Phenomenological Constitutive Modeling of High-Temperature Flow Behavior Incorporating Individual and Coupled Effects of Processing Parameters in Super-austenitic Stainless Steel

NASA Astrophysics Data System (ADS)

Roy, Swagata; Biswas, Srija; Babu, K. Arun; Mandal, Sumantra

2018-05-01

A novel constitutive model has been developed for predicting flow responses of super-austenitic stainless steel over a wide range of strains (0.05-0.6), temperatures (1173-1423 K) and strain rates (0.001-1 s-1). Further, the predictability of this new model has been compared with the existing Johnson-Cook (JC) and modified Zerilli-Armstrong (M-ZA) model. The JC model is not befitted for flow prediction as it is found to be exhibiting very high ( 36%) average absolute error (δ) and low ( 0.92) correlation coefficient (R). On the contrary, the M-ZA model has demonstrated relatively lower δ ( 13%) and higher R ( 0.96) for flow prediction. The incorporation of couplings of processing parameters in M-ZA model has led to exhibit better prediction than JC model. However, the flow analyses of the studied alloy have revealed the additional synergistic influences of strain and strain rate as well as strain, temperature, and strain rate apart from those considered in M-ZA model. Hence, the new phenomenological model has been formulated incorporating all the individual and synergistic effects of processing parameters and a `strain-shifting' parameter. The proposed model predicted the flow behavior of the alloy with much better correlation and generalization than M-ZA model as substantiated by its lower δ ( 7.9%) and higher R ( 0.99) of prediction.

Prediction of human gait trajectories during the SSP using a neuromusculoskeletal modeling: A challenge for parametric optimization.

PubMed

Seyed, Mohammadali Rahmati; Mostafa, Rostami; Borhan, Beigzadeh

2018-04-27

The parametric optimization techniques have been widely employed to predict human gait trajectories; however, their applications to reveal the other aspects of gait are questionable. The aim of this study is to investigate whether or not the gait prediction model is able to justify the movement trajectories for the higher average velocities. A planar, seven-segment model with sixteen muscle groups was used to represent human neuro-musculoskeletal dynamics. At first, the joint angles, ground reaction forces (GRFs) and muscle activations were predicted and validated for normal average velocity (1.55 m/s) in the single support phase (SSP) by minimizing energy expenditure, which is subject to the non-linear constraints of the gait. The unconstrained system dynamics of extended inverse dynamics (USDEID) approach was used to estimate muscle activations. Then by scaling time and applying the same procedure, the movement trajectories were predicted for higher average velocities (from 2.07 m/s to 4.07 m/s) and compared to the pattern of movement with fast walking speed. The comparison indicated a high level of compatibility between the experimental and predicted results, except for the vertical position of the center of gravity (COG). It was concluded that the gait prediction model can be effectively used to predict gait trajectories for higher average velocities.

Application of GA-SVM method with parameter optimization for landslide development prediction

NASA Astrophysics Data System (ADS)

Li, X. Z.; Kong, J. M.

2013-10-01

Prediction of landslide development process is always a hot issue in landslide research. So far, many methods for landslide displacement series prediction have been proposed. Support vector machine (SVM) has been proved to be a novel algorithm with good performance. However, the performance strongly depends on the right selection of the parameters (C and γ) of SVM model. In this study, we presented an application of GA-SVM method with parameter optimization in landslide displacement rate prediction. We selected a typical large-scale landslide in some hydro - electrical engineering area of Southwest China as a case. On the basis of analyzing the basic characteristics and monitoring data of the landslide, a single-factor GA-SVM model and a multi-factor GA-SVM model of the landslide were built. Moreover, the models were compared with single-factor and multi-factor SVM models of the landslide. The results show that, the four models have high prediction accuracies, but the accuracies of GA-SVM models are slightly higher than those of SVM models and the accuracies of multi-factor models are slightly higher than those of single-factor models for the landslide prediction. The accuracy of the multi-factor GA-SVM models is the highest, with the smallest RSME of 0.0009 and the biggest RI of 0.9992.

A comparison of efficacy of six prediction models for intravenous immunoglobulin resistance in Kawasaki disease.

PubMed

Qian, Weiguo; Tang, Yunjia; Yan, Wenhua; Sun, Ling; Lv, Haitao

2018-03-09

Kawasaki disease (KD) is the most common pediatric vasculitis. Several models have been established to predict intravenous immunoglobulin (IVIG) resistance. The present study was aimed to evaluate the efficacy of prediction models using the medical data of KD patients. We collected the medical records of patients hospitalized in the Department of Cardiology in Children's Hospital of Soochow University with a diagnosis of KD from Jan 2015 to Dec 2016. IVIG resistance was defined as recrudescent or persistent fever ≥36 h after the end of their IVIG infusion. Patients with IVIG resistance tended to be younger, have higher occurrence of rash and changes of extremities. They had higher levels of c-reactive protein, aspartate aminotransferase, neutrophils proportion (N%), total bilirubin and lower level of albumin. Our prediction model had a sensitivity of 0.72 and a specificity of 0.75. Sensitivity of Kobayashi, Egami, Kawamura, Sano and Formosa were 0.72, 0.44, 0.48, 0.20, and 0.68, respectively. Specificity of these models were 0.62, 0.82, 0.66, 0.91, and 0.48, respectively. Our prediction model had a powerful predictive value in this area, followed by Kobayashi model while all the other prediction models had less excellent performances than ours.

Association of Coronary Artery Calcification with Estimated Coronary Heart Disease Risk from Prediction Models in a Community-Based Sample of Japanese Men: The Shiga Epidemiological Study of Subclinical Atherosclerosis (SESSA).

PubMed

Fujiyoshi, Akira; Arima, Hisatomi; Tanaka-Mizuno, Sachiko; Hisamatsu, Takahashi; Kadowaki, Sayaka; Kadota, Aya; Zaid, Maryam; Sekikawa, Akira; Yamamoto, Takashi; Horie, Minoru; Miura, Katsuyuki; Ueshima, Hirotsugu

2017-12-05

The clinical significance of coronary artery calcification (CAC) is not fully determined in general East Asian populations where background coronary heart disease (CHD) is less common than in USA/Western countries. We cross-sectionally assessed the association between CAC and estimated CHD risk as well as each major risk factor in general Japanese men. Participants were 996 randomly selected Japanese men aged 40-79 y, free of stroke, myocardial infarction, or revascularization. We examined an independent relationship between each risk factor used in prediction models and CAC score ≥100 by logistic regression. We then divided the participants into quintiles of estimated CHD risk per prediction model to calculate odds ratio of having CAC score ≥100. Receiver operating characteristic curve and c-index were used to examine discriminative ability of prevalent CAC for each prediction model. Age, smoking status, and systolic blood pressure were significantly associated with CAC score ≥100 in the multivariable analysis. The odds of having CAC score ≥100 were higher for those in higher quintiles in all prediction models (p-values for trend across quintiles ＜0.0001 for all models). All prediction models showed fair and similar discriminative abilities to detect CAC score ≥100, with similar c-statistics (around 0.70). In a community-based sample of Japanese men free of CHD and stroke, CAC score ≥100 was significantly associated with higher estimated CHD risk by prediction models. This finding supports the potential utility of CAC as a biomarker for CHD in a general Japanese male population.

Innovative Liner Concepts: Experiments and Impedance Modeling of Liners Including the Effect of Bias Flow

NASA Technical Reports Server (NTRS)

Kelly, Jeff; Betts, Juan Fernando; Fuller, Chris

2000-01-01

The study of normal impedance of perforated plate acoustic liners including the effect of bias flow was studied. Two impedance models were developed by modeling the internal flows of perforate orifices as infinite tubes with the inclusion of end corrections to handle finite length effects. These models assumed incompressible and compressible flows, respectively, between the far field and the perforate orifice. The incompressible model was used to predict impedance results for perforated plates with percent open areas ranging from 5% to 15%. The predicted resistance results showed better agreement with experiments for the higher percent open area samples. The agreement also tended to deteriorate as bias flow was increased. For perforated plates with percent open areas ranging from 1% to 5%, the compressible model was used to predict impedance results. The model predictions were closer to the experimental resistance results for the 2% to 3% open area samples. The predictions tended to deteriorate as bias flow was increased. The reactance results were well predicted by the models for the higher percent open area, but deteriorated as the percent open area was lowered (5%) and bias flow was increased. A fit was done on the incompressible model to the experimental database. The fit was performed using an optimization routine that found the optimal set of multiplication coefficients to the non-dimensional groups that minimized the least squares slope error between predictions and experiments. The result of the fit indicated that terms not associated with bias flow required a greater degree of correction than the terms associated with the bias flow. This model improved agreement with experiments by nearly 15% for the low percent open area (5%) samples when compared to the unfitted model. The fitted model and the unfitted model performed equally well for the higher percent open area (10% and 15%).

A human life-stage physiologically based pharmacokinetic and pharmacodynamic model for chlorpyrifos: development and validation.

PubMed

Smith, Jordan Ned; Hinderliter, Paul M; Timchalk, Charles; Bartels, Michael J; Poet, Torka S

2014-08-01

Sensitivity to some chemicals in animals and humans are known to vary with age. Age-related changes in sensitivity to chlorpyrifos have been reported in animal models. A life-stage physiologically based pharmacokinetic and pharmacodynamic (PBPK/PD) model was developed to predict disposition of chlorpyrifos and its metabolites, chlorpyrifos-oxon (the ultimate toxicant) and 3,5,6-trichloro-2-pyridinol (TCPy), as well as B-esterase inhibition by chlorpyrifos-oxon in humans. In this model, previously measured age-dependent metabolism of chlorpyrifos and chlorpyrifos-oxon were integrated into age-related descriptions of human anatomy and physiology. The life-stage PBPK/PD model was calibrated and tested against controlled adult human exposure studies. Simulations suggest age-dependent pharmacokinetics and response may exist. At oral doses ⩾0.6mg/kg of chlorpyrifos (100- to 1000-fold higher than environmental exposure levels), 6months old children are predicted to have higher levels of chlorpyrifos-oxon in blood and higher levels of red blood cell cholinesterase inhibition compared to adults from equivalent doses. At lower doses more relevant to environmental exposures, simulations predict that adults will have slightly higher levels of chlorpyrifos-oxon in blood and greater cholinesterase inhibition. This model provides a computational framework for age-comparative simulations that can be utilized to predict chlorpyrifos disposition and biological response over various postnatal life stages. Copyright © 2013 Elsevier Inc. All rights reserved.

Predictors of cultural capital on science academic achievement at the 8th grade level

NASA Astrophysics Data System (ADS)

Misner, Johnathan Scott

The purpose of the study was to determine if students' cultural capital is a significant predictor of 8th grade science achievement test scores in urban locales. Cultural capital refers to the knowledge used and gained by the dominant class, which allows social and economic mobility. Cultural capital variables include magazines at home and parental education level. Other variables analyzed include socioeconomic status (SES), gender, and English language learners (ELL). This non-experimental study analyzed the results of the 2011 Eighth Grade Science National Assessment of Educational Progress (NAEP). The researcher analyzed the data using a multivariate stepwise regression analysis. The researcher concluded that the addition of cultural capital factors significantly increased the predictive power of the model where magazines in home, gender, student classified as ELL, parental education level, and SES were the independent variables and science achievement was the dependent variable. For alpha=0.05, the overall test for the model produced a R2 value of 0.232; therefore the model predicted 23.2% of variance in science achievement results. Other major findings include: higher measures of home resources predicted higher 2011 NAEP eighth grade science achievement; males were predicted to have higher 2011 NAEP 8 th grade science achievement; classified ELL students were predicted to score lower on the NAEP eight grade science achievement; higher parent education predicted higher NAEP eighth grade science achievement; lower measures of SES predicted lower 2011 NAEP eighth grade science achievement. This study contributed to the research in this field by identifying cultural capital factors that have been found to have statistical significance on predicting eighth grade science achievement results, which can lead to strategies to help improve science academic achievement among underserved populations.

Application and evaluation of two air quality models for particulate matter for a southeastern U.S. episode.

PubMed

Zhang, Yang; Pun, Betty; Wu, Shiang-Yuh; Vijayaraghavan, Krish; Seigneur, Christian

2004-12-01

The Models-3 Community Multiscale Air Quality (CMAQ) Modeling System and the Particulate Matter Comprehensive Air Quality Model with extensions (PMCAMx) were applied to simulate the period June 29-July 10, 1999, of the Southern Oxidants Study episode with two nested horizontal grid sizes: a coarse resolution of 32 km and a fine resolution of 8 km. The predicted spatial variations of ozone (O3), particulate matter with an aerodynamic diameter less than or equal to 2.5 microm (PM2.5), and particulate matter with an aerodynamic diameter less than or equal to 10 microm (PM10) by both models are similar in rural areas but differ from one another significantly over some urban/suburban areas in the eastern and southern United States, where PMCAMx tends to predict higher values of O3 and PM than CMAQ. Both models tend to predict O3 values that are higher than those observed. For observed O3 values above 60 ppb, O3 performance meets the U.S. Environmental Protection Agency's criteria for CMAQ with both grids and for PMCAMx with the fine grid only. It becomes unsatisfactory for PMCAMx and marginally satisfactory for CMAQ for observed O3 values above 40 ppb. Both models predict similar amounts of sulfate (SO4(2-)) and organic matter, and both predict SO4(2-) to be the largest contributor to PM2.5. PMCAMx generally predicts higher amounts of ammonium (NH4+), nitrate (NO3-), and black carbon (BC) than does CMAQ. PM performance for CMAQ is generally consistent with that of other PM models, whereas PMCAMx predicts higher concentrations of NO3-, NH4+, and BC than observed, which degrades its performance. For PM10 and PM2.5 predictions over the southeastern U.S. domain, the ranges of mean normalized gross errors (MNGEs) and mean normalized bias are 37-43% and -33-4% for CMAQ and 50-59% and 7-30% for PMCAMx. Both models predict the largest MNGEs for NO3- (98-104% for CMAQ 138-338% for PMCAMx). The inaccurate NO3- predictions by both models may be caused by the inaccuracies in the ammonia emission inventory and the uncertainties in the gas/particle partitioning under some conditions. In addition to these uncertainties, the significant PM overpredictions by PMCAMx may be attributed to the lack of wet removal for PM and a likely underprediction in the vertical mixing during the daytime.

The Development of Statistical Models for Predicting Surgical Site Infections in Japan: Toward a Statistical Model-Based Standardized Infection Ratio.

PubMed

Fukuda, Haruhisa; Kuroki, Manabu

2016-03-01

To develop and internally validate a surgical site infection (SSI) prediction model for Japan. Retrospective observational cohort study. We analyzed surveillance data submitted to the Japan Nosocomial Infections Surveillance system for patients who had undergone target surgical procedures from January 1, 2010, through December 31, 2012. Logistic regression analyses were used to develop statistical models for predicting SSIs. An SSI prediction model was constructed for each of the procedure categories by statistically selecting the appropriate risk factors from among the collected surveillance data and determining their optimal categorization. Standard bootstrapping techniques were applied to assess potential overfitting. The C-index was used to compare the predictive performances of the new statistical models with those of models based on conventional risk index variables. The study sample comprised 349,987 cases from 428 participant hospitals throughout Japan, and the overall SSI incidence was 7.0%. The C-indices of the new statistical models were significantly higher than those of the conventional risk index models in 21 (67.7%) of the 31 procedure categories (P<.05). No significant overfitting was detected. Japan-specific SSI prediction models were shown to generally have higher accuracy than conventional risk index models. These new models may have applications in assessing hospital performance and identifying high-risk patients in specific procedure categories.

Alcohol expectancies longitudinally predict drinking and the alcohol myopia effects of relief, self-inflation, and excess.

PubMed

Lac, Andrew; Brack, Nathaniel

2018-02-01

Alcohol myopia theory posits that alcohol consumption attenuates information processing capacity, and that expectancy beliefs together with intake level are responsible for experiences in myopic effects (relief, self-inflation, and excess). Adults (N=413) averaging 36.39 (SD=13.02) years of age completed the Comprehensive Effects of Alcohol questionnaire at baseline, followed by alcohol use measures (frequency and quantity) and the Alcohol Myopia Scale one month later. Three structural equation models based on differing construct manifestations of alcohol expectancies served to longitudinally forecast alcohol use and myopia. In Model 1, overall expectancy predicted greater alcohol use and higher levels of all three myopic effects. In Model 2, specifying separate positive and negative expectancy factors, positive but not negative expectancy predicted greater use. Furthermore, positive expectancy and use explained higher myopic relief and higher self-inflation, whereas positive expectancy, negative expectancy, and use explained higher myopic excess. In Model 3, the seven specific expectancy subscales (sociability, tension reduction, liquid courage, sexuality, cognitive and behavioral impairment, risk and aggression, and self-perception) were simultaneously specified as predictors. Tension reduction expectancy, sexuality expectancy, and use contributed to higher myopic relief; sexuality expectancy and use explained higher myopic self-inflation; and risk and aggression expectancy and use accounted for higher myopic excess. Across all three predictive models, the total variance explained ranged from 12 to 19% for alcohol use, 50 to 51% for relief, 29 to 34% for self-inflation, and 32 to 35% for excess. Findings support that the type of alcohol myopia experienced is a concurrent function of self-fulfilling alcohol prophecies and drinking levels. The interpreted measurement manifestation of expectancy yielded different prevention implications. Copyright © 2017 Elsevier Ltd. All rights reserved.

Incorporating single-side sparing in models for predicting parotid dose sparing in head and neck IMRT

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

Yuan, Lulin, E-mail: lulin.yuan@duke.edu; Wu, Q. Jackie; Yin, Fang-Fang

2014-02-15

Purpose: Sparing of single-side parotid gland is a common practice in head-and-neck (HN) intensity modulated radiation therapy (IMRT) planning. It is a special case of dose sparing tradeoff between different organs-at-risk. The authors describe an improved mathematical model for predicting achievable dose sparing in parotid glands in HN IMRT planning that incorporates single-side sparing considerations based on patient anatomy and learning from prior plan data. Methods: Among 68 HN cases analyzed retrospectively, 35 cases had physician prescribed single-side parotid sparing preferences. The single-side sparing model was trained with cases which had single-side sparing preferences, while the standard model was trainedmore » with the remainder of cases. A receiver operating characteristics (ROC) analysis was performed to determine the best criterion that separates the two case groups using the physician's single-side sparing prescription as ground truth. The final predictive model (combined model) takes into account the single-side sparing by switching between the standard and single-side sparing models according to the single-side sparing criterion. The models were tested with 20 additional cases. The significance of the improvement of prediction accuracy by the combined model over the standard model was evaluated using the Wilcoxon rank-sum test. Results: Using the ROC analysis, the best single-side sparing criterion is (1) the predicted median dose of one parotid is higher than 24 Gy; and (2) that of the other is higher than 7 Gy. This criterion gives a true positive rate of 0.82 and a false positive rate of 0.19, respectively. For the bilateral sparing cases, the combined and the standard models performed equally well, with the median of the prediction errors for parotid median dose being 0.34 Gy by both models (p = 0.81). For the single-side sparing cases, the standard model overestimates the median dose by 7.8 Gy on average, while the predictions by the combined model differ from actual values by only 2.2 Gy (p = 0.005). Similarly, the sum of residues between the modeled and the actual plan DVHs is the same for the bilateral sparing cases by both models (p = 0.67), while the standard model predicts significantly higher DVHs than the combined model for the single-side sparing cases (p = 0.01). Conclusions: The combined model for predicting parotid sparing that takes into account single-side sparing improves the prediction accuracy over the previous model.« less

Incorporating single-side sparing in models for predicting parotid dose sparing in head and neck IMRT

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

Yuan, Lulin, E-mail: lulin.yuan@duke.edu; Wu, Q. Jackie; Yin, Fang-Fang

Purpose: Sparing of single-side parotid gland is a common practice in head-and-neck (HN) intensity modulated radiation therapy (IMRT) planning. It is a special case of dose sparing tradeoff between different organs-at-risk. The authors describe an improved mathematical model for predicting achievable dose sparing in parotid glands in HN IMRT planning that incorporates single-side sparing considerations based on patient anatomy and learning from prior plan data. Methods: Among 68 HN cases analyzed retrospectively, 35 cases had physician prescribed single-side parotid sparing preferences. The single-side sparing model was trained with cases which had single-side sparing preferences, while the standard model was trainedmore » with the remainder of cases. A receiver operating characteristics (ROC) analysis was performed to determine the best criterion that separates the two case groups using the physician's single-side sparing prescription as ground truth. The final predictive model (combined model) takes into account the single-side sparing by switching between the standard and single-side sparing models according to the single-side sparing criterion. The models were tested with 20 additional cases. The significance of the improvement of prediction accuracy by the combined model over the standard model was evaluated using the Wilcoxon rank-sum test. Results: Using the ROC analysis, the best single-side sparing criterion is (1) the predicted median dose of one parotid is higher than 24 Gy; and (2) that of the other is higher than 7 Gy. This criterion gives a true positive rate of 0.82 and a false positive rate of 0.19, respectively. For the bilateral sparing cases, the combined and the standard models performed equally well, with the median of the prediction errors for parotid median dose being 0.34 Gy by both models (p = 0.81). For the single-side sparing cases, the standard model overestimates the median dose by 7.8 Gy on average, while the predictions by the combined model differ from actual values by only 2.2 Gy (p = 0.005). Similarly, the sum of residues between the modeled and the actual plan DVHs is the same for the bilateral sparing cases by both models (p = 0.67), while the standard model predicts significantly higher DVHs than the combined model for the single-side sparing cases (p = 0.01). Conclusions: The combined model for predicting parotid sparing that takes into account single-side sparing improves the prediction accuracy over the previous model.« less

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

Smith, Jordan N.; Hinderliter, Paul M.; Timchalk, Charles

Sensitivity to chemicals in animals and humans are known to vary with age. Age-related changes in sensitivity to chlorpyrifos have been reported in animal models. A life-stage physiologically based pharmacokinetic and pharmacodynamic (PBPK/PD) model was developed to computationally predict disposition of CPF and its metabolites, chlorpyrifos-oxon (the ultimate toxicant) and 3,5,6-trichloro-2-pyridinol (TCPy), as well as B-esterase inhibition by chlorpyrifos-oxon in humans. In this model, age-dependent body weight was calculated from a generalized Gompertz function, and compartments (liver, brain, fat, blood, diaphragm, rapid, and slow) were scaled based on body weight from polynomial functions on a fractional body weight basis. Bloodmore » flows among compartments were calculated as a constant flow per compartment volume. The life-stage PBPK/PD model was calibrated and tested against controlled adult human exposure studies. Model simulations suggest age-dependent pharmacokinetics and response may exist. At oral doses ≥ 0.55 mg/kg of chlorpyrifos (significantly higher than environmental exposure levels), 6 mo old children are predicted to have higher levels of chlorpyrifos-oxon in blood and higher levels of red blood cell cholinesterase inhibition compared to adults from equivalent oral doses of chlorpyrifos. At lower doses that are more relevant to environmental exposures, the model predicts that adults will have slightly higher levels of chlorpyrifos-oxon in blood and greater cholinesterase inhibition. This model provides a computational framework for age-comparative simulations that can be utilized to predict CPF disposition and biological response over various postnatal life-stages.« less

Evaluation of MEGAN predicted biogenic isoprene emissions at urban locations in Southeast Texas

NASA Astrophysics Data System (ADS)

Kota, Sri Harsha; Schade, Gunnar; Estes, Mark; Boyer, Doug; Ying, Qi

2015-06-01

Summertime isoprene emissions in the Houston area predicted by the Model of Emissions of Gases and Aerosol from Nature (MEGAN) version 2.1 during the 2006 TexAQS study were evaluated using a source-oriented Community Multiscale Air Quality (CMAQ) Model. Predicted daytime isoprene concentrations at nine surface sites operated by the Texas Commission of Environmental Quality (TCEQ) were significantly higher than local observations when biogenic emissions dominate the total isoprene concentrations, with mean normalized bias (MNB) ranges from 2.0 to 7.7 and mean normalized error (MNE) ranges from 2.2 to 7.7. Predicted upper air isoprene and its first generation oxidation products of methacrolein (MACR) and methyl vinyl ketone (MVK) were also significantly higher (MNB = 8.6, MNE = 9.1) than observations made onboard of NOAA's WP-3 airplane, which flew over the urban area. Over-prediction of isoprene and its oxidation products both at the surface and the upper air strongly suggests that biogenic isoprene emissions in the Houston area are significantly overestimated. Reducing the emission rates by approximately 3/4 was necessary to reduce the error between predictions and observations. Comparison of gridded leaf area index (LAI), plant functional type (PFT) and gridded isoprene emission factor (EF) used in MEGAN modeling with estimates of the same factors from a field survey north of downtown Houston showed that the isoprene over-prediction is likely caused by the combined effects of a large overestimation of the gridded EF in urban Houston and an underestimation of urban LAI. Nevertheless, predicted ozone concentrations in this region were not significantly affected by the isoprene over-predictions, while predicted isoprene SOA and total SOA concentrations can be higher by as much as 50% and 13% using the higher isoprene emission rates, respectively.

Rapid analysis of composition and reactivity in cellulosic biomass feedstocks with near-infrared spectroscopy.

PubMed

Payne, Courtney E; Wolfrum, Edward J

2015-01-01

Obtaining accurate chemical composition and reactivity (measures of carbohydrate release and yield) information for biomass feedstocks in a timely manner is necessary for the commercialization of biofuels. Our objective was to use near-infrared (NIR) spectroscopy and partial least squares (PLS) multivariate analysis to develop calibration models to predict the feedstock composition and the release and yield of soluble carbohydrates generated by a bench-scale dilute acid pretreatment and enzymatic hydrolysis assay. Major feedstocks included in the calibration models are corn stover, sorghum, switchgrass, perennial cool season grasses, rice straw, and miscanthus. We present individual model statistics to demonstrate model performance and validation samples to more accurately measure predictive quality of the models. The PLS-2 model for composition predicts glucan, xylan, lignin, and ash (wt%) with uncertainties similar to primary measurement methods. A PLS-2 model was developed to predict glucose and xylose release following pretreatment and enzymatic hydrolysis. An additional PLS-2 model was developed to predict glucan and xylan yield. PLS-1 models were developed to predict the sum of glucose/glucan and xylose/xylan for release and yield (grams per gram). The release and yield models have higher uncertainties than the primary methods used to develop the models. It is possible to build effective multispecies feedstock models for composition, as well as carbohydrate release and yield. The model for composition is useful for predicting glucan, xylan, lignin, and ash with good uncertainties. The release and yield models have higher uncertainties; however, these models are useful for rapidly screening sample populations to identify unusual samples.

Elastic velocity models for gas-hydrate-bearing sediments-a comparison

NASA Astrophysics Data System (ADS)

Chand, Shyam; Minshull, Tim A.; Gei, Davide; Carcione, José M.

2004-11-01

The presence of gas hydrate in oceanic sediments is mostly identified by bottom-simulating reflectors (BSRs), reflection events with reversed polarity following the trend of the seafloor. Attempts to quantify the amount of gas hydrate present in oceanic sediments have been based mainly on the presence or absence of a BSR and its relative amplitude. Recent studies have shown that a BSR is not a necessary criterion for the presence of gas hydrates, but rather its presence depends on the type of sediments and the in situ conditions. The influence of hydrate on the physical properties of sediments overlying the BSR is determined by the elastic properties of their constituents and on sediment microstructure. In this context several approaches have been developed to predict the physical properties of sediments, and thereby quantify the amount of gas/gas hydrate present from observed deviations of these properties from those predicted for sediments without gas hydrate. We tested four models: the empirical weighted equation (WE); the three-phase effective-medium theory (TPEM); the three-phase Biot theory (TPB) and the differential effective-medium theory (DEM). We compared these models for a range of variables (porosity and clay content) using standard values for physical parameters. The comparison shows that all the models predict sediment properties comparable to field values except for the WE model at lower porosities and the TPB model at higher porosities. The models differ in the variation of velocity with porosity and clay content. The variation of velocity with hydrate saturation is also different, although the range is similar. We have used these models to predict velocities for field data sets from sediment sections with and without gas hydrates. The first is from the Mallik 2L-38 well, Mackenzie Delta, Canada, and the second is from Ocean Drilling Program (ODP) Leg 164 on Blake Ridge. Both data sets have Vp and Vs information along with the composition and porosity of the matrix. Models are considered successful if predictions from both Vp and Vs match hydrate saturations inferred from other data. Three of the models predict consistent hydrate saturations of 60-80 per cent from both Vp and Vs from log and vertical seismic profiling data for the Mallik 2L-38 well data set, but the TPEM model predicts 20 per cent higher saturations, as does the DEM model with a clay-water starting medium. For the clay-rich sediments of Blake Ridge, the DEM, TPEM and WE models predict 10-20 per cent hydrate saturation from Vp data, comparable to that inferred from resistivity data. The hydrate saturation predicted by the TPB model from Vp is higher. Using Vs data, the DEM and TPEM models predict very low or zero hydrate saturation while the TPB and WE models predict hydrate saturation very much higher than those predicted from Vp data. Low hydrate saturations are observed to have little effect on Vs. The hydrate phase appears to be connected within the sediment microstructure even at low saturations.

QSAR Modeling of Rat Acute Toxicity by Oral Exposure

PubMed Central

Zhu, Hao; Martin, Todd M.; Ye, Lin; Sedykh, Alexander; Young, Douglas M.; Tropsha, Alexander

2009-01-01

Few Quantitative Structure-Activity Relationship (QSAR) studies have successfully modeled large, diverse rodent toxicity endpoints. In this study, a comprehensive dataset of 7,385 compounds with their most conservative lethal dose (LD50) values has been compiled. A combinatorial QSAR approach has been employed to develop robust and predictive models of acute toxicity in rats caused by oral exposure to chemicals. To enable fair comparison between the predictive power of models generated in this study versus a commercial toxicity predictor, TOPKAT (Toxicity Prediction by Komputer Assisted Technology), a modeling subset of the entire dataset was selected that included all 3,472 compounds used in the TOPKAT’s training set. The remaining 3,913 compounds, which were not present in the TOPKAT training set, were used as the external validation set. QSAR models of five different types were developed for the modeling set. The prediction accuracy for the external validation set was estimated by determination coefficient R2 of linear regression between actual and predicted LD50 values. The use of the applicability domain threshold implemented in most models generally improved the external prediction accuracy but expectedly led to the decrease in chemical space coverage; depending on the applicability domain threshold, R2 ranged from 0.24 to 0.70. Ultimately, several consensus models were developed by averaging the predicted LD50 for every compound using all 5 models. The consensus models afforded higher prediction accuracy for the external validation dataset with the higher coverage as compared to individual constituent models. The validated consensus LD50 models developed in this study can be used as reliable computational predictors of in vivo acute toxicity. PMID:19845371

Quantitative structure-activity relationship modeling of rat acute toxicity by oral exposure.

PubMed

Zhu, Hao; Martin, Todd M; Ye, Lin; Sedykh, Alexander; Young, Douglas M; Tropsha, Alexander

2009-12-01

Few quantitative structure-activity relationship (QSAR) studies have successfully modeled large, diverse rodent toxicity end points. In this study, a comprehensive data set of 7385 compounds with their most conservative lethal dose (LD(50)) values has been compiled. A combinatorial QSAR approach has been employed to develop robust and predictive models of acute toxicity in rats caused by oral exposure to chemicals. To enable fair comparison between the predictive power of models generated in this study versus a commercial toxicity predictor, TOPKAT (Toxicity Prediction by Komputer Assisted Technology), a modeling subset of the entire data set was selected that included all 3472 compounds used in TOPKAT's training set. The remaining 3913 compounds, which were not present in the TOPKAT training set, were used as the external validation set. QSAR models of five different types were developed for the modeling set. The prediction accuracy for the external validation set was estimated by determination coefficient R(2) of linear regression between actual and predicted LD(50) values. The use of the applicability domain threshold implemented in most models generally improved the external prediction accuracy but expectedly led to the decrease in chemical space coverage; depending on the applicability domain threshold, R(2) ranged from 0.24 to 0.70. Ultimately, several consensus models were developed by averaging the predicted LD(50) for every compound using all five models. The consensus models afforded higher prediction accuracy for the external validation data set with the higher coverage as compared to individual constituent models. The validated consensus LD(50) models developed in this study can be used as reliable computational predictors of in vivo acute toxicity.

Correlation of Wissler Human Thermal Model Blood Flow and Shiver Algorithms

NASA Technical Reports Server (NTRS)

Bue, Grant; Makinen, Janice; Cognata, Thomas

2010-01-01

The Wissler Human Thermal Model (WHTM) is a thermal math model of the human body that has been widely used to predict the human thermoregulatory response to a variety of cold and hot environments. The model has been shown to predict core temperature and skin temperatures higher and lower, respectively, than in tests of subjects in crew escape suit working in a controlled hot environments. Conversely the model predicts core temperature and skin temperatures lower and higher, respectively, than in tests of lightly clad subjects immersed in cold water conditions. The blood flow algorithms of the model has been investigated to allow for more and less flow, respectively, for the cold and hot case. These changes in the model have yielded better correlation of skin and core temperatures in the cold and hot cases. The algorithm for onset of shiver did not need to be modified to achieve good agreement in cold immersion simulations

Thermal therapy in urologic systems: a comparison of arrhenius and thermal isoeffective dose models in predicting hyperthermic injury.

PubMed

He, Xiaoming; Bhowmick, Sankha; Bischof, John C

2009-07-01

The Arrhenius and thermal isoeffective dose (TID) models are the two most commonly used models for predicting hyperthermic injury. The TID model is essentially derived from the Arrhenius model, but due to a variety of assumptions and simplifications now leads to different predictions, particularly at temperatures higher than 50 degrees C. In the present study, the two models are compared and their appropriateness tested for predicting hyperthermic injury in both the traditional hyperthermia (usually, 43-50 degrees C) and thermal surgery (or thermal therapy/thermal ablation, usually, >50 degrees C) regime. The kinetic parameters of thermal injury in both models were obtained from the literature (or literature data), tabulated, and analyzed for various prostate and kidney systems. It was found that the kinetic parameters vary widely, and were particularly dependent on the cell or tissue type, injury assay used, and the time when the injury assessment was performed. In order to compare the capability of the two models for thermal injury prediction, thermal thresholds for complete killing (i.e., 99% cell or tissue injury) were predicted using the models in two important urologic systems, viz., the benign prostatic hyperplasia tissue and the normal porcine kidney tissue. The predictions of the two models matched well at temperatures below 50 degrees C. At higher temperatures, however, the thermal thresholds predicted using the TID model with a constant R value of 0.5, the value commonly used in the traditional hyperthermia literature, are much lower than those predicted using the Arrhenius model. This suggests that traditional use of the TID model (i.e., R=0.5) is inappropriate for predicting hyperthermic injury in the thermal surgery regime (>50 degrees C). Finally, the time-temperature relationships for complete killing (i.e., 99% injury) were calculated and analyzed using the Arrhenius model for the various prostate and kidney systems.

Modeling posttraumatic growth among cancer patients: The roles of social support, appraisals, and adaptive coping.

PubMed

Cao, Weidan; Qi, Xiaona; Cai, Deborah A; Han, Xuanye

2018-01-01

The purpose of the study was to build a model to explain the relationships between social support, uncontrollability appraisal, adaptive coping, and posttraumatic growth (PTG) among cancer patients in China. The participants who were cancer patients in a cancer hospital in China filled out a survey. The final sample size was 201. Structural equation modeling was used to build a model explaining PTG. Structural equation modeling results indicated that higher levels of social support predicted higher levels of adaptive coping, higher levels of uncontrollability appraisal predicted lower levels of adaptive coping, and higher levels of adaptive coping predicted higher levels of PTG. Moreover, adaptive coping was a mediator between social support and growth, as well as a mediator between uncontrollability and growth. The direct effects of social support and uncontrollability on PTG were insignificant. The model demonstrated the relationships between social support, uncontrollability appraisal, adaptive coping, and PTG. It could be concluded that uncontrollability appraisal was a required but not sufficient condition for PTG. Neither social support nor uncontrollability appraisal had direct influence on PTG. However, social support and uncontrollability might indirectly influence PTG, through adaptive coping. It implies that both internal factors (eg, cognitive appraisal and coping) and external factors (eg, social support) are required in order for growth to happen. Copyright © 2017 John Wiley & Sons, Ltd.

Relationship between Quantitative CT Metrics and Health Status and Bode in COPD

PubMed Central

Martinez, Carlos H.; Chen, Ya-Hong; Westgate, Phillip M.; Liu, Lyrica X.; Murray, Susan; Curtis, Jeffrey L.; Make, Barry J.; Kazerooni, Ella A.; Lynch, David A.; Marchetti, Nathaniel; Washko, George R.; Martinez, Fernando J.; Han, MeiLan K.

2013-01-01

Background The value of quantitative computed tomography (QCT) to identify chronic obstructive pulmonary disease (COPD) phenotypes is increasingly appreciated. We hypothesized that QCT-defined emphysema and airway abnormalities relate to St. George's Respiratory Questionnaire (SGRQ) and BODE. Methods 1,200 COPDGene subjects meeting GOLD criteria for COPD with QCT analysis were included. Total lung emphysema was measured using density mask technique with a -950 HU threshold. An automated program measured mean wall thickness (WT), wall area percent (WA%) and pi10 in six segmental bronchi. Separate multivariate analyses examined the relative influence of airway measures and emphysema on SGRQ and BODE. Results In separate models predicting SGRQ score, a one unit standard deviation (SD) increase in each airway measure predicted higher SGRQ scores (for WT, 1.90 points higher, p=0.002; for WA%, 1.52 points higher, p=0.02; for pi10, 2.83 points higher p<0.001). The comparable increase in SGRQ for a one unit SD increase in percent emphysema in these models was relatively weaker, significant only in the pi10 model (for percent emphysema, 1.45 points higher, p=0.01). In separate models predicting BODE, a one unit SD increase in each airway measure predicted higher BODE scores (for WT, 1.07 fold increase, p<0.001; for WA%, 1.20 fold increase, p<0.001; for pi10, 1.16 fold increase, p<0.001). In these models, emphysema more strongly influenced BODE (range 1.24-1.26 fold increase, p<0.001). Conclusion Emphysema and airway disease both relate to clinically important parameters. The relative influence of airway disease is greater for SGRQ; the relative influence of emphysema is greater for BODE. PMID:22514236

Work and Sleep--A Prospective Study of Psychosocial Work Factors, Physical Work Factors, and Work Scheduling.

PubMed

Åkerstedt, Torbjörn; Garefelt, Johanna; Richter, Anne; Westerlund, Hugo; Magnusson Hanson, Linda L; Sverke, Magnus; Kecklund, Göran

2015-07-01

There is limited knowledge about the prospective relationship between major work characteristics (psychosocial, physical, scheduling) and disturbed sleep. The current study sought to provide such knowledge. Prospective cohort, with measurements on two occasions (T1 and T2) separated by two years. Naturalistic study, Sweden. There were 4,827 participants forming a representative sample of the working population. Questionnaire data on work factors obtained on two occasions were analyzed with structural equation modeling. Competing models were compared in order to investigate temporal relationships. A reciprocal model was found to fit the data best. Sleep disturbances at T2 were predicted by higher work demands at T1 and by lower perceived stress at T1. In addition, sleep disturbances at T1 predicted subsequent higher perception of stress, higher work demands, lower degree of control, and less social support at work at T2. A cross-sectional mediation analysis showed that (higher) perceived stress mediated the relationship between (higher) work demands and sleep disturbances; however, no such association was found longitudinally. Higher work demands predicted disturbed sleep, whereas physical work characteristics, shift work, and overtime did not. In addition, disturbed sleep predicted subsequent higher work demands, perceived stress, less social support, and lower degree of control. The results suggest that remedial interventions against sleep disturbances should focus on psychosocial factors, and that such remedial interventions may improve the psychosocial work situation in the long run. © 2015 Associated Professional Sleep Societies, LLC.

20170312 - In Silico Dynamics: computer simulation in a ...

EPA Pesticide Factsheets

Abstract: Utilizing cell biological information to predict higher order biological processes is a significant challenge in predictive toxicology. This is especially true for highly dynamical systems such as the embryo where morphogenesis, growth and differentiation require precisely orchestrated interactions between diverse cell populations. In patterning the embryo, genetic signals setup spatial information that cells then translate into a coordinated biological response. This can be modeled as ‘biowiring diagrams’ representing genetic signals and responses. Because the hallmark of multicellular organization resides in the ability of cells to interact with one another via well-conserved signaling pathways, multiscale computational (in silico) models that enable these interactions provide a platform to translate cellular-molecular lesions perturbations into higher order predictions. Just as ‘the Cell’ is the fundamental unit of biology so too should it be the computational unit (‘Agent’) for modeling embryogenesis. As such, we constructed multicellular agent-based models (ABM) with ‘CompuCell3D’ (www.compucell3d.org) to simulate kinematics of complex cell signaling networks and enable critical tissue events for use in predictive toxicology. Seeding the ABMs with HTS/HCS data from ToxCast demonstrated the potential to predict, quantitatively, the higher order impacts of chemical disruption at the cellular or bioche

In Silico Dynamics: computer simulation in a Virtual Embryo ...

EPA Pesticide Factsheets

Abstract: Utilizing cell biological information to predict higher order biological processes is a significant challenge in predictive toxicology. This is especially true for highly dynamical systems such as the embryo where morphogenesis, growth and differentiation require precisely orchestrated interactions between diverse cell populations. In patterning the embryo, genetic signals setup spatial information that cells then translate into a coordinated biological response. This can be modeled as ‘biowiring diagrams’ representing genetic signals and responses. Because the hallmark of multicellular organization resides in the ability of cells to interact with one another via well-conserved signaling pathways, multiscale computational (in silico) models that enable these interactions provide a platform to translate cellular-molecular lesions perturbations into higher order predictions. Just as ‘the Cell’ is the fundamental unit of biology so too should it be the computational unit (‘Agent’) for modeling embryogenesis. As such, we constructed multicellular agent-based models (ABM) with ‘CompuCell3D’ (www.compucell3d.org) to simulate kinematics of complex cell signaling networks and enable critical tissue events for use in predictive toxicology. Seeding the ABMs with HTS/HCS data from ToxCast demonstrated the potential to predict, quantitatively, the higher order impacts of chemical disruption at the cellular or biochemical level. This is demonstrate

Modeling the binding affinity of structurally diverse industrial chemicals to carbon using the artificial intelligence approaches.

PubMed

Gupta, Shikha; Basant, Nikita; Rai, Premanjali; Singh, Kunwar P

2015-11-01

Binding affinity of chemical to carbon is an important characteristic as it finds vast industrial applications. Experimental determination of the adsorption capacity of diverse chemicals onto carbon is both time and resource intensive, and development of computational approaches has widely been advocated. In this study, artificial intelligence (AI)-based ten different qualitative and quantitative structure-property relationship (QSPR) models (MLPN, RBFN, PNN/GRNN, CCN, SVM, GEP, GMDH, SDT, DTF, DTB) were established for the prediction of the adsorption capacity of structurally diverse chemicals to activated carbon following the OECD guidelines. Structural diversity of the chemicals and nonlinear dependence in the data were evaluated using the Tanimoto similarity index and Brock-Dechert-Scheinkman statistics. The generalization and prediction abilities of the constructed models were established through rigorous internal and external validation procedures performed employing a wide series of statistical checks. In complete dataset, the qualitative models rendered classification accuracies between 97.04 and 99.93%, while the quantitative models yielded correlation (R(2)) values of 0.877-0.977 between the measured and the predicted endpoint values. The quantitative prediction accuracies for the higher molecular weight (MW) compounds (class 4) were relatively better than those for the low MW compounds. Both in the qualitative and quantitative models, the Polarizability was the most influential descriptor. Structural alerts responsible for the extreme adsorption behavior of the compounds were identified. Higher number of carbon and presence of higher halogens in a molecule rendered higher binding affinity. Proposed QSPR models performed well and outperformed the previous reports. A relatively better performance of the ensemble learning models (DTF, DTB) may be attributed to the strengths of the bagging and boosting algorithms which enhance the predictive accuracies. The proposed AI models can be useful tools in screening the chemicals for their binding affinities toward carbon for their safe management.

The predictive performance of a path-dependent exotic-option credit risk model in the emerging market

NASA Astrophysics Data System (ADS)

Chen, Dar-Hsin; Chou, Heng-Chih; Wang, David; Zaabar, Rim

2011-06-01

Most empirical research of the path-dependent, exotic-option credit risk model focuses on developed markets. Taking Taiwan as an example, this study investigates the bankruptcy prediction performance of the path-dependent, barrier option model in the emerging market. We adopt Duan's (1994) [11], (2000) [12] transformed-data maximum likelihood estimation (MLE) method to directly estimate the unobserved model parameters, and compare the predictive ability of the barrier option model to the commonly adopted credit risk model, Merton's model. Our empirical findings show that the barrier option model is more powerful than Merton's model in predicting bankruptcy in the emerging market. Moreover, we find that the barrier option model predicts bankruptcy much better for highly-leveraged firms. Finally, our findings indicate that the prediction accuracy of the credit risk model can be improved by higher asset liquidity and greater financial transparency.

Probabilistic Forecasting of Coastal Morphodynamic Storm Response at Fire Island, New York

NASA Astrophysics Data System (ADS)

Wilson, K.; Adams, P. N.; Hapke, C. J.; Lentz, E. E.; Brenner, O.

2013-12-01

Site-specific probabilistic models of shoreline change are useful because they are derived from direct observations so that local factors, which greatly influence coastal response, are inherently considered by the model. Fire Island, a 50-km barrier island off Long Island, New York, is periodically subject to large storms, whose waves and storm surge dramatically alter beach morphology. Nor'Ida, which impacted the Fire Island coast in 2009, was one of the larger storms to occur in the early 2000s. In this study, we improve upon a Bayesian Network (BN) model informed with historical data to predict shoreline change from Nor'Ida. We present two BN models, referred to as 'original' model (BNo) and 'revised' model (BNr), designed to predict the most probable magnitude of net shoreline movement (NSM), as measured at 934 cross-shore transects, spanning 46 km. Both are informed with observational data (wave impact hours, shoreline and dune toe change rates, pre-storm beach width, and measured NSM) organized within five nodes, but the revised model contains a sixth node to represent the distribution of material added during an April 2009 nourishment project. We evaluate model success by examining the percentage of transects on which the model chooses the correct (observed) bin value of NSM. Comparisons of observed to model-predicted NSM show BNr has slightly higher predictive success over the total study area and significantly higher success at nourished locations. The BNo, which neglects anthropogenic modification history, correctly predicted the most probable NSM in 66.6% of transects, with ambiguous prediction at 12.7% of the locations. BNr, which incorporates anthropogenic modification history, resulted in 69.4% predictive accuracy and 13.9% ambiguity. However, across nourished transects, BNr reported 72.9% predictive success, while BNo reported 61.5% success. Further, at nourished transects, BNr reported higher ambiguity of 23.5% compared to 9.9% in BNo. These results demonstrate that BNr recognizes that nourished transects may behave differently from the expectation derived from historical data and therefore is more 'cautious' in its predictions at these locations. In contrast, BNo is more confident, but less accurate, demonstrating the risk of ignoring the influences of anthropogenic modification in a probabilistic model. Over the entire study region, both models produced greatest predictive accuracy for low retreat observations (BNo: 77.6%; BNr: 76.0%) and least success at predicting low advance observations, although BNr shows considerable improvement over BNo (39.4% vs. 28.6%, respectively). BNr also was significantly more accurate at predicting observations of no shoreline change (BNo: 56.2%; BNr: 68.93%). Both models were accurate for 60% of high advance observations, and reported high predictive success for high retreat observations (BNo: 69.1%; BNr: 67.6%), the scenario of greatest concern to coastal managers.

Rapid analysis of composition and reactivity in cellulosic biomass feedstocks with near-infrared spectroscopy

DOE PAGES

Payne, Courtney E.; Wolfrum, Edward J.

2015-03-12

Obtaining accurate chemical composition and reactivity (measures of carbohydrate release and yield) information for biomass feedstocks in a timely manner is necessary for the commercialization of biofuels. Our objective was to use near-infrared (NIR) spectroscopy and partial least squares (PLS) multivariate analysis to develop calibration models to predict the feedstock composition and the release and yield of soluble carbohydrates generated by a bench-scale dilute acid pretreatment and enzymatic hydrolysis assay. Major feedstocks included in the calibration models are corn stover, sorghum, switchgrass, perennial cool season grasses, rice straw, and miscanthus. Here are the results: We present individual model statistics tomore » demonstrate model performance and validation samples to more accurately measure predictive quality of the models. The PLS-2 model for composition predicts glucan, xylan, lignin, and ash (wt%) with uncertainties similar to primary measurement methods. A PLS-2 model was developed to predict glucose and xylose release following pretreatment and enzymatic hydrolysis. An additional PLS-2 model was developed to predict glucan and xylan yield. PLS-1 models were developed to predict the sum of glucose/glucan and xylose/xylan for release and yield (grams per gram). The release and yield models have higher uncertainties than the primary methods used to develop the models. In conclusion, it is possible to build effective multispecies feedstock models for composition, as well as carbohydrate release and yield. The model for composition is useful for predicting glucan, xylan, lignin, and ash with good uncertainties. The release and yield models have higher uncertainties; however, these models are useful for rapidly screening sample populations to identify unusual samples.« less

Rapid analysis of composition and reactivity in cellulosic biomass feedstocks with near-infrared spectroscopy

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

Payne, Courtney E.; Wolfrum, Edward J.

Obtaining accurate chemical composition and reactivity (measures of carbohydrate release and yield) information for biomass feedstocks in a timely manner is necessary for the commercialization of biofuels. Our objective was to use near-infrared (NIR) spectroscopy and partial least squares (PLS) multivariate analysis to develop calibration models to predict the feedstock composition and the release and yield of soluble carbohydrates generated by a bench-scale dilute acid pretreatment and enzymatic hydrolysis assay. Major feedstocks included in the calibration models are corn stover, sorghum, switchgrass, perennial cool season grasses, rice straw, and miscanthus. Here are the results: We present individual model statistics tomore » demonstrate model performance and validation samples to more accurately measure predictive quality of the models. The PLS-2 model for composition predicts glucan, xylan, lignin, and ash (wt%) with uncertainties similar to primary measurement methods. A PLS-2 model was developed to predict glucose and xylose release following pretreatment and enzymatic hydrolysis. An additional PLS-2 model was developed to predict glucan and xylan yield. PLS-1 models were developed to predict the sum of glucose/glucan and xylose/xylan for release and yield (grams per gram). The release and yield models have higher uncertainties than the primary methods used to develop the models. In conclusion, it is possible to build effective multispecies feedstock models for composition, as well as carbohydrate release and yield. The model for composition is useful for predicting glucan, xylan, lignin, and ash with good uncertainties. The release and yield models have higher uncertainties; however, these models are useful for rapidly screening sample populations to identify unusual samples.« less

Extension to Higher Mass Numbers of an Improved Knockout-Ablation-Coalescence Model for Secondary Neutron and Light Ion Production in Cosmic Ray Interactions

NASA Astrophysics Data System (ADS)

Indi Sriprisan, Sirikul; Townsend, Lawrence; Cucinotta, Francis A.; Miller, Thomas M.

Purpose: An analytical knockout-ablation-coalescence model capable of making quantitative predictions of the neutron spectra from high-energy nucleon-nucleus and nucleus-nucleus collisions is being developed for use in space radiation protection studies. The FORTRAN computer code that implements this model is called UBERNSPEC. The knockout or abrasion stage of the model is based on Glauber multiple scattering theory. The ablation part of the model uses the classical evaporation model of Weisskopf-Ewing. In earlier work, the knockout-ablation model has been extended to incorporate important coalescence effects into the formalism. Recently, alpha coalescence has been incorporated, and the ability to predict light ion spectra with the coalescence model added. The earlier versions were limited to nuclei with mass numbers less than 69. In this work, the UBERNSPEC code has been extended to make predictions of secondary neutrons and light ion production from the interactions of heavy charged particles with higher mass numbers (as large as 238). The predictions are compared with published measurements of neutron spectra and light ion energy for a variety of collision pairs. Furthermore, the predicted spectra from this work are compared with the predictions from the recently-developed heavy ion event generator incorporated in the Monte Carlo radiation transport code HETC-HEDS.

Extrapolation of a predictive model for growth of a low inoculum size of Salmonella typhimurium DT104 on chicken skin to higher inoculum sizes

USDA-ARS?s Scientific Manuscript database

Validation of model predictions for independent variables not included in model development can save time and money by identifying conditions for which new models are not needed. A single strain of Salmonella Typhimurium DT104 was used to develop a general regression neural network model for growth...

An Overview of Numerical Weather Prediction on Various Scales

NASA Astrophysics Data System (ADS)

Bao, J.-W.

2009-04-01

The increasing public need for detailed weather forecasts, along with the advances in computer technology, has motivated many research institutes and national weather forecasting centers to develop and run global as well as regional numerical weather prediction (NWP) models at high resolutions (i.e., with horizontal resolutions of ~10 km or higher for global models and 1 km or higher for regional models, and with ~60 vertical levels or higher). The need for running NWP models at high horizontal and vertical resolutions requires the implementation of non-hydrostatic dynamic core with a choice of horizontal grid configurations and vertical coordinates that are appropriate for high resolutions. Development of advanced numerics will also be needed for high resolution global and regional models, in particular, when the models are applied to transport problems and air quality applications. In addition to the challenges in numerics, the NWP community is also facing the challenges of developing physics parameterizations that are well suited for high-resolution NWP models. For example, when NWP models are run at resolutions of ~5 km or higher, the use of much more detailed microphysics parameterizations than those currently used in NWP model will become important. Another example is that regional NWP models at ~1 km or higher only partially resolve convective energy containing eddies in the lower troposphere. Parameterizations to account for the subgrid diffusion associated with unresolved turbulence still need to be developed. Further, physically sound parameterizations for air-sea interaction will be a critical component for tropical NWP models, particularly for hurricane predictions models. In this review presentation, the above issues will be elaborated on and the approaches to address them will be discussed.

Hierarchical models for informing general biomass equations with felled tree data

Treesearch

Brian J. Clough; Matthew B. Russell; Christopher W. Woodall; Grant M. Domke; Philip J. Radtke

2015-01-01

We present a hierarchical framework that uses a large multispecies felled tree database to inform a set of general models for predicting tree foliage biomass, with accompanying uncertainty, within the FIA database. Results suggest significant prediction uncertainty for individual trees and reveal higher errors when predicting foliage biomass for larger trees and for...

Predictive genetic testing for the identification of high-risk groups: a simulation study on the impact of predictive ability

PubMed Central

2011-01-01

Background Genetic risk models could potentially be useful in identifying high-risk groups for the prevention of complex diseases. We investigated the performance of this risk stratification strategy by examining epidemiological parameters that impact the predictive ability of risk models. Methods We assessed sensitivity, specificity, and positive and negative predictive value for all possible risk thresholds that can define high-risk groups and investigated how these measures depend on the frequency of disease in the population, the frequency of the high-risk group, and the discriminative accuracy of the risk model, as assessed by the area under the receiver-operating characteristic curve (AUC). In a simulation study, we modeled genetic risk scores of 50 genes with equal odds ratios and genotype frequencies, and varied the odds ratios and the disease frequency across scenarios. We also performed a simulation of age-related macular degeneration risk prediction based on published odds ratios and frequencies for six genetic risk variants. Results We show that when the frequency of the high-risk group was lower than the disease frequency, positive predictive value increased with the AUC but sensitivity remained low. When the frequency of the high-risk group was higher than the disease frequency, sensitivity was high but positive predictive value remained low. When both frequencies were equal, both positive predictive value and sensitivity increased with increasing AUC, but higher AUC was needed to maximize both measures. Conclusions The performance of risk stratification is strongly determined by the frequency of the high-risk group relative to the frequency of disease in the population. The identification of high-risk groups with appreciable combinations of sensitivity and positive predictive value requires higher AUC. PMID:21797996

Exploring Higher Education Business Models ("If Such a Thing Exists")

ERIC Educational Resources Information Center

Harney, John O.

2013-01-01

The global economic recession has caused students, parents, and policymakers to reevaluate personal and societal investments in higher education--and has prompted the realization that traditional higher ed "business models" may be unsustainable. Predicting a shakeout, most presidents expressed confidence for their own school's ability to…

Next-Term Student Performance Prediction: A Recommender Systems Approach

ERIC Educational Resources Information Center

Sweeney, Mack; Rangwala, Huzefa; Lester, Jaime; Johri, Aditya

2016-01-01

An enduring issue in higher education is student retention to successful graduation. National statistics indicate that most higher education institutions have four-year degree completion rates around 50%, or just half of their student populations. While there are prediction models which illuminate what factors assist with college student success,…

Sociodemographic Predictors of Vaccination Exemptions on the Basis of Personal Belief in California.

PubMed

Yang, Y Tony; Delamater, Paul L; Leslie, Timothy F; Mello, Michelle M

2016-01-01

We examined the variability in the percentage of students with personal belief exemptions (PBEs) from mandatory vaccinations in California schools and communities according to income, education, race, and school characteristics. We used spatial lag models to analyze 2007-2013 PBE data from the California Department of Public Health. The analyses included school- and regional-level models, and separately examined the percentage of students with exemptions in 2013 and the change in percentages over time. The percentage of students with PBEs doubled from 2007 to 2013, from 1.54% to 3.06%. Across all models, higher median household income and higher percentage of White race in the population, but not educational attainment, significantly predicted higher percentages of students with PBEs in 2013. Higher income, White population, and private school type significantly predicted greater increases in exemptions from 2007 to 2013, whereas higher educational attainment was associated with smaller increases. Personal belief exemptions are more common in areas with a higher percentage of White race and higher income.

Haplotype-Based Genome-Wide Prediction Models Exploit Local Epistatic Interactions Among Markers

PubMed Central

Jiang, Yong; Schmidt, Renate H.; Reif, Jochen C.

2018-01-01

Genome-wide prediction approaches represent versatile tools for the analysis and prediction of complex traits. Mostly they rely on marker-based information, but scenarios have been reported in which models capitalizing on closely-linked markers that were combined into haplotypes outperformed marker-based models. Detailed comparisons were undertaken to reveal under which circumstances haplotype-based genome-wide prediction models are superior to marker-based models. Specifically, it was of interest to analyze whether and how haplotype-based models may take local epistatic effects between markers into account. Assuming that populations consisted of fully homozygous individuals, a marker-based model in which local epistatic effects inside haplotype blocks were exploited (LEGBLUP) was linearly transformable into a haplotype-based model (HGBLUP). This theoretical derivation formally revealed that haplotype-based genome-wide prediction models capitalize on local epistatic effects among markers. Simulation studies corroborated this finding. Due to its computational efficiency the HGBLUP model promises to be an interesting tool for studies in which ultra-high-density SNP data sets are studied. Applying the HGBLUP model to empirical data sets revealed higher prediction accuracies than for marker-based models for both traits studied using a mouse panel. In contrast, only a small subset of the traits analyzed in crop populations showed such a benefit. Cases in which higher prediction accuracies are observed for HGBLUP than for marker-based models are expected to be of immediate relevance for breeders, due to the tight linkage a beneficial haplotype will be preserved for many generations. In this respect the inheritance of local epistatic effects very much resembles the one of additive effects. PMID:29549092

Haplotype-Based Genome-Wide Prediction Models Exploit Local Epistatic Interactions Among Markers.

PubMed

Jiang, Yong; Schmidt, Renate H; Reif, Jochen C

2018-05-04

Genome-wide prediction approaches represent versatile tools for the analysis and prediction of complex traits. Mostly they rely on marker-based information, but scenarios have been reported in which models capitalizing on closely-linked markers that were combined into haplotypes outperformed marker-based models. Detailed comparisons were undertaken to reveal under which circumstances haplotype-based genome-wide prediction models are superior to marker-based models. Specifically, it was of interest to analyze whether and how haplotype-based models may take local epistatic effects between markers into account. Assuming that populations consisted of fully homozygous individuals, a marker-based model in which local epistatic effects inside haplotype blocks were exploited (LEGBLUP) was linearly transformable into a haplotype-based model (HGBLUP). This theoretical derivation formally revealed that haplotype-based genome-wide prediction models capitalize on local epistatic effects among markers. Simulation studies corroborated this finding. Due to its computational efficiency the HGBLUP model promises to be an interesting tool for studies in which ultra-high-density SNP data sets are studied. Applying the HGBLUP model to empirical data sets revealed higher prediction accuracies than for marker-based models for both traits studied using a mouse panel. In contrast, only a small subset of the traits analyzed in crop populations showed such a benefit. Cases in which higher prediction accuracies are observed for HGBLUP than for marker-based models are expected to be of immediate relevance for breeders, due to the tight linkage a beneficial haplotype will be preserved for many generations. In this respect the inheritance of local epistatic effects very much resembles the one of additive effects. Copyright © 2018 Jiang et al.

Increase in the CO2 exchange rate of leaves of Ilex rotunda with elevated atmospheric CO2 concentration in an urban canyon

NASA Astrophysics Data System (ADS)

Takagi, M.; Gyokusen, Koichiro; Saito, Akira

It was found that the atmospheric carbon dioxide (CO2) concentration in an urban canyon in Fukuoka city, Japan during August 1997 was about 30 µmol mol-1 higher than that in the suburbs. When fully exposed to sunlight, in situ the rate of photosynthesis in single leaves of Ilex rotunda planted in the urban canyon was higher when the atmospheric CO2 concentration was elevated. A biochemically based model was able to predict the in situ rate of photosynthesis well. The model also predicted an increase in the daily CO2 exchange rate for leaves in the urban canyon with an increase in atmospheric CO2 concentration. However, in situ such an increase in the daily CO2 exchange rate may be offset by diminished sunlight, a higher air temperature and a lower relative humidity. Thus, the daily CO2 exchange rate predicted using the model based soleley on the environmental conditions prevailing in the urban canyon was lower than that predicted based only on environmental factors found in the suburbs.

The Mt. Hood challenge: cross-testing two diabetes simulation models.

PubMed

Brown, J B; Palmer, A J; Bisgaard, P; Chan, W; Pedula, K; Russell, A

2000-11-01

Starting from identical patients with type 2 diabetes, we compared the 20-year predictions of two computer simulation models, a 1998 version of the IMIB model and version 2.17 of the Global Diabetes Model (GDM). Primary measures of outcome were 20-year cumulative rates of: survival, first (incident) acute myocardial infarction (AMI), first stroke, proliferative diabetic retinopathy (PDR), macro-albuminuria (gross proteinuria, or GPR), and amputation. Standardized test patients were newly diagnosed males aged 45 or 75, with high and low levels of glycated hemoglobin (HbA(1c)), systolic blood pressure (SBP), and serum lipids. Both models generated realistic results and appropriate responses to changes in risk factors. Compared with the GDM, the IMIB model predicted much higher rates of mortality and AMI, and fewer strokes. These differences can be explained by differences in model architecture (Markov vs. microsimulation), different evidence bases for cardiovascular prediction (Framingham Heart Study cohort vs. Kaiser Permanente patients), and isolated versus interdependent prediction of cardiovascular events. Compared with IMIB, GDM predicted much higher lifetime costs, because of lower mortality and the use of a different costing method. It is feasible to cross-validate and explicate dissimilar diabetes simulation models using standardized patients. The wide differences in the model results that we observed demonstrate the need for cross-validation. We propose to hold a second 'Mt Hood Challenge' in 2001 and invite all diabetes modelers to attend.

A method for grounding grid corrosion rate prediction

NASA Astrophysics Data System (ADS)

Han, Juan; Du, Jingyi

2017-06-01

Involved in a variety of factors, prediction of grounding grid corrosion complex, and uncertainty in the acquisition process, we propose a combination of EAHP (extended AHP) and fuzzy nearness degree of effective grounding grid corrosion rate prediction model. EAHP is used to establish judgment matrix and calculate the weight of each factors corrosion of grounding grid; different sample classification properties have different corrosion rate of contribution, and combining the principle of close to predict corrosion rate.The application result shows, the model can better capture data variation, thus to improve the validity of the model to get higher prediction precision.

Development of a prognostic model for predicting spontaneous singleton preterm birth.

PubMed

Schaaf, Jelle M; Ravelli, Anita C J; Mol, Ben Willem J; Abu-Hanna, Ameen

2012-10-01

To develop and validate a prognostic model for prediction of spontaneous preterm birth. Prospective cohort study using data of the nationwide perinatal registry in The Netherlands. We studied 1,524,058 singleton pregnancies between 1999 and 2007. We developed a multiple logistic regression model to estimate the risk of spontaneous preterm birth based on maternal and pregnancy characteristics. We used bootstrapping techniques to internally validate our model. Discrimination (AUC), accuracy (Brier score) and calibration (calibration graphs and Hosmer-Lemeshow C-statistic) were used to assess the model's predictive performance. Our primary outcome measure was spontaneous preterm birth at <37 completed weeks. Spontaneous preterm birth occurred in 57,796 (3.8%) pregnancies. The final model included 13 variables for predicting preterm birth. The predicted probabilities ranged from 0.01 to 0.71 (IQR 0.02-0.04). The model had an area under the receiver operator characteristic curve (AUC) of 0.63 (95% CI 0.63-0.63), the Brier score was 0.04 (95% CI 0.04-0.04) and the Hosmer Lemeshow C-statistic was significant (p<0.0001). The calibration graph showed overprediction at higher values of predicted probability. The positive predictive value was 26% (95% CI 20-33%) for the 0.4 probability cut-off point. The model's discrimination was fair and it had modest calibration. Previous preterm birth, drug abuse and vaginal bleeding in the first half of pregnancy were the most important predictors for spontaneous preterm birth. Although not applicable in clinical practice yet, this model is a next step towards early prediction of spontaneous preterm birth that enables caregivers to start preventive therapy in women at higher risk. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Dengue forecasting in São Paulo city with generalized additive models, artificial neural networks and seasonal autoregressive integrated moving average models.

PubMed

Baquero, Oswaldo Santos; Santana, Lidia Maria Reis; Chiaravalloti-Neto, Francisco

2018-01-01

Globally, the number of dengue cases has been on the increase since 1990 and this trend has also been found in Brazil and its most populated city-São Paulo. Surveillance systems based on predictions allow for timely decision making processes, and in turn, timely and efficient interventions to reduce the burden of the disease. We conducted a comparative study of dengue predictions in São Paulo city to test the performance of trained seasonal autoregressive integrated moving average models, generalized additive models and artificial neural networks. We also used a naïve model as a benchmark. A generalized additive model with lags of the number of cases and meteorological variables had the best performance, predicted epidemics of unprecedented magnitude and its performance was 3.16 times higher than the benchmark and 1.47 higher that the next best performing model. The predictive models captured the seasonal patterns but differed in their capacity to anticipate large epidemics and all outperformed the benchmark. In addition to be able to predict epidemics of unprecedented magnitude, the best model had computational advantages, since its training and tuning was straightforward and required seconds or at most few minutes. These are desired characteristics to provide timely results for decision makers. However, it should be noted that predictions are made just one month ahead and this is a limitation that future studies could try to reduce.

Learning epistatic interactions from sequence-activity data to predict enantioselectivity

NASA Astrophysics Data System (ADS)

Zaugg, Julian; Gumulya, Yosephine; Malde, Alpeshkumar K.; Bodén, Mikael

2017-12-01

Enzymes with a high selectivity are desirable for improving economics of chemical synthesis of enantiopure compounds. To improve enzyme selectivity mutations are often introduced near the catalytic active site. In this compact environment epistatic interactions between residues, where contributions to selectivity are non-additive, play a significant role in determining the degree of selectivity. Using support vector machine regression models we map mutations to the experimentally characterised enantioselectivities for a set of 136 variants of the epoxide hydrolase from the fungus Aspergillus niger (AnEH). We investigate whether the influence a mutation has on enzyme selectivity can be accurately predicted through linear models, and whether prediction accuracy can be improved using higher-order counterparts. Comparing linear and polynomial degree = 2 models, mean Pearson coefficients (r) from 50 {× } 5 -fold cross-validation increase from 0.84 to 0.91 respectively. Equivalent models tested on interaction-minimised sequences achieve values of r=0.90 and r=0.93 . As expected, testing on a simulated control data set with no interactions results in no significant improvements from higher-order models. Additional experimentally derived AnEH mutants are tested with linear and polynomial degree = 2 models, with values increasing from r=0.51 to r=0.87 respectively. The study demonstrates that linear models perform well, however the representation of epistatic interactions in predictive models improves identification of selectivity-enhancing mutations. The improvement is attributed to higher-order kernel functions that represent epistatic interactions between residues.

Learning epistatic interactions from sequence-activity data to predict enantioselectivity

NASA Astrophysics Data System (ADS)

Zaugg, Julian; Gumulya, Yosephine; Malde, Alpeshkumar K.; Bodén, Mikael

2017-12-01

Enzymes with a high selectivity are desirable for improving economics of chemical synthesis of enantiopure compounds. To improve enzyme selectivity mutations are often introduced near the catalytic active site. In this compact environment epistatic interactions between residues, where contributions to selectivity are non-additive, play a significant role in determining the degree of selectivity. Using support vector machine regression models we map mutations to the experimentally characterised enantioselectivities for a set of 136 variants of the epoxide hydrolase from the fungus Aspergillus niger ( AnEH). We investigate whether the influence a mutation has on enzyme selectivity can be accurately predicted through linear models, and whether prediction accuracy can be improved using higher-order counterparts. Comparing linear and polynomial degree = 2 models, mean Pearson coefficients ( r) from 50 {× } 5-fold cross-validation increase from 0.84 to 0.91 respectively. Equivalent models tested on interaction-minimised sequences achieve values of r=0.90 and r=0.93. As expected, testing on a simulated control data set with no interactions results in no significant improvements from higher-order models. Additional experimentally derived AnEH mutants are tested with linear and polynomial degree = 2 models, with values increasing from r=0.51 to r=0.87 respectively. The study demonstrates that linear models perform well, however the representation of epistatic interactions in predictive models improves identification of selectivity-enhancing mutations. The improvement is attributed to higher-order kernel functions that represent epistatic interactions between residues.

Learning epistatic interactions from sequence-activity data to predict enantioselectivity.

PubMed

Zaugg, Julian; Gumulya, Yosephine; Malde, Alpeshkumar K; Bodén, Mikael

2017-12-01

Enzymes with a high selectivity are desirable for improving economics of chemical synthesis of enantiopure compounds. To improve enzyme selectivity mutations are often introduced near the catalytic active site. In this compact environment epistatic interactions between residues, where contributions to selectivity are non-additive, play a significant role in determining the degree of selectivity. Using support vector machine regression models we map mutations to the experimentally characterised enantioselectivities for a set of 136 variants of the epoxide hydrolase from the fungus Aspergillus niger (AnEH). We investigate whether the influence a mutation has on enzyme selectivity can be accurately predicted through linear models, and whether prediction accuracy can be improved using higher-order counterparts. Comparing linear and polynomial degree = 2 models, mean Pearson coefficients (r) from [Formula: see text]-fold cross-validation increase from 0.84 to 0.91 respectively. Equivalent models tested on interaction-minimised sequences achieve values of [Formula: see text] and [Formula: see text]. As expected, testing on a simulated control data set with no interactions results in no significant improvements from higher-order models. Additional experimentally derived AnEH mutants are tested with linear and polynomial degree = 2 models, with values increasing from [Formula: see text] to [Formula: see text] respectively. The study demonstrates that linear models perform well, however the representation of epistatic interactions in predictive models improves identification of selectivity-enhancing mutations. The improvement is attributed to higher-order kernel functions that represent epistatic interactions between residues.

Progress towards a more predictive model for hohlraum radiation drive and symmetry

NASA Astrophysics Data System (ADS)

Jones, O. S.; Suter, L. J.; Scott, H. A.; Barrios, M. A.; Farmer, W. A.; Hansen, S. B.; Liedahl, D. A.; Mauche, C. W.; Moore, A. S.; Rosen, M. D.; Salmonson, J. D.; Strozzi, D. J.; Thomas, C. A.; Turnbull, D. P.

2017-05-01

For several years, we have been calculating the radiation drive in laser-heated gold hohlraums using flux-limited heat transport with a limiter of 0.15, tabulated values of local thermodynamic equilibrium gold opacity, and an approximate model for not in a local thermodynamic equilibrium (NLTE) gold emissivity (DCA_2010). This model has been successful in predicting the radiation drive in vacuum hohlraums, but for gas-filled hohlraums used to drive capsule implosions, the model consistently predicts too much drive and capsule bang times earlier than measured. In this work, we introduce a new model that brings the calculated bang time into better agreement with the measured bang time. The new model employs (1) a numerical grid that is fully converged in space, energy, and time, (2) a modified approximate NLTE model that includes more physics and is in better agreement with more detailed offline emissivity models, and (3) a reduced flux limiter value of 0.03. We applied this model to gas-filled hohlraum experiments using high density carbon and plastic ablator capsules that had hohlraum He fill gas densities ranging from 0.06 to 1.6 mg/cc and hohlraum diameters of 5.75 or 6.72 mm. The new model predicts bang times to within ±100 ps for most experiments with low to intermediate fill densities (up to 0.85 mg/cc). This model predicts higher temperatures in the plasma than the old model and also predicts that at higher gas fill densities, a significant amount of inner beam laser energy escapes the hohlraum through the opposite laser entrance hole.

Peak expiratory flow profiles delivered by pump systems. Limitations due to wave action.

PubMed

Miller, M R; Jones, B; Xu, Y; Pedersen, O F; Quanjer, P H

2000-06-01

Pump systems are currently used to test the performance of both spirometers and peak expiratory flow (PEF) meters, but for certain flow profiles the input signal (i.e., requested profile) and the output profile can differ. We developed a mathematical model of wave action within a pump and compared the recorded flow profiles with both the input profiles and the output predicted by the model. Three American Thoracic Society (ATS) flow profiles and four artificial flow-versus-time profiles were delivered by a pump, first to a pneumotachograph (PT) on its own, then to the PT with a 32-cm upstream extension tube (which would favor wave action), and lastly with the PT in series with and immediately downstream to a mini-Wright peak flow meter. With the PT on its own, recorded flow for the seven profiles was 2.4 +/- 1.9% (mean +/- SD) higher than the pump's input flow, and similarly was 2.3 +/- 2.3% higher than the pump's output flow as predicted by the model. With the extension tube in place, the recorded flow was 6.6 +/- 6.4% higher than the input flow (range: 0.1 to 18.4%), but was only 1.2 +/- 2.5% higher than the output flow predicted by the model (range: -0.8 to 5.2%). With the mini-Wright meter in series, the flow recorded by the PT was on average 6.1 +/- 9.1% below the input flow (range: -23.8 to 2. 5%), but was only 0.6 +/- 3.3% above the pump's output flow predicted by the model (range: -5.5 to 3.9%). The mini-Wright meter's reading (corrected for its nonlinearity) was on average 1.3 +/- 3.6% below the model's predicted output flow (range: -9.0 to 1. 5%). The mini-Wright meter would be deemed outside ATS limits for accuracy for three of the seven profiles when compared with the pump's input PEF, but this would be true for only one profile when compared with the pump's output PEF as predicted by the model. Our study shows that the output flow from pump systems can differ from the input waveform depending on the operating configuration. This effect can be predicted with reasonable accuracy using a model based on nonsteady flow analysis that takes account of pressure wave reflections within pump systems.

A Conceptual Model for the Development of Externalizing Behavior Problems Among Kindergarten Children of Alcoholic Families: Role of Parenting and Children's Self-Regulation

PubMed Central

Eiden, Rina D.; Edwards, Ellen P.; Leonard, Kenneth E.

2009-01-01

The purpose of this study was to test a conceptual model predicting children's externalizing behavior problems in kindergarten in a sample of children with alcoholic (n = 130) and nonalcoholic (n = 97) parents. The model examined the role of parents' alcohol diagnoses, depression, and antisocial behavior at 12–18 months of child age in predicting parental warmth/sensitivity at 2 years of child age. Parental warmth/sensitivity at 2 years was hypothesized to predict children's self-regulation at 3 years (effortful control and internalization of rules), which in turn was expected to predict externalizing behavior problems in kindergarten. Structural equation modeling was largely supportive of this conceptual model. Fathers' alcohol diagnosis at 12–18 months was associated with lower maternal and paternal warmth/sensitivity at 2 years. Lower maternal warmth/sensitivity was longitudinally predictive of lower child self-regulation at 3 years, which in turn was longitudinally predictive of higher externalizing behavior problems in kindergarten, after controlling for prior behavior problems. There was a direct association between parents' depression and children's externalizing behavior problems. Results indicate that one pathway to higher externalizing behavior problems among children of alcoholics may be via parenting and self-regulation in the toddler to preschool years. PMID:17723044

Validation of Aircraft Noise Prediction Models at Low Levels of Exposure

NASA Technical Reports Server (NTRS)

Page, Juliet A.; Hobbs, Christopher M.; Plotkin, Kenneth J.; Stusnick, Eric; Shepherd, Kevin P. (Technical Monitor)

2000-01-01

Aircraft noise measurements were made at Denver International Airport for a period of four weeks. Detailed operational information was provided by airline operators which enabled noise levels to be predicted using the FAA's Integrated Noise Model. Several thrust prediction techniques were evaluated. Measured sound exposure levels for departure operations were found to be 4 to 10 dB higher than predicted, depending on the thrust prediction technique employed. Differences between measured and predicted levels are shown to be related to atmospheric conditions present at the aircraft altitude.

Study on the medical meteorological forecast of the number of hypertension inpatient based on SVR

NASA Astrophysics Data System (ADS)

Zhai, Guangyu; Chai, Guorong; Zhang, Haifeng

2017-06-01

The purpose of this study is to build a hypertension prediction model by discussing the meteorological factors for hypertension incidence. The research method is selecting the standard data of relative humidity, air temperature, visibility, wind speed and air pressure of Lanzhou from 2010 to 2012(calculating the maximum, minimum and average value with 5 days as a unit ) as the input variables of Support Vector Regression(SVR) and the standard data of hypertension incidence of the same period as the output dependent variables to obtain the optimal prediction parameters by cross validation algorithm, then by SVR algorithm learning and training, a SVR forecast model for hypertension incidence is built. The result shows that the hypertension prediction model is composed of 15 input independent variables, the training accuracy is 0.005, the final error is 0.0026389. The forecast accuracy based on SVR model is 97.1429%, which is higher than statistical forecast equation and neural network prediction method. It is concluded that SVR model provides a new method for hypertension prediction with its simple calculation, small error as well as higher historical sample fitting and Independent sample forecast capability.

Anger and the ABC model underlying Rational-Emotive Behavior Therapy.

PubMed

Ziegler, Daniel J; Smith, Phillip N

2004-06-01

The ABC model underlying Ellis's Rational-Emotive Behavior Therapy predicts that people who think more irrationally should display greater trait anger than do people who think less irrationally. This study tested this prediction regarding the ABC model. 186 college students were administered the Survey of Personal Beliefs and the State-Trait Anger Expression Inventory-Second Edition to measure irrational thinking and trait anger, respectively. Students who scored higher on Overall Irrational Thinking and Low Frustration Tolerance scored significantly higher on Trait Anger than did those who scored lower on Overall Irrational Thinking and Low Frustration Tolerance. This indicates support for the ABC model, especially Ellis's construct of irrational beliefs which is central to the model.

Comparison of different hadron production models for the study of π±, K±, protons and antiprotons production in proton-carbon interactions at 90 GeV/c

NASA Astrophysics Data System (ADS)

Ajaz, M.; Ali, Y.; Ullah, S.; Ali, Q.; Tabassam, U.

2018-05-01

In this research paper, comprehensive results on the double differential yield of π± and K± mesons, protons and antiprotons as a function of laboratory momentum in several polar angle ranges from 0-420 mrad for pions, 0-360 mrad for kaons, proton and antiproton are reported. EPOS 1.99, EPOS-LHC and QGSJETII-04 models are used to perform simulations. The predictions of these models at 90 GeV/c are plotted for comparison, which shows that QGSJETII-04 model gives overall higher yield for π+ mesons in the polar angle interval of 0-40 mrad but for the π‑ the yield is higher only up to 20 mrad. For π+ mesons after 40 mrad, EPOS-LHC predicts higher yield as compared to EPOS 1.99 and QGSJETII-04 while EPOS-LHC and EPOS 1.99 give similar behavior in these two intervals. However, for π‑ mesons EPOS-LHC and EPOS 1.99 give similar behavior in these two intervals. For of K± mesons, QGSJETII-04 model gives higher predictions in all cases from 0-300 mrad, while EPOS 1.99 and EPOS-LHC show similar distributions. In case of protons, all models give similar distribution but this is not true for antiproton. All models are in good agreement for p > 20 GeV/c. EPOS 1.99 produce lower yield compared to the other two models from 60-360 mrad polar angle interval.

Predicting chemically-induced skin reactions. Part I: QSAR models of skin sensitization and their application to identify potentially hazardous compounds

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

Alves, Vinicius M.; Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599; Muratov, Eugene

Repetitive exposure to a chemical agent can induce an immune reaction in inherently susceptible individuals that leads to skin sensitization. Although many chemicals have been reported as skin sensitizers, there have been very few rigorously validated QSAR models with defined applicability domains (AD) that were developed using a large group of chemically diverse compounds. In this study, we have aimed to compile, curate, and integrate the largest publicly available dataset related to chemically-induced skin sensitization, use this data to generate rigorously validated and QSAR models for skin sensitization, and employ these models as a virtual screening tool for identifying putativemore » sensitizers among environmental chemicals. We followed best practices for model building and validation implemented with our predictive QSAR workflow using Random Forest modeling technique in combination with SiRMS and Dragon descriptors. The Correct Classification Rate (CCR) for QSAR models discriminating sensitizers from non-sensitizers was 71–88% when evaluated on several external validation sets, within a broad AD, with positive (for sensitizers) and negative (for non-sensitizers) predicted rates of 85% and 79% respectively. When compared to the skin sensitization module included in the OECD QSAR Toolbox as well as to the skin sensitization model in publicly available VEGA software, our models showed a significantly higher prediction accuracy for the same sets of external compounds as evaluated by Positive Predicted Rate, Negative Predicted Rate, and CCR. These models were applied to identify putative chemical hazards in the Scorecard database of possible skin or sense organ toxicants as primary candidates for experimental validation. - Highlights: • It was compiled the largest publicly-available skin sensitization dataset. • Predictive QSAR models were developed for skin sensitization. • Developed models have higher prediction accuracy than OECD QSAR Toolbox. • Putative chemical hazards in the Scorecard database were found using our models.« less

Climate predictability and prediction skill on seasonal time scales over South America from CHFP models

NASA Astrophysics Data System (ADS)

Osman, Marisol; Vera, C. S.

2017-10-01

This work presents an assessment of the predictability and skill of climate anomalies over South America. The study was made considering a multi-model ensemble of seasonal forecasts for surface air temperature, precipitation and regional circulation, from coupled global circulation models included in the Climate Historical Forecast Project. Predictability was evaluated through the estimation of the signal-to-total variance ratio while prediction skill was assessed computing anomaly correlation coefficients. Both indicators present over the continent higher values at the tropics than at the extratropics for both, surface air temperature and precipitation. Moreover, predictability and prediction skill for temperature are slightly higher in DJF than in JJA while for precipitation they exhibit similar levels in both seasons. The largest values of predictability and skill for both variables and seasons are found over northwestern South America while modest but still significant values for extratropical precipitation at southeastern South America and the extratropical Andes. The predictability levels in ENSO years of both variables are slightly higher, although with the same spatial distribution, than that obtained considering all years. Nevertheless, predictability at the tropics for both variables and seasons diminishes in both warm and cold ENSO years respect to that in all years. The latter can be attributed to changes in signal rather than in the noise. Predictability and prediction skill for low-level winds and upper-level zonal winds over South America was also assessed. Maximum levels of predictability for low-level winds were found were maximum mean values are observed, i.e. the regions associated with the equatorial trade winds, the midlatitudes westerlies and the South American Low-Level Jet. Predictability maxima for upper-level zonal winds locate where the subtropical jet peaks. Seasonal changes in wind predictability are observed that seem to be related to those associated with the signal, especially at the extratropics.

Dynamic Relationships Between Parental Monitoring, Peer Risk Involvement and Sexual Risk Behavior Among Bahamian Mid-Adolescents

PubMed Central

Wang, Bo; Stanton, Bonita; Deveaux, Lynette; Li, Xiaoming; Lunn, Sonja

2015-01-01

CONTEXT Considerable research has examined reciprocal relationships between parenting, peers and adolescent problem behavior; however, such studies have largely considered the influence of peers and parents separately. It is important to examine simultaneously the relationships between parental monitoring, peer risk involvement and adolescent sexual risk behavior, and whether increases in peer risk involvement and changes in parental monitoring longitudinally predict adolescent sexual risk behavior. METHODS Four waves of sexual behavior data were collected between 2008/2009 and 2011 from high school students aged 13–17 in the Bahamas. Structural equation and latent growth curve modeling were used to examine reciprocal relationships between parental monitoring, perceived peer risk involvement and adolescent sexual risk behavior. RESULTS For both male and female youth, greater perceived peer risk involvement predicted higher sexual risk behavior index scores, and greater parental monitoring predicted lower scores. Reciprocal relationships were found between parental monitoring and sexual risk behavior for males and between perceived peer risk involvement and sexual risk behavior for females. For males, greater sexual risk behavior predicted lower parental monitoring; for females, greater sexual risk behavior predicted higher perceived peer risk involvement. According to latent growth curve models, a higher initial level of parental monitoring predicted decreases in sexual risk behavior, whereas both a higher initial level and a higher growth rate of peer risk involvement predicted increases in sexual risk behavior. CONCLUSION Results highlight the important influence of peer risk involvement on youths’ sexual behavior and gender differences in reciprocal relationships between parental monitoring, peer influence and adolescent sexual risk behavior. PMID:26308261

Dynamic Relationships Between Parental Monitoring, Peer Risk Involvement and Sexual Risk Behavior Among Bahamian Mid-Adolescents.

PubMed

Wang, Bo; Stanton, Bonita; Deveaux, Lynette; Li, Xiaoming; Lunn, Sonja

2015-06-01

Considerable research has examined reciprocal relationships between parenting, peers and adolescent problem behavior; however, such studies have largely considered the influence of peers and parents separately. It is important to examine simultaneously the relationships between parental monitoring, peer risk involvement and adolescent sexual risk behavior, and whether increases in peer risk involvement and changes in parental monitoring longitudinally predict adolescent sexual risk behavior. Four waves of sexual behavior data were collected between 2008/2009 and 2011 from high school students aged 13-17 in the Bahamas. Structural equation and latent growth curve modeling were used to examine reciprocal relationships between parental monitoring, perceived peer risk involvement and adolescent sexual risk behavior. For both male and female youth, greater perceived peer risk involvement predicted higher sexual risk behavior index scores, and greater parental monitoring predicted lower scores. Reciprocal relationships were found between parental monitoring and sexual risk behavior for males and between perceived peer risk involvement and sexual risk behavior for females. For males, greater sexual risk behavior predicted lower parental monitoring; for females, greater sexual risk behavior predicted higher perceived peer risk involvement. According to latent growth curve models, a higher initial level of parental monitoring predicted decreases in sexual risk behavior, whereas both a higher initial level and a higher growth rate of peer risk involvement predicted increases in sexual risk behavior. Results highlight the important influence of peer risk involvement on youths' sexual behavior and gender differences in reciprocal relationships between parental monitoring, peer influence and adolescent sexual risk behavior.

Development of estrogen receptor beta binding prediction model using large sets of chemicals.

PubMed

Sakkiah, Sugunadevi; Selvaraj, Chandrabose; Gong, Ping; Zhang, Chaoyang; Tong, Weida; Hong, Huixiao

2017-11-03

We developed an ER β binding prediction model to facilitate identification of chemicals specifically bind ER β or ER α together with our previously developed ER α binding model. Decision Forest was used to train ER β binding prediction model based on a large set of compounds obtained from EADB. Model performance was estimated through 1000 iterations of 5-fold cross validations. Prediction confidence was analyzed using predictions from the cross validations. Informative chemical features for ER β binding were identified through analysis of the frequency data of chemical descriptors used in the models in the 5-fold cross validations. 1000 permutations were conducted to assess the chance correlation. The average accuracy of 5-fold cross validations was 93.14% with a standard deviation of 0.64%. Prediction confidence analysis indicated that the higher the prediction confidence the more accurate the predictions. Permutation testing results revealed that the prediction model is unlikely generated by chance. Eighteen informative descriptors were identified to be important to ER β binding prediction. Application of the prediction model to the data from ToxCast project yielded very high sensitivity of 90-92%. Our results demonstrated ER β binding of chemicals could be accurately predicted using the developed model. Coupling with our previously developed ER α prediction model, this model could be expected to facilitate drug development through identification of chemicals that specifically bind ER β or ER α .

A multimodel approach to interannual and seasonal prediction of Danube discharge anomalies

NASA Astrophysics Data System (ADS)

Rimbu, Norel; Ionita, Monica; Patrut, Simona; Dima, Mihai

2010-05-01

Interannual and seasonal predictability of Danube river discharge is investigated using three model types: 1) time series models 2) linear regression models of discharge with large-scale climate mode indices and 3) models based on stable teleconnections. All models are calibrated using discharge and climatic data for the period 1901-1977 and validated for the period 1978-2008 . Various time series models, like autoregressive (AR), moving average (MA), autoregressive and moving average (ARMA) or singular spectrum analysis and autoregressive moving average (SSA+ARMA) models have been calibrated and their skills evaluated. The best results were obtained using SSA+ARMA models. SSA+ARMA models proved to have the highest forecast skill also for other European rivers (Gamiz-Fortis et al. 2008). Multiple linear regression models using large-scale climatic mode indices as predictors have a higher forecast skill than the time series models. The best predictors for Danube discharge are the North Atlantic Oscillation (NAO) and the East Atlantic/Western Russia patterns during winter and spring. Other patterns, like Polar/Eurasian or Tropical Northern Hemisphere (TNH) are good predictors for summer and autumn discharge. Based on stable teleconnection approach (Ionita et al. 2008) we construct prediction models through a combination of sea surface temperature (SST), temperature (T) and precipitation (PP) from the regions where discharge and SST, T and PP variations are stable correlated. Forecast skills of these models are higher than forecast skills of the time series and multiple regression models. The models calibrated and validated in our study can be used for operational prediction of interannual and seasonal Danube discharge anomalies. References Gamiz-Fortis, S., D. Pozo-Vazquez, R.M. Trigo, and Y. Castro-Diez, Quantifying the predictability of winter river flow in Iberia. Part I: intearannual predictability. J. Climate, 2484-2501, 2008. Gamiz-Fortis, S., D. Pozo-Vazquez, R.M. Trigo, and Y. Castro-Diez, Quantifying the predictability of winter river flow in Iberia. Part II: seasonal predictability. J. Climate, 2503-2518, 2008. Ionita, M., G. Lohmann, and N. Rimbu, Prediction of spring Elbe river discharge based on stable teleconnections with global temperature and precipitation. J. Climate. 6215-6226, 2008.

Medium- and Long-term Prediction of LOD Change by the Leap-step Autoregressive Model

NASA Astrophysics Data System (ADS)

Wang, Qijie

2015-08-01

The accuracy of medium- and long-term prediction of length of day (LOD) change base on combined least-square and autoregressive (LS+AR) deteriorates gradually. Leap-step autoregressive (LSAR) model can significantly reduce the edge effect of the observation sequence. Especially, LSAR model greatly improves the resolution of signals’ low-frequency components. Therefore, it can improve the efficiency of prediction. In this work, LSAR is used to forecast the LOD change. The LOD series from EOP 08 C04 provided by IERS is modeled by both the LSAR and AR models. The results of the two models are analyzed and compared. When the prediction length is between 10-30 days, the accuracy improvement is less than 10%. When the prediction length amounts to above 30 day, the accuracy improved obviously, with the maximum being around 19%. The results show that the LSAR model has higher prediction accuracy and stability in medium- and long-term prediction.

Predicting the stability of nanodevices

NASA Astrophysics Data System (ADS)

Lin, Z. Z.; Yu, W. F.; Wang, Y.; Ning, X. J.

2011-05-01

A simple model based on the statistics of single atoms is developed to predict the stability or lifetime of nanodevices without empirical parameters. Under certain conditions, the model produces the Arrhenius law and the Meyer-Neldel compensation rule. Compared with the classical molecular-dynamics simulations for predicting the stability of monatomic carbon chain at high temperature, the model is proved to be much more accurate than the transition state theory. Based on the ab initio calculation of the static potential, the model can give out a corrected lifetime of monatomic carbon and gold chains at higher temperature, and predict that the monatomic chains are very stable at room temperature.

Teacher characteristics, social classroom relationships, and children's social, emotional, and behavioral classroom adjustment in special education.

PubMed

Breeman, L D; Wubbels, T; van Lier, P A C; Verhulst, F C; van der Ende, J; Maras, A; Hopman, J A B; Tick, N T

2015-02-01

The goal of this study was to explore relations between teacher characteristics (i.e., competence and wellbeing); social classroom relationships (i.e., teacher-child and peer interactions); and children's social, emotional, and behavioral classroom adjustment. These relations were explored at both the individual and classroom levels among 414 children with emotional and behavioral disorders placed in special education. Two models were specified. In the first model, children's classroom adjustment was regressed on social relationships and teacher characteristics. In the second model, reversed links were examined by regressing teacher characteristics on social relationships and children's adjustment. Results of model 1 showed that, at the individual level, better social and emotional adjustment of children was predicted by higher levels of teacher-child closeness and better behavioral adjustment was predicted by both positive teacher-child and peer interactions. At the classroom level, positive social relationships were predicted by higher levels of teacher competence, which in turn were associated with lower classroom levels of social problems. Higher levels of teacher wellbeing were directly associated with classroom adaptive and maladaptive child outcomes. Results of model 2 showed that, at the individual and classroom levels, only the emotional and behavioral problems of children predicted social classroom relationships. At the classroom level, teacher competence was best predicted by positive teacher-child relationships and teacher wellbeing was best predicted by classroom levels of prosocial behavior. We discuss the importance of positive teacher-child and peer interactions for children placed in special education and suggest ways of improving classroom processes by targeting teacher competence. Copyright © 2014 Society for the Study of School Psychology. Published by Elsevier Ltd. All rights reserved.

Free Dendritic Growth of Succinonitrile-Acetone Alloys with Thermosolutal Melt Convection

NASA Technical Reports Server (NTRS)

Beckermann, Christoph; Li, Ben Q.

2003-01-01

A stagnant film model of the effects of thermosolutal convection on free dendritic growth of alloys is developed, and its predictions are compared to available earth-based experimental data for succinonitrileacetone alloys. It is found that the convection model gives excellent agreement with the measured dendrite tip velocities and radii for low solute concentrations. However, at higher solute concentrations the present predictions show some deviations from the measured data, and the measured (thermal) Peclet numbers tend to fall even below the predictions from diffusion theory. Furthermore, the measured selection parameter (sigma*) is significantly above the expected value of 0.02 and exhibits strong scatter. It is shown that convection is not responsible for these discrepancies. Some of the deviations between the predicted and measured data at higher supercoolings could be caused by measurement difficulties. The systematic disagreement in the selection parameter for higher solute concentrations and all supercoolings examined, indicates that the theory for the selection of the dendrite tip operating state in alloys may need to be reexamined.

Detecting Bias in Selection for Higher Education: Three Different Methods

ERIC Educational Resources Information Center

Kennet-Cohen, Tamar; Turvall, Elliot; Oren, Carmel

2014-01-01

This study examined selection bias in Israeli university admissions with respect to test language and gender, using three approaches for the detection of such bias: Cleary's model of differential prediction, boundary conditions for differential prediction and difference between "d's" (the Constant Ratio Model). The university admissions…

Model predictions of higher-order normal alkane ignition from dilute shock-tube experiments

NASA Astrophysics Data System (ADS)

Rotavera, B.; Petersen, E. L.

2013-07-01

Shock-induced oxidation of two higher-order linear alkanes was measured using a heated shock tube facility. Experimental overlap in stoichiometric ignition delay times obtained under dilute (99 % Ar) conditions near atmospheric pressure was observed in the temperature-dependent ignition trends of n-nonane ( n-C9H20) and n-undecane ( n-C11H24). Despite the overlap, model predictions of ignition using two different detailed chemical kinetics mechanisms show discrepancies relative to both the measured data as well as to one another. The present study therefore focuses on the differences observed in the modeled, high-temperature ignition delay times of higher-order n-alkanes, which are generally regarded to have identical ignition behavior for carbon numbers above C7. Comparisons are drawn using experimental data from the present study and from recent work by the authors relative to two existing chemical kinetics mechanisms. Time histories from the shock-tube OH* measurements are also compared to the model predictions; a double-peaked structure observed in the data shows that the time response of the detector electronics is crucial for properly capturing the first, incipient peak near time zero. Calculations using the two mechanisms were carried out at the dilution level employed in the shock-tube experiments for lean {({φ} = 0.5)}, stoichiometric, and rich {({φ} = 2.0)} equivalence ratios, 1230-1620 K, and for both 1.5 and 10 atm. In general, the models show differing trends relative to both measured data and to one another, indicating that agreement among chemical kinetics models for higher-order n-alkanes is not consistent. For example, under certain conditions, one mechanism predicts the ignition delay times to be virtually identical between the n-nonane and n-undecane fuels (in fact, also for all alkanes between at least C8 and C12), which is in agreement with the experiment, while the other mechanism predicts the larger fuels to ignite progressively more slowly.

Improving satellite-driven PM2.5 models with Moderate Resolution Imaging Spectroradiometer fire counts in the southeastern U.S.

PubMed

Hu, Xuefei; Waller, Lance A; Lyapustin, Alexei; Wang, Yujie; Liu, Yang

2014-10-16

Multiple studies have developed surface PM 2.5 (particle size less than 2.5 µm in aerodynamic diameter) prediction models using satellite-derived aerosol optical depth as the primary predictor and meteorological and land use variables as secondary variables. To our knowledge, satellite-retrieved fire information has not been used for PM 2.5 concentration prediction in statistical models. Fire data could be a useful predictor since fires are significant contributors of PM 2.5 . In this paper, we examined whether remotely sensed fire count data could improve PM 2.5 prediction accuracy in the southeastern U.S. in a spatial statistical model setting. A sensitivity analysis showed that when the radius of the buffer zone centered at each PM 2.5 monitoring site reached 75 km, fire count data generally have the greatest predictive power of PM 2.5 across the models considered. Cross validation (CV) generated an R 2 of 0.69, a mean prediction error of 2.75 µg/m 3 , and root-mean-square prediction errors (RMSPEs) of 4.29 µg/m 3 , indicating a good fit between the dependent and predictor variables. A comparison showed that the prediction accuracy was improved more substantially from the nonfire model to the fire model at sites with higher fire counts. With increasing fire counts, CV RMSPE decreased by values up to 1.5 µg/m 3 , exhibiting a maximum improvement of 13.4% in prediction accuracy. Fire count data were shown to have better performance in southern Georgia and in the spring season due to higher fire occurrence. Our findings indicate that fire count data provide a measurable improvement in PM 2.5 concentration estimation, especially in areas and seasons prone to fire events.

Improving satellite-driven PM2.5 models with Moderate Resolution Imaging Spectroradiometer fire counts in the southeastern U.S

PubMed Central

Hu, Xuefei; Waller, Lance A.; Lyapustin, Alexei; Wang, Yujie; Liu, Yang

2017-01-01

Multiple studies have developed surface PM2.5 (particle size less than 2.5 µm in aerodynamic diameter) prediction models using satellite-derived aerosol optical depth as the primary predictor and meteorological and land use variables as secondary variables. To our knowledge, satellite-retrieved fire information has not been used for PM2.5 concentration prediction in statistical models. Fire data could be a useful predictor since fires are significant contributors of PM2.5. In this paper, we examined whether remotely sensed fire count data could improve PM2.5 prediction accuracy in the southeastern U.S. in a spatial statistical model setting. A sensitivity analysis showed that when the radius of the buffer zone centered at each PM2.5 monitoring site reached 75 km, fire count data generally have the greatest predictive power of PM2.5 across the models considered. Cross validation (CV) generated an R2 of 0.69, a mean prediction error of 2.75 µg/m3, and root-mean-square prediction errors (RMSPEs) of 4.29 µg/m3, indicating a good fit between the dependent and predictor variables. A comparison showed that the prediction accuracy was improved more substantially from the nonfire model to the fire model at sites with higher fire counts. With increasing fire counts, CV RMSPE decreased by values up to 1.5 µg/m3, exhibiting a maximum improvement of 13.4% in prediction accuracy. Fire count data were shown to have better performance in southern Georgia and in the spring season due to higher fire occurrence. Our findings indicate that fire count data provide a measurable improvement in PM2.5 concentration estimation, especially in areas and seasons prone to fire events. PMID:28967648

Epigenome-wide cross-tissue predictive modeling and comparison of cord blood and placental methylation in a birth cohort

PubMed Central

De Carli, Margherita M; Baccarelli, Andrea A; Trevisi, Letizia; Pantic, Ivan; Brennan, Kasey JM; Hacker, Michele R; Loudon, Holly; Brunst, Kelly J; Wright, Robert O; Wright, Rosalind J; Just, Allan C

2017-01-01

Aim: We compared predictive modeling approaches to estimate placental methylation using cord blood methylation. Materials & methods: We performed locus-specific methylation prediction using both linear regression and support vector machine models with 174 matched pairs of 450k arrays. Results: At most CpG sites, both approaches gave poor predictions in spite of a misleading improvement in array-wide correlation. CpG islands and gene promoters, but not enhancers, were the genomic contexts where the correlation between measured and predicted placental methylation levels achieved higher values. We provide a list of 714 sites where both models achieved an R2 ≥0.75. Conclusion: The present study indicates the need for caution in interpreting cross-tissue predictions. Few methylation sites can be predicted between cord blood and placenta. PMID:28234020

Climatological Observations for Maritime Prediction and Analysis Support Service (COMPASS)

NASA Astrophysics Data System (ADS)

OConnor, A.; Kirtman, B. P.; Harrison, S.; Gorman, J.

2016-02-01

Current US Navy forecasting systems cannot easily incorporate extended-range forecasts that can improve mission readiness and effectiveness; ensure safety; and reduce cost, labor, and resource requirements. If Navy operational planners had systems that incorporated these forecasts, they could plan missions using more reliable and longer-term weather and climate predictions. Further, using multi-model forecast ensembles instead of single forecasts would produce higher predictive performance. Extended-range multi-model forecast ensembles, such as those available in the North American Multi-Model Ensemble (NMME), are ideal for system integration because of their high skill predictions; however, even higher skill predictions can be produced if forecast model ensembles are combined correctly. While many methods for weighting models exist, the best method in a given environment requires expert knowledge of the models and combination methods.We present an innovative approach that uses machine learning to combine extended-range predictions from multi-model forecast ensembles and generate a probabilistic forecast for any region of the globe up to 12 months in advance. Our machine-learning approach uses 30 years of hindcast predictions to learn patterns of forecast model successes and failures. Each model is assigned a weight for each environmental condition, 100 km2 region, and day given any expected environmental information. These weights are then applied to the respective predictions for the region and time of interest to effectively stitch together a single, coherent probabilistic forecast. Our experimental results demonstrate the benefits of our approach to produce extended-range probabilistic forecasts for regions and time periods of interest that are superior, in terms of skill, to individual NMME forecast models and commonly weighted models. The probabilistic forecast leverages the strengths of three NMME forecast models to predict environmental conditions for an area spanning from San Diego, CA to Honolulu, HI, seven months in-advance. Key findings include: weighted combinations of models are strictly better than individual models; machine-learned combinations are especially better; and forecasts produced using our approach have the highest rank probability skill score most often.

A novel hybrid decomposition-and-ensemble model based on CEEMD and GWO for short-term PM2.5 concentration forecasting

NASA Astrophysics Data System (ADS)

Niu, Mingfei; Wang, Yufang; Sun, Shaolong; Li, Yongwu

2016-06-01

To enhance prediction reliability and accuracy, a hybrid model based on the promising principle of "decomposition and ensemble" and a recently proposed meta-heuristic called grey wolf optimizer (GWO) is introduced for daily PM2.5 concentration forecasting. Compared with existing PM2.5 forecasting methods, this proposed model has improved the prediction accuracy and hit rates of directional prediction. The proposed model involves three main steps, i.e., decomposing the original PM2.5 series into several intrinsic mode functions (IMFs) via complementary ensemble empirical mode decomposition (CEEMD) for simplifying the complex data; individually predicting each IMF with support vector regression (SVR) optimized by GWO; integrating all predicted IMFs for the ensemble result as the final prediction by another SVR optimized by GWO. Seven benchmark models, including single artificial intelligence (AI) models, other decomposition-ensemble models with different decomposition methods and models with the same decomposition-ensemble method but optimized by different algorithms, are considered to verify the superiority of the proposed hybrid model. The empirical study indicates that the proposed hybrid decomposition-ensemble model is remarkably superior to all considered benchmark models for its higher prediction accuracy and hit rates of directional prediction.

Final Technical Report: Increasing Prediction Accuracy.

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

King, Bruce Hardison; Hansen, Clifford; Stein, Joshua

2015-12-01

PV performance models are used to quantify the value of PV plants in a given location. They combine the performance characteristics of the system, the measured or predicted irradiance and weather at a site, and the system configuration and design into a prediction of the amount of energy that will be produced by a PV system. These predictions must be as accurate as possible in order for finance charges to be minimized. Higher accuracy equals lower project risk. The Increasing Prediction Accuracy project at Sandia focuses on quantifying and reducing uncertainties in PV system performance models.

Toward a New Predictive Model of Student Retention in Higher Education: An Application of Classical Sociological Theory

ERIC Educational Resources Information Center

Kerby, Molly B.

2015-01-01

Theoretical models designed to predict whether students will persist or not have been valuable tools for retention efforts relative to the creation of services in academic and student affairs. Some of the early models attempted to explain and measure factors in the "college dropout process." For example, in his seminal work, Tinto…

Experimental evaluation of models for predicting Cherenkov light intensities from short-cooled nuclear fuel assemblies

NASA Astrophysics Data System (ADS)

Branger, E.; Grape, S.; Jansson, P.; Jacobsson Svärd, S.

2018-02-01

The Digital Cherenkov Viewing Device (DCVD) is a tool used by nuclear safeguards inspectors to verify irradiated nuclear fuel assemblies in wet storage based on the recording of Cherenkov light produced by the assemblies. One type of verification involves comparing the measured light intensity from an assembly with a predicted intensity, based on assembly declarations. Crucial for such analyses is the performance of the prediction model used, and recently new modelling methods have been introduced to allow for enhanced prediction capabilities by taking the irradiation history into account, and by including the cross-talk radiation from neighbouring assemblies in the predictions. In this work, the performance of three models for Cherenkov-light intensity prediction is evaluated by applying them to a set of short-cooled PWR 17x17 assemblies for which experimental DCVD measurements and operator-declared irradiation data was available; (1) a two-parameter model, based on total burnup and cooling time, previously used by the safeguards inspectors, (2) a newly introduced gamma-spectrum-based model, which incorporates cycle-wise burnup histories, and (3) the latter gamma-spectrum-based model with the addition to account for contributions from neighbouring assemblies. The results show that the two gamma-spectrum-based models provide significantly higher precision for the measured inventory compared to the two-parameter model, lowering the standard deviation between relative measured and predicted intensities from 15.2 % to 8.1 % respectively 7.8 %. The results show some systematic differences between assemblies of different designs (produced by different manufacturers) in spite of their similar PWR 17x17 geometries, and possible ways are discussed to address such differences, which may allow for even higher prediction capabilities. Still, it is concluded that the gamma-spectrum-based models enable confident verification of the fuel assembly inventory at the currently used detection limit for partial defects, being a 30 % discrepancy between measured and predicted intensities, while some false detection occurs with the two-parameter model. The results also indicate that the gamma-spectrum-based prediction methods are accurate enough that the 30 % discrepancy limit could potentially be lowered.

Cardiovascular risk prediction in HIV-infected patients: comparing the Framingham, atherosclerotic cardiovascular disease risk score (ASCVD), Systematic Coronary Risk Evaluation for the Netherlands (SCORE-NL) and Data Collection on Adverse Events of Anti-HIV Drugs (D:A:D) risk prediction models.

PubMed

Krikke, M; Hoogeveen, R C; Hoepelman, A I M; Visseren, F L J; Arends, J E

2016-04-01

The aim of the study was to compare the predictions of five popular cardiovascular disease (CVD) risk prediction models, namely the Data Collection on Adverse Events of Anti-HIV Drugs (D:A:D) model, the Framingham Heart Study (FHS) coronary heart disease (FHS-CHD) and general CVD (FHS-CVD) models, the American Heart Association (AHA) atherosclerotic cardiovascular disease risk score (ASCVD) model and the Systematic Coronary Risk Evaluation for the Netherlands (SCORE-NL) model. A cross-sectional design was used to compare the cumulative CVD risk predictions of the models. Furthermore, the predictions of the general CVD models were compared with those of the HIV-specific D:A:D model using three categories (< 10%, 10-20% and > 20%) to categorize the risk and to determine the degree to which patients were categorized similarly or in a higher/lower category. A total of 997 HIV-infected patients were included in the study: 81% were male and they had a median age of 46 [interquartile range (IQR) 40-52] years, a known duration of HIV infection of 6.8 (IQR 3.7-10.9) years, and a median time on ART of 6.4 (IQR 3.0-11.5) years. The D:A:D, ASCVD and SCORE-NL models gave a lower cumulative CVD risk, compared with that of the FHS-CVD and FHS-CHD models. Comparing the general CVD models with the D:A:D model, the FHS-CVD and FHS-CHD models only classified 65% and 79% of patients, respectively, in the same category as did the D:A:D model. However, for the ASCVD and SCORE-NL models, this percentage was 89% and 87%, respectively. Furthermore, FHS-CVD and FHS-CHD attributed a higher CVD risk to 33% and 16% of patients, respectively, while this percentage was < 6% for ASCVD and SCORE-NL. When using FHS-CVD and FHS-CHD, a higher overall CVD risk was attributed to the HIV-infected patients than when using the D:A:D, ASCVD and SCORE-NL models. This could have consequences regarding overtreatment, drug-related adverse events and drug-drug interactions. © 2015 British HIV Association.

Optimal Parameter Selection for Support Vector Machine Based on Artificial Bee Colony Algorithm: A Case Study of Grid-Connected PV System Power Prediction.

PubMed

Gao, Xiang-Ming; Yang, Shi-Feng; Pan, San-Bo

2017-01-01

Predicting the output power of photovoltaic system with nonstationarity and randomness, an output power prediction model for grid-connected PV systems is proposed based on empirical mode decomposition (EMD) and support vector machine (SVM) optimized with an artificial bee colony (ABC) algorithm. First, according to the weather forecast data sets on the prediction date, the time series data of output power on a similar day with 15-minute intervals are built. Second, the time series data of the output power are decomposed into a series of components, including some intrinsic mode components IMFn and a trend component Res, at different scales using EMD. The corresponding SVM prediction model is established for each IMF component and trend component, and the SVM model parameters are optimized with the artificial bee colony algorithm. Finally, the prediction results of each model are reconstructed, and the predicted values of the output power of the grid-connected PV system can be obtained. The prediction model is tested with actual data, and the results show that the power prediction model based on the EMD and ABC-SVM has a faster calculation speed and higher prediction accuracy than do the single SVM prediction model and the EMD-SVM prediction model without optimization.

Optimal Parameter Selection for Support Vector Machine Based on Artificial Bee Colony Algorithm: A Case Study of Grid-Connected PV System Power Prediction

PubMed Central

2017-01-01

Predicting the output power of photovoltaic system with nonstationarity and randomness, an output power prediction model for grid-connected PV systems is proposed based on empirical mode decomposition (EMD) and support vector machine (SVM) optimized with an artificial bee colony (ABC) algorithm. First, according to the weather forecast data sets on the prediction date, the time series data of output power on a similar day with 15-minute intervals are built. Second, the time series data of the output power are decomposed into a series of components, including some intrinsic mode components IMFn and a trend component Res, at different scales using EMD. The corresponding SVM prediction model is established for each IMF component and trend component, and the SVM model parameters are optimized with the artificial bee colony algorithm. Finally, the prediction results of each model are reconstructed, and the predicted values of the output power of the grid-connected PV system can be obtained. The prediction model is tested with actual data, and the results show that the power prediction model based on the EMD and ABC-SVM has a faster calculation speed and higher prediction accuracy than do the single SVM prediction model and the EMD-SVM prediction model without optimization. PMID:28912803

Cosmic ray antiprotons in closed galaxy model

NASA Technical Reports Server (NTRS)

Protheroe, R.

1981-01-01

The flux of secondary antiprotons expected for the leaky-box model was calculated as well as that for the closed galaxy model of Peters and Westergard (1977). The antiproton/proton ratio observed at several GeV is a factor of 4 higher than the prediction for the leaky-box model but is consistent with that predicted for the closed galaxy model. New low energy data is not consistent with either model. The possibility of a primary antiproton component is discussed.

Potential predictability and forecast skill in ensemble climate forecast: a skill-persistence rule

NASA Astrophysics Data System (ADS)

Jin, Yishuai; Rong, Xinyao; Liu, Zhengyu

2017-12-01

This study investigates the factors relationship between the forecast skills for the real world (actual skill) and perfect model (perfect skill) in ensemble climate model forecast with a series of fully coupled general circulation model forecast experiments. It is found that the actual skill for sea surface temperature (SST) in seasonal forecast is substantially higher than the perfect skill on a large part of the tropical oceans, especially the tropical Indian Ocean and the central-eastern Pacific Ocean. The higher actual skill is found to be related to the higher observational SST persistence, suggesting a skill-persistence rule: a higher SST persistence in the real world than in the model could overwhelm the model bias to produce a higher forecast skill for the real world than for the perfect model. The relation between forecast skill and persistence is further proved using a first-order autoregressive model (AR1) analytically for theoretical solutions and numerically for analogue experiments. The AR1 model study shows that the skill-persistence rule is strictly valid in the case of infinite ensemble size, but could be distorted by sampling errors and non-AR1 processes. This study suggests that the so called "perfect skill" is model dependent and cannot serve as an accurate estimate of the true upper limit of real world prediction skill, unless the model can capture at least the persistence property of the observation.

The effects of climate change on instream nitrogen transport in the contiguous United States

NASA Astrophysics Data System (ADS)

Alam, M. J.; Goodall, J. L.

2011-12-01

Excessive nitrogen loading has caused significant environmental impacts such as eutrophication and hypoxia in waterbodies around the world. Nitrogen loading is largely dependent on nonpoint source pollution and nitrogen transport from nonpoint source pollution is greatly impacted by climate conditions. For example, increased precipitation leads to more runoff and a higher nitrogen yield. However, higher temperatures also impact nitrogen transport in that higher temperatures increase denitrification and therefore reduce nitrogen yield. The purpose of this research is to quantify potential changes in nitrogen yield for the contiguous United States under predicted climate change scenarios, specifically changes in precipitation and air temperature. The analysis was performed for high (A2) and low (B1) emission scenarios and for the year 2030, 2050 and 2090. We used 11 different IPCC (The Intergovernmental Panel on Climate Change) models predicted precipitation and temperature estimates to capture uncertainty. The SPARROW model was calibrated using historical nitrogen loading data and used to predict nitrogen yields for future climate conditions. We held nitrogen source data constant in order to isolate the impact of predicted precipitation and temperature changes for each model scenario. Preliminary results suggest an overall decrease in nitrogen yield if climate change impacts are considered in isolation. For the A2 scenario, the model results indicated an overall incremental nitrogen yield decrease of 2-17% by the year 2030, 4-26% by the year 2050, and 11-45% by the year 2090. The B1 emission scenario also indicated an incremental yield decrease, but at lesser amounts of 2-18%, 5-21% and 10-38% by the years 2030, 2050, and 2090, respectively. This decrease is mainly due to higher predicted temperatures that result in increased denitrification rates.

Impact of relationships between test and training animals and among training animals on reliability of genomic prediction.

PubMed

Wu, X; Lund, M S; Sun, D; Zhang, Q; Su, G

2015-10-01

One of the factors affecting the reliability of genomic prediction is the relationship among the animals of interest. This study investigated the reliability of genomic prediction in various scenarios with regard to the relationship between test and training animals, and among animals within the training data set. Different training data sets were generated from EuroGenomics data and a group of Nordic Holstein bulls (born in 2005 and afterwards) as a common test data set. Genomic breeding values were predicted using a genomic best linear unbiased prediction model and a Bayesian mixture model. The results showed that a closer relationship between test and training animals led to a higher reliability of genomic predictions for the test animals, while a closer relationship among training animals resulted in a lower reliability. In addition, the Bayesian mixture model in general led to a slightly higher reliability of genomic prediction, especially for the scenario of distant relationships between training and test animals. Therefore, to prevent a decrease in reliability, constant updates of the training population with animals from more recent generations are required. Moreover, a training population consisting of less-related animals is favourable for reliability of genomic prediction. © 2015 Blackwell Verlag GmbH.

Longitudinal structure in temperate stream fish communities: evaluating conceptual models with temporal data

USGS Publications Warehouse

Roberts, James H.; Hitt, Nathaniel P.

2010-01-01

Five conceptual models of longitudinal fish community organization in streams were examined: (1) niche diversity model (NDM), (2) stream continuum model (SCM), (3) immigrant accessibility model (IAM), (4) environmental stability model (ESM), and (5) adventitious stream model (ASM). We used differences among models in their predictions about temporal species turnover, along with five spatiotemporal fish community data sets, to evaluate model applicability. Models were similar in predicting a positive species richness–stream size relationship and longitudinal species nestedness, but differed in predicting either similar temporal species turnover throughout the stream continuum (NDM, SCM), higher turnover upstream (IAM, ESM), or higher turnover downstream (ASM). We calculated measures of spatial and temporal variation from spatiotemporal fish data in five wadeable streams in central and eastern North America spanning 34–68 years (French Creek [New York], Piasa Creek [Illinois], Spruce Run [Virginia], Little Stony Creek [Virginia], and Sinking Creek [Virginia]). All streams exhibited substantial species turnover (i.e., at least 27% turnover in stream-scale species pools), in contrast to the predictions of the SCM. Furthermore, community change was greater in downstream than upstream reaches in four of five streams. This result is most consistent with the ASM and suggests that downstream communities are strongly influenced by migrants to and from species pools outside the focal stream. In Sinking Creek, which is isolated from external species pools, temporal species turnover (via increased richness) was higher upstream than downstream, which is a pattern most consistent with the IAM or ESM. These results corroborate the hypothesis that temperate stream habitats and fish communities are temporally dynamic and that fish migration and environmental disturbances play fundamental roles in stream fish community organization.

Use of mathematic modeling to compare and predict hemodynamic effects of the modified Blalock-Taussig and right ventricle-pulmonary artery shunts for hypoplastic left heart syndrome.

PubMed

Bove, Edward L; Migliavacca, Francesco; de Leval, Marc R; Balossino, Rossella; Pennati, Giancarlo; Lloyd, Thomas R; Khambadkone, Sachin; Hsia, Tain-Yen; Dubini, Gabriele

2008-08-01

Stage one reconstruction (Norwood operation) for hypoplastic left heart syndrome can be performed with either a modified Blalock-Taussig shunt or a right ventricle-pulmonary artery shunt. Both methods have certain inherent characteristics. It is postulated that mathematic modeling could help elucidate these differences. Three-dimensional computer models of the Blalock-Taussig shunt and right ventricle-pulmonary artery shunt modifications of the Norwood operation were developed by using the finite volume method. Conduits of 3, 3.5, and 4 mm were used in the Blalock-Taussig shunt model, whereas conduits of 4, 5, and 6 mm were used in the right ventricle-pulmonary artery shunt model. The hydraulic nets (lumped resistances, compliances, inertances, and elastances) were identical in the 2 models. A multiscale approach was adopted to couple the 3-dimensional models with the circulation net. Computer simulations were compared with postoperative catheterization data. Good correlation was found between predicted and observed data. For the right ventricle-pulmonary artery shunt modification, there was higher aortic diastolic pressure, decreased pulmonary artery pressure, lower Qp/Qs ratio, and higher coronary perfusion pressure. Mathematic modeling predicted minimal regurgitant flow in the right ventricle-pulmonary artery shunt model, which correlated with postoperative Doppler measurements. The right ventricle-pulmonary artery shunt demonstrated lower stroke work and a higher mechanical efficiency (stroke work/total mechanical energy). The close correlation between predicted and observed data supports the use of mathematic modeling in the design and assessment of surgical procedures. The potentially damaging effects of a systemic ventriculotomy in the right ventricle-pulmonary artery shunt modification of the Norwood operation have not been analyzed.

Yellowstone wolf (Canis lupus) denisty predicted by elk (Cervus elaphus) biomass

USGS Publications Warehouse

Mech, L. David; Barber-Meyer, Shannon

2015-01-01

The Northern Range (NR) of Yellowstone National Park (YNP) hosts a higher prey biomass density in the form of elk (Cervus elaphus L., 1758) than any other system of gray wolves (Canis lupus L., 1758) and prey reported. Therefore, it is important to determine whether that wolf–prey system fits a long-standing model relating wolf density to prey biomass. Using data from 2005 to 2012 after elk population fluctuations dampened 10 years subsequent to wolf reintroduction, we found that NR prey biomass predicted wolf density. This finding and the trajectory of the regression extend the validity of the model to prey densities 19% higher than previous data and suggest that the model would apply to wolf–prey systems of even higher prey biomass.

A conceptual model predicting internalizing problems in middle childhood among children of alcoholic and nonalcoholic fathers: the role of marital aggression.

PubMed

Eiden, Rina D; Molnar, Danielle S; Colder, Craig; Edwards, Ellen P; Leonard, Kenneth E

2009-09-01

The purpose of this study was to test a conceptual model predicting children's anxiety/depression in middle childhood in a community sample of children with parents who had alcohol problems (n = 112) and those without alcohol problems (n = 101). The conceptual model examined the role of parents' alcohol diagnoses, depression, and antisocial behavior among parents of children ages 12 months to kindergarten age in predicting marital aggression and parental aggravation. Higher levels of marital aggression and parental aggravation were hypothesized to predict children's depression/anxiety within time (18 months to kindergarten age and, prospectively, to age during fourth grade). The sample was recruited from New York State birth records when the children were 12 months old. Assessments were conducted at 12, 18, 24, and 36 months; at kindergarten age; and during fourth grade. Children with alcoholic fathers had higher depression/anxiety scores according to parental reports but not self-reports. Structural equations modeling was largely supportive of the conceptual model. Fathers' alcoholism was associated with higher child anxiety via greater levels of marital aggression among families with alcohol problems. Results also indicated that there was a significant indirect association between parents' depression symptoms and child anxiety via marital aggression. The results highlight the nested nature of risk characteristics in alcoholic families and the important role of marital aggression in predicting children's anxiety/depression. Interventions targeting both parents' alcohol problems and associated marital aggression are likely to provide the dual benefits of improving family interactions and lowering risk of children's internalizing behavior problems.

Enteric disease episodes and the risk of acquiring a future sexually transmitted infection: a prediction model in Montreal residents.

PubMed

Caron, Melissa; Allard, Robert; Bédard, Lucie; Latreille, Jérôme; Buckeridge, David L

2016-11-01

The sexual transmission of enteric diseases poses an important public health challenge. We aimed to build a prediction model capable of identifying individuals with a reported enteric disease who could be at risk of acquiring future sexually transmitted infections (STIs). Passive surveillance data on Montreal residents with at least 1 enteric disease report was used to construct the prediction model. Cases were defined as all subjects with at least 1 STI report following their initial enteric disease episode. A final logistic regression prediction model was chosen using forward stepwise selection. The prediction model with the greatest validity included age, sex, residential location, number of STI episodes experienced prior to the first enteric disease episode, type of enteric disease acquired, and an interaction term between age and male sex. This model had an area under the curve of 0.77 and had acceptable calibration. A coordinated public health response to the sexual transmission of enteric diseases requires that a distinction be made between cases of enteric diseases transmitted through sexual activity from those transmitted through contaminated food or water. A prediction model can aid public health officials in identifying individuals who may have a higher risk of sexually acquiring a reportable disease. Once identified, these individuals could receive specialized intervention to prevent future infection. The information produced from a prediction model capable of identifying higher risk individuals can be used to guide efforts in investigating and controlling reported cases of enteric diseases and STIs. © The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Individual variation in social aggression and the probability of inheritance: theory and a field test.

PubMed

Cant, Michael A; Llop, Justine B; Field, Jeremy

2006-06-01

Recent theory suggests that much of the wide variation in individual behavior that exists within cooperative animal societies can be explained by variation in the future direct component of fitness, or the probability of inheritance. Here we develop two models to explore the effect of variation in future fitness on social aggression. The models predict that rates of aggression will be highest toward the front of the queue to inherit and will be higher in larger, more productive groups. A third prediction is that, in seasonal animals, aggression will increase as the time available to inherit the breeding position runs out. We tested these predictions using a model social species, the paper wasp Polistes dominulus. We found that rates of both aggressive "displays" (aimed at individuals of lower rank) and aggressive "tests" (aimed at individuals of higher rank) decreased down the hierarchy, as predicted by our models. The only other significant factor affecting aggression rates was date, with more aggression observed later in the season, also as predicted. Variation in future fitness due to inheritance rank is the hidden factor accounting for much of the variation in aggressiveness among apparently equivalent individuals in this species.

Prediction of Airfoil Characteristics With Higher Order Turbulence Models

NASA Technical Reports Server (NTRS)

Gatski, Thomas B.

1996-01-01

This study focuses on the prediction of airfoil characteristics, including lift and drag over a range of Reynolds numbers. Two different turbulence models, which represent two different types of models, are tested. The first is a standard isotropic eddy-viscosity two-equation model, and the second is an explicit algebraic stress model (EASM). The turbulent flow field over a general-aviation airfoil (GA(W)-2) at three Reynolds numbers is studied. At each Reynolds number, predicted lift and drag values at different angles of attack are compared with experimental results, and predicted variations of stall locations with Reynolds number are compared with experimental data. Finally, the size of the separation zone predicted by each model is analyzed, and correlated with the behavior of the lift coefficient near stall. In summary, the EASM model is able to predict the lift and drag coefficients over a wider range of angles of attack than the two-equation model for the three Reynolds numbers studied. However, both models are unable to predict the correct lift and drag behavior near the stall angle, and for the lowest Reynolds number case, the two-equation model did not predict separation on the airfoil near stall.

Verifying the performance of artificial neural network and multiple linear regression in predicting the mean seasonal municipal solid waste generation rate: A case study of Fars province, Iran.

PubMed

Azadi, Sama; Karimi-Jashni, Ayoub

2016-02-01

Predicting the mass of solid waste generation plays an important role in integrated solid waste management plans. In this study, the performance of two predictive models, Artificial Neural Network (ANN) and Multiple Linear Regression (MLR) was verified to predict mean Seasonal Municipal Solid Waste Generation (SMSWG) rate. The accuracy of the proposed models is illustrated through a case study of 20 cities located in Fars Province, Iran. Four performance measures, MAE, MAPE, RMSE and R were used to evaluate the performance of these models. The MLR, as a conventional model, showed poor prediction performance. On the other hand, the results indicated that the ANN model, as a non-linear model, has a higher predictive accuracy when it comes to prediction of the mean SMSWG rate. As a result, in order to develop a more cost-effective strategy for waste management in the future, the ANN model could be used to predict the mean SMSWG rate. Copyright © 2015 Elsevier Ltd. All rights reserved.

Health risk factors as predictors of workers' compensation claim occurrence and cost

PubMed Central

Schwatka, Natalie V; Atherly, Adam; Dally, Miranda J; Fang, Hai; vS Brockbank, Claire; Tenney, Liliana; Goetzel, Ron Z; Jinnett, Kimberly; Witter, Roxana; Reynolds, Stephen; McMillen, James; Newman, Lee S

2017-01-01

Objective The objective of this study was to examine the predictive relationships between employee health risk factors (HRFs) and workers' compensation (WC) claim occurrence and costs. Methods Logistic regression and generalised linear models were used to estimate the predictive association between HRFs and claim occurrence and cost among a cohort of 16 926 employees from 314 large, medium and small businesses across multiple industries. First, unadjusted (HRFs only) models were estimated, and second, adjusted (HRFs plus demographic and work organisation variables) were estimated. Results Unadjusted models demonstrated that several HRFs were predictive of WC claim occurrence and cost. After adjusting for demographic and work organisation differences between employees, many of the relationships previously established did not achieve statistical significance. Stress was the only HRF to display a consistent relationship with claim occurrence, though the type of stress mattered. Stress at work was marginally predictive of a higher odds of incurring a WC claim (p<0.10). Stress at home and stress over finances were predictive of higher and lower costs of claims, respectively (p<0.05). Conclusions The unadjusted model results indicate that HRFs are predictive of future WC claims. However, the disparate findings between unadjusted and adjusted models indicate that future research is needed to examine the multilevel relationship between employee demographics, organisational factors, HRFs and WC claims. PMID:27530688

Seasonal prediction of winter haze days in the north central North China Plain

NASA Astrophysics Data System (ADS)

Yin, Zhicong; Wang, Huijun

2016-11-01

Recently, the winter (December-February) haze pollution over the north central North China Plain (NCP) has become severe. By treating the year-to-year increment as the predictand, two new statistical schemes were established using the multiple linear regression (MLR) and the generalized additive model (GAM). By analyzing the associated increment of atmospheric circulation, seven leading predictors were selected to predict the upcoming winter haze days over the NCP (WHDNCP). After cross validation, the root mean square error and explained variance of the MLR (GAM) prediction model was 3.39 (3.38) and 53 % (54 %), respectively. For the final predicted WHDNCP, both of these models could capture the interannual and interdecadal trends and the extremums successfully. Independent prediction tests for 2014 and 2015 also confirmed the good predictive skill of the new schemes. The predicted bias of the MLR (GAM) prediction model in 2014 and 2015 was 0.09 (-0.07) and -3.33 (-1.01), respectively. Compared to the MLR model, the GAM model had a higher predictive skill in reproducing the rapid and continuous increase of WHDNCP after 2010.

Comparison of two statistical methods for probability prediction of monthly precipitation during summer over Huaihe River Basin in China, and applications in runoff prediction based on hydrological model

NASA Astrophysics Data System (ADS)

Liu, L.; Du, L.; Liao, Y.

2017-12-01

Based on the ensemble hindcast dataset of CSM1.1m by NCC, CMA, Bayesian merging models and a two-step statistical model are developed and employed to predict monthly grid/station precipitation in the Huaihe River China during summer at the lead-time of 1 to 3 months. The hindcast datasets span a period of 1991 to 2014. The skill of the two models is evaluated using area under the ROC curve (AUC) in a leave-one-out cross-validation framework, and is compared to the skill of CSM1.1m. CSM1.1m has highest skill for summer precipitation from April while lowest from May, and has highest skill for precipitation in June but lowest for precipitation in July. Compared with raw outputs of climate models, some schemes of the two approaches have higher skill for the prediction from March and May, but almost schemes have lower skill for prediction from April. Compared to two-step approach, one sampling scheme of Bayesian merging approach has higher skill for the prediction from March, but has lower skill from May. The results suggest that there is potential to apply the two statistical models for monthly precipitation forecast in summer from March and from May over Huaihe River basin, but is potential to apply CSM1.1m forecast from April. Finally, the summer runoff during 1991 to 2014 is simulated based on one hydrological model using the climate hindcast of CSM1.1m and the two statistical models.

A two-component rain model for the prediction of attenuation and diversity improvement

NASA Technical Reports Server (NTRS)

Crane, R. K.

1982-01-01

A new model was developed to predict attenuation statistics for a single Earth-satellite or terrestrial propagation path. The model was extended to provide predictions of the joint occurrences of specified or higher attenuation values on two closely spaced Earth-satellite paths. The joint statistics provide the information required to obtain diversity gain or diversity advantage estimates. The new model is meteorologically based. It was tested against available Earth-satellite beacon observations and terrestrial path measurements. The model employs the rain climate region descriptions of the Global rain model. The rms deviation between the predicted and observed attenuation values for the terrestrial path data was 35 percent, a result consistent with the expectations of the Global model when the rain rate distribution for the path is not used in the calculation. Within the United States the rms deviation between measurement and prediction was 36 percent but worldwide it was 79 percent.

Bayesian decision support for coding occupational injury data.

PubMed

Nanda, Gaurav; Grattan, Kathleen M; Chu, MyDzung T; Davis, Letitia K; Lehto, Mark R

2016-06-01

Studies on autocoding injury data have found that machine learning algorithms perform well for categories that occur frequently but often struggle with rare categories. Therefore, manual coding, although resource-intensive, cannot be eliminated. We propose a Bayesian decision support system to autocode a large portion of the data, filter cases for manual review, and assist human coders by presenting them top k prediction choices and a confusion matrix of predictions from Bayesian models. We studied the prediction performance of Single-Word (SW) and Two-Word-Sequence (TW) Naïve Bayes models on a sample of data from the 2011 Survey of Occupational Injury and Illness (SOII). We used the agreement in prediction results of SW and TW models, and various prediction strength thresholds for autocoding and filtering cases for manual review. We also studied the sensitivity of the top k predictions of the SW model, TW model, and SW-TW combination, and then compared the accuracy of the manually assigned codes to SOII data with that of the proposed system. The accuracy of the proposed system, assuming well-trained coders reviewing a subset of only 26% of cases flagged for review, was estimated to be comparable (86.5%) to the accuracy of the original coding of the data set (range: 73%-86.8%). Overall, the TW model had higher sensitivity than the SW model, and the accuracy of the prediction results increased when the two models agreed, and for higher prediction strength thresholds. The sensitivity of the top five predictions was 93%. The proposed system seems promising for coding injury data as it offers comparable accuracy and less manual coding. Accurate and timely coded occupational injury data is useful for surveillance as well as prevention activities that aim to make workplaces safer. Copyright © 2016 Elsevier Ltd and National Safety Council. All rights reserved.

Underestimated AMOC Variability and Implications for AMV and Predictability in CMIP Models

NASA Astrophysics Data System (ADS)

Yan, Xiaoqin; Zhang, Rong; Knutson, Thomas R.

2018-05-01

The Atlantic Meridional Overturning Circulation (AMOC) has profound impacts on various climate phenomena. Using both observations and simulations from the Coupled Model Intercomparison Project Phase 3 and 5, here we show that most models underestimate the amplitude of low-frequency AMOC variability. We further show that stronger low-frequency AMOC variability leads to stronger linkages between the AMOC and key variables associated with the Atlantic multidecadal variability (AMV), and between the subpolar AMV signal and northern hemisphere surface air temperature. Low-frequency extratropical northern hemisphere surface air temperature variability might increase with the amplitude of low-frequency AMOC variability. Atlantic decadal predictability is much higher in models with stronger low-frequency AMOC variability and much lower in models with weaker or without AMOC variability. Our results suggest that simulating realistic low-frequency AMOC variability is very important, both for simulating realistic linkages between AMOC and AMV-related variables and for achieving substantially higher Atlantic decadal predictability.

[Prediction of regional soil quality based on mutual information theory integrated with decision tree algorithm].

PubMed

Lin, Fen-Fang; Wang, Ke; Yang, Ning; Yan, Shi-Guang; Zheng, Xin-Yu

2012-02-01

In this paper, some main factors such as soil type, land use pattern, lithology type, topography, road, and industry type that affect soil quality were used to precisely obtain the spatial distribution characteristics of regional soil quality, mutual information theory was adopted to select the main environmental factors, and decision tree algorithm See 5.0 was applied to predict the grade of regional soil quality. The main factors affecting regional soil quality were soil type, land use, lithology type, distance to town, distance to water area, altitude, distance to road, and distance to industrial land. The prediction accuracy of the decision tree model with the variables selected by mutual information was obviously higher than that of the model with all variables, and, for the former model, whether of decision tree or of decision rule, its prediction accuracy was all higher than 80%. Based on the continuous and categorical data, the method of mutual information theory integrated with decision tree could not only reduce the number of input parameters for decision tree algorithm, but also predict and assess regional soil quality effectively.

Decadal climate predictions improved by ocean ensemble dispersion filtering

NASA Astrophysics Data System (ADS)

Kadow, C.; Illing, S.; Kröner, I.; Ulbrich, U.; Cubasch, U.

2017-06-01

Decadal predictions by Earth system models aim to capture the state and phase of the climate several years in advance. Atmosphere-ocean interaction plays an important role for such climate forecasts. While short-term weather forecasts represent an initial value problem and long-term climate projections represent a boundary condition problem, the decadal climate prediction falls in-between these two time scales. In recent years, more precise initialization techniques of coupled Earth system models and increased ensemble sizes have improved decadal predictions. However, climate models in general start losing the initialized signal and its predictive skill from one forecast year to the next. Here we show that the climate prediction skill of an Earth system model can be improved by a shift of the ocean state toward the ensemble mean of its individual members at seasonal intervals. We found that this procedure, called ensemble dispersion filter, results in more accurate results than the standard decadal prediction. Global mean and regional temperature, precipitation, and winter cyclone predictions show an increased skill up to 5 years ahead. Furthermore, the novel technique outperforms predictions with larger ensembles and higher resolution. Our results demonstrate how decadal climate predictions benefit from ocean ensemble dispersion filtering toward the ensemble mean.