okun, Michael S.; Krauss, Joachim K.

2017-01-01

The pedunculopontine nucleus (PPN) region has received considerable attention in clinical studies as a target for deep brain stimulation (DBS) in Parkinson disease. These studies have yielded variable results with an overall impression of improvement in falls and freezing in many but not all patients treated. We evaluated the available data on the surgical anatomy and terminology of the PPN region in a companion paper. Here we focus on issues concerning surgical technique, imaging, and early side effects of surgery. The aim of this paper was to gain more insight into the reasoning for choosing specific techniques and to discuss short-comings of available studies. Our data demonstrate the wide range in almost all fields which were investigated. There are a number of important challenges to be resolved, such as identification of the optimal target, the choice of the surgical approach to optimize electrode placement, the impact on the outcome of specific surgical techniques, the reliability of intraoperative confirmation of the target, and methodological differences in postoperative validation of the electrode position. There is considerable variability both within and across groups, the overall experience with PPN DBS is still limited, and there is a lack of controlled trials. Despite these challenges, the procedure seems to provide benefit to selected patients and appears to be relatively safe. One important limitation in comparing studies from different centers and analyzing outcomes is the great variability in targeting and surgical techniques, as shown in our paper. The challenges we identified will be of relevance when designing future studies to better address several controversial issues. We hope that the data we accumulated may facilitate the development of surgical protocols for PPN DBS. PMID:27728909

Comparison of baseline removal methods for laser-induced breakdown spectroscopy of geological samples

NASA Astrophysics Data System (ADS)

Dyar, M. Darby; Giguere, Stephen; Carey, CJ; Boucher, Thomas

2016-12-01

This project examines the causes, effects, and optimization of continuum removal in laser-induced breakdown spectroscopy (LIBS) to produce the best possible prediction accuracy of elemental composition in geological samples. We compare prediction accuracy resulting from several different techniques for baseline removal, including asymmetric least squares (ALS), adaptive iteratively reweighted penalized least squares (Air-PLS), fully automatic baseline correction (FABC), continuous wavelet transformation, median filtering, polynomial fitting, the iterative thresholding Dietrich method, convex hull/rubber band techniques, and a newly-developed technique for Custom baseline removal (BLR). We assess the predictive performance of these methods using partial least-squares analysis for 13 elements of geological interest, expressed as the weight percentages of SiO2, Al2O3, TiO2, FeO, MgO, CaO, Na2O, K2O, and the parts per million concentrations of Ni, Cr, Zn, Mn, and Co. We find that previously published methods for baseline subtraction generally produce equivalent prediction accuracies for major elements. When those pre-existing methods are used, automated optimization of their adjustable parameters is always necessary to wring the best predictive accuracy out of a data set; ideally, it should be done for each individual variable. The new technique of Custom BLR produces significant improvements in prediction accuracy over existing methods across varying geological data sets, instruments, and varying analytical conditions. These results also demonstrate the dual objectives of the continuum removal problem: removing a smooth underlying signal to fit individual peaks (univariate analysis) versus using feature selection to select only those channels that contribute to best prediction accuracy for multivariate analyses. Overall, the current practice of using generalized, one-method-fits-all-spectra baseline removal results in poorer predictive performance for all methods. The extra steps needed to optimize baseline removal for each predicted variable and empower multivariate techniques with the best possible input data for optimal prediction accuracy are shown to be well worth the slight increase in necessary computations and complexity.

Development of a computer-based clinical decision support tool for selecting appropriate rehabilitation interventions for injured workers.

PubMed

Gross, Douglas P; Zhang, Jing; Steenstra, Ivan; Barnsley, Susan; Haws, Calvin; Amell, Tyler; McIntosh, Greg; Cooper, Juliette; Zaiane, Osmar

2013-12-01

To develop a classification algorithm and accompanying computer-based clinical decision support tool to help categorize injured workers toward optimal rehabilitation interventions based on unique worker characteristics. Population-based historical cohort design. Data were extracted from a Canadian provincial workers' compensation database on all claimants undergoing work assessment between December 2009 and January 2011. Data were available on: (1) numerous personal, clinical, occupational, and social variables; (2) type of rehabilitation undertaken; and (3) outcomes following rehabilitation (receiving time loss benefits or undergoing repeat programs). Machine learning, concerned with the design of algorithms to discriminate between classes based on empirical data, was the foundation of our approach to build a classification system with multiple independent and dependent variables. The population included 8,611 unique claimants. Subjects were predominantly employed (85 %) males (64 %) with diagnoses of sprain/strain (44 %). Baseline clinician classification accuracy was high (ROC = 0.86) for selecting programs that lead to successful return-to-work. Classification performance for machine learning techniques outperformed the clinician baseline classification (ROC = 0.94). The final classifiers were multifactorial and included the variables: injury duration, occupation, job attachment status, work status, modified work availability, pain intensity rating, self-rated occupational disability, and 9 items from the SF-36 Health Survey. The use of machine learning classification techniques appears to have resulted in classification performance better than clinician decision-making. The final algorithm has been integrated into a computer-based clinical decision support tool that requires additional validation in a clinical sample.

Development, refinement, and testing of a short term solar flare prediction algorithm

NASA Technical Reports Server (NTRS)

Smith, Jesse B., Jr.

1993-01-01

During the period included in this report, the expenditure of time and effort, and progress toward performance of the tasks and accomplishing the goals set forth in the two year research grant proposal, consisted primarily of calibration and analysis of selected data sets. The heliographic limits of 30 degrees from central meridian were continued. As previously reported, all analyses are interactive and are performed by the Principal Investigator. It should also be noted that the analysis time involved by the Principal Investigator during this reporting period was limited, partially due to illness and partially resulting from other uncontrollable factors. The calibration technique (as developed by MSFC solar scientists), incorporates sets of constants which vary according to the wave length of the observation data set. One input constant is then varied interactively to correct for observing conditions, etc., to result in a maximum magnetic field strength (in the calibrated data), based on a separate analysis. There is some insecurity in the methodology and the selection of variables to yield the most self-consistent results for variable maximum field strengths and for variable observing/atmospheric conditions. Several data sets were analyzed using differing constant sets, and separate analyses to differing maximum field strength - toward standardizing methodology and technique for the most self-consistent results for the large number of cases. It may be necessary to recalibrate some of the analyses, but the sc analyses are retained on the optical disks and can still be used with recalibration where necessary. Only the extracted parameters will be changed.

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.

Image Display and Manipulation System (IDAMS) program documentation, Appendixes A-D. [including routines, convolution filtering, image expansion, and fast Fourier transformation

NASA Technical Reports Server (NTRS)

Cecil, R. W.; White, R. A.; Szczur, M. R.

1972-01-01

The IDAMS Processor is a package of task routines and support software that performs convolution filtering, image expansion, fast Fourier transformation, and other operations on a digital image tape. A unique task control card for that program, together with any necessary parameter cards, selects each processing technique to be applied to the input image. A variable number of tasks can be selected for execution by including the proper task and parameter cards in the input deck. An executive maintains control of the run; it initiates execution of each task in turn and handles any necessary error processing.

Automatic measurement of images on astrometric plates

NASA Astrophysics Data System (ADS)

Ortiz Gil, A.; Lopez Garcia, A.; Martinez Gonzalez, J. M.; Yershov, V.

1994-04-01

We present some results on the process of automatic detection and measurement of objects in overlapped fields of astrometric plates. The main steps of our algorithm are the following: determination of the Scale and Tilt between charge coupled devices (CCD) and microscope coordinate systems and estimation of signal-to-noise ratio in each field;--image identification and improvement of its position and size;--image final centering;--image selection and storage. Several parameters allow the use of variable criteria for image identification, characterization and selection. Problems related with faint images and crowded fields will be approached by special techniques (morphological filters, histogram properties and fitting 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.

Genetic and Psychosocial Predictors of Aggression: Variable Selection and Model Building With Component-Wise Gradient Boosting.

PubMed

Suchting, Robert; Gowin, Joshua L; Green, Charles E; Walss-Bass, Consuelo; Lane, Scott D

2018-01-01

Rationale : Given datasets with a large or diverse set of predictors of aggression, machine learning (ML) provides efficient tools for identifying the most salient variables and building a parsimonious statistical model. ML techniques permit efficient exploration of data, have not been widely used in aggression research, and may have utility for those seeking prediction of aggressive behavior. Objectives : The present study examined predictors of aggression and constructed an optimized model using ML techniques. Predictors were derived from a dataset that included demographic, psychometric and genetic predictors, specifically FK506 binding protein 5 (FKBP5) polymorphisms, which have been shown to alter response to threatening stimuli, but have not been tested as predictors of aggressive behavior in adults. Methods : The data analysis approach utilized component-wise gradient boosting and model reduction via backward elimination to: (a) select variables from an initial set of 20 to build a model of trait aggression; and then (b) reduce that model to maximize parsimony and generalizability. Results : From a dataset of N = 47 participants, component-wise gradient boosting selected 8 of 20 possible predictors to model Buss-Perry Aggression Questionnaire (BPAQ) total score, with R 2 = 0.66. This model was simplified using backward elimination, retaining six predictors: smoking status, psychopathy (interpersonal manipulation and callous affect), childhood trauma (physical abuse and neglect), and the FKBP5_13 gene (rs1360780). The six-factor model approximated the initial eight-factor model at 99.4% of R 2 . Conclusions : Using an inductive data science approach, the gradient boosting model identified predictors consistent with previous experimental work in aggression; specifically psychopathy and trauma exposure. Additionally, allelic variants in FKBP5 were identified for the first time, but the relatively small sample size limits generality of results and calls for replication. This approach provides utility for the prediction of aggression behavior, particularly in the context of large multivariate datasets.

Multi-atlas segmentation of subcortical brain structures via the AutoSeg software pipeline

PubMed Central

Wang, Jiahui; Vachet, Clement; Rumple, Ashley; Gouttard, Sylvain; Ouziel, Clémentine; Perrot, Emilie; Du, Guangwei; Huang, Xuemei; Gerig, Guido; Styner, Martin

2014-01-01

Automated segmenting and labeling of individual brain anatomical regions, in MRI are challenging, due to the issue of individual structural variability. Although atlas-based segmentation has shown its potential for both tissue and structure segmentation, due to the inherent natural variability as well as disease-related changes in MR appearance, a single atlas image is often inappropriate to represent the full population of datasets processed in a given neuroimaging study. As an alternative for the case of single atlas segmentation, the use of multiple atlases alongside label fusion techniques has been introduced using a set of individual “atlases” that encompasses the expected variability in the studied population. In our study, we proposed a multi-atlas segmentation scheme with a novel graph-based atlas selection technique. We first paired and co-registered all atlases and the subject MR scans. A directed graph with edge weights based on intensity and shape similarity between all MR scans is then computed. The set of neighboring templates is selected via clustering of the graph. Finally, weighted majority voting is employed to create the final segmentation over the selected atlases. This multi-atlas segmentation scheme is used to extend a single-atlas-based segmentation toolkit entitled AutoSeg, which is an open-source, extensible C++ based software pipeline employing BatchMake for its pipeline scripting, developed at the Neuro Image Research and Analysis Laboratories of the University of North Carolina at Chapel Hill. AutoSeg performs N4 intensity inhomogeneity correction, rigid registration to a common template space, automated brain tissue classification based skull-stripping, and the multi-atlas segmentation. The multi-atlas-based AutoSeg has been evaluated on subcortical structure segmentation with a testing dataset of 20 adult brain MRI scans and 15 atlas MRI scans. The AutoSeg achieved mean Dice coefficients of 81.73% for the subcortical structures. PMID:24567717

Improvement of near infrared spectroscopic (NIRS) analysis of caffeine in roasted Arabica coffee by variable selection method of stability competitive adaptive reweighted sampling (SCARS).

PubMed

Zhang, Xuan; Li, Wei; Yin, Bin; Chen, Weizhong; Kelly, Declan P; Wang, Xiaoxin; Zheng, Kaiyi; Du, Yiping

2013-10-01

Coffee is the most heavily consumed beverage in the world after water, for which quality is a key consideration in commercial trade. Therefore, caffeine content which has a significant effect on the final quality of the coffee products requires to be determined fast and reliably by new analytical techniques. The main purpose of this work was to establish a powerful and practical analytical method based on near infrared spectroscopy (NIRS) and chemometrics for quantitative determination of caffeine content in roasted Arabica coffees. Ground coffee samples within a wide range of roasted levels were analyzed by NIR, meanwhile, in which the caffeine contents were quantitative determined by the most commonly used HPLC-UV method as the reference values. Then calibration models based on chemometric analyses of the NIR spectral data and reference concentrations of coffee samples were developed. Partial least squares (PLS) regression was used to construct the models. Furthermore, diverse spectra pretreatment and variable selection techniques were applied in order to obtain robust and reliable reduced-spectrum regression models. Comparing the respective quality of the different models constructed, the application of second derivative pretreatment and stability competitive adaptive reweighted sampling (SCARS) variable selection provided a notably improved regression model, with root mean square error of cross validation (RMSECV) of 0.375 mg/g and correlation coefficient (R) of 0.918 at PLS factor of 7. An independent test set was used to assess the model, with the root mean square error of prediction (RMSEP) of 0.378 mg/g, mean relative error of 1.976% and mean relative standard deviation (RSD) of 1.707%. Thus, the results provided by the high-quality calibration model revealed the feasibility of NIR spectroscopy for at-line application to predict the caffeine content of unknown roasted coffee samples, thanks to the short analysis time of a few seconds and non-destructive advantages of NIRS. Copyright © 2013 Elsevier B.V. All rights reserved.

Improvement of near infrared spectroscopic (NIRS) analysis of caffeine in roasted Arabica coffee by variable selection method of stability competitive adaptive reweighted sampling (SCARS)

NASA Astrophysics Data System (ADS)

Zhang, Xuan; Li, Wei; Yin, Bin; Chen, Weizhong; Kelly, Declan P.; Wang, Xiaoxin; Zheng, Kaiyi; Du, Yiping

2013-10-01

Coffee is the most heavily consumed beverage in the world after water, for which quality is a key consideration in commercial trade. Therefore, caffeine content which has a significant effect on the final quality of the coffee products requires to be determined fast and reliably by new analytical techniques. The main purpose of this work was to establish a powerful and practical analytical method based on near infrared spectroscopy (NIRS) and chemometrics for quantitative determination of caffeine content in roasted Arabica coffees. Ground coffee samples within a wide range of roasted levels were analyzed by NIR, meanwhile, in which the caffeine contents were quantitative determined by the most commonly used HPLC-UV method as the reference values. Then calibration models based on chemometric analyses of the NIR spectral data and reference concentrations of coffee samples were developed. Partial least squares (PLS) regression was used to construct the models. Furthermore, diverse spectra pretreatment and variable selection techniques were applied in order to obtain robust and reliable reduced-spectrum regression models. Comparing the respective quality of the different models constructed, the application of second derivative pretreatment and stability competitive adaptive reweighted sampling (SCARS) variable selection provided a notably improved regression model, with root mean square error of cross validation (RMSECV) of 0.375 mg/g and correlation coefficient (R) of 0.918 at PLS factor of 7. An independent test set was used to assess the model, with the root mean square error of prediction (RMSEP) of 0.378 mg/g, mean relative error of 1.976% and mean relative standard deviation (RSD) of 1.707%. Thus, the results provided by the high-quality calibration model revealed the feasibility of NIR spectroscopy for at-line application to predict the caffeine content of unknown roasted coffee samples, thanks to the short analysis time of a few seconds and non-destructive advantages of NIRS.

Statistical optimisation techniques in fatigue signal editing problem

NASA Astrophysics Data System (ADS)

Nopiah, Z. M.; Osman, M. H.; Baharin, N.; Abdullah, S.

2015-02-01

Success in fatigue signal editing is determined by the level of length reduction without compromising statistical constraints. A great reduction rate can be achieved by removing small amplitude cycles from the recorded signal. The long recorded signal sometimes renders the cycle-to-cycle editing process daunting. This has encouraged researchers to focus on the segment-based approach. This paper discusses joint application of the Running Damage Extraction (RDE) technique and single constrained Genetic Algorithm (GA) in fatigue signal editing optimisation.. In the first section, the RDE technique is used to restructure and summarise the fatigue strain. This technique combines the overlapping window and fatigue strain-life models. It is designed to identify and isolate the fatigue events that exist in the variable amplitude strain data into different segments whereby the retention of statistical parameters and the vibration energy are considered. In the second section, the fatigue data editing problem is formulated as a constrained single optimisation problem that can be solved using GA method. The GA produces the shortest edited fatigue signal by selecting appropriate segments from a pool of labelling segments. Challenges arise due to constraints on the segment selection by deviation level over three signal properties, namely cumulative fatigue damage, root mean square and kurtosis values. Experimental results over several case studies show that the idea of solving fatigue signal editing within a framework of optimisation is effective and automatic, and that the GA is robust for constrained segment selection.

Statistical optimisation techniques in fatigue signal editing problem

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

Nopiah, Z. M.; Osman, M. H.; Baharin, N.

Success in fatigue signal editing is determined by the level of length reduction without compromising statistical constraints. A great reduction rate can be achieved by removing small amplitude cycles from the recorded signal. The long recorded signal sometimes renders the cycle-to-cycle editing process daunting. This has encouraged researchers to focus on the segment-based approach. This paper discusses joint application of the Running Damage Extraction (RDE) technique and single constrained Genetic Algorithm (GA) in fatigue signal editing optimisation.. In the first section, the RDE technique is used to restructure and summarise the fatigue strain. This technique combines the overlapping window andmore » fatigue strain-life models. It is designed to identify and isolate the fatigue events that exist in the variable amplitude strain data into different segments whereby the retention of statistical parameters and the vibration energy are considered. In the second section, the fatigue data editing problem is formulated as a constrained single optimisation problem that can be solved using GA method. The GA produces the shortest edited fatigue signal by selecting appropriate segments from a pool of labelling segments. Challenges arise due to constraints on the segment selection by deviation level over three signal properties, namely cumulative fatigue damage, root mean square and kurtosis values. Experimental results over several case studies show that the idea of solving fatigue signal editing within a framework of optimisation is effective and automatic, and that the GA is robust for constrained segment selection.« less

Legitimate Techniques for Improving the R-Square and Related Statistics of a Multiple Regression Model

DTIC Science & Technology

1981-01-01

explanatory variable has been ommitted. Ramsey (1974) has developed a rather interesting test for detecting specification errors using estimates of the...Peter. (1979) A Guide to Econometrics , Cambridge, MA: The MIT Press. Ramsey , J.B. (1974), "Classical Model Selection Through Specification Error... Tests ," in P. Zarembka, Ed. Frontiers in Econometrics , New York: Academia Press. Theil, Henri. (1971), Principles of Econometrics , New York: John Wiley

Machine learning and microsimulation techniques on the prognosis of dementia: A systematic literature review.

PubMed

Dallora, Ana Luiza; Eivazzadeh, Shahryar; Mendes, Emilia; Berglund, Johan; Anderberg, Peter

2017-01-01

Dementia is a complex disorder characterized by poor outcomes for the patients and high costs of care. After decades of research little is known about its mechanisms. Having prognostic estimates about dementia can help researchers, patients and public entities in dealing with this disorder. Thus, health data, machine learning and microsimulation techniques could be employed in developing prognostic estimates for dementia. The goal of this paper is to present evidence on the state of the art of studies investigating and the prognosis of dementia using machine learning and microsimulation techniques. To achieve our goal we carried out a systematic literature review, in which three large databases-Pubmed, Socups and Web of Science were searched to select studies that employed machine learning or microsimulation techniques for the prognosis of dementia. A single backward snowballing was done to identify further studies. A quality checklist was also employed to assess the quality of the evidence presented by the selected studies, and low quality studies were removed. Finally, data from the final set of studies were extracted in summary tables. In total 37 papers were included. The data summary results showed that the current research is focused on the investigation of the patients with mild cognitive impairment that will evolve to Alzheimer's disease, using machine learning techniques. Microsimulation studies were concerned with cost estimation and had a populational focus. Neuroimaging was the most commonly used variable. Prediction of conversion from MCI to AD is the dominant theme in the selected studies. Most studies used ML techniques on Neuroimaging data. Only a few data sources have been recruited by most studies and the ADNI database is the one most commonly used. Only two studies have investigated the prediction of epidemiological aspects of Dementia using either ML or MS techniques. Finally, care should be taken when interpreting the reported accuracy of ML techniques, given studies' different contexts.

Machine learning and microsimulation techniques on the prognosis of dementia: A systematic literature review

PubMed Central

Mendes, Emilia; Berglund, Johan; Anderberg, Peter

2017-01-01

Background Dementia is a complex disorder characterized by poor outcomes for the patients and high costs of care. After decades of research little is known about its mechanisms. Having prognostic estimates about dementia can help researchers, patients and public entities in dealing with this disorder. Thus, health data, machine learning and microsimulation techniques could be employed in developing prognostic estimates for dementia. Objective The goal of this paper is to present evidence on the state of the art of studies investigating and the prognosis of dementia using machine learning and microsimulation techniques. Method To achieve our goal we carried out a systematic literature review, in which three large databases—Pubmed, Socups and Web of Science were searched to select studies that employed machine learning or microsimulation techniques for the prognosis of dementia. A single backward snowballing was done to identify further studies. A quality checklist was also employed to assess the quality of the evidence presented by the selected studies, and low quality studies were removed. Finally, data from the final set of studies were extracted in summary tables. Results In total 37 papers were included. The data summary results showed that the current research is focused on the investigation of the patients with mild cognitive impairment that will evolve to Alzheimer’s disease, using machine learning techniques. Microsimulation studies were concerned with cost estimation and had a populational focus. Neuroimaging was the most commonly used variable. Conclusions Prediction of conversion from MCI to AD is the dominant theme in the selected studies. Most studies used ML techniques on Neuroimaging data. Only a few data sources have been recruited by most studies and the ADNI database is the one most commonly used. Only two studies have investigated the prediction of epidemiological aspects of Dementia using either ML or MS techniques. Finally, care should be taken when interpreting the reported accuracy of ML techniques, given studies’ different contexts. PMID:28662070

Bayesian Techniques for Comparing Time-dependent GRMHD Simulations to Variable Event Horizon Telescope Observations

NASA Astrophysics Data System (ADS)

Kim, Junhan; Marrone, Daniel P.; Chan, Chi-Kwan; Medeiros, Lia; Özel, Feryal; Psaltis, Dimitrios

2016-12-01

The Event Horizon Telescope (EHT) is a millimeter-wavelength, very-long-baseline interferometry (VLBI) experiment that is capable of observing black holes with horizon-scale resolution. Early observations have revealed variable horizon-scale emission in the Galactic Center black hole, Sagittarius A* (Sgr A*). Comparing such observations to time-dependent general relativistic magnetohydrodynamic (GRMHD) simulations requires statistical tools that explicitly consider the variability in both the data and the models. We develop here a Bayesian method to compare time-resolved simulation images to variable VLBI data, in order to infer model parameters and perform model comparisons. We use mock EHT data based on GRMHD simulations to explore the robustness of this Bayesian method and contrast it to approaches that do not consider the effects of variability. We find that time-independent models lead to offset values of the inferred parameters with artificially reduced uncertainties. Moreover, neglecting the variability in the data and the models often leads to erroneous model selections. We finally apply our method to the early EHT data on Sgr A*.

BAYESIAN TECHNIQUES FOR COMPARING TIME-DEPENDENT GRMHD SIMULATIONS TO VARIABLE EVENT HORIZON TELESCOPE OBSERVATIONS

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

Kim, Junhan; Marrone, Daniel P.; Chan, Chi-Kwan

2016-12-01

The Event Horizon Telescope (EHT) is a millimeter-wavelength, very-long-baseline interferometry (VLBI) experiment that is capable of observing black holes with horizon-scale resolution. Early observations have revealed variable horizon-scale emission in the Galactic Center black hole, Sagittarius A* (Sgr A*). Comparing such observations to time-dependent general relativistic magnetohydrodynamic (GRMHD) simulations requires statistical tools that explicitly consider the variability in both the data and the models. We develop here a Bayesian method to compare time-resolved simulation images to variable VLBI data, in order to infer model parameters and perform model comparisons. We use mock EHT data based on GRMHD simulations to explore themore » robustness of this Bayesian method and contrast it to approaches that do not consider the effects of variability. We find that time-independent models lead to offset values of the inferred parameters with artificially reduced uncertainties. Moreover, neglecting the variability in the data and the models often leads to erroneous model selections. We finally apply our method to the early EHT data on Sgr A*.« less

Application of multivariable statistical techniques in plant-wide WWTP control strategies analysis.

PubMed

Flores, X; Comas, J; Roda, I R; Jiménez, L; Gernaey, K V

2007-01-01

The main objective of this paper is to present the application of selected multivariable statistical techniques in plant-wide wastewater treatment plant (WWTP) control strategies analysis. In this study, cluster analysis (CA), principal component analysis/factor analysis (PCA/FA) and discriminant analysis (DA) are applied to the evaluation matrix data set obtained by simulation of several control strategies applied to the plant-wide IWA Benchmark Simulation Model No 2 (BSM2). These techniques allow i) to determine natural groups or clusters of control strategies with a similar behaviour, ii) to find and interpret hidden, complex and casual relation features in the data set and iii) to identify important discriminant variables within the groups found by the cluster analysis. This study illustrates the usefulness of multivariable statistical techniques for both analysis and interpretation of the complex multicriteria data sets and allows an improved use of information for effective evaluation of control strategies.

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.

Regional changes in extreme monsoon rainfall deficit and excess in India

NASA Astrophysics Data System (ADS)

Pal, Indrani; Al-Tabbaa, Abir

2010-04-01

With increasing concerns about climate change, the need to understand the nature and variability of monsoon climatic conditions and to evaluate possible future changes becomes increasingly important. This paper deals with the changes in frequency and magnitudes of extreme monsoon rainfall deficiency and excess in India from 1871 to 2005. Five regions across India comprising variable climates were selected for the study. Apart from changes in individual regions, changing tendencies in extreme monsoon rainfall deficit and excess were also determined for the Indian region as a whole. The trends and their significance were assessed using non-parametric Mann-Kendall technique. The results show that intra-region variability for extreme monsoon seasonal precipitation is large and mostly exhibited a negative tendency leading to increasing frequency and magnitude of monsoon rainfall deficit and decreasing frequency and magnitude of monsoon rainfall excess.

A computing method for sound propagation through a nonuniform jet stream

NASA Technical Reports Server (NTRS)

Padula, S. L.; Liu, C. H.

1974-01-01

The classical formulation of sound propagation through a jet flow was found to be inadequate for computer solutions. Previous investigations selected the phase and amplitude of the acoustic pressure as dependent variables requiring the solution of a system of nonlinear algebraic equations. The nonlinearities complicated both the analysis and the computation. A reformulation of the convective wave equation in terms of a new set of dependent variables is developed with a special emphasis on its suitability for numerical solutions on fast computers. The technique is very attractive because the resulting equations are linear in nonwaving variables. The computer solution to such a linear system of algebraic equations may be obtained by well-defined and direct means which are conservative of computer time and storage space. Typical examples are illustrated and computational results are compared with available numerical and experimental data.

Patent Analysis for Supporting Merger and Acquisition (M&A) Prediction: A Data Mining Approach

NASA Astrophysics Data System (ADS)

Wei, Chih-Ping; Jiang, Yu-Syun; Yang, Chin-Sheng

M&A plays an increasingly important role in the contemporary business environment. Companies usually conduct M&A to pursue complementarity from other companies for preserving and/or extending their competitive advantages. For the given bidder company, a critical first step to the success of M&A activities is the appropriate selection of target companies. However, existing studies on M&A prediction incur several limitations, such as the exclusion of technological variables in M&A prediction models and the omission of the profile of the respective bidder company and its compatibility with candidate target companies. In response to these limitations, we propose an M&A prediction technique which not only encompasses technological variables derived from patent analysis as prediction indictors but also takes into account the profiles of both bidder and candidate target companies when building an M&A prediction model. We collect a set of real-world M&A cases to evaluate the proposed technique. The evaluation results are encouraging and will serve as a basis for future studies.

Trends in non-stationary signal processing techniques applied to vibration analysis of wind turbine drive train - A contemporary survey

NASA Astrophysics Data System (ADS)

Uma Maheswari, R.; Umamaheswari, R.

2017-02-01

Condition Monitoring System (CMS) substantiates potential economic benefits and enables prognostic maintenance in wind turbine-generator failure prevention. Vibration Monitoring and Analysis is a powerful tool in drive train CMS, which enables the early detection of impending failure/damage. In variable speed drives such as wind turbine-generator drive trains, the vibration signal acquired is of non-stationary and non-linear. The traditional stationary signal processing techniques are inefficient to diagnose the machine faults in time varying conditions. The current research trend in CMS for drive-train focuses on developing/improving non-linear, non-stationary feature extraction and fault classification algorithms to improve fault detection/prediction sensitivity and selectivity and thereby reducing the misdetection and false alarm rates. In literature, review of stationary signal processing algorithms employed in vibration analysis is done at great extent. In this paper, an attempt is made to review the recent research advances in non-linear non-stationary signal processing algorithms particularly suited for variable speed wind turbines.

Application of Rosenbrock search technique to reduce the drilling cost of a well in Bai-Hassan oil field

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

Aswad, Z.A.R.; Al-Hadad, S.M.S.

1983-03-01

The powerful Rosenbrock search technique, which optimizes both the search directions using the Gram-Schmidt procedure and the step size using the Fibonacci line search method, has been used to optimize the drilling program of an oil well drilled in Bai-Hassan oil field in Kirkuk, Iran, using the twodimensional drilling model of Galle and Woods. This model shows the effect of the two major controllable variables, weight on bit and rotary speed, on the drilling rate, while considering other controllable variables such as the mud properties, hydrostatic pressure, hydraulic design, and bit selection. The effect of tooth dullness on the drillingmore » rate is also considered. Increasing the weight on the drill bit with a small increase or decrease in ratary speed resulted in a significant decrease in the drilling cost for most bit runs. It was found that a 48% reduction in this cost and a 97-hour savings in the total drilling time was possible under certain conditions.« less

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.

Proxies for soil organic carbon derived from remote sensing

NASA Astrophysics Data System (ADS)

Rasel, S. M. M.; Groen, T. A.; Hussin, Y. A.; Diti, I. J.

2017-07-01

The possibility of carbon storage in soils is of interest because compared to vegetation it contains more carbon. Estimation of soil carbon through remote sensing based techniques can be a cost effective approach, but is limited by available methods. This study aims to develop a model based on remotely sensed variables (elevation, forest type and above ground biomass) to estimate soil carbon stocks. Field observations on soil organic carbon, species composition, and above ground biomass were recorded in the subtropical forest of Chitwan, Nepal. These variables were also estimated using LiDAR data and a WorldView 2 image. Above ground biomass was estimated from the LiDAR image using a novel approach where the image was segmented to identify individual trees, and for these trees estimates of DBH and Height were made. Based on AIC (Akaike Information Criterion) a regression model with above ground biomass derived from LiDAR data, and forest type derived from WorldView 2 imagery was selected to estimate soil organic carbon (SOC) stocks. The selected model had a coefficient of determination (R2) of 0.69. This shows the scope of estimating SOC with remote sensing derived variables in sub-tropical forests.

Sperm selection in natural conception: what can we learn from Mother Nature to improve assisted reproduction outcomes?

PubMed Central

Sakkas, Denny; Ramalingam, Mythili; Garrido, Nicolas; Barratt, Christopher L.R.

2015-01-01

BACKGROUND In natural conception only a few sperm cells reach the ampulla or the site of fertilization. This population is a selected group of cells since only motile cells can pass through cervical mucus and gain initial entry into the female reproductive tract. In animals, some studies indicate that the sperm selected by the reproductive tract and recovered from the uterus and the oviducts have higher fertilization rates but this is not a universal finding. Some species show less discrimination in sperm selection and abnormal sperm do arrive at the oviduct. In contrast, assisted reproductive technologies (ART) utilize a more random sperm population. In this review we contrast the journey of the spermatozoon in vivo and in vitro and discuss this in the context of developing new sperm preparation and selection techniques for ART. METHODS A review of the literature examining characteristics of the spermatozoa selected in vivo is compared with recent developments in in vitro selection and preparation methods. Contrasts and similarities are presented. RESULTS AND CONCLUSIONS New technologies are being developed to aid in the diagnosis, preparation and selection of spermatozoa in ART. To date progress has been frustrating and these methods have provided variable benefits in improving outcomes after ART. It is more likely that examining the mechanisms enforced by nature will provide valuable information in regard to sperm selection and preparation techniques in vitro. Identifying the properties of those spermatozoa which do reach the oviduct will also be important for the development of more effective tests of semen quality. In this review we examine the value of sperm selection to see how much guidance for ART can be gleaned from the natural selection processes in vivo. PMID:26386468

Stochastic mass-reconstruction: a new technique to reconstruct resonance masses of heavy particles decaying into tau lepton pairs

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

Maruyama, Sho

2015-12-15

The invariant mass of tau lepton pairs turns out to be smaller than the resonant mass of their mother particle and the invariant mass distribution is stretched wider than the width of the resonant mass as significant fraction of tau lepton momenta are carried away by neutrinos escaping undetected at collider experiments. This paper describes a new approach to reconstruct resonant masses of heavy particles decaying to tau leptons at such experiments. A typical example is a Z or Higgs boson decaying to a tau pair. Although the new technique can be used for each tau lepton separately, I combinemore » two tau leptons to improve mass resolution by requiring the two tau leptons are lined up in a transverse plane. The method is simple to implement and complementary to the collinear approximation technique that works well when tau leptons are not lined up in a transverse plane. The reconstructed mass can be used as another variable in analyses that already use a visible tau pair mass and missing transverse momentum as these variables are not explicitly used in the stochastic mass-reconstruction to select signal-like events.« less

Sampling in rugged terrain

USGS Publications Warehouse

Dawson, D.K.; Ralph, C. John; Scott, J. Michael

1981-01-01

Work in rugged terrain poses some unique problems that should be considered before research is initiated. Besides the obvious physical difficulties of crossing uneven terrain, topography can influence the bird species? composition of a forest and the observer's ability to detect birds and estimate distances. Census results can also be affected by the slower rate of travel on rugged terrain. Density figures may be higher than results obtained from censuses in similar habitat on level terrain because of the greater likelihood of double-recording of individuals and of recording species that sing infrequently. In selecting a census technique, the researcher should weigh the efficiency and applicability of a technique for the objectives of his study in light of the added difficulties posed by rugged terrain. The variable circular-plot method is probably the most effective technique for estimating bird numbers. Bird counts and distance estimates are facilitated because the observer is stationary, and calculations of species? densities take into account differences in effective area covered amongst stations due to variability in terrain or vegetation structure. Institution of precautions that minimize the risk of injury to field personnel can often enhance the observer?s ability to detect birds.

Estimating stand structure using discrete-return lidar: an example from low density, fire prone ponderosa pine forests

USGS Publications Warehouse

Hall, S. A.; Burke, I.C.; Box, D. O.; Kaufmann, M. R.; Stoker, Jason M.

2005-01-01

The ponderosa pine forests of the Colorado Front Range, USA, have historically been subjected to wildfires. Recent large burns have increased public interest in fire behavior and effects, and scientific interest in the carbon consequences of wildfires. Remote sensing techniques can provide spatially explicit estimates of stand structural characteristics. Some of these characteristics can be used as inputs to fire behavior models, increasing our understanding of the effect of fuels on fire behavior. Others provide estimates of carbon stocks, allowing us to quantify the carbon consequences of fire. Our objective was to use discrete-return lidar to estimate such variables, including stand height, total aboveground biomass, foliage biomass, basal area, tree density, canopy base height and canopy bulk density. We developed 39 metrics from the lidar data, and used them in limited combinations in regression models, which we fit to field estimates of the stand structural variables. We used an information–theoretic approach to select the best model for each variable, and to select the subset of lidar metrics with most predictive potential. Observed versus predicted values of stand structure variables were highly correlated, with r2 ranging from 57% to 87%. The most parsimonious linear models for the biomass structure variables, based on a restricted dataset, explained between 35% and 58% of the observed variability. Our results provide us with useful estimates of stand height, total aboveground biomass, foliage biomass and basal area. There is promise for using this sensor to estimate tree density, canopy base height and canopy bulk density, though more research is needed to generate robust relationships. We selected 14 lidar metrics that showed the most potential as predictors of stand structure. We suggest that the focus of future lidar studies should broaden to include low density forests, particularly systems where the vertical structure of the canopy is important, such as fire prone forests.

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).

Understanding the Effects of Genotype, Growing Year, and Breeding on Tunisian Durum Wheat Allergenicity. 2. The Celiac Disease Case.

PubMed

Boukid, Fatma; Prandi, Barbara; Sforza, Stefano; Sayar, Rhouma; Seo, Yong Weon; Mejri, Mondher; Yacoubi, Ines

2017-07-19

The aim of this study was to compare immunogenic and toxic gluten peptides related to celiac disease (CD). 100 accessions of genotypes selected during the 20th century in Tunisia were in vitro digested and then analyzed by UPLC/ESI-MS technique using an isotopically labeled internal standard. The first MANOVA confirmed a high variability in the content of immunogenic and toxic peptides reflecting high genetic diversity in the germplasm released during the past century in Tunisia, consistently with PCA and clustering analysis results. Our finding showed also important variability in CD epitopes due to growing season's climate scenarios. Moreover, the second MANOVA revealed significant differences between abandoned and modern cultivars' CD-related peptide amounts. Although we could not conclude that there was an augment of allergens in newly selected durum wheat lines compared to abandoned ones, we demonstrated that modern genotype peptides were less sensitive to climate variation, which is a useful indicator for wheat breeders.

Enzyme Polymorphism and Genetic Variability of One Colonized and Several Field Populations of Phlebotomus papatasi (Diptera: Psychodidae)

DTIC Science & Technology

1993-01-01

One Colonized and Several Field Populations of Phlebotomus papatasi (Diptera: Psychodidae) HALA A. KASSEM ,1. 2 DAVID J. FRYAUFF,1., MAGDI G, SHEHATA...were found to erations, and that selection for refractoriness to have atypical genitalia ( Kassem et al. 1988), and infection is associated with a shift... females and has been main- tained for 34 successive generations using the allndt(96) SINAI methods described by Schmid(16) Electrophoretic Techniques

Selective spectroscopic imaging of hyperpolarized pyruvate and its metabolites using a single-echo variable phase advance method in balanced SSFP

PubMed Central

Varma, Gopal; Wang, Xiaoen; Vinogradov, Elena; Bhatt, Rupal S.; Sukhatme, Vikas; Seth, Pankaj; Lenkinski, Robert E.; Alsop, David C.; Grant, Aaron K.

2015-01-01

Purpose In balanced steady state free precession (bSSFP), the signal intensity has a well-known dependence on the off-resonance frequency, or, equivalently, the phase advance between successive radiofrequency (RF) pulses. The signal profile can be used to resolve the contributions from the spectrally separated metabolites. This work describes a method based on use of a variable RF phase advance to acquire spatial and spectral data in a time-efficient manner for hyperpolarized 13C MRI. Theory and Methods The technique relies on the frequency response from a bSSFP acquisition to acquire relatively rapid, high-resolution images that may be reconstructed to separate contributions from different metabolites. The ability to produce images from spectrally separated metabolites was demonstrated in-vitro, as well as in-vivo following administration of hyperpolarized 1-13C pyruvate in mice with xenograft tumors. Results In-vivo images of pyruvate, alanine, pyruvate hydrate and lactate were reconstructed from 4 images acquired in 2 seconds with an in-plane resolution of 1.25 × 1.25mm2 and 5mm slice thickness. Conclusions The phase advance method allowed acquisition of spectroscopically selective images with high spatial and temporal resolution. This method provides an alternative approach to hyperpolarized 13C spectroscopic MRI that can be combined with other techniques such as multi-echo or fluctuating equilibrium bSSFP. PMID:26507361

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.

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.

Sacral nerve stimulation for neuromodulation of the lower urinary tract.

PubMed

Hubsher, Chad P; Jansen, Robert; Riggs, Dale R; Jackson, Barbara J; Zaslau, Stanley

2012-10-01

Sacral neuromodulation (SNM) has become a standard treatment option for patients suffering from urinary urge incontinence, urgency-frequency, and/or nonobstructive urinary retention refractory to conservative and pharmacologic treatment. Since its initial development, the manufacturer of InterStim therapy (Medtronic, Inc., Minneapolis, MN, USA), has introduced technical modifications, while surgeons and researchers have adapted and published various innovations and alterations of the implantation technique. In this article, we feature our SNM technique including patient selection, comprehensive dialogue/evaluation, procedure details, and appropriate follow up. Although there is often great variability in patients with lower urinary tract dysfunction, we maintain that great success can be achieved with a systematic and methodical approach to SNM.

Locating CVBEM collocation points for steady state heat transfer problems

USGS Publications Warehouse

Hromadka, T.V.

1985-01-01

The Complex Variable Boundary Element Method or CVBEM provides a highly accurate means of developing numerical solutions to steady state two-dimensional heat transfer problems. The numerical approach exactly solves the Laplace equation and satisfies the boundary conditions at specified points on the boundary by means of collocation. The accuracy of the approximation depends upon the nodal point distribution specified by the numerical analyst. In order to develop subsequent, refined approximation functions, four techniques for selecting additional collocation points are presented. The techniques are compared as to the governing theory, representation of the error of approximation on the problem boundary, the computational costs, and the ease of use by the numerical analyst. ?? 1985.

Spectral unmixing of urban land cover using a generic library approach

NASA Astrophysics Data System (ADS)

Degerickx, Jeroen; Lordache, Marian-Daniel; Okujeni, Akpona; Hermy, Martin; van der Linden, Sebastian; Somers, Ben

2016-10-01

Remote sensing based land cover classification in urban areas generally requires the use of subpixel classification algorithms to take into account the high spatial heterogeneity. These spectral unmixing techniques often rely on spectral libraries, i.e. collections of pure material spectra (endmembers, EM), which ideally cover the large EM variability typically present in urban scenes. Despite the advent of several (semi-) automated EM detection algorithms, the collection of such image-specific libraries remains a tedious and time-consuming task. As an alternative, we suggest the use of a generic urban EM library, containing material spectra under varying conditions, acquired from different locations and sensors. This approach requires an efficient EM selection technique, capable of only selecting those spectra relevant for a specific image. In this paper, we evaluate and compare the potential of different existing library pruning algorithms (Iterative Endmember Selection and MUSIC) using simulated hyperspectral (APEX) data of the Brussels metropolitan area. In addition, we develop a new hybrid EM selection method which is shown to be highly efficient in dealing with both imagespecific and generic libraries, subsequently yielding more robust land cover classification results compared to existing methods. Future research will include further optimization of the proposed algorithm and additional tests on both simulated and real hyperspectral data.

SVS: data and knowledge integration in computational biology.

PubMed

Zycinski, Grzegorz; Barla, Annalisa; Verri, Alessandro

2011-01-01

In this paper we present a framework for structured variable selection (SVS). The main concept of the proposed schema is to take a step towards the integration of two different aspects of data mining: database and machine learning perspective. The framework is flexible enough to use not only microarray data, but other high-throughput data of choice (e.g. from mass spectrometry, microarray, next generation sequencing). Moreover, the feature selection phase incorporates prior biological knowledge in a modular way from various repositories and is ready to host different statistical learning techniques. We present a proof of concept of SVS, illustrating some implementation details and describing current results on high-throughput microarray data.

A data mining framework for time series estimation.

PubMed

Hu, Xiao; Xu, Peng; Wu, Shaozhi; Asgari, Shadnaz; Bergsneider, Marvin

2010-04-01

Time series estimation techniques are usually employed in biomedical research to derive variables less accessible from a set of related and more accessible variables. These techniques are traditionally built from systems modeling approaches including simulation, blind decovolution, and state estimation. In this work, we define target time series (TTS) and its related time series (RTS) as the output and input of a time series estimation process, respectively. We then propose a novel data mining framework for time series estimation when TTS and RTS represent different sets of observed variables from the same dynamic system. This is made possible by mining a database of instances of TTS, its simultaneously recorded RTS, and the input/output dynamic models between them. The key mining strategy is to formulate a mapping function for each TTS-RTS pair in the database that translates a feature vector extracted from RTS to the dissimilarity between true TTS and its estimate from the dynamic model associated with the same TTS-RTS pair. At run time, a feature vector is extracted from an inquiry RTS and supplied to the mapping function associated with each TTS-RTS pair to calculate a dissimilarity measure. An optimal TTS-RTS pair is then selected by analyzing these dissimilarity measures. The associated input/output model of the selected TTS-RTS pair is then used to simulate the TTS given the inquiry RTS as an input. An exemplary implementation was built to address a biomedical problem of noninvasive intracranial pressure assessment. The performance of the proposed method was superior to that of a simple training-free approach of finding the optimal TTS-RTS pair by a conventional similarity-based search on RTS features. 2009 Elsevier Inc. All rights reserved.

Generalized Dissimilarity Modeling of Late-Quaternary Variations in Pollen-Based Compositional Dissimilarity

NASA Astrophysics Data System (ADS)

Williams, J. W.; Blois, J.; Ferrier, S.; Manion, G.; Fitzpatrick, M.; Veloz, S.; He, F.; Liu, Z.; Otto-Bliesner, B. L.

2011-12-01

In Quaternary paleoecology and paleoclimatology, compositionally dissimilar fossil assemblages usually indicate dissimilar environments; this relationship underpins assemblage-level techniques for paleoenvironmental reconstruction such as mutual climatic ranges or the modern analog technique. However, there has been relatively little investigation into the form of the relationship between compositional dissimilarity and climatic dissimilarity. Here we apply generalized dissimilarity modeling (GDM; Ferrier et al. 2007) as a tool for modeling the expected non-linear relationships between compositional and climatic dissimilarity. We use the CCSM3.0 transient paleoclimatic simulations from the SynTrace working group (Liu et al. 2009) and a new generation of fossil pollen maps from eastern North America (Blois et al. 2011) to 1) assess the spatial relationships between compositional dissimilarity and climatic dissimilarity and 2) whether these spatial relationships change over time. We used a taxonomic list of 106 genus-level pollen types, six climatic variables (winter precipitation and mean temperature, summer precipitation and temperature, seasonality of precipitation, and seasonality of temperature) that were chosen to minimize collinearity, and a cross-referenced pollen and climate dataset mapped for time slices spaced 1000 years apart. When GDM was trained for one time slice, the correlation between predicted and observed spatial patterns of community dissimilarity for other times ranged between 0.3 and 0.73. The selection of climatic predictor variables changed over time, as did the form of the relationship between compositional turnover and climatic predictors. Summer temperature was the only variable selected for all time periods. These results thus suggest that the relationship between compositional dissimilarity in pollen assemblages (and, by implication, beta diversity in plant communities) and climatic dissimilarity can change over time, for reasons to be further studied.

Evaluating uncertainty in predicting spatially variable representative elementary scales in fractured aquifers, with application to Turkey Creek Basin, Colorado

USGS Publications Warehouse

Wellman, Tristan P.; Poeter, Eileen P.

2006-01-01

Computational limitations and sparse field data often mandate use of continuum representation for modeling hydrologic processes in large‐scale fractured aquifers. Selecting appropriate element size is of primary importance because continuum approximation is not valid for all scales. The traditional approach is to select elements by identifying a single representative elementary scale (RES) for the region of interest. Recent advances indicate RES may be spatially variable, prompting unanswered questions regarding the ability of sparse data to spatially resolve continuum equivalents in fractured aquifers. We address this uncertainty of estimating RES using two techniques. In one technique we employ data‐conditioned realizations generated by sequential Gaussian simulation. For the other we develop a new approach using conditioned random walks and nonparametric bootstrapping (CRWN). We evaluate the effectiveness of each method under three fracture densities, three data sets, and two groups of RES analysis parameters. In sum, 18 separate RES analyses are evaluated, which indicate RES magnitudes may be reasonably bounded using uncertainty analysis, even for limited data sets and complex fracture structure. In addition, we conduct a field study to estimate RES magnitudes and resulting uncertainty for Turkey Creek Basin, a crystalline fractured rock aquifer located 30 km southwest of Denver, Colorado. Analyses indicate RES does not correlate to rock type or local relief in several instances but is generally lower within incised creek valleys and higher along mountain fronts. Results of this study suggest that (1) CRWN is an effective and computationally efficient method to estimate uncertainty, (2) RES predictions are well constrained using uncertainty analysis, and (3) for aquifers such as Turkey Creek Basin, spatial variability of RES is significant and complex.

New bandwidth selection criterion for Kernel PCA: approach to dimensionality reduction and classification problems.

PubMed

Thomas, Minta; De Brabanter, Kris; De Moor, Bart

2014-05-10

DNA microarrays are potentially powerful technology for improving diagnostic classification, treatment selection, and prognostic assessment. The use of this technology to predict cancer outcome has a history of almost a decade. Disease class predictors can be designed for known disease cases and provide diagnostic confirmation or clarify abnormal cases. The main input to this class predictors are high dimensional data with many variables and few observations. Dimensionality reduction of these features set significantly speeds up the prediction task. Feature selection and feature transformation methods are well known preprocessing steps in the field of bioinformatics. Several prediction tools are available based on these techniques. Studies show that a well tuned Kernel PCA (KPCA) is an efficient preprocessing step for dimensionality reduction, but the available bandwidth selection method for KPCA was computationally expensive. In this paper, we propose a new data-driven bandwidth selection criterion for KPCA, which is related to least squares cross-validation for kernel density estimation. We propose a new prediction model with a well tuned KPCA and Least Squares Support Vector Machine (LS-SVM). We estimate the accuracy of the newly proposed model based on 9 case studies. Then, we compare its performances (in terms of test set Area Under the ROC Curve (AUC) and computational time) with other well known techniques such as whole data set + LS-SVM, PCA + LS-SVM, t-test + LS-SVM, Prediction Analysis of Microarrays (PAM) and Least Absolute Shrinkage and Selection Operator (Lasso). Finally, we assess the performance of the proposed strategy with an existing KPCA parameter tuning algorithm by means of two additional case studies. We propose, evaluate, and compare several mathematical/statistical techniques, which apply feature transformation/selection for subsequent classification, and consider its application in medical diagnostics. Both feature selection and feature transformation perform well on classification tasks. Due to the dynamic selection property of feature selection, it is hard to define significant features for the classifier, which predicts classes of future samples. Moreover, the proposed strategy enjoys a distinctive advantage with its relatively lesser time complexity.

Actor groups, related needs, and challenges at the climate downscaling interface

NASA Astrophysics Data System (ADS)

Rössler, Ole; Benestad, Rasmus; Diamando, Vlachogannis; Heike, Hübener; Kanamaru, Hideki; Pagé, Christian; Margarida Cardoso, Rita; Soares, Pedro; Maraun, Douglas; Kreienkamp, Frank; Christodoulides, Paul; Fischer, Andreas; Szabo, Peter

2016-04-01

At the climate downscaling interface, numerous downscaling techniques and different philosophies compete on being the best method in their specific terms. Thereby, it remains unclear to what extent and for which purpose these downscaling techniques are valid or even the most appropriate choice. A common validation framework that compares all the different available methods was missing so far. The initiative VALUE closes this gap with such a common validation framework. An essential part of a validation framework for downscaling techniques is the definition of appropriate validation measures. The selection of validation measures should consider the needs of the stakeholder: some might need a temporal or spatial average of a certain variable, others might need temporal or spatial distributions of some variables, still others might need extremes for the variables of interest or even inter-variable dependencies. Hence, a close interaction of climate data providers and climate data users is necessary. Thus, the challenge in formulating a common validation framework mirrors also the challenges between the climate data providers and the impact assessment community. This poster elaborates the issues and challenges at the downscaling interface as it is seen within the VALUE community. It suggests three different actor groups: one group consisting of the climate data providers, the other two groups being climate data users (impact modellers and societal users). Hence, the downscaling interface faces classical transdisciplinary challenges. We depict a graphical illustration of actors involved and their interactions. In addition, we identified four different types of issues that need to be considered: i.e. data based, knowledge based, communication based, and structural issues. They all may, individually or jointly, hinder an optimal exchange of data and information between the actor groups at the downscaling interface. Finally, some possible ways to tackle these issues are discussed.

A firefly algorithm for optimum design of new-generation beams

NASA Astrophysics Data System (ADS)

Erdal, F.

2017-06-01

This research addresses the minimum weight design of new-generation steel beams with sinusoidal openings using a metaheuristic search technique, namely the firefly method. The proposed algorithm is also used to compare the optimum design results of sinusoidal web-expanded beams with steel castellated and cellular beams. Optimum design problems of all beams are formulated according to the design limitations stipulated by the Steel Construction Institute. The design methods adopted in these publications are consistent with BS 5950 specifications. The formulation of the design problem considering the above-mentioned limitations turns out to be a discrete programming problem. The design algorithms based on the technique select the optimum universal beam sections, dimensional properties of sinusoidal, hexagonal and circular holes, and the total number of openings along the beam as design variables. Furthermore, this selection is also carried out such that the behavioural limitations are satisfied. Numerical examples are presented, where the suggested algorithm is implemented to achieve the minimum weight design of these beams subjected to loading combinations.

An overview of techniques for linking high-dimensional molecular data to time-to-event endpoints by risk prediction models.

PubMed

Binder, Harald; Porzelius, Christine; Schumacher, Martin

2011-03-01

Analysis of molecular data promises identification of biomarkers for improving prognostic models, thus potentially enabling better patient management. For identifying such biomarkers, risk prediction models can be employed that link high-dimensional molecular covariate data to a clinical endpoint. In low-dimensional settings, a multitude of statistical techniques already exists for building such models, e.g. allowing for variable selection or for quantifying the added value of a new biomarker. We provide an overview of techniques for regularized estimation that transfer this toward high-dimensional settings, with a focus on models for time-to-event endpoints. Techniques for incorporating specific covariate structure are discussed, as well as techniques for dealing with more complex endpoints. Employing gene expression data from patients with diffuse large B-cell lymphoma, some typical modeling issues from low-dimensional settings are illustrated in a high-dimensional application. First, the performance of classical stepwise regression is compared to stage-wise regression, as implemented by a component-wise likelihood-based boosting approach. A second issues arises, when artificially transforming the response into a binary variable. The effects of the resulting loss of efficiency and potential bias in a high-dimensional setting are illustrated, and a link to competing risks models is provided. Finally, we discuss conditions for adequately quantifying the added value of high-dimensional gene expression measurements, both at the stage of model fitting and when performing evaluation. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Using FT-NIR spectroscopy technique to determine arginine content in fermented Cordyceps sinensis mycelium.

PubMed

Xie, Chuanqi; Xu, Ning; Shao, Yongni; He, Yong

2015-01-01

This research investigated the feasibility of using Fourier transform near-infrared (FT-NIR) spectral technique for determining arginine content in fermented Cordyceps sinensis (C. sinensis) mycelium. Three different models were carried out to predict the arginine content. Wavenumber selection methods such as competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA) were used to identify the most important wavenumbers and reduce the high dimensionality of the raw spectral data. Only a few wavenumbers were selected by CARS and CARS-SPA as the optimal wavenumbers, respectively. Among the prediction models, CARS-least squares-support vector machine (CARS-LS-SVM) model performed best with the highest values of the coefficient of determination of prediction (Rp(2)=0.8370) and residual predictive deviation (RPD=2.4741), the lowest value of root mean square error of prediction (RMSEP=0.0841). Moreover, the number of the input variables was forty-five, which only accounts for 2.04% of that of the full wavenumbers. The results showed that FT-NIR spectral technique has the potential to be an objective and non-destructive method to detect arginine content in fermented C. sinensis mycelium. Copyright © 2015 Elsevier B.V. All rights reserved.

[Theoretical and methodological uses of research in Social and Human Sciences in Health].

PubMed

Deslandes, Suely Ferreira; Iriart, Jorge Alberto Bernstein

2012-12-01

The current article aims to map and critically reflect on the current theoretical and methodological uses of research in the subfield of social and human sciences in health. A convenience sample was used to select three Brazilian public health journals. Based on a reading of 1,128 abstracts published from 2009 to 2010, 266 articles were selected that presented the empirical base of research stemming from social and human sciences in health. The sample was classified thematically as "theoretical/ methodological reference", "study type/ methodological design", "analytical categories", "data production techniques", and "analytical procedures". We analyze the sample's emic categories, drawing on the authors' literal statements. All the classifications and respective variables were tabulated in Excel. Most of the articles were self-described as qualitative and used more than one data production technique. There was a wide variety of theoretical references, in contrast with the almost total predominance of a single type of data analysis (content analysis). In several cases, important gaps were identified in expounding the study methodology and instrumental use of the qualitative research techniques and methods. However, the review did highlight some new objects of study and innovations in theoretical and methodological approaches.

Nonmedical influences on medical decision making: an experimental technique using videotapes, factorial design, and survey sampling.

PubMed Central

Feldman, H A; McKinlay, J B; Potter, D A; Freund, K M; Burns, R B; Moskowitz, M A; Kasten, L E

1997-01-01

OBJECTIVE: To study nonmedical influences on the doctor-patient interaction. A technique using simulated patients and "real" doctors is described. DATA SOURCES: A random sample of physicians, stratified on such characteristics as demographics, specialty, or experience, and selected from commercial and professional listings. STUDY DESIGN: A medical appointment is depicted on videotape by professional actors. The patient's presenting complaint (e.g., chest pain) allows a range of valid interpretation. Several alternative versions are taped, featuring the same script with patient-actors of different age, sex, race, or other characteristics. Fractional factorial design is used to select a balanced subset of patient characteristics, reducing costs without biasing the outcome. DATA COLLECTION: Each physician is shown one version of the videotape appointment and is asked to describe how he or she would diagnose or treat such a patient. PRINCIPAL FINDINGS: Two studies using this technique have been completed to date, one involving chest pain and dyspnea and the other involving breast cancer. The factorial design provided sufficient power, despite limited sample size, to demonstrate with statistical significance various influences of the experimental and stratification variables, including the patient's gender and age and the physician's experience. Persistent recruitment produced a high response rate, minimizing selection bias and enhancing validity. CONCLUSION: These techniques permit us to determine, with a degree of control unattainable in observational studies, whether medical decisions as described by actual physicians and drawn from a demographic or professional group of interest, are influenced by a prescribed set of nonmedical factors. PMID:9240285

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.

Neanderthal hunting strategies inferred from mortality profiles within the Abric Romaní sequence

PubMed Central

Carbonell, Eudald

2017-01-01

Ungulate mortality profiles are commonly used to study Neanderthal subsistence strategies. To assess the hunting strategies used by Neanderthals, we studied the ages at death of the cervids and equids found in levels E, H, I, Ja, Jb, K, L and M of the Abric Romaní sequence. These levels date between 43.2 ± 1.1 ka BP (14C AMS) and 54.5 ± 1.7 ka BP (U-series). The degree of eruption and development of the teeth and their wear stages were used to determine the ages of these animals at death, and mortality profiles were constructed using these data. The equids display prime dominated profiles in all of the analyzed levels, whereas the cervids display variable profiles. These results suggest that the Neanderthals of Abric Romaní employed both selective and non-selective hunting strategies. The selective strategy focused on the hunting of prime adults and generated prime dominated profiles. On the other hand, non-selective strategies, involved the consumption of animals of variable ages, resulting in catastrophic profiles. It is likely that in the selective hunting events were conducted using selective ambushes in which it was possible to select specific prey animals. On the other hand, encounter hunting or non-selective ambush hunting may have also been used at times, based on the abundances of prey animals and encounter rates. Specific hunting strategies would have been developed accordance with the taxa and the age of the individual to be hunted. The hunting groups most likely employed cooperative hunting techniques, especially in the capture of large animals. Thus, it is not possible to uniquely associate a single mortality profile with the predation tactics of Neanderthals at Abric Romaní. PMID:29166384

Neanderthal hunting strategies inferred from mortality profiles within the Abric Romaní sequence.

PubMed

Marín, Juan; Saladié, Palmira; Rodríguez-Hidalgo, Antonio; Carbonell, Eudald

2017-01-01

Ungulate mortality profiles are commonly used to study Neanderthal subsistence strategies. To assess the hunting strategies used by Neanderthals, we studied the ages at death of the cervids and equids found in levels E, H, I, Ja, Jb, K, L and M of the Abric Romaní sequence. These levels date between 43.2 ± 1.1 ka BP (14C AMS) and 54.5 ± 1.7 ka BP (U-series). The degree of eruption and development of the teeth and their wear stages were used to determine the ages of these animals at death, and mortality profiles were constructed using these data. The equids display prime dominated profiles in all of the analyzed levels, whereas the cervids display variable profiles. These results suggest that the Neanderthals of Abric Romaní employed both selective and non-selective hunting strategies. The selective strategy focused on the hunting of prime adults and generated prime dominated profiles. On the other hand, non-selective strategies, involved the consumption of animals of variable ages, resulting in catastrophic profiles. It is likely that in the selective hunting events were conducted using selective ambushes in which it was possible to select specific prey animals. On the other hand, encounter hunting or non-selective ambush hunting may have also been used at times, based on the abundances of prey animals and encounter rates. Specific hunting strategies would have been developed accordance with the taxa and the age of the individual to be hunted. The hunting groups most likely employed cooperative hunting techniques, especially in the capture of large animals. Thus, it is not possible to uniquely associate a single mortality profile with the predation tactics of Neanderthals at Abric Romaní.

Review and classification of variability analysis techniques with clinical applications.

PubMed

Bravi, Andrea; Longtin, André; Seely, Andrew J E

2011-10-10

Analysis of patterns of variation of time-series, termed variability analysis, represents a rapidly evolving discipline with increasing applications in different fields of science. In medicine and in particular critical care, efforts have focussed on evaluating the clinical utility of variability. However, the growth and complexity of techniques applicable to this field have made interpretation and understanding of variability more challenging. Our objective is to provide an updated review of variability analysis techniques suitable for clinical applications. We review more than 70 variability techniques, providing for each technique a brief description of the underlying theory and assumptions, together with a summary of clinical applications. We propose a revised classification for the domains of variability techniques, which include statistical, geometric, energetic, informational, and invariant. We discuss the process of calculation, often necessitating a mathematical transform of the time-series. Our aims are to summarize a broad literature, promote a shared vocabulary that would improve the exchange of ideas, and the analyses of the results between different studies. We conclude with challenges for the evolving science of variability analysis.

Review and classification of variability analysis techniques with clinical applications

PubMed Central

2011-01-01

Analysis of patterns of variation of time-series, termed variability analysis, represents a rapidly evolving discipline with increasing applications in different fields of science. In medicine and in particular critical care, efforts have focussed on evaluating the clinical utility of variability. However, the growth and complexity of techniques applicable to this field have made interpretation and understanding of variability more challenging. Our objective is to provide an updated review of variability analysis techniques suitable for clinical applications. We review more than 70 variability techniques, providing for each technique a brief description of the underlying theory and assumptions, together with a summary of clinical applications. We propose a revised classification for the domains of variability techniques, which include statistical, geometric, energetic, informational, and invariant. We discuss the process of calculation, often necessitating a mathematical transform of the time-series. Our aims are to summarize a broad literature, promote a shared vocabulary that would improve the exchange of ideas, and the analyses of the results between different studies. We conclude with challenges for the evolving science of variability analysis. PMID:21985357

Fourier-transform-infrared-spectroscopy based spectral-biomarker selection towards optimum diagnostic differentiation of oral leukoplakia and cancer.

PubMed

Banerjee, Satarupa; Pal, Mousumi; Chakrabarty, Jitamanyu; Petibois, Cyril; Paul, Ranjan Rashmi; Giri, Amita; Chatterjee, Jyotirmoy

2015-10-01

In search of specific label-free biomarkers for differentiation of two oral lesions, namely oral leukoplakia (OLK) and oral squamous-cell carcinoma (OSCC), Fourier-transform infrared (FTIR) spectroscopy was performed on paraffin-embedded tissue sections from 47 human subjects (eight normal (NOM), 16 OLK, and 23 OSCC). Difference between mean spectra (DBMS), Mann-Whitney's U test, and forward feature selection (FFS) techniques were used for optimising spectral-marker selection. Classification of diseases was performed with linear and quadratic support vector machine (SVM) at 10-fold cross-validation, using different combinations of spectral features. It was observed that six features obtained through FFS enabled differentiation of NOM and OSCC tissue (1782, 1713, 1665, 1545, 1409, and 1161 cm(-1)) and were most significant, able to classify OLK and OSCC with 81.3 % sensitivity, 95.7 % specificity, and 89.7 % overall accuracy. The 43 spectral markers extracted through Mann-Whitney's U Test were the least significant when quadratic SVM was used. Considering the high sensitivity and specificity of the FFS technique, extracting only six spectral biomarkers was thus most useful for diagnosis of OLK and OSCC, and to overcome inter and intra-observer variability experienced in diagnostic best-practice histopathological procedure. By considering the biochemical assignment of these six spectral signatures, this work also revealed altered glycogen and keratin content in histological sections which could able to discriminate OLK and OSCC. The method was validated through spectral selection by the DBMS technique. Thus this method has potential for diagnostic cost minimisation for oral lesions by label-free biomarker identification.

Knowledge Driven Variable Selection (KDVS) – a new approach to enrichment analysis of gene signatures obtained from high–throughput data

PubMed Central

2013-01-01

Background High–throughput (HT) technologies provide huge amount of gene expression data that can be used to identify biomarkers useful in the clinical practice. The most frequently used approaches first select a set of genes (i.e. gene signature) able to characterize differences between two or more phenotypical conditions, and then provide a functional assessment of the selected genes with an a posteriori enrichment analysis, based on biological knowledge. However, this approach comes with some drawbacks. First, gene selection procedure often requires tunable parameters that affect the outcome, typically producing many false hits. Second, a posteriori enrichment analysis is based on mapping between biological concepts and gene expression measurements, which is hard to compute because of constant changes in biological knowledge and genome analysis. Third, such mapping is typically used in the assessment of the coverage of gene signature by biological concepts, that is either score–based or requires tunable parameters as well, limiting its power. Results We present Knowledge Driven Variable Selection (KDVS), a framework that uses a priori biological knowledge in HT data analysis. The expression data matrix is transformed, according to prior knowledge, into smaller matrices, easier to analyze and to interpret from both computational and biological viewpoints. Therefore KDVS, unlike most approaches, does not exclude a priori any function or process potentially relevant for the biological question under investigation. Differently from the standard approach where gene selection and functional assessment are applied independently, KDVS embeds these two steps into a unified statistical framework, decreasing the variability derived from the threshold–dependent selection, the mapping to the biological concepts, and the signature coverage. We present three case studies to assess the usefulness of the method. Conclusions We showed that KDVS not only enables the selection of known biological functionalities with accuracy, but also identification of new ones. An efficient implementation of KDVS was devised to obtain results in a fast and robust way. Computing time is drastically reduced by the effective use of distributed resources. Finally, integrated visualization techniques immediately increase the interpretability of results. Overall, KDVS approach can be considered as a viable alternative to enrichment–based approaches. PMID:23302187

Stochastic model search with binary outcomes for genome-wide association studies.

PubMed

Russu, Alberto; Malovini, Alberto; Puca, Annibale A; Bellazzi, Riccardo

2012-06-01

The spread of case-control genome-wide association studies (GWASs) has stimulated the development of new variable selection methods and predictive models. We introduce a novel Bayesian model search algorithm, Binary Outcome Stochastic Search (BOSS), which addresses the model selection problem when the number of predictors far exceeds the number of binary responses. Our method is based on a latent variable model that links the observed outcomes to the underlying genetic variables. A Markov Chain Monte Carlo approach is used for model search and to evaluate the posterior probability of each predictor. BOSS is compared with three established methods (stepwise regression, logistic lasso, and elastic net) in a simulated benchmark. Two real case studies are also investigated: a GWAS on the genetic bases of longevity, and the type 2 diabetes study from the Wellcome Trust Case Control Consortium. Simulations show that BOSS achieves higher precisions than the reference methods while preserving good recall rates. In both experimental studies, BOSS successfully detects genetic polymorphisms previously reported to be associated with the analyzed phenotypes. BOSS outperforms the other methods in terms of F-measure on simulated data. In the two real studies, BOSS successfully detects biologically relevant features, some of which are missed by univariate analysis and the three reference techniques. The proposed algorithm is an advance in the methodology for model selection with a large number of features. Our simulated and experimental results showed that BOSS proves effective in detecting relevant markers while providing a parsimonious model.

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

Genome-wide association studies on HIV susceptibility, pathogenesis and pharmacogenomics

PubMed Central

2012-01-01

Susceptibility to HIV-1 and the clinical course after infection show a substantial heterogeneity between individuals. Part of this variability can be attributed to host genetic variation. Initial candidate gene studies have revealed interesting host factors that influence HIV infection, replication and pathogenesis. Recently, genome-wide association studies (GWAS) were utilized for unbiased searches at a genome-wide level to discover novel genetic factors and pathways involved in HIV-1 infection. This review gives an overview of findings from the GWAS performed on HIV infection, within different cohorts, with variable patient and phenotype selection. Furthermore, novel techniques and strategies in research that might contribute to the complete understanding of virus-host interactions and its role on the pathogenesis of HIV infection are discussed. PMID:22920050

Do religion and religiosity have anything to do with alcohol consumption patterns? Evidence from two fish landing sites on Lake Victoria Uganda.

PubMed

Tumwesigye, Nazarius M; Atuyambe, Lynn; Kibira, Simon P S; Wabwire-Mangen, Fred; Tushemerirwe, Florence; Wagner, Glenn J

2013-09-01

Fish landing sites have high levels of harmful use of alcohol. This paper examines the role of religion and religiosity on alcohol consumption at two fish landing sites on Lake Victoria in Uganda. Questionnaires were administered to randomly selected people at the sites. Dependent variables included alcohol consumption during the previous 30 days, whereas the key independent variables were religion and religiosity. Bivariate and multivariate analysis techniques were applied. People reporting low religiosity were five times more likely to have consumed alcohol (95% confidence interval: 2.45-10.04) compared with those reporting low/average religiosity. Religion and religiosity are potential channels for controlling alcohol use.

On determining important aspects of mathematical models: Application to problems in physics and chemistry

NASA Technical Reports Server (NTRS)

Rabitz, Herschel

1987-01-01

The use of parametric and functional gradient sensitivity analysis techniques is considered for models described by partial differential equations. By interchanging appropriate dependent and independent variables, questions of inverse sensitivity may be addressed to gain insight into the inversion of observational data for parameter and function identification in mathematical models. It may be argued that the presence of a subset of dominantly strong coupled dependent variables will result in the overall system sensitivity behavior collapsing into a simple set of scaling and self similarity relations amongst elements of the entire matrix of sensitivity coefficients. These general tools are generic in nature, but herein their application to problems arising in selected areas of physics and chemistry is presented.

Usage of machine learning for the separation of electroweak and strong Zγ production at the LHC experiments

NASA Astrophysics Data System (ADS)

Petukhov, A. M.; Soldatov, E. Yu

2017-12-01

Separation of electroweak component from strong component of associated Zγ production on hadron colliders is a very challenging task due to identical final states of such processes. The only difference is the origin of two leading jets in these two processes. Rectangular cuts on jet kinematic variables from ATLAS/CMS 8 TeV Zγ experimental analyses were improved using machine learning techniques. New selection variables were also tested. The expected significance of separation for LHC experiments conditions at the second datataking period (Run2) and 120 fb-1 amount of data reaches more than 5σ. Future experimental observation of electroweak Zγ production can also lead to the observation physics beyond Standard Model.

Techniques of lumbar-sacral spine fusion in spondylosis: systematic literature review and meta-analysis of randomized clinical trials.

PubMed

Umeta, Ricardo S G; Avanzi, Osmar

2011-07-01

Spine fusions can be performed through different techniques and are used to treat a number of vertebral pathologies. However, there seems to be no consensus regarding which technique of fusion is best suited to treat each distinct spinal disease or group of diseases. To study the effectiveness and complications of the different techniques used for spinal fusion in patients with lumbar spondylosis. Systematic literature review and meta-analysis. Randomized clinical studies comparing the most commonly performed surgical techniques for spine fusion in lumbar-sacral spondylosis, as well as those reporting patient outcome were selected. Identify which technique, if any, presents the best clinical, functional, and radiographic outcome. Systematic literature review and meta-analysis based on scientific articles published and indexed to the following databases: PubMed (1966-2009), Cochrane Collaboration-CENTRAL, EMBASE (1980-2009), and LILACS (1982-2009). The general search strategy focused on the surgical treatment of patients with lumbar-sacral spondylosis. Eight studies met the inclusion criteria and were selected with a total of 1,136 patients. Meta-analysis showed that patients who underwent interbody fusion presented a significantly smaller blood loss (p=.001) and a greater rate of bone fusion (p=.02). Patients submitted to fusion using the posterolateral approach had a significantly shorter operative time (p=.007) and less perioperative complications (p=.03). No statistically significant difference was found for the other studied variables (pain, functional impairment, and return to work). The most commonly used techniques for lumbar spine fusion in patients with spondylosis were interbody fusion and posterolateral approach. Both techniques were comparable in final outcome, but the former presented better rates of fusion and the latter the less complications. Copyright © 2011 Elsevier Inc. All rights reserved.

A Comparison of Variable Selection Criteria for Multiple Linear Regression: A Second Simulation Study

DTIC Science & Technology

1993-03-01

statistical mathe- matics, began in the late 1800’s when Sir Francis Galton first attempted to use practical mathematical techniques to investigate the...randomly collected (sampled) many pairs of parent/child height mea- surements (data), Galton observed that for a given parent- height average, the...ty only Maximum Adjusted R2 will be discussed. However, Maximum Adjusted R’ and Minimum MSE test exactly the same 2.thing. Adjusted R is related to R

Improving Students' Learning With Effective Learning Techniques: Promising Directions From Cognitive and Educational Psychology.

PubMed

Dunlosky, John; Rawson, Katherine A; Marsh, Elizabeth J; Nathan, Mitchell J; Willingham, Daniel T

2013-01-01

Many students are being left behind by an educational system that some people believe is in crisis. Improving educational outcomes will require efforts on many fronts, but a central premise of this monograph is that one part of a solution involves helping students to better regulate their learning through the use of effective learning techniques. Fortunately, cognitive and educational psychologists have been developing and evaluating easy-to-use learning techniques that could help students achieve their learning goals. In this monograph, we discuss 10 learning techniques in detail and offer recommendations about their relative utility. We selected techniques that were expected to be relatively easy to use and hence could be adopted by many students. Also, some techniques (e.g., highlighting and rereading) were selected because students report relying heavily on them, which makes it especially important to examine how well they work. The techniques include elaborative interrogation, self-explanation, summarization, highlighting (or underlining), the keyword mnemonic, imagery use for text learning, rereading, practice testing, distributed practice, and interleaved practice. To offer recommendations about the relative utility of these techniques, we evaluated whether their benefits generalize across four categories of variables: learning conditions, student characteristics, materials, and criterion tasks. Learning conditions include aspects of the learning environment in which the technique is implemented, such as whether a student studies alone or with a group. Student characteristics include variables such as age, ability, and level of prior knowledge. Materials vary from simple concepts to mathematical problems to complicated science texts. Criterion tasks include different outcome measures that are relevant to student achievement, such as those tapping memory, problem solving, and comprehension. We attempted to provide thorough reviews for each technique, so this monograph is rather lengthy. However, we also wrote the monograph in a modular fashion, so it is easy to use. In particular, each review is divided into the following sections: General description of the technique and why it should work How general are the effects of this technique?  2a. Learning conditions  2b. Student characteristics  2c. Materials  2d. Criterion tasks Effects in representative educational contexts Issues for implementation Overall assessment The review for each technique can be read independently of the others, and particular variables of interest can be easily compared across techniques. To foreshadow our final recommendations, the techniques vary widely with respect to their generalizability and promise for improving student learning. Practice testing and distributed practice received high utility assessments because they benefit learners of different ages and abilities and have been shown to boost students' performance across many criterion tasks and even in educational contexts. Elaborative interrogation, self-explanation, and interleaved practice received moderate utility assessments. The benefits of these techniques do generalize across some variables, yet despite their promise, they fell short of a high utility assessment because the evidence for their efficacy is limited. For instance, elaborative interrogation and self-explanation have not been adequately evaluated in educational contexts, and the benefits of interleaving have just begun to be systematically explored, so the ultimate effectiveness of these techniques is currently unknown. Nevertheless, the techniques that received moderate-utility ratings show enough promise for us to recommend their use in appropriate situations, which we describe in detail within the review of each technique. Five techniques received a low utility assessment: summarization, highlighting, the keyword mnemonic, imagery use for text learning, and rereading. These techniques were rated as low utility for numerous reasons. Summarization and imagery use for text learning have been shown to help some students on some criterion tasks, yet the conditions under which these techniques produce benefits are limited, and much research is still needed to fully explore their overall effectiveness. The keyword mnemonic is difficult to implement in some contexts, and it appears to benefit students for a limited number of materials and for short retention intervals. Most students report rereading and highlighting, yet these techniques do not consistently boost students' performance, so other techniques should be used in their place (e.g., practice testing instead of rereading). Our hope is that this monograph will foster improvements in student learning, not only by showcasing which learning techniques are likely to have the most generalizable effects but also by encouraging researchers to continue investigating the most promising techniques. Accordingly, in our closing remarks, we discuss some issues for how these techniques could be implemented by teachers and students, and we highlight directions for future research. © The Author(s) 2013.

Application of Spectral Analysis Techniques in the Intercomparison of Aerosol Data. Part II: Using Maximum Covariance Analysis to Effectively Compare Spatiotemporal Variability of Satellite and AERONET Measured Aerosol Optical Depth

NASA Technical Reports Server (NTRS)

Li, Jing; Carlson, Barbara E.; Lacis, Andrew A.

2014-01-01

Moderate Resolution Imaging SpectroRadiometer (MODIS) and Multi-angle Imaging Spectroradiomater (MISR) provide regular aerosol observations with global coverage. It is essential to examine the coherency between space- and ground-measured aerosol parameters in representing aerosol spatial and temporal variability, especially in the climate forcing and model validation context. In this paper, we introduce Maximum Covariance Analysis (MCA), also known as Singular Value Decomposition analysis as an effective way to compare correlated aerosol spatial and temporal patterns between satellite measurements and AERONET data. This technique not only successfully extracts the variability of major aerosol regimes but also allows the simultaneous examination of the aerosol variability both spatially and temporally. More importantly, it well accommodates the sparsely distributed AERONET data, for which other spectral decomposition methods, such as Principal Component Analysis, do not yield satisfactory results. The comparison shows overall good agreement between MODIS/MISR and AERONET AOD variability. The correlations between the first three modes of MCA results for both MODIS/AERONET and MISR/ AERONET are above 0.8 for the full data set and above 0.75 for the AOD anomaly data. The correlations between MODIS and MISR modes are also quite high (greater than 0.9). We also examine the extent of spatial agreement between satellite and AERONET AOD data at the selected stations. Some sites with disagreements in the MCA results, such as Kanpur, also have low spatial coherency. This should be associated partly with high AOD spatial variability and partly with uncertainties in satellite retrievals due to the seasonally varying aerosol types and surface properties.

SIMRAND I- SIMULATION OF RESEARCH AND DEVELOPMENT PROJECTS

NASA Technical Reports Server (NTRS)

Miles, R. F.

1994-01-01

The Simulation of Research and Development Projects program (SIMRAND) aids in the optimal allocation of R&D resources needed to achieve project goals. SIMRAND models the system subsets or project tasks as various network paths to a final goal. Each path is described in terms of task variables such as cost per hour, cost per unit, availability of resources, etc. Uncertainty is incorporated by treating task variables as probabilistic random variables. SIMRAND calculates the measure of preference for each alternative network. The networks yielding the highest utility function (or certainty equivalence) are then ranked as the optimal network paths. SIMRAND has been used in several economic potential studies at NASA's Jet Propulsion Laboratory involving solar dish power systems and photovoltaic array construction. However, any project having tasks which can be reduced to equations and related by measures of preference can be modeled. SIMRAND analysis consists of three phases: reduction, simulation, and evaluation. In the reduction phase, analytical techniques from probability theory and simulation techniques are used to reduce the complexity of the alternative networks. In the simulation phase, a Monte Carlo simulation is used to derive statistics on the variables of interest for each alternative network path. In the evaluation phase, the simulation statistics are compared and the networks are ranked in preference by a selected decision rule. The user must supply project subsystems in terms of equations based on variables (for example, parallel and series assembly line tasks in terms of number of items, cost factors, time limits, etc). The associated cumulative distribution functions and utility functions for each variable must also be provided (allowable upper and lower limits, group decision factors, etc). SIMRAND is written in Microsoft FORTRAN 77 for batch execution and has been implemented on an IBM PC series computer operating under DOS.

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

Use of the partial farm budget technique to predict the economic impact of the flock management decision to use B-mode ultrasonographic pregnancy diagnosis.

PubMed

Sprecher, D J; Ley, W B; Whittier, W D; Bowen, J M; Thatcher, C D; Pelzer, K D; Moore, J M

1989-07-15

A computer spreadsheet was developed to predict the economic impact of a management decision to use B-mode ultrasonographic ovine pregnancy diagnosis. The spreadsheet design and spreadsheet cell formulas are provided. The program used the partial farm budget technique to calculate net return (NR) or cash flow changes that resulted from the decision to use ultrasonography. Using the program, either simple pregnancy diagnosis or pregnancy diagnosis with the ability to determine singleton or multiple pregnancies may be compared with no flock ultrasonographic pregnancy diagnosis. A wide range of user-selected regional variables are used to calculate the cash flow changes associated with the ultrasonography decisions. A variable may be altered through a range of values to conduct a sensitivity analysis of predicted NR. Example sensitivity analyses are included for flock conception rate, veterinary ultrasound fee, and the price of corn. Variables that influence the number of cull animals and the cost of ultrasonography have the greatest impact on predicted NR. Because the determination of singleton or multiple pregnancies is more time consuming, its economic practicality in comparison with simple pregnancy diagnosis is questionable. The value of feed saved by identifying and separately feeding ewes with singleton pregnancies is not offset by the increased ultrasonography cost.

Solubility and bioavailability improvement of pazopanib hydrochloride.

PubMed

Herbrink, Maikel; Groenland, Stefanie L; Huitema, Alwin D R; Schellens, Jan H M; Beijnen, Jos H; Steeghs, Neeltje; Nuijen, Bastiaan

2018-06-10

The anti-cancer drug pazopanib hydrochloride (PZH) has a very low aqueous solubility and a variable oral bioavailability. A new pharmaceutical formulation with an improved solubility may enhance the bioavailability and reduce the variability. A broad selection of polymer excipients was tested for their compatibility and solubilizing properties by conventional microscopic, thermal and spectrometric techniques. A wet milling and mixing technique was used to produce homogenous powder mixtures. The dissolution properties of the formulation were tested by a pH-switch dissolution model. The final formulation was tested in vivo in cancer patient following a dose escalation design. Of the tested mixture formulations, the one containing the co-block polymer Soluplus® in a 8:1 ratio with PZH performed best in terms of in vitro dissolution properties. The in vivo results indicated that 300 mg of the developed formulation yields similar exposure and a lower variability (379 μg/mL∗h (36.7% CV)) than previously reported values for the standard PZH formulation (Votrient®) at the approved dose of 800 mg. Furthermore, the expected plasma-C through levels (27.2 μg/mL) exceeds the defined therapeutic efficacy threshold of 20 μg/mL. Copyright © 2018 Elsevier B.V. All rights reserved.

Predicting non-stationary algal dynamics following changes in hydrometeorological conditions using data assimilation techniques

NASA Astrophysics Data System (ADS)

Kim, S.; Seo, D. J.

2017-12-01

When water temperature (TW) increases due to changes in hydrometeorological conditions, the overall ecological conditions change in the aquatic system. The changes can be harmful to human health and potentially fatal to fish habitat. Therefore, it is important to assess the impacts of thermal disturbances on in-stream processes of water quality variables and be able to predict effectiveness of possible actions that may be taken for water quality protection. For skillful prediction of in-stream water quality processes, it is necessary for the watershed water quality models to be able to reflect such changes. Most of the currently available models, however, assume static parameters for the biophysiochemical processes and hence are not able to capture nonstationaries seen in water quality observations. In this work, we assess the performance of the Hydrological Simulation Program-Fortran (HSPF) in predicting algal dynamics following TW increase. The study area is located in the Republic of Korea where waterway change due to weir construction and drought concurrently occurred around 2012. In this work we use data assimilation (DA) techniques to update model parameters as well as the initial condition of selected state variables for in-stream processes relevant to algal growth. For assessment of model performance and characterization of temporal variability, various goodness-of-fit measures and wavelet analysis are used.

Bayesian LASSO, scale space and decision making in association genetics.

PubMed

Pasanen, Leena; Holmström, Lasse; Sillanpää, Mikko J

2015-01-01

LASSO is a penalized regression method that facilitates model fitting in situations where there are as many, or even more explanatory variables than observations, and only a few variables are relevant in explaining the data. We focus on the Bayesian version of LASSO and consider four problems that need special attention: (i) controlling false positives, (ii) multiple comparisons, (iii) collinearity among explanatory variables, and (iv) the choice of the tuning parameter that controls the amount of shrinkage and the sparsity of the estimates. The particular application considered is association genetics, where LASSO regression can be used to find links between chromosome locations and phenotypic traits in a biological organism. However, the proposed techniques are relevant also in other contexts where LASSO is used for variable selection. We separate the true associations from false positives using the posterior distribution of the effects (regression coefficients) provided by Bayesian LASSO. We propose to solve the multiple comparisons problem by using simultaneous inference based on the joint posterior distribution of the effects. Bayesian LASSO also tends to distribute an effect among collinear variables, making detection of an association difficult. We propose to solve this problem by considering not only individual effects but also their functionals (i.e. sums and differences). Finally, whereas in Bayesian LASSO the tuning parameter is often regarded as a random variable, we adopt a scale space view and consider a whole range of fixed tuning parameters, instead. The effect estimates and the associated inference are considered for all tuning parameters in the selected range and the results are visualized with color maps that provide useful insights into data and the association problem considered. The methods are illustrated using two sets of artificial data and one real data set, all representing typical settings in association genetics.

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.

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.

A systematic approach to parameter selection for CAD-virtual reality data translation using response surface methodology and MOGA-II.

PubMed

Abidi, Mustufa Haider; Al-Ahmari, Abdulrahman; Ahmad, Ali

2018-01-01

Advanced graphics capabilities have enabled the use of virtual reality as an efficient design technique. The integration of virtual reality in the design phase still faces impediment because of issues linked to the integration of CAD and virtual reality software. A set of empirical tests using the selected conversion parameters was found to yield properly represented virtual reality models. The reduced model yields an R-sq (pred) value of 72.71% and an R-sq (adjusted) value of 86.64%, indicating that 86.64% of the response variability can be explained by the model. The R-sq (pred) is 67.45%, which is not very high, indicating that the model should be further reduced by eliminating insignificant terms. The reduced model yields an R-sq (pred) value of 73.32% and an R-sq (adjusted) value of 79.49%, indicating that 79.49% of the response variability can be explained by the model. Using the optimization software MODE Frontier (Optimization, MOGA-II, 2014), four types of response surfaces for the three considered response variables were tested for the data of DOE. The parameter values obtained using the proposed experimental design methodology result in better graphics quality, and other necessary design attributes.

New horizons for study of the cardiopulmonary and circulatory systems. [image reconstruction techniques

NASA Technical Reports Server (NTRS)

Wood, E. H.

1976-01-01

The paper discusses the development of computer-controlled three-dimensional reconstruction techniques designed to determine the dynamic changes in the true shape and dimensions of the epi- and endocardial surfaces of the heart, along with variable time base (stop-action to real-time) displays of the transmural distribution of the coronary microcirculation and the three-dimensional anatomy of the macrovasculature in all regions of the body throughout individual cardiac and/or respiratory cycles. A technique for reconstructing a cross section of the heart from multiplanar videoroentgenograms is outlined. The capability of high spatial and high temporal resolution scanning videodensitometry makes possible measurement of the appearance, mean transit and clearance of roentgen opaque substances in three-dimensional space through the myocardium with a degree of simultaneous anatomic and temporal resolution not obtainable by current isotope techniques. The distribution of a variety of selected chemical elements or biologic materials within a body portion can also be determined.

LIBS Analysis for Coal

NASA Astrophysics Data System (ADS)

E. Romero, Carlos; De Saro, Robert

Coal is a non-uniform material with large inherent variability in composition, and other important properties, such as calorific value and ash fusion temperature. This quality variability is very important when coal is used as fuel in steam generators, since it affects boiler operation and control, maintenance and availability, and the extent and treatment of environmental pollution associated with coal combustion. On-line/in situ monitoring of coal before is fed into a boiler is a necessity. A very few analytical techniques like X-ray fluorescence and prompt gamma neutron activation analysis are available commercially with enough speed and sophistication of data collection for continuous coal monitoring. However, there is still a need for a better on-line/in situ technique that has higher selectivity, sensitivity, accuracy and precision, and that is safer and has a lower installation and operating costs than the other options. Laser induced breakdown spectroscopy (LIBS) is ideal for coal monitoring in boiler applications as it need no sample preparation, it is accurate and precise it is fast, and it can detect all of the elements of concern to the coal-fired boiler industry. LIBS data can also be adapted with advanced data processing techniques to provide real-time information required by boiler operators nowadays. This chapter summarizes development of LIBS for on-line/in situ coal applications in utility boilers.

Biostatistics Series Module 10: Brief Overview of Multivariate Methods.

PubMed

Hazra, Avijit; Gogtay, Nithya

2017-01-01

Multivariate analysis refers to statistical techniques that simultaneously look at three or more variables in relation to the subjects under investigation with the aim of identifying or clarifying the relationships between them. These techniques have been broadly classified as dependence techniques, which explore the relationship between one or more dependent variables and their independent predictors, and interdependence techniques, that make no such distinction but treat all variables equally in a search for underlying relationships. Multiple linear regression models a situation where a single numerical dependent variable is to be predicted from multiple numerical independent variables. Logistic regression is used when the outcome variable is dichotomous in nature. The log-linear technique models count type of data and can be used to analyze cross-tabulations where more than two variables are included. Analysis of covariance is an extension of analysis of variance (ANOVA), in which an additional independent variable of interest, the covariate, is brought into the analysis. It tries to examine whether a difference persists after "controlling" for the effect of the covariate that can impact the numerical dependent variable of interest. Multivariate analysis of variance (MANOVA) is a multivariate extension of ANOVA used when multiple numerical dependent variables have to be incorporated in the analysis. Interdependence techniques are more commonly applied to psychometrics, social sciences and market research. Exploratory factor analysis and principal component analysis are related techniques that seek to extract from a larger number of metric variables, a smaller number of composite factors or components, which are linearly related to the original variables. Cluster analysis aims to identify, in a large number of cases, relatively homogeneous groups called clusters, without prior information about the groups. The calculation intensive nature of multivariate analysis has so far precluded most researchers from using these techniques routinely. The situation is now changing with wider availability, and increasing sophistication of statistical software and researchers should no longer shy away from exploring the applications of multivariate methods to real-life data sets.

Model-based Clustering of High-Dimensional Data in Astrophysics

NASA Astrophysics Data System (ADS)

Bouveyron, C.

2016-05-01

The nature of data in Astrophysics has changed, as in other scientific fields, in the past decades due to the increase of the measurement capabilities. As a consequence, data are nowadays frequently of high dimensionality and available in mass or stream. Model-based techniques for clustering are popular tools which are renowned for their probabilistic foundations and their flexibility. However, classical model-based techniques show a disappointing behavior in high-dimensional spaces which is mainly due to their dramatical over-parametrization. The recent developments in model-based classification overcome these drawbacks and allow to efficiently classify high-dimensional data, even in the "small n / large p" situation. This work presents a comprehensive review of these recent approaches, including regularization-based techniques, parsimonious modeling, subspace classification methods and classification methods based on variable selection. The use of these model-based methods is also illustrated on real-world classification problems in Astrophysics using R packages.

New efficient optimizing techniques for Kalman filters and numerical weather prediction models

NASA Astrophysics Data System (ADS)

Famelis, Ioannis; Galanis, George; Liakatas, Aristotelis

2016-06-01

The need for accurate local environmental predictions and simulations beyond the classical meteorological forecasts are increasing the last years due to the great number of applications that are directly or not affected: renewable energy resource assessment, natural hazards early warning systems, global warming and questions on the climate change can be listed among them. Within this framework the utilization of numerical weather and wave prediction systems in conjunction with advanced statistical techniques that support the elimination of the model bias and the reduction of the error variability may successfully address the above issues. In the present work, new optimization methods are studied and tested in selected areas of Greece where the use of renewable energy sources is of critical. The added value of the proposed work is due to the solid mathematical background adopted making use of Information Geometry and Statistical techniques, new versions of Kalman filters and state of the art numerical analysis tools.

Intelligent evaluation of color sensory quality of black tea by visible-near infrared spectroscopy technology: A comparison of spectra and color data information

NASA Astrophysics Data System (ADS)

Ouyang, Qin; Liu, Yan; Chen, Quansheng; Zhang, Zhengzhu; Zhao, Jiewen; Guo, Zhiming; Gu, Hang

2017-06-01

Instrumental test of black tea samples instead of human panel test is attracting massive attention recently. This study focused on an investigation of the feasibility for estimation of the color sensory quality of black tea samples using the VIS-NIR spectroscopy technique, comparing the performances of models based on the spectra and color information. In model calibration, the variables were first selected by genetic algorithm (GA); then the nonlinear back propagation-artificial neural network (BPANN) models were established based on the optimal variables. In comparison with the other models, GA-BPANN models from spectra data information showed the best performance, with the correlation coefficient of 0.8935, and the root mean square error of 0.392 in the prediction set. In addition, models based on the spectra information provided better performance than that based on the color parameters. Therefore, the VIS-NIR spectroscopy technique is a promising tool for rapid and accurate evaluation of the sensory quality of black tea samples.

Audible acoustics in high-shear wet granulation: application of frequency filtering.

PubMed

Hansuld, Erin M; Briens, Lauren; McCann, Joe A B; Sayani, Amyn

2009-08-13

Previous work has shown analysis of audible acoustic emissions from high-shear wet granulation has potential as a technique for end-point detection. In this research, audible acoustic emissions (AEs) from three different formulations were studied to further develop this technique as a process analytical technology. Condenser microphones were attached to three different locations on a PMA-10 high-shear granulator (air exhaust, bowl and motor) to target different sound sources. Size, flowability and tablet break load data was collected to support formulator end-point ranges and interpretation of AE analysis. Each formulation had a unique total power spectral density (PSD) profile that was sensitive to granule formation and end-point. Analyzing total PSD in 10 Hz segments identified profiles with reduced run variability and distinct maxima and minima suitable for routine granulation monitoring and end-point control. A partial least squares discriminant analysis method was developed to automate selection of key 10 Hz frequency groups using variable importance to projection. The results support use of frequency refinement as a way forward in the development of acoustic emission analysis for granulation monitoring and end-point control.

Intelligent evaluation of color sensory quality of black tea by visible-near infrared spectroscopy technology: A comparison of spectra and color data information.

PubMed

Ouyang, Qin; Liu, Yan; Chen, Quansheng; Zhang, Zhengzhu; Zhao, Jiewen; Guo, Zhiming; Gu, Hang

2017-06-05

Instrumental test of black tea samples instead of human panel test is attracting massive attention recently. This study focused on an investigation of the feasibility for estimation of the color sensory quality of black tea samples using the VIS-NIR spectroscopy technique, comparing the performances of models based on the spectra and color information. In model calibration, the variables were first selected by genetic algorithm (GA); then the nonlinear back propagation-artificial neural network (BPANN) models were established based on the optimal variables. In comparison with the other models, GA-BPANN models from spectra data information showed the best performance, with the correlation coefficient of 0.8935, and the root mean square error of 0.392 in the prediction set. In addition, models based on the spectra information provided better performance than that based on the color parameters. Therefore, the VIS-NIR spectroscopy technique is a promising tool for rapid and accurate evaluation of the sensory quality of black tea samples. Copyright © 2017 Elsevier B.V. All rights reserved.

Value of Exercise Stress Electrocardiography for Risk Stratification in Patients With Suspected or Known Coronary Artery Disease in the Era of Advanced Imaging Technologies

PubMed Central

Bourque, Jamieson M.; Beller, George A.

2015-01-01

Exercise stress electrocardiography (ExECG) is underutilized as the initial test modality in patients with interpretable electrocardiograms able to exercise. Although, stress myocardial imaging techniques provide valuable diagnostic and prognostic information, variables derived from ExECG can yield substantial data for risk stratification, either supplementary to imaging variables, or without concurrent imaging. In addition to exercise-induced ischemic ST depression, such markers as ST segment elevation in lead AVR, abnormal heart rate recovery post-exercise, failure to achieve target heart rate, and poor exercise capacity improve risk stratification of ExECG. For example, patients achieving ≥10 METS on ExECG have a very low prevalence of inducible ischemia and an excellent prognosis. In contrast, cardiac imaging techniques add diagnostic and prognostic value in higher risk populations (e.g. poor functional capacity, diabetes, chronic kidney disease). Optimal test selection for symptomatic patients with suspected coronary artery disease requires a patient-centered approach factoring in the risk/benefit ratio and cost-effectiveness. PMID:26563861

Characterization of Machine Variability and Progressive Heat Treatment in Selective Laser Melting of Inconel 718

NASA Technical Reports Server (NTRS)

Prater, T.; Tilson, W.; Jones, Z.

2015-01-01

The absence of an economy of scale in spaceflight hardware makes additive manufacturing an immensely attractive option for propulsion components. As additive manufacturing techniques are increasingly adopted by government and industry to produce propulsion hardware in human-rated systems, significant development efforts are needed to establish these methods as reliable alternatives to conventional subtractive manufacturing. One of the critical challenges facing powder bed fusion techniques in this application is variability between machines used to perform builds. Even with implementation of robust process controls, it is possible for two machines operating at identical parameters with equivalent base materials to produce specimens with slightly different material properties. The machine variability study presented here evaluates 60 specimens of identical geometry built using the same parameters. 30 samples were produced on machine 1 (M1) and the other 30 samples were built on machine 2 (M2). Each of the 30-sample sets were further subdivided into three subsets (with 10 specimens in each subset) to assess the effect of progressive heat treatment on machine variability. The three categories for post-processing were: stress relief, stress relief followed by hot isostatic press (HIP), and stress relief followed by HIP followed by heat treatment per AMS 5664. Each specimen (a round, smooth tensile) was mechanically tested per ASTM E8. Two formal statistical techniques, hypothesis testing for equivalency of means and one-way analysis of variance (ANOVA), were applied to characterize the impact of machine variability and heat treatment on six material properties: tensile stress, yield stress, modulus of elasticity, fracture elongation, and reduction of area. This work represents the type of development effort that is critical as NASA, academia, and the industrial base work collaboratively to establish a path to certification for additively manufactured parts. For future flight programs, NASA and its commercial partners will procure parts from vendors who will use a diverse range of machines to produce parts and, as such, it is essential that the AM community develop a sound understanding of the degree to which machine variability impacts material properties.

Investigation of the feasibility of optical diagnostic measurements at the exit of the SSME

NASA Technical Reports Server (NTRS)

Shirley, John A.; Boedeker, Laurence R.

1993-01-01

Under Contract NAS8-36861 sponsored by NASA Marshall Space Flight Center, the United Technologies Research Center is conducting an investigation of the feasibility of remote optical diagnostics to measure temperature, species concentration and velocity at the exit of the Space Shuttle Main Engine (SSME). This is a two phase study consisting of a conceptual design phase followed by a laboratory experimental investigation. The first task of the conceptual design studies is to screen and evaluate the techniques which can be used for the measurements. The second task is to select the most promising technique or techniques, if as expected, more than one type of measurement must be used to measure all the flow variables of interest. The third task is to examine in detail analytically the capabilities and limitations of the selected technique(s). The results of this study are described in the section of this report entitled Conceptual Design Investigations. The conceptual design studies identified spontaneous Raman scattering and photodissociative flow-tagging for measurements respectively of gas temperature and major species concentration and for velocity. These techniques and others that were considered are described in the section describing the conceptual design. The objective of the second phase of investigations was to investigate experimentally the techniques identified in the first phase. The first task of the experimental feasibility study is to design and assemble laboratory scale experimental apparatus to evaluate the best approaches for SSME exit optical diagnostics for temperature, species concentrations and velocity, as selected in the Phase I conceptual design study. The second task is to evaluate performance, investigate limitations, and establish actual diagnostic capabilities, accuracies and precision for the selected optical systems. The third task is to evaluate design requirements and system trade-offs of conceptual instruments. Spontaneous Raman scattering excited by a KrF excimer laser pulse was investigated for SSME exit plane temperature and major species concentration measurements. The relative concentrations of molecular hydrogen and water vapor would be determined by measuring the integrated Q-branch scattering signals through narrow bandpass filters in front of photomultipliers. The temperature would be determined by comparing the signal from a single hydrogen rotational Raman line to the total hydrogen Q-branch signal. The rotational Raman line would be isolated by a monochromator and detected with a PMT.

Optical variability properties of the largest AGN sample observed with Kepler/K2

NASA Astrophysics Data System (ADS)

Aranzana, E.; Koerding, E.; Uttley, P.; Scaringi, S.; Steven, B.

2017-10-01

We present the first short time-scale ( hours to days) optical variability study of a large sample of Active Galactic Nuclei (AGN) observed with the Kepler/K2 mission. The sample contains 275 AGN observed over four campaigns with ˜30-minute cadence selected from the Million Quasar Catalogue with R magnitude < 19. We performed time series analysis to determine their variability properties by means of the power spectral densities (PSDs) and applied Monte Carlo techniques to find the best model parameters that fit the observed power spectra. A power-law model is sufficient to describe all the PSDs of the AGN in our sample. The average power-law slope is 2.5±0.5, steeper than the PSDs observed in X-rays, and the rest-frame amplitude variability in the frequency range of 6×10^{-6}-10^{-4} Hz varies from 1-10 % with an average of 2.6 %. We explore correlations between the variability amplitude and key parameters of the AGN, finding a significant correlation of rest-frame short-term variability amplitude with redshift, but no such correlation with luminosity. We attribute these effects to the known 'bluer when brighter variability of quasars combined with the fixed bandpass of Kepler. This study enables us to distinguish between Seyferts and Blazar and confirm AGN candidates.

Elbow joint variability for different hand positions of the round off in gymnastics.

PubMed

Farana, Roman; Irwin, Gareth; Jandacka, Daniel; Uchytil, Jaroslav; Mullineaux, David R

2015-02-01

The aim of the present study was to conduct within-gymnast analyses of biological movement variability in impact forces, elbow joint kinematics and kinetics of expert gymnasts in the execution of the round-off with different hand positions. Six international level female gymnasts performed 10 trials of the round-off from a hurdle step to a back-handspring using two hand potions: parallel and T-shape. Two force plates were used to determine ground reaction forces. Eight infrared cameras were employed to collect the kinematic data automatically. Within gymnast variability was calculated using biological coefficient of variation (BCV) discretely for ground reaction force, kinematic and kinetic measures. Variability of the continuous data was quantified using coefficient of multiple correlations (CMC). Group BCV and CMC were calculated and T-test with effect size statistics determined differences between the variability of the two techniques examined in this study. The major observation was a higher level of biological variability in the elbow joint abduction angle and adduction moment of force in the T-shaped hand position. This finding may lead to a reduced repetitive abduction stress and thus protect the elbow joint from overload. Knowledge of the differences in biological variability can inform clinicians and practitioners with effective skill selection. Copyright © 2014 Elsevier B.V. All rights reserved.

Influence plots for LASSO

DOE PAGES

Jang, Dae -Heung; Anderson-Cook, Christine Michaela

2016-11-22

With many predictors in regression, fitting the full model can induce multicollinearity problems. Least Absolute Shrinkage and Selection Operation (LASSO) is useful when the effects of many explanatory variables are sparse in a high-dimensional dataset. Influential points can have a disproportionate impact on the estimated values of model parameters. Here, this paper describes a new influence plot that can be used to increase understanding of the contributions of individual observations and the robustness of results. This can serve as a complement to other regression diagnostics techniques in the LASSO regression setting. Using this influence plot, we can find influential pointsmore » and their impact on shrinkage of model parameters and model selection. Lastly, we provide two examples to illustrate the methods.« less

Influence plots for LASSO

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

Jang, Dae -Heung; Anderson-Cook, Christine Michaela

With many predictors in regression, fitting the full model can induce multicollinearity problems. Least Absolute Shrinkage and Selection Operation (LASSO) is useful when the effects of many explanatory variables are sparse in a high-dimensional dataset. Influential points can have a disproportionate impact on the estimated values of model parameters. Here, this paper describes a new influence plot that can be used to increase understanding of the contributions of individual observations and the robustness of results. This can serve as a complement to other regression diagnostics techniques in the LASSO regression setting. Using this influence plot, we can find influential pointsmore » and their impact on shrinkage of model parameters and model selection. Lastly, we provide two examples to illustrate the methods.« less

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 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

Econometrics in outcomes research: the use of instrumental variables.

PubMed

Newhouse, J P; McClellan, M

1998-01-01

We describe an econometric technique, instrumental variables, that can be useful in estimating the effectiveness of clinical treatments in situations when a controlled trial has not or cannot be done. This technique relies upon the existence of one or more variables that induce substantial variation in the treatment variable but have no direct effect on the outcome variable of interest. We illustrate the use of the technique with an application to aggressive treatment of acute myocardial infarction in the elderly.

Measuring high-density built environment for public health research: Uncertainty with respect to data, indicator design and spatial scale.

PubMed

Sun, Guibo; Webster, Chris; Ni, Michael Y; Zhang, Xiaohu

2018-05-07

Uncertainty with respect to built environment (BE) data collection, measure conceptualization and spatial scales is evident in urban health research, but most findings are from relatively lowdensity contexts. We selected Hong Kong, an iconic high-density city, as the study area as limited research has been conducted on uncertainty in such areas. We used geocoded home addresses (n=5732) from a large population-based cohort in Hong Kong to extract BE measures for the participants' place of residence based on an internationally recognized BE framework. Variability of the measures was mapped and Spearman's rank correlation calculated to assess how well the relationships among indicators are preserved across variables and spatial scales. We found extreme variations and uncertainties for the 180 measures collected using comprehensive data and advanced geographic information systems modelling techniques. We highlight the implications of methodological selection and spatial scales of the measures. The results suggest that more robust information regarding urban health research in high-density city would emerge if greater consideration were given to BE data, design methods and spatial scales of the BE measures.

An image-based search for pulsars among Fermi unassociated LAT sources

NASA Astrophysics Data System (ADS)

Frail, D. A.; Ray, P. S.; Mooley, K. P.; Hancock, P.; Burnett, T. H.; Jagannathan, P.; Ferrara, E. C.; Intema, H. T.; de Gasperin, F.; Demorest, P. B.; Stovall, K.; McKinnon, M. M.

2018-03-01

We describe an image-based method that uses two radio criteria, compactness, and spectral index, to identify promising pulsar candidates among Fermi Large Area Telescope (LAT) unassociated sources. These criteria are applied to those radio sources from the Giant Metrewave Radio Telescope all-sky survey at 150 MHz (TGSS ADR1) found within the error ellipses of unassociated sources from the 3FGL catalogue and a preliminary source list based on 7 yr of LAT data. After follow-up interferometric observations to identify extended or variable sources, a list of 16 compact, steep-spectrum candidates is generated. An ongoing search for pulsations in these candidates, in gamma rays and radio, has found 6 ms pulsars and one normal pulsar. A comparison of this method with existing selection criteria based on gamma-ray spectral and variability properties suggests that the pulsar discovery space using Fermi may be larger than previously thought. Radio imaging is a hitherto underutilized source selection method that can be used, as with other multiwavelength techniques, in the search for Fermi pulsars.

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.

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.

Fruit Quality Evaluation Using Spectroscopy Technology: A Review

PubMed Central

Wang, Hailong; Peng, Jiyu; Xie, Chuanqi; Bao, Yidan; He, Yong

2015-01-01

An overview is presented with regard to applications of visible and near infrared (Vis/NIR) spectroscopy, multispectral imaging and hyperspectral imaging techniques for quality attributes measurement and variety discrimination of various fruit species, i.e., apple, orange, kiwifruit, peach, grape, strawberry, grape, jujube, banana, mango and others. Some commonly utilized chemometrics including pretreatment methods, variable selection methods, discriminant methods and calibration methods are briefly introduced. The comprehensive review of applications, which concentrates primarily on Vis/NIR spectroscopy, are arranged according to fruit species. Most of the applications are focused on variety discrimination or the measurement of soluble solids content (SSC), acidity and firmness, but also some measurements involving dry matter, vitamin C, polyphenols and pigments have been reported. The feasibility of different spectral modes, i.e., reflectance, interactance and transmittance, are discussed. Optimal variable selection methods and calibration methods for measuring different attributes of different fruit species are addressed. Special attention is paid to sample preparation and the influence of the environment. Areas where further investigation is needed and problems concerning model robustness and model transfer are identified. PMID:26007736

Longitudinal-control design approach for high-angle-of-attack aircraft

NASA Technical Reports Server (NTRS)

Ostroff, Aaron J.; Proffitt, Melissa S.

1993-01-01

This paper describes a control synthesis methodology that emphasizes a variable-gain output feedback technique that is applied to the longitudinal channel of a high-angle-of-attack aircraft. The aircraft is a modified F/A-18 aircraft with thrust-vectored controls. The flight regime covers a range up to a Mach number of 0.7; an altitude range from 15,000 to 35,000 ft; and an angle-of-attack (alpha) range up to 70 deg, which is deep into the poststall region. A brief overview is given of the variable-gain mathematical formulation as well as a description of the discrete control structure used for the feedback controller. This paper also presents an approximate design procedure with relationships for the optimal weights for the selected feedback control structure. These weights are selected to meet control design guidelines for high-alpha flight controls. Those guidelines that apply to the longitudinal-control design are also summarized. A unique approach is presented for the feed-forward command generator to obtain smooth transitions between load factor and alpha commands. Finally, representative linear analysis results and nonlinear batch simulation results are provided.

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.

Enhancing PC Cluster-Based Parallel Branch-and-Bound Algorithms for the Graph Coloring Problem

NASA Astrophysics Data System (ADS)

Taoka, Satoshi; Takafuji, Daisuke; Watanabe, Toshimasa

A branch-and-bound algorithm (BB for short) is the most general technique to deal with various combinatorial optimization problems. Even if it is used, computation time is likely to increase exponentially. So we consider its parallelization to reduce it. It has been reported that the computation time of a parallel BB heavily depends upon node-variable selection strategies. And, in case of a parallel BB, it is also necessary to prevent increase in communication time. So, it is important to pay attention to how many and what kind of nodes are to be transferred (called sending-node selection strategy). In this paper, for the graph coloring problem, we propose some sending-node selection strategies for a parallel BB algorithm by adopting MPI for parallelization and experimentally evaluate how these strategies affect computation time of a parallel BB on a PC cluster network.

Controlling for confounding variables in MS-omics protocol: why modularity matters.

PubMed

Smith, Rob; Ventura, Dan; Prince, John T

2014-09-01

As the field of bioinformatics research continues to grow, more and more novel techniques are proposed to meet new challenges and improvements upon solutions to long-standing problems. These include data processing techniques and wet lab protocol techniques. Although the literature is consistently thorough in experimental detail and variable-controlling rigor for wet lab protocol techniques, bioinformatics techniques tend to be less described and less controlled. As the validation or rejection of hypotheses rests on the experiment's ability to isolate and measure a variable of interest, we urge the importance of reducing confounding variables in bioinformatics techniques during mass spectrometry experimentation. © The Author 2013. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Probing the formation of silicon nano-crystals (Si-ncs) using variable energy positron annihilation spectroscopy

NASA Astrophysics Data System (ADS)

Knights, A. P.; Bradley, J. D. B.; Hulko, O.; Stevanovic, D. V.; Edwards, C. J.; Kallis, A.; Coleman, P. G.; Crowe, I. F.; Halsall, M. P.; Gwilliam, R. M.

2011-01-01

We describe preliminary results from studies of the formation of silicon nano-crystals (Si-ncs) embedded in stoichiometric, thermally grown SiO2 using Variable Energy Positron Annihilation Spectroscopy (VEPAS). We show that the VEPAS technique is able to monitor the introduction of structural damage. In SiO2 through the high dose Si+ ion implantation required to introduce excess silicon as a precursor to Si-nc formation. VEPAS is also able to characterize the rate of the removal of this damage with high temperature annealing, showing strong correlation with photoluminescence. Finally, VEPAS is shown to be able to selectively probe the interface between Si-ncs and the host oxide. Introduction of hydrogen at these interfaces suppresses the trapping of positrons at the interfaces.

Photoswitchable carbohydrate-based fluorosurfactants as tuneable ice recrystallization inhibitors.

PubMed

Adam, Madeleine K; Hu, Yingxue; Poisson, Jessica S; Pottage, Matthew J; Ben, Robert N; Wilkinson, Brendan L

2017-02-01

Cryopreservation is an important technique employed for the storage and preservation of biological tissues and cells. The limited effectiveness and significant toxicity of conventionally-used cryoprotectants, such as DMSO, have prompted efforts toward the rational design of less toxic alternatives, including carbohydrate-based surfactants. In this paper, we report the modular synthesis and ice recrystallization inhibition (IRI) activity of a library of variably substituted, carbohydrate-based fluorosurfactants. Carbohydrate-based fluorosurfactants possessed a variable mono- or disaccharide head group appended to a hydrophobic fluoroalkyl-substituted azobenzene tail group. Light-addressable fluorosurfactants displayed weak-to-moderate IRI activity that could be tuned through selection of carbohydrate head group, position of the trifluoroalkyl group on the azobenzene ring, and isomeric state of the azobenzene tail fragment. Copyright © 2016 Elsevier Ltd. All rights reserved.

Determining effective forecast horizons for multi-purpose reservoirs with short- and long-term operating objectives

NASA Astrophysics Data System (ADS)

Luchner, Jakob; Anghileri, Daniela; Castelletti, Andrea

2017-04-01

Real-time control of multi-purpose reservoirs can benefit significantly from hydro-meteorological forecast products. Because of their reliability, the most used forecasts range on time scales from hours to few days and are suitable for short-term operation targets such as flood control. In recent years, hydro-meteorological forecasts have become more accurate and reliable on longer time scales, which are more relevant to long-term reservoir operation targets such as water supply. While the forecast quality of such products has been studied extensively, the forecast value, i.e. the operational effectiveness of using forecasts to support water management, has been only relatively explored. It is comparatively easy to identify the most effective forecasting information needed to design reservoir operation rules for flood control but it is not straightforward to identify which forecast variable and lead time is needed to define effective hedging rules for operational targets with slow dynamics such as water supply. The task is even more complex when multiple targets, with diverse slow and fast dynamics, are considered at the same time. In these cases, the relative importance of different pieces of information, e.g. magnitude and timing of peak flow rate and accumulated inflow on different time lags, may vary depending on the season or the hydrological conditions. In this work, we analyze the relationship between operational forecast value and streamflow forecast horizon for different multi-purpose reservoir trade-offs. We use the Information Selection and Assessment (ISA) framework to identify the most effective forecast variables and horizons for informing multi-objective reservoir operation over short- and long-term temporal scales. The ISA framework is an automatic iterative procedure to discriminate the information with the highest potential to improve multi-objective reservoir operating performance. Forecast variables and horizons are selected using a feature selection technique. The technique determines the most informative combination in a multi-variate regression model to the optimal reservoir releases based on perfect information at a fixed objective trade-off. The improved reservoir operation is evaluated against optimal reservoir operation conditioned upon perfect information on future disturbances and basic reservoir operation using only the day of the year and the reservoir level. Different objective trade-offs are selected for analyzing resulting differences in improved reservoir operation and selected forecast variables and horizons. For comparison, the effective streamflow forecast horizon determined by the ISA framework is benchmarked against the performances obtained with a deterministic model predictive control (MPC) optimization scheme. Both the ISA framework and the MPC optimization scheme are applied to the real-world case study of Lake Como, Italy, using perfect streamflow forecast information. The principal operation targets for Lake Como are flood control and downstream water supply which makes its operation a suitable case study. Results provide critical feedback to reservoir operators on the use of long-term streamflow forecasts and to the hydro-meteorological forecasting community with respect to the forecast horizon needed from reliable streamflow forecasts.

Assessing sustainability in real urban systems: the Greater Cincinnati Metropolitan Area in Ohio, Kentucky, and Indiana.

PubMed

Gonzalez-Mejía, Alejandra M; Eason, Tarsha N; Cabezas, Heriberto; Suidan, Makram T

2012-09-04

Urban systems have a number of factors (i.e., economic, social, and environmental) that can potentially impact growth, change, and transition. As such, assessing and managing these systems is a complex challenge. While, tracking trends of key variables may provide some insight, identifying the critical characteristics that truly impact the dynamic behavior of these systems is difficult. As an integrated approach to evaluate real urban systems, this work contributes to the research on scientific techniques for assessing sustainability. Specifically, it proposes a practical methodology based on the estimation of dynamic order, for identifying stable and unstable periods of sustainable or unsustainable trends with Fisher Information (FI) metric. As a test case, the dynamic behavior of the City, Suburbs, and Metropolitan Statistical Area (MSA) of Cincinnati was evaluated by using 29 social and 11 economic variables to characterize each system from 1970 to 2009. Air quality variables were also selected to describe the MSA's environmental component (1980-2009). Results indicate systems dynamic started to change from about 1995 for the social variables and about 2000 for the economic and environmental characteristics.

Stochastic model search with binary outcomes for genome-wide association studies

PubMed Central

Malovini, Alberto; Puca, Annibale A; Bellazzi, Riccardo

2012-01-01

Objective The spread of case–control genome-wide association studies (GWASs) has stimulated the development of new variable selection methods and predictive models. We introduce a novel Bayesian model search algorithm, Binary Outcome Stochastic Search (BOSS), which addresses the model selection problem when the number of predictors far exceeds the number of binary responses. Materials and methods Our method is based on a latent variable model that links the observed outcomes to the underlying genetic variables. A Markov Chain Monte Carlo approach is used for model search and to evaluate the posterior probability of each predictor. Results BOSS is compared with three established methods (stepwise regression, logistic lasso, and elastic net) in a simulated benchmark. Two real case studies are also investigated: a GWAS on the genetic bases of longevity, and the type 2 diabetes study from the Wellcome Trust Case Control Consortium. Simulations show that BOSS achieves higher precisions than the reference methods while preserving good recall rates. In both experimental studies, BOSS successfully detects genetic polymorphisms previously reported to be associated with the analyzed phenotypes. Discussion BOSS outperforms the other methods in terms of F-measure on simulated data. In the two real studies, BOSS successfully detects biologically relevant features, some of which are missed by univariate analysis and the three reference techniques. Conclusion The proposed algorithm is an advance in the methodology for model selection with a large number of features. Our simulated and experimental results showed that BOSS proves effective in detecting relevant markers while providing a parsimonious model. PMID:22534080

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.

Multicollinearity in prognostic factor analyses using the EORTC QLQ-C30: identification and impact on model selection.

PubMed

Van Steen, Kristel; Curran, Desmond; Kramer, Jocelyn; Molenberghs, Geert; Van Vreckem, Ann; Bottomley, Andrew; Sylvester, Richard

2002-12-30

Clinical and quality of life (QL) variables from an EORTC clinical trial of first line chemotherapy in advanced breast cancer were used in a prognostic factor analysis of survival and response to chemotherapy. For response, different final multivariate models were obtained from forward and backward selection methods, suggesting a disconcerting instability. Quality of life was measured using the EORTC QLQ-C30 questionnaire completed by patients. Subscales on the questionnaire are known to be highly correlated, and therefore it was hypothesized that multicollinearity contributed to model instability. A correlation matrix indicated that global QL was highly correlated with 7 out of 11 variables. In a first attempt to explore multicollinearity, we used global QL as dependent variable in a regression model with other QL subscales as predictors. Afterwards, standard diagnostic tests for multicollinearity were performed. An exploratory principal components analysis and factor analysis of the QL subscales identified at most three important components and indicated that inclusion of global QL made minimal difference to the loadings on each component, suggesting that it is redundant in the model. In a second approach, we advocate a bootstrap technique to assess the stability of the models. Based on these analyses and since global QL exacerbates problems of multicollinearity, we therefore recommend that global QL be excluded from prognostic factor analyses using the QLQ-C30. The prognostic factor analysis was rerun without global QL in the model, and selected the same significant prognostic factors as before. Copyright 2002 John Wiley & Sons, Ltd.

Process optimization for osmo-dehydrated carambola (Averrhoa carambola L) slices and its storage studies.

PubMed

Roopa, N; Chauhan, O P; Raju, P S; Das Gupta, D K; Singh, R K R; Bawa, A S

2014-10-01

An osmotic-dehydration process protocol for Carambola (Averrhoacarambola L.,), an exotic star shaped tropical fruit, was developed. The process was optimized using Response Surface Methodology (RSM) following Central Composite Rotatable Design (CCRD). The experimental variables selected for the optimization were soak solution concentration (°Brix), soaking temperature (°C) and soaking time (min) with 6 experiments at central point. The effect of process variables was studied on solid gain and water loss during osmotic dehydration process. The data obtained were analyzed employing multiple regression technique to generate suitable mathematical models. Quadratic models were found to fit well (R(2), 95.58 - 98.64 %) in describing the effect of variables on the responses studied. The optimized levels of the process variables were achieved at 70°Brix, 48 °C and 144 min for soak solution concentration, soaking temperature and soaking time, respectively. The predicted and experimental results at optimized levels of variables showed high correlation. The osmo-dehydrated product prepared at optimized conditions showed a shelf-life of 10, 8 and 6 months at 5 °C, ambient (30 ± 2 °C) and 37 °C, respectively.

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…

Psychological Selection of NASA Astronauts for International Space Station Missions

NASA Technical Reports Server (NTRS)

Galarza, Laura

1999-01-01

During the upcoming manned International Space Station (ISS) missions, astronauts will encounter the unique conditions of living and working with a multicultural crew in a confined and isolated space environment. The environmental, social, and mission-related challenges of these missions will require crewmembers to emphasize effective teamwork, leadership, group living and self-management to maintain the morale and productivity of the crew. The need for crew members to possess and display skills and behaviors needed for successful adaptability to ISS missions led us to upgrade the tools and procedures we use for astronaut selection. The upgraded tools include personality and biographical data measures. Content and construct-related validation techniques were used to link upgraded selection tools to critical skills needed for ISS missions. The results of these validation efforts showed that various personality and biographical data variables are related to expert and interview ratings of critical ISS skills. Upgraded and planned selection tools better address the critical skills, demands, and working conditions of ISS missions and facilitate the selection of astronauts who will more easily cope and adapt to ISS flights.

A TRMM-Calibrated Infrared Technique for Global Rainfall Estimation

NASA Technical Reports Server (NTRS)

Negri, Andrew J.; Adler, Robert F.

2002-01-01

The development of a satellite infrared (IR) technique for estimating convective and stratiform rainfall and its application in studying the diurnal variability of rainfall on a global scale is presented. The Convective-Stratiform Technique (CST), calibrated by coincident, physically retrieved rain rates from the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR), is applied over the global tropics during 2001. The technique is calibrated separately over land and ocean, making ingenious use of the IR data from the TRMM Visible/Infrared Scanner (VIRS) before application to global geosynchronous satellite data. The low sampling rate of TRMM PR imposes limitations on calibrating IR-based techniques; however, our research shows that PR observations can be applied to improve IR-based techniques significantly by selecting adequate calibration areas and calibration length. The diurnal cycle of rainfall, as well as the division between convective and stratiform rainfall will be presented. The technique is validated using available data sets and compared to other global rainfall products such as Global Precipitation Climatology Project (GPCP) IR product, calibrated with TRMM Microwave Imager (TMI) data. The calibrated CST technique has the advantages of high spatial resolution (4 km), filtering of non-raining cirrus clouds, and the stratification of the rainfall into its convective and stratiform components, the latter being important for the calculation of vertical profiles of latent heating.

How to find what you don't know: Visualising variability in 3D geological models

NASA Astrophysics Data System (ADS)

Lindsay, Mark; Wellmann, Florian; Jessell, Mark; Ailleres, Laurent

2014-05-01

Uncertainties in input data can have compounding effects on the predictive reliability of three-dimensional (3D) geological models. Resource exploration, tectonic studies and environmental modelling can be compromised by using 3D models that misrepresent the target geology, and drilling campaigns that attempt to intersect particular geological units guided by 3D models are at risk of failure if the exploration geologist is unaware of inherent uncertainties. In addition, the visual inspection of 3D models is often the first contact decision makers have with the geology, thus visually communicating the presence and magnitude of uncertainties contained within geological 3D models is critical. Unless uncertainties are presented early in the relationship between decision maker and model, the model will be considered more truthful than the uncertainties allow with each subsequent viewing. We present a selection of visualisation techniques that provide the viewer with an insight to the location and amount of uncertainty contained within a model, and the geological characteristics which are most affected. A model of the Gippsland Basin, southeastern Australia is used as a case study to demonstrate the concepts of information entropy, stratigraphic variability and geodiversity. Central to the techniques shown here is the creation of a model suite, performed by creating similar (but not the same) version of the original model through perturbation of the input data. Specifically, structural data in the form of strike and dip measurements is perturbed in the creation of the model suite. The visualisation techniques presented are: (i) information entropy; (ii) stratigraphic variability and (iii) geodiversity. Information entropy is used to analyse uncertainty in a spatial context, combining the empirical probability distributions of multiple outcomes with a single quantitative measure. Stratigraphic variability displays the number of possible lithologies that may exist at a given point within the model volume. Geodiversity analyses various model characteristics (or 'geodiveristy metrics'), including the depth, volume of unit, the curvature of an interface, the geological complexity of a contact and the contact relationships units have with each other. Principal component analysis, a multivariate statistical technique, is used to simultaneously examine each of the geodiveristy metrics to determine the boundaries of model space, and identify which metrics contribute most to model uncertainty. The combination of information entropy, stratigraphic variability and geodiversity analysis provides a descriptive and thorough representation of uncertainty with effective visualisation techniques that clearly communicate the geological uncertainty contained within the geological model.

Anterior Urethral Advancement as a Single-Stage Technique for Repair of Anterior Hypospadias: Our Experience.

PubMed

Gite, Venkat A; Nikose, Jayant V; Bote, Sachin M; Patil, Saurabh R

2017-07-02

Many techniques have been described to correct anterior hypospadias with variable results. Anterior urethral advancement as one stage technique was first described by Ti Chang Shing in 1984. It was also used for the repair of strictures and urethrocutaneous fistulae involving distal urethra. We report our experience of using this technique with some modification for the repair of anterior hypospadias. In the period between 2013-2015, 20 cases with anterior hypospadias including 2 cases of glanular, 3 cases of coronal, 12 cases of subcoronal and 3 cases of distal penile hypospadias were treated with anterior urethral advancement technique. Patients' age groups ranged from 18 months to 10 years. Postoperatively, patients were passing urine from tip of neomeatus with satisfactory stream during follow up period of 6 months to 2 years. There were no major complications in any of our patients except in one patient who developed meatal stenosis which was treated by periodic dilatation. Three fold urethral mobilization was sufficient in all cases. Anterior urethral advancement technique is a single-stage procedure with good cosmetic results and least complications for anterior hypospadias repair in properly selected cases.

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.

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.

Consistent Selection towards Low Activity Phenotypes When Catchability Depends on Encounters among Human Predators and Fish

PubMed Central

Alós, Josep; Palmer, Miquel; Arlinghaus, Robert

2012-01-01

Together with life-history and underlying physiology, the behavioural variability among fish is one of the three main trait axes that determines the vulnerability to fishing. However, there are only a few studies that have systematically investigated the strength and direction of selection acting on behavioural traits. Using in situ fish behaviour revealed by telemetry techniques as input, we developed an individual-based model (IBM) that simulated the Lagrangian trajectory of prey (fish) moving within a confined home range (HR). Fishers exhibiting various prototypical fishing styles targeted these fish in the model. We initially hypothesised that more active and more explorative individuals would be systematically removed under all fished conditions, in turn creating negative selection differentials on low activity phenotypes and maybe on small HR. Our results partly supported these general predictions. Standardised selection differentials were, on average, more negative on HR than on activity. However, in many simulation runs, positive selection pressures on HR were also identified, which resulted from the stochastic properties of the fishes’ movement and its interaction with the human predator. In contrast, there was a consistent negative selection on activity under all types of fishing styles. Therefore, in situations where catchability depends on spatial encounters between human predators and fish, we would predict a consistent selection towards low activity phenotypes and have less faith in the direction of the selection on HR size. Our study is the first theoretical investigation on the direction of fishery-induced selection of behaviour using passive fishing gears. The few empirical studies where catchability of fish was measured in relation to passive fishing techniques, such as gill-nets, traps or recreational fishing, support our predictions that fish in highly exploited situations are, on average, characterised by low swimming activity, stemming, in part, from negative selection on swimming activity. PMID:23110164

Communication interval selection in distributed heterogeneous simulation of large-scale dynamical systems

NASA Astrophysics Data System (ADS)

Lucas, Charles E.; Walters, Eric A.; Jatskevich, Juri; Wasynczuk, Oleg; Lamm, Peter T.

2003-09-01

In this paper, a new technique useful for the numerical simulation of large-scale systems is presented. This approach enables the overall system simulation to be formed by the dynamic interconnection of the various interdependent simulations, each representing a specific component or subsystem such as control, electrical, mechanical, hydraulic, or thermal. Each simulation may be developed separately using possibly different commercial-off-the-shelf simulation programs thereby allowing the most suitable language or tool to be used based on the design/analysis needs. These subsystems communicate the required interface variables at specific time intervals. A discussion concerning the selection of appropriate communication intervals is presented herein. For the purpose of demonstration, this technique is applied to a detailed simulation of a representative aircraft power system, such as that found on the Joint Strike Fighter (JSF). This system is comprised of ten component models each developed using MATLAB/Simulink, EASY5, or ACSL. When the ten component simulations were distributed across just four personal computers (PCs), a greater than 15-fold improvement in simulation speed (compared to the single-computer implementation) was achieved.

Laser-Aided Ceramic Bracket Debonding: A Comprehensive Review

PubMed Central

Ghazanfari, Rezvaneh; Nokhbatolfoghahaei, Hanieh; Alikhasi, Marzieh

2016-01-01

Different techniques have been introduced for the removal of ceramic brackets. Since the early 1990s, lasers have been used experimentally for debonding ceramic brackets. The goal of this study is to give a comprehensive literature review on laser-aided ceramic bracket debonding. PubMed and Google Scholar databases were used to identify dental articles with the following combination of key words: Ceramic brackets, Debonding, and Laser. Sixteen English articles from 2004 to 2015 were selected. The selected studies were categorized according to the variables investigated including the intrapulpal temperature, shear bond strength, debonding time, enamel damage and bracket failure. Most articles reported decreased shear bond strength and debonding time following laser irradiation without any critical and irritating increase in pulpal temperature. There were no reports of bracket failure or enamel damage. Laser irradiation is an efficient way to reduce shear bond strength of ceramic bracket and debonding time. This technique is a safe way for removing ceramic bracket with minimal impact on intrapulpal temperature and enamel surface and it reduces ceramic bracket failure. PMID:27330690

Ripple artifact reduction using slice overlap in slice encoding for metal artifact correction.

PubMed

den Harder, J Chiel; van Yperen, Gert H; Blume, Ulrike A; Bos, Clemens

2015-01-01

Multispectral imaging (MSI) significantly reduces metal artifacts. Yet, especially in techniques that use gradient selection, such as slice encoding for metal artifact correction (SEMAC), a residual ripple artifact may be prominent. Here, an analysis is presented of the ripple artifact and of slice overlap as an approach to reduce the artifact. The ripple artifact was analyzed theoretically to clarify its cause. Slice overlap, conceptually similar to spectral bin overlap in multi-acquisition with variable resonances image combination (MAVRIC), was achieved by reducing the selection gradient and, thus, increasing the slice profile width. Time domain simulations and phantom experiments were performed to validate the analyses and proposed solution. Discontinuities between slices are aggravated by signal displacement in the frequency encoding direction in areas with deviating B0. Specifically, it was demonstrated that ripple artifacts appear only where B0 varies both in-plane and through-plane. Simulations and phantom studies of metal implants confirmed the efficacy of slice overlap to reduce the artifact. The ripple artifact is an important limitation of gradient selection based MSI techniques, and can be understood using the presented simulations. At a scan-time penalty, slice overlap effectively addressed the artifact, thereby improving image quality near metal implants. © 2014 Wiley Periodicals, Inc.

Long-range prediction of Indian summer monsoon rainfall using data mining and statistical approaches

NASA Astrophysics Data System (ADS)

H, Vathsala; Koolagudi, Shashidhar G.

2017-10-01

This paper presents a hybrid model to better predict Indian summer monsoon rainfall. The algorithm considers suitable techniques for processing dense datasets. The proposed three-step algorithm comprises closed itemset generation-based association rule mining for feature selection, cluster membership for dimensionality reduction, and simple logistic function for prediction. The application of predicting rainfall into flood, excess, normal, deficit, and drought based on 36 predictors consisting of land and ocean variables is presented. Results show good accuracy in the considered study period of 37years (1969-2005).

Psychological stress measurement through voice output analysis

NASA Technical Reports Server (NTRS)

Older, H. J.; Jenney, L. L.

1975-01-01

Audio tape recordings of selected Skylab communications were processed by a psychological stress evaluator. Strip chart tracings were read blind and scores were assigned based on characteristics reported by the manufacturer to indicate psychological stress. These scores were analyzed for their empirical relationships with operational variables in Skylab judged to represent varying degrees of situational stress. Although some statistically significant relationships were found, the technique was not judged to be sufficiently predictive to warrant its use in assessing the degree of psychological stress of crew members in future space missions.

Analysis of signal to noise enhancement using a highly selective modulation tracking filter

NASA Technical Reports Server (NTRS)

Haden, C. R.; Alworth, C. W.

1972-01-01

Experiments are reported which utilize photodielectric effects in semiconductor loaded superconducting resonant circuits for suppressing noise in RF communication systems. The superconducting tunable cavity acts as a narrow band tracking filter for detecting conventional RF signals. Analytical techniques were developed which lead to prediction of signal-to-noise improvements. Progress is reported in optimization of the experimental variables. These include improved Q, new semiconductors, improved optics, and simplification of the electronics. Information bearing signals were passed through the system, and noise was introduced into the computer model.

Nixtamalized flour from quality protein maize (Zea mays L). optimization of alkaline processing.

PubMed

Milán-Carrillo, J; Gutiérrez-Dorado, R; Cuevas-Rodríguez, E O; Garzón-Tiznado, J A; Reyes-Moreno, C

2004-01-01

Quality of maize proteins is poor, they are deficient in the essential amino acids lysine and tryptophan. Recently, in Mexico were successfully developed nutritionally improved 26 new hybrids and cultivars called quality protein maize (QPM) which contain greater amounts of lysine and tryptophan. Alkaline cooking of maize with lime (nixtamalization) is the first step for producing several maize products (masa, tortillas, flours, snacks). Processors adjust nixtamalization variables based on experience. The objective of this work was to determine the best combination of nixtamalization process variables for producing nixtamalized maize flour (NMF) from QPM V-537 variety. Nixtamalization conditions were selected from factorial combinations of process variables: nixtamalization time (NT, 20-85 min), lime concentration (LC, 3.3-6.7 g Ca(OH)2/l, in distilled water), and steep time (ST, 8-16 hours). Nixtamalization temperature and ratio of grain to cooking medium were 85 degrees C and 1:3 (w/v), respectively. At the end of each cooking treatment the steeping started for the required time. Steeping was finished by draining the cooking liquor (nejayote). Nixtamal (alkaline-cooked maize kernels) was washed with running tap water. Wet nixtamal was dried (24 hours, 55 degrees C) and milled to pass through 80-US mesh screen to obtain NMF. Response surface methodology (RSM) was applied as optimization technique, over four response variables: In vitro protein digestibility (PD), total color difference (deltaE), water absorption index (WAI), and pH. Predictive models for response variables were developed as a function of process variables. Conventional graphical method was applied to obtain maximum PD, WAI and minimum deltaE, pH. Contour plots of each of the response variables were utilized applying superposition surface methodology, to obtain three contour plots for observation and selection of best combination of NT (31 min), LC (5.4 g Ca(OH)2/l), and ST (8.1 hours) for producing optimized NMF from QPM.

Estimation of Flood Discharges at Selected Recurrence Intervals for Streams in New Hampshire

USGS Publications Warehouse

Olson, Scott A.

2009-01-01

This report provides estimates of flood discharges at selected recurrence intervals for streamgages in and adjacent to New Hampshire and equations for estimating flood discharges at recurrence intervals of 2-, 5-, 10-, 25-, 50-, 100-, and 500-years for ungaged, unregulated, rural streams in New Hampshire. The equations were developed using generalized least-squares regression. Flood-frequency and drainage-basin characteristics from 117 streamgages were used in developing the equations. The drainage-basin characteristics used as explanatory variables in the regression equations include drainage area, mean April precipitation, percentage of wetland area, and main channel slope. The average standard error of prediction for estimating the 2-, 5-, 10-, 25-, 50-, 100-, and 500-year recurrence interval flood discharges with these equations are 30.0, 30.8, 32.0, 34.2, 36.0, 38.1, and 43.4 percent, respectively. Flood discharges at selected recurrence intervals for selected streamgages were computed following the guidelines in Bulletin 17B of the U.S. Interagency Advisory Committee on Water Data. To determine the flood-discharge exceedence probabilities at streamgages in New Hampshire, a new generalized skew coefficient map covering the State was developed. The standard error of the data on new map is 0.298. To improve estimates of flood discharges at selected recurrence intervals for 20 streamgages with short-term records (10 to 15 years), record extension using the two-station comparison technique was applied. The two-station comparison method uses data from a streamgage with long-term record to adjust the frequency characteristics at a streamgage with a short-term record. A technique for adjusting a flood-discharge frequency curve computed from a streamgage record with results from the regression equations is described in this report. Also, a technique is described for estimating flood discharge at a selected recurrence interval for an ungaged site upstream or downstream from a streamgage using a drainage-area adjustment. The final regression equations and the flood-discharge frequency data used in this study will be available in StreamStats. StreamStats is a World Wide Web application providing automated regression-equation solutions for user-selected sites on streams.

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

PubMed Central

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. PMID:25493625

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.

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…

Movement variability and skill level of various throwing techniques.

PubMed

Wagner, Herbert; Pfusterschmied, Jürgen; Klous, Miriam; von Duvillard, Serge P; Müller, Erich

2012-02-01

In team-handball, skilled athletes are able to adapt to different game situations that may lead to differences in movement variability. Whether movement variability affects the performance of a team-handball throw and is affected by different skill levels or throwing techniques has not yet been demonstrated. Consequently, the aims of the study were to determine differences in performance and movement variability for several throwing techniques in different phases of the throwing movement, and of different skill levels. Twenty-four team-handball players of different skill levels (n=8) performed 30 throws using various throwing techniques. Upper body kinematics was measured via an 8 camera Vicon motion capture system and movement variability was calculated. Results indicated an increase in movement variability in the distal joint movements during the acceleration phase. In addition, there was a decrease in movement variability in highly skilled and skilled players in the standing throw with run-up, which indicated an increase in the ball release speed, which was highest when using this throwing technique. We assert that team-handball players had the ability to compensate an increase in movement variability in the acceleration phase to throw accurately, and skilled players were able to control the movement, although movement variability decreased in the standing throw with run-up. Copyright © 2011 Elsevier B.V. All rights reserved.

Specifying content and mechanisms of change in interventions to change professionals' practice: an illustration from the Good Goals study in occupational therapy.

PubMed

Kolehmainen, Niina; Francis, Jill J

2012-10-18

It is widely agreed that interventions to change professionals' practice need to be clearly specified. This involves (1) selecting and defining the intervention techniques, (2) operationalising the techniques and deciding their delivery, and (3) formulating hypotheses about the mechanisms through which the techniques are thought to result in change. Descriptions of methods to achieve these objectives are limited. This paper reports methods and illustrates outputs from a study to meet these objectives, specifically from the Good Goals study to improve occupational therapists' caseload management practice. (1) Behaviour change techniques were identified and selected from an existing matrix that maps techniques to determinants. An existing coding manual was used to define the techniques. (2) A team of occupational therapists generated context-relevant, acceptable modes of delivery for the techniques; these data were compared and contrasted with previously collected data, literature on caseload management, and the aims of the intervention. (3) Hypotheses about the mechanisms of change were formulated by drawing on the matrix and on theories of behaviour change. (1) Eight behaviour change techniques were selected: goal specified; self-monitoring; contract; graded tasks; increasing skills (problem solving, decision making, goal setting); coping skills; rehearsal of relevant skills; social processes of encouragement, support, and pressure; demonstration by others; and feedback. (2) A range of modes of delivery were generated (e.g., graded tasks' consisting of series of clinical cases and situations that become increasingly difficult). Conditions for acceptable delivery were identified (e.g., 'self-monitoring' was acceptable only if delivered at team level). The modes of delivery were specified as face-to-face training, task sheets, group tasks, DVDs, and team-based weekly meetings. (3) The eight techniques were hypothesized to target caseload management practice through eleven mediating variables. Three domains were hypothesized to be most likely to change: beliefs about capabilities, motivation and goals, and behavioural regulation. The project provides an exemplar of a systematic and reportable development of a quality-improvement intervention, with its methods likely to be applicable to other projects. A subsequent study of the intervention has provided early indication that use of systematic methods to specify interventions may help to maximize acceptability and effectiveness.

Development of the International Spinal Cord Injury Activities and Participation Basic Data Set.

PubMed

Post, M W; Charlifue, S; Biering-Sørensen, F; Catz, A; Dijkers, M P; Horsewell, J; Noonan, V K; Noreau, L; Tate, D G; Sinnott, K A

2016-07-01

Consensus decision-making process. The objective of this study was to develop an International Spinal Cord Injury (SCI) Activities and Participation (A&P) Basic Data Set. International working group. A committee of experts was established to select and define A&P data elements to be included in this data set. A draft data set was developed and posted on the International Spinal Cord Society (ISCoS) and American Spinal Injury Association websites and was also disseminated among appropriate organizations for review. Suggested revisions were considered, and a final version of the A&P Data Set was completed. Consensus was reached to define A&P and to incorporate both performance and satisfaction ratings. Items that were considered core to each A&P domain were selected from two existing questionnaires. Four items measuring activities were selected from the Spinal Cord Independence Measure III to provide basic data on task execution in activities of daily living. Eight items were selected from the Craig Handicap Assessment and Reporting Technique to provide basic data on the frequency of participation. An additional rating of satisfaction on a three-point scale for each item completes the total of 24 A&P variables. Collection of the International SCI A&P Basic Data Set variables in all future research on SCI outcomes is advised to facilitate comparison of results across published studies from around the world. Additional standardised instruments to assess activities of daily living or participation can be administered, depending on the purpose of a particular study.

Processing techniques for software based SAR processors

NASA Technical Reports Server (NTRS)

Leung, K.; Wu, C.

1983-01-01

Software SAR processing techniques defined to treat Shuttle Imaging Radar-B (SIR-B) data are reviewed. The algorithms are devised for the data processing procedure selection, SAR correlation function implementation, multiple array processors utilization, cornerturning, variable reference length azimuth processing, and range migration handling. The Interim Digital Processor (IDP) originally implemented for handling Seasat SAR data has been adapted for the SIR-B, and offers a resolution of 100 km using a processing procedure based on the Fast Fourier Transformation fast correlation approach. Peculiarities of the Seasat SAR data processing requirements are reviewed, along with modifications introduced for the SIR-B. An Advanced Digital SAR Processor (ADSP) is under development for use with the SIR-B in the 1986 time frame as an upgrade for the IDP, which will be in service in 1984-5.

Logistic regression applied to natural hazards: rare event logistic regression with replications

NASA Astrophysics Data System (ADS)

Guns, M.; Vanacker, V.

2012-06-01

Statistical analysis of natural hazards needs particular attention, as most of these phenomena are rare events. This study shows that the ordinary rare event logistic regression, as it is now commonly used in geomorphologic studies, does not always lead to a robust detection of controlling factors, as the results can be strongly sample-dependent. In this paper, we introduce some concepts of Monte Carlo simulations in rare event logistic regression. This technique, so-called rare event logistic regression with replications, combines the strength of probabilistic and statistical methods, and allows overcoming some of the limitations of previous developments through robust variable selection. This technique was here developed for the analyses of landslide controlling factors, but the concept is widely applicable for statistical analyses of natural hazards.

Open loop model for WDM links

NASA Astrophysics Data System (ADS)

D, Meena; Francis, Fredy; T, Sarath K.; E, Dipin; Srinivas, T.; K, Jayasree V.

2014-10-01

Wavelength Division Multiplexing (WDM) techniques overfibrelinks helps to exploit the high bandwidth capacity of single mode fibres. A typical WDM link consisting of laser source, multiplexer/demultiplexer, amplifier and detectoris considered for obtaining the open loop gain model of the link. The methodology used here is to obtain individual component models using mathematical and different curve fitting techniques. These individual models are then combined to obtain the WDM link model. The objective is to deduce a single variable model for the WDM link in terms of input current to system. Thus it provides a black box solution for a link. The Root Mean Square Error (RMSE) associated with each of the approximated models is given for comparison. This will help the designer to select the suitable WDM link model during a complex link design.

Film/chemistry selection for the earth resources technology satellite /ERTS/ ground data handling system

NASA Technical Reports Server (NTRS)

Shaffer, R. M.

1973-01-01

A detailed description is given of the methods of choose the duplication film and chemistry currently used in the NASA-ERTS Ground Data Handling System. The major ERTS photographic duplication goals are given as background information to justify the specifications for the desirable film/chemistry combination. Once these specifications were defined, a quantitative evaluation program was designed and implemented to determine if any recommended combinations could meet the ERTS laboratory specifications. The specifications include tone reproduction, granularity, MTF and cosmetic effects. A complete description of the techniques used to measure the test response variables is given. It is anticipated that similar quantitative techniques could be used on other programs to determine the optimum film/chemistry consistent with the engineering goals of the program.

Kindergarten predictors of second versus eighth grade reading comprehension impairments.

PubMed

Adlof, Suzanne M; Catts, Hugh W; Lee, Jaehoon

2010-01-01

Multiple studies have shown that kindergarten measures of phonological awareness and alphabet knowledge are good predictors of reading achievement in the primary grades. However, less attention has been given to the early predictors of later reading achievement. This study used a modified best-subsets variable-selection technique to examine kindergarten predictors of early versus later reading comprehension impairments. Participants included 433 children involved in a longitudinal study of language and reading development. The kindergarten test battery assessed various language skills in addition to phonological awareness, alphabet knowledge, naming speed, and nonverbal cognitive ability. Reading comprehension was assessed in second and eighth grades. Results indicated that different combinations of variables were required to optimally predict second versus eighth grade reading impairments. Although some variables effectively predicted reading impairments in both grades, their relative contributions shifted over time. These results are discussed in light of the changing nature of reading comprehension over time. Further research will help to improve the early identification of later reading disabilities.

Modular Construction of Large Non-Immune Human Antibody Phage-Display Libraries from Variable Heavy and Light Chain Gene Cassettes.

PubMed

Lee, Nam-Kyung; Bidlingmaier, Scott; Su, Yang; Liu, Bin

2018-01-01

Monoclonal antibodies and antibody-derived therapeutics have emerged as a rapidly growing class of biological drugs for the treatment of cancer, autoimmunity, infection, and neurological diseases. To support the development of human antibodies, various display techniques based on antibody gene repertoires have been constructed over the last two decades. In particular, scFv-antibody phage display has been extensively utilized to select lead antibodies against a variety of target antigens. To construct a scFv phage display that enables efficient antibody discovery, and optimization, it is desirable to develop a system that allows modular assembly of highly diverse variable heavy chain and light chain (Vκ and Vλ) repertoires. Here, we describe modular construction of large non-immune human antibody phage-display libraries built on variable gene cassettes from heavy chain and light chain repertoires (Vκ- and Vλ-light can be made into independent cassettes). We describe utility of such libraries in antibody discovery and optimization through chain shuffling.

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

PubMed

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

2017-04-15

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

Optimization of porthole die geometrical variables by Taguchi method

NASA Astrophysics Data System (ADS)

Gagliardi, F.; Ciancio, C.; Ambrogio, G.; Filice, L.

2017-10-01

Porthole die extrusion is commonly used to manufacture hollow profiles made of lightweight alloys for numerous industrial applications. The reliability of extruded parts is affected strongly by the quality of the longitudinal and transversal seam welds. According to that, the die geometry must be designed correctly and the process parameters must be selected properly to achieve the desired product quality. In this study, numerical 3D simulations have been created and run to investigate the role of various geometrical variables on punch load and maximum pressure inside the welding chamber. These are important outputs to take into account affecting, respectively, the necessary capacity of the extrusion press and the quality of the welding lines. The Taguchi technique has been used to reduce the number of the required numerical simulations necessary for considering the influence of twelve different geometric variables. Moreover, the Analysis of variance (ANOVA) has been implemented to individually analyze the effect of each input parameter on the two responses. Then, the methodology has been utilized to determine the optimal process configuration individually optimizing the two investigated process outputs. Finally, the responses of the optimized parameters have been verified through finite element simulations approximating the predicted value closely. This study shows the feasibility of the Taguchi technique for predicting performance, optimization and therefore for improving the design of a porthole extrusion process.

Transcutaneous electrical nerve stimulation for spasticity: A systematic review.

PubMed

Fernández-Tenorio, E; Serrano-Muñoz, D; Avendaño-Coy, J; Gómez-Soriano, J

2016-07-26

Although transcutaneous electrical nerve stimulation (TENS) has traditionally been used to treat pain, some studies have observed decreased spasticity after use of this technique. However, its use in clinical practice is still limited. Our purpose was twofold: to determine whether TENS is effective for treating spasticity or associated symptoms in patients with neurological involvement, and to determine which stimulation parameters exert the greatest effect on variables associated with spasticity. Two independent reviewers used PubMed, PEDro, and Cochrane databases to search for randomised clinical trials addressing TENS and spasticity published before 12 May 2015, and selected the articles that met the inclusion criteria. Of the initial 96 articles, 86 were excluded. The remaining 10 articles present results from 207 patients with a cerebrovascular accident, 84 with multiple sclerosis, and 39 with spinal cord lesions. In light of our results, we recommend TENS as a treatment for spasticity due to its low cost, ease of use, and absence of adverse reactions. However, the great variability in the types of stimulation used in the studies, and the differences in parameters and variables, make it difficult to assess and compare any results that might objectively determine the effectiveness of this technique and show how to optimise parameters. Copyright © 2016 Sociedad Española de Neurología. Publicado por Elsevier España, S.L.U. All rights reserved.

On the primary variable switching technique for simulating unsaturated-saturated flows

NASA Astrophysics Data System (ADS)

Diersch, H.-J. G.; Perrochet, P.

Primary variable switching appears as a promising numerical technique for variably saturated flows. While the standard pressure-based form of the Richards equation can suffer from poor mass balance accuracy, the mixed form with its improved conservative properties can possess convergence difficulties for dry initial conditions. On the other hand, variable switching can overcome most of the stated numerical problems. The paper deals with variable switching for finite elements in two and three dimensions. The technique is incorporated in both an adaptive error-controlled predictor-corrector one-step Newton (PCOSN) iteration strategy and a target-based full Newton (TBFN) iteration scheme. Both schemes provide different behaviors with respect to accuracy and solution effort. Additionally, a simplified upstream weighting technique is used. Compared with conventional approaches the primary variable switching technique represents a fast and robust strategy for unsaturated problems with dry initial conditions. The impact of the primary variable switching technique is studied over a wide range of mostly 2D and partly difficult-to-solve problems (infiltration, drainage, perched water table, capillary barrier), where comparable results are available. It is shown that the TBFN iteration is an effective but error-prone procedure. TBFN sacrifices temporal accuracy in favor of accelerated convergence if aggressive time step sizes are chosen.

CORRELATION PURSUIT: FORWARD STEPWISE VARIABLE SELECTION FOR INDEX MODELS

PubMed Central

Zhong, Wenxuan; Zhang, Tingting; Zhu, Yu; Liu, Jun S.

2012-01-01

In this article, a stepwise procedure, correlation pursuit (COP), is developed for variable selection under the sufficient dimension reduction framework, in which the response variable Y is influenced by the predictors X1, X2, …, Xp through an unknown function of a few linear combinations of them. Unlike linear stepwise regression, COP does not impose a special form of relationship (such as linear) between the response variable and the predictor variables. The COP procedure selects variables that attain the maximum correlation between the transformed response and the linear combination of the variables. Various asymptotic properties of the COP procedure are established, and in particular, its variable selection performance under diverging number of predictors and sample size has been investigated. The excellent empirical performance of the COP procedure in comparison with existing methods are demonstrated by both extensive simulation studies and a real example in functional genomics. PMID:23243388

Prediction of solvation enthalpy of gaseous organic compounds in propanol

NASA Astrophysics Data System (ADS)

Golmohammadi, Hassan; Dashtbozorgi, Zahra

2016-09-01

The purpose of this paper is to present a novel way for developing quantitative structure-property relationship (QSPR) models to predict the gas-to-propanol solvation enthalpy (Δ H solv) of 95 organic compounds. Different kinds of descriptors were calculated for each compound using the Dragon software package. The variable selection technique of replacement method (RM) was employed to select the optimal subset of solute descriptors. Our investigation reveals that the dependence of physical chemistry properties of solution on solvation enthalpy is nonlinear and that the RM method is unable to model the solvation enthalpy accurately. The results established that the calculated Δ H solv values by SVM were in good agreement with the experimental ones, and the performances of the SVM models were superior to those obtained by RM model.

Input variable selection and calibration data selection for storm water quality regression models.

PubMed

Sun, Siao; Bertrand-Krajewski, Jean-Luc

2013-01-01

Storm water quality models are useful tools in storm water management. Interest has been growing in analyzing existing data for developing models for urban storm water quality evaluations. It is important to select appropriate model inputs when many candidate explanatory variables are available. Model calibration and verification are essential steps in any storm water quality modeling. This study investigates input variable selection and calibration data selection in storm water quality regression models. The two selection problems are mutually interacted. A procedure is developed in order to fulfil the two selection tasks in order. The procedure firstly selects model input variables using a cross validation method. An appropriate number of variables are identified as model inputs to ensure that a model is neither overfitted nor underfitted. Based on the model input selection results, calibration data selection is studied. Uncertainty of model performances due to calibration data selection is investigated with a random selection method. An approach using the cluster method is applied in order to enhance model calibration practice based on the principle of selecting representative data for calibration. The comparison between results from the cluster selection method and random selection shows that the former can significantly improve performances of calibrated models. It is found that the information content in calibration data is important in addition to the size of calibration data.

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

Soil Cd, Cr, Cu, Ni, Pb and Zn sorption and retention models using SVM: Variable selection and competitive model.

PubMed

González Costa, J J; Reigosa, M J; Matías, J M; Covelo, E F

2017-09-01

The aim of this study was to model the sorption and retention of Cd, Cu, Ni, Pb and Zn in soils. To that extent, the sorption and retention of these metals were studied and the soil characterization was performed separately. Multiple stepwise regression was used to produce multivariate models with linear techniques and with support vector machines, all of which included 15 explanatory variables characterizing soils. When the R-squared values are represented, two different groups are noticed. Cr, Cu and Pb sorption and retention show a higher R-squared; the most explanatory variables being humified organic matter, Al oxides and, in some cases, cation-exchange capacity (CEC). The other group of metals (Cd, Ni and Zn) shows a lower R-squared, and clays are the most explanatory variables, including a percentage of vermiculite and slime. In some cases, quartz, plagioclase or hematite percentages also show some explanatory capacity. Support Vector Machine (SVM) regression shows that the different models are not as regular as in multiple regression in terms of number of variables, the regression for nickel adsorption being the one with the highest number of variables in its optimal model. On the other hand, there are cases where the most explanatory variables are the same for two metals, as it happens with Cd and Cr adsorption. A similar adsorption mechanism is thus postulated. These patterns of the introduction of variables in the model allow us to create explainability sequences. Those which are the most similar to the selectivity sequences obtained by Covelo (2005) are Mn oxides in multiple regression and change capacity in SVM. Among all the variables, the only one that is explanatory for all the metals after applying the maximum parsimony principle is the percentage of sand in the retention process. In the competitive model arising from the aforementioned sequences, the most intense competitiveness for the adsorption and retention of different metals appears between Cr and Cd, Cu and Zn in multiple regression; and between Cr and Cd in SVM regression. Copyright © 2017 Elsevier B.V. All rights reserved.

Measurement of non-sugar solids content in Chinese rice wine using near infrared spectroscopy combined with an efficient characteristic variables selection algorithm.

PubMed

Ouyang, Qin; Zhao, Jiewen; Chen, Quansheng

2015-01-01

The non-sugar solids (NSS) content is one of the most important nutrition indicators of Chinese rice wine. This study proposed a rapid method for the measurement of NSS content in Chinese rice wine using near infrared (NIR) spectroscopy. We also systemically studied the efficient spectral variables selection algorithms that have to go through modeling. A new algorithm of synergy interval partial least square with competitive adaptive reweighted sampling (Si-CARS-PLS) was proposed for modeling. The performance of the final model was back-evaluated using root mean square error of calibration (RMSEC) and correlation coefficient (Rc) in calibration set and similarly tested by mean square error of prediction (RMSEP) and correlation coefficient (Rp) in prediction set. The optimum model by Si-CARS-PLS algorithm was achieved when 7 PLS factors and 18 variables were included, and the results were as follows: Rc=0.95 and RMSEC=1.12 in the calibration set, Rp=0.95 and RMSEP=1.22 in the prediction set. In addition, Si-CARS-PLS algorithm showed its superiority when compared with the commonly used algorithms in multivariate calibration. This work demonstrated that NIR spectroscopy technique combined with a suitable multivariate calibration algorithm has a high potential in rapid measurement of NSS content in Chinese rice wine. Copyright © 2015 Elsevier B.V. All rights reserved.

Structural reanalysis via a mixed method. [using Taylor series for accuracy improvement

NASA Technical Reports Server (NTRS)

Noor, A. K.; Lowder, H. E.

1975-01-01

A study is made of the approximate structural reanalysis technique based on the use of Taylor series expansion of response variables in terms of design variables in conjunction with the mixed method. In addition, comparisons are made with two reanalysis techniques based on the displacement method. These techniques are the Taylor series expansion and the modified reduced basis. It is shown that the use of the reciprocals of the sizing variables as design variables (which is the natural choice in the mixed method) can result in a substantial improvement in the accuracy of the reanalysis technique. Numerical results are presented for a space truss structure.

Strategies to optimize monitoring schemes of recreational waters from Salta, Argentina: a multivariate approach

PubMed Central

Gutiérrez-Cacciabue, Dolores; Teich, Ingrid; Poma, Hugo Ramiro; Cruz, Mercedes Cecilia; Balzarini, Mónica; Rajal, Verónica Beatriz

2014-01-01

Several recreational surface waters in Salta, Argentina, were selected to assess their quality. Seventy percent of the measurements exceeded at least one of the limits established by international legislation becoming unsuitable for their use. To interpret results of complex data, multivariate techniques were applied. Arenales River, due to the variability observed in the data, was divided in two: upstream and downstream representing low and high pollution sites, respectively; and Cluster Analysis supported that differentiation. Arenales River downstream and Campo Alegre Reservoir were the most different environments and Vaqueros and La Caldera Rivers were the most similar. Canonical Correlation Analysis allowed exploration of correlations between physicochemical and microbiological variables except in both parts of Arenales River, and Principal Component Analysis allowed finding relationships among the 9 measured variables in all aquatic environments. Variable’s loadings showed that Arenales River downstream was impacted by industrial and domestic activities, Arenales River upstream was affected by agricultural activities, Campo Alegre Reservoir was disturbed by anthropogenic and ecological effects, and La Caldera and Vaqueros Rivers were influenced by recreational activities. Discriminant Analysis allowed identification of subgroup of variables responsible for seasonal and spatial variations. Enterococcus, dissolved oxygen, conductivity, E. coli, pH, and fecal coliforms are sufficient to spatially describe the quality of the aquatic environments. Regarding seasonal variations, dissolved oxygen, conductivity, fecal coliforms, and pH can be used to describe water quality during dry season, while dissolved oxygen, conductivity, total coliforms, E. coli, and Enterococcus during wet season. Thus, the use of multivariate techniques allowed optimizing monitoring tasks and minimizing costs involved. PMID:25190636

Effects of visual feedback-induced variability on motor learning of handrim wheelchair propulsion.

PubMed

Leving, Marika T; Vegter, Riemer J K; Hartog, Johanneke; Lamoth, Claudine J C; de Groot, Sonja; van der Woude, Lucas H V

2015-01-01

It has been suggested that a higher intra-individual variability benefits the motor learning of wheelchair propulsion. The present study evaluated whether feedback-induced variability on wheelchair propulsion technique variables would also enhance the motor learning process. Learning was operationalized as an improvement in mechanical efficiency and propulsion technique, which are thought to be closely related during the learning process. 17 Participants received visual feedback-based practice (feedback group) and 15 participants received regular practice (natural learning group). Both groups received equal practice dose of 80 min, over 3 weeks, at 0.24 W/kg at a treadmill speed of 1.11 m/s. To compare both groups the pre- and post-test were performed without feedback. The feedback group received real-time visual feedback on seven propulsion variables with instruction to manipulate the presented variable to achieve the highest possible variability (1st 4-min block) and optimize it in the prescribed direction (2nd 4-min block). To increase motor exploration the participants were unaware of the exact variable they received feedback on. Energy consumption and the propulsion technique variables with their respective coefficient of variation were calculated to evaluate the amount of intra-individual variability. The feedback group, which practiced with higher intra-individual variability, improved the propulsion technique between pre- and post-test to the same extent as the natural learning group. Mechanical efficiency improved between pre- and post-test in the natural learning group but remained unchanged in the feedback group. These results suggest that feedback-induced variability inhibited the improvement in mechanical efficiency. Moreover, since both groups improved propulsion technique but only the natural learning group improved mechanical efficiency, it can be concluded that the improvement in mechanical efficiency and propulsion technique do not always appear simultaneously during the motor learning process. Their relationship is most likely modified by other factors such as the amount of the intra-individual variability.

Effects of Visual Feedback-Induced Variability on Motor Learning of Handrim Wheelchair Propulsion

PubMed Central

Leving, Marika T.; Vegter, Riemer J. K.; Hartog, Johanneke; Lamoth, Claudine J. C.; de Groot, Sonja; van der Woude, Lucas H. V.

2015-01-01

Background It has been suggested that a higher intra-individual variability benefits the motor learning of wheelchair propulsion. The present study evaluated whether feedback-induced variability on wheelchair propulsion technique variables would also enhance the motor learning process. Learning was operationalized as an improvement in mechanical efficiency and propulsion technique, which are thought to be closely related during the learning process. Methods 17 Participants received visual feedback-based practice (feedback group) and 15 participants received regular practice (natural learning group). Both groups received equal practice dose of 80 min, over 3 weeks, at 0.24 W/kg at a treadmill speed of 1.11 m/s. To compare both groups the pre- and post-test were performed without feedback. The feedback group received real-time visual feedback on seven propulsion variables with instruction to manipulate the presented variable to achieve the highest possible variability (1st 4-min block) and optimize it in the prescribed direction (2nd 4-min block). To increase motor exploration the participants were unaware of the exact variable they received feedback on. Energy consumption and the propulsion technique variables with their respective coefficient of variation were calculated to evaluate the amount of intra-individual variability. Results The feedback group, which practiced with higher intra-individual variability, improved the propulsion technique between pre- and post-test to the same extent as the natural learning group. Mechanical efficiency improved between pre- and post-test in the natural learning group but remained unchanged in the feedback group. Conclusion These results suggest that feedback-induced variability inhibited the improvement in mechanical efficiency. Moreover, since both groups improved propulsion technique but only the natural learning group improved mechanical efficiency, it can be concluded that the improvement in mechanical efficiency and propulsion technique do not always appear simultaneously during the motor learning process. Their relationship is most likely modified by other factors such as the amount of the intra-individual variability. PMID:25992626

Techniques for estimating monthly mean streamflow at gaged sites and monthly streamflow duration characteristics at ungaged sites in central Nevada

USGS Publications Warehouse

Hess, G.W.; Bohman, L.R.

1996-01-01

Techniques for estimating monthly mean streamflow at gaged sites and monthly streamflow duration characteristics at ungaged sites in central Nevada were developed using streamflow records at six gaged sites and basin physical and climatic characteristics. Streamflow data at gaged sites were related by regression techniques to concurrent flows at nearby gaging stations so that monthly mean streamflows for periods of missing or no record can be estimated for gaged sites in central Nevada. The standard error of estimate for relations at these sites ranged from 12 to 196 percent. Also, monthly streamflow data for selected percent exceedence levels were used in regression analyses with basin and climatic variables to determine relations for ungaged basins for annual and monthly percent exceedence levels. Analyses indicate that the drainage area and percent of drainage area at altitudes greater than 10,000 feet are the most significant variables. For the annual percent exceedence, the standard error of estimate of the relations for ungaged sites ranged from 51 to 96 percent and standard error of prediction for ungaged sites ranged from 96 to 249 percent. For the monthly percent exceedence values, the standard error of estimate of the relations ranged from 31 to 168 percent, and the standard error of prediction ranged from 115 to 3,124 percent. Reliability and limitations of the estimating methods are described.

Effects of implant system, impression technique, and impression material on accuracy of the working cast.

PubMed

Wegner, Kerstin; Weskott, Katharina; Zenginel, Martha; Rehmann, Peter; Wöstmann, Bernd

2013-01-01

This in vitro study aimed to identify the effects of the implant system, impression technique, and impression material on the transfer accuracy of implant impressions. The null hypothesis tested was that, in vitro and within the parameters of the experiment, the spatial relationship of a working cast to the placement of implants is not related to (1) the implant system, (2) the impression technique, or (3) the impression material. A steel maxilla was used as a reference model. Six implants of two different implant systems (Standard Plus, Straumann; Semados, Bego) were fixed in the reference model. The target variables were: three-dimensional (3D) shift in all directions, implant axis direction, and rotation. The target variables were assessed using a 3D coordinate measuring machine, and the respective deviations of the plaster models from the nominal values of the reference model were calculated. Two different impression techniques (reposition/pickup) and four impression materials (Aquasil Ultra, Flexitime, Impregum Penta, P2 Magnum 360) were investigated. In all, 80 implant impressions for each implant system were taken. Statistical analysis was performed using multivariate analysis of variance. The implant system significantly influenced the transfer accuracy for most spatial dimensions, including the overall 3D shift and implant axis direction. There was no significant difference between the two implant systems with regard to rotation. Multivariate analysis of variance showed a significant effect on transfer accuracy only for the implant system. Within the limits of the present study, it can be concluded that the transfer accuracy of the intraoral implant position on the working cast is far more dependent on the implant system than on the selection of a specific impression technique or material.

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.

Predictions of the pathological response to neoadjuvant chemotherapy in patients with primary breast cancer using a data mining technique.

PubMed

Takada, M; Sugimoto, M; Ohno, S; Kuroi, K; Sato, N; Bando, H; Masuda, N; Iwata, H; Kondo, M; Sasano, H; Chow, L W C; Inamoto, T; Naito, Y; Tomita, M; Toi, M

2012-07-01

Nomogram, a standard technique that utilizes multiple characteristics to predict efficacy of treatment and likelihood of a specific status of an individual patient, has been used for prediction of response to neoadjuvant chemotherapy (NAC) in breast cancer patients. The aim of this study was to develop a novel computational technique to predict the pathological complete response (pCR) to NAC in primary breast cancer patients. A mathematical model using alternating decision trees, an epigone of decision tree, was developed using 28 clinicopathological variables that were retrospectively collected from patients treated with NAC (n = 150), and validated using an independent dataset from a randomized controlled trial (n = 173). The model selected 15 variables to predict the pCR with yielding area under the receiver operating characteristics curve (AUC) values of 0.766 [95 % confidence interval (CI)], 0.671-0.861, P value < 0.0001) in cross-validation using training dataset and 0.787 (95 % CI 0.716-0.858, P value < 0.0001) in the validation dataset. Among three subtypes of breast cancer, the luminal subgroup showed the best discrimination (AUC = 0.779, 95 % CI 0.641-0.917, P value = 0.0059). The developed model (AUC = 0.805, 95 % CI 0.716-0.894, P value < 0.0001) outperformed multivariate logistic regression (AUC = 0.754, 95 % CI 0.651-0.858, P value = 0.00019) of validation datasets without missing values (n = 127). Several analyses, e.g. bootstrap analysis, revealed that the developed model was insensitive to missing values and also tolerant to distribution bias among the datasets. Our model based on clinicopathological variables showed high predictive ability for pCR. This model might improve the prediction of the response to NAC in primary breast cancer patients.

Hot spot variability and lithography process window investigation by CDU improvement using CDC technique

NASA Astrophysics Data System (ADS)

Thamm, Thomas; Geh, Bernd; Djordjevic Kaufmann, Marija; Seltmann, Rolf; Bitensky, Alla; Sczyrba, Martin; Samy, Aravind Narayana

2018-03-01

Within the current paper, we will concentrate on the well-known CDC technique from Carl Zeiss to improve the CD distribution of the wafer by improving the reticle CDU and its impact on hotspots and Litho process window. The CDC technique uses an ultra-short pulse laser technology, which generates a micro-level Shade-In-Element (also known as "Pixels") into the mask quartz bulk material. These scatter centers are able to selectively attenuate certain areas of the reticle in higher resolution compared to other methods and thus improve the CD uniformity. In a first section, we compare the CDC technique with scanner dose correction schemes. It becomes obvious, that the CDC technique has unique advantages with respect to spatial resolution and intra-field flexibility over scanner correction schemes, however, due to the scanner flexibility across wafer both methods are rather complementary than competing. In a second section we show that a reference feature based correction scheme can be used to improve the CDU of a full chip with multiple different features that have different MEEF and dose sensitivities. In detail we will discuss the impact of forward scattering light originated by the CDC pixels on the illumination source and the related proximity signature. We will show that the impact on proximity is small compared to the CDU benefit of the CDC technique. Finally we show to which extend the reduced variability across reticle will result in a better common electrical process window of a whole chip design on the whole reticle field on wafer. Finally we will discuss electrical verification results between masks with purposely made bad CDU that got repaired by the CDC technique versus inherently good "golden" masks on a complex logic device. No yield difference is observed between the repaired bad masks and the masks with good CDU.

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.

Patient selection, echocardiographic screening and treatment strategies for interventional tricuspid repair using the edge-to-edge repair technique.

PubMed

Hausleiter, Jörg; Braun, Daniel; Orban, Mathias; Latib, Azeem; Lurz, Philipp; Boekstegers, Peter; von Bardeleben, Ralph Stephan; Kowalski, Marek; Hahn, Rebecca T; Maisano, Francesco; Hagl, Christian; Massberg, Steffen; Nabauer, Michael

2018-04-24

Severe tricuspid regurgitation (TR) has long been neglected despite its well known association with mortality. While surgical mortality rates remain high in isolated tricuspid valve surgery, interventional TR repair is rapidly evolving as an alternative to cardiac surgery in selected patients at high surgical risk. Currently, interventional edge-to-edge repair is the most frequently applied technique for TR repair even though the device has not been developed for this particular indication. Due to the inherent differences in tricuspid and mitral valve anatomy and pathology, percutaneous repair of the tricuspid valve is challenging due to a variety of factors including the complexity and variability of tricuspid valve anatomy, echocardiographic visibility of the valve leaflets, and device steering to the tricuspid valve. Furthermore, it remains to be clarified which patients are suitable for a percutaneous tricuspid repair and which features predict a successful procedure. On the basis of the available experience, we describe criteria for patient selection including morphological valve features, a standardized process for echocardiographic screening, and a strategy for clip placement. These criteria will help to achieve standardization of valve assessment and the procedural approach, and to further develop interventional tricuspid valve repair using either currently available devices or dedicated tricuspid edge-to-edge repair devices in the future. In summary, this manuscript will provide guidance for patient selection and echocardiographic screening when considering edge-to-edge repair for severe TR.

Mediation analysis in nursing research: a methodological review.

PubMed

Liu, Jianghong; Ulrich, Connie

2016-12-01

Mediation statistical models help clarify the relationship between independent predictor variables and dependent outcomes of interest by assessing the impact of third variables. This type of statistical analysis is applicable for many clinical nursing research questions, yet its use within nursing remains low. Indeed, mediational analyses may help nurse researchers develop more effective and accurate prevention and treatment programs as well as help bridge the gap between scientific knowledge and clinical practice. In addition, this statistical approach allows nurse researchers to ask - and answer - more meaningful and nuanced questions that extend beyond merely determining whether an outcome occurs. Therefore, the goal of this paper is to provide a brief tutorial on the use of mediational analyses in clinical nursing research by briefly introducing the technique and, through selected empirical examples from the nursing literature, demonstrating its applicability in advancing nursing science.

[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.

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

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.

Laryngeal reinnervation for bilateral vocal fold paralysis.

PubMed

Marina, Mat B; Marie, Jean-Paul; Birchall, Martin A

2011-12-01

Laryngeal reinnervation for bilateral vocal fold paralysis (BVFP) patients is a promising technique to achieve good airway, although preserving a good quality of voice. On the other hand, the procedure is not simple. This review explores the recent literature on surgical technique and factors that may contribute to the success. Research and literature in this area are limited due to variability and complexity of the nerve supply. The posterior cricoarytenoid (PCA) muscle also receives nerve supply from the interarytenoid branch. Transection of this nerve at the point between interarytenoid and PCA branch may prevent aberrant reinnervation of adductor nerve axons to the PCA muscle. A varying degree of regeneration of injured recurrent laryngeal nerves (RLN) in humans of more than 6 months confirms subclinical reinnervation, which may prevent denervation-induced atrophy. Several promising surgical techniques have been developed for bilateral selective reinnervation for BVFP patients. This involves reinnervation of the abductor and adductor laryngeal muscles. The surgical technique aims at reinnervating the PCA muscle to trigger abduction during the respiratory cycle and preservation of good voice by strengthening the adductor muscles as well as prevention of laryngeal synkinesis.

Convection in containerless processing.

PubMed

Hyers, Robert W; Matson, Douglas M; Kelton, Kenneth F; Rogers, Jan R

2004-11-01

Different containerless processing techniques have different strengths and weaknesses. Applying more than one technique allows various parts of a problem to be solved separately. For two research projects, one on phase selection in steels and the other on nucleation and growth of quasicrystals, a combination of experiments using electrostatic levitation (ESL) and electromagnetic levitation (EML) is appropriate. In both experiments, convection is an important variable. The convective conditions achievable with each method are compared for two very different materials: a low-viscosity, high-temperature stainless steel, and a high-viscosity, low-temperature quasicrystal-forming alloy. It is clear that the techniques are complementary when convection is a parameter to be explored in the experiments. For a number of reasons, including the sample size, temperature, and reactivity, direct measurement of the convective velocity is not feasible. Therefore, we must rely on computation techniques to estimate convection in these experiments. These models are an essential part of almost any microgravity investigation. The methods employed and results obtained for the projects levitation observation of dendrite evolution in steel ternary alloy rapid solidification (LODESTARS) and quasicrystalline undercooled alloys for space investigation (QUASI) are explained.

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/.

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

Modelling approaches: the case of schizophrenia.

PubMed

Heeg, Bart M S; Damen, Joep; Buskens, Erik; Caleo, Sue; de Charro, Frank; van Hout, Ben A

2008-01-01

Schizophrenia is a chronic disease characterized by periods of relative stability interrupted by acute episodes (or relapses). The course of the disease may vary considerably between patients. Patient histories show considerable inter- and even intra-individual variability. We provide a critical assessment of the advantages and disadvantages of three modelling techniques that have been used in schizophrenia: decision trees, (cohort and micro-simulation) Markov models and discrete event simulation models. These modelling techniques are compared in terms of building time, data requirements, medico-scientific experience, simulation time, clinical representation, and their ability to deal with patient heterogeneity, the timing of events, prior events, patient interaction, interaction between co-variates and variability (first-order uncertainty). We note that, depending on the research question, the optimal modelling approach should be selected based on the expected differences between the comparators, the number of co-variates, the number of patient subgroups, the interactions between co-variates, and simulation time. Finally, it is argued that in case micro-simulation is required for the cost-effectiveness analysis of schizophrenia treatments, a discrete event simulation model is best suited to accurately capture all of the relevant interdependencies in this chronic, highly heterogeneous disease with limited long-term follow-up data.

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.

Effects of variable practice on the motor learning outcomes in manual wheelchair propulsion.

PubMed

Leving, Marika T; Vegter, Riemer J K; de Groot, Sonja; van der Woude, Lucas H V

2016-11-23

Handrim wheelchair propulsion is a cyclic skill that needs to be learned during rehabilitation. It has been suggested that more variability in propulsion technique benefits the motor learning process of wheelchair propulsion. The purpose of this study was to determine the influence of variable practice on the motor learning outcomes of wheelchair propulsion in able-bodied participants. Variable practice was introduced in the form of wheelchair basketball practice and wheelchair-skill practice. Motor learning was operationalized as improvements in mechanical efficiency and propulsion technique. Eleven Participants in the variable practice group and 12 participants in the control group performed an identical pre-test and a post-test. Pre- and post-test were performed in a wheelchair on a motor-driven treadmill (1.11 m/s) at a relative power output of 0.23 W/kg. Energy consumption and the propulsion technique variables with their respective coefficient of variation were calculated. Between the pre- and the post-test the variable practice group received 7 practice sessions. During the practice sessions participants performed one-hour of variable practice, consisting of five wheelchair-skill tasks and a 30 min wheelchair basketball game. The control group did not receive any practice between the pre- and the post-test. Comparison of the pre- and the post-test showed that the variable practice group significantly improved the mechanical efficiency (4.5 ± 0.6% → 5.7 ± 0.7%) in contrast to the control group (4.5 ± 0.6% → 4.4 ± 0.5%) (group x time interaction effect p < 0.001).With regard to propulsion technique, both groups significantly reduced the push frequency and increased the contact angle of the hand with the handrim (within group, time effect). No significant group × time interaction effects were found for propulsion technique. With regard to propulsion variability, the variable practice group increased variability when compared to the control group (interaction effect p < 0.001). Compared to a control, variable practice, resulted in an increase in mechanical efficiency and increased variability. Interestingly, the large relative improvement in mechanical efficiency was concomitant with only moderate improvements in the propulsion technique, which were similar in the control group, suggesting that other factors besides propulsion technique contributed to the lower energy expenditure.

Effectiveness of planned teaching program on knowledge regarding Alzheimer's disease among the family members of elderly in a selected urban community at Mangalore

PubMed Central

Rodrigues, Lavina; Mathias, Thereza

2016-01-01

Background: Alzheimer's disease is one of the debilitating chronic diseases among older persons. It is an irreversible condition that leads to progressive deterioration of cognitive, intellectual, physical, and psychosocial functions. The study was aimed to assess the knowledge of the family members of elderly regarding Alzheimer's disease in a selected urban community at Mangalore. Materials and Methods: A preexperimental research design of one group pretest and posttest with an evaluative approach was adopted for the study. A total of 50 family members of elderly who met the inclusion criteria were selected through purposive sampling technique. The researcher developed a planned teaching program on Alzheimer's disease, and structured knowledge questionnaire on Alzheimer's disease was used to collect the data. Results: Descriptive and inferential statistics was used to analyze the data. Analysis revealed that the mean posttest knowledge (20.78 ± 3.31) was higher than mean pretest knowledge scores (12.90 ± 2.43). Significance of difference between pretest and posttest was statistically tested using paired “t” test and it was found very highly significant (t = 40.85, P < 0.05). Majority of the variables showed no significant association between pretest and posttest knowledge score and with demographic variables. Conclusion: The findings revealed that the planned teaching program is an effective strategy for improving the knowledge of the subjects. PMID:26985104

Effectiveness of planned teaching program on knowledge regarding Alzheimer's disease among the family members of elderly in a selected urban community at Mangalore.

PubMed

Rodrigues, Lavina; Mathias, Thereza

2016-01-01

Alzheimer's disease is one of the debilitating chronic diseases among older persons. It is an irreversible condition that leads to progressive deterioration of cognitive, intellectual, physical, and psychosocial functions. The study was aimed to assess the knowledge of the family members of elderly regarding Alzheimer's disease in a selected urban community at Mangalore. A preexperimental research design of one group pretest and posttest with an evaluative approach was adopted for the study. A total of 50 family members of elderly who met the inclusion criteria were selected through purposive sampling technique. The researcher developed a planned teaching program on Alzheimer's disease, and structured knowledge questionnaire on Alzheimer's disease was used to collect the data. Descriptive and inferential statistics was used to analyze the data. Analysis revealed that the mean posttest knowledge (20.78 ± 3.31) was higher than mean pretest knowledge scores (12.90 ± 2.43). Significance of difference between pretest and posttest was statistically tested using paired "t" test and it was found very highly significant (t = 40.85, P < 0.05). Majority of the variables showed no significant association between pretest and posttest knowledge score and with demographic variables. The findings revealed that the planned teaching program is an effective strategy for improving the knowledge of the subjects.

The making of the minibody: an engineered beta-protein for the display of conformationally constrained peptides.

PubMed

Tramontano, A; Bianchi, E; Venturini, S; Martin, F; Pessi, A; Sollazzo, M

1994-03-01

Conformationally constraining selectable peptides onto a suitable scaffold that enables their conformation to be predicted or readily determined by experimental techniques would considerably boost the drug discovery process by reducing the gap between the discovery of a peptide lead and the design of a peptidomimetic with a more desirable pharmacological profile. With this in mind, we designed the minibody, a 61-residue beta-protein aimed at retaining some desirable features of immunoglobulin variable domains, such as tolerance to sequence variability in selected regions of the protein and predictability of the main chain conformation of the same regions, based on the 'canonical structures' model. To test the ability of the minibody scaffold to support functional sites we also designed a metal binding version of the protein by suitably choosing the sequences of its loops. The minibody was produced both by chemical synthesis and expression in E. coli and characterized by size exclusion chromatography, UV CD (circular dichroism) spectroscopy and metal binding activity. All our data supported the model, but a more detailed structural characterization of the molecule was impaired by its low solubility. We were able to overcome this problem both by further mutagenesis of the framework and by addition of a solubilizing motif. The minibody is being used to select constrained human IL-6 peptidic ligands from a library displayed on the surface of the f1 bacteriophage.

Comparative study of shear wave-based elastography techniques in optical coherence tomography

NASA Astrophysics Data System (ADS)

Zvietcovich, Fernando; Rolland, Jannick P.; Yao, Jianing; Meemon, Panomsak; Parker, Kevin J.

2017-03-01

We compare five optical coherence elastography techniques able to estimate the shear speed of waves generated by one and two sources of excitation. The first two techniques make use of one piezoelectric actuator in order to produce a continuous shear wave propagation or a tone-burst propagation (TBP) of 400 Hz over a gelatin tissue-mimicking phantom. The remaining techniques utilize a second actuator located on the opposite side of the region of interest in order to create three types of interference patterns: crawling waves, swept crawling waves, and standing waves, depending on the selection of the frequency difference between the two actuators. We evaluated accuracy, contrast to noise ratio, resolution, and acquisition time for each technique during experiments. Numerical simulations were also performed in order to support the experimental findings. Results suggest that in the presence of strong internal reflections, single source methods are more accurate and less variable when compared to the two-actuator methods. In particular, TBP reports the best performance with an accuracy error <4.1%. Finally, the TBP was tested in a fresh chicken tibialis anterior muscle with a localized thermally ablated lesion in order to evaluate its performance in biological tissue.

Advantages of Synthetic Noise and Machine Learning for Analyzing Radioecological Data Sets.

PubMed

Shuryak, Igor

2017-01-01

The ecological effects of accidental or malicious radioactive contamination are insufficiently understood because of the hazards and difficulties associated with conducting studies in radioactively-polluted areas. Data sets from severely contaminated locations can therefore be small. Moreover, many potentially important factors, such as soil concentrations of toxic chemicals, pH, and temperature, can be correlated with radiation levels and with each other. In such situations, commonly-used statistical techniques like generalized linear models (GLMs) may not be able to provide useful information about how radiation and/or these other variables affect the outcome (e.g. abundance of the studied organisms). Ensemble machine learning methods such as random forests offer powerful alternatives. We propose that analysis of small radioecological data sets by GLMs and/or machine learning can be made more informative by using the following techniques: (1) adding synthetic noise variables to provide benchmarks for distinguishing the performances of valuable predictors from irrelevant ones; (2) adding noise directly to the predictors and/or to the outcome to test the robustness of analysis results against random data fluctuations; (3) adding artificial effects to selected predictors to test the sensitivity of the analysis methods in detecting predictor effects; (4) running a selected machine learning method multiple times (with different random-number seeds) to test the robustness of the detected "signal"; (5) using several machine learning methods to test the "signal's" sensitivity to differences in analysis techniques. Here, we applied these approaches to simulated data, and to two published examples of small radioecological data sets: (I) counts of fungal taxa in samples of soil contaminated by the Chernobyl nuclear power plan accident (Ukraine), and (II) bacterial abundance in soil samples under a ruptured nuclear waste storage tank (USA). We show that the proposed techniques were advantageous compared with the methodology used in the original publications where the data sets were presented. Specifically, our approach identified a negative effect of radioactive contamination in data set I, and suggested that in data set II stable chromium could have been a stronger limiting factor for bacterial abundance than the radionuclides 137Cs and 99Tc. This new information, which was extracted from these data sets using the proposed techniques, can potentially enhance the design of radioactive waste bioremediation.

Advantages of Synthetic Noise and Machine Learning for Analyzing Radioecological Data Sets

PubMed Central

Shuryak, Igor

2017-01-01

The ecological effects of accidental or malicious radioactive contamination are insufficiently understood because of the hazards and difficulties associated with conducting studies in radioactively-polluted areas. Data sets from severely contaminated locations can therefore be small. Moreover, many potentially important factors, such as soil concentrations of toxic chemicals, pH, and temperature, can be correlated with radiation levels and with each other. In such situations, commonly-used statistical techniques like generalized linear models (GLMs) may not be able to provide useful information about how radiation and/or these other variables affect the outcome (e.g. abundance of the studied organisms). Ensemble machine learning methods such as random forests offer powerful alternatives. We propose that analysis of small radioecological data sets by GLMs and/or machine learning can be made more informative by using the following techniques: (1) adding synthetic noise variables to provide benchmarks for distinguishing the performances of valuable predictors from irrelevant ones; (2) adding noise directly to the predictors and/or to the outcome to test the robustness of analysis results against random data fluctuations; (3) adding artificial effects to selected predictors to test the sensitivity of the analysis methods in detecting predictor effects; (4) running a selected machine learning method multiple times (with different random-number seeds) to test the robustness of the detected “signal”; (5) using several machine learning methods to test the “signal’s” sensitivity to differences in analysis techniques. Here, we applied these approaches to simulated data, and to two published examples of small radioecological data sets: (I) counts of fungal taxa in samples of soil contaminated by the Chernobyl nuclear power plan accident (Ukraine), and (II) bacterial abundance in soil samples under a ruptured nuclear waste storage tank (USA). We show that the proposed techniques were advantageous compared with the methodology used in the original publications where the data sets were presented. Specifically, our approach identified a negative effect of radioactive contamination in data set I, and suggested that in data set II stable chromium could have been a stronger limiting factor for bacterial abundance than the radionuclides 137Cs and 99Tc. This new information, which was extracted from these data sets using the proposed techniques, can potentially enhance the design of radioactive waste bioremediation. PMID:28068401

Updated techniques for estimating monthly streamflow-duration characteristics at ungaged and partial-record sites in central Nevada

USGS Publications Warehouse

Hess, Glen W.

2002-01-01

Techniques for estimating monthly streamflow-duration characteristics at ungaged and partial-record sites in central Nevada have been updated. These techniques were developed using streamflow records at six continuous-record sites, basin physical and climatic characteristics, and concurrent streamflow measurements at four partial-record sites. Two methods, the basin-characteristic method and the concurrent-measurement method, were developed to provide estimating techniques for selected streamflow characteristics at ungaged and partial-record sites in central Nevada. In the first method, logarithmic-regression analyses were used to relate monthly mean streamflows (from all months and by month) from continuous-record gaging sites of various percent exceedence levels or monthly mean streamflows (by month) to selected basin physical and climatic variables at ungaged sites. Analyses indicate that the total drainage area and percent of drainage area at altitudes greater than 10,000 feet are the most significant variables. For the equations developed from all months of monthly mean streamflow, the coefficient of determination averaged 0.84 and the standard error of estimate of the relations for the ungaged sites averaged 72 percent. For the equations derived from monthly means by month, the coefficient of determination averaged 0.72 and the standard error of estimate of the relations averaged 78 percent. If standard errors are compared, the relations developed in this study appear generally to be less accurate than those developed in a previous study. However, the new relations are based on additional data and the slight increase in error may be due to the wider range of streamflow for a longer period of record, 1995-2000. In the second method, streamflow measurements at partial-record sites were correlated with concurrent streamflows at nearby gaged sites by the use of linear-regression techniques. Statistical measures of results using the second method typically indicated greater accuracy than for the first method. However, to make estimates for individual months, the concurrent-measurement method requires several years additional streamflow data at more partial-record sites. Thus, exceedence values for individual months are not yet available due to the low number of concurrent-streamflow-measurement data available. Reliability, limitations, and applications of both estimating methods are described herein.

Comparison of imaging characteristics of multiple-beam equalization and storage phosphor direct digitizer radiographic systems

NASA Astrophysics Data System (ADS)

Sankaran, A.; Chuang, Keh-Shih; Yonekawa, Hisashi; Huang, H. K.

1992-06-01

The imaging characteristics of two chest radiographic equipment, Advanced Multiple Beam Equalization Radiography (AMBER) and Konica Direct Digitizer [using a storage phosphor (SP) plate] systems have been compared. The variables affecting image quality and the computer display/reading systems used are detailed. Utilizing specially designed wedge, geometric, and anthropomorphic phantoms, studies were conducted on: exposure and energy response of detectors; nodule detectability; different exposure techniques; various look- up tables (LUTs), gray scale displays and laser printers. Methods for scatter estimation and reduction were investigated. It is concluded that AMBER with screen-film and equalization techniques provides better nodule detectability than SP plates. However, SP plates have other advantages such as flexibility in the selection of exposure techniques, image processing features, and excellent sensitivity when combined with optimum reader operating modes. The equalization feature of AMBER provides better nodule detectability under the denser regions of the chest. Results of diagnostic accuracy are demonstrated with nodule detectability plots and analysis of images obtained with phantoms.

A real time Pegasus propulsion system model for VSTOL piloted simulation evaluation

NASA Technical Reports Server (NTRS)

Mihaloew, J. R.; Roth, S. P.; Creekmore, R.

1981-01-01

A real time propulsion system modeling technique suitable for use in man-in-the-loop simulator studies was developd. This technique provides the system accuracy, stability, and transient response required for integrated aircraft and propulsion control system studies. A Pegasus-Harrier propulsion system was selected as a baseline for developing mathematical modeling and simulation techniques for VSTOL. Initially, static and dynamic propulsion system characteristics were modeled in detail to form a nonlinear aerothermodynamic digital computer simulation of a Pegasus engine. From this high fidelity simulation, a real time propulsion model was formulated by applying a piece-wise linear state variable methodology. A hydromechanical and water injection control system was also simulated. The real time dynamic model includes the detail and flexibility required for the evaluation of critical control parameters and propulsion component limits over a limited flight envelope. The model was programmed for interfacing with a Harrier aircraft simulation. Typical propulsion system simulation results are presented.

Taxi-Out Time Prediction for Departures at Charlotte Airport Using Machine Learning Techniques

NASA Technical Reports Server (NTRS)

Lee, Hanbong; Malik, Waqar; Jung, Yoon C.

2016-01-01

Predicting the taxi-out times of departures accurately is important for improving airport efficiency and takeoff time predictability. In this paper, we attempt to apply machine learning techniques to actual traffic data at Charlotte Douglas International Airport for taxi-out time prediction. To find the key factors affecting aircraft taxi times, surface surveillance data is first analyzed. From this data analysis, several variables, including terminal concourse, spot, runway, departure fix and weight class, are selected for taxi time prediction. Then, various machine learning methods such as linear regression, support vector machines, k-nearest neighbors, random forest, and neural networks model are applied to actual flight data. Different traffic flow and weather conditions at Charlotte airport are also taken into account for more accurate prediction. The taxi-out time prediction results show that linear regression and random forest techniques can provide the most accurate prediction in terms of root-mean-square errors. We also discuss the operational complexity and uncertainties that make it difficult to predict the taxi times accurately.

Flight control system design factors for applying automated testing techniques

NASA Technical Reports Server (NTRS)

Sitz, Joel R.; Vernon, Todd H.

1990-01-01

The principal design features and operational experiences of the X-29 forward-swept-wing aircraft and F-18 high alpha research vehicle (HARV) automated test systems are discussed. It is noted that operational experiences in developing and using these automated testing techniques have highlighted the need for incorporating target system features to improve testability. Improved target system testability can be accomplished with the addition of nonreal-time and real-time features. Online access to target system implementation details, unobtrusive real-time access to internal user-selectable variables, and proper software instrumentation are all desirable features of the target system. Also, test system and target system design issues must be addressed during the early stages of the target system development. Processing speeds of up to 20 million instructions/s and the development of high-bandwidth reflective memory systems have improved the ability to integrate the target system and test system for the application of automated testing techniques. It is concluded that new methods of designing testability into the target systems are required.

Automated vehicle guidance using discrete reference markers. [road surface steering techniques

NASA Technical Reports Server (NTRS)

Johnston, A. R.; Assefi, T.; Lai, J. Y.

1979-01-01

Techniques for providing steering control for an automated vehicle using discrete reference markers fixed to the road surface are investigated analytically. Either optical or magnetic approaches can be used for the sensor, which generates a measurement of the lateral offset of the vehicle path at each marker to form the basic data for steering control. Possible mechanizations of sensor and controller are outlined. Techniques for handling certain anomalous conditions, such as a missing marker, or loss of acquisition, and special maneuvers, such as u-turns and switching, are briefly discussed. A general analysis of the vehicle dynamics and the discrete control system is presented using the state variable formulation. Noise in both the sensor measurement and in the steering servo are accounted for. An optimal controller is simulated on a general purpose computer, and the resulting plots of vehicle path are presented. Parameters representing a small multipassenger tram were selected, and the simulation runs show response to an erroneous sensor measurement and acquisition following large initial path errors.

An Adaptive Mesh Algorithm: Mesh Structure and Generation

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

Scannapieco, Anthony J.

2016-06-21

The purpose of Adaptive Mesh Refinement is to minimize spatial errors over the computational space not to minimize the number of computational elements. The additional result of the technique is that it may reduce the number of computational elements needed to retain a given level of spatial accuracy. Adaptive mesh refinement is a computational technique used to dynamically select, over a region of space, a set of computational elements designed to minimize spatial error in the computational model of a physical process. The fundamental idea is to increase the mesh resolution in regions where the physical variables are represented bymore » a broad spectrum of modes in k-space, hence increasing the effective global spectral coverage of those physical variables. In addition, the selection of the spatially distributed elements is done dynamically by cyclically adjusting the mesh to follow the spectral evolution of the system. Over the years three types of AMR schemes have evolved; block, patch and locally refined AMR. In block and patch AMR logical blocks of various grid sizes are overlaid to span the physical space of interest, whereas in locally refined AMR no logical blocks are employed but locally nested mesh levels are used to span the physical space. The distinction between block and patch AMR is that in block AMR the original blocks refine and coarsen entirely in time, whereas in patch AMR the patches change location and zone size with time. The type of AMR described herein is a locally refi ned AMR. In the algorithm described, at any point in physical space only one zone exists at whatever level of mesh that is appropriate for that physical location. The dynamic creation of a locally refi ned computational mesh is made practical by a judicious selection of mesh rules. With these rules the mesh is evolved via a mesh potential designed to concentrate the nest mesh in regions where the physics is modally dense, and coarsen zones in regions where the physics is modally sparse.« less

Change in BMI accurately predicted by social exposure to acquaintances.

PubMed

Oloritun, Rahman O; Ouarda, Taha B M J; Moturu, Sai; Madan, Anmol; Pentland, Alex Sandy; Khayal, Inas

2013-01-01

Research has mostly focused on obesity and not on processes of BMI change more generally, although these may be key factors that lead to obesity. Studies have suggested that obesity is affected by social ties. However these studies used survey based data collection techniques that may be biased toward select only close friends and relatives. In this study, mobile phone sensing techniques were used to routinely capture social interaction data in an undergraduate dorm. By automating the capture of social interaction data, the limitations of self-reported social exposure data are avoided. This study attempts to understand and develop a model that best describes the change in BMI using social interaction data. We evaluated a cohort of 42 college students in a co-located university dorm, automatically captured via mobile phones and survey based health-related information. We determined the most predictive variables for change in BMI using the least absolute shrinkage and selection operator (LASSO) method. The selected variables, with gender, healthy diet category, and ability to manage stress, were used to build multiple linear regression models that estimate the effect of exposure and individual factors on change in BMI. We identified the best model using Akaike Information Criterion (AIC) and R(2). This study found a model that explains 68% (p<0.0001) of the variation in change in BMI. The model combined social interaction data, especially from acquaintances, and personal health-related information to explain change in BMI. This is the first study taking into account both interactions with different levels of social interaction and personal health-related information. Social interactions with acquaintances accounted for more than half the variation in change in BMI. This suggests the importance of not only individual health information but also the significance of social interactions with people we are exposed to, even people we may not consider as close friends.

ENSO detection and use to inform the operation of large scale water systems

NASA Astrophysics Data System (ADS)

Pham, Vuong; Giuliani, Matteo; Castelletti, Andrea

2016-04-01

El Nino Southern Oscillation (ENSO) is a large-scale, coupled ocean-atmosphere phenomenon occurring in the tropical Pacific Ocean, and is considered one of the most significant factors causing hydro-climatic anomalies throughout the world. Water systems operations could benefit from a better understanding of this global phenomenon, which has the potential for enhancing the accuracy and lead-time of long-range streamflow predictions. In turn, these are key to design interannual water transfers in large scale water systems to contrast increasingly frequent extremes induced by changing climate. Despite the ENSO teleconnection is well defined in some locations such as Western USA and Australia, there is no consensus on how it can be detected and used in other river basins, particularly in Europe, Africa, and Asia. In this work, we contribute a general framework relying on Input Variable Selection techniques for detecting ENSO teleconnection and using this information for improving water reservoir operations. Core of our procedure is the Iterative Input variable Selection (IIS) algorithm, which is employed to find the most relevant determinants of streamflow variability for deriving predictive models based on the selected inputs as well as to find the most valuable information for conditioning operating decisions. Our framework is applied to the multipurpose operations of the Hoa Binh reservoir in the Red River basin (Vietnam), taking into account hydropower production, water supply for irrigation, and flood mitigation during the monsoon season. Numerical results show that our framework is able to quantify the relationship between the ENSO fluctuations and the Red River basin hydrology. Moreover, we demonstrate that such ENSO teleconnection represents valuable information for improving the operations of Hoa Binh reservoir.

An analysis of the relationship of seven selected variables to State Board Test Pool Examination performance of the University of Tennessee, Knoxville, College of Nursing.

PubMed

Sharp, T G

1984-02-01

The study was designed to determine whether any one of seven selected variables or a combination of the variables is predictive of performance on the State Board Test Pool Examination. The selected variables studied were: high school grade point average (HSGPA), The University of Tennessee, Knoxville, College of Nursing grade point average (GPA), and American College Test Assessment (ACT) standard scores (English, ENG; mathematics, MA; social studies, SS; natural sciences, NSC; composite, COMP). Data utilized were from graduates of the baccalaureate program of The University of Tennessee, Knoxville, College of Nursing from 1974 through 1979. The sample of 322 was selected from a total population of 572. The Statistical Analysis System (SAS) was designed to accomplish analysis of the predictive relationship of each of the seven selected variables to State Board Test Pool Examination performance (result of pass or fail), a stepwise discriminant analysis was designed for determining the predictive relationship of the strongest combination of the independent variables to overall State Board Test Pool Examination performance (result of pass or fail), and stepwise multiple regression analysis was designed to determine the strongest predictive combination of selected variables for each of the five subexams of the State Board Test Pool Examination. The selected variables were each found to be predictive of SBTPE performance (result of pass or fail). The strongest combination for predicting SBTPE performance (result of pass or fail) was found to be GPA, MA, and NSC.

Downscaling GCM Output with Genetic Programming Model

NASA Astrophysics Data System (ADS)

Shi, X.; Dibike, Y. B.; Coulibaly, P.

2004-05-01

Climate change impact studies on watershed hydrology require reliable data at appropriate spatial and temporal resolution. However, the outputs of the current global climate models (GCMs) cannot be used directly because GCM do not provide hourly or daily precipitation and temperature reliable enough for hydrological modeling. Nevertheless, we can get more reliable data corresponding to future climate scenarios derived from GCM outputs using the so called 'downscaling techniques'. This study applies Genetic Programming (GP) based technique to downscale daily precipitation and temperature values at the Chute-du-Diable basin of the Saguenay watershed in Canada. In applying GP downscaling technique, the objective is to find a relationship between the large-scale predictor variables (NCEP data which provide daily information concerning the observed large-scale state of the atmosphere) and the predictand (meteorological data which describes conditions at the site scale). The selection of the most relevant predictor variables is achieved using the Pearson's coefficient of determination ( R2) (between the large-scale predictor variables and the daily meteorological data). In this case, the period (1961 - 2000) is identified to represent the current climate condition. For the forty years of data, the first 30 years (1961-1990) are considered for calibrating the models while the remaining ten years of data (1991-2000) are used to validate those models. In general, the R2 between the predictor variables and each predictand is very low in case of precipitation compared to that of maximum and minimum temperature. Moreover, the strength of individual predictors varies for every month and for each GP grammar. Therefore, the most appropriate combination of predictors has to be chosen by looking at the output analysis of all the twelve months and the different GP grammars. During the calibration of the GP model for precipitation downscaling, in addition to the mean daily precipitation and daily precipitation variability for each month, monthly average dry and wet-spell lengths are also considered as performance criteria. For the cases of Tmax and Tmin, means and variances of these variables corresponding to each month were considered as performance criteria. The GP downscaling results show satisfactory agreement between the observed daily temperature (Tmax and Tmin) and the simulated temperature. However, the downscaling results for the daily precipitation still require some improvement - suggesting further investigation of other grammars. KEY WORDS: Climate change; GP downscaling; GCM.

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.

Bayesian Factor Analysis as a Variable Selection Problem: Alternative Priors and Consequences

PubMed Central

Lu, Zhao-Hua; Chow, Sy-Miin; Loken, Eric

2016-01-01

Factor analysis is a popular statistical technique for multivariate data analysis. Developments in the structural equation modeling framework have enabled the use of hybrid confirmatory/exploratory approaches in which factor loading structures can be explored relatively flexibly within a confirmatory factor analysis (CFA) framework. Recently, a Bayesian structural equation modeling (BSEM) approach (Muthén & Asparouhov, 2012) has been proposed as a way to explore the presence of cross-loadings in CFA models. We show that the issue of determining factor loading patterns may be formulated as a Bayesian variable selection problem in which Muthén and Asparouhov’s approach can be regarded as a BSEM approach with ridge regression prior (BSEM-RP). We propose another Bayesian approach, denoted herein as the Bayesian structural equation modeling with spike and slab prior (BSEM-SSP), which serves as a one-stage alternative to the BSEM-RP. We review the theoretical advantages and disadvantages of both approaches and compare their empirical performance relative to two modification indices-based approaches and exploratory factor analysis with target rotation. A teacher stress scale data set (Byrne, 2012; Pettegrew & Wolf, 1982) is used to demonstrate our approach. PMID:27314566

Space-Time Joint Interference Cancellation Using Fuzzy-Inference-Based Adaptive Filtering Techniques in Frequency-Selective Multipath Channels

NASA Astrophysics Data System (ADS)

Hu, Chia-Chang; Lin, Hsuan-Yu; Chen, Yu-Fan; Wen, Jyh-Horng

2006-12-01

An adaptive minimum mean-square error (MMSE) array receiver based on the fuzzy-logic recursive least-squares (RLS) algorithm is developed for asynchronous DS-CDMA interference suppression in the presence of frequency-selective multipath fading. This receiver employs a fuzzy-logic control mechanism to perform the nonlinear mapping of the squared error and squared error variation, denoted by ([InlineEquation not available: see fulltext.],[InlineEquation not available: see fulltext.]), into a forgetting factor[InlineEquation not available: see fulltext.]. For the real-time applicability, a computationally efficient version of the proposed receiver is derived based on the least-mean-square (LMS) algorithm using the fuzzy-inference-controlled step-size[InlineEquation not available: see fulltext.]. This receiver is capable of providing both fast convergence/tracking capability as well as small steady-state misadjustment as compared with conventional LMS- and RLS-based MMSE DS-CDMA receivers. Simulations show that the fuzzy-logic LMS and RLS algorithms outperform, respectively, other variable step-size LMS (VSS-LMS) and variable forgetting factor RLS (VFF-RLS) algorithms at least 3 dB and 1.5 dB in bit-error-rate (BER) for multipath fading channels.

Swimming Speed of The Breaststroke Kick

PubMed Central

Strzała, Marek; Krężałek, Piotr; Kaca, Marcin; Głąb, Grzegorz; Ostrowski, Andrzej; Stanula, Arkadiusz; Tyka, Aleksander

2012-01-01

The breaststroke kick is responsible for a considerable portion of the forward propulsion in breaststroke swimming. The aim of this study was to measure selected anthropometric variables and functional properties of a swimmer’s body: length of body parts; functional range of motion in the leg joints and anaerobic power of the lower limbs. Chosen kinematic variables useful in the evaluation of swimming performance in the breaststroke kick were evaluated. In the present research, swimming speed using breaststroke kicks depended to the largest extent on anaerobic endurance (0.46, p < 0.05 partial correlations with age control). In addition, knee external rotation and swimming technique index had an impact on swimming speed and kick length (both partial correlations with age control 0.35, p < 0.08). A kinematic analysis of the breaststroke kick hip displacement compatible with horizontal body displacement was significantly negatively correlated with foot slip in the water opposite to body displacement (partial correlations: with leg length control −0.43, p < 0.05; with shank length control −0.45, p < 0.05, respectively). Present research and measurements of selected body properties, physical endurance and kinematic movement analysis may help in making a precise determination of an athlete’s talent for breaststroke swimming. PMID:23486737

Screening of the aerodynamic and biophysical properties of barley malt

NASA Astrophysics Data System (ADS)

Ghodsvali, Alireza; Farzaneh, Vahid; Bakhshabadi, Hamid; Zare, Zahra; Karami, Zahra; Mokhtarian, Mohsen; Carvalho, Isabel. S.

2016-10-01

An understanding of the aerodynamic and biophysical properties of barley malt is necessary for the appropriate design of equipment for the handling, shipping, dehydration, grading, sorting and warehousing of this strategic crop. Malting is a complex biotechnological process that includes steeping; germination and finally, the dehydration of cereal grains under controlled temperature and humidity conditions. In this investigation, the biophysical properties of barley malt were predicted using two models of artificial neural networks as well as response surface methodology. Stepping time and germination time were selected as the independent variables and 1 000 kernel weight, kernel density and terminal velocity were selected as the dependent variables (responses). The obtained outcomes showed that the artificial neural network model, with a logarithmic sigmoid activation function, presents more precise results than the response surface model in the prediction of the aerodynamic and biophysical properties of produced barley malt. This model presented the best result with 8 nodes in the hidden layer and significant correlation coefficient values of 0.783, 0.767 and 0.991 were obtained for responses one thousand kernel weight, kernel density, and terminal velocity, respectively. The outcomes indicated that this novel technique could be successfully applied in quantitative and qualitative monitoring within the malting process.

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.

A TRMM-Calibrated Infrared Technique for Global Rainfall Estimation

NASA Technical Reports Server (NTRS)

Negri, Andrew J.; Adler, Robert F.; Xu, Li-Ming

2003-01-01

This paper presents the development of a satellite infrared (IR) technique for estimating convective and stratiform rainfall and its application in studying the diurnal variability of rainfall on a global scale. The Convective-Stratiform Technique (CST), calibrated by coincident, physically retrieved rain rates from the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR), is applied over the global tropics during summer 2001. The technique is calibrated separately over land and ocean, making ingenious use of the IR data from the TRMM Visible/Infrared Scanner (VIRS) before application to global geosynchronous satellite data. The low sampling rate of TRMM PR imposes limitations on calibrating IR- based techniques; however, our research shows that PR observations can be applied to improve IR-based techniques significantly by selecting adequate calibration areas and calibration length. The diurnal cycle of rainfall, as well as the division between convective and t i f m rainfall will be presented. The technique is validated using available data sets and compared to other global rainfall products such as Global Precipitation Climatology Project (GPCP) IR product, calibrated with TRMM Microwave Imager (TMI) data. The calibrated CST technique has the advantages of high spatial resolution (4 km), filtering of non-raining cirrus clouds, and the stratification of the rainfall into its convective and stratiform components, the latter being important for the calculation of vertical profiles of latent heating.

The Fisher-Markov selector: fast selecting maximally separable feature subset for multiclass classification with applications to high-dimensional data.

PubMed

Cheng, Qiang; Zhou, Hongbo; Cheng, Jie

2011-06-01

Selecting features for multiclass classification is a critically important task for pattern recognition and machine learning applications. Especially challenging is selecting an optimal subset of features from high-dimensional data, which typically have many more variables than observations and contain significant noise, missing components, or outliers. Existing methods either cannot handle high-dimensional data efficiently or scalably, or can only obtain local optimum instead of global optimum. Toward the selection of the globally optimal subset of features efficiently, we introduce a new selector--which we call the Fisher-Markov selector--to identify those features that are the most useful in describing essential differences among the possible groups. In particular, in this paper we present a way to represent essential discriminating characteristics together with the sparsity as an optimization objective. With properly identified measures for the sparseness and discriminativeness in possibly high-dimensional settings, we take a systematic approach for optimizing the measures to choose the best feature subset. We use Markov random field optimization techniques to solve the formulated objective functions for simultaneous feature selection. Our results are noncombinatorial, and they can achieve the exact global optimum of the objective function for some special kernels. The method is fast; in particular, it can be linear in the number of features and quadratic in the number of observations. We apply our procedure to a variety of real-world data, including mid--dimensional optical handwritten digit data set and high-dimensional microarray gene expression data sets. The effectiveness of our method is confirmed by experimental results. In pattern recognition and from a model selection viewpoint, our procedure says that it is possible to select the most discriminating subset of variables by solving a very simple unconstrained objective function which in fact can be obtained with an explicit expression.

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

Inter-individual variability and pattern recognition of surface electromyography in front crawl swimming.

PubMed

Martens, Jonas; Daly, Daniel; Deschamps, Kevin; Staes, Filip; Fernandes, Ricardo J

2016-12-01

Variability of electromyographic (EMG) recordings is a complex phenomenon rarely examined in swimming. Our purposes were to investigate inter-individual variability in muscle activation patterns during front crawl swimming and assess if there were clusters of sub patterns present. Bilateral muscle activity of rectus abdominis (RA) and deltoideus medialis (DM) was recorded using wireless surface EMG in 15 adult male competitive swimmers. The amplitude of the median EMG trial of six upper arm movement cycles was used for the inter-individual variability assessment, quantified with the coefficient of variation, coefficient of quartile variation, the variance ratio and mean deviation. Key features were selected based on qualitative and quantitative classification strategies to enter in a k-means cluster analysis to examine the presence of strong sub patterns. Such strong sub patterns were found when clustering in two, three and four clusters. Inter-individual variability in a group of highly skilled swimmers was higher compared to other cyclic movements which is in contrast to what has been reported in the previous 50years of EMG research in swimming. This leads to the conclusion that coaches should be careful in using overall reference EMG information to enhance the individual swimming technique of their athletes. Copyright © 2016 Elsevier Ltd. All rights reserved.

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.

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.

Off-resonance suppression for multispectral MR imaging near metallic implants.

PubMed

den Harder, J Chiel; van Yperen, Gert H; Blume, Ulrike A; Bos, Clemens

2015-01-01

Metal artifact reduction in MRI within clinically feasible scan-times without through-plane aliasing. Existing metal artifact reduction techniques include view angle tilting (VAT), which resolves in-plane distortions, and multispectral imaging (MSI) techniques, such as slice encoding for metal artifact correction (SEMAC) and multi-acquisition with variable resonances image combination (MAVRIC), that further reduce image distortions, but significantly increase scan-time. Scan-time depends on anatomy size and anticipated total spectral content of the signal. Signals outside the anticipated spatial region may cause through-plane back-folding. Off-resonance suppression (ORS), using different gradient amplitudes for excitation and refocusing, is proposed to provide well-defined spatial-spectral selectivity in MSI to allow scan-time reduction and flexibility of scan-orientation. Comparisons of MSI techniques with and without ORS were made in phantom and volunteer experiments. Off-resonance suppressed SEMAC (ORS-SEMAC) and outer-region suppressed MAVRIC (ORS-MAVRIC) required limited through-plane phase encoding steps compared with original MSI. Whereas SEMAC (scan time: 5'46") and MAVRIC (4'12") suffered from through-plane aliasing, ORS-SEMAC and ORS-MAVRIC allowed alias-free imaging in the same scan-times. ORS can be used in MSI to limit the selected spatial-spectral region and contribute to metal artifact reduction in clinically feasible scan-times while avoiding slice aliasing. © 2014 Wiley Periodicals, Inc.

State of the art in marketing hospital foodservice departments.

PubMed

Pickens, C W; Shanklin, C W

1985-11-01

The purposes of this study were to identify the state of the art relative to the utilization of marketing techniques within hospital foodservice departments throughout the United States and to determine whether any relationships existed between the degree of utilization of marketing techniques and selected demographic characteristics of the foodservice administrators and/or operations. A validated questionnaire was mailed to 600 randomly selected hospital foodservice administrators requesting information related to marketing in their facilities. Forty-five percent of the questionnaires were returned and analyzed for frequency of response and significant relationship between variables. Chi-square was used for nominal data and Spearman rho for ranked data. Approximately 73% of the foodservice administrators stated that marketing was extremely important in the success of a hospital foodservice department. Respondents (79%) further indicated that marketing had become more important in their departments in the past 2 years. Departmental records, professional journals, foodservice suppliers, observation, and surveys were the sources most often used to obtain marketing data, a responsibility generally assumed by the foodservice director (86.2%). Merchandising, public relations, and word-of-mouth reputation were regarded as the most important aspects of marketing. Increased sales, participation, good will, departmental recognition, and employee satisfaction were used most frequently to evaluate the success of implemented marketing techniques. Marketing audits as a means of evaluating the success of marketing were used to a limited extent by the respondents.

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

Design and Evaluation of Perceptual-based Object Group Selection Techniques

NASA Astrophysics Data System (ADS)

Dehmeshki, Hoda

Selecting groups of objects is a frequent task in graphical user interfaces. It is required prior to many standard operations such as deletion, movement, or modification. Conventional selection techniques are lasso, rectangle selection, and the selection and de-selection of items through the use of modifier keys. These techniques may become time-consuming and error-prone when target objects are densely distributed or when the distances between target objects are large. Perceptual-based selection techniques can considerably improve selection tasks when targets have a perceptual structure, for example when arranged along a line. Current methods to detect such groups use ad hoc grouping algorithms that are not based on results from perception science. Moreover, these techniques do not allow selecting groups with arbitrary arrangements or permit modifying a selection. This dissertation presents two domain-independent perceptual-based systems that address these issues. Based on established group detection models from perception research, the proposed systems detect perceptual groups formed by the Gestalt principles of good continuation and proximity. The new systems provide gesture-based or click-based interaction techniques for selecting groups with curvilinear or arbitrary structures as well as clusters. Moreover, the gesture-based system is adapted for the graph domain to facilitate path selection. This dissertation includes several user studies that show the proposed systems outperform conventional selection techniques when targets form salient perceptual groups and are still competitive when targets are semi-structured.

Stratification and sample selection for multicrop experiments. [Arkansas, Kentucky, Michigan, Missouri, Mississippi, Ohio, Wisconsin, Illinois, Indiana, Minnesota, Iowa, Louisiana, Nebraska, South Dakota, and North Dakota

NASA Technical Reports Server (NTRS)

Landgrebe, D. A. (Principal Investigator); Hixson, M. M.; Davis, B. J.; Bauer, M. E.

1978-01-01

The author has identified the following significant results. A stratification was performed and sample segments were selected for an initial investigation of multicrop problems in order to support development and evaluation of procedures for using LACIE and other technologies for the classification of corn and soybeans, to identify factors likely to affect classification performance, and to evaluate problems encountered and techniques which are applicable to the crop estimation problem in foreign countries. Two types of samples, low density and high density, supporting these requirements were selected as research data set for an initial evaluation of technical issues. Looking at the geographic location of the strata, the system appears to be logical and the various segments seem to represent different conditions. This result is supportive not only of the variables and the methodology employed in the stratification, but also of the validity of the data sets employed.

Institutional Delivery Service Utilization among Women from Rural Districts of Wolaita and Dawro Zones, Southern Ethiopia; a Community Based Cross-Sectional Study

PubMed Central

Arba, Mihiretu Alemayehu; Darebo, Tadele Dana; Koyira, Mengistu Meskele

2016-01-01

Introduction The highest number of maternal deaths occur during labour, delivery and the first day after delivery highlighting the critical need for good quality care during this period. Therefore, for the strategies of institutional delivery to be effective, it is essential to understand the factors that influence individual and household factors to utilize skilled birth attendance and institutions for delivery. This study was aimed to assess factors affecting the utilization of institutional delivery service of women in rural districts of Wolaita and Dawro Zones. Methods A community based cross-sectional study was done among mothers who gave birth within the past one year preceding the survey in Wolaita and Dawro Zones, from February 01 –April 30, 2015 by using a three stage sampling technique. Initially, 6 districts were selected randomly from the total of 17 eligible districts. Then, 2 kebele from each district was selected randomly cumulating a total of 12 clusters. Finally, study participants were selected from each cluster by using systematic sampling technique. Accordingly, 957 mothers were included in the survey. Data was collected by using a pretested interviewer administered structured questionnaire. The questionnaire was prepared by including socio-demographic variables and variables of maternal health service utilization factors. Data was entered using Epi-data version 1.4.4.0 and exported to SPSS version 20 for analysis. Bivariate and multiple logistic regressions were applied to identify candidate and predictor variables respectively. Result Only 38% of study participants delivered the index child at health facility. Husband’s educational status, wealth index, average distance from nearest health facility, wanted pregnancy, agreement to follow post-natal care, problem faced during delivery, birth order, preference of health professional for ante-natal care and maternity care were predictors of institutional delivery. Conclusion The use of institutional delivery service is low in the study community. Eventhough antenatal care service is high; nearly two in every three mothers delivered their index child out of health facility. Improving socio-economic status of mothers as well as availing modern health facilities to the nearest locality will have a good impact to improve institutional delivery service utilization. Similarly, education is also a tool to improve awareness of mothers and their husbands for the improvement of health care service utilization. PMID:26986563

Institutional Delivery Service Utilization among Women from Rural Districts of Wolaita and Dawro Zones, Southern Ethiopia; a Community Based Cross-Sectional Study.

PubMed

Arba, Mihiretu Alemayehu; Darebo, Tadele Dana; Koyira, Mengistu Meskele

2016-01-01

The highest number of maternal deaths occur during labour, delivery and the first day after delivery highlighting the critical need for good quality care during this period. Therefore, for the strategies of institutional delivery to be effective, it is essential to understand the factors that influence individual and household factors to utilize skilled birth attendance and institutions for delivery. This study was aimed to assess factors affecting the utilization of institutional delivery service of women in rural districts of Wolaita and Dawro Zones. A community based cross-sectional study was done among mothers who gave birth within the past one year preceding the survey in Wolaita and Dawro Zones, from February 01 -April 30, 2015 by using a three stage sampling technique. Initially, 6 districts were selected randomly from the total of 17 eligible districts. Then, 2 kebele from each district was selected randomly cumulating a total of 12 clusters. Finally, study participants were selected from each cluster by using systematic sampling technique. Accordingly, 957 mothers were included in the survey. Data was collected by using a pretested interviewer administered structured questionnaire. The questionnaire was prepared by including socio-demographic variables and variables of maternal health service utilization factors. Data was entered using Epi-data version 1.4.4.0 and exported to SPSS version 20 for analysis. Bivariate and multiple logistic regressions were applied to identify candidate and predictor variables respectively. Only 38% of study participants delivered the index child at health facility. Husband's educational status, wealth index, average distance from nearest health facility, wanted pregnancy, agreement to follow post-natal care, problem faced during delivery, birth order, preference of health professional for ante-natal care and maternity care were predictors of institutional delivery. The use of institutional delivery service is low in the study community. Eventhough antenatal care service is high; nearly two in every three mothers delivered their index child out of health facility. Improving socio-economic status of mothers as well as availing modern health facilities to the nearest locality will have a good impact to improve institutional delivery service utilization. Similarly, education is also a tool to improve awareness of mothers and their husbands for the improvement of health care service utilization.

Analytic Thermoelectric Couple Modeling: Variable Material Properties and Transient Operation

NASA Technical Reports Server (NTRS)

Mackey, Jonathan A.; Sehirlioglu, Alp; Dynys, Fred

2015-01-01

To gain a deeper understanding of the operation of a thermoelectric couple a set of analytic solutions have been derived for a variable material property couple and a transient couple. Using an analytic approach, as opposed to commonly used numerical techniques, results in a set of useful design guidelines. These guidelines can serve as useful starting conditions for further numerical studies, or can serve as design rules for lab built couples. The analytic modeling considers two cases and accounts for 1) material properties which vary with temperature and 2) transient operation of a couple. The variable material property case was handled by means of an asymptotic expansion, which allows for insight into the influence of temperature dependence on different material properties. The variable property work demonstrated the important fact that materials with identical average Figure of Merits can lead to different conversion efficiencies due to temperature dependence of the properties. The transient couple was investigated through a Greens function approach; several transient boundary conditions were investigated. The transient work introduces several new design considerations which are not captured by the classic steady state analysis. The work helps to assist in designing couples for optimal performance, and also helps assist in material selection.

Applying decision tree for identification of a low risk population for type 2 diabetes. Tehran Lipid and Glucose Study.

PubMed

Ramezankhani, Azra; Pournik, Omid; Shahrabi, Jamal; Khalili, Davood; Azizi, Fereidoun; Hadaegh, Farzad

2014-09-01

The aim of this study was to create a prediction model using data mining approach to identify low risk individuals for incidence of type 2 diabetes, using the Tehran Lipid and Glucose Study (TLGS) database. For a 6647 population without diabetes, aged ≥20 years, followed for 12 years, a prediction model was developed using classification by the decision tree technique. Seven hundred and twenty-nine (11%) diabetes cases occurred during the follow-up. Predictor variables were selected from demographic characteristics, smoking status, medical and drug history and laboratory measures. We developed the predictive models by decision tree using 60 input variables and one output variable. The overall classification accuracy was 90.5%, with 31.1% sensitivity, 97.9% specificity; and for the subjects without diabetes, precision and f-measure were 92% and 0.95, respectively. The identified variables included fasting plasma glucose, body mass index, triglycerides, mean arterial blood pressure, family history of diabetes, educational level and job status. In conclusion, decision tree analysis, using routine demographic, clinical, anthropometric and laboratory measurements, created a simple tool to predict individuals at low risk for type 2 diabetes. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

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.

Outcomes of role stress: a multisample constructive replication.

PubMed

Kemery, E R; Bedeian, A G; Mossholder, K W; Touliatos, J

1985-06-01

Responses from four separate samples of accountants and hospital employees provided a constructive replication of the Bedeian and Armenakis (1981) model of the causal nexus between role stress and selected outcome variables. We investigated the relationship between both role ambiguity and role conflict--as specific forms of role stress--and job-related tension, job satisfaction, and propensity to leave, using LISREL IV, a technique capable of providing statistical data for a hypothesized population model, as well as for specific causal paths. Results, which support the Bedeian and Armenakis model, are discussed in light of previous research.

SYSTEM OPTIMIZATION FOR THE AUTOMATIC SIMULTANEOUS DETERMINATION OF ARSENIC, SELENIUM, AND ANTIMONY, USING HYDRIDE GENERATION INTRODUCTION TO AN INDUCTIVELY COUPLED PLASMA.

USGS Publications Warehouse

Pyen, Grace S.; Browner, Richard F.; Long, Stephen

1986-01-01

A fixed-size simplex has been used to determine the optimum conditions for the simultaneous determination of arsenic, selenium, and antimony by hydride generation and inductively coupled plasma emission spectrometry. The variables selected for the simplex were carrier gas flow rate, rf power, viewing height, and reagent conditions. The detection limit for selenium was comparable to the preoptimized case, but there were twofold and fourfold improvements in the detection limits for arsenic and antimony, respectively. Precision of the technique was assessed with the use of artificially prepared water samples.

Circulating tumor cells and miRNAs as prognostic markers in neuroendocrine neoplasms.

PubMed

Zatelli, Maria Chiara; Grossrubatscher, Erika Maria; Guadagno, Elia; Sciammarella, Concetta; Faggiano, Antongiulio; Colao, Annamaria

2017-06-01

The prognosis of neuroendocrine neoplasms (NENs) is widely variable and has been shown to associate with several tissue- and blood-based biomarkers in different settings. The identification of prognostic factors predicting NEN outcome is of paramount importance to select the best clinical management for these patients. Prognostic markers have been intensively investigated, also taking advantage of the most modern techniques, in the perspective of personalized medicine and appropriate resource utilization. This review summarizes the available data on the possible role of circulating tumor cells and microRNAs as prognostic markers in NENs. © 2017 Society for Endocrinology.

Digital robust active control law synthesis for large order flexible structure using parameter optimization

NASA Technical Reports Server (NTRS)

Mukhopadhyay, V.

1988-01-01

A generic procedure for the parameter optimization of a digital control law for a large-order flexible flight vehicle or large space structure modeled as a sampled data system is presented. A linear quadratic Guassian type cost function was minimized, while satisfying a set of constraints on the steady-state rms values of selected design responses, using a constrained optimization technique to meet multiple design requirements. Analytical expressions for the gradients of the cost function and the design constraints on mean square responses with respect to the control law design variables are presented.

Operations planning simulation: Model study

NASA Technical Reports Server (NTRS)

1974-01-01

The use of simulation modeling for the identification of system sensitivities to internal and external forces and variables is discussed. The technique provides a means of exploring alternate system procedures and processes, so that these alternatives may be considered on a mutually comparative basis permitting the selection of a mode or modes of operation which have potential advantages to the system user and the operator. These advantages are measurements is system efficiency are: (1) the ability to meet specific schedules for operations, mission or mission readiness requirements or performance standards and (2) to accomplish the objectives within cost effective limits.

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.

Analog ensemble and Bayesian regression techniques to improve the wind speed prediction during extreme storms in the NE U.S.

NASA Astrophysics Data System (ADS)

Yang, J.; Astitha, M.; Delle Monache, L.; Alessandrini, S.

2016-12-01

Accuracy of weather forecasts in Northeast U.S. has become very important in recent years, given the serious and devastating effects of extreme weather events. Despite the use of evolved forecasting tools and techniques strengthened by increased super-computing resources, the weather forecasting systems still have their limitations in predicting extreme events. In this study, we examine the combination of analog ensemble and Bayesian regression techniques to improve the prediction of storms that have impacted NE U.S., mostly defined by the occurrence of high wind speeds (i.e. blizzards, winter storms, hurricanes and thunderstorms). The predicted wind speed, wind direction and temperature by two state-of-the-science atmospheric models (WRF and RAMS/ICLAMS) are combined using the mentioned techniques, exploring various ways that those variables influence the minimization of the prediction error (systematic and random). This study is focused on retrospective simulations of 146 storms that affected the NE U.S. in the period 2005-2016. In order to evaluate the techniques, leave-one-out cross validation procedure was implemented regarding 145 storms as the training dataset. The analog ensemble method selects a set of past observations that corresponded to the best analogs of the numerical weather prediction and provides a set of ensemble members of the selected observation dataset. The set of ensemble members can then be used in a deterministic or probabilistic way. In the Bayesian regression framework, optimal variances are estimated for the training partition by minimizing the root mean square error and are applied to the out-of-sample storm. The preliminary results indicate a significant improvement in the statistical metrics of 10-m wind speed for 146 storms using both techniques (20-30% bias and error reduction in all observation-model pairs). In this presentation, we discuss the various combinations of atmospheric predictors and techniques and illustrate how the long record of predicted storms is valuable in the improvement of wind speed prediction.

Parallel optimization of signal detection in active magnetospheric signal injection experiments

NASA Astrophysics Data System (ADS)

Gowanlock, Michael; Li, Justin D.; Rude, Cody M.; Pankratius, Victor

2018-05-01

Signal detection and extraction requires substantial manual parameter tuning at different stages in the processing pipeline. Time-series data depends on domain-specific signal properties, necessitating unique parameter selection for a given problem. The large potential search space makes this parameter selection process time-consuming and subject to variability. We introduce a technique to search and prune such parameter search spaces in parallel and select parameters for time series filters using breadth- and depth-first search strategies to increase the likelihood of detecting signals of interest in the field of magnetospheric physics. We focus on studying geomagnetic activity in the extremely and very low frequency ranges (ELF/VLF) using ELF/VLF transmissions from Siple Station, Antarctica, received at Québec, Canada. Our technique successfully detects amplified transmissions and achieves substantial speedup performance gains as compared to an exhaustive parameter search. We present examples where our algorithmic approach reduces the search from hundreds of seconds down to less than 1 s, with a ranked signal detection in the top 99th percentile, thus making it valuable for real-time monitoring. We also present empirical performance models quantifying the trade-off between the quality of signal recovered and the algorithm response time required for signal extraction. In the future, improved signal extraction in scenarios like the Siple experiment will enable better real-time diagnostics of conditions of the Earth's magnetosphere for monitoring space weather activity.

A digital communications system for manned spaceflight applications.

NASA Technical Reports Server (NTRS)

Batson, B. H.; Moorehead, R. W.

1973-01-01

A highly efficient, all-digital communications signal design employing convolutional coding and PN spectrum spreading is described for two-way transmission of voice and data between a manned spacecraft and ground. Variable-slope delta modulation is selected for analog/digital conversion of the voice signal, and a convolutional decoder utilizing the Viterbi decoding algorithm is selected for use at each receiving terminal. A PN spread spectrum technique is implemented to protect against multipath effects and to reduce the energy density (per unit bandwidth) impinging on the earth's surface to a value within the guidelines adopted by international agreement. Performance predictions are presented for transmission via a TDRS (tracking and data relay satellite) system and for direct transmission between the spacecraft and earth. Hardware estimates are provided for a flight-qualified communications system employing the coded digital signal design.

Optimizing data collection for public health decisions: a data mining approach

PubMed Central

2014-01-01

Background Collecting data can be cumbersome and expensive. Lack of relevant, accurate and timely data for research to inform policy may negatively impact public health. The aim of this study was to test if the careful removal of items from two community nutrition surveys guided by a data mining technique called feature selection, can (a) identify a reduced dataset, while (b) not damaging the signal inside that data. Methods The Nutrition Environment Measures Surveys for stores (NEMS-S) and restaurants (NEMS-R) were completed on 885 retail food outlets in two counties in West Virginia between May and November of 2011. A reduced dataset was identified for each outlet type using feature selection. Coefficients from linear regression modeling were used to weight items in the reduced datasets. Weighted item values were summed with the error term to compute reduced item survey scores. Scores produced by the full survey were compared to the reduced item scores using a Wilcoxon rank-sum test. Results Feature selection identified 9 store and 16 restaurant survey items as significant predictors of the score produced from the full survey. The linear regression models built from the reduced feature sets had R2 values of 92% and 94% for restaurant and grocery store data, respectively. Conclusions While there are many potentially important variables in any domain, the most useful set may only be a small subset. The use of feature selection in the initial phase of data collection to identify the most influential variables may be a useful tool to greatly reduce the amount of data needed thereby reducing cost. PMID:24919484

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.

Optimizing data collection for public health decisions: a data mining approach.

PubMed

Partington, Susan N; Papakroni, Vasil; Menzies, Tim

2014-06-12

Collecting data can be cumbersome and expensive. Lack of relevant, accurate and timely data for research to inform policy may negatively impact public health. The aim of this study was to test if the careful removal of items from two community nutrition surveys guided by a data mining technique called feature selection, can (a) identify a reduced dataset, while (b) not damaging the signal inside that data. The Nutrition Environment Measures Surveys for stores (NEMS-S) and restaurants (NEMS-R) were completed on 885 retail food outlets in two counties in West Virginia between May and November of 2011. A reduced dataset was identified for each outlet type using feature selection. Coefficients from linear regression modeling were used to weight items in the reduced datasets. Weighted item values were summed with the error term to compute reduced item survey scores. Scores produced by the full survey were compared to the reduced item scores using a Wilcoxon rank-sum test. Feature selection identified 9 store and 16 restaurant survey items as significant predictors of the score produced from the full survey. The linear regression models built from the reduced feature sets had R2 values of 92% and 94% for restaurant and grocery store data, respectively. While there are many potentially important variables in any domain, the most useful set may only be a small subset. The use of feature selection in the initial phase of data collection to identify the most influential variables may be a useful tool to greatly reduce the amount of data needed thereby reducing cost.

Computation of Sensitivity Derivatives of Navier-Stokes Equations using Complex Variables

NASA Technical Reports Server (NTRS)

Vatsa, Veer N.

2004-01-01

Accurate computation of sensitivity derivatives is becoming an important item in Computational Fluid Dynamics (CFD) because of recent emphasis on using nonlinear CFD methods in aerodynamic design, optimization, stability and control related problems. Several techniques are available to compute gradients or sensitivity derivatives of desired flow quantities or cost functions with respect to selected independent (design) variables. Perhaps the most common and oldest method is to use straightforward finite-differences for the evaluation of sensitivity derivatives. Although very simple, this method is prone to errors associated with choice of step sizes and can be cumbersome for geometric variables. The cost per design variable for computing sensitivity derivatives with central differencing is at least equal to the cost of three full analyses, but is usually much larger in practice due to difficulty in choosing step sizes. Another approach gaining popularity is the use of Automatic Differentiation software (such as ADIFOR) to process the source code, which in turn can be used to evaluate the sensitivity derivatives of preselected functions with respect to chosen design variables. In principle, this approach is also very straightforward and quite promising. The main drawback is the large memory requirement because memory use increases linearly with the number of design variables. ADIFOR software can also be cumber-some for large CFD codes and has not yet reached a full maturity level for production codes, especially in parallel computing environments.

Collective feature selection to identify crucial epistatic variants.

PubMed

Verma, Shefali S; Lucas, Anastasia; Zhang, Xinyuan; Veturi, Yogasudha; Dudek, Scott; Li, Binglan; Li, Ruowang; Urbanowicz, Ryan; Moore, Jason H; Kim, Dokyoon; Ritchie, Marylyn D

2018-01-01

Machine learning methods have gained popularity and practicality in identifying linear and non-linear effects of variants associated with complex disease/traits. Detection of epistatic interactions still remains a challenge due to the large number of features and relatively small sample size as input, thus leading to the so-called "short fat data" problem. The efficiency of machine learning methods can be increased by limiting the number of input features. Thus, it is very important to perform variable selection before searching for epistasis. Many methods have been evaluated and proposed to perform feature selection, but no single method works best in all scenarios. We demonstrate this by conducting two separate simulation analyses to evaluate the proposed collective feature selection approach. Through our simulation study we propose a collective feature selection approach to select features that are in the "union" of the best performing methods. We explored various parametric, non-parametric, and data mining approaches to perform feature selection. We choose our top performing methods to select the union of the resulting variables based on a user-defined percentage of variants selected from each method to take to downstream analysis. Our simulation analysis shows that non-parametric data mining approaches, such as MDR, may work best under one simulation criteria for the high effect size (penetrance) datasets, while non-parametric methods designed for feature selection, such as Ranger and Gradient boosting, work best under other simulation criteria. Thus, using a collective approach proves to be more beneficial for selecting variables with epistatic effects also in low effect size datasets and different genetic architectures. Following this, we applied our proposed collective feature selection approach to select the top 1% of variables to identify potential interacting variables associated with Body Mass Index (BMI) in ~ 44,000 samples obtained from Geisinger's MyCode Community Health Initiative (on behalf of DiscovEHR collaboration). In this study, we were able to show that selecting variables using a collective feature selection approach could help in selecting true positive epistatic variables more frequently than applying any single method for feature selection via simulation studies. We were able to demonstrate the effectiveness of collective feature selection along with a comparison of many methods in our simulation analysis. We also applied our method to identify non-linear networks associated with obesity.

Perceptions of Voice Teachers Regarding Students' Vocal Behaviors During Singing and Speaking.

PubMed

Beeman, Shellie A

2017-01-01

This study examined voice teachers' perceptions of their instruction of healthy singing and speaking voice techniques. An online, researcher-generated questionnaire based on the McClosky technique was administered to college/university voice teachers listed as members in the 2012-2013 College Music Society directory. A majority of participants believed there to be a relationship between the health of the singing voice and the health of the speaking voice. Participants' perception scores were the most positive for variable MBSi, the monitoring of students' vocal behaviors during singing. Perception scores for variable TVB, the teaching of healthy vocal behaviors, and variable MBSp, the monitoring of students' vocal behaviors while speaking, ranked second and third, respectively. Perception scores for variable TVB were primarily associated with participants' familiarity with voice rehabilitation techniques, gender, and familiarity with the McClosky technique. Perception scores for variable MBSi were primarily associated with participants' familiarity with voice rehabilitation techniques, gender, type of student taught, and instruction of a student with a voice disorder. Perception scores for variable MBSp were correlated with the greatest number of characteristics, including participants' familiarity with voice rehabilitation techniques, familiarity with the McClosky technique, type of student taught, years of teaching experience, and instruction of a student with a voice disorder. Voice teachers are purportedly working with injured voices and attempting to include vocal health in their instruction. Although a voice teacher is not obligated to pursue further rehabilitative training, the current study revealed a positive relationship between familiarity with specific rehabilitation techniques and vocal health. Copyright © 2017 The Voice Foundation. Published by Elsevier Inc. All rights reserved.

VO2 and VCO2 variabilities through indirect calorimetry instrumentation.

PubMed

Cadena-Méndez, Miguel; Escalante-Ramírez, Boris; Azpiroz-Leehan, Joaquín; Infante-Vázquez, Oscar

2013-01-01

The aim of this paper is to understand how to measure the VO2 and VCO2 variabilities in indirect calorimetry (IC) since we believe they can explain the high variation in the resting energy expenditure (REE) estimation. We propose that variabilities should be separately measured from the VO2 and VCO2 averages to understand technological differences among metabolic monitors when they estimate the REE. To prove this hypothesis the mixing chamber (MC) and the breath-by-breath (BbB) techniques measured the VO2 and VCO2 averages and their variabilities. Variances and power spectrum energies in the 0-0.5 Hertz band were measured to establish technique differences in steady and non-steady state. A hybrid calorimeter with both IC techniques studied a population of 15 volunteers that underwent the clino-orthostatic maneuver in order to produce the two physiological stages. The results showed that inter-individual VO2 and VCO2 variabilities measured as variances were negligible using the MC while variabilities measured as spectral energies using the BbB underwent 71 and 56% (p < 0.05), increase respectively. Additionally, the energy analysis showed an unexpected cyclic rhythm at 0.025 Hertz only during the orthostatic stage, which is new physiological information, not reported previusly. The VO2 and VCO2 inter-individual averages increased to 63 and 39% by the MC (p < 0.05) and 32 and 40% using the BbB (p < 0.1), respectively, without noticeable statistical differences among techniques. The conclusions are: (a) metabolic monitors should simultaneously include the MC and the BbB techniques to correctly interpret the steady or non-steady state variabilities effect in the REE estimation, (b) the MC is the appropriate technique to compute averages since it behaves as a low-pass filter that minimizes variances, (c) the BbB is the ideal technique to measure the variabilities since it can work as a high-pass filter to generate discrete time series able to accomplish spectral analysis, and (d) the new physiological information in the VO2 and VCO2 variabilities can help to understand why metabolic monitors with dissimilar IC techniques give different results in the REE estimation.

No difference in variability of unique hue selections and binary hue selections.

PubMed

Bosten, J M; Lawrance-Owen, A J

2014-04-01

If unique hues have special status in phenomenological experience as perceptually pure, it seems reasonable to assume that they are represented more precisely by the visual system than are other colors. Following the method of Malkoc et al. (J. Opt. Soc. Am. A22, 2154 [2005]), we gathered unique and binary hue selections from 50 subjects. For these subjects we repeated the measurements in two separate sessions, allowing us to measure test-retest reliabilities (0.52≤ρ≤0.78; p≪0.01). We quantified the within-individual variability for selections of each hue. Adjusting for the differences in variability intrinsic to different regions of chromaticity space, we compared the within-individual variability for unique hues to that for binary hues. Surprisingly, we found that selections of unique hues did not show consistently lower variability than selections of binary hues. We repeated hue measurements in a single session for an independent sample of 58 subjects, using a different relative scaling of the cardinal axes of MacLeod-Boynton chromaticity space. Again, we found no consistent difference in adjusted within-individual variability for selections of unique and binary hues. Our finding does not depend on the particular scaling chosen for the Y axis of MacLeod-Boynton chromaticity space.

Using variable rate models to identify genes under selection in sequence pairs: their validity and limitations for EST sequences.

PubMed

Church, Sheri A; Livingstone, Kevin; Lai, Zhao; Kozik, Alexander; Knapp, Steven J; Michelmore, Richard W; Rieseberg, Loren H

2007-02-01

Using likelihood-based variable selection models, we determined if positive selection was acting on 523 EST sequence pairs from two lineages of sunflower and lettuce. Variable rate models are generally not used for comparisons of sequence pairs due to the limited information and the inaccuracy of estimates of specific substitution rates. However, previous studies have shown that the likelihood ratio test (LRT) is reliable for detecting positive selection, even with low numbers of sequences. These analyses identified 56 genes that show a signature of selection, of which 75% were not identified by simpler models that average selection across codons. Subsequent mapping studies in sunflower show four of five of the positively selected genes identified by these methods mapped to domestication QTLs. We discuss the validity and limitations of using variable rate models for comparisons of sequence pairs, as well as the limitations of using ESTs for identification of positively selected genes.

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

Ideal, nonideal, and no-marker variables: The confirmatory factor analysis (CFA) marker technique works when it matters.

PubMed

Williams, Larry J; O'Boyle, Ernest H

2015-09-01

A persistent concern in the management and applied psychology literature is the effect of common method variance on observed relations among variables. Recent work (i.e., Richardson, Simmering, & Sturman, 2009) evaluated 3 analytical approaches to controlling for common method variance, including the confirmatory factor analysis (CFA) marker technique. Their findings indicated significant problems with this technique, especially with nonideal marker variables (those with theoretical relations with substantive variables). Based on their simulation results, Richardson et al. concluded that not correcting for method variance provides more accurate estimates than using the CFA marker technique. We reexamined the effects of using marker variables in a simulation study and found the degree of error in estimates of a substantive factor correlation was relatively small in most cases, and much smaller than error associated with making no correction. Further, in instances in which the error was large, the correlations between the marker and substantive scales were higher than that found in organizational research with marker variables. We conclude that in most practical settings, the CFA marker technique yields parameter estimates close to their true values, and the criticisms made by Richardson et al. are overstated. (c) 2015 APA, all rights reserved).

A FORTRAN technique for correlating a circular environmental variable with a linear physiological variable in the sugar maple.

PubMed

Pease, J M; Morselli, M F

1987-01-01

This paper deals with a computer program adapted to a statistical method for analyzing an unlimited quantity of binary recorded data of an independent circular variable (e.g. wind direction), and a linear variable (e.g. maple sap flow volume). Circular variables cannot be statistically analyzed with linear methods, unless they have been transformed. The program calculates a critical quantity, the acrophase angle (PHI, phi o). The technique is adapted from original mathematics [1] and is written in Fortran 77 for easier conversion between computer networks. Correlation analysis can be performed following the program or regression which, because of the circular nature of the independent variable, becomes periodic regression. The technique was tested on a file of approximately 4050 data pairs.

Predicting Ecologically Important Vegetation Variables from Remotely Sensed Optical/Radar Data Using Neural Networks

NASA Technical Reports Server (NTRS)

Kimes, Daniel S.; Nelson, Ross F.

1998-01-01

A number of satellite sensor systems will collect large data sets of the Earth's surface during NASA's Earth Observing System (EOS) era. Efforts are being made to develop efficient algorithms that can incorporate a wide variety of spectral data and ancillary data in order to extract vegetation variables required for global and regional studies of ecosystem processes, biosphere-atmosphere interactions, and carbon dynamics. These variables are, for the most part, continuous (e.g. biomass, leaf area index, fraction of vegetation cover, vegetation height, vegetation age, spectral albedo, absorbed photosynthetic active radiation, photosynthetic efficiency, etc.) and estimates may be made using remotely sensed data (e.g. nadir and directional optical wavelengths, multifrequency radar backscatter) and any other readily available ancillary data (e.g., topography, sun angle, ground data, etc.). Using these types of data, neural networks can: 1) provide accurate initial models for extracting vegetation variables when an adequate amount of data is available; 2) provide a performance standard for evaluating existing physically-based models; 3) invert multivariate, physically based models; 4) in a variable selection process, identify those independent variables which best infer the vegetation variable(s) of interest; and 5) incorporate new data sources that would be difficult or impossible to use with conventional techniques. In addition, neural networks employ a more powerful and adaptive nonlinear equation form as compared to traditional linear, index transformations, and simple nonlinear analyses. These neural networks attributes are discussed in the context of the authors' investigations of extracting vegetation variables of ecological interest.

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

NASA Technical Reports Server (NTRS)

Simon, Donald L.; Garg, Sanjay

2011-01-01

An emerging approach in the field of aircraft engine controls and system health management is the inclusion of real-time, onboard models for the inflight estimation of engine performance variations. This technology, typically based on Kalman-filter concepts, enables the estimation of unmeasured engine performance parameters that can be directly utilized by controls, prognostics, and health-management applications. A challenge that complicates this practice is the fact that an aircraft engine s performance is affected by its level of degradation, generally described in terms of unmeasurable health parameters such as efficiencies and flow capacities related to each major engine module. Through Kalman-filter-based estimation techniques, the level of engine performance degradation can be estimated, given that there are at least as many sensors as health parameters to be estimated. However, in an aircraft engine, the number of sensors available is typically less than the number of health parameters, presenting an under-determined estimation problem. A common approach to address this shortcoming is to estimate a subset of the health parameters, referred to as model tuning parameters. The problem/objective is to optimally select the model tuning parameters to minimize Kalman-filterbased estimation error. A tuner selection technique has been developed that specifically addresses the under-determined 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 that seeks to minimize the theoretical mean-squared estimation error of the Kalman filter. This approach can significantly reduce the error in onboard aircraft engine parameter estimation applications such as model-based diagnostic, controls, and life usage calculations. The advantage of the innovation is the significant reduction in estimation errors that it can provide relative to the conventional approach of selecting a subset of health parameters to serve as the model tuning parameter vector. Because this technique needs only to be performed during the system design process, it places no additional computation burden on the onboard Kalman filter implementation. The technique has been developed for aircraft engine onboard estimation applications, as this application typically presents an under-determined estimation problem. However, this generic technique could be applied to other industries using gas turbine engine technology.

[A set of quality and safety indicators for hospitals of the "Agencia Valenciana de Salud"].

PubMed

Nebot-Marzal, C M; Mira-Solves, J J; Guilabert-Mora, M; Pérez-Jover, V; Pablo-Comeche, D; Quirós-Morató, T; Cuesta Peredo, D

2014-01-01

To prepare a set of quality and safety indicators for Hospitals of the «Agencia Valenciana de Salud». The qualitative technique Metaplan® was applied in order to gather proposals on sustainability and nursing. The catalogue of the «Spanish Society of Quality in Healthcare» was adopted as a starting point for clinical indicators. Using the Delphi technique, 207 professionals were invited to participate in the selecting the most reliable and feasible indicators. Lastly, the resulting proposal was validated with the managers of 12 hospitals, taking into account the variability, objectivity, feasibility, reliability and sensitivity, of the indicators. Participation rates varied between 66.67% and 80.71%. Of the 159 initial indicators, 68 were prioritized and selected (21 economic or management indicators, 22 nursing indicators, and 25 clinical or hospital indicators). Three of them were common to all three categories and two did not match the specified criteria during the validation phase, thus obtaining a final catalogue of 63 indicators. A set of quality and safety indicators for Hospitals was prepared. They are currently being monitored using the hospital information systems. Copyright © 2013 SECA. Published by Elsevier Espana. All rights reserved.

An SVM-based solution for fault detection in wind turbines.

PubMed

Santos, Pedro; Villa, Luisa F; Reñones, Aníbal; Bustillo, Andres; Maudes, Jesús

2015-03-09

Research into fault diagnosis in machines with a wide range of variable loads and speeds, such as wind turbines, is of great industrial interest. Analysis of the power signals emitted by wind turbines for the diagnosis of mechanical faults in their mechanical transmission chain is insufficient. A successful diagnosis requires the inclusion of accelerometers to evaluate vibrations. This work presents a multi-sensory system for fault diagnosis in wind turbines, combined with a data-mining solution for the classification of the operational state of the turbine. The selected sensors are accelerometers, in which vibration signals are processed using angular resampling techniques and electrical, torque and speed measurements. Support vector machines (SVMs) are selected for the classification task, including two traditional and two promising new kernels. This multi-sensory system has been validated on a test-bed that simulates the real conditions of wind turbines with two fault typologies: misalignment and imbalance. Comparison of SVM performance with the results of artificial neural networks (ANNs) shows that linear kernel SVM outperforms other kernels and ANNs in terms of accuracy, training and tuning times. The suitability and superior performance of linear SVM is also experimentally analyzed, to conclude that this data acquisition technique generates linearly separable datasets.

Improving the prediction of going concern of Taiwanese listed companies using a hybrid of LASSO with data mining techniques.

PubMed

Goo, Yeung-Ja James; Chi, Der-Jang; Shen, Zong-De

2016-01-01

The purpose of this study is to establish rigorous and reliable going concern doubt (GCD) prediction models. This study first uses the least absolute shrinkage and selection operator (LASSO) to select variables and then applies data mining techniques to establish prediction models, such as neural network (NN), classification and regression tree (CART), and support vector machine (SVM). The samples of this study include 48 GCD listed companies and 124 NGCD (non-GCD) listed companies from 2002 to 2013 in the TEJ database. We conduct fivefold cross validation in order to identify the prediction accuracy. According to the empirical results, the prediction accuracy of the LASSO-NN model is 88.96 % (Type I error rate is 12.22 %; Type II error rate is 7.50 %), the prediction accuracy of the LASSO-CART model is 88.75 % (Type I error rate is 13.61 %; Type II error rate is 14.17 %), and the prediction accuracy of the LASSO-SVM model is 89.79 % (Type I error rate is 10.00 %; Type II error rate is 15.83 %).

The Naïve Overfitting Index Selection (NOIS): A new method to optimize model complexity for hyperspectral data

NASA Astrophysics Data System (ADS)

Rocha, Alby D.; Groen, Thomas A.; Skidmore, Andrew K.; Darvishzadeh, Roshanak; Willemen, Louise

2017-11-01

The growing number of narrow spectral bands in hyperspectral remote sensing improves the capacity to describe and predict biological processes in ecosystems. But it also poses a challenge to fit empirical models based on such high dimensional data, which often contain correlated and noisy predictors. As sample sizes, to train and validate empirical models, seem not to be increasing at the same rate, overfitting has become a serious concern. Overly complex models lead to overfitting by capturing more than the underlying relationship, and also through fitting random noise in the data. Many regression techniques claim to overcome these problems by using different strategies to constrain complexity, such as limiting the number of terms in the model, by creating latent variables or by shrinking parameter coefficients. This paper is proposing a new method, named Naïve Overfitting Index Selection (NOIS), which makes use of artificially generated spectra, to quantify the relative model overfitting and to select an optimal model complexity supported by the data. The robustness of this new method is assessed by comparing it to a traditional model selection based on cross-validation. The optimal model complexity is determined for seven different regression techniques, such as partial least squares regression, support vector machine, artificial neural network and tree-based regressions using five hyperspectral datasets. The NOIS method selects less complex models, which present accuracies similar to the cross-validation method. The NOIS method reduces the chance of overfitting, thereby avoiding models that present accurate predictions that are only valid for the data used, and too complex to make inferences about the underlying process.

Bayesian Group Bridge for Bi-level Variable Selection.

PubMed

Mallick, Himel; Yi, Nengjun

2017-06-01

A Bayesian bi-level variable selection method (BAGB: Bayesian Analysis of Group Bridge) is developed for regularized regression and classification. This new development is motivated by grouped data, where generic variables can be divided into multiple groups, with variables in the same group being mechanistically related or statistically correlated. As an alternative to frequentist group variable selection methods, BAGB incorporates structural information among predictors through a group-wise shrinkage prior. Posterior computation proceeds via an efficient MCMC algorithm. In addition to the usual ease-of-interpretation of hierarchical linear models, the Bayesian formulation produces valid standard errors, a feature that is notably absent in the frequentist framework. Empirical evidence of the attractiveness of the method is illustrated by extensive Monte Carlo simulations and real data analysis. Finally, several extensions of this new approach are presented, providing a unified framework for bi-level variable selection in general models with flexible penalties.

Environmental variability and acoustic signals: a multi-level approach in songbirds.

PubMed

Medina, Iliana; Francis, Clinton D

2012-12-23

Among songbirds, growing evidence suggests that acoustic adaptation of song traits occurs in response to habitat features. Despite extensive study, most research supporting acoustic adaptation has only considered acoustic traits averaged for species or populations, overlooking intraindividual variation of song traits, which may facilitate effective communication in heterogeneous and variable environments. Fewer studies have explicitly incorporated sexual selection, which, if strong, may favour variation across environments. Here, we evaluate the prevalence of acoustic adaptation among 44 species of songbirds by determining how environmental variability and sexual selection intensity are associated with song variability (intraindividual and intraspecific) and short-term song complexity. We show that variability in precipitation can explain short-term song complexity among taxonomically diverse songbirds, and that precipitation seasonality and the intensity of sexual selection are related to intraindividual song variation. Our results link song complexity to environmental variability, something previously found for mockingbirds (Family Mimidae). Perhaps more importantly, our results illustrate that individual variation in song traits may be shaped by both environmental variability and strength of sexual selection.

Dimmable electronic ballasts by variable power density modulation technique

NASA Astrophysics Data System (ADS)

Borekci, Selim; Kesler, Selami

2014-11-01

Dimming can be accomplished commonly by switching frequency and pulse density modulation techniques and a variable inductor. In this study, a variable power density modulation (VPDM) control technique is proposed for dimming applications. A fluorescent lamp is operated in several states to meet the desired lamp power in a modulation period. The proposed technique has the same advantages of magnetic dimming topologies have. In addition, a unique and flexible control technique can be achieved. A prototype dimmable electronic ballast is built and experiments related to it have been conducted. As a result, a 36WT8 fluorescent lamp can be driven for a desired lamp power from several alternatives without modulating the switching frequency.

Complex Morphological Variability in Complex Evaporitic Systems: Thermal Spring Snails from the Chihuahuan Desert, Mexico

NASA Astrophysics Data System (ADS)

Tang, Carol M.; Roopnarine, Peter D.

2003-11-01

Thermal springs in evaporitic environments provide a unique biological laboratory in which to study natural selection and evolutionary diversification. These isolated systems may be an analogue for conditions in early Earth or Mars history. One modern example of such a system can be found in the Chihuahuan Desert of north-central Mexico. The Cuatro Cienegas basin hosts a series of thermal springs that form a complex of aquatic ecosystems under a range of environmental conditions. Using landmark-based morphometric techniques, we have quantified an unusually high level of morphological variability in the endemic gastropod Mexipyrgus from Cuatro Cienegas. The differentiation is seen both within and between hydrological systems. Our results suggest that this type of environmental system is capable of producing and maintaining a high level of morphological diversity on small spatial scales, and thus should be a target for future astrobiological research.