Sample records for hierarchical variable selection
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Panagiotakos, Demosthenes B; Pitsavos, Christos; Chrysohoou, Christine; Stefanadis, Christodoulos
2008-01-01
During 2000 to 2002, 700 men (59 +/- 10 years) and 148 women (65 +/- 9 years) patients with first event of an ACS were randomly selected from cardiology clinics of Greek regions. Afterwards, 1078 population-based, age-matched and sex-matched controls were randomly selected from the same hospitals. The frequency ratio between men and women in the case series of patients was about 4:1, in both south and north Greek areas. Hierarchical classification analysis showed that for north Greek areas family history of coronary heart disease, hypercholesterolemia, hypertension, diabetes (explained variability 35%), and less significantly, dietary habits, smoking, body mass index, and physical activity status (explained variability 4%) were associated with the development of ACS, whereas for south Greek areas hypercholesterolemia, family history of coronary heart disease, diabetes, smoking, hypertension, dietary habits, physical activity (explained variability 34%), and less significantly body mass index (explained variability <1%), were associated with the development of the disease.
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Bayesian Group Bridge for Bi-level Variable Selection.
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.
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Scheel, Ida; Ferkingstad, Egil; Frigessi, Arnoldo; Haug, Ola; Hinnerichsen, Mikkel; Meze-Hausken, Elisabeth
2013-01-01
Climate change will affect the insurance industry. We develop a Bayesian hierarchical statistical approach to explain and predict insurance losses due to weather events at a local geographic scale. The number of weather-related insurance claims is modelled by combining generalized linear models with spatially smoothed variable selection. Using Gibbs sampling and reversible jump Markov chain Monte Carlo methods, this model is fitted on daily weather and insurance data from each of the 319 municipalities which constitute southern and central Norway for the period 1997–2006. Precise out-of-sample predictions validate the model. Our results show interesting regional patterns in the effect of different weather covariates. In addition to being useful for insurance pricing, our model can be used for short-term predictions based on weather forecasts and for long-term predictions based on downscaled climate models. PMID:23396890
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Bayesian Variable Selection for Hierarchical Gene-Environment and Gene-Gene Interactions
Liu, Changlu; Ma, Jianzhong; Amos, Christopher I.
2014-01-01
We propose a Bayesian hierarchical mixture model framework that allows us to investigate the genetic and environmental effects, gene by gene interactions and gene by environment interactions in the same model. Our approach incorporates the natural hierarchical structure between the main effects and interaction effects into a mixture model, such that our methods tend to remove the irrelevant interaction effects more effectively, resulting in more robust and parsimonious models. We consider both strong and weak hierarchical models. For a strong hierarchical model, both of the main effects between interacting factors must be present for the interactions to be considered in the model development, while for a weak hierarchical model, only one of the two main effects is required to be present for the interaction to be evaluated. Our simulation results show that the proposed strong and weak hierarchical mixture models work well in controlling false positive rates and provide a powerful approach for identifying the predisposing effects and interactions in gene-environment interaction studies, in comparison with the naive model that does not impose this hierarchical constraint in most of the scenarios simulated. We illustrated our approach using data for lung cancer and cutaneous melanoma. PMID:25154630
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Variability aware compact model characterization for statistical circuit design optimization
NASA Astrophysics Data System (ADS)
Qiao, Ying; Qian, Kun; Spanos, Costas J.
2012-03-01
Variability modeling at the compact transistor model level can enable statistically optimized designs in view of limitations imposed by the fabrication technology. In this work we propose an efficient variabilityaware compact model characterization methodology based on the linear propagation of variance. Hierarchical spatial variability patterns of selected compact model parameters are directly calculated from transistor array test structures. This methodology has been implemented and tested using transistor I-V measurements and the EKV-EPFL compact model. Calculation results compare well to full-wafer direct model parameter extractions. Further studies are done on the proper selection of both compact model parameters and electrical measurement metrics used in the method.
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LIMO EEG: a toolbox for hierarchical LInear MOdeling of ElectroEncephaloGraphic data.
Pernet, Cyril R; Chauveau, Nicolas; Gaspar, Carl; Rousselet, Guillaume A
2011-01-01
Magnetic- and electric-evoked brain responses have traditionally been analyzed by comparing the peaks or mean amplitudes of signals from selected channels and averaged across trials. More recently, tools have been developed to investigate single trial response variability (e.g., EEGLAB) and to test differences between averaged evoked responses over the entire scalp and time dimensions (e.g., SPM, Fieldtrip). LIMO EEG is a Matlab toolbox (EEGLAB compatible) to analyse evoked responses over all space and time dimensions, while accounting for single trial variability using a simple hierarchical linear modelling of the data. In addition, LIMO EEG provides robust parametric tests, therefore providing a new and complementary tool in the analysis of neural evoked responses.
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LIMO EEG: A Toolbox for Hierarchical LInear MOdeling of ElectroEncephaloGraphic Data
Pernet, Cyril R.; Chauveau, Nicolas; Gaspar, Carl; Rousselet, Guillaume A.
2011-01-01
Magnetic- and electric-evoked brain responses have traditionally been analyzed by comparing the peaks or mean amplitudes of signals from selected channels and averaged across trials. More recently, tools have been developed to investigate single trial response variability (e.g., EEGLAB) and to test differences between averaged evoked responses over the entire scalp and time dimensions (e.g., SPM, Fieldtrip). LIMO EEG is a Matlab toolbox (EEGLAB compatible) to analyse evoked responses over all space and time dimensions, while accounting for single trial variability using a simple hierarchical linear modelling of the data. In addition, LIMO EEG provides robust parametric tests, therefore providing a new and complementary tool in the analysis of neural evoked responses. PMID:21403915
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Detecting and interpreting distortions in hierarchical organization of complex time series
NASA Astrophysics Data System (ADS)
DroŻdŻ, Stanisław; OświÈ©cimka, Paweł
2015-03-01
Hierarchical organization is a cornerstone of complexity and multifractality constitutes its central quantifying concept. For model uniform cascades the corresponding singularity spectra are symmetric while those extracted from empirical data are often asymmetric. Using selected time series representing such diverse phenomena as price changes and intertransaction times in financial markets, sentence length variability in narrative texts, Missouri River discharge, and sunspot number variability as examples, we show that the resulting singularity spectra appear strongly asymmetric, more often left sided but in some cases also right sided. We present a unified view on the origin of such effects and indicate that they may be crucially informative for identifying the composition of the time series. One particularly intriguing case of this latter kind of asymmetry is detected in the daily reported sunspot number variability. This signals that either the commonly used famous Wolf formula distorts the real dynamics in expressing the largest sunspot numbers or, if not, that their dynamics is governed by a somewhat different mechanism.
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Gomez-Uchida, Daniel; Seeb, James E; Smith, Matt J; Habicht, Christopher; Quinn, Thomas P; Seeb, Lisa W
2011-02-18
Disentangling the roles of geography and ecology driving population divergence and distinguishing adaptive from neutral evolution at the molecular level have been common goals among evolutionary and conservation biologists. Using single nucleotide polymorphism (SNP) multilocus genotypes for 31 sockeye salmon (Oncorhynchus nerka) populations from the Kvichak River, Alaska, we assessed the relative roles of geography (discrete boundaries or continuous distance) and ecology (spawning habitat and timing) driving genetic divergence in this species at varying spatial scales within the drainage. We also evaluated two outlier detection methods to characterize candidate SNPs responding to environmental selection, emphasizing which mechanism(s) may maintain the genetic variation of outlier loci. For the entire drainage, Mantel tests suggested a greater role of geographic distance on population divergence than differences in spawn timing when each variable was correlated with pairwise genetic distances. Clustering and hierarchical analyses of molecular variance indicated that the largest genetic differentiation occurred between populations from distinct lakes or subdrainages. Within one population-rich lake, however, Mantel tests suggested a greater role of spawn timing than geographic distance on population divergence when each variable was correlated with pairwise genetic distances. Variable spawn timing among populations was linked to specific spawning habitats as revealed by principal coordinate analyses. We additionally identified two outlier SNPs located in the major histocompatibility complex (MHC) class II that appeared robust to violations of demographic assumptions from an initial pool of eight candidates for selection. First, our results suggest that geography and ecology have influenced genetic divergence between Alaskan sockeye salmon populations in a hierarchical manner depending on the spatial scale. Second, we found consistent evidence for diversifying selection in two loci located in the MHC class II by means of outlier detection methods; yet, alternative scenarios for the evolution of these loci were also evaluated. Both conclusions argue that historical contingency and contemporary adaptation have likely driven differentiation between Kvichak River sockeye salmon populations, as revealed by a suite of SNPs. Our findings highlight the need for conservation of complex population structure, because it provides resilience in the face of environmental change, both natural and anthropogenic.
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2011-01-01
Background Disentangling the roles of geography and ecology driving population divergence and distinguishing adaptive from neutral evolution at the molecular level have been common goals among evolutionary and conservation biologists. Using single nucleotide polymorphism (SNP) multilocus genotypes for 31 sockeye salmon (Oncorhynchus nerka) populations from the Kvichak River, Alaska, we assessed the relative roles of geography (discrete boundaries or continuous distance) and ecology (spawning habitat and timing) driving genetic divergence in this species at varying spatial scales within the drainage. We also evaluated two outlier detection methods to characterize candidate SNPs responding to environmental selection, emphasizing which mechanism(s) may maintain the genetic variation of outlier loci. Results For the entire drainage, Mantel tests suggested a greater role of geographic distance on population divergence than differences in spawn timing when each variable was correlated with pairwise genetic distances. Clustering and hierarchical analyses of molecular variance indicated that the largest genetic differentiation occurred between populations from distinct lakes or subdrainages. Within one population-rich lake, however, Mantel tests suggested a greater role of spawn timing than geographic distance on population divergence when each variable was correlated with pairwise genetic distances. Variable spawn timing among populations was linked to specific spawning habitats as revealed by principal coordinate analyses. We additionally identified two outlier SNPs located in the major histocompatibility complex (MHC) class II that appeared robust to violations of demographic assumptions from an initial pool of eight candidates for selection. Conclusions First, our results suggest that geography and ecology have influenced genetic divergence between Alaskan sockeye salmon populations in a hierarchical manner depending on the spatial scale. Second, we found consistent evidence for diversifying selection in two loci located in the MHC class II by means of outlier detection methods; yet, alternative scenarios for the evolution of these loci were also evaluated. Both conclusions argue that historical contingency and contemporary adaptation have likely driven differentiation between Kvichak River sockeye salmon populations, as revealed by a suite of SNPs. Our findings highlight the need for conservation of complex population structure, because it provides resilience in the face of environmental change, both natural and anthropogenic. PMID:21332997
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Contributions of sociodemographic factors to criminal behavior
Mundia, Lawrence; Matzin, Rohani; Mahalle, Salwa; Hamid, Malai Hayati; Osman, Ratna Suriani
2016-01-01
We explored the extent to which prisoner sociodemographic variables (age, education, marital status, employment, and whether their parents were married or not) influenced offending in 64 randomly selected Brunei inmates, comprising both sexes. A quantitative field survey design ideal for the type of participants used in a prison context was employed to investigate the problem. Hierarchical multiple regression analysis with backward elimination identified prisoner marital status and age groups as significantly related to offending. Furthermore, hierarchical multinomial logistic regression analysis with backward elimination indicated that prisoners’ age, primary level education, marital status, employment status, and parental marital status as significantly related to stealing offenses with high odds ratios. All 29 nonrecidivists were false negatives and predicted to reoffend upon release. Similarly, all 33 recidivists were projected to reoffend after release. Hierarchical binary logistic regression analysis revealed age groups (24–29 years and 30–35 years), employed prisoner, and primary level education as variables with high likelihood trends for reoffending. The results suggested that prisoner interventions (educational, counseling, and psychotherapy) in Brunei should treat not only antisocial personality, psychopathy, and mental health problems but also sociodemographic factors. The study generated offending patterns, trends, and norms that may inform subsequent investigations on Brunei prisoners. PMID:27382342
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Radiograph and passive data analysis using mixed variable optimization
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.
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Social determinants of childhood asthma symptoms: an ecological study in urban Latin America.
Fattore, Gisel L; Santos, Carlos A T; Barreto, Mauricio L
2014-04-01
Asthma is an important public health problem in urban Latin America. This study aimed to analyze the role of socioeconomic and environmental factors as potential determinants of asthma symptoms prevalence in children from Latin American (LA) urban centers. We selected 31 LA urban centers with complete data, and an ecological analysis was performed. According to our theoretical framework, the explanatory variables were classified in three levels: distal, intermediate, and proximate. The association between variables in the three levels and prevalence of asthma symptoms was examined by bivariate and multivariate linear regression analysis weighed by sample size. In a second stage, we fitted several linear regression models introducing sequentially the variables according to the predefined hierarchy. In the final hierarchical model Gini Index, crowding, sanitation, variation in infant mortality rates and homicide rates, explained great part of the variance in asthma prevalence between centers (R(2) = 75.0 %). We found a strong association between socioeconomic and environmental variables and prevalence of asthma symptoms in LA urban children, and according to our hierarchical framework and the results found we suggest that social inequalities (measured by the Gini Index) is a central determinant to explain high prevalence of asthma in LA.
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ERIC Educational Resources Information Center
Choi, Kilchan
2011-01-01
This report explores a new latent variable regression 4-level hierarchical model for monitoring school performance over time using multisite multiple-cohorts longitudinal data. This kind of data set has a 4-level hierarchical structure: time-series observation nested within students who are nested within different cohorts of students. These…
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Crucial nesting habitat for gunnison sage-grouse: A spatially explicit hierarchical approach
Aldridge, Cameron L.; Saher, D.J.; Childers, T.M.; Stahlnecker, K.E.; Bowen, Z.H.
2012-01-01
Gunnison sage-grouse (Centrocercus minimus) is a species of special concern and is currently considered a candidate species under Endangered Species Act. Careful management is therefore required to ensure that suitable habitat is maintained, particularly because much of the species' current distribution is faced with exurban development pressures. We assessed hierarchical nest site selection patterns of Gunnison sage-grouse inhabiting the western portion of the Gunnison Basin, Colorado, USA, at multiple spatial scales, using logistic regression-based resource selection functions. Models were selected using Akaike Information Criterion corrected for small sample sizes (AIC c) and predictive surfaces were generated using model averaged relative probabilities. Landscape-scale factors that had the most influence on nest site selection included the proportion of sagebrush cover >5%, mean productivity, and density of 2 wheel-drive roads. The landscape-scale predictive surface captured 97% of known Gunnison sage-grouse nests within the top 5 of 10 prediction bins, implicating 57% of the basin as crucial nesting habitat. Crucial habitat identified by the landscape model was used to define the extent for patch-scale modeling efforts. Patch-scale variables that had the greatest influence on nest site selection were the proportion of big sagebrush cover >10%, distance to residential development, distance to high volume paved roads, and mean productivity. This model accurately predicted independent nest locations. The unique hierarchical structure of our models more accurately captures the nested nature of habitat selection, and allowed for increased discrimination within larger landscapes of suitable habitat. We extrapolated the landscape-scale model to the entire Gunnison Basin because of conservation concerns for this species. We believe this predictive surface is a valuable tool which can be incorporated into land use and conservation planning as well the assessment of future land-use scenarios. ?? 2011 The Wildlife Society.
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GWASinlps: Nonlocal prior based iterative SNP selection tool for genome-wide association studies.
Sanyal, Nilotpal; Lo, Min-Tzu; Kauppi, Karolina; Djurovic, Srdjan; Andreassen, Ole A; Johnson, Valen E; Chen, Chi-Hua
2018-06-19
Multiple marker analysis of the genome-wide association study (GWAS) data has gained ample attention in recent years. However, because of the ultra high-dimensionality of GWAS data, such analysis is challenging. Frequently used penalized regression methods often lead to large number of false positives, whereas Bayesian methods are computationally very expensive. Motivated to ameliorate these issues simultaneously, we consider the novel approach of using nonlocal priors in an iterative variable selection framework. We develop a variable selection method, named, iterative nonlocal prior based selection for GWAS, or GWASinlps, that combines, in an iterative variable selection framework, the computational efficiency of the screen-and-select approach based on some association learning and the parsimonious uncertainty quantification provided by the use of nonlocal priors. The hallmark of our method is the introduction of 'structured screen-and-select' strategy, that considers hierarchical screening, which is not only based on response-predictor associations, but also based on response-response associations, and concatenates variable selection within that hierarchy. Extensive simulation studies with SNPs having realistic linkage disequilibrium structures demonstrate the advantages of our computationally efficient method compared to several frequentist and Bayesian variable selection methods, in terms of true positive rate, false discovery rate, mean squared error, and effect size estimation error. Further, we provide empirical power analysis useful for study design. Finally, a real GWAS data application was considered with human height as phenotype. An R-package for implementing the GWASinlps method is available at https://cran.r-project.org/web/packages/GWASinlps/index.html. Supplementary data are available at Bioinformatics online.
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NASA Astrophysics Data System (ADS)
Yang, Can; Ma, Cheng; Hu, Linxi; He, Guangqiang
2018-06-01
We present a hierarchical modulation coherent communication protocol, which simultaneously achieves classical optical communication and continuous-variable quantum key distribution. Our hierarchical modulation scheme consists of a quadrature phase-shifting keying modulation for classical communication and a four-state discrete modulation for continuous-variable quantum key distribution. The simulation results based on practical parameters show that it is feasible to transmit both quantum information and classical information on a single carrier. We obtained a secure key rate of 10^{-3} bits/pulse to 10^{-1} bits/pulse within 40 kilometers, and in the meantime the maximum bit error rate for classical information is about 10^{-7}. Because continuous-variable quantum key distribution protocol is compatible with standard telecommunication technology, we think our hierarchical modulation scheme can be used to upgrade the digital communication systems to extend system function in the future.
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Estimation of selection intensity under overdominance by Bayesian methods.
Buzbas, Erkan Ozge; Joyce, Paul; Abdo, Zaid
2009-01-01
A balanced pattern in the allele frequencies of polymorphic loci is a potential sign of selection, particularly of overdominance. Although this type of selection is of some interest in population genetics, there exists no likelihood based approaches specifically tailored to make inference on selection intensity. To fill this gap, we present Bayesian methods to estimate selection intensity under k-allele models with overdominance. Our model allows for an arbitrary number of loci and alleles within a locus. The neutral and selected variability within each locus are modeled with corresponding k-allele models. To estimate the posterior distribution of the mean selection intensity in a multilocus region, a hierarchical setup between loci is used. The methods are demonstrated with data at the Human Leukocyte Antigen loci from world-wide populations.
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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.
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Hierarchical time series bottom-up approach for forecast the export value in Central Java
NASA Astrophysics Data System (ADS)
Mahkya, D. A.; Ulama, B. S.; Suhartono
2017-10-01
The purpose of this study is Getting the best modeling and predicting the export value of Central Java using a Hierarchical Time Series. The export value is one variable injection in the economy of a country, meaning that if the export value of the country increases, the country’s economy will increase even more. Therefore, it is necessary appropriate modeling to predict the export value especially in Central Java. Export Value in Central Java are grouped into 21 commodities with each commodity has a different pattern. One approach that can be used time series is a hierarchical approach. Hierarchical Time Series is used Buttom-up. To Forecast the individual series at all levels using Autoregressive Integrated Moving Average (ARIMA), Radial Basis Function Neural Network (RBFNN), and Hybrid ARIMA-RBFNN. For the selection of the best models used Symmetric Mean Absolute Percentage Error (sMAPE). Results of the analysis showed that for the Export Value of Central Java, Bottom-up approach with Hybrid ARIMA-RBFNN modeling can be used for long-term predictions. As for the short and medium-term predictions, it can be used a bottom-up approach RBFNN modeling. Overall bottom-up approach with RBFNN modeling give the best result.
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Reasons for Hierarchical Linear Modeling: A Reminder.
ERIC Educational Resources Information Center
Wang, Jianjun
1999-01-01
Uses examples of hierarchical linear modeling (HLM) at local and national levels to illustrate proper applications of HLM and dummy variable regression. Raises cautions about the circumstances under which hierarchical data do not need HLM. (SLD)
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Ko, Hao-Wen; Cheng, Ming-Hsiang; Chi, Mu-Huan; Chang, Chun-Wei; Chen, Jiun-Tai
2016-03-01
We demonstrate a novel wetting method to prepare hierarchical polymer films with polymer nanotubes on selective regions. This strategy is based on the selective wetting abilities of polymer chains, annealed in different solvent vapors, into the nanopores of porous templates. Phase-separated films of polystyrene (PS) and poly(methyl methacrylate) (PMMA), two commonly used polymers, are prepared as a model system. After anodic aluminum oxide (AAO) templates are placed on the films, the samples are annealed in vapors of acetic acid, in which the PMMA chains are swollen and wet the nanopores of the AAO templates selectively. As a result, hierarchical polymer films containing PMMA nanotubes can be obtained after the AAO templates are removed. The distribution of the PMMA nanotubes of the hierarchical polymer films can also be controlled by changing the compositions of the polymer blends. This work not only presents a novel method to fabricate hierarchical polymer films with polymer nanotubes on selective regions, but also gives a deeper understanding in the selective wetting ability of polymer chains in solvent vapors.
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Graves, T.A.; Kendall, Katherine C.; Royle, J. Andrew; Stetz, J.B.; Macleod, A.C.
2011-01-01
Few studies link habitat to grizzly bear Ursus arctos abundance and these have not accounted for the variation in detection or spatial autocorrelation. We collected and genotyped bear hair in and around Glacier National Park in northwestern Montana during the summer of 2000. We developed a hierarchical Markov chain Monte Carlo model that extends the existing occupancy and count models by accounting for (1) spatially explicit variables that we hypothesized might influence abundance; (2) separate sub-models of detection probability for two distinct sampling methods (hair traps and rub trees) targeting different segments of the population; (3) covariates to explain variation in each sub-model of detection; (4) a conditional autoregressive term to account for spatial autocorrelation; (5) weights to identify most important variables. Road density and per cent mesic habitat best explained variation in female grizzly bear abundance; spatial autocorrelation was not supported. More female bears were predicted in places with lower road density and with more mesic habitat. Detection rates of females increased with rub tree sampling effort. Road density best explained variation in male grizzly bear abundance and spatial autocorrelation was supported. More male bears were predicted in areas of low road density. Detection rates of males increased with rub tree and hair trap sampling effort and decreased over the sampling period. We provide a new method to (1) incorporate multiple detection methods into hierarchical models of abundance; (2) determine whether spatial autocorrelation should be included in final models. Our results suggest that the influence of landscape variables is consistent between habitat selection and abundance in this system.
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Zhang, Xiaoshuai; Xue, Fuzhong; Liu, Hong; Zhu, Dianwen; Peng, Bin; Wiemels, Joseph L; Yang, Xiaowei
2014-12-10
Genome-wide Association Studies (GWAS) are typically designed to identify phenotype-associated single nucleotide polymorphisms (SNPs) individually using univariate analysis methods. Though providing valuable insights into genetic risks of common diseases, the genetic variants identified by GWAS generally account for only a small proportion of the total heritability for complex diseases. To solve this "missing heritability" problem, we implemented a strategy called integrative Bayesian Variable Selection (iBVS), which is based on a hierarchical model that incorporates an informative prior by considering the gene interrelationship as a network. It was applied here to both simulated and real data sets. Simulation studies indicated that the iBVS method was advantageous in its performance with highest AUC in both variable selection and outcome prediction, when compared to Stepwise and LASSO based strategies. In an analysis of a leprosy case-control study, iBVS selected 94 SNPs as predictors, while LASSO selected 100 SNPs. The Stepwise regression yielded a more parsimonious model with only 3 SNPs. The prediction results demonstrated that the iBVS method had comparable performance with that of LASSO, but better than Stepwise strategies. The proposed iBVS strategy is a novel and valid method for Genome-wide Association Studies, with the additional advantage in that it produces more interpretable posterior probabilities for each variable unlike LASSO and other penalized regression methods.
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Daily Stressors in School-Age Children: A Multilevel Approach
ERIC Educational Resources Information Center
Escobar, Milagros; Alarcón, Rafael; Blanca, María J.; Fernández-Baena, F. Javier; Rosel, Jesús F.; Trianes, María Victoria
2013-01-01
This study uses hierarchical or multilevel modeling to identify variables that contribute to daily stressors in a population of schoolchildren. Four hierarchical levels with several predictive variables were considered: student (age, sex, social adaptation of the student, number of life events and chronic stressors experienced, and educational…
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Evaluation of Validity and Reliability for Hierarchical Scales Using Latent Variable Modeling
ERIC Educational Resources Information Center
Raykov, Tenko; Marcoulides, George A.
2012-01-01
A latent variable modeling method is outlined, which accomplishes estimation of criterion validity and reliability for a multicomponent measuring instrument with hierarchical structure. The approach provides point and interval estimates for the scale criterion validity and reliability coefficients, and can also be used for testing composite or…
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Genetic Algorithm for Initial Orbit Determination with Too Short Arc
NASA Astrophysics Data System (ADS)
Li, Xin-ran; Wang, Xin
2017-01-01
A huge quantity of too-short-arc (TSA) observational data have been obtained in sky surveys of space objects. However, reasonable results for the TSAs can hardly be obtained with the classical methods of initial orbit determination (IOD). In this paper, the IOD is reduced to a two-stage hierarchical optimization problem containing three variables for each stage. Using the genetic algorithm, a new method of the IOD for TSAs is established, through the selections of the optimized variables and the corresponding genetic operators for specific problems. Numerical experiments based on the real measurements show that the method can provide valid initial values for the follow-up work.
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Genetic Algorithm for Initial Orbit Determination with Too Short Arc
NASA Astrophysics Data System (ADS)
Li, X. R.; Wang, X.
2016-01-01
The sky surveys of space objects have obtained a huge quantity of too-short-arc (TSA) observation data. However, the classical method of initial orbit determination (IOD) can hardly get reasonable results for the TSAs. The IOD is reduced to a two-stage hierarchical optimization problem containing three variables for each stage. Using the genetic algorithm, a new method of the IOD for TSAs is established, through the selection of optimizing variables as well as the corresponding genetic operator for specific problems. Numerical experiments based on the real measurements show that the method can provide valid initial values for the follow-up work.
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Nonparametric Hierarchical Bayesian Model for Functional Brain Parcellation
Lashkari, Danial; Sridharan, Ramesh; Vul, Edward; Hsieh, Po-Jang; Kanwisher, Nancy; Golland, Polina
2011-01-01
We develop a method for unsupervised analysis of functional brain images that learns group-level patterns of functional response. Our algorithm is based on a generative model that comprises two main layers. At the lower level, we express the functional brain response to each stimulus as a binary activation variable. At the next level, we define a prior over the sets of activation variables in all subjects. We use a Hierarchical Dirichlet Process as the prior in order to simultaneously learn the patterns of response that are shared across the group, and to estimate the number of these patterns supported by data. Inference based on this model enables automatic discovery and characterization of salient and consistent patterns in functional signals. We apply our method to data from a study that explores the response of the visual cortex to a collection of images. The discovered profiles of activation correspond to selectivity to a number of image categories such as faces, bodies, and scenes. More generally, our results appear superior to the results of alternative data-driven methods in capturing the category structure in the space of stimuli. PMID:21841977
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Mansouri, Mohammad; Teshnehlab, Mohammad; Aliyari Shoorehdeli, Mahdi
2015-05-01
In this paper, a novel adaptive hierarchical fuzzy control system based on the variable structure control is developed for a class of SISO canonical nonlinear systems in the presence of bounded disturbances. It is assumed that nonlinear functions of the systems be completely unknown. Switching surfaces are incorporated into the hierarchical fuzzy control scheme to ensure the system stability. A fuzzy soft switching system decides the operation area of the hierarchical fuzzy control and variable structure control systems. All the nonlinearly appeared parameters of conclusion parts of fuzzy blocks located in different layers of the hierarchical fuzzy control system are adjusted through adaptation laws deduced from the defined Lyapunov function. The proposed hierarchical fuzzy control system reduces the number of rules and consequently the number of tunable parameters with respect to the ordinary fuzzy control system. Global boundedness of the overall adaptive system and the desired precision are achieved using the proposed adaptive control system. In this study, an adaptive hierarchical fuzzy system is used for two objectives; it can be as a function approximator or a control system based on an intelligent-classic approach. Three theorems are proven to investigate the stability of the nonlinear dynamic systems. The important point about the proposed theorems is that they can be applied not only to hierarchical fuzzy controllers with different structures of hierarchical fuzzy controller, but also to ordinary fuzzy controllers. Therefore, the proposed algorithm is more general. To show the effectiveness of the proposed method four systems (two mechanical, one mathematical and one chaotic) are considered in simulations. Simulation results demonstrate the validity, efficiency and feasibility of the proposed approach to control of nonlinear dynamic systems. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.
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Leadership styles across hierarchical levels in nursing departments.
Stordeur, S; Vandenberghe, C; D'hoore, W
2000-01-01
Some researchers have reported on the cascading effect of transformational leadership across hierarchical levels. One study examined this effect in nursing, but it was limited to a single hospital. To examine the cascading effect of leadership styles across hierarchical levels in a sample of nursing departments and to investigate the effect of hierarchical level on the relationships between leadership styles and various work outcomes. Based on a sample of eight hospitals, the cascading effect was tested using correlation analysis. The main sources of variation among leadership scores were determined with analyses of variance (ANOVA), and the interaction effect of hierarchical level and leadership styles on criterion variables was tested with moderated regression analysis. No support was found for a cascading effect of leadership across hierarchical levels. Rather, the variation of leadership scores was explained primarily by the organizational context. Transformational leadership had a stronger impact on criterion variables than transactional leadership. Interaction effects between leadership styles and hierarchical level were observed only for perceived unit effectiveness. The hospital's structure and culture are major determinants of leadership styles.
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Hierarchical Letters in ASD: High Stimulus Variability under Different Attentional Modes
ERIC Educational Resources Information Center
Van der Hallen, Ruth; Vanmarcke, Steven; Noens, Ilse; Wagemans, Johan
2017-01-01
Studies using hierarchical patterns to test global precedence and local-global interference in individuals with ASD have produced mixed results. The current study focused on stimulus variability and locational uncertainty, while using different attentional modes. Two groups of 44 children with and without ASD completed a divided attention task as…
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ERIC Educational Resources Information Center
Raykov, Tenko
2011-01-01
Interval estimation of intraclass correlation coefficients in hierarchical designs is discussed within a latent variable modeling framework. A method accomplishing this aim is outlined, which is applicable in two-level studies where participants (or generally lower-order units) are clustered within higher-order units. The procedure can also be…
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Scale of association: hierarchical linear models and the measurement of ecological systems
Sean M. McMahon; Jeffrey M. Diez
2007-01-01
A fundamental challenge to understanding patterns in ecological systems lies in employing methods that can analyse, test and draw inference from measured associations between variables across scales. Hierarchical linear models (HLM) use advanced estimation algorithms to measure regression relationships and variance-covariance parameters in hierarchically structured...
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A hierarchical framework of aquatic ecological units in North America (Nearctic Zone).
James R. Maxwell; Clayton J. Edwards; Mark E. Jensen; Steven J. Paustian; Harry Parrott; Donley M. Hill
1995-01-01
Proposes a framework for classifying and mapping aquatic systems at various scales using ecologically significant physical and biological criteria. Classification and mapping concepts follow tenets of hierarchical theory, pattern recognition, and driving variables. Criteria are provided for the hierarchical classification and mapping of aquatic ecological units of...
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Missing Data Treatments at the Second Level of Hierarchical Linear Models
ERIC Educational Resources Information Center
St. Clair, Suzanne W.
2011-01-01
The current study evaluated the performance of traditional versus modern MDTs in the estimation of fixed-effects and variance components for data missing at the second level of an hierarchical linear model (HLM) model across 24 different study conditions. Variables manipulated in the analysis included, (a) number of Level-2 variables with missing…
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Couples at risk for transmission of alcoholism: protective influences.
Bennett, L A; Wolin, S J; Reiss, D; Teitelbaum, M A
1987-03-01
A two-generation, sociocultural model of the transmission of alcoholism in families was operationalized and tested. Sixty-eight married children of alcoholic parents and their spouses were interviewed regarding dinner-time and holiday ritual practices in their families of origin, and heritage and ritual practices in the couples' current generation. Coders rated transcribed interviews along 14 theory-derived predictor variables, nine for the family of origin and five for the current nuclear family. Multiple regression analysis was applied in a two-step hierarchical method, with the dependent variable being transmission of alcoholism to the couple. The 14 predictor variables contributed significantly (p less than .01) to the couple's alcoholism outcome. A general theme of selective disengagement and reengagement for couples in families at risk for alcoholism recurrence is discussed.
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Hierarchical Multinomial Processing Tree Models: A Latent-Trait Approach
ERIC Educational Resources Information Center
Klauer, Karl Christoph
2010-01-01
Multinomial processing tree models are widely used in many areas of psychology. A hierarchical extension of the model class is proposed, using a multivariate normal distribution of person-level parameters with the mean and covariance matrix to be estimated from the data. The hierarchical model allows one to take variability between persons into…
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Semmens, Brice X; Ward, Eric J; Moore, Jonathan W; Darimont, Chris T
2009-07-09
Variability in resource use defines the width of a trophic niche occupied by a population. Intra-population variability in resource use may occur across hierarchical levels of population structure from individuals to subpopulations. Understanding how levels of population organization contribute to population niche width is critical to ecology and evolution. Here we describe a hierarchical stable isotope mixing model that can simultaneously estimate both the prey composition of a consumer diet and the diet variability among individuals and across levels of population organization. By explicitly estimating variance components for multiple scales, the model can deconstruct the niche width of a consumer population into relevant levels of population structure. We apply this new approach to stable isotope data from a population of gray wolves from coastal British Columbia, and show support for extensive intra-population niche variability among individuals, social groups, and geographically isolated subpopulations. The analytic method we describe improves mixing models by accounting for diet variability, and improves isotope niche width analysis by quantitatively assessing the contribution of levels of organization to the niche width of a population.
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Strategies of marine dinoflagellate survival and some rules of assembly
NASA Astrophysics Data System (ADS)
Smayda, Theodore J.; Reynolds, Colin S.
2003-03-01
Dinoflagellate ecology is based on multiple adaptive strategies and species having diverse habitat preferences. Nine types of mixing-irradiance-nutrient habitats selecting for specific marine dinoflagellate life-form types are recognised, with five rules of assembly proposed to govern bloom-species selection and community organisation within these habitats. Assembly is moulded around an abiotic template of light energy, nutrient supply and physical mixing in permutative combinations. Species selected will have one of three basic ( C-, S-, R-) strategies: colonist species ( C-) which predominate in chemically disturbed habitats; nutrient stress tolerant species ( S-), and species ( R-) tolerant of shear/stress forces in physically disturbed water masses. This organisational plan of three major habitat variables and three major adaptive strategies is termed the 3-3 plan. The bloom behaviour and habitat specialisation of dinoflagellates and diatoms are compared. Dinoflagellates behave as annual species, bloom soloists, are ecophysiologically diverse, and habitat specialists whose blooms tend to be monospecific. Diatoms behave as perennial species, guild members, are habitat cosmopolites, have a relatively uniform bloom strategy based on species-rich pools and exhibit limited habitat specialisation. Dinoflagellate bloom-species selection follows a taxonomic hierarchical pathway which progresses from phylogenetic to generic to species selection, and in that sequence. Each hierarchical taxonomic level has its own adaptive requirements subject to rules of assembly. Dinoflagellates would appear to be well suited to exploit marine habitats and to be competitive with other phylogenetic groups, yet fail to do so.
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Multicollinearity in hierarchical linear models.
Yu, Han; Jiang, Shanhe; Land, Kenneth C
2015-09-01
This study investigates an ill-posed problem (multicollinearity) in Hierarchical Linear Models from both the data and the model perspectives. We propose an intuitive, effective approach to diagnosing the presence of multicollinearity and its remedies in this class of models. A simulation study demonstrates the impacts of multicollinearity on coefficient estimates, associated standard errors, and variance components at various levels of multicollinearity for finite sample sizes typical in social science studies. We further investigate the role multicollinearity plays at each level for estimation of coefficient parameters in terms of shrinkage. Based on these analyses, we recommend a top-down method for assessing multicollinearity in HLMs that first examines the contextual predictors (Level-2 in a two-level model) and then the individual predictors (Level-1) and uses the results for data collection, research problem redefinition, model re-specification, variable selection and estimation of a final model. Copyright © 2015 Elsevier Inc. All rights reserved.
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NASA Astrophysics Data System (ADS)
Chen, Dongju; Li, Dandan; Li, Xianfeng
2017-06-01
A hierarchical poly (ether sulfone) (PES) porous membrane is facilely fabricated via a hard template method for vanadium flow battery (VFB) application. The construction of this hierarchical porous membrane is prepared via removing templates (phenolphthalein). The pore size can be well controlled by optimizing the template content in the cast solution, ensuring the membrane conductivity and selectively. The prepared hierarchical porous membrane can combine high ion selectivity with high proton conductivity, which renders a good electrochemical performance in a VFB. The optimized hierarchical porous membrane shows a columbic efficiency of 94.52% and energy efficiency of 81.66% along with a superior ability to maintain stable capacity over extended cycling at a current density of 80 mA cm-2. The characteristics of low cost, proven chemical stability and high electrochemical performance afford the hierarchical PES porous membrane great prospect in VFB application.
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Factors associated with preventable infant death: a multiple logistic regression.
Vidal E Silva, Sandra Maria Cunha; Tuon, Rogério Antonio; Probst, Livia Fernandes; Gondinho, Brunna Verna Castro; Pereira, Antonio Carlos; Meneghim, Marcelo de Castro; Cortellazzi, Karine Laura; Ambrosano, Glaucia Maria Bovi
2018-01-01
OBJECTIVE To identify and analyze factors associated with preventable child deaths. METHODS This analytical cross-sectional study had preventable child mortality as dependent variable. From a population of 34,284 live births, we have selected a systematic sample of 4,402 children who did not die compared to 272 children who died from preventable causes during the period studied. The independent variables were analyzed in four hierarchical blocks: sociodemographic factors, the characteristics of the mother, prenatal and delivery care, and health conditions of the patient and neonatal care. We performed a descriptive statistical analysis and estimated multiple hierarchical logistic regression models. RESULTS Approximatelly 35.3% of the deaths could have been prevented with the early diagnosis and treatment of diseases during pregnancy and 26.8% of them could have been prevented with better care conditions for pregnant women. CONCLUSIONS The following characteristics of the mother are determinant for the higher mortality of children before the first year of life: living in neighborhoods with an average family income lower than four minimum wages, being aged ≤ 19 years, having one or more alive children, having a child with low APGAR level at the fifth minute of life, and having a child with low birth weight.
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ERIC Educational Resources Information Center
Nowak, Christoph; Heinrichs, Nina
2008-01-01
A meta-analysis encompassing all studies evaluating the impact of the Triple P-Positive Parenting Program on parent and child outcome measures was conducted in an effort to identify variables that moderate the program's effectiveness. Hierarchical linear models (HLM) with three levels of data were employed to analyze effect sizes. The results (N =…
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Comparing hierarchical models via the marginalized deviance information criterion.
Quintero, Adrian; Lesaffre, Emmanuel
2018-07-20
Hierarchical models are extensively used in pharmacokinetics and longitudinal studies. When the estimation is performed from a Bayesian approach, model comparison is often based on the deviance information criterion (DIC). In hierarchical models with latent variables, there are several versions of this statistic: the conditional DIC (cDIC) that incorporates the latent variables in the focus of the analysis and the marginalized DIC (mDIC) that integrates them out. Regardless of the asymptotic and coherency difficulties of cDIC, this alternative is usually used in Markov chain Monte Carlo (MCMC) methods for hierarchical models because of practical convenience. The mDIC criterion is more appropriate in most cases but requires integration of the likelihood, which is computationally demanding and not implemented in Bayesian software. Therefore, we consider a method to compute mDIC by generating replicate samples of the latent variables that need to be integrated out. This alternative can be easily conducted from the MCMC output of Bayesian packages and is widely applicable to hierarchical models in general. Additionally, we propose some approximations in order to reduce the computational complexity for large-sample situations. The method is illustrated with simulated data sets and 2 medical studies, evidencing that cDIC may be misleading whilst mDIC appears pertinent. Copyright © 2018 John Wiley & Sons, Ltd.
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Hierarchical Bayesian Modeling of Fluid-Induced Seismicity
NASA Astrophysics Data System (ADS)
Broccardo, M.; Mignan, A.; Wiemer, S.; Stojadinovic, B.; Giardini, D.
2017-11-01
In this study, we present a Bayesian hierarchical framework to model fluid-induced seismicity. The framework is based on a nonhomogeneous Poisson process with a fluid-induced seismicity rate proportional to the rate of injected fluid. The fluid-induced seismicity rate model depends upon a set of physically meaningful parameters and has been validated for six fluid-induced case studies. In line with the vision of hierarchical Bayesian modeling, the rate parameters are considered as random variables. We develop both the Bayesian inference and updating rules, which are used to develop a probabilistic forecasting model. We tested the Basel 2006 fluid-induced seismic case study to prove that the hierarchical Bayesian model offers a suitable framework to coherently encode both epistemic uncertainty and aleatory variability. Moreover, it provides a robust and consistent short-term seismic forecasting model suitable for online risk quantification and mitigation.
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Marker-Based Hierarchical Segmentation and Classification Approach for Hyperspectral Imagery
NASA Technical Reports Server (NTRS)
Tarabalka, Yuliya; Tilton, James C.; Benediktsson, Jon Atli; Chanussot, Jocelyn
2011-01-01
The Hierarchical SEGmentation (HSEG) algorithm, which is a combination of hierarchical step-wise optimization and spectral clustering, has given good performances for hyperspectral image analysis. This technique produces at its output a hierarchical set of image segmentations. The automated selection of a single segmentation level is often necessary. We propose and investigate the use of automatically selected markers for this purpose. In this paper, a novel Marker-based HSEG (M-HSEG) method for spectral-spatial classification of hyperspectral images is proposed. First, pixelwise classification is performed and the most reliably classified pixels are selected as markers, with the corresponding class labels. Then, a novel constrained marker-based HSEG algorithm is applied, resulting in a spectral-spatial classification map. The experimental results show that the proposed approach yields accurate segmentation and classification maps, and thus is attractive for hyperspectral image analysis.
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Part 1. Statistical Learning Methods for the Effects of Multiple Air Pollution Constituents.
Coull, Brent A; Bobb, Jennifer F; Wellenius, Gregory A; Kioumourtzoglou, Marianthi-Anna; Mittleman, Murray A; Koutrakis, Petros; Godleski, John J
2015-06-01
The United States Environmental Protection Agency (U.S. EPA*) currently regulates individual air pollutants on a pollutant-by-pollutant basis, adjusted for other pollutants and potential confounders. However, the National Academies of Science concluded that a multipollutant regulatory approach that takes into account the joint effects of multiple constituents is likely to be more protective of human health. Unfortunately, the large majority of existing research had focused on health effects of air pollution for one pollutant or for one pollutant with control for the independent effects of a small number of copollutants. Limitations in existing statistical methods are at least partially responsible for this lack of information on joint effects. The goal of this project was to fill this gap by developing flexible statistical methods to estimate the joint effects of multiple pollutants, while allowing for potential nonlinear or nonadditive associations between a given pollutant and the health outcome of interest. We proposed Bayesian kernel machine regression (BKMR) methods as a way to simultaneously achieve the multifaceted goals of variable selection, flexible estimation of the exposure-response relationship, and inference on the strength of the association between individual pollutants and health outcomes in a health effects analysis of mixtures. We first developed a BKMR variable-selection approach, which we call component-wise variable selection, to make estimating such a potentially complex exposure-response function possible by effectively using two types of penalization (or regularization) of the multivariate exposure-response surface. Next we developed an extension of this first variable-selection approach that incorporates knowledge about how pollutants might group together, such as multiple constituents of particulate matter that might represent a common pollution source category. This second grouped, or hierarchical, variable-selection procedure is applicable when groups of highly correlated pollutants are being studied. To investigate the properties of the proposed methods, we conducted three simulation studies designed to evaluate the ability of BKMR to estimate environmental mixtures responsible for health effects under potentially complex but plausible exposure-response relationships. An attractive feature of our simulation studies is that we used actual exposure data rather than simulated values. This real-data simulation approach allowed us to evaluate the performance of BKMR and several other models under realistic joint distributions of multipollutant exposure. The simulation studies compared the two proposed variable-selection approaches (component-wise and hierarchical variable selection) with each other and with existing frequentist treatments of kernel machine regression (KMR). After the simulation studies, we applied the newly developed methods to an epidemiologic data set and to a toxicologic data set. To illustrate the applicability of the proposed methods to human epidemiologic data, we estimated associations between short-term exposures to fine particulate matter constituents and blood pressure in the Maintenance of Balance, Independent Living, Intellect, and Zest in the Elderly (MOBILIZE) Boston study, a prospective cohort study of elderly subjects. To illustrate the applicability of these methods to animal toxicologic studies, we analyzed data on the associations between both blood pressure and heart rate in canines exposed to a composition of concentrated ambient particles (CAPs) in a study conducted at the Harvard T. H. Chan School of Public Health (the Harvard Chan School; formerly Harvard School of Public Health; Bartoli et al. 2009). We successfully developed the theory and computational tools required to apply the proposed methods to the motivating data sets. Collectively, the three simulation studies showed that component-wise variable selection can identify important pollutants within a mixture as long as the correlations among pollutant concentrations are low to moderate. The hierarchical variable-selection method was more effective in high-dimension, high-correlation settings. Variable selection in existing frequentist KMR models can incur inflated type I error rates, particularly when pollutants are highly correlated. The analyses of the MOBILIZE data yielded evidence of a linear and additive association of black carbon (BC) or Cu exposure with standing diastolic blood pressure (DBP), and a linear association of S exposure with standing systolic blood pressure (SBP). Cu is thought to be a marker of urban road dust associated with traffic; and S is a marker of power plant emissions or regional long-range transported air pollution or both. Therefore, these analyses of the MOBILIZE data set suggest that emissions from these three source categories were most strongly associated with hemodynamic responses in this cohort. In contrast, in the Harvard Chan School canine study, after controlling for an overall effect of CAPs exposure, we did not observe any associations between DBP or SBP and any elemental concentrations. Instead, we observed strong evidence of an association between Mn concentrations and heart rate in that heart rate increased linearly with increasing concentrations of Mn. According to the positive matrix factorization (PMF) source apportionment analyses of the multipollutant data set from the Harvard Chan School Boston Supersite, Mn loads on the two factors that represent the mobile and road dust source categories. The results of the BKMR analyses in both the MOBILIZE and canine studies were similar to those from existing linear mixed model analyses of the same multipollutant data because the effects have linear and additive forms that could also have been detected using standard methods. This work provides several contributions to the KMR literature. First, to our knowledge this is the first time KMR methods have been used to estimate the health effects of multipollutant mixtures. Second, we developed a novel hierarchical variable-selection approach within BKMR that is able to account for the structure of the mixture and systematically handle highly correlated exposures. The analyses of the epidemiologic and toxicologic data on associations between fine particulate matter constituents and blood pressure or heart rate demonstrated associations with constituents that are typically associated with traffic emissions, power plants, and long-range transported pollutants. The simulation studies showed that the BKMR methods proposed here work well for small to moderate data sets; more work is needed to develop computationally fast methods for large data sets. This will be a goal of future work.
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Selective attention deficits in obsessive-compulsive disorder: the role of metacognitive processes.
Koch, Julia; Exner, Cornelia
2015-02-28
While initial studies supported the hypothesis that cognitive characteristics that capture cognitive resources act as underlying mechanisms in memory deficits in obsessive-compulsive disorder (OCD), the influence of those characteristics on selective attention has not been studied, yet. In this study, we examined the influence of cognitive self-consciousness (CSC), rumination and worrying on performance in selective attention in OCD and compared the results to a depressive and a healthy control group. We found that 36 OCD and 36 depressive participants were impaired in selective attention in comparison to 36 healthy controls. In all groups, hierarchical regression analyses demonstrated that age, intelligence and years in school significantly predicted performance in selective attention. But only in OCD, the predictive power of the regression model was improved when CSC, rumination and worrying were implemented as predictor variables. In contrast, in none of the three groups the predictive power improved when indicators of severity of obsessive-compulsive (OC) and depressive symptoms and trait anxiety were introduced as predictor variables. Thus, our results support the assumption that mental characteristics that bind cognitive resources play an important role in the understanding of selective attention deficits in OCD and that this mechanism is especially relevant for OCD. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.
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Thomas, D.L.; Johnson, D.; Griffith, B.
2006-01-01
Modeling the probability of use of land units characterized by discrete and continuous measures, we present a Bayesian random-effects model to assess resource selection. This model provides simultaneous estimation of both individual- and population-level selection. Deviance information criterion (DIC), a Bayesian alternative to AIC that is sample-size specific, is used for model selection. Aerial radiolocation data from 76 adult female caribou (Rangifer tarandus) and calf pairs during 1 year on an Arctic coastal plain calving ground were used to illustrate models and assess population-level selection of landscape attributes, as well as individual heterogeneity of selection. Landscape attributes included elevation, NDVI (a measure of forage greenness), and land cover-type classification. Results from the first of a 2-stage model-selection procedure indicated that there is substantial heterogeneity among cow-calf pairs with respect to selection of the landscape attributes. In the second stage, selection of models with heterogeneity included indicated that at the population-level, NDVI and land cover class were significant attributes for selection of different landscapes by pairs on the calving ground. Population-level selection coefficients indicate that the pairs generally select landscapes with higher levels of NDVI, but the relationship is quadratic. The highest rate of selection occurs at values of NDVI less than the maximum observed. Results for land cover-class selections coefficients indicate that wet sedge, moist sedge, herbaceous tussock tundra, and shrub tussock tundra are selected at approximately the same rate, while alpine and sparsely vegetated landscapes are selected at a lower rate. Furthermore, the variability in selection by individual caribou for moist sedge and sparsely vegetated landscapes is large relative to the variability in selection of other land cover types. The example analysis illustrates that, while sometimes computationally intense, a Bayesian hierarchical discrete-choice model for resource selection can provide managers with 2 components of population-level inference: average population selection and variability of selection. Both components are necessary to make sound management decisions based on animal selection.
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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
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Influence of BMI and dietary restraint on self-selected portions of prepared meals in US women.
Labbe, David; Rytz, Andréas; Brunstrom, Jeffrey M; Forde, Ciarán G; Martin, Nathalie
2017-04-01
The rise of obesity prevalence has been attributed in part to an increase in food and beverage portion sizes selected and consumed among overweight and obese consumers. Nevertheless, evidence from observations of adults is mixed and contradictory findings might reflect the use of small or unrepresentative samples. The objective of this study was i) to determine the extent to which BMI and dietary restraint predict self-selected portion sizes for a range of commercially available prepared savoury meals and ii) to consider the importance of these variables relative to two previously established predictors of portion selection, expected satiation and expected liking. A representative sample of female consumers (N = 300, range 18-55 years) evaluated 15 frozen savoury prepared meals. For each meal, participants rated their expected satiation and expected liking, and selected their ideal portion using a previously validated computer-based task. Dietary restraint was quantified using the Dutch Eating Behaviour Questionnaire (DEBQ-R). Hierarchical multiple regression was performed on self-selected portions with age, hunger level, and meal familiarity entered as control variables in the first step of the model, expected satiation and expected liking as predictor variables in the second step, and DEBQ-R and BMI as exploratory predictor variables in the third step. The second and third steps significantly explained variance in portion size selection (18% and 4%, respectively). Larger portion selections were significantly associated with lower dietary restraint and with lower expected satiation. There was a positive relationship between BMI and portion size selection (p = 0.06) and between expected liking and portion size selection (p = 0.06). Our discussion considers future research directions, the limited variance explained by our model, and the potential for portion size underreporting by overweight participants. Copyright © 2016 Nestec S.A. Published by Elsevier Ltd.. All rights reserved.
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Martin, M D; Mendelson, T C
2016-04-01
Models of speciation by sexual selection propose that male-female coevolution leads to the rapid evolution of behavioural reproductive isolation. Here, we compare the strength of behavioural isolation to ecological isolation, gametic incompatibility and hybrid inviability in a group of dichromatic stream fishes. In addition, we examine whether any of these individual barriers, or a combined measure of total isolation, is predicted by body shape differences, male colour differences, environmental differences or genetic distance. Behavioural isolation reaches the highest values of any barrier and is significantly greater than ecological isolation. No individual reproductive barrier is associated with any of the predictor variables. However, marginally significant relationships between male colour and body shape differences with ecological and behavioural isolation are discussed. Differences in male colour and body shape predict total reproductive isolation between species; hierarchical partitioning of these two variables' effects suggests a stronger role for male colour differences. Together, these results suggest an important role for divergent sexual selection in darter speciation but raise new questions about the mechanisms of sexual selection at play and the role of male nuptial ornaments. © 2015 European Society For Evolutionary Biology. Journal of Evolutionary Biology © 2015 European Society For Evolutionary Biology.
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Learning Behavior Characterization with Multi-Feature, Hierarchical Activity Sequences
ERIC Educational Resources Information Center
Ye, Cheng; Segedy, James R.; Kinnebrew, John S.; Biswas, Gautam
2015-01-01
This paper discusses Multi-Feature Hierarchical Sequential Pattern Mining, MFH-SPAM, a novel algorithm that efficiently extracts patterns from students' learning activity sequences. This algorithm extends an existing sequential pattern mining algorithm by dynamically selecting the level of specificity for hierarchically-defined features…
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The Effect of Hierarchical Task Representations on Task Selection in Voluntary Task Switching
ERIC Educational Resources Information Center
Weaver, Starla M.; Arrington, Catherine M.
2013-01-01
The current study explored the potential for hierarchical representations to influence action selection during voluntary task switching. Participants switched between 4 individual task elements. In Experiment 1, participants were encouraged to represent the task elements as grouped within a hierarchy based on experimental manipulations of varying…
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USDA-ARS?s Scientific Manuscript database
Hyperspectral scattering is a promising technique for rapid and noninvasive measurement of multiple quality attributes of apple fruit. A hierarchical evolutionary algorithm (HEA) approach, in combination with subspace decomposition and partial least squares (PLS) regression, was proposed to select o...
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Michael, P E; Jahncke, J; Hyrenbach, K D
2016-01-01
At-sea surveys facilitate the study of the distribution and abundance of marine birds along standardized transects, in relation to changes in the local environmental conditions and large-scale oceanographic forcing. We analyzed the form and the intensity of black-footed albatross (Phoebastria nigripes: BFAL) spatial dispersion off central California, using five years (2004-2008) of vessel-based surveys of seven replicated survey lines. We related BFAL patchiness to local, regional and basin-wide oceanographic variability using two complementary approaches: a hypothesis-based model and an exploratory analysis. The former tested the strength and sign of hypothesized BFAL responses to environmental variability, within a hierarchical atmosphere-ocean context. The latter explored BFAL cross-correlations with atmospheric / oceanographic variables. While albatross dispersion was not significantly explained by the hierarchical model, the exploratory analysis revealed that aggregations were influenced by static (latitude, depth) and dynamic (wind speed, upwelling) environmental variables. Moreover, the largest BFAL patches occurred along the survey lines with the highest densities, and in association with shallow banks. In turn, the highest BFAL densities occurred during periods of negative Pacific Decadal Oscillation index values and low atmospheric pressure. The exploratory analyses suggest that BFAL dispersion is influenced by basin-wide, regional-scale and local environmental variability. Furthermore, the hypothesis-based model highlights that BFAL do not respond to oceanographic variability in a hierarchical fashion. Instead, their distributions shift more strongly in response to large-scale ocean-atmosphere forcing. Thus, interpreting local changes in BFAL abundance and dispersion requires considering diverse environmental forcing operating at multiple scales.
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Revell, J D; Mirmehdi, M; McNally, D S
2004-04-01
We examine tissue deformations using non-invasive dynamic musculoskeletal ultrasonograhy, and quantify its performance on controlled in vitro gold standard (groundtruth) sequences followed by clinical in vivo data. The proposed approach employs a two-dimensional variable-sized block matching algorithm with a hierarchical full search. We extend this process by refining displacements to sub-pixel accuracy. We show by application that this technique yields quantitatively reliable results.
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NASA Astrophysics Data System (ADS)
Hsieh, Chang-Yu; Cao, Jianshu
2018-01-01
We extend a standard stochastic theory to study open quantum systems coupled to a generic quantum environment. We exemplify the general framework by studying a two-level quantum system coupled bilinearly to the three fundamental classes of non-interacting particles: bosons, fermions, and spins. In this unified stochastic approach, the generalized stochastic Liouville equation (SLE) formally captures the exact quantum dissipations when noise variables with appropriate statistics for different bath models are applied. Anharmonic effects of a non-Gaussian bath are precisely encoded in the bath multi-time correlation functions that noise variables have to satisfy. Starting from the SLE, we devise a family of generalized hierarchical equations by averaging out the noise variables and expand bath multi-time correlation functions in a complete basis of orthonormal functions. The general hierarchical equations constitute systems of linear equations that provide numerically exact simulations of quantum dynamics. For bosonic bath models, our general hierarchical equation of motion reduces exactly to an extended version of hierarchical equation of motion which allows efficient simulation for arbitrary spectral densities and temperature regimes. Similar efficiency and flexibility can be achieved for the fermionic bath models within our formalism. The spin bath models can be simulated with two complementary approaches in the present formalism. (I) They can be viewed as an example of non-Gaussian bath models and be directly handled with the general hierarchical equation approach given their multi-time correlation functions. (II) Alternatively, each bath spin can be first mapped onto a pair of fermions and be treated as fermionic environments within the present formalism.
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Sridharan, Ramesh; Vul, Edward; Hsieh, Po-Jang; Kanwisher, Nancy; Golland, Polina
2012-01-01
Functional MRI studies have uncovered a number of brain areas that demonstrate highly specific functional patterns. In the case of visual object recognition, small, focal regions have been characterized with selectivity for visual categories such as human faces. In this paper, we develop an algorithm that automatically learns patterns of functional specificity from fMRI data in a group of subjects. The method does not require spatial alignment of functional images from different subjects. The algorithm is based on a generative model that comprises two main layers. At the lower level, we express the functional brain response to each stimulus as a binary activation variable. At the next level, we define a prior over sets of activation variables in all subjects. We use a Hierarchical Dirichlet Process as the prior in order to learn the patterns of functional specificity shared across the group, which we call functional systems, and estimate the number of these systems. Inference based on our model enables automatic discovery and characterization of dominant and consistent functional systems. We apply the method to data from a visual fMRI study comprised of 69 distinct stimulus images. The discovered system activation profiles correspond to selectivity for a number of image categories such as faces, bodies, and scenes. Among systems found by our method, we identify new areas that are deactivated by face stimuli. In empirical comparisons with perviously proposed exploratory methods, our results appear superior in capturing the structure in the space of visual categories of stimuli. PMID:21884803
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Winters, Eric R; Petosa, Rick L; Charlton, Thomas E
2003-06-01
To examine whether knowledge of high school students' actions of self-regulation, and perceptions of self-efficacy to overcome exercise barriers, social situation, and outcome expectation will predict non-school related moderate and vigorous physical exercise. High school students enrolled in introductory Physical Education courses completed questionnaires that targeted selected Social Cognitive Theory variables. They also self-reported their typical "leisure-time" exercise participation using a standardized questionnaire. Bivariate correlation statistic and hierarchical regression were conducted on reports of moderate and vigorous exercise frequency. Each predictor variable was significantly associated with measures of moderate and vigorous exercise frequency. All predictor variables were significant in the final regression model used to explain vigorous exercise. After controlling for the effects of gender, the psychosocial variables explained 29% of variance in vigorous exercise frequency. Three of four predictor variables were significant in the final regression equation used to explain moderate exercise. The final regression equation accounted for 11% of variance in moderate exercise frequency. Professionals who attempt to increase the prevalence of physical exercise through educational methods should focus on the psychosocial variables utilized in this study.
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2007-09-17
been proposed; these include a combination of variable fidelity models, parallelisation strategies and hybridisation techniques (Coello, Veldhuizen et...Coello et al (Coello, Veldhuizen et al. 2002). 4.4.2 HIERARCHICAL POPULATION TOPOLOGY A hierarchical population topology, when integrated into...to hybrid parallel Multi-Objective Evolutionary Algorithms (pMOEA) (Cantu-Paz 2000; Veldhuizen , Zydallis et al. 2003); it uses a master slave
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Shoults-Wilson, W. A.; Peterson, J.T.; Unrine, J.M.; Rickard, J.; Black, M.C.
2009-01-01
In the present study, specimens of the invasive clam, Corbicula fluminea, were collected above and below possible sources of potentially toxic trace elements (As, Cd, Cr, Cu, Hg, Pb, and Zn) in the Altamaha River system (Georgia, USA). Bioaccumulation of these elements was quantified, along with environmental (water and sediment) concentrations. Hierarchical linear models were used to account for variability in tissue concentrations related to environmental (site water chemistry and sediment characteristics) and individual (growth metrics) variables while identifying the strongest relations between these variables and trace element accumulation. The present study found significantly elevated concentrations of Cd, Cu, and Hg downstream of the outfall of kaolin-processing facilities, Zn downstream of a tire cording facility, and Cr downstream of both a nuclear power plant and a paper pulp mill. Models of the present study indicated that variation in trace element accumulation was linked to distance upstream from the estuary, dissolved oxygen, percentage of silt and clay in the sediment, elemental concentrations in sediment, shell length, and bivalve condition index. By explicitly modeling environmental variability, the Hierarchical linear modeling procedure allowed the identification of sites showing increased accumulation of trace elements that may have been caused by human activity. Hierarchical linear modeling is a useful tool for accounting for environmental and individual sources of variation in bioaccumulation studies. ?? 2009 SETAC.
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Factors associated with preventable infant death: a multiple logistic regression
Vidal e Silva, Sandra Maria Cunha; Tuon, Rogério Antonio; Probst, Livia Fernandes; Gondinho, Brunna Verna Castro; Pereira, Antonio Carlos; Meneghim, Marcelo de Castro; Cortellazzi, Karine Laura; Ambrosano, Glaucia Maria Bovi
2018-01-01
ABSTRACT OBJECTIVE To identify and analyze factors associated with preventable child deaths. METHODS This analytical cross-sectional study had preventable child mortality as dependent variable. From a population of 34,284 live births, we have selected a systematic sample of 4,402 children who did not die compared to 272 children who died from preventable causes during the period studied. The independent variables were analyzed in four hierarchical blocks: sociodemographic factors, the characteristics of the mother, prenatal and delivery care, and health conditions of the patient and neonatal care. We performed a descriptive statistical analysis and estimated multiple hierarchical logistic regression models. RESULTS Approximatelly 35.3% of the deaths could have been prevented with the early diagnosis and treatment of diseases during pregnancy and 26.8% of them could have been prevented with better care conditions for pregnant women. CONCLUSIONS The following characteristics of the mother are determinant for the higher mortality of children before the first year of life: living in neighborhoods with an average family income lower than four minimum wages, being aged ≤ 19 years, having one or more alive children, having a child with low APGAR level at the fifth minute of life, and having a child with low birth weight. PMID:29723389
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Hierarchical modeling of bycatch rates of sea turtles in the western North Atlantic
Gardner, B.; Sullivan, P.J.; Epperly, S.; Morreale, S.J.
2008-01-01
Previous studies indicate that the locations of the endangered loggerhead Caretta caretta and critically endangered leatherback Dermochelys coriacea sea turtles are influenced by water temperatures, and that incidental catch rates in the pelagic longline fishery vary by region. We present a Bayesian hierarchical model to examine the effects of environmental variables, including water temperature, on the number of sea turtles captured in the US pelagic longline fishery in the western North Atlantic. The modeling structure is highly flexible, utilizes a Bayesian model selection technique, and is fully implemented in the software program WinBUGS. The number of sea turtles captured is modeled as a zero-inflated Poisson distribution and the model incorporates fixed effects to examine region-specific differences in the parameter estimates. Results indicate that water temperature, region, bottom depth, and target species are all significant predictors of the number of loggerhead sea turtles captured. For leatherback sea turtles, the model with only target species had the most posterior model weight, though a re-parameterization of the model indicates that temperature influences the zero-inflation parameter. The relationship between the number of sea turtles captured and the variables of interest all varied by region. This suggests that management decisions aimed at reducing sea turtle bycatch may be more effective if they are spatially explicit. ?? Inter-Research 2008.
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Yu, Rongjie; Abdel-Aty, Mohamed
2013-07-01
The Bayesian inference method has been frequently adopted to develop safety performance functions. One advantage of the Bayesian inference is that prior information for the independent variables can be included in the inference procedures. However, there are few studies that discussed how to formulate informative priors for the independent variables and evaluated the effects of incorporating informative priors in developing safety performance functions. This paper addresses this deficiency by introducing four approaches of developing informative priors for the independent variables based on historical data and expert experience. Merits of these informative priors have been tested along with two types of Bayesian hierarchical models (Poisson-gamma and Poisson-lognormal models). Deviance information criterion (DIC), R-square values, and coefficients of variance for the estimations were utilized as evaluation measures to select the best model(s). Comparison across the models indicated that the Poisson-gamma model is superior with a better model fit and it is much more robust with the informative priors. Moreover, the two-stage Bayesian updating informative priors provided the best goodness-of-fit and coefficient estimation accuracies. Furthermore, informative priors for the inverse dispersion parameter have also been introduced and tested. Different types of informative priors' effects on the model estimations and goodness-of-fit have been compared and concluded. Finally, based on the results, recommendations for future research topics and study applications have been made. Copyright © 2013 Elsevier Ltd. All rights reserved.
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Patterson, Fiona; Lopes, Safiatu; Harding, Stephen; Vaux, Emma; Berkin, Liz; Black, David
2017-02-01
The aim of this study was to follow up a sample of physicians who began core medical training (CMT) in 2009. This paper examines the long-term validity of CMT and GP selection methods in predicting performance in the Membership of Royal College of Physicians (MRCP(UK)) examinations. We performed a longitudinal study, examining the extent to which the GP and CMT selection methods (T1) predict performance in the MRCP(UK) examinations (T2). A total of 2,569 applicants from 2008-09 who completed CMT and GP selection methods were included in the study. Looking at MRCP(UK) part 1, part 2 written and PACES scores, both CMT and GP selection methods show evidence of predictive validity for the outcome variables, and hierarchical regressions show the GP methods add significant value to the CMT selection process. CMT selection methods predict performance in important outcomes and have good evidence of validity; the GP methods may have an additional role alongside the CMT selection methods. © Royal College of Physicians 2017. All rights reserved.
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Learn from every mistake! Hierarchical information combination in astronomy
NASA Astrophysics Data System (ADS)
Süveges, Maria; Fotopoulou, Sotiria; Coupon, Jean; Paltani, Stéphane; Eyer, Laurent; Rimoldini, Lorenzo
2017-06-01
Throughout the processing and analysis of survey data, a ubiquitous issue nowadays is that we are spoilt for choice when we need to select a methodology for some of its steps. The alternative methods usually fail and excel in different data regions, and have various advantages and drawbacks, so a combination that unites the strengths of all while suppressing the weaknesses is desirable. We propose to use a two-level hierarchy of learners. Its first level consists of training and applying the possible base methods on the first part of a known set. At the second level, we feed the output probability distributions from all base methods to a second learner trained on the remaining known objects. Using classification of variable stars and photometric redshift estimation as examples, we show that the hierarchical combination is capable of achieving general improvement over averaging-type combination methods, correcting systematics present in all base methods, is easy to train and apply, and thus, it is a promising tool in the astronomical ``Big Data'' era.
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Hierarchical Synthesis of Coastal Ecosystem Health Indicators at Karimunjawa National Marine Park
NASA Astrophysics Data System (ADS)
Danu Prasetya, Johan; Ambariyanto; Supriharyono; Purwanti, Frida
2018-02-01
The coastal ecosystem of Karimunjawa National Marine Park (KNMP) is facing various pressures, including from human activity. Monitoring the health condition of coastal ecosystems periodically is needed as an evaluation of the ecosystem condition. Systematic and consistent indicators are needed in monitoring of coastal ecosystem health. This paper presents hierarchical synthesis of coastal ecosystem health indicators using Analytic Hierarchy Process (AHP) method. Hierarchical synthesis is obtained from process of weighting by paired comparison based on expert judgments. The variables of coastal ecosystem health indicators in this synthesis consist of 3 level of variable, i.e. main variable, sub-variable and operational variable. As a result of assessment, coastal ecosystem health indicators consist of 3 main variables, i.e. State of Ecosystem, Pressure and Management. Main variables State of Ecosystem and Management obtain the same value i.e. 0.400, while Pressure value was 0.200. Each main variable consist of several sub-variable, i.e. coral reef, reef fish, mangrove and seagrass for State of Ecosystem; fisheries and marine tourism activity for Pressure; planning and regulation, institutional and also infrastructure and financing for Management. The highest value of sub-variable of main variable State of Ecosystem, Pressure and Management were coral reef (0.186); marine tourism pressure (0.133) and institutional (0.171), respectively. The highest value of operational variable of main variable State of Ecosystem, Pressure and Management were percent of coral cover (0.058), marine tourism pressure (0.133) and presence of zonation plan, regulation also socialization of monitoring program (0.53), respectively. Potential pressure from marine tourism activity is the variable that most affect the health of the ecosystem. The results of this research suggest that there is a need to develop stronger conservation strategies to facing with pressures from marine tourism activities.
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Graves, Tabitha A.; Royle, J. Andrew; Kendall, Katherine C.; Beier, Paul; Stetz, Jeffrey B.; Macleod, Amy C.
2012-01-01
Using multiple detection methods can increase the number, kind, and distribution of individuals sampled, which may increase accuracy and precision and reduce cost of population abundance estimates. However, when variables influencing abundance are of interest, if individuals detected via different methods are influenced by the landscape differently, separate analysis of multiple detection methods may be more appropriate. We evaluated the effects of combining two detection methods on the identification of variables important to local abundance using detections of grizzly bears with hair traps (systematic) and bear rubs (opportunistic). We used hierarchical abundance models (N-mixture models) with separate model components for each detection method. If both methods sample the same population, the use of either data set alone should (1) lead to the selection of the same variables as important and (2) provide similar estimates of relative local abundance. We hypothesized that the inclusion of 2 detection methods versus either method alone should (3) yield more support for variables identified in single method analyses (i.e. fewer variables and models with greater weight), and (4) improve precision of covariate estimates for variables selected in both separate and combined analyses because sample size is larger. As expected, joint analysis of both methods increased precision as well as certainty in variable and model selection. However, the single-method analyses identified different variables and the resulting predicted abundances had different spatial distributions. We recommend comparing single-method and jointly modeled results to identify the presence of individual heterogeneity between detection methods in N-mixture models, along with consideration of detection probabilities, correlations among variables, and tolerance to risk of failing to identify variables important to a subset of the population. The benefits of increased precision should be weighed against those risks. The analysis framework presented here will be useful for other species exhibiting heterogeneity by detection method.
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Isolating causal pathways between flow and fish in the regulated river hierarchy
DOE Office of Scientific and Technical Information (OSTI.GOV)
McManamay, Ryan A.; Peoples, Brandon K.; Orth, Donald J.
Unregulated river systems are organized in a hierarchy in which large-scale factors (i.e., landscape and segment scales) influence local habitats (i.e., reach, meso-, and microhabitat scales), and both differentially exert selective pressures on biota. Dams, however, create discontinua in these processes and change the hierarchical structure. We examined the relative roles of hydrology and other instream factors, within a hierarchical landscape context, in organizing fish communities in regulated and unregulated tributaries to the Upper Tennessee River, USA. We also used multivariate regression trees to identify factors that partition fish assemblages based on trait similarities, irrespective of spatial scale. Then, wemore » used classical path analysis and structural equation modeling to evaluate the most plausible hierarchical causal structure of specific trait-based community components, given the data. Both statistical approaches suggested that river regulation affects stream fishes through a variety of reach-scale variables, not always through hydrology itself. Though we observed different changes in flow, temperature, and biotic responses according to regulation types, the most predominant path in which dam regulation affected biota was via temperature alterations. Diversion dams had the strongest effects on fish assemblages. Diversion dams reduced flow magnitudes, leading to declines in fish richness but increased temperatures, leading to lower abundances in equilibrium species and nest guarders. Peaking and run-of-river dams increased flow variability, leading to lower abundances in nest-guarding fishes. Flow displayed direct relationships with biotic responses; however, results indicated that changes in temperature and substrate had equal, if not stronger, effects on fish assemblage composition. The strength and nature of relationships depended on whether flow metrics were standardized for river size. Here, we suggest that restoration efforts in regulated rivers focus on improving flow conditions in conjunction with temperature and substrate restoration.« less
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Isolating causal pathways between flow and fish in the regulated river hierarchy
McManamay, Ryan A.; Peoples, Brandon K.; Orth, Donald J.; ...
2015-07-07
Unregulated river systems are organized in a hierarchy in which large-scale factors (i.e., landscape and segment scales) influence local habitats (i.e., reach, meso-, and microhabitat scales), and both differentially exert selective pressures on biota. Dams, however, create discontinua in these processes and change the hierarchical structure. We examined the relative roles of hydrology and other instream factors, within a hierarchical landscape context, in organizing fish communities in regulated and unregulated tributaries to the Upper Tennessee River, USA. We also used multivariate regression trees to identify factors that partition fish assemblages based on trait similarities, irrespective of spatial scale. Then, wemore » used classical path analysis and structural equation modeling to evaluate the most plausible hierarchical causal structure of specific trait-based community components, given the data. Both statistical approaches suggested that river regulation affects stream fishes through a variety of reach-scale variables, not always through hydrology itself. Though we observed different changes in flow, temperature, and biotic responses according to regulation types, the most predominant path in which dam regulation affected biota was via temperature alterations. Diversion dams had the strongest effects on fish assemblages. Diversion dams reduced flow magnitudes, leading to declines in fish richness but increased temperatures, leading to lower abundances in equilibrium species and nest guarders. Peaking and run-of-river dams increased flow variability, leading to lower abundances in nest-guarding fishes. Flow displayed direct relationships with biotic responses; however, results indicated that changes in temperature and substrate had equal, if not stronger, effects on fish assemblage composition. The strength and nature of relationships depended on whether flow metrics were standardized for river size. Here, we suggest that restoration efforts in regulated rivers focus on improving flow conditions in conjunction with temperature and substrate restoration.« less
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Technique for fast and efficient hierarchical clustering
Stork, Christopher
2013-10-08
A fast and efficient technique for hierarchical clustering of samples in a dataset includes compressing the dataset to reduce a number of variables within each of the samples of the dataset. A nearest neighbor matrix is generated to identify nearest neighbor pairs between the samples based on differences between the variables of the samples. The samples are arranged into a hierarchy that groups the samples based on the nearest neighbor matrix. The hierarchy is rendered to a display to graphically illustrate similarities or differences between the samples.
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NASA Astrophysics Data System (ADS)
Ashe, E.; Kopp, R. E.; Khan, N.; Horton, B.; Engelhart, S. E.
2016-12-01
Sea level varies over of both space and time. Prior to the instrumental period, the sea-level record depends upon geological reconstructions that contain vertical and temporal uncertainty. Spatio-temporal statistical models enable the interpretation of RSL and rates of change as well as the reconstruction of the entire sea-level field from such noisy data. Hierarchical models explicitly distinguish between a process level, which characterizes the spatio-temporal field, and a data level, by which sparse proxy data and its noise is recorded. A hyperparameter level depicts prior expectations about the structure of variability in the spatio-temporal field. Spatio-temporal hierarchical models are amenable to several analysis approaches, with tradeoffs regarding computational efficiency and comprehensiveness of uncertainty characterization. A fully-Bayesian hierarchical model (BHM), which places prior probability distributions upon the hyperparameters, is more computationally intensive than an empirical hierarchical model (EHM), which uses point estimates of hyperparameters, derived from the data [1]. Here, we assess the sensitivity of posterior estimates of relative sea level (RSL) and rates to different statistical approaches by varying prior assumptions about the spatial and temporal structure of sea-level variability and applying multiple analytical approaches to Holocene sea-level proxies along the Atlantic coast of North American and the Caribbean [2]. References: 1. N Cressie, Wikle CK (2011) Statistics for spatio-temporal data (John Wiley & Sons). 2. Kahn N et al. (2016). Quaternary Science Reviews (in revision).
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Hierarchical clusters of phytoplankton variables in dammed water bodies
NASA Astrophysics Data System (ADS)
Silva, Eliana Costa e.; Lopes, Isabel Cristina; Correia, Aldina; Gonçalves, A. Manuela
2017-06-01
In this paper a dataset containing biological variables of the water column of several Portuguese reservoirs is analyzed. Hierarchical cluster analysis is used to obtain clusters of phytoplankton variables of the phylum Cyanophyta, with the objective of validating the classification of Portuguese reservoirs previewly presented in [1] which were divided into three clusters: (1) Interior Tagus and Aguieira; (2) Douro; and (3) Other rivers. Now three new clusters of Cyanophyta variables were found. Kruskal-Wallis and Mann-Whitney tests are used to compare the now obtained Cyanophyta clusters and the previous Reservoirs clusters, in order to validate the classification of the water quality of reservoirs. The amount of Cyanophyta algae present in the reservoirs from the three clusters is significantly different, which validates the previous classification.
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[Violent video games and aggression: long-term impact and selection effects].
Staude-Müller, Frithjof
2011-01-01
This study applied social-cognitive models of aggression in order to examine relations between video game use and aggressive tendencies and biases in social information processing. To this end, 499 secondary school students (aged 12-16) completed a survey on two occasions one year apart. Hierarchical regression analysis probed media effects and selection effects and included relevant contextual variables (parental monitoring of media consumption, impulsivity, and victimization). Results revealed that it was not the consumption of violent video games but rather an uncontrolled pattern of video game use that was associated with increasing aggressive tendencies. This increase was partly mediated by a hostile attribution bias in social information processing. The influence of aggressive tendencies on later video game consumption was also examined (selection path). Adolescents with aggressive traits intensified their video game behavior only in terms of their uncontrolled video game use. This was found even after controlling for sensation seeking and parental media control.
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Wimmer, Klaus; Compte, Albert; Roxin, Alex; Peixoto, Diogo; Renart, Alfonso; de la Rocha, Jaime
2015-01-01
Neuronal variability in sensory cortex predicts perceptual decisions. This relationship, termed choice probability (CP), can arise from sensory variability biasing behaviour and from top-down signals reflecting behaviour. To investigate the interaction of these mechanisms during the decision-making process, we use a hierarchical network model composed of reciprocally connected sensory and integration circuits. Consistent with monkey behaviour in a fixed-duration motion discrimination task, the model integrates sensory evidence transiently, giving rise to a decaying bottom-up CP component. However, the dynamics of the hierarchical loop recruits a concurrently rising top-down component, resulting in sustained CP. We compute the CP time-course of neurons in the medial temporal area (MT) and find an early transient component and a separate late contribution reflecting decision build-up. The stability of individual CPs and the dynamics of noise correlations further support this decomposition. Our model provides a unified understanding of the circuit dynamics linking neural and behavioural variability. PMID:25649611
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NASA Astrophysics Data System (ADS)
Cucchi, K.; Kawa, N.; Hesse, F.; Rubin, Y.
2017-12-01
In order to reduce uncertainty in the prediction of subsurface flow and transport processes, practitioners should use all data available. However, classic inverse modeling frameworks typically only make use of information contained in in-situ field measurements to provide estimates of hydrogeological parameters. Such hydrogeological information about an aquifer is difficult and costly to acquire. In this data-scarce context, the transfer of ex-situ information coming from previously investigated sites can be critical for improving predictions by better constraining the estimation procedure. Bayesian inverse modeling provides a coherent framework to represent such ex-situ information by virtue of the prior distribution and combine them with in-situ information from the target site. In this study, we present an innovative data-driven approach for defining such informative priors for hydrogeological parameters at the target site. Our approach consists in two steps, both relying on statistical and machine learning methods. The first step is data selection; it consists in selecting sites similar to the target site. We use clustering methods for selecting similar sites based on observable hydrogeological features. The second step is data assimilation; it consists in assimilating data from the selected similar sites into the informative prior. We use a Bayesian hierarchical model to account for inter-site variability and to allow for the assimilation of multiple types of site-specific data. We present the application and validation of the presented methods on an established database of hydrogeological parameters. Data and methods are implemented in the form of an open-source R-package and therefore facilitate easy use by other practitioners.
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A management-oriented classification of pinyon-juniper woodlands of the Great Basin
Neil E. West; Robin J. Tausch; Paul T. Tueller
1998-01-01
A hierarchical framework for the classification of Great Basin pinyon-juniper woodlands was based on a systematic sample of 426 stands from a random selection of 66 of the 110 mountain ranges in the region. That is, mountain ranges were randomly selected, but stands were systematically located on mountain ranges. The National Hierarchical Framework of Ecological Units...
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Effects of hierarchical structures and insulating liquid media on adhesion
NASA Astrophysics Data System (ADS)
Yang, Weixu; Wang, Xiaoli; Li, Hanqing; Song, Xintao
2017-11-01
Effects of hierarchical structures and insulating liquid media on adhesion are investigated through a numerical adhesive contact model established in this paper, in which hierarchical structures are considered by introducing the height distribution into the surface gap equation, and media are taken into account through the Hamaker constant in Lifshitz-Hamaker approach. Computational methods such as inexact Newton method, bi-conjugate stabilized (Bi-CGSTAB) method and fast Fourier transform (FFT) technique are employed to obtain the adhesive force. It is shown that hierarchical structured surface exhibits excellent anti-adhesive properties compared with flat, micro or nano structured surfaces. Adhesion force is more dependent on the sizes of nanostructures than those of microstructures, and the optimal ranges of nanostructure pitch and maximum height for small adhesion force are presented. Insulating liquid media effectively decrease the adhesive interaction and 1-bromonaphthalene exhibits the smallest adhesion force among the five selected media. In addition, effects of hierarchical structures with optimal sizes on reducing adhesion are more obvious than those of the selected insulating liquid media.
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Prediction of road accidents: A Bayesian hierarchical approach.
Deublein, Markus; Schubert, Matthias; Adey, Bryan T; Köhler, Jochen; Faber, Michael H
2013-03-01
In this paper a novel methodology for the prediction of the occurrence of road accidents is presented. The methodology utilizes a combination of three statistical methods: (1) gamma-updating of the occurrence rates of injury accidents and injured road users, (2) hierarchical multivariate Poisson-lognormal regression analysis taking into account correlations amongst multiple dependent model response variables and effects of discrete accident count data e.g. over-dispersion, and (3) Bayesian inference algorithms, which are applied by means of data mining techniques supported by Bayesian Probabilistic Networks in order to represent non-linearity between risk indicating and model response variables, as well as different types of uncertainties which might be present in the development of the specific models. Prior Bayesian Probabilistic Networks are first established by means of multivariate regression analysis of the observed frequencies of the model response variables, e.g. the occurrence of an accident, and observed values of the risk indicating variables, e.g. degree of road curvature. Subsequently, parameter learning is done using updating algorithms, to determine the posterior predictive probability distributions of the model response variables, conditional on the values of the risk indicating variables. The methodology is illustrated through a case study using data of the Austrian rural motorway network. In the case study, on randomly selected road segments the methodology is used to produce a model to predict the expected number of accidents in which an injury has occurred and the expected number of light, severe and fatally injured road users. Additionally, the methodology is used for geo-referenced identification of road sections with increased occurrence probabilities of injury accident events on a road link between two Austrian cities. It is shown that the proposed methodology can be used to develop models to estimate the occurrence of road accidents for any road network provided that the required data are available. Copyright © 2012 Elsevier Ltd. All rights reserved.
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Hierarchical Kohonenen net for anomaly detection in network security.
Sarasamma, Suseela T; Zhu, Qiuming A; Huff, Julie
2005-04-01
A novel multilevel hierarchical Kohonen Net (K-Map) for an intrusion detection system is presented. Each level of the hierarchical map is modeled as a simple winner-take-all K-Map. One significant advantage of this multilevel hierarchical K-Map is its computational efficiency. Unlike other statistical anomaly detection methods such as nearest neighbor approach, K-means clustering or probabilistic analysis that employ distance computation in the feature space to identify the outliers, our approach does not involve costly point-to-point computation in organizing the data into clusters. Another advantage is the reduced network size. We use the classification capability of the K-Map on selected dimensions of data set in detecting anomalies. Randomly selected subsets that contain both attacks and normal records from the KDD Cup 1999 benchmark data are used to train the hierarchical net. We use a confidence measure to label the clusters. Then we use the test set from the same KDD Cup 1999 benchmark to test the hierarchical net. We show that a hierarchical K-Map in which each layer operates on a small subset of the feature space is superior to a single-layer K-Map operating on the whole feature space in detecting a variety of attacks in terms of detection rate as well as false positive rate.
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Comparing adult users of public and private dental services in the state of Minas Gerais, Brazil.
Pinto, Rafaela da Silveira; de Abreu, Mauro Henrique Nogueira Guimarães; Vargas, Andrea Maria Duarte
2014-08-06
Studying the factors associated with the use of dental services can provide the necessary knowledge to understand the reasons why individuals seek out public healthcare services and the formulation of more appropriate public policies for the present-day reality. This work was a cross-sectional epidemiological study consisting of a sample of adults found in a research databank concerning the conditions of the oral health of the population of the state of Minas Gerais, Brazil. This study examined both main oral health disorders and relevant socioeconomic aspects. The dependent variable was defined as the type of service used, categorized under public and private use. The independent variables were selected and grouped to be inserted in the analysis model according to an adaptation of the behavioral model described by Andersen and Davidson. A hierarchical model was used to analyze the data. The description of variables and bivariate analyses were performed in an attempt to verify possible associations. For each group of variables at each hierarchical level, the gross and adjusted odds ratios (OR) and the respective 95% confidence intervals (CI) were estimated by means of logistic regression. The Complex Samples model from the SPSS statistics program, version 19.0, was used to analyze the sample framework. In the final model, the factors associated with the use of public healthcare services by adults were directly related to the socioeconomic and demographic conditions of the individuals, including: being of a dark-skinned black race/color, belonging to families with more than four household residents and with a lower income level, residing in small towns, having more teeth that need treatment. According to the findings from this study, socioeconomic and demographic factors, as well as normative treatment needs, are associated with the use of public dental services.
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Strong influence of variable treatment on the performance of numerically defined ecological regions.
Snelder, Ton; Lehmann, Anthony; Lamouroux, Nicolas; Leathwick, John; Allenbach, Karin
2009-10-01
Numerical clustering has frequently been used to define hierarchically organized ecological regionalizations, but there has been little robust evaluation of their performance (i.e., the degree to which regions discriminate areas with similar ecological character). In this study we investigated the effect of the weighting and treatment of input variables on the performance of regionalizations defined by agglomerative clustering across a range of hierarchical levels. For this purpose, we developed three ecological regionalizations of Switzerland of increasing complexity using agglomerative clustering. Environmental data for our analysis were drawn from a 400 m grid and consisted of estimates of 11 environmental variables for each grid cell describing climate, topography and lithology. Regionalization 1 was defined from the environmental variables which were given equal weights. We used the same variables in Regionalization 2 but weighted and transformed them on the basis of a dissimilarity model that was fitted to land cover composition data derived for a random sample of cells from interpretation of aerial photographs. Regionalization 3 was a further two-stage development of Regionalization 2 where specific classifications, also weighted and transformed using dissimilarity models, were applied to 25 small scale "sub-domains" defined by Regionalization 2. Performance was assessed in terms of the discrimination of land cover composition for an independent set of sites using classification strength (CS), which measured the similarity of land cover composition within classes and the dissimilarity between classes. Regionalization 2 performed significantly better than Regionalization 1, but the largest gains in performance, compared to Regionalization 1, occurred at coarse hierarchical levels (i.e., CS did not increase significantly beyond the 25-region level). Regionalization 3 performed better than Regionalization 2 beyond the 25-region level and CS values continued to increase to the 95-region level. The results show that the performance of regionalizations defined by agglomerative clustering are sensitive to variable weighting and transformation. We conclude that large gains in performance can be achieved by training classifications using dissimilarity models. However, these gains are restricted to a narrow range of hierarchical levels because agglomerative clustering is unable to represent the variation in importance of variables at different spatial scales. We suggest that further advances in the numerical definition of hierarchically organized ecological regionalizations will be possible with techniques developed in the field of statistical modeling of the distribution of community composition.
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Hierarchical Bayesian spatial models for alcohol availability, drug "hot spots" and violent crime.
Zhu, Li; Gorman, Dennis M; Horel, Scott
2006-12-07
Ecologic studies have shown a relationship between alcohol outlet densities, illicit drug use and violence. The present study examined this relationship in the City of Houston, Texas, using a sample of 439 census tracts. Neighborhood sociostructural covariates, alcohol outlet density, drug crime density and violent crime data were collected for the year 2000, and analyzed using hierarchical Bayesian models. Model selection was accomplished by applying the Deviance Information Criterion. The counts of violent crime in each census tract were modelled as having a conditional Poisson distribution. Four neighbourhood explanatory variables were identified using principal component analysis. The best fitted model was selected as the one considering both unstructured and spatial dependence random effects. The results showed that drug-law violation explained a greater amount of variance in violent crime rates than alcohol outlet densities. The relative risk for drug-law violation was 2.49 and that for alcohol outlet density was 1.16. Of the neighbourhood sociostructural covariates, males of age 15 to 24 showed an effect on violence, with a 16% decrease in relative risk for each increase the size of its standard deviation. Both unstructured heterogeneity random effect and spatial dependence need to be included in the model. The analysis presented suggests that activity around illicit drug markets is more strongly associated with violent crime than is alcohol outlet density. Unique among the ecological studies in this field, the present study not only shows the direction and magnitude of impact of neighbourhood sociostructural covariates as well as alcohol and illicit drug activities in a neighbourhood, it also reveals the importance of applying hierarchical Bayesian models in this research field as both spatial dependence and heterogeneity random effects need to be considered simultaneously.
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Comparison Between Supervised and Unsupervised Classifications of Neuronal Cell Types: A Case Study
Guerra, Luis; McGarry, Laura M; Robles, Víctor; Bielza, Concha; Larrañaga, Pedro; Yuste, Rafael
2011-01-01
In the study of neural circuits, it becomes essential to discern the different neuronal cell types that build the circuit. Traditionally, neuronal cell types have been classified using qualitative descriptors. More recently, several attempts have been made to classify neurons quantitatively, using unsupervised clustering methods. While useful, these algorithms do not take advantage of previous information known to the investigator, which could improve the classification task. For neocortical GABAergic interneurons, the problem to discern among different cell types is particularly difficult and better methods are needed to perform objective classifications. Here we explore the use of supervised classification algorithms to classify neurons based on their morphological features, using a database of 128 pyramidal cells and 199 interneurons from mouse neocortex. To evaluate the performance of different algorithms we used, as a “benchmark,” the test to automatically distinguish between pyramidal cells and interneurons, defining “ground truth” by the presence or absence of an apical dendrite. We compared hierarchical clustering with a battery of different supervised classification algorithms, finding that supervised classifications outperformed hierarchical clustering. In addition, the selection of subsets of distinguishing features enhanced the classification accuracy for both sets of algorithms. The analysis of selected variables indicates that dendritic features were most useful to distinguish pyramidal cells from interneurons when compared with somatic and axonal morphological variables. We conclude that supervised classification algorithms are better matched to the general problem of distinguishing neuronal cell types when some information on these cell groups, in our case being pyramidal or interneuron, is known a priori. As a spin-off of this methodological study, we provide several methods to automatically distinguish neocortical pyramidal cells from interneurons, based on their morphologies. © 2010 Wiley Periodicals, Inc. Develop Neurobiol 71: 71–82, 2011 PMID:21154911
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Jo, Young-Moo; Kim, Tae-Hyung; Lee, Chul-Soon; Lim, Kyeorei; Na, Chan Woong; Abdel-Hady, Faissal; Wazzan, Abdulaziz A; Lee, Jong-Heun
2018-03-14
Nearly monodisperse hollow hierarchical Co 3 O 4 nanocages of four different sizes (∼0.3, 1.0, 2.0, and 4.0 μm) consisting of nanosheets were prepared by controlled precipitation of zeolitic imidazolate framework-67 (ZIF-67) rhombic dodecahedra, followed by solvothermal synthesis of Co 3 O 4 nanocages using ZIF-67 self-sacrificial templates, and subsequent heat treatment for the development of high-performance methylbenzene sensors. The sensor based on hollow hierarchical Co 3 O 4 nanocages with the size of ∼1.0 μm exhibited not only ultrahigh responses (resistance ratios) to 5 ppm p-xylene (78.6) and toluene (43.8) but also a remarkably high selectivity to methylbenzene over the interference of ubiquitous ethanol at 225 °C. The unprecedented and high response and selectivity to methylbenzenes are attributed to the highly gas-accessible hollow hierarchical morphology with thin shells, abundant mesopores, and high surface area per unit volume as well as the high catalytic activity of Co 3 O 4 . Moreover, the size, shell thickness, mesopores, and hollow/hierarchical morphology of the nanocages, the key parameters determining the gas response and selectivity, could be well-controlled by tuning the precipitation of ZIF-67 rhombic dodecahedra and solvothermal reaction. This method can pave a new pathway for the design of high-performance methylbenzene sensors for monitoring the quality of indoor air.
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Swartz, Michael D; Cai, Yi; Chan, Wenyaw; Symanski, Elaine; Mitchell, Laura E; Danysh, Heather E; Langlois, Peter H; Lupo, Philip J
2015-02-09
While there is evidence that maternal exposure to benzene is associated with spina bifida in offspring, to our knowledge there have been no assessments to evaluate the role of multiple hazardous air pollutants (HAPs) simultaneously on the risk of this relatively common birth defect. In the current study, we evaluated the association between maternal exposure to HAPs identified by the United States Environmental Protection Agency (U.S. EPA) and spina bifida in offspring using hierarchical Bayesian modeling that includes Stochastic Search Variable Selection (SSVS). The Texas Birth Defects Registry provided data on spina bifida cases delivered between 1999 and 2004. The control group was a random sample of unaffected live births, frequency matched to cases on year of birth. Census tract-level estimates of annual HAP levels were obtained from the U.S. EPA's 1999 Assessment System for Population Exposure Nationwide. Using the distribution among controls, exposure was categorized as high exposure (>95(th) percentile), medium exposure (5(th)-95(th) percentile), and low exposure (<5(th) percentile, reference). We used hierarchical Bayesian logistic regression models with SSVS to evaluate the association between HAPs and spina bifida by computing an odds ratio (OR) for each HAP using the posterior mean, and a 95% credible interval (CI) using the 2.5(th) and 97.5(th) quantiles of the posterior samples. Based on previous assessments, any pollutant with a Bayes factor greater than 1 was selected for inclusion in a final model. Twenty-five HAPs were selected in the final analysis to represent "bins" of highly correlated HAPs (ρ > 0.80). We identified two out of 25 HAPs with a Bayes factor greater than 1: quinoline (ORhigh = 2.06, 95% CI: 1.11-3.87, Bayes factor = 1.01) and trichloroethylene (ORmedium = 2.00, 95% CI: 1.14-3.61, Bayes factor = 3.79). Overall there is evidence that quinoline and trichloroethylene may be significant contributors to the risk of spina bifida. Additionally, the use of Bayesian hierarchical models with SSVS is an alternative approach in the evaluation of multiple environmental pollutants on disease risk. This approach can be easily extended to environmental exposures, where novel approaches are needed in the context of multi-pollutant modeling.
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Du, Shuting; Chen, Xiaoxin; Sun, Qiming; Wang, Ning; Jia, Mingjun; Valtchev, Valentin; Yu, Jihong
2016-02-28
Hierarchical TS-1 zeolites with secondary macropores have been successfully prepared by using two different fluoride-containing chemical etching post-treated routes. Hierarchical TS-1 zeolites exhibited a chemical composition similar to that of the parent material and showed remarkably enhanced catalytic activity in oxidative desulfurization reaction.
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The role of multidimensional attentional abilities in academic skills of children with ADHD.
Preston, Andrew S; Heaton, Shelley C; McCann, Sarah J; Watson, William D; Selke, Gregg
2009-01-01
Despite reports of academic difficulties in children with attention-deficit/hyperactivity disorder (ADHD), little is known about the relationship between performance on tests of academic achievement and measures of attention. The current study assessed intellectual ability, parent-reported inattention, academic achievement, and attention in 45 children (ages 7-15) diagnosed with ADHD. Hierarchical regressions were performed with selective, sustained, and attentional control/switching domains of the Test of Everyday Attention for Children as predictor variables and with performance on the Wechsler Individual Achievement Test-Second Edition as dependent variables. It was hypothesized that sustained attention and attentional control/switching would predict performance on achievement tests. Results demonstrate that attentional control/ switching accounted for a significant amount of variance in all academic areas (reading, math, and spelling), even after accounting for verbal IQ and parent-reported inattention. Sustained attention predicted variance only in math, whereas selective attention did not account for variance in any achievement domain. Therefore, attentional control/switching, which involves components of executive functions, plays an important role in academic performance.
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Hierarchical Naive Bayes for genetic association studies.
Malovini, Alberto; Barbarini, Nicola; Bellazzi, Riccardo; de Michelis, Francesca
2012-01-01
Genome Wide Association Studies represent powerful approaches that aim at disentangling the genetic and molecular mechanisms underlying complex traits. The usual "one-SNP-at-the-time" testing strategy cannot capture the multi-factorial nature of this kind of disorders. We propose a Hierarchical Naïve Bayes classification model for taking into account associations in SNPs data characterized by Linkage Disequilibrium. Validation shows that our model reaches classification performances superior to those obtained by the standard Naïve Bayes classifier for simulated and real datasets. In the Hierarchical Naïve Bayes implemented, the SNPs mapping to the same region of Linkage Disequilibrium are considered as "details" or "replicates" of the locus, each contributing to the overall effect of the region on the phenotype. A latent variable for each block, which models the "population" of correlated SNPs, can be then used to summarize the available information. The classification is thus performed relying on the latent variables conditional probability distributions and on the SNPs data available. The developed methodology has been tested on simulated datasets, each composed by 300 cases, 300 controls and a variable number of SNPs. Our approach has been also applied to two real datasets on the genetic bases of Type 1 Diabetes and Type 2 Diabetes generated by the Wellcome Trust Case Control Consortium. The approach proposed in this paper, called Hierarchical Naïve Bayes, allows dealing with classification of examples for which genetic information of structurally correlated SNPs are available. It improves the Naïve Bayes performances by properly handling the within-loci variability.
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Selective hierarchical patterning of silicon nanostructures via soft nanostencil lithography
NASA Astrophysics Data System (ADS)
Du, Ke; Ding, Junjun; Wathuthanthri, Ishan; Choi, Chang-Hwan
2017-11-01
It is challenging to hierarchically pattern high-aspect-ratio nanostructures on microstructures using conventional lithographic techniques, where photoresist (PR) film is not able to uniformly cover on the microstructures as the aspect ratio increases. Such non-uniformity causes poor definition of nanopatterns over the microstructures. Nanostencil lithography can provide an alternative means to hierarchically construct nanostructures on microstructures via direct deposition or plasma etching through a free-standing nanoporous membrane. In this work, we demonstrate the multiscale hierarchical fabrication of high-aspect-ratio nanostructures on microstructures of silicon using a free-standing nanostencil, which is a nanoporous membrane consisting of metal (Cr), PR, and anti-reflective coating. The nanostencil membrane is used as a deposition mask to define Cr nanodot patterns on the predefined silicon microstructures. Then, deep reactive ion etching is used to hierarchically create nanostructures on the microstructures using the Cr nanodots as an etch mask. With simple modification of the main fabrication processes, high-aspect-ratio nanopillars are selectively defined only on top of the microstructures, on bottom, or on both top and bottom.
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Selective hierarchical patterning of silicon nanostructures via soft nanostencil lithography.
Du, Ke; Ding, Junjun; Wathuthanthri, Ishan; Choi, Chang-Hwan
2017-11-17
It is challenging to hierarchically pattern high-aspect-ratio nanostructures on microstructures using conventional lithographic techniques, where photoresist (PR) film is not able to uniformly cover on the microstructures as the aspect ratio increases. Such non-uniformity causes poor definition of nanopatterns over the microstructures. Nanostencil lithography can provide an alternative means to hierarchically construct nanostructures on microstructures via direct deposition or plasma etching through a free-standing nanoporous membrane. In this work, we demonstrate the multiscale hierarchical fabrication of high-aspect-ratio nanostructures on microstructures of silicon using a free-standing nanostencil, which is a nanoporous membrane consisting of metal (Cr), PR, and anti-reflective coating. The nanostencil membrane is used as a deposition mask to define Cr nanodot patterns on the predefined silicon microstructures. Then, deep reactive ion etching is used to hierarchically create nanostructures on the microstructures using the Cr nanodots as an etch mask. With simple modification of the main fabrication processes, high-aspect-ratio nanopillars are selectively defined only on top of the microstructures, on bottom, or on both top and bottom.
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Speck, Thomas; Bohn, Holger F.
2018-01-01
The surfaces of plant leaves are rarely smooth and often possess a species-specific micro- and/or nano-structuring. These structures usually influence the surface functionality of the leaves such as wettability, optical properties, friction and adhesion in insect–plant interactions. This work presents a simple, convenient, inexpensive and precise two-step micro-replication technique to transfer surface microstructures of plant leaves onto highly transparent soft polymer material. Leaves of three different plants with variable size (0.5–100 µm), shape and complexity (hierarchical levels) of their surface microstructures were selected as model bio-templates. A thermoset epoxy resin was used at ambient conditions to produce negative moulds directly from fresh plant leaves. An alkaline chemical treatment was established to remove the entirety of the leaf material from the cured negative epoxy mould when necessary, i.e. for highly complex hierarchical structures. Obtained moulds were filled up afterwards with low viscosity silicone elastomer (PDMS) to obtain positive surface replicas. Comparative scanning electron microscopy investigations (original plant leaves and replicated polymeric surfaces) reveal the high precision and versatility of this replication technique. This technique has promising future application for the development of bioinspired functional surfaces. Additionally, the fabricated polymer replicas provide a model to systematically investigate the structural key points of surface functionalities. PMID:29765666
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Shih, Arthur Chun-Chieh; Lee, DT; Peng, Chin-Lin; Wu, Yu-Wei
2007-01-01
Background When aligning several hundreds or thousands of sequences, such as epidemic virus sequences or homologous/orthologous sequences of some big gene families, to reconstruct the epidemiological history or their phylogenies, how to analyze and visualize the alignment results of many sequences has become a new challenge for computational biologists. Although there are several tools available for visualization of very long sequence alignments, few of them are applicable to the alignments of many sequences. Results A multiple-logo alignment visualization tool, called Phylo-mLogo, is presented in this paper. Phylo-mLogo calculates the variabilities and homogeneities of alignment sequences by base frequencies or entropies. Different from the traditional representations of sequence logos, Phylo-mLogo not only displays the global logo patterns of the whole alignment of multiple sequences, but also demonstrates their local homologous logos for each clade hierarchically. In addition, Phylo-mLogo also allows the user to focus only on the analysis of some important, structurally or functionally constrained sites in the alignment selected by the user or by built-in automatic calculation. Conclusion With Phylo-mLogo, the user can symbolically and hierarchically visualize hundreds of aligned sequences simultaneously and easily check the changes of their amino acid sites when analyzing many homologous/orthologous or influenza virus sequences. More information of Phylo-mLogo can be found at URL . PMID:17319966
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Martín-Loeches, M; Hinojosa, J A; Rubia, F J
1999-11-01
The temporal and hierarchical relationships between the dorsal and the ventral streams in selective attention are known only in relation to the use of spatial location as the attentional cue mediated by the dorsal stream. To improve this state of affairs, event-related brain potentials were recorded while subjects attended simultaneously to motion direction (mediated by the dorsal stream) and to a property mediated by the ventral stream (color or shape). At about the same time, a selection positivity (SP) started for attention mediated by both streams. However, the SP for color and shape peaked about 60 ms later than motion SP. Subsequently, a selection negativity (SN) followed by a late positive component (LPC) were found simultaneously for attention mediated by both streams. A hierarchical relationship between the two streams was not observed, but neither SN nor LPC for one property was completely insensitive to the values of the other property.
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NASA Technical Reports Server (NTRS)
Tarabalka, Y.; Tilton, J. C.; Benediktsson, J. A.; Chanussot, J.
2012-01-01
The Hierarchical SEGmentation (HSEG) algorithm, which combines region object finding with region object clustering, has given good performances for multi- and hyperspectral image analysis. This technique produces at its output a hierarchical set of image segmentations. The automated selection of a single segmentation level is often necessary. We propose and investigate the use of automatically selected markers for this purpose. In this paper, a novel Marker-based HSEG (M-HSEG) method for spectral-spatial classification of hyperspectral images is proposed. Two classification-based approaches for automatic marker selection are adapted and compared for this purpose. Then, a novel constrained marker-based HSEG algorithm is applied, resulting in a spectral-spatial classification map. Three different implementations of the M-HSEG method are proposed and their performances in terms of classification accuracies are compared. The experimental results, presented for three hyperspectral airborne images, demonstrate that the proposed approach yields accurate segmentation and classification maps, and thus is attractive for remote sensing image analysis.
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Chen, Yongsheng; Persaud, Bhagwant
2014-09-01
Crash modification factors (CMFs) for road safety treatments are developed as multiplicative factors that are used to reflect the expected changes in safety performance associated with changes in highway design and/or the traffic control features. However, current CMFs have methodological drawbacks. For example, variability with application circumstance is not well understood, and, as important, correlation is not addressed when several CMFs are applied multiplicatively. These issues can be addressed by developing safety performance functions (SPFs) with components of crash modification functions (CM-Functions), an approach that includes all CMF related variables, along with others, while capturing quantitative and other effects of factors and accounting for cross-factor correlations. CM-Functions can capture the safety impact of factors through a continuous and quantitative approach, avoiding the problematic categorical analysis that is often used to capture CMF variability. There are two formulations to develop such SPFs with CM-Function components - fully specified models and hierarchical models. Based on sample datasets from two Canadian cities, both approaches are investigated in this paper. While both model formulations yielded promising results and reasonable CM-Functions, the hierarchical model was found to be more suitable in retaining homogeneity of first-level SPFs, while addressing CM-Functions in sub-level modeling. In addition, hierarchical models better capture the correlations between different impact factors. Copyright © 2014 Elsevier Ltd. All rights reserved.
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Hierarchical video summarization based on context clustering
NASA Astrophysics Data System (ADS)
Tseng, Belle L.; Smith, John R.
2003-11-01
A personalized video summary is dynamically generated in our video personalization and summarization system based on user preference and usage environment. The three-tier personalization system adopts the server-middleware-client architecture in order to maintain, select, adapt, and deliver rich media content to the user. The server stores the content sources along with their corresponding MPEG-7 metadata descriptions. In this paper, the metadata includes visual semantic annotations and automatic speech transcriptions. Our personalization and summarization engine in the middleware selects the optimal set of desired video segments by matching shot annotations and sentence transcripts with user preferences. Besides finding the desired contents, the objective is to present a coherent summary. There are diverse methods for creating summaries, and we focus on the challenges of generating a hierarchical video summary based on context information. In our summarization algorithm, three inputs are used to generate the hierarchical video summary output. These inputs are (1) MPEG-7 metadata descriptions of the contents in the server, (2) user preference and usage environment declarations from the user client, and (3) context information including MPEG-7 controlled term list and classification scheme. In a video sequence, descriptions and relevance scores are assigned to each shot. Based on these shot descriptions, context clustering is performed to collect consecutively similar shots to correspond to hierarchical scene representations. The context clustering is based on the available context information, and may be derived from domain knowledge or rules engines. Finally, the selection of structured video segments to generate the hierarchical summary efficiently balances between scene representation and shot selection.
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Compiling software for a hierarchical distributed processing system
Archer, Charles J; Blocksome, Michael A; Ratterman, Joseph D; Smith, Brian E
2013-12-31
Compiling software for a hierarchical distributed processing system including providing to one or more compiling nodes software to be compiled, wherein at least a portion of the software to be compiled is to be executed by one or more nodes; compiling, by the compiling node, the software; maintaining, by the compiling node, any compiled software to be executed on the compiling node; selecting, by the compiling node, one or more nodes in a next tier of the hierarchy of the distributed processing system in dependence upon whether any compiled software is for the selected node or the selected node's descendents; sending to the selected node only the compiled software to be executed by the selected node or selected node's descendent.
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Labbe, D; Rytz, A; Godinot, N; Ferrage, A; Martin, N
2017-01-01
Increasing portion sizes over the last 30 years are considered to be one of the factors underlying overconsumption. Past research on the drivers of portion selection for foods showed that larger portions are selected for foods delivering low expected satiation. However, the respective contribution of expected satiation vs. two other potential drivers of portion size selection, i.e. perceived healthfulness and expected tastiness, has never been explored. In this study, we conjointly explored the role of expected satiation, perceived healthfulness and expected tastiness when selecting portions within a range of six commercial pizzas varying in their toppings and brands. For each product, 63 pizza consumers selected a portion size that would satisfy them for lunch and scored their expected satiation, perceived healthfulness and expected tastiness. As six participants selected an entire pizza as ideal portion independently of topping or brand, their data sets were not considered in the data analyses completed on responses from 57 participants. Hierarchical multiple regression analyses showed that portion size variance was predicted by perceived healthiness and expected tastiness variables. Two sub-groups of participants with different portion size patterns across pizzas were identified through post-hoc exploratory analysis. The explanatory power of the regression model was significantly improved by adding interaction terms between sub-group and expected satiation variables and between sub-group and perceived healthfulness variables to the model. Analysis at a sub-group level showed either positive or negative association between portion size and expected satiation depending on sub-groups. For one group, portion size selection was more health-driven and for the other, more hedonic-driven. These results showed that even when considering a well-liked product category, perceived healthfulness can be an important factor influencing portion size decision. Copyright © 2016 Nestec S.A. Published by Elsevier Ltd.. All rights reserved.
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Wang, Yong; State Key Laboratory of Multiphase Complex System, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100080; Zhu, Qingshan, E-mail: qszhu@home.ipe.ac.cn
{beta}-Ni(OH){sub 2} hierarchical micro-flowers, hierarchical hollow microspheres and nanosheets were synthesized via a facile, single-step and selected-control hydrothermal method. Both hierarchical micro-flowers and hierarchical hollow microspheres were built from two-dimensional nanosheets with thickness of 50-100 nm. The as-obtained products were characterized by Brunauer-Emmett-Teller (BET) surface area analysis, X-ray powder diffraction (XRD) and field emission scanning electron microscopy (FESEM). It was observed that marked morphological changes in {beta}-Ni(OH){sub 2} depended on the initial concentrations of Ni{sup 2+} ions and glycine. A possible growth mechanism was proposed based on experimental results. In addition, the effect of morphology on the electrochemical properties wasmore » also investigated. Both hierarchical micro-flowers and hierarchical hollow microspheres exhibited enhanced specific capacity and high-rate discharge ability as compared with pure Ni(OH){sub 2} nanosheets. Investigations confirmed that hierarchical structures had a pronounced influence upon the electrochemical performance of nickel hydroxide.« less
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Real-Time Speech/Music Classification With a Hierarchical Oblique Decision Tree
2008-04-01
REAL-TIME SPEECH/ MUSIC CLASSIFICATION WITH A HIERARCHICAL OBLIQUE DECISION TREE Jun Wang, Qiong Wu, Haojiang Deng, Qin Yan Institute of Acoustics...time speech/ music classification with a hierarchical oblique decision tree. A set of discrimination features in frequency domain are selected...handle signals without discrimination and can not work properly in the existence of multimedia signals. This paper proposes a real-time speech/ music
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Covariates of the Rating Process in Hierarchical Models for Multiple Ratings of Test Items
ERIC Educational Resources Information Center
Mariano, Louis T.; Junker, Brian W.
2007-01-01
When constructed response test items are scored by more than one rater, the repeated ratings allow for the consideration of individual rater bias and variability in estimating student proficiency. Several hierarchical models based on item response theory have been introduced to model such effects. In this article, the authors demonstrate how these…
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Jia, Qianqian; Ji, Huiming; Zhang, Ying; Chen, Yalu; Sun, Xiaohong; Jin, Zhengguo
2014-07-15
Hierarchical nanostructured ZnO dandelion-like spheres were synthesized via solvothermal reaction at 200°C for 4h. The products were pure hexagonal ZnO with large exposure of (002) polar facet. Side-heating gas sensor based on hierarchical ZnO spheres was prepared to evaluate the acetone gas sensing properties. The detection limit to acetone for the ZnO sensor is 0.25ppm. The response (Ra/Rg) toward 100ppm acetone was 33 operated at 230°C and the response time was as short as 3s. The sensor exhibited remarkable acetone selectivity with negligible response toward other hazardous gases and water vapor. The high proportion of electron depletion region and oxygen vacancies contributed to high gas response sensitivity. The hollow and porous structure of dandelion-like ZnO spheres facilitated the diffusion of gas molecules, leading to a rapid response speed. The largely exposed (002) polar facets could adsorb acetone gas molecules easily and efficiently, resulting in a rapid response speed and good selectivity of hierarchical ZnO spheres gas sensor at low operating temperature. Copyright © 2014 Elsevier B.V. All rights reserved.
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Tataranni, Mariella; Lardicci, Claudio
2010-01-01
The aim of this study was to analyse the variability of four different benthic biotic indices (AMBI, BENTIX, H', M-AMBI) in two marine coastal areas of the North-Western Mediterranean Sea. In each coastal area, 36 replicates were randomly selected according to a hierarchical sampling design, which allowed estimating the variance components of the indices associated with four different spatial scales (ranging from metres to kilometres). All the analyses were performed at two different sampling periods in order to evaluate if the observed trends were consistent over the time. The variance components of the four indices revealed complex trends and different patterns in the two sampling periods. These results highlighted that independently from the employed index, a rigorous and appropriate sampling design taking into account different scales should always be used in order to avoid erroneous classifications and to develop effective monitoring programs.
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A multi-criteria index for ecological evaluation of tropical agriculture in southeastern Mexico.
Huerta, Esperanza; Kampichler, Christian; Ochoa-Gaona, Susana; De Jong, Ben; Hernandez-Daumas, Salvador; Geissen, Violette
2014-01-01
The aim of this study was to generate an easy to use index to evaluate the ecological state of agricultural land from a sustainability perspective. We selected environmental indicators, such as the use of organic soil amendments (green manure) versus chemical fertilizers, plant biodiversity (including crop associations), variables which characterize soil conservation of conventional agricultural systems, pesticide use, method and frequency of tillage. We monitored the ecological state of 52 agricultural plots to test the performance of the index. The variables were hierarchically aggregated with simple mathematical algorithms, if-then rules, and rule-based fuzzy models, yielding the final multi-criteria index with values from 0 (worst) to 1 (best conditions). We validated the model through independent evaluation by experts, and we obtained a linear regression with an r2 = 0.61 (p = 2.4e-06, d.f. = 49) between index output and the experts' evaluation.
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A Multi-Criteria Index for Ecological Evaluation of Tropical Agriculture in Southeastern Mexico
Huerta, Esperanza; Kampichler, Christian; Ochoa-Gaona, Susana; De Jong, Ben; Hernandez-Daumas, Salvador; Geissen, Violette
2014-01-01
The aim of this study was to generate an easy to use index to evaluate the ecological state of agricultural land from a sustainability perspective. We selected environmental indicators, such as the use of organic soil amendments (green manure) versus chemical fertilizers, plant biodiversity (including crop associations), variables which characterize soil conservation of conventional agricultural systems, pesticide use, method and frequency of tillage. We monitored the ecological state of 52 agricultural plots to test the performance of the index. The variables were hierarchically aggregated with simple mathematical algorithms, if-then rules, and rule-based fuzzy models, yielding the final multi-criteria index with values from 0 (worst) to 1 (best conditions). We validated the model through independent evaluation by experts, and we obtained a linear regression with an r2 = 0.61 (p = 2.4e-06, d.f. = 49) between index output and the experts’ evaluation. PMID:25405980
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Lipschitz-Elhawi, Racheli; Itzhaky, Haya
2014-03-01
The contribution of selected background variables (age, gender), internal resources (mastery, emotional maturity), external resources (parental and peer support), and emotional distress to alcohol and drug use among 160 Israeli Jewish urban high school students were examined. Analyzing the variables with hierarchical regression, emotional distress contributed most significantly to both alcohol and drug use, and the contribution of age was somewhat less significant for both of them. Emotional distress also contributed indirectly to drug use through an interaction with one's sense of mastery. Gender, internal resources, and external resources contributed differentially to alcohol and drug use. Whereas gender and internal resources contributed only to drug use, external resources contributed only to alcohol use. Specifically, peer support contributed positively to alcohol use whereas parental support contributed negatively. The discussion provides explanations for these research findings and their implications, and the research's limitations are noted.
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Vuong, Nguyen Minh; Chinh, Nguyen Duc; Huy, Bui The; Lee, Yong-Ill
2016-01-01
Highly sensitive hydrogen sulfide (H2S) gas sensors were developed from CuO-decorated ZnO semiconducting hierarchical nanostructures. The ZnO hierarchical nanostructure was fabricated by an electrospinning method following hydrothermal and heat treatment. CuO decoration of ZnO hierarchical structures was carried out by a wet method. The H2S gas-sensing properties were examined at different working temperatures using various quantities of CuO as the variable. CuO decoration of the ZnO hierarchical structure was observed to promote sensitivity for H2S gas higher than 30 times at low working temperature (200 °C) compared with that in the nondecorated hierarchical structure. The sensing mechanism of the hybrid sensor structure is also discussed. The morphology and characteristics of the samples were examined by scanning electron microscopy (SEM), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), UV-vis absorption, photoluminescence (PL), and electrical measurements. PMID:27231026
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The roles of different sources of social support on emotional well-being among Chinese elderly.
Li, Haifeng; Ji, Yang; Chen, Tianyong
2014-01-01
Social support has been widely known as a protective factor for the emotional well-being (EWB) of older adults, but less studies have investigated the roles of different sources of social support (i.e., family and friend support) on different facets of EWB (i.e., positive affect and negative affect) simultaneously. In this study, the associations between family/friend support and positive/negative affect were investigated in a sample of 700 Chinese elderly. The EWB and social support were measured with a 12-item affective wordlist (Kahneman et al., 2004) and a self-prepared questionnaire. The results showed that (1) the order of contact frequency and mutual support followed a hierarchical order from spouse, children, to friends; (2) zero-order correlations of both family support and friend support were associated with more positive affect and less negative affect; and when compared with the relative role of family and friend support, (3) spouse (children if spouse is not available) support had greater contribution on decreasing negative affect, while friend support had greater influence on increasing positive affect, even after controlling the demographic, self-rated health and life events variables. Family and friend support play different roles on the two facets of EWB of the elderly. These results were better explained in light of the task specificity model rather than the hierarchical compensatory model. Moreover, positive affect may be enhanced by friend support (based on personal interests and selectable) rather than family support (bonded by kinship and not selectable), which added evidences to the socioemotional selectivity theory.
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The Roles of Different Sources of Social Support on Emotional Well-Being among Chinese Elderly
Li, Haifeng; Ji, Yang; Chen, Tianyong
2014-01-01
Background Social support has been widely known as a protective factor for the emotional well-being (EWB) of older adults, but less studies have investigated the roles of different sources of social support (i.e., family and friend support) on different facets of EWB (i.e., positive affect and negative affect) simultaneously. Methodology and Findings In this study, the associations between family/friend support and positive/negative affect were investigated in a sample of 700 Chinese elderly. The EWB and social support were measured with a 12-item affective wordlist (Kahneman et al., 2004) and a self-prepared questionnaire. The results showed that (1) the order of contact frequency and mutual support followed a hierarchical order from spouse, children, to friends; (2) zero-order correlations of both family support and friend support were associated with more positive affect and less negative affect; and when compared with the relative role of family and friend support, (3) spouse (children if spouse is not available) support had greater contribution on decreasing negative affect, while friend support had greater influence on increasing positive affect, even after controlling the demographic, self-rated health and life events variables. Conclusion Family and friend support play different roles on the two facets of EWB of the elderly. These results were better explained in light of the task specificity model rather than the hierarchical compensatory model. Moreover, positive affect may be enhanced by friend support (based on personal interests and selectable) rather than family support (bonded by kinship and not selectable), which added evidences to the socioemotional selectivity theory. PMID:24594546
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Ouyang, Xiaoguang; Guo, Fen
2018-04-01
Municipal wastewater discharge is widespread and one of the sources of coastal eutrophication, and is especially uncontrolled in developing and undeveloped coastal regions. Mangrove forests are natural filters of pollutants in wastewater. There are three paradigms of mangroves for municipal wastewater treatment and the selection of the optimal one is a multi-criteria decision-making problem. Combining intuitionistic fuzzy theory, the Fuzzy Delphi Method and the fuzzy analytical hierarchical process (AHP), this study develops an intuitionistic fuzzy AHP (IFAHP) method. For the Fuzzy Delphi Method, the judgments of experts and representatives on criterion weights are made by linguistic variables and quantified by intuitionistic fuzzy theory, which is also used to weight the importance of experts and representatives. This process generates the entropy weights of criteria, which are combined with indices values and weights to rank the alternatives by the fuzzy AHP method. The IFAHP method was used to select the optimal paradigm of mangroves for treating municipal wastewater. The entropy weights were entrained by the valid evaluation of 64 experts and representatives via online survey. Natural mangroves were found to be the optimal paradigm for municipal wastewater treatment. By assigning different weights to the criteria, sensitivity analysis shows that natural mangroves remain to be the optimal paradigm under most scenarios. This study stresses the importance of mangroves for wastewater treatment. Decision-makers need to contemplate mangrove reforestation projects, especially where mangroves are highly deforested but wastewater discharge is uncontrolled. The IFAHP method is expected to be applied in other multi-criteria decision-making cases. Copyright © 2017 Elsevier Ltd. All rights reserved.
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NASA Astrophysics Data System (ADS)
Western, A. W.; Lintern, A.; Liu, S.; Ryu, D.; Webb, J. A.; Leahy, P.; Wilson, P.; Waters, D.; Bende-Michl, U.; Watson, M.
2016-12-01
Many streams, lakes and estuaries are experiencing increasing concentrations and loads of nutrient and sediments. Models that can predict the spatial and temporal variability in water quality of aquatic systems are required to help guide the management and restoration of polluted aquatic systems. We propose that a Bayesian hierarchical modelling framework could be used to predict water quality responses over varying spatial and temporal scales. Stream water quality data and spatial data of catchment characteristics collected throughout Victoria and Queensland (in Australia) over two decades will be used to develop this Bayesian hierarchical model. In this paper, we present the preliminary exploratory data analysis required for the development of the Bayesian hierarchical model. Specifically, we present the results of exploratory data analysis of Total Nitrogen (TN) concentrations in rivers in Victoria (in South-East Australia) to illustrate the catchment characteristics that appear to be influencing spatial variability in (1) mean concentrations of TN; and (2) the relationship between discharge and TN throughout the state. These important catchment characteristics were identified using: (1) monthly TN concentrations measured at 28 water quality gauging stations and (2) climate, land use, topographic and geologic characteristics of the catchments of these 28 sites. Spatial variability in TN concentrations had a positive correlation to fertiliser use in the catchment and average temperature. There were negative correlations between TN concentrations and catchment forest cover, annual runoff, runoff perenniality, soil erosivity and catchment slope. The relationship between discharge and TN concentrations showed spatial variability, possibly resulting from climatic and topographic differences between the sites. The results of this study will feed into the hierarchical Bayesian model of river water quality.
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Spatio-temporal interpolation of precipitation during monsoon periods in Pakistan
NASA Astrophysics Data System (ADS)
Hussain, Ijaz; Spöck, Gunter; Pilz, Jürgen; Yu, Hwa-Lung
2010-08-01
Spatio-temporal estimation of precipitation over a region is essential to the modeling of hydrologic processes for water resources management. The changes of magnitude and space-time heterogeneity of rainfall observations make space-time estimation of precipitation a challenging task. In this paper we propose a Box-Cox transformed hierarchical Bayesian multivariate spatio-temporal interpolation method for the skewed response variable. The proposed method is applied to estimate space-time monthly precipitation in the monsoon periods during 1974-2000, and 27-year monthly average precipitation data are obtained from 51 stations in Pakistan. The results of transformed hierarchical Bayesian multivariate spatio-temporal interpolation are compared to those of non-transformed hierarchical Bayesian interpolation by using cross-validation. The software developed by [11] is used for Bayesian non-stationary multivariate space-time interpolation. It is observed that the transformed hierarchical Bayesian method provides more accuracy than the non-transformed hierarchical Bayesian method.
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Evaluating the Impacts of ICT Use: A Multi-Level Analysis with Hierarchical Linear Modeling
ERIC Educational Resources Information Center
Song, Hae-Deok; Kang, Taehoon
2012-01-01
The purpose of this study is to evaluate the impacts of ICT use on achievements by considering not only ICT use, but also the process and background variables that influence ICT use at both the student- and school-level. This study was conducted using data from the 2010 Survey of Seoul Education Longitudinal Research. A Hierarchical Linear…
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ERIC Educational Resources Information Center
Liu, Xing
2008-01-01
The purpose of this study was to illustrate the use of Hierarchical Linear Models (HLM) to investigate the effects of school and children's attributes on children' reading achievement. In particular, this study was designed to: (1) develop the HLM models to determine the effects of school-level and child-level variables on children's reading…
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Chad Babcock; Andrew O. Finley; John B. Bradford; Randy Kolka; Richard Birdsey; Michael G. Ryan
2015-01-01
Many studies and production inventory systems have shown the utility of coupling covariates derived from Light Detection and Ranging (LiDAR) data with forest variables measured on georeferenced inventory plots through regression models. The objective of this study was to propose and assess the use of a Bayesian hierarchical modeling framework that accommodates both...
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ERIC Educational Resources Information Center
Boker, Steven M.; McArdle, J. J.; Neale, Michael
2002-01-01
Presents an algorithm for the production of a graphical diagram from a matrix formula in such a way that its components are logically and hierarchically arranged. The algorithm, which relies on the matrix equations of J. McArdle and R. McDonald (1984), calculates the individual path components of expected covariance between variables and…
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What are hierarchical models and how do we analyze them?
Royle, Andy
2016-01-01
In this chapter we provide a basic definition of hierarchical models and introduce the two canonical hierarchical models in this book: site occupancy and N-mixture models. The former is a hierarchical extension of logistic regression and the latter is a hierarchical extension of Poisson regression. We introduce basic concepts of probability modeling and statistical inference including likelihood and Bayesian perspectives. We go through the mechanics of maximizing the likelihood and characterizing the posterior distribution by Markov chain Monte Carlo (MCMC) methods. We give a general perspective on topics such as model selection and assessment of model fit, although we demonstrate these topics in practice in later chapters (especially Chapters 5, 6, 7, and 10 Chapter 5 Chapter 6 Chapter 7 Chapter 10)
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Han, S; Humphreys, G W; Chen, L
1999-10-01
The role of perceptual grouping and the encoding of closure of local elements in the processing of hierarchical patterns was studied. Experiments 1 and 2 showed a global advantage over the local level for 2 tasks involving the discrimination of orientation and closure, but there was a local advantage for the closure discrimination task relative to the orientation discrimination task. Experiment 3 showed a local precedence effect for the closure discrimination task when local element grouping was weakened by embedding the stimuli from Experiment 1 in a background made up of cross patterns. Experiments 4A and 4B found that dissimilarity of closure between the local elements of hierarchical stimuli and the background figures could facilitate the grouping of closed local elements and enhanced the perception of global structure. Experiment 5 showed that the advantage for detecting the closure of local elements in hierarchical analysis also held under divided- and selective-attention conditions. Results are consistent with the idea that grouping between local elements takes place in parallel and competes with the computation of closure of local elements in determining the selection between global and local levels of hierarchical patterns for response.
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Cernicchiaro, N; Renter, D G; Xiang, S; White, B J; Bello, N M
2013-06-01
Variability in ADG of feedlot cattle can affect profits, thus making overall returns more unstable. Hence, knowledge of the factors that contribute to heterogeneity of variances in animal performance can help feedlot managers evaluate risks and minimize profit volatility when making managerial and economic decisions in commercial feedlots. The objectives of the present study were to evaluate heteroskedasticity, defined as heterogeneity of variances, in ADG of cohorts of commercial feedlot cattle, and to identify cattle demographic factors at feedlot arrival as potential sources of variance heterogeneity, accounting for cohort- and feedlot-level information in the data structure. An operational dataset compiled from 24,050 cohorts from 25 U. S. commercial feedlots in 2005 and 2006 was used for this study. Inference was based on a hierarchical Bayesian model implemented with Markov chain Monte Carlo, whereby cohorts were modeled at the residual level and feedlot-year clusters were modeled as random effects. Forward model selection based on deviance information criteria was used to screen potentially important explanatory variables for heteroskedasticity at cohort- and feedlot-year levels. The Bayesian modeling framework was preferred as it naturally accommodates the inherently hierarchical structure of feedlot data whereby cohorts are nested within feedlot-year clusters. Evidence for heterogeneity of variance components of ADG was substantial and primarily concentrated at the cohort level. Feedlot-year specific effects were, by far, the greatest contributors to ADG heteroskedasticity among cohorts, with an estimated ∼12-fold change in dispersion between most and least extreme feedlot-year clusters. In addition, identifiable demographic factors associated with greater heterogeneity of cohort-level variance included smaller cohort sizes, fewer days on feed, and greater arrival BW, as well as feedlot arrival during summer months. These results support that heterogeneity of variances in ADG is prevalent in feedlot performance and indicate potential sources of heteroskedasticity. Further investigation of factors associated with heteroskedasticity in feedlot performance is warranted to increase consistency and uniformity in commercial beef cattle production and subsequent profitability.
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Link, William; Sauer, John R.
2016-01-01
The analysis of ecological data has changed in two important ways over the last 15 years. The development and easy availability of Bayesian computational methods has allowed and encouraged the fitting of complex hierarchical models. At the same time, there has been increasing emphasis on acknowledging and accounting for model uncertainty. Unfortunately, the ability to fit complex models has outstripped the development of tools for model selection and model evaluation: familiar model selection tools such as Akaike's information criterion and the deviance information criterion are widely known to be inadequate for hierarchical models. In addition, little attention has been paid to the evaluation of model adequacy in context of hierarchical modeling, i.e., to the evaluation of fit for a single model. In this paper, we describe Bayesian cross-validation, which provides tools for model selection and evaluation. We describe the Bayesian predictive information criterion and a Bayesian approximation to the BPIC known as the Watanabe-Akaike information criterion. We illustrate the use of these tools for model selection, and the use of Bayesian cross-validation as a tool for model evaluation, using three large data sets from the North American Breeding Bird Survey.
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Ranking of Business Process Simulation Software Tools with DEX/QQ Hierarchical Decision Model.
Damij, Nadja; Boškoski, Pavle; Bohanec, Marko; Mileva Boshkoska, Biljana
2016-01-01
The omnipresent need for optimisation requires constant improvements of companies' business processes (BPs). Minimising the risk of inappropriate BP being implemented is usually performed by simulating the newly developed BP under various initial conditions and "what-if" scenarios. An effectual business process simulations software (BPSS) is a prerequisite for accurate analysis of an BP. Characterisation of an BPSS tool is a challenging task due to the complex selection criteria that includes quality of visual aspects, simulation capabilities, statistical facilities, quality reporting etc. Under such circumstances, making an optimal decision is challenging. Therefore, various decision support models are employed aiding the BPSS tool selection. The currently established decision support models are either proprietary or comprise only a limited subset of criteria, which affects their accuracy. Addressing this issue, this paper proposes a new hierarchical decision support model for ranking of BPSS based on their technical characteristics by employing DEX and qualitative to quantitative (QQ) methodology. Consequently, the decision expert feeds the required information in a systematic and user friendly manner. There are three significant contributions of the proposed approach. Firstly, the proposed hierarchical model is easily extendible for adding new criteria in the hierarchical structure. Secondly, a fully operational decision support system (DSS) tool that implements the proposed hierarchical model is presented. Finally, the effectiveness of the proposed hierarchical model is assessed by comparing the resulting rankings of BPSS with respect to currently available results.
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Sato, Naoyuki; Yamaguchi, Yoko
2009-06-01
The human cognitive map is known to be hierarchically organized consisting of a set of perceptually clustered landmarks. Patient studies have demonstrated that these cognitive maps are maintained by the hippocampus, while the neural dynamics are still poorly understood. The authors have shown that the neural dynamic "theta phase precession" observed in the rodent hippocampus may be capable of forming hierarchical cognitive maps in humans. In the model, a visual input sequence consisting of object and scene features in the central and peripheral visual fields, respectively, results in the formation of a hierarchical cognitive map for object-place associations. Surprisingly, it is possible for such a complex memory structure to be formed in a few seconds. In this paper, we evaluate the memory retrieval of object-place associations in the hierarchical network formed by theta phase precession. The results show that multiple object-place associations can be retrieved with the initial cue of a scene input. Importantly, according to the wide-to-narrow unidirectional connections among scene units, the spatial area for object-place retrieval can be controlled by the spatial area of the initial cue input. These results indicate that the hierarchical cognitive maps have computational advantages on a spatial-area selective retrieval of multiple object-place associations. Theta phase precession dynamics is suggested as a fundamental neural mechanism of the human cognitive map.
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Data graphing methods, articles of manufacture, and computing devices
DOE Office of Scientific and Technical Information (OSTI.GOV)
Wong, Pak Chung; Mackey, Patrick S.; Cook, Kristin A.
Data graphing methods, articles of manufacture, and computing devices are described. In one aspect, a method includes accessing a data set, displaying a graphical representation including data of the data set which is arranged according to a first of different hierarchical levels, wherein the first hierarchical level represents the data at a first of a plurality of different resolutions which respectively correspond to respective ones of the hierarchical levels, selecting a portion of the graphical representation wherein the data of the portion is arranged according to the first hierarchical level at the first resolution, modifying the graphical representation by arrangingmore » the data of the portion according to a second of the hierarchal levels at a second of the resolutions, and after the modifying, displaying the graphical representation wherein the data of the portion is arranged according to the second hierarchal level at the second resolution.« less
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Commeau, Natalie; Cornu, Marie; Albert, Isabelle; Denis, Jean-Baptiste; Parent, Eric
2012-03-01
Assessing within-batch and between-batch variability is of major interest for risk assessors and risk managers in the context of microbiological contamination of food. For example, the ratio between the within-batch variability and the between-batch variability has a large impact on the results of a sampling plan. Here, we designed hierarchical Bayesian models to represent such variability. Compatible priors were built mathematically to obtain sound model comparisons. A numeric criterion is proposed to assess the contamination structure comparing the ability of the models to replicate grouped data at the batch level using a posterior predictive loss approach. Models were applied to two case studies: contamination by Listeria monocytogenes of pork breast used to produce diced bacon and contamination by the same microorganism on cold smoked salmon at the end of the process. In the first case study, a contamination structure clearly exists and is located at the batch level, that is, between batches variability is relatively strong, whereas in the second a structure also exists but is less marked. © 2012 Society for Risk Analysis.
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NASA Astrophysics Data System (ADS)
Hu, Jinyan; Li, Li; Yang, Yunfeng
2017-06-01
The hierarchical and successive approximate registration method of non-rigid medical image based on the thin-plate splines is proposed in the paper. There are two major novelties in the proposed method. First, the hierarchical registration based on Wavelet transform is used. The approximate image of Wavelet transform is selected as the registered object. Second, the successive approximation registration method is used to accomplish the non-rigid medical images registration, i.e. the local regions of the couple images are registered roughly based on the thin-plate splines, then, the current rough registration result is selected as the object to be registered in the following registration procedure. Experiments show that the proposed method is effective in the registration process of the non-rigid medical images.
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Wei Wu; James Clark; James Vose
2010-01-01
Hierarchical Bayesian (HB) modeling allows for multiple sources of uncertainty by factoring complex relationships into conditional distributions that can be used to draw inference and make predictions. We applied an HB model to estimate the parameters and state variables of a parsimonious hydrological model â GR4J â by coherently assimilating the uncertainties from the...
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ERIC Educational Resources Information Center
Shin, Nana; Vaughn, Brian E.; Kim, Mina; Krzysik, Lisa; Bost, Kelly K.; McBride, Brent; Santos, Antonio J.; Peceguina, Ines; Coppola, Gabrielle
2011-01-01
Achieving consensus on the definition and measurement of social competence (SC) for preschool children has proven difficult in the developmental sciences. We tested a hierarchical model in which SC is assumed to be a second-order latent variable by using longitudinal data (N = 345). We also tested the degree to which peer SC at Time 1 predicted…
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Sun, Ming-Hui; Huang, Shao-Zhuan; Chen, Li-Hua; Li, Yu; Yang, Xiao-Yu; Yuan, Zhong-Yong; Su, Bao-Lian
2016-06-13
Over the last decade, significant effort has been devoted to the applications of hierarchically structured porous materials owing to their outstanding properties such as high surface area, excellent accessibility to active sites, and enhanced mass transport and diffusion. The hierarchy of porosity, structural, morphological and component levels in these materials is key for their high performance in all kinds of applications. The introduction of hierarchical porosity into materials has led to a significant improvement in the performance of materials. Herein, recent progress in the applications of hierarchically structured porous materials from energy conversion and storage, catalysis, photocatalysis, adsorption, separation, and sensing to biomedicine is reviewed. Their potential future applications are also highlighted. We particularly dwell on the relationship between hierarchically porous structures and properties, with examples of each type of hierarchically structured porous material according to its chemical composition and physical characteristics. The present review aims to open up a new avenue to guide the readers to quickly obtain in-depth knowledge of applications of hierarchically porous materials and to have a good idea about selecting and designing suitable hierarchically porous materials for a specific application. In addition to focusing on the applications of hierarchically porous materials, this comprehensive review could stimulate researchers to synthesize new advanced hierarchically porous solids.
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DOT National Transportation Integrated Search
1995-08-01
Bridge design engineers and local highway officials make bridge replacement decsions across the U.S. The Analytical Hierarchical Process was used to characterize the bridge material selection decisions of these individuals. State Departments of Trans...
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Wang, Yaokun; Yan, Mingyang
2017-01-01
Hierarchical copper shells anchored on magnetic nanoparticles were designed and fabricated to selectively deplete hemoglobin from human blood by immobilized metal affinity chromatography. Briefly, CoFe2O4 nanoparticles coated with polyacrylic acid were first synthesized by a one-pot solvothermal method. Hierarchical copper shells were then deposited by immobilizing Cu2+ on nanoparticles and subsequently by reducing between the solid CoFe2O4@COOH and copper solution with NaBH4. The resulting nanoparticles were characterized by scanning electron microscopy, transmission electron microscopy, Fourier transform infrared spectrometry, X-ray photoelectron spectroscopy, and vibrating sample magnetometry. The particles were also tested against purified bovine hemoglobin over a range of pH, contact time, and initial protein concentration. Hemoglobin adsorption followed pseudo-second-order kinetics and reached equilibrium in 90 min. Isothermal data also fit the Langmuir model well, with calculated maximum adsorption capacity 666 mg g−1. Due to the high density of Cu2+ on the shell, the nanoparticles efficiently and selectively deplete hemoglobin from human blood. Taken together, the results demonstrate that the particles with hierarchical copper shells effectively remove abundant, histidine-rich proteins, such as hemoglobin from human blood, and thereby minimize interference in diagnostic and other assays. PMID:28316987
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Improving Water Quality Assessments through a HierarchicalBayesian Analysis of Variability
Water quality measurement error and variability, while well-documented in laboratory-scale studies, is rarely acknowledged or explicitly resolved in most water body assessments, including those conducted in compliance with the United States Environmental Protection Agency (USEPA)...
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ERIC Educational Resources Information Center
Frees, Edward W.; Kim, Jee-Seon
2006-01-01
Multilevel models are proven tools in social research for modeling complex, hierarchical systems. In multilevel modeling, statistical inference is based largely on quantification of random variables. This paper distinguishes among three types of random variables in multilevel modeling--model disturbances, random coefficients, and future response…
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Bayesian dynamic modeling of time series of dengue disease case counts.
Martínez-Bello, Daniel Adyro; López-Quílez, Antonio; Torres-Prieto, Alexander
2017-07-01
The aim of this study is to model the association between weekly time series of dengue case counts and meteorological variables, in a high-incidence city of Colombia, applying Bayesian hierarchical dynamic generalized linear models over the period January 2008 to August 2015. Additionally, we evaluate the model's short-term performance for predicting dengue cases. The methodology shows dynamic Poisson log link models including constant or time-varying coefficients for the meteorological variables. Calendar effects were modeled using constant or first- or second-order random walk time-varying coefficients. The meteorological variables were modeled using constant coefficients and first-order random walk time-varying coefficients. We applied Markov Chain Monte Carlo simulations for parameter estimation, and deviance information criterion statistic (DIC) for model selection. We assessed the short-term predictive performance of the selected final model, at several time points within the study period using the mean absolute percentage error. The results showed the best model including first-order random walk time-varying coefficients for calendar trend and first-order random walk time-varying coefficients for the meteorological variables. Besides the computational challenges, interpreting the results implies a complete analysis of the time series of dengue with respect to the parameter estimates of the meteorological effects. We found small values of the mean absolute percentage errors at one or two weeks out-of-sample predictions for most prediction points, associated with low volatility periods in the dengue counts. We discuss the advantages and limitations of the dynamic Poisson models for studying the association between time series of dengue disease and meteorological variables. The key conclusion of the study is that dynamic Poisson models account for the dynamic nature of the variables involved in the modeling of time series of dengue disease, producing useful models for decision-making in public health.
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Kashuba, Roxolana; Cha, YoonKyung; Alameddine, Ibrahim; Lee, Boknam; Cuffney, Thomas F.
2010-01-01
Multilevel hierarchical modeling methodology has been developed for use in ecological data analysis. The effect of urbanization on stream macroinvertebrate communities was measured across a gradient of basins in each of nine metropolitan regions across the conterminous United States. The hierarchical nature of this dataset was harnessed in a multi-tiered model structure, predicting both invertebrate response at the basin scale and differences in invertebrate response at the region scale. Ordination site scores, total taxa richness, Ephemeroptera, Plecoptera, Trichoptera (EPT) taxa richness, and richness-weighted mean tolerance of organisms at a site were used to describe invertebrate responses. Percentage of urban land cover was used as a basin-level predictor variable. Regional mean precipitation, air temperature, and antecedent agriculture were used as region-level predictor variables. Multilevel hierarchical models were fit to both levels of data simultaneously, borrowing statistical strength from the complete dataset to reduce uncertainty in regional coefficient estimates. Additionally, whereas non-hierarchical regressions were only able to show differing relations between invertebrate responses and urban intensity separately for each region, the multilevel hierarchical regressions were able to explain and quantify those differences within a single model. In this way, this modeling approach directly establishes the importance of antecedent agricultural conditions in masking the response of invertebrates to urbanization in metropolitan regions such as Milwaukee-Green Bay, Wisconsin; Denver, Colorado; and Dallas-Fort Worth, Texas. Also, these models show that regions with high precipitation, such as Atlanta, Georgia; Birmingham, Alabama; and Portland, Oregon, start out with better regional background conditions of invertebrates prior to urbanization but experience faster negative rates of change with urbanization. Ultimately, this urbanization-invertebrate response example is used to detail the multilevel hierarchical construction methodology, showing how the result is a set of models that are both statistically more rigorous and ecologically more interpretable than simple linear regression models.
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A hierarchical Bayesian method for vibration-based time domain force reconstruction problems
NASA Astrophysics Data System (ADS)
Li, Qiaofeng; Lu, Qiuhai
2018-05-01
Traditional force reconstruction techniques require prior knowledge on the force nature to determine the regularization term. When such information is unavailable, the inappropriate term is easily chosen and the reconstruction result becomes unsatisfactory. In this paper, we propose a novel method to automatically determine the appropriate q as in ℓq regularization and reconstruct the force history. The method incorporates all to-be-determined variables such as the force history, precision parameters and q into a hierarchical Bayesian formulation. The posterior distributions of variables are evaluated by a Metropolis-within-Gibbs sampler. The point estimates of variables and their uncertainties are given. Simulations of a cantilever beam and a space truss under various loading conditions validate the proposed method in providing adaptive determination of q and better reconstruction performance than existing Bayesian methods.
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Ander, Bradley P.; Zhang, Xiaoshuai; Xue, Fuzhong; Sharp, Frank R.; Yang, Xiaowei
2013-01-01
The discovery of genetic or genomic markers plays a central role in the development of personalized medicine. A notable challenge exists when dealing with the high dimensionality of the data sets, as thousands of genes or millions of genetic variants are collected on a relatively small number of subjects. Traditional gene-wise selection methods using univariate analyses face difficulty to incorporate correlational, structural, or functional structures amongst the molecular measures. For microarray gene expression data, we first summarize solutions in dealing with ‘large p, small n’ problems, and then propose an integrative Bayesian variable selection (iBVS) framework for simultaneously identifying causal or marker genes and regulatory pathways. A novel partial least squares (PLS) g-prior for iBVS is developed to allow the incorporation of prior knowledge on gene-gene interactions or functional relationships. From the point view of systems biology, iBVS enables user to directly target the joint effects of multiple genes and pathways in a hierarchical modeling diagram to predict disease status or phenotype. The estimated posterior selection probabilities offer probabilitic and biological interpretations. Both simulated data and a set of microarray data in predicting stroke status are used in validating the performance of iBVS in a Probit model with binary outcomes. iBVS offers a general framework for effective discovery of various molecular biomarkers by combining data-based statistics and knowledge-based priors. Guidelines on making posterior inferences, determining Bayesian significance levels, and improving computational efficiencies are also discussed. PMID:23844055
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Peng, Bin; Zhu, Dianwen; Ander, Bradley P; Zhang, Xiaoshuai; Xue, Fuzhong; Sharp, Frank R; Yang, Xiaowei
2013-01-01
The discovery of genetic or genomic markers plays a central role in the development of personalized medicine. A notable challenge exists when dealing with the high dimensionality of the data sets, as thousands of genes or millions of genetic variants are collected on a relatively small number of subjects. Traditional gene-wise selection methods using univariate analyses face difficulty to incorporate correlational, structural, or functional structures amongst the molecular measures. For microarray gene expression data, we first summarize solutions in dealing with 'large p, small n' problems, and then propose an integrative Bayesian variable selection (iBVS) framework for simultaneously identifying causal or marker genes and regulatory pathways. A novel partial least squares (PLS) g-prior for iBVS is developed to allow the incorporation of prior knowledge on gene-gene interactions or functional relationships. From the point view of systems biology, iBVS enables user to directly target the joint effects of multiple genes and pathways in a hierarchical modeling diagram to predict disease status or phenotype. The estimated posterior selection probabilities offer probabilitic and biological interpretations. Both simulated data and a set of microarray data in predicting stroke status are used in validating the performance of iBVS in a Probit model with binary outcomes. iBVS offers a general framework for effective discovery of various molecular biomarkers by combining data-based statistics and knowledge-based priors. Guidelines on making posterior inferences, determining Bayesian significance levels, and improving computational efficiencies are also discussed.
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Liu, Jianli; Lughofer, Edwin; Zeng, Xianyi
2015-01-01
Modeling human aesthetic perception of visual textures is important and valuable in numerous industrial domains, such as product design, architectural design, and decoration. Based on results from a semantic differential rating experiment, we modeled the relationship between low-level basic texture features and aesthetic properties involved in human aesthetic texture perception. First, we compute basic texture features from textural images using four classical methods. These features are neutral, objective, and independent of the socio-cultural context of the visual textures. Then, we conduct a semantic differential rating experiment to collect from evaluators their aesthetic perceptions of selected textural stimuli. In semantic differential rating experiment, eights pairs of aesthetic properties are chosen, which are strongly related to the socio-cultural context of the selected textures and to human emotions. They are easily understood and connected to everyday life. We propose a hierarchical feed-forward layer model of aesthetic texture perception and assign 8 pairs of aesthetic properties to different layers. Finally, we describe the generation of multiple linear and non-linear regression models for aesthetic prediction by taking dimensionality-reduced texture features and aesthetic properties of visual textures as dependent and independent variables, respectively. Our experimental results indicate that the relationships between each layer and its neighbors in the hierarchical feed-forward layer model of aesthetic texture perception can be fitted well by linear functions, and the models thus generated can successfully bridge the gap between computational texture features and aesthetic texture properties.
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NASA Astrophysics Data System (ADS)
Bafaqeer, Abdullah; Tahir, Muhammad; Amin, Nor Aishah Saidina
2018-03-01
Hierarchical nanostructures have lately garnered enormous attention because of their remarkable performances in energy storage and catalysis applications. In this study, novel hierarchical ZnV2O6 nanosheets, formulated by one-step solvothermal method, for enhanced photocatalytic CO2 reduction with H2O to solar fuels has been investigated. The structure and properties of the catalysts were characterized by XRD, FESEM, TEM, BET, UV-vis, Raman and PL spectroscopy. The hierarchical ZnV2O6 nanosheets show excellent performance towards photoreduction of CO2 with H2O to CH3OH, CH3COOH and HCOOH under visible light. The main product yield, CH3OH of 3253.84 μmol g-cat-1 was obtained over ZnV2O6, 3.4 times the amount of CH3OH produced over the ZnO/V2O5 composite (945.28 μmol g-cat-1). In addition, CH3OH selectivity of 39.96% achieved over ZnO/V2O5, increased to 48.78% in ZnV2O6 nanosheets. This significant improvement in photo-activity over ZnV2O6 structure was due to hierarchical structure with enhanced charge separation by V2O5. The obtained ZnV2O6 hierarchical nanosheets exhibited excellent photocatalytic stability for selective CH3OH production.
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Ranking of Business Process Simulation Software Tools with DEX/QQ Hierarchical Decision Model
2016-01-01
The omnipresent need for optimisation requires constant improvements of companies’ business processes (BPs). Minimising the risk of inappropriate BP being implemented is usually performed by simulating the newly developed BP under various initial conditions and “what-if” scenarios. An effectual business process simulations software (BPSS) is a prerequisite for accurate analysis of an BP. Characterisation of an BPSS tool is a challenging task due to the complex selection criteria that includes quality of visual aspects, simulation capabilities, statistical facilities, quality reporting etc. Under such circumstances, making an optimal decision is challenging. Therefore, various decision support models are employed aiding the BPSS tool selection. The currently established decision support models are either proprietary or comprise only a limited subset of criteria, which affects their accuracy. Addressing this issue, this paper proposes a new hierarchical decision support model for ranking of BPSS based on their technical characteristics by employing DEX and qualitative to quantitative (QQ) methodology. Consequently, the decision expert feeds the required information in a systematic and user friendly manner. There are three significant contributions of the proposed approach. Firstly, the proposed hierarchical model is easily extendible for adding new criteria in the hierarchical structure. Secondly, a fully operational decision support system (DSS) tool that implements the proposed hierarchical model is presented. Finally, the effectiveness of the proposed hierarchical model is assessed by comparing the resulting rankings of BPSS with respect to currently available results. PMID:26871694
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ERIC Educational Resources Information Center
Choi, Kilchan; Seltzer, Michael
2005-01-01
In studies of change in education and numerous other fields, interest often centers on how differences in the status of individuals at the start of a time period of substantive interest relate to differences in subsequent change. This report presents a fully Bayesian approach to estimating three-level hierarchical models in which latent variable…
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Bayesian Hierarchical Random Intercept Model Based on Three Parameter Gamma Distribution
NASA Astrophysics Data System (ADS)
Wirawati, Ika; Iriawan, Nur; Irhamah
2017-06-01
Hierarchical data structures are common throughout many areas of research. Beforehand, the existence of this type of data was less noticed in the analysis. The appropriate statistical analysis to handle this type of data is the hierarchical linear model (HLM). This article will focus only on random intercept model (RIM), as a subclass of HLM. This model assumes that the intercept of models in the lowest level are varied among those models, and their slopes are fixed. The differences of intercepts were suspected affected by some variables in the upper level. These intercepts, therefore, are regressed against those upper level variables as predictors. The purpose of this paper would demonstrate a proven work of the proposed two level RIM of the modeling on per capita household expenditure in Maluku Utara, which has five characteristics in the first level and three characteristics of districts/cities in the second level. The per capita household expenditure data in the first level were captured by the three parameters Gamma distribution. The model, therefore, would be more complex due to interaction of many parameters for representing the hierarchical structure and distribution pattern of the data. To simplify the estimation processes of parameters, the computational Bayesian method couple with Markov Chain Monte Carlo (MCMC) algorithm and its Gibbs Sampling are employed.
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Sun, Junming; Karim, Ayman M; Li, Xiaohong Shari; Rainbolt, James; Kovarik, Libor; Shin, Yongsoon; Wang, Yong
2015-12-04
We report a hierarchically structured catalyst with steam reforming and hydrodeoxygenation functionalities being deposited in the micropores and macropores, respectively. The catalyst is highly efficient to upgrade the pyrolysis vapors of pine forest product residual, resulting in a dramatically decreased acid content and increased hydrocarbon yield without external H2 supply.
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Sun, Junming; Karim, Ayman M.; Li, Xiaohong S.
2015-09-29
We report a hierarchically structured catalyst with steam reforming and hydrodeoxygenation functionalities being deposited in the micropores and macropores, respectively. The catalyst is highly efficient to upgrade the pyrolysis vapors of pine forest product residual, resulting in a dramatically decreased acid content and increased hydrocarbon yield without external H2 supply.
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Hierarchical Hopping through Localized States in a Random Potential
NASA Astrophysics Data System (ADS)
Rajan, Harihar; Srivastava, Vipin
2003-03-01
Generalisation of Mott's idea on (low - temperature, large-time), Variable-range-hopping is considered to include hopping at some what higher temperature(that do not kill localization). These transitions complement the variable- range-hopping in that they do not conserve energy and occur at relatively lower time scales. The hopper picks the next state in a hierarchical fashion in accordance with certain conditions. The results are found to tie up nicely with an interesting property pertaining to the energy dependence of localized states. Acknowlwdgements: One of us(VS) would like to thank Association of Commonwealth Universities and Leverhulme Trust for financial help and to Sir Sam Edwards for hospitality at Cavendish Laboratory,Cambridge CB3 0HE.
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Optimisation by hierarchical search
NASA Astrophysics Data System (ADS)
Zintchenko, Ilia; Hastings, Matthew; Troyer, Matthias
2015-03-01
Finding optimal values for a set of variables relative to a cost function gives rise to some of the hardest problems in physics, computer science and applied mathematics. Although often very simple in their formulation, these problems have a complex cost function landscape which prevents currently known algorithms from efficiently finding the global optimum. Countless techniques have been proposed to partially circumvent this problem, but an efficient method is yet to be found. We present a heuristic, general purpose approach to potentially improve the performance of conventional algorithms or special purpose hardware devices by optimising groups of variables in a hierarchical way. We apply this approach to problems in combinatorial optimisation, machine learning and other fields.
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Vessel Classification in Cosmo-Skymed SAR Data Using Hierarchical Feature Selection
NASA Astrophysics Data System (ADS)
Makedonas, A.; Theoharatos, C.; Tsagaris, V.; Anastasopoulos, V.; Costicoglou, S.
2015-04-01
SAR based ship detection and classification are important elements of maritime monitoring applications. Recently, high-resolution SAR data have opened new possibilities to researchers for achieving improved classification results. In this work, a hierarchical vessel classification procedure is presented based on a robust feature extraction and selection scheme that utilizes scale, shape and texture features in a hierarchical way. Initially, different types of feature extraction algorithms are implemented in order to form the utilized feature pool, able to represent the structure, material, orientation and other vessel type characteristics. A two-stage hierarchical feature selection algorithm is utilized next in order to be able to discriminate effectively civilian vessels into three distinct types, in COSMO-SkyMed SAR images: cargos, small ships and tankers. In our analysis, scale and shape features are utilized in order to discriminate smaller types of vessels present in the available SAR data, or shape specific vessels. Then, the most informative texture and intensity features are incorporated in order to be able to better distinguish the civilian types with high accuracy. A feature selection procedure that utilizes heuristic measures based on features' statistical characteristics, followed by an exhaustive research with feature sets formed by the most qualified features is carried out, in order to discriminate the most appropriate combination of features for the final classification. In our analysis, five COSMO-SkyMed SAR data with 2.2m x 2.2m resolution were used to analyse the detailed characteristics of these types of ships. A total of 111 ships with available AIS data were used in the classification process. The experimental results show that this method has good performance in ship classification, with an overall accuracy reaching 83%. Further investigation of additional features and proper feature selection is currently in progress.
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Brely, Lucas; Bosia, Federico; Pugno, Nicola M
2018-06-20
Contact unit size reduction is a widely studied mechanism as a means to improve adhesion in natural fibrillar systems, such as those observed in beetles or geckos. However, these animals also display complex structural features in the way the contact is subdivided in a hierarchical manner. Here, we study the influence of hierarchical fibrillar architectures on the load distribution over the contact elements of the adhesive system, and the corresponding delamination behaviour. We present an analytical model to derive the load distribution in a fibrillar system loaded in shear, including hierarchical splitting of contacts, i.e. a "hierarchical shear-lag" model that generalizes the well-known shear-lag model used in mechanics. The influence on the detachment process is investigated introducing a numerical procedure that allows the derivation of the maximum delamination force as a function of the considered geometry, including statistical variability of local adhesive energy. Our study suggests that contact splitting generates improved adhesion only in the ideal case of extremely compliant contacts. In real cases, to produce efficient adhesive performance, contact splitting needs to be coupled with hierarchical architectures to counterbalance high load concentrations resulting from contact unit size reduction, generating multiple delamination fronts and helping to avoid detrimental non-uniform load distributions. We show that these results can be summarized in a generalized adhesion scaling scheme for hierarchical structures, proving the beneficial effect of multiple hierarchical levels. The model can thus be used to predict the adhesive performance of hierarchical adhesive structures, as well as the mechanical behaviour of composite materials with hierarchical reinforcements.
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Cook, J.; Derrida, B.
The problem of directed polymers on disordered hierarchical and hypercubic lattices is considered. For the hierarchical lattices the problem can be reduced to the study of the stable laws for combining random variables in a nonlinear way. The authors present the results of numerical simulations of two hierarchical lattices, finding evidence of a phase transition in one case. For a limiting case they extend the perturbation theory developed by Derrida and Griffiths to nonzero temperature and to higher order and use this approach to calculate thermal and geometrical properties (overlaps) of the model. In this limit they obtain an interpolationmore » formula, allowing one to obtain the noninteger moments of the partition function from the integer moments. They obtain bounds for the transition temperature for hierarchical and hypercubic lattices, and some similarities between the problem on the two different types of lattice are discussed.« less
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NASA Astrophysics Data System (ADS)
Gavish, Yoni; O'Connell, Jerome; Marsh, Charles J.; Tarantino, Cristina; Blonda, Palma; Tomaselli, Valeria; Kunin, William E.
2018-02-01
The increasing need for high quality Habitat/Land-Cover (H/LC) maps has triggered considerable research into novel machine-learning based classification models. In many cases, H/LC classes follow pre-defined hierarchical classification schemes (e.g., CORINE), in which fine H/LC categories are thematically nested within more general categories. However, none of the existing machine-learning algorithms account for this pre-defined hierarchical structure. Here we introduce a novel Random Forest (RF) based application of hierarchical classification, which fits a separate local classification model in every branching point of the thematic tree, and then integrates all the different local models to a single global prediction. We applied the hierarchal RF approach in a NATURA 2000 site in Italy, using two land-cover (CORINE, FAO-LCCS) and one habitat classification scheme (EUNIS) that differ from one another in the shape of the class hierarchy. For all 3 classification schemes, both the hierarchical model and a flat model alternative provided accurate predictions, with kappa values mostly above 0.9 (despite using only 2.2-3.2% of the study area as training cells). The flat approach slightly outperformed the hierarchical models when the hierarchy was relatively simple, while the hierarchical model worked better under more complex thematic hierarchies. Most misclassifications came from habitat pairs that are thematically distant yet spectrally similar. In 2 out of 3 classification schemes, the additional constraints of the hierarchical model resulted with fewer such serious misclassifications relative to the flat model. The hierarchical model also provided valuable information on variable importance which can shed light into "black-box" based machine learning algorithms like RF. We suggest various ways by which hierarchical classification models can increase the accuracy and interpretability of H/LC classification maps.
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Building a Lego wall: Sequential action selection.
Arnold, Amy; Wing, Alan M; Rotshtein, Pia
2017-05-01
The present study draws together two distinct lines of enquiry into the selection and control of sequential action: motor sequence production and action selection in everyday tasks. Participants were asked to build 2 different Lego walls. The walls were designed to have hierarchical structures with shared and dissociated colors and spatial components. Participants built 1 wall at a time, under low and high load cognitive states. Selection times for correctly completed trials were measured using 3-dimensional motion tracking. The paradigm enabled precise measurement of the timing of actions, while using real objects to create an end product. The experiment demonstrated that action selection was slowed at decision boundary points, relative to boundaries where no between-wall decision was required. Decision points also affected selection time prior to the actual selection window. Dual-task conditions increased selection errors. Errors mostly occurred at boundaries between chunks and especially when these required decisions. The data support hierarchical control of sequenced behavior. (PsycINFO Database Record (c) 2017 APA, all rights reserved).
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Periodic activations of behaviours and emotional adaptation in behaviour-based robotics
NASA Astrophysics Data System (ADS)
Burattini, Ernesto; Rossi, Silvia
2010-09-01
The possible modulatory influence of motivations and emotions is of great interest in designing robotic adaptive systems. In this paper, an attempt is made to connect the concept of periodic behaviour activations to emotional modulation, in order to link the variability of behaviours to the circumstances in which they are activated. The impact of emotion is studied, described as timed controlled structures, on simple but conflicting reactive behaviours. Through this approach it is shown that the introduction of such asynchronies in the robot control system may lead to an adaptation in the emergent behaviour without having an explicit action selection mechanism. The emergent behaviours of a simple robot designed with both a parallel and a hierarchical architecture are evaluated and compared.
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Bedoya, David; Manolakos, Elias S; Novotny, Vladimir
2011-03-01
Indices of Biological integrity (IBI) are considered valid indicators of the overall health of a water body because the biological community is an endpoint within natural systems. However, prediction of biological integrity using information from multi-parameter environmental observations is a challenging problem due to the hierarchical organization of the natural environment, the existence of nonlinear inter-dependencies among variables as well as natural stochasticity and measurement noise. We present a method for predicting the Fish Index of Biological Integrity (IBI) using multiple environmental observations at the state-scale in Ohio. Instream (chemical and physical quality) and offstream parameters (regional and local upstream land uses, stream fragmentation, and point source density and intensity) are used for this purpose. The IBI predictions are obtained using the environmental site-similarity concept and following a simple to implement leave-one-out cross validation approach. An IBI prediction for a sampling site is calculated by averaging the observed IBI scores of observations clustered in the most similar branch of a dendrogram--a hierarchical clustering tree of environmental observations--built using the rest of the observations. The standardized Euclidean distance is used to assess dissimilarity between observations. The constructed predictive model was able to explain 61% of the IBI variability statewide. Stream fragmentation and regional land use explained 60% of the variability; the remaining 1% was explained by instream habitat quality. Metrics related to local land use, water quality, and point source density and intensity did not improve the predictive model at the state-scale. The impact of local environmental conditions was evaluated by comparing local characteristics between well- and mispredicted sites. Significant differences in local land use patterns and upstream fragmentation density explained some of the model's over-predictions. Local land use conditions explained some of the model's IBI under-predictions at the state-scale since none of the variables within this group were included in the best final predictive model. Under-predicted sites also had higher levels of downstream fragmentation. The proposed variables ranking and predictive modeling methodology is very well suited for the analysis of hierarchical environments, such as natural fresh water systems, with many cross-correlated environmental variables. It is computationally efficient, can be fully automated, does not make any pre-conceived assumptions on the variables interdependency structure (such as linearity), and it is able to rank variables in a database and generate IBI predictions using only non-parametric easy to implement hierarchical clustering. Copyright © 2011 Elsevier Ltd. All rights reserved.
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ERIC Educational Resources Information Center
Choi, Kilchan; Seltzer, Michael
2010-01-01
In studies of change in education and numerous other fields, interest often centers on how differences in the status of individuals at the start of a period of substantive interest relate to differences in subsequent change. In this article, the authors present a fully Bayesian approach to estimating three-level Hierarchical Models in which latent…
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Mykrä, Heikki; Heino, Jani; Muotka, Timo
2004-09-01
Streams are naturally hierarchical systems, and their biota are affected by factors effective at regional to local scales. However, there have been only a few attempts to quantify variation in ecological attributes across multiple spatial scales. We examined the variation in several macroinvertebrate metrics and environmental variables at three hierarchical scales (ecoregions, drainage systems, streams) in boreal headwater streams. In nested analyses of variance, significant spatial variability was observed for most of the macroinvertebrate metrics and environmental variables examined. For most metrics, ecoregions explained more variation than did drainage systems. There was, however, much variation attributable to residuals, suggesting high among-stream variation in macroinvertebrate assemblage characteristics. Nonmetric multidimensional scaling (NMDS) and multiresponse permutation procedure (MRPP) showed that assemblage composition differed significantly among both drainage systems and ecoregions. The associated R-statistics were, however, very low, indicating wide variation among sites within the defined landscape classifications. Regional delineations explained most of the variation in stream water chemistry, ecoregions being clearly more influential than drainage systems. For physical habitat characteristics, by contrast, the among-stream component was the major source of variation. Distinct differences attributable to stream size were observed for several metrics, especially total number of taxa and abundance of algae-scraping invertebrates. Although ecoregions clearly account for a considerable amount of variation in macroinvertebrate assemblage characteristics, we suggest that a three-tiered classification system (stratification through ecoregion and habitat type, followed by assemblage prediction within these ecologically meaningful units) will be needed for effective bioassessment of boreal running waters.
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ERIC Educational Resources Information Center
Staver, John R.; Jacks, Tom
1988-01-01
Investigates the influence of five cognitive variables on high school students' performance on balancing chemical equations by inspection. Reports that reasoning, restructuring, and disembedding variables could be a single variable, and that working memory capacity does not influence overall performance. Results of hierarchical regression analysis…
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ERIC Educational Resources Information Center
Raykov, Tenko; Marcoulides, George A.
2015-01-01
A latent variable modeling procedure that can be used to evaluate intraclass correlation coefficients in two-level settings with discrete response variables is discussed. The approach is readily applied when the purpose is to furnish confidence intervals at prespecified confidence levels for these coefficients in setups with binary or ordinal…
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Biomarkers of Progression after HIV Acute/Early Infection: Nothing Compares to CD4+ T-cell Count?
Ghiglione, Yanina; Hormanstorfer, Macarena; Coloccini, Romina; Salido, Jimena; Trifone, César; Ruiz, María Julia; Falivene, Juliana; Caruso, María Paula; Figueroa, María Inés; Salomón, Horacio; Giavedoni, Luis D.; Pando, María de los Ángeles; Gherardi, María Magdalena; Rabinovich, Roberto Daniel; Sued, Omar
2018-01-01
Progression of HIV infection is variable among individuals, and definition disease progression biomarkers is still needed. Here, we aimed to categorize the predictive potential of several variables using feature selection methods and decision trees. A total of seventy-five treatment-naïve subjects were enrolled during acute/early HIV infection. CD4+ T-cell counts (CD4TC) and viral load (VL) levels were determined at enrollment and for one year. Immune activation, HIV-specific immune response, Human Leukocyte Antigen (HLA) and C-C chemokine receptor type 5 (CCR5) genotypes, and plasma levels of 39 cytokines were determined. Data were analyzed by machine learning and non-parametric methods. Variable hierarchization was performed by Weka correlation-based feature selection and J48 decision tree. Plasma interleukin (IL)-10, interferon gamma-induced protein (IP)-10, soluble IL-2 receptor alpha (sIL-2Rα) and tumor necrosis factor alpha (TNF-α) levels correlated directly with baseline VL, whereas IL-2, TNF-α, fibroblast growth factor (FGF)-2 and macrophage inflammatory protein (MIP)-1β correlated directly with CD4+ T-cell activation (p < 0.05). However, none of these cytokines had good predictive values to distinguish “progressors” from “non-progressors”. Similarly, immune activation, HIV-specific immune responses and HLA/CCR5 genotypes had low discrimination power. Baseline CD4TC was the most potent discerning variable with a cut-off of 438 cells/μL (accuracy = 0.93, κ-Cohen = 0.85). Limited discerning power of the other factors might be related to frequency, variability and/or sampling time. Future studies based on decision trees to identify biomarkers of post-treatment control are warrantied. PMID:29342870
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Biomarkers of Progression after HIV Acute/Early Infection: Nothing Compares to CD4⁺ T-cell Count?
Turk, Gabriela; Ghiglione, Yanina; Hormanstorfer, Macarena; Laufer, Natalia; Coloccini, Romina; Salido, Jimena; Trifone, César; Ruiz, María Julia; Falivene, Juliana; Holgado, María Pía; Caruso, María Paula; Figueroa, María Inés; Salomón, Horacio; Giavedoni, Luis D; Pando, María de Los Ángeles; Gherardi, María Magdalena; Rabinovich, Roberto Daniel; Pury, Pedro A; Sued, Omar
2018-01-13
Progression of HIV infection is variable among individuals, and definition disease progression biomarkers is still needed. Here, we aimed to categorize the predictive potential of several variables using feature selection methods and decision trees. A total of seventy-five treatment-naïve subjects were enrolled during acute/early HIV infection. CD4⁺ T-cell counts (CD4TC) and viral load (VL) levels were determined at enrollment and for one year. Immune activation, HIV-specific immune response, Human Leukocyte Antigen (HLA) and C-C chemokine receptor type 5 (CCR5) genotypes, and plasma levels of 39 cytokines were determined. Data were analyzed by machine learning and non-parametric methods. Variable hierarchization was performed by Weka correlation-based feature selection and J48 decision tree. Plasma interleukin (IL)-10, interferon gamma-induced protein (IP)-10, soluble IL-2 receptor alpha (sIL-2Rα) and tumor necrosis factor alpha (TNF-α) levels correlated directly with baseline VL, whereas IL-2, TNF-α, fibroblast growth factor (FGF)-2 and macrophage inflammatory protein (MIP)-1β correlated directly with CD4⁺ T-cell activation ( p < 0.05). However, none of these cytokines had good predictive values to distinguish "progressors" from "non-progressors". Similarly, immune activation, HIV-specific immune responses and HLA/CCR5 genotypes had low discrimination power. Baseline CD4TC was the most potent discerning variable with a cut-off of 438 cells/μL (accuracy = 0.93, κ-Cohen = 0.85). Limited discerning power of the other factors might be related to frequency, variability and/or sampling time. Future studies based on decision trees to identify biomarkers of post-treatment control are warrantied.
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ERIC Educational Resources Information Center
Hall, John S.
This review analyzes the trend in educational decision making to replace hierarchical authority structures with more rational models for decision making drawn from management science. Emphasis is also placed on alternatives to a hierarchical decision-making model, including governing models, union models, and influence models. A 54-item…
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The Revised Hierarchical Model: A Critical Review and Assessment
ERIC Educational Resources Information Center
Kroll, Judith F.; van Hell, Janet G.; Tokowicz, Natasha; Green, David W.
2010-01-01
Brysbaert and Duyck (this issue) suggest that it is time to abandon the Revised Hierarchical Model (Kroll and Stewart, 1994) in favor of connectionist models such as BIA+ (Dijkstra and Van Heuven, 2002) that more accurately account for the recent evidence on non-selective access in bilingual word recognition. In this brief response, we first…
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ERIC Educational Resources Information Center
Brysbaert, Marc; Duyck, Wouter
2010-01-01
The Revised Hierarchical Model (RHM) of bilingual language processing dominates current thinking on bilingual language processing. Recently, basic tenets of the model have been called into question. First, there is little evidence for separate lexicons. Second, there is little evidence for language selective access. Third, the inclusion of…
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Chen, Sijia; Zhang, Lin; Zhang, Zhao; Qian, Gang; Liu, Zongjian; Cui, Qun; Wang, Haiyan
2018-06-06
UiO-66 (UiO for University of Oslo), is a zirconium-based MOF with reverse shape selectivity, gives an alternative way to produce high purity n-heptane used for the manufacture of high-purity pharmaceuticals. Couple of studies have shown that UiO-66 gives a high selectivity on the separation of n-/iso-alkanes. However, the microporous structure of UiO-66 causes poor mass transport during the desorption process. In this work, hierarchical-pore UiO-66 (H-UiO-66) was synthesized and utilized as an adsorbent of n-heptane (nHEP) and methyl cyclohexane (MCH) for systematically studying the desorption process of n/iso-alkanes. A suite of physical methods, including XRD patterns verified the UiO-66 structures and HRTEM showed the existence of hierarchical pores. N2 adsorption-desorption isotherms further confirmed the size distribution of hierarchical pores in H-UiO-66. Of particular note, the MCH/nHEP selectivity of H-UiO-66 is similar with UiO-66 in the same adsorption conditions, the desorption process of nHEP/MCH from H-UiO-66 is dramatically enhanced, viz, the desorption rates for nHEP/MCH from H-UiO-66 is enhanced by 30%/23% as comparing to UiO-66 at most. Moreover, desorption activation energy (Ed) derived from temperature-programmed desorption (TPD) experiments indicate that the Ed for nHEP/MCH is lower on H-UiO-66, i.e., the Ed of MCH on H-UiO-66 is ~37% lower than that on UiO-66 at most, leading to a milder condition for the desorption process. The introduction of hierarchical structures will be applicable for the optimization of desorption process during separation on porous materials.
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Fattore, Gisel Lorena; Santos, Carlos Antonio de Souza Teles; Barreto, Mauricio Lima
2015-11-01
The prevalence of asthma is high in urban areas of many Latin-American countries where societies show high levels of inequality and different levels of development. This study aimed to examine the relationship between asthma symptoms prevalence in adolescents living in Latin American urban centers and socioeconomic and environmental determinants measured at the ecological level. Asthma prevalence symptoms were obtained from the International Study of Asthma and Allergies in Childhood (ISAAC) phase III. A hierarchical conceptual framework was defined and the explanatory variables were organized in three levels: distal, intermediate, proximal. Linear regression models weighed by sample size were undertaken between asthma prevalence and the selected variables. Asthma prevalence was positively associated with Gini index, water supply and homicide rate, and inversely associated with the Human Development Index, crowding and adequate sanitation. This study provides evidence of the potential influence of poverty and social inequalities on current wheezing in adolescents in a complex social context like Latin America.
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Liu, T.; Gezari, S.; Burgett, W.
We present a systematic search for periodically varying quasars and supermassive black hole binary (SMBHB) candidates in the Pan-STARRS1 (PS1) Medium Deep Survey’s MD09 field. From a color-selected sample of 670 quasars extracted from a multi-band deep-stack catalog of point sources, we locally select variable quasars and look for coherent periods with the Lomb–Scargle periodogram. Three candidates from our sample demonstrate strong variability for more than ∼3 cycles, and their PS1 light curves are well fitted to sinusoidal functions. We test the persistence of the candidates’ apparent periodic variations detected during the 4.2 years of the PS1 survey with archivalmore » photometric data from the SDSS Stripe 82 survey or new monitoring with the Large Monolithic Imager at the Discovery Channel Telescope. None of the three periodic candidates (including PSO J334.2028+1.4075) remain persistent over the extended baseline of 7–14 years, corresponding to a detection rate of <1 in 670 quasars in a search area of ≈5 deg{sup 2}. Even though SMBHBs should be a common product of the hierarchal growth of galaxies, and periodic variability in SMBHBs has been theoretically predicted, a systematic search for such signatures in a large optical survey is strongly limited by its temporal baseline and the “red noise” associated with normal quasar variability. We show that follow-up long-term monitoring (≳5 cycles) is crucial to our search for these systems.« less
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Selecting habitat to survive: the impact of road density on survival in a large carnivore.
Basille, Mathieu; Van Moorter, Bram; Herfindal, Ivar; Martin, Jodie; Linnell, John D C; Odden, John; Andersen, Reidar; Gaillard, Jean-Michel
2013-01-01
Habitat selection studies generally assume that animals select habitat and food resources at multiple scales to maximise their fitness. However, animals sometimes prefer habitats of apparently low quality, especially when considering the costs associated with spatially heterogeneous human disturbance. We used spatial variation in human disturbance, and its consequences on lynx survival, a direct fitness component, to test the Hierarchical Habitat Selection hypothesis from a population of Eurasian lynx Lynx lynx in southern Norway. Data from 46 lynx monitored with telemetry indicated that a high proportion of forest strongly reduced the risk of mortality from legal hunting at the home range scale, while increasing road density strongly increased such risk at the finer scale within the home range. We found hierarchical effects of the impact of human disturbance, with a higher road density at a large scale reinforcing its negative impact at a fine scale. Conversely, we demonstrated that lynx shifted their habitat selection to avoid areas with the highest road densities within their home ranges, thus supporting a compensatory mechanism at fine scale enabling lynx to mitigate the impact of large-scale disturbance. Human impact, positively associated with high road accessibility, was thus a stronger driver of lynx space use at a finer scale, with home range characteristics nevertheless constraining habitat selection. Our study demonstrates the truly hierarchical nature of habitat selection, which aims at maximising fitness by selecting against limiting factors at multiple spatial scales, and indicates that scale-specific heterogeneity of the environment is driving individual spatial behaviour, by means of trade-offs across spatial scales.
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Yue, Yuanyuan; Liu, Haiyan; Yuan, Pei; Yu, Chengzhong; Bao, Xiaojun
2015-01-01
Iron-modified ZSM-5 zeolites (FeZSM-5s) have been considered to be a promising catalyst system to reduce nitrogen oxide emissions, one of the most important global environmental issues, but their synthesis faces enormous economic and environmental challenges. Herein we report a cheap and green strategy to fabricate hierarchical FeZSM-5 zeolites from natural aluminosilicate minerals via a nanoscale depolymerization-reorganization method. Our strategy is featured by neither using any aluminum-, silicon-, or iron-containing inorganic chemical nor involving any mesoscale template and any post-synthetic modification. Compared with the conventional FeZSM-5 synthesized from inorganic chemicals with the similar Fe content, the resulting hierarchical FeZSM-5 with highly-dispersed iron species showed superior catalytic activity in the selective catalytic reduction of NO by NH3. PMID:25791958
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Kinematic gait patterns in healthy runners: A hierarchical cluster analysis.
Phinyomark, Angkoon; Osis, Sean; Hettinga, Blayne A; Ferber, Reed
2015-11-05
Previous studies have demonstrated distinct clusters of gait patterns in both healthy and pathological groups, suggesting that different movement strategies may be represented. However, these studies have used discrete time point variables and usually focused on only one specific joint and plane of motion. Therefore, the first purpose of this study was to determine if running gait patterns for healthy subjects could be classified into homogeneous subgroups using three-dimensional kinematic data from the ankle, knee, and hip joints. The second purpose was to identify differences in joint kinematics between these groups. The third purpose was to investigate the practical implications of clustering healthy subjects by comparing these kinematics with runners experiencing patellofemoral pain (PFP). A principal component analysis (PCA) was used to reduce the dimensionality of the entire gait waveform data and then a hierarchical cluster analysis (HCA) determined group sets of similar gait patterns and homogeneous clusters. The results show two distinct running gait patterns were found with the main between-group differences occurring in frontal and sagittal plane knee angles (P<0.001), independent of age, height, weight, and running speed. When these two groups were compared to PFP runners, one cluster exhibited greater while the other exhibited reduced peak knee abduction angles (P<0.05). The variability observed in running patterns across this sample could be the result of different gait strategies. These results suggest care must be taken when selecting samples of subjects in order to investigate the pathomechanics of injured runners. Copyright © 2015 Elsevier Ltd. All rights reserved.
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2012-01-01
Background Childhood depression affects the morbidity, mortality and life functions of children. Individual, family and environmental factors have been documented as psychosocial risk factors for childhood depression, especially family violence, which results in inadequate support, low family cohesion and poor communication. This study investigates the association between psychosocial depression factors in low-income schoolchildren and reveals the potential trouble spots, highlighting several forms of violence that take place within the family context. Methods The study was based on a cross-sectional analysis of 464 schoolchildren aged between 6 and 10, selected by random sampling from a city in the state of Rio de Janeiro, Brazil. Socio-economic, family and individual variables were investigated on the strength of the caregivers’ information and organized in blocks for analysis. A binary logistic regression model was applied, according to hierarchical blocks. Results The final hierarchical regression analysis showed that the following variables are potential psychosocial factors associated with depression in childhood: average/poor relationship with the father (OR 3.24, 95% CI 1.32-7.94), high frequency of victimization by psychological violence (humiliation) (OR 6.13, 95% CI 2.06-18.31), parental divorce (OR 2.89, 95% CI 1.14-7.32) and externalizing behavior problems (OR 3.53 IC 1.51-8.23). Conclusions The results point to multiple determinants of depressive behavior in children, as well as the potential contribution of psychological family violence. The study also reveals potential key targets for early intervention, especially for children from highly vulnerable families. PMID:22776354
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Developmental Screening Referrals: Child and Family Factors that Predict Referral Completion
ERIC Educational Resources Information Center
Jennings, Danielle J.; Hanline, Mary Frances
2013-01-01
This study researched the predictive impact of developmental screening results and the effects of child and family characteristics on completion of referrals given for evaluation. Logistical and hierarchical logistic regression analyses were used to determine the significance of 10 independent variables on the predictor variable. The number of…
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Centering Effects in HLM Level-1 Predictor Variables.
ERIC Educational Resources Information Center
Schumacker, Randall E.; Bembry, Karen
Research has suggested that important research questions can be addressed with meaningful interpretations using hierarchical linear modeling (HLM). The proper interpretation of results, however, is invariably linked to the choice of centering for the Level-1 predictor variables that produce the outcome measure for the Level-2 regression analysis.…
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Turkson, Anthony Joe; Otchey, James Eric
2015-01-14
Various psychosocial studies on health related lifestyles lay emphasis on the fact that the perception one has of himself as being at risk of HIV/AIDS infection was a necessary condition for preventive behaviors to be adopted. Hierarchical Multiple Regression models was used to examine the relationship between eight independent variables and one dependent variable to isolate predictors which have significant influence on behavior and sexual practices. A Cross-sectional design was used for the study. Structured close-ended interviewer-administered questionnaire was used to collect primary data. Multistage stratified technique was used to sample views from 380 students from Takoradi Polytechnic, Ghana. A Hierarchical multiple regression model was used to ascertain the significance of certain predictors of sexual behavior and practices. The variables that were extracted from the multiple regression were; for the constant; Beta=14.202, t=2.279, p=0.023, variable is significant; for the marital status; Beta=0.092, t=1.996, p<0.05, variable is significant; for the knowledge on AIDs; Beta=0.090, t=1.996, p<0.05, variable is significant; for the attitude towards HIV/AIDs; =0.486, t=10.575, p<0.001, variable is highly significant. Thus, the best fitting model for predicting behavior and sexual practices was a linear combination of the constant, one's marital status, knowledge on HIV/AIDs and Attitude towards HIV/AIDs., Y(Behavior and sexual practies)= Beta0+Beta1(Marital status)+Beta2(Knowledge on HIV/AIDs issues)+Beta3(Attitude towards HIV/AIDs issues) Beta0, Beta1, Beta2 and Beta3 are respectively 14.201, 2.038, 0.148 and 0.486; the higher the better. Attitude and behavior change education on HIV/AIDs should be intensified in the institution so that students could adopt better lifestyles.
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Shared neural coding for social hierarchy and reward value in primate amygdala.
Munuera, Jérôme; Rigotti, Mattia; Salzman, C Daniel
2018-03-01
The social brain hypothesis posits that dedicated neural systems process social information. In support of this, neurophysiological data have shown that some brain regions are specialized for representing faces. It remains unknown, however, whether distinct anatomical substrates also represent more complex social variables, such as the hierarchical rank of individuals within a social group. Here we show that the primate amygdala encodes the hierarchical rank of individuals in the same neuronal ensembles that encode the rewards associated with nonsocial stimuli. By contrast, orbitofrontal and anterior cingulate cortices lack strong representations of hierarchical rank while still representing reward values. These results challenge the conventional view that dedicated neural systems process social information. Instead, information about hierarchical rank-which contributes to the assessment of the social value of individuals within a group-is linked in the amygdala to representations of rewards associated with nonsocial stimuli.
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Aquino, Karl; Tripp, Thomas M; Bies, Robert J
2006-05-01
A field study and an experimental study examined relationships among organizational variables and various responses of victims to perceived wrongdoing. Both studies showed that procedural justice climate moderates the effect of organizational variables on the victim's revenge, forgiveness, reconciliation, or avoidance behaviors. In Study 1, a field study, absolute hierarchical status enhanced forgiveness and reconciliation, but only when perceptions of procedural justice climate were high; relative hierarchical status increased revenge, but only when perceptions of procedural justice climate were low. In Study 2, a laboratory experiment, victims were less likely to endorse vengeance or avoidance depending on the type of wrongdoing, but only when perceptions of procedural justice climate were high.
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Matsuoka, Takeshi; Tanaka, Shigenori; Ebina, Kuniyoshi
2014-03-01
We propose a hierarchical reduction scheme to cope with coupled rate equations that describe the dynamics of multi-time-scale photosynthetic reactions. To numerically solve nonlinear dynamical equations containing a wide temporal range of rate constants, we first study a prototypical three-variable model. Using a separation of the time scale of rate constants combined with identified slow variables as (quasi-)conserved quantities in the fast process, we achieve a coarse-graining of the dynamical equations reduced to those at a slower time scale. By iteratively employing this reduction method, the coarse-graining of broadly multi-scale dynamical equations can be performed in a hierarchical manner. We then apply this scheme to the reaction dynamics analysis of a simplified model for an illuminated photosystem II, which involves many processes of electron and excitation-energy transfers with a wide range of rate constants. We thus confirm a good agreement between the coarse-grained and fully (finely) integrated results for the population dynamics. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.
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Improved Model Fitting for the Empirical Green's Function Approach Using Hierarchical Models
NASA Astrophysics Data System (ADS)
Van Houtte, Chris; Denolle, Marine
2018-04-01
Stress drops calculated from source spectral studies currently show larger variability than what is implied by empirical ground motion models. One of the potential origins of the inflated variability is the simplified model-fitting techniques used in most source spectral studies. This study examines a variety of model-fitting methods and shows that the choice of method can explain some of the discrepancy. The preferred method is Bayesian hierarchical modeling, which can reduce bias, better quantify uncertainties, and allow additional effects to be resolved. Two case study earthquakes are examined, the 2016 MW7.1 Kumamoto, Japan earthquake and a MW5.3 aftershock of the 2016 MW7.8 Kaikōura earthquake. By using hierarchical models, the variation of the corner frequency, fc, and the falloff rate, n, across the focal sphere can be retrieved without overfitting the data. Other methods commonly used to calculate corner frequencies may give substantial biases. In particular, if fc was calculated for the Kumamoto earthquake using an ω-square model, the obtained fc could be twice as large as a realistic value.
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Hierarchical organization in the temporal structure of infant-direct speech and song.
Falk, Simone; Kello, Christopher T
2017-06-01
Caregivers alter the temporal structure of their utterances when talking and singing to infants compared with adult communication. The present study tested whether temporal variability in infant-directed registers serves to emphasize the hierarchical temporal structure of speech. Fifteen German-speaking mothers sang a play song and told a story to their 6-months-old infants, or to an adult. Recordings were analyzed using a recently developed method that determines the degree of nested clustering of temporal events in speech. Events were defined as peaks in the amplitude envelope, and clusters of various sizes related to periods of acoustic speech energy at varying timescales. Infant-directed speech and song clearly showed greater event clustering compared with adult-directed registers, at multiple timescales of hundreds of milliseconds to tens of seconds. We discuss the relation of this newly discovered acoustic property to temporal variability in linguistic units and its potential implications for parent-infant communication and infants learning the hierarchical structures of speech and language. Copyright © 2017 Elsevier B.V. All rights reserved.
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Chen, Zhe; Cao, Minhua, E-mail: caomh@bit.edu.cn; Key Laboratory of Cluster Science, Ministry of Education of China, Department of Chemistry, Beijing Institute of Technology, Beijing 100081
Research highlights: {yields} Novel Bi{sub 2}S{sub 3} hierarchical nanostructures self-assembled by nanorods are successfully synthesized in mild benzyl alcohol system under hydrothermal conditions. {yields} The hierarchical nanostructures exhibit a flower-like shape. {yields} PVP plays an important role for the formation of the hierarchical nanostructures. {yields} Bi{sub 2}S{sub 3} film prepared from the flower-like hierarchical nanostructures exhibits good hydrophobic properties. -- Abstract: Novel Bi{sub 2}S{sub 3} hierarchical nanostructures self-assembled by nanorods are successfully synthesized in mild benzyl alcohol system under hydrothermal conditions. The hierarchical nanostructures exhibit a flower-like shape. X-ray diffraction (XRD), X-ray photoelectron spectra (XPS), scanning electron microscopy (SEM), transmissionmore » electron microscopy (TEM), high-resolution transmission electron microscopy (HRTEM), and selected area electron diffraction (SAED) were used to characterize the as-synthesized samples. Meanwhile, the effect of various experimental parameters including the concentration of reagents and reaction time on final product has been investigated. In our experiment, PVP plays an important role for the formation of the hierarchical nanostructures and the possible mechanism was proposed. In addition, Bi{sub 2}S{sub 3} film prepared from the flower-like hierarchical nanostructures exhibits good hydrophobic properties, which may bring nontrivial functionalities and may have some promising applications in the future.« less
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Activity recognition using dynamic multiple sensor fusion in body sensor networks.
Gao, Lei; Bourke, Alan K; Nelson, John
2012-01-01
Multiple sensor fusion is a main research direction for activity recognition. However, there are two challenges in those systems: the energy consumption due to the wireless transmission and the classifier design because of the dynamic feature vector. This paper proposes a multi-sensor fusion framework, which consists of the sensor selection module and the hierarchical classifier. The sensor selection module adopts the convex optimization to select the sensor subset in real time. The hierarchical classifier combines the Decision Tree classifier with the Naïve Bayes classifier. The dataset collected from 8 subjects, who performed 8 scenario activities, was used to evaluate the proposed system. The results show that the proposed system can obviously reduce the energy consumption while guaranteeing the recognition accuracy.
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Conversion of methanol to propylene over hierarchical HZSM-5: the effect of Al spatial distribution.
Li, Jianwen; Ma, Hongfang; Chen, Yan; Xu, Zhiqiang; Li, Chunzhong; Ying, Weiyong
2018-06-08
Different silicon sources caused diverse Al spatial distribution in HZSM-5, and this affected the hierarchical structures and catalytic performance of desilicated zeolites. After being treated with 0.1 M NaOH, HZSM-5 zeolites synthesized with silica sol exhibited relatively widely distributed mesopores and channels, and possessed highly improved propylene selectivity and activity stability.
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Pan, Yang; Hou, Zhaohui; Yi, Wei; Zhu, Wei; Zeng, Fanyan; Liu, You-Nian
2015-08-15
Hierarchical hybrid films of MnO2 nanoparticles/multi-walled fullerene nanotubes-graphene (MNPs/MWFNTs-GS) have been prepared via a simple wet-chemical method. For this purpose, MWFNTs (~300nm in length) are fabricated from tailoring multi-walled carbon nanotubes (MWCNTs), and then inserted into GS to pile up into a hierarchical hybrid film with the in situ formative MNPs. Scanning electron microscope, transmission electron microscope and X-ray diffraction are used to confirm the morphology and structure of the as-obtained film. The electrochemical studies reveal that MNPs/MWFNTs-GS exhibit significantly enhanced electrocatalytic activity compared with MNPs/GS, and show a rapid response to H2O2 over a wide linear range of 2.0μM-8.44mM with a high sensitivity of 206.3μA mM(-1)cm(-2) and an excellent selectivity. These favorable electrochemical detection properties may be mainly attributed to the introduction of MWFNTs, which helps to promote the electron/ion transport between MNPs and GS and form the hierarchical film structure. Copyright © 2015 Elsevier B.V. All rights reserved.
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Sun, Xiaoqiang; Xian, Huifang; Tian, Shuo; Sun, Tingzhe; Qin, Yunfei; Zhang, Shoutao; Cui, Jun
2016-07-08
RIG-I is an essential receptor in the initiation of the type I interferon (IFN) signaling pathway upon viral infection. Although K63-linked ubiquitination plays an important role in RIG-I activation, the optimal modulation of conjugated and unanchored ubiquitination of RIG-I as well as its functional implications remains unclear. In this study, we determined that, in contrast to the RIG-I CARD domain, full-length RIG-I must undergo K63-linked ubiquitination at multiple sites to reach full activity. A systems biology approach was designed based on experiments using full-length RIG-I. Model selection for 7 candidate mechanisms of RIG-I ubiquitination inferred a hierarchical architecture of the RIG-I ubiquitination mode, which was then experimentally validated. Compared with other mechanisms, the selected hierarchical mechanism exhibited superior sensitivity and robustness in RIG-I-induced type I IFN activation. Furthermore, our model analysis and experimental data revealed that TRIM4 and TRIM25 exhibited dose-dependent synergism. These results demonstrated that the hierarchical mechanism of multi-site/type ubiquitination of RIG-I provides an efficient, robust and optimal synergistic regulatory module in antiviral immune responses.
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Sun, Xiaoqiang; Xian, Huifang; Tian, Shuo; Sun, Tingzhe; Qin, Yunfei; Zhang, Shoutao; Cui, Jun
2016-01-01
RIG-I is an essential receptor in the initiation of the type I interferon (IFN) signaling pathway upon viral infection. Although K63-linked ubiquitination plays an important role in RIG-I activation, the optimal modulation of conjugated and unanchored ubiquitination of RIG-I as well as its functional implications remains unclear. In this study, we determined that, in contrast to the RIG-I CARD domain, full-length RIG-I must undergo K63-linked ubiquitination at multiple sites to reach full activity. A systems biology approach was designed based on experiments using full-length RIG-I. Model selection for 7 candidate mechanisms of RIG-I ubiquitination inferred a hierarchical architecture of the RIG-I ubiquitination mode, which was then experimentally validated. Compared with other mechanisms, the selected hierarchical mechanism exhibited superior sensitivity and robustness in RIG-I-induced type I IFN activation. Furthermore, our model analysis and experimental data revealed that TRIM4 and TRIM25 exhibited dose-dependent synergism. These results demonstrated that the hierarchical mechanism of multi-site/type ubiquitination of RIG-I provides an efficient, robust and optimal synergistic regulatory module in antiviral immune responses. PMID:27387525
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NASA Astrophysics Data System (ADS)
Sun, Xiaoqiang; Xian, Huifang; Tian, Shuo; Sun, Tingzhe; Qin, Yunfei; Zhang, Shoutao; Cui, Jun
2016-07-01
RIG-I is an essential receptor in the initiation of the type I interferon (IFN) signaling pathway upon viral infection. Although K63-linked ubiquitination plays an important role in RIG-I activation, the optimal modulation of conjugated and unanchored ubiquitination of RIG-I as well as its functional implications remains unclear. In this study, we determined that, in contrast to the RIG-I CARD domain, full-length RIG-I must undergo K63-linked ubiquitination at multiple sites to reach full activity. A systems biology approach was designed based on experiments using full-length RIG-I. Model selection for 7 candidate mechanisms of RIG-I ubiquitination inferred a hierarchical architecture of the RIG-I ubiquitination mode, which was then experimentally validated. Compared with other mechanisms, the selected hierarchical mechanism exhibited superior sensitivity and robustness in RIG-I-induced type I IFN activation. Furthermore, our model analysis and experimental data revealed that TRIM4 and TRIM25 exhibited dose-dependent synergism. These results demonstrated that the hierarchical mechanism of multi-site/type ubiquitination of RIG-I provides an efficient, robust and optimal synergistic regulatory module in antiviral immune responses.
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Patterned Diblock Co-Polymer Thin Films as Templates for Advanced Anisotropic Metal Nanostructures.
Roth, Stephan V; Santoro, Gonzalo; Risch, Johannes F H; Yu, Shun; Schwartzkopf, Matthias; Boese, Torsten; Döhrmann, Ralph; Zhang, Peng; Besner, Bastian; Bremer, Philipp; Rukser, Dieter; Rübhausen, Michael A; Terrill, Nick J; Staniec, Paul A; Yao, Yuan; Metwalli, Ezzeldin; Müller-Buschbaum, Peter
2015-06-17
We demonstrate glancing-angle deposition of gold on a nanostructured diblock copolymer, namely polystyrene-block-poly(methyl methacrylate) thin film. Exploiting the selective wetting of gold on the polystyrene block, we are able to fabricate directional hierarchical structures. We prove the asymmetric growth of the gold nanoparticles and are able to extract the different growth laws by in situ scattering methods. The optical anisotropy of these hierarchical hybrid materials is further probed by angular resolved spectroscopic methods. This approach enables us to tailor functional hierarchical layers in nanodevices, such as nanoantennae arrays, organic photovoltaics, and sensor electronics.
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What software tools can I use to view ERBE HDF data products?
Atmospheric Science Data Center
2014-12-08
Visualize ERBE data with view_hdf: view_hdf a visualization and analysis tool for accessing data stored in Hierarchical Data Format (HDF) and HDF-EOS. ... Start HDFView Select File Select Open Select the file to be viewed ERBE: Data Access ...
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Boccolini, Cristiano Siqueira; de Carvalho, Márcia Lazaro; de Oliveira, Maria Inês Couto
2015-01-01
ABSTRACT OBJECTIVE To identify factors associated with exclusive breastfeeding in the first six months of life in Brazil. METHODS Systematic review of epidemiological studies conducted in Brazil with exclusive breastfeeding as outcome. Medline and LILACS databases were used. After the selection of articles, a hierarchical theoretical model was proposed according to the proximity of the variable to the outcome. RESULTS Of the 67 articles identified, we selected 20 cross-sectional studies and seven cohort studies, conducted between 1998 and 2010, comprising 77,866 children. We identified 36 factors associated with exclusive breastfeeding, being more often associated the distal factors: place of residence, maternal age and education, and the proximal factors: maternal labor, age of the child, use of a pacifier, and financing of primary health care. CONCLUSIONS The theoretical model developed may contribute to future research, and factors associated with exclusive breastfeeding may subsidize public policies on health and nutrition. PMID:26759970
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NASA Astrophysics Data System (ADS)
Weber, K. C.; Honório, K. M.; da Silva, S. L.; Mercadante, R.; da Silva, A. B. F.
In the present study, the aim was to select electronic properties responsible for free radical scavenging ability of a set of 25 flavonoid compounds employing chemometric methods. Electronic parameters were calculated using the AM1 semiempirical method, and chemometric methods (principal component analysis, hierarchical cluster analysis, and k-nearest neighbor) were used with the aim to build models able to find relationships between electronic features and the antioxidant activity presented by the compounds studied. According to these models, four electronic variables can be considered important to discriminate more and less antioxidant flavonoid compounds: polarizability (α), charge at carbon 3 (QC3), total charge at substituent 5 (QS5), and total charge at substituent 3' (QS3'). The features found as being responsible for the antioxidant activity of the flavonoid compounds studied are consistent with previous results found in the literature. The results obtained can also bring improvements in the search for better antioxidant flavonoid compounds.
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Tucker, Jalie A.; Reed, Geoffrey M.
2008-01-01
This paper examines the utility of evidentiary pluralism, a research strategy that selects methods in service of content questions, in the context of rehabilitation psychology. Hierarchical views that favor randomized controlled clinical trials (RCTs) over other evidence are discussed, and RCTs are considered as they intersect with issues in the field. RCTs are vital for establishing treatment efficacy, but whether they are uniformly the best evidence to inform practice is critically evaluated. We argue that because treatment is only one of several variables that influence functioning, disability, and participation over time, an expanded set of conceptual and data analytic approaches should be selected in an informed way to support an expanded research agenda that investigates therapeutic and extra-therapeutic influences on rehabilitation processes and outcomes. The benefits of evidentiary pluralism are considered, including helping close the gap between the narrower clinical rehabilitation model and a public health disability model. KEY WORDS: evidence-based practice, evidentiary pluralism, rehabilitation psychology, randomized controlled trials PMID:19649150
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NASA Astrophysics Data System (ADS)
Shiklomanov, A. N.; Cowdery, E.; Dietze, M.
2016-12-01
Recent syntheses of global trait databases have revealed that although the functional diversity among plant species is immense, this diversity is constrained by trade-offs between plant strategies. However, the use of among-trait and trait-environment correlations at the global scale for both qualitative ecological inference and land surface modeling has several important caveats. An alternative approach is to preserve the existing PFT-based model structure while using statistical analyses to account for uncertainty and variability in model parameters. In this study, we used a hierarchical Bayesian model of foliar traits in the TRY database to test the following hypotheses: (1) Leveraging the covariance between foliar traits will significantly constrain our uncertainty in their distributions; and (2) Among-trait covariance patterns are significantly different among and within PFTs, reflecting differences in trade-offs associated with biome-level evolution, site-level community assembly, and individual-level ecophysiological acclimation. We found that among-trait covariance significantly constrained estimates of trait means, and the additional information provided by across-PFT covariance led to more constraint still, especially for traits and PFTs with low sample sizes. We also found that among-trait correlations were highly variable among PFTs, and were generally inconsistent with correlations within PFTs. The hierarchical multivariate framework developed in our study can readily be enhanced with additional levels of hierarchy to account for geographic, species, and individual-level variability.
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ERIC Educational Resources Information Center
Tchumtchoua, Sylvie; Dey, Dipak K.
2012-01-01
This paper proposes a semiparametric Bayesian framework for the analysis of associations among multivariate longitudinal categorical variables in high-dimensional data settings. This type of data is frequent, especially in the social and behavioral sciences. A semiparametric hierarchical factor analysis model is developed in which the…
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ERIC Educational Resources Information Center
Strayhorn, Terrell L.
2010-01-01
Using data from the National Education Longitudinal Study (NELS;1988/2000), the author conducted hierarchical linear regression analyses, with a nested design, to estimate the influence of affective variables--parent involvement, teacher perceptions, and school environments--on Black students' math achievement in grade 10. Drawing on…
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Students' Self-Regulation for Interaction with Others in Online Learning Environments
ERIC Educational Resources Information Center
Cho, Moon-Heum; Kim, B. Joon
2013-01-01
The purpose of this study was to explore variables explaining students' self-regulation (SR) for interaction with others, specifically peers and instructors, in online learning environments. A total of 407 students participated in the study. With hierarchical regression model (HRM), several variables were regressed on students' SR for interaction…
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Association between prolonged breast-feeding and early childhood caries: a hierarchical approach.
Nunes, Ana Margarida Melo; Alves, Claudia Maria Coelho; Borba de Araújo, Fernando; Ortiz, Tânia Mara Lopes; Ribeiro, Marizélia Rodrigues Costa; Silva, Antônio Augusto Moura da; Ribeiro, Cecília Claudia Costa
2012-12-01
This study was conducted to investigate the association between prolonged breastfeeding and early childhood caries(ECC) with adjustment for important confounders, using hieraschical approach. This retrospective cohort study involved 260 low-income children (18-42 months). The number of decayed teeth was used as a measure of caries. Following a theoretical framework, the hierarchical model was built in a forward fashion, by adding the following levels in succession: level 1: age; level 2: social variables; level 3: health variables; level 4: behavioral variables; level 5: oral hygiene-related variables; level 6: oral hygiene quality measured by visible plaque; and level 7: contamination by mutans streptococci. Sequential forward multiple Poisson regression analysis was employed. Breast-feeding was not a risk factor for ECC after adjustment for some confounders (incidence density ratio, 1.15; 95% confidence interval, 0.84-1.59, P = 0.363). Prolonged breast-feeding was not a risk factor for ECC while age, high sucrose comption between main meals and the quality of oral higiene were associated with disease in children. © 2012 John Wiley & Sons A/S.
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Scarduelli, Lucia; Giacchini, Roberto; Parenti, Paolo; Migliorati, Sonia; Di Brisco, Agnese Maria; Vighi, Marco
2017-11-01
Biomarkers are widely used in ecotoxicology as indicators of exposure to toxicants. However, their ability to provide ecologically relevant information remains controversial. One of the major problems is understanding whether the measured responses are determined by stress factors or lie within the natural variability range. In a previous work, the natural variability of enzymatic levels in invertebrates sampled in pristine rivers was proven to be relevant across both space and time. In the present study, the experimental design was improved by considering different life stages of the selected taxa and by measuring more environmental parameters. The experimental design considered sampling sites in 2 different rivers, 8 sampling dates covering the whole seasonal cycle, 4 species from 3 different taxonomic groups (Plecoptera, Perla grandis; Ephemeroptera, Baetis alpinus and Epeorus alpicula; Tricoptera, Hydropsyche pellucidula), different life stages for each species, and 4 enzymes (acetylcholinesterase, glutathione S-transferase, alkaline phosphatase, and catalase). Biomarker levels were related to environmental (physicochemical) parameters to verify any kind of dependence. Data were statistically elaborated using hierarchical multilevel Bayesian models. Natural variability was found to be relevant across both space and time. The results of the present study proved that care should be paid when interpreting biomarker results. Further research is needed to better understand the dependence of the natural variability on environmental parameters. Environ Toxicol Chem 2017;36:3158-3167. © 2017 SETAC. © 2017 SETAC.
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Multi-scale habitat selection modeling: A review and outlook
Kevin McGarigal; Ho Yi Wan; Kathy A. Zeller; Brad C. Timm; Samuel A. Cushman
2016-01-01
Scale is the lens that focuses ecological relationships. Organisms select habitat at multiple hierarchical levels and at different spatial and/or temporal scales within each level. Failure to properly address scale dependence can result in incorrect inferences in multi-scale habitat selection modeling studies.
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On the application of multilevel modeling in environmental and ecological studies
Qian, Song S.; Cuffney, Thomas F.; Alameddine, Ibrahim; McMahon, Gerard; Reckhow, Kenneth H.
2010-01-01
This paper illustrates the advantages of a multilevel/hierarchical approach for predictive modeling, including flexibility of model formulation, explicitly accounting for hierarchical structure in the data, and the ability to predict the outcome of new cases. As a generalization of the classical approach, the multilevel modeling approach explicitly models the hierarchical structure in the data by considering both the within- and between-group variances leading to a partial pooling of data across all levels in the hierarchy. The modeling framework provides means for incorporating variables at different spatiotemporal scales. The examples used in this paper illustrate the iterative process of model fitting and evaluation, a process that can lead to improved understanding of the system being studied.
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Hierarchic models for laminated plates. Ph.D. Thesis
NASA Technical Reports Server (NTRS)
Actis, Ricardo Luis
1991-01-01
Structural plates and shells are three-dimensional bodies, one dimension of which happens to be much smaller than the other two. Thus, the quality of a plate or shell model must be judged on the basis of how well its exact solution approximates the corresponding three-dimensional problem. Of course, the exact solution depends not only on the choice of the model but also on the topology, material properties, loading and constraints. The desired degree of approximation depends on the analyst's goals in performing the analysis. For these reasons models have to be chosen adaptively. Hierarchic sequences of models make adaptive selection of the model which is best suited for the purposes of a particular analysis possible. The principles governing the formulation of hierarchic models for laminated plates are presented. The essential features of the hierarchic models described models are: (1) the exact solutions corresponding to the hierarchic sequence of models converge to the exact solution of the corresponding problem of elasticity for a fixed laminate thickness; and (2) the exact solution of each model converges to the same limit as the exact solution of the corresponding problem of elasticity with respect to the laminate thickness approaching zero. The formulation is based on one parameter (beta) which characterizes the hierarchic sequence of models, and a set of constants whose influence was assessed by a numerical sensitivity study. The recommended selection of these constants results in the number of fields increasing by three for each increment in the power of beta. Numerical examples analyzed with the proposed sequence of models are included and good correlation with the reference solutions was found. Results were obtained for laminated strips (plates in cylindrical bending) and for square and rectangular plates with uniform loading and with homogeneous boundary conditions. Cross-ply and angle-ply laminates were evaluated and the results compared with those of MSC/PROBE. Hierarchic models make the computation of any engineering data possible to an arbitrary level of precision within the framework of the theory of elasticity.
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ERIC Educational Resources Information Center
Usta, H. Gonca
2016-01-01
This study aims to analyze the student and school level variables that affect students' self-efficacy levels in mathematics in China-Shanghai, Turkey, and Greece based on PISA 2012 results. In line with this purpose, the hierarchical linear regression model (HLM) was employed. The interschool variability is estimated at approximately 17% in…
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Drivers of Variability in Public-Supply Water Use Across the Contiguous United States
NASA Astrophysics Data System (ADS)
Worland, Scott C.; Steinschneider, Scott; Hornberger, George M.
2018-03-01
This study explores the relationship between municipal water use and an array of climate, economic, behavioral, and policy variables across the contiguous U.S. The relationship is explored using Bayesian-hierarchical regression models for over 2,500 counties, 18 covariates, and three higher-level grouping variables. Additionally, a second analysis is included for 83 cities where water price and water conservation policy information is available. A hierarchical model using the nine climate regions (product of National Oceanic and Atmospheric Administration) as the higher-level groups results in the best out-of-sample performance, as estimated by the Widely Available Information Criterion, compared to counties grouped by urban continuum classification or primary economic activity. The regression coefficients indicate that the controls on water use are not uniform across the nation: e.g., counties in the Northeast and Northwest climate regions are more sensitive to social variables, whereas counties in the Southwest and East North Central climate regions are more sensitive to environmental variables. For the national city-level model, it appears that arid cities with a high cost of living and relatively low water bills sell more water per customer, but as with the county-level model, the effect of each variable depends heavily on where a city is located.
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2010-01-01
Background Cluster analysis, and in particular hierarchical clustering, is widely used to extract information from gene expression data. The aim is to discover new classes, or sub-classes, of either individuals or genes. Performing a cluster analysis commonly involve decisions on how to; handle missing values, standardize the data and select genes. In addition, pre-processing, involving various types of filtration and normalization procedures, can have an effect on the ability to discover biologically relevant classes. Here we consider cluster analysis in a broad sense and perform a comprehensive evaluation that covers several aspects of cluster analyses, including normalization. Result We evaluated 2780 cluster analysis methods on seven publicly available 2-channel microarray data sets with common reference designs. Each cluster analysis method differed in data normalization (5 normalizations were considered), missing value imputation (2), standardization of data (2), gene selection (19) or clustering method (11). The cluster analyses are evaluated using known classes, such as cancer types, and the adjusted Rand index. The performances of the different analyses vary between the data sets and it is difficult to give general recommendations. However, normalization, gene selection and clustering method are all variables that have a significant impact on the performance. In particular, gene selection is important and it is generally necessary to include a relatively large number of genes in order to get good performance. Selecting genes with high standard deviation or using principal component analysis are shown to be the preferred gene selection methods. Hierarchical clustering using Ward's method, k-means clustering and Mclust are the clustering methods considered in this paper that achieves the highest adjusted Rand. Normalization can have a significant positive impact on the ability to cluster individuals, and there are indications that background correction is preferable, in particular if the gene selection is successful. However, this is an area that needs to be studied further in order to draw any general conclusions. Conclusions The choice of cluster analysis, and in particular gene selection, has a large impact on the ability to cluster individuals correctly based on expression profiles. Normalization has a positive effect, but the relative performance of different normalizations is an area that needs more research. In summary, although clustering, gene selection and normalization are considered standard methods in bioinformatics, our comprehensive analysis shows that selecting the right methods, and the right combinations of methods, is far from trivial and that much is still unexplored in what is considered to be the most basic analysis of genomic data. PMID:20937082
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NASA Astrophysics Data System (ADS)
Chowdhury, A. F. M. K.; Lockart, N.; Willgoose, G. R.; Kuczera, G. A.; Kiem, A.; Nadeeka, P. M.
2016-12-01
One of the key objectives of stochastic rainfall modelling is to capture the full variability of climate system for future drought and flood risk assessment. However, it is not clear how well these models can capture the future climate variability when they are calibrated to Global/Regional Climate Model data (GCM/RCM) as these datasets are usually available for very short future period/s (e.g. 20 years). This study has assessed the ability of two stochastic daily rainfall models to capture climate variability by calibrating them to a dynamically downscaled RCM dataset in an east Australian catchment for 1990-2010, 2020-2040, and 2060-2080 epochs. The two stochastic models are: (1) a hierarchical Markov Chain (MC) model, which we developed in a previous study and (2) a semi-parametric MC model developed by Mehrotra and Sharma (2007). Our hierarchical model uses stochastic parameters of MC and Gamma distribution, while the semi-parametric model uses a modified MC process with memory of past periods and kernel density estimation. This study has generated multiple realizations of rainfall series by using parameters of each model calibrated to the RCM dataset for each epoch. The generated rainfall series are used to generate synthetic streamflow by using a SimHyd hydrology model. Assessing the synthetic rainfall and streamflow series, this study has found that both stochastic models can incorporate a range of variability in rainfall as well as streamflow generation for both current and future periods. However, the hierarchical model tends to overestimate the multiyear variability of wet spell lengths (therefore, is less likely to simulate long periods of drought and flood), while the semi-parametric model tends to overestimate the mean annual rainfall depths and streamflow volumes (hence, simulated droughts are likely to be less severe). Sensitivity of these limitations of both stochastic models in terms of future drought and flood risk assessment will be discussed.
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Complex Patterns of Local Adaptation in Teosinte
Pyhäjärvi, Tanja; Hufford, Matthew B.; Mezmouk, Sofiane; Ross-Ibarra, Jeffrey
2013-01-01
Populations of widely distributed species encounter and must adapt to local environmental conditions. However, comprehensive characterization of the genetic basis of adaptation is demanding, requiring genome-wide genotype data, multiple sampled populations, and an understanding of population structure and potential selection pressures. Here, we used single-nucleotide polymorphism genotyping and data on numerous environmental variables to describe the genetic basis of local adaptation in 21 populations of teosinte, the wild ancestor of maize. We found complex hierarchical genetic structure created by altitude, dispersal events, and admixture among subspecies, which complicated identification of locally beneficial alleles. Patterns of linkage disequilibrium revealed four large putative inversion polymorphisms showing clinal patterns of frequency. Population differentiation and environmental correlations suggest that both inversions and intergenic polymorphisms are involved in local adaptation. PMID:23902747
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Religion and suicide acceptability: a cross-national analysis.
Stack, Steven; Kposowa, Augustine J
2011-01-01
Four perspectives (moral community thesis, religious integration, religious commitment, and social networks) guide the selection of variables in this study. Data are from the combined World Values/European Values Surveys for 2000 (50,547 individuals nested in 56 nations). The results of a multivariate hierarchical linear model support all four perspectives. Persons residing in nations with relatively high levels of religiosity, who are affiliated with one of four major faiths, are religiously committed, and are engaged with a religious network are found to be lower in suicide acceptability. The religious integration perspective, in particular, is empirically supported; affiliation with Islam is associated with low suicide acceptability. The findings provide strong support for an integrated model and demonstrate the usefulness of the moral community thesis in understanding suicide acceptability.
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Depression in non-Korean women residing in South Korea following marriage to Korean men.
Kim, Hyun-Sil; Kim, Hun-Soo
2013-06-01
The purpose of the study was to examine the roles of acculturative stress, life satisfaction, and language literacy in depression in non-Korean women residing in South Korea following marriage to Korean men. A cross-sectional study was performed, using an anonymous, self-reporting questionnaire. A total of 173 women were selected using a proportional stratified random sampling method. The relation between acculturation, depression, language literacy, life satisfaction and socio-demographic variables and the predictors of depression among participants were analyzed. The analysis included descriptive statistics and hierarchical multiple regression. Of the participants, 9.2% had depression, which was almost twice the rate of depression found in the general Korean population. In hierarchical multiple regression analysis, acculturative stress (beta=-.325, P<.001) and life satisfaction (beta=-.282, P=.003) were significantly associated with the level of depression. This final model was statistically significant and life satisfaction, acculturative stress, language literacy accounted for 31.0% (adjusted R(2)) of the variance in the depression score (P<.001). Elevated acculturative stress and less life satisfaction were significantly associated with a higher level of depression in migrant wives in Korea. Implications for practice and research are discussed. Copyright © 2013 Elsevier Inc. All rights reserved.
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Advances in Applications of Hierarchical Bayesian Methods with Hydrological Models
NASA Astrophysics Data System (ADS)
Alexander, R. B.; Schwarz, G. E.; Boyer, E. W.
2017-12-01
Mechanistic and empirical watershed models are increasingly used to inform water resource decisions. Growing access to historical stream measurements and data from in-situ sensor technologies has increased the need for improved techniques for coupling models with hydrological measurements. Techniques that account for the intrinsic uncertainties of both models and measurements are especially needed. Hierarchical Bayesian methods provide an efficient modeling tool for quantifying model and prediction uncertainties, including those associated with measurements. Hierarchical methods can also be used to explore spatial and temporal variations in model parameters and uncertainties that are informed by hydrological measurements. We used hierarchical Bayesian methods to develop a hybrid (statistical-mechanistic) SPARROW (SPAtially Referenced Regression On Watershed attributes) model of long-term mean annual streamflow across diverse environmental and climatic drainages in 18 U.S. hydrological regions. Our application illustrates the use of a new generation of Bayesian methods that offer more advanced computational efficiencies than the prior generation. Evaluations of the effects of hierarchical (regional) variations in model coefficients and uncertainties on model accuracy indicates improved prediction accuracies (median of 10-50%) but primarily in humid eastern regions, where model uncertainties are one-third of those in arid western regions. Generally moderate regional variability is observed for most hierarchical coefficients. Accounting for measurement and structural uncertainties, using hierarchical state-space techniques, revealed the effects of spatially-heterogeneous, latent hydrological processes in the "localized" drainages between calibration sites; this improved model precision, with only minor changes in regional coefficients. Our study can inform advances in the use of hierarchical methods with hydrological models to improve their integration with stream measurements.
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Concept Selection and Developmental Effects in Bilingual Speech Production
ERIC Educational Resources Information Center
Schwieter, John; Sunderman, Gretchen
2009-01-01
The present study investigates the locus of language selection in less and more proficient language learners, specifically testing differential predictions of La Heij's (2005) concept selection model (CSM) and Kroll and Stewart's (1994) revised hierarchical model (RHM). Less and more proficient English dominant learners of Spanish participated in…
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Bayesian dynamic modeling of time series of dengue disease case counts
López-Quílez, Antonio; Torres-Prieto, Alexander
2017-01-01
The aim of this study is to model the association between weekly time series of dengue case counts and meteorological variables, in a high-incidence city of Colombia, applying Bayesian hierarchical dynamic generalized linear models over the period January 2008 to August 2015. Additionally, we evaluate the model’s short-term performance for predicting dengue cases. The methodology shows dynamic Poisson log link models including constant or time-varying coefficients for the meteorological variables. Calendar effects were modeled using constant or first- or second-order random walk time-varying coefficients. The meteorological variables were modeled using constant coefficients and first-order random walk time-varying coefficients. We applied Markov Chain Monte Carlo simulations for parameter estimation, and deviance information criterion statistic (DIC) for model selection. We assessed the short-term predictive performance of the selected final model, at several time points within the study period using the mean absolute percentage error. The results showed the best model including first-order random walk time-varying coefficients for calendar trend and first-order random walk time-varying coefficients for the meteorological variables. Besides the computational challenges, interpreting the results implies a complete analysis of the time series of dengue with respect to the parameter estimates of the meteorological effects. We found small values of the mean absolute percentage errors at one or two weeks out-of-sample predictions for most prediction points, associated with low volatility periods in the dengue counts. We discuss the advantages and limitations of the dynamic Poisson models for studying the association between time series of dengue disease and meteorological variables. The key conclusion of the study is that dynamic Poisson models account for the dynamic nature of the variables involved in the modeling of time series of dengue disease, producing useful models for decision-making in public health. PMID:28671941
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Our Selections and Decisions: Inherent Features of the Nervous System?
NASA Astrophysics Data System (ADS)
Rösler, Frank
The chapter summarizes findings on the neuronal bases of decisionmaking. Taking the phenomenon of selection it will be explained that systems built only from excitatory and inhibitory neuron (populations) have the emergent property of selecting between different alternatives. These considerations suggest that there exists a hierarchical architecture with central selection switches. However, in such a system, functions of selection and decision-making are not localized, but rather emerge from an interaction of several participating networks. These are, on the one hand, networks that process specific input and output representations and, on the other hand, networks that regulate the relative activation/inhibition of the specific input and output networks. These ideas are supported by recent empirical evidence. Moreover, other studies show that rather complex psychological variables, like subjective probability estimates, expected gains and losses, prediction errors, etc., do have biological correlates, i.e., they can be localized in time and space as activation states of neural networks and single cells. These findings suggest that selections and decisions are consequences of an architecture which, seen from a biological perspective, is fully deterministic. However, a transposition of such nomothetic functional principles into the idiographic domain, i.e., using them as elements for comprehensive 'mechanistic' explanations of individual decisions, seems not to be possible because of principle limitations. Therefore, individual decisions will remain predictable by means of probabilistic models alone.
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An integrated fuzzy approach for strategic alliance partner selection in third-party logistics.
Erkayman, Burak; Gundogar, Emin; Yilmaz, Aysegul
2012-01-01
Outsourcing some of the logistic activities is a useful strategy for companies in recent years. This makes it possible for firms to concentrate on their main issues and processes and presents facility to improve logistics performance, to reduce costs, and to improve quality. Therefore provider selection and evaluation in third-party logistics become important activities for companies. Making a strategic decision like this is significantly hard and crucial. In this study we proposed a fuzzy multicriteria decision making (MCDM) approach to effectively select the most appropriate provider. First we identify the provider selection criteria and build the hierarchical structure of decision model. After building the hierarchical structure we determined the selection criteria weights by using fuzzy analytical hierarchy process (AHP) technique. Then we applied fuzzy technique for order preference by similarity to ideal solution (TOPSIS) to obtain final rankings for providers. And finally an illustrative example is also given to demonstrate the effectiveness of the proposed model.
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An Integrated Fuzzy Approach for Strategic Alliance Partner Selection in Third-Party Logistics
Gundogar, Emin; Yılmaz, Aysegul
2012-01-01
Outsourcing some of the logistic activities is a useful strategy for companies in recent years. This makes it possible for firms to concentrate on their main issues and processes and presents facility to improve logistics performance, to reduce costs, and to improve quality. Therefore provider selection and evaluation in third-party logistics become important activities for companies. Making a strategic decision like this is significantly hard and crucial. In this study we proposed a fuzzy multicriteria decision making (MCDM) approach to effectively select the most appropriate provider. First we identify the provider selection criteria and build the hierarchical structure of decision model. After building the hierarchical structure we determined the selection criteria weights by using fuzzy analytical hierarchy process (AHP) technique. Then we applied fuzzy technique for order preference by similarity to ideal solution (TOPSIS) to obtain final rankings for providers. And finally an illustrative example is also given to demonstrate the effectiveness of the proposed model. PMID:23365520
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Bioinspired Diatomite Membrane with Selective Superwettability for Oil/Water Separation.
Lo, Yu-Hsiang; Yang, Ching-Yu; Chang, Haw-Kai; Hung, Wei-Chen; Chen, Po-Yu
2017-05-03
Membranes with selective superwettability for oil/water separation have received significant attention during the past decades. Hierarchical structures and surface roughness are believed to improve the oil repellency and the stability of Cassie-Baxter state. Diatoms, unicellular photosynthetic algae, possess sophisticated skeletal shells (called frustules) which are made of hydrated silica. Motivated by the hierarchical micro- and nanoscale features of diatom, we fabricate a hierarchical diatomite membrane which consists of aligned micro-sized channels by the freeze casting process. The fine nano-porous structures of frustules are well preserved after the post sintering process. The bioinspired diatomite membrane performs both underwater superoleophobicity and superhydrophobicity under various oils. Additionally, we demonstrate the highly efficient oil/water separation capabililty of the membranes in various harsh environments. The water flux can be further adjusted by tuning the cooling rates. The eco-friendly and robust bioinspired membranes produced by the simple, cost-effective freeze casting method can be potentially applied for large scale and efficient oil/water separation.
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Distribution of cavity trees in midwestern old-growth and second-growth forests
Zhaofei Fan; Stephen R. Shifley; Martin A. Spetich; Frank R. Thompson; David R. Larsen
2003-01-01
We used classification and regression tree analysis to determine the primary variables associated with the occurrence of cavity trees and the hierarchical structure among those variables. We applied that information to develop logistic models predicting cavity tree probability as a function of diameter, species group, and decay class. Inventories of cavity abundance in...
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Distribution of cavity trees in midwesternold-growth and second-growth forests
Zhaofei Fan; Stephen R. Shifley; Martin A. Spetich; Frank R., III Thompson; David R. Larsen
2003-01-01
We used classification and regression tree analysis to determine the primary variables associated with the occurrence of cavity trees and the hierarchical structure among those variables. We applied that information to develop logistic models predicting cavity tree probability as a function of diameter, species group, and decay class. Inventories of cavity abundance in...
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ERIC Educational Resources Information Center
Blau, Gary; Drennan, Rob B.; Hochner, Arthur; Kapanjie, Darin
2016-01-01
An online survey tested the impact of background, technological, and course-related variables on perceived learning and timely graduation for a complete data sample of 263 business undergraduates taking at least one online or hybrid course in the fall of 2015. Hierarchical regression results showed that course-related variables (instructor…
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Modeling Signal-Noise Processes Supports Student Construction of a Hierarchical Image of Sample
ERIC Educational Resources Information Center
Lehrer, Richard
2017-01-01
Grade 6 (modal age 11) students invented and revised models of the variability generated as each measured the perimeter of a table in their classroom. To construct models, students represented variability as a linear composite of true measure (signal) and multiple sources of random error. Students revised models by developing sampling…
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Evans, Samuel; Davis, Matthew H.
2015-01-01
How humans extract the identity of speech sounds from highly variable acoustic signals remains unclear. Here, we use searchlight representational similarity analysis (RSA) to localize and characterize neural representations of syllables at different levels of the hierarchically organized temporo-frontal pathways for speech perception. We asked participants to listen to spoken syllables that differed considerably in their surface acoustic form by changing speaker and degrading surface acoustics using noise-vocoding and sine wave synthesis while we recorded neural responses with functional magnetic resonance imaging. We found evidence for a graded hierarchy of abstraction across the brain. At the peak of the hierarchy, neural representations in somatomotor cortex encoded syllable identity but not surface acoustic form, at the base of the hierarchy, primary auditory cortex showed the reverse. In contrast, bilateral temporal cortex exhibited an intermediate response, encoding both syllable identity and the surface acoustic form of speech. Regions of somatomotor cortex associated with encoding syllable identity in perception were also engaged when producing the same syllables in a separate session. These findings are consistent with a hierarchical account of how variable acoustic signals are transformed into abstract representations of the identity of speech sounds. PMID:26157026
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NASA Astrophysics Data System (ADS)
Hu, Jiexiang; Zhou, Qi; Jiang, Ping; Shao, Xinyu; Xie, Tingli
2018-01-01
Variable-fidelity (VF) modelling methods have been widely used in complex engineering system design to mitigate the computational burden. Building a VF model generally includes two parts: design of experiments and metamodel construction. In this article, an adaptive sampling method based on improved hierarchical kriging (ASM-IHK) is proposed to refine the improved VF model. First, an improved hierarchical kriging model is developed as the metamodel, in which the low-fidelity model is varied through a polynomial response surface function to capture the characteristics of a high-fidelity model. Secondly, to reduce local approximation errors, an active learning strategy based on a sequential sampling method is introduced to make full use of the already required information on the current sampling points and to guide the sampling process of the high-fidelity model. Finally, two numerical examples and the modelling of the aerodynamic coefficient for an aircraft are provided to demonstrate the approximation capability of the proposed approach, as well as three other metamodelling methods and two sequential sampling methods. The results show that ASM-IHK provides a more accurate metamodel at the same simulation cost, which is very important in metamodel-based engineering design problems.
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New Materials and Methods for Hierarchically Structured Tissue Scaffolds
2005-01-01
to the fabrication of hierarchically structured scaffolds. In order to achieve this goal, photopolymerizable materials must be developed that are... photopolymerizable materials that can also be selectively chemically modified during the SL part building process. This paper provides an update on our work...which uses a laser to "write" patterns into a vat containing a photopolymerizable resin. The first step in performing SL is generating a computer
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ERIC Educational Resources Information Center
De los Santos, Saturnino; Norland, Emmalou Van Tilburg
A study evaluated the cacao farmer training program in the Dominican Republic by testing hypothesized relationships among reactions, knowledge and skills, attitudes, aspirations, and some selected demographic characteristics of farmers who attended programs. Bennett's hierarchical model of program evaluation was used as the framework of the study.…
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NASA Technical Reports Server (NTRS)
Litomisky, Krystof
2012-01-01
Even though NASA's space missions are many and varied, there are some tasks that are common to all of them. For example, all spacecraft need to communicate with other entities, and all spacecraft need to know where they are. These tasks use tools and services that can be inherited and reused between missions, reducing systems engineering effort and therefore reducing cost.The Advanced Multi-Mission Operations System, or AMMOS, is a collection of multimission tools and services, whose development and maintenance are funded by NASA. I created HierarchThis, a plugin designed to provide an interactive interface to help customers identify mission-relevant tools and services. HierarchThis automatically creates diagrams of the AMMOS database, and then allows users to show/hide specific details through a graphical interface. Once customers identify tools and services they want for a specific mission, HierarchThis can automatically generate a contract between the Multimission Ground Systems and Services Office, which manages AMMOS, and the customer. The document contains the selected AMMOS components, along with their capabilities and satisfied requirements. HierarchThis reduces the time needed for the process from service selections to having a mission-specific contract from the order of days to the order of minutes.
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Control, responses and modularity of cellular regulatory networks: a control analysis perspective.
Bruggeman, F J; Snoep, J L; Westerhoff, H V
2008-11-01
Cells adapt to changes in environmental conditions through the concerted action of signalling, gene expression and metabolic subsystems. The authors will discuss a theoretical framework addressing such integrated systems. This 'hierarchical analysis' was first developed as an extension to a metabolic control analysis. It builds on the phenomenon that often the communication between signalling, gene expression and metabolic subsystems is almost exclusively via regulatory interactions and not via mass flow interactions. This allows for the treatment of the said subsystems as 'levels' in a hierarchical view of the organisation of the molecular reaction network of cells. Such a hierarchical approach has as a major advantage that levels can be analysed conceptually in isolation of each other (from a local intra-level perspective) and at a later stage integrated via their interactions (from a global inter-level perspective). Hereby, it allows for a modular approach with variable scope. A number of different approaches have been developed for the analysis of hierarchical systems, for example hierarchical control analysis and modular response analysis. The authors, here, review these methods and illustrate the strength of these types of analyses using a core model of a system with gene expression, metabolic and signal transduction levels.
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Word Order and Voice Influence the Timing of Verb Planning in German Sentence Production.
Sauppe, Sebastian
2017-01-01
Theories of incremental sentence production make different assumptions about when speakers encode information about described events and when verbs are selected, accordingly. An eye tracking experiment on German testing the predictions from linear and hierarchical incrementality about the timing of event encoding and verb planning is reported. In the experiment, participants described depictions of two-participant events with sentences that differed in voice and word order. Verb-medial active sentences and actives and passives with sentence-final verbs were compared. Linear incrementality predicts that sentences with verbs placed early differ from verb-final sentences because verbs are assumed to only be planned shortly before they are articulated. By contrast, hierarchical incrementality assumes that speakers start planning with relational encoding of the event. A weak version of hierarchical incrementality assumes that only the action is encoded at the outset of formulation and selection of lexical verbs only occurs shortly before they are articulated, leading to the prediction of different fixation patterns for verb-medial and verb-final sentences. A strong version of hierarchical incrementality predicts no differences between verb-medial and verb-final sentences because it assumes that verbs are always lexically selected early in the formulation process. Based on growth curve analyses of fixations to agent and patient characters in the described pictures, and the influence of character humanness and the lack of an influence of the visual salience of characters on speakers' choice of active or passive voice, the current results suggest that while verb planning does not necessarily occur early during formulation, speakers of German always create an event representation early.
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Fesharaki, Nooshin Jafari; Pourghassem, Hossein
2013-07-01
Due to the daily mass production and the widespread variation of medical X-ray images, it is necessary to classify these for searching and retrieving proposes, especially for content-based medical image retrieval systems. In this paper, a medical X-ray image hierarchical classification structure based on a novel merging and splitting scheme and using shape and texture features is proposed. In the first level of the proposed structure, to improve the classification performance, similar classes with regard to shape contents are grouped based on merging measures and shape features into the general overlapped classes. In the next levels of this structure, the overlapped classes split in smaller classes based on the classification performance of combination of shape and texture features or texture features only. Ultimately, in the last levels, this procedure is also continued forming all the classes, separately. Moreover, to optimize the feature vector in the proposed structure, we use orthogonal forward selection algorithm according to Mahalanobis class separability measure as a feature selection and reduction algorithm. In other words, according to the complexity and inter-class distance of each class, a sub-space of the feature space is selected in each level and then a supervised merging and splitting scheme is applied to form the hierarchical classification. The proposed structure is evaluated on a database consisting of 2158 medical X-ray images of 18 classes (IMAGECLEF 2005 database) and accuracy rate of 93.6% in the last level of the hierarchical structure for an 18-class classification problem is obtained.
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NASA Astrophysics Data System (ADS)
León, Madeleine; Escalante-Ramirez, Boris
2013-11-01
Knee osteoarthritis (OA) is characterized by the morphological degeneration of cartilage. Efficient segmentation of cartilage is important for cartilage damage diagnosis and to support therapeutic responses. We present a method for knee cartilage segmentation in magnetic resonance images (MRI). Our method incorporates the Hermite Transform to obtain a hierarchical decomposition of contours which describe knee cartilage shapes. Then, we compute a statistical model of the contour of interest from a set of training images. Thereby, our Hierarchical Active Shape Model (HASM) captures a large range of shape variability even from a small group of training samples, improving segmentation accuracy. The method was trained with a training set of 16- MRI of knee and tested with leave-one-out method.
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Establishing an efficient way to utilize the drought resistance germplasm population in wheat.
Wang, Jiancheng; Guan, Yajing; Wang, Yang; Zhu, Liwei; Wang, Qitian; Hu, Qijuan; Hu, Jin
2013-01-01
Drought resistance breeding provides a hopeful way to improve yield and quality of wheat in arid and semiarid regions. Constructing core collection is an efficient way to evaluate and utilize drought-resistant germplasm resources in wheat. In the present research, 1,683 wheat varieties were divided into five germplasm groups (high resistant, HR; resistant, R; moderate resistant, MR; susceptible, S; and high susceptible, HS). The least distance stepwise sampling (LDSS) method was adopted to select core accessions. Six commonly used genetic distances (Euclidean distance, Euclid; Standardized Euclidean distance, Seuclid; Mahalanobis distance, Mahal; Manhattan distance, Manhat; Cosine distance, Cosine; and Correlation distance, Correlation) were used to assess genetic distances among accessions. Unweighted pair-group average (UPGMA) method was used to perform hierarchical cluster analysis. Coincidence rate of range (CR) and variable rate of coefficient of variation (VR) were adopted to evaluate the representativeness of the core collection. A method for selecting the ideal constructing strategy was suggested in the present research. A wheat core collection for the drought resistance breeding programs was constructed by the strategy selected in the present research. The principal component analysis showed that the genetic diversity was well preserved in that core collection.
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NASA Astrophysics Data System (ADS)
Singh, Santosh Kumar; Ghatak Choudhuri, Sumit
2018-05-01
Parallel connection of UPS inverters to enhance power rating is a widely accepted practice. Inter-modular circulating currents appear when multiple inverter modules are connected in parallel to supply variable critical load. Interfacing of modules henceforth requires an intensive design, using proper control strategy. The potentiality of human intuitive Fuzzy Logic (FL) control with imprecise system model is well known and thus can be utilised in parallel-connected UPS systems. Conventional FL controller is computational intensive, especially with higher number of input variables. This paper proposes application of Hierarchical-Fuzzy Logic control for parallel connected Multi-modular inverters system for reduced computational burden on the processor for a given switching frequency. Simulated results in MATLAB environment and experimental verification using Texas TMS320F2812 DSP are included to demonstrate feasibility of the proposed control scheme.
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Kowal, J; Fortier, M S
2000-06-01
The purpose of this study was to test a motivational model based on Vallerand's (1997) Hierarchical Model of Intrinsic and Extrinsic Motivation. This model incorporates situational and contextual motivational variables, and was tested using a time-lagged design. Master's level swimmers (N = 104) completed a questionnaire on two separate occasions. At Time 1, situational social factors (perceptions of success and perceptions of the motivational climate), situational motivational mediators (perceptions of autonomy, competence, and relatedness), situational motivation, and flow were assessed immediately following a swim practice. Contextual measures of these same variables were assessed at Time 2, 1 week later, with the exception of flow. Results of a path analysis supported numerous links in the hypothesized model. Findings are discussed in light of research and theory on motivation and flow.
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Hierarchical Spatial Concept Formation Based on Multimodal Information for Human Support Robots.
Hagiwara, Yoshinobu; Inoue, Masakazu; Kobayashi, Hiroyoshi; Taniguchi, Tadahiro
2018-01-01
In this paper, we propose a hierarchical spatial concept formation method based on the Bayesian generative model with multimodal information e.g., vision, position and word information. Since humans have the ability to select an appropriate level of abstraction according to the situation and describe their position linguistically, e.g., "I am in my home" and "I am in front of the table," a hierarchical structure of spatial concepts is necessary in order for human support robots to communicate smoothly with users. The proposed method enables a robot to form hierarchical spatial concepts by categorizing multimodal information using hierarchical multimodal latent Dirichlet allocation (hMLDA). Object recognition results using convolutional neural network (CNN), hierarchical k-means clustering result of self-position estimated by Monte Carlo localization (MCL), and a set of location names are used, respectively, as features in vision, position, and word information. Experiments in forming hierarchical spatial concepts and evaluating how the proposed method can predict unobserved location names and position categories are performed using a robot in the real world. Results verify that, relative to comparable baseline methods, the proposed method enables a robot to predict location names and position categories closer to predictions made by humans. As an application example of the proposed method in a home environment, a demonstration in which a human support robot moves to an instructed place based on human speech instructions is achieved based on the formed hierarchical spatial concept.
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Hierarchical Spatial Concept Formation Based on Multimodal Information for Human Support Robots
Hagiwara, Yoshinobu; Inoue, Masakazu; Kobayashi, Hiroyoshi; Taniguchi, Tadahiro
2018-01-01
In this paper, we propose a hierarchical spatial concept formation method based on the Bayesian generative model with multimodal information e.g., vision, position and word information. Since humans have the ability to select an appropriate level of abstraction according to the situation and describe their position linguistically, e.g., “I am in my home” and “I am in front of the table,” a hierarchical structure of spatial concepts is necessary in order for human support robots to communicate smoothly with users. The proposed method enables a robot to form hierarchical spatial concepts by categorizing multimodal information using hierarchical multimodal latent Dirichlet allocation (hMLDA). Object recognition results using convolutional neural network (CNN), hierarchical k-means clustering result of self-position estimated by Monte Carlo localization (MCL), and a set of location names are used, respectively, as features in vision, position, and word information. Experiments in forming hierarchical spatial concepts and evaluating how the proposed method can predict unobserved location names and position categories are performed using a robot in the real world. Results verify that, relative to comparable baseline methods, the proposed method enables a robot to predict location names and position categories closer to predictions made by humans. As an application example of the proposed method in a home environment, a demonstration in which a human support robot moves to an instructed place based on human speech instructions is achieved based on the formed hierarchical spatial concept. PMID:29593521
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Generating global network structures by triad types
Ferligoj, Anuška; Žiberna, Aleš
2018-01-01
This paper addresses the question of whether one can generate networks with a given global structure (defined by selected blockmodels, i.e., cohesive, core-periphery, hierarchical, and transitivity), considering only different types of triads. Two methods are used to generate networks: (i) the newly proposed method of relocating links; and (ii) the Monte Carlo Multi Chain algorithm implemented in the ergm package in R. Most of the selected blockmodel types can be generated by considering all types of triads. The selection of only a subset of triads can improve the generated networks’ blockmodel structure. Yet, in the case of a hierarchical blockmodel without complete blocks on the diagonal, additional local structures are needed to achieve the desired global structure of generated networks. This shows that blockmodels can emerge based only on local processes that do not take attributes into account. PMID:29847563
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A hierarchical two-phase framework for selecting genes in cancer datasets with a neuro-fuzzy system.
Lim, Jongwoo; Wang, Bohyun; Lim, Joon S
2016-04-29
Finding the minimum number of appropriate biomarkers for specific targets such as a lung cancer has been a challenging issue in bioinformatics. We propose a hierarchical two-phase framework for selecting appropriate biomarkers that extracts candidate biomarkers from the cancer microarray datasets and then selects the minimum number of appropriate biomarkers from the extracted candidate biomarkers datasets with a specific neuro-fuzzy algorithm, which is called a neural network with weighted fuzzy membership function (NEWFM). In this context, as the first phase, the proposed framework is to extract candidate biomarkers by using a Bhattacharyya distance method that measures the similarity of two discrete probability distributions. Finally, the proposed framework is able to reduce the cost of finding biomarkers by not receiving medical supplements and improve the accuracy of the biomarkers in specific cancer target datasets.
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Nanowebs and nanocables of silicon carbide
NASA Astrophysics Data System (ADS)
Shim, Hyun Woo; Huang, Hanchen
2007-08-01
This paper presents two novel hierarchical structures of SiC-SiO2 core-shell nanowires: (a) nanocables in the form of multi-core and single shell and (b) nanowebs in the form of intersecting nanowires and nanocables, augmented by variable amounts of SiO2 membranes. The two structures are controllable through variations of substrate temperature and source chemistry. The hierarchical nanostructures, together with the controllability, may offer superb mechanical properties in composite applications. Finally, the authors propose a model of nanowebs and nanocables formation, as a result of nanowires intersection and alignment.
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Hierarchical image-based rendering using texture mapping hardware
DOE Office of Scientific and Technical Information (OSTI.GOV)
Max, N
1999-01-15
Multi-layered depth images containing color and normal information for subobjects in a hierarchical scene model are precomputed with standard z-buffer hardware for six orthogonal views. These are adaptively selected according to the proximity of the viewpoint, and combined using hardware texture mapping to create ''reprojected'' output images for new viewpoints. (If a subobject is too close to the viewpoint, the polygons in the original model are rendered.) Specific z-ranges are selected from the textures with the hardware alpha test to give accurate 3D reprojection. The OpenGL color matrix is used to transform the precomputed normals into their orientations in themore » final view, for hardware shading.« less
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Marjanovic, Jovana; Mulder, Han A; Khaw, Hooi L; Bijma, Piter
2016-06-10
Animal breeding programs have been very successful in improving the mean levels of traits through selection. However, in recent decades, reducing the variability of trait levels between individuals has become a highly desirable objective. Reaching this objective through genetic selection requires that there is genetic variation in the variability of trait levels, a phenomenon known as genetic heterogeneity of environmental (residual) variance. The aim of our study was to investigate the potential for genetic improvement of uniformity of harvest weight and body size traits (length, depth, and width) in the genetically improved farmed tilapia (GIFT) strain. In order to quantify the genetic variation in uniformity of traits and estimate the genetic correlations between level and variance of the traits, double hierarchical generalized linear models were applied to individual trait values. Our results showed substantial genetic variation in uniformity of all analyzed traits, with genetic coefficients of variation for residual variance ranging from 39 to 58 %. Genetic correlation between trait level and variance was strongly positive for harvest weight (0.60 ± 0.09), moderate and positive for body depth (0.37 ± 0.13), but not significantly different from 0 for body length and width. Our results on the genetic variation in uniformity of harvest weight and body size traits show good prospects for the genetic improvement of uniformity in the GIFT strain. A high and positive genetic correlation was estimated between level and variance of harvest weight, which suggests that selection for heavier fish will also result in more variation in harvest weight. Simultaneous improvement of harvest weight and its uniformity will thus require index selection.
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ERIC Educational Resources Information Center
Peters, Christina D.; Kranzler, John H.; Algina, James; Smith, Stephen W.; Daunic, Ann P.
2014-01-01
The aim of the current study was to examine mean-group differences on behavior rating scales and variables that may predict such differences. Sixty-five teachers completed the Clinical Assessment of Behavior-Teacher Form (CAB-T) for a sample of 982 students. Four outcome variables from the CAB-T were assessed. Hierarchical linear modeling was used…
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Motor-sensory confluence in tactile perception.
Saig, Avraham; Gordon, Goren; Assa, Eldad; Arieli, Amos; Ahissar, Ehud
2012-10-03
Perception involves motor control of sensory organs. However, the dynamics underlying emergence of perception from motor-sensory interactions are not yet known. Two extreme possibilities are as follows: (1) motor and sensory signals interact within an open-loop scheme in which motor signals determine sensory sampling but are not affected by sensory processing and (2) motor and sensory signals are affected by each other within a closed-loop scheme. We studied the scheme of motor-sensory interactions in humans using a novel object localization task that enabled monitoring the relevant overt motor and sensory variables. We found that motor variables were dynamically controlled within each perceptual trial, such that they gradually converged to steady values. Training on this task resulted in improvement in perceptual acuity, which was achieved solely by changes in motor variables, without any change in the acuity of sensory readout. The within-trial dynamics is captured by a hierarchical closed-loop model in which lower loops actively maintain constant sensory coding, and higher loops maintain constant sensory update flow. These findings demonstrate interchangeability of motor and sensory variables in perception, motor convergence during perception, and a consistent hierarchical closed-loop perceptual model.
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Self Assembled Structures by Directional Solidification of Eutectics
NASA Technical Reports Server (NTRS)
Dynys, Frederick W.; Sayir, Ali
2004-01-01
Interest in ordered porous structures has grown because of there unique properties such as photonic bandgaps, high backing packing density and high surface to volume ratio. Inspired by nature, biometric strategies using self assembled organic molecules dominate the development of hierarchical inorganic structures. Directional solidification of eutectics (DSE) also exhibit self assembly characteristics to form hierarchical metallic and inorganic structures. Crystallization of diphasic materials by DSE can produce two dimensional ordered structures consisting of rods or lamella. By selective removal of phases, DSE is capable to fabricate ordered pore arrays or ordered pin arrays. Criteria and limitations to fabricate hierarchical structures will be presented. Porous structures in silicon base alloys and ceramic systems will be reported.
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Mollenhauer, Robert; Brewer, Shannon K.
2017-01-01
Failure to account for variable detection across survey conditions constrains progressive stream ecology and can lead to erroneous stream fish management and conservation decisions. In addition to variable detection’s confounding long-term stream fish population trends, reliable abundance estimates across a wide range of survey conditions are fundamental to establishing species–environment relationships. Despite major advancements in accounting for variable detection when surveying animal populations, these approaches remain largely ignored by stream fish scientists, and CPUE remains the most common metric used by researchers and managers. One notable advancement for addressing the challenges of variable detection is the multinomial N-mixture model. Multinomial N-mixture models use a flexible hierarchical framework to model the detection process across sites as a function of covariates; they also accommodate common fisheries survey methods, such as removal and capture–recapture. Effective monitoring of stream-dwelling Smallmouth Bass Micropterus dolomieu populations has long been challenging; therefore, our objective was to examine the use of multinomial N-mixture models to improve the applicability of electrofishing for estimating absolute abundance. We sampled Smallmouth Bass populations by using tow-barge electrofishing across a range of environmental conditions in streams of the Ozark Highlands ecoregion. Using an information-theoretic approach, we identified effort, water clarity, wetted channel width, and water depth as covariates that were related to variable Smallmouth Bass electrofishing detection. Smallmouth Bass abundance estimates derived from our top model consistently agreed with baseline estimates obtained via snorkel surveys. Additionally, confidence intervals from the multinomial N-mixture models were consistently more precise than those of unbiased Petersen capture–recapture estimates due to the dependency among data sets in the hierarchical framework. We demonstrate the application of this contemporary population estimation method to address a longstanding stream fish management issue. We also detail the advantages and trade-offs of hierarchical population estimation methods relative to CPUE and estimation methods that model each site separately.
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Novel Two-Step Hierarchical Screening of Mutant Pools Reveals Mutants under Selection in Chicks
Yang, Hee-Jeong; Bogomolnaya, Lydia M.; Elfenbein, Johanna R.; Endicott-Yazdani, Tiana; Reynolds, M. Megan; Porwollik, Steffen; Cheng, Pui; Xia, Xiao-Qin
2016-01-01
Contaminated chicken/egg products are major sources of human salmonellosis, yet the strategies used by Salmonella to colonize chickens are poorly understood. We applied a novel two-step hierarchical procedure to identify new genes important for colonization and persistence of Salmonella enterica serotype Typhimurium in chickens. A library of 182 S. Typhimurium mutants each containing a targeted deletion of a group of contiguous genes (for a total of 2,069 genes deleted) was used to identify regions under selection at 1, 3, and 9 days postinfection in chicks. Mutants in 11 regions were under selection at all assayed times (colonization mutants), and mutants in 15 regions were under selection only at day 9 (persistence mutants). We assembled a pool of 92 mutants, each deleted for a single gene, representing nearly all genes in nine regions under selection. Twelve single gene deletion mutants were under selection in this assay, and we confirmed 6 of 9 of these candidate mutants via competitive infections and complementation analysis in chicks. STM0580, STM1295, STM1297, STM3612, STM3615, and STM3734 are needed for Salmonella to colonize and persist in chicks and were not previously associated with this ability. One of these key genes, STM1297 (selD), is required for anaerobic growth and supports the ability to utilize formate under these conditions, suggesting that metabolism of formate is important during infection. We report a hierarchical screening strategy to interrogate large portions of the genome during infection of animals using pools of mutants of low complexity. Using this strategy, we identified six genes not previously known to be needed during infection in chicks, and one of these (STM1297) suggests an important role for formate metabolism during infection. PMID:26857572
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Kannan, Palanisamy; Maiyalagan, Thandavarayan; Marsili, Enrico; Ghosh, Srabanti; Niedziolka-Jönsson, Joanna; Jönsson-Niedziolka, Martin
2016-01-14
Three-dimensional nickel-iron (3-D/Ni-Fe) nanostructures are exciting candidates for various applications because they produce more reaction-active sites than 1-D and 2-D nanostructured materials and exhibit attractive optical, electrical and catalytic properties. In this work, freestanding 3-D/Ni-Fe interconnected hierarchical nanosheets, hierarchical nanospheres, and porous nanospheres are directly grown on a flexible carbon fiber paper (CFP) substrate by a single-step hydrothermal process. Among the nanostructures, 3-D/Ni-Fe interconnected hierarchical nanosheets show excellent electrochemical properties because of its high conductivity, large specific active surface area, and mesopores on its walls (vide infra). The 3-D/Ni-Fe hierarchical nanosheet array modified CFP substrate is further explored as a novel electrode for electrochemical non-enzymatic glucose sensor application. The 3-D/Ni-Fe hierarchical nanosheet arrays exhibit significant catalytic activity towards the electrochemical oxidation of glucose, as compared to the 3-D/Ni-Fe hierarchical nanospheres, and porous nanospheres. The 3-D/Ni-Fe hierarchical nanosheet arrays can access a large amount of glucose molecules on their surface (mesopore walls) for an efficient electrocatalytic oxidation process. Moreover, 3-D/Ni-Fe hierarchical nanosheet arrays showed higher sensitivity (7.90 μA μM(-1) cm(-2)) with wide linear glucose concentration ranging from 0.05 μM to 0.2 mM, and the low detection limit (LOD) of 0.031 μM (S/N = 3) is achieved by the amperometry method. Further, the 3-D/Ni-Fe hierarchical nanosheet array modified CFP electrode can be demonstrated to have excellent selectivity towards the detection of glucose in the presence of 500-fold excess of major important interferents. All these results indicate that 3-D/Ni-Fe hierarchical nanosheet arrays are promising candidates for non-enzymatic glucose sensing.
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Estimations of population density for selected periods between the Neolithic and AD 1800.
Zimmermann, Andreas; Hilpert, Johanna; Wendt, Karl Peter
2009-04-01
Abstract We describe a combination of methods applied to obtain reliable estimations of population density using archaeological data. The combination is based on a hierarchical model of scale levels. The necessary data and methods used to obtain the results are chosen so as to define transfer functions from one scale level to another. We apply our method to data sets from western Germany that cover early Neolithic, Iron Age, Roman, and Merovingian times as well as historical data from AD 1800. Error margins and natural and historical variability are discussed. Our results for nonstate societies are always lower than conventional estimations compiled from the literature, and we discuss the reasons for this finding. At the end, we compare the calculated local and global population densities with other estimations from different parts of the world.
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A hierarchical nest survival model integrating incomplete temporally varying covariates
Converse, Sarah J; Royle, J Andrew; Adler, Peter H; Urbanek, Richard P; Barzen, Jeb A
2013-01-01
Nest success is a critical determinant of the dynamics of avian populations, and nest survival modeling has played a key role in advancing avian ecology and management. Beginning with the development of daily nest survival models, and proceeding through subsequent extensions, the capacity for modeling the effects of hypothesized factors on nest survival has expanded greatly. We extend nest survival models further by introducing an approach to deal with incompletely observed, temporally varying covariates using a hierarchical model. Hierarchical modeling offers a way to separate process and observational components of demographic models to obtain estimates of the parameters of primary interest, and to evaluate structural effects of ecological and management interest. We built a hierarchical model for daily nest survival to analyze nest data from reintroduced whooping cranes (Grus americana) in the Eastern Migratory Population. This reintroduction effort has been beset by poor reproduction, apparently due primarily to nest abandonment by breeding birds. We used the model to assess support for the hypothesis that nest abandonment is caused by harassment from biting insects. We obtained indices of blood-feeding insect populations based on the spatially interpolated counts of insects captured in carbon dioxide traps. However, insect trapping was not conducted daily, and so we had incomplete information on a temporally variable covariate of interest. We therefore supplemented our nest survival model with a parallel model for estimating the values of the missing insect covariates. We used Bayesian model selection to identify the best predictors of daily nest survival. Our results suggest that the black fly Simulium annulus may be negatively affecting nest survival of reintroduced whooping cranes, with decreasing nest survival as abundance of S. annulus increases. The modeling framework we have developed will be applied in the future to a larger data set to evaluate the biting-insect hypothesis and other hypotheses for nesting failure in this reintroduced population; resulting inferences will support ongoing efforts to manage this population via an adaptive management approach. Wider application of our approach offers promise for modeling the effects of other temporally varying, but imperfectly observed covariates on nest survival, including the possibility of modeling temporally varying covariates collected from incubating adults. PMID:24340185
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A hierarchical nest survival model integrating incomplete temporally varying covariates
Converse, Sarah J.; Royle, J. Andrew; Adler, Peter H.; Urbanek, Richard P.; Barzan, Jeb A.
2013-01-01
Nest success is a critical determinant of the dynamics of avian populations, and nest survival modeling has played a key role in advancing avian ecology and management. Beginning with the development of daily nest survival models, and proceeding through subsequent extensions, the capacity for modeling the effects of hypothesized factors on nest survival has expanded greatly. We extend nest survival models further by introducing an approach to deal with incompletely observed, temporally varying covariates using a hierarchical model. Hierarchical modeling offers a way to separate process and observational components of demographic models to obtain estimates of the parameters of primary interest, and to evaluate structural effects of ecological and management interest. We built a hierarchical model for daily nest survival to analyze nest data from reintroduced whooping cranes (Grus americana) in the Eastern Migratory Population. This reintroduction effort has been beset by poor reproduction, apparently due primarily to nest abandonment by breeding birds. We used the model to assess support for the hypothesis that nest abandonment is caused by harassment from biting insects. We obtained indices of blood-feeding insect populations based on the spatially interpolated counts of insects captured in carbon dioxide traps. However, insect trapping was not conducted daily, and so we had incomplete information on a temporally variable covariate of interest. We therefore supplemented our nest survival model with a parallel model for estimating the values of the missing insect covariates. We used Bayesian model selection to identify the best predictors of daily nest survival. Our results suggest that the black fly Simulium annulus may be negatively affecting nest survival of reintroduced whooping cranes, with decreasing nest survival as abundance of S. annulus increases. The modeling framework we have developed will be applied in the future to a larger data set to evaluate the biting-insect hypothesis and other hypotheses for nesting failure in this reintroduced population; resulting inferences will support ongoing efforts to manage this population via an adaptive management approach. Wider application of our approach offers promise for modeling the effects of other temporally varying, but imperfectly observed covariates on nest survival, including the possibility of modeling temporally varying covariates collected from incubating adults.
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Diffusion of aromatic hydrocarbons in hierarchical mesoporous H-ZSM-5 zeolite
Bu, Lintao; Nimlos, Mark R.; Robichaud, David J.; ...
2018-02-08
Hierarchical mesoporous zeolites exhibit higher catalytic activities and longer lifetime compared to the traditional microporous zeolites due to improved diffusivity of substrate molecules and their enhanced access to the zeolite active sites. Understanding diffusion of biomass pyrolysis vapors and their upgraded products in such materials is fundamentally important during catalytic fast pyrolysis (CFP) of lignocellulosic biomass, since diffusion makes major contribution to determine shape selectivity and product distribution. However, diffusivities of biomass relevant species in hierarchical mesoporous zeolites are poorly characterized, primarily due to the limitations of the available experimental technology. In this work, molecular dynamics (MD) simulations are utilizedmore » to investigate the diffusivities of several selected coke precursor molecules, benzene, naphthalene, and anthracene, in hierarchical mesoporous H-ZSM-5 zeolite. The effects of temperature and size of mesopores on the diffusivity of the chosen model compounds are examined. The simulation results demonstrate that diffusion within the microspores as well as on the external surface of mesoporous H-ZSM-5 dominates only at low temperature. At pyrolysis relevant temperatures, mass transfer is essentially conducted via diffusion along the mesopores. Additionally, the results illustrate the heuristic diffusion model, such as the extensively used Knudsen diffusion, overestimates the diffusion of bulky molecules in the mesopores, thus making MD simulation a powerful and compulsory approach to explore diffusion in zeolites.« less
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Edmands, William M B; Barupal, Dinesh K; Scalbert, Augustin
2015-03-01
MetMSLine represents a complete collection of functions in the R programming language as an accessible GUI for biomarker discovery in large-scale liquid-chromatography high-resolution mass spectral datasets from acquisition through to final metabolite identification forming a backend to output from any peak-picking software such as XCMS. MetMSLine automatically creates subdirectories, data tables and relevant figures at the following steps: (i) signal smoothing, normalization, filtration and noise transformation (PreProc.QC.LSC.R); (ii) PCA and automatic outlier removal (Auto.PCA.R); (iii) automatic regression, biomarker selection, hierarchical clustering and cluster ion/artefact identification (Auto.MV.Regress.R); (iv) Biomarker-MS/MS fragmentation spectra matching and fragment/neutral loss annotation (Auto.MS.MS.match.R) and (v) semi-targeted metabolite identification based on a list of theoretical masses obtained from public databases (DBAnnotate.R). All source code and suggested parameters are available in an un-encapsulated layout on http://wmbedmands.github.io/MetMSLine/. Readme files and a synthetic dataset of both X-variables (simulated LC-MS data), Y-variables (simulated continuous variables) and metabolite theoretical masses are also available on our GitHub repository. © The Author 2014. Published by Oxford University Press.
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Edmands, William M. B.; Barupal, Dinesh K.; Scalbert, Augustin
2015-01-01
Summary: MetMSLine represents a complete collection of functions in the R programming language as an accessible GUI for biomarker discovery in large-scale liquid-chromatography high-resolution mass spectral datasets from acquisition through to final metabolite identification forming a backend to output from any peak-picking software such as XCMS. MetMSLine automatically creates subdirectories, data tables and relevant figures at the following steps: (i) signal smoothing, normalization, filtration and noise transformation (PreProc.QC.LSC.R); (ii) PCA and automatic outlier removal (Auto.PCA.R); (iii) automatic regression, biomarker selection, hierarchical clustering and cluster ion/artefact identification (Auto.MV.Regress.R); (iv) Biomarker—MS/MS fragmentation spectra matching and fragment/neutral loss annotation (Auto.MS.MS.match.R) and (v) semi-targeted metabolite identification based on a list of theoretical masses obtained from public databases (DBAnnotate.R). Availability and implementation: All source code and suggested parameters are available in an un-encapsulated layout on http://wmbedmands.github.io/MetMSLine/. Readme files and a synthetic dataset of both X-variables (simulated LC–MS data), Y-variables (simulated continuous variables) and metabolite theoretical masses are also available on our GitHub repository. Contact: ScalbertA@iarc.fr PMID:25348215
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Design Rules for Tailoring Antireflection Properties of Hierarchical Optical Structures
Leon, Juan J. Diaz; Hiszpanski, Anna M.; Bond, Tiziana C.; ...
2017-05-18
Hierarchical structures consisting of small sub-wavelength features stacked atop larger structures have been demonstrated as an effective means of reducing the reflectance of surfaces. However, optical devices require different antireflective properties depending on the application, and general unifying guidelines on hierarchical structures' design to attain a desired antireflection spectral response are still lacking. The type of reflectivity (diffuse, specular, or total/hemispherical) and its angular- and spectral-dependence are all dictated by the structural parameters. Through computational and experimental studies, guidelines have been devised to modify these various aspects of reflectivity across the solar spectrum by proper selection of the features ofmore » hierarchical structures. In this wavelength regime, micrometer-scale substructures dictate the long-wavelength spectral response and effectively reduce specular reflectance, whereas nanometer-scale substructures dictate primarily the visible wavelength spectral response and reduce diffuse reflectance. Coupling structures having these two length scales into hierarchical arrays impressively reduces surfaces' hemispherical reflectance across a broad spectrum of wavelengths and angles. Furthermore, such hierarchical structures in silicon are demonstrated having an average total reflectance across the solar spectrum of 1.1% (average weighted reflectance of 1% in the 280–2500 nm range of the AM 1.5 G spectrum) and specular reflectance <1% even at angles of incidence as high as 67°.« less
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Hierarchical competitions subserving multi-attribute choice
Hunt, Laurence T; Dolan, Raymond J; Behrens, Timothy EJ
2015-01-01
Valuation is a key tenet of decision neuroscience, where it is generally assumed that different attributes of competing options are assimilated into unitary values. Such values are central to current neural models of choice. By contrast, psychological studies emphasize complex interactions between choice and valuation. Principles of neuronal selection also suggest competitive inhibition may occur in early valuation stages, before option selection. Here, we show behavior in multi-attribute choice is best explained by a model involving competition at multiple levels of representation. This hierarchical model also explains neural signals in human brain regions previously linked to valuation, including striatum, parietal and prefrontal cortex, where activity represents competition within-attribute, competition between attributes, and option selection. This multi-layered inhibition framework challenges the assumption that option values are computed before choice. Instead our results indicate a canonical competition mechanism throughout all stages of a processing hierarchy, not simply at a final choice stage. PMID:25306549
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Mechanism by which BMI influences leisure-time physical activity behavior.
Godin, Gaston; Bélanger-Gravel, Ariane; Nolin, Bertrand
2008-06-01
The objective of this prospective study was to clarify the mechanism by which BMI influences leisure-time physical activity. This was achieved in accordance with the assumptions underlying the Theory of Planned Behavior (TPB), considered as one of the most useful theories to predict behavior adoption. At baseline, a sample of 1,530 respondents completed a short questionnaire to measure intention and perceived behavioral control (PBC), the two proximal determinants of behavior of TPB. Past behavior, sociodemographic variables, and weight and height were also assessed. The dependent variable, leisure-time physical activity was assessed 3 months later. Hierarchical multiple regression analyses revealed that BMI is a direct predictor of future leisure-time physical activity, not mediated by the variables of TPB. Additional hierarchical analyses indicated that BMI was not a moderator of the intention-behavior and PBC-behavior relationships. The results of this study suggest that high BMI is a significant negative determinant of leisure-time physical activity. This observation reinforces the importance of preventing weight gain as a health promotion strategy for avoiding a sedentary lifestyle.
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Water quality assessment with hierarchical cluster analysis based on Mahalanobis distance.
Du, Xiangjun; Shao, Fengjing; Wu, Shunyao; Zhang, Hanlin; Xu, Si
2017-07-01
Water quality assessment is crucial for assessment of marine eutrophication, prediction of harmful algal blooms, and environment protection. Previous studies have developed many numeric modeling methods and data driven approaches for water quality assessment. The cluster analysis, an approach widely used for grouping data, has also been employed. However, there are complex correlations between water quality variables, which play important roles in water quality assessment but have always been overlooked. In this paper, we analyze correlations between water quality variables and propose an alternative method for water quality assessment with hierarchical cluster analysis based on Mahalanobis distance. Further, we cluster water quality data collected form coastal water of Bohai Sea and North Yellow Sea of China, and apply clustering results to evaluate its water quality. To evaluate the validity, we also cluster the water quality data with cluster analysis based on Euclidean distance, which are widely adopted by previous studies. The results show that our method is more suitable for water quality assessment with many correlated water quality variables. To our knowledge, it is the first attempt to apply Mahalanobis distance for coastal water quality assessment.
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Huang, Xiaobi; Elliott, Michael R.; Harlow, Siobán D.
2013-01-01
SUMMARY As women approach menopause, the patterns of their menstrual cycle lengths change. To study these changes, we need to jointly model both the mean and variability of cycle length. Our proposed model incorporates separate mean and variance change points for each woman and a hierarchical model to link them together, along with regression components to include predictors of menopausal onset such as age at menarche and parity. Additional complexity arises from the fact that the calendar data have substantial missingness due to hormone use, surgery, and failure to report. We integrate multiple imputation and time-to event modeling in a Bayesian estimation framework to deal with different forms of the missingness. Posterior predictive model checks are applied to evaluate the model fit. Our method successfully models patterns of women’s menstrual cycle trajectories throughout their late reproductive life and identifies change points for mean and variability of segment length, providing insight into the menopausal process. More generally, our model points the way toward increasing use of joint mean-variance models to predict health outcomes and better understand disease processes. PMID:24729638
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Soft Mixer Assignment in a Hierarchical Generative Model of Natural Scene Statistics
Schwartz, Odelia; Sejnowski, Terrence J.; Dayan, Peter
2010-01-01
Gaussian scale mixture models offer a top-down description of signal generation that captures key bottom-up statistical characteristics of filter responses to images. However, the pattern of dependence among the filters for this class of models is prespecified. We propose a novel extension to the gaussian scale mixture model that learns the pattern of dependence from observed inputs and thereby induces a hierarchical representation of these inputs. Specifically, we propose that inputs are generated by gaussian variables (modeling local filter structure), multiplied by a mixer variable that is assigned probabilistically to each input from a set of possible mixers. We demonstrate inference of both components of the generative model, for synthesized data and for different classes of natural images, such as a generic ensemble and faces. For natural images, the mixer variable assignments show invariances resembling those of complex cells in visual cortex; the statistics of the gaussian components of the model are in accord with the outputs of divisive normalization models. We also show how our model helps interrelate a wide range of models of image statistics and cortical processing. PMID:16999575
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A Simulation Study of Methods for Selecting Subgroup-Specific Doses in Phase I Trials
Morita, Satoshi; Thall, Peter F.; Takeda, Kentaro
2016-01-01
Summary Patient heterogeneity may complicate dose-finding in phase I clinical trials if the dose-toxicity curves differ between subgroups. Conducting separate trials within subgroups may lead to infeasibly small sample sizes in subgroups having low prevalence. Alternatively, it is not obvious how to conduct a single trial while accounting for heterogeneity. To address this problem, we consider a generalization of the continual reassessment method (O’Quigley, et al., 1990) based on a hierarchical Bayesian dose-toxicity model that borrows strength between subgroups under the assumption that the subgroups are exchangeable. We evaluate a design using this model that includes subgroup-specific dose selection and safety rules. A simulation study is presented that includes comparison of this method to three alternative approaches, based on non-hierarchical models, that make different types of assumptions about within-subgroup dose-toxicity curves. The simulations show that the hierarchical model-based method is recommended in settings where the dose-toxicity curves are exchangeable between subgroups. We present practical guidelines for application, and provide computer programs for trial simulation and conduct. PMID:28111916
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Tian, Ting; McLachlan, Geoffrey J.; Dieters, Mark J.; Basford, Kaye E.
2015-01-01
It is a common occurrence in plant breeding programs to observe missing values in three-way three-mode multi-environment trial (MET) data. We proposed modifications of models for estimating missing observations for these data arrays, and developed a novel approach in terms of hierarchical clustering. Multiple imputation (MI) was used in four ways, multiple agglomerative hierarchical clustering, normal distribution model, normal regression model, and predictive mean match. The later three models used both Bayesian analysis and non-Bayesian analysis, while the first approach used a clustering procedure with randomly selected attributes and assigned real values from the nearest neighbour to the one with missing observations. Different proportions of data entries in six complete datasets were randomly selected to be missing and the MI methods were compared based on the efficiency and accuracy of estimating those values. The results indicated that the models using Bayesian analysis had slightly higher accuracy of estimation performance than those using non-Bayesian analysis but they were more time-consuming. However, the novel approach of multiple agglomerative hierarchical clustering demonstrated the overall best performances. PMID:26689369
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Tian, Ting; McLachlan, Geoffrey J; Dieters, Mark J; Basford, Kaye E
2015-01-01
It is a common occurrence in plant breeding programs to observe missing values in three-way three-mode multi-environment trial (MET) data. We proposed modifications of models for estimating missing observations for these data arrays, and developed a novel approach in terms of hierarchical clustering. Multiple imputation (MI) was used in four ways, multiple agglomerative hierarchical clustering, normal distribution model, normal regression model, and predictive mean match. The later three models used both Bayesian analysis and non-Bayesian analysis, while the first approach used a clustering procedure with randomly selected attributes and assigned real values from the nearest neighbour to the one with missing observations. Different proportions of data entries in six complete datasets were randomly selected to be missing and the MI methods were compared based on the efficiency and accuracy of estimating those values. The results indicated that the models using Bayesian analysis had slightly higher accuracy of estimation performance than those using non-Bayesian analysis but they were more time-consuming. However, the novel approach of multiple agglomerative hierarchical clustering demonstrated the overall best performances.
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Radhika, N P; Selvin, Rosilda; Kakkar, Rita; Roselin, L Selva
2018-08-01
In this paper, authors report the synthesis of nanocrystalline hierarchical zeolite ZSM-5 and its application as a heterogeneous catalyst in the alkylation of phenol with cyclohexene. The catalyst was synthesized by vacuum-concentration coupled hydrothermal technique in the presence of two templates. This synthetic route could successfully introduce pores of higher hierarchy in the zeolite ZSM-5 structure. Hierarchical ZSM-5 could catalyse effectively the industrially important reaction of cyclohexene with phenol. We ascribe the high efficiency of the catalyst to its conducive structural features such as nanoscale size, high surface area, presence of hierarchy of pores and existence of Lewis sites along with Brønsted acid sites. The effect of various reaction parameters like duration, catalyst amount, reactant mole ratio and temperature were assessed. Under optimum reaction conditions, the catalyst showed up to 65% selectivity towards the major product, cyclohexyl phenyl ether. There was no discernible decline in percent conversion or selectivity even when the catalyst was re-used for up to four runs. Kinetic studies were done through regression analysis and a mechanistic route based on LHHW model was suggested.
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A multiscale, hierarchical model of pulse dynamics in arid-land ecosystems
Collins, Scott L.; Belnap, Jayne; Grimm, N. B.; Rudgers, J. A.; Dahm, Clifford N.; D'Odorico, P.; Litvak, M.; Natvig, D. O.; Peters, Douglas C.; Pockman, W. T.; Sinsabaugh, R. L.; Wolf, B. O.
2014-01-01
Ecological processes in arid lands are often described by the pulse-reserve paradigm, in which rain events drive biological activity until moisture is depleted, leaving a reserve. This paradigm is frequently applied to processes stimulated by one or a few precipitation events within a growing season. Here we expand the original framework in time and space and include other pulses that interact with rainfall. This new hierarchical pulse-dynamics framework integrates space and time through pulse-driven exchanges, interactions, transitions, and transfers that occur across individual to multiple pulses extending from micro to watershed scales. Climate change will likely alter the size, frequency, and intensity of precipitation pulses in the future, and arid-land ecosystems are known to be highly sensitive to climate variability. Thus, a more comprehensive understanding of arid-land pulse dynamics is needed to determine how these ecosystems will respond to, and be shaped by, increased climate variability.
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NASA Astrophysics Data System (ADS)
Udupa, Jayaram K.; Odhner, Dewey; Falcao, Alexandre X.; Ciesielski, Krzysztof C.; Miranda, Paulo A. V.; Vaideeswaran, Pavithra; Mishra, Shipra; Grevera, George J.; Saboury, Babak; Torigian, Drew A.
2011-03-01
To make Quantitative Radiology (QR) a reality in routine clinical practice, computerized automatic anatomy recognition (AAR) becomes essential. As part of this larger goal, we present in this paper a novel fuzzy strategy for building bodywide group-wise anatomic models. They have the potential to handle uncertainties and variability in anatomy naturally and to be integrated with the fuzzy connectedness framework for image segmentation. Our approach is to build a family of models, called the Virtual Quantitative Human, representing normal adult subjects at a chosen resolution of the population variables (gender, age). Models are represented hierarchically, the descendents representing organs contained in parent organs. Based on an index of fuzziness of the models, 32 thorax data sets, and 10 organs defined in them, we found that the hierarchical approach to modeling can effectively handle the non-linear relationships in position, scale, and orientation that exist among organs in different patients.
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Age-related decline in cognitive control: the role of fluid intelligence and processing speed
2014-01-01
Background Research on cognitive control suggests an age-related decline in proactive control abilities whereas reactive control seems to remain intact. However, the reason of the differential age effect on cognitive control efficiency is still unclear. This study investigated the potential influence of fluid intelligence and processing speed on the selective age-related decline in proactive control. Eighty young and 80 healthy older adults were included in this study. The participants were submitted to a working memory recognition paradigm, assessing proactive and reactive cognitive control by manipulating the interference level across items. Results Repeated measures ANOVAs and hierarchical linear regressions indicated that the ability to appropriately use cognitive control processes during aging seems to be at least partially affected by the amount of available cognitive resources (assessed by fluid intelligence and processing speed abilities). Conclusions This study highlights the potential role of cognitive resources on the selective age-related decline in proactive control, suggesting the importance of a more exhaustive approach considering the confounding variables during cognitive control assessment. PMID:24401034
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Tree structure and cavity microclimate: implications for bats and birds.
Clement, Matthew J; Castleberry, Steven B
2013-05-01
It is widely assumed that tree cavity structure and microclimate affect cavity selection and use in cavity-dwelling bats and birds. Despite the interest in tree structure and microclimate, the relationship between the two has rarely been quantified. Currently available data often comes from artificial structures that may not accurately represent conditions in natural cavities. We collected data on tree cavity structure and microclimate from 45 trees in five cypress-gum swamps in the Coastal Plain of Georgia in the United States in 2008. We used hierarchical linear models to predict cavity microclimate from tree structure and ambient temperature and humidity, and used Aikaike's information criterion to select the most parsimonious models. We found large differences in microclimate among trees, but tree structure variables explained <28% of the variation, while ambient conditions explained >80% of variation common to all trees. We argue that the determinants of microclimate are complex and multidimensional, and therefore cavity microclimate cannot be deduced easily from simple tree structures. Furthermore, we found that daily fluctuations in ambient conditions strongly affect microclimate, indicating that greater weather fluctuations will cause greater differences among tree cavities.
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The Influence of the Student Mobility Rate on the Graduation Rate in the State of New Jersey
ERIC Educational Resources Information Center
Ross, Lavetta S.
2016-01-01
This study examined the influence of the student mobility rate on the high school graduation rate of schools in the state of New Jersey. Variables found to have an influence on the graduation rate in the extant literature were evaluated and reported. The analysis included multiple and hierarchical regression models for school variables (i.e.,…
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NASA Astrophysics Data System (ADS)
Abdelguerfi, Mahdi; Wynne, Chris; Cooper, Edgar; Ladner, Roy V.; Shaw, Kevin B.
1997-08-01
Three-dimensional terrain representation plays an important role in a number of terrain database applications. Hierarchical triangulated irregular networks (TINs) provide a variable-resolution terrain representation that is based on a nested triangulation of the terrain. This paper compares and analyzes existing hierarchical triangulation techniques. The comparative analysis takes into account how aesthetically appealing and accurate the resulting terrain representation is. Parameters, such as adjacency, slivers, and streaks, are used to provide a measure on how aesthetically appealing the terrain representation is. Slivers occur when the triangulation produces thin and slivery triangles. Streaks appear when there are too many triangulations done at a given vertex. Simple mathematical expressions are derived for these parameters, thereby providing a fairer and a more easily duplicated comparison. In addition to meeting the adjacency requirement, an aesthetically pleasant hierarchical TINs generation algorithm is expected to reduce both slivers and streaks while maintaining accuracy. A comparative analysis of a number of existing approaches shows that a variant of a method originally proposed by Scarlatos exhibits better overall performance.
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Hierarchical models and the analysis of bird survey information
Sauer, J.R.; Link, W.A.
2003-01-01
Management of birds often requires analysis of collections of estimates. We describe a hierarchical modeling approach to the analysis of these data, in which parameters associated with the individual species estimates are treated as random variables, and probability statements are made about the species parameters conditioned on the data. A Markov-Chain Monte Carlo (MCMC) procedure is used to fit the hierarchical model. This approach is computer intensive, and is based upon simulation. MCMC allows for estimation both of parameters and of derived statistics. To illustrate the application of this method, we use the case in which we are interested in attributes of a collection of estimates of population change. Using data for 28 species of grassland-breeding birds from the North American Breeding Bird Survey, we estimate the number of species with increasing populations, provide precision-adjusted rankings of species trends, and describe a measure of population stability as the probability that the trend for a species is within a certain interval. Hierarchical models can be applied to a variety of bird survey applications, and we are investigating their use in estimation of population change from survey data.
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Effect of Professional Development on Classroom Practices in Some Selected Saudi Universities
ERIC Educational Resources Information Center
Alghamdi, AbdulKhaliq Hajjad; Bin Sihes, Ahmad Johari
2016-01-01
"Scientific studies found the impact of professional development on effective classroom practices in Higher Education." This paper hypothesizes no statistically significant effect of lecturers' professional development on classroom practices in some selected Saudi Universities not as highlighted in the model. Hierarchical multiple…
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CROSS-SCALE CORRELATIONS AND THE DESIGN AND ANALYSIS OF AVIAN HABITAT SELECTION STUDIES
It has long been suggested that birds select habitat hierarchically, progressing from coarser to finer spatial scales. This hypothesis, in conjunction with the realization that many organisms likely respond to environmental patterns at multiple spatial scales, has led to a large ...
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A Bayesian hierarchical approach to comparative audit for carotid surgery.
Kuhan, G; Marshall, E C; Abidia, A F; Chetter, I C; McCollum, P T
2002-12-01
the aim of this study was to illustrate how a Bayesian hierarchical modelling approach can aid the reliable comparison of outcome rates between surgeons. retrospective analysis of prospective and retrospective data. binary outcome data (death/stroke within 30 days), together with information on 15 possible risk factors specific for CEA were available on 836 CEAs performed by four vascular surgeons from 1992-99. The median patient age was 68 (range 38-86) years and 60% were men. the model was developed using the WinBUGS software. After adjusting for patient-level risk factors, a cross-validatory approach was adopted to identify "divergent" performance. A ranking exercise was also carried out. the overall observed 30-day stroke/death rate was 3.9% (33/836). The model found diabetes, stroke and heart disease to be significant risk factors. There was no significant difference between the predicted and observed outcome rates for any surgeon (Bayesian p -value>0.05). Each surgeon had a median rank of 3 with associated 95% CI 1.0-5.0, despite the variability of observed stroke/death rate from 2.9-4.4%. After risk adjustment, there was very little residual between-surgeon variability in outcome rate. Bayesian hierarchical models can help to accurately quantify the uncertainty associated with surgeons' performance and rank.
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Evans, Samuel; Davis, Matthew H
2015-12-01
How humans extract the identity of speech sounds from highly variable acoustic signals remains unclear. Here, we use searchlight representational similarity analysis (RSA) to localize and characterize neural representations of syllables at different levels of the hierarchically organized temporo-frontal pathways for speech perception. We asked participants to listen to spoken syllables that differed considerably in their surface acoustic form by changing speaker and degrading surface acoustics using noise-vocoding and sine wave synthesis while we recorded neural responses with functional magnetic resonance imaging. We found evidence for a graded hierarchy of abstraction across the brain. At the peak of the hierarchy, neural representations in somatomotor cortex encoded syllable identity but not surface acoustic form, at the base of the hierarchy, primary auditory cortex showed the reverse. In contrast, bilateral temporal cortex exhibited an intermediate response, encoding both syllable identity and the surface acoustic form of speech. Regions of somatomotor cortex associated with encoding syllable identity in perception were also engaged when producing the same syllables in a separate session. These findings are consistent with a hierarchical account of how variable acoustic signals are transformed into abstract representations of the identity of speech sounds. © The Author 2015. Published by Oxford University Press.
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Cavalcante, Milady Cutrim Vieira; Lamy, Fernando; França, Ana Karina Teixeira da Cunha; Lamy, Zeni Carvalho
2017-05-01
Several factors can interfere in the mother-child relationship. Studies about different maternal characteristics and this relationship are scarce; they mainly evaluate women with psychopathology and use simultaneous regression models with adjustment for multiple confounders. This study aimed to assess factors associated with losses in the mother-child relationship through a cohort of 3,215 mothers of children between 15 and 36 months of age. Losses in the mother-child relationship, assessed by the Postpartum Bonding Questionnaire, was the outcome variable and the explanatory variables were demographic, socioeconomic, reproductive health and mental health of mothers as well as the conditions of the birth of children. It used multivariate regression analysis with a hierarchical approach in which the hierarchical blocks were structured according to the influence on the mother-child relationship. The prevalence of losses in the mother-child relationship was high (12.6%) and associated risk factors to lower maternal education (RR = 1.64), having unplanned pregnancy (RR = 1.42), consumption of alcoholic beverages during pregnancy (RR = 1.42) and maternal stress symptoms (RR = 1.88) and depression (RR = 2.00). Education and elements related to mental health were risks for damage in the mother-child relationship.
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Nee, K.; Bryan, S.; Levitskaia, T.
The reliability of chemical processes can be greatly improved by implementing inline monitoring systems. Combining multivariate analysis with non-destructive sensors can enhance the process without interfering with the operation. Here, we present here hierarchical models using both principal component analysis and partial least square analysis developed for different chemical components representative of solvent extraction process streams. A training set of 380 samples and an external validation set of 95 samples were prepared and Near infrared and Raman spectral data as well as conductivity under variable temperature conditions were collected. The results from the models indicate that careful selection of themore » spectral range is important. By compressing the data through Principal Component Analysis (PCA), we lower the rank of the data set to its most dominant features while maintaining the key principal components to be used in the regression analysis. Within the studied data set, concentration of five chemical components were modeled; total nitrate (NO 3 -), total acid (H +), neodymium (Nd 3+), sodium (Na +), and ionic strength (I.S.). The best overall model prediction for each of the species studied used a combined data set comprised of complementary techniques including NIR, Raman, and conductivity. Finally, our study shows that chemometric models are powerful but requires significant amount of carefully analyzed data to capture variations in the chemistry.« less
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Nee, K.; Bryan, S.; Levitskaia, T.; ...
2017-12-28
The reliability of chemical processes can be greatly improved by implementing inline monitoring systems. Combining multivariate analysis with non-destructive sensors can enhance the process without interfering with the operation. Here, we present here hierarchical models using both principal component analysis and partial least square analysis developed for different chemical components representative of solvent extraction process streams. A training set of 380 samples and an external validation set of 95 samples were prepared and Near infrared and Raman spectral data as well as conductivity under variable temperature conditions were collected. The results from the models indicate that careful selection of themore » spectral range is important. By compressing the data through Principal Component Analysis (PCA), we lower the rank of the data set to its most dominant features while maintaining the key principal components to be used in the regression analysis. Within the studied data set, concentration of five chemical components were modeled; total nitrate (NO 3 -), total acid (H +), neodymium (Nd 3+), sodium (Na +), and ionic strength (I.S.). The best overall model prediction for each of the species studied used a combined data set comprised of complementary techniques including NIR, Raman, and conductivity. Finally, our study shows that chemometric models are powerful but requires significant amount of carefully analyzed data to capture variations in the chemistry.« less
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Merging information from multi-model flood projections in a hierarchical Bayesian framework
NASA Astrophysics Data System (ADS)
Le Vine, Nataliya
2016-04-01
Multi-model ensembles are becoming widely accepted for flood frequency change analysis. The use of multiple models results in large uncertainty around estimates of flood magnitudes, due to both uncertainty in model selection and natural variability of river flow. The challenge is therefore to extract the most meaningful signal from the multi-model predictions, accounting for both model quality and uncertainties in individual model estimates. The study demonstrates the potential of a recently proposed hierarchical Bayesian approach to combine information from multiple models. The approach facilitates explicit treatment of shared multi-model discrepancy as well as the probabilistic nature of the flood estimates, by treating the available models as a sample from a hypothetical complete (but unobserved) set of models. The advantages of the approach are: 1) to insure an adequate 'baseline' conditions with which to compare future changes; 2) to reduce flood estimate uncertainty; 3) to maximize use of statistical information in circumstances where multiple weak predictions individually lack power, but collectively provide meaningful information; 4) to adjust multi-model consistency criteria when model biases are large; and 5) to explicitly consider the influence of the (model performance) stationarity assumption. Moreover, the analysis indicates that reducing shared model discrepancy is the key to further reduction of uncertainty in the flood frequency analysis. The findings are of value regarding how conclusions about changing exposure to flooding are drawn, and to flood frequency change attribution studies.
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Yang, Mingxing; Li, Xiumin; Li, Zhibin; Ou, Zhimin; Liu, Ming; Liu, Suhuan; Li, Xuejun; Yang, Shuyu
2013-01-01
DNA microarray analysis is characterized by obtaining a large number of gene variables from a small number of observations. Cluster analysis is widely used to analyze DNA microarray data to make classification and diagnosis of disease. Because there are so many irrelevant and insignificant genes in a dataset, a feature selection approach must be employed in data analysis. The performance of cluster analysis of this high-throughput data depends on whether the feature selection approach chooses the most relevant genes associated with disease classes. Here we proposed a new method using multiple Orthogonal Partial Least Squares-Discriminant Analysis (mOPLS-DA) models and S-plots to select the most relevant genes to conduct three-class disease classification and prediction. We tested our method using Golub's leukemia microarray data. For three classes with subtypes, we proposed hierarchical orthogonal partial least squares-discriminant analysis (OPLS-DA) models and S-plots to select features for two main classes and their subtypes. For three classes in parallel, we employed three OPLS-DA models and S-plots to choose marker genes for each class. The power of feature selection to classify and predict three-class disease was evaluated using cluster analysis. Further, the general performance of our method was tested using four public datasets and compared with those of four other feature selection methods. The results revealed that our method effectively selected the most relevant features for disease classification and prediction, and its performance was better than that of the other methods.
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Attention to Hierarchical Level Influences Attentional Selection of Spatial Scale
ERIC Educational Resources Information Center
Flevaris, Anastasia V.; Bentin, Shlomo; Robertson, Lynn C.
2011-01-01
Ample evidence suggests that global perception may involve low spatial frequency (LSF) processing and that local perception may involve high spatial frequency (HSF) processing (Shulman, Sullivan, Gish, & Sakoda, 1986; Shulman & Wilson, 1987; Robertson, 1996). It is debated whether SF selection is a low-level mechanism associating global…
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Banerjee, Debasis; Elsaidi, Sameh K.; Aguila, Briana
2016-10-20
Efficient and cost-effective removal of radioactive pertechnetate anions from nuclear waste is a key challenge to mitigate long-term nuclear waste storage issues. Traditional materials such as resins and layered double hydroxides (LDHs) were evaluated for their pertechnetate or perrhenate (the non-radioactive surrogate) removal capacity, but there is room for improvement in terms of capacity, selectivity and kinetics. A series of functionalized hierarchical porous frameworks were evaluated for their perrhenate removal capacity in the presence of other competing anions.
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DNA Barcode Sequence Identification Incorporating Taxonomic Hierarchy and within Taxon Variability
Little, Damon P.
2011-01-01
For DNA barcoding to succeed as a scientific endeavor an accurate and expeditious query sequence identification method is needed. Although a global multiple–sequence alignment can be generated for some barcoding markers (e.g. COI, rbcL), not all barcoding markers are as structurally conserved (e.g. matK). Thus, algorithms that depend on global multiple–sequence alignments are not universally applicable. Some sequence identification methods that use local pairwise alignments (e.g. BLAST) are unable to accurately differentiate between highly similar sequences and are not designed to cope with hierarchic phylogenetic relationships or within taxon variability. Here, I present a novel alignment–free sequence identification algorithm–BRONX–that accounts for observed within taxon variability and hierarchic relationships among taxa. BRONX identifies short variable segments and corresponding invariant flanking regions in reference sequences. These flanking regions are used to score variable regions in the query sequence without the production of a global multiple–sequence alignment. By incorporating observed within taxon variability into the scoring procedure, misidentifications arising from shared alleles/haplotypes are minimized. An explicit treatment of more inclusive terminals allows for separate identifications to be made for each taxonomic level and/or for user–defined terminals. BRONX performs better than all other methods when there is imperfect overlap between query and reference sequences (e.g. mini–barcode queries against a full–length barcode database). BRONX consistently produced better identifications at the genus–level for all query types. PMID:21857897
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Vickers, T. Winston; Ernest, Holly B.; Boyce, Walter M.
2017-01-01
The importance of examining multiple hierarchical levels when modeling resource use for wildlife has been acknowledged for decades. Multi-level resource selection functions have recently been promoted as a method to synthesize resource use across nested organizational levels into a single predictive surface. Analyzing multiple scales of selection within each hierarchical level further strengthens multi-level resource selection functions. We extend this multi-level, multi-scale framework to modeling resistance for wildlife by combining multi-scale resistance surfaces from two data types, genetic and movement. Resistance estimation has typically been conducted with one of these data types, or compared between the two. However, we contend it is not an either/or issue and that resistance may be better-modeled using a combination of resistance surfaces that represent processes at different hierarchical levels. Resistance surfaces estimated from genetic data characterize temporally broad-scale dispersal and successful breeding over generations, whereas resistance surfaces estimated from movement data represent fine-scale travel and contextualized movement decisions. We used telemetry and genetic data from a long-term study on pumas (Puma concolor) in a highly developed landscape in southern California to develop a multi-level, multi-scale resource selection function and a multi-level, multi-scale resistance surface. We used these multi-level, multi-scale surfaces to identify resource use patches and resistant kernel corridors. Across levels, we found puma avoided urban, agricultural areas, and roads and preferred riparian areas and more rugged terrain. For other landscape features, selection differed among levels, as did the scales of selection for each feature. With these results, we developed a conservation plan for one of the most isolated puma populations in the U.S. Our approach captured a wide spectrum of ecological relationships for a population, resulted in effective conservation planning, and can be readily applied to other wildlife species. PMID:28609466
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Zeller, Katherine A; Vickers, T Winston; Ernest, Holly B; Boyce, Walter M
2017-01-01
The importance of examining multiple hierarchical levels when modeling resource use for wildlife has been acknowledged for decades. Multi-level resource selection functions have recently been promoted as a method to synthesize resource use across nested organizational levels into a single predictive surface. Analyzing multiple scales of selection within each hierarchical level further strengthens multi-level resource selection functions. We extend this multi-level, multi-scale framework to modeling resistance for wildlife by combining multi-scale resistance surfaces from two data types, genetic and movement. Resistance estimation has typically been conducted with one of these data types, or compared between the two. However, we contend it is not an either/or issue and that resistance may be better-modeled using a combination of resistance surfaces that represent processes at different hierarchical levels. Resistance surfaces estimated from genetic data characterize temporally broad-scale dispersal and successful breeding over generations, whereas resistance surfaces estimated from movement data represent fine-scale travel and contextualized movement decisions. We used telemetry and genetic data from a long-term study on pumas (Puma concolor) in a highly developed landscape in southern California to develop a multi-level, multi-scale resource selection function and a multi-level, multi-scale resistance surface. We used these multi-level, multi-scale surfaces to identify resource use patches and resistant kernel corridors. Across levels, we found puma avoided urban, agricultural areas, and roads and preferred riparian areas and more rugged terrain. For other landscape features, selection differed among levels, as did the scales of selection for each feature. With these results, we developed a conservation plan for one of the most isolated puma populations in the U.S. Our approach captured a wide spectrum of ecological relationships for a population, resulted in effective conservation planning, and can be readily applied to other wildlife species.
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unmarked: An R package for fitting hierarchical models of wildlife occurrence and abundance
Fiske, Ian J.; Chandler, Richard B.
2011-01-01
Ecological research uses data collection techniques that are prone to substantial and unique types of measurement error to address scientific questions about species abundance and distribution. These data collection schemes include a number of survey methods in which unmarked individuals are counted, or determined to be present, at spatially- referenced sites. Examples include site occupancy sampling, repeated counts, distance sampling, removal sampling, and double observer sampling. To appropriately analyze these data, hierarchical models have been developed to separately model explanatory variables of both a latent abundance or occurrence process and a conditional detection process. Because these models have a straightforward interpretation paralleling mechanisms under which the data arose, they have recently gained immense popularity. The common hierarchical structure of these models is well-suited for a unified modeling interface. The R package unmarked provides such a unified modeling framework, including tools for data exploration, model fitting, model criticism, post-hoc analysis, and model comparison.
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ERIC Educational Resources Information Center
Chang, Edward C.; Yu, Elizabeth A.; Yu, Tina; Kahle, Emma R.; Hernandez, Viviana; Kim, Jean M.; Jeglic, Elizabeth L.; Hirsch, Jameson K.
2016-01-01
In the present study, we examined ethnic variables (viz., multigroup ethnic identity and other group orientation) along with negative life events as predictors of depressive symptoms and suicidal behaviors in a sample of 156 (38 male and 118 female) Latino college students. Results of conducting hierarchical regression analyses indicated that the…
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Chen, Yu Ming; Yu, Le; Lou, Xiong Wen David
2016-05-10
Hierarchical tubular structures composed of Co3 O4 hollow nanoparticles and carbon nanotubes (CNTs) have been synthesized by an efficient multi-step route. Starting from polymer-cobalt acetate (Co(Ac)2 ) composite nanofibers, uniform polymer-Co(Ac)2 @zeolitic imidazolate framework-67 (ZIF-67) core-shell nanofibers are first synthesized via partial phase transformation with 2-methylimidazole in ethanol. After the selective dissolution of polymer-Co(Ac)2 cores, the resulting ZIF-67 tubular structures can be converted into hierarchical CNTs/Co-carbon hybrids by annealing in Ar/H2 atmosphere. Finally, the hierarchical CNT/Co3 O4 microtubes are obtained by a subsequent thermal treatment in air. Impressively, the as-prepared nanocomposite delivers a high reversible capacity of 1281 mAh g(-1) at 0.1 A g(-1) with exceptional rate capability and long cycle life over 200 cycles as an anode material for lithium-ion batteries. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
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Long, Hu; Harley-Trochimczyk, Anna; Cheng, Siyi; Hu, Hao; Chi, Won Seok; Rao, Ameya; Carraro, Carlo; Shi, Tielin; Tang, Zirong; Maboudian, Roya
2016-11-23
Nanowire-assembled 3D hierarchical ZnCo 2 O 4 microstructure is synthesized by a facile hydrothermal route and a subsequent annealing process. In comparison to simple nanowires, the resulting dandelion-like structure yields more open spaces between nanowires, which allow for better gas diffusion and provide more active sites for gas adsorption while maintaining good electrical conductivity. The hierarchical ZnCo 2 O 4 microstructure is integrated on a low-power microheater platform without using binders or conductive additives. The hierarchical structure of the ZnCo 2 O 4 sensing material provides reliable electrical connection across the sensing electrodes. The resulting sensor exhibits an ultralow detection limit of 3 ppb toward formaldehyde with fast response and recovery as well as good selectivity to CO, H 2 , and hydrocarbons such as n-pentane, propane, and CH 4 . The sensor only consumes ∼5.7 mW for continuous operation at 300 °C with good long-term stability. The excellent sensing performance of this hierarchical structure based sensor suggests the advantages of combining such structures with microfabricated heaters for practical low-power sensing applications.
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Boehler, Christian E H; Lord, Joanne
2016-01-01
Published cost-effectiveness estimates can vary considerably, both within and between countries. Despite extensive discussion, little is known empirically about factors relating to these variations. To use multilevel statistical modeling to integrate cost-effectiveness estimates from published economic evaluations to investigate potential causes of variation. Cost-effectiveness studies of statins for cardiovascular disease prevention were identified by systematic review. Estimates of incremental costs and effects were extracted from reported base case, sensitivity, and subgroup analyses, with estimates grouped in studies and in countries. Three bivariate models were developed: a cross-classified model to accommodate data from multinational studies, a hierarchical model with multinational data allocated to a single category at country level, and a hierarchical model excluding multinational data. Covariates at different levels were drawn from a long list of factors suggested in the literature. We found 67 studies reporting 2094 cost-effectiveness estimates relating to 23 countries (6 studies reporting for more than 1 country). Data and study-level covariates included patient characteristics, intervention and comparator cost, and some study methods (e.g., discount rates and time horizon). After adjusting for these factors, the proportion of variation attributable to countries was negligible in the cross-classified model but moderate in the hierarchical models (14%-19% of total variance). Country-level variables that improved the fit of the hierarchical models included measures of income and health care finance, health care resources, and population risks. Our analysis suggested that variability in published cost-effectiveness estimates is related more to differences in study methods than to differences in national context. Multinational studies were associated with much lower country-level variation than single-country studies. These findings are for a single clinical question and may be atypical. © The Author(s) 2015.
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Bayesian state space models for dynamic genetic network construction across multiple tissues.
Liang, Yulan; Kelemen, Arpad
2016-08-01
Construction of gene-gene interaction networks and potential pathways is a challenging and important problem in genomic research for complex diseases while estimating the dynamic changes of the temporal correlations and non-stationarity are the keys in this process. In this paper, we develop dynamic state space models with hierarchical Bayesian settings to tackle this challenge for inferring the dynamic profiles and genetic networks associated with disease treatments. We treat both the stochastic transition matrix and the observation matrix time-variant and include temporal correlation structures in the covariance matrix estimations in the multivariate Bayesian state space models. The unevenly spaced short time courses with unseen time points are treated as hidden state variables. Hierarchical Bayesian approaches with various prior and hyper-prior models with Monte Carlo Markov Chain and Gibbs sampling algorithms are used to estimate the model parameters and the hidden state variables. We apply the proposed Hierarchical Bayesian state space models to multiple tissues (liver, skeletal muscle, and kidney) Affymetrix time course data sets following corticosteroid (CS) drug administration. Both simulation and real data analysis results show that the genomic changes over time and gene-gene interaction in response to CS treatment can be well captured by the proposed models. The proposed dynamic Hierarchical Bayesian state space modeling approaches could be expanded and applied to other large scale genomic data, such as next generation sequence (NGS) combined with real time and time varying electronic health record (EHR) for more comprehensive and robust systematic and network based analysis in order to transform big biomedical data into predictions and diagnostics for precision medicine and personalized healthcare with better decision making and patient outcomes.
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Self-efficacy for controlling upsetting thoughts and emotional eating in family caregivers.
MacDougall, Megan; Steffen, Ann
2017-10-01
Self-efficacy for controlling upsetting thoughts was examined as a predictor of emotional eating by family caregivers of physically and cognitively impaired older adults. Adult women (N = 158) providing healthcare assistance for an older family member completed an online survey about caregiving stressors, depressive symptoms, self-efficacy, and emotional eating. A stress process framework was used as a conceptual model to guide selection of variables predicting emotional eating scores. A hierarchical multiple regression was conducted and the overall model was significant (R 2 = .21, F(4,153) = 10.02, p < .01); self-efficacy for controlling upsetting thoughts was a significant predictor of caregivers' emotional eating scores after accounting for IADL, role overload, and depression scores. These findings replicate previous research demonstrating the relationship between managing cognitions about caregiving and behavioral responses to stressors, and point to the importance of addressing cognitive processes in efforts to improve caregiver health behaviors.
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Gómez, Aina G; Ondiviela, Bárbara; Puente, Araceli; Juanes, José A
2015-05-15
This work presents a standard and unified procedure for assessment of environmental risks at the contaminant source level in port aquatic systems. Using this method, port managers and local authorities will be able to hierarchically classify environmental hazards and proceed with the most suitable management actions. This procedure combines rigorously selected parameters and indicators to estimate the environmental risk of each contaminant source based on its probability, consequences and vulnerability. The spatio-temporal variability of multiple stressors (agents) and receptors (endpoints) is taken into account to provide accurate estimations for application of precisely defined measures. The developed methodology is tested on a wide range of different scenarios via application in six European ports. The validation process confirms its usefulness, versatility and adaptability as a management tool for port water quality in Europe and worldwide. Copyright © 2015 Elsevier Ltd. All rights reserved.
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NASA Astrophysics Data System (ADS)
Hoell, Simon; Omenzetter, Piotr
2017-07-01
Considering jointly damage sensitive features (DSFs) of signals recorded by multiple sensors, applying advanced transformations to these DSFs and assessing systematically their contribution to damage detectability and localisation can significantly enhance the performance of structural health monitoring systems. This philosophy is explored here for partial autocorrelation coefficients (PACCs) of acceleration responses. They are interrogated with the help of the linear discriminant analysis based on the Fukunaga-Koontz transformation using datasets of the healthy and selected reference damage states. Then, a simple but efficient fast forward selection procedure is applied to rank the DSF components with respect to statistical distance measures specialised for either damage detection or localisation. For the damage detection task, the optimal feature subsets are identified based on the statistical hypothesis testing. For damage localisation, a hierarchical neuro-fuzzy tool is developed that uses the DSF ranking to establish its own optimal architecture. The proposed approaches are evaluated experimentally on data from non-destructively simulated damage in a laboratory scale wind turbine blade. The results support our claim of being able to enhance damage detectability and localisation performance by transforming and optimally selecting DSFs. It is demonstrated that the optimally selected PACCs from multiple sensors or their Fukunaga-Koontz transformed versions can not only improve the detectability of damage via statistical hypothesis testing but also increase the accuracy of damage localisation when used as inputs into a hierarchical neuro-fuzzy network. Furthermore, the computational effort of employing these advanced soft computing models for damage localisation can be significantly reduced by using transformed DSFs.
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Explicit Encoding of Multimodal Percepts by Single Neurons in the Human Brain
Quiroga, Rodrigo Quian; Kraskov, Alexander; Koch, Christof; Fried, Itzhak
2010-01-01
Summary Different pictures of Marilyn Monroe can evoke the same percept, even if greatly modified as in Andy Warhol’s famous portraits. But how does the brain recognize highly variable pictures as the same percept? Various studies have provided insights into how visual information is processed along the “ventral pathway,” via both single-cell recordings in monkeys [1, 2] and functional imaging in humans [3, 4]. Interestingly, in humans, the same “concept” of Marilyn Monroe can be evoked with other stimulus modalities, for instance by hearing or reading her name. Brain imaging studies have identified cortical areas selective to voices [5, 6] and visual word forms [7, 8]. However, how visual, text, and sound information can elicit a unique percept is still largely unknown. By using presentations of pictures and of spoken and written names, we show that (1) single neurons in the human medial temporal lobe (MTL) respond selectively to representations of the same individual across different sensory modalities; (2) the degree of multimodal invariance increases along the hierarchical structure within the MTL; and (3) such neuronal representations can be generated within less than a day or two. These results demonstrate that single neurons can encode percepts in an explicit, selective, and invariant manner, even if evoked by different sensory modalities. PMID:19631538
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Thomson, James R; Kimmerer, Wim J; Brown, Larry R; Newman, Ken B; Mac Nally, Ralph; Bennett, William A; Feyrer, Frederick; Fleishman, Erica
2010-07-01
We examined trends in abundance of four pelagic fish species (delta smelt, longfin smelt, striped bass, and threadfin shad) in the upper San Francisco Estuary, California, USA, over 40 years using Bayesian change point models. Change point models identify times of abrupt or unusual changes in absolute abundance (step changes) or in rates of change in abundance (trend changes). We coupled Bayesian model selection with linear regression splines to identify biotic or abiotic covariates with the strongest associations with abundances of each species. We then refitted change point models conditional on the selected covariates to explore whether those covariates could explain statistical trends or change points in species abundances. We also fitted a multispecies change point model that identified change points common to all species. All models included hierarchical structures to model data uncertainties, including observation errors and missing covariate values. There were step declines in abundances of all four species in the early 2000s, with a likely common decline in 2002. Abiotic variables, including water clarity, position of the 2 per thousand isohaline (X2), and the volume of freshwater exported from the estuary, explained some variation in species' abundances over the time series, but no selected covariates could explain statistically the post-2000 change points for any species.
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Response moderation models for conditional dependence between response time and response accuracy.
Bolsinova, Maria; Tijmstra, Jesper; Molenaar, Dylan
2017-05-01
It is becoming more feasible and common to register response times in the application of psychometric tests. Researchers thus have the opportunity to jointly model response accuracy and response time, which provides users with more relevant information. The most common choice is to use the hierarchical model (van der Linden, 2007, Psychometrika, 72, 287), which assumes conditional independence between response time and accuracy, given a person's speed and ability. However, this assumption may be violated in practice if, for example, persons vary their speed or differ in their response strategies, leading to conditional dependence between response time and accuracy and confounding measurement. We propose six nested hierarchical models for response time and accuracy that allow for conditional dependence, and discuss their relationship to existing models. Unlike existing approaches, the proposed hierarchical models allow for various forms of conditional dependence in the model and allow the effect of continuous residual response time on response accuracy to be item-specific, person-specific, or both. Estimation procedures for the models are proposed, as well as two information criteria that can be used for model selection. Parameter recovery and usefulness of the information criteria are investigated using simulation, indicating that the procedure works well and is likely to select the appropriate model. Two empirical applications are discussed to illustrate the different types of conditional dependence that may occur in practice and how these can be captured using the proposed hierarchical models. © 2016 The British Psychological Society.
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Braverman, Ami; Berger, Andrea; Meiran, Nachshon
2014-07-01
According to "hierarchical" multi-step theories, response selection is preceded by a decision regarding which task rule should be executed. Other theories assume a "flat" single-step architecture in which task information and stimulus information are simultaneously considered. Using task switching, the authors independently manipulated two kinds of conflict: task conflict (with information that potentially triggers the relevant or the competing task rule/identity) and response conflict (with information that potentially triggers the relevant or the competing response code/motor response). Event related potentials indicated that the task conflict effect began before the response conflict effect and carried on in parallel with it. These results are more in line with the hierarchical view. Copyright © 2014 Elsevier Inc. All rights reserved.
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A hierarchical, retinotopic proto-organization of the primate visual system at birth
Arcaro, Michael J; Livingstone, Margaret S
2017-01-01
The adult primate visual system comprises a series of hierarchically organized areas. Each cortical area contains a topographic map of visual space, with different areas extracting different kinds of information from the retinal input. Here we asked to what extent the newborn visual system resembles the adult organization. We find that hierarchical, topographic organization is present at birth and therefore constitutes a proto-organization for the entire primate visual system. Even within inferior temporal cortex, this proto-organization was already present, prior to the emergence of category selectivity (e.g., faces or scenes). We propose that this topographic organization provides the scaffolding for the subsequent development of visual cortex that commences at the onset of visual experience DOI: http://dx.doi.org/10.7554/eLife.26196.001 PMID:28671063
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NASA Astrophysics Data System (ADS)
Alexander, R. B.; Boyer, E. W.; Schwarz, G. E.; Smith, R. A.
2013-12-01
Estimating water and material stores and fluxes in watershed studies is frequently complicated by uncertainties in quantifying hydrological and biogeochemical effects of factors such as land use, soils, and climate. Although these process-related effects are commonly measured and modeled in separate catchments, researchers are especially challenged by their complexity across catchments and diverse environmental settings, leading to a poor understanding of how model parameters and prediction uncertainties vary spatially. To address these concerns, we illustrate the use of Bayesian hierarchical modeling techniques with a dynamic version of the spatially referenced watershed model SPARROW (SPAtially Referenced Regression On Watershed attributes). The dynamic SPARROW model is designed to predict streamflow and other water cycle components (e.g., evapotranspiration, soil and groundwater storage) for monthly varying hydrological regimes, using mechanistic functions, mass conservation constraints, and statistically estimated parameters. In this application, the model domain includes nearly 30,000 NHD (National Hydrologic Data) stream reaches and their associated catchments in the Susquehanna River Basin. We report the results of our comparisons of alternative models of varying complexity, including models with different explanatory variables as well as hierarchical models that account for spatial and temporal variability in model parameters and variance (error) components. The model errors are evaluated for changes with season and catchment size and correlations in time and space. The hierarchical models consist of a two-tiered structure in which climate forcing parameters are modeled as random variables, conditioned on watershed properties. Quantification of spatial and temporal variations in the hydrological parameters and model uncertainties in this approach leads to more efficient (lower variance) and less biased model predictions throughout the river network. Moreover, predictions of water-balance components are reported according to probabilistic metrics (e.g., percentiles, prediction intervals) that include both parameter and model uncertainties. These improvements in predictions of streamflow dynamics can inform the development of more accurate predictions of spatial and temporal variations in biogeochemical stores and fluxes (e.g., nutrients and carbon) in watersheds.
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Developmental Trajectories of Form Perception: A Story of Attention
ERIC Educational Resources Information Center
Kovshoff, Hanna; Iarocci, Grace; Shore, David I.; Burack, Jacob A.
2015-01-01
The developmental trajectories of selective and divided attention were examined in relation to the processing of hierarchically integrated stimuli. The participants included children in 4 age groups (6, 8, 10, and 12 years) and a group of young adults (24 years) who completed 2 computer-based attention tasks. In the selective attention task, the…
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NASA Astrophysics Data System (ADS)
Widayatno, W. B.
2017-04-01
This paper reports the surface modification of high silica ZSM-5 zeolite, particularly emphasizing the effect of cation type on selective conversion of biomass-derived oil. XRD spectra of the NaOH-treated HZSM-5 showed notable crystallinity decrease at specific crystal plane orientation. The N2-physisorption tests confirmed mesoporosity evolution as NaOH concentration was increased. NH3-desorption tests revealed a significant change on surface acidity which involved realumination and cation replacement processes. The utilization of untreated HZSM-5 as well as hierarchical NaZSM-5 for catalytic conversion of bio-oil showed the effect of cation type and mesoporosity on chemicals distribution. The untreated HZSM-5 showed high selectivity to aromatics, which degraded gradually due to deactivation and poisoning of the acid sites. Meanwhile, hierarchical NaZSM-5 showed high selectivity to phenolic compound, which became more stable for 0.4M NaOH-treated zeolite (Na04). The current findings provide an additional insight on the potentials of NaZSM-5 for bio-oil valorization.
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Marzaro, Giovanni; Ferrarese, Alessandro; Chilin, Adriana
2014-08-01
The selection of the most appropriate protein conformation is a crucial aspect in molecular docking experiments. In order to reduce the errors arising from the use of a single protein conformation, several authors suggest the use of several tridimensional structures for the target. However, the selection of the most appropriate protein conformations still remains a challenging goal. The protein 3D-structures selection is mainly performed based on pairwise root-mean-square-deviation (RMSD) values computation, followed by hierarchical clustering. Herein we report an alternative strategy, based on the computation of only two atom affinity map for each protein conformation, followed by multivariate analysis and hierarchical clustering. This methodology was applied on seven different kinases of pharmaceutical interest. The comparison with the classical RMSD-based strategy was based on cross-docking of co-crystallized ligands. In the case of epidermal growth factor receptor kinase, also the docking performance on 220 known ligands were evaluated, followed by 3D-QSAR studies. In all the cases, the herein proposed methodology outperformed the RMSD-based one.
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Kong, Xiangqian; Qin, Jie; Li, Zeng; Vultur, Adina; Tong, Linjiang; Feng, Enguang; Rajan, Geena; Liu, Shien; Lu, Junyan; Liang, Zhongjie; Zheng, Mingyue; Zhu, Weiliang; Jiang, Hualiang; Herlyn, Meenhard; Liu, Hong; Marmorstein, Ronen; Luo, Cheng
2012-01-01
Oncogenic mutations in critical nodes of cellular signaling pathways have been associated with tumorigenesis and progression. The B-Raf protein kinase, a key hub in the canonical MAPK signaling cascade, is mutated in a broad range of human cancers and especially in malignant melanoma. The most prevalent B-RafV600E mutant exhibits elevated kinase activity and results in constitutive activation of the MAPK pathway, thus making it a promising drug target for cancer therapy. Herein, we described the development of novel B-RafV600E selective inhibitors via multi-step virtual screening and hierarchical hit optimization. Nine hit compounds with low micromolar IC50 values were identified as B-RafV600E inhibitors through virtual screening. Subsequent scaffold-based analogue searching and medicinal chemistry efforts significantly improved both the inhibitor potency and oncogene selectivity. In particular, compounds 22f and 22q possess nanomolar IC50 values with selectivity for B-RafV600E in vitro and exclusive cytotoxicity against B-RafV600E harboring cancer cells. PMID:22875039
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Kong, Xiangqian; Qin, Jie; Li, Zeng; Vultur, Adina; Tong, Linjiang; Feng, Enguang; Rajan, Geena; Liu, Shien; Lu, Junyan; Liang, Zhongjie; Zheng, Mingyue; Zhu, Weiliang; Jiang, Hualiang; Herlyn, Meenhard; Liu, Hong; Marmorstein, Ronen; Luo, Cheng
2012-09-28
Oncogenic mutations in critical nodes of cellular signaling pathways have been associated with tumorigenesis and progression. The B-Raf protein kinase, a key hub in the canonical MAPK signaling cascade, is mutated in a broad range of human cancers and especially in malignant melanoma. The most prevalent B-Raf(V600E) mutant exhibits elevated kinase activity and results in constitutive activation of the MAPK pathway, thus making it a promising drug target for cancer therapy. Herein, we describe the development of novel B-Raf(V600E) selective inhibitors via multi-step virtual screening and hierarchical hit optimization. Nine hit compounds with low micromolar IC(50) values were identified as B-Raf(V600E) inhibitors through virtual screening. Subsequent scaffold-based analogue searching and medicinal chemistry efforts significantly improved both the inhibitor potency and oncogene selectivity. In particular, compounds 22f and 22q possess nanomolar IC(50) values with selectivity for B-Raf(V600E)in vitro and exclusive cytotoxicity against B-Raf(V600E) harboring cancer cells.
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A bayesian hierarchical model for classification with selection of functional predictors.
Zhu, Hongxiao; Vannucci, Marina; Cox, Dennis D
2010-06-01
In functional data classification, functional observations are often contaminated by various systematic effects, such as random batch effects caused by device artifacts, or fixed effects caused by sample-related factors. These effects may lead to classification bias and thus should not be neglected. Another issue of concern is the selection of functions when predictors consist of multiple functions, some of which may be redundant. The above issues arise in a real data application where we use fluorescence spectroscopy to detect cervical precancer. In this article, we propose a Bayesian hierarchical model that takes into account random batch effects and selects effective functions among multiple functional predictors. Fixed effects or predictors in nonfunctional form are also included in the model. The dimension of the functional data is reduced through orthonormal basis expansion or functional principal components. For posterior sampling, we use a hybrid Metropolis-Hastings/Gibbs sampler, which suffers slow mixing. An evolutionary Monte Carlo algorithm is applied to improve the mixing. Simulation and real data application show that the proposed model provides accurate selection of functional predictors as well as good classification.
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A Bayesian Approach to Model Selection in Hierarchical Mixtures-of-Experts Architectures.
Tanner, Martin A.; Peng, Fengchun; Jacobs, Robert A.
1997-03-01
There does not exist a statistical model that shows good performance on all tasks. Consequently, the model selection problem is unavoidable; investigators must decide which model is best at summarizing the data for each task of interest. This article presents an approach to the model selection problem in hierarchical mixtures-of-experts architectures. These architectures combine aspects of generalized linear models with those of finite mixture models in order to perform tasks via a recursive "divide-and-conquer" strategy. Markov chain Monte Carlo methodology is used to estimate the distribution of the architectures' parameters. One part of our approach to model selection attempts to estimate the worth of each component of an architecture so that relatively unused components can be pruned from the architecture's structure. A second part of this approach uses a Bayesian hypothesis testing procedure in order to differentiate inputs that carry useful information from nuisance inputs. Simulation results suggest that the approach presented here adheres to the dictum of Occam's razor; simple architectures that are adequate for summarizing the data are favored over more complex structures. Copyright 1997 Elsevier Science Ltd. All Rights Reserved.
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Cognitive factors contributing to spelling performance in children with prenatal alcohol exposure.
Glass, Leila; Graham, Diana M; Akshoomoff, Natacha; Mattson, Sarah N
2015-11-01
Heavy prenatal alcohol exposure is associated with impaired school functioning. Spelling performance has not been comprehensively evaluated. We examined whether children with heavy prenatal alcohol exposure demonstrate deficits in spelling and related abilities, including reading, and tested whether there are unique underlying mechanisms for observed deficits in this population. Ninety-six school-age children made up 2 groups: children with heavy prenatal alcohol exposure (AE, n = 49) and control children (CON, n = 47). Children completed select subtests from the Wechsler Individual Achievement Test-Second Edition and the NEPSY-II. Group differences and relations between spelling and theoretically related cognitive variables were evaluated using multivariate analysis of variance and Pearson correlations. Hierarchical regression analyses were used to assess contributions of group membership and cognitive variables to spelling performance. The specificity of these deficits and underlying mechanisms was tested by examining the relations between reading ability, group membership, and cognitive variables. Groups differed significantly on all variables. Group membership and phonological processing significantly contributed to spelling performance, whereas for reading, group membership and all cognitive variables contributed significantly. For both reading and spelling, group × working memory interactions revealed that working memory contributed independently only for alcohol-exposed children. Alcohol-exposed children demonstrated a unique pattern of spelling deficits. The relation of working memory to spelling and reading was specific to the AE group, suggesting that if prenatal alcohol exposure is known or suspected, working memory ability should be considered in the development and implementation of explicit instruction. (c) 2015 APA, all rights reserved).
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Cognitive Factors Contributing to Spelling Performance in Children with Prenatal Alcohol Exposure
Glass, Leila; Graham, Diana M.; Akshoomoff, Natacha; Mattson, Sarah N.
2015-01-01
Objective Heavy prenatal alcohol exposure is associated with impaired school functioning. Spelling performance has not been comprehensively evaluated. We examined whether children with heavy prenatal alcohol exposure demonstrate deficits in spelling and related abilities, including reading, and tested whether there are unique underlying mechanisms for observed deficits in this population. Method Ninety-six school-age children comprised two groups: children with heavy prenatal alcohol exposure (AE, n=49) and control children (CON, n=47). Children completed select subtests from the WIAT-II and NEPSY-II. Group differences and relations between spelling and theoretically-related cognitive variables were evaluated using MANOVA and Pearson correlations. Hierarchical regression analyses were utilized to assess contributions of group membership and cognitive variables to spelling performance. The specificity of these deficits and underlying mechanisms was tested by examining the relations between reading ability, group membership, and cognitive variables. Results Groups differed significantly on all variables. Group membership and phonological processing significantly contributed to spelling performance. In addition, a significant group*working memory interaction revealed that working memory independently contributed significantly to spelling only for the AE group. All cognitive variables contributed to reading across groups and a group*working memory interaction revealed that working memory contributed independently to reading only for alcohol-exposed children. Conclusion Alcohol-exposed children demonstrated a unique pattern of spelling deficits. The relation of working memory to spelling and reading was specific to the AE group, suggesting that if prenatal alcohol exposure is known or suspected, working memory ability should be considered in the development and implementation of explicit instruction. PMID:25643217
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Bae, Hyoung Won; Ji, Yongwoo; Lee, Hye Sun; Lee, Naeun; Hong, Samin; Seong, Gong Je; Sung, Kyung Rim; Kim, Chan Yun
2015-01-01
Normal-tension glaucoma (NTG) is a heterogenous disease, and there is still controversy about subclassifications of this disorder. On the basis of spectral-domain optical coherence tomography (SD-OCT), we subdivided NTG with hierarchical cluster analysis using optic nerve head (ONH) parameters and retinal nerve fiber layer (RNFL) thicknesses. A total of 200 eyes of 200 NTG patients between March 2011 and June 2012 underwent SD-OCT scans to measure ONH parameters and RNFL thicknesses. We classified NTG into homogenous subgroups based on these variables using a hierarchical cluster analysis, and compared clusters to evaluate diverse NTG characteristics. Three clusters were found after hierarchical cluster analysis. Cluster 1 (62 eyes) had the thickest RNFL and widest rim area, and showed early glaucoma features. Cluster 2 (60 eyes) was characterized by the largest cup/disc ratio and cup volume, and showed advanced glaucomatous damage. Cluster 3 (78 eyes) had small disc areas in SD-OCT and were comprised of patients with significantly younger age, longer axial length, and greater myopia than the other 2 groups. A hierarchical cluster analysis of SD-OCT scans divided NTG patients into 3 groups based upon ONH parameters and RNFL thicknesses. It is anticipated that the small disc area group comprised of younger and more myopic patients may show unique features unlike the other 2 groups.
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Clustering of Variables for Mixed Data
NASA Astrophysics Data System (ADS)
Saracco, J.; Chavent, M.
2016-05-01
This chapter presents clustering of variables which aim is to lump together strongly related variables. The proposed approach works on a mixed data set, i.e. on a data set which contains numerical variables and categorical variables. Two algorithms of clustering of variables are described: a hierarchical clustering and a k-means type clustering. A brief description of PCAmix method (that is a principal component analysis for mixed data) is provided, since the calculus of the synthetic variables summarizing the obtained clusters of variables is based on this multivariate method. Finally, the R packages ClustOfVar and PCAmixdata are illustrated on real mixed data. The PCAmix and ClustOfVar approaches are first used for dimension reduction (step 1) before applying in step 2 a standard clustering method to obtain groups of individuals.
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Bayesian Hierarchical Modeling of Cardiac Response to Particulate Matter Exposure
Studies have linked increased levels of particulate air pollution to decreased autonomic control, as measured by heart rate variability (HRV), particularly in populations such as the elderly. In this study, we use data obtained from the 1998 USEPA epidemiology-exposure longitudin...
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NASA Astrophysics Data System (ADS)
An, Le; Adeli, Ehsan; Liu, Mingxia; Zhang, Jun; Lee, Seong-Whan; Shen, Dinggang
2017-03-01
Classification is one of the most important tasks in machine learning. Due to feature redundancy or outliers in samples, using all available data for training a classifier may be suboptimal. For example, the Alzheimer’s disease (AD) is correlated with certain brain regions or single nucleotide polymorphisms (SNPs), and identification of relevant features is critical for computer-aided diagnosis. Many existing methods first select features from structural magnetic resonance imaging (MRI) or SNPs and then use those features to build the classifier. However, with the presence of many redundant features, the most discriminative features are difficult to be identified in a single step. Thus, we formulate a hierarchical feature and sample selection framework to gradually select informative features and discard ambiguous samples in multiple steps for improved classifier learning. To positively guide the data manifold preservation process, we utilize both labeled and unlabeled data during training, making our method semi-supervised. For validation, we conduct experiments on AD diagnosis by selecting mutually informative features from both MRI and SNP, and using the most discriminative samples for training. The superior classification results demonstrate the effectiveness of our approach, as compared with the rivals.
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Hierarchical prisoner’s dilemma in hierarchical game for resource competition
NASA Astrophysics Data System (ADS)
Fujimoto, Yuma; Sagawa, Takahiro; Kaneko, Kunihiko
2017-07-01
Dilemmas in cooperation are one of the major concerns in game theory. In a public goods game, each individual cooperates by paying a cost or defecting without paying it, and receives a reward from the group out of the collected cost. Thus, defecting is beneficial for each individual, while cooperation is beneficial for the group. Now, groups (say, countries) consisting of individuals also play games. To study such a multi-level game, we introduce a hierarchical game in which multiple groups compete for limited resources by utilizing the collected cost in each group, where the power to appropriate resources increases with the population of the group. Analyzing this hierarchical game, we found a hierarchical prisoner’s dilemma, in which groups choose the defecting policy (say, armament) as a Nash strategy to optimize each group’s benefit, while cooperation optimizes the total benefit. On the other hand, for each individual, refusing to pay the cost (say, tax) is a Nash strategy, which turns out to be a cooperation policy for the group, thus leading to a hierarchical dilemma. Here the group reward increases with the group size. However, we find that there exists an optimal group size that maximizes the individual payoff. Furthermore, when the population asymmetry between two groups is large, the smaller group will choose a cooperation policy (say, disarmament) to avoid excessive response from the larger group, and the prisoner’s dilemma between the groups is resolved. Accordingly, the relevance of this hierarchical game on policy selection in society and the optimal size of human or animal groups are discussed.
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Genomic Prediction Accounting for Residual Heteroskedasticity
Ou, Zhining; Tempelman, Robert J.; Steibel, Juan P.; Ernst, Catherine W.; Bates, Ronald O.; Bello, Nora M.
2015-01-01
Whole-genome prediction (WGP) models that use single-nucleotide polymorphism marker information to predict genetic merit of animals and plants typically assume homogeneous residual variance. However, variability is often heterogeneous across agricultural production systems and may subsequently bias WGP-based inferences. This study extends classical WGP models based on normality, heavy-tailed specifications and variable selection to explicitly account for environmentally-driven residual heteroskedasticity under a hierarchical Bayesian mixed-models framework. WGP models assuming homogeneous or heterogeneous residual variances were fitted to training data generated under simulation scenarios reflecting a gradient of increasing heteroskedasticity. Model fit was based on pseudo-Bayes factors and also on prediction accuracy of genomic breeding values computed on a validation data subset one generation removed from the simulated training dataset. Homogeneous vs. heterogeneous residual variance WGP models were also fitted to two quantitative traits, namely 45-min postmortem carcass temperature and loin muscle pH, recorded in a swine resource population dataset prescreened for high and mild residual heteroskedasticity, respectively. Fit of competing WGP models was compared using pseudo-Bayes factors. Predictive ability, defined as the correlation between predicted and observed phenotypes in validation sets of a five-fold cross-validation was also computed. Heteroskedastic error WGP models showed improved model fit and enhanced prediction accuracy compared to homoskedastic error WGP models although the magnitude of the improvement was small (less than two percentage points net gain in prediction accuracy). Nevertheless, accounting for residual heteroskedasticity did improve accuracy of selection, especially on individuals of extreme genetic merit. PMID:26564950
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Zeinab, Jalambadani; Gholamreza, Garmaroudi; Mehdi, Yaseri; Mahmood, Tavousi; Korush, Jafarian
2017-09-21
The Trans-Theoretical model (TTM) and Theory of Planned Behaviour (TPB) may be promising models for understanding and predicting reduction in the consumption of fast food. The aim of this study was to examine the applicability of the Trans-Theoretical model (TTM) and the additional predictive role of the subjective norms and perceived behavioural control in predicting reduction consumption of fast food in obese Iranian adolescent girls. A cross sectional study design was conducted among twelve randomly selected schools in Sabzevar, Iran from 2015 to 2017. Four hundred eighty five randomly selected students consented to participate in the study. Hierarchical regression models used to predict the role of important variables that can influence the reduction in the consumption of fast food among students. using SPSS version 22. Variables Perceived behavioural control (r=0.58, P<0.001), Subjective norms (r=0.51, P<0.001), self-efficacy (r=0.49, P<0.001), decisional balance (pros) (r=0.29, P<0.001), decisional balance (cons) (r=0.25, P<0.001), stage of change (r=0.38, P<0.001), were significantly and positively correlated while experiential processes of change (r=0.08, P=0.135) and behavioural processes of change (r=0.09, P=0.145), were not significant. The study demonstrated that the TTM (except the experiential and behavioural processes of change) focusing on the perceived behavioural control and subjective norms are useful models for reduction in the consumption of fast food.
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Qian, Wuyong; Wang, Zhou-Jing; Li, Kevin W.
2016-01-01
Although medical waste usually accounts for a small fraction of urban municipal waste, its proper disposal has been a challenging issue as it often contains infectious, radioactive, or hazardous waste. This article proposes a two-level hierarchical multicriteria decision model to address medical waste disposal method selection (MWDMS), where disposal methods are assessed against different criteria as intuitionistic fuzzy preference relations and criteria weights are furnished as real values. This paper first introduces new operations for a special class of intuitionistic fuzzy values, whose membership and non-membership information is cross ratio based ]0, 1[-values. New score and accuracy functions are defined in order to develop a comparison approach for ]0, 1[-valued intuitionistic fuzzy numbers. A weighted geometric operator is then put forward to aggregate a collection of ]0, 1[-valued intuitionistic fuzzy values. Similar to Saaty’s 1–9 scale, this paper proposes a cross-ratio-based bipolar 0.1–0.9 scale to characterize pairwise comparison results. Subsequently, a two-level hierarchical structure is formulated to handle multicriteria decision problems with intuitionistic preference relations. Finally, the proposed decision framework is applied to MWDMS to illustrate its feasibility and effectiveness. PMID:27618082
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Qian, Wuyong; Wang, Zhou-Jing; Li, Kevin W
2016-09-09
Although medical waste usually accounts for a small fraction of urban municipal waste, its proper disposal has been a challenging issue as it often contains infectious, radioactive, or hazardous waste. This article proposes a two-level hierarchical multicriteria decision model to address medical waste disposal method selection (MWDMS), where disposal methods are assessed against different criteria as intuitionistic fuzzy preference relations and criteria weights are furnished as real values. This paper first introduces new operations for a special class of intuitionistic fuzzy values, whose membership and non-membership information is cross ratio based ]0, 1[-values. New score and accuracy functions are defined in order to develop a comparison approach for ]0, 1[-valued intuitionistic fuzzy numbers. A weighted geometric operator is then put forward to aggregate a collection of ]0, 1[-valued intuitionistic fuzzy values. Similar to Saaty's 1-9 scale, this paper proposes a cross-ratio-based bipolar 0.1-0.9 scale to characterize pairwise comparison results. Subsequently, a two-level hierarchical structure is formulated to handle multicriteria decision problems with intuitionistic preference relations. Finally, the proposed decision framework is applied to MWDMS to illustrate its feasibility and effectiveness.
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Hierarchical feature selection for erythema severity estimation
NASA Astrophysics Data System (ADS)
Wang, Li; Shi, Chenbo; Shu, Chang
2014-10-01
At present PASI system of scoring is used for evaluating erythema severity, which can help doctors to diagnose psoriasis [1-3]. The system relies on the subjective judge of doctors, where the accuracy and stability cannot be guaranteed [4]. This paper proposes a stable and precise algorithm for erythema severity estimation. Our contributions are twofold. On one hand, in order to extract the multi-scale redness of erythema, we design the hierarchical feature. Different from traditional methods, we not only utilize the color statistical features, but also divide the detect window into small window and extract hierarchical features. Further, a feature re-ranking step is introduced, which can guarantee that extracted features are irrelevant to each other. On the other hand, an adaptive boosting classifier is applied for further feature selection. During the step of training, the classifier will seek out the most valuable feature for evaluating erythema severity, due to its strong learning ability. Experimental results demonstrate the high precision and robustness of our algorithm. The accuracy is 80.1% on the dataset which comprise 116 patients' images with various kinds of erythema. Now our system has been applied for erythema medical efficacy evaluation in Union Hosp, China.
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Counseling psychology trainees' perceptions of training and commitments to social justice.
Beer, Amanda M; Spanierman, Lisa B; Greene, Jennifer C; Todd, Nathan R
2012-01-01
This mixed methods study examined social justice commitments of counseling psychology graduate trainees. In the quantitative portion of the study, a national sample of trainees (n = 260) completed a web-based survey assessing their commitments to social justice and related personal and training variables. Results suggested that students desired greater social justice training than what they experienced in their programs. In the qualitative portion, we used a phenomenological approach to expand and elaborate upon quantitative results. A subsample (n = 7) of trainees who identified as strong social justice activists were interviewed regarding their personal, professional, and training experiences. Eleven themes related to participants' meanings of and experiences with social justice emerged within 4 broad categories: nature of social justice, motivation for activism, role of training, and personal and professional integration. Thematic findings as well as descriptive statistics informed the selection and ordering of variables in a hierarchical regression analysis that examined predictors of social justice commitment. Results indicated that trainees' perceptions of training environment significantly predicted their social justice commitment over and above their general activist orientation and spirituality. Findings are discussed collectively, and implications for training and future research are provided. (c) 2012 APA, all rights reserved.
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A Machine Learning Framework for Plan Payment Risk Adjustment.
Rose, Sherri
2016-12-01
To introduce cross-validation and a nonparametric machine learning framework for plan payment risk adjustment and then assess whether they have the potential to improve risk adjustment. 2011-2012 Truven MarketScan database. We compare the performance of multiple statistical approaches within a broad machine learning framework for estimation of risk adjustment formulas. Total annual expenditure was predicted using age, sex, geography, inpatient diagnoses, and hierarchical condition category variables. The methods included regression, penalized regression, decision trees, neural networks, and an ensemble super learner, all in concert with screening algorithms that reduce the set of variables considered. The performance of these methods was compared based on cross-validated R 2 . Our results indicate that a simplified risk adjustment formula selected via this nonparametric framework maintains much of the efficiency of a traditional larger formula. The ensemble approach also outperformed classical regression and all other algorithms studied. The implementation of cross-validated machine learning techniques provides novel insight into risk adjustment estimation, possibly allowing for a simplified formula, thereby reducing incentives for increased coding intensity as well as the ability of insurers to "game" the system with aggressive diagnostic upcoding. © Health Research and Educational Trust.
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Diurnal variability of CO2 flux at coastal zone of Taiwan based on eddy covariance observation
NASA Astrophysics Data System (ADS)
Chien, Hwa; Zhong, Yao-Zhao; Yang, Kang-Hung; Cheng, Hao-Yuan
2018-06-01
In this study, we employed shore-based eddy covariance systems for a continuous measurement of the coastal CO2 flux near the northwestern coast of Taiwan from 2011 to 2015. To ensure the validity of the analysis, the data was selected and filtered with a footprint model and an empirical mode decomposition method. The results indicate that the nearshore air-sea and air-land CO2 fluxes exhibited a significant diurnal variability and a substantial day-night difference. The net air-sea CO2 flux was -1.75 ± 0.98 μmol-C m-2 s-1, whereas the net air-land CO2 flux was 0.54 ± 7.35 μmol-C m-2 s-1, which indicated that in northwestern Taiwan, the coastal water acts as a sink of atmospheric CO2 but the coastal land acts as a source. The Random Forest Method was applied to hierarchize the influence of Chl-a, SST, DO, pH and U10 on air-sea CO2 fluxes. The result suggests that the strength of the diurnal air-sea CO2 flux is strongly influenced by the local wind speed.
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Hierarchical Bayesian spatial models for multispecies conservation planning and monitoring.
Carroll, Carlos; Johnson, Devin S; Dunk, Jeffrey R; Zielinski, William J
2010-12-01
Biologists who develop and apply habitat models are often familiar with the statistical challenges posed by their data's spatial structure but are unsure of whether the use of complex spatial models will increase the utility of model results in planning. We compared the relative performance of nonspatial and hierarchical Bayesian spatial models for three vertebrate and invertebrate taxa of conservation concern (Church's sideband snails [Monadenia churchi], red tree voles [Arborimus longicaudus], and Pacific fishers [Martes pennanti pacifica]) that provide examples of a range of distributional extents and dispersal abilities. We used presence-absence data derived from regional monitoring programs to develop models with both landscape and site-level environmental covariates. We used Markov chain Monte Carlo algorithms and a conditional autoregressive or intrinsic conditional autoregressive model framework to fit spatial models. The fit of Bayesian spatial models was between 35 and 55% better than the fit of nonspatial analogue models. Bayesian spatial models outperformed analogous models developed with maximum entropy (Maxent) methods. Although the best spatial and nonspatial models included similar environmental variables, spatial models provided estimates of residual spatial effects that suggested how ecological processes might structure distribution patterns. Spatial models built from presence-absence data improved fit most for localized endemic species with ranges constrained by poorly known biogeographic factors and for widely distributed species suspected to be strongly affected by unmeasured environmental variables or population processes. By treating spatial effects as a variable of interest rather than a nuisance, hierarchical Bayesian spatial models, especially when they are based on a common broad-scale spatial lattice (here the national Forest Inventory and Analysis grid of 24 km(2) hexagons), can increase the relevance of habitat models to multispecies conservation planning. Journal compilation © 2010 Society for Conservation Biology. No claim to original US government works.
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Chen, Cong; Zhang, Guohui; Liu, Xiaoyue Cathy; Ci, Yusheng; Huang, Helai; Ma, Jianming; Chen, Yanyan; Guan, Hongzhi
2016-12-01
There is a high potential of severe injury outcomes in traffic crashes on rural interstate highways due to the significant amount of high speed traffic on these corridors. Hierarchical Bayesian models are capable of incorporating between-crash variance and within-crash correlations into traffic crash data analysis and are increasingly utilized in traffic crash severity analysis. This paper applies a hierarchical Bayesian logistic model to examine the significant factors at crash and vehicle/driver levels and their heterogeneous impacts on driver injury severity in rural interstate highway crashes. Analysis results indicate that the majority of the total variance is induced by the between-crash variance, showing the appropriateness of the utilized hierarchical modeling approach. Three crash-level variables and six vehicle/driver-level variables are found significant in predicting driver injury severities: road curve, maximum vehicle damage in a crash, number of vehicles in a crash, wet road surface, vehicle type, driver age, driver gender, driver seatbelt use and driver alcohol or drug involvement. Among these variables, road curve, functional and disabled vehicle damage in crash, single-vehicle crashes, female drivers, senior drivers, motorcycles and driver alcohol or drug involvement tend to increase the odds of drivers being incapably injured or killed in rural interstate crashes, while wet road surface, male drivers and driver seatbelt use are more likely to decrease the probability of severe driver injuries. The developed methodology and estimation results provide insightful understanding of the internal mechanism of rural interstate crashes and beneficial references for developing effective countermeasures for rural interstate crash prevention. Copyright © 2016 Elsevier Ltd. All rights reserved.
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Wang, Xiao; Gu, Jinghua; Hilakivi-Clarke, Leena; Clarke, Robert; Xuan, Jianhua
2017-01-15
The advent of high-throughput DNA methylation profiling techniques has enabled the possibility of accurate identification of differentially methylated genes for cancer research. The large number of measured loci facilitates whole genome methylation study, yet posing great challenges for differential methylation detection due to the high variability in tumor samples. We have developed a novel probabilistic approach, D: ifferential M: ethylation detection using a hierarchical B: ayesian model exploiting L: ocal D: ependency (DM-BLD), to detect differentially methylated genes based on a Bayesian framework. The DM-BLD approach features a joint model to capture both the local dependency of measured loci and the dependency of methylation change in samples. Specifically, the local dependency is modeled by Leroux conditional autoregressive structure; the dependency of methylation changes is modeled by a discrete Markov random field. A hierarchical Bayesian model is developed to fully take into account the local dependency for differential analysis, in which differential states are embedded as hidden variables. Simulation studies demonstrate that DM-BLD outperforms existing methods for differential methylation detection, particularly when the methylation change is moderate and the variability of methylation in samples is high. DM-BLD has been applied to breast cancer data to identify important methylated genes (such as polycomb target genes and genes involved in transcription factor activity) associated with breast cancer recurrence. A Matlab package of DM-BLD is available at http://www.cbil.ece.vt.edu/software.htm CONTACT: Xuan@vt.eduSupplementary information: Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.
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Cecala, Kristen K.; Maerz, John C.; Halstead, Brian J.; Frisch, John R.; Gragson, Ted L.; Hepinstall-Cymerman, Jeffrey; Leigh, David S.; Jackson, C. Rhett; Peterson, James T.; Pringle, Catherine M.
2018-01-01
Understanding how factors that vary in spatial scale relate to population abundance is vital to forecasting species responses to environmental change. Stream and river ecosystems are inherently hierarchical, potentially resulting in organismal responses to fine‐scale changes in patch characteristics that are conditional on the watershed context. Here, we address how populations of two salamander species are affected by interactions among hierarchical processes operating at different scales within a rapidly changing landscape of the southern Appalachian Mountains. We modeled reach‐level occupancy of larval and adult black‐bellied salamanders (Desmognathus quadramaculatus) and larval Blue Ridge two‐lined salamanders (Eurycea wilderae) as a function of 17 different terrestrial and aquatic predictor variables that varied in spatial extent. We found that salamander occurrence varied widely among streams within fully forested catchments, but also exhibited species‐specific responses to changes in local conditions. While D. quadramaculatus declined predictably in relation to losses in forest cover, larval occupancy exhibited the strongest negative response to forest loss as well as decreases in elevation. Conversely, occupancy of E. wilderae was unassociated with watershed conditions, only responding negatively to higher proportions of fast‐flowing stream habitat types. Evaluation of hierarchical relationships demonstrated that most fine‐scale variables were closely correlated with broad watershed‐scale variables, suggesting that local reach‐scale factors have relatively smaller effects within the context of the larger landscape. Our results imply that effective management of southern Appalachian stream salamanders must first focus on the larger scale condition of watersheds before management of local‐scale conditions should proceed. Our findings confirm the results of some studies while refuting the results of others, which may indicate that prescriptive recommendations for range‐wide management of species or the application of a single management focus across large geographic areas is inappropriate.
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da Silva, Natal Santos; Undurraga, Eduardo A; da Silva Ferreira, Elis Regina; Estofolete, Cássia Fernanda; Nogueira, Maurício Lacerda
2018-01-01
In Brazil, the incidence of hospitalization due to dengue, as an indicator of severity, has drastically increased since 1998. The objective of our study was to identify risk factors associated with subsequent hospitalization related to dengue. We analyzed 7613 dengue confirmed via serology (ELISA), non-structural protein 1, or polymerase chain reaction amplification. We used a hierarchical framework to generate a multivariate logistic regression based on a variety of risk variables. This was followed by multiple statistical analyses to assess hierarchical model accuracy, variance, goodness of fit, and whether or not this model reliably represented the population. The final model, which included age, sex, ethnicity, previous dengue infection, hemorrhagic manifestations, plasma leakage, and organ failure, showed that all measured parameters, with the exception of previous dengue, were statistically significant. The presence of organ failure was associated with the highest risk of subsequent dengue hospitalization (OR=5·75; CI=3·53-9·37). Therefore, plasma leakage and organ failure were the main indicators of hospitalization due to dengue, although other variables of minor importance should also be considered to refer dengue patients to hospital treatment, which may lead to a reduction in avoidable deaths as well as costs related to dengue. Copyright © 2017 Elsevier B.V. All rights reserved.
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Using GOMS models and hypertext to create representations of medical procedures for online display
NASA Technical Reports Server (NTRS)
Gugerty, Leo; Halgren, Shannon; Gosbee, John; Rudisill, Marianne
1991-01-01
This study investigated two methods to improve organization and presentation of computer-based medical procedures. A literature review suggested that the GOMS (goals, operators, methods, and selecton rules) model can assist in rigorous task analysis, which can then help generate initial design ideas for the human-computer interface. GOMS model are hierarchical in nature, so this study also investigated the effect of hierarchical, hypertext interfaces. We used a 2 x 2 between subjects design, including the following independent variables: procedure organization - GOMS model based vs. medical-textbook based; navigation type - hierarchical vs. linear (booklike). After naive subjects studies the online procedures, measures were taken of their memory for the content and the organization of the procedures. This design was repeated for two medical procedures. For one procedure, subjects who studied GOMS-based and hierarchical procedures remembered more about the procedures than other subjects. The results for the other procedure were less clear. However, data for both procedures showed a 'GOMSification effect'. That is, when asked to do a free recall of a procedure, subjects who had studies a textbook procedure often recalled key information in a location inconsistent with the procedure they actually studied, but consistent with the GOMS-based procedure.
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Predicting U.S. food demand in the 20th century: a new look at system dynamics
NASA Astrophysics Data System (ADS)
Moorthy, Mukund; Cellier, Francois E.; LaFrance, Jeffrey T.
1998-08-01
The paper describes a new methodology for predicting the behavior of macroeconomic variables. The approach is based on System Dynamics and Fuzzy Inductive Reasoning. A four- layer pseudo-hierarchical model is proposed. The bottom layer makes predications about population dynamics, age distributions among the populace, as well as demographics. The second layer makes predications about the general state of the economy, including such variables as inflation and unemployment. The third layer makes predictions about the demand for certain goods or services, such as milk products, used cars, mobile telephones, or internet services. The fourth and top layer makes predictions about the supply of such goods and services, both in terms of their prices. Each layer can be influenced by control variables the values of which are only determined at higher levels. In this sense, the model is not strictly hierarchical. For example, the demand for goods at level three depends on the prices of these goods, which are only determined at level four. Yet, the prices are themselves influenced by the expected demand. The methodology is exemplified by means of a macroeconomic model that makes predictions about US food demand during the 20th century.
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Olea, Pedro P.; Mateo-Tomás, Patricia; de Frutos, Ángel
2010-01-01
Background Hierarchical partitioning (HP) is an analytical method of multiple regression that identifies the most likely causal factors while alleviating multicollinearity problems. Its use is increasing in ecology and conservation by its usefulness for complementing multiple regression analysis. A public-domain software “hier.part package” has been developed for running HP in R software. Its authors highlight a “minor rounding error” for hierarchies constructed from >9 variables, however potential bias by using this module has not yet been examined. Knowing this bias is pivotal because, for example, the ranking obtained in HP is being used as a criterion for establishing priorities of conservation. Methodology/Principal Findings Using numerical simulations and two real examples, we assessed the robustness of this HP module in relation to the order the variables have in the analysis. Results indicated a considerable effect of the variable order on the amount of independent variance explained by predictors for models with >9 explanatory variables. For these models the nominal ranking of importance of the predictors changed with variable order, i.e. predictors declared important by its contribution in explaining the response variable frequently changed to be either most or less important with other variable orders. The probability of changing position of a variable was best explained by the difference in independent explanatory power between that variable and the previous one in the nominal ranking of importance. The lesser is this difference, the more likely is the change of position. Conclusions/Significance HP should be applied with caution when more than 9 explanatory variables are used to know ranking of covariate importance. The explained variance is not a useful parameter to use in models with more than 9 independent variables. The inconsistency in the results obtained by HP should be considered in future studies as well as in those already published. Some recommendations to improve the analysis with this HP module are given. PMID:20657734
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Aiello, Marilena; Merola, Sheila; Lasaponara, Stefano; Pinto, Mario; Tomaiuolo, Francesco; Doricchi, Fabrizio
2018-01-31
The possibility of allocating attentional resources to the "global" shape or to the "local" details of pictorial stimuli helps visual processing. Investigations with hierarchical Navon letters, that are large "global" letters made up of small "local" ones, consistently demonstrate a right hemisphere advantage for global processing and a left hemisphere advantage for local processing. Here we investigated how the visual and phonological features of the global and local components of Navon letters influence these hemispheric advantages. In a first study in healthy participants, we contrasted the hemispheric processing of hierarchical letters with global and local items competing for response selection, to the processing of hierarchical letters in which a letter, a false-letter conveying no phonological information or a geometrical shape presented at the unattended level did not compete for response selection. In a second study, we investigated the hemispheric processing of hierarchical stimuli in which global and local letters were both visually and phonologically congruent (e.g. large uppercase G made of smaller uppercase G), visually incongruent and phonologically congruent (e.g. large uppercase G made of small lowercase g) or visually incongruent and phonologically incongruent (e.g. large uppercase G made of small lowercase or uppercase M). In a third study, we administered the same tasks to a right brain damaged patient with a lesion involving pre-striate areas engaged by global processing. The results of the first two experiments showed that the global abilities of the left hemisphere are limited because of its strong susceptibility to interference from local letters even when these are irrelevant to the task. Phonological features played a crucial role in this interference because the interference was entirely maintained also when letters at the global and local level were presented in different uppercase vs. lowercase formats. In contrast, when local features conveyed no phonological information, the left hemisphere showed preserved global processing abilities. These findings were supported by the study of the right brain damaged patient. These results offer a new look at the hemispheric dominance in the attentional processing of the global and local levels of hierarchical stimuli. Copyright © 2017 Elsevier Ltd. All rights reserved.
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A hierarchical modeling methodology for the definition and selection of requirements
NASA Astrophysics Data System (ADS)
Dufresne, Stephane
This dissertation describes the development of a requirements analysis methodology that takes into account the concept of operations and the hierarchical decomposition of aerospace systems. At the core of the methodology, the Analytic Network Process (ANP) is used to ensure the traceability between the qualitative and quantitative information present in the hierarchical model. The proposed methodology is implemented to the requirements definition of a hurricane tracker Unmanned Aerial Vehicle. Three research objectives are identified in this work; (1) improve the requirements mapping process by matching the stakeholder expectations with the concept of operations, systems and available resources; (2) reduce the epistemic uncertainty surrounding the requirements and requirements mapping; and (3) improve the requirements down-selection process by taking into account the level of importance of the criteria and the available resources. Several challenges are associated with the identification and definition of requirements. The complexity of the system implies that a large number of requirements are needed to define the systems. These requirements are defined early in the conceptual design, where the level of knowledge is relatively low and the level of uncertainty is large. The proposed methodology intends to increase the level of knowledge and reduce the level of uncertainty by guiding the design team through a structured process. To address these challenges, a new methodology is created to flow-down the requirements from the stakeholder expectations to the systems alternatives. A taxonomy of requirements is created to classify the information gathered during the problem definition. Subsequently, the operational and systems functions and measures of effectiveness are integrated to a hierarchical model to allow the traceability of the information. Monte Carlo methods are used to evaluate the variations of the hierarchical model elements and consequently reduce the epistemic uncertainty. The proposed methodology is applied to the design of a hurricane tracker Unmanned Aerial Vehicles to demonstrate the origin and impact of requirements on the concept of operations and systems alternatives. This research demonstrates that the hierarchical modeling methodology provides a traceable flow-down of the requirements from the problem definition to the systems alternatives phases of conceptual design.
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Extension of mixture-of-experts networks for binary classification of hierarchical data.
Ng, Shu-Kay; McLachlan, Geoffrey J
2007-09-01
For many applied problems in the context of medically relevant artificial intelligence, the data collected exhibit a hierarchical or clustered structure. Ignoring the interdependence between hierarchical data can result in misleading classification. In this paper, we extend the mechanism for mixture-of-experts (ME) networks for binary classification of hierarchical data. Another extension is to quantify cluster-specific information on data hierarchy by random effects via the generalized linear mixed-effects model (GLMM). The extension of ME networks is implemented by allowing for correlation in the hierarchical data in both the gating and expert networks via the GLMM. The proposed model is illustrated using a real thyroid disease data set. In our study, we consider 7652 thyroid diagnosis records from 1984 to early 1987 with complete information on 20 attribute values. We obtain 10 independent random splits of the data into a training set and a test set in the proportions 85% and 15%. The test sets are used to assess the generalization performance of the proposed model, based on the percentage of misclassifications. For comparison, the results obtained from the ME network with independence assumption are also included. With the thyroid disease data, the misclassification rate on test sets for the extended ME network is 8.9%, compared to 13.9% for the ME network. In addition, based on model selection methods described in Section 2, a network with two experts is selected. These two expert networks can be considered as modeling two groups of patients with high and low incidence rates. Significant variation among the predicted cluster-specific random effects is detected in the patient group with low incidence rate. It is shown that the extended ME network outperforms the ME network for binary classification of hierarchical data. With the thyroid disease data, useful information on the relative log odds of patients with diagnosed conditions at different periods can be evaluated. This information can be taken into consideration for the assessment of treatment planning of the disease. The proposed extended ME network thus facilitates a more general approach to incorporate data hierarchy mechanism in network modeling.
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Nice, Chris C; Forister, Matthew L; Gompert, Zachariah; Fordyce, James A; Shapiro, Arthur M
2014-08-01
An important and largely unaddressed issue in studies of biotic-abiotic relationships is the extent to which closely related species, or species living in similar habitats, have similar responses to weather. We addressed this by applying a hierarchical, Bayesian analytical framework to a long-term data set for butterflies which allowed us to simultaneously investigate responses of the entire fauna and individual species. A small number of variables had community-level effects. In particular, higher total annual snow depth had a positive effect on butterfly occurrences, while spring minimum temperature and El Niño-Southern Oscillation (ENSO) sea-surface variables for April-May had negative standardized coefficients. Our most important finding was that variables with large impacts at the community-level did not necessarily have a consistent response across all species. Species-level responses were much more similar to each other for snow depth compared to the other variables with strong community effects. This variation in species-level responses to weather variables raises important complications for the prediction of biotic responses to shifting climatic conditions. In addition, we found that clear associations with weather can be detected when considering ecologically delimited subsets of the community. For example, resident species and non-ruderal species had a much more unified response to weather variables compared to non-resident species and ruderal species, which suggests local adaptation to climate. These results highlight the complexity of biotic-abiotic interactions and confront that complexity with methodological advances that allow ecologists to understand communities and shifting climates while simultaneously revealing species-specific variation in response to climate.
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Patterns of Hierarchy in Formal and Principled Moral Reasoning.
ERIC Educational Resources Information Center
Zeidler, Dana Lewis
Measurements of formal reasoning and principled moral reasoning ability were obtained from a sample of 99 tenth grade students. Specific modes of formal reasoning (proportional reasoning, controlling variables, probabilistic, correlational and combinatorial reasoning) were first examined. Findings support the notion of hierarchical relationships…
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A Cognitive Complexity Metric Applied to Cognitive Development
ERIC Educational Resources Information Center
Andrews, Glenda; Halford, Graeme S.
2002-01-01
Two experiments tested predictions from a theory in which processing load depends on relational complexity (RC), the number of variables related in a single decision. Tasks from six domains (transitivity, hierarchical classification, class inclusion, cardinality, relative-clause sentence comprehension, and hypothesis testing) were administered to…
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Cool, Geneviève; Lebel, Alexandre; Sadiq, Rehan; Rodriguez, Manuel J
2015-12-01
The regional variability of the probability of occurrence of high total trihalomethane (TTHM) levels was assessed using multilevel logistic regression models that incorporate environmental and infrastructure characteristics. The models were structured in a three-level hierarchical configuration: samples (first level), drinking water utilities (DWUs, second level) and natural regions, an ecological hierarchical division from the Quebec ecological framework of reference (third level). They considered six independent variables: precipitation, temperature, source type, seasons, treatment type and pH. The average probability of TTHM concentrations exceeding the targeted threshold was 18.1%. The probability was influenced by seasons, treatment type, precipitations and temperature. The variance at all levels was significant, showing that the probability of TTHM concentrations exceeding the threshold is most likely to be similar if located within the same DWU and within the same natural region. However, most of the variance initially attributed to natural regions was explained by treatment types and clarified by spatial aggregation on treatment types. Nevertheless, even after controlling for treatment type, there was still significant regional variability of the probability of TTHM concentrations exceeding the threshold. Regional variability was particularly important for DWUs using chlorination alone since they lack the appropriate treatment required to reduce the amount of natural organic matter (NOM) in source water prior to disinfection. Results presented herein could be of interest to authorities in identifying regions with specific needs regarding drinking water quality and for epidemiological studies identifying geographical variations in population exposure to disinfection by-products (DBPs).
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Time-Hierarchical Clustering and Visualization of Weather Forecast Ensembles.
Ferstl, Florian; Kanzler, Mathias; Rautenhaus, Marc; Westermann, Rudiger
2017-01-01
We propose a new approach for analyzing the temporal growth of the uncertainty in ensembles of weather forecasts which are started from perturbed but similar initial conditions. As an alternative to traditional approaches in meteorology, which use juxtaposition and animation of spaghetti plots of iso-contours, we make use of contour clustering and provide means to encode forecast dynamics and spread in one single visualization. Based on a given ensemble clustering in a specified time window, we merge clusters in time-reversed order to indicate when and where forecast trajectories start to diverge. We present and compare different visualizations of the resulting time-hierarchical grouping, including space-time surfaces built by connecting cluster representatives over time, and stacked contour variability plots. We demonstrate the effectiveness of our visual encodings with forecast examples of the European Centre for Medium-Range Weather Forecasts, which convey the evolution of specific features in the data as well as the temporally increasing spatial variability.
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Self-esteem and optimism in men and women infected with HIV.
Anderson, E H
2000-01-01
Self-esteem and optimism have been associated with appraisal and outcomes in a variety of situations. The degree to which the contribution of self-esteem and optimism to outcomes over time is accounted for by the differences in threat (primary) or resource (secondary) appraisal has not been established in persons with human immunodeficiency virus (HIV). To examine the longitudinal relationship of personality (self-esteem and optimism) on primary and secondary appraisal and outcomes of well-being, mood, CD4+ T-lymphocyte count, and selected activities. Men (n = 56) and women (n = 42) infected with HIV completed eight self-report measures twice over 18 months. Hierarchical Multiple Regressions were used to examine the relationship of personality variables on appraisals and outcomes. The mediating effects of primary and secondary appraisals were explored. Self-esteem uniquely accounted for 6% of the variance in primary appraisal and 5% in secondary appraisal. Optimism accounted for 8% of the unique variance in secondary appraisal. Primary and secondary appraisal mediated differently between personality and outcome variables. A strong predictor of well-being, mood disturbance, and activity disruption at Time 2 was participants' initial level of these variables. Socioeconomic status was a strong predictor of mood. Self-esteem and optimism are important but different resources for adapting to HIV disease. Strategies for reducing threats and increasing resources associated with HIV may improve an individual's mood and sense of well-being.
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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.
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Stratification of habitats for identifying habitat selection by Merriam's turkeys
Mark A. Rumble; Stanley H. Anderson
1992-01-01
Habitat selection patterns of Merriamâs Turkeys were compared in hierarchical analyses of three levels of habitat stratification. Habitat descriptions in first-level analyses were based on dominant species of vegetation. Habitat descriptions in second-level analyses were based on dominant species of vegetation and overstory canopy cover. Habitat descriptions in third-...
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Using Response Times for Item Selection in Adaptive Testing
ERIC Educational Resources Information Center
van der Linden, Wim J.
2008-01-01
Response times on items can be used to improve item selection in adaptive testing provided that a probabilistic model for their distribution is available. In this research, the author used a hierarchical modeling framework with separate first-level models for the responses and response times and a second-level model for the distribution of the…
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Hierarchical den selection of Canada lynx in western Montana
John R. Squires; Nicholas J. Decesare; Jay A. Kolbe; Leonard F. Ruggiero
2008-01-01
We studied den selection of Canada lynx (Lynx canadensis; hereafter lynx) at multiple ecological scales based on 57 dens from 19 females located in western Montana, USA, between 1999 and 2006. We considered 3 spatial scales in this analysis, including den site (11-m-radius circle surrounding dens), den area (100-m-radius circle), and den environ (1-...
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Badre, David
2012-01-01
Growing evidence suggests that the prefrontal cortex (PFC) is organized hierarchically, with more anterior regions having increasingly abstract representations. How does this organization support hierarchical cognitive control and the rapid discovery of abstract action rules? We present computational models at different levels of description. A neural circuit model simulates interacting corticostriatal circuits organized hierarchically. In each circuit, the basal ganglia gate frontal actions, with some striatal units gating the inputs to PFC and others gating the outputs to influence response selection. Learning at all of these levels is accomplished via dopaminergic reward prediction error signals in each corticostriatal circuit. This functionality allows the system to exhibit conditional if–then hypothesis testing and to learn rapidly in environments with hierarchical structure. We also develop a hybrid Bayesian-reinforcement learning mixture of experts (MoE) model, which can estimate the most likely hypothesis state of individual participants based on their observed sequence of choices and rewards. This model yields accurate probabilistic estimates about which hypotheses are attended by manipulating attentional states in the generative neural model and recovering them with the MoE model. This 2-pronged modeling approach leads to multiple quantitative predictions that are tested with functional magnetic resonance imaging in the companion paper. PMID:21693490
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Hierarchical content-based image retrieval by dynamic indexing and guided search
NASA Astrophysics Data System (ADS)
You, Jane; Cheung, King H.; Liu, James; Guo, Linong
2003-12-01
This paper presents a new approach to content-based image retrieval by using dynamic indexing and guided search in a hierarchical structure, and extending data mining and data warehousing techniques. The proposed algorithms include: a wavelet-based scheme for multiple image feature extraction, the extension of a conventional data warehouse and an image database to an image data warehouse for dynamic image indexing, an image data schema for hierarchical image representation and dynamic image indexing, a statistically based feature selection scheme to achieve flexible similarity measures, and a feature component code to facilitate query processing and guide the search for the best matching. A series of case studies are reported, which include a wavelet-based image color hierarchy, classification of satellite images, tropical cyclone pattern recognition, and personal identification using multi-level palmprint and face features.
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Shinjo, Daisuke; Fushimi, Kiyohide
2015-11-17
To determine the effect of preoperative patient and hospital factors on resource use, cost and length of stay (LOS) among patients undergoing off-pump coronary artery bypass grafting (OPCAB). Observational retrospective study. Data from the Japanese Administrative Database. Patients who underwent isolated, elective OPCAB between April 2011 and March 2012. The primary outcomes of this study were inpatient cost and LOS associated with OPCAB. A two-level hierarchical linear model was used to examine the effects of patient and hospital characteristics on inpatient costs and LOS. The independent variables were patient and hospital factors. We identified 2491 patients who underwent OPCAB at 268 hospitals. The mean cost of OPCAB was $40 665 ±7774, and the mean LOS was 23.4±8.2 days. The study found that select patient factors and certain comorbidities were associated with a high cost and long LOS. A high hospital OPCAB volume was associated with a low cost (-6.6%; p=0.024) as well as a short LOS (-17.6%, p<0.001). The hospital OPCAB volume is associated with efficient resource use. The findings of the present study indicate the need to focus on hospital elective OPCAB volume in Japan in order to improve cost and LOS. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/
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Kim, Jeong-Im; Humphreys, Glyn W
2010-08-01
Previous research has shown that stimuli held in working memory (WM) can influence spatial attention. Using Navon stimuli, we explored whether and how items in WM affect the perception of visual targets at local and global levels in compound letters. Participants looked for a target letter presented at a local or global level while holding a regular block letter as a memory item. An effect of holding the target's identity in WM was found. When memory items and targets were the same, performance was better than in a neutral condition when the memory item did not appear in the hierarchical letter (a benefit from valid cuing). When the memory item matched the distractor in the hierarchical stimulus, performance was worse than in the neutral baseline (a cost on invalid trials). These effects were greatest when the WM cue matched the global level of the hierarchical stimulus, suggesting that WM biases attention to the global level of form. Interestingly, in a no-memory priming condition, target perception was faster in the invalid condition than in the neutral baseline, reversing the effect in the WM condition. A further control experiment ruled out the effects of WM being due to participants' refreshing their memory from the hierarchical stimulus display. The data show that information in WM biases the selection of hierarchical forms, whereas priming does not. Priming alters the perceptual processing of repeated stimuli without biasing attention.
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Feng, Liang; Yuan, Shuai; Zhang, Liang-Liang; Tan, Kui; Li, Jia-Luo; Kirchon, Angelo; Liu, Ling-Mei; Zhang, Peng; Han, Yu; Chabal, Yves J; Zhou, Hong-Cai
2018-02-14
Sufficient pore size, appropriate stability, and hierarchical porosity are three prerequisites for open frameworks designed for drug delivery, enzyme immobilization, and catalysis involving large molecules. Herein, we report a powerful and general strategy, linker thermolysis, to construct ultrastable hierarchically porous metal-organic frameworks (HP-MOFs) with tunable pore size distribution. Linker instability, usually an undesirable trait of MOFs, was exploited to create mesopores by generating crystal defects throughout a microporous MOF crystal via thermolysis. The crystallinity and stability of HP-MOFs remain after thermolabile linkers are selectively removed from multivariate metal-organic frameworks (MTV-MOFs) through a decarboxylation process. A domain-based linker spatial distribution was found to be critical for creating hierarchical pores inside MTV-MOFs. Furthermore, linker thermolysis promotes the formation of ultrasmall metal oxide nanoparticles immobilized in an open framework that exhibits high catalytic activity for Lewis acid-catalyzed reactions. Most importantly, this work provides fresh insights into the connection between linker apportionment and vacancy distribution, which may shed light on probing the disordered linker apportionment in multivariate systems, a long-standing challenge in the study of MTV-MOFs.
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Hierarchical video summarization
NASA Astrophysics Data System (ADS)
Ratakonda, Krishna; Sezan, M. Ibrahim; Crinon, Regis J.
1998-12-01
We address the problem of key-frame summarization of vide in the absence of any a priori information about its content. This is a common problem that is encountered in home videos. We propose a hierarchical key-frame summarization algorithm where a coarse-to-fine key-frame summary is generated. A hierarchical key-frame summary facilitates multi-level browsing where the user can quickly discover the content of the video by accessing its coarsest but most compact summary and then view a desired segment of the video with increasingly more detail. At the finest level, the summary is generated on the basis of color features of video frames, using an extension of a recently proposed key-frame extraction algorithm. The finest level key-frames are recursively clustered using a novel pairwise K-means clustering approach with temporal consecutiveness constraint. We also address summarization of MPEG-2 compressed video without fully decoding the bitstream. We also propose efficient mechanisms that facilitate decoding the video when the hierarchical summary is utilized in browsing and playback of video segments starting at selected key-frames.
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Yao, Dongren; Calhoun, Vince D; Fu, Zening; Du, Yuhui; Sui, Jing
2018-05-15
Discriminating Alzheimer's disease (AD) from its prodromal form, mild cognitive impairment (MCI), is a significant clinical problem that may facilitate early diagnosis and intervention, in which a more challenging issue is to classify MCI subtypes, i.e., those who eventually convert to AD (cMCI) versus those who do not (MCI). To solve this difficult 4-way classification problem (AD, MCI, cMCI and healthy controls), a competition was hosted by Kaggle to invite the scientific community to apply their machine learning approaches on pre-processed sets of T1-weighted magnetic resonance images (MRI) data and the demographic information from the international Alzheimer's disease neuroimaging initiative (ADNI) database. This paper summarizes our competition results. We first proposed a hierarchical process by turning the 4-way classification into five binary classification problems. A new feature selection technology based on relative importance was also proposed, aiming to identify a more informative and concise subset from 426 sMRI morphometric and 3 demographic features, to ensure each binary classifier to achieve its highest accuracy. As a result, about 2% of the original features were selected to build a new feature space, which can achieve the final four-way classification with a 54.38% accuracy on testing data through hierarchical grouping, higher than several alternative methods in comparison. More importantly, the selected discriminative features such as hippocampal volume, parahippocampal surface area, and medial orbitofrontal thickness, etc. as well as the MMSE score, are reasonable and consistent with those reported in AD/MCI deficits. In summary, the proposed method provides a new framework for multi-way classification using hierarchical grouping and precise feature selection. Copyright © 2018 Elsevier B.V. All rights reserved.
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College Tuition and Perceptions of Private University Quality
ERIC Educational Resources Information Center
Tang, Thomas Li-Ping; Tang, David Shin-Hsiung; Tang, Cindy Shin-Yi
2004-01-01
This research employs institutional characteristics and market-related factors to predict undergraduate students' tuition at 190 private colleges and universities in the USA. Results showed that the strongest correlations among variables for college tuition were reputation ranking and SAT scores. Results of a hierarchical multiple regression…
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Li, Ru; Sit, Cindy Hui-Ping; Yu, Jane Jie; Sum, Raymond Kim-Wai; Wong, Stephen Heung-Sang; Cheng, Kenneth Chik-Chi; McKenzie, Thomas L
2017-06-25
The purpose of this study was to assess the physical activity (PA) of children with physical disabilities (PD) in school and home settings and to simultaneously examine selected contextual characteristics in relation to PA in those settings. Children with PD (N = 35; Mean age = 15.67 ± 4.30 years; 26 boys) were systematically observed using BEACHES (Behaviors of Eating and Activity for Children's Health: Evaluation System) at school (before school, recess, lunch break, after class) and at home (before dinner) during four normal school days. The children spent most of their time in all five settings being physically inactive, but had slightly more PA during recess and lunch break periods. Hierarchical multiple regression revealed that selected contextual characteristics explained 18.9-56.0% ( p < 0.01) of the variance predicting moderate-to-vigorous physical activity (MVPA) after controlling for demographic variables. Prompts to be active were positively associated with MVPA at school and the presence of fathers and fathers being motivators at home. This study highlights how little PA that children with PD receive and identifies the importance of the provision of prompts for PA at both school and home with this special population.
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A SAR and QSAR study of new artemisinin compounds with antimalarial activity.
Santos, Cleydson Breno R; Vieira, Josinete B; Lobato, Cleison C; Hage-Melim, Lorane I S; Souto, Raimundo N P; Lima, Clarissa S; Costa, Elizabeth V M; Brasil, Davi S B; Macêdo, Williams Jorge C; Carvalho, José Carlos T
2013-12-30
The Hartree-Fock method and the 6-31G** basis set were employed to calculate the molecular properties of artemisinin and 20 derivatives with antimalarial activity. Maps of molecular electrostatic potential (MEPs) and molecular docking were used to investigate the interaction between ligands and the receptor (heme). Principal component analysis and hierarchical cluster analysis were employed to select the most important descriptors related to activity. The correlation between biological activity and molecular properties was obtained using the partial least squares and principal component regression methods. The regression PLS and PCR models built in this study were also used to predict the antimalarial activity of 30 new artemisinin compounds with unknown activity. The models obtained showed not only statistical significance but also predictive ability. The significant molecular descriptors related to the compounds with antimalarial activity were the hydration energy (HE), the charge on the O11 oxygen atom (QO11), the torsion angle O1-O2-Fe-N2 (D2) and the maximum rate of R/Sanderson Electronegativity (RTe+). These variables led to a physical and structural explanation of the molecular properties that should be selected for when designing new ligands to be used as antimalarial agents.
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Unmarked: An R package for fitting hierarchical models of wildlife occurrence and abundance
Fiske, I.J.; Chandler, R.B.
2011-01-01
Ecological research uses data collection techniques that are prone to substantial and unique types of measurement error to address scientic questions about species abundance and distribution. These data collection schemes include a number of survey methods in which unmarked individuals are counted, or determined to be present, at spatially- referenced sites. Examples include site occupancy sampling, repeated counts, distance sampling, removal sampling, and double observer sampling. To appropriately analyze these data, hierarchical models have been developed to separately model explanatory variables of both a latent abundance or occurrence process and a conditional detection process. Because these models have a straightforward interpretation paralleling mecha- nisms under which the data arose, they have recently gained immense popularity. The common hierarchical structure of these models is well-suited for a unied modeling in- terface. The R package unmarked provides such a unied modeling framework, including tools for data exploration, model tting, model criticism, post-hoc analysis, and model comparison.
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School system evaluation by value added analysis under endogeneity.
Manzi, Jorge; San Martín, Ernesto; Van Bellegem, Sébastien
2014-01-01
Value added is a common tool in educational research on effectiveness. It is often modeled as a (prediction of a) random effect in a specific hierarchical linear model. This paper shows that this modeling strategy is not valid when endogeneity is present. Endogeneity stems, for instance, from a correlation between the random effect in the hierarchical model and some of its covariates. This paper shows that this phenomenon is far from exceptional and can even be a generic problem when the covariates contain the prior score attainments, a typical situation in value added modeling. Starting from a general, model-free definition of value added, the paper derives an explicit expression of the value added in an endogeneous hierarchical linear Gaussian model. Inference on value added is proposed using an instrumental variable approach. The impact of endogeneity on the value added and the estimated value added is calculated accurately. This is also illustrated on a large data set of individual scores of about 200,000 students in Chile.
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Castillo, Ramon D.; Kloos, Heidi; Holden, John G.; Richardson, Michael J.
2015-01-01
In order to make sense of a scene, a person must pay attention to several levels of nested order, ranging from the most differentiated details of the display to the integrated whole. In adults, research shows that the processes of integration and differentiation have the signature of self-organization. Does the same hold for children? The current study addresses this question with children between 6 and 9 years of age, using two tasks that require attention to hierarchical displays. A group of adults were tested as well, for control purposes. To get at the question of self-organization, reaction times were submitted to a detrended fluctuation analysis and a recurrence quantification analysis. H exponents show a long-range correlations (1/f noise), and recurrence measures (percent determinism, maximum line, entropy, and trend), show a deterministic structure of variability being characteristic of self-organizing systems. Findings are discussed in terms of organism-environment coupling that gives rise to fluid attention to hierarchical displays. PMID:25999862
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Application of growing hierarchical SOM for visualisation of network forensics traffic data.
Palomo, E J; North, J; Elizondo, D; Luque, R M; Watson, T
2012-08-01
Digital investigation methods are becoming more and more important due to the proliferation of digital crimes and crimes involving digital evidence. Network forensics is a research area that gathers evidence by collecting and analysing network traffic data logs. This analysis can be a difficult process, especially because of the high variability of these attacks and large amount of data. Therefore, software tools that can help with these digital investigations are in great demand. In this paper, a novel approach to analysing and visualising network traffic data based on growing hierarchical self-organising maps (GHSOM) is presented. The self-organising map (SOM) has been shown to be successful for the analysis of highly-dimensional input data in data mining applications as well as for data visualisation in a more intuitive and understandable manner. However, the SOM has some problems related to its static topology and its inability to represent hierarchical relationships in the input data. The GHSOM tries to overcome these limitations by generating a hierarchical architecture that is automatically determined according to the input data and reflects the inherent hierarchical relationships among them. Moreover, the proposed GHSOM has been modified to correctly treat the qualitative features that are present in the traffic data in addition to the quantitative features. Experimental results show that this approach can be very useful for a better understanding of network traffic data, making it easier to search for evidence of attacks or anomalous behaviour in a network environment. Copyright © 2012 Elsevier Ltd. All rights reserved.
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Understanding seasonal variability of uncertainty in hydrological prediction
NASA Astrophysics Data System (ADS)
Li, M.; Wang, Q. J.
2012-04-01
Understanding uncertainty in hydrological prediction can be highly valuable for improving the reliability of streamflow prediction. In this study, a monthly water balance model, WAPABA, in a Bayesian joint probability with error models are presented to investigate the seasonal dependency of prediction error structure. A seasonal invariant error model, analogous to traditional time series analysis, uses constant parameters for model error and account for no seasonal variations. In contrast, a seasonal variant error model uses a different set of parameters for bias, variance and autocorrelation for each individual calendar month. Potential connection amongst model parameters from similar months is not considered within the seasonal variant model and could result in over-fitting and over-parameterization. A hierarchical error model further applies some distributional restrictions on model parameters within a Bayesian hierarchical framework. An iterative algorithm is implemented to expedite the maximum a posterior (MAP) estimation of a hierarchical error model. Three error models are applied to forecasting streamflow at a catchment in southeast Australia in a cross-validation analysis. This study also presents a number of statistical measures and graphical tools to compare the predictive skills of different error models. From probability integral transform histograms and other diagnostic graphs, the hierarchical error model conforms better to reliability when compared to the seasonal invariant error model. The hierarchical error model also generally provides the most accurate mean prediction in terms of the Nash-Sutcliffe model efficiency coefficient and the best probabilistic prediction in terms of the continuous ranked probability score (CRPS). The model parameters of the seasonal variant error model are very sensitive to each cross validation, while the hierarchical error model produces much more robust and reliable model parameters. Furthermore, the result of the hierarchical error model shows that most of model parameters are not seasonal variant except for error bias. The seasonal variant error model is likely to use more parameters than necessary to maximize the posterior likelihood. The model flexibility and robustness indicates that the hierarchical error model has great potential for future streamflow predictions.
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Cascade process modeling with mechanism-based hierarchical neural networks.
Cong, Qiumei; Yu, Wen; Chai, Tianyou
2010-02-01
Cascade process, such as wastewater treatment plant, includes many nonlinear sub-systems and many variables. When the number of sub-systems is big, the input-output relation in the first block and the last block cannot represent the whole process. In this paper we use two techniques to overcome the above problem. Firstly we propose a new neural model: hierarchical neural networks to identify the cascade process; then we use serial structural mechanism model based on the physical equations to connect with neural model. A stable learning algorithm and theoretical analysis are given. Finally, this method is used to model a wastewater treatment plant. Real operational data of wastewater treatment plant is applied to illustrate the modeling approach.
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VR Employment Outcomes of Individuals with Autism Spectrum Disorders: A Decade in the Making
ERIC Educational Resources Information Center
Alverson, Charlotte Y.; Yamamoto, Scott H.
2018-01-01
This study utilized hierarchical linear modeling analysis of a 10-year extant dataset from Rehabilitation Services Administration to investigate significant predictors of employment outcomes for vocational rehabilitation (VR) clients with autism. Predictor variables were gender, ethnicity, attained education level, IEP status in high school,…
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ERIC Educational Resources Information Center
Lester, Regan; Petrie, Trent A.
1995-01-01
Examined the relationship of personality and physical variables to bulimic symptoms. Hierarchical regression analysis of a sample of Mexican American female students revealed that body mass and endorsement of United States societal values concerning attractiveness were related positively to bulimic symptomatology; age, body satisfaction, and…
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Economic and Demographic Factors Impacting Placement of Students with Autism
ERIC Educational Resources Information Center
Kurth, Jennifer A.; Mastergeorge, Ann M.; Paschall, Katherine
2016-01-01
Educational placement of students with autism is often associated with child factors, such as IQ and communication skills. However, variability in placement patterns across states suggests that other factors are at play. This study used hierarchical cluster analysis techniques to identify demographic, economic, and educational covariates…
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ERIC Educational Resources Information Center
Bahr, Peter Riley
2009-01-01
Variables that address student enrollment patterns (e.g., persistence, enrollment inconsistency, completed credit hours, course credit load, course completion rate, procrastination) constitute a longstanding fixture of analytical strategies in educational research, particularly research that focuses on explaining variation in academic outcomes.…
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USDA-ARS?s Scientific Manuscript database
Psychosocial and demographic correlates of fruit, juice, and vegetable (FJV) consumption were investigated to guide how to increase FJV intake. Experimental design consisted of hierarchical multiple regression analysis of FJV consumption on demographics and psychosocial variables. Subjects were boys...
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Knowing When to Retire: The First Step towards Financial Planning in Malaysia
ERIC Educational Resources Information Center
Kock, Tan Hoe; Yoong, Folk Jee
2011-01-01
This article draws upon expected retirement age cohorts as a main determinant to financial planning preparation in Malaysia. The return rate was 55% from 600 questionnaires distributed. Five hypotheses were analyzed using hierarchical and stepwise regression analysis. The results revealed that expected retirement age cohort variables made…
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Functions of Marijuana Use in College Students
ERIC Educational Resources Information Center
Bates, Julie K.; Accordino, Michael P.; Hewes, Robert L.
2010-01-01
Hierarchical regression analysis was used to test the hypothesis that specific functional factors of marijuana use would predict past 30-day marijuana use in 425 college students more precisely than demographic variables alone. This hypothesis was confirmed. Functional factors of personal/physical enhancement as well as activity enhancement were…
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2013-01-01
Background In 2009, the Tobacco Hazards Prevention Act (Taiwan) was amended to more effectively restrict smoking in indoor public places and workplaces in Taiwan. However, the lack of prohibitions for smoking in private homes may place family members at increased risk for exposure to environmental tobacco smoke (ETS). The aim of our study was to determine the factors associated with parental smoking in the presence of children at home. Methods In 2010, we performed a cross-sectional study of factors associated with parental smoking in the presence of children at home in Taiwan using self-administered questionnaires. Quota sampling was used to select five primary schools from four different regions of Taiwan. Parents were surveyed to identify parental smokers and 307 parental smokers were selected for participation in our study. Questionnaire data regarding parental smoking in the presence of children at home and related interactions among family members were analyzed. Hierarchical logistic regression was used to determine the best-fit model for examining the relationships among the variables related to parental smoking in the presence of children at home. Results Two-thirds of parents who smoked reported smoking in the presence of their children. The results of the hierarchical logistic regression analysis identified the smokers’ compliance with their family’s antismoking responses, mutual agreement with smoking bans, daily smoking, smoking more than 20 cigarettes per day, the education level of the parental smoker, and the annual family income as determinants of smoking in the presence of children at home. Conclusions Households with smoking parents should be targeted for interventions to encourage the adoption and enforcement of home smoking bans. Educational interventions that promote smoke-free homes for children and provide support to help parents stop smoking are critical factors in reducing the frequency of children’s ETS exposure in the home. PMID:24015810
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Casals, Martí; Girabent-Farrés, Montserrat; Carrasco, Josep L
2014-01-01
Modeling count and binary data collected in hierarchical designs have increased the use of Generalized Linear Mixed Models (GLMMs) in medicine. This article presents a systematic review of the application and quality of results and information reported from GLMMs in the field of clinical medicine. A search using the Web of Science database was performed for published original articles in medical journals from 2000 to 2012. The search strategy included the topic "generalized linear mixed models","hierarchical generalized linear models", "multilevel generalized linear model" and as a research domain we refined by science technology. Papers reporting methodological considerations without application, and those that were not involved in clinical medicine or written in English were excluded. A total of 443 articles were detected, with an increase over time in the number of articles. In total, 108 articles fit the inclusion criteria. Of these, 54.6% were declared to be longitudinal studies, whereas 58.3% and 26.9% were defined as repeated measurements and multilevel design, respectively. Twenty-two articles belonged to environmental and occupational public health, 10 articles to clinical neurology, 8 to oncology, and 7 to infectious diseases and pediatrics. The distribution of the response variable was reported in 88% of the articles, predominantly Binomial (n = 64) or Poisson (n = 22). Most of the useful information about GLMMs was not reported in most cases. Variance estimates of random effects were described in only 8 articles (9.2%). The model validation, the method of covariate selection and the method of goodness of fit were only reported in 8.0%, 36.8% and 14.9% of the articles, respectively. During recent years, the use of GLMMs in medical literature has increased to take into account the correlation of data when modeling qualitative data or counts. According to the current recommendations, the quality of reporting has room for improvement regarding the characteristics of the analysis, estimation method, validation, and selection of the model.
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Bu, Lintao; Nimlos, Mark R.; Robichaud, David J.
Hierarchical mesoporous zeolites exhibit higher catalytic activities and longer lifetime compared to the traditional microporous zeolites due to improved diffusivity of substrate molecules and their enhanced access to the zeolite active sites. Understanding diffusion of biomass pyrolysis vapors and their upgraded products in such materials is fundamentally important during catalytic fast pyrolysis (CFP) of lignocellulosic biomass, since diffusion makes major contribution to determine shape selectivity and product distribution. However, diffusivities of biomass relevant species in hierarchical mesoporous zeolites are poorly characterized, primarily due to the limitations of the available experimental technology. In this work, molecular dynamics (MD) simulations are utilizedmore » to investigate the diffusivities of several selected coke precursor molecules, benzene, naphthalene, and anthracene, in hierarchical mesoporous H-ZSM-5 zeolite. The effects of temperature and size of mesopores on the diffusivity of the chosen model compounds are examined. The simulation results demonstrate that diffusion within the microspores as well as on the external surface of mesoporous H-ZSM-5 dominates only at low temperature. At pyrolysis relevant temperatures, mass transfer is essentially conducted via diffusion along the mesopores. Additionally, the results illustrate the heuristic diffusion model, such as the extensively used Knudsen diffusion, overestimates the diffusion of bulky molecules in the mesopores, thus making MD simulation a powerful and compulsory approach to explore diffusion in zeolites.« less
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Safdari, Reza; Ghazisaeedi, Marjan; Mirzaee, Mahboobeh; Farzi, Jebrail; Goodini, Azadeh
2014-01-01
Dynamic reporting tools, such as dashboards, should be developed to measure emergency department (ED) performance. However, choosing an effective balanced set of performance measures and key performance indicators (KPIs) is a main challenge to accomplish this. The aim of this study was to develop a balanced set of KPIs for use in ED strategic dashboards following an analytic hierarchical process. The study was carried out in 2 phases: constructing ED performance measures based on balanced scorecard perspectives and incorporating them into analytic hierarchical process framework to select the final KPIs. The respondents placed most importance on ED internal processes perspective especially on measures related to timeliness and accessibility of care in ED. Some measures from financial, customer, and learning and growth perspectives were also selected as other top KPIs. Measures of care effectiveness and care safety were placed as the next priorities too. The respondents placed least importance on disease-/condition-specific "time to" measures. The methodology can be presented as a reference model for development of KPIs in various performance related areas based on a consistent and fair approach. Dashboards that are designed based on such a balanced set of KPIs will help to establish comprehensive performance measurements and fair benchmarks and comparisons.
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Saha, Dibakar; Alluri, Priyanka; Gan, Albert; Wu, Wanyang
2018-02-21
The objective of this study was to investigate the relationship between bicycle crash frequency and their contributing factors at the census block group level in Florida, USA. Crashes aggregated over the census block groups tend to be clustered (i.e., spatially dependent) rather than randomly distributed. To account for the effect of spatial dependence across the census block groups, the class of conditional autoregressive (CAR) models were employed within the hierarchical Bayesian framework. Based on four years (2011-2014) of crash data, total and fatal-and-severe injury bicycle crash frequencies were modeled as a function of a large number of variables representing demographic and socio-economic characteristics, roadway infrastructure and traffic characteristics, and bicycle activity characteristics. This study explored and compared the performance of two CAR models, namely the Besag's model and the Leroux's model, in crash prediction. The Besag's models, which differ from the Leroux's models by the structure of how spatial autocorrelation are specified in the models, were found to fit the data better. A 95% Bayesian credible interval was selected to identify the variables that had credible impact on bicycle crashes. A total of 21 variables were found to be credible in the total crash model, while 18 variables were found to be credible in the fatal-and-severe injury crash model. Population, daily vehicle miles traveled, age cohorts, household automobile ownership, density of urban roads by functional class, bicycle trip miles, and bicycle trip intensity had positive effects in both the total and fatal-and-severe crash models. Educational attainment variables, truck percentage, and density of rural roads by functional class were found to be negatively associated with both total and fatal-and-severe bicycle crash frequencies. Published by Elsevier Ltd.
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Taha, Zahari; Musa, Rabiu Muazu; P P Abdul Majeed, Anwar; Alim, Muhammad Muaz; Abdullah, Mohamad Razali
2018-02-01
Support Vector Machine (SVM) has been shown to be an effective learning algorithm for classification and prediction. However, the application of SVM for prediction and classification in specific sport has rarely been used to quantify/discriminate low and high-performance athletes. The present study classified and predicted high and low-potential archers from a set of fitness and motor ability variables trained on different SVMs kernel algorithms. 50 youth archers with the mean age and standard deviation of 17.0 ± 0.6 years drawn from various archery programmes completed a six arrows shooting score test. Standard fitness and ability measurements namely hand grip, vertical jump, standing broad jump, static balance, upper muscle strength and the core muscle strength were also recorded. Hierarchical agglomerative cluster analysis (HACA) was used to cluster the archers based on the performance variables tested. SVM models with linear, quadratic, cubic, fine RBF, medium RBF, as well as the coarse RBF kernel functions, were trained based on the measured performance variables. The HACA clustered the archers into high-potential archers (HPA) and low-potential archers (LPA), respectively. The linear, quadratic, cubic, as well as the medium RBF kernel functions models, demonstrated reasonably excellent classification accuracy of 97.5% and 2.5% error rate for the prediction of the HPA and the LPA. The findings of this investigation can be valuable to coaches and sports managers to recognise high potential athletes from a combination of the selected few measured fitness and motor ability performance variables examined which would consequently save cost, time and effort during talent identification programme. Copyright © 2017 Elsevier B.V. All rights reserved.
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NASA Astrophysics Data System (ADS)
Djallel Dilmi, Mohamed; Mallet, Cécile; Barthes, Laurent; Chazottes, Aymeric
2017-04-01
Rain time series records are generally studied using rainfall rate or accumulation parameters, which are estimated for a fixed duration (typically 1 min, 1 h or 1 day). In this study we use the concept of
rain events
. The aim of the first part of this paper is to establish a parsimonious characterization of rain events, using a minimal set of variables selected among those normally used for the characterization of these events. A methodology is proposed, based on the combined use of a genetic algorithm (GA) and self-organizing maps (SOMs). It can be advantageous to use an SOM, since it allows a high-dimensional data space to be mapped onto a two-dimensional space while preserving, in an unsupervised manner, most of the information contained in the initial space topology. The 2-D maps obtained in this way allow the relationships between variables to be determined and redundant variables to be removed, thus leading to a minimal subset of variables. We verify that such 2-D maps make it possible to determine the characteristics of all events, on the basis of only five features (the event duration, the peak rain rate, the rain event depth, the standard deviation of the rain rate event and the absolute rain rate variation of the order of 0.5). From this minimal subset of variables, hierarchical cluster analyses were carried out. We show that clustering into two classes allows the conventional convective and stratiform classes to be determined, whereas classification into five classes allows this convective-stratiform classification to be further refined. Finally, our study made it possible to reveal the presence of some specific relationships between these five classes and the microphysics of their associated rain events. -
Mohammed, Mohammed A; Manktelow, Bradley N; Hofer, Timothy P
2016-04-01
There is interest in deriving case-mix adjusted standardised mortality ratios so that comparisons between healthcare providers, such as hospitals, can be undertaken in the controversial belief that variability in standardised mortality ratios reflects quality of care. Typically standardised mortality ratios are derived using a fixed effects logistic regression model, without a hospital term in the model. This fails to account for the hierarchical structure of the data - patients nested within hospitals - and so a hierarchical logistic regression model is more appropriate. However, four methods have been advocated for deriving standardised mortality ratios from a hierarchical logistic regression model, but their agreement is not known and neither do we know which is to be preferred. We found significant differences between the four types of standardised mortality ratios because they reflect a range of underlying conceptual issues. The most subtle issue is the distinction between asking how an average patient fares in different hospitals versus how patients at a given hospital fare at an average hospital. Since the answers to these questions are not the same and since the choice between these two approaches is not obvious, the extent to which profiling hospitals on mortality can be undertaken safely and reliably, without resolving these methodological issues, remains questionable. © The Author(s) 2012.
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A hierarchical model for spatial capture-recapture data
Royle, J. Andrew; Young, K.V.
2008-01-01
Estimating density is a fundamental objective of many animal population studies. Application of methods for estimating population size from ostensibly closed populations is widespread, but ineffective for estimating absolute density because most populations are subject to short-term movements or so-called temporary emigration. This phenomenon invalidates the resulting estimates because the effective sample area is unknown. A number of methods involving the adjustment of estimates based on heuristic considerations are in widespread use. In this paper, a hierarchical model of spatially indexed capture recapture data is proposed for sampling based on area searches of spatial sample units subject to uniform sampling intensity. The hierarchical model contains explicit models for the distribution of individuals and their movements, in addition to an observation model that is conditional on the location of individuals during sampling. Bayesian analysis of the hierarchical model is achieved by the use of data augmentation, which allows for a straightforward implementation in the freely available software WinBUGS. We present results of a simulation study that was carried out to evaluate the operating characteristics of the Bayesian estimator under variable densities and movement patterns of individuals. An application of the model is presented for survey data on the flat-tailed horned lizard (Phrynosoma mcallii) in Arizona, USA.
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Sharafi, Zahra
2017-01-01
Background The purpose of this study was to evaluate the effectiveness of two methods of detecting differential item functioning (DIF) in the presence of multilevel data and polytomously scored items. The assessment of DIF with multilevel data (e.g., patients nested within hospitals, hospitals nested within districts) from large-scale assessment programs has received considerable attention but very few studies evaluated the effect of hierarchical structure of data on DIF detection for polytomously scored items. Methods The ordinal logistic regression (OLR) and hierarchical ordinal logistic regression (HOLR) were utilized to assess DIF in simulated and real multilevel polytomous data. Six factors (DIF magnitude, grouping variable, intraclass correlation coefficient, number of clusters, number of participants per cluster, and item discrimination parameter) with a fully crossed design were considered in the simulation study. Furthermore, data of Pediatric Quality of Life Inventory™ (PedsQL™) 4.0 collected from 576 healthy school children were analyzed. Results Overall, results indicate that both methods performed equivalently in terms of controlling Type I error and detection power rates. Conclusions The current study showed negligible difference between OLR and HOLR in detecting DIF with polytomously scored items in a hierarchical structure. Implications and considerations while analyzing real data were also discussed. PMID:29312463
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Sharafi, Zahra; Mousavi, Amin; Ayatollahi, Seyyed Mohammad Taghi; Jafari, Peyman
2017-01-01
The purpose of this study was to evaluate the effectiveness of two methods of detecting differential item functioning (DIF) in the presence of multilevel data and polytomously scored items. The assessment of DIF with multilevel data (e.g., patients nested within hospitals, hospitals nested within districts) from large-scale assessment programs has received considerable attention but very few studies evaluated the effect of hierarchical structure of data on DIF detection for polytomously scored items. The ordinal logistic regression (OLR) and hierarchical ordinal logistic regression (HOLR) were utilized to assess DIF in simulated and real multilevel polytomous data. Six factors (DIF magnitude, grouping variable, intraclass correlation coefficient, number of clusters, number of participants per cluster, and item discrimination parameter) with a fully crossed design were considered in the simulation study. Furthermore, data of Pediatric Quality of Life Inventory™ (PedsQL™) 4.0 collected from 576 healthy school children were analyzed. Overall, results indicate that both methods performed equivalently in terms of controlling Type I error and detection power rates. The current study showed negligible difference between OLR and HOLR in detecting DIF with polytomously scored items in a hierarchical structure. Implications and considerations while analyzing real data were also discussed.
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Hierarchical Heteroclinics in Dynamical Model of Cognitive Processes: Chunking
NASA Astrophysics Data System (ADS)
Afraimovich, Valentin S.; Young, Todd R.; Rabinovich, Mikhail I.
Combining the results of brain imaging and nonlinear dynamics provides a new hierarchical vision of brain network functionality that is helpful in understanding the relationship of the network to different mental tasks. Using these ideas it is possible to build adequate models for the description and prediction of different cognitive activities in which the number of variables is usually small enough for analysis. The dynamical images of different mental processes depend on their temporal organization and, as a rule, cannot be just simple attractors since cognition is characterized by transient dynamics. The mathematical image for a robust transient is a stable heteroclinic channel consisting of a chain of saddles connected by unstable separatrices. We focus here on hierarchical chunking dynamics that can represent several cognitive activities. Chunking is the dynamical phenomenon that means dividing a long information chain into shorter items. Chunking is known to be important in many processes of perception, learning, memory and cognition. We prove that in the phase space of the model that describes chunking there exists a new mathematical object — heteroclinic sequence of heteroclinic cycles — using the technique of slow-fast approximations. This new object serves as a skeleton of motions reflecting sequential features of hierarchical chunking dynamics and is an adequate image of the chunking processing.
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Sergentanis, Theodoros N; Sakelliadis, Emmanouil I; Vlachodimitropoulos, Dimitrios; Goutas, Nikolaos; Sergentanis, Ioannis N; Spiliopoulou, Chara A; Papadodima, StavroulaA
2014-12-30
This study attempts to assess childhood maltreatment in prison through a hierarchical approach. The hierarchical approach principally aims to disentangle the independent effects of childhood maltreatment upon psychiatric morbidity/personality traits, if any, from the burden that the adverse family conditions have already imposed to the mental health of the maltreated individual-prisoner. To this direction, a conceptual framework with five hierarchical levels was constructed, namely: immutable demographic factors; family conditions; childhood maltreatment (physical abuse, neglect and sexual abuse); personality traits, habits and psychiatric morbidity; prison-related variables. A self-administered, anonymous set (battery) of questionnaires was administered to 173 male prisoners in the Chalkida prison, Greece; 26% of prisoners disclosed childhood maltreatment. Psychiatric condition in the family, parental alcoholism and parental divorce correlated with childhood maltreatment. After adjustment for immutable demographic factors and family conditions, childhood maltreatment was associated with aggression (both in terms of Lifetime History of Aggression and Buss–Perry Aggression Questionnaire scores), illicit substance use, personal history of psychiatric condition, current smoking, impulsivity and alcohol abuse. In conclusion, childhood maltreatment represents a pivotal, determining factor in the life course of male prisoners. Delinquents seem to suffer from long-term consequences of childhood maltreatment in terms of numerous mental health aspects.
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Singharoy, Abhishek; Sereda, Yuriy
2012-01-01
Macromolecular assemblies often display a hierarchical organization of macromolecules or their sub-assemblies. To model this, we have formulated a space warping method that enables capturing overall macromolecular structure and dynamics via a set of coarse-grained order parameters (OPs). This article is the first of two describing the construction and computational implementation of an additional class of OPs that has built into them the hierarchical architecture of macromolecular assemblies. To accomplish this, first, the system is divided into subsystems, each of which is described via a representative set of OPs. Then, a global set of variables is constructed from these subsystem-centered OPs to capture overall system organization. Dynamical properties of the resulting OPs are compared to those of our previous nonhierarchical ones, and implied conceptual and computational advantages are discussed for a 100ns, 2 million atom solvated Human Papillomavirus-like particle simulation. In the second article, the hierarchical OPs are shown to enable a multiscale analysis that starts with the N-atom Liouville equation and yields rigorous Langevin equations of stochastic OP dynamics. The latter is demonstrated via a force-field based simulation algorithm that probes key structural transition pathways, simultaneously accounting for all-atom details and overall structure. PMID:22661911
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Kang, Xinchen; Zhang, Jianling; Shang, Wenting; Wu, Tianbin; Zhang, Peng; Han, Buxing; Wu, Zhonghua; Mo, Guang; Xing, Xueqing
2014-03-12
Stable porous ionic liquid-water gel induced by inorganic salts was created for the first time. The porous gel was used to develop a one-step method to synthesize supported metal nanocatalysts. Au/SiO2, Ru/SiO2, Pd/Cu(2-pymo)2 metal-organic framework (Cu-MOF), and Au/polyacrylamide (PAM) were synthesized, in which the supports had hierarchical meso- and macropores, the size of the metal nanocatalysts could be very small (<1 nm), and the size distribution was very narrow even when the metal loading amount was as high as 8 wt %. The catalysts were extremely active, selective, and stable for oxidative esterification of benzyl alcohol to methyl benzoate, benzene hydrogenation to cyclohexane, and oxidation of benzyl alcohol to benzaldehyde because they combined the advantages of the nanocatalysts of small size and hierarchical porosity of the supports. In addition, this method is very simple.
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A new anisotropic mesh adaptation method based upon hierarchical a posteriori error estimates
NASA Astrophysics Data System (ADS)
Huang, Weizhang; Kamenski, Lennard; Lang, Jens
2010-03-01
A new anisotropic mesh adaptation strategy for finite element solution of elliptic differential equations is presented. It generates anisotropic adaptive meshes as quasi-uniform ones in some metric space, with the metric tensor being computed based on hierarchical a posteriori error estimates. A global hierarchical error estimate is employed in this study to obtain reliable directional information of the solution. Instead of solving the global error problem exactly, which is costly in general, we solve it iteratively using the symmetric Gauß-Seidel method. Numerical results show that a few GS iterations are sufficient for obtaining a reasonably good approximation to the error for use in anisotropic mesh adaptation. The new method is compared with several strategies using local error estimators or recovered Hessians. Numerical results are presented for a selection of test examples and a mathematical model for heat conduction in a thermal battery with large orthotropic jumps in the material coefficients.
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Sharifahmadian, Ershad
2006-01-01
The set partitioning in hierarchical trees (SPIHT) algorithm is very effective and computationally simple technique for image and signal compression. Here the author modified the algorithm which provides even better performance than the SPIHT algorithm. The enhanced set partitioning in hierarchical trees (ESPIHT) algorithm has performance faster than the SPIHT algorithm. In addition, the proposed algorithm reduces the number of bits in a bit stream which is stored or transmitted. I applied it to compression of multichannel ECG data. Also, I presented a specific procedure based on the modified algorithm for more efficient compression of multichannel ECG data. This method employed on selected records from the MIT-BIH arrhythmia database. According to experiments, the proposed method attained the significant results regarding compression of multichannel ECG data. Furthermore, in order to compress one signal which is stored for a long time, the proposed multichannel compression method can be utilized efficiently.
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Hierarchical fuzzy control of low-energy building systems
DOE Office of Scientific and Technical Information (OSTI.GOV)
Yu, Zhen; Dexter, Arthur
2010-04-15
A hierarchical fuzzy supervisory controller is described that is capable of optimizing the operation of a low-energy building, which uses solar energy to heat and cool its interior spaces. The highest level fuzzy rules choose the most appropriate set of lower level rules according to the weather and occupancy information; the second level fuzzy rules determine an optimal energy profile and the overall modes of operation of the heating, ventilating and air-conditioning system (HVAC); the third level fuzzy rules select the mode of operation of specific equipment, and assign schedules to the local controllers so that the optimal energy profilemore » can be achieved in the most efficient way. Computer simulation is used to compare the hierarchical fuzzy control scheme with a supervisory control scheme based on expert rules. The performance is evaluated by comparing the energy consumption and thermal comfort. (author)« less
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Hierarchical Bayes approach for subgroup analysis.
Hsu, Yu-Yi; Zalkikar, Jyoti; Tiwari, Ram C
2017-01-01
In clinical data analysis, both treatment effect estimation and consistency assessment are important for a better understanding of the drug efficacy for the benefit of subjects in individual subgroups. The linear mixed-effects model has been used for subgroup analysis to describe treatment differences among subgroups with great flexibility. The hierarchical Bayes approach has been applied to linear mixed-effects model to derive the posterior distributions of overall and subgroup treatment effects. In this article, we discuss the prior selection for variance components in hierarchical Bayes, estimation and decision making of the overall treatment effect, as well as consistency assessment of the treatment effects across the subgroups based on the posterior predictive p-value. Decision procedures are suggested using either the posterior probability or the Bayes factor. These decision procedures and their properties are illustrated using a simulated example with normally distributed response and repeated measurements.
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Hierarchical Diagnosis of Vocal Fold Disorders
NASA Astrophysics Data System (ADS)
Nikkhah-Bahrami, Mansour; Ahmadi-Noubari, Hossein; Seyed Aghazadeh, Babak; Khadivi Heris, Hossein
This paper explores the use of hierarchical structure for diagnosis of vocal fold disorders. The hierarchical structure is initially used to train different second-level classifiers. At the first level normal and pathological signals have been distinguished. Next, pathological signals have been classified into neurogenic and organic vocal fold disorders. At the final level, vocal fold nodules have been distinguished from polyps in organic disorders category. For feature selection at each level of hierarchy, the reconstructed signal at each wavelet packet decomposition sub-band in 5 levels of decomposition with mother wavelet of (db10) is used to extract the nonlinear features of self-similarity and approximate entropy. Also, wavelet packet coefficients are used to measure energy and Shannon entropy features at different spectral sub-bands. Davies-Bouldin criterion has been employed to find the most discriminant features. Finally, support vector machines have been adopted as classifiers at each level of hierarchy resulting in the diagnosis accuracy of 92%.
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Stability of glassy hierarchical networks
NASA Astrophysics Data System (ADS)
Zamani, M.; Camargo-Forero, L.; Vicsek, T.
2018-02-01
The structure of interactions in most animal and human societies can be best represented by complex hierarchical networks. In order to maintain close-to-optimal function both stability and adaptability are necessary. Here we investigate the stability of hierarchical networks that emerge from the simulations of an organization type with an efficiency function reminiscent of the Hamiltonian of spin glasses. Using this quantitative approach we find a number of expected (from everyday observations) and highly non-trivial results for the obtained locally optimal networks, including, for example: (i) stability increases with growing efficiency and level of hierarchy; (ii) the same perturbation results in a larger change for more efficient states; (iii) networks with a lower level of hierarchy become more efficient after perturbation; (iv) due to the huge number of possible optimal states only a small fraction of them exhibit resilience and, finally, (v) ‘attacks’ targeting the nodes selectively (regarding their position in the hierarchy) can result in paradoxical outcomes.
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A Policy Representation Using Weighted Multiple Normal Distribution
NASA Astrophysics Data System (ADS)
Kimura, Hajime; Aramaki, Takeshi; Kobayashi, Shigenobu
In this paper, we challenge to solve a reinforcement learning problem for a 5-linked ring robot within a real-time so that the real-robot can stand up to the trial and error. On this robot, incomplete perception problems are caused from noisy sensors and cheap position-control motor systems. This incomplete perception also causes varying optimum actions with the progress of the learning. To cope with this problem, we adopt an actor-critic method, and we propose a new hierarchical policy representation scheme, that consists of discrete action selection on the top level and continuous action selection on the low level of the hierarchy. The proposed hierarchical scheme accelerates learning on continuous action space, and it can pursue the optimum actions varying with the progress of learning on our robotics problem. This paper compares and discusses several learning algorithms through simulations, and demonstrates the proposed method showing application for the real robot.
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Hierarchical planning for a surface mounting machine placement.
Zeng, You-jiao; Ma, Deng-ze; Jin, Ye; Yan, Jun-qi
2004-11-01
For a surface mounting machine (SMM) in printed circuit board (PCB) assembly line, there are four problems, e.g. CAD data conversion, nozzle selection, feeder assignment and placement sequence determination. A hierarchical planning for them to maximize the throughput rate of an SMM is presented here. To minimize set-up time, a CAD data conversion system was first applied that could automatically generate the data for machine placement from CAD design data files. Then an effective nozzle selection approach implemented to minimize the time of nozzle changing. And then, to minimize picking time, an algorithm for feeder assignment was used to make picking multiple components simultaneously as much as possible. Finally, in order to shorten pick-and-place time, a heuristic algorithm was used to determine optimal component placement sequence according to the decided feeder positions. Experiments were conducted on a four head SMM. The experimental results were used to analyse the assembly line performance.
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NASA Astrophysics Data System (ADS)
Jimenez, H.; Dumas, P.; Ponton, D.; Ferraris, J.
2012-03-01
Invertebrates represent an essential component of coral reef ecosystems; they are ecologically important and a major resource, but their assemblages remain largely unknown, particularly on Pacific islands. Understanding their distribution and building predictive models of community composition as a function of environmental variables therefore constitutes a key issue for resource management. The goal of this study was to define and classify the main environmental factors influencing tropical invertebrate distributions in New Caledonian reef flats and to test the resulting predictive model. Invertebrate assemblages were sampled by visual counting during 2 years and 2 seasons, then coupled to different environmental conditions (habitat composition, hydrodynamics and sediment characteristics) and harvesting status (MPA vs. non-MPA and islets vs. coastal flats). Environmental conditions were described by a principal component analysis (PCA), and contributing variables were selected. Permutational analysis of variance (PERMANOVA) was used to test the effects of different factors (status, flat, year and season) on the invertebrate assemblage composition. Multivariate regression trees (MRT) were then used to hierarchically classify the effects of environmental and harvesting variables. MRT model explained at least 60% of the variation in structure of invertebrate communities. Results highlighted the influence of status (MPA vs. non-MPA) and location (islet vs. coastal flat), followed by habitat composition, organic matter content, hydrodynamics and sampling year. Predicted assemblages defined by indicator families were very different for each environment-exploitation scenario and correctly matched a calibration data matrix. Predictions from MRT including both environmental variables and harvesting pressure can be useful for management of invertebrates in coral reef environments.
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[Factors Influencing Quality of Life of Alcoholics Anonymous Members in Korea].
Yoo, Jae Soon; Lee, Jongeun; Park, Woo Young
2016-04-01
The purpose of this study was to determine quality of life (QOL) related factors in Alcoholics Anonymous (AA) members based on PRECEDE Model. A cross sectional survey was conducted with participants (N =203) from AA meeting in 11 alcohol counsel centers all over South Korea. Data were collected using a specially designed questionnaire based on the PRECEDE model and including QOL, epidemiological factors (including depression and perceived health status), behavioral factors (continuous abstinence and physical health status and practice), predisposing factors (abstinence self-efficacy and self-esteem), reinforcing factors (social capital and family functioning), and enabling factors. Data were analyzed using t-test, one way ANOVA, Tukey HSD test and hierarchical multiple regression analysis with SPSS (ver. 21.0). Of the educational diagnostic variables, self-esteem (β=.23), family functioning (β=.12), abstinence self-efficacy (β=.12) and social capital (β=.11) were strong influential factors in AA members' QOL. In addition, epidemiological diagnostic variables such as depression (β=-.44) and perceived health status (β=.35) were the main factors in QOL. Also, physical health status and practice (β=.106), one of behavioral diagnostic variables was a beneficial factor in QOL. Hierarchical multiple regression analysis showed the determinant variables accounted for 44.0% of the variation in QOL (F=25.76, p<.001). The finding of the study can be used as a framework for planning interventions in order to promote the quality of life of AA members. It is necessary to develop nursing intervention strategies for strengthening educational and epidemiological diagnostic variables in order to improve AA members' QOL.
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Cruz, Bruna B.; Miranda, Leandro E.; Cetra, Mauricio
2013-01-01
We hypothesised and tested a hierarchical organisation model where riparian landcover would influence bank composition and light availability, which in turn would influence instream environments and control fish assemblages. The study was conducted during the dry season in 11 headwater tributaries of the Sorocaba River in the upper Paraná River Basin, south-eastern Brazil. We focused on seven environmental factors each represented by one or multiple environmental variables and seven fish functional traits each represented by two or more classes. Multivariate direct gradient analyses suggested that riparian zone landcover can be considered a higher level causal factor in a network of relations that control instream characteristics and fish assemblages. Our results provide a framework for a hierarchical conceptual model that identifies singular and collective influences of variables from different scales on each other and ultimately on different aspects related to stream fish functional composition. This conceptual model is focused on the relationships between riparian landcover and instream variables as causal factors on the organisation of stream fish assemblages. Our results can also be viewed as a model for headwater stream management in that landcover can be manipulated to influence factors such as bank composition, substrates and water quality, whereas fish assemblage composition can be used as indicators to monitor the success of such efforts.
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Boehler, Christian E. H.; Lord, Joanne
2016-01-01
Background. Published cost-effectiveness estimates can vary considerably, both within and between countries. Despite extensive discussion, little is known empirically about factors relating to these variations. Objectives. To use multilevel statistical modeling to integrate cost-effectiveness estimates from published economic evaluations to investigate potential causes of variation. Methods. Cost-effectiveness studies of statins for cardiovascular disease prevention were identified by systematic review. Estimates of incremental costs and effects were extracted from reported base case, sensitivity, and subgroup analyses, with estimates grouped in studies and in countries. Three bivariate models were developed: a cross-classified model to accommodate data from multinational studies, a hierarchical model with multinational data allocated to a single category at country level, and a hierarchical model excluding multinational data. Covariates at different levels were drawn from a long list of factors suggested in the literature. Results. We found 67 studies reporting 2094 cost-effectiveness estimates relating to 23 countries (6 studies reporting for more than 1 country). Data and study-level covariates included patient characteristics, intervention and comparator cost, and some study methods (e.g., discount rates and time horizon). After adjusting for these factors, the proportion of variation attributable to countries was negligible in the cross-classified model but moderate in the hierarchical models (14%−19% of total variance). Country-level variables that improved the fit of the hierarchical models included measures of income and health care finance, health care resources, and population risks. Conclusions. Our analysis suggested that variability in published cost-effectiveness estimates is related more to differences in study methods than to differences in national context. Multinational studies were associated with much lower country-level variation than single-country studies. These findings are for a single clinical question and may be atypical. PMID:25878194
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Genomic Prediction Accounting for Residual Heteroskedasticity.
Ou, Zhining; Tempelman, Robert J; Steibel, Juan P; Ernst, Catherine W; Bates, Ronald O; Bello, Nora M
2015-11-12
Whole-genome prediction (WGP) models that use single-nucleotide polymorphism marker information to predict genetic merit of animals and plants typically assume homogeneous residual variance. However, variability is often heterogeneous across agricultural production systems and may subsequently bias WGP-based inferences. This study extends classical WGP models based on normality, heavy-tailed specifications and variable selection to explicitly account for environmentally-driven residual heteroskedasticity under a hierarchical Bayesian mixed-models framework. WGP models assuming homogeneous or heterogeneous residual variances were fitted to training data generated under simulation scenarios reflecting a gradient of increasing heteroskedasticity. Model fit was based on pseudo-Bayes factors and also on prediction accuracy of genomic breeding values computed on a validation data subset one generation removed from the simulated training dataset. Homogeneous vs. heterogeneous residual variance WGP models were also fitted to two quantitative traits, namely 45-min postmortem carcass temperature and loin muscle pH, recorded in a swine resource population dataset prescreened for high and mild residual heteroskedasticity, respectively. Fit of competing WGP models was compared using pseudo-Bayes factors. Predictive ability, defined as the correlation between predicted and observed phenotypes in validation sets of a five-fold cross-validation was also computed. Heteroskedastic error WGP models showed improved model fit and enhanced prediction accuracy compared to homoskedastic error WGP models although the magnitude of the improvement was small (less than two percentage points net gain in prediction accuracy). Nevertheless, accounting for residual heteroskedasticity did improve accuracy of selection, especially on individuals of extreme genetic merit. Copyright © 2016 Ou et al.
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NASA Astrophysics Data System (ADS)
Choi, Hak-Jong; Choi, Seon-Jin; Choo, Soyoung; Kim, Il-Doo; Lee, Heon
2016-01-01
We propose a novel synthetic route by combining imprinting transfer of a Sb-doped SnO2 (ATO)-ZnO composite micrograting pattern (MP), i.e., microstrip lines, on a sensor substrate and subsequent hydrothermal growth of ZnO nanowires (NWs) for producing a hierarchical ZnO NW-loaded ATO-ZnO MP as an improved chemo-resistive sensing layer. Here, ATO-ZnO MP structure with 3-μm line width, 9-μm pitch, and 6-μm height was fabricated by direct transfer of mixed ATO and ZnO nanoparticle (NP)-dispersed resists, which are pre-patterned on a polydimethylsiloxane (PDMS) mold. ZnO NWs with an average diameter of less than 50 nm and a height of 250 nm were quasi-vertically grown on the ATO-ZnO MP, leading to markedly enhanced surface area and heterojunction composites between each ATO NP, ZnO NP, and ZnO NW. A ZnO NW-loaded MP sensor with a relative ratio of 1:9 between ATO and ZnO (1:9 ATO-ZnO), exhibited highly sensitive and selective acetone sensing performance with 2.84-fold higher response (Rair/Rgas = 12.8) compared to that (Rair/Rgas = 4.5) of pristine 1:9 ATO-ZnO MP sensor at 5 ppm. Our results demonstrate the processing advantages of direct imprinting-assisted hydrothermal growth for large-scale homogeneous coating of hierarchical oxide layers, particularly for applications in highly sensitive and selective chemical sensors.
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Choi, Hak-Jong; Choi, Seon-Jin; Choo, Soyoung; Kim, Il-Doo; Lee, Heon
2016-01-08
We propose a novel synthetic route by combining imprinting transfer of a Sb-doped SnO2 (ATO)-ZnO composite micrograting pattern (MP), i.e., microstrip lines, on a sensor substrate and subsequent hydrothermal growth of ZnO nanowires (NWs) for producing a hierarchical ZnO NW-loaded ATO-ZnO MP as an improved chemo-resistive sensing layer. Here, ATO-ZnO MP structure with 3-μm line width, 9-μm pitch, and 6-μm height was fabricated by direct transfer of mixed ATO and ZnO nanoparticle (NP)-dispersed resists, which are pre-patterned on a polydimethylsiloxane (PDMS) mold. ZnO NWs with an average diameter of less than 50 nm and a height of 250 nm were quasi-vertically grown on the ATO-ZnO MP, leading to markedly enhanced surface area and heterojunction composites between each ATO NP, ZnO NP, and ZnO NW. A ZnO NW-loaded MP sensor with a relative ratio of 1:9 between ATO and ZnO (1:9 ATO-ZnO), exhibited highly sensitive and selective acetone sensing performance with 2.84-fold higher response (R air/R gas = 12.8) compared to that (R air/R gas = 4.5) of pristine 1:9 ATO-ZnO MP sensor at 5 ppm. Our results demonstrate the processing advantages of direct imprinting-assisted hydrothermal growth for large-scale homogeneous coating of hierarchical oxide layers, particularly for applications in highly sensitive and selective chemical sensors.
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Choi, Hak-Jong; Choi, Seon-Jin; Choo, Soyoung; Kim, Il-Doo; Lee, Heon
2016-01-01
We propose a novel synthetic route by combining imprinting transfer of a Sb-doped SnO2 (ATO)-ZnO composite micrograting pattern (MP), i.e., microstrip lines, on a sensor substrate and subsequent hydrothermal growth of ZnO nanowires (NWs) for producing a hierarchical ZnO NW-loaded ATO-ZnO MP as an improved chemo-resistive sensing layer. Here, ATO-ZnO MP structure with 3-μm line width, 9-μm pitch, and 6-μm height was fabricated by direct transfer of mixed ATO and ZnO nanoparticle (NP)-dispersed resists, which are pre-patterned on a polydimethylsiloxane (PDMS) mold. ZnO NWs with an average diameter of less than 50 nm and a height of 250 nm were quasi-vertically grown on the ATO-ZnO MP, leading to markedly enhanced surface area and heterojunction composites between each ATO NP, ZnO NP, and ZnO NW. A ZnO NW-loaded MP sensor with a relative ratio of 1:9 between ATO and ZnO (1:9 ATO-ZnO), exhibited highly sensitive and selective acetone sensing performance with 2.84-fold higher response (Rair/Rgas = 12.8) compared to that (Rair/Rgas = 4.5) of pristine 1:9 ATO-ZnO MP sensor at 5 ppm. Our results demonstrate the processing advantages of direct imprinting-assisted hydrothermal growth for large-scale homogeneous coating of hierarchical oxide layers, particularly for applications in highly sensitive and selective chemical sensors. PMID:26743814
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Miranda, M; Díaz, L; Sicilia, M; Cristóbal, I; Cassinello, J
2011-01-01
We report evidence of hierarchical resource selection by large herbivores and plant neighbouring effects in a Mediterranean ecosystem. Plant palatability was assessed according to herbivore foraging decisions. We hypothesize that under natural conditions large herbivores follow a hierarchical foraging pattern, starting at the landscape scale, and then selecting patches and individual plants. A between- and within-patch selection study was carried out in an area formed by scrubland and pasture patches, connected by habitat edges. With regard to between-patch selection, quality-dependent resource selection is reported: herbivores mainly consume pasture in spring and woody plants in winter. Within-patch selection was also observed in scrub habitats, influenced by season, relative patch palatability and edge effect. We defined a Proximity Index (PI) between palatable and unpalatable plants, which allowed verification of neighbouring effects. In spring, when the preferred food resource (i.e. herbs) is abundant, we observed that in habitat edges large herbivores basically select the relatively scarce palatable shrubs, whereas inside scrubland, unpalatable shrub consumption was related to increasing PI. In winter, a very different picture was observed; there was low consumption of palatable species surrounded by unpalatable species in habitat edges, where the latter were more abundant. These outcomes could be explained though different plant associations described in the literature. We conclude that optimal foraging theory provides a conceptual framework behind the observed interactions between plants and large herbivores in Mediterranean ecosystems. © 2010 German Botanical Society and The Royal Botanical Society of the Netherlands.
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ERIC Educational Resources Information Center
Bowers, Alex J.
2015-01-01
School districts in the USA are an active area of study in education research as findings have shown that some districts find success in certain contexts while others struggle. However, the research domain has had few actionable methods for site selection for in-depth qualitative studies. This study analyses all districts in the state of Ohio (n =…
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Bustamante-Ubilla, Miguel Alejandro; del Río-Rivero, María Carolina; Lobos-Andrade, Germán Enrique; Villarreal-Navarrete, Patricia Isabel
2009-01-01
In this work, a questionnaire was designed and perceptions of motivation and demotivation of middle managers in three hospitals in the Region del Maule, Chile were measured. The fieldwork was carried out between September and October, 2006. A questionnaire that included 57 statements to measure attitude was administered and qualified according to a five-point Likert-type scale. The population studied included l25 professionals that supervise roughly 3 800 employees. Ten variables were identified, 5 motivational and 5 demotivational. Notable among the motivational variables are vocation and service-oriented spirit; among the demotivational variables are lack of recognition and commitment. It is affirmed that both motivational variables as well as demotivational variables are essentially qualitative and that economic and salary variables are less relevant and less hierarchical.
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NASA Astrophysics Data System (ADS)
Hockaday, W. C.; Kane, E. S.; Ohlson, M.; Huang, R.; Von Bargen, J.; Davis, R.
2014-12-01
Efforts have been made by various scientific disciplines to study hyporheic zones and characterize their associated processes. One way to approach the study of the hyporheic zone is to define facies, which are elements of a (hydrobio) geologic classification scheme that groups components of a complex system with high variability into a manageable set of discrete classes. In this study, we try to classify the hyporheic zone based on the geology, geochemistry, microbiology, and understand their interactive influences on the integrated biogeochemical distributions and processes. A number of measurements have been taken for 21 freeze core samples along the Columbia River bank in the Hanford 300 Area, and unique datasets have been obtained on biomass, pH, number of microbial taxa, percentage of N/C/H/S, microbial activity parameters, as well as microbial community attributes/modules. In order to gain a complete understanding of the geological control on these variables and processes, the explanatory variables are set to include quantitative gravel/sand/mud/silt/clay percentages, statistical moments of grain size distributions, as well as geological (e.g., Folk-Wentworth) and statistical (e.g., hierarchical) clusters. The dominant factors for major microbial and geochemical variables are identified and summarized using exploratory data analysis approaches (e.g., principal component analysis, hierarchical clustering, factor analysis, multivariate analysis of variance). The feasibility of extending the facies definition and its control of microbial and geochemical properties to larger scales is discussed.
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Microbial facies distribution and its geological and geochemical controls at the Hanford 300 area
NASA Astrophysics Data System (ADS)
Hou, Z.; Nelson, W.; Stegen, J.; Murray, C. J.; Arntzen, E.
2015-12-01
Efforts have been made by various scientific disciplines to study hyporheic zones and characterize their associated processes. One way to approach the study of the hyporheic zone is to define facies, which are elements of a (hydrobio) geologic classification scheme that groups components of a complex system with high variability into a manageable set of discrete classes. In this study, we try to classify the hyporheic zone based on the geology, geochemistry, microbiology, and understand their interactive influences on the integrated biogeochemical distributions and processes. A number of measurements have been taken for 21 freeze core samples along the Columbia River bank in the Hanford 300 Area, and unique datasets have been obtained on biomass, pH, number of microbial taxa, percentage of N/C/H/S, microbial activity parameters, as well as microbial community attributes/modules. In order to gain a complete understanding of the geological control on these variables and processes, the explanatory variables are set to include quantitative gravel/sand/mud/silt/clay percentages, statistical moments of grain size distributions, as well as geological (e.g., Folk-Wentworth) and statistical (e.g., hierarchical) clusters. The dominant factors for major microbial and geochemical variables are identified and summarized using exploratory data analysis approaches (e.g., principal component analysis, hierarchical clustering, factor analysis, multivariate analysis of variance). The feasibility of extending the facies definition and its control of microbial and geochemical properties to larger scales is discussed.
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River science and management can be conducted at a range of spatiotemporal scales from reach to basin levels as long as the project goals and questions are matched correctly with the study design’s spatiotemporal scales and dependent variables. These project goals should also inc...
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ERIC Educational Resources Information Center
Fischer, Donald V.; Wielkiewicz, Richard M.; Stelzner, Stephen P.; Overland, Maribeth; Meuwissen, Alyssa S.
2015-01-01
Incoming first-year college students completed a leadership survey prior to any formal leadership education. These students were reassessed during the spring of their senior year; 386 students completed both surveys. The differential effect of 33 leadership and demographic variables on change in hierarchical and systemic leadership beliefs were…
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ERIC Educational Resources Information Center
Choi, Namok; Chang, Mido
2011-01-01
This research examined the important factors influencing the mathematics achievement of students in middle schools by hierarchically specifying the personal and contextual variables. The study focused on the effect of school climate at the class level and the effects of student gender, attitude toward mathematics, educational aspiration, parent…
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A Multilevel Study of the Role of Environment in Adolescent Substance Use
ERIC Educational Resources Information Center
Steen, Julie A.
2010-01-01
The purpose of this study is to assess the relationships between county-level characteristics and adolescent use of alcohol, cigarettes, and marijuana. The study consisted of a hierarchical generalized linear analysis of secondary data from the Florida Youth Substance Abuse Survey. Variables on the county level included the percent of adolescents…
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Predictors of College Readiness: An Analysis of the Student Readiness Inventory
ERIC Educational Resources Information Center
Wilson, James K., III
2012-01-01
The purpose of this study was to better predict how a first semester college freshman becomes prepared for college. The theoretical framework guiding this study is Vrooms' expectancy theory, motivation plays a key role in success. This study used a hierarchical multiple regression model. The independent variables of interest included high school…
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ERIC Educational Resources Information Center
Barkoukis, Vassilis; Ntoumanis, Nikos; Nikitaras, Nikitas
2007-01-01
Background: It is commonly assumed that there is conceptual equivalence between the task and ego achievement goals proposed by Nicholl's (1989) dichotomous achievement goal theory (Nicholls, 1989), and the mastery and performance approach goals advanced by Elliot's (1997) trichotomous hierarchical model of approach and avoidance achievement…
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The land-cover cascade: relationships coupling land and water
C.L. Burcher; H.M. Valett; E.F. Benfield
2007-01-01
We introduce the land-cover cascade (LCC) as a conceptual framework to quantify the transfer of land-cover-disturbance effects to stream biota. We hypothesize that disturbance is propagated through multivariate systems through key variables that transform a disturbance and pass a reorganized disturbance effect to the next hierarchical level where the process repeats...
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Investigating the Incremental Validity of Cognitive Variables in Early Mathematics Screening
ERIC Educational Resources Information Center
Clarke, Ben; Shanley, Lina; Kosty, Derek; Baker, Scott K.; Cary, Mari Strand; Fien, Hank; Smolkowski, Keith
2018-01-01
The purpose of this study was to investigate the incremental validity of a set of domain general cognitive measures added to a traditional screening battery of early numeracy measures. The sample consisted of 458 kindergarten students of whom 285 were designated as severely at-risk for mathematics difficulty. Hierarchical multiple regression…
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ERIC Educational Resources Information Center
Liu, Ou Lydia; Lee, Hee-Sun; Linn, Marcia C.
2010-01-01
Teachers play a central role in inquiry science classrooms. In this study, we investigate how seven teacher variables (i.e., gender, experience, perceived importance of inquiry and traditional teaching, workshop attendance, partner teacher, use of technology) affect student knowledge integration understanding of science topics drawing on previous…
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ERIC Educational Resources Information Center
Pajak, Bozena; Fine, Alex B.; Kleinschmidt, Dave F.; Jaeger, T. Florian
2016-01-01
We present a framework of second and additional language (L2/L"n") acquisition motivated by recent work on socio-indexical knowledge in first language (L1) processing. The distribution of linguistic categories covaries with socio-indexical variables (e.g., talker identity, gender, dialects). We summarize evidence that implicit…
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Suppressor Variables and Multilevel Mixture Modelling
ERIC Educational Resources Information Center
Darmawan, I Gusti Ngurah; Keeves, John P.
2006-01-01
A major issue in educational research involves taking into consideration the multilevel nature of the data. Since the late 1980s, attempts have been made to model social science data that conform to a nested structure. Among other models, two-level structural equation modelling or two-level path modelling and hierarchical linear modelling are two…
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ERIC Educational Resources Information Center
Shin, Jongho; Lee, Hyunjoo; Kim, Yongnam
2009-01-01
The purpose of the study was to comparatively investigate student- and school-level factors affecting mathematics achievement of Korean, Japanese and American students. For international comparisons, the PISA 2003 data were analysed by using the Hierarchical Linear Modeling method. The variables of competitive-learning preference, instrumental…
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Variability, Negative Evidence, and the Acquisition of Verb Argument Constructions
ERIC Educational Resources Information Center
Perfors, Amy; Tenenbaum, Joshua B.; Wonnacott, Elizabeth
2010-01-01
We present a hierarchical Bayesian framework for modeling the acquisition of verb argument constructions. It embodies a domain-general approach to learning higher-level knowledge in the form of inductive constraints (or overhypotheses), and has been used to explain other aspects of language development such as the shape bias in learning object…
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Assessing and monitoring urban resilience using COPD in Porto.
Monteiro, Ana; Carvalho, Vânia; Velho, Sara; Sousa, Carlos
2012-01-01
COPD morbidity is a good example of how the urban form may interfere with a disease's severity. Then, it may play an important role as a stimulus to increase the acceptability of several policy actions that aim to upgrade urban resilience. Despite the multiple dimensions of wellbeing, health is surely a key variable attracting everyone's attention, which is thus more likely to be able to persuade people that actions that may at first seem undesirable are fundamental in improving urban sustainability and well-being. After creating a short list of socio-economic and environmental factors relating to the onset and aggravation of COPD, daily admissions distributions were compared using both a non-weighted and a weighted multi-criteria hierarchical analysis procedure. Porto's COPD Social and Environmental Inequalities Index (SEII), calculated with a hierarchical analysis procedure, accurately illustrates a great relationship between COPD admissions and adverse urban form variables. COPD may be an important communication tool to stimulate the acceptability of some otherwise unpopular planning measures to improve urban resilience (sustainability and well-being). Copyright © 2011 Elsevier B.V. All rights reserved.
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Interactive visual exploration and analysis of origin-destination data
NASA Astrophysics Data System (ADS)
Ding, Linfang; Meng, Liqiu; Yang, Jian; Krisp, Jukka M.
2018-05-01
In this paper, we propose a visual analytics approach for the exploration of spatiotemporal interaction patterns of massive origin-destination data. Firstly, we visually query the movement database for data at certain time windows. Secondly, we conduct interactive clustering to allow the users to select input variables/features (e.g., origins, destinations, distance, and duration) and to adjust clustering parameters (e.g. distance threshold). The agglomerative hierarchical clustering method is applied for the multivariate clustering of the origin-destination data. Thirdly, we design a parallel coordinates plot for visualizing the precomputed clusters and for further exploration of interesting clusters. Finally, we propose a gradient line rendering technique to show the spatial and directional distribution of origin-destination clusters on a map view. We implement the visual analytics approach in a web-based interactive environment and apply it to real-world floating car data from Shanghai. The experiment results show the origin/destination hotspots and their spatial interaction patterns. They also demonstrate the effectiveness of our proposed approach.
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A new clustering algorithm applicable to multispectral and polarimetric SAR images
NASA Technical Reports Server (NTRS)
Wong, Yiu-Fai; Posner, Edward C.
1993-01-01
We describe an application of a scale-space clustering algorithm to the classification of a multispectral and polarimetric SAR image of an agricultural site. After the initial polarimetric and radiometric calibration and noise cancellation, we extracted a 12-dimensional feature vector for each pixel from the scattering matrix. The clustering algorithm was able to partition a set of unlabeled feature vectors from 13 selected sites, each site corresponding to a distinct crop, into 13 clusters without any supervision. The cluster parameters were then used to classify the whole image. The classification map is much less noisy and more accurate than those obtained by hierarchical rules. Starting with every point as a cluster, the algorithm works by melting the system to produce a tree of clusters in the scale space. It can cluster data in any multidimensional space and is insensitive to variability in cluster densities, sizes and ellipsoidal shapes. This algorithm, more powerful than existing ones, may be useful for remote sensing for land use.
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A novel program to design siRNAs simultaneously effective to highly variable virus genomes.
Lee, Hui Sun; Ahn, Jeonghyun; Jun, Eun Jung; Yang, Sanghwa; Joo, Chul Hyun; Kim, Yoo Kyum; Lee, Heuiran
2009-07-10
A major concern of antiviral therapy using small interfering RNAs (siRNAs) targeting RNA viral genome is high sequence diversity and mutation rate due to genetic instability. To overcome this problem, it is indispensable to design siRNAs targeting highly conserved regions. We thus designed CAPSID (Convenient Application Program for siRNA Design), a novel bioinformatics program to identify siRNAs targeting highly conserved regions within RNA viral genomes. From a set of input RNAs of diverse sequences, CAPSID rapidly searches conserved patterns and suggests highly potent siRNA candidates in a hierarchical manner. To validate the usefulness of this novel program, we investigated the antiviral potency of universal siRNA for various Human enterovirus B (HEB) serotypes. Assessment of antiviral efficacy using Hela cells, clearly demonstrates that HEB-specific siRNAs exhibit protective effects against all HEBs examined. These findings strongly indicate that CAPSID can be applied to select universal antiviral siRNAs against highly divergent viral genomes.
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The uses and gratifications of online care pages: a study of CaringBridge.
Anderson, Isolde K
2011-09-01
This study investigated how online care pages help people connect with others and gain social support during a health care event. It reports the results of a survey of 1035 CaringBridge authors who set up personalized web pages because of hospitalization, serious illness, or other reasons, regarding the uses and gratifications obtained from their sites. Four primary benefits were found to be important to all authors of CaringBridge sites: providing information, receiving encouragement from messages, convenience, and psychological support. Hierarchical multiple regression revealed significant effects for six demographic and health-related variables: gender, age, religiosity, Internet usage, the purpose for which the site was set up, and sufficiency of information received from health care providers. Support was obtained for the perspective that online care pages provide new media gratifications for authors, and that health-related antecedents of media use may affect media selection and gratifications. The implications of this study for communication researchers and support services like CaringBridge are also discussed.
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Prado, M. S.; Strina, A.; Barreto, M. L.; Oliveira-Assis, Ana Marlúcia; Paz, Lívia Maria; Cairncross, S.
2003-01-01
A cross-sectional study of 694 children aged 2 to 45 months selected from 30 clusters throughout the city of Salvador, Bahia (pop. 2.3 million) was carried out as part of a longitudinal study of diarrhoea in order to identify risk factors for infection with Giardia duodenalis. Variables studied included three social and demographic factors (such as mother's education and marital status), five relating to the peri-domestic environment (rubbish disposal, open sewers, paving of the street), seven relating to the home itself (house construction, susceptibility to flooding, water supply and sanitation) as well as a score for hygiene behaviour based on structured observation. After multivariate analysis using a hierarchical model, only four significant risk factors were found: (a) number of children in the household under five years (b) rubbish not collected from the house (c) presence of visible sewage nearby, and (d) absence of a toilet. All four were significant at the 1% level. PMID:14596531
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Approximation abilities of neuro-fuzzy networks
NASA Astrophysics Data System (ADS)
Mrówczyńska, Maria
2010-01-01
The paper presents the operation of two neuro-fuzzy systems of an adaptive type, intended for solving problems of the approximation of multi-variable functions in the domain of real numbers. Neuro-fuzzy systems being a combination of the methodology of artificial neural networks and fuzzy sets operate on the basis of a set of fuzzy rules "if-then", generated by means of the self-organization of data grouping and the estimation of relations between fuzzy experiment results. The article includes a description of neuro-fuzzy systems by Takaga-Sugeno-Kang (TSK) and Wang-Mendel (WM), and in order to complement the problem in question, a hierarchical structural self-organizing method of teaching a fuzzy network. A multi-layer structure of the systems is a structure analogous to the structure of "classic" neural networks. In its final part the article presents selected areas of application of neuro-fuzzy systems in the field of geodesy and surveying engineering. Numerical examples showing how the systems work concerned: the approximation of functions of several variables to be used as algorithms in the Geographic Information Systems (the approximation of a terrain model), the transformation of coordinates, and the prediction of a time series. The accuracy characteristics of the results obtained have been taken into consideration.
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Hierarchical Bayesian Logistic Regression to forecast metabolic control in type 2 DM patients.
Dagliati, Arianna; Malovini, Alberto; Decata, Pasquale; Cogni, Giulia; Teliti, Marsida; Sacchi, Lucia; Cerra, Carlo; Chiovato, Luca; Bellazzi, Riccardo
2016-01-01
In this work we present our efforts in building a model able to forecast patients' changes in clinical conditions when repeated measurements are available. In this case the available risk calculators are typically not applicable. We propose a Hierarchical Bayesian Logistic Regression model, which allows taking into account individual and population variability in model parameters estimate. The model is used to predict metabolic control and its variation in type 2 diabetes mellitus. In particular we have analyzed a population of more than 1000 Italian type 2 diabetic patients, collected within the European project Mosaic. The results obtained in terms of Matthews Correlation Coefficient are significantly better than the ones gathered with standard logistic regression model, based on data pooling.
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Improving the Calibration of the SN Ia Anchor Datasets with a Bayesian Hierarchal Model
NASA Astrophysics Data System (ADS)
Currie, Miles; Rubin, David
2018-01-01
Inter-survey calibration remains one of the largest systematic uncertainties in SN Ia cosmology today. Ideally, each survey would measure their system throughputs and observe well characterized spectrophotometric standard stars, but many important surveys have not done so. For these surveys, we calibrate using tertiary survey stars tied to SDSS and Pan-STARRS. We improve on previous efforts by taking the spatially variable response of each telescope/camera into account, and using improved color transformations in the surveys’ natural instrumental photometric system. We use a global hierarchical model of the data, automatically providing a covariance matrix of magnitude offsets and bandpass shifts which reduces the systematic uncertainty in inter-survey calibration, thereby providing better cosmological constraints.
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Housing environment and mental health outcomes: A levels of analysis perspective
Wright, Patricia Ann; Kloos, Bret
2008-01-01
This study examines the effects of perceived housing environment on selected well-being outcomes of a seriously mentally ill population in supported housing programs. Individuals live independently in their own apartments and use supportive mental health services as needed. The study conceptualizes one’s housing environment as existing at the apartment, neighborhood and the surrounding community levels of analysis that, taken together, form a multi-dimensional construct of housing environment. Self-report data from interviews with a sample of seriously mentally ill adults is paired with (a) observer ratings of housing environments, (b) census profiles of the surrounding community and (c) case manager ratings of clients’ functioning in order to explore the effects of supported housing environments on well-being outcomes. Well-being is operationalized here as levels of psychiatric distress, recovery orientation, residential satisfaction, and adaptive functioning. Hierarchical regression models posit that apartment, neighborhood and census tract level variables are unique predictors of these domains of well-being. Results show that neighborhood level variables, especially those relating to the social environment, are the most influential predictors for understanding variance in well-being, with apartment level variables also contributing to understanding of housing environment effects. The census tract level predictors did not contribute a significant amount of explanation of the variance in well-being outcomes. Implications for supported housing programs and the role of ecological levels of analysis in conceptualizing and measuring housing environment influence are discussed. PMID:19183703
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Quantum chemical and statistical study of megazol-derived compounds with trypanocidal activity
NASA Astrophysics Data System (ADS)
Rosselli, F. P.; Albuquerque, C. N.; da Silva, A. B. F.
In this work we performed a structure-activity relationship (SAR) study with the aim to correlate molecular properties of the megazol compound and 10 of its analogs with the biological activity against Trypanosoma cruzi (trypanocidal or antichagasic activity) presented by these molecules. The biological activity indication was obtained from in vitro tests and the molecular properties (variables or descriptors) were obtained from the optimized chemical structures by using the PM3 semiempirical method. It was calculated ˜80 molecular properties selected among steric, constitutional, electronic, and lipophilicity properties. In order to reduce dimensionality and investigate which subset of variables (descriptors) would be more effective in classifying the compounds studied, according to their degree of trypanocidal activity, we employed statistical methodologies (pattern recognition and classification techniques) such as principal component analysis (PCA), hierarchical cluster analysis (HCA), K-nearest neighbor (KNN), and discriminant function analysis (DFA). These methods showed that the descriptors molecular mass (MM), energy of the second lowest unoccupied molecular orbital (LUMO+1), charge on the first nitrogen at substituent 2 (qN'), dihedral angles (D1 and D2), bond length between atom C4 and its substituent (L4), Moriguchi octanol-partition coefficient (MLogP), and length-to-breadth ratio (L/Bw) were the variables responsible for the separation between active and inactive compounds against T. cruzi. Afterwards, the PCA, KNN, and DFA models built in this work were used to perform trypanocidal activity predictions for eight new megazol analog compounds.
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Yamaguchi, Motonori; Logan, Gordon D; Li, Vanessa
2013-08-01
Does response selection select words or letters in skilled typewriting? Typing performance involves hierarchically organized control processes: an outer loop that controls word level processing, and an inner loop that controls letter (or keystroke) level processing. The present study addressed whether response selection occurs in the outer loop or the inner loop by using the psychological refractory period (PRP) paradigm in which Task1 required typing single words and Task2 required vocal responses to tones. The number of letters (string length) in the words was manipulated to discriminate selection of words from selection of keystrokes. In Experiment 1, the PRP effect depended on string length of words in Task1, suggesting that response selection occurs in the inner loop. To assess contributions of the outer loop, the influence of string length was examined in a lexical-decision task that also involves word encoding and lexical access (Experiment 2), or to-be-typed words were preexposed so outer-loop processing could finish before typing started (Experiment 3). Response time for Task2 (RT2) did not depend on string length with lexical decision, and RT2 still depended on string length with typing preexposed strings. These results support the inner-loop locus of the PRP effect. In Experiment 4, typing was performed as Task2, and the effect of string length on typing RT interacted with stimulus onset asynchrony superadditively, implying that another bottleneck also exists in the outer loop. We conclude that there are at least two bottleneck processes in skilled typewriting. 2013 APA, all rights reserved
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Sentence processing selectivity in Broca's area: evident for structure but not syntactic movement.
Rogalsky, Corianne; Almeida, Diogo; Sprouse, Jon; Hickok, Gregory
The role of Broca's area in sentence processing is hotly debated. Prominent hypotheses include that Broca's area supports sentence comprehension via syntax-specific processes ("syntactic movement" in particular), hierarchical structure building or working memory. In the present fMRI study we adopt a within subject, across task approach using targeted sentence-level contrasts and non-sentential comparison tasks to address these hypotheses regarding the role of Broca's area in sentence processing. For clarity, we have presented findings as three experiments: (i) Experiment 1 examines selectivity for a particular type of sentence construction, namely those containing syntactic movement. Standard syntactic movement distance effects in Broca's area were replicated but no difference was found between movement and non-movement sentences in Broca's area at the group level or consistently in individual subjects. (ii) Experiment 2 examines selectivity for sentences versus non-sentences, to assess claims regarding the role of Broca's area in hierarchical structure building. Group and individual results differ, but both identify subregions of Broca's area that are selective for sentence structure. (iii) Experiment 3 assesses whether activations in Broca's area are selective for sentences when contrasted with simple subvocal articulation. Group results suggest shared resources for sentence processing and articulation in Broca's area, but individual subject analyses contradict this finding. We conclude that Broca's area is not selectively involved in processing syntactic movement, but that subregions are selectively responsive to sentence structure. Our findings also reinforce Fedorenko & Kanwishser's call for the use of more individual subject analyses in functional imaging studies of sentence processing in Broca's area, as group findings can obscure selective response patterns.
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Probabilistic assessment of compliance with the numerical criteria for fecal coliforms in rivers
NASA Astrophysics Data System (ADS)
Cha, YoonKyung
2017-04-01
Most guidelines for assessing fecal contamination in surface waters suggest that a waterbody is impaired if a certain percent or the geometric mean of samples exceeds the numerical criteria for fecal indicator organisms. However, this raw score approach is not able to account for the uncertainty and variability in the sample statistics. In a Bayesian hierarchical modeling approach, the uncertainty in the mean parameter is expressed as a posterior distribution, and the probability of not violating the criterion is referred to as the confidence of compliance (COC). Further, the spatiotemporal variability in the mean parameter can be quantified by imposing the hierarchical structure on the model. The monitoring data spanning 91 sites across the four major rivers (the Han, Geum, Yeongsan, and Nakdong) of South Korea for the years 2007-2016 were used. The Bayesian hierarchical model was developed for each river to predict the COC with the criteria for fecal coliforms. The established criteria for fecal coliforms are less than 10, 100, 200, and 1,000 CFU/100mL in the river whose water quality goal corresponds to Class Ia, Ib, II, and III, respectively. The model results suggested that the COC varied significantly by site, ranging from 0.0 to 98.9 percent across the four rivers. In the Geum, Yeongsan, and Nakdong Rivers, COC values in the upper river sections were substantially lower than those in the upper river sections. The model suggested that for all four rivers the spatial component, compared with annual and seasonal components, made the largest contribution to the variability in mean fecal coliforms. In all four rivers, mean levels for fecal coliform during the summer (July to September) were distinctly higher than those during other seasons. A decreasing pattern was clearly shown in the Yeongsan River over the recent decade, while monotonic increases or decreases were not shown in other three rivers.
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Walter, W. David; Smith, Rick; Vanderklok, Mike; VerCauterren, Kurt C.
2014-01-01
Bovine tuberculosis is a bacterial disease caused by Mycobacterium bovis in livestock and wildlife with hosts that include Eurasian badgers (Meles meles), brushtail possum (Trichosurus vulpecula), and white-tailed deer (Odocoileus virginianus). Risk-assessment efforts in Michigan have been initiated on farms to minimize interactions of cattle with wildlife hosts but research onM. bovis on cattle farms has not investigated the spatial context of disease epidemiology. To incorporate spatially explicit data, initial likelihood of infection probabilities for cattle farms tested for M. bovis, prevalence of M. bovis in white-tailed deer, deer density, and environmental variables for each farm were modeled in a Bayesian hierarchical framework. We used geo-referenced locations of 762 cattle farms that have been tested for M. bovis, white-tailed deer prevalence, and several environmental variables that may lead to long-term survival and viability of M. bovis on farms and surrounding habitats (i.e., soil type, habitat type). Bayesian hierarchical analyses identified deer prevalence and proportion of sandy soil within our sampling grid as the most supported model. Analysis of cattle farms tested for M. bovisidentified that for every 1% increase in sandy soil resulted in an increase in odds of infection by 4%. Our analysis revealed that the influence of prevalence of M. bovis in white-tailed deer was still a concern even after considerable efforts to prevent cattle interactions with white-tailed deer through on-farm mitigation and reduction in the deer population. Cattle farms test positive for M. bovis annually in our study area suggesting that the potential for an environmental source either on farms or in the surrounding landscape may contributing to new or re-infections with M. bovis. Our research provides an initial assessment of potential environmental factors that could be incorporated into additional modeling efforts as more knowledge of deer herd factors and cattle farm prevalence is documented.
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Risk-adjusted hospital outcomes for children's surgery.
Saito, Jacqueline M; Chen, Li Ern; Hall, Bruce L; Kraemer, Kari; Barnhart, Douglas C; Byrd, Claudia; Cohen, Mark E; Fei, Chunyuan; Heiss, Kurt F; Huffman, Kristopher; Ko, Clifford Y; Latus, Melissa; Meara, John G; Oldham, Keith T; Raval, Mehul V; Richards, Karen E; Shah, Rahul K; Sutton, Laura C; Vinocur, Charles D; Moss, R Lawrence
2013-09-01
BACKGROUND The American College of Surgeons National Surgical Quality Improvement Program-Pediatric was initiated in 2008 to drive quality improvement in children's surgery. Low mortality and morbidity in previous analyses limited differentiation of hospital performance. Participating institutions included children's units within general hospitals and free-standing children's hospitals. Cases selected by Current Procedural Terminology codes encompassed procedures within pediatric general, otolaryngologic, orthopedic, urologic, plastic, neurologic, thoracic, and gynecologic surgery. Trained personnel abstracted demographic, surgical profile, preoperative, intraoperative, and postoperative variables. Incorporating procedure-specific risk, hierarchical models for 30-day mortality and morbidities were developed with significant predictors identified by stepwise logistic regression. Reliability was estimated to assess the balance of information versus error within models. In 2011, 46 281 patients from 43 hospitals were accrued; 1467 codes were aggregated into 226 groupings. Overall mortality was 0.3%, composite morbidity 5.8%, and surgical site infection (SSI) 1.8%. Hierarchical models revealed outlier hospitals with above or below expected performance for composite morbidity in the entire cohort, pediatric abdominal subgroup, and spine subgroup; SSI in the entire cohort and pediatric abdominal subgroup; and urinary tract infection in the entire cohort. Based on reliability estimates, mortality discriminates performance poorly due to very low event rate; however, reliable model construction for composite morbidity and SSI that differentiate institutions is feasible. The National Surgical Quality Improvement Program-Pediatric expansion has yielded risk-adjusted models to differentiate hospital performance in composite and specific morbidities. However, mortality has low utility as a children's surgery performance indicator. Programmatic improvements have resulted in actionable data.
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Gélin, Pauline; Fauvelot, Cécile; Bigot, Lionel; Baly, Joseph; Magalon, Hélène
2018-01-01
Here, we examined the genetic variability in the coral genus Pocillopora , in particular within the Primary Species Hypothesis PSH09, identified by Gélin, Postaire, Fauvelot and Magalon (2017) using species delimitation methods [also named Pocillopora eydouxi/meandrina complex sensu , Schmidt-Roach, Miller, Lundgren, & Andreakis (2014)] and which was found to split into three secondary species hypotheses (SSH09a, SSH09b, and SSH09c) according to assignment tests using multi-locus genotypes (13 microsatellites). From a large sampling (2,507 colonies) achieved in three marine provinces [Western Indian Ocean (WIO), Tropical Southwestern Pacific (TSP), and Southeast Polynesia (SEP)], genetic structuring analysis conducted with two clustering analyses (structure and DAPC) using 13 microsatellites revealed that SSH09a was restricted to the WIO while SSH09b and SSH09c were almost exclusively in the TSP and SEP. More surprisingly, each SSH split into two to three genetically differentiated clusters, found in sympatry at the reef scale, leading to a pattern of nested hierarchical levels (PSH > SSH > cluster), each level hiding highly differentiated genetic groups. Thus, rather than structured populations within a single species, these three SSHs, and even the eight clusters, likely represent distinct genetic lineages engaged in a speciation process or real species. The issue is now to understand which hierarchical level (SSH, cluster, or even below) corresponds to the species one. Several hypotheses are discussed on the processes leading to this pattern of mixed clusters in sympatry, evoking formation of reproductive barriers, either by allopatric speciation or habitat selection.
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The Partition of Multi-Resolution LOD Based on Qtm
NASA Astrophysics Data System (ADS)
Hou, M.-L.; Xing, H.-Q.; Zhao, X.-S.; Chen, J.
2011-08-01
The partition hierarch of Quaternary Triangular Mesh (QTM) determine the accuracy of spatial analysis and application based on QTM. In order to resolve the problem that the partition hierarch of QTM is limited by the level of the computer hardware, the new method that Multi- Resolution LOD (Level of Details) based on QTM will be discussed in this paper. This method can make the resolution of the cells varying with the viewpoint position by partitioning the cells of QTM, selecting the particular area according to the viewpoint; dealing with the cracks caused by different subdivisions, it satisfies the request of unlimited partition in part.
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Security clustering algorithm based on reputation in hierarchical peer-to-peer network
NASA Astrophysics Data System (ADS)
Chen, Mei; Luo, Xin; Wu, Guowen; Tan, Yang; Kita, Kenji
2013-03-01
For the security problems of the hierarchical P2P network (HPN), the paper presents a security clustering algorithm based on reputation (CABR). In the algorithm, we take the reputation mechanism for ensuring the security of transaction and use cluster for managing the reputation mechanism. In order to improve security, reduce cost of network brought by management of reputation and enhance stability of cluster, we select reputation, the historical average online time, and the network bandwidth as the basic factors of the comprehensive performance of node. Simulation results showed that the proposed algorithm improved the security, reduced the network overhead, and enhanced stability of cluster.
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Ning, P; Guo, Y F; Sun, T Y; Zhang, H S; Chai, D; Li, X M
2016-09-01
To study the distinct clinical phenotype of chronic airway diseases by hierarchical cluster analysis and two-step cluster analysis. A population sample of adult patients in Donghuamen community, Dongcheng district and Qinghe community, Haidian district, Beijing from April 2012 to January 2015, who had wheeze within the last 12 months, underwent detailed investigation, including a clinical questionnaire, pulmonary function tests, total serum IgE levels, blood eosinophil level and a peak flow diary. Nine variables were chosen as evaluating parameters, including pre-salbutamol forced expired volume in one second(FEV1)/forced vital capacity(FVC) ratio, pre-salbutamol FEV1, percentage of post-salbutamol change in FEV1, residual capacity, diffusing capacity of the lung for carbon monoxide/alveolar volume adjusted for haemoglobin level, peak expiratory flow(PEF) variability, serum IgE level, cumulative tobacco cigarette consumption (pack-years) and respiratory symptoms (cough and expectoration). Subjects' different clinical phenotype by hierarchical cluster analysis and two-step cluster analysis was identified. (1) Four clusters were identified by hierarchical cluster analysis. Cluster 1 was chronic bronchitis in smokers with normal pulmonary function. Cluster 2 was chronic bronchitis or mild chronic obstructive pulmonary disease (COPD) patients with mild airflow limitation. Cluster 3 included COPD patients with heavy smoking, poor quality of life and severe airflow limitation. Cluster 4 recognized atopic patients with mild airflow limitation, elevated serum IgE and clinical features of asthma. Significant differences were revealed regarding pre-salbutamol FEV1/FVC%, pre-salbutamol FEV1% pred, post-salbutamol change in FEV1%, maximal mid-expiratory flow curve(MMEF)% pred, carbon monoxide diffusing capacity per liter of alveolar(DLCO)/(VA)% pred, residual volume(RV)% pred, total serum IgE level, smoking history (pack-years), St.George's respiratory questionnaire(SGRQ) score, acute exacerbation in the past one year, PEF variability and allergic dermatitis (P<0.05). (2) Four clusters were also identified by two-step cluster analysis as followings, cluster 1, COPD patients with moderate to severe airflow limitation; cluster 2, asthma and COPD patients with heavy smoking, airflow limitation and increased airways reversibility; cluster 3, patients having less smoking and normal pulmonary function with wheezing but no chronic cough; cluster 4, chronic bronchitis patients with normal pulmonary function and chronic cough. Significant differences were revealed regarding gender distribution, respiratory symptoms, pre-salbutamol FEV1/FVC%, pre-salbutamol FEV1% pred, post-salbutamol change in FEV1%, MMEF% pred, DLCO/VA% pred, RV% pred, PEF variability, total serum IgE level, cumulative tobacco cigarette consumption (pack-years), and SGRQ score (P<0.05). By different cluster analyses, distinct clinical phenotypes of chronic airway diseases are identified. Thus, individualized treatments may guide doctors to provide based on different phenotypes.
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Tom, Jennifer A; Sinsheimer, Janet S; Suchard, Marc A
Massive datasets in the gigabyte and terabyte range combined with the availability of increasingly sophisticated statistical tools yield analyses at the boundary of what is computationally feasible. Compromising in the face of this computational burden by partitioning the dataset into more tractable sizes results in stratified analyses, removed from the context that justified the initial data collection. In a Bayesian framework, these stratified analyses generate intermediate realizations, often compared using point estimates that fail to account for the variability within and correlation between the distributions these realizations approximate. However, although the initial concession to stratify generally precludes the more sensible analysis using a single joint hierarchical model, we can circumvent this outcome and capitalize on the intermediate realizations by extending the dynamic iterative reweighting MCMC algorithm. In doing so, we reuse the available realizations by reweighting them with importance weights, recycling them into a now tractable joint hierarchical model. We apply this technique to intermediate realizations generated from stratified analyses of 687 influenza A genomes spanning 13 years allowing us to revisit hypotheses regarding the evolutionary history of influenza within a hierarchical statistical framework.
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Tom, Jennifer A.; Sinsheimer, Janet S.; Suchard, Marc A.
2015-01-01
Massive datasets in the gigabyte and terabyte range combined with the availability of increasingly sophisticated statistical tools yield analyses at the boundary of what is computationally feasible. Compromising in the face of this computational burden by partitioning the dataset into more tractable sizes results in stratified analyses, removed from the context that justified the initial data collection. In a Bayesian framework, these stratified analyses generate intermediate realizations, often compared using point estimates that fail to account for the variability within and correlation between the distributions these realizations approximate. However, although the initial concession to stratify generally precludes the more sensible analysis using a single joint hierarchical model, we can circumvent this outcome and capitalize on the intermediate realizations by extending the dynamic iterative reweighting MCMC algorithm. In doing so, we reuse the available realizations by reweighting them with importance weights, recycling them into a now tractable joint hierarchical model. We apply this technique to intermediate realizations generated from stratified analyses of 687 influenza A genomes spanning 13 years allowing us to revisit hypotheses regarding the evolutionary history of influenza within a hierarchical statistical framework. PMID:26681992
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Forbes, Miriam K.; Tackett, Jennifer L.; Markon, Kristian E.; Krueger, Robert F.
2016-01-01
In this review, we propose a novel developmentally informed framework to push research beyond a focus on comorbidity between discrete diagnostic categories, and to move towards research based on the well-validated dimensional and hierarchical structure of psychopathology. For example, a large body of research speaks to the validity and utility of the Internalizing and Externalizing (IE) spectra as organizing constructs for research on common forms of psychopathology. The IE spectra act as powerful explanatory variables that channel the psychopathological effects of genetic and environmental risk factors, predict adaptive functioning, and account for the likelihood of disorder-level manifestations of psychopathology. As such, our proposed theoretical framework uses the IE spectra as central constructs to guide future psychopathology research across the lifespan. The framework is particularly flexible, as any of the facets or factors from the dimensional and hierarchical structure of psychopathology can form the focus of research. We describe the utility and strengths of this framework for developmental psychopathology in particular, and explore avenues for future research. PMID:27739384
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Yet another method for triangulation and contouring for automated cartography
NASA Technical Reports Server (NTRS)
De Floriani, L.; Falcidieno, B.; Nasy, G.; Pienovi, C.
1982-01-01
An algorithm is presented for hierarchical subdivision of a set of three-dimensional surface observations. The data structure used for obtaining the desired triangulation is also singularly appropriate for extracting contours. Some examples are presented, and the results obtained are compared with those given by Delaunay triangulation. The data points selected by the algorithm provide a better approximation to the desired surface than do randomly selected points.
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Novel Catalyst for the Chirality Selective Synthesis of Single Walled Carbon Nanotubes
2015-05-12
hierarchical structures comprising nitrogen- doped reduced GO (rGO) and acid- oxidized SWCNTs was produced using a linear hydrothermal microreactor. Fiber...structures comprising nitrogen- doped reduced GO (rGO) and acidoxidized SWCNTs was produced using a linear hydrothermal microreactor. Fiber micro... doped into Co/SiO2 catalysts to change their chirality selectivity. Further, enrichment of (9,8) nanotubes was carried out by extraction using fluorene
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Strecker, Angela L; Casselman, John M; Fortin, Marie-Josée; Jackson, Donald A; Ridgway, Mark S; Abrams, Peter A; Shuter, Brian J
2011-07-01
Species present in communities are affected by the prevailing environmental conditions, and the traits that these species display may be sensitive indicators of community responses to environmental change. However, interpretation of community responses may be confounded by environmental variation at different spatial scales. Using a hierarchical approach, we assessed the spatial and temporal variation of traits in coastal fish communities in Lake Huron over a 5-year time period (2001-2005) in response to biotic and abiotic environmental factors. The association of environmental and spatial variables with trophic, life-history, and thermal traits at two spatial scales (regional basin-scale, local site-scale) was quantified using multivariate statistics and variation partitioning. We defined these two scales (regional, local) on which to measure variation and then applied this measurement framework identically in all 5 study years. With this framework, we found that there was no change in the spatial scales of fish community traits over the course of the study, although there were small inter-annual shifts in the importance of regional basin- and local site-scale variables in determining community trait composition (e.g., life-history, trophic, and thermal). The overriding effects of regional-scale variables may be related to inter-annual variation in average summer temperature. Additionally, drivers of fish community traits were highly variable among study years, with some years dominated by environmental variation and others dominated by spatially structured variation. The influence of spatial factors on trait composition was dynamic, which suggests that spatial patterns in fish communities over large landscapes are transient. Air temperature and vegetation were significant variables in most years, underscoring the importance of future climate change and shoreline development as drivers of fish community structure. Overall, a trait-based hierarchical framework may be a useful conservation tool, as it highlights the multi-scaled interactive effect of variables over a large landscape.
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MIL-100 derived nitrogen-embodied carbon shells embedded with iron nanoparticles
NASA Astrophysics Data System (ADS)
Mao, Chengyu; Kong, Aiguo; Wang, Yuan; Bu, Xianhui; Feng, Pingyun
2015-06-01
The use of metal-organic frameworks (MOFs) as templates and precursors to synthesize new carbon materials with controllable morphology and pre-selected heteroatom doping holds promise for applications as efficient non-precious metal catalysts. Here, we report a facile pyrolysis pathway to convert MIL-100 into nitrogen-doped carbon shells encapsulating Fe nanoparticles in a comparative study involving multiple selected nitrogen sources. The hierarchical porous architecture, embedded Fe nanoparticles, and nitrogen decoration endow this composite with a superior oxygen reduction activity. Furthermore, the excellent durability and high methanol tolerance even outperform the commercial Pt-C catalyst.The use of metal-organic frameworks (MOFs) as templates and precursors to synthesize new carbon materials with controllable morphology and pre-selected heteroatom doping holds promise for applications as efficient non-precious metal catalysts. Here, we report a facile pyrolysis pathway to convert MIL-100 into nitrogen-doped carbon shells encapsulating Fe nanoparticles in a comparative study involving multiple selected nitrogen sources. The hierarchical porous architecture, embedded Fe nanoparticles, and nitrogen decoration endow this composite with a superior oxygen reduction activity. Furthermore, the excellent durability and high methanol tolerance even outperform the commercial Pt-C catalyst. Electronic supplementary information (ESI) available: Material synthesis and elemental analysis, electrochemistry measurements, and additional figures. See DOI: 10.1039/c5nr02346g
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Meng, Lala; Zhang, Xiaofei; Tang, Yusheng; Su, Kehe; Kong, Jie
2015-01-01
The hierarchically macro/micro-porous silicon–carbon–nitrogen (Si–C–N) hybrid material was presented with novel functionalities of totally selective and highly efficient adsorption for organic dyes. The hybrid material was conveniently generated by the pyrolysis of commercial polysilazane precursors using polydivinylbenzene microspheres as sacrificial templates. Owing to the Van der Waals force between sp2-hybridized carbon domains and triphenyl structure of dyes, and electrostatic interaction between dyes and Si-C-N matrix, it exhibites high adsorption capacity and good regeneration and recycling ability for the dyes with triphenyl structure, such as methyl blue (MB), acid fuchsin (AF), basic fuchsin and malachite green. The adsorption process is determined by both surface adsorption and intraparticle diffusion. According to the Langmuir model, the adsorption capacity is 1327.7 mg·g−1 and 1084.5 mg·g−1 for MB and AF, respectively, which is much higher than that of many other adsorbents. On the contrary, the hybrid materials do not adsorb the dyes with azo benzene structures, such as methyl orange, methyl red and congro red. Thus, the hierarchically porous Si–C–N hybrid material from a facile and low cost polymer-derived strategy provides a new perspective and possesses a significant potential in the treatment of wastewater with complex organic pollutants. PMID:25604334
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Cross-frequency power coupling between hierarchically organized face-selective areas.
Furl, Nicholas; Coppola, Richard; Averbeck, Bruno B; Weinberger, Daniel R
2014-09-01
Neural oscillations are linked to perception and behavior and may reflect mechanisms for long-range communication between brain areas. We developed a causal model of oscillatory dynamics in the face perception network using magnetoencephalographic data from 51 normal volunteers. This model predicted induced responses to faces by estimating oscillatory power coupling between source locations corresponding to bilateral occipital and fusiform face areas (OFA and FFA) and the right superior temporal sulcus (STS). These sources showed increased alpha and theta and decreased beta power as well as selective responses to fearful facial expressions. We then used Bayesian model comparison to compare hypothetical models, which were motivated by previous connectivity data and a well-known theory of temporal lobe function. We confirmed this theory in detail by showing that the OFA bifurcated into 2 independent, hierarchical, feedforward pathways, with fearful expressions modulating power coupling only in the more dorsal (STS) pathway. The power coupling parameters showed a common pattern over connections. Low-frequency bands showed same-frequency power coupling, which, in the dorsal pathway, was modulated by fearful faces. Also, theta power showed a cross-frequency suppression of beta power. This combination of linear and nonlinear mechanisms could reflect computational mechanisms in hierarchical feedforward networks. © The Author 2013. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.
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Miner, Michael H.; Romine, Rebecca Swinburne; Raymond, Nancy; Janssen, Erick; MacDonald, Angus; Coleman, Eli
2016-01-01
Objective The purpose of this study was to investigate personality factors and behavioral mechanisms that are relevant to hypersexuality in men who have sex with men. Method A sample of 242 men who have sex with men were recruited from various sites in a moderate size mid-western city. Participants were assigned to hypersexuality or control group using a SCID-type interview. Self-report inventories were administered that measured the broad band personality constructs of positive emotionality, negative emotionality and constraint, and more narrow constructs related to sexual behavioral control, behavioral activation, behavioral inhibition, sexual excitation, sexual inhibition, impulsivity, ADHD, and sexual behavior. Hierarchical logistic regression was used to determine the relationship between these personality and behavioral variables and group membership. Results A hierarchical logistic regression, controlling for age, revealed a significant positive relationship between hypersexuality and negative emotionality and a negative relationship with constraint. None of the behavioral mechanism variables entered this equation. However, a hierarchical multiple regression predicting sexual behavioral control indicated that lack of such control was positively related to sexual excitation and sexual inhibition due to the threat of performance failure and negatively related to sexual inhibition due to the threat of performance consequences and general behavioral inhibition Conclusions Hypersexuality was found to be related to two broad personality factors that are characterized by emotional reactivity, risk-taking, and impulsivity. The associated lack of sexual behavior control is influenced by both sexual excitatory and inhibitory mechanisms, but not general behavioral activation and inhibitory mechanisms. PMID:27486137
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Category search speeds up face-selective fMRI responses in a non-hierarchical cortical face network.
Jiang, Fang; Badler, Jeremy B; Righi, Giulia; Rossion, Bruno
2015-05-01
The human brain is extremely efficient at detecting faces in complex visual scenes, but the spatio-temporal dynamics of this remarkable ability, and how it is influenced by category-search, remain largely unknown. In the present study, human subjects were shown gradually-emerging images of faces or cars in visual scenes, while neural activity was recorded using functional magnetic resonance imaging (fMRI). Category search was manipulated by the instruction to indicate the presence of either a face or a car, in different blocks, as soon as an exemplar of the target category was detected in the visual scene. The category selectivity of most face-selective areas was enhanced when participants were instructed to report the presence of faces in gradually decreasing noise stimuli. Conversely, the same regions showed much less selectivity when participants were instructed instead to detect cars. When "face" was the target category, the fusiform face area (FFA) showed consistently earlier differentiation of face versus car stimuli than did the "occipital face area" (OFA). When "car" was the target category, only the FFA showed differentiation of face versus car stimuli. These observations provide further challenges for hierarchical models of cortical face processing and show that during gradual revealing of information, selective category-search may decrease the required amount of information, enhancing and speeding up category-selective responses in the human brain. Copyright © 2015 Elsevier Ltd. All rights reserved.
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Bayman, Emine O; Chaloner, Kathryn M; Hindman, Bradley J; Todd, Michael M
2013-01-16
To quantify the variability among centers and to identify centers whose performance are potentially outside of normal variability in the primary outcome and to propose a guideline that they are outliers. Novel statistical methodology using a Bayesian hierarchical model is used. Bayesian methods for estimation and outlier detection are applied assuming an additive random center effect on the log odds of response: centers are similar but different (exchangeable). The Intraoperative Hypothermia for Aneurysm Surgery Trial (IHAST) is used as an example. Analyses were adjusted for treatment, age, gender, aneurysm location, World Federation of Neurological Surgeons scale, Fisher score and baseline NIH stroke scale scores. Adjustments for differences in center characteristics were also examined. Graphical and numerical summaries of the between-center standard deviation (sd) and variability, as well as the identification of potential outliers are implemented. In the IHAST, the center-to-center variation in the log odds of favorable outcome at each center is consistent with a normal distribution with posterior sd of 0.538 (95% credible interval: 0.397 to 0.726) after adjusting for the effects of important covariates. Outcome differences among centers show no outlying centers. Four potential outlying centers were identified but did not meet the proposed guideline for declaring them as outlying. Center characteristics (number of subjects enrolled from the center, geographical location, learning over time, nitrous oxide, and temporary clipping use) did not predict outcome, but subject and disease characteristics did. Bayesian hierarchical methods allow for determination of whether outcomes from a specific center differ from others and whether specific clinical practices predict outcome, even when some centers/subgroups have relatively small sample sizes. In the IHAST no outlying centers were found. The estimated variability between centers was moderately large.
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Salehpoor, Ghasem; Rezaei, Sajjad; Hosseininezhad, Mozaffar
2014-11-01
Although studies have demonstrated significant negative relationships between quality of life (QOL), fatigue, and the most common psychological symptoms (depression, anxiety, stress), the main ambiguity of previous studies on QOL is in the relative importance of these predictors. Also, there is lack of adequate knowledge about the actual contribution of each of them in the prediction of QOL dimensions. Thus, the main objective of this study is to assess the role of fatigue, depression, anxiety, and stress in relation to QOL of multiple sclerosis (MS) patients. One hundred and sixty-two MS patients completed the questionnaire on demographic variables, and then they were evaluated by the Persian versions of Short-Form Health Survey Questionnaire (SF-36), Fatigue Survey Scale (FSS), and Depression, Anxiety, Stress Scale-21 (DASS-21). Data were analyzed by Pearson correlation coefficient and hierarchical regression. Correlation analysis showed a significant relationship between QOL elements in SF-36 (physical component summary and mental component summary) and depression, fatigue, stress, and anxiety (P < 0.01). Hierarchical regression analysis indicated that among the predictor variables in the final step, fatigue, depression, and anxiety were identified as the physical component summary predictor variables. Anxiety was found to be the most powerful predictor variable amongst all (β = -0.46, P < 0.001). Furthermore, results have shown depression as the only significant mental component summary predictor variable (β = -0.39, P < 0.001). This study has highlighted the role of anxiety, fatigue, and depression in physical dimensions and the role of depression in psychological dimensions of the lives of MS patients. In addition, the findings of this study indirectly suggest that psychological interventions for reducing fatigue, depression, and anxiety can lead to improved QOL of MS patients.
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Bayesian Models for Streamflow and River Network Reconstruction using Tree Rings
NASA Astrophysics Data System (ADS)
Ravindranath, A.; Devineni, N.
2016-12-01
Water systems face non-stationary, dynamically shifting risks due to shifting societal conditions and systematic long-term variations in climate manifesting as quasi-periodic behavior on multi-decadal time scales. Water systems are thus vulnerable to long periods of wet or dry hydroclimatic conditions. Streamflow is a major component of water systems and a primary means by which water is transported to serve ecosystems' and human needs. Thus, our concern is in understanding streamflow variability. Climate variability and impacts on water resources are crucial factors affecting streamflow, and multi-scale variability increases risk to water sustainability and systems. Dam operations are necessary for collecting water brought by streamflow while maintaining downstream ecological health. Rules governing dam operations are based on streamflow records that are woefully short compared to periods of systematic variation present in the climatic factors driving streamflow variability and non-stationarity. We use hierarchical Bayesian regression methods in order to reconstruct paleo-streamflow records for dams within a basin using paleoclimate proxies (e.g. tree rings) to guide the reconstructions. The riverine flow network for the entire basin is subsequently modeled hierarchically using feeder stream and tributary flows. This is a starting point in analyzing streamflow variability and risks to water systems, and developing a scientifically-informed dynamic risk management framework for formulating dam operations and water policies to best hedge such risks. We will apply this work to the Missouri and Delaware River Basins (DRB). Preliminary results of streamflow reconstructions for eight dams in the upper DRB using standard Gaussian regression with regional tree ring chronologies give streamflow records that now span two to two and a half centuries, and modestly smoothed versions of these reconstructed flows indicate physically-justifiable trends in the time series.
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Success through Inattention in School Administration and Elsewhere.
ERIC Educational Resources Information Center
Lindblom, Charles E.
1994-01-01
Examines two ways of achieving social coordination: unilateral/hierarchical controls and multilateral controls. Discusses advantages of using mutual adjustment as an alternative to central coordination. Mutual adjustment occurs variously through language creation, moral codes, biological self-selection, market systems, and politics. Although…
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Potential of sustainable hierarchical zeolites in the valorization of α-pinene.
Nuttens, Nicolas; Verboekend, Danny; Deneyer, Aron; Van Aelst, Joost; Sels, Bert F
2015-04-13
In the valorization of α-pinene, which is an important biomass intermediate derived from turpentine oil, hierarchical (mesoporous) zeolites represent a superior class of catalysts. Hierarchical USY, ZSM-5, and beta zeolites have been prepared, characterized, and catalytically evaluated, with the aim of combining the highest catalytic performance with the most sustainable synthetic protocol. These zeolites are prepared by alkaline treatment in aqueous solutions of NH4 OH, NaOH, diethylamine, and NaOH complemented with tetrapropylammonium bromide. The hierarchical USY zeolite is the most attractive catalyst of the tested series, and is able to combine an overall organic-free synthesis with an up to sixfold activity enhancement and comparable selectivity over the conventional USY zeolite. This superior performance relates to a threefold greater activity than that of the commercial standard, namely, H2 SO4 /TiO2 . Correlation of the obtained benefits to the amount of solid lost during the postsynthetic modifications highlights that the highest activity gains are obtained with minor leaching. Furthermore, a highly zeolitic character, as determined by bulk XRD, is beneficial, but not crucial, in the conversion of α-pinene. The alkaline treatments not only result in a higher overall activity, but also a more functional external surface area, attaining up to four times the pinene conversions per square nanometer. The efficiency of the hierarchical USY zeolite is concomitantly demonstrated in the conversion of limonene and turpentine oil, which emphasizes its industrial potential. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
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NASA Astrophysics Data System (ADS)
Jouannic, G.; Kolli, Z.; Legendre, T.; Marchetti, M.; Gastaud, P.; Gargani, J.; Lermet, R.; Augeard, C.; Felts, D.; Arki, F.
2015-12-01
Recent studies have shown that the national flood risk exposure is high in France, with one fourth of the total population and a third of jobs located in risk areas. In this context, a global vulnerability assessment methodology is currently being developed in France to bring adequate tools for local territories to manage flood risk. This study addresses the question of the quantification, the qualification and the choice of these vulnerability indicators for a given territory. This work aims to propose a classification of nearly 40 of these indicators in terms of their relative impacts on the risk level estimated on two territories: Chalon-sur-Saône (Saône river) Garonne estuary (Garonne and Dordogne rivers, and Atlantic ocean) Through these cases study, 3 different spatial scales have been compared: the Prés-Saint-Jean district inside Chalon (0.6 km²), the city of Ambès (28.8 km²) and Chalon with its suburbs (72.2 km²). A principal component analysis (PCA) was applied and indicated a threshold in terms of urban impacts between the different flood scenarios. On Chalon, the PCA discriminates 2 groups of flood and highlighted a threshold between T20 and T50. A partial least-square regression (PLS) was computed to make predictions on vulnerability indicators values modelled on new flood scenarios. Their results were is useful to identify the most relevant vulnerability indicators as a function of their flood exposure. These statistical analysis aims to highlight the relationship between a variable of exposure level (hydrologic impact: water levels and flow velocity) with spatialized vulnerability indicators in a 100 m grid (e.g., population, job, etc.). Finally, to get a hierarchy of variables depending on their impact on the risk level, an ANOVA was computed. The selection of variables was performed with a stepwise selection to assess contributions of each dependant variable on the F-statistic as they are added to or removed from the model.
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Yang, Lanlin; Cai, Sufen; Zhang, Shuoping; Kong, Xiangyi; Gu, Yifan; Lu, Changfu; Dai, Jing; Gong, Fei; Lu, Guangxiu; Lin, Ge
2018-05-01
Does single cleavage-stage (Day 3) embryo transfer using a time-lapse (TL) hierarchical classification model achieve comparable ongoing pregnancy rates (OPR) to single blastocyst (Day 5) transfer by conventional morphological (CM) selection? Day 3 single embryo transfer (SET) with a hierarchical classification model had a significantly lower OPR compared with Day 5 SET with CM selection. Cleavage-stage SET is an alternative to blastocyst SET. Time-lapse imaging assists better embryo selection, based on studies of pregnancy outcomes when adding time-lapse imaging to CM selection at the cleavage or blastocyst stage. This single-centre, randomized, open-label, active-controlled, non-inferiority study included 600 women between October 2015 and April 2017. Eligible patients were Chinese females, aged ≤36 years, who were undergoing their first or second fresh IVF cycle using their own oocytes, and who had FSH levels ≤12 IU/mL on Day 3 of the cycle and 10 or more oocytes retrieved. Patients who had underlying uterine conditions, oocyte donation, recurrent pregnancy loss, abnormal oocytes or <6 normally fertilized embryos (2PN) were excluded from the study participation. Patients were randomized 1:1 to either the cleavage-stage SET with a time-lapse hierarchical classification model for selection (D3 + TL) or blastocyst SET with CM selection (D5 + CM). All normally fertilized zygotes were cultured in Primo Vision. The study was conducted at a tertiary IVF centre (CITIC-Xiangya) and OPR was the primary outcome. A total of 600 patients were randomized to the two groups, among which 585 (D3 + TL = 290, D5 + CM = 295) were included in the Modified-intention-to-treat (mITT) population and 517 (D3 + TL = 261, D5 + CM = 256) were included in the PP population. In the per protocol (PP) population, OPR was significantly lower in the D3 group (59.4%, 155/261) than in the D5 group (68.4%, 175/256) (difference: -9.0%, 95% CI: -17.1%, -0.7%, P = 0.03). Analysis in mITT population showed a marginally significant difference in the OPR between the D3 + TL and D5 + CM groups (56.6 versus 64.1%, difference: -7.5%, 95% CI: -15.4%, 0.4%, P = 0.06). The D3 + TL group resulted in a markedly lower implantation rate than the D5 + CM group (64.4 versus 77.0%; P = 0.002) in the PP analysis, however, the early miscarriage rate did not significantly differ between the two groups. The study lacked a direct comparison between time-lapse and CM selections at cleavage-stage SET and was statistically underpowered to detect non-inferiority. The subject's eligibility criteria favouring women with a good prognosis for IVF weakened the generalizability of the results. The OPR from Day 3 cleavage-stage SET using hierarchical classification time-lapse selection was significantly lower compared with that from Day 5 blastocyst SET using conventional morphology, yet it appeared to be clinically acceptable in women underwent IVF. This study is supported by grants from Ferring Pharmaceuticals and the Program for New Century Excellent Talents in University, China. ChiCTR-ICR-15006600. 16 June 2015. 1 October 2015.
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Posada, David
2006-01-01
ModelTest server is a web-based application for the selection of models of nucleotide substitution using the program ModelTest. The server takes as input a text file with likelihood scores for the set of candidate models. Models can be selected with hierarchical likelihood ratio tests, or with the Akaike or Bayesian information criteria. The output includes several statistics for the assessment of model selection uncertainty, for model averaging or to estimate the relative importance of model parameters. The server can be accessed at . PMID:16845102
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A Multidimensional Approach to Explore the Understanding of the Notion of Absolute Value
ERIC Educational Resources Information Center
Gagatsis, Athanasios; Panaoura, Areti
2014-01-01
The study aimed to investigate students' conceptions on the notion of absolute value and their abilities in applying the specific notion in routine and non-routine situations. A questionnaire was constructed and administered to 17-year-old students. Data were analysed using the hierarchical clustering of variables and the implicative method, while…
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Maximum Likelihood Analysis of a Two-Level Nonlinear Structural Equation Model with Fixed Covariates
ERIC Educational Resources Information Center
Lee, Sik-Yum; Song, Xin-Yuan
2005-01-01
In this article, a maximum likelihood (ML) approach for analyzing a rather general two-level structural equation model is developed for hierarchically structured data that are very common in educational and/or behavioral research. The proposed two-level model can accommodate nonlinear causal relations among latent variables as well as effects…
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A multivariate decision tree analysis of biophysical factors in tropical forest fire occurrence
Rey S. Ofren; Edward Harvey
2000-01-01
A multivariate decision tree model was used to quantify the relative importance of complex hierarchical relationships between biophysical variables and the occurrence of tropical forest fires. The study site is the Huai Kha Kbaeng wildlife sanctuary, a World Heritage Site in northwestern Thailand where annual fires are common and particularly destructive. Thematic...
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ERIC Educational Resources Information Center
Vaughn, Brandon K.; Wang, Qui
2009-01-01
Many areas in educational and psychological research involve the use of classification statistical analysis. For example, school districts might be interested in attaining variables that provide optimal prediction of school dropouts. In psychology, a researcher might be interested in the classification of a subject into a particular psychological…
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Analysis of Student Performance in Large-Enrollment Life Science Courses
ERIC Educational Resources Information Center
Creech, Leah Renee; Sweeder, Ryan D.
2012-01-01
This study examined the historical performance of students at Michigan State University in 12 life sciences courses over 13 yr to find variables impacting student success. Hierarchical linear modeling predicted 25.0-62.8% of the variance in students' grades in the courses analyzed. The primary predictor of a student's course grade was his or her…
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A Spreadsheet for a 2 x 3 x 2 Log-Linear Analysis. AIR 1991 Annual Forum Paper.
ERIC Educational Resources Information Center
Saupe, Joe L.
This paper describes a personal computer spreadsheet set up to carry out hierarchical log-linear analyses, a type of analysis useful for institutional research into multidimensional frequency tables formed from categorical variables such as faculty rank, student class level, gender, or retention status. The spreadsheet provides a concrete vehicle…
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ERIC Educational Resources Information Center
Teeroovengadum, Viraiyan; Heeraman, Nabeel; Jugurnath, Bhavish
2017-01-01
This study assesses the determinants of ICT adoption by educators in the teaching and learning process in the context of a developing country, Mauritius. A hierarchical regression analysis is used, to firstly determine the incremental effects of factors from the technology acceptance model (TAM) while controlling for demographic variables such as…
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ERIC Educational Resources Information Center
Schmitt, Michael T.; Wirth, James H.
2009-01-01
Numerous studies have found that, compared to women, men express higher levels of social dominance orientation (SDO), an individual difference variable reflecting support for unequal, hierarchical relationships between groups. Recent research suggests that the often-observed gender difference in SDO results from processes related to gender group…
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Illustration of a Multilevel Model for Meta-Analysis
ERIC Educational Resources Information Center
de la Torre, Jimmy; Camilli, Gregory; Vargas, Sadako; Vernon, R. Fox
2007-01-01
In this article, the authors present a multilevel (or hierarchical linear) model that illustrates issues in the application of the model to data from meta-analytic studies. In doing so, several issues are discussed that typically arise in the course of a meta-analysis. These include the presence of non-zero between-study variability, how multiple…
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HLM in Cluster-Randomised Trials--Measuring Efficacy across Diverse Populations of Learners
ERIC Educational Resources Information Center
Hegedus, Stephen; Tapper, John; Dalton, Sara; Sloane, Finbarr
2013-01-01
We describe the application of Hierarchical Linear Modelling (HLM) in a cluster-randomised study to examine learning algebraic concepts and procedures in an innovative, technology-rich environment in the US. HLM is applied to measure the impact of such treatment on learning and on contextual variables. We provide a detailed description of such…
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ERIC Educational Resources Information Center
Myers, Carrie B.
2011-01-01
This study tests the association between union status and job satisfaction using 8,000+ U.S. faculty at four-year public institutions surveyed in the 2004 National Study of Postsecondary Faculty. The results from hierarchical linear models that included individual and institutional variables found that nonunion faculty reported significantly…
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ERIC Educational Resources Information Center
O'Dwyer, Laura M.
2005-01-01
International studies in education provide researchers with opportunities to examine how students with both similar and dissimilar formal education systems perform on a single test and provide rich information about the relationships among student outcomes and the factors that affect them. Using hierarchical linear regression techniques and TIMSS…
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Predicting summer monsoon of Bhutan based on SST and teleconnection indices
NASA Astrophysics Data System (ADS)
Dorji, Singay; Herath, Srikantha; Mishra, Binaya Kumar; Chophel, Ugyen
2018-02-01
The paper uses a statistical method of predicting summer monsoon over Bhutan using the ocean-atmospheric circulation variables of sea surface temperature (SST), mean sea-level pressure (MSLP), and selected teleconnection indices. The predictors are selected based on the correlation. They are the SST and MSLP of the Bay of Bengal and the Arabian Sea and the MSLP of Bangladesh and northeast India. The Northern Hemisphere teleconnections of East Atlantic Pattern (EA), West Pacific Pattern (WP), Pacific/North American Pattern, and East Atlantic/West Russia Pattern (EA/WR). The rainfall station data are grouped into two regions with principal components analysis and Ward's hierarchical clustering algorithm. A support vector machine for regression model is proposed to predict the monsoon. The model shows improved skills over traditional linear regression. The model was able to predict the summer monsoon for the test data from 2011 to 2015 with a total monthly root mean squared error of 112 mm for region A and 33 mm for region B. Model could also forecast the 2016 monsoon of the South Asia Monsoon Outlook of World Meteorological Organization (WMO) for Bhutan. The reliance on agriculture and hydropower economy makes the prediction of summer monsoon highly valuable information for farmers and various other sectors. The proposed method can predict summer monsoon for operational forecasting.
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Profile-Based LC-MS Data Alignment—A Bayesian Approach
Tsai, Tsung-Heng; Tadesse, Mahlet G.; Wang, Yue; Ressom, Habtom W.
2014-01-01
A Bayesian alignment model (BAM) is proposed for alignment of liquid chromatography-mass spectrometry (LC-MS) data. BAM belongs to the category of profile-based approaches, which are composed of two major components: a prototype function and a set of mapping functions. Appropriate estimation of these functions is crucial for good alignment results. BAM uses Markov chain Monte Carlo (MCMC) methods to draw inference on the model parameters and improves on existing MCMC-based alignment methods through 1) the implementation of an efficient MCMC sampler and 2) an adaptive selection of knots. A block Metropolis-Hastings algorithm that mitigates the problem of the MCMC sampler getting stuck at local modes of the posterior distribution is used for the update of the mapping function coefficients. In addition, a stochastic search variable selection (SSVS) methodology is used to determine the number and positions of knots. We applied BAM to a simulated data set, an LC-MS proteomic data set, and two LC-MS metabolomic data sets, and compared its performance with the Bayesian hierarchical curve registration (BHCR) model, the dynamic time-warping (DTW) model, and the continuous profile model (CPM). The advantage of applying appropriate profile-based retention time correction prior to performing a feature-based approach is also demonstrated through the metabolomic data sets. PMID:23929872
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Gray, B.R.; Rogala, J.R.; Houser, J.N.
2013-01-01
Contiguous floodplain lakes ('lakes') have historically been used as study units for comparative studies of limnological variables that vary within lakes. The hierarchical nature of these studies implies that study variables may be correlated within lakes and that covariate associations may differ not only among lakes but also by spatial scale. We evaluated the utility of treating lakes as study units for limnological variables that vary within lakes based on the criteria of important levels of among-lake variation in study variables and the observation of covariate associations that vary among lakes. These concerns were selected, respectively, to ensure that lake signatures were distinguishable from within-lake variation and that lake-scale effects on covariate associations might provide inferences not available by ignoring those effects. Study data represented chlorophyll a (CHL) and inorganic suspended solids (ISS) data from lakes within three reaches of the Upper Mississippi River. Sampling occurred in summer from 1993 through 2005 (except 2003); numbers of lakes per reach varied from 7 to 19, and median lake area varied from 53 to 101 ha. CHL and ISS levels were modelled linearly, with lake, year and lake x year effects treated as random. For all reaches, the proportions of variation in CHL and ISS attributable to differences among lakes (including lake and lake x year effects) were substantial (range: 18%-73%). Finally, among-lake variation in CHL and ISS was strongly associated with covariates and covariate effects that varied by lakes or lake-years (including with vegetation levels and, for CHL, log(ISS)). These findings demonstrate the utility of treating floodplain lakes as study units for the study of limnological variables and the importance of addressing hierarchy within study designs when making inferences from data collected within floodplain lakes.
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Improving stability of prediction models based on correlated omics data by using network approaches.
Tissier, Renaud; Houwing-Duistermaat, Jeanine; Rodríguez-Girondo, Mar
2018-01-01
Building prediction models based on complex omics datasets such as transcriptomics, proteomics, metabolomics remains a challenge in bioinformatics and biostatistics. Regularized regression techniques are typically used to deal with the high dimensionality of these datasets. However, due to the presence of correlation in the datasets, it is difficult to select the best model and application of these methods yields unstable results. We propose a novel strategy for model selection where the obtained models also perform well in terms of overall predictability. Several three step approaches are considered, where the steps are 1) network construction, 2) clustering to empirically derive modules or pathways, and 3) building a prediction model incorporating the information on the modules. For the first step, we use weighted correlation networks and Gaussian graphical modelling. Identification of groups of features is performed by hierarchical clustering. The grouping information is included in the prediction model by using group-based variable selection or group-specific penalization. We compare the performance of our new approaches with standard regularized regression via simulations. Based on these results we provide recommendations for selecting a strategy for building a prediction model given the specific goal of the analysis and the sizes of the datasets. Finally we illustrate the advantages of our approach by application of the methodology to two problems, namely prediction of body mass index in the DIetary, Lifestyle, and Genetic determinants of Obesity and Metabolic syndrome study (DILGOM) and prediction of response of each breast cancer cell line to treatment with specific drugs using a breast cancer cell lines pharmacogenomics dataset.
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Determinants of postpartum weight variation in a cohort of adult women; a hierarchical approach.
Monteiro da Silva, Maria da Conceição; Marlúcia Oliveira, Ana; Pereira Magalhães de Oliveira, Lucivalda; Silva dos Santos Fonseca, Dra Nedja; Portela de Santana, Mônica Leila; de Araújo Góes Neto, Edgar; Rodrigues Porto da Cruz, Thomaz
2013-01-01
Retention of the weight gained during pregnancy or the weight gain postpartum has been associated with increased prevalence of obesity in women of childbearing age. To identify determinants of weight variation at 24 months postpartum in women from 2 towns in Bahia, Brazil. Dynamic cohort data of 325 adult women were collected for 24 months postpartum. Weight variation at 24 months postpartum was considered a response variable. Socioeconomic, demographic, reproductive, related with childbirth variables and lifestyle conditions were considered exposure variables. A linear mixed-effects regression model with a hierarchical approach was used for data analysis. Suitable sanitary conditions in the household (2.175 kg; p = 0.001) and participation social programs for income transfer (1.300 kg; p = 0.018) contributed to weight gain in distal level of determinants, while at intermediate level, pre gestational overweight and surgical delivery had effects on postpartum weight, causing an average increase of 3.380 kg (p < 0.001) and loss of 2.451 kg (p < 0.001), respectively. At proximal level, a score point increase for breastfeeding yielded an average postpartum loss of 70 g (p = 0.002). Our results indicate the need to promote weight control during and after pregnancy, encourage extended breastfeeding, and improve living conditions through intersectoral interventions. Copyright © AULA MEDICA EDICIONES 2013. Published by AULA MEDICA. All rights reserved.
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Parameter estimation and prediction for the course of a single epidemic outbreak of a plant disease.
Kleczkowski, A; Gilligan, C A
2007-10-22
Many epidemics of plant diseases are characterized by large variability among individual outbreaks. However, individual epidemics often follow a well-defined trajectory which is much more predictable in the short term than the ensemble (collection) of potential epidemics. In this paper, we introduce a modelling framework that allows us to deal with individual replicated outbreaks, based upon a Bayesian hierarchical analysis. Information about 'similar' replicate epidemics can be incorporated into a hierarchical model, allowing both ensemble and individual parameters to be estimated. The model is used to analyse the data from a replicated experiment involving spread of Rhizoctonia solani on radish in the presence or absence of a biocontrol agent, Trichoderma viride. The rate of primary (soil-to-plant) infection is found to be the most variable factor determining the final size of epidemics. Breakdown of biological control in some replicates results in high levels of primary infection and increased variability. The model can be used to predict new outbreaks of disease based upon knowledge from a 'library' of previous epidemics and partial information about the current outbreak. We show that forecasting improves significantly with knowledge about the history of a particular epidemic, whereas the precision of hindcasting to identify the past course of the epidemic is largely independent of detailed knowledge of the epidemic trajectory. The results have important consequences for parameter estimation, inference and prediction for emerging epidemic outbreaks.
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NASA Astrophysics Data System (ADS)
Zulvia, Pepi; Kurnia, Anang; Soleh, Agus M.
2017-03-01
Individual and environment are a hierarchical structure consist of units grouped at different levels. Hierarchical data structures are analyzed based on several levels, with the lowest level nested in the highest level. This modeling is commonly call multilevel modeling. Multilevel modeling is widely used in education research, for example, the average score of National Examination (UN). While in Indonesia UN for high school student is divided into natural science and social science. The purpose of this research is to develop multilevel and panel data modeling using linear mixed model on educational data. The first step is data exploration and identification relationships between independent and dependent variable by checking correlation coefficient and variance inflation factor (VIF). Furthermore, we use a simple model approach with highest level of the hierarchy (level-2) is regency/city while school is the lowest of hierarchy (level-1). The best model was determined by comparing goodness-of-fit and checking assumption from residual plots and predictions for each model. Our finding that for natural science and social science, the regression with random effects of regency/city and fixed effects of the time i.e multilevel model has better performance than the linear mixed model in explaining the variability of the dependent variable, which is the average scores of UN.
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Hierarchical and Multidimensional Academic Self-Concept of Commercial Students.
Yeung; Chui; Lau
1999-10-01
Adapting the Marsh (1990) Academic Self-Description Questionnaire (ASDQ), this study examined the academic self-concept of students in a school of commerce in Hong Kong (N = 212). Confirmatory factor analysis found that students clearly distinguished among self-concept constructs in English, Chinese, Math and Statistics, Economics, and Principles of Accounting, and each of these constructs was highly associated with a global Academic self-concept construct, reflecting the validity of each construct in measuring an academic component of self-concept. Domain-specific self-concepts were more highly related with students' intention of course selection in corresponding areas than in nonmatching areas, further supporting the multidimensionality of the students' academic self-concept. Students' self-concepts in the five curriculum domains can be represented by the global Academic self-concept, supporting the hierarchical structure of students' academic self-concept in an educational institution with a specific focus, such as commercial studies. The academic self-concepts of the commercial students are both multidimensional and hierarchical. Copyright 1999 Academic Press.
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Liu, Xiao; Qi, Wei; Wang, Yuefei; Su, Rongxin; He, Zhimin
2017-11-16
Metal-organic frameworks (MOFs) have drawn extensive research interest as candidates for enzyme immobilization owing to their tunable porosity, high surface area, and excellent chemical/thermal stability. Herein, we report a facile and universal strategy for enzyme immobilization using highly stable hierarchically porous metal-organic frameworks (HP-MOFs). The HP-MOFs were stable over a wide pH range (pH = 2-11 for HP-DUT-5) and met the catalysis conditions of most enzymes. The as-prepared hierarchical micro/mesoporous MOFs with mesoporous defects showed a superior adsorption capacity towards enzymes. The maximum adsorption capacity of HP-DUT-5 for glucose oxidase (GOx) and uricase was 208 mg g -1 and 225 mg g -1 , respectively. Furthermore, we constructed two multi-enzyme biosensors for glucose and uric acid (UA) by immobilizing GOx and uricase with horseradish peroxidase (HRP) on HP-DUT-5, respectively. These sensors were efficiently applied in the colorimetric detection of glucose and UA and showed good sensitivity, selectivity, and recyclability.
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Xu, Caixia; Sun, Fenglei; Gao, Hua; Wang, Jinping
2013-05-30
Nanoporous platinum-cobalt (NP-PtCo) alloy with hierarchical nanostructure is straightforwardly fabricated by dealloying PtCoAl alloy in a mild alkaline solution. Selectively etching Al resulted in a hierarchical three-dimensional network nanostructure with a narrow size distribution at 3 nm. The as-prepared NP-PtCo alloy shows superior performance toward ethanol and hydrogen peroxide (H2O2) with highly sensitive response due to its unique electrocatalytic activity. In addition, NP-PtCo also exhibits excellent amperometric durability and long-term stability for H2O2 as well as a good anti-interference toward ascorbic acid, uric acid, and dopamine. The hierarchical nanoporous architecture in PtCo alloy is also highly active for glucose sensing electrooxidation and sensing in a wide linear range. The NP-PtCo alloy holds great application potential for electrochemical sensing with simple preparation, unique catalytic activity, and high structure stability. Copyright © 2013 Elsevier B.V. All rights reserved.
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Ikeda, Masato; Nobori, Tadahito; Schmutz, Marc; Lehn, Jean-Marie
2005-01-07
The bow-shaped molecule 1 bearing a self-complementary DAAD-ADDA (D=donor A=acceptor) hydrogen-bonding array generates, in hydrocarbon solvents, highly ordered supramolecular sheet aggregates that subsequently give rise to gels by formation of an entangled network. The process of hierarchical self-assembly of compound 1 was investigated by the concentration and temperature dependence of UV-visible and (1)H NMR spectra, fluorescence spectra, and electron microscopy data. The temperature dependence of the UV-visible spectra indicates a highly cooperative process for the self-assembly of compound 1 in decaline. The electron micrograph of the decaline solution of compound 1 (1.0 mM) revealed supramolecular sheet aggregates forming an entangled network. The selected area electronic diffraction patterns of the supramolecular sheet aggregates were typical for single crystals, indicative of a highly ordered assembly. The results exemplify the generation, by hierarchical self-assembly, of highly organized supramolecular materials presenting novel collective properties at each level of organization.
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Active Interaction Mapping as a tool to elucidate hierarchical functions of biological processes.
Farré, Jean-Claude; Kramer, Michael; Ideker, Trey; Subramani, Suresh
2017-07-03
Increasingly, various 'omics data are contributing significantly to our understanding of novel biological processes, but it has not been possible to iteratively elucidate hierarchical functions in complex phenomena. We describe a general systems biology approach called Active Interaction Mapping (AI-MAP), which elucidates the hierarchy of functions for any biological process. Existing and new 'omics data sets can be iteratively added to create and improve hierarchical models which enhance our understanding of particular biological processes. The best datatypes to further improve an AI-MAP model are predicted computationally. We applied this approach to our understanding of general and selective autophagy, which are conserved in most eukaryotes, setting the stage for the broader application to other cellular processes of interest. In the particular application to autophagy-related processes, we uncovered and validated new autophagy and autophagy-related processes, expanded known autophagy processes with new components, integrated known non-autophagic processes with autophagy and predict other unexplored connections.
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Selection of higher order regression models in the analysis of multi-factorial transcription data.
Prazeres da Costa, Olivia; Hoffman, Arthur; Rey, Johannes W; Mansmann, Ulrich; Buch, Thorsten; Tresch, Achim
2014-01-01
Many studies examine gene expression data that has been obtained under the influence of multiple factors, such as genetic background, environmental conditions, or exposure to diseases. The interplay of multiple factors may lead to effect modification and confounding. Higher order linear regression models can account for these effects. We present a new methodology for linear model selection and apply it to microarray data of bone marrow-derived macrophages. This experiment investigates the influence of three variable factors: the genetic background of the mice from which the macrophages were obtained, Yersinia enterocolitica infection (two strains, and a mock control), and treatment/non-treatment with interferon-γ. We set up four different linear regression models in a hierarchical order. We introduce the eruption plot as a new practical tool for model selection complementary to global testing. It visually compares the size and significance of effect estimates between two nested models. Using this methodology we were able to select the most appropriate model by keeping only relevant factors showing additional explanatory power. Application to experimental data allowed us to qualify the interaction of factors as either neutral (no interaction), alleviating (co-occurring effects are weaker than expected from the single effects), or aggravating (stronger than expected). We find a biologically meaningful gene cluster of putative C2TA target genes that appear to be co-regulated with MHC class II genes. We introduced the eruption plot as a tool for visual model comparison to identify relevant higher order interactions in the analysis of expression data obtained under the influence of multiple factors. We conclude that model selection in higher order linear regression models should generally be performed for the analysis of multi-factorial microarray data.
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Chen, Yun; Yang, Hui
2016-01-01
In the era of big data, there are increasing interests on clustering variables for the minimization of data redundancy and the maximization of variable relevancy. Existing clustering methods, however, depend on nontrivial assumptions about the data structure. Note that nonlinear interdependence among variables poses significant challenges on the traditional framework of predictive modeling. In the present work, we reformulate the problem of variable clustering from an information theoretic perspective that does not require the assumption of data structure for the identification of nonlinear interdependence among variables. Specifically, we propose the use of mutual information to characterize and measure nonlinear correlation structures among variables. Further, we develop Dirichlet process (DP) models to cluster variables based on the mutual-information measures among variables. Finally, orthonormalized variables in each cluster are integrated with group elastic-net model to improve the performance of predictive modeling. Both simulation and real-world case studies showed that the proposed methodology not only effectively reveals the nonlinear interdependence structures among variables but also outperforms traditional variable clustering algorithms such as hierarchical clustering. PMID:27966581
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Chen, Yun; Yang, Hui
2016-12-14
In the era of big data, there are increasing interests on clustering variables for the minimization of data redundancy and the maximization of variable relevancy. Existing clustering methods, however, depend on nontrivial assumptions about the data structure. Note that nonlinear interdependence among variables poses significant challenges on the traditional framework of predictive modeling. In the present work, we reformulate the problem of variable clustering from an information theoretic perspective that does not require the assumption of data structure for the identification of nonlinear interdependence among variables. Specifically, we propose the use of mutual information to characterize and measure nonlinear correlation structures among variables. Further, we develop Dirichlet process (DP) models to cluster variables based on the mutual-information measures among variables. Finally, orthonormalized variables in each cluster are integrated with group elastic-net model to improve the performance of predictive modeling. Both simulation and real-world case studies showed that the proposed methodology not only effectively reveals the nonlinear interdependence structures among variables but also outperforms traditional variable clustering algorithms such as hierarchical clustering.
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VR Employment Outcomes of Individuals with Autism Spectrum Disorders: A Decade in the Making.
Alverson, Charlotte Y; Yamamoto, Scott H
2018-01-01
This study utilized hierarchical linear modeling analysis of a 10-year extant dataset from Rehabilitation Services Administration to investigate significant predictors of employment outcomes for vocational rehabilitation (VR) clients with autism. Predictor variables were gender, ethnicity, attained education level, IEP status in high school, secondary disability status, and total number of VR services. Competitive employment was the criterion variable. Only one predictor variable, Total Number of VR Services, was significant across all 10 years. IEP status in high school was not significant in any year. The remaining predictors were significant in one or more years. Further research and implications for researchers and practitioners are included.
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ERIC Educational Resources Information Center
Dickson, Ginger L.; Jepsen, David A.
2007-01-01
The authors surveyed a national sample of master's-level counseling students regarding their multicultural training experiences and their multicultural counseling competencies. A series of hierarchical regression models tested the prediction of inventoried competencies from measures of selected training experiences: (a) program cultural ambience…
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The Relationship between Counselors' Multicultural Counseling Competence and Poverty Beliefs
ERIC Educational Resources Information Center
Clark, Madeline; Moe, Jeff; Hays, Danica G.
2017-01-01
The authors explored the relationship between counselors' multicultural counseling competence (MCC), poverty beliefs, and select demographic factors. Results of hierarchical linear regressions indicate that MCC is predictive of counselor individualistic and structural poverty beliefs. Implications for counselor multicultural training and immersion…
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Structure Strategies for Comprehending Expository Text.
ERIC Educational Resources Information Center
Muth, K. Denise
1987-01-01
Examines three strategies designed to help middle school students use text structures to comprehend expository text: (1) hierarchical summaries, (2) conceptual maps, and (3) thematic organizers. Summarizes advantages and disadvantages of each strategy and recommends that teachers consider the outcomes they want and select the most appropriate…
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A study of undue pain and surfing: using hierarchical criteria to assess website quality.
Lorence, Daniel; Abraham, Joanna
2008-09-01
In studies of web-based consumer health information, scant attention has been paid to the selective development of differential methodologies for website quality evaluation, or to selective grouping and analysis of specific ;domains of uncertainty' in healthcare. Our objective is to introduce a more refined model for website evaluation, and illustrate its application using assessment of websites within an area of ongoing medical uncertainty, back pain. In this exploratory technology assessment, we suggest a model for assessing these ;domains of uncertainty' within healthcare, using qualitative assessment of websites and hierarchical concepts. Using such a hierarchy of quality criteria, we review medical information provided by the most frequently accessed websites related to back pain. Websites are evaluated using standardized criteria, with results rated from the viewpoint of the consumer. Results show that standardization of quality rating across subjective content, and between commercial and niche search results, can provide a consumer-friendly dimension to health information.
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Tan, Wui Siew; Lewis, Christina L; Horelik, Nicholas E; Pregibon, Daniel C; Doyle, Patrick S; Yi, Hyunmin
2008-11-04
We demonstrate hierarchical assembly of tobacco mosaic virus (TMV)-based nanotemplates with hydrogel-based encoded microparticles via nucleic acid hybridization. TMV nanotemplates possess a highly defined structure and a genetically engineered high density thiol functionality. The encoded microparticles are produced in a high throughput microfluidic device via stop-flow lithography (SFL) and consist of spatially discrete regions containing encoded identity information, an internal control, and capture DNAs. For the hybridization-based assembly, partially disassembled TMVs were programmed with linker DNAs that contain sequences complementary to both the virus 5' end and a selected capture DNA. Fluorescence microscopy, atomic force microscopy (AFM), and confocal microscopy results clearly indicate facile assembly of TMV nanotemplates onto microparticles with high spatial and sequence selectivity. We anticipate that our hybridization-based assembly strategy could be employed to create multifunctional viral-synthetic hybrid materials in a rapid and high-throughput manner. Additionally, we believe that these viral-synthetic hybrid microparticles may find broad applications in high capacity, multiplexed target sensing.
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NASA Astrophysics Data System (ADS)
Liu, Qing; Wen, Dafen; Yang, Yanran; Fei, Zhaoyang; Zhang, Zhuxiu; Chen, Xian; Tang, Jihai; Cui, Mifen; Qiao, Xu
2018-03-01
Hierarchical porous ZSM-5 (HP-ZSM-5) zeolites were synthesized by hydrothermal crystallization method adding triethoxyvinylsilane as the growth-inhibitor at different hydrothermal crystallized temperatures. The properties of the obtained samples were characterized by XRD, SEM, N2-sorption, uptake of ethylene, 27Al MAS NMR, NH3-TPD, and Py-IR. It was found that the mesopore was introduced and the acidity was adjusted over HP-ZSM-5 samples successfully. The hydrothermal crystallized temperature had an important influence on the porous structure and surface properties. The catalytic performance for chloromethane to light-olefins (CMTO) were also investigated. Compared with ZSM-5 samples, HP-ZSM-5 samples exhibited enhanced stability and increased selectivity of light-olefins for CMTO reaction because of the introduction of the abundant mesopore and appropriate acidity. The lifetime (the duration of chloromethane conversion >98%) and selectivity of light-olefins reached 115 h and 69.3%, respectively.
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Approximation, abstraction and decomposition in search and optimization
NASA Technical Reports Server (NTRS)
Ellman, Thomas
1992-01-01
In this paper, I discuss four different areas of my research. One portion of my research has focused on automatic synthesis of search control heuristics for constraint satisfaction problems (CSPs). I have developed techniques for automatically synthesizing two types of heuristics for CSPs: Filtering functions are used to remove portions of a search space from consideration. Another portion of my research is focused on automatic synthesis of hierarchic algorithms for solving constraint satisfaction problems (CSPs). I have developed a technique for constructing hierarchic problem solvers based on numeric interval algebra. Another portion of my research is focused on automatic decomposition of design optimization problems. We are using the design of racing yacht hulls as a testbed domain for this research. Decomposition is especially important in the design of complex physical shapes such as yacht hulls. Another portion of my research is focused on intelligent model selection in design optimization. The model selection problem results from the difficulty of using exact models to analyze the performance of candidate designs.
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Kapa, Leah L; Plante, Elena; Doubleday, Kevin
2017-08-16
The first goal of this research was to compare verbal and nonverbal executive function abilities between preschoolers with and without specific language impairment (SLI). The second goal was to assess the group differences on 4 executive function components in order to determine if the components may be hierarchically related as suggested within a developmental integrative framework of executive function. This study included 26 4- and 5-year-olds diagnosed with SLI and 26 typically developing age- and sex-matched peers. Participants were tested on verbal and nonverbal measures of sustained selective attention, working memory, inhibition, and shifting. The SLI group performed worse compared with typically developing children on both verbal and nonverbal measures of sustained selective attention and working memory, the verbal inhibition task, and the nonverbal shifting task. Comparisons of standardized group differences between executive function measures revealed a linear increase with the following order: working memory, inhibition, shifting, and sustained selective attention. The pattern of results suggests that preschoolers with SLI have deficits in executive functioning compared with typical peers, and deficits are not limited to verbal tasks. A significant linear relationship between group differences across executive function components supports the possibility of a hierarchical relationship between executive function skills.
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Development of hierarchical, tunable pore size polymer foams for ICF targets
Hamilton, Christopher E.; Lee, Matthew Nicholson; Parra-Vasquez, A. Nicholas Gerardo
2016-08-01
In this study, one of the great challenges of inertial confinement fusion experiments is poor understanding of the effects of reactant heterogeneity on fusion reactions. The Marble campaign, conceived at Los Alamos National Laboratory, aims to gather new insights into this issue by utilizing target capsules containing polymer foams of variable pore sizes, tunable over an order of magnitude. Here, we describe recent and ongoing progress in the development of CH and CH/CD polymer foams in support of Marble. Hierarchical and tunable pore sizes have been achieved by utilizing a sacrificial porogen template within an open-celled poly(divinylbenzene) or poly(divinylbenzene-co-styrene) aerogelmore » matrix, resulting in low-density foams (~30 mg/ml) with continuous multimodal pore networks.« less
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Groundwater Quality: Analysis of Its Temporal and Spatial Variability in a Karst Aquifer.
Pacheco Castro, Roger; Pacheco Ávila, Julia; Ye, Ming; Cabrera Sansores, Armando
2018-01-01
This study develops an approach based on hierarchical cluster analysis for investigating the spatial and temporal variation of water quality governing processes. The water quality data used in this study were collected in the karst aquifer of Yucatan, Mexico, the only source of drinking water for a population of nearly two million people. Hierarchical cluster analysis was applied to the quality data of all the sampling periods lumped together. This was motivated by the observation that, if water quality does not vary significantly in time, two samples from the same sampling site will belong to the same cluster. The resulting distribution maps of clusters and box-plots of the major chemical components reveal the spatial and temporal variability of groundwater quality. Principal component analysis was used to verify the results of cluster analysis and to derive the variables that explained most of the variation of the groundwater quality data. Results of this work increase the knowledge about how precipitation and human contamination impact groundwater quality in Yucatan. Spatial variability of groundwater quality in the study area is caused by: a) seawater intrusion and groundwater rich in sulfates at the west and in the coast, b) water rock interactions and the average annual precipitation at the middle and east zones respectively, and c) human contamination present in two localized zones. Changes in the amount and distribution of precipitation cause temporal variation by diluting groundwater in the aquifer. This approach allows to analyze the variation of groundwater quality controlling processes efficiently and simultaneously. © 2017, National Ground Water Association.
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Pursuit of STEM: Factors shaping degree completion for African American females in STEM
NASA Astrophysics Data System (ADS)
Wilkins, Ashlee N.
The primary purpose of the study was to examine secondary data from the Cooperative Institutional Research Program (CIRP) Freshman and College Senior Surveys to investigate factors shaping degree aspirations for African American female undergraduates partaking in science, technology, engineering, and mathematics (STEM) majors. Hierarchical multiple regression was used to analyze the data and identify relationships between independent variables in relation to the dependent variable. The findings of the study reveal four key variables that were predictive of degree completion for African American females in STEM. Father's education, SAT composite, highest degree planned, and self-perception were positive predictors for females; while independent variable overall sense of community among students remained a negative predictor. Lastly implications for education and recommendations for future research were discussed.
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Availability-Based Importance Framework for Supplier Selection
2015-04-30
IMA Journal of Management Math, 15(2), 161– 174. Chen, C . -T., Lin, C . -T., & Huang, S. -F. (2006). A fuzzy approach for supplier evaluation and...reliability modeling: Principles and applications. Hoboken, NJ: Wiley. Liao, C . -N., & Kao, H. -P. (2011). An integrated fuzzy TOPSIS and MCGP approach to...5307–5326. Wang, J. -W., Cheng, C . -H., & Huang, K.- C . (2009). Fuzzy hierarchical TOPSIS for supplier selection. Applied Soft Computing, 9(1), 377
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Koley, Pradyot; Sakurai, Makoto; Aono, Masakazu
2016-01-27
Fabrication of protein-inorganic hybrid materials of innumerable hierarchical patterns plays a major role in the development of multifunctional advanced materials with their improved features in synergistic way. However, effective fabrication and applications of the hybrid structures is limited due to the difficulty in control and production cost. Here, we report the controlled fabrication of complex hybrid flowers with hierarchical porosity through a green and facile coprecipitation method by using industrial waste natural silk protein sericin. The large surface areas and porosity of the microsize hybrid flowers enable water purification through adsorption of different heavy metal ions. The high adsorption capacity depends on their morphology, which is changed largely by sericin concentration in their fabrication. Superior adsorption and greater selectivity of the Pb(II) ions have been confirmed by the characteristic growth of needle-shaped nanowires on the hierarchical surface of the hybrid flowers. These hybrid flowers show excellent thermal stability even after complete evaporation of the protein molecules, significantly increasing the porosity of the flower petals. A simple, cost-effective and environmental friendly fabrication method of the porous flowers will lead to a new solution to water pollution required in the modern industrial society.
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Zhang, Rui; Zhou, Tingting; Wang, Lili; Zhang, Tong
2018-03-21
Highly sensitive and stable gas sensors have attracted much attention because they are the key to innovations in the fields of environment, health, energy savings and security, etc. Sensing materials, which influence the practical sensing performance, are the crucial parts for gas sensors. Metal-organic frameworks (MOFs) are considered as alluring sensing materials for gas sensors because of the possession of high specific surface area, unique morphology, abundant metal sites, and functional linkers. Herein, four kinds of porous hierarchical Co 3 O 4 structures have been selectively controlled by optimizing the thermal decomposition (temperature, rate, and atmosphere) using ZIF-67 as precursor that was obtained from coprecipitation method with the co-assistance of cobalt salt and 2-methylimidazole in the solution of methanol. These hierarchical Co 3 O 4 structures, with controllable cross-linked channels, meso-/micropores, and adjustable surface area, are efficient catalytic materials for gas sensing. Benefits from structural advantages, core-shell, and porous core-shell Co 3 O 4 exhibit enhanced sensing performance compared to those of porous popcorn and nanoparticle Co 3 O 4 to acetone gas. These novel MOF-templated Co 3 O 4 hierarchical structures are so fantastic that they can be expected to be efficient sensing materials for development of low-temperature operating gas sensors.
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Hahus, Ian; Migliaccio, Kati; Douglas-Mankin, Kyle; Klarenberg, Geraldine; Muñoz-Carpena, Rafael
2018-04-27
Hierarchical and partitional cluster analyses were used to compartmentalize Water Conservation Area 1, a managed wetland within the Arthur R. Marshall Loxahatchee National Wildlife Refuge in southeast Florida, USA, based on physical, biological, and climatic geospatial attributes. Single, complete, average, and Ward's linkages were tested during the hierarchical cluster analyses, with average linkage providing the best results. In general, the partitional method, partitioning around medoids, found clusters that were more evenly sized and more spatially aggregated than those resulting from the hierarchical analyses. However, hierarchical analysis appeared to be better suited to identify outlier regions that were significantly different from other areas. The clusters identified by geospatial attributes were similar to clusters developed for the interior marsh in a separate study using water quality attributes, suggesting that similar factors have influenced variations in both the set of physical, biological, and climatic attributes selected in this study and water quality parameters. However, geospatial data allowed further subdivision of several interior marsh clusters identified from the water quality data, potentially indicating zones with important differences in function. Identification of these zones can be useful to managers and modelers by informing the distribution of monitoring equipment and personnel as well as delineating regions that may respond similarly to future changes in management or climate.
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Wasson, Anton P; Chiu, Grace S; Zwart, Alexander B; Binns, Timothy R
2017-01-01
Ensuring future food security for a growing population while climate change and urban sprawl put pressure on agricultural land will require sustainable intensification of current farming practices. For the crop breeder this means producing higher crop yields with less resources due to greater environmental stresses. While easy gains in crop yield have been made mostly "above ground," little progress has been made "below ground"; and yet it is these root system traits that can improve productivity and resistance to drought stress. Wheat pre-breeders use soil coring and core-break counts to phenotype root architecture traits, with data collected on rooting density for hundreds of genotypes in small increments of depth. The measured densities are both large datasets and highly variable even within the same genotype, hence, any rigorous, comprehensive statistical analysis of such complex field data would be technically challenging. Traditionally, most attributes of the field data are therefore discarded in favor of simple numerical summary descriptors which retain much of the high variability exhibited by the raw data. This poses practical challenges: although plant scientists have established that root traits do drive resource capture in crops, traits that are more randomly (rather than genetically) determined are difficult to breed for. In this paper we develop a hierarchical nonlinear mixed modeling approach that utilizes the complete field data for wheat genotypes to fit, under the Bayesian paradigm, an "idealized" relative intensity function for the root distribution over depth. Our approach was used to determine heritability : how much of the variation between field samples was purely random vs. being mechanistically driven by the plant genetics? Based on the genotypic intensity functions, the overall heritability estimate was 0.62 (95% Bayesian confidence interval was 0.52 to 0.71). Despite root count profiles that were statistically very noisy, our approach led to denoised profiles which exhibited rigorously discernible phenotypic traits. Profile-specific traits could be representative of a genotype, and thus, used as a quantitative tool to associate phenotypic traits with specific genotypes. This would allow breeders to select for whole root system distributions appropriate for sustainable intensification, and inform policy for mitigating crop yield risk and food insecurity.
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Liu, Chuan; Wang, Shu; Shen, Xue; Li, Mengyao; Wang, Lie
2015-06-20
College teachers in China are confronted with a lot of pressure from teaching, researching and living. They are suffering from impaired physical and mental health. The purpose of this study was to investigate the relationships between organizational behavior factors and college teachers' health related quality of life (HRQOL), and to confirm whether they are positive resources for improving teachers' HRQOL. A cross-sectional survey was conducted in Shenyang, China, from January to April 2014. Participants were composed of 965 teachers randomly selected from five representative colleges in Shenyang. Self-administrated questionnaires containing the 36-item Short-Form Health Survey (SF-36), the Chinese version Psychological Capital Questionnaire (PCQ), and scales assessing group identification, POS, and psychological empowerment were used to measure HRQOL and organizational behavior variables of college teachers. Hierarchical multiple regression analysis (HMR) was performed to explore the effects of organizational behavior variables on college teachers' HRQOL. The mean (SD) scores of physical component summary (PCS) and mental component summary (MCS) among college teachers were 71.43 (14.70) and 65.46 (16.55) respectively in the study population. Hierarchical multiple regression analysis showed that group identification (β = 0.121, P < 0.001) and PsyCap (β = 0.336, P < 0.001) were significant predictors of PCS, while group identification (β = 0.107, P < 0.001), POS (β = 0.124, P = 0.003), psychological empowerment (β = 0.093, P = 0.017) and PsyCap (β = 0.421, P < 0.001) were significant predictors of MCS. Chinese college teachers experienced relatively low level of HRQOL and their mental quality of life (QOL) were impaired more seriously than physical QOL. Organizational behavior factors (PsyCap, group identification, POS and psychological empowerment) were strong predictors of college teachers' HRQOL and are positive resources for improving teachers' HRQOL. The enhancement of college teachers' PsyCap, group identification, POS and psychological empowerment at work should be incorporated in the strategy of protecting and improving college teachers' physical and mental QOL.
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Breaking down hierarchies of decision-making in primates
Hyafil, Alexandre; Moreno-Bote, Rubén
2017-01-01
Possible options in a decision often organize as a hierarchy of subdecisions. A recent study concluded that perceptual processes in primates mimic this hierarchical structure and perform subdecisions in parallel. We argue that a flat model that directly selects between final choices accounts more parsimoniously for the reported behavioral and neural data. Critically, a flat model is characterized by decision signals integrating evidence at different hierarchical levels, in agreement with neural recordings showing this integration in localized neural populations. Our results point to the role of experience for building integrated perceptual categories where sensory evidence is merged prior to decision. DOI: http://dx.doi.org/10.7554/eLife.16650.001 PMID:28648171
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Decentralized cooperative TOA/AOA target tracking for hierarchical wireless sensor networks.
Chen, Ying-Chih; Wen, Chih-Yu
2012-11-08
This paper proposes a distributed method for cooperative target tracking in hierarchical wireless sensor networks. The concept of leader-based information processing is conducted to achieve object positioning, considering a cluster-based network topology. Random timers and local information are applied to adaptively select a sub-cluster for the localization task. The proposed energy-efficient tracking algorithm allows each sub-cluster member to locally estimate the target position with a Bayesian filtering framework and a neural networking model, and further performs estimation fusion in the leader node with the covariance intersection algorithm. This paper evaluates the merits and trade-offs of the protocol design towards developing more efficient and practical algorithms for object position estimation.
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DOT National Transportation Integrated Search
1995-08-01
Bridge design engineers and local highway officials make bridge replacement decisions across the : United States. The Analytical Hierarchy Process was used to characterize the bridge material selection : decision of these individuals. State Departmen...
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Developing a Framework for Communication Management Competencies
ERIC Educational Resources Information Center
Jeffrey, Lynn Maud; Brunton, Margaret Ann
2011-01-01
Using a hierarchical needs assessment model developed by Hunt we identified the essential competencies of communication management practitioners for the purpose of curriculum development and selection. We found that the underlying values of the profession were embodied in two superordinate goals. Six major competencies were identified, which were…
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ERIC Educational Resources Information Center
Lee, Jaekyung; Reeves, Todd
2012-01-01
This study examines the impact of high-stakes school accountability, capacity, and resources under NCLB on reading and math achievement outcomes through comparative interrupted time-series analyses of 1990-2009 NAEP state assessment data. Through hierarchical linear modeling latent variable regression with inverse probability of treatment…