Using CV-GLUE procedure in analysis of wetland model predictive uncertainty.

PubMed

Huang, Chun-Wei; Lin, Yu-Pin; Chiang, Li-Chi; Wang, Yung-Chieh

2014-07-01

This study develops a procedure that is related to Generalized Likelihood Uncertainty Estimation (GLUE), called the CV-GLUE procedure, for assessing the predictive uncertainty that is associated with different model structures with varying degrees of complexity. The proposed procedure comprises model calibration, validation, and predictive uncertainty estimation in terms of a characteristic coefficient of variation (characteristic CV). The procedure first performed two-stage Monte-Carlo simulations to ensure predictive accuracy by obtaining behavior parameter sets, and then the estimation of CV-values of the model outcomes, which represent the predictive uncertainties for a model structure of interest with its associated behavior parameter sets. Three commonly used wetland models (the first-order K-C model, the plug flow with dispersion model, and the Wetland Water Quality Model; WWQM) were compared based on data that were collected from a free water surface constructed wetland with paddy cultivation in Taipei, Taiwan. The results show that the first-order K-C model, which is simpler than the other two models, has greater predictive uncertainty. This finding shows that predictive uncertainty does not necessarily increase with the complexity of the model structure because in this case, the more simplistic representation (first-order K-C model) of reality results in a higher uncertainty in the prediction made by the model. The CV-GLUE procedure is suggested to be a useful tool not only for designing constructed wetlands but also for other aspects of environmental management. Copyright © 2014 Elsevier Ltd. All rights reserved.

A Comparison of Height-Accumulation and Volume-Equation Methods for Estimating Tree and Stand Volumes

Treesearch

R.B. Ferguson; V. Clark Baldwin

1995-01-01

Estimating tree and stand volume in mature plantations is time consuming, involving much manpower and equipment; however, several sampling and volume-prediction techniques are available. This study showed that a well-constructed, volume-equation method yields estimates comparable to those of the often more time-consuming, height-accumulation method, even though the...

When relationships estimated in the past cannot be used to predict the future: using mechanistic models to predict landscape ecological dynamics in a changing world

Treesearch

Eric J. Gustafson

2013-01-01

Researchers and natural resource managers need predictions of how multiple global changes (e.g., climate change, rising levels of air pollutants, exotic invasions) will affect landscape composition and ecosystem function. Ecological predictive models used for this purpose are constructed using either a mechanistic (process-based) or a phenomenological (empirical)...

Tracking and Predicting Fine Scale Sea Ice Motion by Constructing Super-Resolution Images and Fusing Multiple Satellite Sensors

DTIC Science & Technology

2013-09-30

COVERED 00-00-2013 to 00-00-2013 4. TITLE AND SUBTITLE Tracking and Predicting Fine Scale Sea Ice Motion by Constructing Super-Resolution Images...limited, but potentially provide more detailed data. Initial assessments have been made on MODIS data in terms of its suitability. While clouds obscure...estimates. 2 Data from Aqua, Terra, and Suomi NPP satellites were investigated. Aqua and Terra are older satellites that fly the MODIS instrument

PockDrug: A Model for Predicting Pocket Druggability That Overcomes Pocket Estimation Uncertainties.

PubMed

Borrel, Alexandre; Regad, Leslie; Xhaard, Henri; Petitjean, Michel; Camproux, Anne-Claude

2015-04-27

Predicting protein druggability is a key interest in the target identification phase of drug discovery. Here, we assess the pocket estimation methods' influence on druggability predictions by comparing statistical models constructed from pockets estimated using different pocket estimation methods: a proximity of either 4 or 5.5 Å to a cocrystallized ligand or DoGSite and fpocket estimation methods. We developed PockDrug, a robust pocket druggability model that copes with uncertainties in pocket boundaries. It is based on a linear discriminant analysis from a pool of 52 descriptors combined with a selection of the most stable and efficient models using different pocket estimation methods. PockDrug retains the best combinations of three pocket properties which impact druggability: geometry, hydrophobicity, and aromaticity. It results in an average accuracy of 87.9% ± 4.7% using a test set and exhibits higher accuracy (∼5-10%) than previous studies that used an identical apo set. In conclusion, this study confirms the influence of pocket estimation on pocket druggability prediction and proposes PockDrug as a new model that overcomes pocket estimation variability.

Development of models to estimate the subgrade and subbase layers' resilient modulus from in-situ devices test results for construction control.

DOT National Transportation Integrated Search

2009-05-01

The primary objective of this research was to develop models that predict the resilient modulus of cohesive and granular soils from the test results of various in-situ test devices for possible application in QA/QC during construction of pavement str...

PREDICTION OF RELIABILITY IN BIOGRAPHICAL QUESTIONNAIRES.

ERIC Educational Resources Information Center

STARRY, ALLAN R.

THE OBJECTIVES OF THIS STUDY WERE (1) TO DEVELOP A GENERAL CLASSIFICATION SYSTEM FOR LIFE HISTORY ITEMS, (2) TO DETERMINE TEST-RETEST RELIABILITY ESTIMATES, AND (3) TO ESTIMATE RESISTANCE TO EXAMINEE FAKING, FOR REPRESENTATIVE BIOGRAPHICAL QUESTIONNAIRES. TWO 100-ITEM QUESTIONNAIRES WERE CONSTRUCTED THROUGH RANDOM ASSIGNMENT BY CONTENT AREA OF 200…

Measurement versus prediction in the construction of patient-reported outcome questionnaires: can we have our cake and eat it?

PubMed

Smits, Niels; van der Ark, L Andries; Conijn, Judith M

2017-11-02

Two important goals when using questionnaires are (a) measurement: the questionnaire is constructed to assign numerical values that accurately represent the test taker's attribute, and (b) prediction: the questionnaire is constructed to give an accurate forecast of an external criterion. Construction methods aimed at measurement prescribe that items should be reliable. In practice, this leads to questionnaires with high inter-item correlations. By contrast, construction methods aimed at prediction typically prescribe that items have a high correlation with the criterion and low inter-item correlations. The latter approach has often been said to produce a paradox concerning the relation between reliability and validity [1-3], because it is often assumed that good measurement is a prerequisite of good prediction. To answer four questions: (1) Why are measurement-based methods suboptimal for questionnaires that are used for prediction? (2) How should one construct a questionnaire that is used for prediction? (3) Do questionnaire-construction methods that optimize measurement and prediction lead to the selection of different items in the questionnaire? (4) Is it possible to construct a questionnaire that can be used for both measurement and prediction? An empirical data set consisting of scores of 242 respondents on questionnaire items measuring mental health is used to select items by means of two methods: a method that optimizes the predictive value of the scale (i.e., forecast a clinical diagnosis), and a method that optimizes the reliability of the scale. We show that for the two scales different sets of items are selected and that a scale constructed to meet the one goal does not show optimal performance with reference to the other goal. The answers are as follows: (1) Because measurement-based methods tend to maximize inter-item correlations by which predictive validity reduces. (2) Through selecting items that correlate highly with the criterion and lowly with the remaining items. (3) Yes, these methods may lead to different item selections. (4) For a single questionnaire: Yes, but it is problematic because reliability cannot be estimated accurately. For a test battery: Yes, but it is very costly. Implications for the construction of patient-reported outcome questionnaires are discussed.

A computer-based matrix for rapid calculation of pulmonary hemodynamic parameters in congenital heart disease

PubMed Central

Lopes, Antonio Augusto; dos Anjos Miranda, Rogério; Gonçalves, Rilvani Cavalcante; Thomaz, Ana Maria

2009-01-01

BACKGROUND: In patients with congenital heart disease undergoing cardiac catheterization for hemodynamic purposes, parameter estimation by the indirect Fick method using a single predicted value of oxygen consumption has been a matter of criticism. OBJECTIVE: We developed a computer-based routine for rapid estimation of replicate hemodynamic parameters using multiple predicted values of oxygen consumption. MATERIALS AND METHODS: Using Microsoft® Excel facilities, we constructed a matrix containing 5 models (equations) for prediction of oxygen consumption, and all additional formulas needed to obtain replicate estimates of hemodynamic parameters. RESULTS: By entering data from 65 patients with ventricular septal defects, aged 1 month to 8 years, it was possible to obtain multiple predictions for oxygen consumption, with clear between-age groups (P <.001) and between-methods (P <.001) differences. Using these predictions in the individual patient, it was possible to obtain the upper and lower limits of a likely range for any given parameter, which made estimation more realistic. CONCLUSION: The organized matrix allows for rapid obtainment of replicate parameter estimates, without error due to exhaustive calculations. PMID:19641642

Silica exposure during construction activities: statistical modeling of task-based measurements from the literature.

PubMed

Sauvé, Jean-François; Beaudry, Charles; Bégin, Denis; Dion, Chantal; Gérin, Michel; Lavoué, Jérôme

2013-05-01

Many construction activities can put workers at risk of breathing silica containing dusts, and there is an important body of literature documenting exposure levels using a task-based strategy. In this study, statistical modeling was used to analyze a data set containing 1466 task-based, personal respirable crystalline silica (RCS) measurements gathered from 46 sources to estimate exposure levels during construction tasks and the effects of determinants of exposure. Monte-Carlo simulation was used to recreate individual exposures from summary parameters, and the statistical modeling involved multimodel inference with Tobit models containing combinations of the following exposure variables: sampling year, sampling duration, construction sector, project type, workspace, ventilation, and controls. Exposure levels by task were predicted based on the median reported duration by activity, the year 1998, absence of source control methods, and an equal distribution of the other determinants of exposure. The model containing all the variables explained 60% of the variability and was identified as the best approximating model. Of the 27 tasks contained in the data set, abrasive blasting, masonry chipping, scabbling concrete, tuck pointing, and tunnel boring had estimated geometric means above 0.1mg m(-3) based on the exposure scenario developed. Water-fed tools and local exhaust ventilation were associated with a reduction of 71 and 69% in exposure levels compared with no controls, respectively. The predictive model developed can be used to estimate RCS concentrations for many construction activities in a wide range of circumstances.

Further Improvements to Linear Mixed Models for Genome-Wide Association Studies

PubMed Central

Widmer, Christian; Lippert, Christoph; Weissbrod, Omer; Fusi, Nicolo; Kadie, Carl; Davidson, Robert; Listgarten, Jennifer; Heckerman, David

2014-01-01

We examine improvements to the linear mixed model (LMM) that better correct for population structure and family relatedness in genome-wide association studies (GWAS). LMMs rely on the estimation of a genetic similarity matrix (GSM), which encodes the pairwise similarity between every two individuals in a cohort. These similarities are estimated from single nucleotide polymorphisms (SNPs) or other genetic variants. Traditionally, all available SNPs are used to estimate the GSM. In empirical studies across a wide range of synthetic and real data, we find that modifications to this approach improve GWAS performance as measured by type I error control and power. Specifically, when only population structure is present, a GSM constructed from SNPs that well predict the phenotype in combination with principal components as covariates controls type I error and yields more power than the traditional LMM. In any setting, with or without population structure or family relatedness, a GSM consisting of a mixture of two component GSMs, one constructed from all SNPs and another constructed from SNPs that well predict the phenotype again controls type I error and yields more power than the traditional LMM. Software implementing these improvements and the experimental comparisons are available at http://microsoft.com/science. PMID:25387525

Further Improvements to Linear Mixed Models for Genome-Wide Association Studies

NASA Astrophysics Data System (ADS)

Widmer, Christian; Lippert, Christoph; Weissbrod, Omer; Fusi, Nicolo; Kadie, Carl; Davidson, Robert; Listgarten, Jennifer; Heckerman, David

2014-11-01

We examine improvements to the linear mixed model (LMM) that better correct for population structure and family relatedness in genome-wide association studies (GWAS). LMMs rely on the estimation of a genetic similarity matrix (GSM), which encodes the pairwise similarity between every two individuals in a cohort. These similarities are estimated from single nucleotide polymorphisms (SNPs) or other genetic variants. Traditionally, all available SNPs are used to estimate the GSM. In empirical studies across a wide range of synthetic and real data, we find that modifications to this approach improve GWAS performance as measured by type I error control and power. Specifically, when only population structure is present, a GSM constructed from SNPs that well predict the phenotype in combination with principal components as covariates controls type I error and yields more power than the traditional LMM. In any setting, with or without population structure or family relatedness, a GSM consisting of a mixture of two component GSMs, one constructed from all SNPs and another constructed from SNPs that well predict the phenotype again controls type I error and yields more power than the traditional LMM. Software implementing these improvements and the experimental comparisons are available at http://microsoft.com/science.

Further improvements to linear mixed models for genome-wide association studies.

PubMed

Widmer, Christian; Lippert, Christoph; Weissbrod, Omer; Fusi, Nicolo; Kadie, Carl; Davidson, Robert; Listgarten, Jennifer; Heckerman, David

2014-11-12

We examine improvements to the linear mixed model (LMM) that better correct for population structure and family relatedness in genome-wide association studies (GWAS). LMMs rely on the estimation of a genetic similarity matrix (GSM), which encodes the pairwise similarity between every two individuals in a cohort. These similarities are estimated from single nucleotide polymorphisms (SNPs) or other genetic variants. Traditionally, all available SNPs are used to estimate the GSM. In empirical studies across a wide range of synthetic and real data, we find that modifications to this approach improve GWAS performance as measured by type I error control and power. Specifically, when only population structure is present, a GSM constructed from SNPs that well predict the phenotype in combination with principal components as covariates controls type I error and yields more power than the traditional LMM. In any setting, with or without population structure or family relatedness, a GSM consisting of a mixture of two component GSMs, one constructed from all SNPs and another constructed from SNPs that well predict the phenotype again controls type I error and yields more power than the traditional LMM. Software implementing these improvements and the experimental comparisons are available at http://microsoft.com/science.

FOOTPRINT: A New Tool to Predict the Potential Impact of Biofuels on BTEX Plumes

EPA Science Inventory

Ahsanuzzaman et al. (2008) used the Deeb et al. (2002) conceptual model to construct a simple screening model to estimate the area of a plume of benzene produced from a release of gasoline containing ethanol. The screening model estimates the plume area, or footprint of the plum...

The effect of using genealogy-based haplotypes for genomic prediction

PubMed Central

2013-01-01

Background Genomic prediction uses two sources of information: linkage disequilibrium between markers and quantitative trait loci, and additive genetic relationships between individuals. One way to increase the accuracy of genomic prediction is to capture more linkage disequilibrium by regression on haplotypes instead of regression on individual markers. The aim of this study was to investigate the accuracy of genomic prediction using haplotypes based on local genealogy information. Methods A total of 4429 Danish Holstein bulls were genotyped with the 50K SNP chip. Haplotypes were constructed using local genealogical trees. Effects of haplotype covariates were estimated with two types of prediction models: (1) assuming that effects had the same distribution for all haplotype covariates, i.e. the GBLUP method and (2) assuming that a large proportion (π) of the haplotype covariates had zero effect, i.e. a Bayesian mixture method. Results About 7.5 times more covariate effects were estimated when fitting haplotypes based on local genealogical trees compared to fitting individuals markers. Genealogy-based haplotype clustering slightly increased the accuracy of genomic prediction and, in some cases, decreased the bias of prediction. With the Bayesian method, accuracy of prediction was less sensitive to parameter π when fitting haplotypes compared to fitting markers. Conclusions Use of haplotypes based on genealogy can slightly increase the accuracy of genomic prediction. Improved methods to cluster the haplotypes constructed from local genealogy could lead to additional gains in accuracy. PMID:23496971

The effect of using genealogy-based haplotypes for genomic prediction.

PubMed

Edriss, Vahid; Fernando, Rohan L; Su, Guosheng; Lund, Mogens S; Guldbrandtsen, Bernt

2013-03-06

Genomic prediction uses two sources of information: linkage disequilibrium between markers and quantitative trait loci, and additive genetic relationships between individuals. One way to increase the accuracy of genomic prediction is to capture more linkage disequilibrium by regression on haplotypes instead of regression on individual markers. The aim of this study was to investigate the accuracy of genomic prediction using haplotypes based on local genealogy information. A total of 4429 Danish Holstein bulls were genotyped with the 50K SNP chip. Haplotypes were constructed using local genealogical trees. Effects of haplotype covariates were estimated with two types of prediction models: (1) assuming that effects had the same distribution for all haplotype covariates, i.e. the GBLUP method and (2) assuming that a large proportion (π) of the haplotype covariates had zero effect, i.e. a Bayesian mixture method. About 7.5 times more covariate effects were estimated when fitting haplotypes based on local genealogical trees compared to fitting individuals markers. Genealogy-based haplotype clustering slightly increased the accuracy of genomic prediction and, in some cases, decreased the bias of prediction. With the Bayesian method, accuracy of prediction was less sensitive to parameter π when fitting haplotypes compared to fitting markers. Use of haplotypes based on genealogy can slightly increase the accuracy of genomic prediction. Improved methods to cluster the haplotypes constructed from local genealogy could lead to additional gains in accuracy.

Objective Analysis of Oceanic Data for Coast Guard Trajectory Models Phase II

DTIC Science & Technology

1997-12-01

as outliers depends on the desired probability of false alarm, Pfa values, which is the probability of marking a valid point as an outlier. Table 2-2...constructed to minimize the mean-squared prediction error of the grid point estimate under the constraint that the estimate is unbiased . The...prediction error, e= Zl(S) _oizl(Si)+oC1iZz(S) (2.44) subject to the constraints of unbiasedness , • c/1 = 1,and (2.45) i SCC12 = 0. (2.46) Denoting

Brownian motion with adaptive drift for remaining useful life prediction: Revisited

NASA Astrophysics Data System (ADS)

Wang, Dong; Tsui, Kwok-Leung

2018-01-01

Linear Brownian motion with constant drift is widely used in remaining useful life predictions because its first hitting time follows the inverse Gaussian distribution. State space modelling of linear Brownian motion was proposed to make the drift coefficient adaptive and incorporate on-line measurements into the first hitting time distribution. Here, the drift coefficient followed the Gaussian distribution, and it was iteratively estimated by using Kalman filtering once a new measurement was available. Then, to model nonlinear degradation, linear Brownian motion with adaptive drift was extended to nonlinear Brownian motion with adaptive drift. However, in previous studies, an underlying assumption used in the state space modelling was that in the update phase of Kalman filtering, the predicted drift coefficient at the current time exactly equalled the posterior drift coefficient estimated at the previous time, which caused a contradiction with the predicted drift coefficient evolution driven by an additive Gaussian process noise. In this paper, to alleviate such an underlying assumption, a new state space model is constructed. As a result, in the update phase of Kalman filtering, the predicted drift coefficient at the current time evolves from the posterior drift coefficient at the previous time. Moreover, the optimal Kalman filtering gain for iteratively estimating the posterior drift coefficient at any time is mathematically derived. A discussion that theoretically explains the main reasons why the constructed state space model can result in high remaining useful life prediction accuracies is provided. Finally, the proposed state space model and its associated Kalman filtering gain are applied to battery prognostics.

Obtaining appropriate interval estimates for age when multiple indicators are used: evaluation of an ad-hoc procedure.

PubMed

Fieuws, Steffen; Willems, Guy; Larsen-Tangmose, Sara; Lynnerup, Niels; Boldsen, Jesper; Thevissen, Patrick

2016-03-01

When an estimate of age is needed, typically multiple indicators are present as found in skeletal or dental information. There exists a vast literature on approaches to estimate age from such multivariate data. Application of Bayes' rule has been proposed to overcome drawbacks of classical regression models but becomes less trivial as soon as the number of indicators increases. Each of the age indicators can lead to a different point estimate ("the most plausible value for age") and a prediction interval ("the range of possible values"). The major challenge in the combination of multiple indicators is not the calculation of a combined point estimate for age but the construction of an appropriate prediction interval. Ignoring the correlation between the age indicators results in intervals being too small. Boldsen et al. (2002) presented an ad-hoc procedure to construct an approximate confidence interval without the need to model the multivariate correlation structure between the indicators. The aim of the present paper is to bring under attention this pragmatic approach and to evaluate its performance in a practical setting. This is all the more needed since recent publications ignore the need for interval estimation. To illustrate and evaluate the method, Köhler et al. (1995) third molar scores are used to estimate the age in a dataset of 3200 male subjects in the juvenile age range.

Time prediction of failure a type of lamps by using general composite hazard rate model

NASA Astrophysics Data System (ADS)

Riaman; Lesmana, E.; Subartini, B.; Supian, S.

2018-03-01

This paper discusses the basic survival model estimates to obtain the average predictive value of lamp failure time. This estimate is for the parametric model, General Composite Hazard Level Model. The random time variable model used is the exponential distribution model, as the basis, which has a constant hazard function. In this case, we discuss an example of survival model estimation for a composite hazard function, using an exponential model as its basis. To estimate this model is done by estimating model parameters, through the construction of survival function and empirical cumulative function. The model obtained, will then be used to predict the average failure time of the model, for the type of lamp. By grouping the data into several intervals and the average value of failure at each interval, then calculate the average failure time of a model based on each interval, the p value obtained from the tes result is 0.3296.

Quantifying and estimating the predictive accuracy for censored time-to-event data with competing risks.

PubMed

Wu, Cai; Li, Liang

2018-05-15

This paper focuses on quantifying and estimating the predictive accuracy of prognostic models for time-to-event outcomes with competing events. We consider the time-dependent discrimination and calibration metrics, including the receiver operating characteristics curve and the Brier score, in the context of competing risks. To address censoring, we propose a unified nonparametric estimation framework for both discrimination and calibration measures, by weighting the censored subjects with the conditional probability of the event of interest given the observed data. The proposed method can be extended to time-dependent predictive accuracy metrics constructed from a general class of loss functions. We apply the methodology to a data set from the African American Study of Kidney Disease and Hypertension to evaluate the predictive accuracy of a prognostic risk score in predicting end-stage renal disease, accounting for the competing risk of pre-end-stage renal disease death, and evaluate its numerical performance in extensive simulation studies. Copyright © 2018 John Wiley & Sons, Ltd.

Real-time emissions from construction equipment compared with model predictions.

PubMed

Heidari, Bardia; Marr, Linsey C

2015-02-01

The construction industry is a large source of greenhouse gases and other air pollutants. Measuring and monitoring real-time emissions will provide practitioners with information to assess environmental impacts and improve the sustainability of construction. We employed a portable emission measurement system (PEMS) for real-time measurement of carbon dioxide (CO), nitrogen oxides (NOx), hydrocarbon, and carbon monoxide (CO) emissions from construction equipment to derive emission rates (mass of pollutant emitted per unit time) and emission factors (mass of pollutant emitted per unit volume of fuel consumed) under real-world operating conditions. Measurements were compared with emissions predicted by methodologies used in three models: NONROAD2008, OFFROAD2011, and a modal statistical model. Measured emission rates agreed with model predictions for some pieces of equipment but were up to 100 times lower for others. Much of the difference was driven by lower fuel consumption rates than predicted. Emission factors during idling and hauling were significantly different from each other and from those of other moving activities, such as digging and dumping. It appears that operating conditions introduce considerable variability in emission factors. Results of this research will aid researchers and practitioners in improving current emission estimation techniques, frameworks, and databases.

Incorporating weather impacts in traffic estimation and prediction systems (TREPS)

DOT National Transportation Integrated Search

2009-09-01

This document provides quantitative benefits of using Intelligent Transportation Systems in highway construction and maintenance work zones. The technical report covers case study sites in the District of Columbia, Texas, Michigan, Arkansas, and Nort...

Acoustic fatigue life prediction for nonlinear structures with multiple resonant modes

NASA Technical Reports Server (NTRS)

Miles, R. N.

1992-01-01

This report documents an effort to develop practical and accurate methods for estimating the fatigue lives of complex aerospace structures subjected to intense random excitations. The emphasis of the current program is to construct analytical schemes for performing fatigue life estimates for structures that exhibit nonlinear vibration behavior and that have numerous resonant modes contributing to the response.

Predicting future forestland area: a comparison of econometric approaches.

Treesearch

SoEun Ahn; Andrew J. Plantinga; Ralph J. Alig

2000-01-01

Predictions of future forestland area are an important component of forest policy analyses. In this article, we test the ability of econometric land use models to accurately forecast forest area. We construct a panel data set for Alabama consisting of county and time-series observation for the period 1964 to 1992. We estimate models using restricted data sets-namely,...

Comparison of Methods for Estimating Prevalence of Chronic Diseases and Health Behaviors for Small Geographic Areas: Boston Validation Study, 2013

PubMed Central

Holt, James B.; Zhang, Xingyou; Lu, Hua; Shah, Snehal N.; Dooley, Daniel P.; Matthews, Kevin A.; Croft, Janet B.

2017-01-01

Introduction Local health authorities need small-area estimates for prevalence of chronic diseases and health behaviors for multiple purposes. We generated city-level and census-tract–level prevalence estimates of 27 measures for the 500 largest US cities. Methods To validate the methodology, we constructed multilevel logistic regressions to predict 10 selected health indicators among adults aged 18 years or older by using 2013 Behavioral Risk Factor Surveillance System (BRFSS) data; we applied their predicted probabilities to census population data to generate city-level, neighborhood-level, and zip-code–level estimates for the city of Boston, Massachusetts. Results By comparing the predicted estimates with their corresponding direct estimates from a locally administered survey (Boston BRFSS 2010 and 2013), we found that our model-based estimates for most of the selected health indicators at the city level were close to the direct estimates from the local survey. We also found strong correlation between the model-based estimates and direct survey estimates at neighborhood and zip code levels for most indicators. Conclusion Findings suggest that our model-based estimates are reliable and valid at the city level for certain health outcomes. Local health authorities can use the neighborhood-level estimates if high quality local health survey data are not otherwise available. PMID:29049020

Predictive modelling of Lactobacillus casei KN291 survival in fermented soy beverage.

PubMed

Zielińska, Dorota; Dorota, Zielińska; Kołożyn-Krajewska, Danuta; Danuta, Kołożyn-Krajewska; Goryl, Antoni; Antoni, Goryl; Motyl, Ilona

2014-02-01

The aim of the study was to construct and verify predictive growth and survival models of a potentially probiotic bacteria in fermented soy beverage. The research material included natural soy beverage (Polgrunt, Poland) and the strain of lactic acid bacteria (LAB) - Lactobacillus casei KN291. To construct predictive models for the growth and survival of L. casei KN291 bacteria in the fermented soy beverage we design an experiment which allowed the collection of CFU data. Fermented soy beverage samples were stored at various temperature conditions (5, 10, 15, and 20°C) for 28 days. On the basis of obtained data concerning the survival of L. casei KN291 bacteria in soy beverage at different temperature and time conditions, two non-linear models (r(2)= 0.68-0.93) and two surface models (r(2)=0.76-0.79) were constructed; these models described the behaviour of the bacteria in the product to a satisfactory extent. Verification of the surface models was carried out utilizing the validation data - at 7°C during 28 days. It was found that applied models were well fitted and charged with small systematic errors, which is evidenced by accuracy factor - Af, bias factor - Bf and mean squared error - MSE. The constructed microbiological growth and survival models of L. casei KN291 in fermented soy beverage enable the estimation of products shelf life period, which in this case is defined by the requirement for the level of the bacteria to be above 10(6) CFU/cm(3). The constructed models may be useful as a tool for the manufacture of probiotic foods to estimate of their shelf life period.

A collision risk model to predict avian fatalities at wind facilities: an example using golden eagles, Aquila chrysaetos

USGS Publications Warehouse

New, Leslie; Bjerre, Emily; Millsap, Brian A.; Otto, Mark C.; Runge, Michael C.

2015-01-01

Wind power is a major candidate in the search for clean, renewable energy. Beyond the technical and economic challenges of wind energy development are environmental issues that may restrict its growth. Avian fatalities due to collisions with rotating turbine blades are a leading concern and there is considerable uncertainty surrounding avian collision risk at wind facilities. This uncertainty is not reflected in many models currently used to predict the avian fatalities that would result from proposed wind developments. We introduce a method to predict fatalities at wind facilities, based on pre-construction monitoring. Our method can directly incorporate uncertainty into the estimates of avian fatalities and can be updated if information on the true number of fatalities becomes available from post-construction carcass monitoring. Our model considers only three parameters: hazardous footprint, bird exposure to turbines and collision probability. By using a Bayesian analytical framework we account for uncertainties in these values, which are then reflected in our predictions and can be reduced through subsequent data collection. The simplicity of our approach makes it accessible to ecologists concerned with the impact of wind development, as well as to managers, policy makers and industry interested in its implementation in real-world decision contexts. We demonstrate the utility of our method by predicting golden eagle (Aquila chrysaetos) fatalities at a wind installation in the United States. Using pre-construction data, we predicted 7.48 eagle fatalities year-1 (95% CI: (1.1, 19.81)). The U.S. Fish and Wildlife Service uses the 80th quantile (11.0 eagle fatalities year-1) in their permitting process to ensure there is only a 20% chance a wind facility exceeds the authorized fatalities. Once data were available from two-years of post-construction monitoring, we updated the fatality estimate to 4.8 eagle fatalities year-1 (95% CI: (1.76, 9.4); 80th quantile, 6.3). In this case, the increased precision in the fatality prediction lowered the level of authorized take, and thus lowered the required amount of compensatory mitigation.

A Collision Risk Model to Predict Avian Fatalities at Wind Facilities: An Example Using Golden Eagles, Aquila chrysaetos

PubMed Central

New, Leslie; Bjerre, Emily; Millsap, Brian; Otto, Mark C.; Runge, Michael C.

2015-01-01

Wind power is a major candidate in the search for clean, renewable energy. Beyond the technical and economic challenges of wind energy development are environmental issues that may restrict its growth. Avian fatalities due to collisions with rotating turbine blades are a leading concern and there is considerable uncertainty surrounding avian collision risk at wind facilities. This uncertainty is not reflected in many models currently used to predict the avian fatalities that would result from proposed wind developments. We introduce a method to predict fatalities at wind facilities, based on pre-construction monitoring. Our method can directly incorporate uncertainty into the estimates of avian fatalities and can be updated if information on the true number of fatalities becomes available from post-construction carcass monitoring. Our model considers only three parameters: hazardous footprint, bird exposure to turbines and collision probability. By using a Bayesian analytical framework we account for uncertainties in these values, which are then reflected in our predictions and can be reduced through subsequent data collection. The simplicity of our approach makes it accessible to ecologists concerned with the impact of wind development, as well as to managers, policy makers and industry interested in its implementation in real-world decision contexts. We demonstrate the utility of our method by predicting golden eagle (Aquila chrysaetos) fatalities at a wind installation in the United States. Using pre-construction data, we predicted 7.48 eagle fatalities year-1 (95% CI: (1.1, 19.81)). The U.S. Fish and Wildlife Service uses the 80th quantile (11.0 eagle fatalities year-1) in their permitting process to ensure there is only a 20% chance a wind facility exceeds the authorized fatalities. Once data were available from two-years of post-construction monitoring, we updated the fatality estimate to 4.8 eagle fatalities year-1 (95% CI: (1.76, 9.4); 80th quantile, 6.3). In this case, the increased precision in the fatality prediction lowered the level of authorized take, and thus lowered the required amount of compensatory mitigation. PMID:26134412

A Collision Risk Model to Predict Avian Fatalities at Wind Facilities: An Example Using Golden Eagles, Aquila chrysaetos.

PubMed

New, Leslie; Bjerre, Emily; Millsap, Brian; Otto, Mark C; Runge, Michael C

2015-01-01

Wind power is a major candidate in the search for clean, renewable energy. Beyond the technical and economic challenges of wind energy development are environmental issues that may restrict its growth. Avian fatalities due to collisions with rotating turbine blades are a leading concern and there is considerable uncertainty surrounding avian collision risk at wind facilities. This uncertainty is not reflected in many models currently used to predict the avian fatalities that would result from proposed wind developments. We introduce a method to predict fatalities at wind facilities, based on pre-construction monitoring. Our method can directly incorporate uncertainty into the estimates of avian fatalities and can be updated if information on the true number of fatalities becomes available from post-construction carcass monitoring. Our model considers only three parameters: hazardous footprint, bird exposure to turbines and collision probability. By using a Bayesian analytical framework we account for uncertainties in these values, which are then reflected in our predictions and can be reduced through subsequent data collection. The simplicity of our approach makes it accessible to ecologists concerned with the impact of wind development, as well as to managers, policy makers and industry interested in its implementation in real-world decision contexts. We demonstrate the utility of our method by predicting golden eagle (Aquila chrysaetos) fatalities at a wind installation in the United States. Using pre-construction data, we predicted 7.48 eagle fatalities year-1 (95% CI: (1.1, 19.81)). The U.S. Fish and Wildlife Service uses the 80th quantile (11.0 eagle fatalities year-1) in their permitting process to ensure there is only a 20% chance a wind facility exceeds the authorized fatalities. Once data were available from two-years of post-construction monitoring, we updated the fatality estimate to 4.8 eagle fatalities year-1 (95% CI: (1.76, 9.4); 80th quantile, 6.3). In this case, the increased precision in the fatality prediction lowered the level of authorized take, and thus lowered the required amount of compensatory mitigation.

THE DEVELOPMENT OF A PREDICTIVE TOLL USING LARGEMOUTH BASS (MICROPTERUS SALMOIDES) SCALES TO ESTIMATE MERCURY (HG) CONCENTRATIONS AND STABLE-NITROGEN (15N/14N) ISOTOPE RATIOS IN FISH MUSCLE TISSUE

EPA Science Inventory

Knowledge of the trophic structure of biota in aquatic sites offers potential for the construction of models to allow the prediction of contaminant bioaccumulation. Measurements of trophic position have been conducted using stable-nitrogen isotope ratios ( 15N) measured in fish m...

The prediction of engineering cost for green buildings based on information entropy

NASA Astrophysics Data System (ADS)

Liang, Guoqiang; Huang, Jinglian

2018-03-01

Green building is the developing trend in the world building industry. Additionally, construction costs are an essential consideration in building constructions. Therefore, it is necessary to investigate the problems of cost prediction in green building. On the basis of analyzing the cost of green building, this paper proposes the forecasting method of actual cost in green building based on information entropy and provides the forecasting working procedure. Using the probability density obtained from statistical data, such as labor costs, material costs, machinery costs, administration costs, profits, risk costs a unit project quotation and etc., situations can be predicted which lead to cost variations between budgeted cost and actual cost in constructions, through estimating the information entropy of budgeted cost and actual cost. The research results of this article have a practical significance in cost control of green building. Additionally, the method proposed in this article can be generalized and applied to a variety of other aspects in building management.

Role of learning potential in cognitive remediation: Construct and predictive validity.

PubMed

Davidson, Charlie A; Johannesen, Jason K; Fiszdon, Joanna M

2016-03-01

The construct, convergent, discriminant, and predictive validity of Learning Potential (LP) was evaluated in a trial of cognitive remediation for adults with schizophrenia-spectrum disorders. LP utilizes a dynamic assessment approach to prospectively estimate an individual's learning capacity if provided the opportunity for specific related learning. LP was assessed in 75 participants at study entry, of whom 41 completed an eight-week cognitive remediation (CR) intervention, and 22 received treatment-as-usual (TAU). LP was assessed in a "test-train-test" verbal learning paradigm. Incremental predictive validity was assessed as the degree to which LP predicted memory skill acquisition above and beyond prediction by static verbal learning ability. Examination of construct validity confirmed that LP scores reflected use of trained semantic clustering strategy. LP scores correlated with executive functioning and education history, but not other demographics or symptom severity. Following the eight-week active phase, TAU evidenced little substantial change in skill acquisition outcomes, which related to static baseline verbal learning ability but not LP. For the CR group, LP significantly predicted skill acquisition in domains of verbal and visuospatial memory, but not auditory working memory. Furthermore, LP predicted skill acquisition incrementally beyond relevant background characteristics, symptoms, and neurocognitive abilities. Results suggest that LP assessment can significantly improve prediction of specific skill acquisition with cognitive training, particularly for the domain assessed, and thereby may prove useful in individualization of treatment. Published by Elsevier B.V.

BMP COST ANALYSIS FOR SOURCE WATER PROTECTION

EPA Science Inventory

Cost equations are developed to estimate capital, and operations and maintenance (O&M) costs for commonly used best management practices (BMPs). Total BMP volume and/or surface area is used to predict these costs. Engineering News Record (ENR) construction cost index was used t...

Estimating Effects with Rare Outcomes and High Dimensional Covariates: Knowledge is Power

PubMed Central

Ahern, Jennifer; Galea, Sandro; van der Laan, Mark

2016-01-01

Many of the secondary outcomes in observational studies and randomized trials are rare. Methods for estimating causal effects and associations with rare outcomes, however, are limited, and this represents a missed opportunity for investigation. In this article, we construct a new targeted minimum loss-based estimator (TMLE) for the effect or association of an exposure on a rare outcome. We focus on the causal risk difference and statistical models incorporating bounds on the conditional mean of the outcome, given the exposure and measured confounders. By construction, the proposed estimator constrains the predicted outcomes to respect this model knowledge. Theoretically, this bounding provides stability and power to estimate the exposure effect. In finite sample simulations, the proposed estimator performed as well, if not better, than alternative estimators, including a propensity score matching estimator, inverse probability of treatment weighted (IPTW) estimator, augmented-IPTW and the standard TMLE algorithm. The new estimator yielded consistent estimates if either the conditional mean outcome or the propensity score was consistently estimated. As a substitution estimator, TMLE guaranteed the point estimates were within the parameter range. We applied the estimator to investigate the association between permissive neighborhood drunkenness norms and alcohol use disorder. Our results highlight the potential for double robust, semiparametric efficient estimation with rare events and high dimensional covariates. PMID:28529839

Predicting the effectiveness of road safety campaigns through alternative research designs.

PubMed

Adamos, Giannis; Nathanail, Eftihia

2016-12-01

A large number of road safety communication campaigns have been designed and implemented in the recent years; however their explicit impact on driving behavior and road accident rates has been estimated in a rather low proportion. Based on the findings of the evaluation of three road safety communication campaigns addressing the issues of drinking and driving, seat belt usage, and driving fatigue, this paper applies different types of research designs (i.e., experimental, quasi-experimental, and non-experimental designs), when estimating the effectiveness of road safety campaigns, implements a cross-design assessment, and conducts a cross-campaign evaluation. An integrated evaluation plan was developed, taking into account the structure of evaluation questions, the definition of measurable variables, the separation of the target audience into intervention (exposed to the campaign) and control (not exposed to the campaign) groups, the selection of alternative research designs, and the appropriate data collection methods and techniques. Evaluating the implementation of different research designs in estimating the effectiveness of road safety campaigns, results showed that the separate pre-post samples design demonstrated better predictability than other designs, especially in data obtained from the intervention group after the realization of the campaign. The more constructs that were added to the independent variables, the higher the values of the predictability were. The construct that most affects behavior is intention, whereas the rest of the constructs have a lower impact on behavior. This is particularly significant in the Health Belief Model (HBM). On the other hand, behavioral beliefs, normative beliefs, and descriptive norms, are significant parameters for predicting intention according to the Theory of Planned Behavior (TPB). The theoretical and applied implications of alternative research designs and their applicability in the evaluation of road safety campaigns are provided by this study. Copyright © 2016 Elsevier Ltd and National Safety Council. All rights reserved.

A physically-based method for predicting peak discharge of floods caused by failure of natural and constructed earthen dams

USGS Publications Warehouse

Walder, J.S.; O'Connor, J. E.; Costa, J.E.; ,

1997-01-01

We analyse a simple, physically-based model of breach formation in natural and constructed earthen dams to elucidate the principal factors controlling the flood hydrograph at the breach. Formation of the breach, which is assumed trapezoidal in cross-section, is parameterized by the mean rate of downcutting, k, the value of which is constrained by observations. A dimensionless formulation of the model leads to the prediction that the breach hydrograph depends upon lake shape, the ratio r of breach width to depth, the side slope ?? of the breach, and the parameter ?? = (V.D3)(k/???gD), where V = lake volume, D = lake depth, and g is the acceleration due to gravity. Calculations show that peak discharge Qp depends weakly on lake shape r and ??, but strongly on ??, which is the product of a dimensionless lake volume and a dimensionless erosion rate. Qp(??) takes asymptotically distinct forms depending on whether < ??? 1 or < ??? 1. Theoretical predictions agree well with data from dam failures for which k could be reasonably estimated. The analysis provides a rapid and in many cases graphical way to estimate plausible values of Qp at the breach.We analyze a simple, physically-based model of breach formation in natural and constructed earthen dams to elucidate the principal factors controlling the flood hydrograph at the breach. Formation of the breach, which is assumed trapezoidal in cross-section, is parameterized by the mean rate of downcutting, k, the value of which is constrained by observations. A dimensionless formulation of the model leads to the prediction that the breach hydrograph depends upon lake shape, the ratio r of breach width to depth, the side slope ?? of the breach, and the parameter ?? = (V/D3)(k/???gD), where V = lake volume, D = lake depth, and g is the acceleration due to gravity. Calculations show that peak discharge Qp depends weakly on lake shape r and ??, but strongly on ??, which is the product of a dimensionless lake volume and a dimensionless erosion rate. Qp(??) takes asymptotically distinct forms depending on whether ?????1 or ?????1. Theoretical predictions agree well with data from dam failures for which k could be reasonably estimated. The analysis provides a rapid and in many cases graphical way to estimate plausible values of Qp at the breach.

BMP COST ANALYSIS FOR SOURCE WATER PROTECTION

EPA Science Inventory

Cost equations are developed to estimate capital and operations and maintenance (O&M) for commonly used best management practices (BMPS). Total BMP volume and/or surface area is used to predict these costs. ENR construction cost index was used to adjust cost data to December 2000...

Estimating Additive and Non-Additive Genetic Variances and Predicting Genetic Merits Using Genome-Wide Dense Single Nucleotide Polymorphism Markers

PubMed Central

Su, Guosheng; Christensen, Ole F.; Ostersen, Tage; Henryon, Mark; Lund, Mogens S.

2012-01-01

Non-additive genetic variation is usually ignored when genome-wide markers are used to study the genetic architecture and genomic prediction of complex traits in human, wild life, model organisms or farm animals. However, non-additive genetic effects may have an important contribution to total genetic variation of complex traits. This study presented a genomic BLUP model including additive and non-additive genetic effects, in which additive and non-additive genetic relation matrices were constructed from information of genome-wide dense single nucleotide polymorphism (SNP) markers. In addition, this study for the first time proposed a method to construct dominance relationship matrix using SNP markers and demonstrated it in detail. The proposed model was implemented to investigate the amounts of additive genetic, dominance and epistatic variations, and assessed the accuracy and unbiasedness of genomic predictions for daily gain in pigs. In the analysis of daily gain, four linear models were used: 1) a simple additive genetic model (MA), 2) a model including both additive and additive by additive epistatic genetic effects (MAE), 3) a model including both additive and dominance genetic effects (MAD), and 4) a full model including all three genetic components (MAED). Estimates of narrow-sense heritability were 0.397, 0.373, 0.379 and 0.357 for models MA, MAE, MAD and MAED, respectively. Estimated dominance variance and additive by additive epistatic variance accounted for 5.6% and 9.5% of the total phenotypic variance, respectively. Based on model MAED, the estimate of broad-sense heritability was 0.506. Reliabilities of genomic predicted breeding values for the animals without performance records were 28.5%, 28.8%, 29.2% and 29.5% for models MA, MAE, MAD and MAED, respectively. In addition, models including non-additive genetic effects improved unbiasedness of genomic predictions. PMID:23028912

Relatedness predicts multiple measures of investment in cooperative nest construction in sociable weavers

PubMed Central

Leighton, Gavin M.; Echeverri, Sebastian; Heinrich, Dirk; Kolberg, Holger

2015-01-01

Although communal goods are often critical to society, they are simultaneously susceptible to exploitation and are evolutionarily stable only if mechanisms exist to curtail exploitation. Mechanisms such as punishment and kin selection have been offered as general explanations for how communal resources can be maintained. Evidence for these mechanisms comes largely from humans and social insects, leaving their generality in question. To assess how communal resources are maintained, we observed cooperative nest construction in sociable weavers (Philetairus socius). The communal nest of sociable weavers provides thermal benefits for all individuals but requires continual maintenance. We observed cooperative nest construction and also recorded basic morphological characteristics. We also collected blood samples, performed next-generation sequencing, and isolated 2358 variable single nucleotide polymorphisms (SNPs) to estimate relatedness. We find that relatedness predicts investment in cooperative nest construction, while no other morphological characters significantly explain cooperative output. We argue that indirect benefits are a critical fitness component for maintaining the cooperative behavior that maintains the communal good. PMID:26726282

Optimal prediction of the number of unseen species.

PubMed

Orlitsky, Alon; Suresh, Ananda Theertha; Wu, Yihong

2016-11-22

Estimating the number of unseen species is an important problem in many scientific endeavors. Its most popular formulation, introduced by Fisher et al. [Fisher RA, Corbet AS, Williams CB (1943) J Animal Ecol 12(1):42-58], uses n samples to predict the number U of hitherto unseen species that would be observed if [Formula: see text] new samples were collected. Of considerable interest is the largest ratio t between the number of new and existing samples for which U can be accurately predicted. In seminal works, Good and Toulmin [Good I, Toulmin G (1956) Biometrika 43(102):45-63] constructed an intriguing estimator that predicts U for all [Formula: see text] Subsequently, Efron and Thisted [Efron B, Thisted R (1976) Biometrika 63(3):435-447] proposed a modification that empirically predicts U even for some [Formula: see text], but without provable guarantees. We derive a class of estimators that provably predict U all of the way up to [Formula: see text] We also show that this range is the best possible and that the estimator's mean-square error is near optimal for any t Our approach yields a provable guarantee for the Efron-Thisted estimator and, in addition, a variant with stronger theoretical and experimental performance than existing methodologies on a variety of synthetic and real datasets. The estimators are simple, linear, computationally efficient, and scalable to massive datasets. Their performance guarantees hold uniformly for all distributions, and apply to all four standard sampling models commonly used across various scientific disciplines: multinomial, Poisson, hypergeometric, and Bernoulli product.

Nomograms for predicting graft function and survival in living donor kidney transplantation based on the UNOS Registry.

PubMed

Tiong, H Y; Goldfarb, D A; Kattan, M W; Alster, J M; Thuita, L; Yu, C; Wee, A; Poggio, E D

2009-03-01

We developed nomograms that predict transplant renal function at 1 year (Modification of Diet in Renal Disease equation [estimated glomerular filtration rate]) and 5-year graft survival after living donor kidney transplantation. Data for living donor renal transplants were obtained from the United Network for Organ Sharing registry for 2000 to 2003. Nomograms were designed using linear or Cox regression models to predict 1-year estimated glomerular filtration rate and 5-year graft survival based on pretransplant information including demographic factors, immunosuppressive therapy, immunological factors and organ procurement technique. A third nomogram was constructed to predict 5-year graft survival using additional information available by 6 months after transplantation. These data included delayed graft function, any treated rejection episodes and the 6-month estimated glomerular filtration rate. The nomograms were internally validated using 10-fold cross-validation. The renal function nomogram had an r-square value of 0.13. It worked best when predicting estimated glomerular filtration rate values between 50 and 70 ml per minute per 1.73 m(2). The 5-year graft survival nomograms had a concordance index of 0.71 for the pretransplant nomogram and 0.78 for the 6-month posttransplant nomogram. Calibration was adequate for all nomograms. Nomograms based on data from the United Network for Organ Sharing registry have been validated to predict the 1-year estimated glomerular filtration rate and 5-year graft survival. These nomograms may facilitate individualized patient care in living donor kidney transplantation.

A physically-based method for predicting peak discharge of floods caused by failure of natural and constructed earthen dams

USGS Publications Warehouse

Walder, J.S.

1997-01-01

We analyse a simple, physically-based model of breach formation in natural and constructed earthen dams to elucidate the principal factors controlling the flood hydrograph at the breach. Formation of the breach, which is assumed trapezoidal in cross-section, is parameterized by the mean rate of downcutting, k, the value of which is constrained by observations. A dimensionless formulation of the model leads to the prediction that the breach hydrograph depends upon lake shape, the ratio r of breach width to depth, the side slope ?? of the breach, and the parameter ?? = (V/ D3)(k/???gD), where V = lake volume, D = lake depth, and g is the acceleration due to gravity. Calculations show that peak discharge Qp depends weakly on lake shape r and ??, but strongly on ??, which is the product of a dimensionless lake volume and a dimensionless erosion rate. Qp(??) takes asymptotically distinct forms depending on whether ?? > 1. Theoretical predictions agree well with data from dam failures for which k could be reasonably estimated. The analysis provides a rapid and in many cases graphical way to estimate plausible values of Qp at the breach.

Social network models predict movement and connectivity in ecological landscapes

USGS Publications Warehouse

Fletcher, R.J.; Acevedo, M.A.; Reichert, Brian E.; Pias, Kyle E.; Kitchens, W.M.

2011-01-01

Network analysis is on the rise across scientific disciplines because of its ability to reveal complex, and often emergent, patterns and dynamics. Nonetheless, a growing concern in network analysis is the use of limited data for constructing networks. This concern is strikingly relevant to ecology and conservation biology, where network analysis is used to infer connectivity across landscapes. In this context, movement among patches is the crucial parameter for interpreting connectivity but because of the difficulty of collecting reliable movement data, most network analysis proceeds with only indirect information on movement across landscapes rather than using observed movement to construct networks. Statistical models developed for social networks provide promising alternatives for landscape network construction because they can leverage limited movement information to predict linkages. Using two mark-recapture datasets on individual movement and connectivity across landscapes, we test whether commonly used network constructions for interpreting connectivity can predict actual linkages and network structure, and we contrast these approaches to social network models. We find that currently applied network constructions for assessing connectivity consistently, and substantially, overpredict actual connectivity, resulting in considerable overestimation of metapopulation lifetime. Furthermore, social network models provide accurate predictions of network structure, and can do so with remarkably limited data on movement. Social network models offer a flexible and powerful way for not only understanding the factors influencing connectivity but also for providing more reliable estimates of connectivity and metapopulation persistence in the face of limited data.

A pretreatment nomogram for prediction of biochemical failure after primary cryoablation of the prostate.

PubMed

Elshafei, Ahmed; Kovac, Evan; Dhar, Nivedita; Levy, David; Polascik, Thomas; Mouraviev, Vladimir; Yu, Changhong; Jones, J Stephen

2015-09-01

To create a predictive nomogram for biochemical failure following primary whole-gland cryoablation of the prostate for localized prostate cancer (LPCa). We retrospectively analyzed 2,242 patients from the Cryo On-Line Database (COLD) who were treatment naive and had undergone primary whole gland cryoablation of the prostate for biopsy-confirmed LPCa. Kaplan-Meier (KM) curves estimating 5 year biochemical progression-free survival (bPFS) were generated. Multivariable Cox proportional hazards analysis (CoxPH) was performed in order to construct the nomogram. The nomogram was internally validated using the bootstrap technique. Overall, the KM estimated 5 year bPFS was 72.8%. Stratified by D'Amico risk, The KM estimated 5 year bPFS was 82.6%, 71.1%, and 57.8% for low-, intermediate-, and high-risk groups, respectively. Statistically significant predictors of biochemical outcomes from CoxPH analysis were pre-treatment prostate specific antigen (PTPSA) (P < 0.001), total prostate volume (P = 0.004), clinical stage (P = 0.034), and Gleason score (0.004). A nomogram for predicted 5 year biochemical progression free probability was constructed with a concordance index of 0.652. An online risk calculator was also generated. To the best of our knowledge, this is the first predictive nomogram for biochemical outcomes after primary whole gland cryoablation of the prostate using socio-demographic, pretreatment, clinical, and prostate biopsy data. Our nomogram and online risk calculator can guide both patients and urologists for shared decision making regarding definitive treatment options. © 2015 Wiley Periodicals, Inc.

Estimating the concrete compressive strength using hard clustering and fuzzy clustering based regression techniques.

PubMed

Nagwani, Naresh Kumar; Deo, Shirish V

2014-01-01

Understanding of the compressive strength of concrete is important for activities like construction arrangement, prestressing operations, and proportioning new mixtures and for the quality assurance. Regression techniques are most widely used for prediction tasks where relationship between the independent variables and dependent (prediction) variable is identified. The accuracy of the regression techniques for prediction can be improved if clustering can be used along with regression. Clustering along with regression will ensure the more accurate curve fitting between the dependent and independent variables. In this work cluster regression technique is applied for estimating the compressive strength of the concrete and a novel state of the art is proposed for predicting the concrete compressive strength. The objective of this work is to demonstrate that clustering along with regression ensures less prediction errors for estimating the concrete compressive strength. The proposed technique consists of two major stages: in the first stage, clustering is used to group the similar characteristics concrete data and then in the second stage regression techniques are applied over these clusters (groups) to predict the compressive strength from individual clusters. It is found from experiments that clustering along with regression techniques gives minimum errors for predicting compressive strength of concrete; also fuzzy clustering algorithm C-means performs better than K-means algorithm.

Estimating the Concrete Compressive Strength Using Hard Clustering and Fuzzy Clustering Based Regression Techniques

PubMed Central

Nagwani, Naresh Kumar; Deo, Shirish V.

2014-01-01

Understanding of the compressive strength of concrete is important for activities like construction arrangement, prestressing operations, and proportioning new mixtures and for the quality assurance. Regression techniques are most widely used for prediction tasks where relationship between the independent variables and dependent (prediction) variable is identified. The accuracy of the regression techniques for prediction can be improved if clustering can be used along with regression. Clustering along with regression will ensure the more accurate curve fitting between the dependent and independent variables. In this work cluster regression technique is applied for estimating the compressive strength of the concrete and a novel state of the art is proposed for predicting the concrete compressive strength. The objective of this work is to demonstrate that clustering along with regression ensures less prediction errors for estimating the concrete compressive strength. The proposed technique consists of two major stages: in the first stage, clustering is used to group the similar characteristics concrete data and then in the second stage regression techniques are applied over these clusters (groups) to predict the compressive strength from individual clusters. It is found from experiments that clustering along with regression techniques gives minimum errors for predicting compressive strength of concrete; also fuzzy clustering algorithm C-means performs better than K-means algorithm. PMID:25374939

A predictive model of avian natal dispersal distance provides prior information for investigating response to landscape change.

PubMed

Garrard, Georgia E; McCarthy, Michael A; Vesk, Peter A; Radford, James Q; Bennett, Andrew F

2012-01-01

1. Informative Bayesian priors can improve the precision of estimates in ecological studies or estimate parameters for which little or no information is available. While Bayesian analyses are becoming more popular in ecology, the use of strongly informative priors remains rare, perhaps because examples of informative priors are not readily available in the published literature. 2. Dispersal distance is an important ecological parameter, but is difficult to measure and estimates are scarce. General models that provide informative prior estimates of dispersal distances will therefore be valuable. 3. Using a world-wide data set on birds, we develop a predictive model of median natal dispersal distance that includes body mass, wingspan, sex and feeding guild. This model predicts median dispersal distance well when using the fitted data and an independent test data set, explaining up to 53% of the variation. 4. Using this model, we predict a priori estimates of median dispersal distance for 57 woodland-dependent bird species in northern Victoria, Australia. These estimates are then used to investigate the relationship between dispersal ability and vulnerability to landscape-scale changes in habitat cover and fragmentation. 5. We find evidence that woodland bird species with poor predicted dispersal ability are more vulnerable to habitat fragmentation than those species with longer predicted dispersal distances, thus improving the understanding of this important phenomenon. 6. The value of constructing informative priors from existing information is also demonstrated. When used as informative priors for four example species, predicted dispersal distances reduced the 95% credible intervals of posterior estimates of dispersal distance by 8-19%. Further, should we have wished to collect information on avian dispersal distances and relate it to species' responses to habitat loss and fragmentation, data from 221 individuals across 57 species would have been required to obtain estimates with the same precision as those provided by the general model. © 2011 The Authors. Journal of Animal Ecology © 2011 British Ecological Society.

Extending Theory-Based Quantitative Predictions to New Health Behaviors.

PubMed

Brick, Leslie Ann D; Velicer, Wayne F; Redding, Colleen A; Rossi, Joseph S; Prochaska, James O

2016-04-01

Traditional null hypothesis significance testing suffers many limitations and is poorly adapted to theory testing. A proposed alternative approach, called Testing Theory-based Quantitative Predictions, uses effect size estimates and confidence intervals to directly test predictions based on theory. This paper replicates findings from previous smoking studies and extends the approach to diet and sun protection behaviors using baseline data from a Transtheoretical Model behavioral intervention (N = 5407). Effect size predictions were developed using two methods: (1) applying refined effect size estimates from previous smoking research or (2) using predictions developed by an expert panel. Thirteen of 15 predictions were confirmed for smoking. For diet, 7 of 14 predictions were confirmed using smoking predictions and 6 of 16 using expert panel predictions. For sun protection, 3 of 11 predictions were confirmed using smoking predictions and 5 of 19 using expert panel predictions. Expert panel predictions and smoking-based predictions poorly predicted effect sizes for diet and sun protection constructs. Future studies should aim to use previous empirical data to generate predictions whenever possible. The best results occur when there have been several iterations of predictions for a behavior, such as with smoking, demonstrating that expected values begin to converge on the population effect size. Overall, the study supports necessity in strengthening and revising theory with empirical data.

DEVELOPING ALTERNATIVE POWER TO DRIVE A PARTITIONED AQUACULTURE SYSTEM FOR INTENSIVE FOOD FISH PRODUCTION

EPA Science Inventory

Data collected, such as fish production rates and construction/operating costs, will allow us to evaluate the predictions generated by our modeling activities as well as provide real estimates of cost/benefit for the application of alternative PAS technology in temperate water...

Rebar: Reinforcing a Matching Estimator with Predictions from High-Dimensional Covariates

ERIC Educational Resources Information Center

Sales, Adam C.; Hansen, Ben B.; Rowan, Brian

2018-01-01

In causal matching designs, some control subjects are often left unmatched, and some covariates are often left unmodeled. This article introduces "rebar," a method using high-dimensional modeling to incorporate these commonly discarded data without sacrificing the integrity of the matching design. After constructing a match, a researcher…

A unified internal model theory to resolve the paradox of active versus passive self-motion sensation

PubMed Central

Angelaki, Dora E

2017-01-01

Brainstem and cerebellar neurons implement an internal model to accurately estimate self-motion during externally generated (‘passive’) movements. However, these neurons show reduced responses during self-generated (‘active’) movements, indicating that predicted sensory consequences of motor commands cancel sensory signals. Remarkably, the computational processes underlying sensory prediction during active motion and their relationship to internal model computations during passive movements remain unknown. We construct a Kalman filter that incorporates motor commands into a previously established model of optimal passive self-motion estimation. The simulated sensory error and feedback signals match experimentally measured neuronal responses during active and passive head and trunk rotations and translations. We conclude that a single sensory internal model can combine motor commands with vestibular and proprioceptive signals optimally. Thus, although neurons carrying sensory prediction error or feedback signals show attenuated modulation, the sensory cues and internal model are both engaged and critically important for accurate self-motion estimation during active head movements. PMID:29043978

[The therapeutic drug monitoring network server of tacrolimus for Chinese renal transplant patients].

PubMed

Deng, Chen-Hui; Zhang, Guan-Min; Bi, Shan-Shan; Zhou, Tian-Yan; Lu, Wei

2011-07-01

This study is to develop a therapeutic drug monitoring (TDM) network server of tacrolimus for Chinese renal transplant patients, which can facilitate doctor to manage patients' information and provide three levels of predictions. Database management system MySQL was employed to build and manage the database of patients and doctors' information, and hypertext mark-up language (HTML) and Java server pages (JSP) technology were employed to construct network server for database management. Based on the population pharmacokinetic model of tacrolimus for Chinese renal transplant patients, above program languages were used to construct the population prediction and subpopulation prediction modules. Based on Bayesian principle and maximization of the posterior probability function, an objective function was established, and minimized by an optimization algorithm to estimate patient's individual pharmacokinetic parameters. It is proved that the network server has the basic functions for database management and three levels of prediction to aid doctor to optimize the regimen of tacrolimus for Chinese renal transplant patients.

Neuro-genetic non-invasive temperature estimation: intensity and spatial prediction.

PubMed

Teixeira, César A; Ruano, M Graça; Ruano, António E; Pereira, Wagner C A

2008-06-01

The existence of proper non-invasive temperature estimators is an essential aspect when thermal therapy applications are envisaged. These estimators must be good predictors to enable temperature estimation at different operational situations, providing better control of the therapeutic instrumentation. In this work, radial basis functions artificial neural networks were constructed to access temperature evolution on an ultrasound insonated medium. The employed models were radial basis functions neural networks with external dynamics induced by their inputs. Both the most suited set of model inputs and number of neurons in the network were found using the multi-objective genetic algorithm. The neural models were validated in two situations: the operating ones, as used in the construction of the network; and in 11 unseen situations. The new data addressed two new spatial locations and a new intensity level, assessing the intensity and space prediction capacity of the proposed model. Good performance was obtained during the validation process both in terms of the spatial points considered and whenever the new intensity level was within the range of applied intensities. A maximum absolute error of 0.5 degrees C+/-10% (0.5 degrees C is the gold-standard threshold in hyperthermia/diathermia) was attained with low computationally complex models. The results confirm that the proposed neuro-genetic approach enables foreseeing temperature propagation, in connection to intensity and space parameters, thus enabling the assessment of different operating situations with proper temperature resolution.

Estimation of typhoon rainfall in GaoPing River: A Multivariate Maximum Entropy Method

NASA Astrophysics Data System (ADS)

Pei-Jui, Wu; Hwa-Lung, Yu

2016-04-01

The heavy rainfall from typhoons is the main factor of the natural disaster in Taiwan, which causes the significant loss of human lives and properties. Statistically average 3.5 typhoons invade Taiwan every year, and the serious typhoon, Morakot in 2009, impacted Taiwan in recorded history. Because the duration, path and intensity of typhoon, also affect the temporal and spatial rainfall type in specific region , finding the characteristics of the typhoon rainfall type is advantageous when we try to estimate the quantity of rainfall. This study developed a rainfall prediction model and can be divided three parts. First, using the EEOF(extended empirical orthogonal function) to classify the typhoon events, and decompose the standard rainfall type of all stations of each typhoon event into the EOF and PC(principal component). So we can classify the typhoon events which vary similarly in temporally and spatially as the similar typhoon types. Next, according to the classification above, we construct the PDF(probability density function) in different space and time by means of using the multivariate maximum entropy from the first to forth moment statistically. Therefore, we can get the probability of each stations of each time. Final we use the BME(Bayesian Maximum Entropy method) to construct the typhoon rainfall prediction model , and to estimate the rainfall for the case of GaoPing river which located in south of Taiwan.This study could be useful for typhoon rainfall predictions in future and suitable to government for the typhoon disaster prevention .

Optimal prediction of the number of unseen species

PubMed Central

Orlitsky, Alon; Suresh, Ananda Theertha; Wu, Yihong

2016-01-01

Estimating the number of unseen species is an important problem in many scientific endeavors. Its most popular formulation, introduced by Fisher et al. [Fisher RA, Corbet AS, Williams CB (1943) J Animal Ecol 12(1):42−58], uses n samples to predict the number U of hitherto unseen species that would be observed if t⋅n new samples were collected. Of considerable interest is the largest ratio t between the number of new and existing samples for which U can be accurately predicted. In seminal works, Good and Toulmin [Good I, Toulmin G (1956) Biometrika 43(102):45−63] constructed an intriguing estimator that predicts U for all t≤1. Subsequently, Efron and Thisted [Efron B, Thisted R (1976) Biometrika 63(3):435−447] proposed a modification that empirically predicts U even for some t>1, but without provable guarantees. We derive a class of estimators that provably predict U all of the way up to t∝log⁡n. We also show that this range is the best possible and that the estimator’s mean-square error is near optimal for any t. Our approach yields a provable guarantee for the Efron−Thisted estimator and, in addition, a variant with stronger theoretical and experimental performance than existing methodologies on a variety of synthetic and real datasets. The estimators are simple, linear, computationally efficient, and scalable to massive datasets. Their performance guarantees hold uniformly for all distributions, and apply to all four standard sampling models commonly used across various scientific disciplines: multinomial, Poisson, hypergeometric, and Bernoulli product. PMID:27830649

Reader reaction on estimation of treatment effects in all-comers randomized clinical trials with a predictive marker.

PubMed

Korn, Edward L; Freidlin, Boris

2017-06-01

For a fallback randomized clinical trial design with a marker, Choai and Matsui (2015, Biometrics 71, 25-32) estimate the bias of the estimator of the treatment effect in the marker-positive subgroup conditional on the treatment effect not being statistically significant in the overall population. This is used to construct and examine conditionally bias-corrected estimators of the treatment effect for the marker-positive subgroup. We argue that it may not be appropriate to correct for conditional bias in this setting. Instead, we consider the unconditional bias of estimators of the treatment effect for marker-positive patients. © 2016, The International Biometric Society.

eMolTox: prediction of molecular toxicity with confidence.

PubMed

Ji, Changge; Svensson, Fredrik; Zoufir, Azedine; Bender, Andreas

2018-03-07

In this work we present eMolTox, a web server for the prediction of potential toxicity associated with a given molecule. 174 toxicology-related in vitro/vivo experimental datasets were used for model construction and Mondrian conformal prediction was used to estimate the confidence of the resulting predictions. Toxic substructure analysis is also implemented in eMolTox. eMolTox predicts and displays a wealth of information of potential molecular toxicities for safety analysis in drug development. The eMolTox Server is freely available for use on the web at http://xundrug.cn/moltox. chicago.ji@gmail.com or ab454@cam.ac.uk. Supplementary data are available at Bioinformatics online.

Exploring structural relationships between blood alcohol concentration and signs and clinical assessment of intoxication in alcohol-involved injury cases.

PubMed

Bond, Jason; Witbrodt, Jane; Ye, Yu; Cherpitel, Cheryl J; Room, Robin; Monteiro, Maristela G

2014-01-01

Although the relationship between the Y90 (blood alcohol concentration, BAC) and Y91 (clinician intoxication assessment) ICD-10 codes has received attention recently, the role of 10 signs of intoxication in the Y91-Y90 relationship has not been studied yet. This work examines these signs in the estimation of alcohol intoxication levels of patients in medical settings. Collected and analyzed were data on 1997 injured emergency room patients from 17 countries worldwide reporting drinking prior to injury or presenting with a non-zero BAC from 17 countries worldwide. A model is estimated describing how the 10 signs inform the Y91, Y90 prediction with the goal of the use of observations on patients in place of a biological measure. Signs were consistent with a single underlying construct that strongly predicted Y91. Smell of alcohol on breath predicted Y91 above its contribution through the construct and was stronger for those with tolerance to alcohol than for those without. Controlling for Y91, no sign further contributed to prediction of Y90 indicating that Y91 incorporated all intoxication sign information in predicting Y90. Variance explained was high for Y91 (R(2) = 0.84) and intoxication signs (above 0.72 for all but smell on the breath, 0.57) and lower for Y90 (0.38). Intoxication assessments are well predicted by overall intoxication severity, which itself is well represented by intoxication signs along with differential emphasis on smell of alcohol on breath, especially for those with alcohol tolerance. However, BAC levels remain largely unexplained by intoxication signs with a clinician's assessment serving as the primary predictive measure. © The Author 2014. Medical Council on Alcohol and Oxford University Press. All rights reserved.

Exploring Structural Relationships Between Blood Alcohol Concentration and Signs and Clinical Assessment of Intoxication in Alcohol-Involved Injury Cases

PubMed Central

Bond, Jason; Witbrodt, Jane; Ye, Yu; Cherpitel, Cheryl J.; Room, Robin; Monteiro, Maristela G.

2014-01-01

Aims: Although the relationship between the Y90 (blood alcohol concentration, BAC) and Y91 (clinician intoxication assessment) ICD-10 codes has received attention recently, the role of 10 signs of intoxication in the Y91–Y90 relationship has not been studied yet. This work examines these signs in the estimation of alcohol intoxication levels of patients in medical settings. Methods: Collected and analyzed were data on 1997 injured emergency room patients from 17 countries worldwide reporting drinking prior to injury or presenting with a non-zero BAC from 17 countries worldwide. A model is estimated describing how the 10 signs inform the Y91, Y90 prediction with the goal of the use of observations on patients in place of a biological measure. Results: Signs were consistent with a single underlying construct that strongly predicted Y91. Smell of alcohol on breath predicted Y91 above its contribution through the construct and was stronger for those with tolerance to alcohol than for those without. Controlling for Y91, no sign further contributed to prediction of Y90 indicating that Y91 incorporated all intoxication sign information in predicting Y90. Variance explained was high for Y91 (R2 = 0.84) and intoxication signs (above 0.72 for all but smell on the breath, 0.57) and lower for Y90 (0.38). Conclusion: Intoxication assessments are well predicted by overall intoxication severity, which itself is well represented by intoxication signs along with differential emphasis on smell of alcohol on breath, especially for those with alcohol tolerance. However, BAC levels remain largely unexplained by intoxication signs with a clinician's assessment serving as the primary predictive measure. PMID:24705784

Can administrative health utilisation data provide an accurate diabetes prevalence estimate for a geographical region?

PubMed

Chan, Wing Cheuk; Papaconstantinou, Dean; Lee, Mildred; Telfer, Kendra; Jo, Emmanuel; Drury, Paul L; Tobias, Martin

2018-05-01

To validate the New Zealand Ministry of Health (MoH) Virtual Diabetes Register (VDR) using longitudinal laboratory results and to develop an improved algorithm for estimating diabetes prevalence at a population level. The assigned diabetes status of individuals based on the 2014 version of the MoH VDR is compared to the diabetes status based on the laboratory results stored in the Auckland regional laboratory result repository (TestSafe) using the New Zealand diabetes diagnostic criteria. The existing VDR algorithm is refined by reviewing the sensitivity and positive predictive value of the each of the VDR algorithm rules individually and as a combination. The diabetes prevalence estimate based on the original 2014 MoH VDR was 17% higher (n = 108,505) than the corresponding TestSafe prevalence estimate (n = 92,707). Compared to the diabetes prevalence based on TestSafe, the original VDR has a sensitivity of 89%, specificity of 96%, positive predictive value of 76% and negative predictive value of 98%. The modified VDR algorithm has improved the positive predictive value by 6.1% and the specificity by 1.4% with modest reductions in sensitivity of 2.2% and negative predictive value of 0.3%. At an aggregated level the overall diabetes prevalence estimated by the modified VDR is 5.7% higher than the corresponding estimate based on TestSafe. The Ministry of Health Virtual Diabetes Register algorithm has been refined to provide a more accurate diabetes prevalence estimate at a population level. The comparison highlights the potential value of a national population long term condition register constructed from both laboratory results and administrative data. Copyright © 2018 Elsevier B.V. All rights reserved.

Service quality, satisfaction, and behavioral intention in home delivered meals program

PubMed Central

Joung, Hyun-Woo; Yuan, Jingxue Jessica; Huffman, Lynn

2011-01-01

This study was conducted to evaluate recipients' perception of service quality, satisfaction, and behavioral intention in home delivered meals program in the US. Out of 398 questionnaires, 265 (66.6%) were collected, and 209 questionnaires (52.5%) were used for the statistical analysis. A Confirmatory Factor Analysis (CFA) with a maximum likelihood was first conducted to estimate the measurement model by verifying the underlying structure of constructs. The level of internal consistency in each construct was acceptable, with Cronbach's alpha estimates ranging from 0.7 to 0.94. All of the composite reliabilities of the constructs were over the cutoff value of 0.50, ensuring adequate internal consistency of multiple items for each construct. As a second step, a Meals-On-Wheels (MOW) recipient perception model was estimated. The model's fit as indicated by these indexes was satisfactory and path coefficients were analyzed. Two paths between (1) volunteer issues and behavioral intention and (2) responsiveness and behavioral intention were not significant. The path for predicting a positive relationship between food quality and satisfaction was supported. The results show that having high food quality may create recipient satisfaction. The findings suggest that food quality and responsiveness are significant predictors of positive satisfaction. Moreover, satisfied recipients have positive behavioral intention toward MOW programs. PMID:21556231

Service quality, satisfaction, and behavioral intention in home delivered meals program.

PubMed

Joung, Hyun-Woo; Kim, Hak-Seon; Yuan, Jingxue Jessica; Huffman, Lynn

2011-04-01

This study was conducted to evaluate recipients' perception of service quality, satisfaction, and behavioral intention in home delivered meals program in the US. Out of 398 questionnaires, 265 (66.6%) were collected, and 209 questionnaires (52.5%) were used for the statistical analysis. A Confirmatory Factor Analysis (CFA) with a maximum likelihood was first conducted to estimate the measurement model by verifying the underlying structure of constructs. The level of internal consistency in each construct was acceptable, with Cronbach's alpha estimates ranging from 0.7 to 0.94. All of the composite reliabilities of the constructs were over the cutoff value of 0.50, ensuring adequate internal consistency of multiple items for each construct. As a second step, a Meals-On-Wheels (MOW) recipient perception model was estimated. The model's fit as indicated by these indexes was satisfactory and path coefficients were analyzed. Two paths between (1) volunteer issues and behavioral intention and (2) responsiveness and behavioral intention were not significant. The path for predicting a positive relationship between food quality and satisfaction was supported. The results show that having high food quality may create recipient satisfaction. The findings suggest that food quality and responsiveness are significant predictors of positive satisfaction. Moreover, satisfied recipients have positive behavioral intention toward MOW programs.

Ultrasound Elastography for Estimation of Regional Strain of Multilayered Hydrogels and Tissue-Engineered Cartilage

PubMed Central

Chung, Chen-Yuan; Heebner, Joseph; Baskaran, Harihara; Welter, Jean F.; Mansour, Joseph M.

2015-01-01

Tissue-engineered (TE) cartilage constructs tend to develop inhomogeneously, thus, to predict the mechanical performance of the tissue, conventional biomechanical testing, which yields average material properties, is of limited value. Rather, techniques for evaluating regional and depth-dependent properties of TE cartilage, preferably non-destructively, are required. The purpose of this study was to build upon our previous results and to investigate the feasibility of using ultrasound elastography to non-destructively assess the depth-dependent biomechanical characteristics of TE cartilage while in a sterile bioreactor. As a proof-of-concept, and to standardize an assessment protocol, a well-characterized three-layered hydrogel construct was used as a surrogate for TE cartilage, and was studied under controlled incremental compressions. The strain field of the construct predicted by elastography was then validated by comparison with a poroelastic finite-element analysis (FEA). On average, the differences between the strains predicted by elastography and the FEA were within 10%. Subsequently engineered cartilage tissue was evaluated in the same test fixture. Results from these examinations showed internal regions where the local strain was 1–2 orders of magnitude greater than that near the surface. These studies document the feasibility of using ultrasound to evaluate the mechanical behaviors of maturing TE constructs in a sterile environment. PMID:26077987

Predicting fundamental and realized distributions based on thermal niche: A case study of a freshwater turtle

NASA Astrophysics Data System (ADS)

Rodrigues, João Fabrício Mota; Coelho, Marco Túlio Pacheco; Ribeiro, Bruno R.

2018-04-01

Species distribution models (SDM) have been broadly used in ecology to address theoretical and practical problems. Currently, there are two main approaches to generate SDMs: (i) correlative, which is based on species occurrences and environmental predictor layers and (ii) process-based models, which are constructed based on species' functional traits and physiological tolerances. The distributions estimated by each approach are based on different components of species niche. Predictions of correlative models approach species realized niches, while predictions of process-based are more akin to species fundamental niche. Here, we integrated the predictions of fundamental and realized distributions of the freshwater turtle Trachemys dorbigni. Fundamental distribution was estimated using data of T. dorbigni's egg incubation temperature, and realized distribution was estimated using species occurrence records. Both types of distributions were estimated using the same regression approaches (logistic regression and support vector machines), both considering macroclimatic and microclimatic temperatures. The realized distribution of T. dorbigni was generally nested in its fundamental distribution reinforcing theoretical assumptions that the species' realized niche is a subset of its fundamental niche. Both modelling algorithms produced similar results but microtemperature generated better results than macrotemperature for the incubation model. Finally, our results reinforce the conclusion that species realized distributions are constrained by other factors other than just thermal tolerances.

Gambling score in earthquake prediction analysis

NASA Astrophysics Data System (ADS)

Molchan, G.; Romashkova, L.

2011-03-01

The number of successes and the space-time alarm rate are commonly used to characterize the strength of an earthquake prediction method and the significance of prediction results. It has been recently suggested to use a new characteristic to evaluate the forecaster's skill, the gambling score (GS), which incorporates the difficulty of guessing each target event by using different weights for different alarms. We expand parametrization of the GS and use the M8 prediction algorithm to illustrate difficulties of the new approach in the analysis of the prediction significance. We show that the level of significance strongly depends (1) on the choice of alarm weights, (2) on the partitioning of the entire alarm volume into component parts and (3) on the accuracy of the spatial rate measure of target events. These tools are at the disposal of the researcher and can affect the significance estimate. Formally, all reasonable GSs discussed here corroborate that the M8 method is non-trivial in the prediction of 8.0 ≤M < 8.5 events because the point estimates of the significance are in the range 0.5-5 per cent. However, the conservative estimate 3.7 per cent based on the number of successes seems preferable owing to two circumstances: (1) it is based on relative values of the spatial rate and hence is more stable and (2) the statistic of successes enables us to construct analytically an upper estimate of the significance taking into account the uncertainty of the spatial rate measure.

Funnel plot control limits to identify poorly performing healthcare providers when there is uncertainty in the value of the benchmark.

PubMed

Manktelow, Bradley N; Seaton, Sarah E; Evans, T Alun

2016-12-01

There is an increasing use of statistical methods, such as funnel plots, to identify poorly performing healthcare providers. Funnel plots comprise the construction of control limits around a benchmark and providers with outcomes falling outside the limits are investigated as potential outliers. The benchmark is usually estimated from observed data but uncertainty in this estimate is usually ignored when constructing control limits. In this paper, the use of funnel plots in the presence of uncertainty in the value of the benchmark is reviewed for outcomes from a Binomial distribution. Two methods to derive the control limits are shown: (i) prediction intervals; (ii) tolerance intervals Tolerance intervals formally include the uncertainty in the value of the benchmark while prediction intervals do not. The probability properties of 95% control limits derived using each method were investigated through hypothesised scenarios. Neither prediction intervals nor tolerance intervals produce funnel plot control limits that satisfy the nominal probability characteristics when there is uncertainty in the value of the benchmark. This is not necessarily to say that funnel plots have no role to play in healthcare, but that without the development of intervals satisfying the nominal probability characteristics they must be interpreted with care. © The Author(s) 2014.

College Influence on Student Intentions toward International Competence. ASHE Annual Meeting Paper.

ERIC Educational Resources Information Center

English, Susan Lewis

This study attempted to test the concept of international competence as a construct and to estimate the extent to which college experience predicts variance on student intentions toward international competence. Relying on Lambert's model of global competence, the study tested five components of international competence for validity and…

Whole stand volume tables for quaking aspen in the Rocky Mountains

Treesearch

Wayne D. Shepperd; H. Todd Mowrer

1984-01-01

Linear regression equations were developed to predict stand volumes for aspen given average stand basal area and average stand height. Tables constructed from these equations allow easy field estimation of gross merchantable cubic and board foot Scribner Rules per acre, and cubic meters per hectare using simple prism cruise data.

Failure Assessment Diagram for Brazed 304 Stainless Steel Joints

NASA Technical Reports Server (NTRS)

Flom, Yory

2011-01-01

Interaction equations were proposed earlier to predict failure in Albemet 162 brazed joints. Present study demonstrates that the same interaction equations can be used for lower bound estimate of the failure criterion in 304 stainless steel joints brazed with silver-based filler metals as well as for construction of the Failure Assessment Diagrams (FAD).

Precision Interval Estimation of the Response Surface by Means of an Integrated Algorithm of Neural Network and Linear Regression

NASA Technical Reports Server (NTRS)

Lo, Ching F.

1999-01-01

The integration of Radial Basis Function Networks and Back Propagation Neural Networks with the Multiple Linear Regression has been accomplished to map nonlinear response surfaces over a wide range of independent variables in the process of the Modem Design of Experiments. The integrated method is capable to estimate the precision intervals including confidence and predicted intervals. The power of the innovative method has been demonstrated by applying to a set of wind tunnel test data in construction of response surface and estimation of precision interval.

Power flow prediction in vibrating systems via model reduction

NASA Astrophysics Data System (ADS)

Li, Xianhui

This dissertation focuses on power flow prediction in vibrating systems. Reduced order models (ROMs) are built based on rational Krylov model reduction which preserve power flow information in the original systems over a specified frequency band. Stiffness and mass matrices of the ROMs are obtained by projecting the original system matrices onto the subspaces spanned by forced responses. A matrix-free algorithm is designed to construct ROMs directly from the power quantities at selected interpolation frequencies. Strategies for parallel implementation of the algorithm via message passing interface are proposed. The quality of ROMs is iteratively refined according to the error estimate based on residual norms. Band capacity is proposed to provide a priori estimate of the sizes of good quality ROMs. Frequency averaging is recast as ensemble averaging and Cauchy distribution is used to simplify the computation. Besides model reduction for deterministic systems, details of constructing ROMs for parametric and nonparametric random systems are also presented. Case studies have been conducted on testbeds from Harwell-Boeing collections. Input and coupling power flow are computed for the original systems and the ROMs. Good agreement is observed in all cases.

Perceptual color difference metric including a CSF based on the perception threshold

NASA Astrophysics Data System (ADS)

Rosselli, Vincent; Larabi, Mohamed-Chaker; Fernandez-Maloigne, Christine

2008-01-01

The study of the Human Visual System (HVS) is very interesting to quantify the quality of a picture, to predict which information will be perceived on it, to apply adapted tools ... The Contrast Sensitivity Function (CSF) is one of the major ways to integrate the HVS properties into an imaging system. It characterizes the sensitivity of the visual system to spatial and temporal frequencies and predicts the behavior for the three channels. Common constructions of the CSF have been performed by estimating the detection threshold beyond which it is possible to perceive a stimulus. In this work, we developed a novel approach for spatio-chromatic construction based on matching experiments to estimate the perception threshold. It consists in matching the contrast of a test stimulus with that of a reference one. The obtained results are quite different in comparison with the standard approaches as the chromatic CSFs have band-pass behavior and not low pass. The obtained model has been integrated in a perceptual color difference metric inspired by the s-CIELAB. The metric is then evaluated with both objective and subjective procedures.

The design, analysis and experimental evaluation of an elastic model wing

NASA Technical Reports Server (NTRS)

Cavin, R. K., III; Thisayakorn, C.

1974-01-01

An elastic orbiter model was developed to evaluate the effectiveness of aeroelasticity computer programs. The elasticity properties were introduced by constructing beam-like straight wings for the wind tunnel model. A standard influence coefficient mathematical model was used to estimate aeroelastic effects analytically. In general good agreement was obtained between the empirical and analytical estimates of the deformed shape. However, in the static aeroelasticity case, it was found that the physical wing exhibited less bending and more twist than was predicted by theory.

Estimating Building Age with 3d GIS

NASA Astrophysics Data System (ADS)

Biljecki, F.; Sindram, M.

2017-10-01

Building datasets (e.g. footprints in OpenStreetMap and 3D city models) are becoming increasingly available worldwide. However, the thematic (attribute) aspect is not always given attention, as many of such datasets are lacking in completeness of attributes. A prominent attribute of buildings is the year of construction, which is useful for some applications, but its availability may be scarce. This paper explores the potential of estimating the year of construction (or age) of buildings from other attributes using random forest regression. The developed method has a two-fold benefit: enriching datasets and quality control (verification of existing attributes). Experiments are carried out on a semantically rich LOD1 dataset of Rotterdam in the Netherlands using 9 attributes. The results are mixed: the accuracy in the estimation of building age depends on the available information used in the regression model. In the best scenario we have achieved predictions with an RMSE of 11 years, but in more realistic situations with limited knowledge about buildings the error is much larger (RMSE = 26 years). Hence the main conclusion of the paper is that inferring building age with 3D city models is possible to a certain extent because it reveals the approximate period of construction, but precise estimations remain a difficult task.

Methods for predicting peak discharge of floods caused by failure of natural and constructed earthen dams

USGS Publications Warehouse

Walder, Joseph S.; O'Connor, Jim E.

1997-01-01

Floods from failures of natural and constructed dams constitute a widespread hazard to people and property. Expeditious means of assessing flood hazards are necessary, particularly in the case of natural dams, which may form suddenly and unexpectedly. We revise statistical relations (derived from data for past constructed and natural dam failures) between peak discharge (Qp) and water volume released (V0) or drop in lake level (d) but assert that such relations, even when cast into a dimensionless form, are of limited utility because they fail to portray the effect of breach-formation rate. We then analyze a simple, physically based model of dam-breach formation to show that the hydrograph at the breach depends primarily on a dimensionless parameter η=kV0/gl/2d7/2, where k is the mean erosion rate of the breach and g is acceleration due to gravity. The functional relationship between Qp and η takes asymptotically distinct forms depending on whether η ≪ 1 (relatively slow breach formation or small lake volume) or η ≫ 1 (relatively fast breach formation or large lake volume). Theoretical predictions agree well with data from dam failures for which k, and thus η, can be estimated. The theory thus provides a rapid means of predicting the plausible range of values of peak discharge at the breach in an earthen dam as long as the impounded water volume and the water depth at the dam face can be estimated.

ESTIMATING THE RADIUS OF THE CONVECTIVE CORE OF MAIN-SEQUENCE STARS FROM OBSERVED OSCILLATION FREQUENCIES

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

Yang, Wuming, E-mail: yangwuming@bnu.edu.cn, E-mail: yangwuming@ynao.ac.cn

The determination of the size of the convective core of main-sequence stars is usually dependent on the construction of models of stars. Here we introduce a method to estimate the radius of the convective core of main-sequence stars with masses between about 1.1 and 1.5 M {sub ⊙} from observed frequencies of low-degree p -modes. A formula is proposed to achieve the estimation. The values of the radius of the convective core of four known stars are successfully estimated by the formula. The radius of the convective core of KIC 9812850 estimated by the formula is 0.140 ± 0.028 Rmore » {sub ⊙}. In order to confirm this prediction, a grid of evolutionary models was computed. The value of the convective-core radius of the best-fit model of KIC 9812850 is 0.149 R {sub ⊙}, which is in good agreement with that estimated by the formula from observed frequencies. The formula aids in understanding the interior structure of stars directly from observed frequencies. The understanding is not dependent on the construction of models.« less

Shrinkage Estimators for a Composite Measure of Quality Conceptualized as a Formative Construct

PubMed Central

Shwartz, Michael; Peköz, Erol A; Christiansen, Cindy L; Burgess, James F; Berlowitz, Dan

2013-01-01

Objective To demonstrate the value of shrinkage estimators when calculating a composite quality measure as the weighted average of a set of individual quality indicators. Data Sources Rates of 28 quality indicators (QIs) calculated from the minimum dataset from residents of 112 Veterans Health Administration nursing homes in fiscal years 2005–2008. Study Design We compared composite scores calculated from the 28 QIs using both observed rates and shrunken rates derived from a Bayesian multivariate normal-binomial model. Principal Findings Shrunken-rate composite scores, because they take into account unreliability of estimates from small samples and the correlation among QIs, have more intuitive appeal than observed-rate composite scores. Facilities can be profiled based on more policy-relevant measures than point estimates of composite scores, and interval estimates can be calculated without assuming the QIs are independent. Usually, shrunken-rate composite scores in 1 year are better able to predict the observed total number of QI events or the observed-rate composite scores in the following year than the initial year observed-rate composite scores. Conclusion Shrinkage estimators can be useful when a composite measure is conceptualized as a formative construct. PMID:22716650

Unchained Melody: Revisiting the Estimation of SF-6D Values

PubMed Central

Craig, Benjamin M.

2015-01-01

Purpose In the original SF-6D valuation study, the analytical design inherited conventions that detrimentally affected its ability to predict values on a quality-adjusted life year (QALY) scale. Our objective is to estimate UK values for SF-6D states using the original data and multi-attribute utility (MAU) regression after addressing its limitations and to compare the revised SF-6D and EQ-5D value predictions. Methods Using the unaltered data (611 respondents, 3503 SG responses), the parameters of the original MAU model were re-estimated under 3 alternative error specifications, known as the instant, episodic, and angular random utility models. Value predictions on a QALY scale were compared to EQ-5D3L predictions using the 1996 Health Survey for England. Results Contrary to the original results, the revised SF-6D value predictions range below 0 QALYs (i.e., worse than death) and agree largely with EQ-5D predictions after adjusting for scale. Although a QALY is defined as a year in optimal health, the SF-6D sets a higher standard for optimal health than the EQ-5D-3L; therefore, it has larger units on a QALY scale by construction (20.9% more). Conclusions Much of the debate in health valuation has focused on differences between preference elicitation tasks, sampling, and instruments. After correcting errant econometric practices and adjusting for differences in QALY scale between the EQ-5D and SF-6D values, the revised predictions demonstrate convergent validity, making them more suitable for UK economic evaluations compared to original estimates. PMID:26359242

Nuclear mass formula with the shell energies obtained by a new method

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

Koura, H.; Tachibana, T.; Yamada, M.

1998-12-21

Nuclear shapes and masses are estimated by a new method. The main feature of this method lies in estimating shell energies of deformed nuclei from spherical shell energies by mixing them with appropriate weights. The spherical shell energies are calculated from single-particle potentials, and, till now, two mass formulas have been constructed from two different sets of potential parameters. The standard deviation of the calculated masses from all the experimental masses of the 1995 Mass Evaluation is about 760 keV. Contrary to the mass formula by Tachibana, Uno, Yamada and Yamada in the 1987-1988 Atomic Mass Predictions, the present formulasmore » can give nuclear shapes and predict on super-heavy elements.« less

Vision-guided gripping of a cylinder

NASA Technical Reports Server (NTRS)

Nicewarner, Keith E.; Kelley, Robert B.

1991-01-01

The motivation for vision-guided servoing is taken from tasks in automated or telerobotic space assembly and construction. Vision-guided servoing requires the ability to perform rapid pose estimates and provide predictive feature tracking. Monocular information from a gripper-mounted camera is used to servo the gripper to grasp a cylinder. The procedure is divided into recognition and servo phases. The recognition stage verifies the presence of a cylinder in the camera field of view. Then an initial pose estimate is computed and uncluttered scan regions are selected. The servo phase processes only the selected scan regions of the image. Given the knowledge, from the recognition phase, that there is a cylinder in the image and knowing the radius of the cylinder, 4 of the 6 pose parameters can be estimated with minimal computation. The relative motion of the cylinder is obtained by using the current pose and prior pose estimates. The motion information is then used to generate a predictive feature-based trajectory for the path of the gripper.

Gaussian functional regression for output prediction: Model assimilation and experimental design

NASA Astrophysics Data System (ADS)

Nguyen, N. C.; Peraire, J.

2016-03-01

In this paper, we introduce a Gaussian functional regression (GFR) technique that integrates multi-fidelity models with model reduction to efficiently predict the input-output relationship of a high-fidelity model. The GFR method combines the high-fidelity model with a low-fidelity model to provide an estimate of the output of the high-fidelity model in the form of a posterior distribution that can characterize uncertainty in the prediction. A reduced basis approximation is constructed upon the low-fidelity model and incorporated into the GFR method to yield an inexpensive posterior distribution of the output estimate. As this posterior distribution depends crucially on a set of training inputs at which the high-fidelity models are simulated, we develop a greedy sampling algorithm to select the training inputs. Our approach results in an output prediction model that inherits the fidelity of the high-fidelity model and has the computational complexity of the reduced basis approximation. Numerical results are presented to demonstrate the proposed approach.

A Comparison of Machine Learning Approaches for Corn Yield Estimation

NASA Astrophysics Data System (ADS)

Kim, N.; Lee, Y. W.

2017-12-01

Machine learning is an efficient empirical method for classification and prediction, and it is another approach to crop yield estimation. The objective of this study is to estimate corn yield in the Midwestern United States by employing the machine learning approaches such as the support vector machine (SVM), random forest (RF), and deep neural networks (DNN), and to perform the comprehensive comparison for their results. We constructed the database using satellite images from MODIS, the climate data of PRISM climate group, and GLDAS soil moisture data. In addition, to examine the seasonal sensitivities of corn yields, two period groups were set up: May to September (MJJAS) and July and August (JA). In overall, the DNN showed the highest accuracies in term of the correlation coefficient for the two period groups. The differences between our predictions and USDA yield statistics were about 10-11 %.

Earthquake prediction analysis based on empirical seismic rate: the M8 algorithm

NASA Astrophysics Data System (ADS)

Molchan, G.; Romashkova, L.

2010-12-01

The quality of space-time earthquake prediction is usually characterized by a 2-D error diagram (n, τ), where n is the fraction of failures-to-predict and τ is the local rate of alarm averaged in space. The most reasonable averaging measure for analysis of a prediction strategy is the normalized rate of target events λ(dg) in a subarea dg. In that case the quantity H = 1 - (n + τ) determines the prediction capability of the strategy. The uncertainty of λ(dg) causes difficulties in estimating H and the statistical significance, α, of prediction results. We investigate this problem theoretically and show how the uncertainty of the measure can be taken into account in two situations, viz., the estimation of α and the construction of a confidence zone for the (n, τ)-parameters of the random strategies. We use our approach to analyse the results from prediction of M >= 8.0 events by the M8 method for the period 1985-2009 (the M8.0+ test). The model of λ(dg) based on the events Mw >= 5.5, 1977-2004, and the magnitude range of target events 8.0 <= M < 8.5 are considered as basic to this M8 analysis. We find the point and upper estimates of α and show that they are still unstable because the number of target events in the experiment is small. However, our results argue in favour of non-triviality of the M8 prediction algorithm.

Which factors are most predictive for live birth after in vitro fertilization and intracytoplasmic sperm injection (IVF/ICSI) treatments? Analysis of 100 prospectively recorded variables in 8,400 IVF/ICSI single-embryo transfers.

PubMed

Vaegter, Katarina Kebbon; Lakic, Tatevik Ghukasyan; Olovsson, Matts; Berglund, Lars; Brodin, Thomas; Holte, Jan

2017-03-01

To construct a prediction model for live birth after in vitro fertilization/intracytoplasmic sperm injection (IVF/ICSI) treatment and single-embryo transfer (SET) after 2 days of embryo culture. Prospective observational cohort study. University-affiliated private infertility center. SET in 8,451 IVF/ICSI treatments in 5,699 unselected consecutive couples during 1999-2014. A total of 100 basal patient characteristics and treatment data were analyzed for associations with live birth after IVF/ICSI (adjusted for repeated treatments) and subsequently combined for prediction model construction. Live birth rate (LBR) and performance of live birth prediction model. Embryo score, treatment history, ovarian sensitivity index (OSI; number of oocytes/total dose of FSH administered), female age, infertility cause, endometrial thickness, and female height were all independent predictors of live birth. A prediction model (training data set; n = 5,722) based on these variables showed moderate discrimination, but predicted LBR with high accuracy in subgroups of patients, with LBR estimates ranging from <10% to >40%. Outcomes were similar in an internal validation data set (n = 2,460). Based on 100 variables prospectively recorded during a 15-year period, a model for live birth prediction after strict SET was constructed and showed excellent calibration in internal validation. For the first time, female height qualified as a predictor of live birth after IVF/ICSI. Copyright © 2016 American Society for Reproductive Medicine. Published by Elsevier Inc. All rights reserved.

Prediction of major complications after hepatectomy using liver stiffness values determined by magnetic resonance elastography.

PubMed

Sato, N; Kenjo, A; Kimura, T; Okada, R; Ishigame, T; Kofunato, Y; Shimura, T; Abe, K; Ohira, H; Marubashi, S

2018-04-23

Liver fibrosis is a risk factor for hepatectomy but cannot be determined accurately before hepatectomy because diagnostic procedures are too invasive. Magnetic resonance elastography (MRE) can determine liver stiffness (LS), a surrogate marker for assessing liver fibrosis, non-invasively. The aim of this study was to investigate whether the LS value determined by MRE is predictive of major complications after hepatectomy. This prospective study enrolled consecutive patients who underwent hepatic resection between April 2013 and August 2016. LS values were measured by imaging shear waves by MRE in the liver before hepatectomy. The primary endpoint was major complications, defined as Clavien-Dindo grade IIIa or above. Logistic regression analysis identified independent predictive factors, from which a logistic model to estimate the probability of major complications was constructed. A total of 96 patients were included in the study. Major complications were observed in 15 patients (16 per cent). Multivariable logistic analysis confirmed that higher LS value (P = 0·021) and serum albumin level (P = 0·009) were independent predictive factors for major complications after hepatectomy. Receiver operating characteristic (ROC) analysis showed that the best LS cut-off value was 4·3 kPa for detecting major complications, comparable to liver fibrosis grade F4, with a sensitivity of 80 per cent and specificity of 82 per cent. A logistic model using the LS value and serum albumin level to estimate the probability of major complications was constructed; the area under the ROC curve for predicting major complications was 0·84. The LS value determined by MRE in patients undergoing hepatectomy was an independent predictive factor for major complications. © 2018 BJS Society Ltd Published by John Wiley & Sons Ltd.

A preliminary study on postmortem interval estimation of suffocated rats by GC-MS/MS-based plasma metabolic profiling.

PubMed

Sato, Takako; Zaitsu, Kei; Tsuboi, Kento; Nomura, Masakatsu; Kusano, Maiko; Shima, Noriaki; Abe, Shuntaro; Ishii, Akira; Tsuchihashi, Hitoshi; Suzuki, Koichi

2015-05-01

Estimation of postmortem interval (PMI) is an important goal in judicial autopsy. Although many approaches can estimate PMI through physical findings and biochemical tests, accurate PMI calculation by these conventional methods remains difficult because PMI is readily affected by surrounding conditions, such as ambient temperature and humidity. In this study, Sprague-Dawley (SD) rats (10 weeks) were sacrificed by suffocation, and blood was collected by dissection at various time intervals (0, 3, 6, 12, 24, and 48 h; n = 6) after death. A total of 70 endogenous metabolites were detected in plasma by gas chromatography-tandem mass spectrometry (GC-MS/MS). Each time group was separated from each other on the principal component analysis (PCA) score plot, suggesting that the various endogenous metabolites changed with time after death. To prepare a prediction model of a PMI, a partial least squares (or projection to latent structure, PLS) regression model was constructed using the levels of significantly different metabolites determined by variable importance in the projection (VIP) score and the Kruskal-Wallis test (P < 0.05). Because the constructed PLS regression model could successfully predict each PMI, this model was validated with another validation set (n = 3). In conclusion, plasma metabolic profiling demonstrated its ability to successfully estimate PMI under a certain condition. This result can be considered to be the first step for using the metabolomics method in future forensic casework.

A BIM-based system for demolition and renovation waste estimation and planning.

PubMed

Cheng, Jack C P; Ma, Lauren Y H

2013-06-01

Due to the rising worldwide awareness of green environment, both government and contractors have to consider effective construction and demolition (C&D) waste management practices. The last two decades have witnessed the growing importance of demolition and renovation (D&R) works and the growing amount of D&R waste disposed to landfills every day, especially in developed cities like Hong Kong. Quantitative waste prediction is crucial for waste management. It can enable contractors to pinpoint critical waste generation processes and to plan waste control strategies. In addition, waste estimation could also facilitate some government waste management policies, such as the waste disposal charging scheme in Hong Kong. Currently, tools that can accurately and conveniently estimate the amount of waste from construction, renovation, and demolition projects are lacking. In the light of this research gap, this paper presents a building information modeling (BIM) based system that we have developed for estimation and planning of D&R waste. BIM allows multi-disciplinary information to be superimposed within one digital building model. Our system can extract material and volume information through the BIM model and integrate the information for detailed waste estimation and planning. Waste recycling and reuse are also considered in our system. Extracted material information can be provided to recyclers before demolition or renovation to make recycling stage more cooperative and more efficient. Pick-up truck requirements and waste disposal charging fee for different waste facilities will also be predicted through our system. The results could provide alerts to contractors ahead of time at project planning stage. This paper also presents an example scenario with a 47-floor residential building in Hong Kong to demonstrate our D&R waste estimation and planning system. As the BIM technology has been increasingly adopted in the architectural, engineering and construction industry and digital building information models will likely to be available for most buildings (including historical buildings) in the future, our system can be used in various demolition and renovation projects and be extended to facilitate project control. Copyright © 2013 Elsevier Ltd. All rights reserved.

Comparing Forest/Nonforest Classifications of Landsat TM Imagery for Stratifying FIA Estimates of Forest Land Area

Treesearch

Mark D. Nelson; Ronald E. McRoberts; Greg C. Liknes; Geoffrey R. Holden

2005-01-01

Landsat Thematic Mapper (TM) satellite imagery and Forest Inventory and Analysis (FIA) plot data were used to construct forest/nonforest maps of Mapping Zone 41, National Land Cover Dataset 2000 (NLCD 2000). Stratification approaches resulting from Maximum Likelihood, Fuzzy Convolution, Logistic Regression, and k-Nearest Neighbors classification/prediction methods were...

Teaching Neurophysiology, Neuropharmacology, and Experimental Design Using Animal Models of Psychiatric and Neurological Disorders

ERIC Educational Resources Information Center

Morsink, Maarten C.; Dukers, Danny F.

2009-01-01

Animal models have been widely used for studying the physiology and pharmacology of psychiatric and neurological diseases. The concepts of face, construct, and predictive validity are used as indicators to estimate the extent to which the animal model mimics the disease. Currently, we used these three concepts to design a theoretical assignment to…

Assessing Potential Climate Change Effects on Loblolly Pine Growth: A Probabilistic Regional Modeling Approach

Treesearch

Peter B. Woodbury; James E. Smith; David A. Weinstein; John A. Laurence

1998-01-01

Most models of the potential effects of climate change on forest growth have produced deterministic predictions. However, there are large uncertainties in data on regional forest condition, estimates of future climate, and quantitative relationships between environmental conditions and forest growth rate. We constructed a new model to analyze these uncertainties...

Consumer product chemical weight fractions from ingredient lists.

PubMed

Isaacs, Kristin K; Phillips, Katherine A; Biryol, Derya; Dionisio, Kathie L; Price, Paul S

2018-05-01

Assessing human exposures to chemicals in consumer products requires composition information. However, comprehensive composition data for products in commerce are not generally available. Many consumer products have reported ingredient lists that are constructed using specific guidelines. A probabilistic model was developed to estimate quantitative weight fraction (WF) values that are consistent with the rank of an ingredient in the list, the number of reported ingredients, and labeling rules. The model provides the mean, median, and 95% upper and lower confidence limit WFs for ingredients of any rank in lists of any length. WFs predicted by the model compared favorably with those reported on Material Safety Data Sheets. Predictions for chemicals known to provide specific functions in products were also found to reasonably agree with reported WFs. The model was applied to a selection of publicly available ingredient lists, thereby estimating WFs for 1293 unique ingredients in 1123 products in 81 product categories. Predicted WFs, although less precise than reported values, can be estimated for large numbers of product-chemical combinations and thus provide a useful source of data for high-throughput or screening-level exposure assessments.

Impact of measurement invariance on construct correlations, mean differences, and relations with external correlates: an illustrative example using Big Five and RIASEC measures.

PubMed

Schmitt, Neal; Golubovich, Juliya; Leong, Frederick T L

2011-12-01

The impact of measurement invariance and the provision for partial invariance in confirmatory factor analytic models on factor intercorrelations, latent mean differences, and estimates of relations with external variables is investigated for measures of two sets of widely assessed constructs: Big Five personality and the six Holland interests (RIASEC). In comparing models that include provisions for partial invariance with models that do not, the results indicate quite small differences in parameter estimates involving the relations between factors, one relatively large standardized mean difference in factors between the subgroups compared and relatively small differences in the regression coefficients when the factors are used to predict external variables. The results provide support for the use of partially invariant models, but there does not seem to be a great deal of difference between structural coefficients when the measurement model does or does not include separate estimates of subgroup parameters that differ across subgroups. Future research should include simulations in which the impact of various factors related to invariance is estimated.

Developmental Times of Chrysomya megacephala (Fabricius) (Diptera: Calliphoridae) at Constant Temperatures and Applications in Forensic Entomology.

PubMed

Yang, Yong-Qiang; Li, Xue-Bo; Shao, Ru-Yue; Lyu, Zhou; Li, Hong-Wei; Li, Gen-Ping; Xu, Lyu-Zi; Wan, Li-Hua

2016-09-01

The characteristic life stages of infesting blowflies (Calliphoridae) such as Chrysomya megacephala (Fabricius) are powerful evidence for estimating the death time of a corpse, but an established reference of developmental times for local blowfly species is required. We determined the developmental rates of C. megacephala from southwest China at seven constant temperatures (16-34°C). Isomegalen and isomorphen diagrams were constructed based on the larval length and time for each developmental event (first ecdysis, second ecdysis, wandering, pupariation, and eclosion), at each temperature. A thermal summation model was constructed by estimating the developmental threshold temperature D0 and the thermal summation constant K. The thermal summation model indicated that, for complete development from egg hatching to eclosion, D0 = 9.07 ± 0.54°C and K = 3991.07 ± 187.26 h °C. This reference can increase the accuracy of estimations of postmortem intervals in China by predicting the growth of C. megacephala. © 2016 American Academy of Forensic Sciences.

Estimation of available global solar radiation using sunshine duration over South Korea

NASA Astrophysics Data System (ADS)

Das, Amrita; Park, Jin-ki; Park, Jong-hwa

2015-11-01

Besides designing a solar energy system, accurate insolation data is also a key component for many biological and atmospheric studies. But solar radiation stations are not widely available due to financial and technical limitations; this insufficient number affects the spatial resolution whenever an attempt is made to construct a solar radiation map. There are several models in literature for estimating incoming solar radiation using sunshine fraction. Seventeen of such models among which 6 are linear and 11 non-linear, have been chosen for studying and estimating solar radiation on a horizontal surface over South Korea. The better performance of a non-linear model signifies the fact that the relationship between sunshine duration and clearness index does not follow a straight line. With such a model solar radiation over 79 stations measuring sunshine duration is computed and used as input for spatial interpolation. Finally monthly solar radiation maps are constructed using the Ordinary Kriging method. The cross validation results show good agreement between observed and predicted data.

Prediction of whole-genome risk for selection and management of hyperketonemia in Holstein dairy cattle.

PubMed

Weigel, K A; Pralle, R S; Adams, H; Cho, K; Do, C; White, H M

2017-06-01

Hyperketonemia (HYK), a common early postpartum health disorder characterized by elevated blood concentrations of β-hydroxybutyrate (BHB), affects millions of dairy cows worldwide and leads to significant economic losses and animal welfare concerns. In this study, blood concentrations of BHB were assessed for 1,453 Holstein cows using electronic handheld meters at four time points between 5 and 18 days postpartum. Incidence rates of subclinical (1.2 ≤ maximum BHB ≤ 2.9 mmol/L) and clinical ketosis (maximum BHB ≥ 3.0 mmol/L) were 24.0 and 2.4%, respectively. Variance components, estimated breeding values, and predicted HYK phenotypes were computed on the original, square-root, and binary scales. Heritability estimates for HYK ranged from 0.058 to 0.072 in pedigree-based analyses, as compared to estimates that ranged from 0.071 to 0.093 when pedigrees were augmented with 60,671 single nucleotide polymorphism genotypes of 959 cows and 801 male ancestors. On average, predicted HYK phenotypes from the genome-enhanced analysis ranged from 0.55 mmol/L for first-parity cows in the best contemporary group to 1.40 mmol/L for fourth-parity cows in the worst contemporary group. Genome-enhanced predictions of HYK phenotypes were more closely associated with actual phenotypes than pedigree-based predictions in five-fold cross-validation, and transforming phenotypes to reduce skewness and kurtosis also improved predictive ability. This study demonstrates the feasibility of using repeated cowside measurement of blood BHB concentration in early lactation to construct a reference population that can be used to estimate HYK breeding values for genomic selection programmes and predict HYK phenotypes for genome-guided management decisions. © 2017 Blackwell Verlag GmbH.

Development and validation of anthropometric prediction equations for estimation of lean body mass and appendicular lean soft tissue in Indian men and women.

PubMed

Kulkarni, Bharati; Kuper, Hannah; Taylor, Amy; Wells, Jonathan C; Radhakrishna, K V; Kinra, Sanjay; Ben-Shlomo, Yoav; Smith, George Davey; Ebrahim, Shah; Byrne, Nuala M; Hills, Andrew P

2013-10-15

Lean body mass (LBM) and muscle mass remain difficult to quantify in large epidemiological studies due to the unavailability of inexpensive methods. We therefore developed anthropometric prediction equations to estimate the LBM and appendicular lean soft tissue (ALST) using dual-energy X-ray absorptiometry (DXA) as a reference method. Healthy volunteers (n = 2,220; 36% women; age 18-79 yr), representing a wide range of body mass index (14-44 kg/m(2)), participated in this study. Their LBM, including ALST, was assessed by DXA along with anthropometric measurements. The sample was divided into prediction (60%) and validation (40%) sets. In the prediction set, a number of prediction models were constructed using DXA-measured LBM and ALST estimates as dependent variables and a combination of anthropometric indices as independent variables. These equations were cross-validated in the validation set. Simple equations using age, height, and weight explained >90% variation in the LBM and ALST in both men and women. Additional variables (hip and limb circumferences and sum of skinfold thicknesses) increased the explained variation by 5-8% in the fully adjusted models predicting LBM and ALST. More complex equations using all of the above anthropometric variables could predict the DXA-measured LBM and ALST accurately, as indicated by low standard error of the estimate (LBM: 1.47 kg and 1.63 kg for men and women, respectively), as well as good agreement by Bland-Altman analyses (Bland JM, Altman D. Lancet 1: 307-310, 1986). These equations could be a valuable tool in large epidemiological studies assessing these body compartments in Indians and other population groups with similar body composition.

Development and validation of anthropometric prediction equations for estimation of lean body mass and appendicular lean soft tissue in Indian men and women

PubMed Central

Kuper, Hannah; Taylor, Amy; Wells, Jonathan C.; Radhakrishna, K. V.; Kinra, Sanjay; Ben-Shlomo, Yoav; Smith, George Davey; Ebrahim, Shah; Byrne, Nuala M.; Hills, Andrew P.

2013-01-01

Lean body mass (LBM) and muscle mass remain difficult to quantify in large epidemiological studies due to the unavailability of inexpensive methods. We therefore developed anthropometric prediction equations to estimate the LBM and appendicular lean soft tissue (ALST) using dual-energy X-ray absorptiometry (DXA) as a reference method. Healthy volunteers (n = 2,220; 36% women; age 18-79 yr), representing a wide range of body mass index (14–44 kg/m2), participated in this study. Their LBM, including ALST, was assessed by DXA along with anthropometric measurements. The sample was divided into prediction (60%) and validation (40%) sets. In the prediction set, a number of prediction models were constructed using DXA-measured LBM and ALST estimates as dependent variables and a combination of anthropometric indices as independent variables. These equations were cross-validated in the validation set. Simple equations using age, height, and weight explained >90% variation in the LBM and ALST in both men and women. Additional variables (hip and limb circumferences and sum of skinfold thicknesses) increased the explained variation by 5–8% in the fully adjusted models predicting LBM and ALST. More complex equations using all of the above anthropometric variables could predict the DXA-measured LBM and ALST accurately, as indicated by low standard error of the estimate (LBM: 1.47 kg and 1.63 kg for men and women, respectively), as well as good agreement by Bland-Altman analyses (Bland JM, Altman D. Lancet 1: 307–310, 1986). These equations could be a valuable tool in large epidemiological studies assessing these body compartments in Indians and other population groups with similar body composition. PMID:23950165

Updating the Standard Spatial Observer for Contrast Detection

NASA Technical Reports Server (NTRS)

Ahumada, Albert J.; Watson, Andrew B.

2011-01-01

Watson and Ahmuada (2005) constructed a Standard Spatial Observer (SSO) model for foveal luminance contrast signal detection based on the Medelfest data (Watson, 1999). Here we propose two changes to the model, dropping the oblique effect from the CSF and using the cone density data of Curcio et al. (1990) to estimate the variation of sensitivity with eccentricity. Dropping the complex images, and using medians to exclude outlier data points, the SSO model now accounts for essentially all the predictable variance in the data, with an RMS prediction error of only 0.67 dB.

Mass Uncertainty and Application For Space Systems

NASA Technical Reports Server (NTRS)

Beech, Geoffrey

2013-01-01

Expected development maturity under contract (spec) should correlate with Project/Program Approved MGA Depletion Schedule in Mass Properties Control Plan. If specification NTE, MGA is inclusive of Actual MGA (A5 & A6). If specification is not an NTE Actual MGA (e.g. nominal), then MGA values are reduced by A5 values and A5 is representative of remaining uncertainty. Basic Mass = Engineering Estimate based on design and construction principles with NO embedded margin MGA Mass = Basic Mass * assessed % from approved MGA schedule. Predicted Mass = Basic + MGA. Aggregate MGA % = (Aggregate Predicted - Aggregate Basic) /Aggregate Basic.

Anger, hostility, and hospitalizations in patients with heart failure.

PubMed

Keith, Felicia; Krantz, David S; Chen, Rusan; Harris, Kristie M; Ware, Catherine M; Lee, Amy K; Bellini, Paula G; Gottlieb, Stephen S

2017-09-01

Heart failure patients have a high hospitalization rate, and anger and hostility are associated with coronary heart disease morbidity and mortality. Using structural equation modeling, this prospective study assessed the predictive validity of anger and hostility traits for cardiovascular and all-cause rehospitalizations in patients with heart failure. 146 heart failure patients were administered the STAXI and Cook-Medley Hostility Inventory to measure anger, hostility, and their component traits. Hospitalizations were recorded for up to 3 years following baseline. Causes of hospitalizations were categorized as heart failure, total cardiac, noncardiac, and all-cause (sum of cardiac and noncardiac). Measurement models were separately fit for Anger and Hostility, followed by a Confirmatory Factor Analysis to estimate the relationship between the Anger and Hostility constructs. An Anger model consisted of State Anger, Trait Anger, Anger Expression Out, and Anger Expression In, and a Hostility model included Cynicism, Hostile Affect, Aggressive Responding, and Hostile Attribution. The latent construct of Anger did not predict any of the hospitalization outcomes, but Hostility significantly predicted all-cause hospitalizations. Analyses of individual trait components of each of the 2 models indicated that Anger Expression Out predicted all-cause and noncardiac hospitalizations, and Trait Anger predicted noncardiac hospitalizations. None of the individual components of Hostility were related to rehospitalizations or death. The construct of Hostility and several components of Anger are predictive of hospitalizations that were not specific to cardiac causes. Mechanisms common to a variety of health problems, such as self-care and risky health behaviors, may be involved in these associations. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

IPMP Global Fit - A one-step direct data analysis tool for predictive microbiology.

PubMed

Huang, Lihan

2017-12-04

The objective of this work is to develop and validate a unified optimization algorithm for performing one-step global regression analysis of isothermal growth and survival curves for determination of kinetic parameters in predictive microbiology. The algorithm is incorporated with user-friendly graphical interfaces (GUIs) to develop a data analysis tool, the USDA IPMP-Global Fit. The GUIs are designed to guide the users to easily navigate through the data analysis process and properly select the initial parameters for different combinations of mathematical models. The software is developed for one-step kinetic analysis to directly construct tertiary models by minimizing the global error between the experimental observations and mathematical models. The current version of the software is specifically designed for constructing tertiary models with time and temperature as the independent model parameters in the package. The software is tested with a total of 9 different combinations of primary and secondary models for growth and survival of various microorganisms. The results of data analysis show that this software provides accurate estimates of kinetic parameters. In addition, it can be used to improve the experimental design and data collection for more accurate estimation of kinetic parameters. IPMP-Global Fit can be used in combination with the regular USDA-IPMP for solving the inverse problems and developing tertiary models in predictive microbiology. Published by Elsevier B.V.

Predictive Modeling of Risk Associated with Temperature Extremes over Continental US

NASA Astrophysics Data System (ADS)

Kravtsov, S.; Roebber, P.; Brazauskas, V.

2016-12-01

We build an extremely statistically accurate, essentially bias-free empirical emulator of atmospheric surface temperature and apply it for meteorological risk assessment over the domain of continental US. The resulting prediction scheme achieves an order-of-magnitude or larger gain of numerical efficiency compared with the schemes based on high-resolution dynamical atmospheric models, leading to unprecedented accuracy of the estimated risk distributions. The empirical model construction methodology is based on our earlier work, but is further modified to account for the influence of large-scale, global climate change on regional US weather and climate. The resulting estimates of the time-dependent, spatially extended probability of temperature extremes over the simulation period can be used as a risk management tool by insurance companies and regulatory governmental agencies.

A simple equation to estimate body fat percentage in children with overweightness or obesity: a retrospective study.

PubMed

Cortés-Castell, Ernesto; Juste, Mercedes; Palazón-Bru, Antonio; Monge, Laura; Sánchez-Ferrer, Francisco; Rizo-Baeza, María Mercedes

2017-01-01

Dual-energy X-ray absorptiometry (DXA) provides separate measurements of fat mass, fat-free mass and bone mass, and is a quick, accurate, and safe technique, yet one that is not readily available in routine clinical practice. Consequently, we aimed to develop statistical formulas to predict fat mass (%) and fat mass index (FMI) with simple parameters (age, sex, weight and height). We conducted a retrospective observational cross-sectional study in 416 overweight or obese patients aged 4-18 years that involved assessing adiposity by DXA (fat mass percentage and FMI), body mass index (BMI), sex and age. We randomly divided the sample into two parts (construction and validation). In the construction sample, we developed formulas to predict fat mass and FMI using linear multiple regression models. The formulas were validated in the other sample, calculating the intraclass correlation coefficient via bootstrapping. The fat mass percentage formula had a coefficient of determination of 0.65. This value was 0.86 for FMI. In the validation, the constructed formulas had an intraclass correlation coefficient of 0.77 for fat mass percentage and 0.92 for FMI. Our predictive formulas accurately predicted fat mass and FMI with simple parameters (BMI, sex and age) in children with overweight and obesity. The proposed methodology could be applied in other fields. Further studies are needed to externally validate these formulas.

Multinomial Logistic Regression & Bootstrapping for Bayesian Estimation of Vertical Facies Prediction in Heterogeneous Sandstone Reservoirs

NASA Astrophysics Data System (ADS)

Al-Mudhafar, W. J.

2013-12-01

Precisely prediction of rock facies leads to adequate reservoir characterization by improving the porosity-permeability relationships to estimate the properties in non-cored intervals. It also helps to accurately identify the spatial facies distribution to perform an accurate reservoir model for optimal future reservoir performance. In this paper, the facies estimation has been done through Multinomial logistic regression (MLR) with respect to the well logs and core data in a well in upper sandstone formation of South Rumaila oil field. The entire independent variables are gamma rays, formation density, water saturation, shale volume, log porosity, core porosity, and core permeability. Firstly, Robust Sequential Imputation Algorithm has been considered to impute the missing data. This algorithm starts from a complete subset of the dataset and estimates sequentially the missing values in an incomplete observation by minimizing the determinant of the covariance of the augmented data matrix. Then, the observation is added to the complete data matrix and the algorithm continues with the next observation with missing values. The MLR has been chosen to estimate the maximum likelihood and minimize the standard error for the nonlinear relationships between facies & core and log data. The MLR is used to predict the probabilities of the different possible facies given each independent variable by constructing a linear predictor function having a set of weights that are linearly combined with the independent variables by using a dot product. Beta distribution of facies has been considered as prior knowledge and the resulted predicted probability (posterior) has been estimated from MLR based on Baye's theorem that represents the relationship between predicted probability (posterior) with the conditional probability and the prior knowledge. To assess the statistical accuracy of the model, the bootstrap should be carried out to estimate extra-sample prediction error by randomly drawing datasets with replacement from the training data. Each sample has the same size of the original training set and it can be conducted N times to produce N bootstrap datasets to re-fit the model accordingly to decrease the squared difference between the estimated and observed categorical variables (facies) leading to decrease the degree of uncertainty.

Automated Transition State Theory Calculations for High-Throughput Kinetics.

PubMed

Bhoorasingh, Pierre L; Slakman, Belinda L; Seyedzadeh Khanshan, Fariba; Cain, Jason Y; West, Richard H

2017-09-21

A scarcity of known chemical kinetic parameters leads to the use of many reaction rate estimates, which are not always sufficiently accurate, in the construction of detailed kinetic models. To reduce the reliance on these estimates and improve the accuracy of predictive kinetic models, we have developed a high-throughput, fully automated, reaction rate calculation method, AutoTST. The algorithm integrates automated saddle-point geometry search methods and a canonical transition state theory kinetics calculator. The automatically calculated reaction rates compare favorably to existing estimated rates. Comparison against high level theoretical calculations show the new automated method performs better than rate estimates when the estimate is made by a poor analogy. The method will improve by accounting for internal rotor contributions and by improving methods to determine molecular symmetry.

Trust from the past: Bayesian Personalized Ranking based Link Prediction in Knowledge Graphs

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

Zhang, Baichuan; Choudhury, Sutanay; Al-Hasan, Mohammad

2016-02-01

Estimating the confidence for a link is a critical task for Knowledge Graph construction. Link prediction, or predicting the likelihood of a link in a knowledge graph based on prior state is a key research direction within this area. We propose a Latent Feature Embedding based link recommendation model for prediction task and utilize Bayesian Personalized Ranking based optimization technique for learning models for each predicate. Experimental results on large-scale knowledge bases such as YAGO2 show that our approach achieves substantially higher performance than several state-of-art approaches. Furthermore, we also study the performance of the link prediction algorithm in termsmore » of topological properties of the Knowledge Graph and present a linear regression model to reason about its expected level of accuracy.« less

A Proposal for Phase 4 of the Forest Inventory and Analysis Program

Treesearch

Ronald E. McRoberts

2005-01-01

Maps of forest cover were constructed using observations from forest inventory plots, Landsat Thematic Mapper satellite imagery, and a logistic regression model. Estimates of mean proportion forest area and the variance of the mean were calculated for circular study areas with radii ranging from 1 km to 15 km. The spatial correlation among pixel predictions was...

Network Analysis on Attitudes: A Brief Tutorial.

PubMed

Dalege, Jonas; Borsboom, Denny; van Harreveld, Frenk; van der Maas, Han L J

2017-07-01

In this article, we provide a brief tutorial on the estimation, analysis, and simulation on attitude networks using the programming language R. We first discuss what a network is and subsequently show how one can estimate a regularized network on typical attitude data. For this, we use open-access data on the attitudes toward Barack Obama during the 2012 American presidential election. Second, we show how one can calculate standard network measures such as community structure, centrality, and connectivity on this estimated attitude network. Third, we show how one can simulate from an estimated attitude network to derive predictions from attitude networks. By this, we highlight that network theory provides a framework for both testing and developing formalized hypotheses on attitudes and related core social psychological constructs.

Network Analysis on Attitudes

PubMed Central

Borsboom, Denny; van Harreveld, Frenk; van der Maas, Han L. J.

2017-01-01

In this article, we provide a brief tutorial on the estimation, analysis, and simulation on attitude networks using the programming language R. We first discuss what a network is and subsequently show how one can estimate a regularized network on typical attitude data. For this, we use open-access data on the attitudes toward Barack Obama during the 2012 American presidential election. Second, we show how one can calculate standard network measures such as community structure, centrality, and connectivity on this estimated attitude network. Third, we show how one can simulate from an estimated attitude network to derive predictions from attitude networks. By this, we highlight that network theory provides a framework for both testing and developing formalized hypotheses on attitudes and related core social psychological constructs. PMID:28919944

Generalised equations for the prediction of percentage body fat by anthropometry in adult men and women aged 18-81 years.

PubMed

Leahy, Siobhan; O'Neill, Cian; Sohun, Rhoda; Toomey, Clodagh; Jakeman, Philip

2013-02-28

Anthropometric data indicate that the human phenotype is changing. Today's adult is greater in stature, body mass and fat mass. Accurate measurement of body composition is necessary to maintain surveillance of obesity within the population and to evaluate associated interventions. The aim of the present study was to construct and validate generalised equations for percentage body fat (%BF) prediction from anthropometry in 1136 adult men and women. Reference values for %BF were obtained using dual-energy X-ray absorptiometry. Skinfold thickness (SF) at ten sites and girth (G) at seven sites were measured on 736 men and women aged 18-81 years (%BF 5·1-56·8%). Quantile regression was employed to construct prediction equations from age and log-transformed SF and G measures. These equations were then cross-validated on a cohort of 400 subjects of similar age and fatness. The following generalised equations were found to most accurately predict %BF: Men: (age x 0·1) + (logtricepsSF x 7·6) + (logmidaxillaSF x 8·8) + (logsuprspinaleSF x 11·9) - 11·3 (standard error of the estimate: 2·5%, 95% limits of agreement: - 4·8, + 4·9) Women: (age x 0·1) + (logabdominalG x 39·4) + (logmidaxillaSF x 4·9) + (logbicepsSF x 11·0) + (logmedialcalfSF x 9·1) - 73·5 (standard error of the estimate: 3·0%, 95% limits of agreement: - 5·7, + 5·9) These generalised anthropometric equations accurately predict %BF and are suitable for the measurement of %BF in adult men and women of varying levels of fatness across the lifespan.

Thickness distribution of a cooling pyroclastic flow deposit on Augustine Volcano, Alaska: Optimization using InSAR, FEMs, and an adaptive mesh algorithm

USGS Publications Warehouse

Masterlark, Timothy; Lu, Zhong; Rykhus, Russell P.

2006-01-01

Interferometric synthetic aperture radar (InSAR) imagery documents the consistent subsidence, during the interval 1992–1999, of a pyroclastic flow deposit (PFD) emplaced during the 1986 eruption of Augustine Volcano, Alaska. We construct finite element models (FEMs) that simulate thermoelastic contraction of the PFD to account for the observed subsidence. Three-dimensional problem domains of the FEMs include a thermoelastic PFD embedded in an elastic substrate. The thickness of the PFD is initially determined from the difference between post- and pre-eruption digital elevation models (DEMs). The initial excess temperature of the PFD at the time of deposition, 640 °C, is estimated from FEM predictions and an InSAR image via standard least-squares inverse methods. Although the FEM predicts the major features of the observed transient deformation, systematic prediction errors (RMSE = 2.2 cm) are most likely associated with errors in the a priori PFD thickness distribution estimated from the DEM differences. We combine an InSAR image, FEMs, and an adaptive mesh algorithm to iteratively optimize the geometry of the PFD with respect to a minimized misfit between the predicted thermoelastic deformation and observed deformation. Prediction errors from an FEM, which includes an optimized PFD geometry and the initial excess PFD temperature estimated from the least-squares analysis, are sub-millimeter (RMSE = 0.3 mm). The average thickness (9.3 m), maximum thickness (126 m), and volume (2.1 × 107m3) of the PFD, estimated using the adaptive mesh algorithm, are about twice as large as the respective estimations for the a priori PFD geometry. Sensitivity analyses suggest unrealistic PFD thickness distributions are required for initial excess PFD temperatures outside of the range 500–800 °C.

Predictive Power Estimation Algorithm (PPEA) - A New Algorithm to Reduce Overfitting for Genomic Biomarker Discovery

PubMed Central

Liu, Jiangang; Jolly, Robert A.; Smith, Aaron T.; Searfoss, George H.; Goldstein, Keith M.; Uversky, Vladimir N.; Dunker, Keith; Li, Shuyu; Thomas, Craig E.; Wei, Tao

2011-01-01

Toxicogenomics promises to aid in predicting adverse effects, understanding the mechanisms of drug action or toxicity, and uncovering unexpected or secondary pharmacology. However, modeling adverse effects using high dimensional and high noise genomic data is prone to over-fitting. Models constructed from such data sets often consist of a large number of genes with no obvious functional relevance to the biological effect the model intends to predict that can make it challenging to interpret the modeling results. To address these issues, we developed a novel algorithm, Predictive Power Estimation Algorithm (PPEA), which estimates the predictive power of each individual transcript through an iterative two-way bootstrapping procedure. By repeatedly enforcing that the sample number is larger than the transcript number, in each iteration of modeling and testing, PPEA reduces the potential risk of overfitting. We show with three different cases studies that: (1) PPEA can quickly derive a reliable rank order of predictive power of individual transcripts in a relatively small number of iterations, (2) the top ranked transcripts tend to be functionally related to the phenotype they are intended to predict, (3) using only the most predictive top ranked transcripts greatly facilitates development of multiplex assay such as qRT-PCR as a biomarker, and (4) more importantly, we were able to demonstrate that a small number of genes identified from the top-ranked transcripts are highly predictive of phenotype as their expression changes distinguished adverse from nonadverse effects of compounds in completely independent tests. Thus, we believe that the PPEA model effectively addresses the over-fitting problem and can be used to facilitate genomic biomarker discovery for predictive toxicology and drug responses. PMID:21935387

Bayesian source term estimation of atmospheric releases in urban areas using LES approach.

PubMed

Xue, Fei; Kikumoto, Hideki; Li, Xiaofeng; Ooka, Ryozo

2018-05-05

The estimation of source information from limited measurements of a sensor network is a challenging inverse problem, which can be viewed as an assimilation process of the observed concentration data and the predicted concentration data. When dealing with releases in built-up areas, the predicted data are generally obtained by the Reynolds-averaged Navier-Stokes (RANS) equations, which yields building-resolving results; however, RANS-based models are outperformed by large-eddy simulation (LES) in the predictions of both airflow and dispersion. Therefore, it is important to explore the possibility of improving the estimation of the source parameters by using the LES approach. In this paper, a novel source term estimation method is proposed based on LES approach using Bayesian inference. The source-receptor relationship is obtained by solving the adjoint equations constructed using the time-averaged flow field simulated by the LES approach based on the gradient diffusion hypothesis. A wind tunnel experiment with a constant point source downwind of a single building model is used to evaluate the performance of the proposed method, which is compared with that of the existing method using a RANS model. The results show that the proposed method reduces the errors of source location and releasing strength by 77% and 28%, respectively. Copyright © 2018 Elsevier B.V. All rights reserved.

Using High-Resolution Satellite Aerosol Optical Depth To Estimate Daily PM2.5 Geographical Distribution in Mexico City.

PubMed

Just, Allan C; Wright, Robert O; Schwartz, Joel; Coull, Brent A; Baccarelli, Andrea A; Tellez-Rojo, Martha María; Moody, Emily; Wang, Yujie; Lyapustin, Alexei; Kloog, Itai

2015-07-21

Recent advances in estimating fine particle (PM2.5) ambient concentrations use daily satellite measurements of aerosol optical depth (AOD) for spatially and temporally resolved exposure estimates. Mexico City is a dense megacity that differs from other previously modeled regions in several ways: it has bright land surfaces, a distinctive climatological cycle, and an elevated semi-enclosed air basin with a unique planetary boundary layer dynamic. We extend our previous satellite methodology to the Mexico City area, a region with higher PM2.5 than most U.S. and European urban areas. Using a novel 1 km resolution AOD product from the MODIS instrument, we constructed daily predictions across the greater Mexico City area for 2004-2014. We calibrated the association of AOD to PM2.5 daily using municipal ground monitors, land use, and meteorological features. Predictions used spatial and temporal smoothing to estimate AOD when satellite data were missing. Our model performed well, resulting in an out-of-sample cross-validation R(2) of 0.724. Cross-validated root-mean-squared prediction error (RMSPE) of the model was 5.55 μg/m(3). This novel model reconstructs long- and short-term spatially resolved exposure to PM2.5 for epidemiological studies in Mexico City.

Using high-resolution satellite aerosol optical depth to estimate daily PM2.5 geographical distribution in Mexico City

PubMed Central

Just, Allan C.; Wright, Robert O.; Schwartz, Joel; Coull, Brent A.; Baccarelli, Andrea A.; Tellez-Rojo, Martha María; Moody, Emily; Wang, Yujie; Lyapustin, Alexei; Kloog, Itai

2015-01-01

Recent advances in estimating fine particle (PM2.5) ambient concentrations use daily satellite measurements of aerosol optical depth (AOD) for spatially and temporally resolved exposure estimates. Mexico City is a dense megacity that differs from other previously modeled regions in several ways: it has bright land surfaces, a distinctive climatological cycle, and an elevated semi-enclosed air basin with a unique planetary boundary layer dynamic. We extend our previous satellite methodology to the Mexico City area, a region with higher PM2.5 than most US and European urban areas. Using a novel 1 km resolution AOD product from the MODIS instrument, we constructed daily predictions across the greater Mexico City area for 2004–2014. We calibrated the association of AOD to PM2.5 daily using municipal ground monitors, land use, and meteorological features. Predictions used spatial and temporal smoothing to estimate AOD when satellite data were missing. Our model performed well, resulting in an out-of-sample cross validation R2 of 0.724. Cross-validated root mean squared prediction error (RMSPE) of the model was 5.55 μg/m3. This novel model reconstructs long- and short-term spatially resolved exposure to PM2.5 for epidemiological studies in Mexico City. PMID:26061488

Cryptosporidiosis susceptibility and risk: a case study.

PubMed

Makri, Anna; Modarres, Reza; Parkin, Rebecca

2004-02-01

Regional estimates of cryptosporidiosis risks from drinking water exposure were developed and validated, accounting for AIDS status and age. We constructed a model with probability distributions and point estimates representing Cryptosporidium in tap water, tap water consumed per day (exposure characterization); dose response, illness given infection, prolonged illness given illness; and three conditional probabilities describing the likelihood of case detection by active surveillance (health effects characterization). The model predictions were combined with population data to derive expected case numbers and incidence rates per 100,000 population, by age and AIDS status, borough specific and for New York City overall in 2000 (risk characterization). They were compared with same-year surveillance data to evaluate predictive ability, assumed to represent true incidence of waterborne cryptosporidiosis. The predicted mean risks, similar to previously published estimates for this region, overpredicted observed incidence-most extensively when accounting for AIDS status. The results suggest that overprediction may be due to conservative parameters applied to both non-AIDS and AIDS populations, and that biological differences for children need to be incorporated. Interpretations are limited by the unknown accuracy of available surveillance data, in addition to variability and uncertainty of model predictions. The model appears sensitive to geographical differences in AIDS prevalence. The use of surveillance data for validation and model parameters pertinent to susceptibility are discussed.

Overview of the 1986--1987 atomic mass predictions

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

Haustein, P.E.

1988-07-01

The need for a comprehensive update of earlier sets of atomic mass predictions is documented. A project that grew from this need and which resulted in the preparation of the 1986--1987 Atomic Mass Predictions is summarized. Ten sets of new mass predictions and expository text from a variety of types of mass models are combined with the latest evaluation of experimentally determined atomic masses. The methodology employed in constructing these mass predictions is outlined. The models are compared with regard to their reproduction of the experimental mass surface and their use of varying numbers of adjustable parameters. Plots are presented,more » for each set of predictions, of differences between model calculations and the measured masses. These plots may be used to estimate the reliability of the new mass predictions in unmeasured regions that border the experimetally known mass surface. copyright 1988 Academic Press, Inc.« less

Measurement and control of bias in patient reported outcomes using multidimensional item response theory.

PubMed

Dowling, N Maritza; Bolt, Daniel M; Deng, Sien; Li, Chenxi

2016-05-26

Patient-reported outcome (PRO) measures play a key role in the advancement of patient-centered care research. The accuracy of inferences, relevance of predictions, and the true nature of the associations made with PRO data depend on the validity of these measures. Errors inherent to self-report measures can seriously bias the estimation of constructs assessed by the scale. A well-documented disadvantage of self-report measures is their sensitivity to response style (RS) effects such as the respondent's tendency to select the extremes of a rating scale. Although the biasing effect of extreme responding on constructs measured by self-reported tools has been widely acknowledged and studied across disciplines, little attention has been given to the development and systematic application of methodologies to assess and control for this effect in PRO measures. We review the methodological approaches that have been proposed to study extreme RS effects (ERS). We applied a multidimensional item response theory model to simultaneously estimate and correct for the impact of ERS on trait estimation in a PRO instrument. Model estimates were used to study the biasing effects of ERS on sum scores for individuals with the same amount of the targeted trait but different levels of ERS. We evaluated the effect of joint estimation of multiple scales and ERS on trait estimates and demonstrated the biasing effects of ERS on these trait estimates when used as explanatory variables. A four-dimensional model accounting for ERS bias provided a better fit to the response data. Increasing levels of ERS showed bias in total scores as a function of trait estimates. The effect of ERS was greater when the pattern of extreme responding was the same across multiple scales modeled jointly. The estimated item category intercepts provided evidence of content independent category selection. Uncorrected trait estimates used as explanatory variables in prediction models showed downward bias. A comprehensive evaluation of the psychometric quality and soundness of PRO assessment measures should incorporate the study of ERS as a potential nuisance dimension affecting the accuracy and validity of scores and the impact of PRO data in clinical research and decision making.

Are EMS call volume predictions based on demand pattern analysis accurate?

PubMed

Brown, Lawrence H; Lerner, E Brooke; Larmon, Baxter; LeGassick, Todd; Taigman, Michael

2007-01-01

Most EMS systems determine the number of crews they will deploy in their communities and when those crews will be scheduled based on anticipated call volumes. Many systems use historical data to calculate their anticipated call volumes, a method of prediction known as demand pattern analysis. To evaluate the accuracy of call volume predictions calculated using demand pattern analysis. Seven EMS systems provided 73 consecutive weeks of hourly call volume data. The first 20 weeks of data were used to calculate three common demand pattern analysis constructs for call volume prediction: average peak demand (AP), smoothed average peak demand (SAP), and 90th percentile rank (90%R). The 21st week served as a buffer. Actual call volumes in the last 52 weeks were then compared to the predicted call volumes by using descriptive statistics. There were 61,152 hourly observations in the test period. All three constructs accurately predicted peaks and troughs in call volume but not exact call volume. Predictions were accurate (+/-1 call) 13% of the time using AP, 10% using SAP, and 19% using 90%R. Call volumes were overestimated 83% of the time using AP, 86% using SAP, and 74% using 90%R. When call volumes were overestimated, predictions exceeded actual call volume by a median (Interquartile range) of 4 (2-6) calls for AP, 4 (2-6) for SAP, and 3 (2-5) for 90%R. Call volumes were underestimated 4% of time using AP, 4% using SAP, and 7% using 90%R predictions. When call volumes were underestimated, call volumes exceeded predictions by a median (Interquartile range; maximum under estimation) of 1 (1-2; 18) call for AP, 1 (1-2; 18) for SAP, and 2 (1-3; 20) for 90%R. Results did not vary between systems. Generally, demand pattern analysis estimated or overestimated call volume, making it a reasonable predictor for ambulance staffing patterns. However, it did underestimate call volume between 4% and 7% of the time. Communities need to determine if these rates of over-and underestimation are acceptable given their resources and local priorities.

An improved method for predicting the evolution of the characteristic parameters of an information system

NASA Astrophysics Data System (ADS)

Dushkin, A. V.; Kasatkina, T. I.; Novoseltsev, V. I.; Ivanov, S. V.

2018-03-01

The article proposes a forecasting method that allows, based on the given values of entropy and error level of the first and second kind, to determine the allowable time for forecasting the development of the characteristic parameters of a complex information system. The main feature of the method under consideration is the determination of changes in the characteristic parameters of the development of the information system in the form of the magnitude of the increment in the ratios of its entropy. When a predetermined value of the prediction error ratio is reached, that is, the entropy of the system, the characteristic parameters of the system and the depth of the prediction in time are estimated. The resulting values of the characteristics and will be optimal, since at that moment the system possessed the best ratio of entropy as a measure of the degree of organization and orderliness of the structure of the system. To construct a method for estimating the depth of prediction, it is expedient to use the maximum principle of the value of entropy.

Synthesis of capillary pressure curves from post-stack seismic data with the use of intelligent estimators: A case study from the Iranian part of the South Pars gas field, Persian Gulf Basin

NASA Astrophysics Data System (ADS)

Golsanami, Naser; Kadkhodaie-Ilkhchi, Ali; Erfani, Amir

2015-01-01

Capillary pressure curves are important data for reservoir rock typing, analyzing pore throat distribution, determining height above free water level, and reservoir simulation. Laboratory experiments provide accurate data, however they are expensive, time-consuming and discontinuous through the reservoir intervals. The current study focuses on synthesizing artificial capillary pressure (Pc) curves from seismic attributes with the use of artificial intelligent systems including Artificial Neural Networks (ANNs), Fuzzy logic (FL) and Adaptive Neuro-Fuzzy Inference Systems (ANFISs). The synthetic capillary pressure curves were achieved by estimating pressure values at six mercury saturation points. These points correspond to mercury filled pore volumes of core samples (Hg-saturation) at 5%, 20%, 35%, 65%, 80%, and 90% saturations. To predict the synthetic Pc curve at each saturation point, various FL, ANFIS and ANN models were constructed. The varying neural network models differ in their training algorithm. Based on the performance function, the most accurately functioning models were selected as the final solvers to do the prediction process at each of the above-mentioned mercury saturation points. The constructed models were then tested at six depth points of the studied well which were already unforeseen by the models. The results show that the Fuzzy logic and neuro-fuzzy models were not capable of making reliable estimations, while the predictions from the ANN models were satisfyingly trustworthy. The obtained results showed a good agreement between the laboratory derived and synthetic capillary pressure curves. Finally, a 3D seismic cube was captured for which the required attributes were extracted and the capillary pressure cube was estimated by using the developed models. In the next step, the synthesized Pc cube was compared with the seismic cube and an acceptable correspondence was observed.

Estimating daily PM2.5 and PM10 across the complex geo-climate region of Israel using MAIAC satellite-based AOD data.

PubMed

Kloog, Itai; Sorek-Hamer, Meytar; Lyapustin, Alexei; Coull, Brent; Wang, Yujie; Just, Allan C; Schwartz, Joel; Broday, David M

2015-12-01

Estimates of exposure to PM 2.5 are often derived from geographic characteristics based on land-use regression or from a limited number of fixed ground monitors. Remote sensing advances have integrated these approaches with satellite-based measures of aerosol optical depth (AOD), which is spatially and temporally resolved, allowing greater coverage for PM 2.5 estimations. Israel is situated in a complex geo-climatic region with contrasting geographic and weather patterns, including both dark and bright surfaces within a relatively small area. Our goal was to examine the use of MODIS-based MAIAC data in Israel, and to explore the reliability of predicted PM 2.5 and PM 10 at a high spatiotemporal resolution. We applied a three stage process, including a daily calibration method based on a mixed effects model, to predict ground PM 2.5 and PM 10 over Israel. We later constructed daily predictions across Israel for 2003-2013 using spatial and temporal smoothing, to estimate AOD when satellite data were missing. Good model performance was achieved, with out-of-sample cross validation R 2 values of 0.79 and 0.72 for PM 10 and PM 2.5 , respectively. Model predictions had little bias, with cross-validated slopes (predicted vs. observed) of 0.99 for both the PM 2.5 and PM 10 models. To our knowledge, this is the first study that utilizes high resolution 1km MAIAC AOD retrievals for PM prediction while accounting for geo-climate complexities, such as experienced in Israel. This novel model allowed the reconstruction of long- and short-term spatially resolved exposure to PM 2.5 and PM 10 in Israel, which could be used in the future for epidemiological studies.

Genome-Enabled Estimates of Additive and Nonadditive Genetic Variances and Prediction of Apple Phenotypes Across Environments

PubMed Central

Kumar, Satish; Molloy, Claire; Muñoz, Patricio; Daetwyler, Hans; Chagné, David; Volz, Richard

2015-01-01

The nonadditive genetic effects may have an important contribution to total genetic variation of phenotypes, so estimates of both the additive and nonadditive effects are desirable for breeding and selection purposes. Our main objectives were to: estimate additive, dominance and epistatic variances of apple (Malus × domestica Borkh.) phenotypes using relationship matrices constructed from genome-wide dense single nucleotide polymorphism (SNP) markers; and compare the accuracy of genomic predictions using genomic best linear unbiased prediction models with or without including nonadditive genetic effects. A set of 247 clonally replicated individuals was assessed for six fruit quality traits at two sites, and also genotyped using an Illumina 8K SNP array. Across several fruit quality traits, the additive, dominance, and epistatic effects contributed about 30%, 16%, and 19%, respectively, to the total phenotypic variance. Models ignoring nonadditive components yielded upwardly biased estimates of additive variance (heritability) for all traits in this study. The accuracy of genomic predicted genetic values (GEGV) varied from about 0.15 to 0.35 for various traits, and these were almost identical for models with or without including nonadditive effects. However, models including nonadditive genetic effects further reduced the bias of GEGV. Between-site genotypic correlations were high (>0.85) for all traits, and genotype-site interaction accounted for <10% of the phenotypic variability. The accuracy of prediction, when the validation set was present only at one site, was generally similar for both sites, and varied from about 0.50 to 0.85. The prediction accuracies were strongly influenced by trait heritability, and genetic relatedness between the training and validation families. PMID:26497141

Estimating daily PM2.5 and PM10 across the complex geo-climate region of Israel using MAIAC satellite-based AOD data

PubMed Central

Kloog, Itai; Sorek-Hamer, Meytar; Lyapustin, Alexei; Coull, Brent; Wang, Yujie; Just, Allan C.; Schwartz, Joel; Broday, David M.

2017-01-01

Estimates of exposure to PM2.5 are often derived from geographic characteristics based on land-use regression or from a limited number of fixed ground monitors. Remote sensing advances have integrated these approaches with satellite-based measures of aerosol optical depth (AOD), which is spatially and temporally resolved, allowing greater coverage for PM2.5 estimations. Israel is situated in a complex geo-climatic region with contrasting geographic and weather patterns, including both dark and bright surfaces within a relatively small area. Our goal was to examine the use of MODIS-based MAIAC data in Israel, and to explore the reliability of predicted PM2.5 and PM10 at a high spatiotemporal resolution. We applied a three stage process, including a daily calibration method based on a mixed effects model, to predict ground PM2.5 and PM10 over Israel. We later constructed daily predictions across Israel for 2003–2013 using spatial and temporal smoothing, to estimate AOD when satellite data were missing. Good model performance was achieved, with out-of-sample cross validation R2 values of 0.79 and 0.72 for PM10 and PM2.5, respectively. Model predictions had little bias, with cross-validated slopes (predicted vs. observed) of 0.99 for both the PM2.5 and PM10 models. To our knowledge, this is the first study that utilizes high resolution 1km MAIAC AOD retrievals for PM prediction while accounting for geo-climate complexities, such as experienced in Israel. This novel model allowed the reconstruction of long- and short-term spatially resolved exposure to PM2.5 and PM10 in Israel, which could be used in the future for epidemiological studies. PMID:28966551

Validity of the alcohol purchase task: a meta-analysis.

PubMed

Kiselica, Andrew M; Webber, Troy A; Bornovalova, Marina A

2016-05-01

Behavioral economists assess alcohol consumption as a function of unit price. This method allows construction of demand curves and demand indices, which are thought to provide precise numerical estimates of risk for alcohol problems. One of the more commonly used behavioral economic measures is the Alcohol Purchase Task (APT). Although the APT has shown promise as a measure of risk for alcohol problems, the construct validity and incremental utility of the APT remain unclear. This paper presents a meta-analysis of the APT literature. Sixteen studies were included in the meta-analysis. Studies were gathered via searches of the PsycInfo, PubMed, Web of Science and EconLit research databases. Random-effects meta-analyses with inverse variance weighting were used to calculate summary effect sizes for each demand index-drinking outcome relationship. Moderation of these effects by drinking status (regular versus heavy drinkers) was examined. Additionally, tests of the incremental utility of the APT indices in predicting drinking problems above and beyond measuring alcohol consumption were performed. The APT indices were correlated in the expected directions with drinking outcomes, although many effects were small in size. These effects were typically not moderated by the drinking status of the samples. Additionally, the intensity metric demonstrated incremental utility in predicting alcohol use disorder symptoms beyond measuring drinking. The Alcohol Purchase Task appears to have good construct validity, but limited incremental utility in estimating risk for alcohol problems. © 2015 Society for the Study of Addiction.

Desired Accuracy Estimation of Noise Function from ECG Signal by Fuzzy Approach

PubMed Central

Vahabi, Zahra; Kermani, Saeed

2012-01-01

Unknown noise and artifacts present in medical signals with non-linear fuzzy filter will be estimated and then removed. An adaptive neuro-fuzzy interference system which has a non-linear structure presented for the noise function prediction by before Samples. This paper is about a neuro-fuzzy method to estimate unknown noise of Electrocardiogram signal. Adaptive neural combined with Fuzzy System to construct a fuzzy Predictor. For this system setting parameters such as the number of Membership Functions for each input and output, training epochs, type of MFs for each input and output, learning algorithm and etc. is determined by learning data. At the end simulated experimental results are presented for proper validation. PMID:23717810

Estimation of potential maximum biomass of trout in Wyoming streams to assist management decisions

USGS Publications Warehouse

Hubert, W.A.; Marwitz, T.D.; Gerow, K.G.; Binns, N.A.; Wiley, R.W.

1996-01-01

Fishery managers can benefit from knowledge of the potential maximum biomass (PMB) of trout in streams when making decisions on the allocation of resources to improve fisheries. Resources are most likely to he expended on streams with high PMB and with large differences between PMB and currently measured biomass. We developed and tested a model that uses four easily measured habitat variables to estimate PMB (upper 90th percentile of predicted mean bid mass) of trout (Oncorhynchus spp., Salmo trutta, and Salvelinus fontinalis) in Wyoming streams. The habitat variables were proportion of cover, elevation, wetted width, and channel gradient. The PMB model was constructed from data on 166 stream reaches throughout Wyoming and validated on an independent data set of 50 stream reaches. Prediction of PMB in combination with estimation of current biomass and information on habitat quality can provide managers with insight into the extent to which management actions may enhance trout biomass.

Predicting the risk of suicide by analyzing the text of clinical notes.

PubMed

Poulin, Chris; Shiner, Brian; Thompson, Paul; Vepstas, Linas; Young-Xu, Yinong; Goertzel, Benjamin; Watts, Bradley; Flashman, Laura; McAllister, Thomas

2014-01-01

We developed linguistics-driven prediction models to estimate the risk of suicide. These models were generated from unstructured clinical notes taken from a national sample of U.S. Veterans Administration (VA) medical records. We created three matched cohorts: veterans who committed suicide, veterans who used mental health services and did not commit suicide, and veterans who did not use mental health services and did not commit suicide during the observation period (n = 70 in each group). From the clinical notes, we generated datasets of single keywords and multi-word phrases, and constructed prediction models using a machine-learning algorithm based on a genetic programming framework. The resulting inference accuracy was consistently 65% or more. Our data therefore suggests that computerized text analytics can be applied to unstructured medical records to estimate the risk of suicide. The resulting system could allow clinicians to potentially screen seemingly healthy patients at the primary care level, and to continuously evaluate the suicide risk among psychiatric patients.

Capillary waves' dynamics at the nanoscale

NASA Astrophysics Data System (ADS)

Delgado-Buscalioni, Rafael; Chacón, Enrique; Tarazona, Pedro

2008-12-01

We study the dynamics of thermally excited capillary waves (CW) at molecular scales, using molecular dynamics simulations of simple liquid slabs. The analysis is based on the Fourier modes of the liquid surface, constructed via the intrinsic sampling method (Chacón and Tarazona 2003 Phys. Rev. Lett. 91 166103). We obtain the time autocorrelation of the Fourier modes to get the frequency and damping rate Γd(q) of each mode, with wavenumber q. Continuum hydrodynamics predicts \\Gamma (q) \\propto q\\gamma (q) and thus provides a dynamic measure of the q-dependent surface tension, γd(q). The dynamical estimation is much more robust than the structural prediction based on the amplitude of the Fourier mode, γs(q). Using the optimal estimation of the intrinsic surface, we obtain quantitative agreement between the structural and dynamic pictures. Quite surprisingly, the hydrodynamic prediction for CW remains valid up to wavelengths of about four molecular diameters. Surface tension hydrodynamics break down at shorter scales, whereby a transition to a molecular diffusion regime is observed.

Two-Tiered Violence Risk Estimates: a validation study of an integrated-actuarial risk assessment instrument.

PubMed

Mills, Jeremy F; Gray, Andrew L

2013-11-01

This study is an initial validation study of the Two-Tiered Violence Risk Estimates instrument (TTV), a violence risk appraisal instrument designed to support an integrated-actuarial approach to violence risk assessment. The TTV was scored retrospectively from file information on a sample of violent offenders. Construct validity was examined by comparing the TTV with instruments that have shown utility to predict violence that were prospectively scored: The Historical-Clinical-Risk Management-20 (HCR-20) and Lifestyle Criminality Screening Form (LCSF). Predictive validity was examined through a long-term follow-up of 12.4 years with a sample of 78 incarcerated offenders. Results show the TTV to be highly correlated with the HCR-20 and LCSF. The base rate for violence over the follow-up period was 47.4%, and the TTV was equally predictive of violent recidivism relative to the HCR-20 and LCSF. Discussion centers on the advantages of an integrated-actuarial approach to the assessment of violence risk.

Predicting the Risk of Suicide by Analyzing the Text of Clinical Notes

PubMed Central

Thompson, Paul; Vepstas, Linas; Young-Xu, Yinong; Goertzel, Benjamin; Watts, Bradley; Flashman, Laura; McAllister, Thomas

2014-01-01

We developed linguistics-driven prediction models to estimate the risk of suicide. These models were generated from unstructured clinical notes taken from a national sample of U.S. Veterans Administration (VA) medical records. We created three matched cohorts: veterans who committed suicide, veterans who used mental health services and did not commit suicide, and veterans who did not use mental health services and did not commit suicide during the observation period (n = 70 in each group). From the clinical notes, we generated datasets of single keywords and multi-word phrases, and constructed prediction models using a machine-learning algorithm based on a genetic programming framework. The resulting inference accuracy was consistently 65% or more. Our data therefore suggests that computerized text analytics can be applied to unstructured medical records to estimate the risk of suicide. The resulting system could allow clinicians to potentially screen seemingly healthy patients at the primary care level, and to continuously evaluate the suicide risk among psychiatric patients. PMID:24489669

Development and evaluation of a semi-empirical two-zone dust exposure model for a dusty construction trade.

PubMed

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

2011-06-01

Drywall finishing is a dusty construction activity. We describe a mathematical model that predicts the time-weighted average concentration of respirable and total dusts in the personal breathing zone of the sander, and in the area surrounding joint compound sanding activities. The model represents spatial variation in dust concentrations using two-zones, and temporal variation using an exponential function. Interzone flux and the relationships between respirable and total dusts are described using empirical factors. For model evaluation, we measured dust concentrations in two field studies, including three workers from a commercial contracting crew, and one unskilled worker. Data from the field studies confirm that the model assumptions and parameterization are reasonable and thus validate the modeling approach. Predicted dust C(twa) were in concordance with measured values for the contracting crew, but under estimated measured values for the unskilled worker. Further characterization of skill-related exposure factors is indicated.

Design space construction of multiple dose-strength tablets utilizing bayesian estimation based on one set of design-of-experiments.

PubMed

Maeda, Jin; Suzuki, Tatsuya; Takayama, Kozo

2012-01-01

Design spaces for multiple dose strengths of tablets were constructed using a Bayesian estimation method with one set of design of experiments (DoE) of only the highest dose-strength tablet. The lubricant blending process for theophylline tablets with dose strengths of 100, 50, and 25 mg is used as a model manufacturing process in order to construct design spaces. The DoE was conducted using various Froude numbers (X(1)) and blending times (X(2)) for theophylline 100-mg tablet. The response surfaces, design space, and their reliability of the compression rate of the powder mixture (Y(1)), tablet hardness (Y(2)), and dissolution rate (Y(3)) of the 100-mg tablet were calculated using multivariate spline interpolation, a bootstrap resampling technique, and self-organizing map clustering. Three experiments under an optimal condition and two experiments under other conditions were performed using 50- and 25-mg tablets, respectively. The response surfaces of the highest-strength tablet were corrected to those of the lower-strength tablets by Bayesian estimation using the manufacturing data of the lower-strength tablets. Experiments under three additional sets of conditions of lower-strength tablets showed that the corrected design space made it possible to predict the quality of lower-strength tablets more precisely than the design space of the highest-strength tablet. This approach is useful for constructing design spaces of tablets with multiple strengths.

Estimating the state of a geophysical system with sparse observations: time delay methods to achieve accurate initial states for prediction

NASA Astrophysics Data System (ADS)

An, Zhe; Rey, Daniel; Ye, Jingxin; Abarbanel, Henry D. I.

2017-01-01

The problem of forecasting the behavior of a complex dynamical system through analysis of observational time-series data becomes difficult when the system expresses chaotic behavior and the measurements are sparse, in both space and/or time. Despite the fact that this situation is quite typical across many fields, including numerical weather prediction, the issue of whether the available observations are "sufficient" for generating successful forecasts is still not well understood. An analysis by Whartenby et al. (2013) found that in the context of the nonlinear shallow water equations on a β plane, standard nudging techniques require observing approximately 70 % of the full set of state variables. Here we examine the same system using a method introduced by Rey et al. (2014a), which generalizes standard nudging methods to utilize time delayed measurements. We show that in certain circumstances, it provides a sizable reduction in the number of observations required to construct accurate estimates and high-quality predictions. In particular, we find that this estimate of 70 % can be reduced to about 33 % using time delays, and even further if Lagrangian drifter locations are also used as measurements.

Re-using biological devices: a model-aided analysis of interconnected transcriptional cascades designed from the bottom-up.

PubMed

Pasotti, Lorenzo; Bellato, Massimo; Casanova, Michela; Zucca, Susanna; Cusella De Angelis, Maria Gabriella; Magni, Paolo

2017-01-01

The study of simplified, ad-hoc constructed model systems can help to elucidate if quantitatively characterized biological parts can be effectively re-used in composite circuits to yield predictable functions. Synthetic systems designed from the bottom-up can enable the building of complex interconnected devices via rational approach, supported by mathematical modelling. However, such process is affected by different, usually non-modelled, unpredictability sources, like cell burden. Here, we analyzed a set of synthetic transcriptional cascades in Escherichia coli . We aimed to test the predictive power of a simple Hill function activation/repression model (no-burden model, NBM) and of a recently proposed model, including Hill functions and the modulation of proteins expression by cell load (burden model, BM). To test the bottom-up approach, the circuit collection was divided into training and test sets, used to learn individual component functions and test the predicted output of interconnected circuits, respectively. Among the constructed configurations, two test set circuits showed unexpected logic behaviour. Both NBM and BM were able to predict the quantitative output of interconnected devices with expected behaviour, but only the BM was also able to predict the output of one circuit with unexpected behaviour. Moreover, considering training and test set data together, the BM captures circuits output with higher accuracy than the NBM, which is unable to capture the experimental output exhibited by some of the circuits even qualitatively. Finally, resource usage parameters, estimated via BM, guided the successful construction of new corrected variants of the two circuits showing unexpected behaviour. Superior descriptive and predictive capabilities were achieved considering resource limitation modelling, but further efforts are needed to improve the accuracy of models for biological engineering.

Robust neural network with applications to credit portfolio data analysis.

PubMed

Feng, Yijia; Li, Runze; Sudjianto, Agus; Zhang, Yiyun

2010-01-01

In this article, we study nonparametric conditional quantile estimation via neural network structure. We proposed an estimation method that combines quantile regression and neural network (robust neural network, RNN). It provides good smoothing performance in the presence of outliers and can be used to construct prediction bands. A Majorization-Minimization (MM) algorithm was developed for optimization. Monte Carlo simulation study is conducted to assess the performance of RNN. Comparison with other nonparametric regression methods (e.g., local linear regression and regression splines) in real data application demonstrate the advantage of the newly proposed procedure.

Estimating the Lineage Dynamics of Human Influenza B Viruses.

PubMed

Nyirenda, Mayumbo; Omori, Ryosuke; Tessmer, Heidi L; Arimura, Hiroki; Ito, Kimihito

2016-01-01

The prediction of the lineage dynamics of influenza B viruses for the next season is one of the largest obstacles for constructing an appropriate influenza trivalent vaccine. Seasonal fluctuation of transmissibility and epidemiological interference between the two major influenza B lineages make the lineage dynamics complicated. Here we construct a parsimonious model describing the lineage dynamics while taking into account seasonal fluctuation of transmissibility and epidemiological interference. Using this model we estimated the epidemiological and evolutional parameters with the time-series data of the lineage specific isolates in Japan from the 2010-2011 season to the 2014-2015 season. The basic reproduction number is similar between Victoria and Yamagata, with a minimum value during one year as 0.82 (95% highest posterior density (HPD): 0.77-0.87) for the Yamagata and 0.83 (95% HPD: 0.74-0.92) for Victoria, the amplitude of seasonal variation of the basic reproduction number is 0.77 (95% HPD:0.66-0.87) for Yamagata and 1.05 (95% HPD: 0.89-1.02) for Victoria. The duration for which the acquired immunity is effective against infection by the Yamagata lineage is shorter than the acquired immunity for Victoria, 424.1days (95% HPD:317.4-561.5days). The reduction rate of susceptibility due to immune cross-reaction is 0.51 (95% HPD: 0.084-0.92) for the immunity obtained from the infection with Yamagata against the infection with Victoria and 0.62 (95% HPD: 0.42-0.80) for the immunity obtained from the infection with Victoria against the infection with Yamagata. Using estimated parameters, we predicted the dominant lineage in 2015-2016 season. The accuracy of this prediction is 68.8% if the emergence timings of the two lineages are known and 61.4% if the emergence timings are unknown. Estimated seasonal variation of the lineage specific reproduction number can narrow down the range of emergence timing, with an accuracy of 64.6% if the emergence times are assumed to be the time at which the estimated reproduction number exceeds one.

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

Cheng, Jack C.P., E-mail: cejcheng@ust.hk; Ma, Lauren Y.H., E-mail: yingzi@ust.hk

Highlights: ► We developed a waste estimation system leveraging the BIM technology. ► The system can calculate waste disposal charging fee and pick-up truck demand. ► We presented an example scenario demonstrating this system. ► Automatic, time-saving and wide applicability are the features of the system. - Abstract: Due to the rising worldwide awareness of green environment, both government and contractors have to consider effective construction and demolition (C and D) waste management practices. The last two decades have witnessed the growing importance of demolition and renovation (D and R) works and the growing amount of D and R wastemore » disposed to landfills every day, especially in developed cities like Hong Kong. Quantitative waste prediction is crucial for waste management. It can enable contractors to pinpoint critical waste generation processes and to plan waste control strategies. In addition, waste estimation could also facilitate some government waste management policies, such as the waste disposal charging scheme in Hong Kong. Currently, tools that can accurately and conveniently estimate the amount of waste from construction, renovation, and demolition projects are lacking. In the light of this research gap, this paper presents a building information modeling (BIM) based system that we have developed for estimation and planning of D and R waste. BIM allows multi-disciplinary information to be superimposed within one digital building model. Our system can extract material and volume information through the BIM model and integrate the information for detailed waste estimation and planning. Waste recycling and reuse are also considered in our system. Extracted material information can be provided to recyclers before demolition or renovation to make recycling stage more cooperative and more efficient. Pick-up truck requirements and waste disposal charging fee for different waste facilities will also be predicted through our system. The results could provide alerts to contractors ahead of time at project planning stage. This paper also presents an example scenario with a 47-floor residential building in Hong Kong to demonstrate our D and R waste estimation and planning system. As the BIM technology has been increasingly adopted in the architectural, engineering and construction industry and digital building information models will likely to be available for most buildings (including historical buildings) in the future, our system can be used in various demolition and renovation projects and be extended to facilitate project control.« less

[A site index model for Larix principis-rupprechtii plantation in Saihanba, north China].

PubMed

Wang, Dong-zhi; Zhang, Dong-yan; Jiang, Feng-ling; Bai, Ye; Zhang, Zhi-dong; Huang, Xuan-rui

2015-11-01

It is often difficult to estimate site indices for different types of plantation by using an ordinary site index model. The objective of this paper was to establish a site index model for plantations in varied site conditions, and assess the site qualities. In this study, a nonlinear mixed site index model was constructed based on data from the second class forest resources inventory and 173 temporary sample plots. The results showed that the main limiting factors for height growth of Larix principis-rupprechtii were elevation, slope, soil thickness and soil type. A linear regression model was constructed for the main constraining site factors and dominant tree height, with the coefficient of determination being 0.912, and the baseline age of Larix principis-rupprechtii determined as 20 years. The nonlinear mixed site index model parameters for the main site types were estimated (R2 > 0.85, the error between the predicted value and the actual value was in the range of -0.43 to 0.45, with an average root mean squared error (RMSE) in the range of 0.907 to 1.148). The estimation error between the predicted value and the actual value of dominant tree height for the main site types was in the confidence interval of [-0.95, 0.95]. The site quality of the high altitude-shady-sandy loam-medium soil layer was the highest and that of low altitude-sunny-sandy loam-medium soil layer was the lowest, while the other two sites were moderate.

Statistical modeling of crystalline silica exposure by trade in the construction industry using a database compiled from the literature.

PubMed

Sauvé, Jean-François; Beaudry, Charles; Bégin, Denis; Dion, Chantal; Gérin, Michel; Lavoué, Jérôme

2012-09-01

A quantitative determinants-of-exposure analysis of respirable crystalline silica (RCS) levels in the construction industry was performed using a database compiled from an extensive literature review. Statistical models were developed to predict work-shift exposure levels by trade. Monte Carlo simulation was used to recreate exposures derived from summarized measurements which were combined with single measurements for analysis. Modeling was performed using Tobit models within a multimodel inference framework, with year, sampling duration, type of environment, project purpose, project type, sampling strategy and use of exposure controls as potential predictors. 1346 RCS measurements were included in the analysis, of which 318 were non-detects and 228 were simulated from summary statistics. The model containing all the variables explained 22% of total variability. Apart from trade, sampling duration, year and strategy were the most influential predictors of RCS levels. The use of exposure controls was associated with an average decrease of 19% in exposure levels compared to none, and increased concentrations were found for industrial, demolition and renovation projects. Predicted geometric means for year 1999 were the highest for drilling rig operators (0.238 mg m(-3)) and tunnel construction workers (0.224 mg m(-3)), while the estimated exceedance fraction of the ACGIH TLV by trade ranged from 47% to 91%. The predicted geometric means in this study indicated important overexposure compared to the TLV. However, the low proportion of variability explained by the models suggests that the construction trade is only a moderate predictor of work-shift exposure levels. The impact of the different tasks performed during a work shift should also be assessed to provide better management and control of RCS exposure levels on construction sites.

Carbon stock estimation in the catchment of Kotli Bhel 1A hydroelectric reservoir, Uttarakhand, India.

PubMed

Kumar, Amit; Sharma, M P

2016-12-01

Constructions of dams/reservoirs all over the world are reported to emit significant amount of greenhouse gases (GHGs) and are considered as environmental polluters. Organic carbon is contributed by the forest in the catchment, part of soil organic carbon is transported through the runoffs to the reservoir and undergoes aerobic and anaerobic degradation with time to release GHGs to the atmosphere. Literature reveals that no work is available on the estimation of 'C' stock of trees of forest catchment for assessing/predicting the GHGs emissions from the reservoirs to atmosphere. To assess the GHGs emission potential of the reservoir, an attempt is made in the study to estimate the 'C' stock in the forest catchment of Kotli Bhel 1A hydroelectric reservoir located in Tehri Garhwal district of Uttarakhand, India. For this purpose, the selected area was categorized into the site-I, II and III along the Bhagirathi River based on type of forest available in the catchment. The total carbon density (TCD) of tree species of different forest types was calculated using diameter at breast height (dbh) and trees height. The results found that the TCD of forest catchment was found 76.96MgCha -1 as the highest at the site-II and 29.93MgCha -1 as lowest at site-I with mean of 51.50MgCha -1 . The estimated forest 'C' stock shall be used to know the amount of carbon present before and after construction of the dam and to predict net GHGs emissions. The results may be helpful to study the potential of a given reservoir to release GHG and its subsequent impacts on global warming/climate challenges. Copyright © 2015 Elsevier Inc. All rights reserved.

Developing particle emission inventories using remote sensing (PEIRS).

PubMed

Tang, Chia-Hsi; Coull, Brent A; Schwartz, Joel; Lyapustin, Alexei I; Di, Qian; Koutrakis, Petros

2017-01-01

Information regarding the magnitude and distribution of PM 2.5 emissions is crucial in establishing effective PM regulations and assessing the associated risk to human health and the ecosystem. At present, emission data is obtained from measured or estimated emission factors of various source types. Collecting such information for every known source is costly and time-consuming. For this reason, emission inventories are reported periodically and unknown or smaller sources are often omitted or aggregated at large spatial scale. To address these limitations, we have developed and evaluated a novel method that uses remote sensing data to construct spatially resolved emission inventories for PM 2.5 . This approach enables us to account for all sources within a fixed area, which renders source classification unnecessary. We applied this method to predict emissions in the northeastern United States during the period 2002-2013 using high-resolution 1 km × 1 km aerosol optical depth (AOD). Emission estimates moderately agreed with the EPA National Emission Inventory (R 2 = 0.66-0.71, CV = 17.7-20%). Predicted emissions are found to correlate with land use parameters, suggesting that our method can capture emissions from land-use-related sources. In addition, we distinguished small-scale intra-urban variation in emissions reflecting distribution of metropolitan sources. In essence, this study demonstrates the great potential of remote sensing data to predict particle source emissions cost-effectively. We present a novel method, particle emission inventories using remote sensing (PEIRS), using remote sensing data to construct spatially resolved PM 2.5 emission inventories. Both primary emissions and secondary formations are captured and predicted at a high spatial resolution of 1 km × 1 km. Using PEIRS, large and comprehensive data sets can be generated cost-effectively and can inform development of air quality regulations.

AMP: Assembly Matching Pursuit.

PubMed

Biswas, S; Jojic, V

2013-01-01

Metagenomics, the study of the total genetic material isolated from a biological host, promises to reveal host-microbe or microbe-microbe interactions that may help to personalize medicine or improve agronomic practice. We introduce a method that discovers metagenomic units (MGUs) relevant for phenotype prediction through sequence-based dictionary learning. The method aggregates patient-specific dictionaries and estimates MGU abundances in order to summarize a whole population and yield universally predictive biomarkers. We analyze the impact of Gaussian, Poisson, and Negative Binomial read count models in guiding dictionary construction by examining classification efficiency on a number of synthetic datasets and a real dataset from Ref. 1. Each outperforms standard methods of dictionary composition, such as random projection and orthogonal matching pursuit. Additionally, the predictive MGUs they recover are biologically relevant.

Prediction of high-dimensional states subject to respiratory motion: a manifold learning approach

NASA Astrophysics Data System (ADS)

Liu, Wenyang; Sawant, Amit; Ruan, Dan

2016-07-01

The development of high-dimensional imaging systems in image-guided radiotherapy provides important pathways to the ultimate goal of real-time full volumetric motion monitoring. Effective motion management during radiation treatment usually requires prediction to account for system latency and extra signal/image processing time. It is challenging to predict high-dimensional respiratory motion due to the complexity of the motion pattern combined with the curse of dimensionality. Linear dimension reduction methods such as PCA have been used to construct a linear subspace from the high-dimensional data, followed by efficient predictions on the lower-dimensional subspace. In this study, we extend such rationale to a more general manifold and propose a framework for high-dimensional motion prediction with manifold learning, which allows one to learn more descriptive features compared to linear methods with comparable dimensions. Specifically, a kernel PCA is used to construct a proper low-dimensional feature manifold, where accurate and efficient prediction can be performed. A fixed-point iterative pre-image estimation method is used to recover the predicted value in the original state space. We evaluated and compared the proposed method with a PCA-based approach on level-set surfaces reconstructed from point clouds captured by a 3D photogrammetry system. The prediction accuracy was evaluated in terms of root-mean-squared-error. Our proposed method achieved consistent higher prediction accuracy (sub-millimeter) for both 200 ms and 600 ms lookahead lengths compared to the PCA-based approach, and the performance gain was statistically significant.

Composite Intelligent Learning Control of Strict-Feedback Systems With Disturbance.

PubMed

Xu, Bin; Sun, Fuchun

2018-02-01

This paper addresses the dynamic surface control of uncertain nonlinear systems on the basis of composite intelligent learning and disturbance observer in presence of unknown system nonlinearity and time-varying disturbance. The serial-parallel estimation model with intelligent approximation and disturbance estimation is built to obtain the prediction error and in this way the composite law for weights updating is constructed. The nonlinear disturbance observer is developed using intelligent approximation information while the disturbance estimation is guaranteed to converge to a bounded compact set. The highlight is that different from previous work directly toward asymptotic stability, the transparency of the intelligent approximation and disturbance estimation is included in the control scheme. The uniformly ultimate boundedness stability is analyzed via Lyapunov method. Through simulation verification, the composite intelligent learning with disturbance observer can efficiently estimate the effect caused by system nonlinearity and disturbance while the proposed approach obtains better performance with higher accuracy.

Predicting disease progression from short biomarker series using expert advice algorithm

NASA Astrophysics Data System (ADS)

Morino, Kai; Hirata, Yoshito; Tomioka, Ryota; Kashima, Hisashi; Yamanishi, Kenji; Hayashi, Norihiro; Egawa, Shin; Aihara, Kazuyuki

2015-05-01

Well-trained clinicians may be able to provide diagnosis and prognosis from very short biomarker series using information and experience gained from previous patients. Although mathematical methods can potentially help clinicians to predict the progression of diseases, there is no method so far that estimates the patient state from very short time-series of a biomarker for making diagnosis and/or prognosis by employing the information of previous patients. Here, we propose a mathematical framework for integrating other patients' datasets to infer and predict the state of the disease in the current patient based on their short history. We extend a machine-learning framework of ``prediction with expert advice'' to deal with unstable dynamics. We construct this mathematical framework by combining expert advice with a mathematical model of prostate cancer. Our model predicted well the individual biomarker series of patients with prostate cancer that are used as clinical samples.

Predicting disease progression from short biomarker series using expert advice algorithm.

PubMed

Morino, Kai; Hirata, Yoshito; Tomioka, Ryota; Kashima, Hisashi; Yamanishi, Kenji; Hayashi, Norihiro; Egawa, Shin; Aihara, Kazuyuki

2015-05-20

Well-trained clinicians may be able to provide diagnosis and prognosis from very short biomarker series using information and experience gained from previous patients. Although mathematical methods can potentially help clinicians to predict the progression of diseases, there is no method so far that estimates the patient state from very short time-series of a biomarker for making diagnosis and/or prognosis by employing the information of previous patients. Here, we propose a mathematical framework for integrating other patients' datasets to infer and predict the state of the disease in the current patient based on their short history. We extend a machine-learning framework of "prediction with expert advice" to deal with unstable dynamics. We construct this mathematical framework by combining expert advice with a mathematical model of prostate cancer. Our model predicted well the individual biomarker series of patients with prostate cancer that are used as clinical samples.

Geometric Metamorphosis

PubMed Central

Niethammer, Marc; Hart, Gabriel L.; Pace, Danielle F.; Vespa, Paul M.; Irimia, Andrei; Van Horn, John D.; Aylward, Stephen R.

2013-01-01

Standard image registration methods do not account for changes in image appearance. Hence, metamorphosis approaches have been developed which jointly estimate a space deformation and a change in image appearance to construct a spatio-temporal trajectory smoothly transforming a source to a target image. For standard metamorphosis, geometric changes are not explicitly modeled. We propose a geometric metamorphosis formulation, which explains changes in image appearance by a global deformation, a deformation of a geometric model, and an image composition model. This work is motivated by the clinical challenge of predicting the long-term effects of traumatic brain injuries based on time-series images. This work is also applicable to the quantification of tumor progression (e.g., estimating its infiltrating and displacing components) and predicting chronic blood perfusion changes after stroke. We demonstrate the utility of the method using simulated data as well as scans from a clinical traumatic brain injury patient. PMID:21995083

Estimating the domain of applicability for machine learning QSAR models: a study on aqueous solubility of drug discovery molecules.

PubMed

Schroeter, Timon Sebastian; Schwaighofer, Anton; Mika, Sebastian; Ter Laak, Antonius; Suelzle, Detlev; Ganzer, Ursula; Heinrich, Nikolaus; Müller, Klaus-Robert

2007-12-01

We investigate the use of different Machine Learning methods to construct models for aqueous solubility. Models are based on about 4000 compounds, including an in-house set of 632 drug discovery molecules of Bayer Schering Pharma. For each method, we also consider an appropriate method to obtain error bars, in order to estimate the domain of applicability (DOA) for each model. Here, we investigate error bars from a Bayesian model (Gaussian Process (GP)), an ensemble based approach (Random Forest), and approaches based on the Mahalanobis distance to training data (for Support Vector Machine and Ridge Regression models). We evaluate all approaches in terms of their prediction accuracy (in cross-validation, and on an external validation set of 536 molecules) and in how far the individual error bars can faithfully represent the actual prediction error.

Estimating the domain of applicability for machine learning QSAR models: a study on aqueous solubility of drug discovery molecules.

PubMed

Schroeter, Timon Sebastian; Schwaighofer, Anton; Mika, Sebastian; Ter Laak, Antonius; Suelzle, Detlev; Ganzer, Ursula; Heinrich, Nikolaus; Müller, Klaus-Robert

2007-09-01

We investigate the use of different Machine Learning methods to construct models for aqueous solubility. Models are based on about 4000 compounds, including an in-house set of 632 drug discovery molecules of Bayer Schering Pharma. For each method, we also consider an appropriate method to obtain error bars, in order to estimate the domain of applicability (DOA) for each model. Here, we investigate error bars from a Bayesian model (Gaussian Process (GP)), an ensemble based approach (Random Forest), and approaches based on the Mahalanobis distance to training data (for Support Vector Machine and Ridge Regression models). We evaluate all approaches in terms of their prediction accuracy (in cross-validation, and on an external validation set of 536 molecules) and in how far the individual error bars can faithfully represent the actual prediction error.

Estimating the domain of applicability for machine learning QSAR models: a study on aqueous solubility of drug discovery molecules

NASA Astrophysics Data System (ADS)

Schroeter, Timon Sebastian; Schwaighofer, Anton; Mika, Sebastian; Ter Laak, Antonius; Suelzle, Detlev; Ganzer, Ursula; Heinrich, Nikolaus; Müller, Klaus-Robert

2007-12-01

We investigate the use of different Machine Learning methods to construct models for aqueous solubility. Models are based on about 4000 compounds, including an in-house set of 632 drug discovery molecules of Bayer Schering Pharma. For each method, we also consider an appropriate method to obtain error bars, in order to estimate the domain of applicability (DOA) for each model. Here, we investigate error bars from a Bayesian model (Gaussian Process (GP)), an ensemble based approach (Random Forest), and approaches based on the Mahalanobis distance to training data (for Support Vector Machine and Ridge Regression models). We evaluate all approaches in terms of their prediction accuracy (in cross-validation, and on an external validation set of 536 molecules) and in how far the individual error bars can faithfully represent the actual prediction error.

Estimating the domain of applicability for machine learning QSAR models: a study on aqueous solubility of drug discovery molecules

NASA Astrophysics Data System (ADS)

Schroeter, Timon Sebastian; Schwaighofer, Anton; Mika, Sebastian; Ter Laak, Antonius; Suelzle, Detlev; Ganzer, Ursula; Heinrich, Nikolaus; Müller, Klaus-Robert

2007-09-01

We investigate the use of different Machine Learning methods to construct models for aqueous solubility. Models are based on about 4000 compounds, including an in-house set of 632 drug discovery molecules of Bayer Schering Pharma. For each method, we also consider an appropriate method to obtain error bars, in order to estimate the domain of applicability (DOA) for each model. Here, we investigate error bars from a Bayesian model (Gaussian Process (GP)), an ensemble based approach (Random Forest), and approaches based on the Mahalanobis distance to training data (for Support Vector Machine and Ridge Regression models). We evaluate all approaches in terms of their prediction accuracy (in cross-validation, and on an external validation set of 536 molecules) and in how far the individual error bars can faithfully represent the actual prediction error.

Use of statistical and neural net approaches in predicting toxicity of chemicals.

PubMed

Basak, S C; Grunwald, G D; Gute, B D; Balasubramanian, K; Opitz, D

2000-01-01

Hierarchical quantitative structure-activity relationships (H-QSAR) have been developed as a new approach in constructing models for estimating physicochemical, biomedicinal, and toxicological properties of interest. This approach uses increasingly more complex molecular descriptors in a graduated approach to model building. In this study, statistical and neural network methods have been applied to the development of H-QSAR models for estimating the acute aquatic toxicity (LC50) of 69 benzene derivatives to Pimephales promelas (fathead minnow). Topostructural, topochemical, geometrical, and quantum chemical indices were used as the four levels of the hierarchical method. It is clear from both the statistical and neural network models that topostructural indices alone cannot adequately model this set of congeneric chemicals. Not surprisingly, topochemical indices greatly increase the predictive power of both statistical and neural network models. Quantum chemical indices also add significantly to the modeling of this set of acute aquatic toxicity data.

Estimation of stream conditions in tributaries of the Klamath River, northern California

USGS Publications Warehouse

Manhard, Christopher V.; Som, Nicholas A.; Jones, Edward C.; Perry, Russell W.

2018-01-01

Because of their critical ecological role, stream temperature and discharge are requisite inputs for models of salmonid population dynamics. Coho Salmon inhabiting the Klamath Basin spend much of their freshwater life cycle inhabiting tributaries, but environmental data are often absent or only seasonally available at these locations. To address this information gap, we constructed daily averaged water temperature models that used simulated meteorological data to estimate daily tributary temperatures, and we used flow differentials recorded on the mainstem Klamath River to estimate daily tributary discharge. Observed temperature data were available for fourteen of the major salmon bearing tributaries, which enabled estimation of tributary-specific model parameters at those locations. Water temperature data from six mid-Klamath Basin tributaries were used to estimate a global set of parameters for predicting water temperatures in the remaining tributaries. The resulting parameter sets were used to simulate water temperatures for each of 75 tributaries from 1980-2015. Goodness-of-fit statistics computed from a cross-validation analysis demonstrated a high precision of the tributary-specific models in predicting temperature in unobserved years and of the global model in predicting temperatures in unobserved streams. Klamath River discharge has been monitored by four gages that broadly intersperse the 292 kilometers from the Iron Gate Dam to the Klamath River mouth. These gages defined the upstream and downstream margins of three reaches. Daily discharge of tributaries within a reach was estimated from 1980-2015 based on drainage-area proportionate allocations of the discharge differential between the upstream and downstream margin. Comparisons with measured discharge on Indian Creek, a moderate-sized tributary with naturally regulated flows, revealed that the estimates effectively approximated both the variability and magnitude of discharge.

Age prediction formulae from radiographic assessment of skeletal maturation at the knee in an Irish population.

PubMed

O'Connor, Jean E; Coyle, Joseph; Bogue, Conor; Spence, Liam D; Last, Jason

2014-01-01

Age estimation in living subjects is primarily achieved through assessment of a hand-wrist radiograph and comparison with a standard reference atlas. Recently, maturation of other regions of the skeleton has also been assessed in an attempt to refine the age estimates. The current study presents a method to predict bone age directly from the knee in a modern Irish sample. Ten maturity indicators (A-J) at the knee were examined from radiographs of 221 subjects (137 males; 84 females). Each indicator was assigned a maturity score. Scores for indicators A-G, H-J and A-J, respectively, were totalled to provide a cumulative maturity score for change in morphology of the epiphyses (AG), epiphyseal union (HJ) and the combination of both (AJ). Linear regression equations to predict age from the maturity scores (AG, HJ, AJ) were constructed for males and females. For males, equation-AJ demonstrated the greatest predictive capability (R(2)=0.775) while for females equation-HJ had the strongest capacity for prediction (R(2)=0.815). When equation-AJ for males and equation-HJ for females were applied to the current sample, the predicted age of 90% of subjects was within ±1.5 years of actual age for male subjects and within +2.0 to -1.9 years of actual age for female subjects. The regression formulae and associated charts represent the most contemporary method of age prediction currently available for an Irish population, and provide a further technique which can contribute to a multifactorial approach to age estimation in non-adults. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Solidarity-conflict and ambivalence: testing two conceptual frameworks and their impact on quality of life for older family members.

PubMed

Lowenstein, Ariela

2007-03-01

The purpose of this study was to test empirically two major conceptualizations of parent-child relations in later adulthood-intergenerational solidarity-conflict and ambivalence paradigms-and their predictive validity on elders' quality of life using comparative cross-national data. Data were from a sample of 2,064 elders (aged 75 and older) from the five-country OASIS study (Old Age and Autonomy: The Role of Service Systems and Intergenerational Family Solidarity; Norway, England, Germany, Spain, and Israel). Multivariate and block-recursive regression models estimated the predictivity of the two conceptualizations of family dynamics on quality of life controlling for country, personal characteristics, and activity of daily living functioning. Descriptive analyses indicated that family solidarity, especially the affective/cognitive component (called Solidarity A), was high in all five countries, whereas conflict and ambivalence were low. When I entered all three constructs into the regression Solidarity A, reciprocal intergenerational support and ambivalence predicted quality of life. Controlling for activity of daily living functioning, socioeconomics status, and country, intergenerational relations had only a weak explanatory power, and personal resources explained most of the variance. The data suggest that the three constructs exist simultaneously but in varying combinations, confirming that in cross-cultural contexts family cohesion predominates, albeit with low degrees of conflict and ambivalence. The solidarity construct evidenced relatively robust measurement. More work is required to enhance the ambivalence measurement.

Bragg peak prediction from quantitative proton computed tomography using different path estimates

PubMed Central

Wang, Dongxu; Mackie, T Rockwell

2015-01-01

This paper characterizes the performance of the straight-line path (SLP) and cubic spline path (CSP) as path estimates used in reconstruction of proton computed tomography (pCT). The GEANT4 Monte Carlo simulation toolkit is employed to simulate the imaging phantom and proton projections. SLP, CSP and the most-probable path (MPP) are constructed based on the entrance and exit information of each proton. The physical deviations of SLP, CSP and MPP from the real path are calculated. Using a conditional proton path probability map, the relative probability of SLP, CSP and MPP are calculated and compared. The depth dose and Bragg peak are predicted on the pCT images reconstructed using SLP, CSP, and MPP and compared with the simulation result. The root-mean-square physical deviations and the cumulative distribution of the physical deviations show that the performance of CSP is comparable to MPP while SLP is slightly inferior. About 90% of the SLP pixels and 99% of the CSP pixels lie in the 99% relative probability envelope of the MPP. Even at an imaging dose of ~0.1 mGy the proton Bragg peak for a given incoming energy can be predicted on the pCT image reconstructed using SLP, CSP, or MPP with 1 mm accuracy. This study shows that SLP and CSP, like MPP, are adequate path estimates for pCT reconstruction, and therefore can be chosen as the path estimation method for pCT reconstruction, which can aid the treatment planning and range prediction of proton radiation therapy. PMID:21212472

Sampling-based real-time motion planning under state uncertainty for autonomous micro-aerial vehicles in GPS-denied environments.

PubMed

Li, Dachuan; Li, Qing; Cheng, Nong; Song, Jingyan

2014-11-18

This paper presents a real-time motion planning approach for autonomous vehicles with complex dynamics and state uncertainty. The approach is motivated by the motion planning problem for autonomous vehicles navigating in GPS-denied dynamic environments, which involves non-linear and/or non-holonomic vehicle dynamics, incomplete state estimates, and constraints imposed by uncertain and cluttered environments. To address the above motion planning problem, we propose an extension of the closed-loop rapid belief trees, the closed-loop random belief trees (CL-RBT), which incorporates predictions of the position estimation uncertainty, using a factored form of the covariance provided by the Kalman filter-based estimator. The proposed motion planner operates by incrementally constructing a tree of dynamically feasible trajectories using the closed-loop prediction, while selecting candidate paths with low uncertainty using efficient covariance update and propagation. The algorithm can operate in real-time, continuously providing the controller with feasible paths for execution, enabling the vehicle to account for dynamic and uncertain environments. Simulation results demonstrate that the proposed approach can generate feasible trajectories that reduce the state estimation uncertainty, while handling complex vehicle dynamics and environment constraints.

Sampling-Based Real-Time Motion Planning under State Uncertainty for Autonomous Micro-Aerial Vehicles in GPS-Denied Environments

PubMed Central

Li, Dachuan; Li, Qing; Cheng, Nong; Song, Jingyan

2014-01-01

This paper presents a real-time motion planning approach for autonomous vehicles with complex dynamics and state uncertainty. The approach is motivated by the motion planning problem for autonomous vehicles navigating in GPS-denied dynamic environments, which involves non-linear and/or non-holonomic vehicle dynamics, incomplete state estimates, and constraints imposed by uncertain and cluttered environments. To address the above motion planning problem, we propose an extension of the closed-loop rapid belief trees, the closed-loop random belief trees (CL-RBT), which incorporates predictions of the position estimation uncertainty, using a factored form of the covariance provided by the Kalman filter-based estimator. The proposed motion planner operates by incrementally constructing a tree of dynamically feasible trajectories using the closed-loop prediction, while selecting candidate paths with low uncertainty using efficient covariance update and propagation. The algorithm can operate in real-time, continuously providing the controller with feasible paths for execution, enabling the vehicle to account for dynamic and uncertain environments. Simulation results demonstrate that the proposed approach can generate feasible trajectories that reduce the state estimation uncertainty, while handling complex vehicle dynamics and environment constraints. PMID:25412217

Mortality Predicted Accuracy for Hepatocellular Carcinoma Patients with Hepatic Resection Using Artificial Neural Network

PubMed Central

Chiu, Herng-Chia; Ho, Te-Wei; Lee, King-Teh; Chen, Hong-Yaw; Ho, Wen-Hsien

2013-01-01

The aim of this present study is firstly to compare significant predictors of mortality for hepatocellular carcinoma (HCC) patients undergoing resection between artificial neural network (ANN) and logistic regression (LR) models and secondly to evaluate the predictive accuracy of ANN and LR in different survival year estimation models. We constructed a prognostic model for 434 patients with 21 potential input variables by Cox regression model. Model performance was measured by numbers of significant predictors and predictive accuracy. The results indicated that ANN had double to triple numbers of significant predictors at 1-, 3-, and 5-year survival models as compared with LR models. Scores of accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC) of 1-, 3-, and 5-year survival estimation models using ANN were superior to those of LR in all the training sets and most of the validation sets. The study demonstrated that ANN not only had a great number of predictors of mortality variables but also provided accurate prediction, as compared with conventional methods. It is suggested that physicians consider using data mining methods as supplemental tools for clinical decision-making and prognostic evaluation. PMID:23737707

Estimation of Biomass and Canopy Height in Bermudagrass, Alfalfa, and Wheat Using Ultrasonic, Laser, and Spectral Sensors

PubMed Central

Pittman, Jeremy Joshua; Arnall, Daryl Brian; Interrante, Sindy M.; Moffet, Corey A.; Butler, Twain J.

2015-01-01

Non-destructive biomass estimation of vegetation has been performed via remote sensing as well as physical measurements. An effective method for estimating biomass must have accuracy comparable to the accepted standard of destructive removal. Estimation or measurement of height is commonly employed to create a relationship between height and mass. This study examined several types of ground-based mobile sensing strategies for forage biomass estimation. Forage production experiments consisting of alfalfa (Medicago sativa L.), bermudagrass [Cynodon dactylon (L.) Pers.], and wheat (Triticum aestivum L.) were employed to examine sensor biomass estimation (laser, ultrasonic, and spectral) as compared to physical measurements (plate meter and meter stick) and the traditional harvest method (clipping). Predictive models were constructed via partial least squares regression and modeled estimates were compared to the physically measured biomass. Least significant difference separated mean estimates were examined to evaluate differences in the physical measurements and sensor estimates for canopy height and biomass. Differences between methods were minimal (average percent error of 11.2% for difference between predicted values versus machine and quadrat harvested biomass values (1.64 and 4.91 t·ha−1, respectively), except at the lowest measured biomass (average percent error of 89% for harvester and quad harvested biomass < 0.79 t·ha−1) and greatest measured biomass (average percent error of 18% for harvester and quad harvested biomass >6.4 t·ha−1). These data suggest that using mobile sensor-based biomass estimation models could be an effective alternative to the traditional clipping method for rapid, accurate in-field biomass estimation. PMID:25635415

The ACCE method: an approach for obtaining quantitative or qualitative estimates of residual confounding that includes unmeasured confounding

PubMed Central

Smith, Eric G.

2015-01-01

Background:  Nonrandomized studies typically cannot account for confounding from unmeasured factors.  Method:  A method is presented that exploits the recently-identified phenomenon of  “confounding amplification” to produce, in principle, a quantitative estimate of total residual confounding resulting from both measured and unmeasured factors.  Two nested propensity score models are constructed that differ only in the deliberate introduction of an additional variable(s) that substantially predicts treatment exposure.  Residual confounding is then estimated by dividing the change in treatment effect estimate between models by the degree of confounding amplification estimated to occur, adjusting for any association between the additional variable(s) and outcome. Results:  Several hypothetical examples are provided to illustrate how the method produces a quantitative estimate of residual confounding if the method’s requirements and assumptions are met.  Previously published data is used to illustrate that, whether or not the method routinely provides precise quantitative estimates of residual confounding, the method appears to produce a valuable qualitative estimate of the likely direction and general size of residual confounding. Limitations:  Uncertainties exist, including identifying the best approaches for: 1) predicting the amount of confounding amplification, 2) minimizing changes between the nested models unrelated to confounding amplification, 3) adjusting for the association of the introduced variable(s) with outcome, and 4) deriving confidence intervals for the method’s estimates (although bootstrapping is one plausible approach). Conclusions:  To this author’s knowledge, it has not been previously suggested that the phenomenon of confounding amplification, if such amplification is as predictable as suggested by a recent simulation, provides a logical basis for estimating total residual confounding. The method's basic approach is straightforward.  The method's routine usefulness, however, has not yet been established, nor has the method been fully validated. Rapid further investigation of this novel method is clearly indicated, given the potential value of its quantitative or qualitative output. PMID:25580226

Uncertainty analysis of neural network based flood forecasting models: An ensemble based approach for constructing prediction interval

NASA Astrophysics Data System (ADS)

Kasiviswanathan, K.; Sudheer, K.

2013-05-01

Artificial neural network (ANN) based hydrologic models have gained lot of attention among water resources engineers and scientists, owing to their potential for accurate prediction of flood flows as compared to conceptual or physics based hydrologic models. The ANN approximates the non-linear functional relationship between the complex hydrologic variables in arriving at the river flow forecast values. Despite a large number of applications, there is still some criticism that ANN's point prediction lacks in reliability since the uncertainty of predictions are not quantified, and it limits its use in practical applications. A major concern in application of traditional uncertainty analysis techniques on neural network framework is its parallel computing architecture with large degrees of freedom, which makes the uncertainty assessment a challenging task. Very limited studies have considered assessment of predictive uncertainty of ANN based hydrologic models. In this study, a novel method is proposed that help construct the prediction interval of ANN flood forecasting model during calibration itself. The method is designed to have two stages of optimization during calibration: at stage 1, the ANN model is trained with genetic algorithm (GA) to obtain optimal set of weights and biases vector, and during stage 2, the optimal variability of ANN parameters (obtained in stage 1) is identified so as to create an ensemble of predictions. During the 2nd stage, the optimization is performed with multiple objectives, (i) minimum residual variance for the ensemble mean, (ii) maximum measured data points to fall within the estimated prediction interval and (iii) minimum width of prediction interval. The method is illustrated using a real world case study of an Indian basin. The method was able to produce an ensemble that has an average prediction interval width of 23.03 m3/s, with 97.17% of the total validation data points (measured) lying within the interval. The derived prediction interval for a selected hydrograph in the validation data set is presented in Fig 1. It is noted that most of the observed flows lie within the constructed prediction interval, and therefore provides information about the uncertainty of the prediction. One specific advantage of the method is that when ensemble mean value is considered as a forecast, the peak flows are predicted with improved accuracy by this method compared to traditional single point forecasted ANNs. Fig. 1 Prediction Interval for selected hydrograph

Estimating overall exposure effects for the clustered and censored outcome using random effect Tobit regression models.

PubMed

Wang, Wei; Griswold, Michael E

2016-11-30

The random effect Tobit model is a regression model that accommodates both left- and/or right-censoring and within-cluster dependence of the outcome variable. Regression coefficients of random effect Tobit models have conditional interpretations on a constructed latent dependent variable and do not provide inference of overall exposure effects on the original outcome scale. Marginalized random effects model (MREM) permits likelihood-based estimation of marginal mean parameters for the clustered data. For random effect Tobit models, we extend the MREM to marginalize over both the random effects and the normal space and boundary components of the censored response to estimate overall exposure effects at population level. We also extend the 'Average Predicted Value' method to estimate the model-predicted marginal means for each person under different exposure status in a designated reference group by integrating over the random effects and then use the calculated difference to assess the overall exposure effect. The maximum likelihood estimation is proposed utilizing a quasi-Newton optimization algorithm with Gauss-Hermite quadrature to approximate the integration of the random effects. We use these methods to carefully analyze two real datasets. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Reducing process delays for real-time earthquake parameter estimation - An application of KD tree to large databases for Earthquake Early Warning

NASA Astrophysics Data System (ADS)

Yin, Lucy; Andrews, Jennifer; Heaton, Thomas

2018-05-01

Earthquake parameter estimations using nearest neighbor searching among a large database of observations can lead to reliable prediction results. However, in the real-time application of Earthquake Early Warning (EEW) systems, the accurate prediction using a large database is penalized by a significant delay in the processing time. We propose to use a multidimensional binary search tree (KD tree) data structure to organize large seismic databases to reduce the processing time in nearest neighbor search for predictions. We evaluated the performance of KD tree on the Gutenberg Algorithm, a database-searching algorithm for EEW. We constructed an offline test to predict peak ground motions using a database with feature sets of waveform filter-bank characteristics, and compare the results with the observed seismic parameters. We concluded that large database provides more accurate predictions of the ground motion information, such as peak ground acceleration, velocity, and displacement (PGA, PGV, PGD), than source parameters, such as hypocenter distance. Application of the KD tree search to organize the database reduced the average searching process by 85% time cost of the exhaustive method, allowing the method to be feasible for real-time implementation. The algorithm is straightforward and the results will reduce the overall time of warning delivery for EEW.

Using Uncertainty Quantification to Guide Development and Improvements of a Regional-Scale Model of the Coastal Lowlands Aquifer System Spanning Texas, Louisiana, Mississippi, Alabama and Florida

NASA Astrophysics Data System (ADS)

Foster, L. K.; Clark, B. R.; Duncan, L. L.; Tebo, D. T.; White, J.

2017-12-01

Several historical groundwater models exist within the Coastal Lowlands Aquifer System (CLAS), which spans the Gulf Coastal Plain in Texas, Louisiana, Mississippi, Alabama, and Florida. The largest of these models, called the Gulf Coast Regional Aquifer System Analysis (RASA) model, has been brought into a new framework using the Newton formulation for MODFLOW-2005 (MODFLOW-NWT) and serves as the starting point of a new investigation underway by the U.S. Geological Survey to improve understanding of the CLAS and provide predictions of future groundwater availability within an uncertainty quantification (UQ) framework. The use of an UQ framework will not only provide estimates of water-level observation worth, hydraulic parameter uncertainty, boundary-condition uncertainty, and uncertainty of future potential predictions, but it will also guide the model development process. Traditionally, model development proceeds from dataset construction to the process of deterministic history matching, followed by deterministic predictions using the model. This investigation will combine the use of UQ with existing historical models of the study area to assess in a quantitative framework the effect model package and property improvements have on the ability to represent past-system states, as well as the effect on the model's ability to make certain predictions of water levels, water budgets, and base-flow estimates. Estimates of hydraulic property information and boundary conditions from the existing models and literature, forming the prior, will be used to make initial estimates of model forecasts and their corresponding uncertainty, along with an uncalibrated groundwater model run within an unconstrained Monte Carlo analysis. First-Order Second-Moment (FOSM) analysis will also be used to investigate parameter and predictive uncertainty, and guide next steps in model development prior to rigorous history matching by using PEST++ parameter estimation code.

Voxel inversion of airborne electromagnetic data for improved groundwater model construction and prediction accuracy

NASA Astrophysics Data System (ADS)

Kruse Christensen, Nikolaj; Ferre, Ty Paul A.; Fiandaca, Gianluca; Christensen, Steen

2017-03-01

We present a workflow for efficient construction and calibration of large-scale groundwater models that includes the integration of airborne electromagnetic (AEM) data and hydrological data. In the first step, the AEM data are inverted to form a 3-D geophysical model. In the second step, the 3-D geophysical model is translated, using a spatially dependent petrophysical relationship, to form a 3-D hydraulic conductivity distribution. The geophysical models and the hydrological data are used to estimate spatially distributed petrophysical shape factors. The shape factors primarily work as translators between resistivity and hydraulic conductivity, but they can also compensate for structural defects in the geophysical model. The method is demonstrated for a synthetic case study with sharp transitions among various types of deposits. Besides demonstrating the methodology, we demonstrate the importance of using geophysical regularization constraints that conform well to the depositional environment. This is done by inverting the AEM data using either smoothness (smooth) constraints or minimum gradient support (sharp) constraints, where the use of sharp constraints conforms best to the environment. The dependency on AEM data quality is also tested by inverting the geophysical model using data corrupted with four different levels of background noise. Subsequently, the geophysical models are used to construct competing groundwater models for which the shape factors are calibrated. The performance of each groundwater model is tested with respect to four types of prediction that are beyond the calibration base: a pumping well's recharge area and groundwater age, respectively, are predicted by applying the same stress as for the hydrologic model calibration; and head and stream discharge are predicted for a different stress situation. As expected, in this case the predictive capability of a groundwater model is better when it is based on a sharp geophysical model instead of a smoothness constraint. This is true for predictions of recharge area, head change, and stream discharge, while we find no improvement for prediction of groundwater age. Furthermore, we show that the model prediction accuracy improves with AEM data quality for predictions of recharge area, head change, and stream discharge, while there appears to be no accuracy improvement for the prediction of groundwater age.

The use of copulas to practical estimation of multivariate stochastic differential equation mixed effects models

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

Rupšys, P.

A system of stochastic differential equations (SDE) with mixed-effects parameters and multivariate normal copula density function were used to develop tree height model for Scots pine trees in Lithuania. A two-step maximum likelihood parameter estimation method is used and computational guidelines are given. After fitting the conditional probability density functions to outside bark diameter at breast height, and total tree height, a bivariate normal copula distribution model was constructed. Predictions from the mixed-effects parameters SDE tree height model calculated during this research were compared to the regression tree height equations. The results are implemented in the symbolic computational language MAPLE.

Submillimeter, millimeter, and microwave spectral line catalogue

NASA Technical Reports Server (NTRS)

Poynter, R. L.; Pickett, H. M.

1980-01-01

A computer accessible catalogue of submillimeter, millimeter, and microwave spectral lines in the frequency range between O and 3000 GHz (such as; wavelengths longer than 100 m) is discussed. The catalogue was used as a planning guide and as an aid in the identification and analysis of observed spectral lines. The information listed for each spectral line includes the frequency and its estimated error, the intensity, lower state energy, and quantum number assignment. The catalogue was constructed by using theoretical least squares fits of published spectral lines to accepted molecular models. The associated predictions and their estimated errors are based upon the resultant fitted parameters and their covariances.

Estimation of the monthly average daily solar radiation using geographic information system and advanced case-based reasoning.

PubMed

Koo, Choongwan; Hong, Taehoon; Lee, Minhyun; Park, Hyo Seon

2013-05-07

The photovoltaic (PV) system is considered an unlimited source of clean energy, whose amount of electricity generation changes according to the monthly average daily solar radiation (MADSR). It is revealed that the MADSR distribution in South Korea has very diverse patterns due to the country's climatic and geographical characteristics. This study aimed to develop a MADSR estimation model for the location without the measured MADSR data, using an advanced case based reasoning (CBR) model, which is a hybrid methodology combining CBR with artificial neural network, multiregression analysis, and genetic algorithm. The average prediction accuracy of the advanced CBR model was very high at 95.69%, and the standard deviation of the prediction accuracy was 3.67%, showing a significant improvement in prediction accuracy and consistency. A case study was conducted to verify the proposed model. The proposed model could be useful for owner or construction manager in charge of determining whether or not to introduce the PV system and where to install it. Also, it would benefit contractors in a competitive bidding process to accurately estimate the electricity generation of the PV system in advance and to conduct an economic and environmental feasibility study from the life cycle perspective.

Detecting Presymptomatic Infection Is Necessary to Forecast Major Epidemics in the Earliest Stages of Infectious Disease Outbreaks

PubMed Central

Thompson, Robin N.; Gilligan, Christopher A.; Cunniffe, Nik J.

2016-01-01

We assess how presymptomatic infection affects predictability of infectious disease epidemics. We focus on whether or not a major outbreak (i.e. an epidemic that will go on to infect a large number of individuals) can be predicted reliably soon after initial cases of disease have appeared within a population. For emerging epidemics, significant time and effort is spent recording symptomatic cases. Scientific attention has often focused on improving statistical methodologies to estimate disease transmission parameters from these data. Here we show that, even if symptomatic cases are recorded perfectly, and disease spread parameters are estimated exactly, it is impossible to estimate the probability of a major outbreak without ambiguity. Our results therefore provide an upper bound on the accuracy of forecasts of major outbreaks that are constructed using data on symptomatic cases alone. Accurate prediction of whether or not an epidemic will occur requires records of symptomatic individuals to be supplemented with data concerning the true infection status of apparently uninfected individuals. To forecast likely future behavior in the earliest stages of an emerging outbreak, it is therefore vital to develop and deploy accurate diagnostic tests that can determine whether asymptomatic individuals are actually uninfected, or instead are infected but just do not yet show detectable symptoms. PMID:27046030

Entropy-based gene ranking without selection bias for the predictive classification of microarray data.

PubMed

Furlanello, Cesare; Serafini, Maria; Merler, Stefano; Jurman, Giuseppe

2003-11-06

We describe the E-RFE method for gene ranking, which is useful for the identification of markers in the predictive classification of array data. The method supports a practical modeling scheme designed to avoid the construction of classification rules based on the selection of too small gene subsets (an effect known as the selection bias, in which the estimated predictive errors are too optimistic due to testing on samples already considered in the feature selection process). With E-RFE, we speed up the recursive feature elimination (RFE) with SVM classifiers by eliminating chunks of uninteresting genes using an entropy measure of the SVM weights distribution. An optimal subset of genes is selected according to a two-strata model evaluation procedure: modeling is replicated by an external stratified-partition resampling scheme, and, within each run, an internal K-fold cross-validation is used for E-RFE ranking. Also, the optimal number of genes can be estimated according to the saturation of Zipf's law profiles. Without a decrease of classification accuracy, E-RFE allows a speed-up factor of 100 with respect to standard RFE, while improving on alternative parametric RFE reduction strategies. Thus, a process for gene selection and error estimation is made practical, ensuring control of the selection bias, and providing additional diagnostic indicators of gene importance.

Estimating the state of a geophysical system with sparse observations: time delay methods to achieve accurate initial states for prediction

DOE PAGES

An, Zhe; Rey, Daniel; Ye, Jingxin; ...

2017-01-16

The problem of forecasting the behavior of a complex dynamical system through analysis of observational time-series data becomes difficult when the system expresses chaotic behavior and the measurements are sparse, in both space and/or time. Despite the fact that this situation is quite typical across many fields, including numerical weather prediction, the issue of whether the available observations are "sufficient" for generating successful forecasts is still not well understood. An analysis by Whartenby et al. (2013) found that in the context of the nonlinear shallow water equations on a β plane, standard nudging techniques require observing approximately 70 % of themore » full set of state variables. Here we examine the same system using a method introduced by Rey et al. (2014a), which generalizes standard nudging methods to utilize time delayed measurements. Here, we show that in certain circumstances, it provides a sizable reduction in the number of observations required to construct accurate estimates and high-quality predictions. In particular, we find that this estimate of 70 % can be reduced to about 33 % using time delays, and even further if Lagrangian drifter locations are also used as measurements.« less

Estimating the state of a geophysical system with sparse observations: time delay methods to achieve accurate initial states for prediction

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

An, Zhe; Rey, Daniel; Ye, Jingxin

The problem of forecasting the behavior of a complex dynamical system through analysis of observational time-series data becomes difficult when the system expresses chaotic behavior and the measurements are sparse, in both space and/or time. Despite the fact that this situation is quite typical across many fields, including numerical weather prediction, the issue of whether the available observations are "sufficient" for generating successful forecasts is still not well understood. An analysis by Whartenby et al. (2013) found that in the context of the nonlinear shallow water equations on a β plane, standard nudging techniques require observing approximately 70 % of themore » full set of state variables. Here we examine the same system using a method introduced by Rey et al. (2014a), which generalizes standard nudging methods to utilize time delayed measurements. Here, we show that in certain circumstances, it provides a sizable reduction in the number of observations required to construct accurate estimates and high-quality predictions. In particular, we find that this estimate of 70 % can be reduced to about 33 % using time delays, and even further if Lagrangian drifter locations are also used as measurements.« less

A closer look at cross-validation for assessing the accuracy of gene regulatory networks and models.

PubMed

Tabe-Bordbar, Shayan; Emad, Amin; Zhao, Sihai Dave; Sinha, Saurabh

2018-04-26

Cross-validation (CV) is a technique to assess the generalizability of a model to unseen data. This technique relies on assumptions that may not be satisfied when studying genomics datasets. For example, random CV (RCV) assumes that a randomly selected set of samples, the test set, well represents unseen data. This assumption doesn't hold true where samples are obtained from different experimental conditions, and the goal is to learn regulatory relationships among the genes that generalize beyond the observed conditions. In this study, we investigated how the CV procedure affects the assessment of supervised learning methods used to learn gene regulatory networks (or in other applications). We compared the performance of a regression-based method for gene expression prediction estimated using RCV with that estimated using a clustering-based CV (CCV) procedure. Our analysis illustrates that RCV can produce over-optimistic estimates of the model's generalizability compared to CCV. Next, we defined the 'distinctness' of test set from training set and showed that this measure is predictive of performance of the regression method. Finally, we introduced a simulated annealing method to construct partitions with gradually increasing distinctness and showed that performance of different gene expression prediction methods can be better evaluated using this method.

Report of Naval Commander Western Task Force (CTF 122). Report of Normandy Invasion

DTIC Science & Technology

1944-09-15

duplication of effort, general directives for the provision of intellignece were laid down by the Chief of Staff Supreme Allied Commander (COSSAC) and...yards inland. Artificially inundated lowlands lie inland behind the entire beach area. Across these flooded meadows from the beach are 6 roads, only three...and beached craft and on the construction as well as the permanence of artificial harbor installations. (f) Estimation and prediction of

Reduced order modeling of fluid/structure interaction.

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

Barone, Matthew Franklin; Kalashnikova, Irina; Segalman, Daniel Joseph

2009-11-01

This report describes work performed from October 2007 through September 2009 under the Sandia Laboratory Directed Research and Development project titled 'Reduced Order Modeling of Fluid/Structure Interaction.' This project addresses fundamental aspects of techniques for construction of predictive Reduced Order Models (ROMs). A ROM is defined as a model, derived from a sequence of high-fidelity simulations, that preserves the essential physics and predictive capability of the original simulations but at a much lower computational cost. Techniques are developed for construction of provably stable linear Galerkin projection ROMs for compressible fluid flow, including a method for enforcing boundary conditions that preservesmore » numerical stability. A convergence proof and error estimates are given for this class of ROM, and the method is demonstrated on a series of model problems. A reduced order method, based on the method of quadratic components, for solving the von Karman nonlinear plate equations is developed and tested. This method is applied to the problem of nonlinear limit cycle oscillations encountered when the plate interacts with an adjacent supersonic flow. A stability-preserving method for coupling the linear fluid ROM with the structural dynamics model for the elastic plate is constructed and tested. Methods for constructing efficient ROMs for nonlinear fluid equations are developed and tested on a one-dimensional convection-diffusion-reaction equation. These methods are combined with a symmetrization approach to construct a ROM technique for application to the compressible Navier-Stokes equations.« less

Multiple Damage Progression Paths in Model-Based Prognostics

NASA Technical Reports Server (NTRS)

Daigle, Matthew; Goebel, Kai Frank

2011-01-01

Model-based prognostics approaches employ domain knowledge about a system, its components, and how they fail through the use of physics-based models. Component wear is driven by several different degradation phenomena, each resulting in their own damage progression path, overlapping to contribute to the overall degradation of the component. We develop a model-based prognostics methodology using particle filters, in which the problem of characterizing multiple damage progression paths is cast as a joint state-parameter estimation problem. The estimate is represented as a probability distribution, allowing the prediction of end of life and remaining useful life within a probabilistic framework that supports uncertainty management. We also develop a novel variance control mechanism that maintains an uncertainty bound around the hidden parameters to limit the amount of estimation uncertainty and, consequently, reduce prediction uncertainty. We construct a detailed physics-based model of a centrifugal pump, to which we apply our model-based prognostics algorithms. We illustrate the operation of the prognostic solution with a number of simulation-based experiments and demonstrate the performance of the chosen approach when multiple damage mechanisms are active

Benefit Estimates of Terminal Area Productivity Program Technologies

NASA Technical Reports Server (NTRS)

Hemm, Robert; Shapiro, Gerald; Lee, David; Gribko, Joana; Glaser, Bonnie

1999-01-01

This report documents benefit analyses for the NASA Terminal Area Technology (TAP) technology programs. Benefits are based on reductions in arrival delays at ten major airports over the 10 years from 2006 through 2015. Detailed analytic airport capacity and delay models were constructed to produce the estimates. The goal of TAP is enable good weather operations tempos in all weather conditions. The TAP program includes technologies to measure and predict runway occupancy times, reduce runway occupancy times in bad weather, accurately predict wake vortex hazards, and couple controller automation with aircraft flight management systems. The report presents and discusses the estimate results and describes the models. Three appendixes document the model algorithms and discuss the input parameters selected for the TAP technologies. The fourth appendix is the user's guide for the models. The results indicate that the combined benefits for all TAP technologies at all 10 airports range from $550 to $650 million per year (in constant 1997 dollars). Additional benefits will accrue from reductions in departure delays. Departure delay benefits are calculated by the current models.

Use of vegetation health data for estimation of aus rice yield in bangladesh.

PubMed

Rahman, Atiqur; Roytman, Leonid; Krakauer, Nir Y; Nizamuddin, Mohammad; Goldberg, Mitch

2009-01-01

Rice is a vital staple crop for Bangladesh and surrounding countries, with interannual variation in yields depending on climatic conditions. We compared Bangladesh yield of aus rice, one of the main varieties grown, from official agricultural statistics with Vegetation Health (VH) Indices [Vegetation Condition Index (VCI), Temperature Condition Index (TCI) and Vegetation Health Index (VHI)] computed from Advanced Very High Resolution Radiometer (AVHRR) data covering a period of 15 years (1991-2005). A strong correlation was found between aus rice yield and VCI and VHI during the critical period of aus rice development that occurs during March-April (weeks 8-13 of the year), several months in advance of the rice harvest. Stepwise principal component regression (PCR) was used to construct a model to predict yield as a function of critical-period VHI. The model reduced the yield prediction error variance by 62% compared with a prediction of average yield for each year. Remote sensing is a valuable tool for estimating rice yields well in advance of harvest and at a low cost.

Use of Vegetation Health Data for Estimation of Aus Rice Yield in Bangladesh

PubMed Central

Rahman, Atiqur; Roytman, Leonid; Krakauer, Nir Y.; Nizamuddin, Mohammad; Goldberg, Mitch

2009-01-01

Rice is a vital staple crop for Bangladesh and surrounding countries, with interannual variation in yields depending on climatic conditions. We compared Bangladesh yield of aus rice, one of the main varieties grown, from official agricultural statistics with Vegetation Health (VH) Indices [Vegetation Condition Index (VCI), Temperature Condition Index (TCI) and Vegetation Health Index (VHI)] computed from Advanced Very High Resolution Radiometer (AVHRR) data covering a period of 15 years (1991–2005). A strong correlation was found between aus rice yield and VCI and VHI during the critical period of aus rice development that occurs during March–April (weeks 8–13 of the year), several months in advance of the rice harvest. Stepwise principal component regression (PCR) was used to construct a model to predict yield as a function of critical-period VHI. The model reduced the yield prediction error variance by 62% compared with a prediction of average yield for each year. Remote sensing is a valuable tool for estimating rice yields well in advance of harvest and at a low cost. PMID:22574057

What is preexisting strength? Predicting free association probabilities, similarity ratings, and cued recall probabilities.

PubMed

Nelson, Douglas L; Dyrdal, Gunvor M; Goodmon, Leilani B

2005-08-01

Measuring lexical knowledge poses a challenge to the study of the influence of preexisting knowledge on the retrieval of new memories. Many tasks focus on word pairs, but words are embedded in associative networks, so how should preexisting pair strength be measured? It has been measured by free association, similarity ratings, and co-occurrence statistics. Researchers interpret free association response probabilities as unbiased estimates of forward cue-to-target strength. In Study 1, analyses of large free association and extralist cued recall databases indicate that this interpretation is incorrect. Competitor and backward strengths bias free association probabilities, and as with other recall tasks, preexisting strength is described by a ratio rule. In Study 2, associative similarity ratings are predicted by forward and backward, but not by competitor, strength. Preexisting strength is not a unitary construct, because its measurement varies with method. Furthermore, free association probabilities predict extralist cued recall better than do ratings and co-occurrence statistics. The measure that most closely matches the criterion task may provide the best estimate of the identity of preexisting strength.

Empirical Assessment of Spatial Prediction Methods for Location Cost Adjustment Factors

PubMed Central

Migliaccio, Giovanni C.; Guindani, Michele; D'Incognito, Maria; Zhang, Linlin

2014-01-01

In the feasibility stage, the correct prediction of construction costs ensures that budget requirements are met from the start of a project's lifecycle. A very common approach for performing quick-order-of-magnitude estimates is based on using Location Cost Adjustment Factors (LCAFs) that compute historically based costs by project location. Nowadays, numerous LCAF datasets are commercially available in North America, but, obviously, they do not include all locations. Hence, LCAFs for un-sampled locations need to be inferred through spatial interpolation or prediction methods. Currently, practitioners tend to select the value for a location using only one variable, namely the nearest linear-distance between two sites. However, construction costs could be affected by socio-economic variables as suggested by macroeconomic theories. Using a commonly used set of LCAFs, the City Cost Indexes (CCI) by RSMeans, and the socio-economic variables included in the ESRI Community Sourcebook, this article provides several contributions to the body of knowledge. First, the accuracy of various spatial prediction methods in estimating LCAF values for un-sampled locations was evaluated and assessed in respect to spatial interpolation methods. Two Regression-based prediction models were selected, a Global Regression Analysis and a Geographically-weighted regression analysis (GWR). Once these models were compared against interpolation methods, the results showed that GWR is the most appropriate way to model CCI as a function of multiple covariates. The outcome of GWR, for each covariate, was studied for all the 48 states in the contiguous US. As a direct consequence of spatial non-stationarity, it was possible to discuss the influence of each single covariate differently from state to state. In addition, the article includes a first attempt to determine if the observed variability in cost index values could be, at least partially explained by independent socio-economic variables. PMID:25018582

Year-round spatiotemporal distribution of harbour porpoises within and around the Maryland wind energy area

PubMed Central

O’Brien, Michael; Lyubchich, Vyacheslav; Roberts, Jason J.; Halpin, Patrick N.; Rice, Aaron N.; Bailey, Helen

2017-01-01

Offshore windfarms provide renewable energy, but activities during the construction phase can affect marine mammals. To understand how the construction of an offshore windfarm in the Maryland Wind Energy Area (WEA) off Maryland, USA, might impact harbour porpoises (Phocoena phocoena), it is essential to determine their poorly understood year-round distribution. Although habitat-based models can help predict the occurrence of species in areas with limited or no sampling, they require validation to determine the accuracy of the predictions. Incorporating more than 18 months of harbour porpoise detection data from passive acoustic monitoring, generalized auto-regressive moving average and generalized additive models were used to investigate harbour porpoise occurrence within and around the Maryland WEA in relation to temporal and environmental variables. Acoustic detection metrics were compared to habitat-based density estimates derived from aerial and boat-based sightings to validate the model predictions. Harbour porpoises occurred significantly more frequently during January to May, and foraged significantly more often in the evenings to early mornings at sites within and outside the Maryland WEA. Harbour porpoise occurrence peaked at sea surface temperatures of 5°C and chlorophyll a concentrations of 4.5 to 7.4 mg m-3. The acoustic detections were significantly correlated with the predicted densities, except at the most inshore site. This study provides insight into previously unknown fine-scale spatial and temporal patterns in distribution of harbour porpoises offshore of Maryland. The results can be used to help inform future monitoring and mitigate the impacts of windfarm construction and other human activities. PMID:28467455

Year-round spatiotemporal distribution of harbour porpoises within and around the Maryland wind energy area.

PubMed

Wingfield, Jessica E; O'Brien, Michael; Lyubchich, Vyacheslav; Roberts, Jason J; Halpin, Patrick N; Rice, Aaron N; Bailey, Helen

2017-01-01

Offshore windfarms provide renewable energy, but activities during the construction phase can affect marine mammals. To understand how the construction of an offshore windfarm in the Maryland Wind Energy Area (WEA) off Maryland, USA, might impact harbour porpoises (Phocoena phocoena), it is essential to determine their poorly understood year-round distribution. Although habitat-based models can help predict the occurrence of species in areas with limited or no sampling, they require validation to determine the accuracy of the predictions. Incorporating more than 18 months of harbour porpoise detection data from passive acoustic monitoring, generalized auto-regressive moving average and generalized additive models were used to investigate harbour porpoise occurrence within and around the Maryland WEA in relation to temporal and environmental variables. Acoustic detection metrics were compared to habitat-based density estimates derived from aerial and boat-based sightings to validate the model predictions. Harbour porpoises occurred significantly more frequently during January to May, and foraged significantly more often in the evenings to early mornings at sites within and outside the Maryland WEA. Harbour porpoise occurrence peaked at sea surface temperatures of 5°C and chlorophyll a concentrations of 4.5 to 7.4 mg m-3. The acoustic detections were significantly correlated with the predicted densities, except at the most inshore site. This study provides insight into previously unknown fine-scale spatial and temporal patterns in distribution of harbour porpoises offshore of Maryland. The results can be used to help inform future monitoring and mitigate the impacts of windfarm construction and other human activities.

APPLICATION OF THE 3D MODEL OF RAILWAY VIADUCTS TO COST ESTIMATION AND CONSTRUCTION

NASA Astrophysics Data System (ADS)

Fujisawa, Yasuo; Yabuki, Nobuyoshi; Igarashi, Zenichi; Yoshino, Hiroyuki

Three dimensional models of civil engineering structures are only partially used in either design or construction but not both. Research on integration of design, cost estimation and construction by 3Dmodels has not been heard in civil engineering domain yet. Using continuously a 3D product model of a structure from design to construction through estimation should improve the efficiency and decrease the occurrence of mistakes, hence enhancing the quality. In this research, we investigated the current practices of flow from design to construction, particularly focusing on cost estimation. Then, we identified advantages and issues on utilization of 3D design models to estimation and construction by applying 3D models to an actual railway construction project.

Estimating the Uncertain Mathematical Structure of Hydrological Model via Bayesian Data Assimilation

NASA Astrophysics Data System (ADS)

Bulygina, N.; Gupta, H.; O'Donell, G.; Wheater, H.

2008-12-01

The structure of hydrological model at macro scale (e.g. watershed) is inherently uncertain due to many factors, including the lack of a robust hydrological theory at the macro scale. In this work, we assume that a suitable conceptual model for the hydrologic system has already been determined - i.e., the system boundaries have been specified, the important state variables and input and output fluxes to be included have been selected, and the major hydrological processes and geometries of their interconnections have been identified. The structural identification problem then is to specify the mathematical form of the relationships between the inputs, state variables and outputs, so that a computational model can be constructed for making simulations and/or predictions of system input-state-output behaviour. We show how Bayesian data assimilation can be used to merge both prior beliefs in the form of pre-assumed model equations with information derived from the data to construct a posterior model. The approach, entitled Bayesian Estimation of Structure (BESt), is used to estimate a hydrological model for a small basin in England, at hourly time scales, conditioned on the assumption of 3-dimensional state - soil moisture storage, fast and slow flow stores - conceptual model structure. Inputs to the system are precipitation and potential evapotranspiration, and outputs are actual evapotranspiration and streamflow discharge. Results show the difference between prior and posterior mathematical structures, as well as provide prediction confidence intervals that reflect three types of uncertainty: due to initial conditions, due to input and due to mathematical structure.

Linking Field and Satellite Observations to Reveal Differences in Single vs. Double-Cropped Soybean Yields in Central Brazil

NASA Astrophysics Data System (ADS)

Jeffries, G. R.; Cohn, A.

2016-12-01

Soy-corn double cropping (DC) has been widely adopted in Central Brazil alongside single cropped (SC) soybean production. DC involves different cropping calendars, soy varieties, and may be associated with different crop yield patterns and volatility than SC. Study of the performance of the region's agriculture in a changing climate depends on tracking differences in the productivity of SC vs. DC, but has been limited by crop yield data that conflate the two systems. We predicted SC and DC yields across Central Brazil, drawing on field observations and remotely sensed data. We first modeled field yield estimates as a function of remotely sensed DC status and vegetation index (VI) metrics, and other management and biophysical factors. We then used the statistical model estimated to predict SC and DC soybean yields at each 500 m2 grid cell of Central Brazil for harvest years 2001 - 2015. The yield estimation model was constructed using 1) a repeated cross-sectional survey of soybean yields and management factors for years 2007-2015, 2) a custom agricultural land cover classification dataset which assimilates earlier datasets for the region, and 3) 500m 8-day MODIS image composites used to calculate the wide dynamic range vegetation index (WDRVI) and derivative metrics such as area under the curve for WDRVI values in critical crop development periods. A statistical yield estimation model which primarily entails WDRVI metrics, DC status, and spatial fixed effects was developed on a subset of the yield dataset. Model validation was conducted by predicting previously withheld yield records, and then assessing error and goodness-of-fit for predicted values with metrics including root mean squared error (RMSE), mean squared error (MSE), and R2. We found a statistical yield estimation model which incorporates WDRVI and DC status to be way to estimate crop yields over the region. Statistical properties of the resulting gridded yield dataset may be valuable for understanding linkages between crop yields, farm management factors, and climate.

Intervals for posttest probabilities: a comparison of 5 methods.

PubMed

Mossman, D; Berger, J O

2001-01-01

Several medical articles discuss methods of constructing confidence intervals for single proportions and the likelihood ratio, but scant attention has been given to the systematic study of intervals for the posterior odds, or the positive predictive value, of a test. The authors describe 5 methods of constructing confidence intervals for posttest probabilities when estimates of sensitivity, specificity, and the pretest probability of a disorder are derived from empirical data. They then evaluate each method to determine how well the intervals' coverage properties correspond to their nominal value. When the estimates of pretest probabilities, sensitivity, and specificity are derived from more than 80 subjects and are not close to 0 or 1, all methods generate intervals with appropriate coverage properties. When these conditions are not met, however, the best-performing method is an objective Bayesian approach implemented by a simple simulation using a spreadsheet. Physicians and investigators can generate accurate confidence intervals for posttest probabilities in small-sample situations using the objective Bayesian approach.

Student engagement and its relationship with early high school dropout.

PubMed

Archambault, Isabelle; Janosz, Michel; Fallu, Jean-Sébastien; Pagani, Linda S

2009-06-01

Although the concept of school engagement figures prominently in most school dropout theories, there has been little empirical research conducted on its nature and course and, more importantly, the association with dropout. Information on the natural development of school engagement would greatly benefit those interested in preventing student alienation during adolescence. Using a longitudinal sample of 11,827 French-Canadian high school students, we tested behavioral, affective, cognitive indices of engagement both separately and as a global construct. We then assessed their contribution as prospective predictors of school dropout using factor analysis and structural equation modeling. Global engagement reliably predicted school dropout. Among its three specific dimensions, only behavioral engagement made a significant contribution in the prediction equation. Our findings confirm the robustness of the overall multidimensional construct of school engagement, which reflects both cognitive and psychosocial characteristics, and underscore the importance attributed to basic participation and compliance issues in reliably estimating risk of not completing basic schooling during adolescence.

A transcriptomics data-driven gene space accurately predicts liver cytopathology and drug-induced liver injury

PubMed Central

Kohonen, Pekka; Parkkinen, Juuso A.; Willighagen, Egon L.; Ceder, Rebecca; Wennerberg, Krister; Kaski, Samuel; Grafström, Roland C.

2017-01-01

Predicting unanticipated harmful effects of chemicals and drug molecules is a difficult and costly task. Here we utilize a ‘big data compacting and data fusion’—concept to capture diverse adverse outcomes on cellular and organismal levels. The approach generates from transcriptomics data set a ‘predictive toxicogenomics space’ (PTGS) tool composed of 1,331 genes distributed over 14 overlapping cytotoxicity-related gene space components. Involving ∼2.5 × 108 data points and 1,300 compounds to construct and validate the PTGS, the tool serves to: explain dose-dependent cytotoxicity effects, provide a virtual cytotoxicity probability estimate intrinsic to omics data, predict chemically-induced pathological states in liver resulting from repeated dosing of rats, and furthermore, predict human drug-induced liver injury (DILI) from hepatocyte experiments. Analysing 68 DILI-annotated drugs, the PTGS tool outperforms and complements existing tests, leading to a hereto-unseen level of DILI prediction accuracy. PMID:28671182

Modified linear predictive coding approach for moving target tracking by Doppler radar

NASA Astrophysics Data System (ADS)

Ding, Yipeng; Lin, Xiaoyi; Sun, Ke-Hui; Xu, Xue-Mei; Liu, Xi-Yao

2016-07-01

Doppler radar is a cost-effective tool for moving target tracking, which can support a large range of civilian and military applications. A modified linear predictive coding (LPC) approach is proposed to increase the target localization accuracy of the Doppler radar. Based on the time-frequency analysis of the received echo, the proposed approach first real-time estimates the noise statistical parameters and constructs an adaptive filter to intelligently suppress the noise interference. Then, a linear predictive model is applied to extend the available data, which can help improve the resolution of the target localization result. Compared with the traditional LPC method, which empirically decides the extension data length, the proposed approach develops an error array to evaluate the prediction accuracy and thus, adjust the optimum extension data length intelligently. Finally, the prediction error array is superimposed with the predictor output to correct the prediction error. A series of experiments are conducted to illustrate the validity and performance of the proposed techniques.

Simulation-Based Prediction of Equivalent Continuous Noises during Construction Processes

PubMed Central

Zhang, Hong; Pei, Yun

2016-01-01

Quantitative prediction of construction noise is crucial to evaluate construction plans to help make decisions to address noise levels. Considering limitations of existing methods for measuring or predicting the construction noise and particularly the equivalent continuous noise level over a period of time, this paper presents a discrete-event simulation method for predicting the construction noise in terms of equivalent continuous level. The noise-calculating models regarding synchronization, propagation and equivalent continuous level are presented. The simulation framework for modeling the noise-affected factors and calculating the equivalent continuous noise by incorporating the noise-calculating models into simulation strategy is proposed. An application study is presented to demonstrate and justify the proposed simulation method in predicting the equivalent continuous noise during construction. The study contributes to provision of a simulation methodology to quantitatively predict the equivalent continuous noise of construction by considering the relevant uncertainties, dynamics and interactions. PMID:27529266

Simulation-Based Prediction of Equivalent Continuous Noises during Construction Processes.

PubMed

Zhang, Hong; Pei, Yun

2016-08-12

Quantitative prediction of construction noise is crucial to evaluate construction plans to help make decisions to address noise levels. Considering limitations of existing methods for measuring or predicting the construction noise and particularly the equivalent continuous noise level over a period of time, this paper presents a discrete-event simulation method for predicting the construction noise in terms of equivalent continuous level. The noise-calculating models regarding synchronization, propagation and equivalent continuous level are presented. The simulation framework for modeling the noise-affected factors and calculating the equivalent continuous noise by incorporating the noise-calculating models into simulation strategy is proposed. An application study is presented to demonstrate and justify the proposed simulation method in predicting the equivalent continuous noise during construction. The study contributes to provision of a simulation methodology to quantitatively predict the equivalent continuous noise of construction by considering the relevant uncertainties, dynamics and interactions.

Iterative random vs. Kennard-Stone sampling for IR spectrum-based classification task using PLS2-DA

NASA Astrophysics Data System (ADS)

Lee, Loong Chuen; Liong, Choong-Yeun; Jemain, Abdul Aziz

2018-04-01

External testing (ET) is preferred over auto-prediction (AP) or k-fold-cross-validation in estimating more realistic predictive ability of a statistical model. With IR spectra, Kennard-stone (KS) sampling algorithm is often used to split the data into training and test sets, i.e. respectively for model construction and for model testing. On the other hand, iterative random sampling (IRS) has not been the favored choice though it is theoretically more likely to produce reliable estimation. The aim of this preliminary work is to compare performances of KS and IRS in sampling a representative training set from an attenuated total reflectance - Fourier transform infrared spectral dataset (of four varieties of blue gel pen inks) for PLS2-DA modeling. The `best' performance achievable from the dataset is estimated with AP on the full dataset (APF, error). Both IRS (n = 200) and KS were used to split the dataset in the ratio of 7:3. The classic decision rule (i.e. maximum value-based) is employed for new sample prediction via partial least squares - discriminant analysis (PLS2-DA). Error rate of each model was estimated repeatedly via: (a) AP on full data (APF, error); (b) AP on training set (APS, error); and (c) ET on the respective test set (ETS, error). A good PLS2-DA model is expected to produce APS, error and EVS, error that is similar to the APF, error. Bearing that in mind, the similarities between (a) APS, error vs. APF, error; (b) ETS, error vs. APF, error and; (c) APS, error vs. ETS, error were evaluated using correlation tests (i.e. Pearson and Spearman's rank test), using series of PLS2-DA models computed from KS-set and IRS-set, respectively. Overall, models constructed from IRS-set exhibits more similarities between the internal and external error rates than the respective KS-set, i.e. less risk of overfitting. In conclusion, IRS is more reliable than KS in sampling representative training set.

A novel body circumferences-based estimation of percentage body fat.

PubMed

Lahav, Yair; Epstein, Yoram; Kedem, Ron; Schermann, Haggai

2018-03-01

Anthropometric measures of body composition are often used for rapid and cost-effective estimation of percentage body fat (%BF) in field research, serial measurements and screening. Our aim was to develop a validated estimate of %BF for the general population, based on simple body circumferences measures. The study cohort consisted of two consecutive samples of health club members, designated as 'development' (n 476, 61 % men, 39 % women) and 'validation' (n 224, 50 % men, 50 % women) groups. All subjects underwent anthropometric measurements as part of their registration to a health club. Dual-energy X-ray absorptiometry (DEXA) scan was used as the 'gold standard' estimate of %BF. Linear regressions where used to construct the predictive equation (%BFcal). Bland-Altman statistics, Lin concordance coefficients and percentage of subjects falling within 5 % of %BF estimate by DEXA were used to evaluate accuracy and precision of the equation. The variance inflation factor was used to check multicollinearity. Two distinct equations were developed for men and women: %BFcal (men)=10·1-0·239H+0·8A-0·5N; %BFcal (women)=19·2-0·239H+0·8A-0·5N (H, height; A, abdomen; N, neck, all in cm). Bland-Altman differences were randomly distributed and showed no fixed bias. Lin concordance coefficients of %BFcal were 0·89 in men and 0·86 in women. About 79·5 % of %BF predictions in both sexes were within ±5 % of the DEXA value. The Durnin-Womersley skinfolds equation was less accurate in our study group for prediction of %BF than %BFcal. We conclude that %BFcal offers the advantage of obtaining a reliable estimate of %BF from simple measurements that require no sophisticated tools and only a minimal prior training and experience.

Comparative dynamics of avian communities across edges and interiors of North American ecoregions

USGS Publications Warehouse

Karanth, K.K.; Nichols, J.D.; Sauer, J.R.; Hines, J.E.

2006-01-01

Aim Based on a priori hypotheses, we developed predictions about how avian communities might differ at the edges vs. interiors of ecoregions. Specifically, we predicted lower species richness and greater local turnover and extinction probabilities for regional edges. We tested these predictions using North American Breeding Bird Survey (BBS) data across nine ecoregions over a 20-year time period. Location Data from 2238 BBS routes within nine ecoregions of the United States were used. Methods The estimation methods used accounted for species detection probabilities < 1. Parameter estimates for species richness, local turnover and extinction probabilities were obtained using the program COMDYN. We examined the difference in community-level parameters estimated from within exterior edges (the habitat interface between ecoregions), interior edges (the habitat interface between two bird conservation regions within the same ecoregion) and interior (habitat excluding interfaces). General linear models were constructed to examine sources of variation in community parameters for five ecoregions (containing all three habitat types) and all nine ecoregions (containing two habitat types). Results Analyses provided evidence that interior habitats and interior edges had on average higher bird species richness than exterior edges, providing some evidence of reduced species richness near habitat edges. Lower average extinction probabilities and turnover rates in interior habitats (five-region analysis) provided some support for our predictions about these quantities. However, analyses directed at all three response variables, i.e. species richness, local turnover, and local extinction probability, provided evidence of an interaction between habitat and region, indicating that the relationships did not hold in all regions. Main conclusions The overall predictions of lower species richness, higher local turnover and extinction probabilities in regional edge habitats, as opposed to interior habitats, were generally supported. However, these predicted tendencies did not hold in all regions.

Comparison of techniques for correction of magnification of pelvic X-rays for hip surgery planning.

PubMed

The, Bertram; Kootstra, Johan W J; Hosman, Anton H; Verdonschot, Nico; Gerritsma, Carina L E; Diercks, Ron L

2007-12-01

The aim of this study was to develop an accurate method for correction of magnification of pelvic x-rays to enhance accuracy of hip surgery planning. All investigated methods aim at estimating the anteroposterior location of the hip joint in supine position to correctly position a reference object for correction of magnification. An existing method-which is currently being used in clinical practice in our clinics-is based on estimating the position of the hip joint by palpation of the greater trochanter. It is only moderately accurate and difficult to execute reliably in clinical practice. To develop a new method, 99 patients who already had a hip implant in situ were included; this enabled determining the true location of the hip joint deducted from the magnification of the prosthesis. Physical examination was used to obtain predictor variables possibly associated with the height of the hip joint. This included a simple dynamic hip joint examination to estimate the position of the center of rotation. Prediction equations were then constructed using regression analysis. The performance of these prediction equations was compared with the performance of the existing protocol. The mean absolute error in predicting the height of the hip joint center using the old method was 20 mm (range -79 mm to +46 mm). This was 11 mm for the new method (-32 mm to +39 mm). The prediction equation is: height (mm) = 34 + 1/2 abdominal circumference (cm). The newly developed prediction equation is a superior method for predicting the height of the hip joint center for correction of magnification of pelvic x-rays. We recommend its implementation in the departments of radiology and orthopedic surgery.

Does early second-trimester sonography predict adverse perinatal outcomes in monochorionic diamniotic twin pregnancies?

PubMed

Allaf, M Baraa; Campbell, Winston A; Vintzileos, Anthony M; Haeri, Sina; Javadian, Pouya; Shamshirsaz, Amir A; Ogburn, Paul; Figueroa, Reinaldo; Wax, Joseph; Markenson, Glenn; Chavez, Martin R; Ravangard, Samadh F; Ruano, Rodrigo; Sangi-Haghpeykar, Haleh; Salmanian, Bahram; Meyer, Marjorie; Johnson, Jeffery; Ozhand, Ali; Davis, Sarah; Borgida, Adam; Belfort, Michael A; Shamshirsaz, Alireza A

2014-09-01

To determine whether intertwin discordant abdominal circumference, femur length, head circumference, and estimated fetal weight sonographic measurements in early second-trimester monochorionic diamniotic twins predict adverse obstetric and neonatal outcomes. We conducted a multicenter retrospective cohort study involving 9 regional perinatal centers in the United States. We examined the records of all monochorionic diamniotic twin pregnancies with two live fetuses at the 16- to 18-week sonographic examination who had serial follow-up sonography until delivery. The intertwin discordance in abdominal circumference, femur length, head circumference, and estimated fetal weight was calculated as the difference between the two fetuses, expressed as a percentage of the larger using the 16- to 18-week sonographic measurements. An adverse composite obstetric outcome was defined as the occurrence of 1 or more of the following in either fetus: intrauterine growth restriction, twin-twin transfusion syndrome, intrauterine fetal death, abnormal growth discordance (≥20% difference), and very preterm birth at or before 28 weeks. An adverse composite neonatal outcome was defined as the occurrence of 1 or more of the following: respiratory distress syndrome, any stage of intraventricular hemorrhage, 5-minute Apgar score less than 7, necrotizing enterocolitis, culture-proven early-onset sepsis, and neonatal death. Receiver operating characteristic and logistic regression-with-generalized estimating equation analyses were constructed. Among the 177 monochorionic diamniotic twin pregnancies analyzed, intertwin abdominal circumference and estimated fetal weight discordances were only predictive of adverse composite obstetric outcomes (areas under the curve, 79% and 80%, respectively). Receiver operating characteristic curves showed that intertwin discordances in abdominal circumference, femur length, head circumference, and estimated fetal weight were not acceptable predictors of twin-twin transfusion syndrome or adverse neonatal outcomes. In our cohort, only second-trimester abdominal circumference and estimated fetal weight discordances in monochorionic diamniotic twin pregnancies were predictive of adverse composite obstetric outcomes. Twin-twin transfusion syndrome and adverse neonatal outcomes were not predicted by any of the intertwin discordances measured. © 2014 by the American Institute of Ultrasound in Medicine.

Non-additive genetic variation in growth, carcass and fertility traits of beef cattle.

PubMed

Bolormaa, Sunduimijid; Pryce, Jennie E; Zhang, Yuandan; Reverter, Antonio; Barendse, William; Hayes, Ben J; Goddard, Michael E

2015-04-02

A better understanding of non-additive variance could lead to increased knowledge on the genetic control and physiology of quantitative traits, and to improved prediction of the genetic value and phenotype of individuals. Genome-wide panels of single nucleotide polymorphisms (SNPs) have been mainly used to map additive effects for quantitative traits, but they can also be used to investigate non-additive effects. We estimated dominance and epistatic effects of SNPs on various traits in beef cattle and the variance explained by dominance, and quantified the increase in accuracy of phenotype prediction by including dominance deviations in its estimation. Genotype data (729 068 real or imputed SNPs) and phenotypes on up to 16 traits of 10 191 individuals from Bos taurus, Bos indicus and composite breeds were used. A genome-wide association study was performed by fitting the additive and dominance effects of single SNPs. The dominance variance was estimated by fitting a dominance relationship matrix constructed from the 729 068 SNPs. The accuracy of predicted phenotypic values was evaluated by best linear unbiased prediction using the additive and dominance relationship matrices. Epistatic interactions (additive × additive) were tested between each of the 28 SNPs that are known to have additive effects on multiple traits, and each of the other remaining 729 067 SNPs. The number of significant dominance effects was greater than expected by chance and most of them were in the direction that is presumed to increase fitness and in the opposite direction to inbreeding depression. Estimates of dominance variance explained by SNPs varied widely between traits, but had large standard errors. The median dominance variance across the 16 traits was equal to 5% of the phenotypic variance. Including a dominance deviation in the prediction did not significantly increase its accuracy for any of the phenotypes. The number of additive × additive epistatic effects that were statistically significant was greater than expected by chance. Significant dominance and epistatic effects occur for growth, carcass and fertility traits in beef cattle but they are difficult to estimate precisely and including them in phenotype prediction does not increase its accuracy.

A strategy to apply machine learning to small datasets in materials science

NASA Astrophysics Data System (ADS)

Zhang, Ying; Ling, Chen

2018-12-01

There is growing interest in applying machine learning techniques in the research of materials science. However, although it is recognized that materials datasets are typically smaller and sometimes more diverse compared to other fields, the influence of availability of materials data on training machine learning models has not yet been studied, which prevents the possibility to establish accurate predictive rules using small materials datasets. Here we analyzed the fundamental interplay between the availability of materials data and the predictive capability of machine learning models. Instead of affecting the model precision directly, the effect of data size is mediated by the degree of freedom (DoF) of model, resulting in the phenomenon of association between precision and DoF. The appearance of precision-DoF association signals the issue of underfitting and is characterized by large bias of prediction, which consequently restricts the accurate prediction in unknown domains. We proposed to incorporate the crude estimation of property in the feature space to establish ML models using small sized materials data, which increases the accuracy of prediction without the cost of higher DoF. In three case studies of predicting the band gap of binary semiconductors, lattice thermal conductivity, and elastic properties of zeolites, the integration of crude estimation effectively boosted the predictive capability of machine learning models to state-of-art levels, demonstrating the generality of the proposed strategy to construct accurate machine learning models using small materials dataset.

Selecting an Informative/Discriminating Multivariate Response for Inverse Prediction

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

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

The inverse prediction is important in a variety of scientific and engineering applications, such as to predict properties/characteristics of an object by using multiple measurements obtained from it. Inverse prediction can be accomplished by inverting parameterized forward models that relate the measurements (responses) to the properties/characteristics of interest. Sometimes forward models are computational/science based; but often, forward models are empirically based response surface models, obtained by using the results of controlled experimentation. For empirical models, it is important that the experiments provide a sound basis to develop accurate forward models in terms of the properties/characteristics (factors). And while nature dictatesmore » the causal relationships between factors and responses, experimenters can control the complexity, accuracy, and precision of forward models constructed via selection of factors, factor levels, and the set of trials that are performed. Recognition of the uncertainty in the estimated forward models leads to an errors-in-variables approach for inverse prediction. The forward models (estimated by experiments or science based) can also be used to analyze how well candidate responses complement one another for inverse prediction over the range of the factor space of interest. Furthermore, one may find that some responses are complementary, redundant, or noninformative. Simple analysis and examples illustrate how an informative and discriminating subset of responses could be selected among candidates in cases where the number of responses that can be acquired during inverse prediction is limited by difficulty, expense, and/or availability of material.« less

Selecting an Informative/Discriminating Multivariate Response for Inverse Prediction

DOE PAGES

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

2017-07-01

The inverse prediction is important in a variety of scientific and engineering applications, such as to predict properties/characteristics of an object by using multiple measurements obtained from it. Inverse prediction can be accomplished by inverting parameterized forward models that relate the measurements (responses) to the properties/characteristics of interest. Sometimes forward models are computational/science based; but often, forward models are empirically based response surface models, obtained by using the results of controlled experimentation. For empirical models, it is important that the experiments provide a sound basis to develop accurate forward models in terms of the properties/characteristics (factors). And while nature dictatesmore » the causal relationships between factors and responses, experimenters can control the complexity, accuracy, and precision of forward models constructed via selection of factors, factor levels, and the set of trials that are performed. Recognition of the uncertainty in the estimated forward models leads to an errors-in-variables approach for inverse prediction. The forward models (estimated by experiments or science based) can also be used to analyze how well candidate responses complement one another for inverse prediction over the range of the factor space of interest. Furthermore, one may find that some responses are complementary, redundant, or noninformative. Simple analysis and examples illustrate how an informative and discriminating subset of responses could be selected among candidates in cases where the number of responses that can be acquired during inverse prediction is limited by difficulty, expense, and/or availability of material.« less

Kinetic microplate bioassays for relative potency of antibiotics improved by partial Least Square (PLS) regression.

PubMed

Francisco, Fabiane Lacerda; Saviano, Alessandro Morais; Almeida, Túlia de Souza Botelho; Lourenço, Felipe Rebello

2016-05-01

Microbiological assays are widely used to estimate the relative potencies of antibiotics in order to guarantee the efficacy, safety, and quality of drug products. Despite of the advantages of turbidimetric bioassays when compared to other methods, it has limitations concerning the linearity and range of the dose-response curve determination. Here, we proposed to use partial least squares (PLS) regression to solve these limitations and to improve the prediction of relative potencies of antibiotics. Kinetic-reading microplate turbidimetric bioassays for apramacyin and vancomycin were performed using Escherichia coli (ATCC 8739) and Bacillus subtilis (ATCC 6633), respectively. Microbial growths were measured as absorbance up to 180 and 300min for apramycin and vancomycin turbidimetric bioassays, respectively. Conventional dose-response curves (absorbances or area under the microbial growth curve vs. log of antibiotic concentration) showed significant regression, however there were significant deviation of linearity. Thus, they could not be used for relative potency estimations. PLS regression allowed us to construct a predictive model for estimating the relative potencies of apramycin and vancomycin without over-fitting and it improved the linear range of turbidimetric bioassay. In addition, PLS regression provided predictions of relative potencies equivalent to those obtained from agar diffusion official methods. Therefore, we conclude that PLS regression may be used to estimate the relative potencies of antibiotics with significant advantages when compared to conventional dose-response curve determination. Copyright © 2016 Elsevier B.V. All rights reserved.

Using Simplistic Shape/Surface Models to Predict Brightness in Estimation Filters

NASA Astrophysics Data System (ADS)

Wetterer, C.; Sheppard, D.; Hunt, B.

The prerequisite for using brightness (radiometric flux intensity) measurements in an estimation filter is to have a measurement function that accurately predicts a space objects brightness for variations in the parameters of interest. These parameters include changes in attitude and articulations of particular components (e.g. solar panel east-west offsets to direct sun-tracking). Typically, shape models and bidirectional reflectance distribution functions are combined to provide this forward light curve modeling capability. To achieve precise orbit predictions with the inclusion of shape/surface dependent forces such as radiation pressure, relatively complex and sophisticated modeling is required. Unfortunately, increasing the complexity of the models makes it difficult to estimate all those parameters simultaneously because changes in light curve features can now be explained by variations in a number of different properties. The classic example of this is the connection between the albedo and the area of a surface. If, however, the desire is to extract information about a single and specific parameter or feature from the light curve, a simple shape/surface model could be used. This paper details an example of this where a complex model is used to create simulated light curves, and then a simple model is used in an estimation filter to extract out a particular feature of interest. In order for this to be successful, however, the simple model must be first constructed using training data where the feature of interest is known or at least known to be constant.

GIS-based spatial regression and prediction of water quality in river networks: A case study in Iowa

USGS Publications Warehouse

Yang, X.; Jin, W.

2010-01-01

Nonpoint source pollution is the leading cause of the U.S.'s water quality problems. One important component of nonpoint source pollution control is an understanding of what and how watershed-scale conditions influence ambient water quality. This paper investigated the use of spatial regression to evaluate the impacts of watershed characteristics on stream NO3NO2-N concentration in the Cedar River Watershed, Iowa. An Arc Hydro geodatabase was constructed to organize various datasets on the watershed. Spatial regression models were developed to evaluate the impacts of watershed characteristics on stream NO3NO2-N concentration and predict NO3NO2-N concentration at unmonitored locations. Unlike the traditional ordinary least square (OLS) method, the spatial regression method incorporates the potential spatial correlation among the observations in its coefficient estimation. Study results show that NO3NO2-N observations in the Cedar River Watershed are spatially correlated, and by ignoring the spatial correlation, the OLS method tends to over-estimate the impacts of watershed characteristics on stream NO3NO2-N concentration. In conjunction with kriging, the spatial regression method not only makes better stream NO3NO2-N concentration predictions than the OLS method, but also gives estimates of the uncertainty of the predictions, which provides useful information for optimizing the design of stream monitoring network. It is a promising tool for better managing and controlling nonpoint source pollution. ?? 2010 Elsevier Ltd.

Distributed Prognostics based on Structural Model Decomposition

NASA Technical Reports Server (NTRS)

Daigle, Matthew J.; Bregon, Anibal; Roychoudhury, I.

2014-01-01

Within systems health management, prognostics focuses on predicting the remaining useful life of a system. In the model-based prognostics paradigm, physics-based models are constructed that describe the operation of a system and how it fails. Such approaches consist of an estimation phase, in which the health state of the system is first identified, and a prediction phase, in which the health state is projected forward in time to determine the end of life. Centralized solutions to these problems are often computationally expensive, do not scale well as the size of the system grows, and introduce a single point of failure. In this paper, we propose a novel distributed model-based prognostics scheme that formally describes how to decompose both the estimation and prediction problems into independent local subproblems whose solutions may be easily composed into a global solution. The decomposition of the prognostics problem is achieved through structural decomposition of the underlying models. The decomposition algorithm creates from the global system model a set of local submodels suitable for prognostics. Independent local estimation and prediction problems are formed based on these local submodels, resulting in a scalable distributed prognostics approach that allows the local subproblems to be solved in parallel, thus offering increases in computational efficiency. Using a centrifugal pump as a case study, we perform a number of simulation-based experiments to demonstrate the distributed approach, compare the performance with a centralized approach, and establish its scalability. Index Terms-model-based prognostics, distributed prognostics, structural model decomposition ABBREVIATIONS

Prediction and discovery of new geothermal resources in the Great Basin: Multiple evidence of a large undiscovered resource base

USGS Publications Warehouse

Coolbaugh, M.F.; Raines, G.L.; Zehner, R.E.; Shevenell, L.; Williams, C.F.

2006-01-01

Geothermal potential maps by themselves cannot directly be used to estimate undiscovered resources. To address the undiscovered resource base in the Great Basin, a new and relatively quantitative methodology is presented. The methodology involves three steps, the first being the construction of a data-driven probabilistic model of the location of known geothermal systems using weights of evidence. The second step is the construction of a degree-of-exploration model. This degree-of-exploration model uses expert judgment in a fuzzy logic context to estimate how well each spot in the state has been explored, using as constraints digital maps of the depth to the water table, presence of the carbonate aquifer, and the location, depth, and type of drill-holes. Finally, the exploration model and the data-driven occurrence model are combined together quantitatively using area-weighted modifications to the weights-of-evidence equations. Using this methodology in the state of Nevada, the number of undiscovered geothermal systems with reservoir temperatures ???100??C is estimated at 157, which is 3.2 times greater than the 69 known systems. Currently, nine of the 69 known systems are producing electricity. If it is conservatively assumed that an additional nine for a total of 18 of the known systems will eventually produce electricity, then the model predicts 59 known and undiscovered geothermal systems are capable of producing electricity under current economic conditions in the state, a figure that is more than six times higher than the current number. Many additional geothermal systems could potentially become economic under improved economic conditions or with improved methods of reservoir stimulation (Enhanced Geothermal Systems).This large predicted geothermal resource base appears corroborated by recent grass-roots geothermal discoveries in the state of Nevada. At least two and possibly three newly recognized geothermal systems with estimated reservoir temperatures ???150??C have been identified on the Pyramid Lake Paiute Reservation in west-central Nevada. Evidence of three blind geothermal systems has recently been uncovered near the borate-bearing playas at Rhodes, Teels, and Columbus Marshes in southwestern Nevada. Recent gold exploration drilling has resulted in at least four new geothermal discoveries, including the McGinness Hills geothermal system with an estimated reservoir temperature of roughly 200??C. All of this evidence suggests that the potential for expansion of geothermal power production in Nevada is significant.

Spatial distribution estimation of malaria in northern China and its scenarios in 2020, 2030, 2040 and 2050.

PubMed

Song, Yongze; Ge, Yong; Wang, Jinfeng; Ren, Zhoupeng; Liao, Yilan; Peng, Junhuan

2016-07-07

Malaria is one of the most severe parasitic diseases in the world. Spatial distribution estimation of malaria and its future scenarios are important issues for malaria control and elimination. Furthermore, sophisticated nonlinear relationships for prediction between malaria incidence and potential variables have not been well constructed in previous research. This study aims to estimate these nonlinear relationships and predict future malaria scenarios in northern China. Nonlinear relationships between malaria incidence and predictor variables were constructed using a genetic programming (GP) method, to predict the spatial distributions of malaria under climate change scenarios. For this, the examples of monthly average malaria incidence were used in each county of northern China from 2004 to 2010. Among the five variables at county level, precipitation rate and temperature are used for projections, while elevation, water density index, and gross domestic product are held at their present-day values. Average malaria incidence was 0.107 ‰ per annum in northern China, with incidence characteristics in significant spatial clustering. A GP-based model fit the relationships with average relative error (ARE) = 8.127 % for training data (R(2) = 0.825) and 17.102 % for test data (R(2) = 0.532). The fitness of GP results are significantly improved compared with those by generalized additive models (GAM) and linear regressions. With the future precipitation rate and temperature conditions in Special Report on Emission Scenarios (SRES) family B1, A1B and A2 scenarios, spatial distributions and changes in malaria incidences in 2020, 2030, 2040 and 2050 were predicted and mapped. The GP method increases the precision of predicting the spatial distribution of malaria incidence. With the assumption of varied precipitation rate and temperature, and other variables controlled, the relationships between incidence and the varied variables appear sophisticated nonlinearity and spatially differentiation. Using the future fluctuated precipitation and the increased temperature, median malaria incidence in 2020, 2030, 2040 and 2050 would significantly increase that it might increase 19 to 29 % in 2020, but currently China is in the malaria elimination phase, indicating that the effective strategies and actions had been taken. While the mean incidences will not increase even reduce due to the incidence reduction in high-risk regions but the simultaneous expansion of the high-risk areas.

The link between judgments of comparative risk and own risk: further evidence.

PubMed

Gold, Ron S

2007-03-01

Individuals typically believe that they are less likely than the average person to experience negative events, a phenomenon termed "unrealistic optimism". The direct method of assessing unrealistic optimism employs a question of the form, "Compared with the average person, what is the chance that X will occur to you?". However, it has been proposed that responses to such a question (direct-estimates) are based essentially just on estimates that X will occur to the self (self-estimates). If this is so, any factors that affect one of these estimates should also affect the other. This prediction was tested in two experiments. In each, direct- and self-estimates for an unfamiliar health threat - homocysteine-related heart problems - were recorded. It was found that both types of estimate were affected in the same way by varying the stated probability of having unsafe levels of homocysteine (Study 1, N=149) and varying the stated probability that unsafe levels of homocysteine will lead to heart problems (Study 2, N=111). The results are consistent with the proposal that direct-estimates are constructed just from self-estimates.

Prediction Model for Impulsive Noise on Structures

DTIC Science & Technology

2012-09-01

construction usually have an interior wall finish of: a) gypsum wallboard (also called plasterboard or drywall), b) plaster or c) wood paneling... Gypsum Plaster , Wall Board 11,67 0.04 NA For simply-supported beams vibrating in their fundamental mode, the value of KS is needed for...Dev of log10(f0) for wood panel interior to be average for wood walls with plaster or gypsum board interior. (8) L(w) based on estimated standard

Identifying Factors That Predict Promotion Time to E-4 and Re-Enlistment Eligibility for U.S. Marine Corps Field Radio Operators

DTIC Science & Technology

2014-12-01

Primary Military Occupational Specialty PRO Proficiency Q-Q Quantile - Quantile RSS Residual Sum of Squares SI Shop Information T&R Training and...construct multivariate linear regression models to estimate Marines’ Computed Tier Score and time to achieve E-4 based on their individual personal...Science (GS) score, ASVAB Mathematics Knowledge (MK) score, ASVAB Paragraph Comprehension (PC) score, weight , and whether a Marine receives a weight

Stereotype Threat and College Academic Performance: A Latent Variables Approach*

PubMed Central

Owens, Jayanti; Massey, Douglas S.

2013-01-01

Stereotype threat theory has gained experimental and survey-based support in helping explain the academic underperformance of minority students at selective colleges and universities. Stereotype threat theory states that minority students underperform because of pressures created by negative stereotypes about their racial group. Past survey-based studies, however, are characterized by methodological inefficiencies and potential biases: key theoretical constructs have only been measured using summed indicators and predicted relationships modeled using ordinary least squares. Using the National Longitudinal Survey of Freshman, this study overcomes previous methodological shortcomings by developing a latent construct model of stereotype threat. Theoretical constructs and equations are estimated simultaneously from multiple indicators, yielding a more reliable, valid, and parsimonious test of key propositions. Findings additionally support the view that social stigma can indeed have strong negative effects on the academic performance of pejoratively stereotyped racial-minority group members, not only in laboratory settings, but also in the real world. PMID:23950616

Children's construction task performance and spatial ability: controlling task complexity and predicting mathematics performance.

PubMed

Richardson, Miles; Hunt, Thomas E; Richardson, Cassandra

2014-12-01

This paper presents a methodology to control construction task complexity and examined the relationships between construction performance and spatial and mathematical abilities in children. The study included three groups of children (N = 96); ages 7-8, 10-11, and 13-14 years. Each group constructed seven pre-specified objects. The study replicated and extended previous findings that indicated that the extent of component symmetry and variety, and the number of components for each object and available for selection, significantly predicted construction task difficulty. Results showed that this methodology is a valid and reliable technique for assessing and predicting construction play task difficulty. Furthermore, construction play performance predicted mathematical attainment independently of spatial ability.

Construct-level predictive validity of educational attainment and intellectual aptitude tests in medical student selection: meta-regression of six UK longitudinal studies.

PubMed

McManus, I C; Dewberry, Chris; Nicholson, Sandra; Dowell, Jonathan S; Woolf, Katherine; Potts, Henry W W

2013-11-14

Measures used for medical student selection should predict future performance during training. A problem for any selection study is that predictor-outcome correlations are known only in those who have been selected, whereas selectors need to know how measures would predict in the entire pool of applicants. That problem of interpretation can be solved by calculating construct-level predictive validity, an estimate of true predictor-outcome correlation across the range of applicant abilities. Construct-level predictive validities were calculated in six cohort studies of medical student selection and training (student entry, 1972 to 2009) for a range of predictors, including A-levels, General Certificates of Secondary Education (GCSEs)/O-levels, and aptitude tests (AH5 and UK Clinical Aptitude Test (UKCAT)). Outcomes included undergraduate basic medical science and finals assessments, as well as postgraduate measures of Membership of the Royal Colleges of Physicians of the United Kingdom (MRCP(UK)) performance and entry in the Specialist Register. Construct-level predictive validity was calculated with the method of Hunter, Schmidt and Le (2006), adapted to correct for right-censorship of examination results due to grade inflation. Meta-regression analyzed 57 separate predictor-outcome correlations (POCs) and construct-level predictive validities (CLPVs). Mean CLPVs are substantially higher (.450) than mean POCs (.171). Mean CLPVs for first-year examinations, were high for A-levels (.809; CI: .501 to .935), and lower for GCSEs/O-levels (.332; CI: .024 to .583) and UKCAT (mean = .245; CI: .207 to .276). A-levels had higher CLPVs for all undergraduate and postgraduate assessments than did GCSEs/O-levels and intellectual aptitude tests. CLPVs of educational attainment measures decline somewhat during training, but continue to predict postgraduate performance. Intellectual aptitude tests have lower CLPVs than A-levels or GCSEs/O-levels. Educational attainment has strong CLPVs for undergraduate and postgraduate performance, accounting for perhaps 65% of true variance in first year performance. Such CLPVs justify the use of educational attainment measure in selection, but also raise a key theoretical question concerning the remaining 35% of variance (and measurement error, range restriction and right-censorship have been taken into account). Just as in astrophysics, 'dark matter' and 'dark energy' are posited to balance various theoretical equations, so medical student selection must also have its 'dark variance', whose nature is not yet properly characterized, but explains a third of the variation in performance during training. Some variance probably relates to factors which are unpredictable at selection, such as illness or other life events, but some is probably also associated with factors such as personality, motivation or study skills.

Construct-level predictive validity of educational attainment and intellectual aptitude tests in medical student selection: meta-regression of six UK longitudinal studies

PubMed Central

2013-01-01

Background Measures used for medical student selection should predict future performance during training. A problem for any selection study is that predictor-outcome correlations are known only in those who have been selected, whereas selectors need to know how measures would predict in the entire pool of applicants. That problem of interpretation can be solved by calculating construct-level predictive validity, an estimate of true predictor-outcome correlation across the range of applicant abilities. Methods Construct-level predictive validities were calculated in six cohort studies of medical student selection and training (student entry, 1972 to 2009) for a range of predictors, including A-levels, General Certificates of Secondary Education (GCSEs)/O-levels, and aptitude tests (AH5 and UK Clinical Aptitude Test (UKCAT)). Outcomes included undergraduate basic medical science and finals assessments, as well as postgraduate measures of Membership of the Royal Colleges of Physicians of the United Kingdom (MRCP(UK)) performance and entry in the Specialist Register. Construct-level predictive validity was calculated with the method of Hunter, Schmidt and Le (2006), adapted to correct for right-censorship of examination results due to grade inflation. Results Meta-regression analyzed 57 separate predictor-outcome correlations (POCs) and construct-level predictive validities (CLPVs). Mean CLPVs are substantially higher (.450) than mean POCs (.171). Mean CLPVs for first-year examinations, were high for A-levels (.809; CI: .501 to .935), and lower for GCSEs/O-levels (.332; CI: .024 to .583) and UKCAT (mean = .245; CI: .207 to .276). A-levels had higher CLPVs for all undergraduate and postgraduate assessments than did GCSEs/O-levels and intellectual aptitude tests. CLPVs of educational attainment measures decline somewhat during training, but continue to predict postgraduate performance. Intellectual aptitude tests have lower CLPVs than A-levels or GCSEs/O-levels. Conclusions Educational attainment has strong CLPVs for undergraduate and postgraduate performance, accounting for perhaps 65% of true variance in first year performance. Such CLPVs justify the use of educational attainment measure in selection, but also raise a key theoretical question concerning the remaining 35% of variance (and measurement error, range restriction and right-censorship have been taken into account). Just as in astrophysics, ‘dark matter’ and ‘dark energy’ are posited to balance various theoretical equations, so medical student selection must also have its ‘dark variance’, whose nature is not yet properly characterized, but explains a third of the variation in performance during training. Some variance probably relates to factors which are unpredictable at selection, such as illness or other life events, but some is probably also associated with factors such as personality, motivation or study skills. PMID:24229353

Economic Impacts of Wind Turbine Development in U.S. Counties

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

J., Brown; B., Hoen; E., Lantz

2011-07-25

The objective is to address the research question using post-project construction, county-level data, and econometric evaluation methods. Wind energy is expanding rapidly in the United States: Over the last 4 years, wind power has contributed approximately 35 percent of all new electric power capacity. Wind power plants are often developed in rural areas where local economic development impacts from the installation are projected, including land lease and property tax payments and employment growth during plant construction and operation. Wind energy represented 2.3 percent of the U.S. electricity supply in 2010, but studies show that penetrations of at least 20 percentmore » are feasible. Several studies have used input-output models to predict direct, indirect, and induced economic development impacts. These analyses have often been completed prior to project construction. Available studies have not yet investigated the economic development impacts of wind development at the county level using post-construction econometric evaluation methods. Analysis of county-level impacts is limited. However, previous county-level analyses have estimated operation-period employment at 0.2 to 0.6 jobs per megawatt (MW) of power installed and earnings at $9,000/MW to $50,000/MW. We find statistically significant evidence of positive impacts of wind development on county-level per capita income from the OLS and spatial lag models when they are applied to the full set of wind and non-wind counties. The total impact on annual per capita income of wind turbine development (measured in MW per capita) in the spatial lag model was $21,604 per MW. This estimate is within the range of values estimated in the literature using input-output models. OLS results for the wind-only counties and matched samples are similar in magnitude, but are not statistically significant at the 10-percent level. We find a statistically significant impact of wind development on employment in the OLS analysis for wind counties only, but not in the other models. Our estimates of employment impacts are not precise enough to assess the validity of employment impacts from input-output models applied in advance of wind energy project construction. The analysis provides empirical evidence of positive income effects at the county level from cumulative wind turbine development, consistent with the range of impacts estimated using input-output models. Employment impacts are less clear.« less

Analysis and prediction of Doppler noise during solar conjunctions

NASA Technical Reports Server (NTRS)

Berman, A. L.; Rockwell, S. T.

1975-01-01

The results of a study of Doppler data noise during solar conjunctions were presented. During the first half of 1975, a sizeable data base of Doppler data noise (estimates) for the Pioneer 10, Pioneer 11, and Helios 1 solar conjunctions was accumulated. To analyze this data, certain physical assumptions are made, leading to the development of a geometric parameter ("ISI") which correlates strongly with Doppler data noise under varying sun-earth-spacecraft geometries. Doppler noise models are then constructed from this parameter, resulting in the newfound ability to predict Doppler data noise during solar conjunctions, and hence to additionally be in a position to validate Doppler data acquired during solar conjunctions.

Variability in wood-frame building damage using broad-band synthetic ground motions: a comparative numerical study with recorded motions

USGS Publications Warehouse

Pei, Shiling; van de Lindt, John W.; Hartzell, Stephen; Luco, Nicolas

2014-01-01

Earthquake damage to light-frame wood buildings is a major concern for North America because of the volume of this construction type. In order to estimate wood building damage using synthetic ground motions, we need to verify the ability of synthetically generated ground motions to simulate realistic damage for this structure type. Through a calibrated damage potential indicator, four different synthetic ground motion models are compared with the historically recorded ground motions at corresponding sites. We conclude that damage for sites farther from the fault (>20 km) is under-predicted on average and damage at closer sites is sometimes over-predicted.

Estimation of Random Medium Parameters from 2D Post-Stack Seismic Data and Its Application in Seismic Inversion

NASA Astrophysics Data System (ADS)

Yang, X.; Zhu, P.; Gu, Y.; Xu, Z.

2015-12-01

Small scale heterogeneities of subsurface medium can be characterized conveniently and effectively using a few simple random medium parameters (RMP), such as autocorrelation length, angle and roughness factor, etc. The estimation of these parameters is significant in both oil reservoir prediction and metallic mine exploration. Poor accuracy and low stability existed in current estimation approaches limit the application of random medium theory in seismic exploration. This study focuses on improving the accuracy and stability of RMP estimation from post-stacked seismic data and its application in the seismic inversion. Experiment and theory analysis indicate that, although the autocorrelation of random medium is related to those of corresponding post-stacked seismic data, the relationship is obviously affected by the seismic dominant frequency, the autocorrelation length, roughness factor and so on. Also the error of calculation of autocorrelation in the case of finite and discrete model decreases the accuracy. In order to improve the precision of estimation of RMP, we design two improved approaches. Firstly, we apply region growing algorithm, which often used in image processing, to reduce the influence of noise in the autocorrelation calculated by the power spectrum method. Secondly, the orientation of autocorrelation is used as a new constraint in the estimation algorithm. The numerical experiments proved that it is feasible. In addition, in post-stack seismic inversion of random medium, the estimated RMP may be used to constrain inverse procedure and to construct the initial model. The experiment results indicate that taking inversed model as random medium and using relatively accurate estimated RMP to construct initial model can get better inversion result, which contained more details conformed to the actual underground medium.

Comparing two correlated C indices with right-censored survival outcome: a one-shot nonparametric approach

PubMed Central

Kang, Le; Chen, Weijie; Petrick, Nicholas A.; Gallas, Brandon D.

2014-01-01

The area under the receiver operating characteristic (ROC) curve (AUC) is often used as a summary index of the diagnostic ability in evaluating biomarkers when the clinical outcome (truth) is binary. When the clinical outcome is right-censored survival time, the C index, motivated as an extension of AUC, has been proposed by Harrell as a measure of concordance between a predictive biomarker and the right-censored survival outcome. In this work, we investigate methods for statistical comparison of two diagnostic or predictive systems, of which they could either be two biomarkers or two fixed algorithms, in terms of their C indices. We adopt a U-statistics based C estimator that is asymptotically normal and develop a nonparametric analytical approach to estimate the variance of the C estimator and the covariance of two C estimators. A z-score test is then constructed to compare the two C indices. We validate our one-shot nonparametric method via simulation studies in terms of the type I error rate and power. We also compare our one-shot method with resampling methods including the jackknife and the bootstrap. Simulation results show that the proposed one-shot method provides almost unbiased variance estimations and has satisfactory type I error control and power. Finally, we illustrate the use of the proposed method with an example from the Framingham Heart Study. PMID:25399736

Artificial Neural Networks applied to estimate permeability, porosity and intrinsic attenuation using seismic attributes and well-log data

NASA Astrophysics Data System (ADS)

Iturrarán-Viveros, Ursula; Parra, Jorge O.

2014-08-01

Permeability and porosity are two fundamental reservoir properties which relate to the amount of fluid contained in a reservoir and its ability to flow. The intrinsic attenuation is another important parameter since it is related to porosity, permeability, oil and gas saturation and these parameters significantly affect the seismic signature of a reservoir. We apply Artificial Neural Network (ANN) models to predict permeability (k) and porosity (ϕ) for a carbonate aquifer in southeastern Florida and to predict intrinsic attenuation (1/Q) for a sand-shale oil reservoir in northeast Texas. In this study, the Gamma test (a revolutionary estimator of the noise in a data set) has been used as a mathematically non-parametric nonlinear smooth modeling tool to choose the best input combination of seismic attributes to estimate k and ϕ, and the best combination of well-logs to estimate 1/Q. This saves time during the construction and training of ANN models and also sets a lower bound for the mean squared error to prevent over-training. The Neural Network method successfully delineates a highly permeable zone that corresponds to a high water production in the aquifer. The Gamma test found nonlinear relations that were not visible to linear regression allowing us to generalize the ANN estimations of k, ϕ and 1/Q for their respective sets of patterns that were not used during the learning phase.

A new self-report inventory of dyslexia for students: criterion and construct validity.

PubMed

Tamboer, Peter; Vorst, Harrie C M

2015-02-01

The validity of a Dutch self-report inventory of dyslexia was ascertained in two samples of students. Six biographical questions, 20 general language statements and 56 specific language statements were based on dyslexia as a multi-dimensional deficit. Dyslexia and non-dyslexia were assessed with two criteria: identification with test results (Sample 1) and classification using biographical information (both samples). Using discriminant analyses, these criteria were predicted with various groups of statements. All together, 11 discriminant functions were used to estimate classification accuracy of the inventory. In Sample 1, 15 statements predicted the test criterion with classification accuracy of 98%, and 18 statements predicted the biographical criterion with classification accuracy of 97%. In Sample 2, 16 statements predicted the biographical criterion with classification accuracy of 94%. Estimations of positive and negative predictive value were 89% and 99%. Items of various discriminant functions were factor analysed to find characteristic difficulties of students with dyslexia, resulting in a five-factor structure in Sample 1 and a four-factor structure in Sample 2. Answer bias was investigated with measures of internal consistency reliability. Less than 20 self-report items are sufficient to accurately classify students with and without dyslexia. This supports the usefulness of self-assessment of dyslexia as a valid alternative to diagnostic test batteries. Copyright © 2015 John Wiley & Sons, Ltd.

Estimating the outcome of a pregnancy test: women's judgements in foresight and hindsight.

PubMed

Pennington, D C; Rutter, D R; McKenna, K; Morley, I E

1980-11-01

Previous research on judgement under uncertainty has suggested that, when we know the outcome of some event, we perceive that outcome as more likely than when we do not have outcome knowledge. That is, in comparison with judgements made in foresight, judgements made in hindsight are biased in the direction of the outcome the judge believes to have happened. While the effect appears to be robust in the laboratory, it has very seldom been tested in real life. This experiment therefore went outside the laboratory, and examined women's estimates of the outcome of a pregnacy test. It was predicted that those who knew the result of their test (hindsight) would perceive that outcome as more likely than those asked to make the estimate before they knew the result (foresight). The prediction was supported only for women whose result was positive and, furthermore, the positive group made consistently higher estimates than the negative group, both in hindsight and foresight. The findings were therefore less marked and more complex than in previous laboratory research, and support the argument that experiments and materials must be constructed with salience for the subjects. The findings are interpreted in the light ot Tversky & Kahneman's (1974) work on heuristic rules of thinking.

Using prediction uncertainty analysis to design hydrologic monitoring networks: Example applications from the Great Lakes water availability pilot project

USGS Publications Warehouse

Fienen, Michael N.; Doherty, John E.; Hunt, Randall J.; Reeves, Howard W.

2010-01-01

The importance of monitoring networks for resource-management decisions is becoming more recognized, in both theory and application. Quantitative computer models provide a science-based framework to evaluate the efficacy and efficiency of existing and possible future monitoring networks. In the study described herein, two suites of tools were used to evaluate the worth of new data for specific predictions, which in turn can support efficient use of resources needed to construct a monitoring network. The approach evaluates the uncertainty of a model prediction and, by using linear propagation of uncertainty, estimates how much uncertainty could be reduced if the model were calibrated with addition information (increased a priori knowledge of parameter values or new observations). The theoretical underpinnings of the two suites of tools addressing this technique are compared, and their application to a hypothetical model based on a local model inset into the Great Lakes Water Availability Pilot model are described. Results show that meaningful guidance for monitoring network design can be obtained by using the methods explored. The validity of this guidance depends substantially on the parameterization as well; hence, parameterization must be considered not only when designing the parameter-estimation paradigm but also-importantly-when designing the prediction-uncertainty paradigm.

Use seismic colored inversion and power law committee machine based on imperial competitive algorithm for improving porosity prediction in a heterogeneous reservoir

NASA Astrophysics Data System (ADS)

Ansari, Hamid Reza

2014-09-01

In this paper we propose a new method for predicting rock porosity based on a combination of several artificial intelligence systems. The method focuses on one of the Iranian carbonate fields in the Persian Gulf. Because there is strong heterogeneity in carbonate formations, estimation of rock properties experiences more challenge than sandstone. For this purpose, seismic colored inversion (SCI) and a new approach of committee machine are used in order to improve porosity estimation. The study comprises three major steps. First, a series of sample-based attributes is calculated from 3D seismic volume. Acoustic impedance is an important attribute that is obtained by the SCI method in this study. Second, porosity log is predicted from seismic attributes using common intelligent computation systems including: probabilistic neural network (PNN), radial basis function network (RBFN), multi-layer feed forward network (MLFN), ε-support vector regression (ε-SVR) and adaptive neuro-fuzzy inference system (ANFIS). Finally, a power law committee machine (PLCM) is constructed based on imperial competitive algorithm (ICA) to combine the results of all previous predictions in a single solution. This technique is called PLCM-ICA in this paper. The results show that PLCM-ICA model improved the results of neural networks, support vector machine and neuro-fuzzy system.

Ecological risk assessment on heavy metals in soils: Use of soil diffuse reflectance mid-infrared Fourier-transform spectroscopy

PubMed Central

Wang, Cheng; Li, Wei; Guo, Mingxing; Ji, Junfeng

2017-01-01

The bioavailability of heavy metals in soil is controlled by their concentrations and soil properties. Diffuse reflectance mid-infrared Fourier-transform spectroscopy (DRIFTS) is capable of detecting specific organic and inorganic bonds in metal complexes and minerals and therefore, has been employed to predict soil composition and heavy metal contents. The present study explored the potential of DRIFTS for estimating soil heavy metal bioavailability. Soil and corresponding wheat grain samples from the Yangtze River Delta region were analyzed by DRIFTS and chemical methods. Statistical regression analyses were conducted to correlate the soil spectral information to the concentrations of Cd, Cr, Cu, Zn, Pb, Ni, Hg and Fe in wheat grains. The principal components in the spectra influencing soil heavy metal bioavailability were identified and used in prediction model construction. The established soil DRIFTS-based prediction models were applied to estimate the heavy metal concentrations in wheat grains in the mid-Yangtze River Delta area. The predicted heavy metal concentrations of wheat grain were highly consistent with the measured levels by chemical analysis, showing a significant correlation (r2 > 0.72) with acceptable root mean square error RMSE. In conclusion, DRIFTS is a promising technique for assessing the bioavailability of soil heavy metals and related ecological risk. PMID:28198802

Chaos synchronization and Nelder-Mead search for parameter estimation in nonlinear pharmacological systems: Estimating tumor antigenicity in a model of immunotherapy.

PubMed

Pillai, Nikhil; Craig, Morgan; Dokoumetzidis, Aristeidis; Schwartz, Sorell L; Bies, Robert; Freedman, Immanuel

2018-06-19

In mathematical pharmacology, models are constructed to confer a robust method for optimizing treatment. The predictive capability of pharmacological models depends heavily on the ability to track the system and to accurately determine parameters with reference to the sensitivity in projected outcomes. To closely track chaotic systems, one may choose to apply chaos synchronization. An advantageous byproduct of this methodology is the ability to quantify model parameters. In this paper, we illustrate the use of chaos synchronization combined with Nelder-Mead search to estimate parameters of the well-known Kirschner-Panetta model of IL-2 immunotherapy from noisy data. Chaos synchronization with Nelder-Mead search is shown to provide more accurate and reliable estimates than Nelder-Mead search based on an extended least squares (ELS) objective function. Our results underline the strength of this approach to parameter estimation and provide a broader framework of parameter identification for nonlinear models in pharmacology. Copyright © 2018 Elsevier Ltd. All rights reserved.

Statistical methods of estimating mining costs

USGS Publications Warehouse

Long, K.R.

2011-01-01

Until it was defunded in 1995, the U.S. Bureau of Mines maintained a Cost Estimating System (CES) for prefeasibility-type economic evaluations of mineral deposits and estimating costs at producing and non-producing mines. This system had a significant role in mineral resource assessments to estimate costs of developing and operating known mineral deposits and predicted undiscovered deposits. For legal reasons, the U.S. Geological Survey cannot update and maintain CES. Instead, statistical tools are under development to estimate mining costs from basic properties of mineral deposits such as tonnage, grade, mineralogy, depth, strip ratio, distance from infrastructure, rock strength, and work index. The first step was to reestimate "Taylor's Rule" which relates operating rate to available ore tonnage. The second step was to estimate statistical models of capital and operating costs for open pit porphyry copper mines with flotation concentrators. For a sample of 27 proposed porphyry copper projects, capital costs can be estimated from three variables: mineral processing rate, strip ratio, and distance from nearest railroad before mine construction began. Of all the variables tested, operating costs were found to be significantly correlated only with strip ratio.

Predicting work-related disability and medical cost outcomes: a comparison of injury severity scoring methods.

PubMed

Sears, Jeanne M; Blanar, Laura; Bowman, Stephen M

2014-01-01

Acute work-related trauma is a leading cause of death and disability among U.S. workers. Occupational health services researchers have described the pressing need to identify valid injury severity measures for purposes such as case-mix adjustment and the construction of appropriate comparison groups in programme evaluation, intervention, quality improvement, and outcome studies. The objective of this study was to compare the performance of several injury severity scores and scoring methods in the context of predicting work-related disability and medical cost outcomes. Washington State Trauma Registry (WTR) records for injuries treated from 1998 to 2008 were linked with workers' compensation claims. Several Abbreviated Injury Scale (AIS)-based injury severity measures (ISS, New ISS, maximum AIS) were estimated directly from ICD-9-CM codes using two software packages: (1) ICDMAP-90, and (2) Stata's user-written ICDPIC programme (ICDPIC). ICDMAP-90 and ICDPIC scores were compared with existing WTR scores using the Akaike Information Criterion, amount of variance explained, and estimated effects on outcomes. Competing risks survival analysis was used to evaluate work disability outcomes. Adjusted total medical costs were modelled using linear regression. The linked sample contained 6052 work-related injury events. There was substantial agreement between WTR scores and those estimated by ICDMAP-90 (kappa=0.73), and between WTR scores and those estimated by ICDPIC (kappa=0.68). Work disability and medical costs increased monotonically with injury severity, and injury severity was a significant predictor of work disability and medical cost outcomes in all models. WTR and ICDMAP-90 scores performed better with regard to predicting outcomes than did ICDPIC scores, but effect estimates were similar. Of the three severity measures, maxAIS was usually weakest, except when predicting total permanent disability. Injury severity was significantly associated with work disability and medical cost outcomes for work-related injuries. Injury severity can be estimated using either ICDMAP-90 or ICDPIC when ICD-9-CM codes are available. We observed little practical difference between severity measures or scoring methods. This study demonstrated that using existing software to estimate injury severity may be useful to enhance occupational injury surveillance and research. Copyright © 2013 Elsevier Ltd. All rights reserved.

Prediction of 222Rn in Danish dwellings using geology and house construction information from central databases.

PubMed

Andersen, Claus E; Raaschou-Nielsen, Ole; Andersen, Helle Primdal; Lind, Morten; Gravesen, Peter; Thomsen, Birthe L; Ulbak, Kaare

2007-01-01

A linear regression model has been developed for the prediction of indoor (222)Rn in Danish houses. The model provides proxy radon concentrations for about 21,000 houses in a Danish case-control study on the possible association between residential radon and childhood cancer (primarily leukaemia). The model was calibrated against radon measurements in 3116 houses. An independent dataset with 788 house measurements was used for model performance assessment. The model includes nine explanatory variables, of which the most important ones are house type and geology. All explanatory variables are available from central databases. The model was fitted to log-transformed radon concentrations and it has an R(2) of 40%. The uncertainty associated with individual predictions of (untransformed) radon concentrations is about a factor of 2.0 (one standard deviation). The comparison with the independent test data shows that the model makes sound predictions and that errors of radon predictions are only weakly correlated with the estimates themselves (R(2) = 10%).

Improving estimation of tree carbon stocks by harvesting aboveground woody biomass within airborne LiDAR flight areas

NASA Astrophysics Data System (ADS)

Colgan, M.; Asner, G. P.; Swemmer, A. M.

2011-12-01

The accurate estimation of carbon stored in a tree is essential to accounting for the carbon emissions due to deforestation and degradation. Airborne LiDAR (Light Detection and Ranging) has been successful in estimating aboveground carbon density (ACD) by correlating airborne metrics, such as canopy height, to field-estimated biomass. This latter step is reliant on field allometry which is applied to forest inventory quantities, such as stem diameter and height, to predict the biomass of a given tree stem. Constructing such allometry is expensive, time consuming, and requires destructive sampling. Consequently, the sample sizes used to construct such allometry are often small, and the largest tree sampled is often much smaller than the largest in the forest population. The uncertainty resulting from these sampling errors can lead to severe biases when the allometry is applied to stems larger than those harvested to construct the allometry, which is then subsequently propagated to airborne ACD estimates. The Kruger National Park (KNP) mission of maintaining biodiversity coincides with preserving ecosystem carbon stocks. However, one hurdle to accurately quantifying carbon density in savannas is that small stems are typically harvested to construct woody biomass allometry, yet they are not representative of Kruger's distribution of biomass. Consequently, these equations inadequately capture large tree variation in sapwood/hardwood composition, root/shoot/leaf allocation, branch fall, and stem rot. This study eliminates the "middleman" of field allometry by directly measuring, or harvesting, tree biomass within the extent of airborne LiDAR. This enables comparisons of field and airborne ACD estimates, and also enables creation of new airborne algorithms to estimate biomass at the scale of individual trees. A field campaign was conducted at Pompey Silica Mine 5km outside Kruger National Park, South Africa, in Mar-Aug 2010 to harvest and weigh tree mass. Since harvesting of trees is not possible within KNP, this was a unique opportunity to fell trees already scheduled to be cleared for mining operations. The area was first flown by the Carnegie Airborne Observatory in early May, prior to harvest, to enable correlation of LiDAR-measured tree height and crown diameter to harvested tree mass. Results include over 4,000 harvested stems and 13 species-specific biomass equations, including seven Kruger woody species previously without allometry. We found existing biomass stem allometry over-estimates ACD in the field, whereas airborne estimates based on harvest data avoid this bias while maintaining similar precision to field-based estimates. Lastly, a new airborne algorithm estimating biomass at the tree-level reduced error from tree canopies "leaning" into field plots but whose stems are outside plot boundaries. These advances pave the way to better understanding of savanna and forest carbon density at landscape and regional scales.

Distributed and decentralized state estimation in gas networks as distributed parameter systems.

PubMed

Ahmadian Behrooz, Hesam; Boozarjomehry, R Bozorgmehry

2015-09-01

In this paper, a framework for distributed and decentralized state estimation in high-pressure and long-distance gas transmission networks (GTNs) is proposed. The non-isothermal model of the plant including mass, momentum and energy balance equations are used to simulate the dynamic behavior. Due to several disadvantages of implementing a centralized Kalman filter for large-scale systems, the continuous/discrete form of extended Kalman filter for distributed and decentralized estimation (DDE) has been extended for these systems. Accordingly, the global model is decomposed into several subsystems, called local models. Some heuristic rules are suggested for system decomposition in gas pipeline networks. In the construction of local models, due to the existence of common states and interconnections among the subsystems, the assimilation and prediction steps of the Kalman filter are modified to take the overlapping and external states into account. However, dynamic Riccati equation for each subsystem is constructed based on the local model, which introduces a maximum error of 5% in the estimated standard deviation of the states in the benchmarks studied in this paper. The performance of the proposed methodology has been shown based on the comparison of its accuracy and computational demands against their counterparts in centralized Kalman filter for two viable benchmarks. In a real life network, it is shown that while the accuracy is not significantly decreased, the real-time factor of the state estimation is increased by a factor of 10. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Comparison of Measured to Predicted Estimations of Nonpoint Source Contaminants Using Conservation Practices in an Agriculturally-Dominated Watershed in Northeast Arkansas, USA.

PubMed

Frasher, Sarah K; Woodruff, Tracy M; Bouldin, Jennifer L

2016-06-01

In efforts to reduce nonpoint source runoff and improve water quality, Best Management Practices (BMPs) were implemented in the Outlet Larkin Creek Watershed. Farmers need to make scientifically informed decisions concerning BMPs addressing contaminants from agricultural fields. The BMP Tool was developed from previous studies to estimate BMP effectiveness at reducing nonpoint source contaminants. The purpose of this study was to compare the measured percent reduction of dissolved phosphorus (DP) and total suspended solids to the reported percent reductions from the BMP Tool for validation. Similarities were measured between the BMP Tool and the measured water quality parameters. Construction of a sedimentation pond resulted in 74 %-76 % reduction in DP as compared to 80 % as predicted with the BMP Tool. However, further research is needed to validate the tool for additional water quality parameters. The BMP Tool is recommended for future BMP implementation as a useful predictor for farmers.

Inference and Prediction of Metabolic Network Fluxes

PubMed Central

Nikoloski, Zoran; Perez-Storey, Richard; Sweetlove, Lee J.

2015-01-01

In this Update, we cover the basic principles of the estimation and prediction of the rates of the many interconnected biochemical reactions that constitute plant metabolic networks. This includes metabolic flux analysis approaches that utilize the rates or patterns of redistribution of stable isotopes of carbon and other atoms to estimate fluxes, as well as constraints-based optimization approaches such as flux balance analysis. Some of the major insights that have been gained from analysis of fluxes in plants are discussed, including the functioning of metabolic pathways in a network context, the robustness of the metabolic phenotype, the importance of cell maintenance costs, and the mechanisms that enable energy and redox balancing at steady state. We also discuss methodologies to exploit 'omic data sets for the construction of tissue-specific metabolic network models and to constrain the range of permissible fluxes in such models. Finally, we consider the future directions and challenges faced by the field of metabolic network flux phenotyping. PMID:26392262

A prediction method of ice breaking resistance using a multiple regression analysis

NASA Astrophysics Data System (ADS)

Cho, Seong-Rak; Lee, Sungsu

2015-07-01

The two most important tasks of icebreakers are first to secure a sailing route by breaking the thick sea ice and second to sail efficiently herself for purposes of exploration and transportation in the polar seas. The resistance of icebreakers is a priority factor at the preliminary design stage; not only must their sailing efficiency be satisfied, but the design of the propulsion system will be directly affected. Therefore, the performance of icebreakers must be accurately calculated and evaluated through the use of model tests in an ice tank before construction starts. In this paper, a new procedure is developed, based on model tests, to estimate a ship's ice breaking resistance during continuous ice-breaking in ice. Some of the factors associated with crushing failures are systematically considered in order to correctly estimate her ice-breaking resistance. This study is intended to contribute to the improvement of the techniques for ice resistance prediction with ice breaking ships.

Auditing of suppliers as the requirement of quality management systems in construction

NASA Astrophysics Data System (ADS)

Harasymiuk, Jolanta; Barski, Janusz

2017-07-01

The choice of a supplier of construction materials can be important factor of increase or reduction of building works costs. Construction materials present from 40 for 70% of investment task depending on kind of works being provided for realization. There is necessity of estimate of suppliers from the point of view of effectiveness of construction undertaking and necessity from the point of view of conformity of taken operation by executives of construction job and objects within the confines of systems of managements quality being initiated in their organizations. The estimate of suppliers of construction materials and subexecutives of special works is formal requirement in quality management systems, which meets the requirements of the ISO 9001 standard. The aim of this paper is to show possibilities of making use of anaudit for estimate of credibility and reliability of the supplier of construction materials. The article describes kinds of audits, that were carried in quality management systems, with particular taking into consideration audits called as second-site. One characterizes the estimate criterions of qualitative ability and method of choice of the supplier of construction materials. The paper shows also propositions of exemplary questions, that would be estimated in audit process, the way of conducting of this estimate and conditionality of estimate.

Single neuron modeling and data assimilation in BNST neurons

NASA Astrophysics Data System (ADS)

Farsian, Reza

Neurons, although tiny in size, are vastly complicated systems, which are responsible for the most basic yet essential functions of any nervous system. Even the most simple models of single neurons are usually high dimensional, nonlinear, and contain many parameters and states which are unobservable in a typical neurophysiological experiment. One of the most fundamental problems in experimental neurophysiology is the estimation of these parameters and states, since knowing their values is essential in identification, model construction, and forward prediction of biological neurons. Common methods of parameter and state estimation do not perform well for neural models due to their high dimensionality and nonlinearity. In this dissertation, two alternative approaches for parameters and state estimation of biological neurons have been demonstrated: dynamical parameter estimation (DPE) and a Markov Chain Monte Carlo (MCMC) method. The first method uses elements of chaos control and synchronization theory for parameter and state estimation. MCMC is a statistical approach which uses a path integral formulation to evaluate a mean and an error bound for these unobserved parameters and states. These methods have been applied to biological system of neurons in Bed Nucleus of Stria Termialis neurons (BNST) of rats. State and parameters of neurons in both systems were estimated, and their value were used for recreating a realistic model and predicting the behavior of the neurons successfully. The knowledge of biological parameters can ultimately provide a better understanding of the internal dynamics of a neuron in order to build robust models of neuron networks.

Efficient Construction of Free Energy Profiles of Breathing Metal–Organic Frameworks Using Advanced Molecular Dynamics Simulations

PubMed Central

2017-01-01

In order to reliably predict and understand the breathing behavior of highly flexible metal–organic frameworks from thermodynamic considerations, an accurate estimation of the free energy difference between their different metastable states is a prerequisite. Herein, a variety of free energy estimation methods are thoroughly tested for their ability to construct the free energy profile as a function of the unit cell volume of MIL-53(Al). The methods comprise free energy perturbation, thermodynamic integration, umbrella sampling, metadynamics, and variationally enhanced sampling. A series of molecular dynamics simulations have been performed in the frame of each of the five methods to describe structural transformations in flexible materials with the volume as the collective variable, which offers a unique opportunity to assess their computational efficiency. Subsequently, the most efficient method, umbrella sampling, is used to construct an accurate free energy profile at different temperatures for MIL-53(Al) from first principles at the PBE+D3(BJ) level of theory. This study yields insight into the importance of the different aspects such as entropy contributions and anharmonic contributions on the resulting free energy profile. As such, this thorough study provides unparalleled insight in the thermodynamics of the large structural deformations of flexible materials. PMID:29131647

Efficient Construction of Free Energy Profiles of Breathing Metal-Organic Frameworks Using Advanced Molecular Dynamics Simulations.

PubMed

Demuynck, Ruben; Rogge, Sven M J; Vanduyfhuys, Louis; Wieme, Jelle; Waroquier, Michel; Van Speybroeck, Veronique

2017-12-12

In order to reliably predict and understand the breathing behavior of highly flexible metal-organic frameworks from thermodynamic considerations, an accurate estimation of the free energy difference between their different metastable states is a prerequisite. Herein, a variety of free energy estimation methods are thoroughly tested for their ability to construct the free energy profile as a function of the unit cell volume of MIL-53(Al). The methods comprise free energy perturbation, thermodynamic integration, umbrella sampling, metadynamics, and variationally enhanced sampling. A series of molecular dynamics simulations have been performed in the frame of each of the five methods to describe structural transformations in flexible materials with the volume as the collective variable, which offers a unique opportunity to assess their computational efficiency. Subsequently, the most efficient method, umbrella sampling, is used to construct an accurate free energy profile at different temperatures for MIL-53(Al) from first principles at the PBE+D3(BJ) level of theory. This study yields insight into the importance of the different aspects such as entropy contributions and anharmonic contributions on the resulting free energy profile. As such, this thorough study provides unparalleled insight in the thermodynamics of the large structural deformations of flexible materials.

Development of a hybrid model to predict construction and demolition waste: China as a case study.

PubMed

Song, Yiliao; Wang, Yong; Liu, Feng; Zhang, Yixin

2017-01-01

Construction and demolition waste (C&DW) is currently a worldwide issue, and the situation is the worst in China due to a rapid increase in the construction industry and the short life span of China's buildings. To create an opportunity out of this problem, comprehensive prevention measures and effective management strategies are urgently needed. One major gap in the literature of waste management is a lack of estimations on future C&DW generation. Therefore, this paper presents a forecasting procedure for C&DW in China that can forecast the quantity of each component in such waste. The proposed approach is based on a GM-SVR model that improves the forecasting effectiveness of the gray model (GM), which is achieved by adjusting the residual series by a support vector regression (SVR) method and a transition matrix that aims to estimate the discharge of each component in the C&DW. Through the proposed method, future C&DW volume are listed and analyzed containing their potential components and distribution in different provinces in China. Besides, model testing process provides mathematical evidence to validate the proposed model is an effective way to give future information of C&DW for policy makers. Copyright © 2016 Elsevier Ltd. All rights reserved.

A robust method for estimating motorbike count based on visual information learning

NASA Astrophysics Data System (ADS)

Huynh, Kien C.; Thai, Dung N.; Le, Sach T.; Thoai, Nam; Hamamoto, Kazuhiko

2015-03-01

Estimating the number of vehicles in traffic videos is an important and challenging task in traffic surveillance, especially with a high level of occlusions between vehicles, e.g.,in crowded urban area with people and/or motorbikes. In such the condition, the problem of separating individual vehicles from foreground silhouettes often requires complicated computation [1][2][3]. Thus, the counting problem is gradually shifted into drawing statistical inferences of target objects density from their shape [4], local features [5], etc. Those researches indicate a correlation between local features and the number of target objects. However, they are inadequate to construct an accurate model for vehicles density estimation. In this paper, we present a reliable method that is robust to illumination changes and partial affine transformations. It can achieve high accuracy in case of occlusions. Firstly, local features are extracted from images of the scene using Speed-Up Robust Features (SURF) method. For each image, a global feature vector is computed using a Bag-of-Words model which is constructed from the local features above. Finally, a mapping between the extracted global feature vectors and their labels (the number of motorbikes) is learned. That mapping provides us a strong prediction model for estimating the number of motorbikes in new images. The experimental results show that our proposed method can achieve a better accuracy in comparison to others.

A mass-energy preserving Galerkin FEM for the coupled nonlinear fractional Schrödinger equations

NASA Astrophysics Data System (ADS)

Zhang, Guoyu; Huang, Chengming; Li, Meng

2018-04-01

We consider the numerical simulation of the coupled nonlinear space fractional Schrödinger equations. Based on the Galerkin finite element method in space and the Crank-Nicolson (CN) difference method in time, a fully discrete scheme is constructed. Firstly, we focus on a rigorous analysis of conservation laws for the discrete system. The definitions of discrete mass and energy here correspond with the original ones in physics. Then, we prove that the fully discrete system is uniquely solvable. Moreover, we consider the unconditionally convergent properties (that is to say, we complete the error estimates without any mesh ratio restriction). We derive L2-norm error estimates for the nonlinear equations and L^{∞}-norm error estimates for the linear equations. Finally, some numerical experiments are included showing results in agreement with the theoretical predictions.

A Model for Predicting Spring Emergence of Monochamus saltuarius (Coleoptera: Cerambycidae) from Korean white pine, Pinus koraiensis.

PubMed

Jung, Chan Sik; Koh, Sang-Hyun; Nam, Youngwoo; Ahn, Jeong Joon; Lee, Cha Young; Choi, Won I L

2015-08-01

Monochamus saltuarius Gebler is a vector that transmits the pine wood nematode, Bursaphelenchus xylophilus, to Korean white pine, Pinus koraiensis, in Korea. To reduce the damage caused by this nematode in pine forests, timely control measures are needed to suppress the cerambycid beetle population. This study sought to construct a forecasting model to predict beetle emergence based on spring temperature. Logs of Korean white pine were infested with M. saltuarius in 2009, and the infested logs were overwintered. In February 2010, infested logs were then moved into incubators held at constant temperature conditions of 16, 20, 23, 25, 27, 30 or 34°C until all adults had emerged. The developmental rate of the beetles was estimated by linear and nonlinear equations and a forecasting model for emergence of the beetle was constructed by pooling data based on normalized developmental rate. The lower threshold temperature for development was 8.3°C. The forecasting model relatively well predicted the emergence pattern of M. saltuarius collected from four areas in northern Republic of Korea. The median emergence dates predicted by the model were 2.2-5.9 d earlier than the observed median dates. © The Authors 2015. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

A Comparison of Center/TRACON Automation System and Airline Time of Arrival Predictions

NASA Technical Reports Server (NTRS)

Heere, Karen R.; Zelenka, Richard E.

2000-01-01

Benefits from information sharing between an air traffic service provider and a major air carrier are evaluated. Aircraft arrival time schedules generated by the NASA/FAA Center/TRACON Automation System (CTAS) were provided to the American Airlines System Operations Control Center in Fort Worth, Texas, during a field trial of a specialized CTAS display. A statistical analysis indicates that the CTAS schedules, based on aircraft trajectories predicted from real-time radar and weather data, are substantially more accurate than the traditional airline arrival time estimates, constructed from flight plans and en route crew updates. The improvement offered by CTAS is especially advantageous during periods of heavy traffic and substantial terminal area delay, allowing the airline to avoid large predictive errors with serious impact on the efficiency and profitability of flight operations.

Converting wood volume to biomass for pinyon and juniper. Forest Service research note

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

Chojnacky, D.C.; Moisen, G.G.

1993-03-01

A technique was developed to convert pinyon-juniper volume equation predictions to weights. The method uses specific gravity and biomass conversion equations to obtain foliage weight and total wood weight of all stems, branches, and bark. Specific gravity data are given for several Arizona pinyon-juniper species. Biomass conversion equations are constructed from pinyon-juniper data collected in Nevada. Results provide an interim means of estimating pinyon-juniper aboveground biomass from available volume inventory data.

Chiral Lagrangian with Heavy Quark-Diquark Symmetry

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

Jie Hu; Thomas Mehen

2005-11-29

We construct a chiral Lagrangian for doubly heavy baryons and heavy mesons that is invariant under heavy quark-diquark symmetry at leading order and includes the leading O(1/m{sub Q}) symmetry violating operators. The theory is used to predict the electromagnetic decay width of the J=3/2 member of the ground state doubly heavy baryon doublet. Numerical estimates are provided for doubly charm baryons. We also calculate chiral corrections to doubly heavy baryon masses and strong decay widths of low lying excited doubly heavy baryons.

Comparison Of Downscaled CMIP5 Precipitation Datasets For Projecting Changes In Extreme Precipitation In The San Francisco Bay Area.

NASA Technical Reports Server (NTRS)

Milesi, Cristina; Costa-Cabral, Mariza; Rath, John; Mills, William; Roy, Sujoy; Thrasher, Bridget; Wang, Weile; Chiang, Felicia; Loewenstein, Max; Podolske, James

2014-01-01

Water resource managers planning for the adaptation to future events of extreme precipitation now have access to high resolution downscaled daily projections derived from statistical bias correction and constructed analogs. We also show that along the Pacific Coast the Northern Oscillation Index (NOI) is a reliable predictor of storm likelihood, and therefore a predictor of seasonal precipitation totals and likelihood of extremely intense precipitation. Such time series can be used to project intensity duration curves into the future or input into stormwater models. However, few climate projection studies have explored the impact of the type of downscaling method used on the range and uncertainty of predictions for local flood protection studies. Here we present a study of the future climate flood risk at NASA Ames Research Center, located in South Bay Area, by comparing the range of predictions in extreme precipitation events calculated from three sets of time series downscaled from CMIP5 data: 1) the Bias Correction Constructed Analogs method dataset downscaled to a 1/8 degree grid (12km); 2) the Bias Correction Spatial Disaggregation method downscaled to a 1km grid; 3) a statistical model of extreme daily precipitation events and projected NOI from CMIP5 models. In addition, predicted years of extreme precipitation are used to estimate the risk of overtopping of the retention pond located on the site through simulations of the EPA SWMM hydrologic model. Preliminary results indicate that the intensity of extreme precipitation events is expected to increase and flood the NASA Ames retention pond. The results from these estimations will assist flood protection managers in planning for infrastructure adaptations.

Predicting Space Weather Effects on Close Approach Events

NASA Technical Reports Server (NTRS)

Hejduk, Matthew D.; Newman, Lauri K.; Besser, Rebecca L.; Pachura, Daniel A.

2015-01-01

The NASA Robotic Conjunction Assessment Risk Analysis (CARA) team sends ephemeris data to the Joint Space Operations Center (JSpOC) for conjunction assessment screening against the JSpOC high accuracy catalog and then assesses risk posed to protected assets from predicted close approaches. Since most spacecraft supported by the CARA team are located in LEO orbits, atmospheric drag is the primary source of state estimate uncertainty. Drag magnitude and uncertainty is directly governed by atmospheric density and thus space weather. At present the actual effect of space weather on atmospheric density cannot be accurately predicted because most atmospheric density models are empirical in nature, which do not perform well in prediction. The Jacchia-Bowman-HASDM 2009 (JBH09) atmospheric density model used at the JSpOC employs a solar storm active compensation feature that predicts storm sizes and arrival times and thus the resulting neutral density alterations. With this feature, estimation errors can occur in either direction (i.e., over- or under-estimation of density and thus drag). Although the exact effect of a solar storm on atmospheric drag cannot be determined, one can explore the effects of JBH09 model error on conjuncting objects' trajectories to determine if a conjunction is likely to become riskier, less risky, or pass unaffected. The CARA team has constructed a Space Weather Trade-Space tool that systematically alters the drag situation for the conjuncting objects and recalculates the probability of collision for each case to determine the range of possible effects on the collision risk. In addition to a review of the theory and the particulars of the tool, the different types of observed output will be explained, along with statistics of their frequency.

Estimating gestational age at birth from fundal height and additional anthropometrics: a prospective cohort study.

PubMed

Pugh, S J; Ortega-Villa, A M; Grobman, W; Newman, R B; Owen, J; Wing, D A; Albert, P S; Grantz, K L

2018-02-23

Accurate assessment of gestational age (GA) is critical to paediatric care, but is limited in developing countries without access to ultrasound. Our objectives were to assess the accuracy of prediction of GA at birth and preterm birth classification using routinely collected anthropometry measures. Prospective cohort study. United States. A total of 2334 non-obese and 468 obese pregnant women. Enrolment GA was determined based on last menstrual period, confirmed by first-trimester ultrasound. Maternal anthropometry and fundal height (FH) were measured by a standardised protocol at study visits; FH alone was additionally abstracted from medical charts. Neonatal anthropometry measurements were obtained at birth. To estimate GA at delivery, we developed three predictor models using longitudinal FH alone and with maternal and neonatal anthropometry. For all predictors, we repeatedly sampled observations to construct training (60%) and test (40%) sets. Linear mixed models incorporated longitudinal maternal anthropometry and a shared parameter model incorporated neonatal anthropometry. We assessed models' accuracy under varied scenarios. Estimated GA at delivery. Prediction error for various combinations of anthropometric measures ranged between 13.9 and 14.9 days. Longitudinal FH alone predicted GA within 14.9 days with relatively stable prediction errors across individual race/ethnicities [whites (13.9 days), blacks (15.1 days), Hispanics (15.5 days) and Asians (13.1 days)], and correctly identified 75% of preterm births. The model was robust to additional scenarios. In low-risk, non-obese women, longitudinal FH measures alone can provide a reasonably accurate assessment of GA when ultrasound measures are not available. Longitudinal fundal height alone predicts gestational age at birth when ultrasound measures are unavailable. © 2018 Royal College of Obstetricians and Gynaecologists.

Copula based prediction models: an application to an aortic regurgitation study

PubMed Central

Kumar, Pranesh; Shoukri, Mohamed M

2007-01-01

Background: An important issue in prediction modeling of multivariate data is the measure of dependence structure. The use of Pearson's correlation as a dependence measure has several pitfalls and hence application of regression prediction models based on this correlation may not be an appropriate methodology. As an alternative, a copula based methodology for prediction modeling and an algorithm to simulate data are proposed. Methods: The method consists of introducing copulas as an alternative to the correlation coefficient commonly used as a measure of dependence. An algorithm based on the marginal distributions of random variables is applied to construct the Archimedean copulas. Monte Carlo simulations are carried out to replicate datasets, estimate prediction model parameters and validate them using Lin's concordance measure. Results: We have carried out a correlation-based regression analysis on data from 20 patients aged 17–82 years on pre-operative and post-operative ejection fractions after surgery and estimated the prediction model: Post-operative ejection fraction = - 0.0658 + 0.8403 (Pre-operative ejection fraction); p = 0.0008; 95% confidence interval of the slope coefficient (0.3998, 1.2808). From the exploratory data analysis, it is noted that both the pre-operative and post-operative ejection fractions measurements have slight departures from symmetry and are skewed to the left. It is also noted that the measurements tend to be widely spread and have shorter tails compared to normal distribution. Therefore predictions made from the correlation-based model corresponding to the pre-operative ejection fraction measurements in the lower range may not be accurate. Further it is found that the best approximated marginal distributions of pre-operative and post-operative ejection fractions (using q-q plots) are gamma distributions. The copula based prediction model is estimated as: Post -operative ejection fraction = - 0.0933 + 0.8907 × (Pre-operative ejection fraction); p = 0.00008 ; 95% confidence interval for slope coefficient (0.4810, 1.3003). For both models differences in the predicted post-operative ejection fractions in the lower range of pre-operative ejection measurements are considerably different and prediction errors due to copula model are smaller. To validate the copula methodology we have re-sampled with replacement fifty independent bootstrap samples and have estimated concordance statistics 0.7722 (p = 0.0224) for the copula model and 0.7237 (p = 0.0604) for the correlation model. The predicted and observed measurements are concordant for both models. The estimates of accuracy components are 0.9233 and 0.8654 for copula and correlation models respectively. Conclusion: Copula-based prediction modeling is demonstrated to be an appropriate alternative to the conventional correlation-based prediction modeling since the correlation-based prediction models are not appropriate to model the dependence in populations with asymmetrical tails. Proposed copula-based prediction model has been validated using the independent bootstrap samples. PMID:17573974

Predicting Response to Neoadjuvant Chemoradiotherapy in Esophageal Cancer with Textural Features Derived from Pretreatment 18F-FDG PET/CT Imaging.

PubMed

Beukinga, Roelof J; Hulshoff, Jan B; van Dijk, Lisanne V; Muijs, Christina T; Burgerhof, Johannes G M; Kats-Ugurlu, Gursah; Slart, Riemer H J A; Slump, Cornelis H; Mul, Véronique E M; Plukker, John Th M

2017-05-01

Adequate prediction of tumor response to neoadjuvant chemoradiotherapy (nCRT) in esophageal cancer (EC) patients is important in a more personalized treatment. The current best clinical method to predict pathologic complete response is SUV max in 18 F-FDG PET/CT imaging. To improve the prediction of response, we constructed a model to predict complete response to nCRT in EC based on pretreatment clinical parameters and 18 F-FDG PET/CT-derived textural features. Methods: From a prospectively maintained single-institution database, we reviewed 97 consecutive patients with locally advanced EC and a pretreatment 18 F-FDG PET/CT scan between 2009 and 2015. All patients were treated with nCRT (carboplatin/paclitaxel/41.4 Gy) followed by esophagectomy. We analyzed clinical, geometric, and pretreatment textural features extracted from both 18 F-FDG PET and CT. The current most accurate prediction model with SUV max as a predictor variable was compared with 6 different response prediction models constructed using least absolute shrinkage and selection operator regularized logistic regression. Internal validation was performed to estimate the model's performances. Pathologic response was defined as complete versus incomplete response (Mandard tumor regression grade system 1 vs. 2-5). Results: Pathologic examination revealed 19 (19.6%) complete and 78 (80.4%) incomplete responders. Least absolute shrinkage and selection operator regularization selected the clinical parameters: histologic type and clinical T stage, the 18 F-FDG PET-derived textural feature long run low gray level emphasis, and the CT-derived textural feature run percentage. Introducing these variables to a logistic regression analysis showed areas under the receiver-operating-characteristic curve (AUCs) of 0.78 compared with 0.58 in the SUV max model. The discrimination slopes were 0.17 compared with 0.01, respectively. After internal validation, the AUCs decreased to 0.74 and 0.54, respectively. Conclusion: The predictive values of the constructed models were superior to the standard method (SUV max ). These results can be considered as an initial step in predicting tumor response to nCRT in locally advanced EC. Further research in refining the predictive value of these models is needed to justify omission of surgery. © 2017 by the Society of Nuclear Medicine and Molecular Imaging.

SNP-based heritability estimates of the personality dimensions and polygenic prediction of both neuroticism and major depression: findings from CONVERGE.

PubMed

Docherty, A R; Moscati, A; Peterson, R; Edwards, A C; Adkins, D E; Bacanu, S A; Bigdeli, T B; Webb, B T; Flint, J; Kendler, K S

2016-10-25

Biometrical genetic studies suggest that the personality dimensions, including neuroticism, are moderately heritable (~0.4 to 0.6). Quantitative analyses that aggregate the effects of many common variants have recently further informed genetic research on European samples. However, there has been limited research to date on non-European populations. This study examined the personality dimensions in a large sample of Han Chinese descent (N=10 064) from the China, Oxford, and VCU Experimental Research on Genetic Epidemiology study, aimed at identifying genetic risk factors for recurrent major depression among a rigorously ascertained cohort. Heritability of neuroticism as measured by the Eysenck Personality Questionnaire (EPQ) was estimated to be low but statistically significant at 10% (s.e.=0.03, P=0.0001). In addition to EPQ, neuroticism based on a three-factor model, data for the Big Five (BF) personality dimensions (neuroticism, openness, conscientiousness, extraversion and agreeableness) measured by the Big Five Inventory were available for controls (n=5596). Heritability estimates of the BF were not statistically significant despite high power (>0.85) to detect heritabilities of 0.10. Polygenic risk scores constructed by best linear unbiased prediction weights applied to split-half samples failed to significantly predict any of the personality traits, but polygenic risk for neuroticism, calculated with LDpred and based on predictive variants previously identified from European populations (N=171 911), significantly predicted major depressive disorder case-control status (P=0.0004) after false discovery rate correction. The scores also significantly predicted EPQ neuroticism (P=6.3 × 10 -6 ). Factor analytic results of the measures indicated that any differences in heritabilities across samples may be due to genetic variation or variation in haplotype structure between samples, rather than measurement non-invariance. Findings demonstrate that neuroticism can be significantly predicted across ancestry, and highlight the importance of studying polygenic contributions to personality in non-European populations.

Gene regulatory network inference from multifactorial perturbation data using both regression and correlation analyses.

PubMed

Xiong, Jie; Zhou, Tong

2012-01-01

An important problem in systems biology is to reconstruct gene regulatory networks (GRNs) from experimental data and other a priori information. The DREAM project offers some types of experimental data, such as knockout data, knockdown data, time series data, etc. Among them, multifactorial perturbation data are easier and less expensive to obtain than other types of experimental data and are thus more common in practice. In this article, a new algorithm is presented for the inference of GRNs using the DREAM4 multifactorial perturbation data. The GRN inference problem among [Formula: see text] genes is decomposed into [Formula: see text] different regression problems. In each of the regression problems, the expression level of a target gene is predicted solely from the expression level of a potential regulation gene. For different potential regulation genes, different weights for a specific target gene are constructed by using the sum of squared residuals and the Pearson correlation coefficient. Then these weights are normalized to reflect effort differences of regulating distinct genes. By appropriately choosing the parameters of the power law, we constructe a 0-1 integer programming problem. By solving this problem, direct regulation genes for an arbitrary gene can be estimated. And, the normalized weight of a gene is modified, on the basis of the estimation results about the existence of direct regulations to it. These normalized and modified weights are used in queuing the possibility of the existence of a corresponding direct regulation. Computation results with the DREAM4 In Silico Size 100 Multifactorial subchallenge show that estimation performances of the suggested algorithm can even outperform the best team. Using the real data provided by the DREAM5 Network Inference Challenge, estimation performances can be ranked third. Furthermore, the high precision of the obtained most reliable predictions shows the suggested algorithm may be helpful in guiding biological experiment designs.

Copula-based prediction of economic movements

NASA Astrophysics Data System (ADS)

García, J. E.; González-López, V. A.; Hirsh, I. D.

2016-06-01

In this paper we model the discretized returns of two paired time series BM&FBOVESPA Dividend Index and BM&FBOVESPA Public Utilities Index using multivariate Markov models. The discretization corresponds to three categories, high losses, high profits and the complementary periods of the series. In technical terms, the maximal memory that can be considered for a Markov model, can be derived from the size of the alphabet and dataset. The number of parameters needed to specify a discrete multivariate Markov chain grows exponentially with the order and dimension of the chain. In this case the size of the database is not large enough for a consistent estimation of the model. We apply a strategy to estimate a multivariate process with an order greater than the order achieved using standard procedures. The new strategy consist on obtaining a partition of the state space which is constructed from a combination, of the partitions corresponding to the two marginal processes and the partition corresponding to the multivariate Markov chain. In order to estimate the transition probabilities, all the partitions are linked using a copula. In our application this strategy provides a significant improvement in the movement predictions.

A Game Theoretical Approach to Hacktivism: Is Attack Likelihood a Product of Risks and Payoffs?

PubMed

Bodford, Jessica E; Kwan, Virginia S Y

2018-02-01

The current study examines hacktivism (i.e., hacking to convey a moral, ethical, or social justice message) through a general game theoretic framework-that is, as a product of costs and benefits. Given the inherent risk of carrying out a hacktivist attack (e.g., legal action, imprisonment), it would be rational for the user to weigh these risks against perceived benefits of carrying out the attack. As such, we examined computer science students' estimations of risks, payoffs, and attack likelihood through a game theoretic design. Furthermore, this study aims at constructing a descriptive profile of potential hacktivists, exploring two predicted covariates of attack decision making, namely, peer prevalence of hacking and sex differences. Contrary to expectations, results suggest that participants' estimations of attack likelihood stemmed solely from expected payoffs, rather than subjective risks. Peer prevalence significantly predicted increased payoffs and attack likelihood, suggesting an underlying descriptive norm in social networks. Notably, we observed no sex differences in the decision to attack, nor in the factors predicting attack likelihood. Implications for policymakers and the understanding and prevention of hacktivism are discussed, as are the possible ramifications of widely communicated payoffs over potential risks in hacking communities.

Assessing the validity of commercial and municipal food environment data sets in Vancouver, Canada.

PubMed

Daepp, Madeleine Ig; Black, Jennifer

2017-10-01

The present study assessed systematic bias and the effects of data set error on the validity of food environment measures in two municipal and two commercial secondary data sets. Sensitivity, positive predictive value (PPV) and concordance were calculated by comparing two municipal and two commercial secondary data sets with ground-truthed data collected within 800 m buffers surrounding twenty-six schools. Logistic regression examined associations of sensitivity and PPV with commercial density and neighbourhood socio-economic deprivation. Kendall's τ estimated correlations between density and proximity of food outlets near schools constructed with secondary data sets v. ground-truthed data. Vancouver, Canada. Food retailers located within 800 m of twenty-six schools RESULTS: All data sets scored relatively poorly across validity measures, although, overall, municipal data sets had higher levels of validity than did commercial data sets. Food outlets were more likely to be missing from municipal health inspections lists and commercial data sets in neighbourhoods with higher commercial density. Still, both proximity and density measures constructed from all secondary data sets were highly correlated (Kendall's τ>0·70) with measures constructed from ground-truthed data. Despite relatively low levels of validity in all secondary data sets examined, food environment measures constructed from secondary data sets remained highly correlated with ground-truthed data. Findings suggest that secondary data sets can be used to measure the food environment, although estimates should be treated with caution in areas with high commercial density.

Risks of a lifetime in construction. Part II: Chronic occupational diseases.

PubMed

Ringen, Knut; Dement, John; Welch, Laura; Dong, Xiuwen Sue; Bingham, Eula; Quinn, Patricia S

2014-11-01

We developed working-life estimates of risk for dust-related occupational lung disease, COPD, and hearing loss based on the experience of the Building Trades National Medical Screening Program in order to (1) demonstrate the value of estimates of lifetime risk, and (2) make lifetime risk estimates for common conditions among construction workers. Estimates of lifetime risk were performed based on 12,742 radiographic evaluations, 12,679 spirometry tests, and 11,793 audiograms. Over a 45-year working life, 16% of construction workers developed COPD, 11% developed parenchymal radiological abnormality, and 73.8% developed hearing loss. The risk for occupationally related disease over a lifetime in a construction trade was 2-6 times greater than the risk in non-construction workers. When compared with estimates from annualized cross-sectional data, lifetime risk estimates are highly useful for risk expression, and should help to inform stakeholders in the construction industry as well as policy-makers about magnitudes of risk. © 2014 Wiley Periodicals, Inc.

[Development and validation of an algorithm to identify cancer recurrences from hospital data bases].

PubMed

Manzanares-Laya, S; Burón, A; Murta-Nascimento, C; Servitja, S; Castells, X; Macià, F

2014-01-01

Hospital cancer registries and hospital databases are valuable and efficient sources of information for research into cancer recurrences. The aim of this study was to develop and validate algorithms for the detection of breast cancer recurrence. A retrospective observational study was conducted on breast cancer cases from the cancer registry of a third level university hospital diagnosed between 2003 and 2009. Different probable cancer recurrence algorithms were obtained by linking the hospital databases and the construction of several operational definitions, with their corresponding sensitivity, specificity, positive predictive value and negative predictive value. A total of 1,523 patients were diagnosed of breast cancer between 2003 and 2009. A request for bone gammagraphy after 6 months from the first oncological treatment showed the highest sensitivity (53.8%) and negative predictive value (93.8%), and a pathology test after 6 months after the diagnosis showed the highest specificity (93.8%) and negative predictive value (92.6%). The combination of different definitions increased the specificity and the positive predictive value, but decreased the sensitivity. Several diagnostic algorithms were obtained, and the different definitions could be useful depending on the interest and resources of the researcher. A higher positive predictive value could be interesting for a quick estimation of the number of cases, and a higher negative predictive value for a more exact estimation if more resources are available. It is a versatile and adaptable tool for other types of tumors, as well as for the needs of the researcher. Copyright © 2014 SECA. Published by Elsevier Espana. All rights reserved.

Design and Implementation of an Intelligent Cost Estimation Model for Decision Support System Software

DTIC Science & Technology

1990-09-01

following two chapters. 28 V. COCOMO MODEL A. OVERVIEW The COCOMO model which stands for COnstructive COst MOdel was developed by Barry Boehm and is...estimation model which uses an expert system to automate the Intermediate COnstructive Cost Estimation MOdel (COCOMO), developed by Barry W. Boehm and...cost estimation model which uses an expert system to automate the Intermediate COnstructive Cost Estimation MOdel (COCOMO), developed by Barry W

Reliability and validity of Arabic Rapid Estimate of Adult Literacy in Dentistry (AREALD-30) in Saudi Arabia.

PubMed

Tadakamadla, Santosh Kumar; Quadri, Mir Faeq Ali; Pakpour, Amir H; Zailai, Abdulaziz M; Sayed, Mohammed E; Mashyakhy, Mohammed; Inamdar, Aadil S; Tadakamadla, Jyothi

2014-09-29

To evaluate the reliability and validity of Arabic Rapid Estimate of Adult Literacy in Dentistry (AREALD-30) in Saudi Arabia. A convenience sample of 200 subjects was approached, of which 177 agreed to participate giving a response rate of 88.5%. Rapid Estimate of Adult Literacy in Dentistry (REALD-99), was translated into Arabic to prepare the longer and shorter versions of Arabic Rapid Estimate of Adult Literacy in Dentistry (AREALD-99 and AREALD-30). Each participant was provided with AREALD-99 which also includes words from AREALD-30. A questionnaire containing socio-behavioral information and Arabic Oral Health Impact Profile (A-OHIP-14) was also administered. Reliability of the AREALD-30 was assessed by re-administering it to 20 subjects after two weeks. Convergent and predictive validity of AREALD-30 was evaluated by its correlations with AREALD-99 and self-perceived oral health status, dental visiting habits and A-OHIP-14 respectively. Discriminant validity was assessed in relation to the educational level while construct validity was evaluated by confirmatory factor analysis (CFA). Reliability of AREALD-30 was excellent with intraclass correlation coefficient of 0.99. It exhibited good convergent and discriminant validity but poor predictive validity. CFA showed presence of two factors and infit mean-square statistics for AREALD-30 were all within the desired range of 0.50 - 2.0 in Rasch analysis. AREALD-30 showed excellent reliability, good convergent and concurrent validity, but failed to predict the differences between the subjects categorized based on their oral health outcomes.

Generation of 3-D hydrostratigraphic zones from dense airborne electromagnetic data to assess groundwater model prediction error

USGS Publications Warehouse

Christensen, Nikolaj K; Minsley, Burke J.; Christensen, Steen

2017-01-01

We present a new methodology to combine spatially dense high-resolution airborne electromagnetic (AEM) data and sparse borehole information to construct multiple plausible geological structures using a stochastic approach. The method developed allows for quantification of the performance of groundwater models built from different geological realizations of structure. Multiple structural realizations are generated using geostatistical Monte Carlo simulations that treat sparse borehole lithological observations as hard data and dense geophysically derived structural probabilities as soft data. Each structural model is used to define 3-D hydrostratigraphical zones of a groundwater model, and the hydraulic parameter values of the zones are estimated by using nonlinear regression to fit hydrological data (hydraulic head and river discharge measurements). Use of the methodology is demonstrated for a synthetic domain having structures of categorical deposits consisting of sand, silt, or clay. It is shown that using dense AEM data with the methodology can significantly improve the estimated accuracy of the sediment distribution as compared to when borehole data are used alone. It is also shown that this use of AEM data can improve the predictive capability of a calibrated groundwater model that uses the geological structures as zones. However, such structural models will always contain errors because even with dense AEM data it is not possible to perfectly resolve the structures of a groundwater system. It is shown that when using such erroneous structures in a groundwater model, they can lead to biased parameter estimates and biased model predictions, therefore impairing the model's predictive capability.

Generation of 3-D hydrostratigraphic zones from dense airborne electromagnetic data to assess groundwater model prediction error

NASA Astrophysics Data System (ADS)

Christensen, N. K.; Minsley, B. J.; Christensen, S.

2017-02-01

We present a new methodology to combine spatially dense high-resolution airborne electromagnetic (AEM) data and sparse borehole information to construct multiple plausible geological structures using a stochastic approach. The method developed allows for quantification of the performance of groundwater models built from different geological realizations of structure. Multiple structural realizations are generated using geostatistical Monte Carlo simulations that treat sparse borehole lithological observations as hard data and dense geophysically derived structural probabilities as soft data. Each structural model is used to define 3-D hydrostratigraphical zones of a groundwater model, and the hydraulic parameter values of the zones are estimated by using nonlinear regression to fit hydrological data (hydraulic head and river discharge measurements). Use of the methodology is demonstrated for a synthetic domain having structures of categorical deposits consisting of sand, silt, or clay. It is shown that using dense AEM data with the methodology can significantly improve the estimated accuracy of the sediment distribution as compared to when borehole data are used alone. It is also shown that this use of AEM data can improve the predictive capability of a calibrated groundwater model that uses the geological structures as zones. However, such structural models will always contain errors because even with dense AEM data it is not possible to perfectly resolve the structures of a groundwater system. It is shown that when using such erroneous structures in a groundwater model, they can lead to biased parameter estimates and biased model predictions, therefore impairing the model's predictive capability.

Estimation of construction and demolition waste using waste generation rates in Chennai, India.

PubMed

Ram, V G; Kalidindi, Satyanarayana N

2017-06-01

A large amount of construction and demolition waste is being generated owing to rapid urbanisation in Indian cities. A reliable estimate of construction and demolition waste generation is essential to create awareness about this stream of solid waste among the government bodies in India. However, the required data to estimate construction and demolition waste generation in India are unavailable or not explicitly documented. This study proposed an approach to estimate construction and demolition waste generation using waste generation rates and demonstrated it by estimating construction and demolition waste generation in Chennai city. The demolition waste generation rates of primary materials were determined through regression analysis using waste generation data from 45 case studies. Materials, such as wood, electrical wires, doors, windows and reinforcement steel, were found to be salvaged and sold on the secondary market. Concrete and masonry debris were dumped in either landfills or unauthorised places. The total quantity of construction and demolition debris generated in Chennai city in 2013 was estimated to be 1.14 million tonnes. The proportion of masonry debris was found to be 76% of the total quantity of demolition debris. Construction and demolition debris forms about 36% of the total solid waste generated in Chennai city. A gross underestimation of construction and demolition waste generation in some earlier studies in India has also been shown. The methodology proposed could be utilised by government bodies, policymakers and researchers to generate reliable estimates of construction and demolition waste in other developing countries facing similar challenges of limited data availability.

Predicting the Distribution of Vibrio spp. in the Chesapeake Bay: A Vibrio cholerae Case Study

PubMed Central

Magny, Guillaume Constantin de; Long, Wen; Brown, Christopher W.; Hood, Raleigh R.; Huq, Anwar; Murtugudde, Raghu; Colwell, Rita R.

2010-01-01

Vibrio cholerae, the causative agent of cholera, is a naturally occurring inhabitant of the Chesapeake Bay and serves as a predictor for other clinically important vibrios, including Vibrio parahaemolyticus and Vibrio vulnificus. A system was constructed to predict the likelihood of the presence of V. cholerae in surface waters of the Chesapeake Bay, with the goal to provide forecasts of the occurrence of this and related pathogenic Vibrio spp. Prediction was achieved by driving an available multivariate empirical habitat model estimating the probability of V. cholerae within a range of temperatures and salinities in the Bay, with hydrodynamically generated predictions of ambient temperature and salinity. The experimental predictions provided both an improved understanding of the in situ variability of V. cholerae, including identification of potential hotspots of occurrence, and usefulness as an early warning system. With further development of the system, prediction of the probability of the occurrence of related pathogenic vibrios in the Chesapeake Bay, notably V. parahaemolyticus and V. vulnificus, will be possible, as well as its transport to any geographical location where sufficient relevant data are available. PMID:20145974

Where is The Dark Matter: The Flow-field From 2MASS

NASA Astrophysics Data System (ADS)

Crook, Aidan; Huchra, J.; Macri, L.; Masters, K.; Jarrett, T.

2009-01-01

We present a map of the flow-field constructed from groups of galaxies in the 2MASS Redshift Survey. Previous efforts have suffered because the underlying surveys either did not penetrate to low galactic latitudes or were not sensitive to elliptical galaxies, thereby missing a significant fraction of the mass. The 2MASS Redshift Survey provides a uniform all-sky magnitude-limited sample in the J, H and Ks bands, 97% complete to Ks<11.75 and |b|>10°, sensitive to both ellipticals and spirals. We demonstrate how utilizing the properties of galaxy groups leads to improved predictions of peculiar velocities in the nearby Universe, and use dynamical mass estimates to construct a reliable flow-field to 12,000 km/s. We demonstrate its effectiveness in providing distance estimates, and discuss the advantages of this model over earlier work. With independent knowledge of the peculiar velocity of the Local Group, we discuss the implications for the matter density parameter and bias. This work is supported by a Whiteman Fellowship and NSF grant AST-0406906.

Data Analysis & Statistical Methods for Command File Errors

NASA Technical Reports Server (NTRS)

Meshkat, Leila; Waggoner, Bruce; Bryant, Larry

2014-01-01

This paper explains current work on modeling for managing the risk of command file errors. It is focused on analyzing actual data from a JPL spaceflight mission to build models for evaluating and predicting error rates as a function of several key variables. We constructed a rich dataset by considering the number of errors, the number of files radiated, including the number commands and blocks in each file, as well as subjective estimates of workload and operational novelty. We have assessed these data using different curve fitting and distribution fitting techniques, such as multiple regression analysis, and maximum likelihood estimation to see how much of the variability in the error rates can be explained with these. We have also used goodness of fit testing strategies and principal component analysis to further assess our data. Finally, we constructed a model of expected error rates based on the what these statistics bore out as critical drivers to the error rate. This model allows project management to evaluate the error rate against a theoretically expected rate as well as anticipate future error rates.

Ball milling: An experimental support to the energy transfer evaluated by the collision model

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

Magini, M.; Iasonna, A.; Padella, F.

1996-01-01

In recent years several attempts have been made in order to understand the fundamentals of the ball milling process. The aim of these approaches is to establish predictive capabilities for this process, i.e. the possibility of obtaining a given product by suitable choosing the proper milling conditions. Maurice and Courtney have modeled ball milling in a planetary and in a vibratory mill including parameters like impact times, areas of the colliding surfaces (derived from hertzian collision theory), powder strain rates and pressure peak during collision. Burgio et al derived the kinematic equations of a ball moving on a planetary millmore » and the consequent ball-to-powder energy transfer occurring in a single collision event. The fraction of input energy transferred to the powder was subsequently estimated by an analysis of the collision event. Finally an energy map was constructed which was the basis for a model with predictive capabilities. The aim of the present article is to show that the arguments used to construct the model of the milling process has substantial experimental support.« less

Mortality table construction

NASA Astrophysics Data System (ADS)

Sutawanir

2015-12-01

Mortality tables play important role in actuarial studies such as life annuities, premium determination, premium reserve, valuation pension plan, pension funding. Some known mortality tables are CSO mortality table, Indonesian Mortality Table, Bowers mortality table, Japan Mortality table. For actuary applications some tables are constructed with different environment such as single decrement, double decrement, and multiple decrement. There exist two approaches in mortality table construction : mathematics approach and statistical approach. Distribution model and estimation theory are the statistical concepts that are used in mortality table construction. This article aims to discuss the statistical approach in mortality table construction. The distributional assumptions are uniform death distribution (UDD) and constant force (exponential). Moment estimation and maximum likelihood are used to estimate the mortality parameter. Moment estimation methods are easier to manipulate compared to maximum likelihood estimation (mle). However, the complete mortality data are not used in moment estimation method. Maximum likelihood exploited all available information in mortality estimation. Some mle equations are complicated and solved using numerical methods. The article focus on single decrement estimation using moment and maximum likelihood estimation. Some extension to double decrement will introduced. Simple dataset will be used to illustrated the mortality estimation, and mortality table.

Predicting phenotype from genotype: Improving accuracy through more robust experimental and computational modeling

PubMed Central

Gallion, Jonathan; Koire, Amanda; Katsonis, Panagiotis; Schoenegge, Anne‐Marie; Bouvier, Michel

2017-01-01

Abstract Computational prediction yields efficient and scalable initial assessments of how variants of unknown significance may affect human health. However, when discrepancies between these predictions and direct experimental measurements of functional impact arise, inaccurate computational predictions are frequently assumed as the source. Here, we present a methodological analysis indicating that shortcomings in both computational and biological data can contribute to these disagreements. We demonstrate that incomplete assaying of multifunctional proteins can affect the strength of correlations between prediction and experiments; a variant's full impact on function is better quantified by considering multiple assays that probe an ensemble of protein functions. Additionally, many variants predictions are sensitive to protein alignment construction and can be customized to maximize relevance of predictions to a specific experimental question. We conclude that inconsistencies between computation and experiment can often be attributed to the fact that they do not test identical hypotheses. Aligning the design of the computational input with the design of the experimental output will require cooperation between computational and biological scientists, but will also lead to improved estimations of computational prediction accuracy and a better understanding of the genotype–phenotype relationship. PMID:28230923

Predicting phenotype from genotype: Improving accuracy through more robust experimental and computational modeling.

PubMed

Gallion, Jonathan; Koire, Amanda; Katsonis, Panagiotis; Schoenegge, Anne-Marie; Bouvier, Michel; Lichtarge, Olivier

2017-05-01

Computational prediction yields efficient and scalable initial assessments of how variants of unknown significance may affect human health. However, when discrepancies between these predictions and direct experimental measurements of functional impact arise, inaccurate computational predictions are frequently assumed as the source. Here, we present a methodological analysis indicating that shortcomings in both computational and biological data can contribute to these disagreements. We demonstrate that incomplete assaying of multifunctional proteins can affect the strength of correlations between prediction and experiments; a variant's full impact on function is better quantified by considering multiple assays that probe an ensemble of protein functions. Additionally, many variants predictions are sensitive to protein alignment construction and can be customized to maximize relevance of predictions to a specific experimental question. We conclude that inconsistencies between computation and experiment can often be attributed to the fact that they do not test identical hypotheses. Aligning the design of the computational input with the design of the experimental output will require cooperation between computational and biological scientists, but will also lead to improved estimations of computational prediction accuracy and a better understanding of the genotype-phenotype relationship. © 2017 The Authors. **Human Mutation published by Wiley Periodicals, Inc.

Dengue prediction by the web: Tweets are a useful tool for estimating and forecasting Dengue at country and city level.

PubMed

Marques-Toledo, Cecilia de Almeida; Degener, Carolin Marlen; Vinhal, Livia; Coelho, Giovanini; Meira, Wagner; Codeço, Claudia Torres; Teixeira, Mauro Martins

2017-07-01

Infectious diseases are a leading threat to public health. Accurate and timely monitoring of disease risk and progress can reduce their impact. Mentioning a disease in social networks is correlated with physician visits by patients, and can be used to estimate disease activity. Dengue is the fastest growing mosquito-borne viral disease, with an estimated annual incidence of 390 million infections, of which 96 million manifest clinically. Dengue burden is likely to increase in the future owing to trends toward increased urbanization, scarce water supplies and, possibly, environmental change. The epidemiological dynamic of Dengue is complex and difficult to predict, partly due to costly and slow surveillance systems. In this study, we aimed to quantitatively assess the usefulness of data acquired by Twitter for the early detection and monitoring of Dengue epidemics, both at country and city level at a weekly basis. Here, we evaluated and demonstrated the potential of tweets modeling for Dengue estimation and forecast, in comparison with other available web-based data, Google Trends and Wikipedia access logs. Also, we studied the factors that might influence the goodness-of-fit of the model. We built a simple model based on tweets that was able to 'nowcast', i.e. estimate disease numbers in the same week, but also 'forecast' disease in future weeks. At the country level, tweets are strongly associated with Dengue cases, and can estimate present and future Dengue cases until 8 weeks in advance. At city level, tweets are also useful for estimating Dengue activity. Our model can be applied successfully to small and less developed cities, suggesting a robust construction, even though it may be influenced by the incidence of the disease, the activity of Twitter locally, and social factors, including human development index and internet access. Tweets association with Dengue cases is valuable to assist traditional Dengue surveillance at real-time and low-cost. Tweets are able to successfully nowcast, i.e. estimate Dengue in the present week, but also forecast, i.e. predict Dengue at until 8 weeks in the future, both at country and city level with high estimation capacity.

Assessment of predictive performance in incomplete data by combining internal validation and multiple imputation.

PubMed

Wahl, Simone; Boulesteix, Anne-Laure; Zierer, Astrid; Thorand, Barbara; van de Wiel, Mark A

2016-10-26

Missing values are a frequent issue in human studies. In many situations, multiple imputation (MI) is an appropriate missing data handling strategy, whereby missing values are imputed multiple times, the analysis is performed in every imputed data set, and the obtained estimates are pooled. If the aim is to estimate (added) predictive performance measures, such as (change in) the area under the receiver-operating characteristic curve (AUC), internal validation strategies become desirable in order to correct for optimism. It is not fully understood how internal validation should be combined with multiple imputation. In a comprehensive simulation study and in a real data set based on blood markers as predictors for mortality, we compare three combination strategies: Val-MI, internal validation followed by MI on the training and test parts separately, MI-Val, MI on the full data set followed by internal validation, and MI(-y)-Val, MI on the full data set omitting the outcome followed by internal validation. Different validation strategies, including bootstrap und cross-validation, different (added) performance measures, and various data characteristics are considered, and the strategies are evaluated with regard to bias and mean squared error of the obtained performance estimates. In addition, we elaborate on the number of resamples and imputations to be used, and adopt a strategy for confidence interval construction to incomplete data. Internal validation is essential in order to avoid optimism, with the bootstrap 0.632+ estimate representing a reliable method to correct for optimism. While estimates obtained by MI-Val are optimistically biased, those obtained by MI(-y)-Val tend to be pessimistic in the presence of a true underlying effect. Val-MI provides largely unbiased estimates, with a slight pessimistic bias with increasing true effect size, number of covariates and decreasing sample size. In Val-MI, accuracy of the estimate is more strongly improved by increasing the number of bootstrap draws rather than the number of imputations. With a simple integrated approach, valid confidence intervals for performance estimates can be obtained. When prognostic models are developed on incomplete data, Val-MI represents a valid strategy to obtain estimates of predictive performance measures.

Comparison of aquifer characterization approaches through steady state groundwater model validation: A controlled laboratory sandbox study

USGS Publications Warehouse

Illman, W.A.; Zhu, J.; Craig, A.J.; Yin, D.

2010-01-01

Groundwater modeling has become a vital component to water supply and contaminant transport investigations. An important component of groundwater modeling under steady state conditions is selecting a representative hydraulic conductivity (K) estimate or set of estimates which defines the K field of the studied region. Currently, there are a number of characterization approaches to obtain K at various scales and in varying degrees of detail, but there is a paucity of information in terms of which characterization approach best predicts flow through aquifers or drawdowns caused by some drawdown inducing events. The main objective of this paper is to assess K estimates obtained by various approaches by predicting drawdowns from independent cross-hole pumping tests and total flow rates through a synthetic heterogeneous aquifer from flow-through tests. Specifically, we (1) characterize a synthetic heterogeneous aquifer built in the sandbox through various techniques (permeameter analyses of core samples, single-hole, cross-hole, and flow-through testing), (2) obtain mean K fields through traditional analysis of test data by treating the medium to be homogeneous, (3) obtain heterogeneous K fields through kriging and steady state hydraulic tomography, and (4) conduct forward simulations of 16 independent pumping tests and six flowthrough tests using these homogeneous and heterogeneous K fields and comparing them to actual data. Results show that the mean K and heterogeneous K fields estimated through kriging of small-scale K data (core and single-hole tests) yield biased predictions of drawdowns and flow rates in this synthetic heterogeneous aquifer. In contrast, the heterogeneous K distribution or ?K tomogram? estimated via steady state hydraulic tomography yields excellent predictions of drawdowns of pumping tests not used in the construction of the tomogram and very good estimates of total flow rates from the flowthrough tests. These results suggest that steady state groundwater model validation is possible in this laboratory sandbox aquifer if the heterogeneous K distribution and forcing functions (boundary conditions and source/sink terms) are characterized sufficiently. ?? 2010 by the American Geophysical Union.

Prediction equation for estimating total daily energy requirements of special operations personnel.

PubMed

Barringer, N D; Pasiakos, S M; McClung, H L; Crombie, A P; Margolis, L M

2018-01-01

Special Operations Forces (SOF) engage in a variety of military tasks with many producing high energy expenditures, leading to undesired energy deficits and loss of body mass. Therefore, the ability to accurately estimate daily energy requirements would be useful for accurate logistical planning. Generate a predictive equation estimating energy requirements of SOF. Retrospective analysis of data collected from SOF personnel engaged in 12 different SOF training scenarios. Energy expenditure and total body water were determined using the doubly-labeled water technique. Physical activity level was determined as daily energy expenditure divided by resting metabolic rate. Physical activity level was broken into quartiles (0 = mission prep, 1 = common warrior tasks, 2 = battle drills, 3 = specialized intense activity) to generate a physical activity factor (PAF). Regression analysis was used to construct two predictive equations (Model A; body mass and PAF, Model B; fat-free mass and PAF) estimating daily energy expenditures. Average measured energy expenditure during SOF training was 4468 (range: 3700 to 6300) Kcal·d- 1 . Regression analysis revealed that physical activity level ( r  = 0.91; P  < 0.05) and body mass ( r  = 0.28; P  < 0.05; Model A), or fat-free mass (FFM; r  = 0.32; P  < 0.05; Model B) were the factors that most highly predicted energy expenditures. Predictive equations coupling PAF with body mass (Model A) and FFM (Model B), were correlated ( r  = 0.74 and r  = 0.76, respectively) and did not differ [mean ± SEM: Model A; 4463 ± 65 Kcal·d - 1 , Model B; 4462 ± 61 Kcal·d - 1 ] from DLW measured energy expenditures. By quantifying and grouping SOF training exercises into activity factors, SOF energy requirements can be predicted with reasonable accuracy and these equations used by dietetic/logistical personnel to plan appropriate feeding regimens to meet SOF nutritional requirements across their mission profile.

A test to evaluate the earthquake prediction algorithm, M8

USGS Publications Warehouse

Healy, John H.; Kossobokov, Vladimir G.; Dewey, James W.

1992-01-01

A test of the algorithm M8 is described. The test is constructed to meet four rules, which we propose to be applicable to the test of any method for earthquake prediction:  1. An earthquake prediction technique should be presented as a well documented, logical algorithm that can be used by  investigators without restrictions. 2. The algorithm should be coded in a common programming language and implementable on widely available computer systems. 3. A test of the earthquake prediction technique should involve future predictions with a black box version of the algorithm in which potentially adjustable parameters are fixed in advance. The source of the input data must be defined and ambiguities in these data must be resolved automatically by the algorithm. 4. At least one reasonable null hypothesis should be stated in advance of testing the earthquake prediction method, and it should be stated how this null hypothesis will be used to estimate the statistical significance of the earthquake predictions. The M8 algorithm has successfully predicted several destructive earthquakes, in the sense that the earthquakes occurred inside regions with linear dimensions from 384 to 854 km that the algorithm had identified as being in times of increased probability for strong earthquakes. In addition, M8 has successfully "post predicted" high percentages of strong earthquakes in regions to which it has been applied in retroactive studies. The statistical significance of previous predictions has not been established, however, and post-prediction studies in general are notoriously subject to success-enhancement through hindsight. Nor has it been determined how much more precise an M8 prediction might be than forecasts and probability-of-occurrence estimates made by other techniques. We view our test of M8 both as a means to better determine the effectiveness of M8 and as an experimental structure within which to make observations that might lead to improvements in the algorithm or conceivably lead to a radically different approach to earthquake prediction.

Performance of third-trimester ultrasound for prediction of small-for-gestational-age neonates and evaluation of contingency screening policies.

PubMed

Souka, A P; Papastefanou, I; Pilalis, A; Michalitsi, V; Kassanos, D

2012-05-01

To assess the performance of third-trimester fetal biometry and fetal Doppler studies for the prediction of small-for-gestational-age (SGA) neonates, and to explore contingency strategies using a first-trimester prediction model based on maternal and fetal parameters and third-trimester ultrasound. This was an observational cross-sectional study of uncomplicated singleton pregnancies. Risk assessment for chromosomal abnormality was carried out in 4702 pregnancies using a combination of ultrasound markers (fetal nuchal translucency thickness (NT) and nasal bone assessment) and biochemistry (free beta-human chorionic gonadotropin (β-hCG) and pregnancy-associated plasma protein-A (PAPP-A)) at 11 to 13 + 6 weeks. Maternal demographic characteristics and method of conception were recorded. Third-trimester (30-34 weeks) fetal biometry (biparietal diameter (BPD), head circumference (HC), abdominal circumference (AC) and femur length (FL)) and umbilical artery (UA) and middle cerebral artery Doppler studies were performed routinely in a subgroup (n = 2310). Reference ranges for birth weight were constructed using the cohort of 4702 women, and neonates were classified as small (SGA, ≤ 5th centile) or appropriate (AGA) for gestational age. First-trimester, third-trimester and integrated first- and third-trimester prediction models for SGA were constructed using regression analysis and three different contingency strategies of rescanning in the third trimester were investigated. According to the areas under the receiver-operating characteristics curves (AUCs), AC (AUC = 0.85) and ultrasound-estimated fetal weight (EFW, AUC = 0.87) were equally good predictors of SGA. The model was marginally improved by the addition of UA Doppler, smoking status and first-trimester indices (free β-hCG and PAPP-A multiples of the median) (combined model, AUC = 0.88), but the difference was not statistically significant. A contingency strategy of rescanning 50% of the population in the third trimester according to the risk estimated by a first-trimester prediction model yielded a detection rate of 79% for a 25% screen-positive rate. Third-trimester ultrasound is effective in screening for SGA in uncomplicated pregnancies. The use of a contingency screening policy can reduce the need for unnecessary examinations. Copyright © 2012 ISUOG. Published by John Wiley & Sons, Ltd.

Near infrared spectroscopy to estimate the temperature reached on burned soils: strategies to develop robust models.

NASA Astrophysics Data System (ADS)

Guerrero, César; Pedrosa, Elisabete T.; Pérez-Bejarano, Andrea; Keizer, Jan Jacob

2014-05-01

The temperature reached on soils is an important parameter needed to describe the wildfire effects. However, the methods for measure the temperature reached on burned soils have been poorly developed. Recently, the use of the near-infrared (NIR) spectroscopy has been pointed as a valuable tool for this purpose. The NIR spectrum of a soil sample contains information of the organic matter (quantity and quality), clay (quantity and quality), minerals (such as carbonates and iron oxides) and water contents. Some of these components are modified by the heat, and each temperature causes a group of changes, leaving a typical fingerprint on the NIR spectrum. This technique needs the use of a model (or calibration) where the changes in the NIR spectra are related with the temperature reached. For the development of the model, several aliquots are heated at known temperatures, and used as standards in the calibration set. This model offers the possibility to make estimations of the temperature reached on a burned sample from its NIR spectrum. However, the estimation of the temperature reached using NIR spectroscopy is due to changes in several components, and cannot be attributed to changes in a unique soil component. Thus, we can estimate the temperature reached by the interaction between temperature and the thermo-sensible soil components. In addition, we cannot expect the uniform distribution of these components, even at small scale. Consequently, the proportion of these soil components can vary spatially across the site. This variation will be present in the samples used to construct the model and also in the samples affected by the wildfire. Therefore, the strategies followed to develop robust models should be focused to manage this expected variation. In this work we compared the prediction accuracy of models constructed with different approaches. These approaches were designed to provide insights about how to distribute the efforts needed for the development of robust models, since this step is the bottle-neck of this technique. In the first approach, a plot-scale model was used to predict the temperature reached in samples collected in other plots from the same site. In a plot-scale model, all the heated aliquots come from a unique plot-scale sample. As expected, the results obtained with this approach were deceptive, because this approach was assuming that a plot-scale model would be enough to represent the whole variability of the site. The accuracy (measured as the root mean square error of prediction, thereinafter RMSEP) was 86ºC, and the bias was also high (>30ºC). In the second approach, the temperatures predicted through several plot-scale models were averaged. The accuracy was improved (RMSEP=65ºC) respect the first approach, because the variability from several plots was considered and biased predictions were partially counterbalanced. However, this approach implies more efforts, since several plot-scale models are needed. In the third approach, the predictions were obtained with site-scale models. These models were constructed with aliquots from several plots. In this case, the results were accurate, since the RMSEP was around 40ºC, the bias was very small (<1ºC) and the R2 was 0.92. As expected, this approach clearly outperformed the second approach, in spite of the fact that the same efforts were needed. In a plot-scale model, only one interaction between temperature and soil components was modelled. However, several different interactions between temperature and soil components were present in the calibration matrix of a site-scale model. Consequently, the site-scale models were able to model the temperature reached excluding the influence of the differences in soil composition, resulting in more robust models respect that variation. Summarizing, the results were highlighting the importance of an adequate strategy to develop robust and accurate models with moderate efforts, and how a wrong strategy can result in deceptive predictions.

Detecting non-orthology in the COGs database and other approaches grouping orthologs using genome-specific best hits.

PubMed

Dessimoz, Christophe; Boeckmann, Brigitte; Roth, Alexander C J; Gonnet, Gaston H

2006-01-01

Correct orthology assignment is a critical prerequisite of numerous comparative genomics procedures, such as function prediction, construction of phylogenetic species trees and genome rearrangement analysis. We present an algorithm for the detection of non-orthologs that arise by mistake in current orthology classification methods based on genome-specific best hits, such as the COGs database. The algorithm works with pairwise distance estimates, rather than computationally expensive and error-prone tree-building methods. The accuracy of the algorithm is evaluated through verification of the distribution of predicted cases, case-by-case phylogenetic analysis and comparisons with predictions from other projects using independent methods. Our results show that a very significant fraction of the COG groups include non-orthologs: using conservative parameters, the algorithm detects non-orthology in a third of all COG groups. Consequently, sequence analysis sensitive to correct orthology assignments will greatly benefit from these findings.

A prediction model for cognitive performance in health ageing using diffusion tensor imaging with graph theory.

PubMed

Yun, Ruijuan; Lin, Chung-Chih; Wu, Shuicai; Huang, Chu-Chung; Lin, Ching-Po; Chao, Yi-Ping

2013-01-01

In this study, we employed diffusion tensor imaging (DTI) to construct brain structural network and then derive the connection matrices from 96 healthy elderly subjects. The correlation analysis between these topological properties of network based on graph theory and the Cognitive Abilities Screening Instrument (CASI) index were processed to extract the significant network characteristics. These characteristics were then integrated to estimate the models by various machine-learning algorithms to predict user's cognitive performance. From the results, linear regression model and Gaussian processes model showed presented better abilities with lower mean absolute errors of 5.8120 and 6.25 to predict the cognitive performance respectively. Moreover, these extracted topological properties of brain structural network derived from DTI also could be regarded as the bio-signatures for further evaluation of brain degeneration in healthy aged and early diagnosis of mild cognitive impairment (MCI).

Learning representative features for facial images based on a modified principal component analysis

NASA Astrophysics Data System (ADS)

Averkin, Anton; Potapov, Alexey

2013-05-01

The paper is devoted to facial image analysis and particularly deals with the problem of automatic evaluation of the attractiveness of human faces. We propose a new approach for automatic construction of feature space based on a modified principal component analysis. Input data sets for the algorithm are the learning data sets of facial images, which are rated by one person. The proposed approach allows one to extract features of the individual subjective face beauty perception and to predict attractiveness values for new facial images, which were not included into a learning data set. The Pearson correlation coefficient between values predicted by our method for new facial images and personal attractiveness estimation values equals to 0.89. This means that the new approach proposed is promising and can be used for predicting subjective face attractiveness values in real systems of the facial images analysis.

A new method for the prediction of chatter stability lobes based on dynamic cutting force simulation model and support vector machine

NASA Astrophysics Data System (ADS)

Peng, Chong; Wang, Lun; Liao, T. Warren

2015-10-01

Currently, chatter has become the critical factor in hindering machining quality and productivity in machining processes. To avoid cutting chatter, a new method based on dynamic cutting force simulation model and support vector machine (SVM) is presented for the prediction of chatter stability lobes. The cutting force is selected as the monitoring signal, and the wavelet energy entropy theory is used to extract the feature vectors. A support vector machine is constructed using the MATLAB LIBSVM toolbox for pattern classification based on the feature vectors derived from the experimental cutting data. Then combining with the dynamic cutting force simulation model, the stability lobes diagram (SLD) can be estimated. Finally, the predicted results are compared with existing methods such as zero-order analytical (ZOA) and semi-discretization (SD) method as well as actual cutting experimental results to confirm the validity of this new method.

Effects of Data Anonymization by Cell Suppression on Descriptive Statistics and Predictive Modeling Performance

PubMed Central

Ohno-Machado, Lucila; Vinterbo, Staal; Dreiseitl, Stephan

2002-01-01

Protecting individual data in disclosed databases is essential. Data anonymization strategies can produce table ambiguation by suppression of selected cells. Using table ambiguation, different degrees of anonymization can be achieved, depending on the number of individuals that a particular case must become indistinguishable from. This number defines the level of anonymization. Anonymization by cell suppression does not necessarily prevent inferences from being made from the disclosed data. Preventing inferences may be important to preserve confidentiality. We show that anonymized data sets can preserve descriptive characteristics of the data, but might also be used for making inferences on particular individuals, which is a feature that may not be desirable. The degradation of predictive performance is directly proportional to the degree of anonymity. As an example, we report the effect of anonymization on the predictive performance of a model constructed to estimate the probability of disease given clinical findings.

Effects of data anonymization by cell suppression on descriptive statistics and predictive modeling performance.

PubMed Central

Ohno-Machado, L.; Vinterbo, S. A.; Dreiseitl, S.

2001-01-01

Protecting individual data in disclosed databases is essential. Data anonymization strategies can produce table ambiguation by suppression of selected cells. Using table ambiguation, different degrees of anonymization can be achieved, depending on the number of individuals that a particular case must become indistinguishable from. This number defines the level of anonymization. Anonymization by cell suppression does not necessarily prevent inferences from being made from the disclosed data. Preventing inferences may be important to preserve confidentiality. We show that anonymized data sets can preserve descriptive characteristics of the data, but might also be used for making inferences on particular individuals, which is a feature that may not be desirable. The degradation of predictive performance is directly proportional to the degree of anonymity. As an example, we report the effect of anonymization on the predictive performance of a model constructed to estimate the probability of disease given clinical findings. PMID:11825239

The relationship between language use and depression: illuminating the importance of self-reflection, self-rumination, and the need for absolute truth.

PubMed

Şimşek, Ömer Faruk

2013-01-01

The main aim of the present study was to provide additional knowledge about the mediatory processes through which language relates to depression. Although previous research gave clear evidence that language is closely related to depression, the research on intervening variables in the relationship has been limited. The present investigation tested a structural equation model in which self-concept clarity and self-consciousness mediated the relationship between personal perceptions of language and depression. Since "the need for absolute truth" construct has been shown to be important in providing greater consistency in estimates of the relationships among the variables, it has been added to the model as a control variable. The results supported the model and showed that personal perceptions of language predicted self-concept clarity, which in turn predicted the participants' self-reflection and self-rumination. Self-reflection and self-rumination, in turn, predicted depression.

75 FR 27574 - Agency Information Collection Activities: Comment Request for the Production Estimate, Quarterly...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-05-17

... Request for the Production Estimate, Quarterly Construction Sand and Gravel and Crushed and Broken Stone... Production Estimate, Quarterly Construction Sand and Gravel and Crushed and Broken Stone. This collection... Construction Sand and Gravel and Crushed and Broken Stone. Type of Request: Extension of a currently approved...

Test Framing Generates a Stability Bias for Predictions of Learning by Causing People to Discount their Learning Beliefs

PubMed Central

Ariel, Robert; Hines, Jarrod C.; Hertzog, Christopher

2014-01-01

People estimate minimal changes in learning when making predictions of learning (POLs) for future study opportunities despite later showing increased performance and an awareness of that increase (Kornell & Bjork, 2009). This phenomenon is conceptualized as a stability bias in judgments about learning. We investigated the malleability of this effect, and whether it reflected people’s underlying beliefs about learning. We manipulated prediction framing to emphasize the role of testing vs. studying on memory and directly measured beliefs about multi-trial study effects on learning by having participants construct predicted learning curves before and after the experiment. Mean POLs were more sensitive to the number of study-test opportunities when performance was framed in terms of study benefits rather than testing benefits and POLs reflected pre-existing beliefs about learning. The stability bias is partially due to framing and reflects discounted beliefs about learning benefits rather than inherent belief in the stability of performance. PMID:25067885

Test Framing Generates a Stability Bias for Predictions of Learning by Causing People to Discount their Learning Beliefs.

PubMed

Ariel, Robert; Hines, Jarrod C; Hertzog, Christopher

2014-08-01

People estimate minimal changes in learning when making predictions of learning (POLs) for future study opportunities despite later showing increased performance and an awareness of that increase (Kornell & Bjork, 2009). This phenomenon is conceptualized as a stability bias in judgments about learning. We investigated the malleability of this effect, and whether it reflected people's underlying beliefs about learning. We manipulated prediction framing to emphasize the role of testing vs. studying on memory and directly measured beliefs about multi-trial study effects on learning by having participants construct predicted learning curves before and after the experiment. Mean POLs were more sensitive to the number of study-test opportunities when performance was framed in terms of study benefits rather than testing benefits and POLs reflected pre-existing beliefs about learning. The stability bias is partially due to framing and reflects discounted beliefs about learning benefits rather than inherent belief in the stability of performance.

Predicting the size of individual and group differences on speeded cognitive tasks.

PubMed

Chen, Jing; Hale, Sandra; Myerson, Joel

2007-06-01

An a priori test of the difference engine model (Myerson, Hale, Zheng, Jenkins, & Widaman, 2003) was conducted using a large, diverse sample of individuals who performed three speeded verbal tasks and three speeded visuospatial tasks. Results demonstrated that, as predicted by the model, the group standard deviation (SD) on any task was proportional to the amount of processing required by that task. Both individual performances as well as those of fast and slow subgroups could be accurately predicted by the model using no free parameters, just an individual or subgroup's mean z-score and the values of theoretical constructs estimated from fits to the group SDs. Taken together, these results are consistent with post hoc analyses reported by Myerson et al. and provide even stronger supporting evidence. In particular, the ability to make quantitative predictions without using any free parameters provides the clearest demonstration to date of the power of an analytic approach on the basis of the difference engine.

36 CFR 223.83 - Contents of prospectus.

Code of Federal Regulations, 2010 CFR

2010-07-01

... specified roads to be constructed. (16) The estimated road construction cost and the estimated public works..., the prospectus shall also include: (1) The road standards applicable to construction of permanent... permanent roads. (3) A statement explaining how the Forest Service intends to perform road construction by...

Comparing two correlated C indices with right-censored survival outcome: a one-shot nonparametric approach.

PubMed

Kang, Le; Chen, Weijie; Petrick, Nicholas A; Gallas, Brandon D

2015-02-20

The area under the receiver operating characteristic curve is often used as a summary index of the diagnostic ability in evaluating biomarkers when the clinical outcome (truth) is binary. When the clinical outcome is right-censored survival time, the C index, motivated as an extension of area under the receiver operating characteristic curve, has been proposed by Harrell as a measure of concordance between a predictive biomarker and the right-censored survival outcome. In this work, we investigate methods for statistical comparison of two diagnostic or predictive systems, of which they could either be two biomarkers or two fixed algorithms, in terms of their C indices. We adopt a U-statistics-based C estimator that is asymptotically normal and develop a nonparametric analytical approach to estimate the variance of the C estimator and the covariance of two C estimators. A z-score test is then constructed to compare the two C indices. We validate our one-shot nonparametric method via simulation studies in terms of the type I error rate and power. We also compare our one-shot method with resampling methods including the jackknife and the bootstrap. Simulation results show that the proposed one-shot method provides almost unbiased variance estimations and has satisfactory type I error control and power. Finally, we illustrate the use of the proposed method with an example from the Framingham Heart Study. Copyright © 2014 John Wiley & Sons, Ltd.

Remaining useful life prediction of degrading systems subjected to imperfect maintenance: Application to draught fans

NASA Astrophysics Data System (ADS)

Wang, Zhao-Qiang; Hu, Chang-Hua; Si, Xiao-Sheng; Zio, Enrico

2018-02-01

Current degradation modeling and remaining useful life prediction studies share a common assumption that the degrading systems are not maintained or maintained perfectly (i.e., to an as-good-as new state). This paper concerns the issues of how to model the degradation process and predict the remaining useful life of degrading systems subjected to imperfect maintenance activities, which can restore the health condition of a degrading system to any degradation level between as-good-as new and as-bad-as old. Toward this end, a nonlinear model driven by Wiener process is first proposed to characterize the degradation trajectory of the degrading system subjected to imperfect maintenance, where negative jumps are incorporated to quantify the influence of imperfect maintenance activities on the system's degradation. Then, the probability density function of the remaining useful life is derived analytically by a space-scale transformation, i.e., transforming the constructed degradation model with negative jumps crossing a constant threshold level to a Wiener process model crossing a random threshold level. To implement the proposed method, unknown parameters in the degradation model are estimated by the maximum likelihood estimation method. Finally, the proposed degradation modeling and remaining useful life prediction method are applied to a practical case of draught fans belonging to a kind of mechanical systems from steel mills. The results reveal that, for a degrading system subjected to imperfect maintenance, our proposed method can obtain more accurate remaining useful life predictions than those of the benchmark model in literature.

Estimation of trabecular bone parameters in children from multisequence MRI using texture-based regression

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

Lekadir, Karim, E-mail: karim.lekadir@upf.edu; Hoogendoorn, Corné; Armitage, Paul

Purpose: This paper presents a statistical approach for the prediction of trabecular bone parameters from low-resolution multisequence magnetic resonance imaging (MRI) in children, thus addressing the limitations of high-resolution modalities such as HR-pQCT, including the significant exposure of young patients to radiation and the limited applicability of such modalities to peripheral bones in vivo. Methods: A statistical predictive model is constructed from a database of MRI and HR-pQCT datasets, to relate the low-resolution MRI appearance in the cancellous bone to the trabecular parameters extracted from the high-resolution images. The description of the MRI appearance is achieved between subjects by usingmore » a collection of feature descriptors, which describe the texture properties inside the cancellous bone, and which are invariant to the geometry and size of the trabecular areas. The predictive model is built by fitting to the training data a nonlinear partial least square regression between the input MRI features and the output trabecular parameters. Results: Detailed validation based on a sample of 96 datasets shows correlations >0.7 between the trabecular parameters predicted from low-resolution multisequence MRI based on the proposed statistical model and the values extracted from high-resolution HRp-QCT. Conclusions: The obtained results indicate the promise of the proposed predictive technique for the estimation of trabecular parameters in children from multisequence MRI, thus reducing the need for high-resolution radiation-based scans for a fragile population that is under development and growth.« less

The relationship between offspring size and fitness: integrating theory and empiricism.

PubMed

Rollinson, Njal; Hutchings, Jeffrey A

2013-02-01

How parents divide the energy available for reproduction between size and number of offspring has a profound effect on parental reproductive success. Theory indicates that the relationship between offspring size and offspring fitness is of fundamental importance to the evolution of parental reproductive strategies: this relationship predicts the optimal division of resources between size and number of offspring, it describes the fitness consequences for parents that deviate from optimality, and its shape can predict the most viable type of investment strategy in a given environment (e.g., conservative vs. diversified bet-hedging). Many previous attempts to estimate this relationship and the corresponding value of optimal offspring size have been frustrated by a lack of integration between theory and empiricism. In the present study, we draw from C. Smith and S. Fretwell's classic model to explain how a sound estimate of the offspring size--fitness relationship can be derived with empirical data. We evaluate what measures of fitness can be used to model the offspring size--fitness curve and optimal size, as well as which statistical models should and should not be used to estimate offspring size--fitness relationships. To construct the fitness curve, we recommend that offspring fitness be measured as survival up to the age at which the instantaneous rate of offspring mortality becomes random with respect to initial investment. Parental fitness is then expressed in ecologically meaningful, theoretically defensible, and broadly comparable units: the number of offspring surviving to independence. Although logistic and asymptotic regression have been widely used to estimate offspring size-fitness relationships, the former provides relatively unreliable estimates of optimal size when offspring survival and sample sizes are low, and the latter is unreliable under all conditions. We recommend that the Weibull-1 model be used to estimate this curve because it provides modest improvements in prediction accuracy under experimentally relevant conditions.

Highway Cost Index Estimator Tool

DOT National Transportation Integrated Search

2017-10-01

To plan and program highway construction projects, the Texas Department of Transportation requires accurate construction cost data. However, due to the number of, and uncertainty of, variables that affect highway construction costs, estimating future...

Risk Prediction of New Adjacent Vertebral Fractures After PVP for Patients with Vertebral Compression Fractures: Development of a Prediction Model

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

Zhong, Bin-Yan; He, Shi-Cheng; Zhu, Hai-Dong

PurposeWe aim to determine the predictors of new adjacent vertebral fractures (AVCFs) after percutaneous vertebroplasty (PVP) in patients with osteoporotic vertebral compression fractures (OVCFs) and to construct a risk prediction score to estimate a 2-year new AVCF risk-by-risk factor condition.Materials and MethodsPatients with OVCFs who underwent their first PVP between December 2006 and December 2013 at Hospital A (training cohort) and Hospital B (validation cohort) were included in this study. In training cohort, we assessed the independent risk predictors and developed the probability of new adjacent OVCFs (PNAV) score system using the Cox proportional hazard regression analysis. The accuracy ofmore » this system was then validated in both training and validation cohorts by concordance (c) statistic.Results421 patients (training cohort: n = 256; validation cohort: n = 165) were included in this study. In training cohort, new AVCFs after the first PVP treatment occurred in 33 (12.9%) patients. The independent risk factors were intradiscal cement leakage and preexisting old vertebral compression fracture(s). The estimated 2-year absolute risk of new AVCFs ranged from less than 4% in patients with neither independent risk factors to more than 45% in individuals with both factors.ConclusionsThe PNAV score is an objective and easy approach to predict the risk of new AVCFs.« less

Prediction of recombinant protein overexpression in Escherichia coli using a machine learning based model (RPOLP).

PubMed

Habibi, Narjeskhatoon; Norouzi, Alireza; Mohd Hashim, Siti Z; Shamsir, Mohd Shahir; Samian, Razip

2015-11-01

Recombinant protein overexpression, an important biotechnological process, is ruled by complex biological rules which are mostly unknown, is in need of an intelligent algorithm so as to avoid resource-intensive lab-based trial and error experiments in order to determine the expression level of the recombinant protein. The purpose of this study is to propose a predictive model to estimate the level of recombinant protein overexpression for the first time in the literature using a machine learning approach based on the sequence, expression vector, and expression host. The expression host was confined to Escherichia coli which is the most popular bacterial host to overexpress recombinant proteins. To provide a handle to the problem, the overexpression level was categorized as low, medium and high. A set of features which were likely to affect the overexpression level was generated based on the known facts (e.g. gene length) and knowledge gathered from related literature. Then, a representative sub-set of features generated in the previous objective was determined using feature selection techniques. Finally a predictive model was developed using random forest classifier which was able to adequately classify the multi-class imbalanced small dataset constructed. The result showed that the predictive model provided a promising accuracy of 80% on average, in estimating the overexpression level of a recombinant protein. Copyright © 2015 Elsevier Ltd. All rights reserved.

Construction schedule simulation of a diversion tunnel based on the optimized ventilation time.

PubMed

Wang, Xiaoling; Liu, Xuepeng; Sun, Yuefeng; An, Juan; Zhang, Jing; Chen, Hongchao

2009-06-15

Former studies, the methods for estimating the ventilation time are all empirical in construction schedule simulation. However, in many real cases of construction schedule, the many factors have impact on the ventilation time. Therefore, in this paper the 3D unsteady quasi-single phase models are proposed to optimize the ventilation time with different tunneling lengths. The effect of buoyancy is considered in the momentum equation of the CO transport model, while the effects of inter-phase drag, lift force, and virtual mass force are taken into account in the momentum source of the dust transport model. The prediction by the present model for airflow in a diversion tunnel is confirmed by the experimental values reported by Nakayama [Nakayama, In-situ measurement and simulation by CFD of methane gas distribution at a heading faces, Shigen-to-Sozai 114 (11) (1998) 769-775]. The construction ventilation of the diversion tunnel of XinTangfang power station in China is used as a case. The distributions of airflow, CO and dust in the diversion tunnel are analyzed. A theory method for GIS-based dynamic visual simulation for the construction processes of underground structure groups is presented that combines cyclic operation network simulation, system simulation, network plan optimization, and GIS-based construction processes' 3D visualization. Based on the ventilation time the construction schedule of the diversion tunnel is simulated by the above theory method.

Accounting for trait architecture in genomic predictions of US Holstein cattle using a weighted realized relationship matrix.

PubMed

Tiezzi, Francesco; Maltecca, Christian

2015-04-02

Genomic BLUP (GBLUP) can predict breeding values for non-phenotyped individuals based on the identity-by-state genomic relationship matrix (G). The G matrix can be constructed from thousands of markers spread across the genome. The strongest assumption of G and consequently of GBLUP is that all markers contribute equally to the genetic variance of a trait. This assumption is violated for traits that are controlled by a small number of quantitative trait loci (QTL) or individual QTL with large effects. In this paper, we investigate the performance of using a weighted genomic relationship matrix (wG) that takes into consideration the genetic architecture of the trait in order to improve predictive ability for a wide range of traits. Multiple methods were used to calculate weights for several economically relevant traits in US Holstein dairy cattle. Predictive performance was tested by k-means cross-validation. Relaxing the GBLUP assumption of equal marker contribution by increasing the weight that is given to a specific marker in the construction of the trait-specific G resulted in increased predictive performance. The increase was strongest for traits that are controlled by a small number of QTL (e.g. fat and protein percentage). Furthermore, bias in prediction estimates was reduced compared to that resulting from the use of regular G. Even for traits with low heritability and lower general predictive performance (e.g. calving ease traits), weighted G still yielded a gain in accuracy. Genomic relationship matrices weighted by marker realized variance yielded more accurate and less biased predictions for traits regulated by few QTL. Genome-wide association analyses were used to derive marker weights for creating weighted genomic relationship matrices. However, this can be cumbersome and prone to low stability over generations because of erosion of linkage disequilibrium between markers and QTL. Future studies may include other sources of information, such as functional annotation and gene networks, to better exploit the genetic architecture of traits and produce more stable predictions.

Assessment of MRI-Based Automated Fetal Cerebral Cortical Folding Measures in Prediction of Gestational Age in the Third Trimester.

PubMed

Wu, J; Awate, S P; Licht, D J; Clouchoux, C; du Plessis, A J; Avants, B B; Vossough, A; Gee, J C; Limperopoulos, C

2015-07-01

Traditional methods of dating a pregnancy based on history or sonographic assessment have a large variation in the third trimester. We aimed to assess the ability of various quantitative measures of brain cortical folding on MR imaging in determining fetal gestational age in the third trimester. We evaluated 8 different quantitative cortical folding measures to predict gestational age in 33 healthy fetuses by using T2-weighted fetal MR imaging. We compared the accuracy of the prediction of gestational age by these cortical folding measures with the accuracy of prediction by brain volume measurement and by a previously reported semiquantitative visual scale of brain maturity. Regression models were constructed, and measurement biases and variances were determined via a cross-validation procedure. The cortical folding measures are accurate in the estimation and prediction of gestational age (mean of the absolute error, 0.43 ± 0.45 weeks) and perform better than (P = .024) brain volume (mean of the absolute error, 0.72 ± 0.61 weeks) or sonography measures (SDs approximately 1.5 weeks, as reported in literature). Prediction accuracy is comparable with that of the semiquantitative visual assessment score (mean, 0.57 ± 0.41 weeks). Quantitative cortical folding measures such as global average curvedness can be an accurate and reliable estimator of gestational age and brain maturity for healthy fetuses in the third trimester and have the potential to be an indicator of brain-growth delays for at-risk fetuses and preterm neonates. © 2015 by American Journal of Neuroradiology.

The development and validation of the Youth Actuarial Care Needs Assessment Tool for Non-Offenders (Y-ACNAT-NO).

PubMed

Assink, Mark; van der Put, Claudia E; Oort, Frans J; Stams, Geert Jan J M

2015-03-04

In The Netherlands, police officers not only come into contact with juvenile offenders, but also with a large number of juveniles who were involved in a criminal offense, but not in the role of a suspect (i.e., juvenile non-offenders). Until now, no valid and reliable instrument was available that can be used by Dutch police officers for estimating the risk for future care needs of juvenile non-offenders. In the present study, the Youth Actuarial Care Needs Assessment Tool for Non-Offenders (Y-ACNAT-NO) was developed for predicting the risk for future care needs that consisted of (1) a future supervision order as imposed by a juvenile court judge and (2) future worrisome incidents involving child abuse, domestic violence/strife, and/or sexual offensive behavior at the juvenile's living address (i.e., problems in the child-rearing environment). Police records of 3,200 juveniles were retrieved from the Dutch police registration system after which the sample was randomly split in a construction (n = 1,549) and validation sample (n = 1,651). The Y-ACNAT-NO was developed by performing an Exhaustive CHAID analysis using the construction sample. The predictive validity of the instrument was examined in the validation sample by calculating several performance indicators that assess discrimination and calibration. The CHAID output yielded an instrument that consisted of six variables and eleven different risk groups. The risk for future care needs ranged from 0.06 in the lowest risk group to 0.83 in the highest risk group. The AUC value in the validation sample was .764 (95% CI [.743, .784]) and Sander's calibration score indicated an average assessment error of 3.74% in risk estimates per risk category. The Y-ACNAT-NO is the first instrument that can be used by Dutch police officers for estimating the risk for future care needs of juvenile non-offenders. The predictive validity of the Y-ACNAT-NO in terms of discrimination and calibration was sufficient to justify its use as an initial screening instrument when a decision is needed about referring a juvenile for further assessment of care needs.

An Estimation of Construction and Demolition Debris in Seoul, Korea: Waste Amount, Type, and Estimating Model.

PubMed

Seo, Seongwon; Hwang, Yongwoo

1999-08-01

Construction and demolition (C&D) debris is generated at the site of various construction activities. However, the amount of the debris is usually so large that it is necessary to estimate the amount of C&D debris as accurately as possible for effective waste management and control in urban areas. In this paper, an effective estimation method using a statistical model was proposed. The estimation process was composed of five steps: estimation of the life span of buildings; estimation of the floor area of buildings to be constructed and demolished; calculation of individual intensity units of C&D debris; and estimation of the future C&D debris production. This method was also applied in the city of Seoul as an actual case, and the estimated amount of C&D debris in Seoul in 2021 was approximately 24 million tons. Of this total amount, 98% was generated by demolition, and the main components of debris were concrete and brick.

Design and analysis of forward and reverse models for predicting defect accumulation, defect energetics, and irradiation conditions

DOE PAGES

Stewart, James A.; Kohnert, Aaron A.; Capolungo, Laurent; ...

2018-03-06

The complexity of radiation effects in a material’s microstructure makes developing predictive models a difficult task. In principle, a complete list of all possible reactions between defect species being considered can be used to elucidate damage evolution mechanisms and its associated impact on microstructure evolution. However, a central limitation is that many models use a limited and incomplete catalog of defect energetics and associated reactions. Even for a given model, estimating its input parameters remains a challenge, especially for complex material systems. Here, we present a computational analysis to identify the extent to which defect accumulation, energetics, and irradiation conditionsmore » can be determined via forward and reverse regression models constructed and trained from large data sets produced by cluster dynamics simulations. A global sensitivity analysis, via Sobol’ indices, concisely characterizes parameter sensitivity and demonstrates how this can be connected to variability in defect evolution. Based on this analysis and depending on the definition of what constitutes the input and output spaces, forward and reverse regression models are constructed and allow for the direct calculation of defect accumulation, defect energetics, and irradiation conditions. Here, this computational analysis, exercised on a simplified cluster dynamics model, demonstrates the ability to design predictive surrogate and reduced-order models, and provides guidelines for improving model predictions within the context of forward and reverse engineering of mathematical models for radiation effects in a materials’ microstructure.« less

Machine health prognostics using the Bayesian-inference-based probabilistic indication and high-order particle filtering framework

NASA Astrophysics Data System (ADS)

Yu, Jianbo

2015-12-01

Prognostics is much efficient to achieve zero-downtime performance, maximum productivity and proactive maintenance of machines. Prognostics intends to assess and predict the time evolution of machine health degradation so that machine failures can be predicted and prevented. A novel prognostics system is developed based on the data-model-fusion scheme using the Bayesian inference-based self-organizing map (SOM) and an integration of logistic regression (LR) and high-order particle filtering (HOPF). In this prognostics system, a baseline SOM is constructed to model the data distribution space of healthy machine under an assumption that predictable fault patterns are not available. Bayesian inference-based probability (BIP) derived from the baseline SOM is developed as a quantification indication of machine health degradation. BIP is capable of offering failure probability for the monitored machine, which has intuitionist explanation related to health degradation state. Based on those historic BIPs, the constructed LR and its modeling noise constitute a high-order Markov process (HOMP) to describe machine health propagation. HOPF is used to solve the HOMP estimation to predict the evolution of the machine health in the form of a probability density function (PDF). An on-line model update scheme is developed to adapt the Markov process changes to machine health dynamics quickly. The experimental results on a bearing test-bed illustrate the potential applications of the proposed system as an effective and simple tool for machine health prognostics.

Predicting inpatient hypoglycaemia in hospitalized patients with diabetes: a retrospective analysis of 9584 admissions with diabetes.

PubMed

Stuart, K; Adderley, N J; Marshall, T; Rayman, G; Sitch, A; Manley, S; Ghosh, S; Toulis, K A; Nirantharakumar, K

2017-10-01

To explore whether a quantitative approach to identifying hospitalized patients with diabetes at risk of hypoglycaemia would be feasible through incorporation of routine biochemical, haematological and prescription data. A retrospective cross-sectional analysis of all diabetic admissions (n=9584) from 1 January 2014 to 31 December 2014 was performed. Hypoglycaemia was defined as a blood glucose level of <4 mmol/l. The prediction model was constructed using multivariable logistic regression, populated by clinically important variables and routine laboratory data. Using a prespecified variable selection strategy, it was shown that the occurrence of inpatient hypoglycaemia could be predicted by a combined model taking into account background medication (type of insulin, use of sulfonylureas), ethnicity (black and Asian), age (≥75 years), type of admission (emergency) and laboratory measurements (estimated GFR, C-reactive protein, sodium and albumin). Receiver-operating curve analysis showed that the area under the curve was 0.733 (95% CI 0.719 to 0.747). The threshold chosen to maximize both sensitivity and specificity was 0.15. The area under the curve obtained from internal validation did not differ from the primary model [0.731 (95% CI 0.717 to 0.746)]. The inclusion of routine biochemical data, available at the time of admission, can add prognostic value to demographic and medication history. The predictive performance of the constructed model indicates potential clinical utility for the identification of patients at risk of hypoglycaemia during their inpatient stay. © 2017 Diabetes UK.

Design and analysis of forward and reverse models for predicting defect accumulation, defect energetics, and irradiation conditions

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

Stewart, James A.; Kohnert, Aaron A.; Capolungo, Laurent

The complexity of radiation effects in a material’s microstructure makes developing predictive models a difficult task. In principle, a complete list of all possible reactions between defect species being considered can be used to elucidate damage evolution mechanisms and its associated impact on microstructure evolution. However, a central limitation is that many models use a limited and incomplete catalog of defect energetics and associated reactions. Even for a given model, estimating its input parameters remains a challenge, especially for complex material systems. Here, we present a computational analysis to identify the extent to which defect accumulation, energetics, and irradiation conditionsmore » can be determined via forward and reverse regression models constructed and trained from large data sets produced by cluster dynamics simulations. A global sensitivity analysis, via Sobol’ indices, concisely characterizes parameter sensitivity and demonstrates how this can be connected to variability in defect evolution. Based on this analysis and depending on the definition of what constitutes the input and output spaces, forward and reverse regression models are constructed and allow for the direct calculation of defect accumulation, defect energetics, and irradiation conditions. Here, this computational analysis, exercised on a simplified cluster dynamics model, demonstrates the ability to design predictive surrogate and reduced-order models, and provides guidelines for improving model predictions within the context of forward and reverse engineering of mathematical models for radiation effects in a materials’ microstructure.« less

A Locally Weighted Fixation Density-Based Metric for Assessing the Quality of Visual Saliency Predictions

NASA Astrophysics Data System (ADS)

Gide, Milind S.; Karam, Lina J.

2016-08-01

With the increased focus on visual attention (VA) in the last decade, a large number of computational visual saliency methods have been developed over the past few years. These models are traditionally evaluated by using performance evaluation metrics that quantify the match between predicted saliency and fixation data obtained from eye-tracking experiments on human observers. Though a considerable number of such metrics have been proposed in the literature, there are notable problems in them. In this work, we discuss shortcomings in existing metrics through illustrative examples and propose a new metric that uses local weights based on fixation density which overcomes these flaws. To compare the performance of our proposed metric at assessing the quality of saliency prediction with other existing metrics, we construct a ground-truth subjective database in which saliency maps obtained from 17 different VA models are evaluated by 16 human observers on a 5-point categorical scale in terms of their visual resemblance with corresponding ground-truth fixation density maps obtained from eye-tracking data. The metrics are evaluated by correlating metric scores with the human subjective ratings. The correlation results show that the proposed evaluation metric outperforms all other popular existing metrics. Additionally, the constructed database and corresponding subjective ratings provide an insight into which of the existing metrics and future metrics are better at estimating the quality of saliency prediction and can be used as a benchmark.

Innovative second-generation wavelets construction with recurrent neural networks for solar radiation forecasting.

PubMed

Capizzi, Giacomo; Napoli, Christian; Bonanno, Francesco

2012-11-01

Solar radiation prediction is an important challenge for the electrical engineer because it is used to estimate the power developed by commercial photovoltaic modules. This paper deals with the problem of solar radiation prediction based on observed meteorological data. A 2-day forecast is obtained by using novel wavelet recurrent neural networks (WRNNs). In fact, these WRNNS are used to exploit the correlation between solar radiation and timescale-related variations of wind speed, humidity, and temperature. The input to the selected WRNN is provided by timescale-related bands of wavelet coefficients obtained from meteorological time series. The experimental setup available at the University of Catania, Italy, provided this information. The novelty of this approach is that the proposed WRNN performs the prediction in the wavelet domain and, in addition, also performs the inverse wavelet transform, giving the predicted signal as output. The obtained simulation results show a very low root-mean-square error compared to the results of the solar radiation prediction approaches obtained by hybrid neural networks reported in the recent literature.

Melting line of polymeric nitrogen

NASA Astrophysics Data System (ADS)

Yakub, L. N.

2013-05-01

We made an attempt to predict location of the melting line of polymeric nitrogen using two equations for Helmholtz free energy: proposed earlier for cubic gauche-structure and developed recently for liquid polymerized nitrogen. The P-T relation, orthobaric densities and latent heat of melting were determined using a standard double tangent construction. The estimated melting temperature decreases with increasing pressure, alike the temperature of molecular-nonmolecular transition in solid. We discuss the possibility of a triple point (solid-molecular fluid-polymeric fluid) at ˜80 GPa and observed maximum of melting temperature of nitrogen.

A method for producing digital probabilistic seismic landslide hazard maps

USGS Publications Warehouse

Jibson, R.W.; Harp, E.L.; Michael, J.A.

2000-01-01

The 1994 Northridge, California, earthquake is the first earthquake for which we have all of the data sets needed to conduct a rigorous regional analysis of seismic slope instability. These data sets include: (1) a comprehensive inventory of triggered landslides, (2) about 200 strong-motion records of the mainshock, (3) 1:24 000-scale geologic mapping of the region, (4) extensive data on engineering properties of geologic units, and (5) high-resolution digital elevation models of the topography. All of these data sets have been digitized and rasterized at 10 m grid spacing using ARC/INFO GIS software on a UNIX computer. Combining these data sets in a dynamic model based on Newmark's permanent-deformation (sliding-block) analysis yields estimates of coseismic landslide displacement in each grid cell from the Northridge earthquake. The modeled displacements are then compared with the digital inventory of landslides triggered by the Northridge earthquake to construct a probability curve relating predicted displacement to probability of failure. This probability function can be applied to predict and map the spatial variability in failure probability in any ground-shaking conditions of interest. We anticipate that this mapping procedure will be used to construct seismic landslide hazard maps that will assist in emergency preparedness planning and in making rational decisions regarding development and construction in areas susceptible to seismic slope failure. ?? 2000 Elsevier Science B.V. All rights reserved.

A method for producing digital probabilistic seismic landslide hazard maps; an example from the Los Angeles, California, area

USGS Publications Warehouse

Jibson, Randall W.; Harp, Edwin L.; Michael, John A.

1998-01-01

The 1994 Northridge, California, earthquake is the first earthquake for which we have all of the data sets needed to conduct a rigorous regional analysis of seismic slope instability. These data sets include (1) a comprehensive inventory of triggered landslides, (2) about 200 strong-motion records of the mainshock, (3) 1:24,000-scale geologic mapping of the region, (4) extensive data on engineering properties of geologic units, and (5) high-resolution digital elevation models of the topography. All of these data sets have been digitized and rasterized at 10-m grid spacing in the ARC/INFO GIS platform. Combining these data sets in a dynamic model based on Newmark's permanent-deformation (sliding-block) analysis yields estimates of coseismic landslide displacement in each grid cell from the Northridge earthquake. The modeled displacements are then compared with the digital inventory of landslides triggered by the Northridge earthquake to construct a probability curve relating predicted displacement to probability of failure. This probability function can be applied to predict and map the spatial variability in failure probability in any ground-shaking conditions of interest. We anticipate that this mapping procedure will be used to construct seismic landslide hazard maps that will assist in emergency preparedness planning and in making rational decisions regarding development and construction in areas susceptible to seismic slope failure.

Considerations on the Use of 3-D Geophysical Models to Predict Test Ban Monitoring Observables

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

Harris, D B; Zucca, J J; McCallen, D B

2007-07-09

The use of 3-D geophysical models to predict nuclear test ban monitoring observables (phase travel times, amplitudes, dispersion, etc.) is widely anticipated to provide improvements in the basic seismic monitoring functions of detection, association, location, discrimination and yield estimation. A number of questions arise when contemplating a transition from 1-D, 2-D and 2.5-D models to constructing and using 3-D models, among them: (1) Can a 3-D geophysical model or a collection of 3-D models provide measurably improved predictions of seismic monitoring observables over existing 1-D models, or 2-D and 2 1/2-D models currently under development? (2) Is a single modelmore » that can predict all observables achievable, or must separate models be devised for each observable? How should joint inversion of disparate observable data be performed, if required? (3) What are the options for model representation? Are multi-resolution models essential? How does representation affect the accuracy and speed of observable predictions? (4) How should model uncertainty be estimated, represented and how should it be used? Are stochastic models desirable? (5) What data types should be used to construct the models? What quality control regime should be established? (6) How will 3-D models be used in operations? Will significant improvements in the basic monitoring functions result from the use of 3-D models? Will the calculation of observables through 3-D models be fast enough for real-time use or must a strategy of pre-computation be employed? (7) What are the theoretical limits to 3-D model development (resolution, uncertainty) and performance in predicting monitoring observables? How closely can those limits be approached with projected data availability, station distribution and inverse methods? (8) What priorities should be placed on the acquisition of event ground truth information, deployment of new stations, development of new inverse techniques, exploitation of large-scale computing and other activities in the pursuit of 3-D model development and use? In this paper, we examine what technical issues must be addressed to answer these questions. Although convened for a somewhat broader purpose, the June 2007 Workshop on Multi-resolution 3D Earth Models held in Berkeley, CA also touched on this topic. Results from the workshop are summarized in this paper.« less

Population Pharmacokinetics of Intravenous Paracetamol (Acetaminophen) in Preterm and Term Neonates: Model Development and External Evaluation.

PubMed

Cook, Sarah F; Roberts, Jessica K; Samiee-Zafarghandy, Samira; Stockmann, Chris; King, Amber D; Deutsch, Nina; Williams, Elaine F; Allegaert, Karel; Wilkins, Diana G; Sherwin, Catherine M T; van den Anker, John N

2016-01-01

The aims of this study were to develop a population pharmacokinetic model for intravenous paracetamol in preterm and term neonates and to assess the generalizability of the model by testing its predictive performance in an external dataset. Nonlinear mixed-effects models were constructed from paracetamol concentration-time data in NONMEM 7.2. Potential covariates included body weight, gestational age, postnatal age, postmenstrual age, sex, race, total bilirubin, and estimated glomerular filtration rate. An external dataset was used to test the predictive performance of the model through calculation of bias, precision, and normalized prediction distribution errors. The model-building dataset included 260 observations from 35 neonates with a mean gestational age of 33.6 weeks [standard deviation (SD) 6.6]. Data were well-described by a one-compartment model with first-order elimination. Weight predicted paracetamol clearance and volume of distribution, which were estimated as 0.348 L/h (5.5 % relative standard error; 30.8 % coefficient of variation) and 2.46 L (3.5 % relative standard error; 14.3 % coefficient of variation), respectively, at the mean subject weight of 2.30 kg. An external evaluation was performed on an independent dataset that included 436 observations from 60 neonates with a mean gestational age of 35.6 weeks (SD 4.3). The median prediction error was 10.1 % [95 % confidence interval (CI) 6.1-14.3] and the median absolute prediction error was 25.3 % (95 % CI 23.1-28.1). Weight predicted intravenous paracetamol pharmacokinetics in neonates ranging from extreme preterm to full-term gestational status. External evaluation suggested that these findings should be generalizable to other similar patient populations.

Population Pharmacokinetics of Intravenous Paracetamol (Acetaminophen) in Preterm and Term Neonates: Model Development and External Evaluation

PubMed Central

Cook, Sarah F.; Roberts, Jessica K.; Samiee-Zafarghandy, Samira; Stockmann, Chris; King, Amber D.; Deutsch, Nina; Williams, Elaine F.; Allegaert, Karel; Sherwin, Catherine M. T.; van den Anker, John N.

2017-01-01

Objectives The aims of this study were to develop a population pharmacokinetic model for intravenous paracetamol in preterm and term neonates and to assess the generalizability of the model by testing its predictive performance in an external dataset. Methods Nonlinear mixed-effects models were constructed from paracetamol concentration–time data in NONMEM 7.2. Potential covariates included body weight, gestational age, postnatal age, postmenstrual age, sex, race, total bilirubin, and estimated glomerular filtration rate. An external dataset was used to test the predictive performance of the model through calculation of bias, precision, and normalized prediction distribution errors. Results The model-building dataset included 260 observations from 35 neonates with a mean gestational age of 33.6 weeks [standard deviation (SD) 6.6]. Data were well-described by a one-compartment model with first-order elimination. Weight predicted paracetamol clearance and volume of distribution, which were estimated as 0.348 L/h (5.5 % relative standard error; 30.8 % coefficient of variation) and 2.46 L (3.5 % relative standard error; 14.3 % coefficient of variation), respectively, at the mean subject weight of 2.30 kg. An external evaluation was performed on an independent dataset that included 436 observations from 60 neonates with a mean gestational age of 35.6 weeks (SD 4.3). The median prediction error was 10.1 % [95 % confidence interval (CI) 6.1–14.3] and the median absolute prediction error was 25.3 % (95 % CI 23.1–28.1). Conclusions Weight predicted intravenous paracetamol pharmacokinetics in neonates ranging from extreme preterm to full-term gestational status. External evaluation suggested that these findings should be generalizable to other similar patient populations. PMID:26201306

Risk model for estimating the 1-year risk of deferred lesion intervention following deferred revascularization after fractional flow reserve assessment.

PubMed

Depta, Jeremiah P; Patel, Jayendrakumar S; Novak, Eric; Gage, Brian F; Masrani, Shriti K; Raymer, David; Facey, Gabrielle; Patel, Yogesh; Zajarias, Alan; Lasala, John M; Amin, Amit P; Kurz, Howard I; Singh, Jasvindar; Bach, Richard G

2015-02-21

Although lesions deferred revascularization following fractional flow reserve (FFR) assessment have a low risk of adverse cardiac events, variability in risk for deferred lesion intervention (DLI) has not been previously evaluated. The aim of this study was to develop a prediction model to estimate 1-year risk of DLI for coronary lesions where revascularization was not performed following FFR assessment. A prediction model for DLI was developed from a cohort of 721 patients with 882 coronary lesions where revascularization was deferred based on FFR between 10/2002 and 7/2010. Deferred lesion intervention was defined as any revascularization of a lesion previously deferred following FFR. The final DLI model was developed using stepwise Cox regression and validated using bootstrapping techniques. An algorithm was constructed to predict the 1-year risk of DLI. During a mean (±SD) follow-up period of 4.0 ± 2.3 years, 18% of lesions deferred after FFR underwent DLI; the 1-year incidence of DLI was 5.3%, while the predicted risk of DLI varied from 1 to 40%. The final Cox model included the FFR value, age, current or former smoking, history of coronary artery disease (CAD) or prior percutaneous coronary intervention, multi-vessel CAD, and serum creatinine. The c statistic for the DLI prediction model was 0.66 (95% confidence interval, CI: 0.61-0.70). Patients deferred revascularization based on FFR have variation in their risk for DLI. A clinical prediction model consisting of five clinical variables and the FFR value can help predict the risk of DLI in the first year following FFR assessment. Published on behalf of the European Society of Cardiology. All rights reserved. © The Author 2014. For permissions please email: journals.permissions@oup.com.

An Evaluation of Population Density Mapping and Built up Area Estimates in Sri Lanka Using Multiple Methodologies

NASA Astrophysics Data System (ADS)

Engstrom, R.; Soundararajan, V.; Newhouse, D.

2017-12-01

In this study we examine how well multiple population density and built up estimates that utilize satellite data compare in Sri Lanka. The population relationship is examined at the Gram Niladhari (GN) level, the lowest administrative unit in Sri Lanka from the 2011 census. For this study we have two spatial domains, the whole country and a 3,500km2 sub-sample, for which we have complete high spatial resolution imagery coverage. For both the entire country and the sub-sample we examine how consistent are the existing publicly available measures of population constructed from satellite imagery at predicting population density? For just the sub-sample we examine how well do a suite of values derived from high spatial resolution satellite imagery predict population density and how does our built up area estimate compare to other publicly available estimates. Population measures were obtained from the Sri Lankan census, and were downloaded from Facebook, WorldPoP, GPW, and Landscan. Percentage built-up area at the GN level was calculated from three sources: Facebook, Global Urban Footprint (GUF), and the Global Human Settlement Layer (GHSL). For the sub-sample we have derived a variety of indicators from the high spatial resolution imagery. Using deep learning convolutional neural networks, an object oriented, and a non-overlapping block, spatial feature approach. Variables calculated include: cars, shadows (a proxy for building height), built up area, and buildings, roof types, roads, type of agriculture, NDVI, Pantex, and Histogram of Oriented Gradients (HOG) and others. Results indicate that population estimates are accurate at the higher, DS Division level but not necessarily at the GN level. Estimates from Facebook correlated well with census population (GN correlation of 0.91) but measures from GPW and WorldPop are more weakly correlated (0.64 and 0.34). Estimates of built-up area appear to be reliable. In the 32 DSD-subsample, Facebook's built- up area measure is highly correlated with our built-up measure (correlation of 0.9). Preliminary regression results based on variables selected from Lasso-regressions indicate that satellite indicators have exceptionally strong predictive power in predicting GN level population level and density with an out of sample r-squared of 0.75 and 0.72 respectively.

Nonlinear experimental dye-doped nematic liquid crystal optical transmission spectra estimated by neural network empirical physical formulas

NASA Astrophysics Data System (ADS)

Yildiz, Nihat; San, Sait Eren; Köysal, Oğuz

2010-09-01

In this paper, two complementary objectives related to optical transmission spectra of nematic liquid crystals (NLCs) were achieved. First, at room temperature, for both pure and dye (DR9) doped E7 NLCs, the 10-250 W halogen lamp transmission spectra (wavelength 400-1200 nm) were measured at various bias voltages. Second, because the measured spectra were inherently highly nonlinear, it was difficult to construct explicit empirical physical formulas (EPFs) to employ as transmittance functions. To avoid this difficulty, layered feedforward neural networks (LFNNs) were used to construct explicit EPFs for these theoretically unknown nonlinear NLC transmittance functions. As we theoretically showed in a previous work, a LFNN, as an excellent nonlinear function approximator, is highly relevant to EPF construction. The LFNN-EPFs efficiently and consistently estimated both the measured and yet-to-be-measured nonlinear transmittance response values. The experimentally obtained doping ratio dependencies and applied bias voltage responses of transmittance were also confirmed by LFFN-EPFs. This clearly indicates that physical laws embedded in the physical data can be faithfully extracted by the suitable LFNNs. The extraordinary success achieved with LFNN here suggests two potential applications. First, although not attempted here, these LFNN-EPFs, by such mathematical operations as derivation, integration, minimization etc., can be used to obtain further transmittance related functions of NLCs. Second, for a given NLC response function, whose theoretical nonlinear functional form is yet unknown, a suitable experimental data based LFNN-EPF can be constructed to predict the yet-to-be-measured values.

A global predictive model of carbon in mangrove soils

NASA Astrophysics Data System (ADS)

Jardine, Sunny L.; Siikamäki, Juha V.

2014-10-01

Mangroves are among the most threatened and rapidly vanishing natural environments worldwide. They provide a wide range of ecosystem services and have recently become known for their exceptional capacity to store carbon. Research shows that mangrove conservation may be a low-cost means of reducing CO2 emissions. Accordingly, there is growing interest in developing market mechanisms to credit mangrove conservation projects for associated CO2 emissions reductions. These efforts depend on robust and readily applicable, but currently unavailable, localized estimates of soil carbon. Here, we use over 900 soil carbon measurements, collected in 28 countries by 61 independent studies, to develop a global predictive model for mangrove soil carbon. Using climatological and locational data as predictors, we explore several predictive modeling alternatives, including machine-learning methods. With our predictive model, we construct a global dataset of estimated soil carbon concentrations and stocks on a high-resolution grid (5 arc min). We estimate that the global mangrove soil carbon stock is 5.00 ± 0.94 Pg C (assuming a 1 meter soil depth) and find this stock is highly variable over space. The amount of carbon per hectare in the world’s most carbon-rich mangroves (approximately 703 ± 38 Mg C ha-1) is roughly a 2.6 ± 0.14 times the amount of carbon per hectare in the world’s most carbon-poor mangroves (approximately 272 ± 49 Mg C ha-1). Considerable within country variation in mangrove soil carbon also exists. In Indonesia, the country with the largest mangrove soil carbon stock, we estimate that the most carbon-rich mangroves contain 1.5 ± 0.12 times as much carbon per hectare as the most carbon-poor mangroves. Our results can aid in evaluating benefits from mangrove conservation and designing mangrove conservation policy. Additionally, the results can be used to project changes in mangrove soil carbon stocks based on changing climatological predictors, e.g. to assess the impacts of climate change on mangrove soil carbon stocks.

Bleeding impacting mortality after noncardiac surgery: a protocol to establish diagnostic criteria, estimate prognostic importance, and develop and validate a prediction guide in an international prospective cohort study

PubMed Central

Roshanov, Pavel S.; Eikelboom, John W.; Crowther, Mark; Tandon, Vikas; Borges, Flavia K.; Kearon, Clive; Lamy, Andre; Whitlock, Richard; Biccard, Bruce M.; Szczeklik, Wojciech; Guyatt, Gordon H.; Panju, Mohamed; Spence, Jessica; Garg, Amit X.; McGillion, Michael; VanHelder, Tomas; Kavsak, Peter A.; de Beer, Justin; Winemaker, Mitchell; Sessler, Daniel I.; Le Manach, Yannick; Sheth, Tej; Pinthus, Jehonathan H.; Thabane, Lehana; Simunovic, Marko R.I.; Mizera, Ryszard; Ribas, Sebastian; Devereaux, P.J.

2017-01-01

Introduction: Various definitions of bleeding have been used in perioperative studies without systematic assessment of the diagnostic criteria for their independent association with outcomes important to patients. Our proposed definition of bleeding impacting mortality after noncardiac surgery (BIMS) is bleeding that is independently associated with death during or within 30 days after noncardiac surgery. We describe our analysis plan to sequentially 1) establish the diagnostic criteria for BIMS, 2) estimate the independent contribution of BIMS to 30-day mortality and 3) develop and internally validate a clinical prediction guide to estimate patient-specific risk of BIMS. Methods: In the Vascular Events In Noncardiac Surgery Patients Cohort Evaluation (VISION) study, we prospectively collected bleeding data for 16 079 patients aged 45 years or more who had noncardiac inpatient surgery between 2007 and 2011 at 12 centres in 8 countries across 5 continents. We will include bleeding features independently associated with 30-day mortality in the diagnostic criteria for BIMS. Candidate features will include the need for reoperation due to bleeding, the number of units of erythrocytes transfused, the lowest postoperative hemoglobin concentration, and the absolute and relative decrements in hemoglobin concentration from the preoperative value. We will then estimate the incidence of BIMS and its independent association with 30-day mortality. Last, we will construct and internally validate a clinical prediction guide for BIMS. Interpretation: This study will address an important gap in our knowledge about perioperative bleeding, with implications for the 200 million patients who undergo noncardiac surgery globally every year. Trial registration: ClinicalTrials.gov, no NCT00512109. PMID:28943515

Application of Boosting Regression Trees to Preliminary Cost Estimation in Building Construction Projects

PubMed Central

2015-01-01

Among the recent data mining techniques available, the boosting approach has attracted a great deal of attention because of its effective learning algorithm and strong boundaries in terms of its generalization performance. However, the boosting approach has yet to be used in regression problems within the construction domain, including cost estimations, but has been actively utilized in other domains. Therefore, a boosting regression tree (BRT) is applied to cost estimations at the early stage of a construction project to examine the applicability of the boosting approach to a regression problem within the construction domain. To evaluate the performance of the BRT model, its performance was compared with that of a neural network (NN) model, which has been proven to have a high performance in cost estimation domains. The BRT model has shown results similar to those of NN model using 234 actual cost datasets of a building construction project. In addition, the BRT model can provide additional information such as the importance plot and structure model, which can support estimators in comprehending the decision making process. Consequently, the boosting approach has potential applicability in preliminary cost estimations in a building construction project. PMID:26339227

Application of Boosting Regression Trees to Preliminary Cost Estimation in Building Construction Projects.

PubMed

Shin, Yoonseok

2015-01-01

Among the recent data mining techniques available, the boosting approach has attracted a great deal of attention because of its effective learning algorithm and strong boundaries in terms of its generalization performance. However, the boosting approach has yet to be used in regression problems within the construction domain, including cost estimations, but has been actively utilized in other domains. Therefore, a boosting regression tree (BRT) is applied to cost estimations at the early stage of a construction project to examine the applicability of the boosting approach to a regression problem within the construction domain. To evaluate the performance of the BRT model, its performance was compared with that of a neural network (NN) model, which has been proven to have a high performance in cost estimation domains. The BRT model has shown results similar to those of NN model using 234 actual cost datasets of a building construction project. In addition, the BRT model can provide additional information such as the importance plot and structure model, which can support estimators in comprehending the decision making process. Consequently, the boosting approach has potential applicability in preliminary cost estimations in a building construction project.

Modelling and Prediction of Spark-ignition Engine Power Performance Using Incremental Least Squares Support Vector Machines

NASA Astrophysics Data System (ADS)

Wong, Pak-kin; Vong, Chi-man; Wong, Hang-cheong; Li, Ke

2010-05-01

Modern automotive spark-ignition (SI) power performance usually refers to output power and torque, and they are significantly affected by the setup of control parameters in the engine management system (EMS). EMS calibration is done empirically through tests on the dynamometer (dyno) because no exact mathematical engine model is yet available. With an emerging nonlinear function estimation technique of Least squares support vector machines (LS-SVM), the approximate power performance model of a SI engine can be determined by training the sample data acquired from the dyno. A novel incremental algorithm based on typical LS-SVM is also proposed in this paper, so the power performance models built from the incremental LS-SVM can be updated whenever new training data arrives. With updating the models, the model accuracies can be continuously increased. The predicted results using the estimated models from the incremental LS-SVM are good agreement with the actual test results and with the almost same average accuracy of retraining the models from scratch, but the incremental algorithm can significantly shorten the model construction time when new training data arrives.

A robust nonparametric framework for reconstruction of stochastic differential equation models

NASA Astrophysics Data System (ADS)

Rajabzadeh, Yalda; Rezaie, Amir Hossein; Amindavar, Hamidreza

2016-05-01

In this paper, we employ a nonparametric framework to robustly estimate the functional forms of drift and diffusion terms from discrete stationary time series. The proposed method significantly improves the accuracy of the parameter estimation. In this framework, drift and diffusion coefficients are modeled through orthogonal Legendre polynomials. We employ the least squares regression approach along with the Euler-Maruyama approximation method to learn coefficients of stochastic model. Next, a numerical discrete construction of mean squared prediction error (MSPE) is established to calculate the order of Legendre polynomials in drift and diffusion terms. We show numerically that the new method is robust against the variation in sample size and sampling rate. The performance of our method in comparison with the kernel-based regression (KBR) method is demonstrated through simulation and real data. In case of real dataset, we test our method for discriminating healthy electroencephalogram (EEG) signals from epilepsy ones. We also demonstrate the efficiency of the method through prediction in the financial data. In both simulation and real data, our algorithm outperforms the KBR method.

Model inversion via multi-fidelity Bayesian optimization: a new paradigm for parameter estimation in haemodynamics, and beyond.

PubMed

Perdikaris, Paris; Karniadakis, George Em

2016-05-01

We present a computational framework for model inversion based on multi-fidelity information fusion and Bayesian optimization. The proposed methodology targets the accurate construction of response surfaces in parameter space, and the efficient pursuit to identify global optima while keeping the number of expensive function evaluations at a minimum. We train families of correlated surrogates on available data using Gaussian processes and auto-regressive stochastic schemes, and exploit the resulting predictive posterior distributions within a Bayesian optimization setting. This enables a smart adaptive sampling procedure that uses the predictive posterior variance to balance the exploration versus exploitation trade-off, and is a key enabler for practical computations under limited budgets. The effectiveness of the proposed framework is tested on three parameter estimation problems. The first two involve the calibration of outflow boundary conditions of blood flow simulations in arterial bifurcations using multi-fidelity realizations of one- and three-dimensional models, whereas the last one aims to identify the forcing term that generated a particular solution to an elliptic partial differential equation. © 2016 The Author(s).

Model inversion via multi-fidelity Bayesian optimization: a new paradigm for parameter estimation in haemodynamics, and beyond

PubMed Central

Perdikaris, Paris; Karniadakis, George Em

2016-01-01

We present a computational framework for model inversion based on multi-fidelity information fusion and Bayesian optimization. The proposed methodology targets the accurate construction of response surfaces in parameter space, and the efficient pursuit to identify global optima while keeping the number of expensive function evaluations at a minimum. We train families of correlated surrogates on available data using Gaussian processes and auto-regressive stochastic schemes, and exploit the resulting predictive posterior distributions within a Bayesian optimization setting. This enables a smart adaptive sampling procedure that uses the predictive posterior variance to balance the exploration versus exploitation trade-off, and is a key enabler for practical computations under limited budgets. The effectiveness of the proposed framework is tested on three parameter estimation problems. The first two involve the calibration of outflow boundary conditions of blood flow simulations in arterial bifurcations using multi-fidelity realizations of one- and three-dimensional models, whereas the last one aims to identify the forcing term that generated a particular solution to an elliptic partial differential equation. PMID:27194481

Motion Predicts Clinical Callus Formation

PubMed Central

Elkins, Jacob; Marsh, J. Lawrence; Lujan, Trevor; Peindl, Richard; Kellam, James; Anderson, Donald D.; Lack, William

2016-01-01

Background: Mechanotransduction is theorized to influence fracture-healing, but optimal fracture-site motion is poorly defined. We hypothesized that three-dimensional (3-D) fracture-site motion as estimated by finite element (FE) analysis would influence callus formation for a clinical series of supracondylar femoral fractures treated with locking-plate fixation. Methods: Construct-specific FE modeling simulated 3-D fracture-site motion for sixty-six supracondylar femoral fractures (OTA/AO classification of 33A or 33C) treated at a single institution. Construct stiffness and directional motion through the fracture were investigated to assess the validity of construct stiffness as a surrogate measure of 3-D motion at the fracture site. Callus formation was assessed radiographically for all patients at six, twelve, and twenty-four weeks postoperatively. Univariate and multivariate linear regression analyses examined the effects of longitudinal motion, shear (transverse motion), open fracture, smoking, and diabetes on callus formation. Construct types were compared to determine whether their 3-D motion profile was associated with callus formation. Results: Shear disproportionately increased relative to longitudinal motion with increasing bridge span, which was not predicted by our assessment of construct stiffness alone. Callus formation was not associated with open fracture, smoking, or diabetes at six, twelve, or twenty-four weeks. However, callus formation was associated with 3-D fracture-site motion at twelve and twenty-four weeks. Longitudinal motion promoted callus formation at twelve and twenty-four weeks (p = 0.017 for both). Shear inhibited callus formation at twelve and twenty-four weeks (p = 0.017 and p = 0.022, respectively). Titanium constructs with a short bridge span demonstrated greater longitudinal motion with less shear than did the other constructs, and this was associated with greater callus formation (p < 0.001). Conclusions: In this study of supracondylar femoral fractures treated with locking-plate fixation, longitudinal motion promoted callus formation, while shear inhibited callus formation. Construct stiffness was found to be a poor surrogate of fracture-site motion. Future implant design and operative fixation strategies should seek to optimize 3-D fracture-site motion rather than rely on surrogate measures such as axial stiffness. PMID:26888675

Quantifying pollen-vegetation relationships to reconstruct ancient forests using 19th-century forest composition and pollen data

USGS Publications Warehouse

Dawson, Andria; Paciorek, Christopher J.; McLachlan, Jason S.; Goring, Simon; Williams, John W.; Jackson, Stephen T.

2016-01-01

Mitigation of climate change and adaptation to its effects relies partly on how effectively land-atmosphere interactions can be quantified. Quantifying composition of past forest ecosystems can help understand processes governing forest dynamics in a changing world. Fossil pollen data provide information about past forest composition, but rigorous interpretation requires development of pollen-vegetation models (PVMs) that account for interspecific differences in pollen production and dispersal. Widespread and intensified land-use over the 19th and 20th centuries may have altered pollen-vegetation relationships. Here we use STEPPS, a Bayesian hierarchical spatial PVM, to estimate key process parameters and associated uncertainties in the pollen-vegetation relationship. We apply alternate dispersal kernels, and calibrate STEPPS using a newly developed Euro-American settlement-era calibration data set constructed from Public Land Survey data and fossil pollen samples matched to the settlement-era using expert elicitation. Models based on the inverse power-law dispersal kernel outperformed those based on the Gaussian dispersal kernel, indicating that pollen dispersal kernels are fat tailed. Pine and birch have the highest pollen productivities. Pollen productivity and dispersal estimates are generally consistent with previous understanding from modern data sets, although source area estimates are larger. Tests of model predictions demonstrate the ability of STEPPS to predict regional compositional patterns.

UV–Vis and ATR–FTIR spectroscopic investigations of postmortem interval based on the changes in rabbit plasma

PubMed Central

Wang, Qi; He, Haijun; Li, Bing; Lin, Hancheng; Zhang, Yinming; Zhang, Ji

2017-01-01

Estimating PMI is of great importance in forensic investigations. Although many methods are used to estimate the PMI, a few investigations focus on the postmortem redistribution. In this study, ultraviolet–visible (UV–Vis) measurement combined with visual inspection indicated a regular diffusion of hemoglobin into plasma after death showing the redistribution of postmortem components in blood. Thereafter, attenuated total reflection–Fourier transform infrared (ATR–FTIR) spectroscopy was used to confirm the variations caused by this phenomenon. First, full-spectrum partial least-squares (PLS) and genetic algorithm combined with PLS (GA-PLS) models were constructed to predict the PMI. The performance of GA-PLS model was better than that of full-spectrum PLS model based on its root mean square error (RMSE) of cross-validation of 3.46 h (R2 = 0.95) and the RMSE of prediction of 3.46 h (R2 = 0.94). The investigation on the similarity of spectra between blood plasma and formed elements also supported the role of redistribution of components in spectral changes in postmortem plasma. These results demonstrated that ATR-FTIR spectroscopy coupled with the advanced mathematical methods could serve as a convenient and reliable tool to study the redistribution of postmortem components and estimate the PMI. PMID:28753641

Quantifying pollen-vegetation relationships to reconstruct ancient forests using 19th-century forest composition and pollen data

NASA Astrophysics Data System (ADS)

Dawson, Andria; Paciorek, Christopher J.; McLachlan, Jason S.; Goring, Simon; Williams, John W.; Jackson, Stephen T.

2016-04-01

Mitigation of climate change and adaptation to its effects relies partly on how effectively land-atmosphere interactions can be quantified. Quantifying composition of past forest ecosystems can help understand processes governing forest dynamics in a changing world. Fossil pollen data provide information about past forest composition, but rigorous interpretation requires development of pollen-vegetation models (PVMs) that account for interspecific differences in pollen production and dispersal. Widespread and intensified land-use over the 19th and 20th centuries may have altered pollen-vegetation relationships. Here we use STEPPS, a Bayesian hierarchical spatial PVM, to estimate key process parameters and associated uncertainties in the pollen-vegetation relationship. We apply alternate dispersal kernels, and calibrate STEPPS using a newly developed Euro-American settlement-era calibration data set constructed from Public Land Survey data and fossil pollen samples matched to the settlement-era using expert elicitation. Models based on the inverse power-law dispersal kernel outperformed those based on the Gaussian dispersal kernel, indicating that pollen dispersal kernels are fat tailed. Pine and birch have the highest pollen productivities. Pollen productivity and dispersal estimates are generally consistent with previous understanding from modern data sets, although source area estimates are larger. Tests of model predictions demonstrate the ability of STEPPS to predict regional compositional patterns.

ANN Surface Roughness Optimization of AZ61 Magnesium Alloy Finish Turning: Minimum Machining Times at Prime Machining Costs

PubMed Central

Erdakov, Ivan Nikolaevich; Taha, Mohamed~Adel; Soliman, Mahmoud Sayed; El Rayes, Magdy Mostafa

2018-01-01

Magnesium alloys are widely used in aerospace vehicles and modern cars, due to their rapid machinability at high cutting speeds. A novel Edgeworth–Pareto optimization of an artificial neural network (ANN) is presented in this paper for surface roughness (Ra) prediction of one component in computer numerical control (CNC) turning over minimal machining time (Tm) and at prime machining costs (C). An ANN is built in the Matlab programming environment, based on a 4-12-3 multi-layer perceptron (MLP), to predict Ra, Tm, and C, in relation to cutting speed, vc, depth of cut, ap, and feed per revolution, fr. For the first time, a profile of an AZ61 alloy workpiece after finish turning is constructed using an ANN for the range of experimental values vc, ap, and fr. The global minimum length of a three-dimensional estimation vector was defined with the following coordinates: Ra = 0.087 μm, Tm = 0.358 min/cm3, C = $8.2973. Likewise, the corresponding finish-turning parameters were also estimated: cutting speed vc = 250 m/min, cutting depth ap = 1.0 mm, and feed per revolution fr = 0.08 mm/rev. The ANN model achieved a reliable prediction accuracy of ±1.35% for surface roughness. PMID:29772670

Youth Actuarial Risk Assessment Tool (Y-ARAT): The development of an actuarial risk assessment instrument for predicting general offense recidivism on the basis of police records.

PubMed

van der Put, Claudia E

2014-06-01

Estimating the risk for recidivism is important for many areas of the criminal justice system. In the present study, the Youth Actuarial Risk Assessment Tool (Y-ARAT) was developed for juvenile offenders based solely on police records, with the aim to estimate the risk of general recidivism among large groups of juvenile offenders by police officers without clinical expertise. On the basis of the Y-ARAT, juvenile offenders are classified into five risk groups based on (combinations of) 10 variables including different types of incidents in which the juvenile was a suspect, total number of incidents in which the juvenile was a suspect, total number of other incidents, total number of incidents in which co-occupants at the youth's address were suspects, gender, and age at first incident. The Y-ARAT was developed on a sample of 2,501 juvenile offenders and validated on another sample of 2,499 juvenile offenders, showing moderate predictive accuracy (area under the receiver-operating-characteristic curve = .73), with little variation between the construction and validation sample. The predictive accuracy of the Y-ARAT was considered sufficient to justify its use as a screening instrument for the police. © The Author(s) 2013.

Relationships between response surfaces for tablet characteristics of placebo and API-containing tablets manufactured by direct compression method.

PubMed

Hayashi, Yoshihiro; Tsuji, Takahiro; Shirotori, Kaede; Oishi, Takuya; Kosugi, Atsushi; Kumada, Shungo; Hirai, Daijiro; Takayama, Kozo; Onuki, Yoshinori

2017-10-30

In this study, we evaluated the correlation between the response surfaces for the tablet characteristics of placebo and active pharmaceutical ingredient (API)-containing tablets. The quantities of lactose, cornstarch, and microcrystalline cellulose were chosen as the formulation factors. Ten tablet formulations were prepared. The tensile strength (TS) and disintegration time (DT) of tablets were measured as tablet characteristics. The response surfaces for TS and DT were estimated using a nonlinear response surface method incorporating multivariate spline interpolation, and were then compared with those of placebo tablets. A correlation was clearly observed for TS and DT of all APIs, although the value of the response surfaces for TS and DT was highly dependent on the type of API used. Based on this knowledge, the response surfaces for TS and DT of API-containing tablets were predicted from only two and four formulations using regression expression and placebo tablet data, respectively. The results from the evaluation of prediction accuracy showed that this method accurately predicted TS and DT, suggesting that it could construct a reliable response surface for TS and DT with a small number of samples. This technique assists in the effective estimation of the relationships between design variables and pharmaceutical responses during pharmaceutical development. Copyright © 2017 Elsevier B.V. All rights reserved.

Sequence similarity is more relevant than species specificity in probabilistic backtranslation.

PubMed

Ferro, Alfredo; Giugno, Rosalba; Pigola, Giuseppe; Pulvirenti, Alfredo; Di Pietro, Cinzia; Purrello, Michele; Ragusa, Marco

2007-02-21

Backtranslation is the process of decoding a sequence of amino acids into the corresponding codons. All synthetic gene design systems include a backtranslation module. The degeneracy of the genetic code makes backtranslation potentially ambiguous since most amino acids are encoded by multiple codons. The common approach to overcome this difficulty is based on imitation of codon usage within the target species. This paper describes EasyBack, a new parameter-free, fully-automated software for backtranslation using Hidden Markov Models. EasyBack is not based on imitation of codon usage within the target species, but instead uses a sequence-similarity criterion. The model is trained with a set of proteins with known cDNA coding sequences, constructed from the input protein by querying the NCBI databases with BLAST. Unlike existing software, the proposed method allows the quality of prediction to be estimated. When tested on a group of proteins that show different degrees of sequence conservation, EasyBack outperforms other published methods in terms of precision. The prediction quality of a protein backtranslation methis markedly increased by replacing the criterion of most used codon in the same species with a Hidden Markov Model trained with a set of most similar sequences from all species. Moreover, the proposed method allows the quality of prediction to be estimated probabilistically.

Time-integrated activity coefficient estimation for radionuclide therapy using PET and a pharmacokinetic model: A simulation study on the effect of sampling schedule and noise

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

Hardiansyah, Deni

2016-09-15

Purpose: The aim of this study was to investigate the accuracy of PET-based treatment planning for predicting the time-integrated activity coefficients (TIACs). Methods: The parameters of a physiologically based pharmacokinetic (PBPK) model were fitted to the biokinetic data of 15 patients to derive assumed true parameters and were used to construct true mathematical patient phantoms (MPPs). Biokinetics of 150 MBq {sup 68}Ga-DOTATATE-PET was simulated with different noise levels [fractional standard deviation (FSD) 10%, 1%, 0.1%, and 0.01%], and seven combinations of measurements at 30 min, 1 h, and 4 h p.i. PBPK model parameters were fitted to the simulated noisymore » PET data using population-based Bayesian parameters to construct predicted MPPs. Therapy simulations were performed as 30 min infusion of {sup 90}Y-DOTATATE of 3.3 GBq in both true and predicted MPPs. Prediction accuracy was then calculated as relative variability v{sub organ} between TIACs from both MPPs. Results: Large variability values of one time-point protocols [e.g., FSD = 1%, 240 min p.i., v{sub kidneys} = (9 ± 6)%, and v{sub tumor} = (27 ± 26)%] show inaccurate prediction. Accurate TIAC prediction of the kidneys was obtained for the case of two measurements (1 and 4 h p.i.), e.g., FSD = 1%, v{sub kidneys} = (7 ± 3)%, and v{sub tumor} = (22 ± 10)%, or three measurements, e.g., FSD = 1%, v{sub kidneys} = (7 ± 3)%, and v{sub tumor} = (22 ± 9)%. Conclusions: {sup 68}Ga-DOTATATE-PET measurements could possibly be used to predict the TIACs of {sup 90}Y-DOTATATE when using a PBPK model and population-based Bayesian parameters. The two time-point measurement at 1 and 4 h p.i. with a noise up to FSD = 1% allows an accurate prediction of the TIACs in kidneys.« less

Water movement through an experimental soil liner

USGS Publications Warehouse

Krapac, I.G.; Cartwright, K.; Panno, S.V.; Hensel, B.R.; Rehfeldt, K.R.; Herzog, B.L.

1991-01-01

A field-scale soil liner was constructed to test whether compacted soil barriers in cover and liner systems could be built to meet the U.S. EPA saturated hydraulic conductivity requirement (???1 x 10-7 cm s-1). The 8 x 15 x 0.9m liner was constructed in 15 cm compacted lifts using a 20,037 kg pad-foot compactor and standard engineering practices. Water infiltration into the liner has been monitored for one year. Monitoring will continue until water break through at the base of the liner occurs. Estimated saturated hydraulic conductivities were 2.5 x 10-9, 4.0 x 10-8, and 5.0 x 10-8 cm s-1 based on measurements of water infiltration into the liner by large- and small-ring infiltrometers and a water balance analysis, respectively. Also investigated in this research was the variability of the liner's hydraulic properties and estimates of the transit times for water and tracers. Small variances exhibited by small-ring flux data suggested that the liner was homogeneous with respect to infiltration fluxes. The predictions of water and tracer breakthrough at the base of the liner ranged from 2.4-12.6 y, depending on the method of calculation and assumptions made. The liner appeared to be saturated to a depth between 18 and 33 cm at the end of the first year of monitoring. Transit time calculations cannot be verified yet, since breakthrough has not occurred. The work conducted so far indicates that compacted soil barriers can be constructed to meet the saturated hydraulic conductivity requirement established by the U.S. EPA.A field-scale soil liner was constructed to test whether compacted soil barriers in cover and liner systems could be built to meet the U.S. EPA saturated hydraulic conductivity requirement (??? 1 ?? 10-7 cm s-1). The 8 ?? 15 ?? 0.9 m liner was constructed in 15 cm compacted lifts using a 20.037 kg pad-foot compactor and standard engineering practices. Water infiltration into the liner has been monitored for one year. Monitoring will continue until water break through at the base of the liner occurs. Estimated saturated hydraulic conductivities were 2.5 ?? 10-9, 4.0 ?? 10-8, and 5.0 ?? 10-8 cm s-1 based on measurements of water infiltration into the liner by large- and small-ring infiltrometers and a water balance analysis, respectively. Also investigated in this research was the variability of the liner's hydraulic properties and estimates of the transit times for water and tracers. Small variances exhibited by small-ring flux data suggested that the liner was homogeneous with respect to infiltration fluxes. The predictions of water and tracer breakthrough at the base of the liner ranged from 2.4-12.6 y, depending on the method of calculation and assumptions made. The liner appeared to be saturated to a depth between 18 and 33 cm at the end of the first year of monitoring. Transit time calculations cannot be verified yet, since breakthrough has not occurred. The work conducted so far indicates that compacted soil barriers can be constructed to meet the saturated hydraulic conductivity requirement established by the U.S. EPA.

Estimation of Noise Properties for TV-regularized Image Reconstruction in Computed Tomography

PubMed Central

Sánchez, Adrian A.

2016-01-01

A method for predicting the image covariance resulting from total-variation-penalized iterative image reconstruction (TV-penalized IIR) is presented and demonstrated in a variety of contexts. The method is validated against the sample covariance from statistical noise realizations for a small image using a variety of comparison metrics. Potential applications for the covariance approximation include investigation of image properties such as object- and signal-dependence of noise, and noise stationarity. These applications are demonstrated, along with the construction of image pixel variance maps for two-dimensional 128 × 128 pixel images. Methods for extending the proposed covariance approximation to larger images and improving computational efficiency are discussed. Future work will apply the developed methodology to the construction of task-based image quality metrics such as the Hotelling observer detectability for TV-based IIR. PMID:26308968

Estimation of noise properties for TV-regularized image reconstruction in computed tomography.

PubMed

Sánchez, Adrian A

2015-09-21

A method for predicting the image covariance resulting from total-variation-penalized iterative image reconstruction (TV-penalized IIR) is presented and demonstrated in a variety of contexts. The method is validated against the sample covariance from statistical noise realizations for a small image using a variety of comparison metrics. Potential applications for the covariance approximation include investigation of image properties such as object- and signal-dependence of noise, and noise stationarity. These applications are demonstrated, along with the construction of image pixel variance maps for two-dimensional 128 × 128 pixel images. Methods for extending the proposed covariance approximation to larger images and improving computational efficiency are discussed. Future work will apply the developed methodology to the construction of task-based image quality metrics such as the Hotelling observer detectability for TV-based IIR.

Estimation of noise properties for TV-regularized image reconstruction in computed tomography

NASA Astrophysics Data System (ADS)

Sánchez, Adrian A.

2015-09-01

A method for predicting the image covariance resulting from total-variation-penalized iterative image reconstruction (TV-penalized IIR) is presented and demonstrated in a variety of contexts. The method is validated against the sample covariance from statistical noise realizations for a small image using a variety of comparison metrics. Potential applications for the covariance approximation include investigation of image properties such as object- and signal-dependence of noise, and noise stationarity. These applications are demonstrated, along with the construction of image pixel variance maps for two-dimensional 128× 128 pixel images. Methods for extending the proposed covariance approximation to larger images and improving computational efficiency are discussed. Future work will apply the developed methodology to the construction of task-based image quality metrics such as the Hotelling observer detectability for TV-based IIR.

Shuttle Space Suit: Fabric/LCVG Model Validation. Chapter 8

NASA Technical Reports Server (NTRS)

Wilson, J. W.; Tweed, J.; Zeitlin, C.; Kim, M.-H. Y.; Anderson, B. M.; Cucinotta, F. A.; Ware, J.; Persans, A. E.

2003-01-01

A detailed space suit computational model is being developed at the Langley Research Center for radiation exposure evaluation studies. The details of the construction of the space suit are critical to estimation of exposures and assessing the risk to the astronaut on EVA. Past evaluations of space suit shielding properties assumed the basic fabric layup (Thermal Micrometeoroid Garment, fabric restraints, and pressure envelope) and LCVG could be homogenized as a single layer overestimating the protective properties over 60 percent of the fabric area. The present space suit model represents the inhomogeneous distributions of LCVG materials (mainly the water filled cooling tubes). An experimental test is performed using a 34-MeV proton beam and high-resolution detectors to compare with model-predicted transmission factors. Some suggestions are made on possible improved construction methods to improve the space suit s protection properties.

Shuttle Spacesuit: Fabric/LCVG Model Validation

NASA Technical Reports Server (NTRS)

Wilson, J. W.; Tweed, J.; Zeitlin, C.; Kim, M.-H. Y.; Anderson, B. M.; Cucinotta, F. A.; Ware, J.; Persans, A. E.

2001-01-01

A detailed spacesuit computational model is being developed at the Langley Research Center for radiation exposure evaluation studies. The details of the construction of the spacesuit are critical to estimation of exposures and assessing the risk to the astronaut on EVA. Past evaluations of spacesuit shielding properties assumed the basic fabric lay-up (Thermal Micrometeroid Garment, fabric restraints, and pressure envelope) and Liquid Cooling and Ventilation Garment (LCVG) could be homogenized as a single layer overestimating the protective properties over 60 percent of the fabric area. The present spacesuit model represents the inhomogeneous distributions of LCVG materials (mainly the water filled cooling tubes). An experimental test is performed using a 34-MeV proton beam and highresolution detectors to compare with model-predicted transmission factors. Some suggestions are made on possible improved construction methods to improve the spacesuit's protection properties.

Thermodynamic geometry of minimum-dissipation driven barrier crossing

NASA Astrophysics Data System (ADS)

Sivak, David A.; Crooks, Gavin E.

2016-11-01

We explore the thermodynamic geometry of a simple system that models the bistable dynamics of nucleic acid hairpins in single molecule force-extension experiments. Near equilibrium, optimal (minimum-dissipation) driving protocols are governed by a generalized linear response friction coefficient. Our analysis demonstrates that the friction coefficient of the driving protocols is sharply peaked at the interface between metastable regions, which leads to minimum-dissipation protocols that drive rapidly within a metastable basin, but then linger longest at the interface, giving thermal fluctuations maximal time to kick the system over the barrier. Intuitively, the same principle applies generically in free energy estimation (both in steered molecular dynamics simulations and in single-molecule experiments), provides a design principle for the construction of thermodynamically efficient coupling between stochastic objects, and makes a prediction regarding the construction of evolved biomolecular motors.

Thermodynamic geometry of minimum-dissipation driven barrier crossing

NASA Astrophysics Data System (ADS)

Sivak, David; Crooks, Gavin

We explore the thermodynamic geometry of a simple system that models the bistable dynamics of nucleic acid hairpins in single molecule force-extension experiments. Near equilibrium, optimal (minimum-dissipation) driving protocols are governed by a generalized linear response friction coefficient. Our analysis demonstrates that the friction coefficient of the driving protocols is sharply peaked at the interface between metastable regions, which leads to minimum-dissipation protocols that drive rapidly within a metastable basin, but then linger longest at the interface, giving thermal fluctuations maximal time to kick the system over the barrier. Intuitively, the same principle applies generically in free energy estimation (both in steered molecular dynamics simulations and in single-molecule experiments), provides a design principle for the construction of thermodynamically efficient coupling between stochastic objects, and makes a prediction regarding the construction of evolved biomolecular motors.

Development and Validation of a New Air Carrier Block Time Prediction Model and Methodology

NASA Astrophysics Data System (ADS)

Litvay, Robyn Olson

Commercial airline operations rely on predicted block times as the foundation for critical, successive decisions that include fuel purchasing, crew scheduling, and airport facility usage planning. Small inaccuracies in the predicted block times have the potential to result in huge financial losses, and, with profit margins for airline operations currently almost nonexistent, potentially negate any possible profit. Although optimization techniques have resulted in many models targeting airline operations, the challenge of accurately predicting and quantifying variables months in advance remains elusive. The objective of this work is the development of an airline block time prediction model and methodology that is practical, easily implemented, and easily updated. Research was accomplished, and actual U.S., domestic, flight data from a major airline was utilized, to develop a model to predict airline block times with increased accuracy and smaller variance in the actual times from the predicted times. This reduction in variance represents tens of millions of dollars (U.S.) per year in operational cost savings for an individual airline. A new methodology for block time prediction is constructed using a regression model as the base, as it has both deterministic and probabilistic components, and historic block time distributions. The estimation of the block times for commercial, domestic, airline operations requires a probabilistic, general model that can be easily customized for a specific airline’s network. As individual block times vary by season, by day, and by time of day, the challenge is to make general, long-term estimations representing the average, actual block times while minimizing the variation. Predictions of block times for the third quarter months of July and August of 2011 were calculated using this new model. The resulting, actual block times were obtained from the Research and Innovative Technology Administration, Bureau of Transportation Statistics (Airline On-time Performance Data, 2008-2011) for comparison and analysis. Future block times are shown to be predicted with greater accuracy, without exception and network-wide, for a major, U.S., domestic airline.

Building a 40% Energy Saving House in the Mixed-Humid Climate

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

Christian, Jeffrey E; Bonar, Jacob

2011-10-01

This report describes a home that uses 40% less energy than the energy-efficient Building America standard - a giant step in the pursuit of affordable near-zero-energy housing through the evolution of five near-zero-energy research houses. This four-bedroom, two-bath, 1232-ft2 house has a Home Energy Rating System (HERS) index of 35 (a HERS rating of 0 is a zero-energy house, a conventional new house would have a HERS rating of 100), which qualifies it for federal energy efficiency and solar incentives. The house is leading to the planned construction of a similar home in Greensburg, Kansas, and 21 staff houses inmore » the Walden Reserve, a 7000-unit "deep green" community in Cookville, Tennessee. Discussions are underway for construction of similar houses in Charleston, South Carolina, Seattle, Washington, Knoxville and Oak Ridge, Tennessee, and upstate New York. This house should lead to a 40% and 50% Gate-3, Mixed-Humid-Climate Joule for the DOE Building America Program. The house is constructed with structurally-insulated-panel walls and roof, raised metal-seam roof with infrared reflective coating, airtight envelope (1.65 air changes per hour at 50 Pascal), supply mechanical ventilation, ducts inside the conditioned space, extensive moisture control package, foundation geothermal space heating and cooling system, ZEHcor wall, solar water heater, and a 2.2 kWp grid-connected photovoltaic (PV) system. The detailed specifications for the envelope and the equipment used in ZEH5 compared to all the houses in this series are shown in Tables 1 and 2. Based on a validated computer simulation of ZEH5 with typical occupancy patterns and energy services for four occupants, energy for this all-electric house is predicted to cost only $0.66/day ($0.86/day counting the hookup charges). By contrast, the benchmark house would require $3.56/day, including hookup charges (these costs are based on a 2006 residential rates of $0.07/kWh and solar buyback at $0.15/kWh). The solar fraction for this home located in Lenoir City, Tennessee, is predicted to be as high as 41%(accounting for both solar PV and the solar water heater). This all-electric home is predicted to use 25 kWh/day based on the one year of measured data used to calibrate a whole-building simulation model. Based on two years of measured data, the roof-mounted 2.2 kWp PV system is predicted to generate 7.5 kWh/day. The 2005 cost to commercially construct ZEH5, including builder profit and overhead, is estimated at about $150,000. This cost - for ZEH5's panelized construction, premanufactured utility wall (ZEHcor), foundation geothermal system, and the addition of the walkout lower level, and considering the falling cost for PV - suggests that the construction cost per ft2 for a ZEH5 two-story will be even more cost-competitive. The 2005 construction cost estimate for a finished-out ZEH5 with 2632 ft2 is $222,000 or $85/ft2. The intention of this report is to help builders and homeowners make the decision to build zero-energy-ready homes. Detailed drawings, specifications, and lessons learned in the construction and analysis of data from about 100 sensors monitoring thermal performance for a one-year period are presented. This information should be specifically useful to those considering structural insulated panel walls and roof, foundation geothermal space heating and cooling, solar water heater and roof-mounted, photovoltaic, grid-tied systems.« less

Estimation of k-ε parameters using surrogate models and jet-in-crossflow data

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

Lefantzi, Sophia; Ray, Jaideep; Arunajatesan, Srinivasan

2014-11-01

We demonstrate a Bayesian method that can be used to calibrate computationally expensive 3D RANS (Reynolds Av- eraged Navier Stokes) models with complex response surfaces. Such calibrations, conditioned on experimental data, can yield turbulence model parameters as probability density functions (PDF), concisely capturing the uncertainty in the parameter estimates. Methods such as Markov chain Monte Carlo (MCMC) estimate the PDF by sampling, with each sample requiring a run of the RANS model. Consequently a quick-running surrogate is used instead to the RANS simulator. The surrogate can be very difficult to design if the model's response i.e., the dependence of themore » calibration variable (the observable) on the parameter being estimated is complex. We show how the training data used to construct the surrogate can be employed to isolate a promising and physically realistic part of the parameter space, within which the response is well-behaved and easily modeled. We design a classifier, based on treed linear models, to model the "well-behaved region". This classifier serves as a prior in a Bayesian calibration study aimed at estimating 3 k - ε parameters ( C μ, C ε2 , C ε1 ) from experimental data of a transonic jet-in-crossflow interaction. The robustness of the calibration is investigated by checking its predictions of variables not included in the cal- ibration data. We also check the limit of applicability of the calibration by testing at off-calibration flow regimes. We find that calibration yield turbulence model parameters which predict the flowfield far better than when the nomi- nal values of the parameters are used. Substantial improvements are still obtained when we use the calibrated RANS model to predict jet-in-crossflow at Mach numbers and jet strengths quite different from those used to generate the ex- perimental (calibration) data. Thus the primary reason for poor predictive skill of RANS, when using nominal values of the turbulence model parameters, was parametric uncertainty, which was rectified by calibration. Post-calibration, the dominant contribution to model inaccuraries are due to the structural errors in RANS.« less

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

PubMed

Ishwaran, Hemant; Lu, Min

2018-06-04

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

Gaussian covariance graph models accounting for correlated marker effects in genome-wide prediction.

PubMed

Martínez, C A; Khare, K; Rahman, S; Elzo, M A

2017-10-01

Several statistical models used in genome-wide prediction assume uncorrelated marker allele substitution effects, but it is known that these effects may be correlated. In statistics, graphical models have been identified as a useful tool for covariance estimation in high-dimensional problems and it is an area that has recently experienced a great expansion. In Gaussian covariance graph models (GCovGM), the joint distribution of a set of random variables is assumed to be Gaussian and the pattern of zeros of the covariance matrix is encoded in terms of an undirected graph G. In this study, methods adapting the theory of GCovGM to genome-wide prediction were developed (Bayes GCov, Bayes GCov-KR and Bayes GCov-H). In simulated data sets, improvements in correlation between phenotypes and predicted breeding values and accuracies of predicted breeding values were found. Our models account for correlation of marker effects and permit to accommodate general structures as opposed to models proposed in previous studies, which consider spatial correlation only. In addition, they allow incorporation of biological information in the prediction process through its use when constructing graph G, and their extension to the multi-allelic loci case is straightforward. © 2017 Blackwell Verlag GmbH.

Gaussian Process Regression (GPR) Representation in Predictive Model Markup Language (PMML)

PubMed Central

Lechevalier, D.; Ak, R.; Ferguson, M.; Law, K. H.; Lee, Y.-T. T.; Rachuri, S.

2017-01-01

This paper describes Gaussian process regression (GPR) models presented in predictive model markup language (PMML). PMML is an extensible-markup-language (XML) -based standard language used to represent data-mining and predictive analytic models, as well as pre- and post-processed data. The previous PMML version, PMML 4.2, did not provide capabilities for representing probabilistic (stochastic) machine-learning algorithms that are widely used for constructing predictive models taking the associated uncertainties into consideration. The newly released PMML version 4.3, which includes the GPR model, provides new features: confidence bounds and distribution for the predictive estimations. Both features are needed to establish the foundation for uncertainty quantification analysis. Among various probabilistic machine-learning algorithms, GPR has been widely used for approximating a target function because of its capability of representing complex input and output relationships without predefining a set of basis functions, and predicting a target output with uncertainty quantification. GPR is being employed to various manufacturing data-analytics applications, which necessitates representing this model in a standardized form for easy and rapid employment. In this paper, we present a GPR model and its representation in PMML. Furthermore, we demonstrate a prototype using a real data set in the manufacturing domain. PMID:29202125

Gaussian Process Regression (GPR) Representation in Predictive Model Markup Language (PMML).

PubMed

Park, J; Lechevalier, D; Ak, R; Ferguson, M; Law, K H; Lee, Y-T T; Rachuri, S

2017-01-01

This paper describes Gaussian process regression (GPR) models presented in predictive model markup language (PMML). PMML is an extensible-markup-language (XML) -based standard language used to represent data-mining and predictive analytic models, as well as pre- and post-processed data. The previous PMML version, PMML 4.2, did not provide capabilities for representing probabilistic (stochastic) machine-learning algorithms that are widely used for constructing predictive models taking the associated uncertainties into consideration. The newly released PMML version 4.3, which includes the GPR model, provides new features: confidence bounds and distribution for the predictive estimations. Both features are needed to establish the foundation for uncertainty quantification analysis. Among various probabilistic machine-learning algorithms, GPR has been widely used for approximating a target function because of its capability of representing complex input and output relationships without predefining a set of basis functions, and predicting a target output with uncertainty quantification. GPR is being employed to various manufacturing data-analytics applications, which necessitates representing this model in a standardized form for easy and rapid employment. In this paper, we present a GPR model and its representation in PMML. Furthermore, we demonstrate a prototype using a real data set in the manufacturing domain.

Performance of comorbidity, risk adjustment, and functional status measures in expenditure prediction for patients with diabetes.

PubMed

Maciejewski, Matthew L; Liu, Chuan-Fen; Fihn, Stephan D

2009-01-01

To compare the ability of generic comorbidity and risk adjustment measures, a diabetes-specific measure, and a self-reported functional status measure to explain variation in health care expenditures for individuals with diabetes. This study included a retrospective cohort of 3,092 diabetic veterans participating in a multisite trial. Two comorbidity measures, four risk adjusters, a functional status measure, a diabetes complication count, and baseline expenditures were constructed from administrative and survey data. Outpatient, inpatient, and total expenditure models were estimated using ordinary least squares regression. Adjusted R(2) statistics and predictive ratios were compared across measures to assess overall explanatory power and explanatory power of low- and high-cost subgroups. Administrative data-based risk adjusters performed better than the comorbidity, functional status, and diabetes-specific measures in all expenditure models. The diagnostic cost groups (DCGs) measure had the greatest predictive power overall and for the low- and high-cost subgroups, while the diabetes-specific measure had the lowest predictive power. A model with DCGs and the diabetes-specific measure modestly improved predictive power. Existing generic measures can be useful for diabetes-specific research and policy applications, but more predictive diabetes-specific measures are needed.

A framework for testing the use of electric and electromagnetic data to reduce the prediction error of groundwater models

NASA Astrophysics Data System (ADS)

Christensen, N. K.; Christensen, S.; Ferre, T. P. A.

2015-09-01

Despite geophysics is being used increasingly, it is still unclear how and when the integration of geophysical data improves the construction and predictive capability of groundwater models. Therefore, this paper presents a newly developed HYdrogeophysical TEst-Bench (HYTEB) which is a collection of geological, groundwater and geophysical modeling and inversion software wrapped to make a platform for generation and consideration of multi-modal data for objective hydrologic analysis. It is intentionally flexible to allow for simple or sophisticated treatments of geophysical responses, hydrologic processes, parameterization, and inversion approaches. It can also be used to discover potential errors that can be introduced through petrophysical models and approaches to correlating geophysical and hydrologic parameters. With HYTEB we study alternative uses of electromagnetic (EM) data for groundwater modeling in a hydrogeological environment consisting of various types of glacial deposits with typical hydraulic conductivities and electrical resistivities covering impermeable bedrock with low resistivity. It is investigated to what extent groundwater model calibration and, often more importantly, model predictions can be improved by including in the calibration process electrical resistivity estimates obtained from TEM data. In all calibration cases, the hydraulic conductivity field is highly parameterized and the estimation is stabilized by regularization. For purely hydrologic inversion (HI, only using hydrologic data) we used Tikhonov regularization combined with singular value decomposition. For joint hydrogeophysical inversion (JHI) and sequential hydrogeophysical inversion (SHI) the resistivity estimates from TEM are used together with a petrophysical relationship to formulate the regularization term. In all cases, the regularization stabilizes the inversion, but neither the HI nor the JHI objective function could be minimized uniquely. SHI or JHI with regularization based on the use of TEM data produced estimated hydraulic conductivity fields that bear more resemblance to the reference fields than when using HI with Tikhonov regularization. However, for the studied system the resistivities estimated by SHI or JHI must be used with caution as estimators of hydraulic conductivity or as regularization means for subsequent hydrological inversion. Much of the lack of value of the geophysical data arises from a mistaken faith in the power of the petrophysical model in combination with geophysical data of low sensitivity, thereby propagating geophysical estimation errors into the hydrologic model parameters. With respect to reducing model prediction error, it depends on the type of prediction whether it has value to include geophysical data in the model calibration. It is found that all calibrated models are good predictors of hydraulic head. When the stress situation is changed from that of the hydrologic calibration data, then all models make biased predictions of head change. All calibrated models turn out to be a very poor predictor of the pumping well's recharge area and groundwater age. The reason for this is that distributed recharge is parameterized as depending on estimated hydraulic conductivity of the upper model layer which tends to be underestimated. Another important insight from the HYTEB analysis is thus that either recharge should be parameterized and estimated in a different way, or other types of data should be added to better constrain the recharge estimates.

Testing the predictive power of the transtheoretical model of behavior change applied to dietary fat intake

PubMed Central

Wright, Julie A.; Velicer, Wayne F.; Prochaska, James O.

2009-01-01

This study evaluated how well predictions from the transtheoretical model (TTM) generalized from smoking to diet. Longitudinal data were used from a randomized control trial on reducing dietary fat consumption in adults (n =1207) recruited from primary care practices. Predictive power was evaluated by making a priori predictions of the magnitude of change expected in the TTM constructs of temptation, pros and cons, and 10 processes of change when an individual transitions between the stages of change. Generalizability was evaluated by testing predictions based on smoking data. Three sets of predictions were made for each stage: Precontemplation (PC), Contemplation (C) and Preparation (PR) based on stage transition categories of no progress, progress and regression determined by stage at baseline versus stage at the 12-month follow-up. Univariate analysis of variance between stage transition groups was used to calculate the effect size [omega squared (ω2)]. For diet predictions based on diet data, there was a high degree of confirmation: 92%, 95% and 92% for PC, C and PR, respectively. For diet predictions based on smoking data, 77%, 79% and 85% were confirmed, respectively, suggesting a moderate degree of generalizability. This study revised effect size estimates for future theory testing on the TTM applied to dietary fat. PMID:18400785

Multi-model ensembles for assessment of flood losses and associated uncertainty

NASA Astrophysics Data System (ADS)

Figueiredo, Rui; Schröter, Kai; Weiss-Motz, Alexander; Martina, Mario L. V.; Kreibich, Heidi

2018-05-01

Flood loss modelling is a crucial part of risk assessments. However, it is subject to large uncertainty that is often neglected. Most models available in the literature are deterministic, providing only single point estimates of flood loss, and large disparities tend to exist among them. Adopting any one such model in a risk assessment context is likely to lead to inaccurate loss estimates and sub-optimal decision-making. In this paper, we propose the use of multi-model ensembles to address these issues. This approach, which has been applied successfully in other scientific fields, is based on the combination of different model outputs with the aim of improving the skill and usefulness of predictions. We first propose a model rating framework to support ensemble construction, based on a probability tree of model properties, which establishes relative degrees of belief between candidate models. Using 20 flood loss models in two test cases, we then construct numerous multi-model ensembles, based both on the rating framework and on a stochastic method, differing in terms of participating members, ensemble size and model weights. We evaluate the performance of ensemble means, as well as their probabilistic skill and reliability. Our results demonstrate that well-designed multi-model ensembles represent a pragmatic approach to consistently obtain more accurate flood loss estimates and reliable probability distributions of model uncertainty.

Development and control of a magnetorheological haptic device for robot assisted surgery.

PubMed

Shokrollahi, Elnaz; Goldenberg, Andrew A; Drake, James M; Eastwood, Kyle W; Kang, Matthew

2017-07-01

A prototype magnetorheological (MR) fluid-based actuator has been designed for tele-robotic surgical applications. This device is capable of generating forces up to 47 N, with input currents ranging from 0 to 1.5 A. We begin by outlining the physical design of the device, and then discuss a novel nonlinear model of the device's behavior. The model was developed using the Hammerstein-Wiener (H-W) nonlinear black-box technique and is intended to accurately capture the hysteresis behavior of the MR-fluid. Several experiments were conducted on the device to collect estimation and validation datasets to construct the model and assess its performance. Different estimating functions were used to construct the model, and their effectiveness is assessed based on goodness-of-fit and final-prediction-error measurements. A sigmoid network was found to have a goodness-of-fit of 95%. The model estimate was then used to tune a PID controller. Two control schemes were proposed to eliminate the hysteresis behavior present in the MR fluid device. One method uses a traditional force feedback control loop and the other is based on measuring the magnetic field using a Hall-effect sensor embedded within the device. The Hall-effect sensor scheme was found to be superior in terms of cost, simplicity and real-time control performance compared to the force control strategy.

Evolution of the cerebellum as a neuronal machine for Bayesian state estimation

NASA Astrophysics Data System (ADS)

Paulin, M. G.

2005-09-01

The cerebellum evolved in association with the electric sense and vestibular sense of the earliest vertebrates. Accurate information provided by these sensory systems would have been essential for precise control of orienting behavior in predation. A simple model shows that individual spikes in electrosensory primary afferent neurons can be interpreted as measurements of prey location. Using this result, I construct a computational neural model in which the spatial distribution of spikes in a secondary electrosensory map forms a Monte Carlo approximation to the Bayesian posterior distribution of prey locations given the sense data. The neural circuit that emerges naturally to perform this task resembles the cerebellar-like hindbrain electrosensory filtering circuitry of sharks and other electrosensory vertebrates. The optimal filtering mechanism can be extended to handle dynamical targets observed from a dynamical platform; that is, to construct an optimal dynamical state estimator using spiking neurons. This may provide a generic model of cerebellar computation. Vertebrate motion-sensing neurons have specific fractional-order dynamical characteristics that allow Bayesian state estimators to be implemented elegantly and efficiently, using simple operations with asynchronous pulses, i.e. spikes. The computational neural models described in this paper represent a novel kind of particle filter, using spikes as particles. The models are specific and make testable predictions about computational mechanisms in cerebellar circuitry, while providing a plausible explanation of cerebellar contributions to aspects of motor control, perception and cognition.

Estimating diesel fuel consumption and carbon dioxide emissions from forest road construction

Treesearch

Dan Loeffler; Greg Jones; Nikolaus Vonessen; Sean Healey; Woodam Chung

2009-01-01

Forest access road construction is a necessary component of many on-the-ground forest vegetation treatment projects. However, the fuel energy requirements and associated carbon dioxide emissions from forest road construction are unknown. We present a method for estimating diesel fuel consumed and related carbon dioxide emissions from constructing forest roads using...

Dynamic modeling of green algae cultivation in a photobioreactor for sustainable biodiesel production.

PubMed

Del Rio-Chanona, Ehecatl A; Liu, Jiao; Wagner, Jonathan L; Zhang, Dongda; Meng, Yingying; Xue, Song; Shah, Nilay

2018-02-01

Biodiesel produced from microalgae has been extensively studied due to its potentially outstanding advantages over traditional transportation fuels. In order to facilitate its industrialization and improve the process profitability, it is vital to construct highly accurate models capable of predicting the complex behavior of the investigated biosystem for process optimization and control, which forms the current research goal. Three original contributions are described in this paper. Firstly, a dynamic model is constructed to simulate the complicated effect of light intensity, nutrient supply and light attenuation on both biomass growth and biolipid production. Secondly, chlorophyll fluorescence, an instantly measurable variable and indicator of photosynthetic activity, is embedded into the model to monitor and update model accuracy especially for the purpose of future process optimal control, and its correlation between intracellular nitrogen content is quantified, which to the best of our knowledge has never been addressed so far. Thirdly, a thorough experimental verification is conducted under different scenarios including both continuous illumination and light/dark cycle conditions to testify the model predictive capability particularly for long-term operation, and it is concluded that the current model is characterized by a high level of predictive capability. Based on the model, the optimal light intensity for algal biomass growth and lipid synthesis is estimated. This work, therefore, paves the way to forward future process design and real-time optimization. © 2017 Wiley Periodicals, Inc.

Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements.

PubMed

Bai, Wenming; Yoshimura, Norio; Takayanagi, Masao; Che, Jingai; Horiuchi, Naomi; Ogiwara, Isao

2016-06-28

Nondestructive prediction of ingredient contents of farm products is useful to ship and sell the products with guaranteed qualities. Here, near-infrared spectroscopy is used to predict nondestructively total sugar, total organic acid, and total anthocyanin content in each blueberry. The technique is expected to enable the selection of only delicious blueberries from all harvested ones. The near-infrared absorption spectra of blueberries are measured with the diffuse reflectance mode at the positions not on the calyx. The ingredient contents of a blueberry determined by high-performance liquid chromatography are used to construct models to predict the ingredient contents from observed spectra. Partial least squares regression is used for the construction of the models. It is necessary to properly select the pretreatments for the observed spectra and the wavelength regions of the spectra used for analyses. Validations are necessary for the constructed models to confirm that the ingredient contents are predicted with practical accuracies. Here we present a protocol to construct and validate the models for nondestructive prediction of ingredient contents in blueberries by near-infrared spectroscopy.

Re-constructing historical Adélie penguin abundance estimates by retrospectively accounting for detection bias.

PubMed

Southwell, Colin; Emmerson, Louise; Newbery, Kym; McKinlay, John; Kerry, Knowles; Woehler, Eric; Ensor, Paul

2015-01-01

Seabirds and other land-breeding marine predators are considered to be useful and practical indicators of the state of marine ecosystems because of their dependence on marine prey and the accessibility of their populations at breeding colonies. Historical counts of breeding populations of these higher-order marine predators are one of few data sources available for inferring past change in marine ecosystems. However, historical abundance estimates derived from these population counts may be subject to unrecognised bias and uncertainty because of variable attendance of birds at breeding colonies and variable timing of past population surveys. We retrospectively accounted for detection bias in historical abundance estimates of the colonial, land-breeding Adélie penguin through an analysis of 222 historical abundance estimates from 81 breeding sites in east Antarctica. The published abundance estimates were de-constructed to retrieve the raw count data and then re-constructed by applying contemporary adjustment factors obtained from remotely operating time-lapse cameras. The re-construction process incorporated spatial and temporal variation in phenology and attendance by using data from cameras deployed at multiple sites over multiple years and propagating this uncertainty through to the final revised abundance estimates. Our re-constructed abundance estimates were consistently higher and more uncertain than published estimates. The re-constructed estimates alter the conclusions reached for some sites in east Antarctica in recent assessments of long-term Adélie penguin population change. Our approach is applicable to abundance data for a wide range of colonial, land-breeding marine species including other penguin species, flying seabirds and marine mammals.

Predictive value and construct validity of the work functioning screener-healthcare (WFS-H).

PubMed

Boezeman, Edwin J; Nieuwenhuijsen, Karen; Sluiter, Judith K

2016-05-25

To test the predictive value and convergent construct validity of a 6-item work functioning screener (WFS-H). Healthcare workers (249 nurses) completed a questionnaire containing the work functioning screener (WFS-H) and a work functioning instrument (NWFQ) measuring the following: cognitive aspects of task execution and general incidents, avoidance behavior, conflicts and irritation with colleagues, impaired contact with patients and their family, and level of energy and motivation. Productivity and mental health were also measured. Negative and positive predictive values, AUC values, and sensitivity and specificity were calculated to examine the predictive value of the screener. Correlation analysis was used to examine the construct validity. The screener had good predictive value, since the results showed that a negative screener score is a strong indicator of work functioning not hindered by mental health problems (negative predictive values: 94%-98%; positive predictive values: 21%-36%; AUC:.64-.82; sensitivity: 42%-76%; and specificity 85%-87%). The screener has good construct validity due to moderate, but significant (p<.001), associations with productivity (r=.51), mental health (r=.48), and distress (r=.47). The screener (WFS-H) had good predictive value and good construct validity. Its score offers occupational health professionals a helpful preliminary insight into the work functioning of healthcare workers.

A Semantics-Based Approach to Construction Cost Estimating

ERIC Educational Resources Information Center

Niknam, Mehrdad

2015-01-01

A construction project requires collaboration of different organizations such as owner, designer, contractor, and resource suppliers. These organizations need to exchange information to improve their teamwork. Understanding the information created in other organizations requires specialized human resources. Construction cost estimating is one of…

Exploring the Predictive Validity of the Susceptibility to Smoking Construct for Tobacco Cigarettes, Alternative Tobacco Products, and E-Cigarettes.

PubMed

Cole, Adam G; Kennedy, Ryan David; Chaurasia, Ashok; Leatherdale, Scott T

2017-12-06

Within tobacco prevention programming, it is useful to identify youth that are at risk for experimenting with various tobacco products and e-cigarettes. The susceptibility to smoking construct is a simple method to identify never-smoking students that are less committed to remaining smoke-free. However, the predictive validity of this construct has not been tested within the Canadian context or for the use of other tobacco products and e-cigarettes. This study used a large, longitudinal sample of secondary school students that reported never using tobacco cigarettes and non-current use of alternative tobacco products or e-cigarettes at baseline in Ontario, Canada. The sensitivity, specificity, and positive and negative predictive values of the susceptibility construct for predicting tobacco cigarette, e-cigarette, cigarillo or little cigar, cigar, hookah, and smokeless tobacco use one and two years after baseline measurement were calculated. At baseline, 29.4% of the sample was susceptible to future tobacco product or e-cigarette use. The sensitivity of the construct ranged from 43.2% (smokeless tobacco) to 59.5% (tobacco cigarettes), the specificity ranged from 70.9% (smokeless tobacco) to 75.9% (tobacco cigarettes), and the positive predictive value ranged from 2.6% (smokeless tobacco) to 32.2% (tobacco cigarettes). Similar values were calculated for each measure of the susceptibility construct. A significant number of youth that did not currently use tobacco products or e-cigarettes at baseline reported using tobacco products and e-cigarettes over a two-year follow-up period. The predictive validity of the susceptibility construct was high and the construct can be used to predict other tobacco product and e-cigarette use among youth. This study presents the predictive validity of the susceptibility construct for the use of tobacco cigarettes among secondary school students in Ontario, Canada. It also presents a novel use of the susceptibility construct for predicting the use of e-cigarettes, cigarillos or little cigars, cigars, hookah, and smokeless tobacco among secondary school students in Ontario, Canada. © The Author(s) 2017. Published by Oxford University Press on behalf of the Society for Research on Nicotine and Tobacco. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Multiple environment single system quantum mechanical/molecular mechanical (MESS-QM/MM) calculations. 1. Estimation of polarization energies.

PubMed

Sodt, Alexander J; Mei, Ye; König, Gerhard; Tao, Peng; Steele, Ryan P; Brooks, Bernard R; Shao, Yihan

2015-03-05

In combined quantum mechanical/molecular mechanical (QM/MM) free energy calculations, it is often advantageous to have a frozen geometry for the quantum mechanical (QM) region. For such multiple-environment single-system (MESS) cases, two schemes are proposed here for estimating the polarization energy: the first scheme, termed MESS-E, involves a Roothaan step extrapolation of the self-consistent field (SCF) energy; whereas the other scheme, termed MESS-H, employs a Newton-Raphson correction using an approximate inverse electronic Hessian of the QM region (which is constructed only once). Both schemes are extremely efficient, because the expensive Fock updates and SCF iterations in standard QM/MM calculations are completely avoided at each configuration. They produce reasonably accurate QM/MM polarization energies: MESS-E can predict the polarization energy within 0.25 kcal/mol in terms of the mean signed error for two of our test cases, solvated methanol and solvated β-alanine, using the M06-2X or ωB97X-D functionals; MESS-H can reproduce the polarization energy within 0.2 kcal/mol for these two cases and for the oxyluciferin-luciferase complex, if the approximate inverse electronic Hessians are constructed with sufficient accuracy.

Genetic Modification of the Relationship between Parental Rejection and Adolescent Alcohol Use.

PubMed

Stogner, John M; Gibson, Chris L

2016-07-01

Parenting practices are associated with adolescents' alcohol consumption, however not all youth respond similarly to challenging family situations and harsh environments. This study examines the relationship between perceived parental rejection and adolescent alcohol use, and specifically evaluates whether youth who possess greater genetic sensitivity to their environment are more susceptible to negative parental relationships. Analyzing data from the National Longitudinal Study of Adolescent Health, we estimated a series of regression models predicting alcohol use during adolescence. A multiplicative interaction term between parental rejection and a genetic index was constructed to evaluate this potential gene-environment interaction. Results from logistic regression analyses show a statistically significant gene-environment interaction predicting alcohol use. The relationship between parental rejection and alcohol use was moderated by the genetic index, indicating that adolescents possessing more 'risk alleles' for five candidate genes were affected more by stressful parental relationships. Feelings of parental rejection appear to influence the alcohol use decisions of youth, but they do not do so equally for all. Higher scores on the constructed genetic sensitivity measure are related to increased susceptibility to negative parental relationships. © The Author 2016. Medical Council on Alcohol and Oxford University Press. All rights reserved.

Gyrokinetic modelling of the quasilinear particle flux for plasmas with neutral-beam fuelling

NASA Astrophysics Data System (ADS)

Narita, E.; Honda, M.; Nakata, M.; Yoshida, M.; Takenaga, H.; Hayashi, N.

2018-02-01

A quasilinear particle flux is modelled based on gyrokinetic calculations. The particle flux is estimated by determining factors, namely, coefficients of off-diagonal terms and a particle diffusivity. In this paper, the methodology to estimate the factors is presented using a subset of JT-60U plasmas. First, the coefficients of off-diagonal terms are estimated by linear gyrokinetic calculations. Next, to obtain the particle diffusivity, a semi-empirical approach is taken. Most experimental analyses for particle transport have assumed that turbulent particle fluxes are zero in the core region. On the other hand, even in the stationary state, the plasmas in question have a finite turbulent particle flux due to neutral-beam fuelling. By combining estimates of the experimental turbulent particle flux and the coefficients of off-diagonal terms calculated earlier, the particle diffusivity is obtained. The particle diffusivity should reflect a saturation amplitude of instabilities. The particle diffusivity is investigated in terms of the effects of the linear instability and linear zonal flow response, and it is found that a formula including these effects roughly reproduces the particle diffusivity. The developed framework for prediction of the particle flux is flexible to add terms neglected in the current model. The methodology to estimate the quasilinear particle flux requires so low computational cost that a database consisting of the resultant coefficients of off-diagonal terms and particle diffusivity can be constructed to train a neural network. The development of the methodology is the first step towards a neural-network-based particle transport model for fast prediction of the particle flux.

Exploring the contribution of patient-reported and clinician based variables for the prediction of low back work status.

PubMed

Heymans, Martijn W; Ford, Jon J; McMeeken, Joan M; Chan, Alexander; de Vet, Henrica C W; van Mechelen, Willem

2007-09-01

Successful management of workers on sick leave due to low back pain by the general physician and physiotherapist depends on reliable prognostic information on the course of low back pain and work resumption. Retrospective cohort study in 194 patients who were compensated because of chronic low back pain and who were treated by a physiotherapy functional restoration program. Patient-reported and clinician based prognostic indicators were assessed at baseline before patients entered the functional restoration program. We investigated the predictive value of these indicators on work status at 6 months. Relationships were studied using logistic regression analysis in a 2-step bootstrap modelling approach and a nomogram was developed. Discrimination and calibration of the nomogram was evaluated internally and the explained variation of the nomogram calculated. Seventy percent of workers were back to work at 6 months. We found that including duration of complaints, functional disability, disc herniation and fear avoidance beliefs resulted in the "best" prognostic model. All these factors delayed work resumption. This model was used to construct a nomogram. The explained variation of the nomogram was 23.7%. Discrimination was estimated by the area under the receiver operating characteristic curve and was 0.76 and for calibration we used the slope estimate that was 0.91. The positive predictive values of the nomogram at different cut-off levels of predicted probability were good. Knowledge of the predictive value of these indicators by physicians and physiotherapists will help to identify subgroups of patients and will thus enhance clinical decision-making.

SNP Discovery in the Transcriptome of White Pacific Shrimp Litopenaeus vannamei by Next Generation Sequencing

PubMed Central

Yu, Yang; Wei, Jiankai; Zhang, Xiaojun; Liu, Jingwen; Liu, Chengzhang; Li, Fuhua; Xiang, Jianhai

2014-01-01

The application of next generation sequencing technology has greatly facilitated high throughput single nucleotide polymorphism (SNP) discovery and genotyping in genetic research. In the present study, SNPs were discovered based on two transcriptomes of Litopenaeus vannamei (L. vannamei) generated from Illumina sequencing platform HiSeq 2000. One transcriptome of L. vannamei was obtained through sequencing on the RNA from larvae at mysis stage and its reference sequence was de novo assembled. The data from another transcriptome were downloaded from NCBI and the reads of the two transcriptomes were mapped separately to the assembled reference by BWA. SNP calling was performed using SAMtools. A total of 58,717 and 36,277 SNPs with high quality were predicted from the two transcriptomes, respectively. SNP calling was also performed using the reads of two transcriptomes together, and a total of 96,040 SNPs with high quality were predicted. Among these 96,040 SNPs, 5,242 and 29,129 were predicted as non-synonymous and synonymous SNPs respectively. Characterization analysis of the predicted SNPs in L. vannamei showed that the estimated SNP frequency was 0.21% (one SNP per 476 bp) and the estimated ratio for transition to transversion was 2.0. Fifty SNPs were randomly selected for validation by Sanger sequencing after PCR amplification and 76% of SNPs were confirmed, which indicated that the SNPs predicted in this study were reliable. These SNPs will be very useful for genetic study in L. vannamei, especially for the high density linkage map construction and genome-wide association studies. PMID:24498047

Forecasting extinction risk with nonstationary matrix models.

PubMed

Gotelli, Nicholas J; Ellison, Aaron M

2006-02-01

Matrix population growth models are standard tools for forecasting population change and for managing rare species, but they are less useful for predicting extinction risk in the face of changing environmental conditions. Deterministic models provide point estimates of lambda, the finite rate of increase, as well as measures of matrix sensitivity and elasticity. Stationary matrix models can be used to estimate extinction risk in a variable environment, but they assume that the matrix elements are randomly sampled from a stationary (i.e., non-changing) distribution. Here we outline a method for using nonstationary matrix models to construct realistic forecasts of population fluctuation in changing environments. Our method requires three pieces of data: (1) field estimates of transition matrix elements, (2) experimental data on the demographic responses of populations to altered environmental conditions, and (3) forecasting data on environmental drivers. These three pieces of data are combined to generate a series of sequential transition matrices that emulate a pattern of long-term change in environmental drivers. Realistic estimates of population persistence and extinction risk can be derived from stochastic permutations of such a model. We illustrate the steps of this analysis with data from two populations of Sarracenia purpurea growing in northern New England. Sarracenia purpurea is a perennial carnivorous plant that is potentially at risk of local extinction because of increased nitrogen deposition. Long-term monitoring records or models of environmental change can be used to generate time series of driver variables under different scenarios of changing environments. Both manipulative and natural experiments can be used to construct a linking function that describes how matrix parameters change as a function of the environmental driver. This synthetic modeling approach provides quantitative estimates of extinction probability that have an explicit mechanistic basis.

Cost Risk Analysis Based on Perception of the Engineering Process

NASA Technical Reports Server (NTRS)

Dean, Edwin B.; Wood, Darrell A.; Moore, Arlene A.; Bogart, Edward H.

1986-01-01

In most cost estimating applications at the NASA Langley Research Center (LaRC), it is desirable to present predicted cost as a range of possible costs rather than a single predicted cost. A cost risk analysis generates a range of cost for a project and assigns a probability level to each cost value in the range. Constructing a cost risk curve requires a good estimate of the expected cost of a project. It must also include a good estimate of expected variance of the cost. Many cost risk analyses are based upon an expert's knowledge of the cost of similar projects in the past. In a common scenario, a manager or engineer, asked to estimate the cost of a project in his area of expertise, will gather historical cost data from a similar completed project. The cost of the completed project is adjusted using the perceived technical and economic differences between the two projects. This allows errors from at least three sources. The historical cost data may be in error by some unknown amount. The managers' evaluation of the new project and its similarity to the old project may be in error. The factors used to adjust the cost of the old project may not correctly reflect the differences. Some risk analyses are based on untested hypotheses about the form of the statistical distribution that underlies the distribution of possible cost. The usual problem is not just to come up with an estimate of the cost of a project, but to predict the range of values into which the cost may fall and with what level of confidence the prediction is made. Risk analysis techniques that assume the shape of the underlying cost distribution and derive the risk curve from a single estimate plus and minus some amount usually fail to take into account the actual magnitude of the uncertainty in cost due to technical factors in the project itself. This paper addresses a cost risk method that is based on parametric estimates of the technical factors involved in the project being costed. The engineering process parameters are elicited from the engineer/expert on the project and are based on that expert's technical knowledge. These are converted by a parametric cost model into a cost estimate. The method discussed makes no assumptions about the distribution underlying the distribution of possible costs, and is not tied to the analysis of previous projects, except through the expert calibrations performed by the parametric cost analyst.

Integrating non-colocated well and geophysical data to capture subsurface heterogeneity at an aquifer recharge and recovery site

NASA Astrophysics Data System (ADS)

Gottschalk, Ian P.; Hermans, Thomas; Knight, Rosemary; Caers, Jef; Cameron, David A.; Regnery, Julia; McCray, John E.

2017-12-01

Geophysical data have proven to be very useful for lithological characterization. However, quantitatively integrating the information gained from acquiring geophysical data generally requires colocated lithological and geophysical data for constructing a rock-physics relationship. In this contribution, the issue of integrating noncolocated geophysical and lithological data is addressed, and the results are applied to simulate groundwater flow in a heterogeneous aquifer in the Prairie Waters Project North Campus aquifer recharge site, Colorado. Two methods of constructing a rock-physics transform between electrical resistivity tomography (ERT) data and lithology measurements are assessed. In the first approach, a maximum likelihood estimation (MLE) is used to fit a bimodal lognormal distribution to horizontal crosssections of the ERT resistivity histogram. In the second approach, a spatial bootstrap is applied to approximate the rock-physics relationship. The rock-physics transforms provide soft data for multiple point statistics (MPS) simulations. Subsurface models are used to run groundwater flow and tracer test simulations. Each model's uncalibrated, predicted breakthrough time is evaluated based on its agreement with measured subsurface travel time values from infiltration basins to selected groundwater recovery wells. We find that incorporating geophysical information into uncalibrated flow models reduces the difference with observed values, as compared to flow models without geophysical information incorporated. The integration of geophysical data also narrows the variance of predicted tracer breakthrough times substantially. Accuracy is highest and variance is lowest in breakthrough predictions generated by the MLE-based rock-physics transform. Calibrating the ensemble of geophysically constrained models would help produce a suite of realistic flow models for predictive purposes at the site. We find that the success of breakthrough predictions is highly sensitive to the definition of the rock-physics transform; it is therefore important to model this transfer function accurately.

Development of a Multicomponent Prediction Model for Acute Esophagitis in Lung Cancer Patients Receiving Chemoradiotherapy

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

De Ruyck, Kim, E-mail: kim.deruyck@UGent.be; Sabbe, Nick; Oberije, Cary

2011-10-01

Purpose: To construct a model for the prediction of acute esophagitis in lung cancer patients receiving chemoradiotherapy by combining clinical data, treatment parameters, and genotyping profile. Patients and Methods: Data were available for 273 lung cancer patients treated with curative chemoradiotherapy. Clinical data included gender, age, World Health Organization performance score, nicotine use, diabetes, chronic disease, tumor type, tumor stage, lymph node stage, tumor location, and medical center. Treatment parameters included chemotherapy, surgery, radiotherapy technique, tumor dose, mean fractionation size, mean and maximal esophageal dose, and overall treatment time. A total of 332 genetic polymorphisms were considered in 112 candidatemore » genes. The predicting model was achieved by lasso logistic regression for predictor selection, followed by classic logistic regression for unbiased estimation of the coefficients. Performance of the model was expressed as the area under the curve of the receiver operating characteristic and as the false-negative rate in the optimal point on the receiver operating characteristic curve. Results: A total of 110 patients (40%) developed acute esophagitis Grade {>=}2 (Common Terminology Criteria for Adverse Events v3.0). The final model contained chemotherapy treatment, lymph node stage, mean esophageal dose, gender, overall treatment time, radiotherapy technique, rs2302535 (EGFR), rs16930129 (ENG), rs1131877 (TRAF3), and rs2230528 (ITGB2). The area under the curve was 0.87, and the false-negative rate was 16%. Conclusion: Prediction of acute esophagitis can be improved by combining clinical, treatment, and genetic factors. A multicomponent prediction model for acute esophagitis with a sensitivity of 84% was constructed with two clinical parameters, four treatment parameters, and four genetic polymorphisms.« less

Predicting waist circumference from body mass index.

PubMed

Bozeman, Samuel R; Hoaglin, David C; Burton, Tanya M; Pashos, Chris L; Ben-Joseph, Rami H; Hollenbeak, Christopher S

2012-08-03

Being overweight or obese increases risk for cardiometabolic disorders. Although both body mass index (BMI) and waist circumference (WC) measure the level of overweight and obesity, WC may be more important because of its closer relationship to total body fat. Because WC is typically not assessed in clinical practice, this study sought to develop and verify a model to predict WC from BMI and demographic data, and to use the predicted WC to assess cardiometabolic risk. Data were obtained from the Third National Health and Nutrition Examination Survey (NHANES) and the Atherosclerosis Risk in Communities Study (ARIC). We developed linear regression models for men and women using NHANES data, fitting waist circumference as a function of BMI. For validation, those regressions were applied to ARIC data, assigning a predicted WC to each individual. We used the predicted WC to assess abdominal obesity and cardiometabolic risk. The model correctly classified 88.4% of NHANES subjects with respect to abdominal obesity. Median differences between actual and predicted WC were -0.07 cm for men and 0.11 cm for women. In ARIC, the model closely estimated the observed WC (median difference: -0.34 cm for men, +3.94 cm for women), correctly classifying 86.1% of ARIC subjects with respect to abdominal obesity and 91.5% to 99.5% as to cardiometabolic risk.The model is generalizable to Caucasian and African-American adult populations because it was constructed from data on a large, population-based sample of men and women in the United States, and then validated in a population with a larger representation of African-Americans. The model accurately estimates WC and identifies cardiometabolic risk. It should be useful for health care practitioners and public health officials who wish to identify individuals and populations at risk for cardiometabolic disease when WC data are unavailable.

Utilization of BIM for automation of quantity takeoffs and cost estimation in transport infrastructure construction projects in the Czech Republic

NASA Astrophysics Data System (ADS)

Vitásek, Stanislav; Matějka, Petr

2017-09-01

The article deals with problematic parts of automated processing of quantity takeoff (QTO) from data generated in BIM model. It focuses on models of road constructions, and uses volumes and dimensions of excavation work to create an estimate of construction costs. The article uses a case study and explorative methods to discuss possibilities and problems of data transfer from a model to a price system of construction production when such transfer is used for price estimates of construction works. Current QTOs and price tenders are made with 2D documents. This process is becoming obsolete because more modern tools can be used. The BIM phenomenon enables partial automation in processing volumes and dimensions of construction units and matching the data to units in a given price scheme. Therefore price of construction can be estimated and structured without lengthy and often imprecise manual calculations. The use of BIM for QTO is highly dependent on local market budgeting systems, therefore proper push/pull strategy is required. It also requires proper requirements specification, compatible pricing database and software.

Source Model of Huge Subduction Earthquakes for Strong Ground Motion Prediction

NASA Astrophysics Data System (ADS)

Iwata, T.; Asano, K.

2012-12-01

It is a quite important issue for strong ground motion prediction to construct the source model of huge subduction earthquakes. Irikura and Miyake (2001, 2011) proposed the characterized source model for strong ground motion prediction, which consists of plural strong ground motion generation area (SMGA, Miyake et al., 2003) patches on the source fault. We obtained the SMGA source models for many events using the empirical Green's function method and found the SMGA size has an empirical scaling relationship with seismic moment. Therefore, the SMGA size can be assumed from that empirical relation under giving the seismic moment for anticipated earthquakes. Concerning to the setting of the SMGAs position, the information of the fault segment is useful for inland crustal earthquakes. For the 1995 Kobe earthquake, three SMGA patches are obtained and each Nojima, Suma, and Suwayama segment respectively has one SMGA from the SMGA modeling (e.g. Kamae and Irikura, 1998). For the 2011 Tohoku earthquake, Asano and Iwata (2012) estimated the SMGA source model and obtained four SMGA patches on the source fault. Total SMGA area follows the extension of the empirical scaling relationship between the seismic moment and the SMGA area for subduction plate-boundary earthquakes, and it shows the applicability of the empirical scaling relationship for the SMGA. The positions of two SMGAs are in Miyagi-Oki segment and those other two SMGAs are in Fukushima-Oki and Ibaraki-Oki segments, respectively. Asano and Iwata (2012) also pointed out that all SMGAs are corresponding to the historical source areas of 1930's. Those SMGAs do not overlap the huge slip area in the shallower part of the source fault which estimated by teleseismic data, long-period strong motion data, and/or geodetic data during the 2011 mainshock. This fact shows the huge slip area does not contribute to strong ground motion generation (10-0.1s). The information of the fault segment in the subduction zone, or historical earthquake source area is also applicable for the construction of SMGA settings for strong ground motion prediction for future earthquakes.

Dengue prediction by the web: Tweets are a useful tool for estimating and forecasting Dengue at country and city level

PubMed Central

Degener, Carolin Marlen; Vinhal, Livia; Coelho, Giovanini; Meira, Wagner; Codeço, Claudia Torres; Teixeira, Mauro Martins

2017-01-01

Background Infectious diseases are a leading threat to public health. Accurate and timely monitoring of disease risk and progress can reduce their impact. Mentioning a disease in social networks is correlated with physician visits by patients, and can be used to estimate disease activity. Dengue is the fastest growing mosquito-borne viral disease, with an estimated annual incidence of 390 million infections, of which 96 million manifest clinically. Dengue burden is likely to increase in the future owing to trends toward increased urbanization, scarce water supplies and, possibly, environmental change. The epidemiological dynamic of Dengue is complex and difficult to predict, partly due to costly and slow surveillance systems. Methodology / Principal findings In this study, we aimed to quantitatively assess the usefulness of data acquired by Twitter for the early detection and monitoring of Dengue epidemics, both at country and city level at a weekly basis. Here, we evaluated and demonstrated the potential of tweets modeling for Dengue estimation and forecast, in comparison with other available web-based data, Google Trends and Wikipedia access logs. Also, we studied the factors that might influence the goodness-of-fit of the model. We built a simple model based on tweets that was able to ‘nowcast’, i.e. estimate disease numbers in the same week, but also ‘forecast’ disease in future weeks. At the country level, tweets are strongly associated with Dengue cases, and can estimate present and future Dengue cases until 8 weeks in advance. At city level, tweets are also useful for estimating Dengue activity. Our model can be applied successfully to small and less developed cities, suggesting a robust construction, even though it may be influenced by the incidence of the disease, the activity of Twitter locally, and social factors, including human development index and internet access. Conclusions Tweets association with Dengue cases is valuable to assist traditional Dengue surveillance at real-time and low-cost. Tweets are able to successfully nowcast, i.e. estimate Dengue in the present week, but also forecast, i.e. predict Dengue at until 8 weeks in the future, both at country and city level with high estimation capacity. PMID:28719659

36 CFR 223.82 - Contents of advertisement.

Code of Federal Regulations, 2010 CFR

2010-07-01

... sale which includes specified road construction with total estimated construction costs of $50,000 or more, the advertisement shall also include: (1) The total estimated construction cost of the permanent roads. (2) A statement extending to small business concerns qualified for preferential bidding on timber...

A rapid estimation of tsunami run-up based on finite fault models

NASA Astrophysics Data System (ADS)

Campos, J.; Fuentes, M. A.; Hayes, G. P.; Barrientos, S. E.; Riquelme, S.

2014-12-01

Many efforts have been made to estimate the maximum run-up height of tsunamis associated with large earthquakes. This is a difficult task, because of the time it takes to construct a tsunami model using real time data from the source. It is possible to construct a database of potential seismic sources and their corresponding tsunami a priori. However, such models are generally based on uniform slip distributions and thus oversimplify our knowledge of the earthquake source. Instead, we can use finite fault models of earthquakes to give a more accurate prediction of the tsunami run-up. Here we show how to accurately predict tsunami run-up from any seismic source model using an analytic solution found by Fuentes et al, 2013 that was especially calculated for zones with a very well defined strike, i.e, Chile, Japan, Alaska, etc. The main idea of this work is to produce a tool for emergency response, trading off accuracy for quickness. Our solutions for three large earthquakes are promising. Here we compute models of the run-up for the 2010 Mw 8.8 Maule Earthquake, the 2011 Mw 9.0 Tohoku Earthquake, and the recent 2014 Mw 8.2 Iquique Earthquake. Our maximum rup-up predictions are consistent with measurements made inland after each event, with a peak of 15 to 20 m for Maule, 40 m for Tohoku, and 2,1 m for the Iquique earthquake. Considering recent advances made in the analysis of real time GPS data and the ability to rapidly resolve the finiteness of a large earthquake close to existing GPS networks, it will be possible in the near future to perform these calculations within the first five minutes after the occurrence of any such event. Such calculations will thus provide more accurate run-up information than is otherwise available from existing uniform-slip seismic source databases.

Quantifying the impact of current and future concentrations of air pollutants on respiratory disease risk in England.

PubMed

Pannullo, Francesca; Lee, Duncan; Neal, Lucy; Dalvi, Mohit; Agnew, Paul; O'Connor, Fiona M; Mukhopadhyay, Sabyasachi; Sahu, Sujit; Sarran, Christophe

2017-03-27

Estimating the long-term health impact of air pollution in a spatio-temporal ecological study requires representative concentrations of air pollutants to be constructed for each geographical unit and time period. Averaging concentrations in space and time is commonly carried out, but little is known about how robust the estimated health effects are to different aggregation functions. A second under researched question is what impact air pollution is likely to have in the future. We conducted a study for England between 2007 and 2011, investigating the relationship between respiratory hospital admissions and different pollutants: nitrogen dioxide (NO 2 ); ozone (O 3 ); particulate matter, the latter including particles with an aerodynamic diameter less than 2.5 micrometers (PM 2.5 ), and less than 10 micrometers (PM 10 ); and sulphur dioxide (SO 2 ). Bayesian Poisson regression models accounting for localised spatio-temporal autocorrelation were used to estimate the relative risks (RRs) of pollution on disease risk, and for each pollutant four representative concentrations were constructed using combinations of spatial and temporal averages and maximums. The estimated RRs were then used to make projections of the numbers of likely respiratory hospital admissions in the 2050s attributable to air pollution, based on emission projections from a number of Representative Concentration Pathways (RCP). NO 2 exhibited the largest association with respiratory hospital admissions out of the pollutants considered, with estimated increased risks of between 0.9 and 1.6% for a one standard deviation increase in concentrations. In the future the projected numbers of respiratory hospital admissions attributable to NO 2 in the 2050s are lower than present day rates under 3 Representative Concentration Pathways (RCPs): 2.6, 6.0, and 8.5, which is due to projected reductions in future NO 2 emissions and concentrations. NO 2 concentrations exhibit consistent substantial present-day health effects regardless of how a representative concentration is constructed in space and time. Thus as concentrations are predicted to remain above limits set by European Union Legislation until the 2030s in parts of urban England, it will remain a substantial health risk for some time.

The construction of a decision tool to analyse local demand and local supply for GP care using a synthetic estimation model

PubMed Central

2013-01-01

Background This study addresses the growing academic and policy interest in the appropriate provision of local healthcare services to the healthcare needs of local populations to increase health status and decrease healthcare costs. However, for most local areas information on the demand for primary care and supply is missing. The research goal is to examine the construction of a decision tool which enables healthcare planners to analyse local supply and demand in order to arrive at a better match. Methods National sample-based medical record data of general practitioners (GPs) were used to predict the local demand for GP care based on local populations using a synthetic estimation technique. Next, the surplus or deficit in local GP supply were calculated using the national GP registry. Subsequently, a dynamic internet tool was built to present demand, supply and the confrontation between supply and demand regarding GP care for local areas and their surroundings in the Netherlands. Results Regression analysis showed a significant relationship between sociodemographic predictors of postcode areas and GP consultation time (F [14, 269,467] = 2,852.24; P <0.001). The statistical model could estimate GP consultation time for every postcode area with >1,000 inhabitants in the Netherlands covering 97% of the total population. Confronting these estimated demand figures with the actual GP supply resulted in the average GP workload and the number of full-time equivalent (FTE) GP too much/too few for local areas to cover the demand for GP care. An estimated shortage of one FTE GP or more was prevalent in about 19% of the postcode areas with >1,000 inhabitants if the surrounding postcode areas were taken into consideration. Underserved areas were mainly found in rural regions. Conclusions The constructed decision tool is freely accessible on the Internet and can be used as a starting point in the discussion on primary care service provision in local communities and it can make a considerable contribution to a primary care system which provides care when and where people need it. PMID:24161015

Accurate secondary structure prediction and fold recognition for circular dichroism spectroscopy

PubMed Central

Micsonai, András; Wien, Frank; Kernya, Linda; Lee, Young-Ho; Goto, Yuji; Réfrégiers, Matthieu; Kardos, József

2015-01-01

Circular dichroism (CD) spectroscopy is a widely used technique for the study of protein structure. Numerous algorithms have been developed for the estimation of the secondary structure composition from the CD spectra. These methods often fail to provide acceptable results on α/β-mixed or β-structure–rich proteins. The problem arises from the spectral diversity of β-structures, which has hitherto been considered as an intrinsic limitation of the technique. The predictions are less reliable for proteins of unusual β-structures such as membrane proteins, protein aggregates, and amyloid fibrils. Here, we show that the parallel/antiparallel orientation and the twisting of the β-sheets account for the observed spectral diversity. We have developed a method called β-structure selection (BeStSel) for the secondary structure estimation that takes into account the twist of β-structures. This method can reliably distinguish parallel and antiparallel β-sheets and accurately estimates the secondary structure for a broad range of proteins. Moreover, the secondary structure components applied by the method are characteristic to the protein fold, and thus the fold can be predicted to the level of topology in the CATH classification from a single CD spectrum. By constructing a web server, we offer a general tool for a quick and reliable structure analysis using conventional CD or synchrotron radiation CD (SRCD) spectroscopy for the protein science research community. The method is especially useful when X-ray or NMR techniques fail. Using BeStSel on data collected by SRCD spectroscopy, we investigated the structure of amyloid fibrils of various disease-related proteins and peptides. PMID:26038575

Development of a prediction model for residual disease in newly diagnosed advanced ovarian cancer.

PubMed

Janco, Jo Marie Tran; Glaser, Gretchen; Kim, Bohyun; McGree, Michaela E; Weaver, Amy L; Cliby, William A; Dowdy, Sean C; Bakkum-Gamez, Jamie N

2015-07-01

To construct a tool, using computed tomography (CT) imaging and preoperative clinical variables, to estimate successful primary cytoreduction for advanced epithelial ovarian cancer (EOC). Women who underwent primary cytoreductive surgery for stage IIIC/IV EOC at Mayo Clinic between 1/2/2003 and 12/30/2011 and had preoperative CT images of the abdomen and pelvis within 90days prior to their surgery available for review were included. CT images were reviewed for large-volume ascites, diffuse peritoneal thickening (DPT), omental cake, lymphadenopathy (LP), and spleen or liver involvement. Preoperative factors included age, body mass index (BMI), Eastern Cooperative Oncology Group performance status (ECOG PS), American Society of Anesthesiologists (ASA) score, albumin, CA-125, and thrombocytosis. Two prediction models were developed to estimate the probability of (i) complete and (ii) suboptimal cytoreduction (residual disease (RD) >1cm) using multivariable logistic analysis with backward and stepwise variable selection methods. Internal validation was assessed using bootstrap resampling to derive an optimism-corrected estimate of the c-index. 279 patients met inclusion criteria: 143 had complete cytoreduction, 26 had suboptimal cytoreduction (RD>1cm), and 110 had measurable RD ≤1cm. On multivariable analysis, age, absence of ascites, omental cake, and DPT on CT imaging independently predicted complete cytoreduction (c-index=0.748). Conversely, predictors of suboptimal cytoreduction were ECOG PS, DPT, and LP on preoperative CT imaging (c-index=0.685). The generated models serve as preoperative evaluation tools that may improve counseling and selection for primary surgery, but need to be externally validated. Copyright © 2015 Elsevier Inc. All rights reserved.

Use of the Flory-Huggins theory to predict the solubility of nifedipine and sulfamethoxazole in the triblock, graft copolymer Soluplus.

PubMed

Altamimi, Mohammad A; Neau, Steven H

2016-01-01

Drug dispersed in a polymer can improve bioavailability; dispersed amorphous drug undergoes recrystallization. Solid solutions eliminate amorphous regions, but require a measure of the solubility. Use the Flory-Huggins Theory to predict crystalline drugs solubility in the triblock, graft copolymer Soluplus® to provide a solid solution. Physical mixtures of the two drugs with similar melting points but different glass forming ability, sulfamethoxazole and nifedipine, were prepared with Soluplus® using a quick technique. Drug melting point depression (MPD) was measured using differential scanning calorimetry. The Flory-Huggins Theory allowed: (1) interaction parameter, χ, calculation using MPD data to provide a measure of drug-polymer interaction strength and (2) estimation of the free energy of mixing. A phase diagram was constructed with the MPD data and glass transition temperature (Tg) curves. The interaction parameters with Soluplus® and the free energy of mixing were estimated. Drug solubility was calculated by the intersection of solubility equations and that of MPD and Tg curves in the phase diagram. Negative interaction parameters indicated strong drug-polymer interactions. The phase diagram and solubility equations provided comparable solubility estimates for each drug in Soluplus®. Results using the onset of melting rather than the end of melting support the use of the onset of melting. The Flory-Huggins Theory indicates that Soluplus® interacts effectively with each drug, making solid solution formation feasible. The predicted solubility of the drugs in Soluplus® compared favorably across the methods and supports the use of the onset of melting.

Use of the Flory-Huggins theory to predict the solubility of nifedipine and sulfamethoxazole in the triblock, graft copolymer Soluplus.

PubMed

Altamimi, Mohammad A; Neau, Steven H

2016-03-01

Drug dispersed in a polymer can improve bioavailability; dispersed amorphous drug undergoes recrystallization. Solid solutions eliminate amorphous regions, but require a measure of the solubility. Use the Flory-Huggins Theory to predict crystalline drugs solubility in the triblock, graft copolymer Soluplus® to provide a solid solution. Physical mixtures of the two drugs with similar melting points but different glass forming ability, sulfamethoxazole and nifedipine, were prepared with Soluplus® using a quick technique. Drug melting point depression (MPD) was measured using differential scanning calorimetry. The Flory-Huggins Theory allowed: (1) interaction parameter, χ, calculation using MPD data to provide a measure of drug-polymer interaction strength and (2) estimation of the free energy of mixing. A phase diagram was constructed with the MPD data and glass transition temperature (T g ) curves. The interaction parameters with Soluplus® and the free energy of mixing were estimated. Drug solubility was calculated by the intersection of solubility equations and that of MPD and T g curves in the phase diagram. Negative interaction parameters indicated strong drug-polymer interactions. The phase diagram and solubility equations provided comparable solubility estimates for each drug in Soluplus®. Results using the onset of melting rather than the end of melting support the use of the onset of melting. The Flory-Huggins Theory indicates that Soluplus® interacts effectively with each drug, making solid solution formation feasible. The predicted solubility of the drugs in Soluplus® compared favorably across the methods and supports the use of the onset of melting.

Conceptual Cost Estimating

NASA Technical Reports Server (NTRS)

Brown, J. A.

1983-01-01

Kennedy Space Center data aid in efficient construction-cost managment. Report discusses development and use of NASA TR-1508, Kennedy Space Center Aerospace Construction price book for preparing conceptual budget, funding cost estimating, and preliminary cost engineering reports. Report based on actual bid prices and Government estimates.

Control surface hinge moment prediction using computational fluid dynamics

NASA Astrophysics Data System (ADS)

Simpson, Christopher David

The following research determines the feasibility of predicting control surface hinge moments using various computational methods. A detailed analysis is conducted using a 2D GA(W)-1 airfoil with a 20% plain flap. Simple hinge moment prediction methods are tested, including empirical Datcom relations and XFOIL. Steady-state and time-accurate turbulent, viscous, Navier-Stokes solutions are computed using Fun3D. Hinge moment coefficients are computed. Mesh construction techniques are discussed. An adjoint-based mesh adaptation case is also evaluated. An NACA 0012 45-degree swept horizontal stabilizer with a 25% elevator is also evaluated using Fun3D. Results are compared with experimental wind-tunnel data obtained from references. Finally, the costs of various solution methods are estimated. Results indicate that while a steady-state Navier-Stokes solution can accurately predict control surface hinge moments for small angles of attack and deflection angles, a time-accurate solution is necessary to accurately predict hinge moments in the presence of flow separation. The ability to capture the unsteady vortex shedding behavior present in moderate to large control surface deflections is found to be critical to hinge moment prediction accuracy. Adjoint-based mesh adaptation is shown to give hinge moment predictions similar to a globally-refined mesh for a steady-state 2D simulation.

[Development and Application of a Performance Prediction Model for Home Care Nursing Based on a Balanced Scorecard using the Bayesian Belief Network].

PubMed

Noh, Wonjung; Seomun, Gyeongae

2015-06-01

This study was conducted to develop key performance indicators (KPIs) for home care nursing (HCN) based on a balanced scorecard, and to construct a performance prediction model of strategic objectives using the Bayesian Belief Network (BBN). This methodological study included four steps: establishment of KPIs, performance prediction modeling, development of a performance prediction model using BBN, and simulation of a suggested nursing management strategy. An HCN expert group and a staff group participated. The content validity index was analyzed using STATA 13.0, and BBN was analyzed using HUGIN 8.0. We generated a list of KPIs composed of 4 perspectives, 10 strategic objectives, and 31 KPIs. In the validity test of the performance prediction model, the factor with the greatest variance for increasing profit was maximum cost reduction of HCN services. The factor with the smallest variance for increasing profit was a minimum image improvement for HCN. During sensitivity analysis, the probability of the expert group did not affect the sensitivity. Furthermore, simulation of a 10% image improvement predicted the most effective way to increase profit. KPIs of HCN can estimate financial and non-financial performance. The performance prediction model for HCN will be useful to improve performance.

Individual and population pharmacokinetic compartment analysis: a graphic procedure for quantification of predictive performance.

PubMed

Eksborg, Staffan

2013-01-01

Pharmacokinetic studies are important for optimizing of drug dosing, but requires proper validation of the used pharmacokinetic procedures. However, simple and reliable statistical methods suitable for evaluation of the predictive performance of pharmacokinetic analysis are essentially lacking. The aim of the present study was to construct and evaluate a graphic procedure for quantification of predictive performance of individual and population pharmacokinetic compartment analysis. Original data from previously published pharmacokinetic compartment analyses after intravenous, oral, and epidural administration, and digitized data, obtained from published scatter plots of observed vs predicted drug concentrations from population pharmacokinetic studies using the NPEM algorithm and NONMEM computer program and Bayesian forecasting procedures, were used for estimating the predictive performance according to the proposed graphical method and by the method of Sheiner and Beal. The graphical plot proposed in the present paper proved to be a useful tool for evaluation of predictive performance of both individual and population compartment pharmacokinetic analysis. The proposed method is simple to use and gives valuable information concerning time- and concentration-dependent inaccuracies that might occur in individual and population pharmacokinetic compartment analysis. Predictive performance can be quantified by the fraction of concentration ratios within arbitrarily specified ranges, e.g. within the range 0.8-1.2.

Application of neural networks to prediction of fish diversity and salmonid production in the Lake Ontario basin

USGS Publications Warehouse

McKenna, James E.

2005-01-01

Diversity and fish productivity are important measures of the health and status of aquatic systems. Being able to predict the values of these indices as a function of environmental variables would be valuable to management. Diversity and productivity have been related to environmental conditions by multiple linear regression and discriminant analysis, but such methods have several shortcomings. In an effort to predict fish species diversity and estimate salmonid production for streams in the eastern basin of Lake Ontario, I constructed neural networks and trained them on a data set containing abiotic information and either fish diversity or juvenile salmonid abundance. Twenty percent of the original data were retained as a test data set and used in the training. The ability to extend these neural networks to conditions throughout the streams was tested with data not involved in the network training. The resulting neural networks were able to predict the number of salmonids with more than 84% accuracy and diversity with more than 73% accuracy, which was far superior to the performance of multiple regression. The networks also identified the environmental variables with the greatest predictive power, namely, those describing water movement, stream size, and water chemistry. Thirteen input variables were used to predict diversity and 17 to predict salmonid abundance.

Teaching neurophysiology, neuropharmacology, and experimental design using animal models of psychiatric and neurological disorders.

PubMed

Morsink, Maarten C; Dukers, Danny F

2009-03-01

Animal models have been widely used for studying the physiology and pharmacology of psychiatric and neurological diseases. The concepts of face, construct, and predictive validity are used as indicators to estimate the extent to which the animal model mimics the disease. Currently, we used these three concepts to design a theoretical assignment to integrate the teaching of neurophysiology, neuropharmacology, and experimental design. For this purpose, seven case studies were developed in which animal models for several psychiatric and neurological diseases were described and in which neuroactive drugs used to treat or study these diseases were introduced. Groups of undergraduate students were assigned to one of these case studies and asked to give a classroom presentation in which 1) the disease and underlying pathophysiology are described, 2) face and construct validity of the animal model are discussed, and 3) a pharmacological experiment with the associated neuroactive drug to assess predictive validity is presented. After evaluation of the presentations, we found that the students had gained considerable insight into disease phenomenology, its underlying neurophysiology, and the mechanism of action of the neuroactive drug. Moreover, the assignment was very useful in the teaching of experimental design, allowing an in-depth discussion of experimental control groups and the prediction of outcomes in these groups if the animal model were to display predictive validity. Finally, the highly positive responses in the student evaluation forms indicated that the assignment was of great interest to the students. Hence, the currently developed case studies constitute a very useful tool for teaching neurophysiology, neuropharmacology, and experimental design.

Verification Techniques for Parameter Selection and Bayesian Model Calibration Presented for an HIV Model

NASA Astrophysics Data System (ADS)

Wentworth, Mami Tonoe

Uncertainty quantification plays an important role when making predictive estimates of model responses. In this context, uncertainty quantification is defined as quantifying and reducing uncertainties, and the objective is to quantify uncertainties in parameter, model and measurements, and propagate the uncertainties through the model, so that one can make a predictive estimate with quantified uncertainties. Two of the aspects of uncertainty quantification that must be performed prior to propagating uncertainties are model calibration and parameter selection. There are several efficient techniques for these processes; however, the accuracy of these methods are often not verified. This is the motivation for our work, and in this dissertation, we present and illustrate verification frameworks for model calibration and parameter selection in the context of biological and physical models. First, HIV models, developed and improved by [2, 3, 8], describe the viral infection dynamics of an HIV disease. These are also used to make predictive estimates of viral loads and T-cell counts and to construct an optimal control for drug therapy. Estimating input parameters is an essential step prior to uncertainty quantification. However, not all the parameters are identifiable, implying that they cannot be uniquely determined by the observations. These unidentifiable parameters can be partially removed by performing parameter selection, a process in which parameters that have minimal impacts on the model response are determined. We provide verification techniques for Bayesian model calibration and parameter selection for an HIV model. As an example of a physical model, we employ a heat model with experimental measurements presented in [10]. A steady-state heat model represents a prototypical behavior for heat conduction and diffusion process involved in a thermal-hydraulic model, which is a part of nuclear reactor models. We employ this simple heat model to illustrate verification techniques for model calibration. For Bayesian model calibration, we employ adaptive Metropolis algorithms to construct densities for input parameters in the heat model and the HIV model. To quantify the uncertainty in the parameters, we employ two MCMC algorithms: Delayed Rejection Adaptive Metropolis (DRAM) [33] and Differential Evolution Adaptive Metropolis (DREAM) [66, 68]. The densities obtained using these methods are compared to those obtained through the direct numerical evaluation of the Bayes' formula. We also combine uncertainties in input parameters and measurement errors to construct predictive estimates for a model response. A significant emphasis is on the development and illustration of techniques to verify the accuracy of sampling-based Metropolis algorithms. We verify the accuracy of DRAM and DREAM by comparing chains, densities and correlations obtained using DRAM, DREAM and the direct evaluation of Bayes formula. We also perform similar analysis for credible and prediction intervals for responses. Once the parameters are estimated, we employ energy statistics test [63, 64] to compare the densities obtained by different methods for the HIV model. The energy statistics are used to test the equality of distributions. We also consider parameter selection and verification techniques for models having one or more parameters that are noninfluential in the sense that they minimally impact model outputs. We illustrate these techniques for a dynamic HIV model but note that the parameter selection and verification framework is applicable to a wide range of biological and physical models. To accommodate the nonlinear input to output relations, which are typical for such models, we focus on global sensitivity analysis techniques, including those based on partial correlations, Sobol indices based on second-order model representations, and Morris indices, as well as a parameter selection technique based on standard errors. A significant objective is to provide verification strategies to assess the accuracy of those techniques, which we illustrate in the context of the HIV model. Finally, we examine active subspace methods as an alternative to parameter subset selection techniques. The objective of active subspace methods is to determine the subspace of inputs that most strongly affect the model response, and to reduce the dimension of the input space. The major difference between active subspace methods and parameter selection techniques is that parameter selection identifies influential parameters whereas subspace selection identifies a linear combination of parameters that impacts the model responses significantly. We employ active subspace methods discussed in [22] for the HIV model and present a verification that the active subspace successfully reduces the input dimensions.

A unified procedure for meta-analytic evaluation of surrogate end points in randomized clinical trials

PubMed Central

Dai, James Y.; Hughes, James P.

2012-01-01

The meta-analytic approach to evaluating surrogate end points assesses the predictiveness of treatment effect on the surrogate toward treatment effect on the clinical end point based on multiple clinical trials. Definition and estimation of the correlation of treatment effects were developed in linear mixed models and later extended to binary or failure time outcomes on a case-by-case basis. In a general regression setting that covers nonnormal outcomes, we discuss in this paper several metrics that are useful in the meta-analytic evaluation of surrogacy. We propose a unified 3-step procedure to assess these metrics in settings with binary end points, time-to-event outcomes, or repeated measures. First, the joint distribution of estimated treatment effects is ascertained by an estimating equation approach; second, the restricted maximum likelihood method is used to estimate the means and the variance components of the random treatment effects; finally, confidence intervals are constructed by a parametric bootstrap procedure. The proposed method is evaluated by simulations and applications to 2 clinical trials. PMID:22394448

Airborne sound transmission loss characteristics of wood-frame construction

NASA Astrophysics Data System (ADS)

Rudder, F. F., Jr.

1985-03-01

This report summarizes the available data on the airborne sound transmission loss properties of wood-frame construction and evaluates the methods for predicting the airborne sound transmission loss. The first part of the report comprises a summary of sound transmission loss data for wood-frame interior walls and floor-ceiling construction. Data bases describing the sound transmission loss characteristics of other building components, such as windows and doors, are discussed. The second part of the report presents the prediction of the sound transmission loss of wood-frame construction. Appropriate calculation methods are described both for single-panel and for double-panel construction with sound absorption material in the cavity. With available methods, single-panel construction and double-panel construction with the panels connected by studs may be adequately characterized. Technical appendices are included that summarize laboratory measurements, compare measurement with theory, describe details of the prediction methods, and present sound transmission loss data for common building materials.

Simultaneous construction of PCR-DGGE-based predictive models of Listeria monocytogenes and Vibrio parahaemolyticus on cooked shrimps.

PubMed

Liao, C; Peng, Z Y; Li, J B; Cui, X W; Zhang, Z H; Malakar, P K; Zhang, W J; Pan, Y J; Zhao, Y

2015-03-01

The aim of this study was to simultaneously construct PCR-DGGE-based predictive models of Listeria monocytogenes and Vibrio parahaemolyticus on cooked shrimps at 4 and 10°C. Calibration curves were established to correlate peak density of DGGE bands with microbial counts. Microbial counts derived from PCR-DGGE and plate methods were fitted by Baranyi model to obtain molecular and traditional predictive models. For L. monocytogenes, growing at 4 and 10°C, molecular predictive models were constructed. It showed good evaluations of correlation coefficients (R(2) > 0.92), bias factors (Bf ) and accuracy factors (Af ) (1.0 ≤ Bf ≤ Af ≤ 1.1). Moreover, no significant difference was found between molecular and traditional predictive models when analysed on lag phase (λ), maximum growth rate (μmax ) and growth data (P > 0.05). But for V. parahaemolyticus, inactivated at 4 and 10°C, molecular models show significant difference when compared with traditional models. Taken together, these results suggest that PCR-DGGE based on DNA can be used to construct growth models, but it is inappropriate for inactivation models yet. This is the first report of developing PCR-DGGE to simultaneously construct multiple molecular models. It has been known for a long time that microbial predictive models based on traditional plate methods are time-consuming and labour-intensive. Denaturing gradient gel electrophoresis (DGGE) has been widely used as a semiquantitative method to describe complex microbial community. In our study, we developed DGGE to quantify bacterial counts and simultaneously established two molecular predictive models to describe the growth and survival of two bacteria (Listeria monocytogenes and Vibrio parahaemolyticus) at 4 and 10°C. We demonstrated that PCR-DGGE could be used to construct growth models. This work provides a new approach to construct molecular predictive models and thereby facilitates predictive microbiology and QMRA (Quantitative Microbial Risk Assessment). © 2014 The Society for Applied Microbiology.

Failure Assessment Diagram for Titanium Brazed Joints

NASA Technical Reports Server (NTRS)

Flom, Yury; Jones, Justin S.; Powell, Mollie M.; Puckett, David F.

2011-01-01

The interaction equation was used to predict failure in Ti-4V-6Al joints brazed with Al 1100 filler metal. The joints used in this study were geometrically similar to the joints in the brazed beryllium metering structure considered for the ATLAS telescope. This study confirmed that the interaction equation R(sub sigma) + R(sub Tau) = 1, where R(sub sigma) and R(sub Tau)are normal and shear stress ratios, can be used as conservative lower bound estimate of the failure criterion in ATLAS brazed joints as well as for construction of the Failure Assessment Diagram (FAD).

Neural networks for structural design - An integrated system implementation

NASA Technical Reports Server (NTRS)

Berke, Laszlo; Hafez, Wassim; Pao, Yoh-Han

1992-01-01

The development of powerful automated procedures to aid the creative designer is becoming increasingly critical for complex design tasks. In the work described here Artificial Neural Nets are applied to acquire structural analysis and optimization domain expertise. Based on initial instructions from the user an automated procedure generates random instances of structural analysis and/or optimization 'experiences' that cover a desired domain. It extracts training patterns from the created instances, constructs and trains an appropriate network architecture and checks the accuracy of net predictions. The final product is a trained neural net that can estimate analysis and/or optimization results instantaneously.

The contribution to future flood risk in the Severn Estuary from extreme sea level rise due to ice sheet mass loss

NASA Astrophysics Data System (ADS)

Quinn, N.; Bates, P. D.; Siddall, M.

2013-12-01

The rate at which sea levels will rise in the coming century is of great interest to decision makers tasked with developing mitigation policies to cope with the risk of coastal inundation. Accurate estimates of future sea levels are vital in the provision of effective policy. Recent reports from UK Climate Impacts Programme (UKCIP) suggest that mean sea levels in the UK may rise by as much as 80 cm by 2100; however, a great deal of uncertainty surrounds model predictions, particularly the contribution from ice sheets responding to climatic warming. For this reason, the application of semi-empirical modelling approaches for sea level rise predictions has increased of late, the results from which suggest that the rate of sea level rise may be greater than previously thought, exceeding 1 m by 2100. Furthermore, studies in the Red Sea indicate that rapid sea level rise beyond 1m per century has occurred in the past. In light of such research, the latest UKCIP assessment has included a H++ scenario for sea level rise in the UK of up to 1.9 m which is defined as improbable but, crucially, physically plausible. The significance of such low-probability sea level rise scenarios upon the estimation of future flood risk is assessed using the Somerset levels (UK) as a case study. A simple asymmetric probability distribution is constructed to include sea level rise scenarios of up to 1.9 m by 2100 which are added to a current 1:200 year event water level to force a two-dimensional hydrodynamic model of coastal inundation. From the resulting ensemble predictions an estimation of risk by 2100 is established. The results indicate that although the likelihood of extreme sea level rise due to rapid ice sheet mass loss is low, the resulting hazard can be large, resulting in a significant (27%) increase to the projected annual risk. Furthermore, current defence construction guidelines for the coming century in the UK are expected to account for 95% of the sea level rise distribution presented in this research, while the larger, low probability scenarios beyond this level are estimated to contribute a residual annual risk of approximately £0.45 million. These findings clearly demonstrate that uncertainty in future sea level rise is a vital component of coastal flood risk, and therefore, needs to be accounted for by decision makers when considering mitigation policies related to coastal flooding.

Construction of hydrodynamic bead models from high-resolution X-ray crystallographic or nuclear magnetic resonance data.

PubMed Central

Byron, O

1997-01-01

Computer software such as HYDRO, based upon a comprehensive body of theoretical work, permits the hydrodynamic modeling of macromolecules in solution, which are represented to the computer interface as an assembly of spheres. The uniqueness of any satisfactory resultant model is optimized by incorporating into the modeling procedure the maximal possible number of criteria to which the bead model must conform. An algorithm (AtoB, for atoms to beads) that permits the direct construction of bead models from high resolution x-ray crystallographic or nuclear magnetic resonance data has now been formulated and tested. Models so generated then act as informed starting estimates for the subsequent iterative modeling procedure, thereby hastening the convergence to reasonable representations of solution conformation. Successful application of this algorithm to several proteins shows that predictions of hydrodynamic parameters, including those concerning solvation, can be confirmed. PMID:8994627

48 CFR 36.214 - Special procedures for price negotiation in construction contracting.

Code of Federal Regulations, 2013 CFR

2013-10-01

... price negotiation in construction contracting. 36.214 Section 36.214 Federal Acquisition Regulations... negotiation in construction contracting. (a) Agencies shall follow the policies and procedures in part 15 when... scope of the work. If negotiations reveal errors in the Government estimate, the estimate shall be...

48 CFR 36.214 - Special procedures for price negotiation in construction contracting.

Code of Federal Regulations, 2011 CFR

2011-10-01

... price negotiation in construction contracting. 36.214 Section 36.214 Federal Acquisition Regulations... negotiation in construction contracting. (a) Agencies shall follow the policies and procedures in part 15 when... scope of the work. If negotiations reveal errors in the Government estimate, the estimate shall be...

48 CFR 36.214 - Special procedures for price negotiation in construction contracting.

Code of Federal Regulations, 2012 CFR

2012-10-01

... price negotiation in construction contracting. 36.214 Section 36.214 Federal Acquisition Regulations... negotiation in construction contracting. (a) Agencies shall follow the policies and procedures in part 15 when... scope of the work. If negotiations reveal errors in the Government estimate, the estimate shall be...

48 CFR 36.214 - Special procedures for price negotiation in construction contracting.

Code of Federal Regulations, 2014 CFR

2014-10-01

... price negotiation in construction contracting. 36.214 Section 36.214 Federal Acquisition Regulations... negotiation in construction contracting. (a) Agencies shall follow the policies and procedures in part 15 when... scope of the work. If negotiations reveal errors in the Government estimate, the estimate shall be...

48 CFR 36.214 - Special procedures for price negotiation in construction contracting.

Code of Federal Regulations, 2010 CFR

2010-10-01

... price negotiation in construction contracting. 36.214 Section 36.214 Federal Acquisition Regulations... negotiation in construction contracting. (a) Agencies shall follow the policies and procedures in part 15 when... scope of the work. If negotiations reveal errors in the Government estimate, the estimate shall be...

Stability basin estimates fall risk from observed kinematics, demonstrated on the Sit-to-Stand task.

PubMed

Shia, Victor; Moore, Talia Yuki; Holmes, Patrick; Bajcsy, Ruzena; Vasudevan, Ram

2018-04-27

The ability to quantitatively measure stability is essential to ensuring the safety of locomoting systems. While the response to perturbation directly reflects the stability of a motion, this experimental method puts human subjects at risk. Unfortunately, existing indirect methods for estimating stability from unperturbed motion have been shown to have limited predictive power. This paper leverages recent advances in dynamical systems theory to accurately estimate the stability of human motion without requiring perturbation. This approach relies on kinematic observations of a nominal Sit-to-Stand motion to construct an individual-specific dynamic model, input bounds, and feedback control that are then used to compute the set of perturbations from which the model can recover. This set, referred to as the stability basin, was computed for 14 individuals, and was able to successfully differentiate between less and more stable Sit-to-Stand strategies for each individual with greater accuracy than existing methods. Copyright © 2018 Elsevier Ltd. All rights reserved.

Empirical Approach for Determining Axial Strength of Circular Concrete Filled Steel Tubular Columns

NASA Astrophysics Data System (ADS)

Jayalekshmi, S.; Jegadesh, J. S. Sankar; Goel, Abhishek

2018-06-01

The concrete filled steel tubular (CFST) columns are highly regarded in recent years as an interesting option in the construction field by designers and structural engineers, due to their exquisite structural performance, with enhanced load bearing capacity and energy absorption capacity. This study presents a new approach to simulate the capacity of circular CFST columns under axial loading condition, using a large database of experimental results by applying artificial neural network (ANN). A well trained network is established and is used to simulate the axial capacity of CFST columns. The validation and testing of the ANN is carried out. The current study is focused on proposing a simplified equation that can predict the ultimate strength of the axially loaded columns with high level of accuracy. The predicted results are compared with five existing analytical models which estimate the strength of the CFST column. The ANN-based equation has good prediction with experimental data, when compared with the analytical models.

Estimating the average length of hospitalization due to pneumonia: a fuzzy approach.

PubMed

Nascimento, L F C; Rizol, P M S R; Peneluppi, A P

2014-08-29

Exposure to air pollutants is associated with hospitalizations due to pneumonia in children. We hypothesized the length of hospitalization due to pneumonia may be dependent on air pollutant concentrations. Therefore, we built a computational model using fuzzy logic tools to predict the mean time of hospitalization due to pneumonia in children living in São José dos Campos, SP, Brazil. The model was built with four inputs related to pollutant concentrations and effective temperature, and the output was related to the mean length of hospitalization. Each input had two membership functions and the output had four membership functions, generating 16 rules. The model was validated against real data, and a receiver operating characteristic (ROC) curve was constructed to evaluate model performance. The values predicted by the model were significantly correlated with real data. Sulfur dioxide and particulate matter significantly predicted the mean length of hospitalization in lags 0, 1, and 2. This model can contribute to the care provided to children with pneumonia.

Estimating the average length of hospitalization due to pneumonia: a fuzzy approach.

PubMed

Nascimento, L F C; Rizol, P M S R; Peneluppi, A P

2014-11-01

Exposure to air pollutants is associated with hospitalizations due to pneumonia in children. We hypothesized the length of hospitalization due to pneumonia may be dependent on air pollutant concentrations. Therefore, we built a computational model using fuzzy logic tools to predict the mean time of hospitalization due to pneumonia in children living in São José dos Campos, SP, Brazil. The model was built with four inputs related to pollutant concentrations and effective temperature, and the output was related to the mean length of hospitalization. Each input had two membership functions and the output had four membership functions, generating 16 rules. The model was validated against real data, and a receiver operating characteristic (ROC) curve was constructed to evaluate model performance. The values predicted by the model were significantly correlated with real data. Sulfur dioxide and particulate matter significantly predicted the mean length of hospitalization in lags 0, 1, and 2. This model can contribute to the care provided to children with pneumonia.

spMC: an R-package for 3D lithological reconstructions based on spatial Markov chains

NASA Astrophysics Data System (ADS)

Sartore, Luca; Fabbri, Paolo; Gaetan, Carlo

2016-09-01

The paper presents the spatial Markov Chains (spMC) R-package and a case study of subsoil simulation/prediction located in a plain site of Northeastern Italy. spMC is a quite complete collection of advanced methods for data inspection, besides spMC implements Markov Chain models to estimate experimental transition probabilities of categorical lithological data. Furthermore, simulation methods based on most known prediction methods (as indicator Kriging and CoKriging) were implemented in spMC package. Moreover, other more advanced methods are available for simulations, e.g. path methods and Bayesian procedures, that exploit the maximum entropy. Since the spMC package was developed for intensive geostatistical computations, part of the code is implemented for parallel computations via the OpenMP constructs. A final analysis of this computational efficiency compares the simulation/prediction algorithms by using different numbers of CPU cores, and considering the example data set of the case study included in the package.

In Vivo Validation of Numerical Prediction for Turbulence Intensity in an Aortic Coarctation

PubMed Central

Arzani, Amirhossein; Dyverfeldt, Petter; Ebbers, Tino; Shadden, Shawn C.

2013-01-01

This paper compares numerical predictions of turbulence intensity with in vivo measurement. Magnetic resonance imaging (MRI) was carried out on a 60-year-old female with a restenosed aortic coarctation. Time-resolved three-directional phase-contrast (PC) MRI data was acquired to enable turbulence intensity estimation. A contrast-enhanced MR angiography (MRA) and a time-resolved 2D PCMRI measurement were also performed to acquire data needed to perform subsequent image-based computational fluid dynamics (CFD) modeling. A 3D model of the aortic coarctation and surrounding vasculature was constructed from the MRA data, and physiologic boundary conditions were modeled to match 2D PCMRI and pressure pulse measurements. Blood flow velocity data was subsequently obtained by numerical simulation. Turbulent kinetic energy (TKE) was computed from the resulting CFD data. Results indicate relative agreement (error ≈10%) between the in vivo measurements and the CFD predictions of TKE. The discrepancies in modeled vs. measured TKE values were within expectations due to modeling and measurement errors. PMID:22016327

Adding-point strategy for reduced-order hypersonic aerothermodynamics modeling based on fuzzy clustering

NASA Astrophysics Data System (ADS)

Chen, Xin; Liu, Li; Zhou, Sida; Yue, Zhenjiang

2016-09-01

Reduced order models(ROMs) based on the snapshots on the CFD high-fidelity simulations have been paid great attention recently due to their capability of capturing the features of the complex geometries and flow configurations. To improve the efficiency and precision of the ROMs, it is indispensable to add extra sampling points to the initial snapshots, since the number of sampling points to achieve an adequately accurate ROM is generally unknown in prior, but a large number of initial sampling points reduces the parsimony of the ROMs. A fuzzy-clustering-based adding-point strategy is proposed and the fuzzy clustering acts an indicator of the region in which the precision of ROMs is relatively low. The proposed method is applied to construct the ROMs for the benchmark mathematical examples and a numerical example of hypersonic aerothermodynamics prediction for a typical control surface. The proposed method can achieve a 34.5% improvement on the efficiency than the estimated mean squared error prediction algorithm and shows same-level prediction accuracy.

Empirical Approach for Determining Axial Strength of Circular Concrete Filled Steel Tubular Columns

NASA Astrophysics Data System (ADS)

Jayalekshmi, S.; Jegadesh, J. S. Sankar; Goel, Abhishek

2018-03-01

The concrete filled steel tubular (CFST) columns are highly regarded in recent years as an interesting option in the construction field by designers and structural engineers, due to their exquisite structural performance, with enhanced load bearing capacity and energy absorption capacity. This study presents a new approach to simulate the capacity of circular CFST columns under axial loading condition, using a large database of experimental results by applying artificial neural network (ANN). A well trained network is established and is used to simulate the axial capacity of CFST columns. The validation and testing of the ANN is carried out. The current study is focused on proposing a simplified equation that can predict the ultimate strength of the axially loaded columns with high level of accuracy. The predicted results are compared with five existing analytical models which estimate the strength of the CFST column. The ANN-based equation has good prediction with experimental data, when compared with the analytical models.

Estimating NOA Health Risks from Selected Construction Activities at the Calaveras Dam Replacement Project (CDRP)

NASA Astrophysics Data System (ADS)

Hernandez, D. W.

2012-12-01

The CDRP is a major construction project involving up to 400 workers using heavy earth moving equipment, blasting, drilling, rock crushing, and other techniques designed to move 7 million yards of earth. Much of this material is composed of serpentinite, blueschist, and other rocks that contain chrysotile, crocidolite, actinolite, tremolite, and Libby-class amphiboles. To date, over 1,000 personal, work area, and emission inventory related samples have been collected and analyzed by NIOSH 7400, NIOSH 7402, and CARB-AHERA methodology. Data indicate that various CDRP construction activities have the potential to generate significant mineral fibers and structures that could represent elevated on site and off site health risks. This presentation will review the Contractors air monitoring program for this major project, followed by a discussion of predictive methods to evaluate potential onsite and offsite risks. Ultimately, the data are used for planning control strategies designed to achieve a Project Action Level of 0.01 f/cc (one tenth the Cal/OSHA PEL) and risk-based offsite target levels.

The nature and development of hypothetico-predictive argumentation with implications for science teaching

NASA Astrophysics Data System (ADS)

Lawson, Anton E.

2003-11-01

This paper explicates a pattern of scientific argumentation in which scientists respond to causal questions with the generation and test of alternative hypotheses through cycles of hypothetico-predictive argumentation. Hypothetico-predictive arguments are employed to test causal claims that exist on at least two levels (designated stage 4 in which the causal claims are perceptible, and stage 5 in which the causal claims are imperceptible). Origins of the ability to construct and comprehend hypothetico-predictive arguments at the highest level can be traced to pre-verbal reasoning of the sensory-motor child and the gradual internalization of verbally mediated arguments involving nominal, categorical, causal and, finally, theoretical propositions. Presumably, the ability to construct and comprehend hypothetico-predictive arguments (an aspect of procedural knowledge) is necessary for the construction of conceptual knowledge (an aspect of declarative knowledge) because such arguments are used during concept construction and conceptual change. Science instruction that focuses on the generation and debate of hypothetico-predictive arguments should improve students' conceptual understanding and their argumentative/reasoning skills.

Metaheuristic optimization approaches to predict shear-wave velocity from conventional well logs in sandstone and carbonate case studies

NASA Astrophysics Data System (ADS)

Emami Niri, Mohammad; Amiri Kolajoobi, Rasool; Khodaiy Arbat, Mohammad; Shahbazi Raz, Mahdi

2018-06-01

Seismic wave velocities, along with petrophysical data, provide valuable information during the exploration and development stages of oil and gas fields. The compressional-wave velocity (VP ) is acquired using conventional acoustic logging tools in many drilled wells. But the shear-wave velocity (VS ) is recorded using advanced logging tools only in a limited number of wells, mainly because of the high operational costs. In addition, laboratory measurements of seismic velocities on core samples are expensive and time consuming. So, alternative methods are often used to estimate VS . Heretofore, several empirical correlations that predict VS by using well logging measurements and petrophysical data such as VP , porosity and density are proposed. However, these empirical relations can only be used in limited cases. The use of intelligent systems and optimization algorithms are inexpensive, fast and efficient approaches for predicting VS. In this study, in addition to the widely used Greenberg–Castagna empirical method, we implement three relatively recently developed metaheuristic algorithms to construct linear and nonlinear models for predicting VS : teaching–learning based optimization, imperialist competitive and artificial bee colony algorithms. We demonstrate the applicability and performance of these algorithms to predict Vs using conventional well logs in two field data examples, a sandstone formation from an offshore oil field and a carbonate formation from an onshore oil field. We compared the estimated VS using each of the employed metaheuristic approaches with observed VS and also with those predicted by Greenberg–Castagna relations. The results indicate that, for both sandstone and carbonate case studies, all three implemented metaheuristic algorithms are more efficient and reliable than the empirical correlation to predict VS . The results also demonstrate that in both sandstone and carbonate case studies, the performance of an artificial bee colony algorithm in VS prediction is slightly higher than two other alternative employed approaches.

Effect of sexual motivation on men's risk perception for sexually transmitted disease: there must be 50 ways to justify a lover.

PubMed

Blanton, H; Gerrard, M

1997-07-01

Recent research has incorporated situational factors into assessment of risk. Working from a rational appraisal framework, however, these studies have not emphasized contextual features that might introduce motivated risk assessment. In the current study, participants (N = 40 male undergraduates) lowered their risk perceptions for STDs following the induction of a sexual motivation. In an initial baseline condition, participants estimated the risk of contracting STDs from partners with relatively high- or low-risk sexual histories. In a subsequent trial, participants repeated the imagery task while viewing photographs that were high or low in sex appeal. As predicted, participants reduced their risk perceptions when they viewed photographs high in sex appeal. The only necessary precondition was the presence of nondiagnostic information from which they could construct biased risk estimates.

Field Telemetry of Blade-rotor Coupled Torsional Vibration at Matuura Power Station Number 1 Unit

NASA Technical Reports Server (NTRS)

Isii, Kuniyoshi; Murakami, Hideaki; Otawara, Yasuhiko; Okabe, Akira

1991-01-01

The quasi-modal reduction technique and finite element model (FEM) were used to construct an analytical model for the blade-rotor coupled torsional vibration of a steam turbine generator of the Matuura Power Station. A single rotor test was executed in order to evaluate umbrella vibration characteristics. Based on the single rotor test results and the quasi-modal procedure, the total rotor system was analyzed to predict coupled torsional frequencies. Finally, field measurement of the vibration of the last stage buckets was made, which confirmed that the double synchronous resonance was 124.2 Hz, meaning that the machine can be safely operated. The measured eigen values are very close to the predicted value. The single rotor test and this analytical procedure thus proved to be a valid technique to estimate coupled torsional vibration.

New methodology for the heat flow prediction for different construction materials

NASA Astrophysics Data System (ADS)

Benachour, Elhadj; Draoui, Belkacem; Imine, Bachir; Asnoune, Khadidja; Boumediene, Allaoua; Mebarki, Brahim

2018-06-01

Among the ways of energy transfer by conduction is that of molecular interaction, in which the greater motion of a molecule at a higher energy level (temperature) imparts energy to adjacent molecules at lower energy levels. This type of transfer is present, to some degree, in all systems in which a temperature gradient exists and in which molecules of a solid, liquid, or gas are present. So, in heat transfer, the thermal conductivity of a substance is an intensive property that indicates its ability to conduct heat In particular in the building sector. The thermal flux is often measured with a mathematical analysis but for the same material, on the other hand the estimate will be disruptive and sometimes very difficult when the material changes. In this paper, a single equation for predicting heat flux of different materials is given.

[Application of decision curve on evaluation of MRI predictive model for early assessing pathological complete response to neoadjuvant therapy in breast cancer].

PubMed

He, Y J; Li, X T; Fan, Z Q; Li, Y L; Cao, K; Sun, Y S; Ouyang, T

2018-01-23

Objective: To construct a dynamic enhanced MR based predictive model for early assessing pathological complete response (pCR) to neoadjuvant therapy in breast cancer, and to evaluate the clinical benefit of the model by using decision curve. Methods: From December 2005 to December 2007, 170 patients with breast cancer treated with neoadjuvant therapy were identified and their MR images before neoadjuvant therapy and at the end of the first cycle of neoadjuvant therapy were collected. Logistic regression model was used to detect independent factors for predicting pCR and construct the predictive model accordingly, then receiver operating characteristic (ROC) curve and decision curve were used to evaluate the predictive model. Results: ΔArea(max) and Δslope(max) were independent predictive factors for pCR, OR =0.942 (95% CI : 0.918-0.967) and 0.961 (95% CI : 0.940-0.987), respectively. The area under ROC curve (AUC) for the constructed model was 0.886 (95% CI : 0.820-0.951). Decision curve showed that in the range of the threshold probability above 0.4, the predictive model presented increased net benefit as the threshold probability increased. Conclusions: The constructed predictive model for pCR is of potential clinical value, with an AUC>0.85. Meanwhile, decision curve analysis indicates the constructed predictive model has net benefit from 3 to 8 percent in the likely range of probability threshold from 80% to 90%.

Comparison of Model Predictions and Laboratory Observations of Transgene Frequencies in Continuously-Breeding Mosquito Populations

PubMed Central

Valerio, Laura; North, Ace; Collins, C. Matilda; Mumford, John D.; Facchinelli, Luca; Spaccapelo, Roberta; Benedict, Mark Q.

2016-01-01

The persistence of transgenes in the environment is a consideration in risk assessments of transgenic organisms. Combining mathematical models that predict the frequency of transgenes and experimental demonstrations can validate the model predictions, or can detect significant biological deviations that were neither apparent nor included as model parameters. In order to assess the correlation between predictions and observations, models were constructed to estimate the frequency of a transgene causing male sexual sterility in simulated populations of a malaria mosquito Anopheles gambiae that were seeded with transgenic females at various proportions. Concurrently, overlapping-generation laboratory populations similar to those being modeled were initialized with various starting transgene proportions, and the subsequent proportions of transgenic individuals in populations were determined weekly until the transgene disappeared. The specific transgene being tested contained a homing endonuclease gene expressed in testes, I-PpoI, that cleaves the ribosomal DNA and results in complete male sexual sterility with no effect on female fertility. The transgene was observed to disappear more rapidly than the model predicted in all cases. The period before ovipositions that contained no transgenic progeny ranged from as little as three weeks after cage initiation to as long as 11 weeks. PMID:27669312

Wood products used in constructing conservation and development projects by the Corps of Engineers in the United States, 1962 and 1978

Treesearch

W. H. Reid; D. B. McKeever

Estimates of the amounts of wood products used in constructing civil conservation and development projects by the Corps of Engineers in the United States are presented for the years 1962 and 1978. Amounts of lumber, laminated lumber, poles and piling, and plywood used in construction are stratified by five construction categories, and three types of uses. Estimates of...

Predictive models of glucose control: roles for glucose-sensing neurones.

PubMed

Kosse, C; Gonzalez, A; Burdakov, D

2015-01-01

The brain can be viewed as a sophisticated control module for stabilizing blood glucose. A review of classical behavioural evidence indicates that central circuits add predictive (feedforward/anticipatory) control to the reactive (feedback/compensatory) control by peripheral organs. The brain/cephalic control is constructed and engaged, via associative learning, by sensory cues predicting energy intake or expenditure (e.g. sight, smell, taste, sound). This allows rapidly measurable sensory information (rather than slowly generated internal feedback signals, e.g. digested nutrients) to control food selection, glucose supply for fight-or-flight responses or preparedness for digestion/absorption. Predictive control is therefore useful for preventing large glucose fluctuations. We review emerging roles in predictive control of two classes of widely projecting hypothalamic neurones, orexin/hypocretin (ORX) and melanin-concentrating hormone (MCH) cells. Evidence is cited that ORX neurones (i) are activated by sensory cues (e.g. taste, sound), (ii) drive hepatic production, and muscle uptake, of glucose, via sympathetic nerves, (iii) stimulate wakefulness and exploration via global brain projections and (iv) are glucose-inhibited. MCH neurones are (i) glucose-excited, (ii) innervate learning and reward centres to promote synaptic plasticity, learning and memory and (iii) are critical for learning associations useful for predictive control (e.g. using taste to predict nutrient value of food). This evidence is unified into a model for predictive glucose control. During associative learning, inputs from some glucose-excited neurones may promote connections between the 'fast' senses and reward circuits, constructing neural shortcuts for efficient action selection. In turn, glucose-inhibited neurones may engage locomotion/exploration and coordinate the required fuel supply. Feedback inhibition of the latter neurones by glucose would ensure that glucose fluxes they stimulate (from liver, into muscle) are balanced. Estimating nutrient challenges from indirect sensory cues may become more difficult when the cues become complex and variable (e.g. like human foods today). Consequent errors of predictive glucose control may contribute to obesity and diabetes. © 2014 The Authors. Acta Physiologica published by John Wiley & Sons Ltd on behalf of Scandinavian Physiological Society.

The use of patient factors to improve the prediction of operative duration using laparoscopic cholecystectomy.

PubMed

Thiels, Cornelius A; Yu, Denny; Abdelrahman, Amro M; Habermann, Elizabeth B; Hallbeck, Susan; Pasupathy, Kalyan S; Bingener, Juliane

2017-01-01

Reliable prediction of operative duration is essential for improving patient and care team satisfaction, optimizing resource utilization and reducing cost. Current operative scheduling systems are unreliable and contribute to costly over- and underestimation of operative time. We hypothesized that the inclusion of patient-specific factors would improve the accuracy in predicting operative duration. We reviewed all elective laparoscopic cholecystectomies performed at a single institution between 01/2007 and 06/2013. Concurrent procedures were excluded. Univariate analysis evaluated the effect of age, gender, BMI, ASA, laboratory values, smoking, and comorbidities on operative duration. Multivariable linear regression models were constructed using the significant factors (p < 0.05). The patient factors model was compared to the traditional surgical scheduling system estimates, which uses historical surgeon-specific and procedure-specific operative duration. External validation was done using the ACS-NSQIP database (n = 11,842). A total of 1801 laparoscopic cholecystectomy patients met inclusion criteria. Female sex was associated with reduced operative duration (-7.5 min, p < 0.001 vs. male sex) while increasing BMI (+5.1 min BMI 25-29.9, +6.9 min BMI 30-34.9, +10.4 min BMI 35-39.9, +17.0 min BMI 40 + , all p < 0.05 vs. normal BMI), increasing ASA (+7.4 min ASA III, +38.3 min ASA IV, all p < 0.01 vs. ASA I), and elevated liver function tests (+7.9 min, p < 0.01 vs. normal) were predictive of increased operative duration on univariate analysis. A model was then constructed using these predictive factors. The traditional surgical scheduling system was poorly predictive of actual operative duration (R 2  = 0.001) compared to the patient factors model (R 2  = 0.08). The model remained predictive on external validation (R 2  = 0.14).The addition of surgeon as a variable in the institutional model further improved predictive ability of the model (R 2  = 0.18). The use of routinely available pre-operative patient factors improves the prediction of operative duration during cholecystectomy.

Adolescents' protection motivation and smoking behaviour.

PubMed

Thrul, Johannes; Stemmler, Mark; Bühler, Anneke; Kuntsche, Emmanuel

2013-08-01

The protection motivation theory (PMT) is a well-known theory of behaviour change. This study tested the applicability of the sub-constructs of threat and coping appraisal in predicting adolescents' smoking-related behavioural intentions and smoking behaviour longitudinally. Adolescents (N = 494) aged 11-16 years and not currently smoking at baseline participated in the study. Predictive validity of PMT constructs was tested in a path analysis model. Self-efficacy significantly predicted behavioural intention at baseline, which significantly predicted behavioural intention at follow-up, which in turn predicted smoking behaviour at follow-up. The effect of self-efficacy on behavioural intention at follow-up was mediated by behavioural intention at baseline and the effect of self-efficacy on smoking behaviour was mediated by behavioural intention at baseline and follow-up. In conclusion, we found support for one part of the PMT, namely for the predictive validity of the coping appraisal construct self-efficacy in predicting adolescents' smoking-related behavioural intention and smoking behaviour. These results fail to support the appropriateness of the PMT's construct threat appraisal in longitudinally predicting adolescents' smoking as well as the applicability of communicating fear and negative information as preventive interventions for this target group.

Comparing motor-vehicle crash risk of EU and US vehicles.

PubMed

Flannagan, Carol A C; Bálint, András; Klinich, Kathleen D; Sander, Ulrich; Manary, Miriam A; Cuny, Sophie; McCarthy, Michael; Phan, Vuthy; Wallbank, Caroline; Green, Paul E; Sui, Bo; Forsman, Åsa; Fagerlind, Helen

2018-08-01

This study examined the hypotheses that passenger vehicles meeting European Union (EU) safety standards have similar crashworthiness to United States (US) -regulated vehicles in the US driving environment, and vice versa. The first step involved identifying appropriate databases of US and EU crashes that include in-depth crash information, such as estimation of crash severity using Delta-V and injury outcome based on medical records. The next step was to harmonize variable definitions and sampling criteria so that the EU data could be combined and compared to the US data using the same or equivalent parameters. Logistic regression models of the risk of a Maximum injury according to the Abbreviated Injury Scale of 3 or greater, or fatality (MAIS3+F) in EU-regulated and US-regulated vehicles were constructed. The injury risk predictions of the EU model and the US model were each applied to both the US and EU standard crash populations. Frontal, near-side, and far-side crashes were analyzed together (termed "front/side crashes") and a separate model was developed for rollover crashes. For the front/side model applied to the US standard population, the mean estimated risk for the US-vehicle model is 0.035 (sd = 0.012), and the mean estimated risk for the EU-vehicle model is 0.023 (sd = 0.016). When applied to the EU front/side population, the US model predicted a 0.065 risk (sd = 0.027), and the EU model predicted a 0.052 risk (sd = 0.025). For the rollover model applied to the US standard population, the US model predicted a risk of 0.071 (sd = 0.024), and the EU model predicted 0.128 risk (sd = 0.057). When applied to the EU rollover standard population, the US model predicted a 0.067 risk (sd = 0.024), and the EU model predicted 0.103 risk (sd = 0.040). The results based on these methods indicate that EU vehicles most likely have a lower risk of MAIS3+F injury in front/side impacts, while US vehicles most likely have a lower risk of MAIS3+F injury in llroovers. These results should be interpreted with an understanding of the uncertainty of the estimates, the study limitations, and our recommendations for further study detailed in the report. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

On the Efficacy and Mediation of a One-on-One HIV Risk-Reduction Intervention for African American Men Who Have Sex with Men: A Randomized Controlled Trial.

PubMed

Jemmott, John B; Jemmott, Loretta Sweet; O'Leary, Ann; Icard, Larry D; Rutledge, Scott E; Stevens, Robin; Hsu, Janet; Stephens, Alisa J

2015-07-01

We examined the efficacy and mediation of Being Responsible for Ourselves (BRO), an HIV/STI risk-reduction intervention for African American men who have sex with men (MSM), the population with the highest HIV-diagnosis rate in the US. We randomized African American MSM to one of two interventions: BRO HIV/STI risk-reduction, targeting condom use; or attention-matched control, targeting physical activity and healthy diet. The interventions were based on social cognitive theory, the reasoned-action approach, and qualitative research. Men reporting anal intercourse with other men in the past 90 days were eligible and completed pre-intervention, immediately post-intervention, and 6 and 12 months post-intervention surveys. Of 595 participants, 503 (85 %) completed the 12-month follow-up. Generalized-estimating-equations analysis indicated that, compared with the attention-matched control intervention, the BRO intervention did not increase consistent condom use averaged over the 6- and 12-month follow-ups, which was the primary outcome. Although BRO did not affect the proportion of condom-protected intercourse acts, unprotected sexual intercourse, multiple partners, or insertive anal intercourse, it did reduce receptive anal intercourse compared with the control, a behavior linked to incident HIV infection. Mediation analysis using the product-of-coefficients approach revealed that although BRO increased seven of nine theoretical constructs it was designed to affect, it increased only one of three theoretical constructs that predicted consistent condom use: condom-use impulse-control self-efficacy. Thus, BRO indirectly increased consistent condom use through condom-use impulse-control self-efficacy. In conclusion, although BRO increased several theoretical constructs, most of those constructs did not predict consistent condom use; hence, the intervention did not increase it. Theoretical constructs that interventions should target to increase African American MSM's condom use are discussed.

Assessing Water Level Changes in Lake, Reservoir, Wetland, and River Systems with Remote Sensing Tools and Hydrological Model

NASA Astrophysics Data System (ADS)

Ricko, M.; Birkett, C. M.; Beckley, B. D.

2017-12-01

The NASA/USDA Global Reservoir and Lake Monitor (G-REALM) offers multi-mission satellite radar altimetry derived surface water level products for a subset of large reservoirs, lakes, and wetlands. These products complement the in situ networks by providing stage information at un-gauged locations, and filling existing data gaps. The availability of both satellite-based rainfall (e.g., TRMM, GPCP) and surface water level products offers great opportunities to estimate and monitor additional hydrologic properties of the lake/reservoir systems. A simple water balance model relating the net freshwater flux over a catchment basin to the lake/reservoir level has been previously utilized (Ricko et al., 2011). The applicability of this approach enables the construction of a longer record of surface water level, i.e. improving the climate data record. As instrument technology and data availability evolve, this method can be used to estimate the water level of a greater number of water bodies, and a greater number of much smaller targets. In addition, such information can improve water balance estimation in different lake, reservoir, wetland, and river systems, and be very useful for assessment of improved prediction of surface water availability. Connections to climatic variations on inter-annual to inter-decadal time-scales are explored here, with a focus on a future ability to predict changes in storage volume for water resources or natural hazards concerns.

Ranking and combining multiple predictors without labeled data

PubMed Central

Parisi, Fabio; Strino, Francesco; Nadler, Boaz; Kluger, Yuval

2014-01-01

In a broad range of classification and decision-making problems, one is given the advice or predictions of several classifiers, of unknown reliability, over multiple questions or queries. This scenario is different from the standard supervised setting, where each classifier’s accuracy can be assessed using available labeled data, and raises two questions: Given only the predictions of several classifiers over a large set of unlabeled test data, is it possible to (i) reliably rank them and (ii) construct a metaclassifier more accurate than most classifiers in the ensemble? Here we present a spectral approach to address these questions. First, assuming conditional independence between classifiers, we show that the off-diagonal entries of their covariance matrix correspond to a rank-one matrix. Moreover, the classifiers can be ranked using the leading eigenvector of this covariance matrix, because its entries are proportional to their balanced accuracies. Second, via a linear approximation to the maximum likelihood estimator, we derive the Spectral Meta-Learner (SML), an unsupervised ensemble classifier whose weights are equal to these eigenvector entries. On both simulated and real data, SML typically achieves a higher accuracy than most classifiers in the ensemble and can provide a better starting point than majority voting for estimating the maximum likelihood solution. Furthermore, SML is robust to the presence of small malicious groups of classifiers designed to veer the ensemble prediction away from the (unknown) ground truth. PMID:24474744

Integrating Nonadditive Genomic Relationship Matrices into the Study of Genetic Architecture of Complex Traits.

PubMed

Nazarian, Alireza; Gezan, Salvador A

2016-03-01

The study of genetic architecture of complex traits has been dramatically influenced by implementing genome-wide analytical approaches during recent years. Of particular interest are genomic prediction strategies which make use of genomic information for predicting phenotypic responses instead of detecting trait-associated loci. In this work, we present the results of a simulation study to improve our understanding of the statistical properties of estimation of genetic variance components of complex traits, and of additive, dominance, and genetic effects through best linear unbiased prediction methodology. Simulated dense marker information was used to construct genomic additive and dominance matrices, and multiple alternative pedigree- and marker-based models were compared to determine if including a dominance term into the analysis may improve the genetic analysis of complex traits. Our results showed that a model containing a pedigree- or marker-based additive relationship matrix along with a pedigree-based dominance matrix provided the best partitioning of genetic variance into its components, especially when some degree of true dominance effects was expected to exist. Also, we noted that the use of a marker-based additive relationship matrix along with a pedigree-based dominance matrix had the best performance in terms of accuracy of correlations between true and estimated additive, dominance, and genetic effects. © The American Genetic Association 2015. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

ANN Surface Roughness Optimization of AZ61 Magnesium Alloy Finish Turning: Minimum Machining Times at Prime Machining Costs.

PubMed

Abbas, Adel Taha; Pimenov, Danil Yurievich; Erdakov, Ivan Nikolaevich; Taha, Mohamed Adel; Soliman, Mahmoud Sayed; El Rayes, Magdy Mostafa

2018-05-16

Magnesium alloys are widely used in aerospace vehicles and modern cars, due to their rapid machinability at high cutting speeds. A novel Edgeworth⁻Pareto optimization of an artificial neural network (ANN) is presented in this paper for surface roughness ( Ra ) prediction of one component in computer numerical control (CNC) turning over minimal machining time ( T m ) and at prime machining costs ( C ). An ANN is built in the Matlab programming environment, based on a 4-12-3 multi-layer perceptron (MLP), to predict Ra , T m , and C , in relation to cutting speed, v c , depth of cut, a p , and feed per revolution, f r . For the first time, a profile of an AZ61 alloy workpiece after finish turning is constructed using an ANN for the range of experimental values v c , a p , and f r . The global minimum length of a three-dimensional estimation vector was defined with the following coordinates: Ra = 0.087 μm, T m = 0.358 min/cm³, C = $8.2973. Likewise, the corresponding finish-turning parameters were also estimated: cutting speed v c = 250 m/min, cutting depth a p = 1.0 mm, and feed per revolution f r = 0.08 mm/rev. The ANN model achieved a reliable prediction accuracy of ±1.35% for surface roughness.

Modeling and Prediction of Monthly Total Ozone Concentrations by Use of an Artificial Neural Network Based on Principal Component Analysis

NASA Astrophysics Data System (ADS)

Chattopadhyay, Surajit; Chattopadhyay, Goutami

2012-10-01

In the work discussed in this paper we considered total ozone time series over Kolkata (22°34'10.92″N, 88°22'10.92″E), an urban area in eastern India. Using cloud cover, average temperature, and rainfall as the predictors, we developed an artificial neural network, in the form of a multilayer perceptron with sigmoid non-linearity, for prediction of monthly total ozone concentrations from values of the predictors in previous months. We also estimated total ozone from values of the predictors in the same month. Before development of the neural network model we removed multicollinearity by means of principal component analysis. On the basis of the variables extracted by principal component analysis, we developed three artificial neural network models. By rigorous statistical assessment it was found that cloud cover and rainfall can act as good predictors for monthly total ozone when they are considered as the set of input variables for the neural network model constructed in the form of a multilayer perceptron. In general, the artificial neural network has good potential for predicting and estimating monthly total ozone on the basis of the meteorological predictors. It was further observed that during pre-monsoon and winter seasons, the proposed models perform better than during and after the monsoon.

Developing Particle Emission Inventories Using Remote Sensing (PEIRS)

NASA Technical Reports Server (NTRS)

Tang, Chia-Hsi; Coull, Brent A.; Schwartz, Joel; Lyapustin, Alexei I.; Di, Qian; Koutrakis, Petros

2016-01-01

Information regarding the magnitude and distribution of PM(sub 2.5) emissions is crucial in establishing effective PM regulations and assessing the associated risk to human health and the ecosystem. At present, emission data is obtained from measured or estimated emission factors of various source types. Collecting such information for every known source is costly and time consuming. For this reason, emission inventories are reported periodically and unknown or smaller sources are often omitted or aggregated at large spatial scale. To address these limitations, we have developed and evaluated a novel method that uses remote sensing data to construct spatially-resolved emission inventories for PM(sub 2.5). This approach enables us to account for all sources within a fixed area, which renders source classification unnecessary. We applied this method to predict emissions in the northeast United States during the period of 2002-2013 using high- resolution 1 km x 1 km Aerosol Optical Depth (AOD). Emission estimates moderately agreed with the EPA National Emission Inventory (R(sup2) = 0.66 approx. 0.71, CV = 17.7 approx. 20%). Predicted emissions are found to correlate with land use parameters suggesting that our method can capture emissions from land use-related sources. In addition, we distinguished small-scale intra-urban variation in emissions reflecting distribution of metropolitan sources. In essence, this study demonstrates the great potential of remote sensing data to predict particle source emissions cost-effectively.

Developing Particle Emission Inventories Using Remote Sensing (PEIRS)

PubMed Central

Tang, Chia-Hsi; Coull, Brent A.; Schwartz, Joel; Lyapustin, Alexei I.; Di, Qian; Koutrakis, Petros

2018-01-01

Information regarding the magnitude and distribution of PM2.5 emissions is crucial in establishing effective PM regulations and assessing the associated risk to human health and the ecosystem. At present, emission data is obtained from measured or estimated emission factors of various source types. Collecting such information for every known source is costly and time consuming. For this reason, emission inventories are reported periodically and unknown or smaller sources are often omitted or aggregated at large spatial scale. To address these limitations, we have developed and evaluated a novel method that uses remote sensing data to construct spatially-resolved emission inventories for PM2.5. This approach enables us to account for all sources within a fixed area, which renders source classification unnecessary. We applied this method to predict emissions in the northeast United States during the period of 2002–2013 using high- resolution 1 km × 1km Aerosol Optical Depth (AOD). Emission estimates moderately agreed with the EPA National Emission Inventory (R2=0.66~0.71, CV = 17.7~20%). Predicted emissions are found to correlate with land use parameters suggesting that our method can capture emissions from land use-related sources. In addition, we distinguished small-scale intra-urban variation in emissions reflecting distribution of metropolitan sources. In essence, this study demonstrates the great potential of remote sensing data to predict particle source emissions cost-effectively. PMID:27653469

Immobilized Cytochrome P450 2C9 (CYP2C9): Applications for Metabolite Generation, Monitoring Protein-Protein Interactions, and Improving In-vivo Predictions Using Enhanced In-vitro Models

NASA Astrophysics Data System (ADS)

Wollenberg, Lance A.

Cytochrome P450 (P450) enzymes are a family of oxoferroreductase enzymes containing a heme moiety and are well known to be involved in the metabolism of a wide variety of endogenous and xenobiotic materials. It is estimated that roughly 75% of all pharmaceutical compounds are metabolized by these enzymes. Traditional reconstituted in-vitro incubation studies using recombinant P450 enzymes are often used to predict in-vivo kinetic parameters of a drug early in development. However, in many cases, these reconstituted incubations are prone to aggregation which has been shown to affect the catalytic activity of an enzyme. Moreover, the presence of other isoforms of P450 enzymes present in a metabolic incubation, as is the case with microsomal systems, may affect the catalytic activity of an enzyme through isoform-specific protein-protein interactions. Both of these effects may result in inaccurate prediction of in-vivo drug metabolism using in-vitro experiments. Here we described the development of immobilized P450 constructs designed to elucidate the effects of aggregation and protein-protein interactions between P450 isoforms on catalytic activities. The long term objective of this project is to develop a system to control the oligomeric state of Cytochrome P450 enzymes to accurately elucidate discrepancies between in vitro reconstituted systems and actual in vivo drug metabolism for the precise prediction of metabolic activity. This approach will serve as a system to better draw correlations between in-vivo and in-vitro drug metabolism data. The central hypothesis is that Cytochrome P450 enzymes catalytic activity can be altered by protein-protein interactions occurring between Cytochrome P450 enzymes involved in drug metabolism, and is dependent on varying states of protein aggregation. This dissertation explains the details of the construction and characterization of a nanostructure device designed to control the state of aggregation of a P450 enzyme. Moreover, applications of immobilized P450 enzyme constructs will also be used for monitoring protein-protein interaction and metabolite production with the use of immobilized-P450 bioreactor constructs. This work provides insight into the effect on catalytic activity caused by both P450 aggregation as well as isoform-specific protein-protein interactions and provides insight in the production of biosynthetically produced drug metabolites

Selection of emission factor standards for estimating emissions from diesel construction equipment in building construction in the Australian context.

PubMed

Zhang, Guomin; Sandanayake, Malindu; Setunge, Sujeeva; Li, Chunqing; Fang, Jun

2017-02-01

Emissions from equipment usage and transportation at the construction stage are classified as the direct emissions which include both greenhouse gas (GHG) and non-GHG emissions due to partial combustion of fuel. Unavailability of a reliable and complete inventory restricts an accurate emission evaluation on construction work. The study attempts to review emission factor standards readily available worldwide for estimating emissions from construction equipment. Emission factors published by United States Environmental Protection Agency (US EPA), Australian National Greenhouse Accounts (AUS NGA), Intergovernmental Panel on Climate Change (IPCC) and European Environmental Agency (EEA) are critically reviewed to identify their strengths and weaknesses. A selection process based on the availability and applicability is then developed to help identify the most suitable emission factor standards for estimating emissions from construction equipment in the Australian context. A case study indicates that a fuel based emission factor is more suitable for GHG emission estimation and a time based emission factor is more appropriate for estimation of non-GHG emissions. However, the selection of emission factor standards also depends on factors like the place of analysis (country of origin), data availability and the scope of analysis. Therefore, suitable modifications and assumptions should be incorporated in order to represent these factors. Copyright © 2016 Elsevier Ltd. All rights reserved.

Adaptive framework to better characterize errors of apriori fluxes and observational residuals in a Bayesian setup for the urban flux inversions.

NASA Astrophysics Data System (ADS)

Ghosh, S.; Lopez-Coto, I.; Prasad, K.; Karion, A.; Mueller, K.; Gourdji, S.; Martin, C.; Whetstone, J. R.

2017-12-01

The National Institute of Standards and Technology (NIST) supports the North-East Corridor Baltimore Washington (NEC-B/W) project and Indianapolis Flux Experiment (INFLUX) aiming to quantify sources of Greenhouse Gas (GHG) emissions as well as their uncertainties. These projects employ different flux estimation methods including top-down inversion approaches. The traditional Bayesian inversion method estimates emission distributions by updating prior information using atmospheric observations of Green House Gases (GHG) coupled to an atmospheric and dispersion model. The magnitude of the update is dependent upon the observed enhancement along with the assumed errors such as those associated with prior information and the atmospheric transport and dispersion model. These errors are specified within the inversion covariance matrices. The assumed structure and magnitude of the specified errors can have large impact on the emission estimates from the inversion. The main objective of this work is to build a data-adaptive model for these covariances matrices. We construct a synthetic data experiment using a Kalman Filter inversion framework (Lopez et al., 2017) employing different configurations of transport and dispersion model and an assumed prior. Unlike previous traditional Bayesian approaches, we estimate posterior emissions using regularized sample covariance matrices associated with prior errors to investigate whether the structure of the matrices help to better recover our hypothetical true emissions. To incorporate transport model error, we use ensemble of transport models combined with space-time analytical covariance to construct a covariance that accounts for errors in space and time. A Kalman Filter is then run using these covariances along with Maximum Likelihood Estimates (MLE) of the involved parameters. Preliminary results indicate that specifying sptio-temporally varying errors in the error covariances can improve the flux estimates and uncertainties. We also demonstrate that differences between the modeled and observed meteorology can be used to predict uncertainties associated with atmospheric transport and dispersion modeling which can help improve the skill of an inversion at urban scales.

Performance of Comorbidity, Risk Adjustment, and Functional Status Measures in Expenditure Prediction for Patients With Diabetes

PubMed Central

Maciejewski, Matthew L.; Liu, Chuan-Fen; Fihn, Stephan D.

2009-01-01

OBJECTIVE—To compare the ability of generic comorbidity and risk adjustment measures, a diabetes-specific measure, and a self-reported functional status measure to explain variation in health care expenditures for individuals with diabetes. RESEARCH DESIGN AND METHODS—This study included a retrospective cohort of 3,092 diabetic veterans participating in a multisite trial. Two comorbidity measures, four risk adjusters, a functional status measure, a diabetes complication count, and baseline expenditures were constructed from administrative and survey data. Outpatient, inpatient, and total expenditure models were estimated using ordinary least squares regression. Adjusted R2 statistics and predictive ratios were compared across measures to assess overall explanatory power and explanatory power of low- and high-cost subgroups. RESULTS—Administrative data–based risk adjusters performed better than the comorbidity, functional status, and diabetes-specific measures in all expenditure models. The diagnostic cost groups (DCGs) measure had the greatest predictive power overall and for the low- and high-cost subgroups, while the diabetes-specific measure had the lowest predictive power. A model with DCGs and the diabetes-specific measure modestly improved predictive power. CONCLUSIONS—Existing generic measures can be useful for diabetes-specific research and policy applications, but more predictive diabetes-specific measures are needed. PMID:18945927

Linking Forests and Fish: The Relationship Between Productivities of Salmonids and Forest Stands in Northern California

NASA Astrophysics Data System (ADS)

Wilzbach, P.; Frazey, S.

2005-05-01

Productivities of resident salmonid populations, upland, and riparian areas in 25 small watersheds of coastal northern California were estimated and compared to determine if: 1) upland site productivity predicted riparian site productivity; 2) either upland or riparian site productivity predicted salmonid productivity; and 3) other parameters explained more of the variance in salmonid productivity than upland or riparian site productivity. Salmonid productivity was indexed by total salmonid biomass, length of age 1 fish, and percent habitat saturation. Upland and riparian site productivities were estimated using site indices for redwood (Sequoia sempervirens) and red alder (Alnus rubra), respectively. Upland and riparian site indices were correlated, but neither factor contributed to the best approximating models of salmonid biomass or fish length at age one. Salmonid biomass was best described by a positive relationship with drainage area, and length at age was best described by a positive relationship with percent of riparian hardwoods. Percent habitat saturation was not well described by any of the models constructed. Lack of a relationship between upland conifer and salmonid productivity suggests that management of land for timber productivity and component streams for salmonid production in these sites will require separate, albeit integrated, strategies.

Quantification of Uncertainty in the Flood Frequency Analysis

NASA Astrophysics Data System (ADS)

Kasiapillai Sudalaimuthu, K.; He, J.; Swami, D.

2017-12-01

Flood frequency analysis (FFA) is usually carried out for planning and designing of water resources and hydraulic structures. Owing to the existence of variability in sample representation, selection of distribution and estimation of distribution parameters, the estimation of flood quantile has been always uncertain. Hence, suitable approaches must be developed to quantify the uncertainty in the form of prediction interval as an alternate to deterministic approach. The developed framework in the present study to include uncertainty in the FFA discusses a multi-objective optimization approach to construct the prediction interval using ensemble of flood quantile. Through this approach, an optimal variability of distribution parameters is identified to carry out FFA. To demonstrate the proposed approach, annual maximum flow data from two gauge stations (Bow river at Calgary and Banff, Canada) are used. The major focus of the present study was to evaluate the changes in magnitude of flood quantiles due to the recent extreme flood event occurred during the year 2013. In addition, the efficacy of the proposed method was further verified using standard bootstrap based sampling approaches and found that the proposed method is reliable in modeling extreme floods as compared to the bootstrap methods.

Salience Assignment for Multiple-Instance Data and Its Application to Crop Yield Prediction

NASA Technical Reports Server (NTRS)

Wagstaff, Kiri L.; Lane, Terran

2010-01-01

An algorithm was developed to generate crop yield predictions from orbital remote sensing observations, by analyzing thousands of pixels per county and the associated historical crop yield data for those counties. The algorithm determines which pixels contain which crop. Since each known yield value is associated with thousands of individual pixels, this is a multiple instance learning problem. Because individual crop growth is related to the resulting yield, this relationship has been leveraged to identify pixels that are individually related to corn, wheat, cotton, and soybean yield. Those that have the strongest relationship to a given crop s yield values are most likely to contain fields with that crop. Remote sensing time series data (a new observation every 8 days) was examined for each pixel, which contains information for that pixel s growth curve, peak greenness, and other relevant features. An alternating-projection (AP) technique was used to first estimate the "salience" of each pixel, with respect to the given target (crop yield), and then those estimates were used to build a regression model that relates input data (remote sensing observations) to the target. This is achieved by constructing an exemplar for each crop in each county that is a weighted average of all the pixels within the county; the pixels are weighted according to the salience values. The new regression model estimate then informs the next estimate of the salience values. By iterating between these two steps, the algorithm converges to a stable estimate of both the salience of each pixel and the regression model. The salience values indicate which pixels are most relevant to each crop under consideration.

Multicomponent seismic loss estimation on the North Anatolian Fault Zone (Turkey)

NASA Astrophysics Data System (ADS)

karimzadeh Naghshineh, S.; Askan, A.; Erberik, M. A.; Yakut, A.

2015-12-01

Seismic loss estimation is essential to incorporate seismic risk of structures into an efficient decision-making framework. Evaluation of seismic damage of structures requires a multidisciplinary approach including earthquake source characterization, seismological prediction of earthquake-induced ground motions, prediction of structural responses exposed to ground shaking, and finally estimation of induced damage to structures. As the study region, Erzincan, a city on the eastern part of Turkey is selected which is located in the conjunction of three active strike-slip faults as North Anatolian Fault, North East Anatolian Fault and Ovacik fault. Erzincan city center is in a pull-apart basin underlain by soft sediments that has experienced devastating earthquakes such as the 27 December 1939 (Ms=8.0) and the 13 March 1992 (Mw=6.6) events, resulting in extensive amount of physical as well as economical losses. These losses are attributed to not only the high seismicity of the area but also as a result of the seismic vulnerability of the constructed environment. This study focuses on the seismic damage estimation of Erzincan using both regional seismicity and local building information. For this purpose, first, ground motion records are selected from a set of scenario events simulated with the stochastic finite fault methodology using regional seismicity parameters. Then, existing building stock are classified into specified groups represented with equivalent single-degree-of-freedom systems. Through these models, the inelastic dynamic structural responses are investigated with non-linear time history analysis. To assess the potential seismic damage in the study area, fragility curves for the classified structural types are derived. Finally, the estimated damage is compared with the observed damage during the 1992 Erzincan earthquake. The results are observed to have a reasonable match indicating the efficiency of the ground motion simulations and building analyses.

A global goodness-of-fit test for receiver operating characteristic curve analysis via the bootstrap method.

PubMed

Zou, Kelly H; Resnic, Frederic S; Talos, Ion-Florin; Goldberg-Zimring, Daniel; Bhagwat, Jui G; Haker, Steven J; Kikinis, Ron; Jolesz, Ferenc A; Ohno-Machado, Lucila

2005-10-01

Medical classification accuracy studies often yield continuous data based on predictive models for treatment outcomes. A popular method for evaluating the performance of diagnostic tests is the receiver operating characteristic (ROC) curve analysis. The main objective was to develop a global statistical hypothesis test for assessing the goodness-of-fit (GOF) for parametric ROC curves via the bootstrap. A simple log (or logit) and a more flexible Box-Cox normality transformations were applied to untransformed or transformed data from two clinical studies to predict complications following percutaneous coronary interventions (PCIs) and for image-guided neurosurgical resection results predicted by tumor volume, respectively. We compared a non-parametric with a parametric binormal estimate of the underlying ROC curve. To construct such a GOF test, we used the non-parametric and parametric areas under the curve (AUCs) as the metrics, with a resulting p value reported. In the interventional cardiology example, logit and Box-Cox transformations of the predictive probabilities led to satisfactory AUCs (AUC=0.888; p=0.78, and AUC=0.888; p=0.73, respectively), while in the brain tumor resection example, log and Box-Cox transformations of the tumor size also led to satisfactory AUCs (AUC=0.898; p=0.61, and AUC=0.899; p=0.42, respectively). In contrast, significant departures from GOF were observed without applying any transformation prior to assuming a binormal model (AUC=0.766; p=0.004, and AUC=0.831; p=0.03), respectively. In both studies the p values suggested that transformations were important to consider before applying any binormal model to estimate the AUC. Our analyses also demonstrated and confirmed the predictive values of different classifiers for determining the interventional complications following PCIs and resection outcomes in image-guided neurosurgery.

An Open-Access Modeled Passenger Flow Matrix for the Global Air Network in 2010

PubMed Central

Huang, Zhuojie; Wu, Xiao; Garcia, Andres J.; Fik, Timothy J.; Tatem, Andrew J.

2013-01-01

The expanding global air network provides rapid and wide-reaching connections accelerating both domestic and international travel. To understand human movement patterns on the network and their socioeconomic, environmental and epidemiological implications, information on passenger flow is required. However, comprehensive data on global passenger flow remain difficult and expensive to obtain, prompting researchers to rely on scheduled flight seat capacity data or simple models of flow. This study describes the construction of an open-access modeled passenger flow matrix for all airports with a host city-population of more than 100,000 and within two transfers of air travel from various publicly available air travel datasets. Data on network characteristics, city population, and local area GDP amongst others are utilized as covariates in a spatial interaction framework to predict the air transportation flows between airports. Training datasets based on information from various transportation organizations in the United States, Canada and the European Union were assembled. A log-linear model controlling the random effects on origin, destination and the airport hierarchy was then built to predict passenger flows on the network, and compared to the results produced using previously published models. Validation analyses showed that the model presented here produced improved predictive power and accuracy compared to previously published models, yielding the highest successful prediction rate at the global scale. Based on this model, passenger flows between 1,491 airports on 644,406 unique routes were estimated in the prediction dataset. The airport node characteristics and estimated passenger flows are freely available as part of the Vector-Borne Disease Airline Importation Risk (VBD-Air) project at: www.vbd-air.com/data. PMID:23691194

Transferrin saturation phenotype and HFE genotype screening for hemochromatosis and primary iron overload: predictions from a model based on national, racial, and ethnic group composition in central Alabama.

PubMed

Barton, J C; Acton, R T

2000-01-01

There is interest in general population screening for hemochromatosis and other primary iron overload disorders, although not all persons are at equal risk. We developed a model to estimate the numbers of persons in national, racial, or ethnic population subgroups in Jefferson County, Alabama, who would be detected using transferrin saturation (phenotype) or HFE mutation analysis (genotype) screening. Approximately 62% are Caucasians, 37% are African Americans, and the remainder are Hispanics, Asians, or Native Americans. The predicted phenotype frequencies are greatest in a Caucasian subgroup, ethnicity unspecified, which consists predominantly of persons of Scotch and Irish descent (0.0065 men, 0.0046 women), and in African Americans (0.0089 men, 0.0085 women). Frequencies of the HFE genotype C282Y/C282Y > or = 0.0001 are predicted to occur only among Caucasians; the greatest frequency (0.0080) was predicted to occur in the ethnicity-unspecified Caucasian population. C282Y/C282Y frequency estimates were lower in Italian, Greek, and Jewish subgroups. There is excellent agreement in the numbers of the ethnicity-unspecified Caucasians who would be detected using phenotype and genotype criteria. Our model also indicates that phenotyping would identify more persons with primary iron overload than would genotyping in our Italian Caucasian, Hispanic, and African American subgroups. This is consistent with previous observations that indicate that primary iron overload disorders in persons of southern Italian descent and African Americans are largely attributable to non-HFE alleles. Because the proportions of population subgroups and their genetic constitution may differ significantly in other geographic regions, we suggest that models similar to the present one be constructed to predict optimal screening strategies for primary iron overload disorders.

Ventricular Cycle Length Characteristics Estimative of Prolonged RR Interval during Atrial Fibrillation

PubMed Central

CIACCIO, EDWARD J.; BIVIANO, ANGELO B.; GAMBHIR, ALOK; EINSTEIN, ANDREW J.; GARAN, HASAN

2014-01-01

Background When atrial fibrillation (AF) is incessant, imaging during a prolonged ventricular RR interval may improve image quality. It was hypothesized that long RR intervals could be predicted from preceding RR values. Methods From the PhysioNet database, electrocardiogram RR intervals were obtained from 74 persistent AF patients. An RR interval lengthened by at least 250 ms beyond the immediately preceding RR interval (termed T0 and T1, respectively) was considered prolonged. A two-parameter scatterplot was used to predict the occurrence of a prolonged interval T0. The scatterplot parameters were: (1) RR variability (RRv) estimated as the average second derivative from 10 previous pairs of RR differences, T13–T2, and (2) Tm–T1, the difference between Tm, the mean from T13 to T2, and T1. For each patient, scatterplots were constructed using preliminary data from the first hour. The ranges of parameters 1 and 2 were adjusted to maximize the proportion of prolonged RR intervals within range. These constraints were used for prediction of prolonged RR in test data collected during the second hour. Results The mean prolonged event was 1.0 seconds in duration. Actual prolonged events were identified with a mean positive predictive value (PPV) of 80% in the test set. PPV was >80% in 36 of 74 patients. An average of 10.8 prolonged RR intervals per 60 minutes was correctly identified. Conclusions A method was developed to predict prolonged RR intervals using two parameters and prior statistical sampling for each patient. This or similar methodology may help improve cardiac imaging in many longstanding persistent AF patients. PMID:23998759

An open-access modeled passenger flow matrix for the global air network in 2010.

PubMed

Huang, Zhuojie; Wu, Xiao; Garcia, Andres J; Fik, Timothy J; Tatem, Andrew J

2013-01-01

The expanding global air network provides rapid and wide-reaching connections accelerating both domestic and international travel. To understand human movement patterns on the network and their socioeconomic, environmental and epidemiological implications, information on passenger flow is required. However, comprehensive data on global passenger flow remain difficult and expensive to obtain, prompting researchers to rely on scheduled flight seat capacity data or simple models of flow. This study describes the construction of an open-access modeled passenger flow matrix for all airports with a host city-population of more than 100,000 and within two transfers of air travel from various publicly available air travel datasets. Data on network characteristics, city population, and local area GDP amongst others are utilized as covariates in a spatial interaction framework to predict the air transportation flows between airports. Training datasets based on information from various transportation organizations in the United States, Canada and the European Union were assembled. A log-linear model controlling the random effects on origin, destination and the airport hierarchy was then built to predict passenger flows on the network, and compared to the results produced using previously published models. Validation analyses showed that the model presented here produced improved predictive power and accuracy compared to previously published models, yielding the highest successful prediction rate at the global scale. Based on this model, passenger flows between 1,491 airports on 644,406 unique routes were estimated in the prediction dataset. The airport node characteristics and estimated passenger flows are freely available as part of the Vector-Borne Disease Airline Importation Risk (VBD-Air) project at: www.vbd-air.com/data.

Reserve growth in oil fields of the North Sea

USGS Publications Warehouse

Klett, T.R.; Gautier, D.L.

2005-01-01

The assessment of petroleum resources of the North Sea, as well as other areas of the world, requires a viable means to forecast the amount of growth of reserve estimates (reserve growth) for discovered fields and to predict the potential fully developed sizes of undiscovered fields. This study investigates the utility of North Sea oil field data to construct reserve-growth models. Oil fields of the North Sea provide an excellent dataset in which to examine the mechanisms, characteristics, rates and quantities of reserve growth because of the high level of capital investments, implementation of sophisticated technologies and careful data collection. Additionally, these field data are well reported and available publicly. Increases in successive annual estimat es of recoverable crude oil volumes indicate that oil fields in the North Sea, collectively and in each country, experience reserve growth. Specific patterns of reserve growth are observed among countries and primary producing reservoir-rock types. Since 1985, Norwegian oil fields had the greatest volume increase; Danish oil fields increased by the greatest percentage relative to 1985 estimates; and British oil fields experienced an increase in recoverable oil estimates for the first ten years since 1985, followed by a slight reduction. Fields producing primarily from clastic reservoirs account for the majority of the estimated recoverable oil and, therefore, these fields had the largest volumetric increase. Fields producing primarily from chalk (limestone) reservoirs increased by a greater percentage relative to 1985 estimates than did fields producing primarily from clastic reservoirs. Additionally, the largest oil fields had the greatest volumetric increases. Although different reserve-growth patterns are observed among oil fields located in different countries, the small number of fields in Denmark precludes construction of reserve-growth models for that country. However, differences in reserve-growth patterns among oil fields that produce from primarily clastic and primarily chalk reservoirs, in addition to a greater number of fields in each of the two categories, allow separate reserve-growth models to be constructed based on reservoir-rock type. Reserve-growth models referenced to the date of discovery and to the date of first production may be constructed from North Sea field data. Years since discovery or years since first production are used as surrogates for, or measures of, field-development effort that is applied to promote reserve growth. Better estimates of recoverable oil are made as fields are developed. Because much of the field development occurs some time later than the field discovery date, reserve-growth models referenced to the date of first production may provide a more appropriate measure of development than does date of discovery. ?? 2005 EAGE/Geological Society of London.

Lung cancer risk among workers in the construction industry: results from two case-control studies in Montreal.

PubMed

Lacourt, Aude; Pintos, Javier; Lavoué, Jérôme; Richardson, Lesley; Siemiatycki, Jack

2015-09-22

Given the large number of workers in the construction industry, it is important to derive accurate and valid estimates of cancer risk, and in particular lung cancer risk. In most previous studies, risks among construction workers were compared with general populations including blue and white collar workers. The main objectives of this study were to assess whether construction workers experience excess lung cancer risk, and whether exposure to selected construction industry exposures carries excess risks. We wished to address these objectives within the sub-population of blue collar workers. Two case-control studies were conducted in Montreal. Combined, they included 1593 lung cancer cases and 1427 controls, of whom 1304 cases and 1081 controls had been blue collar workers. Detailed lifetime job histories were obtained and translated by experts into histories of exposure to chemical agents. The two key analyses were to estimate odds ratio (OR) estimates of lung cancer risk: a) for all blue-collar construction workers compared with other blue-collar workers, and b) for construction workers exposed to each of 20 exposure agents found in the construction industry compared with construction workers unexposed to those agents. All analyses were conducted using unconditional logistic regression adjusted for socio-demographic factors and smoking history. The OR for all construction workers combined was 1.11 (95 % CI: 0.90-1.38), based on 381 blue collar construction workers. Analyses of specific exposures were hampered by small numbers and imprecise estimates. While none of 20 occupational agents examined was significantly associated with lung cancer, the following agents manifested non-significantly elevated ORs: asbestos, silica, Portland cement, soil dust, calcium oxide and calcium sulfate. Compared with other blue collar workers, there was only a slight increased risk of lung cancer for subjects who ever held an occupation in the construction industry. The analyses of agents within the construction industry produced imprecise estimates of risk, but nevertheless pointed to some plausible associations. Excess risks for asbestos and silica were in line with previous knowledge. The possible excess risks with the other inorganic dusts require further corroboration.

HCV prevalence can predict HIV epidemic potential among people who inject drugs: mathematical modeling analysis.

PubMed

Akbarzadeh, Vajiheh; Mumtaz, Ghina R; Awad, Susanne F; Weiss, Helen A; Abu-Raddad, Laith J

2016-12-03

Hepatitis C virus (HCV) and HIV are both transmitted through percutaneous exposures among people who inject drugs (PWID). Ecological analyses on global epidemiological data have identified a positive association between HCV and HIV prevalence among PWID. Our objective was to demonstrate how HCV prevalence can be used to predict HIV epidemic potential among PWID. Two population-level models were constructed to simulate the evolution of HCV and HIV epidemics among PWID. The models described HCV and HIV parenteral transmission, and were solved both deterministically and stochastically. The modeling results provided a good fit to the epidemiological data describing the ecological HCV and HIV association among PWID. HCV was estimated to be eight times more transmissible per shared injection than HIV. A threshold HCV prevalence of 29.0% (95% uncertainty interval (UI): 20.7-39.8) and 46.5% (95% UI: 37.6-56.6) were identified for a sustainable HIV epidemic (HIV prevalence >1%) and concentrated HIV epidemic (HIV prevalence >5%), respectively. The association between HCV and HIV was further described with six dynamical regimes depicting the overlapping epidemiology of the two infections, and was quantified using defined and estimated measures of association. Modeling predictions across a wide range of HCV prevalence indicated overall acceptable precision in predicting HIV prevalence at endemic equilibrium. Modeling predictions were found to be robust with respect to stochasticity and behavioral and biological parameter uncertainty. In an illustrative application of the methodology, the modeling predictions of endemic HIV prevalence in Iran agreed with the scale and time course of the HIV epidemic in this country. Our results show that HCV prevalence can be used as a proxy biomarker of HIV epidemic potential among PWID, and that the scale and evolution of HIV epidemic expansion can be predicted with sufficient precision to inform HIV policy, programming, and resource allocation.

On decentralized estimation. [for large linear systems

NASA Technical Reports Server (NTRS)

Siljak, D. D.; Vukcevic, M. B.

1978-01-01

A multilevel scheme is proposed to construct decentralized estimators for large linear systems. The scheme is numerically attractive since only observability tests of low-order subsystems are required. Equally important is the fact that the constructed estimators are reliable under structural perturbations and can tolerate a wide range of nonlinearities in coupling among the subsystems.

Reliability of mobile systems in construction

NASA Astrophysics Data System (ADS)

Narezhnaya, Tamara; Prykina, Larisa

2017-10-01

The purpose of the article is to analyze the influence of the mobility of construction production in the article taking into account the properties of reliability and readiness. Basing on the studied systems the effectiveness and efficiency is estimated. The construction system is considered to be the complete organizational structure providing creation or updating of construction facilities. At the same time the production sphere of these systems joins the production on the building site itself, material and technical resources of the construction production and live labour in these spheres within the construction dynamics. The author concludes, that the estimation of the degree of mobility of systems the of construction production makes a great positive effect in the project.

A computer program for predicting recharge with a master recession curve

USGS Publications Warehouse

Heppner, Christopher S.; Nimmo, John R.

2005-01-01

Water-table fluctuations occur in unconfined aquifers owing to ground-water recharge following precipitation and infiltration, and ground-water discharge to streams between storm events. Ground-water recharge can be estimated from well hydrograph data using the water-table fluctuation (WTF) principle, which states that recharge is equal to the product of the water-table rise and the specific yield of the subsurface porous medium. The water-table rise, however, must be expressed relative to the water level that would have occurred in the absence of recharge. This requires a means for estimating the recession pattern of the water-table at the site. For a given site there is often a characteristic relation between the water-table elevation and the water-table decline rate following a recharge event. A computer program was written which extracts the relation between decline rate and water-table elevation from well hydrograph data and uses it to construct a master recession curve (MRC). The MRC is a characteristic water-table recession hydrograph, representing the average behavior for a declining water-table at that site. The program then calculates recharge using the WTF method by comparing the measured well hydrograph with the hydrograph predicted by the MRC and multiplying the difference at each time step by the specific yield. This approach can be used to estimate recharge in a continuous fashion from long-term well records. Presented here is a description of the code including the WTF theory and instructions for running it to estimate recharge with continuous well hydrograph data.

Combining the ASA Physical Classification System and Continuous Intraoperative Surgical Apgar Score Measurement in Predicting Postoperative Risk.

PubMed

Jering, Monika Zdenka; Marolen, Khensani N; Shotwell, Matthew S; Denton, Jason N; Sandberg, Warren S; Ehrenfeld, Jesse Menachem

2015-11-01

The surgical Apgar score predicts major 30-day postoperative complications using data assessed at the end of surgery. We hypothesized that evaluating the surgical Apgar score continuously during surgery may identify patients at high risk for postoperative complications. We retrospectively identified general, vascular, and general oncology patients at Vanderbilt University Medical Center. Logistic regression methods were used to construct a series of predictive models in order to continuously estimate the risk of major postoperative complications, and to alert care providers during surgery should the risk exceed a given threshold. Area under the receiver operating characteristic curve (AUROC) was used to evaluate the discriminative ability of a model utilizing a continuously measured surgical Apgar score relative to models that use only preoperative clinical factors or continuously monitored individual constituents of the surgical Apgar score (i.e. heart rate, blood pressure, and blood loss). AUROC estimates were validated internally using a bootstrap method. 4,728 patients were included. Combining the ASA PS classification with continuously measured surgical Apgar score demonstrated improved discriminative ability (AUROC 0.80) in the pooled cohort compared to ASA (0.73) and the surgical Apgar score alone (0.74). To optimize the tradeoff between inadequate and excessive alerting with future real-time notifications, we recommend a threshold probability of 0.24. Continuous assessment of the surgical Apgar score is predictive for major postoperative complications. In the future, real-time notifications might allow for detection and mitigation of changes in a patient's accumulating risk of complications during a surgical procedure.

Recognition of Atypical Symptoms of Acute Myocardial Infarction: Development and Validation of a Risk Scoring System.

PubMed

Li, Polly W C; Yu, Doris S F

Atypical symptom presentation in patients with acute myocardial infarction (AMI) is associated with longer delay in care seeking and poorer prognosis. Symptom recognition in these patients is a challenging task. Our purpose in this risk prediction model development study was to develop and validate a risk scoring system for estimating cumulative risk for atypical AMI presentation. A consecutive sample was recruited for the developmental (n = 300) and validation (n = 97) cohorts. Symptom experience was measured with the validated Chinese version of the Symptoms of Acute Coronary Syndromes Inventory. Potential predictors were identified from the literature. Multivariable logistic regression was performed to identify significant predictors. A risk scoring system was then constructed by assigning weights to each significant predictor according to their b coefficients. Five independent predictors for atypical symptom presentation were older age (≥75 years), female gender, diabetes mellitus, history of AMI, and absence of hyperlipidemia. The Hosmer and Lemeshow test (χ6 = 4.47, P = .62) indicated that this predictive model was adequate to predict the outcome. Acceptable discrimination was demonstrated, with area under the receiver operating characteristic curve as 0.74 (95% confidence interval, 0.67-0.82) (P < .001). The predictive power of this risk scoring system was confirmed in the validation cohort. Atypical AMI presentation is common. A simple risk scoring system developed on the basis of the 5 identified predictors can raise awareness of atypical AMI presentation and promote symptom recognition by estimating the cumulative risk for an individual to present with atypical AMI symptoms.

Linear models for airborne-laser-scanning-based operational forest inventory with small field sample size and highly correlated LiDAR data

USGS Publications Warehouse

Junttila, Virpi; Kauranne, Tuomo; Finley, Andrew O.; Bradford, John B.

2015-01-01

Modern operational forest inventory often uses remotely sensed data that cover the whole inventory area to produce spatially explicit estimates of forest properties through statistical models. The data obtained by airborne light detection and ranging (LiDAR) correlate well with many forest inventory variables, such as the tree height, the timber volume, and the biomass. To construct an accurate model over thousands of hectares, LiDAR data must be supplemented with several hundred field sample measurements of forest inventory variables. This can be costly and time consuming. Different LiDAR-data-based and spatial-data-based sampling designs can reduce the number of field sample plots needed. However, problems arising from the features of the LiDAR data, such as a large number of predictors compared with the sample size (overfitting) or a strong correlation among predictors (multicollinearity), may decrease the accuracy and precision of the estimates and predictions. To overcome these problems, a Bayesian linear model with the singular value decomposition of predictors, combined with regularization, is proposed. The model performance in predicting different forest inventory variables is verified in ten inventory areas from two continents, where the number of field sample plots is reduced using different sampling designs. The results show that, with an appropriate field plot selection strategy and the proposed linear model, the total relative error of the predicted forest inventory variables is only 5%–15% larger using 50 field sample plots than the error of a linear model estimated with several hundred field sample plots when we sum up the error due to both the model noise variance and the model’s lack of fit.

A model for nematode locomotion in soil

USGS Publications Warehouse

Hunt, H. William; Wall, Diana H.; DeCrappeo, Nicole; Brenner, John S.

2001-01-01

Locomotion of nematodes in soil is important for both practical and theoretical reasons. We constructed a model for rate of locomotion. The first model component is a simple simulation of nematode movement among finite cells by both random and directed behaviours. Optimisation procedures were used to fit the simulation output to data from published experiments on movement along columns of soil or washed sand, and thus to estimate the values of the model's movement coefficients. The coefficients then provided an objective means to compare rates of locomotion among studies done under different experimental conditions. The second component of the model is an equation to predict the movement coefficients as a function of controlling factors that have been addressed experimentally: soil texture, bulk density, water potential, temperature, trophic group of nematode, presence of an attractant or physical gradient and the duration of the experiment. Parameters of the equation were estimated by optimisation to achieve a good fit to the estimated movement coefficients. Bulk density, which has been reported in a minority of published studies, is predicted to have an important effect on rate of locomotion, at least in fine-textured soils. Soil sieving, which appears to be a universal practice in laboratory studies of nematode movement, is predicted to negatively affect locomotion. Slower movement in finer textured soils would be expected to increase isolation among local populations, and thus to promote species richness. Future additions to the model that might improve its utility include representing heterogeneity within populations in rate of movement, development of gradients of chemical attractants, trade-offs between random and directed components of movement, species differences in optimal temperature and water potential, and interactions among factors controlling locomotion.

Validation of a 20-year forecast of US childhood lead poisoning: Updated prospects for 2010.

PubMed

Jacobs, David E; Nevin, Rick

2006-11-01

We forecast childhood lead poisoning and residential lead paint hazard prevalence for 1990-2010, based on a previously unvalidated model that combines national blood lead data with three different housing data sets. The housing data sets, which describe trends in housing demolition, rehabilitation, window replacement, and lead paint, are the American Housing Survey, the Residential Energy Consumption Survey, and the National Lead Paint Survey. Blood lead data are principally from the National Health and Nutrition Examination Survey. New data now make it possible to validate the midpoint of the forecast time period. For the year 2000, the model predicted 23.3 million pre-1960 housing units with lead paint hazards, compared to an empirical HUD estimate of 20.6 million units. Further, the model predicted 498,000 children with elevated blood lead levels (EBL) in 2000, compared to a CDC empirical estimate of 434,000. The model predictions were well within 95% confidence intervals of empirical estimates for both residential lead paint hazard and blood lead outcome measures. The model shows that window replacement explains a large part of the dramatic reduction in lead poisoning that occurred from 1990 to 2000. Here, the construction of the model is described and updated through 2010 using new data. Further declines in childhood lead poisoning are achievable, but the goal of eliminating children's blood lead levels > or =10 microg/dL by 2010 is unlikely to be achieved without additional action. A window replacement policy will yield multiple benefits of lead poisoning prevention, increased home energy efficiency, decreased power plant emissions, improved housing affordability, and other previously unrecognized benefits. Finally, combining housing and health data could be applied to forecasting other housing-related diseases and injuries.

Validation of a 20-year forecast of US childhood lead poisoning: Updated prospects for 2010

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

Jacobs, David E.; Nevin, Rick

2006-11-15

We forecast childhood lead poisoning and residential lead paint hazard prevalence for 1990-2010, based on a previously unvalidated model that combines national blood lead data with three different housing data sets. The housing data sets, which describe trends in housing demolition, rehabilitation, window replacement, and lead paint, are the American Housing Survey, the Residential Energy Consumption Survey, and the National Lead Paint Survey. Blood lead data are principally from the National Health and Nutrition Examination Survey. New data now make it possible to validate the midpoint of the forecast time period. For the year 2000, the model predicted 23.3 millionmore » pre-1960 housing units with lead paint hazards, compared to an empirical HUD estimate of 20.6 million units. Further, the model predicted 498,000 children with elevated blood lead levels (EBL) in 2000, compared to a CDC empirical estimate of 434,000. The model predictions were well within 95% confidence intervals of empirical estimates for both residential lead paint hazard and blood lead outcome measures. The model shows that window replacement explains a large part of the dramatic reduction in lead poisoning that occurred from 1990 to 2000. Here, the construction of the model is described and updated through 2010 using new data. Further declines in childhood lead poisoning are achievable, but the goal of eliminating children's blood lead levels {>=}10 {mu}g/dL by 2010 is unlikely to be achieved without additional action. A window replacement policy will yield multiple benefits of lead poisoning prevention, increased home energy efficiency, decreased power plant emissions, improved housing affordability, and other previously unrecognized benefits. Finally, combining housing and health data could be applied to forecasting other housing-related diseases and injuries.« less

Toward a consistent model for strain accrual and release for the New Madrid Seismic Zone, central United States

USGS Publications Warehouse

Hough, S.E.; Page, M.

2011-01-01

At the heart of the conundrum of seismogenesis in the New Madrid Seismic Zone is the apparently substantial discrepancy between low strain rate and high recent seismic moment release. In this study we revisit the magnitudes of the four principal 1811–1812 earthquakes using intensity values determined from individual assessments from four experts. Using these values and the grid search method of Bakun and Wentworth (1997), we estimate magnitudes around 7.0 for all four events, values that are significantly lower than previously published magnitude estimates based on macroseismic intensities. We further show that the strain rate predicted from postglacial rebound is sufficient to produce a sequence with the moment release of one Mmax6.8 every 500 years, a rate that is much lower than previous estimates of late Holocene moment release. However, Mw6.8 is at the low end of the uncertainty range inferred from analysis of intensities for the largest 1811–1812 event. We show that Mw6.8 is also a reasonable value for the largest main shock given a plausible rupture scenario. One can also construct a range of consistent models that permit a somewhat higher Mmax, with a longer average recurrence rate. It is thus possible to reconcile predicted strain and seismic moment release rates with alternative models: one in which 1811–1812 sequences occur every 500 years, with the largest events being Mmax∼6.8, or one in which sequences occur, on average, less frequently, with Mmax of ∼7.0. Both models predict that the late Holocene rate of activity will continue for the next few to 10 thousand years.

Improvement of Predictive Ability by Uniform Coverage of the Target Genetic Space

PubMed Central

Bustos-Korts, Daniela; Malosetti, Marcos; Chapman, Scott; Biddulph, Ben; van Eeuwijk, Fred

2016-01-01

Genome-enabled prediction provides breeders with the means to increase the number of genotypes that can be evaluated for selection. One of the major challenges in genome-enabled prediction is how to construct a training set of genotypes from a calibration set that represents the target population of genotypes, where the calibration set is composed of a training and validation set. A random sampling protocol of genotypes from the calibration set will lead to low quality coverage of the total genetic space by the training set when the calibration set contains population structure. As a consequence, predictive ability will be affected negatively, because some parts of the genotypic diversity in the target population will be under-represented in the training set, whereas other parts will be over-represented. Therefore, we propose a training set construction method that uniformly samples the genetic space spanned by the target population of genotypes, thereby increasing predictive ability. To evaluate our method, we constructed training sets alongside with the identification of corresponding genomic prediction models for four genotype panels that differed in the amount of population structure they contained (maize Flint, maize Dent, wheat, and rice). Training sets were constructed using uniform sampling, stratified-uniform sampling, stratified sampling and random sampling. We compared these methods with a method that maximizes the generalized coefficient of determination (CD). Several training set sizes were considered. We investigated four genomic prediction models: multi-locus QTL models, GBLUP models, combinations of QTL and GBLUPs, and Reproducing Kernel Hilbert Space (RKHS) models. For the maize and wheat panels, construction of the training set under uniform sampling led to a larger predictive ability than under stratified and random sampling. The results of our methods were similar to those of the CD method. For the rice panel, all training set construction methods led to similar predictive ability, a reflection of the very strong population structure in this panel. PMID:27672112

Statistical Modeling of Occupational Exposure to Polycyclic Aromatic Hydrocarbons Using OSHA Data.

PubMed

Lee, Derrick G; Lavoué, Jérôme; Spinelli, John J; Burstyn, Igor

2015-01-01

Polycyclic aromatic hydrocarbons (PAHs) are a group of pollutants with multiple variants classified as carcinogenic. The Occupational Safety and Health Administration (OSHA) provided access to two PAH exposure databanks of United States workplace compliance testing data collected between 1979 and 2010. Mixed-effects logistic models were used to predict the exceedance fraction (EF), i.e., the probability of exceeding OSHA's Permissible Exposure Limit (PEL = 0.2 mg/m3) for PAHs based on industry and occupation. Measurements of coal tar pitch volatiles were used as a surrogate for PAHs. Time, databank, occupation, and industry were included as fixed-effects while an identifier for the compliance inspection number was included as a random effect. Analyses involved 2,509 full-shift personal measurements. Results showed that the majority of industries had an estimated EF < 0.5, although several industries, including Standardized Industry Classification codes 1623 (Water, Sewer, Pipeline, and Communication and Powerline Construction), 1711 (Plumbing, Heating, and Air-Conditioning), 2824 (Manmade Organic Fibres), 3496 (Misc. Fabricated Wire products), and 5812 (Eating Places), and Major group's 13 (Oil and Gas Extraction) and 30 (Rubber and Miscellaneous Plastic Products), were estimated to have more than an 80% likelihood of exceeding the PEL. There was an inverse temporal trend of exceeding the PEL, with lower risk in most recent years, albeit not statistically significant. Similar results were shown when incorporating occupation, but varied depending on the occupation as the majority of industries predicted at the administrative level, e.g., managers, had an estimated EF < 0.5 while at the minimally skilled/laborer level there was a substantial increase in the estimated EF. These statistical models allow the prediction of PAH exposure risk through individual occupational histories and will be used to create a job-exposure matrix for use in a population-based case-control study exploring PAH exposure and breast cancer risk.

Inhibition by ultraviolet and photosynthetically available radiation lowers model estimates of depth-integrated picophytoplankton photosynthesis: global predictions for Prochlorococcus and Synechococcus.

PubMed

Neale, Patrick J; Thomas, Brian C

2017-01-01

Phytoplankton photosynthesis is often inhibited by ultraviolet (UV) and intense photosynthetically available radiation (PAR), but the effects on ocean productivity have received little consideration aside from polar areas subject to periodic enhanced UV-B due to depletion of stratospheric ozone. A more comprehensive assessment is important for understanding the contribution of phytoplankton production to the global carbon budget, present and future. Here, we consider responses in the temperate and tropical mid-ocean regions typically dominated by picophytoplankton including the prokaryotic lineages, Prochlorococcus and Synechococcus. Spectral models of photosynthetic response for each lineage were constructed using model strains cultured at different growth irradiances and temperatures. In the model, inhibition becomes more severe once exposure exceeds a threshold (E max ) related to repair capacity. Model parameters are presented for Prochlorococcus adding to those previously presented for Synechococcus. The models were applied to estimate midday, water column photosynthesis based on an atmospheric model of spectral radiation, satellite-derived spectral water transparency and temperature. Based on a global survey of inhibitory exposure severity, a full-latitude section of the mid-Pacific and near-equatorial region of the east Pacific were identified as representative regions for prediction of responses over the entire water column. Comparing predictions integrated over the water column including versus excluding inhibition, production was 7-28% lower due to inhibition depending on strain and site conditions. Inhibition was consistently greater for Prochlorococcus compared to two strains of Synechococcus. Considering only the surface mixed layer, production was inhibited 7-73%. On average, including inhibition lowered estimates of midday productivity around 20% for the modeled region of the Pacific with UV accounting for two-thirds of the reduction. In contrast, most other productivity models either ignore inhibition or only include PAR inhibition. Incorporation of E max model responses into an existing spectral model of depth-integrated, daily production will enable efficient global predictions of picophytoplankton productivity including inhibition. © 2016 John Wiley & Sons Ltd.

Integrating auxiliary data and geophysical techniques for the estimation of soil clay content using CHAID algorithm

NASA Astrophysics Data System (ADS)

Abbaszadeh Afshar, Farideh; Ayoubi, Shamsollah; Besalatpour, Ali Asghar; Khademi, Hossein; Castrignano, Annamaria

2016-03-01

This study was conducted to estimate soil clay content in two depths using geophysical techniques (Ground Penetration Radar-GPR and Electromagnetic Induction-EMI) and ancillary variables (remote sensing and topographic data) in an arid region of the southeastern Iran. GPR measurements were performed throughout ten transects of 100 m length with the line spacing of 10 m, and the EMI measurements were done every 10 m on the same transect in six sites. Ten soil cores were sampled randomly in each site and soil samples were taken from the depth of 0-20 and 20-40 cm, and then the clay fraction of each of sixty soil samples was measured in the laboratory. Clay content was predicted using three different sets of properties including geophysical data, ancillary data, and a combination of both as inputs to multiple linear regressions (MLR) and decision tree-based algorithm of Chi-Squared Automatic Interaction Detection (CHAID) models. The results of the CHAID and MLR models with all combined data showed that geophysical data were the most important variables for the prediction of clay content in two depths in the study area. The proposed MLR model, using the combined data, could explain only 0.44 and 0.31% of the total variability of clay content in 0-20 and 20-40 cm depths, respectively. Also, the coefficient of determination (R2) values for the clay content prediction, using the constructed CHAID model with the combined data, was 0.82 and 0.76 in 0-20 and 20-40 cm depths, respectively. CHAID models, therefore, showed a greater potential in predicting soil clay content from geophysical and ancillary data, while traditional regression methods (i.e. the MLR models) did not perform as well. Overall, the results may encourage researchers in using georeferenced GPR and EMI data as ancillary variables and CHAID algorithm to improve the estimation of soil clay content.

Multiple Environment Single System Quantum Mechanical/Molecular Mechanical (MESS-QM/MM) Calculations. 1. Estimation of Polarization Energies

PubMed Central

2015-01-01

In combined quantum mechanical/molecular mechanical (QM/MM) free energy calculations, it is often advantageous to have a frozen geometry for the quantum mechanical (QM) region. For such multiple-environment single-system (MESS) cases, two schemes are proposed here for estimating the polarization energy: the first scheme, termed MESS-E, involves a Roothaan step extrapolation of the self-consistent field (SCF) energy; whereas the other scheme, termed MESS-H, employs a Newton–Raphson correction using an approximate inverse electronic Hessian of the QM region (which is constructed only once). Both schemes are extremely efficient, because the expensive Fock updates and SCF iterations in standard QM/MM calculations are completely avoided at each configuration. They produce reasonably accurate QM/MM polarization energies: MESS-E can predict the polarization energy within 0.25 kcal/mol in terms of the mean signed error for two of our test cases, solvated methanol and solvated β-alanine, using the M06-2X or ωB97X-D functionals; MESS-H can reproduce the polarization energy within 0.2 kcal/mol for these two cases and for the oxyluciferin–luciferase complex, if the approximate inverse electronic Hessians are constructed with sufficient accuracy. PMID:25321186

Integrated Model for Performance Analysis of All-Optical Multihop Packet Switches

NASA Astrophysics Data System (ADS)

Jeong, Han-You; Seo, Seung-Woo

2000-09-01

The overall performance of an all-optical packet switching system is usually determined by two criteria, i.e., switching latency and packet loss rate. In some real-time applications, however, in which packets arriving later than a timeout period are discarded as loss, the packet loss rate becomes the most dominant criterion for system performance. Here we focus on evaluating the performance of all-optical packet switches in terms of the packet loss rate, which normally arises from the insufficient hardware or the degradation of an optical signal. Considering both aspects, we propose what we believe is a new analysis model for the packet loss rate that reflects the complicated interactions between physical impairments and system-level parameters. On the basis of the estimation model for signal quality degradation in a multihop path we construct an equivalent analysis model of a switching network for evaluating an average bit error rate. With the model constructed we then propose an integrated model for estimating the packet loss rate in three architectural examples of multihop packet switches, each of which is based on a different switching concept. We also derive the bounds on the packet loss rate induced by bit errors. Finally, it is verified through simulation studies that our analysis model accurately predicts system performance.

[Estimation of urban non-point source pollution loading and its factor analysis in the Pearl River Delta].

PubMed

Liao, Yi-Shan; Zhuo, Mu-Ning; Li, Ding-Qiang; Guo, Tai-Long

2013-08-01

In the Pearl Delta region, urban rivers have been seriously polluted, and the input of non-point source pollution materials, such as chemical oxygen demand (COD), into rivers cannot be neglected. During 2009-2010, the water qualities at eight different catchments in the Fenjiang River of Foshan city were monitored, and the COD loads for eight rivulet sewages were calculated in respect of different rainfall conditions. Interesting results were concluded in our paper. The rainfall and landuse type played important roles in the COD loading, with greater influence of rainfall than landuse type. Consequently, a COD loading formula was constructed that was defined as a function of runoff and landuse type that were derived SCS model and land use map. Loading of COD could be evaluated and predicted with the constructed formula. The mean simulation accuracy for single rainfall event was 75.51%. Long-term simulation accuracy was better than that of single rainfall. In 2009, the estimated COD loading and its loading intensity were 8 053 t and 339 kg x (hm2 x a)(-1), and the industrial land was regarded as the main source of COD pollution area. The severe non-point source pollution such as COD in Fenjiang River must be paid more attention in the future.

Multiple environment single system quantum mechanical/molecular mechanical (MESS-QM/MM) calculations. 1. Estimation of polarization energies

DOE PAGES

Sodt, Alexander J.; Mei, Ye; Konig, Gerhard; ...

2014-10-16

In combined quantum mechanical/molecular mechanical (QM/MM) free energy calculations, it is often advantageous to have a frozen geometry for the quantum mechanical (QM) region. For such multiple-environment single-system (MESS) cases, two schemes are proposed here for estimating the polarization energy: the first scheme, termed MESS-E, involves a Roothaan step extrapolation of the self-consistent field (SCF) energy; whereas the other scheme, termed MESS-H, employs a Newton–Raphson correction using an approximate inverse electronic Hessian of the QM region (which is constructed only once). Both schemes are extremely efficient, because the expensive Fock updates and SCF iterations in standard QM/MM calculations are completelymore » avoided at each configuration. Here, they produce reasonably accurate QM/MM polarization energies: MESS-E can predict the polarization energy within 0.25 kcal/mol in terms of the mean signed error for two of our test cases, solvated methanol and solvated β-alanine, using the M06-2X or ωB97X-D functionals; MESS-H can reproduce the polarization energy within 0.2 kcal/mol for these two cases and for the oxyluciferin–luciferase complex, if the approximate inverse electronic Hessians are constructed with sufficient accuracy.« less

Short-term load and wind power forecasting using neural network-based prediction intervals.

PubMed

Quan, Hao; Srinivasan, Dipti; Khosravi, Abbas

2014-02-01

Electrical power systems are evolving from today's centralized bulk systems to more decentralized systems. Penetrations of renewable energies, such as wind and solar power, significantly increase the level of uncertainty in power systems. Accurate load forecasting becomes more complex, yet more important for management of power systems. Traditional methods for generating point forecasts of load demands cannot properly handle uncertainties in system operations. To quantify potential uncertainties associated with forecasts, this paper implements a neural network (NN)-based method for the construction of prediction intervals (PIs). A newly introduced method, called lower upper bound estimation (LUBE), is applied and extended to develop PIs using NN models. A new problem formulation is proposed, which translates the primary multiobjective problem into a constrained single-objective problem. Compared with the cost function, this new formulation is closer to the primary problem and has fewer parameters. Particle swarm optimization (PSO) integrated with the mutation operator is used to solve the problem. Electrical demands from Singapore and New South Wales (Australia), as well as wind power generation from Capital Wind Farm, are used to validate the PSO-based LUBE method. Comparative results show that the proposed method can construct higher quality PIs for load and wind power generation forecasts in a short time.

Production and characterization of cellulose reinforced starch (CRT) films.

PubMed

Sudharsan, K; Chandra Mohan, C; Azhagu Saravana Babu, P; Archana, G; Sabina, K; Sivarajan, M; Sukumar, M

2016-02-01

Starch from Tamarind seed is considered to be a nonedible and inexpensive component, with many industrial applications. Extraction and characterization of tamarind seed starch was carried out for the synthesis of biopolymer. Tamarind seeds were collected, cleaned and further roasted, decorticated, and pulverized to get starch powder. Total starch content present in each tamarind seed is estimated to be around 65-70%. About 84.68% purified starch can be recovered from the tamarind seed. Defatted Tamarind seed starch has an amylose content of 27.55 wt.% and 72.45 wt.% of amylopectin. Morphological (SEM) and X-ray diffraction were used to evaluate crystallinity. Likewise, TGA and DSC of starch have also been analyzed. Thermal properties of starch obtained from tamarind seeds showed good thermal stability when compared to other starch sources such as Mesquite seed and Mango kernel. This study proved that the tamarind seed starch can be used as a potential biopolymer material. Thermo-stable biofilms were produced through initial optimization studies. Predictive response surface quadratic models were constructed for prediction and optimization of biofilm mechanical properties. Correlation coefficient values were calculated to me more than 0.90 for mechanical responses which implies the fitness of constructed model with experimental data. Copyright © 2015 Elsevier B.V. All rights reserved.

A model to predict radon exhalation from walls to indoor air based on the exhalation from building material samples.

PubMed

Sahoo, B K; Sapra, B K; Gaware, J J; Kanse, S D; Mayya, Y S

2011-06-01

In recognition of the fact that building materials are an important source of indoor radon, second only to soil, surface radon exhalation fluxes have been extensively measured from the samples of these materials. Based on this flux data, several researchers have attempted to predict the inhalation dose attributable to radon emitted from walls and ceilings made up of these materials. However, an important aspect not considered in this methodology is the enhancement of the radon flux from the wall or the ceiling constructed using the same building material. This enhancement occurs mainly because of the change in the radon diffusion process from the former to the latter configuration. To predict the true radon flux from the wall based on the flux data of building material samples, we now propose a semi-empirical model involving radon diffusion length and the physical dimensions of the samples as well as wall thickness as other input parameters. This model has been established by statistically fitting the ratio of the solution to radon diffusion equations for the cases of three-dimensional cuboidal shaped building materials (such as brick, concrete block) and one dimensional wall system to a simple mathematical function. The model predictions have been validated against the measurements made at a new construction site. This model provides an alternative tool (substitute to conventional 1-D model) to estimate radon flux from a wall without relying on ²²⁶Ra content, radon emanation factor and bulk density of the samples. Moreover, it may be very useful in the context of developing building codes for radon regulation in new buildings. Copyright © 2011 Elsevier B.V. All rights reserved.

General inattentiveness is a long-term reliable trait independently predictive of psychological health: Danish validation studies of the Mindful Attention Awareness Scale.

PubMed

Jensen, Christian Gaden; Niclasen, Janni; Vangkilde, Signe Allerup; Petersen, Anders; Hasselbalch, Steen Gregers

2016-05-01

The Mindful Attention Awareness Scale (MAAS) measures perceived degree of inattentiveness in different contexts and is often used as a reversed indicator of mindfulness. MAAS is hypothesized to reflect a psychological trait or disposition when used outside attentional training contexts, but the long-term test-retest reliability of MAAS scores is virtually untested. It is unknown whether MAAS predicts psychological health after controlling for standardized socioeconomic status classifications. First, MAAS translated to Danish was validated psychometrically within a randomly invited healthy adult community sample (N = 490). Factor analysis confirmed that MAAS scores quantified a unifactorial construct of excellent composite reliability and consistent convergent validity. Structural equation modeling revealed that MAAS scores contributed independently to predicting psychological distress and mental health, after controlling for age, gender, income, socioeconomic occupational class, stressful life events, and social desirability (β = 0.32-.42, ps < .001). Second, MAAS scores showed satisfactory short-term test-retest reliability in 100 retested healthy university students. Finally, MAAS sample mean scores as well as individuals' scores demonstrated satisfactory test-retest reliability across a 6 months interval in the adult community (retested N = 407), intraclass correlations ≥ .74. MAAS scores displayed significantly stronger long-term test-retest reliability than scores measuring psychological distress (z = 2.78, p = .005). Test-retest reliability estimates did not differ within demographic and socioeconomic strata. Scores on the Danish MAAS were psychometrically validated in healthy adults. MAAS's inattentiveness scores reflected a unidimensional construct, long-term reliable disposition, and a factor of independent significance for predicting psychological health. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Calculations of rate constants for the three-body recombination of H2 in the presence of H2

NASA Technical Reports Server (NTRS)

Schwenke, David W.

1988-01-01

A new global potential energy hypersurface for H2 + H2 is constructed and quasiclassical trajectory calculations performed using the resonance complex theory and energy transfer mechanism to estimate the rate of three body recombination over the temperature range 100 to 5000 K. The new potential is a faithful representation of ab initio electron structure calculations, is unchanged under the operation of exchanging H atoms, and reproduces the accurate H3 potential as one H atom is pulled away. Included in the fitting procedure are geometries expected to be important when one H2 is near or above the dissociation limit. The dynamics calculations explicitly include the motion of all four atoms and are performed efficiently using a vectorized variable-stepsize integrator. The predicted rate constants are approximately a factor of two smaller than experimental estimates over a broad temperature range.

Enterprise architecture availability analysis using fault trees and stakeholder interviews

NASA Astrophysics Data System (ADS)

Närman, Per; Franke, Ulrik; König, Johan; Buschle, Markus; Ekstedt, Mathias

2014-01-01

The availability of enterprise information systems is a key concern for many organisations. This article describes a method for availability analysis based on Fault Tree Analysis and constructs from the ArchiMate enterprise architecture (EA) language. To test the quality of the method, several case-studies within the banking and electrical utility industries were performed. Input data were collected through stakeholder interviews. The results from the case studies were compared with availability of log data to determine the accuracy of the method's predictions. In the five cases where accurate log data were available, the yearly downtime estimates were within eight hours from the actual downtimes. The cost of performing the analysis was low; no case study required more than 20 man-hours of work, making the method ideal for practitioners with an interest in obtaining rapid availability estimates of their enterprise information systems.

Submillimeter, millimeter, and microwave spectral line catalogue

NASA Technical Reports Server (NTRS)

Poynter, R. L.; Pickett, H. M.

1984-01-01

This report describes a computer accessible catalogue of submillimeter, millimeter, and microwave spectral lines in the frequency range between 0 and 10000 GHz (i.e., wavelengths longer than 30 micrometers). The catalogue can be used as a planning guide or as an aid in the identification and analysis of observed spectral lines. The information listed for each spectral line includes the frequency and its estimated error, the intensity, lower state energy, and quantum number assignment. The catalogue has been constructed using theoretical least squares fits of published spectral lines to accepted molecular models. The associated predictions and their estimated errors are based upon the resultant fitted parameters and their covariances. Future versions of this catalogue will add more atoms and molecules and update the present listings (151 species) as new data appear. The catalogue is available from the authors as a magnetic tape recorded in card images and as a set of microfiche records.

Submillimeter, millimeter, and microwave spectral line catalogue

NASA Technical Reports Server (NTRS)

Poynter, R. L.; Pickett, H. M.

1981-01-01

A computer accessible catalogue of submillimeter, millimeter and microwave spectral lines in the frequency range between 0 and 3000 GHZ (i.e., wavelengths longer than 100 mu m) is presented which can be used a planning guide or as an aid in the identification and analysis of observed spectral lines. The information listed for each spectral line includes the frequency and its estimated error, the intensity, lower state energy, and quantum number assignment. The catalogue was constructed by using theoretical least squares fits of published spectral lines to accepted molecular models. The associated predictions and their estimated errors are based upon the resultant fitted parameters and their covariances. Future versions of this catalogue will add more atoms and molecules and update the present listings (133 species) as new data appear. The catalogue is available as a magnetic tape recorded in card images and as a set of microfiche records.

Table Rock Lake Water-Clarity Assessment Using Landsat Thematic Mapper Satellite Data

USGS Publications Warehouse

Krizanich, Gary; Finn, Michael P.

2009-01-01

Water quality of Table Rock Lake in southwestern Missouri is assessed using Landsat Thematic Mapper satellite data. A pilot study uses multidate satellite image scenes in conjunction with physical measurements of secchi disk transparency collected by the Lakes of Missouri Volunteer Program to construct a regression model used to estimate water clarity. The natural log of secchi disk transparency is the dependent variable in the regression and the independent variables are Thematic Mapper band 1 (blue) reflectance and a ratio of the band 1 and band 3 (red) reflectance. The regression model can be used to reliably predict water clarity anywhere within the lake. A pixel-level lake map of predicted water clarity or computed trophic state can be produced from the model output. Information derived from this model can be used by water-resource managers to assess water quality and evaluate effects of changes in the watershed on water quality.

Numerical solution of open string field theory in Schnabl gauge

NASA Astrophysics Data System (ADS)

Arroyo, E. Aldo; Fernandes-Silva, A.; Szitas, R.

2018-01-01

Using traditional Virasoro L 0 level-truncation computations, we evaluate the open bosonic string field theory action up to level (10 , 30). Extremizing this level-truncated potential, we construct a numerical solution for tachyon condensation in Schnabl gauge. We find that the energy associated to the numerical solution overshoots the expected value -1 at level L = 6. Extrapolating the level-truncation data for L ≤ 10 to estimate the vacuum energies for L > 10, we predict that the energy reaches a minimum value at L ˜ 12, and then turns back to approach -1 asymptotically as L → ∞. Furthermore, we analyze the tachyon vacuum expectation value (vev), for which by extrapolating its corresponding level-truncation data, we predict that the tachyon vev reaches a minimum value at L ˜ 26, and then turns back to approach the expected analytical result as L → ∞.

Depositional sequence analysis and sedimentologic modeling for improved prediction of Pennsylvanian reservoirs (Annex 1). Annual report, February 1, 1991--January 31, 1992

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

Watney, W.L.

1992-08-01

Interdisciplinary studies of the Upper Pennsylvanian Lansing and Kansas City groups have been undertaken in order to improve the geologic characterization of petroleum reservoirs and to develop a quantitative understanding of the processes responsible for formation of associated depositional sequences. To this end, concepts and methods of sequence stratigraphy are being used to define and interpret the three-dimensional depositional framework of the Kansas City Group. The investigation includes characterization of reservoir rocks in oil fields in western Kansas, description of analog equivalents in near-surface and surface sites in southeastern Kansas, and construction of regional structural and stratigraphic framework to linkmore » the site specific studies. Geologic inverse and simulation models are being developed to integrate quantitative estimates of controls on sedimentation to produce reconstructions of reservoir-bearing strata in an attempt to enhance our ability to predict reservoir characteristics.« less

Depositional sequence analysis and sedimentologic modeling for improved prediction of Pennsylvanian reservoirs (Annex 1)

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

Watney, W.L.

1992-01-01

Interdisciplinary studies of the Upper Pennsylvanian Lansing and Kansas City groups have been undertaken in order to improve the geologic characterization of petroleum reservoirs and to develop a quantitative understanding of the processes responsible for formation of associated depositional sequences. To this end, concepts and methods of sequence stratigraphy are being used to define and interpret the three-dimensional depositional framework of the Kansas City Group. The investigation includes characterization of reservoir rocks in oil fields in western Kansas, description of analog equivalents in near-surface and surface sites in southeastern Kansas, and construction of regional structural and stratigraphic framework to linkmore » the site specific studies. Geologic inverse and simulation models are being developed to integrate quantitative estimates of controls on sedimentation to produce reconstructions of reservoir-bearing strata in an attempt to enhance our ability to predict reservoir characteristics.« less

Prediction of the compression ratio for municipal solid waste using decision tree.

PubMed

Heshmati R, Ali Akbar; Mokhtari, Maryam; Shakiba Rad, Saeed

2014-01-01

The compression ratio of municipal solid waste (MSW) is an essential parameter for evaluation of waste settlement and landfill design. However, no appropriate model has been proposed to estimate the waste compression ratio so far. In this study, a decision tree method was utilized to predict the waste compression ratio (C'c). The tree was constructed using Quinlan's M5 algorithm. A reliable database retrieved from the literature was used to develop a practical model that relates C'c to waste composition and properties, including dry density, dry weight water content, and percentage of biodegradable organic waste using the decision tree method. The performance of the developed model was examined in terms of different statistical criteria, including correlation coefficient, root mean squared error, mean absolute error and mean bias error, recommended by researchers. The obtained results demonstrate that the suggested model is able to evaluate the compression ratio of MSW effectively.

Modeling of the reactant conversion rate in a turbulent shear flow

NASA Technical Reports Server (NTRS)

Frankel, S. H.; Madnia, C. K.; Givi, P.

1992-01-01

Results are presented of direct numerical simulations (DNS) of spatially developing shear flows under the influence of infinitely fast chemical reactions of the type A + B yields Products. The simulation results are used to construct the compositional structure of the scalar field in a statistical manner. The results of this statistical analysis indicate that the use of a Beta density for the probability density function (PDF) of an appropriate Shvab-Zeldovich mixture fraction provides a very good estimate of the limiting bounds of the reactant conversion rate within the shear layer. This provides a strong justification for the implementation of this density in practical modeling of non-homogeneous turbulent reacting flows. However, the validity of the model cannot be generalized for predictions of higher order statistical quantities. A closed form analytical expression is presented for predicting the maximum rate of reactant conversion in non-homogeneous reacting turbulence.