Comparison of Models for Ball Bearing Dynamic Capacity and Life

NASA Technical Reports Server (NTRS)

Gupta, Pradeep K.; Oswald, Fred B.; Zaretsky, Erwin V.

2015-01-01

Generalized formulations for dynamic capacity and life of ball bearings, based on the models introduced by Lundberg and Palmgren and Zaretsky, have been developed and implemented in the bearing dynamics computer code, ADORE. Unlike the original Lundberg-Palmgren dynamic capacity equation, where the elastic properties are part of the life constant, the generalized formulations permit variation of elastic properties of the interacting materials. The newly updated Lundberg-Palmgren model allows prediction of life as a function of elastic properties. For elastic properties similar to those of AISI 52100 bearing steel, both the original and updated Lundberg-Palmgren models provide identical results. A comparison between the Lundberg-Palmgren and the Zaretsky models shows that at relatively light loads the Zaretsky model predicts a much higher life than the Lundberg-Palmgren model. As the load increases, the Zaretsky model provides a much faster drop off in life. This is because the Zaretsky model is much more sensitive to load than the Lundberg-Palmgren model. The generalized implementation where all model parameters can be varied provides an effective tool for future model validation and enhancement in bearing life prediction capabilities.

A generalized predictive model for direct gain

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

Givoni, B.

In the correlational model for direct gain developed by the Los Alamos National Laboratory, a list of constants applicable to different types of buildings or passive solar systems was specified separately for each type. In its original form, the model was applicable only to buildings similar in their heat capacity, type of glazing, or night insulation to the types specified by the model. While maintaining the general form of the predictive equations, the new model, the predictive model for direct gain (PMDG), replaces the constants with functions dependent upon the thermal properties of the building, or the components of themore » solar system, or both. By this transformation, the LANL model for direct gain becomes a generalized one. The new model predicts the performance of buildings heated by direct gain with any heat capacity, glazing, and night insulation as functions of their thermophysical properties and climatic conditions.« less

Remaining Useful Life Prediction for Lithium-Ion Batteries Based on Gaussian Processes Mixture

PubMed Central

Li, Lingling; Wang, Pengchong; Chao, Kuei-Hsiang; Zhou, Yatong; Xie, Yang

2016-01-01

The remaining useful life (RUL) prediction of Lithium-ion batteries is closely related to the capacity degeneration trajectories. Due to the self-charging and the capacity regeneration, the trajectories have the property of multimodality. Traditional prediction models such as the support vector machines (SVM) or the Gaussian Process regression (GPR) cannot accurately characterize this multimodality. This paper proposes a novel RUL prediction method based on the Gaussian Process Mixture (GPM). It can process multimodality by fitting different segments of trajectories with different GPR models separately, such that the tiny differences among these segments can be revealed. The method is demonstrated to be effective for prediction by the excellent predictive result of the experiments on the two commercial and chargeable Type 1850 Lithium-ion batteries, provided by NASA. The performance comparison among the models illustrates that the GPM is more accurate than the SVM and the GPR. In addition, GPM can yield the predictive confidence interval, which makes the prediction more reliable than that of traditional models. PMID:27632176

Reference values for pulmonary diffusing capacity for adult native Finns.

PubMed

Kainu, Annette; Toikka, Jyri; Vanninen, Esko; Timonen, Kirsi L

2017-04-01

Measurement standards for pulmonary diffusing capacity were updated in 2005 by the ATS/ERS Task Force. However, in Finland reference values published in 1982 by Viljanen et al. have been used to date. The main aim of this study was to produce updated reference models for single-breath diffusing capacity for carbon monoxide for Finnish adults. Single-breath diffusing capacity for carbon monoxide was measured in 631 healthy non-smoking volunteers (41.5% male). Reference values for diffusing capacity (DLCO), alveolar volume (VA), diffusing capacity per unit of lung volume (DLCO/VA), and lung volumes were calculated using a linear regression model. Previously used Finnish reference values were found to produce too low predicted values, with mean predicted DLCO 111.0 and 104.4%, and DLCO/VA of 103.5 and 102.7% in males and females, respectively. With the European Coalition for Steel and Coal (ECSC) reference values there was a significant sex difference in DLCO/VA with mean predicted 105.4% in males and 92.8% in females (p < .001). New reference values for DLCO, DLCO/VA, VA, vital capacity (VC), inspiratory vital capacity (IVC), and inspiratory capacity (IC) are suggested for clinical use to replace technically outdated reference values for clinical applications.

Life history trade-off moderates model predictions of diversity loss from climate change

PubMed Central

2017-01-01

Climate change can trigger species range shifts, local extinctions and changes in diversity. Species interactions and dispersal capacity are important mediators of community responses to climate change. The interaction between multispecies competition and variation in dispersal capacity has recently been shown to exacerbate the effects of climate change on diversity and to increase predictions of extinction risk dramatically. Dispersal capacity, however, is part of a species’ overall ecological strategy and are likely to trade off with other aspects of its life history that influence population growth and persistence. In plants, a well-known example is the trade-off between seed mass and seed number. The presence of such a trade-off might buffer the diversity loss predicted by models with random but neutral (i.e. not impacting fitness otherwise) differences in dispersal capacity. Using a trait-based metacommunity model along a warming climatic gradient the effect of three different dispersal scenarios on model predictions of diversity change were compared. Adding random variation in species dispersal capacity caused extinctions by the introduction of strong fitness differences due an inherent property of the dispersal kernel. Simulations including a fitness-equalising trade-off based on empirical relationships between seed mass (here affecting dispersal distance, establishment probability, and seedling biomass) and seed number (fecundity) maintained higher initial species diversity and predicted lower extinction risk and diversity loss during climate change than simulations with variable dispersal capacity. Large seeded species persisted during climate change, but developed lags behind their climate niche that may cause extinction debts. Small seeded species were more extinction-prone during climate change but tracked their niches through dispersal and colonisation, despite competitive resistance from residents. Life history trade-offs involved in coexistence mechanisms may increase community resilience to future climate change and are useful guides for model development. PMID:28520770

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

PubMed

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

2008-10-01

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

Prediction of Weather Impacted Airport Capacity using Ensemble Learning

NASA Technical Reports Server (NTRS)

Wang, Yao Xun

2011-01-01

Ensemble learning with the Bagging Decision Tree (BDT) model was used to assess the impact of weather on airport capacities at selected high-demand airports in the United States. The ensemble bagging decision tree models were developed and validated using the Federal Aviation Administration (FAA) Aviation System Performance Metrics (ASPM) data and weather forecast at these airports. The study examines the performance of BDT, along with traditional single Support Vector Machines (SVM), for airport runway configuration selection and airport arrival rates (AAR) prediction during weather impacts. Testing of these models was accomplished using observed weather, weather forecast, and airport operation information at the chosen airports. The experimental results show that ensemble methods are more accurate than a single SVM classifier. The airport capacity ensemble method presented here can be used as a decision support model that supports air traffic flow management to meet the weather impacted airport capacity in order to reduce costs and increase safety.

Oscillatory dynamics of investment and capacity utilization

NASA Astrophysics Data System (ADS)

Greenblatt, R. E.

2017-01-01

Capitalist economic systems display a wide variety of oscillatory phenomena whose underlying causes are often not well understood. In this paper, I consider a very simple model of the reciprocal interaction between investment, capacity utilization, and their time derivatives. The model, which gives rise periodic oscillations, predicts qualitatively the phase relations between these variables. These predictions are observed to be consistent in a statistical sense with econometric data from the US economy.

Improvement and Application of the Softened Strut-and-Tie Model

NASA Astrophysics Data System (ADS)

Fan, Guoxi; Wang, Debin; Diao, Yuhong; Shang, Huaishuai; Tang, Xiaocheng; Sun, Hai

2017-11-01

Previous experimental researches indicate that reinforced concrete beam-column joints play an important role in the mechanical properties of moment resisting frame structures, so as to require proper design. The aims of this paper are to predict the joint carrying capacity and cracks development theoretically. Thus, a rational model needs to be developed. Based on the former considerations, the softened strut-and-tie model is selected to be introduced and analyzed. Four adjustments including modifications of the depth of the diagonal strut, the inclination angle of diagonal compression strut, the smeared stress of mild steel bars embedded in concrete, as well as the softening coefficient are made. After that, the carrying capacity of beam-column joint and cracks development are predicted using the improved softened strut-and-tie model. Based on the test results, it is not difficult to find that the improved softened strut-and-tie model can be used to predict the joint carrying capacity and cracks development with sufficient accuracy.

Load Carriage Capacity of the Dismounted Combatant - A Commanders’ Guide

DTIC Science & Technology

2012-10-01

predictive model has been used throughout this document to predict the physiological burden (i.e. energy cost ) of representative load carriage...scenarios. As a general guide this model indicates that a 10 kg increase in external load is metabolically equivalent (i.e. energy cost ) to an increase...larger increases in energy cost for a load carriage task. The multi-factorial nature of human load carriage capacity makes it difficult to set

Determination of heat capacity of ionic liquid based nanofluids using group method of data handling technique

NASA Astrophysics Data System (ADS)

Sadi, Maryam

2018-01-01

In this study a group method of data handling model has been successfully developed to predict heat capacity of ionic liquid based nanofluids by considering reduced temperature, acentric factor and molecular weight of ionic liquids, and nanoparticle concentration as input parameters. In order to accomplish modeling, 528 experimental data points extracted from the literature have been divided into training and testing subsets. The training set has been used to predict model coefficients and the testing set has been applied for model validation. The ability and accuracy of developed model, has been evaluated by comparison of model predictions with experimental values using different statistical parameters such as coefficient of determination, mean square error and mean absolute percentage error. The mean absolute percentage error of developed model for training and testing sets are 1.38% and 1.66%, respectively, which indicate excellent agreement between model predictions and experimental data. Also, the results estimated by the developed GMDH model exhibit a higher accuracy when compared to the available theoretical correlations.

Impact of limited solvent capacity on metabolic rate, enzyme activities, and metabolite concentrations of S. cerevisiae glycolysis.

PubMed

Vazquez, Alexei; de Menezes, Marcio A; Barabási, Albert-László; Oltvai, Zoltan N

2008-10-01

The cell's cytoplasm is crowded by its various molecular components, resulting in a limited solvent capacity for the allocation of new proteins, thus constraining various cellular processes such as metabolism. Here we study the impact of the limited solvent capacity constraint on the metabolic rate, enzyme activities, and metabolite concentrations using a computational model of Saccharomyces cerevisiae glycolysis as a case study. We show that given the limited solvent capacity constraint, the optimal enzyme activities and the metabolite concentrations necessary to achieve a maximum rate of glycolysis are in agreement with their experimentally measured values. Furthermore, the predicted maximum glycolytic rate determined by the solvent capacity constraint is close to that measured in vivo. These results indicate that the limited solvent capacity is a relevant constraint acting on S. cerevisiae at physiological growth conditions, and that a full kinetic model together with the limited solvent capacity constraint can be used to predict both metabolite concentrations and enzyme activities in vivo.

Impact of Limited Solvent Capacity on Metabolic Rate, Enzyme Activities, and Metabolite Concentrations of S. cerevisiae Glycolysis

PubMed Central

Vazquez, Alexei; de Menezes, Marcio A.; Barabási, Albert-László; Oltvai, Zoltan N.

2008-01-01

The cell's cytoplasm is crowded by its various molecular components, resulting in a limited solvent capacity for the allocation of new proteins, thus constraining various cellular processes such as metabolism. Here we study the impact of the limited solvent capacity constraint on the metabolic rate, enzyme activities, and metabolite concentrations using a computational model of Saccharomyces cerevisiae glycolysis as a case study. We show that given the limited solvent capacity constraint, the optimal enzyme activities and the metabolite concentrations necessary to achieve a maximum rate of glycolysis are in agreement with their experimentally measured values. Furthermore, the predicted maximum glycolytic rate determined by the solvent capacity constraint is close to that measured in vivo. These results indicate that the limited solvent capacity is a relevant constraint acting on S. cerevisiae at physiological growth conditions, and that a full kinetic model together with the limited solvent capacity constraint can be used to predict both metabolite concentrations and enzyme activities in vivo. PMID:18846199

[Population pharmacokinetics applied to optimising cisplatin doses in cancer patients].

PubMed

Ramón-López, A; Escudero-Ortiz, V; Carbonell, V; Pérez-Ruixo, J J; Valenzuela, B

2012-01-01

To develop and internally validate a population pharmacokinetics model for cisplatin and assess its prediction capacity for personalising doses in cancer patients. Cisplatin plasma concentrations in forty-six cancer patients were used to determine the pharmacokinetic parameters of a two-compartment pharmacokinetic model implemented in NONMEN VI software. Pharmacokinetic parameter identification capacity was assessed using the parametric bootstrap method and the model was validated using the nonparametric bootstrap method and standardised visual and numerical predictive checks. The final model's prediction capacity was evaluated in terms of accuracy and precision during the first (a priori) and second (a posteriori) chemotherapy cycles. Mean population cisplatin clearance is 1.03 L/h with an interpatient variability of 78.0%. Estimated distribution volume at steady state was 48.3 L, with inter- and intrapatient variabilities of 31,3% and 11,7%, respectively. Internal validation confirmed that the population pharmacokinetics model is appropriate to describe changes over time in cisplatin plasma concentrations, as well as its variability in the study population. The accuracy and precision of a posteriori prediction of cisplatin concentrations improved by 21% and 54% compared to a priori prediction. The population pharmacokinetic model developed adequately described the changes in cisplatin plasma concentrations in cancer patients and can be used to optimise cisplatin dosing regimes accurately and precisely. Copyright © 2011 SEFH. Published by Elsevier Espana. All rights reserved.

External prognostic validations and comparisons of age- and gender-adjusted exercise capacity predictions.

PubMed

Kim, Esther S H; Ishwaran, Hemant; Blackstone, Eugene; Lauer, Michael S

2007-11-06

The purpose of this study was to externally validate the prognostic value of age- and gender-based nomograms and categorical definitions of impaired exercise capacity (EC). Exercise capacity predicts death, but its use in routine clinical practice is hampered by its close correlation with age and gender. For a median of 5 years, we followed 22,275 patients without known heart disease who underwent symptom-limited stress testing. Models for predicted or impaired EC were identified by literature search. Gender-specific multivariable proportional hazards models were constructed. Four methods were used to assess validity: Akaike Information Criterion (AIC), right-censored c-index in 100 out-of-bootstrap samples, the Nagelkerke Index R2, and calculation of calibration error in 100 bootstrap samples. There were 646 and 430 deaths in 13,098 men and 9,177 women, respectively. Of the 7 models tested in men, a model based on a Veterans Affairs cohort (predicted metabolic equivalents [METs] = 18 - [0.15 x age]) had the highest AIC and R2. In women, a model based on the St. James Take Heart Project (predicted METs = 14.7 - [0.13 x age]) performed best. Categorical definitions of fitness performed less well. Even after accounting for age and gender, there was still an important interaction with age, whereby predicted EC was a weaker predictor in older subjects (p for interaction <0.001 in men and 0.003 in women). Several methods describe EC accounting for age and gender-related differences, but their ability to predict mortality differ. Simple cutoff values fail to fully describe EC's strong predictive value.

Echocardiography service provision in New Zealand: The implications of capacity modelling for the cardiac sonographer workforce.

PubMed

Buckley, Belinda; Farnworth, Mark J; Whalley, Gillian

2016-01-08

Regional disparity in both utilisation and the cardiac sonographer workforce has previously been identified. We sought to model the capacity of the cardiac sonographer workforce at a national and District Health Board level to better understand these regional differences. In 2013, surveys were distributed to 18 hospitals who employ cardiac sonographers (return rate 100%). Questions related to cardiac sonographer demographics, echo utilisation and workflow. Actual clinical capacity was calculated from scan duration and annual scan volumes. New Zealand national actual capacity was compared to predicted capacity from three international models. Potential clinical capacity was calculated from the workforce size in fulltime equivalent (FTE) and clinical availability. In New Zealand, scan duration and population-based clinical capacity varies between centres. The New Zealand capacity is similar to the UK 30:70 model, and consistently less than the US model for all scan types. There are marked regional differences in potential versus actual capacity, with 10/16 DHBs demonstrating excess potential capacity. There is regional disparity in the capacity of the cardiac sonographer workforce, which appears to be strongly related to scan duration. Workforce capacity modelling should be used with need and demand modelling to plan adequate levels of service provision.

Empirical Approach for Determining Axial Strength of Circular Concrete Filled Steel Tubular Columns

NASA Astrophysics Data System (ADS)

Jayalekshmi, S.; Jegadesh, J. S. Sankar; Goel, Abhishek

2018-06-01

The concrete filled steel tubular (CFST) columns are highly regarded in recent years as an interesting option in the construction field by designers and structural engineers, due to their exquisite structural performance, with enhanced load bearing capacity and energy absorption capacity. This study presents a new approach to simulate the capacity of circular CFST columns under axial loading condition, using a large database of experimental results by applying artificial neural network (ANN). A well trained network is established and is used to simulate the axial capacity of CFST columns. The validation and testing of the ANN is carried out. The current study is focused on proposing a simplified equation that can predict the ultimate strength of the axially loaded columns with high level of accuracy. The predicted results are compared with five existing analytical models which estimate the strength of the CFST column. The ANN-based equation has good prediction with experimental data, when compared with the analytical models.

Empirical Approach for Determining Axial Strength of Circular Concrete Filled Steel Tubular Columns

NASA Astrophysics Data System (ADS)

Jayalekshmi, S.; Jegadesh, J. S. Sankar; Goel, Abhishek

2018-03-01

The concrete filled steel tubular (CFST) columns are highly regarded in recent years as an interesting option in the construction field by designers and structural engineers, due to their exquisite structural performance, with enhanced load bearing capacity and energy absorption capacity. This study presents a new approach to simulate the capacity of circular CFST columns under axial loading condition, using a large database of experimental results by applying artificial neural network (ANN). A well trained network is established and is used to simulate the axial capacity of CFST columns. The validation and testing of the ANN is carried out. The current study is focused on proposing a simplified equation that can predict the ultimate strength of the axially loaded columns with high level of accuracy. The predicted results are compared with five existing analytical models which estimate the strength of the CFST column. The ANN-based equation has good prediction with experimental data, when compared with the analytical models.

Leaf nitrogen from first principles: field evidence for adaptive variation with climate

NASA Astrophysics Data System (ADS)

Dong, Ning; Prentice, Iain Colin; Evans, Bradley J.; Caddy-Retalic, Stefan; Lowe, Andrew J.; Wright, Ian J.

2017-01-01

Nitrogen content per unit leaf area (Narea) is a key variable in plant functional ecology and biogeochemistry. Narea comprises a structural component, which scales with leaf mass per area (LMA), and a metabolic component, which scales with Rubisco capacity. The co-ordination hypothesis, as implemented in LPJ and related global vegetation models, predicts that Rubisco capacity should be directly proportional to irradiance but should decrease with increases in ci : ca and temperature because the amount of Rubisco required to achieve a given assimilation rate declines with increases in both. We tested these predictions using LMA, leaf δ13C, and leaf N measurements on complete species assemblages sampled at sites on a north-south transect from tropical to temperate Australia. Partial effects of mean canopy irradiance, mean annual temperature, and ci : ca (from δ13C) on Narea were all significant and their directions and magnitudes were in line with predictions. Over 80 % of the variance in community-mean (ln) Narea was accounted for by these predictors plus LMA. Moreover, Narea could be decomposed into two components, one proportional to LMA (slightly steeper in N-fixers), and the other to Rubisco capacity as predicted by the co-ordination hypothesis. Trait gradient analysis revealed ci : ca to be perfectly plastic, while species turnover contributed about half the variation in LMA and Narea. Interest has surged in methods to predict continuous leaf-trait variation from environmental factors, in order to improve ecosystem models. Coupled carbon-nitrogen models require a method to predict Narea that is more realistic than the widespread assumptions that Narea is proportional to photosynthetic capacity, and/or that Narea (and photosynthetic capacity) are determined by N supply from the soil. Our results indicate that Narea has a useful degree of predictability, from a combination of LMA and ci : ca - themselves in part environmentally determined - with Rubisco activity, as predicted from local growing conditions. This finding is consistent with a plant-centred approach to modelling, emphasizing the adaptive regulation of traits. Models that account for biodiversity will also need to partition community-level trait variation into components due to phenotypic plasticity and/or genotypic differentiation within species vs. progressive species replacement, along environmental gradients. Our analysis suggests that variation in Narea is about evenly split between these two modes.

Divergent predictions of carbon storage between two global land models: attribution of the causes through traceability analysis

NASA Astrophysics Data System (ADS)

Rafique, Rashid; Xia, Jianyang; Hararuk, Oleksandra; Asrar, Ghassem R.; Leng, Guoyong; Wang, Yingping; Luo, Yiqi

2016-07-01

Representations of the terrestrial carbon cycle in land models are becoming increasingly complex. It is crucial to develop approaches for critical assessment of the complex model properties in order to understand key factors contributing to models' performance. In this study, we applied a traceability analysis which decomposes carbon cycle models into traceable components, for two global land models (CABLE and CLM-CASA') to diagnose the causes of their differences in simulating ecosystem carbon storage capacity. Driven with similar forcing data, CLM-CASA' predicted ˜ 31 % larger carbon storage capacity than CABLE. Since ecosystem carbon storage capacity is a product of net primary productivity (NPP) and ecosystem residence time (τE), the predicted difference in the storage capacity between the two models results from differences in either NPP or τE or both. Our analysis showed that CLM-CASA' simulated 37 % higher NPP than CABLE. On the other hand, τE, which was a function of the baseline carbon residence time (τ'E) and environmental effect on carbon residence time, was on average 11 years longer in CABLE than CLM-CASA'. This difference in τE was mainly caused by longer τ'E of woody biomass (23 vs. 14 years in CLM-CASA'), and higher proportion of NPP allocated to woody biomass (23 vs. 16 %). Differences in environmental effects on carbon residence times had smaller influences on differences in ecosystem carbon storage capacities compared to differences in NPP and τ'E. Overall, the traceability analysis showed that the major causes of different carbon storage estimations were found to be parameters setting related to carbon input and baseline carbon residence times between two models.

Divergent predictions of carbon storage between two global land models: Attribution of the causes through traceability analysis

DOE PAGES

Rafique, Rashid; Xia, Jianyang; Hararuk, Oleksandra; ...

2016-07-29

Representations of the terrestrial carbon cycle in land models are becoming increasingly complex. It is crucial to develop approaches for critical assessment of the complex model properties in order to understand key factors contributing to models' performance. In this study, we applied a traceability analysis which decomposes carbon cycle models into traceable components, for two global land models (CABLE and CLM-CASA') to diagnose the causes of their differences in simulating ecosystem carbon storage capacity. Driven with similar forcing data, CLM-CASA' predicted – 31 % larger carbon storage capacity than CABLE. Since ecosystem carbon storage capacity is a product of net primary productivitymore » (NPP) and ecosystem residence time ( τ E), the predicted difference in the storage capacity between the two models results from differences in either NPP or τ E or both. Our analysis showed that CLM-CASA'simulated 37 % higher NPP than CABLE. On the other hand, τ E, which was a function of the baseline carbon residence time ( τ' E) and environmental effect on carbon residence time, was on average 11 years longer in CABLE than CLM-CASA'. This difference in τ E was mainly caused by longer τ' E of woody biomass (23 vs. 14 years in CLM-CASA'), and higher proportion of NPP allocated to woody biomass (23 vs. 16 %). Differences in environmental effects on carbon residence times had smaller influences on differences in ecosystem carbon storage capacities compared to differences in NPP and τ' E. Altogether, the traceability analysis showed that the major causes of different carbon storage estimations were found to be parameters setting related to carbon input and baseline carbon residence times between two models.« less

Divergent predictions of carbon storage between two global land models: Attribution of the causes through traceability analysis

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

Rafique, Rashid; Xia, Jianyang; Hararuk, Oleksandra

Representations of the terrestrial carbon cycle in land models are becoming increasingly complex. It is crucial to develop approaches for critical assessment of the complex model properties in order to understand key factors contributing to models' performance. In this study, we applied a traceability analysis which decomposes carbon cycle models into traceable components, for two global land models (CABLE and CLM-CASA') to diagnose the causes of their differences in simulating ecosystem carbon storage capacity. Driven with similar forcing data, CLM-CASA' predicted – 31 % larger carbon storage capacity than CABLE. Since ecosystem carbon storage capacity is a product of net primary productivitymore » (NPP) and ecosystem residence time ( τ E), the predicted difference in the storage capacity between the two models results from differences in either NPP or τ E or both. Our analysis showed that CLM-CASA'simulated 37 % higher NPP than CABLE. On the other hand, τ E, which was a function of the baseline carbon residence time ( τ' E) and environmental effect on carbon residence time, was on average 11 years longer in CABLE than CLM-CASA'. This difference in τ E was mainly caused by longer τ' E of woody biomass (23 vs. 14 years in CLM-CASA'), and higher proportion of NPP allocated to woody biomass (23 vs. 16 %). Differences in environmental effects on carbon residence times had smaller influences on differences in ecosystem carbon storage capacities compared to differences in NPP and τ' E. Altogether, the traceability analysis showed that the major causes of different carbon storage estimations were found to be parameters setting related to carbon input and baseline carbon residence times between two models.« less

Tools for Early Prediction of Drug Loading in Lipid-Based Formulations

PubMed Central

2015-01-01

Identification of the usefulness of lipid-based formulations (LBFs) for delivery of poorly water-soluble drugs is at date mainly experimentally based. In this work we used a diverse drug data set, and more than 2,000 solubility measurements to develop experimental and computational tools to predict the loading capacity of LBFs. Computational models were developed to enable in silico prediction of solubility, and hence drug loading capacity, in the LBFs. Drug solubility in mixed mono-, di-, triglycerides (Maisine 35-1 and Capmul MCM EP) correlated (R2 0.89) as well as the drug solubility in Carbitol and other ethoxylated excipients (PEG400, R2 0.85; Polysorbate 80, R2 0.90; Cremophor EL, R2 0.93). A melting point below 150 °C was observed to result in a reasonable solubility in the glycerides. The loading capacity in LBFs was accurately calculated from solubility data in single excipients (R2 0.91). In silico models, without the demand of experimentally determined solubility, also gave good predictions of the loading capacity in these complex formulations (R2 0.79). The framework established here gives a better understanding of drug solubility in single excipients and of LBF loading capacity. The large data set studied revealed that experimental screening efforts can be rationalized by solubility measurements in key excipients or from solid state information. For the first time it was shown that loading capacity in complex formulations can be accurately predicted using molecular information extracted from calculated descriptors and thermal properties of the crystalline drug. PMID:26568134

Tools for Early Prediction of Drug Loading in Lipid-Based Formulations.

PubMed

Alskär, Linda C; Porter, Christopher J H; Bergström, Christel A S

2016-01-04

Identification of the usefulness of lipid-based formulations (LBFs) for delivery of poorly water-soluble drugs is at date mainly experimentally based. In this work we used a diverse drug data set, and more than 2,000 solubility measurements to develop experimental and computational tools to predict the loading capacity of LBFs. Computational models were developed to enable in silico prediction of solubility, and hence drug loading capacity, in the LBFs. Drug solubility in mixed mono-, di-, triglycerides (Maisine 35-1 and Capmul MCM EP) correlated (R(2) 0.89) as well as the drug solubility in Carbitol and other ethoxylated excipients (PEG400, R(2) 0.85; Polysorbate 80, R(2) 0.90; Cremophor EL, R(2) 0.93). A melting point below 150 °C was observed to result in a reasonable solubility in the glycerides. The loading capacity in LBFs was accurately calculated from solubility data in single excipients (R(2) 0.91). In silico models, without the demand of experimentally determined solubility, also gave good predictions of the loading capacity in these complex formulations (R(2) 0.79). The framework established here gives a better understanding of drug solubility in single excipients and of LBF loading capacity. The large data set studied revealed that experimental screening efforts can be rationalized by solubility measurements in key excipients or from solid state information. For the first time it was shown that loading capacity in complex formulations can be accurately predicted using molecular information extracted from calculated descriptors and thermal properties of the crystalline drug.

Analysis on the Oversize Blast Furnace Desulfurization and a Sulfide Capacity Prediction Model Based on Congregated Electron Phase

NASA Astrophysics Data System (ADS)

Zhenyang, Wang; Jianliang, Zhang; Gang, An; Zhengjian, Liu; Zhengming, Cheng; Junjie, Huang; Jingwei, Zhang

2016-02-01

Through analyzed and regressed the actual productive desulfurization data from the oversize blast furnace (5500 m3) in north China, the relationship between the sulfur distribution parameters and the slag composition in actual production situation was investigated. As the slag and hot metal phases have their own balance sulfur content or sulfur partial pressure in gas phase, respectively, the non-equilibrium of sulfur among gas, slag, and metal phases leads to the transmission and distribution of sulfur. Combined with sulfur transmission reactions between gas, slag and metal phases, C/CO pairs equilibrium, and Wagner model, the measured sulfide capacity can be acquired using sulfur distribution ratio, sulfur activity coefficient, and oxygen activity in hot metal. Based on the theory of congregated electron phase, a new sulfide capacity prediction model (CEPM) has been developed, which has a good liner relationship with the measured sulfide capacity. Thus, using the burden structure for BF, the ironmaking slag composition can be obtained simply and can be used to reliably predict the ironmaking slag desulfurization ability a few hours later after charging under a certain temperature by CEPM.

Adsorption Isotherms for Xenon and Krypton using INL HZ-PAN and AgZ-PAN Sorbents

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

Garn, Troy G.; Greenhalgh, Mitchell; Rutledge, Veronica J.

2014-08-01

The generation of adsorption isotherms compliments the scale-up of off-gas processes used to control the emission of encapsulated radioactive volatile fission and activation products released during Used Nuclear Fuel (UNF) reprocessing activities. A series of experiments were conducted to obtain capacity results for varying Kr and Xe gas concentrations using HZ-PAN and AgZ-PAN engineered form sorbents. Gas compositions for Kr ranged from 150-40,000 ppmv and 250-5020 ppmv for Xe in a helium balance. The experiments were all performed at 220 K at a flowrate of 50 sccm. Acquired capacities were then respectively fit to the Langmuir equation using the Langmuirmore » linear regression method to obtain the equilibrium parameters Qmax and Keq. Generated experimental adsorption isotherms were then plotted with the Langmuir predicted isotherms to illustrate agreement between the two. The Langmuir parameters were provided for input into the OSPREY model to predict breakthrough of single component adsorption of Kr and Xe on HZ-PAN and AgZ-PAN sorbents at the experimental conditions tested. Kr and Xe capacities resulting from model breakthrough predictions were then compared to experimental capacities for model validation.« less

Predicting the distribution of bed material accumulation using river network sediment budgets

NASA Astrophysics Data System (ADS)

Wilkinson, Scott N.; Prosser, Ian P.; Hughes, Andrew O.

2006-10-01

Assessing the spatial distribution of bed material accumulation in river networks is important for determining the impacts of erosion on downstream channel form and habitat and for planning erosion and sediment management. A model that constructs spatially distributed budgets of bed material sediment is developed to predict the locations of accumulation following land use change. For each link in the river network, GIS algorithms are used to predict bed material supply from gullies, river banks, and upstream tributaries and to compare total supply with transport capacity. The model is tested in the 29,000 km2 Murrumbidgee River catchment in southeast Australia. It correctly predicts the presence or absence of accumulation in 71% of river links, which is significantly better performance than previous models, which do not account for spatial variability in sediment supply and transport capacity. Representing transient sediment storage is important for predicting smaller accumulations. Bed material accumulation is predicted in 25% of the river network, indicating its importance as an environmental problem in Australia.

An objective analysis of the dynamic nature of field capacity

NASA Astrophysics Data System (ADS)

Twarakavi, Navin K. C.; Sakai, Masaru; Å Imå¯Nek, Jirka

2009-10-01

Field capacity is one of the most commonly used, and yet poorly defined, soil hydraulic properties. Traditionally, field capacity has been defined as the amount of soil moisture after excess water has drained away and the rate of downward movement has materially decreased. Unfortunately, this qualitative definition does not lend itself to an unambiguous quantitative approach for estimation. Because of the vagueness in defining what constitutes "drainage of excess water" from a soil, the estimation of field capacity has often been based upon empirical guidelines. These empirical guidelines are either time, pressure, or flux based. In this paper, we developed a numerical approach to estimate field capacity using a flux-based definition. The resulting approach was implemented on the soil parameter data set used by Schaap et al. (2001), and the estimated field capacity was compared to traditional definitions of field capacity. The developed modeling approach was implemented using the HYDRUS-1D software with the capability of simultaneously estimating field capacity for multiple soils with soil hydraulic parameter data. The Richards equation was used in conjunction with the van Genuchten-Mualem model to simulate variably saturated flow in a soil. Using the modeling approach to estimate field capacity also resulted in additional information such as (1) the pressure head, at which field capacity is attained, and (2) the drainage time needed to reach field capacity from saturated conditions under nonevaporative conditions. We analyzed the applicability of the modeling-based approach to estimate field capacity on real-world soils data. We also used the developed method to create contour diagrams showing the variation of field capacity with texture. It was found that using benchmark pressure heads to estimate field capacity from the retention curve leads to inaccurate results. Finally, a simple analytical equation was developed to predict field capacity from soil hydraulic parameter information. The analytical equation was found to be effective in its ability to predict field capacities.

Predicting Alumni/ae Gift Giving Behavior: A Structural Equation Model Approach.

ERIC Educational Resources Information Center

Mosser, John Wayne

This dissertation focuses on predicting alumni gift giving behavior at a large public research university (University of Michigan). A conceptual model was developed for predicting alumni giving behavior in order to advance the theoretical understanding of how capacity to give, motivation to give, and their interaction effect gift giving behavior.…

Neurocognitive Models of Medical Decision-Making Capacity in Traumatic Brain Injury Across Injury Severity.

PubMed

Triebel, Kristen L; Novack, Thomas A; Kennedy, Richard; Martin, Roy C; Dreer, Laura E; Raman, Rema; Marson, Daniel C

2016-01-01

To identify neurocognitive predictors of medical decision-making capacity (MDC) in participants with mild and moderate/severe traumatic brain injury (TBI). Academic medical center. Sixty adult controls and 104 adults with TBI (49 mild, 55 moderate/severe) evaluated within 6 weeks of injury. Prospective cross-sectional study. Participants completed the Capacity to Consent to Treatment Instrument to assess MDC and a neuropsychological test battery. We used factor analysis to reduce the battery test measures into 4 cognitive composite scores (verbal memory, verbal fluency, academic skills, and processing speed/executive function). We identified cognitive predictors of the 3 most clinically relevant Capacity to Consent to Treatment Instrument consent standards (appreciation, reasoning, and understanding). In controls, academic skills (word reading, arithmetic) and verbal memory predicted understanding; verbal fluency predicted reasoning; and no predictors emerged for appreciation. In the mild TBI group, verbal memory predicted understanding and reasoning, whereas academic skills predicted appreciation. In the moderate/severe TBI group, verbal memory and academic skills predicted understanding; academic skills predicted reasoning; and academic skills and verbal fluency predicted appreciation. Verbal memory was a predictor of MDC in controls and persons with mild and moderate/severe TBI. In clinical practice, impaired verbal memory could serve as a "red flag" for diminished consent capacity in persons with recent TBI.

From neural oscillations to reasoning ability: Simulating the effect of the theta-to-gamma cycle length ratio on individual scores in a figural analogy test.

PubMed

Chuderski, Adam; Andrelczyk, Krzysztof

2015-02-01

Several existing computational models of working memory (WM) have predicted a positive relationship (later confirmed empirically) between WM capacity and the individual ratio of theta to gamma oscillatory band lengths. These models assume that each gamma cycle represents one WM object (e.g., a binding of its features), whereas the theta cycle integrates such objects into the maintained list. As WM capacity strongly predicts reasoning, it might be expected that this ratio also predicts performance in reasoning tasks. However, no computational model has yet explained how the differences in the theta-to-gamma ratio found among adult individuals might contribute to their scores on a reasoning test. Here, we propose a novel model of how WM capacity constraints figural analogical reasoning, aimed at explaining inter-individual differences in reasoning scores in terms of the characteristics of oscillatory patterns in the brain. In the model, the gamma cycle encodes the bindings between objects/features and the roles they play in the relations processed. Asynchrony between consecutive gamma cycles results from lateral inhibition between oscillating bindings. Computer simulations showed that achieving the highest WM capacity required reaching the optimal level of inhibition. When too strong, this inhibition eliminated some bindings from WM, whereas, when inhibition was too weak, the bindings became unstable and fell apart or became improperly grouped. The model aptly replicated several empirical effects and the distribution of individual scores, as well as the patterns of correlations found in the 100-people sample attempting the same reasoning task. Most importantly, the model's reasoning performance strongly depended on its theta-to-gamma ratio in same way as the performance of human participants depended on their WM capacity. The data suggest that proper regulation of oscillations in the theta and gamma bands may be crucial for both high WM capacity and effective complex cognition. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Signal detection evidence for limited capacity in visual search

PubMed Central

Fencsik, David E.; Flusberg, Stephen J.; Horowitz, Todd S.; Wolfe, Jeremy M.

2014-01-01

The nature of capacity limits (if any) in visual search has been a topic of controversy for decades. In 30 years of work, researchers have attempted to distinguish between two broad classes of visual search models. Attention-limited models have proposed two stages of perceptual processing: an unlimited-capacity preattentive stage, and a limited-capacity selective attention stage. Conversely, noise-limited models have proposed a single, unlimited-capacity perceptual processing stage, with decision processes influenced only by stochastic noise. Here, we use signal detection methods to test a strong prediction of attention-limited models. In standard attention-limited models, performance of some searches (feature searches) should only be limited by a preattentive stage. Other search tasks (e.g., spatial configuration search for a “2” among “5”s) should be additionally limited by an attentional bottleneck. We equated average accuracies for a feature and a spatial configuration search over set sizes of 1–8 for briefly presented stimuli. The strong prediction of attention-limited models is that, given overall equivalence in performance, accuracy should be better on the spatial configuration search than on the feature search for set size 1, and worse for set size 8. We confirm this crossover interaction and show that it is problematic for at least one class of one-stage decision models. PMID:21901574

Predictive equations for total lung capacity and residual volume calculated from radiographs in a random sample of the Michigan population.

PubMed Central

Kilburn, K H; Warshaw, R H; Thornton, J C; Thornton, K; Miller, A

1992-01-01

BACKGROUND: Published predicted values for total lung capacity and residual volume are often based on a small number of subjects and derive from different populations from predicted spirometric values. Equations from the only two large studies gave smaller predicted values for total lung capacity than the smaller studies. A large number of subjects have been studied from a population which has already provided predicted values for spirometry and transfer factor for carbon monoxide. METHODS: Total lung capacity was measured from standard posteroanterior and lateral chest radiographs and forced vital capacity by spirometry in a population sample of 771 subjects. Prediction equations were developed for total lung capacity (TLC), residual volume (RV) and RV/TLC in two groups--normal and total. Subjects with signs or symptoms of cardiopulmonary disease were combined with the normal subjects and equations for all subjects were also modelled. RESULTS: Prediction equations for TLC and RV in non-smoking normal men and women were square root transformations which included height and weight but not age. They included a coefficient for duration of smoking in current smokers. The predictive equation for RV/TLC included weight, age, age and duration of smoking for current smokers and ex-smokers of both sexes. For the total population the equations took the same form but the height coefficients and constants were slightly different. CONCLUSION: These population based prediction equations for TLC, RV and RV/TLC provide reference standards in a population that has provided reference standards for spirometry and single breath transfer factor for carbon monoxide. PMID:1412094

Modeling "Throughput Capacity": Using Computational Thinking to Envision More Graduates without Investing More Resources

ERIC Educational Resources Information Center

Wick, Michael R.; Kleine, Patricia A.; Nelson, Andrew J.

2011-01-01

This article presents the development, testing, and application of an enrollment model. The model incorporates incoming freshman enrollment class size and historical persistence, transfer, and graduation rates to predict a six-year enrollment window and associated annual graduate production. The model predicts six-year enrollment to within 0.67…

Computer model for electrochemical cell performance loss over time in terms of capacity, power, and conductance (CPC)

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

Gering, Kevin L.

2015-09-01

Available capacity, power, and cell conductance figure centrally into performance characterization of electrochemical cells (such as Li-ion cells) over their service life. For example, capacity loss in Li-ion cells is due to a combination of mechanisms, including loss of free available lithium, loss of active host sites, shifts in the potential-capacity curve, etc. Further distinctions can be made regarding irreversible and reversible capacity loss mechanisms. There are tandem needs for accurate interpretation of capacity at characterization conditions (cycling rate, temperature, etc.) and for robust self-consistent modeling techniques that can be used for diagnostic analysis of cell data as well asmore » forecasting of future performance. Analogous issues exist for aging effects on cell conductance and available power. To address these needs, a modeling capability was developed that provides a systematic analysis of the contributing factors to battery performance loss over aging and to act as a regression/prediction platform for cell performance. The modeling basis is a summation of self-consistent chemical kinetics rate expressions, which as individual expressions each covers a distinct mechanism (e.g., loss of active host sites, lithium loss), but collectively account for the net loss of premier metrics (e.g., capacity) over time for a particular characterization condition. Specifically, sigmoid-based rate expressions are utilized to describe each contribution to performance loss. Through additional mathematical development another tier of expressions is derived and used to perform differential analyses and segregate irreversible versus reversible contributions, as well as to determine concentration profiles over cell aging for affected Li+ ion inventory and fraction of active sites that remain at each time step. Reversible fade components are surmised by comparing fade rates at fast versus slow cycling conditions. The model is easily utilized for predictive calculations so that future capacity performance can be estimated. The invention covers mathematical and theoretical frameworks, and demonstrates application to various Li-ion cells covering test periods that vary in duration, and shows model predictions well past the end of test periods. Version 2.0 Enhancements: the code now covers path-dependent aging scenarios, wherein the framework allows for arbitrarily-chosen aging conditions over a timeline to accommodate prediction of battery aging over a multiplicity of changing conditions. The code framework also allows for cell conductance and power loss evaluations over cell aging, analysis of series strings that contain a thermal anomaly (hot spot), and evaluation of battery thermal management parameters that impact battery lifetimes. Lastly, a comprehensive GUI now resides in the Ver. 2.0 code.« less

High capacity demonstration of honeycomb panel heat pipes

NASA Technical Reports Server (NTRS)

Tanzer, H. J.

1989-01-01

The feasibility of performance enhancing the sandwich panel heat pipe was investigated for moderate temperature range heat rejection radiators on future-high-power spacecraft. The hardware development program consisted of performance prediction modeling, fabrication, ground test, and data correlation. Using available sandwich panel materials, a series of subscale test panels were augumented with high-capacity sideflow and temperature control variable conductance features, and test evaluated for correlation with performance prediction codes. Using the correlated prediction model, a 50-kW full size radiator was defined using methanol working fluid and closely spaced sideflows. A new concept called the hybrid radiator individually optimizes heat pipe components. A 2.44-m long hybrid test vehicle demonstrated proof-of-principle performance.

Comparing large-scale hydrological model predictions with observed streamflow in the Pacific Northwest: effects of climate and groundwater

Treesearch

Mohammad Safeeq; Guillaume S. Mauger; Gordon E. Grant; Ivan Arismendi; Alan F. Hamlet; Se-Yeun Lee

2014-01-01

Assessing uncertainties in hydrologic models can improve accuracy in predicting future streamflow. Here, simulated streamflows using the Variable Infiltration Capacity (VIC) model at coarse (1/16°) and fine (1/120°) spatial resolutions were evaluated against observed streamflows from 217 watersheds. In...

Modeling the capacity of riverscapes to support beaver dams

NASA Astrophysics Data System (ADS)

Macfarlane, William W.; Wheaton, Joseph M.; Bouwes, Nicolaas; Jensen, Martha L.; Gilbert, Jordan T.; Hough-Snee, Nate; Shivik, John A.

2017-01-01

The construction of beaver dams facilitates a suite of hydrologic, hydraulic, geomorphic, and ecological feedbacks that increase stream complexity and channel-floodplain connectivity that benefit aquatic and terrestrial biota. Depending on where beaver build dams within a drainage network, they impact lateral and longitudinal connectivity by introducing roughness elements that fundamentally change the timing, delivery, and storage of water, sediment, nutrients, and organic matter. While the local effects of beaver dams on streams are well understood, broader coverage network models that predict where beaver dams can be built and highlight their impacts on connectivity across diverse drainage networks are lacking. Here we present a capacity model to assess the limits of riverscapes to support dam-building activities by beaver across physiographically diverse landscapes. We estimated dam capacity with freely and nationally-available inputs to evaluate seven lines of evidence: (1) reliable water source, (2) riparian vegetation conducive to foraging and dam building, (3) vegetation within 100 m of edge of stream to support expansion of dam complexes and maintain large colonies, (4) likelihood that channel-spanning dams could be built during low flows, (5) the likelihood that a beaver dam is likely to withstand typical floods, (6) a suitable stream gradient that is neither too low to limit dam density nor too high to preclude the building or persistence of dams, and (7) a suitable river that is not too large to restrict dam building or persistence. Fuzzy inference systems were used to combine these controlling factors in a framework that explicitly also accounts for model uncertainty. The model was run for 40,561 km of streams in Utah, USA, and portions of surrounding states, predicting an overall network capacity of 356,294 dams at an average capacity of 8.8 dams/km. We validated model performance using 2852 observed dams across 1947 km of streams. The model showed excellent agreement with observed dam densities where beaver dams were present. Model performance was spatially coherent and logical, with electivity indices that effectively segregated capacity categories. That is, beaver dams were not found where the model predicted no dams could be supported, beaver avoided segments that were predicted to support rare or occasional densities, and beaver preferentially occupied and built dams in areas predicted to have pervasive dam densities. The resulting spatially explicit reach-scale (250 m long reaches) data identifies where dam-building activity is sustainable, and at what densities dams can occur across a landscape. As such, model outputs can be used to determine where channel-floodplain and wetland connectivity are likely to persist or expand by promoting increases in beaver dam densities.

A framework for streamflow prediction in the world's most severely data-limited regions: Test of applicability and performance in a poorly-gauged region of China

NASA Astrophysics Data System (ADS)

Alipour, M. H.; Kibler, Kelly M.

2018-02-01

A framework methodology is proposed for streamflow prediction in poorly-gauged rivers located within large-scale regions of sparse hydrometeorologic observation. A multi-criteria model evaluation is developed to select models that balance runoff efficiency with selection of accurate parameter values. Sparse observed data are supplemented by uncertain or low-resolution information, incorporated as 'soft' data, to estimate parameter values a priori. Model performance is tested in two catchments within a data-poor region of southwestern China, and results are compared to models selected using alternative calibration methods. While all models perform consistently with respect to runoff efficiency (NSE range of 0.67-0.78), models selected using the proposed multi-objective method may incorporate more representative parameter values than those selected by traditional calibration. Notably, parameter values estimated by the proposed method resonate with direct estimates of catchment subsurface storage capacity (parameter residuals of 20 and 61 mm for maximum soil moisture capacity (Cmax), and 0.91 and 0.48 for soil moisture distribution shape factor (B); where a parameter residual is equal to the centroid of a soft parameter value minus the calibrated parameter value). A model more traditionally calibrated to observed data only (single-objective model) estimates a much lower soil moisture capacity (residuals of Cmax = 475 and 518 mm and B = 1.24 and 0.7). A constrained single-objective model also underestimates maximum soil moisture capacity relative to a priori estimates (residuals of Cmax = 246 and 289 mm). The proposed method may allow managers to more confidently transfer calibrated models to ungauged catchments for streamflow predictions, even in the world's most data-limited regions.

LRFD software for design and actual ultimate capacity of confined rectangular columns.

DOT National Transportation Integrated Search

2013-04-01

The analysis of concrete columns using unconfined concrete models is a well established practice. On the : other hand, prediction of the actual ultimate capacity of confined concrete columns requires specialized nonlinear : analysis. Modern codes and...

Geochemical Modeling of Reactions and Partitioning of Trace Metals and Radionuclides during Titration of Contaminated Acidic Sediments

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

Zhang, Fan; Parker, Jack C.; Luo, Wensui

2008-01-01

Many geochemical reactions that control aqueous metal concentrations are directly affected by solution pH. However, changes in solution pH are strongly buffered by various aqueous phase and solid phase precipitation/dissolution and adsorption/desorption reactions. The ability to predict acid-base behavior of the soil-solution system is thus critical to predict metal transport under variable pH conditions. This study was undertaken to develop a practical generic geochemical modeling approach to predict aqueous and solid phase concentrations of metals and anions during conditions of acid or base additions. The method of Spalding and Spalding was utilized to model soil buffer capacity and pH-dependent cationmore » exchange capacity by treating aquifer solids as a polyprotic acid. To simulate the dynamic and pH-dependent anion exchange capacity, the aquifer solids were simultaneously treated as a polyprotic base controlled by mineral precipitation/dissolution reactions. An equilibrium reaction model that describes aqueous complexation, precipitation, sorption and soil buffering with pH-dependent ion exchange was developed using HydroGeoChem v5.0 (HGC5). Comparison of model results with experimental titration data of pH, Al, Ca, Mg, Sr, Mn, Ni, Co, and SO{sub 4}{sup 2-} for contaminated sediments indicated close agreement, suggesting that the model could potentially be used to predict the acid-base behavior of the sediment-solution system under variable pH conditions.« less

Does Domain Knowledge Moderate Involvement of Working Memory Capacity in Higher-Level Cognition? A Test of Three Models

ERIC Educational Resources Information Center

Hambrick, D.Z.; Oswald, F.L.

2005-01-01

Research suggests that both working memory capacity and domain knowledge contribute to individual differences in higher-level cognition. This study evaluated three hypotheses concerning the interplay between these factors. The compensation hypothesis predicts that domain knowledge attenuates the influence of working memory capacity on higher-level…

Extending the Simultaneous-Sequential Paradigm to Measure Perceptual Capacity for Features and Words

ERIC Educational Resources Information Center

Scharff, Alec; Palmer, John; Moore, Cathleen M.

2011-01-01

In perception, divided attention refers to conditions in which multiple stimuli are relevant to an observer. To measure the effect of divided attention in terms of perceptual capacity, we introduce an extension of the simultaneous-sequential paradigm. The extension makes predictions for fixed-capacity models as well as for unlimited-capacity…

Stimulus discriminability in visual search.

PubMed

Verghese, P; Nakayama, K

1994-09-01

We measured the probability of detecting the target in a visual search task, as a function of the following parameters: the discriminability of the target from the distractors, the duration of the display, and the number of elements in the display. We examined the relation between these parameters at criterion performance (80% correct) to determine if the parameters traded off according to the predictions of a limited capacity model. For the three dimensions that we studied, orientation, color, and spatial frequency, the observed relationship between the parameters deviates significantly from a limited capacity model. The data relating discriminability to display duration are better than predicted over the entire range of orientation and color differences that we examined, and are consistent with the prediction for only a limited range of spatial frequency differences--from 12 to 23%. The relation between discriminability and number varies considerably across the three dimensions and is better than the limited capacity prediction for two of the three dimensions that we studied. Orientation discrimination shows a strong number effect, color discrimination shows almost no effect, and spatial frequency discrimination shows an intermediate effect. The different trading relationships in each dimension are more consistent with early filtering in that dimension, than with a common limited capacity stage. Our results indicate that higher-level processes that group elements together also play a strong role. Our experiments provide little support for limited capacity mechanisms over the range of stimulus differences that we examined in three different dimensions.

Development of an aerobic capacity prediction model from one-mile run/walk performance in adolescents aged 13-16 years.

PubMed

Burns, Ryan D; Hannon, James C; Brusseau, Timothy A; Eisenman, Patricia A; Shultz, Barry B; Saint-Maurice, Pedro F; Welk, Gregory J; Mahar, Matthew T

2016-01-01

A popular algorithm to predict VO2Peak from the one-mile run/walk test (1MRW) includes body mass index (BMI), which manifests practical issues in school settings. The purpose of this study was to develop an aerobic capacity model from 1MRW in adolescents independent of BMI. Cardiorespiratory endurance data were collected on 90 adolescents aged 13-16 years. The 1MRW was administered on an outside track and a laboratory VO2Peak test was conducted using a maximal treadmill protocol. Multiple linear regression was employed to develop the prediction model. Results yielded the following algorithm: VO2Peak = 7.34 × (1MRW speed in m s(-1)) + 0.23 × (age × sex) + 17.75. The New Model displayed a multiple correlation and prediction error of R = 0.81, standard error of the estimate = 4.78 ml kg(-1) · min(-1), with measured VO2Peak and good criterion-referenced (CR) agreement into FITNESSGRAM's Healthy Fitness Zone (Kappa = 0.62; percentage agreement = 84.4%; Φ = 0.62). The New Model was validated using k-fold cross-validation and showed homoscedastic residuals across the range of predicted scores. The omission of BMI did not compromise accuracy of the model. In conclusion, the New Model displayed good predictive accuracy and good CR agreement with measured VO2Peak in adolescents aged 13-16 years.

A Coupled Probabilistic Wake Vortex and Aircraft Response Prediction Model

NASA Technical Reports Server (NTRS)

Gloudemans, Thijs; Van Lochem, Sander; Ras, Eelco; Malissa, Joel; Ahmad, Nashat N.; Lewis, Timothy A.

2016-01-01

Wake vortex spacing standards along with weather and runway occupancy time, restrict terminal area throughput and impose major constraints on the overall capacity and efficiency of the National Airspace System (NAS). For more than two decades, the National Aeronautics and Space Administration (NASA) has been conducting research on characterizing wake vortex behavior in order to develop fast-time wake transport and decay prediction models. It is expected that the models can be used in the systems level design of advanced air traffic management (ATM) concepts that safely increase the capacity of the NAS. It is also envisioned that at a later stage of maturity, these models could potentially be used operationally, in groundbased spacing and scheduling systems as well as on the flight deck.

An evaluation of the predictive performance of distributional models for flora and fauna in north-east New South Wales.

PubMed

Pearce, J; Ferrier, S; Scotts, D

2001-06-01

To use models of species distributions effectively in conservation planning, it is important to determine the predictive accuracy of such models. Extensive modelling of the distribution of vascular plant and vertebrate fauna species within north-east New South Wales has been undertaken by linking field survey data to environmental and geographical predictors using logistic regression. These models have been used in the development of a comprehensive and adequate reserve system within the region. We evaluate the predictive accuracy of models for 153 small reptile, arboreal marsupial, diurnal bird and vascular plant species for which independent evaluation data were available. The predictive performance of each model was evaluated using the relative operating characteristic curve to measure discrimination capacity. Good discrimination ability implies that a model's predictions provide an acceptable index of species occurrence. The discrimination capacity of 89% of the models was significantly better than random, with 70% of the models providing high levels of discrimination. Predictions generated by this type of modelling therefore provide a reasonably sound basis for regional conservation planning. The discrimination ability of models was highest for the less mobile biological groups, particularly the vascular plants and small reptiles. In the case of diurnal birds, poor performing models tended to be for species which occur mainly within specific habitats not well sampled by either the model development or evaluation data, highly mobile species, species that are locally nomadic or those that display very broad habitat requirements. Particular care needs to be exercised when employing models for these types of species in conservation planning.

I. WORKING MEMORY CAPACITY IN CONTEXT: MODELING DYNAMIC PROCESSES OF BEHAVIOR, MEMORY, AND DEVELOPMENT.

PubMed

Simmering, Vanessa R

2016-09-01

Working memory is a vital cognitive skill that underlies a broad range of behaviors. Higher cognitive functions are reliably predicted by working memory measures from two domains: children's performance on complex span tasks, and infants' performance in looking paradigms. Despite the similar predictive power across these research areas, theories of working memory development have not connected these different task types and developmental periods. The current project takes a first step toward bridging this gap by presenting a process-oriented theory, focusing on two tasks designed to assess visual working memory capacity in infants (the change-preference task) versus children and adults (the change detection task). Previous studies have shown inconsistent results, with capacity estimates increasing from one to four items during infancy, but only two to three items during early childhood. A probable source of this discrepancy is the different task structures used with each age group, but prior theories were not sufficiently specific to explain how performance relates across tasks. The current theory focuses on cognitive dynamics, that is, how memory representations are formed, maintained, and used within specific task contexts over development. This theory was formalized in a computational model to generate three predictions: 1) capacity estimates in the change-preference task should continue to increase beyond infancy; 2) capacity estimates should be higher in the change-preference versus change detection task when tested within individuals; and 3) performance should correlate across tasks because both rely on the same underlying memory system. I also tested a fourth prediction, that development across tasks could be explained through increasing real-time stability, realized computationally as strengthening connectivity within the model. Results confirmed these predictions, supporting the cognitive dynamics account of performance and developmental changes in real-time stability. The monograph concludes with implications for understanding memory, behavior, and development in a broader range of cognitive development. © 2016 The Society for Research in Child Development, Inc.

Statistical prediction with Kanerva's sparse distributed memory

NASA Technical Reports Server (NTRS)

Rogers, David

1989-01-01

A new viewpoint of the processing performed by Kanerva's sparse distributed memory (SDM) is presented. In conditions of near- or over-capacity, where the associative-memory behavior of the model breaks down, the processing performed by the model can be interpreted as that of a statistical predictor. Mathematical results are presented which serve as the framework for a new statistical viewpoint of sparse distributed memory and for which the standard formulation of SDM is a special case. This viewpoint suggests possible enhancements to the SDM model, including a procedure for improving the predictiveness of the system based on Holland's work with genetic algorithms, and a method for improving the capacity of SDM even when used as an associative memory.

Predicting the Productive Capacity of Air Force Aerospace Ground Equipment Personnel Using Aptitude and Experience Measures

DTIC Science & Technology

1993-03-01

Study of the Productive Capacity Project 40 4. 454X1 Job Duty Areas ....... ........................ ......... 41 5. Bases Visited in the Initial Study of...101 21. Correlttion Matrix of the Other Job Performance Measures ................. 102 22 454X1 Tasks...mentioned, the goal of the thesis is to develop an experimental mathematical model for predicting the job performance of enlisted personnel in AFS 454X1

Predictive capacity of a non-radioisotopic local lymph node assay using flow cytometry, LLNA:BrdU-FCM: Comparison of a cutoff approach and inferential statistics.

PubMed

Kim, Da-Eun; Yang, Hyeri; Jang, Won-Hee; Jung, Kyoung-Mi; Park, Miyoung; Choi, Jin Kyu; Jung, Mi-Sook; Jeon, Eun-Young; Heo, Yong; Yeo, Kyung-Wook; Jo, Ji-Hoon; Park, Jung Eun; Sohn, Soo Jung; Kim, Tae Sung; Ahn, Il Young; Jeong, Tae-Cheon; Lim, Kyung-Min; Bae, SeungJin

2016-01-01

In order for a novel test method to be applied for regulatory purposes, its reliability and relevance, i.e., reproducibility and predictive capacity, must be demonstrated. Here, we examine the predictive capacity of a novel non-radioisotopic local lymph node assay, LLNA:BrdU-FCM (5-bromo-2'-deoxyuridine-flow cytometry), with a cutoff approach and inferential statistics as a prediction model. 22 reference substances in OECD TG429 were tested with a concurrent positive control, hexylcinnamaldehyde 25%(PC), and the stimulation index (SI) representing the fold increase in lymph node cells over the vehicle control was obtained. The optimal cutoff SI (2.7≤cutoff <3.5), with respect to predictive capacity, was obtained by a receiver operating characteristic curve, which produced 90.9% accuracy for the 22 substances. To address the inter-test variability in responsiveness, SI values standardized with PC were employed to obtain the optimal percentage cutoff (42.6≤cutoff <57.3% of PC), which produced 86.4% accuracy. A test substance may be diagnosed as a sensitizer if a statistically significant increase in SI is elicited. The parametric one-sided t-test and non-parametric Wilcoxon rank-sum test produced 77.3% accuracy. Similarly, a test substance could be defined as a sensitizer if the SI means of the vehicle control, and of the low, middle, and high concentrations were statistically significantly different, which was tested using ANOVA or Kruskal-Wallis, with post hoc analysis, Dunnett, or DSCF (Dwass-Steel-Critchlow-Fligner), respectively, depending on the equal variance test, producing 81.8% accuracy. The absolute SI-based cutoff approach produced the best predictive capacity, however the discordant decisions between prediction models need to be examined further. Copyright © 2015 Elsevier Inc. All rights reserved.

Predicting change in symptoms of depression during the transition to university: the roles of BDNF and working memory capacity.

PubMed

LeMoult, Joelle; Carver, Charles S; Johnson, Sheri L; Joormann, Jutta

2015-03-01

Studies on depression risk emphasize the importance of both cognitive and genetic vulnerability factors. The present study has provided the first examination of whether working memory capacity, the BDNF Val66Met polymorphism, and their interaction predict changes in symptoms of depression during the transition to university. Early in the semester, students completed a self-report measure of depressive symptoms and a modified version of the reading span task to assess working memory capacity in the presence of both neutral and negative distractors. Whole blood was genotyped for the BDNF Val66Met polymorphism. Students returned at the end of the semester to complete additional self-report questionnaires. Neither working memory capacity nor the BDNF Val66Met polymorphism predicted change in depressive symptoms either independently or in interaction with self-reported semester difficulty. The BDNF Val66Met polymorphism, however, moderated the association between working memory capacity and symptom change. Among met carriers, lower working memory capacity in the presence of negative-but not neutral-distractors was associated with increased symptoms of depression over the semester. For the val/val group, working memory capacity did not predict symptom change. These findings contribute directly to biological and cognitive models of depression and highlight the importance of examining Gene × Cognition interactions when investigating risk for depression.

Controls on SOC across space and time: Models with different acclimation schemes make similar spatial predictions but divergent warming predictions

NASA Astrophysics Data System (ADS)

Abramoff, R. Z.; Torn, M. S.; Georgiou, K.; Tang, J.; Riley, W. J.

2017-12-01

Researchers use spatial gradients to estimate long-term ecosystem responses to perturbations. This approach is commonly applied to soil organic carbon (SOC) stocks which change slowly but store the majority of terrestrial carbon. Climate warming may reduce SOC stocks if higher temperatures increase decomposition rates. Yet, it is uncertain how vulnerable SOC is to warming, and whether the same factors - such as organo-mineral associations, climate, or plant inputs - determine SOC stocks across space and time. In order to test the "space for time" concept, we developed two versions of the Substrate-Mineral-Microbe Soil (SuMMS) model - one with microbial temperature acclimation and one without - to analyze observed SOC stocks at 24 sites spanning a wide range of soil types and climate. Both model predictions of SOC were strongly correlated with observations (R2 > 0.9), because mineral sorption capacity was the dominant control over steady-state SOC stock as determined by a Random Forest model. However, the two model versions made fundamentally different predictions of the change in SOC following 5°C soil warming from 2016 to 2100 because the initial mean annual temperature (MAT) was the dominant control over the SOC response. The model with microbial acclimation predicted that SOC would decline 10% at all sites along the transect, while the model with no acclimation predicted large surface SOC losses at high latitude sites and SOC gains at low latitude sites where microbial exoenzymes were already at or near their temperature optimum. These simulations suggest that gradient studies cannot be used to infer site-level responses to warming, because the dominant controls on SOC at steady state (i.e., mineral sorption capacity) are different than the dominant controls on the SOC response to a warming perturbation (i.e., initial MAT, capacity for acclimation).

High-performance heat pipes for heat recovery applications

NASA Technical Reports Server (NTRS)

Saaski, E. W.; Hartl, J. H.

1980-01-01

Methods to improve the performance of reflux heat pipes for heat recovery applications were examined both analytically and experimentally. Various models for the estimation of reflux heat pipe transport capacity were surveyed in the literature and compared with experimental data. A high transport capacity reflux heat pipe was developed that provides up to a factor of 10 capacity improvement over conventional open tube designs; analytical models were developed for this device and incorporated into a computer program HPIPE. Good agreement of the model predictions with data for R-11 and benzene reflux heat pipes was obtained.

Assessing the capacity of social determinants of health data to augment predictive models identifying patients in need of wraparound social services.

PubMed

Kasthurirathne, Suranga N; Vest, Joshua R; Menachemi, Nir; Halverson, Paul K; Grannis, Shaun J

2018-01-01

A growing variety of diverse data sources is emerging to better inform health care delivery and health outcomes. We sought to evaluate the capacity for clinical, socioeconomic, and public health data sources to predict the need for various social service referrals among patients at a safety-net hospital. We integrated patient clinical data and community-level data representing patients' social determinants of health (SDH) obtained from multiple sources to build random forest decision models to predict the need for any, mental health, dietitian, social work, or other SDH service referrals. To assess the impact of SDH on improving performance, we built separate decision models using clinical and SDH determinants and clinical data only. Decision models predicting the need for any, mental health, and dietitian referrals yielded sensitivity, specificity, and accuracy measures ranging between 60% and 75%. Specificity and accuracy scores for social work and other SDH services ranged between 67% and 77%, while sensitivity scores were between 50% and 63%. Area under the receiver operating characteristic curve values for the decision models ranged between 70% and 78%. Models for predicting the need for any services reported positive predictive values between 65% and 73%. Positive predictive values for predicting individual outcomes were below 40%. The need for various social service referrals can be predicted with considerable accuracy using a wide range of readily available clinical and community data that measure socioeconomic and public health conditions. While the use of SDH did not result in significant performance improvements, our approach represents a novel and important application of risk predictive modeling. © The Author 2017. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Geochemical modeling of reactions and partitioning of trace metals and radionuclides during titration of contaminated acidic sediments.

PubMed

Zhang, Fan; Luo, Wensui; Parker, Jack C; Spalding, Brian P; Brooks, Scott C; Watson, David B; Jardine, Philip M; Gu, Baohua

2008-11-01

Many geochemical reactions that control aqueous metal concentrations are directly affected by solution pH. However, changes in solution pH are strongly buffered by various aqueous phase and solid phase precipitation/dissolution and adsorption/desorption reactions. The ability to predict acid-base behavior of the soil-solution system is thus critical to predict metal transport under variable pH conditions. This studywas undertaken to develop a practical generic geochemical modeling approach to predict aqueous and solid phase concentrations of metals and anions during conditions of acid or base additions. The method of Spalding and Spalding was utilized to model soil buffer capacity and pH-dependent cation exchange capacity by treating aquifer solids as a polyprotic acid. To simulate the dynamic and pH-dependent anion exchange capacity, the aquifer solids were simultaneously treated as a polyprotic base controlled by mineral precipitation/ dissolution reactions. An equilibrium reaction model that describes aqueous complexation, precipitation, sorption and soil buffering with pH-dependent ion exchange was developed using HydroGeoChem v5.0 (HGC5). Comparison of model results with experimental titration data of pH, Al, Ca, Mg, Sr, Mn, Ni, Co, and SO4(2-) for contaminated sediments indicated close agreement suggesting that the model could potentially be used to predictthe acid-base behavior of the sediment-solution system under variable pH conditions.

Predictor characteristics necessary for building a clinically useful risk prediction model: a simulation study.

PubMed

Schummers, Laura; Himes, Katherine P; Bodnar, Lisa M; Hutcheon, Jennifer A

2016-09-21

Compelled by the intuitive appeal of predicting each individual patient's risk of an outcome, there is a growing interest in risk prediction models. While the statistical methods used to build prediction models are increasingly well understood, the literature offers little insight to researchers seeking to gauge a priori whether a prediction model is likely to perform well for their particular research question. The objective of this study was to inform the development of new risk prediction models by evaluating model performance under a wide range of predictor characteristics. Data from all births to overweight or obese women in British Columbia, Canada from 2004 to 2012 (n = 75,225) were used to build a risk prediction model for preeclampsia. The data were then augmented with simulated predictors of the outcome with pre-set prevalence values and univariable odds ratios. We built 120 risk prediction models that included known demographic and clinical predictors, and one, three, or five of the simulated variables. Finally, we evaluated standard model performance criteria (discrimination, risk stratification capacity, calibration, and Nagelkerke's r 2 ) for each model. Findings from our models built with simulated predictors demonstrated the predictor characteristics required for a risk prediction model to adequately discriminate cases from non-cases and to adequately classify patients into clinically distinct risk groups. Several predictor characteristics can yield well performing risk prediction models; however, these characteristics are not typical of predictor-outcome relationships in many population-based or clinical data sets. Novel predictors must be both strongly associated with the outcome and prevalent in the population to be useful for clinical prediction modeling (e.g., one predictor with prevalence ≥20 % and odds ratio ≥8, or 3 predictors with prevalence ≥10 % and odds ratios ≥4). Area under the receiver operating characteristic curve values of >0.8 were necessary to achieve reasonable risk stratification capacity. Our findings provide a guide for researchers to estimate the expected performance of a prediction model before a model has been built based on the characteristics of available predictors.

Modelling the distributions and spatial coincidence of bluetongue vectors Culicoides imicola and the Culicoides obsoletus group throughout the Iberian peninsula.

PubMed

Calvete, C; Estrada, R; Miranda, M A; Borrás, D; Calvo, J H; Lucientes, J

2008-06-01

Data obtained by a Spanish national surveillance programme in 2005 were used to develop climatic models for predictions of the distribution of the bluetongue virus (BTV) vectors Culicoides imicola Kieffer (Diptera: Ceratopogonidae) and the Culicoides obsoletus group Meigen throughout the Iberian peninsula. Models were generated using logistic regression to predict the probability of species occurrence at an 8-km spatial resolution. Predictor variables included the annual mean values and seasonalities of a remotely sensed normalized difference vegetation index (NDVI), a sun index, interpolated precipitation and temperature. Using an information-theoretic paradigm based on Akaike's criterion, a set of best models accounting for 95% of model selection certainty were selected and used to generate an average predictive model for each vector. The predictive performances (i.e. the discrimination capacity and calibration) of the average models were evaluated by both internal and external validation. External validation was achieved by comparing average model predictions with surveillance programme data obtained in 2004 and 2006. The discriminatory capacity of both models was found to be reasonably high. The estimated areas under the receiver operating characteristic (ROC) curve (AUC) were 0.78 and 0.70 for the C. imicola and C. obsoletus group models, respectively, in external validation, and 0.81 and 0.75, respectively, in internal validation. The predictions of both models were in close agreement with the observed distribution patterns of both vectors. Both models, however, showed a systematic bias in their predicted probability of occurrence: observed occurrence was systematically overestimated for C. imicola and underestimated for the C. obsoletus group. Average models were used to determine the areas of spatial coincidence of the two vectors. Although their spatial distributions were highly complementary, areas of spatial coincidence were identified, mainly in Portugal and in the southwest of peninsular Spain. In a hypothetical scenario in which both Culicoides members had similar vectorial capacity for a BTV strain, these areas should be considered of special epidemiological concern because any epizootic event could be intensified by consecutive vector activity developed for both species during the year; consequently, the probability of BTV spreading to remaining areas occupied by both vectors might also be higher.

Analyzing the Impact of Storage Shortage on Data Availability in Decentralized Online Social Networks

PubMed Central

He, Ligang; Liao, Xiangke; Huang, Chenlin

2014-01-01

Maintaining data availability is one of the biggest challenges in decentralized online social networks (DOSNs). The existing work often assumes that the friends of a user can always contribute to the sufficient storage capacity to store all data. However, this assumption is not always true in today's online social networks (OSNs) due to the fact that nowadays the users often use the smart mobile devices to access the OSNs. The limitation of the storage capacity in mobile devices may jeopardize the data availability. Therefore, it is desired to know the relation between the storage capacity contributed by the OSN users and the level of data availability that the OSNs can achieve. This paper addresses this issue. In this paper, the data availability model over storage capacity is established. Further, a novel method is proposed to predict the data availability on the fly. Extensive simulation experiments have been conducted to evaluate the effectiveness of the data availability model and the on-the-fly prediction. PMID:24892095

Analyzing the impact of storage shortage on data availability in decentralized online social networks.

PubMed

Fu, Songling; He, Ligang; Liao, Xiangke; Li, Kenli; Huang, Chenlin

2014-01-01

Maintaining data availability is one of the biggest challenges in decentralized online social networks (DOSNs). The existing work often assumes that the friends of a user can always contribute to the sufficient storage capacity to store all data. However, this assumption is not always true in today's online social networks (OSNs) due to the fact that nowadays the users often use the smart mobile devices to access the OSNs. The limitation of the storage capacity in mobile devices may jeopardize the data availability. Therefore, it is desired to know the relation between the storage capacity contributed by the OSN users and the level of data availability that the OSNs can achieve. This paper addresses this issue. In this paper, the data availability model over storage capacity is established. Further, a novel method is proposed to predict the data availability on the fly. Extensive simulation experiments have been conducted to evaluate the effectiveness of the data availability model and the on-the-fly prediction.

Nice Guys Finish Fast and Bad Guys Finish Last: Facilitatory vs. Inhibitory Interaction in Parallel Systems

PubMed Central

Eidels, Ami; Houpt, Joseph W.; Altieri, Nicholas; Pei, Lei; Townsend, James T.

2011-01-01

Systems Factorial Technology is a powerful framework for investigating the fundamental properties of human information processing such as architecture (i.e., serial or parallel processing) and capacity (how processing efficiency is affected by increased workload). The Survivor Interaction Contrast (SIC) and the Capacity Coefficient are effective measures in determining these underlying properties, based on response-time data. Each of the different architectures, under the assumption of independent processing, predicts a specific form of the SIC along with some range of capacity. In this study, we explored SIC predictions of discrete-state (Markov process) and continuous-state (Linear Dynamic) models that allow for certain types of cross-channel interaction. The interaction can be facilitatory or inhibitory: one channel can either facilitate, or slow down processing in its counterpart. Despite the relative generality of these models, the combination of the architecture-oriented plus the capacity oriented analyses provide for precise identification of the underlying system. PMID:21516183

Nice Guys Finish Fast and Bad Guys Finish Last: Facilitatory vs. Inhibitory Interaction in Parallel Systems.

PubMed

Eidels, Ami; Houpt, Joseph W; Altieri, Nicholas; Pei, Lei; Townsend, James T

2011-04-01

Systems Factorial Technology is a powerful framework for investigating the fundamental properties of human information processing such as architecture (i.e., serial or parallel processing) and capacity (how processing efficiency is affected by increased workload). The Survivor Interaction Contrast (SIC) and the Capacity Coefficient are effective measures in determining these underlying properties, based on response-time data. Each of the different architectures, under the assumption of independent processing, predicts a specific form of the SIC along with some range of capacity. In this study, we explored SIC predictions of discrete-state (Markov process) and continuous-state (Linear Dynamic) models that allow for certain types of cross-channel interaction. The interaction can be facilitatory or inhibitory: one channel can either facilitate, or slow down processing in its counterpart. Despite the relative generality of these models, the combination of the architecture-oriented plus the capacity oriented analyses provide for precise identification of the underlying system.

Elaboration of the α-model derived from the BCS theory of superconductivity

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

Johnston, David C.

2013-10-14

The single-band α-model of superconductivity (Padamsee et al 1973 J. Low Temp. Phys. 12 387) is a popular model that was adapted from the single-band Bardeen–Cooper–Schrieffer (BCS) theory of superconductivity mainly to allow fits to electronic heat capacity versus temperature T data that deviate from the BCS prediction. The model assumes that the normalized superconducting order parameter Δ(T)/Δ(0) and therefore the normalized London penetration depth λL(T)/λL(0) are the same as in BCS theory, calculated using the BCS value αBCS ≈ 1.764 of α ≡ Δ(0)/kBTc, where kB is The single-band α-model of superconductivity (Padamsee et al 1973 J. Low Temp.more » Phys. 12 387) is a popular model that was adapted from the single-band Bardeen–Cooper–Schrieffer (BCS) theory of superconductivity mainly to allow fits to electronic heat capacity versus temperature T data that deviate from the BCS prediction. The model assumes that the normalized superconducting order parameter Δ(T)/Δ(0) and therefore the normalized London penetration depth λL(T)/λL(0) are the same as in BCS theory, calculated using the BCS value αBCS ≈ 1.764 of α ≡ Δ(0)/kBTc, where kB is Boltzmann's constant and Tc is the superconducting transition temperature. On the other hand, to calculate the electronic free energy, entropy, heat capacity and thermodynamic critical field versus T, the α-model takes α to be an adjustable parameter. Here we write the BCS equations and limiting behaviors for the superconducting state thermodynamic properties explicitly in terms of α, as needed for calculations within the α-model, and present plots of the results versus T and α that are compared with the respective BCS predictions. Mechanisms such as gap anisotropy and strong coupling that can cause deviations of the thermodynamics from the BCS predictions, especially the heat capacity jump at Tc, are considered. Extensions of the α-model that have appeared in the literature, such as the two-band model, are also discussed. Tables of values of Δ(T)/Δ(0), the normalized London parameter Λ(T)/Λ(0) and λL(T)/λL(0) calculated from the BCS theory using α = αBCS are provided, which are the same in the α-model by assumption. Tables of values of the entropy, heat capacity and thermodynamic critical field versus T for seven values of α, including αBCS, are also presented.« less

A new model to improve aggregate air traffic demand predictions

DOT National Transportation Integrated Search

2007-08-20

Federal Aviation Administration (FAA) air traffic flow management (TFM) : decision-making is based primarily on a comparison of predictions of traffic demand and : available capacity at various National Airspace System (NAS) elements such as airports...

Technical Consultation of the Hubble Space Telescope (HST) Nickel Hydrogen (NiH2) Battery Charge Capacity Prediction. Version 1.0

NASA Technical Reports Server (NTRS)

Gentz, Steven J.; Pandipati, Radha; Ling, Jerri; Miller, Thomas; Jeevarajan, Judith; Halpert, Gerald; Zimmerman, Albert

2005-01-01

The purpose of the GSFC position paper is to identify critical HST milestone dates for continued science studies followed by the attachment of a re-entry module or a robotic servicing mission. The paper examines the viability of the HST with respect to the NiH2 continued battery charge capacity. In the course of the assessment, it was recognized that the HST battery thermal control system has an average heat dissipation limitation of 30 W per bay per orbit cycle. This thermal constraint will continue to govern options for battery capacity maintenance. In addition, the HST usage represents the longest exposure ofNiH2 batteries to Low Earth Orbit (LEO) at the current level of Depth of Discharge (DOD). Finally, the current battery life is at the limit predicted by the manufacturer, Eaglepicher. Therefore, given these factors, the potential exists that the HST battery capacities could radically degrade at any point. Given this caveat on any life extrapolations, the conservative model proposed in the GSFC position paper was viewed by the NESC as having several technical assumptions such as limited utilization of flight battery capacity data, the susceptibility of the proposed prediction method to large variations when supplemented with additional information, and the failure to qualitatively or quantitatively assess life prediction sensitivities. The NESC conducted an independent evaluation of the supporting information and assumptions to generate the predictions for battery capacity loss and practicality of on-orbit battery conditioning.

Monitoring apparatus and method for battery power supply

DOEpatents

Martin, Harry L.; Goodson, Raymond E.

1983-01-01

A monitoring apparatus and method are disclosed for monitoring and/or indicating energy that a battery power source has then remaining and/or can deliver for utilization purposes as, for example, to an electric vehicle. A battery mathematical model forms the basis for monitoring with a capacity prediction determined from measurement of the discharge current rate and stored battery parameters. The predicted capacity is used to provide a state-of-charge indication. Self-calibration over the life of the battery power supply is enacted through use of a feedback voltage based upon the difference between predicted and measured voltages to correct the battery mathematical model. Through use of a microprocessor with central information storage of temperature, current and voltage, system behavior is monitored, and system flexibility is enhanced.

Human short-term spatial memory: precision predicts capacity.

PubMed

Banta Lavenex, Pamela; Boujon, Valérie; Ndarugendamwo, Angélique; Lavenex, Pierre

2015-03-01

Here, we aimed to determine the capacity of human short-term memory for allocentric spatial information in a real-world setting. Young adults were tested on their ability to learn, on a trial-unique basis, and remember over a 1-min interval the location(s) of 1, 3, 5, or 7 illuminating pads, among 23 pads distributed in a 4m×4m arena surrounded by curtains on three sides. Participants had to walk to and touch the pads with their foot to illuminate the goal locations. In contrast to the predictions from classical slot models of working memory capacity limited to a fixed number of items, i.e., Miller's magical number 7 or Cowan's magical number 4, we found that the number of visited locations to find the goals was consistently about 1.6 times the number of goals, whereas the number of correct choices before erring and the number of errorless trials varied with memory load even when memory load was below the hypothetical memory capacity. In contrast to resource models of visual working memory, we found no evidence that memory resources were evenly distributed among unlimited numbers of items to be remembered. Instead, we found that memory for even one individual location was imprecise, and that memory performance for one location could be used to predict memory performance for multiple locations. Our findings are consistent with a theoretical model suggesting that the precision of the memory for individual locations might determine the capacity of human short-term memory for spatial information. Copyright © 2015 Elsevier Inc. All rights reserved.

Predictive factors of functional capacity and real-world functioning in patients with schizophrenia.

PubMed

Menendez-Miranda, I; Garcia-Portilla, M P; Garcia-Alvarez, L; Arrojo, M; Sanchez, P; Sarramea, F; Gomar, J; Bobes-Bascaran, M T; Sierra, P; Saiz, P A; Bobes, J

2015-07-01

This study was performed to identify the predictive factors of functional capacity assessed by the Spanish University of California Performance Skills Assessment (Sp-UPSA) and real-world functioning assessed by the Spanish Personal and Social Performance scale (PSP) in outpatients with schizophrenia. Naturalistic, 6-month follow-up, multicentre, validation study. Here, we report data on 139 patients with schizophrenia at their baseline visit. Positive and Negative Syndrome Scale (PANSS), Clinical Global Impression-Severity (CGI-S), Sp-UPSA and PSP. Pearson's correlation coefficient (r) was used to determine the relationships between variables, and multivariable stepwise linear regression analyses to identify predictive variables of Sp-UPSA and PSP total scores. Functional capacity: scores on the PSP and PANSS-GP entered first and second at P<0.0001 and accounted for 21% of variance (R(2)=0.208, model df=2, F=15.724, P<0.0001). Real-world functioning: scores on the CGI-S (B=-5.406), PANSS-N (B=-0.657) and Sp-UPSA (B=0.230) entered first, second and third, and accounted for 51% of variance (model df=3, F=37.741, P<0.0001). In patients with schizophrenia, functional capacity and real-world functioning are two related but different constructs. Each one predicts the other along with other factors; general psychopathology for functional capacity, and severity of the illness and negative symptoms for real-world functioning. These findings have important clinical implications: (1) both types of functioning should be assessed in patients with schizophrenia and (2) strategies for improving them should be different. Copyright © 2015 Elsevier Masson SAS. All rights reserved.

A simulation of dementia epidemiology and resource use in Australia.

PubMed

Standfield, Lachlan B; Comans, Tracy; Scuffham, Paul

2018-06-01

The number of people in the developed world who have dementia is predicted to rise markedly. This study presents a validated predictive model to assist decision-makers to determine this population's future resource requirements and target scarce health and welfare resources appropriately. A novel individual patient discrete event simulation was developed to estimate the future prevalence of dementia and related health and welfare resource use in Australia. When compared to other published results, the simulation generated valid estimates of dementia prevalence and resource use. The analysis predicted 298,000, 387,000 and 928,000 persons in Australia will have dementia in 2011, 2020 and 2050, respectively. Health and welfare resource use increased markedly over the simulated time-horizon and was affected by capacity constraints. This simulation provides useful estimates of future demands on dementia-related services allowing the exploration of the effects of capacity constraints. Implications for public health: The model demonstrates that under-resourcing of residential aged care may lead to inappropriate and inefficient use of hospital resources. To avoid these capacity constraints it is predicted that the number of aged care beds for persons with dementia will need to increase more than threefold from 2011 to 2050. © 2017 The Authors.

Prediction of functional aerobic capacity without exercise testing

NASA Technical Reports Server (NTRS)

Jackson, A. S.; Blair, S. N.; Mahar, M. T.; Wier, L. T.; Ross, R. M.; Stuteville, J. E.

1990-01-01

The purpose of this study was to develop functional aerobic capacity prediction models without using exercise tests (N-Ex) and to compare the accuracy with Astrand single-stage submaximal prediction methods. The data of 2,009 subjects (9.7% female) were randomly divided into validation (N = 1,543) and cross-validation (N = 466) samples. The validation sample was used to develop two N-Ex models to estimate VO2peak. Gender, age, body composition, and self-report activity were used to develop two N-Ex prediction models. One model estimated percent fat from skinfolds (N-Ex %fat) and the other used body mass index (N-Ex BMI) to represent body composition. The multiple correlations for the developed models were R = 0.81 (SE = 5.3 ml.kg-1.min-1) and R = 0.78 (SE = 5.6 ml.kg-1.min-1). This accuracy was confirmed when applied to the cross-validation sample. The N-Ex models were more accurate than what was obtained from VO2peak estimated from the Astrand prediction models. The SEs of the Astrand models ranged from 5.5-9.7 ml.kg-1.min-1. The N-Ex models were cross-validated on 59 men on hypertensive medication and 71 men who were found to have a positive exercise ECG. The SEs of the N-Ex models ranged from 4.6-5.4 ml.kg-1.min-1 with these subjects.(ABSTRACT TRUNCATED AT 250 WORDS).

Predicting Reactive Intermediate Quantum Yields from Dissolved Organic Matter Photolysis Using Optical Properties and Antioxidant Capacity.

PubMed

Mckay, Garrett; Huang, Wenxi; Romera-Castillo, Cristina; Crouch, Jenna E; Rosario-Ortiz, Fernando L; Jaffé, Rudolf

2017-05-16

The antioxidant capacity and formation of photochemically produced reactive intermediates (RI) was studied for water samples collected from the Florida Everglades with different spatial (marsh versus estuarine) and temporal (wet versus dry season) characteristics. Measured RI included triplet excited states of dissolved organic matter ( 3 DOM*), singlet oxygen ( 1 O 2 ), and the hydroxyl radical ( • OH). Single and multiple linear regression modeling were performed using a broad range of extrinsic (to predict RI formation rates, R RI ) and intrinsic (to predict RI quantum yields, Φ RI ) parameters. Multiple linear regression models consistently led to better predictions of R RI and Φ RI for our data set but poor prediction of Φ RI for a previously published data set,1 probably because the predictors are intercorrelated (Pearson's r > 0.5). Single linear regression models were built with data compiled from previously published studies (n ≈ 120) in which E2:E3, S, and Φ RI values were measured, which revealed a high degree of similarity between RI-optical property relationships across DOM samples of diverse sources. This study reveals that • OH formation is, in general, decoupled from 3 DOM* and 1 O 2 formation, providing supporting evidence that 3 DOM* is not a • OH precursor. Finally, Φ RI for 1 O 2 and 3 DOM* correlated negatively with antioxidant activity (a surrogate for electron donating capacity) for the collected samples, which is consistent with intramolecular oxidation of DOM moieties by 3 DOM*.

Towards a universal trait-based model of terrestrial primary production

NASA Astrophysics Data System (ADS)

Wang, H.; Prentice, I. C.; Cornwell, W.; Keenan, T. F.; Davis, T.; Wright, I. J.; Evans, B. J.; Peng, C.

2015-12-01

Systematic variations of plant traits along environmental gradients have been observed for decades. For example, the tendencies of leaf nitrogen per unit area to increase, and of the leaf-internal to ambient CO2 concentration ratio (ci:ca) to decrease, with aridity are well established. But ecosystem models typically represent trait variation based purely on empirical relationships, or on untested conjectures, or not at all. Neglect of quantitative trait variation and its adapative significance probably contributes to the persistent large uncertainties among models in predicting the response of the carbon cycle to environmental change. However, advances in ecological theory and the accumulation of extensive data sets during recent decades suggest that theoretically based and testable predictions of trait variation could be achieved. Based on well-established ecophysiological principles and consideration of the adaptive significance of traits, we propose universal relationships between photosynthetic traits (ci:ca, carbon fixation capacity, and the ratio of electron transport capacity to carbon fixation capacity) and primary environmental variables, which capture observed trait variations both within and between plant functional types. Moreover, incorporating these traits into the standard model of C3photosynthesis allows gross primary production (GPP) of natural vegetation to be predicted by a single equation with just two free parameters, which can be estimated from independent observations. The resulting model performs as well as much more complex models. Our results provide a fresh perspective with potentially high reward: the possibility of a deeper understanding of the relationships between plant traits and environment, simpler and more robust and reliable representation of land processes in Earth system models, and thus improved predictability for biosphere-atmosphere interactions and climate feedbacks.

Life Prediction Model for Grid-Connected Li-ion Battery Energy Storage System: Preprint

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

Smith, Kandler A; Saxon, Aron R; Keyser, Matthew A

Life Prediction Model for Grid-Connected Li-ion Battery Energy Storage System: Preprint Lithium-ion (Li-ion) batteries are being deployed on the electrical grid for a variety of purposes, such as to smooth fluctuations in solar renewable power generation. The lifetime of these batteries will vary depending on their thermal environment and how they are charged and discharged. To optimal utilization of a battery over its lifetime requires characterization of its performance degradation under different storage and cycling conditions. Aging tests were conducted on commercial graphite/nickel-manganese-cobalt (NMC) Li-ion cells. A general lifetime prognostic model framework is applied to model changes in capacity andmore » resistance as the battery degrades. Across 9 aging test conditions from 0oC to 55oC, the model predicts capacity fade with 1.4 percent RMS error and resistance growth with 15 percent RMS error. The model, recast in state variable form with 8 states representing separate fade mechanisms, is used to extrapolate lifetime for example applications of the energy storage system integrated with renewable photovoltaic (PV) power generation.« less

Predicting CH4 adsorption capacity of microporous carbon using N2 isotherm and a new analytical model

USGS Publications Warehouse

Sun, Jielun; Chen, S.; Rostam-Abadi, M.; Rood, M.J.

1998-01-01

A new analytical pore size distribution (PSD) model was developed to predict CH4 adsorption (storage) capacity of microporous adsorbent carbon. The model is based on a 3-D adsorption isotherm equation, derived from statistical mechanical principles. Least squares error minimization is used to solve the PSD without any pre-assumed distribution function. In comparison with several well-accepted analytical methods from the literature, this 3-D model offers relatively realistic PSD description for select reference materials, including activated carbon fibers. N2 and CH4 adsorption data were correlated using the 3-D model for commercial carbons BPL and AX-21. Predicted CH4 adsorption isotherms, based on N2 adsorption at 77 K, were in reasonable agreement with the experimental CH4 isotherms. Modeling results indicate that not all the pores contribute the same percentage Vm/Vs for CH4 storage due to different adsorbed CH4 densities. Pores near 8-9 A?? shows higher Vm/Vs on the equivalent volume basis than does larger pores.

Artificial neural network (ANN)-based prediction of depth filter loading capacity for filter sizing.

PubMed

Agarwal, Harshit; Rathore, Anurag S; Hadpe, Sandeep Ramesh; Alva, Solomon J

2016-11-01

This article presents an application of artificial neural network (ANN) modelling towards prediction of depth filter loading capacity for clarification of a monoclonal antibody (mAb) product during commercial manufacturing. The effect of operating parameters on filter loading capacity was evaluated based on the analysis of change in the differential pressure (DP) as a function of time. The proposed ANN model uses inlet stream properties (feed turbidity, feed cell count, feed cell viability), flux, and time to predict the corresponding DP. The ANN contained a single output layer with ten neurons in hidden layer and employed a sigmoidal activation function. This network was trained with 174 training points, 37 validation points, and 37 test points. Further, a pressure cut-off of 1.1 bar was used for sizing the filter area required under each operating condition. The modelling results showed that there was excellent agreement between the predicted and experimental data with a regression coefficient (R 2 ) of 0.98. The developed ANN model was used for performing variable depth filter sizing for different clarification lots. Monte-Carlo simulation was performed to estimate the cost savings by using different filter areas for different clarification lots rather than using the same filter area. A 10% saving in cost of goods was obtained for this operation. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:1436-1443, 2016. © 2016 American Institute of Chemical Engineers.

The attention-weighted sample-size model of visual short-term memory: Attention capture predicts resource allocation and memory load.

PubMed

Smith, Philip L; Lilburn, Simon D; Corbett, Elaine A; Sewell, David K; Kyllingsbæk, Søren

2016-09-01

We investigated the capacity of visual short-term memory (VSTM) in a phase discrimination task that required judgments about the configural relations between pairs of black and white features. Sewell et al. (2014) previously showed that VSTM capacity in an orientation discrimination task was well described by a sample-size model, which views VSTM as a resource comprised of a finite number of noisy stimulus samples. The model predicts the invariance of [Formula: see text] , the sum of squared sensitivities across items, for displays of different sizes. For phase discrimination, the set-size effect significantly exceeded that predicted by the sample-size model for both simultaneously and sequentially presented stimuli. Instead, the set-size effect and the serial position curves with sequential presentation were predicted by an attention-weighted version of the sample-size model, which assumes that one of the items in the display captures attention and receives a disproportionate share of resources. The choice probabilities and response time distributions from the task were well described by a diffusion decision model in which the drift rates embodied the assumptions of the attention-weighted sample-size model. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

The Application of Modeling and Simulation in Capacity Management within the ITIL Framework

NASA Technical Reports Server (NTRS)

Rahmani, Sonya; vonderHoff, Otto

2010-01-01

Tightly integrating modeling and simulation techniques into Information Technology Infrastructure Library (ITIL) practices can be one of the driving factors behind a successful and cost-effective capacity management effort for any Information Technology (IT) system. ITIL is a best practices framework for managing IT infrastructure, development and operations. Translating ITIL theory into operational reality can be a challenge. This paper aims to highlight how to best integrate modeling and simulation into an ITIL implementation. For cases where the project team initially has difficulty gaining consensus on investing in modeling and simulation resources, a clear definition for M&S implementation into the ITIL framework, specifically its role in supporting Capacity Management, is critical to gaining the support required to garner these resources. This implementation should also help to clearly define M&S support to the overall system mission. This paper will describe the development of an integrated modeling approach and how best to tie M&S to definitive goals for evaluating system capacity and performance requirements. Specifically the paper will discuss best practices for implementing modeling and simulation into ITIL. These practices hinge on implementing integrated M&S methods that 1) encompass at least two or more predictive modeling techniques, 2) complement each one's respective strengths and weaknesses to support the validation of predicted results, and 3) are tied to the system's performance and workload monitoring efforts. How to structure two forms of modeling: statistical and simUlation in the development of "As Is" and "To Be" efforts will be used to exemplify the integrated M&S methods. The paper will show how these methods can better support the project's overall capacity management efforts.

An empirical evaluation of landscape energetic models: Mallard and American black duck space use during the non-breeding period

USGS Publications Warehouse

Beatty, William S.; Webb, Elisabeth B.; Kesler, Dylan C.; Naylor, Luke W.; Raedeke, Andrew H.; Humburg, Dale D.; Coluccy, John M.; Soulliere, G.

2015-01-01

Bird conservation Joint Ventures are collaborative partnerships between public agencies and private organizations that facilitate habitat management to support waterfowl and other bird populations. A subset of Joint Ventures has developed energetic carrying capacity models (ECCs) to translate regional waterfowl population goals into habitat objectives during the non-breeding period. Energetic carrying capacity models consider food biomass, metabolism, and available habitat to estimate waterfowl carrying capacity within an area. To evaluate Joint Venture ECCs in the context of waterfowl space use, we monitored 33 female mallards (Anas platyrhynchos) and 55 female American black ducks (A. rubripes) using global positioning system satellite telemetry in the central and eastern United States. To quantify space use, we measured first-passage time (FPT: time required for an individual to transit across a circle of a given radius) at biologically relevant spatial scales for mallards (3.46 km) and American black ducks (2.30 km) during the non-breeding period, which included autumn migration, winter, and spring migration. We developed a series of models to predict FPT using Joint Venture ECCs and compared them to a biological null model that quantified habitat composition and a statistical null model, which included intercept and random terms. Energetic carrying capacity models predicted mallard space use more efficiently during autumn and spring migrations, but the statistical null was the top model for winter. For American black ducks, ECCs did not improve predictions of space use; the biological null was top ranked for winter and the statistical null was top ranked for spring migration. Thus, ECCs provided limited insight into predicting waterfowl space use during the non-breeding season. Refined estimates of spatial and temporal variation in food abundance, habitat conditions, and anthropogenic disturbance will likely improve ECCs and benefit conservation planners in linking non-breeding waterfowl habitat objectives with distribution and population parameters. Published 2015. This article is a U.S. Government work and is in the public domain in the USA.

Revel8or: Model Driven Capacity Planning Tool Suite

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

Zhu, Liming; Liu, Yan; Bui, Ngoc B.

2007-05-31

Designing complex multi-tier applications that must meet strict performance requirements is a challenging software engineering problem. Ideally, the application architect could derive accurate performance predictions early in the project life-cycle, leveraging initial application design-level models and a description of the target software and hardware platforms. To this end, we have developed a capacity planning tool suite for component-based applications, called Revel8tor. The tool adheres to the model driven development paradigm and supports benchmarking and performance prediction for J2EE, .Net and Web services platforms. The suite is composed of three different tools: MDAPerf, MDABench and DSLBench. MDAPerf allows annotation of designmore » diagrams and derives performance analysis models. MDABench allows a customized benchmark application to be modeled in the UML 2.0 Testing Profile and automatically generates a deployable application, with measurement automatically conducted. DSLBench allows the same benchmark modeling and generation to be conducted using a simple performance engineering Domain Specific Language (DSL) in Microsoft Visual Studio. DSLBench integrates with Visual Studio and reuses its load testing infrastructure. Together, the tool suite can assist capacity planning across platforms in an automated fashion.« less

Female Adolescent Contraceptive Decision Making and Risk Taking.

ERIC Educational Resources Information Center

Johnson, Sharon A.; Green, Vicki

1993-01-01

Findings from 60 sexually active, unmarried females, ages 14 through 18, revealed that cognitive capacity and cognitive egocentrism variables as well as age, grade, and ethnic status significantly predicted 6 of 7 decision-making variables in contraceptive use model. One cognitive capacity variable and one sexual contraceptive behavior variable…

Visible/near-infrared spectroscopy to predict water holding capacity in broiler breast meat

USDA-ARS?s Scientific Manuscript database

Visible/Near-infrared spectroscopy (Vis/NIRS) was examined as a tool for rapidly determining water holding capacity (WHC) in broiler breast meat. Both partial least squares (PLS) and principal component analysis (PCA) models were developed to relate Vis/NIRS spectra of 85 broiler breast meat sample...

Consumer Search, Rationing Rules, and the Consequence for Competition

NASA Astrophysics Data System (ADS)

Ruebeck, Christopher S.

Firms' conjectures about demand are consequential in oligopoly games. Through agent-based modeling of consumers' search for products, we can study the rationing of demand between capacity-constrained firms offering homogeneous products and explore the robustness of analytically solvable models' results. After algorithmically formalizing short-run search behavior rather than assuming a long-run average, this study predicts stronger competition in a two-stage capacity-price game.

An Agent-Based Model for Analyzing Control Policies and the Dynamic Service-Time Performance of a Capacity-Constrained Air Traffic Management Facility

NASA Technical Reports Server (NTRS)

Conway, Sheila R.

2006-01-01

Simple agent-based models may be useful for investigating air traffic control strategies as a precursory screening for more costly, higher fidelity simulation. Of concern is the ability of the models to capture the essence of the system and provide insight into system behavior in a timely manner and without breaking the bank. The method is put to the test with the development of a model to address situations where capacity is overburdened and potential for propagation of the resultant delay though later flights is possible via flight dependencies. The resultant model includes primitive representations of principal air traffic system attributes, namely system capacity, demand, airline schedules and strategy, and aircraft capability. It affords a venue to explore their interdependence in a time-dependent, dynamic system simulation. The scope of the research question and the carefully-chosen modeling fidelity did allow for the development of an agent-based model in short order. The model predicted non-linear behavior given certain initial conditions and system control strategies. Additionally, a combination of the model and dimensionless techniques borrowed from fluid systems was demonstrated that can predict the system s dynamic behavior across a wide range of parametric settings.

Comparison of two weighted integration models for the cueing task: linear and likelihood

NASA Technical Reports Server (NTRS)

Shimozaki, Steven S.; Eckstein, Miguel P.; Abbey, Craig K.

2003-01-01

In a task in which the observer must detect a signal at two locations, presenting a precue that predicts the location of a signal leads to improved performance with a valid cue (signal location matches the cue), compared to an invalid cue (signal location does not match the cue). The cue validity effect has often been explained with a limited capacity attentional mechanism improving the perceptual quality at the cued location. Alternatively, the cueing effect can also be explained by unlimited capacity models that assume a weighted combination of noisy responses across the two locations. We compare two weighted integration models, a linear model and a sum of weighted likelihoods model based on a Bayesian observer. While qualitatively these models are similar, quantitatively they predict different cue validity effects as the signal-to-noise ratios (SNR) increase. To test these models, 3 observers performed in a cued discrimination task of Gaussian targets with an 80% valid precue across a broad range of SNR's. Analysis of a limited capacity attentional switching model was also included and rejected. The sum of weighted likelihoods model best described the psychophysical results, suggesting that human observers approximate a weighted combination of likelihoods, and not a weighted linear combination.

Predicting solvation free energies and thermodynamics in polar solvents and mixtures using a solvation-layer interface condition

PubMed Central

Goossens, Spencer; Mehdizadeh Rahimi, Ali

2017-01-01

We demonstrate that with two small modifications, the popular dielectric continuum model is capable of predicting, with high accuracy, ion solvation thermodynamics (Gibbs free energies, entropies, and heat capacities) in numerous polar solvents. We are also able to predict ion solvation free energies in water–co-solvent mixtures over available concentration series. The first modification to the classical dielectric Poisson model is a perturbation of the macroscopic dielectric-flux interface condition at the solute–solvent interface: we add a nonlinear function of the local electric field, giving what we have called a solvation-layer interface condition (SLIC). The second modification is including the microscopic interface potential (static potential) in our model. We show that the resulting model exhibits high accuracy without the need for fitting solute atom radii in a state-dependent fashion. Compared to experimental results in nine water–co-solvent mixtures, SLIC predicts transfer free energies to within 2.5 kJ/mol. The co-solvents include both protic and aprotic species, as well as biologically relevant denaturants such as urea and dimethylformamide. Furthermore, our results indicate that the interface potential is essential to reproduce entropies and heat capacities. These and previous tests of the SLIC model indicate that it is a promising dielectric continuum model for accurate predictions in a wide range of conditions.

Lung function parameters improve prediction of VO2peak in an elderly population: The Generation 100 study.

PubMed

Hassel, Erlend; Stensvold, Dorthe; Halvorsen, Thomas; Wisløff, Ulrik; Langhammer, Arnulf; Steinshamn, Sigurd

2017-01-01

Peak oxygen uptake (VO2peak) is an indicator of cardiovascular health and a useful tool for risk stratification. Direct measurement of VO2peak is resource-demanding and may be contraindicated. There exist several non-exercise models to estimate VO2peak that utilize easily obtainable health parameters, but none of them includes lung function measures or hemoglobin concentrations. We aimed to test whether addition of these parameters could improve prediction of VO2peak compared to an established model that includes age, waist circumference, self-reported physical activity and resting heart rate. We included 1431 subjects aged 69-77 years that completed a laboratory test of VO2peak, spirometry, and a gas diffusion test. Prediction models for VO2peak were developed with multiple linear regression, and goodness of fit was evaluated. Forced expiratory volume in one second (FEV1), diffusing capacity of the lung for carbon monoxide and blood hemoglobin concentration significantly improved the ability of the established model to predict VO2peak. The explained variance of the model increased from 31% to 48% for men and from 32% to 38% for women (p<0.001). FEV1, diffusing capacity of the lungs for carbon monoxide and hemoglobin concentration substantially improved the accuracy of VO2peak prediction when added to an established model in an elderly population.

Predicting solvation free energies and thermodynamics in polar solvents and mixtures using a solvation-layer interface condition

NASA Astrophysics Data System (ADS)

Molavi Tabrizi, Amirhossein; Goossens, Spencer; Mehdizadeh Rahimi, Ali; Knepley, Matthew; Bardhan, Jaydeep P.

2017-03-01

We demonstrate that with two small modifications, the popular dielectric continuum model is capable of predicting, with high accuracy, ion solvation thermodynamics (Gibbs free energies, entropies, and heat capacities) in numerous polar solvents. We are also able to predict ion solvation free energies in water-co-solvent mixtures over available concentration series. The first modification to the classical dielectric Poisson model is a perturbation of the macroscopic dielectric-flux interface condition at the solute-solvent interface: we add a nonlinear function of the local electric field, giving what we have called a solvation-layer interface condition (SLIC). The second modification is including the microscopic interface potential (static potential) in our model. We show that the resulting model exhibits high accuracy without the need for fitting solute atom radii in a state-dependent fashion. Compared to experimental results in nine water-co-solvent mixtures, SLIC predicts transfer free energies to within 2.5 kJ/mol. The co-solvents include both protic and aprotic species, as well as biologically relevant denaturants such as urea and dimethylformamide. Furthermore, our results indicate that the interface potential is essential to reproduce entropies and heat capacities. These and previous tests of the SLIC model indicate that it is a promising dielectric continuum model for accurate predictions in a wide range of conditions.

Predictive power of theoretical modelling of the nuclear mean field: examples of improving predictive capacities

NASA Astrophysics Data System (ADS)

Dedes, I.; Dudek, J.

2018-03-01

We examine the effects of the parametric correlations on the predictive capacities of the theoretical modelling keeping in mind the nuclear structure applications. The main purpose of this work is to illustrate the method of establishing the presence and determining the form of parametric correlations within a model as well as an algorithm of elimination by substitution (see text) of parametric correlations. We examine the effects of the elimination of the parametric correlations on the stabilisation of the model predictions further and further away from the fitting zone. It follows that the choice of the physics case and the selection of the associated model are of secondary importance in this case. Under these circumstances we give priority to the relative simplicity of the underlying mathematical algorithm, provided the model is realistic. Following such criteria, we focus specifically on an important but relatively simple case of doubly magic spherical nuclei. To profit from the algorithmic simplicity we chose working with the phenomenological spherically symmetric Woods–Saxon mean-field. We employ two variants of the underlying Hamiltonian, the traditional one involving both the central and the spin orbit potential in the Woods–Saxon form and the more advanced version with the self-consistent density-dependent spin–orbit interaction. We compare the effects of eliminating of various types of correlations and discuss the improvement of the quality of predictions (‘predictive power’) under realistic parameter adjustment conditions.

Influence of landscape-scale factors in limiting brook trout populations in Pennsylvania streams

USGS Publications Warehouse

Kocovsky, P.M.; Carline, R.F.

2006-01-01

Landscapes influence the capacity of streams to produce trout through their effect on water chemistry and other factors at the reach scale. Trout abundance also fluctuates over time; thus, to thoroughly understand how spatial factors at landscape scales affect trout populations, one must assess the changes in populations over time to provide a context for interpreting the importance of spatial factors. We used data from the Pennsylvania Fish and Boat Commission's fisheries management database to investigate spatial factors that affect the capacity of streams to support brook trout Salvelinus fontinalis and to provide models useful for their management. We assessed the relative importance of spatial and temporal variation by calculating variance components and comparing relative standard errors for spatial and temporal variation. We used binary logistic regression to predict the presence of harvestable-length brook trout and multiple linear regression to assess the mechanistic links between landscapes and trout populations and to predict population density. The variance in trout density among streams was equal to or greater than the temporal variation for several streams, indicating that differences among sites affect population density. Logistic regression models correctly predicted the absence of harvestable-length brook trout in 60% of validation samples. The r 2-value for the linear regression model predicting density was 0.3, indicating low predictive ability. Both logistic and linear regression models supported buffering capacity against acid episodes as an important mechanistic link between landscapes and trout populations. Although our models fail to predict trout densities precisely, their success at elucidating the mechanistic links between landscapes and trout populations, in concert with the importance of spatial variation, increases our understanding of factors affecting brook trout abundance and will help managers and private groups to protect and enhance populations of wild brook trout. ?? Copyright by the American Fisheries Society 2006.

Predicting site locations for biomass using facilities with Bayesian methods

Treesearch

Timothy M. Young; James H. Perdue; Xia Huang

2017-01-01

Logistic regression models combined with Bayesian inference were developed to predict locations and quantify factors that influence the siting of biomass-using facilities that use woody biomass in the Southeastern United States. Predictions were developed for two groups of mills, one representing larger capacity mills similar to pulp and paper mills (Group II...

Non-constant link tension coefficient in the tumbling-snake model subjected to simple shear

NASA Astrophysics Data System (ADS)

Stephanou, Pavlos S.; Kröger, Martin

2017-11-01

The authors of the present study have recently presented evidence that the tumbling-snake model for polymeric systems has the necessary capacity to predict the appearance of pronounced undershoots in the time-dependent shear viscosity as well as an absence of equally pronounced undershoots in the transient two normal stress coefficients. The undershoots were found to appear due to the tumbling behavior of the director u when a rotational Brownian diffusion term is considered within the equation of motion of polymer segments, and a theoretical basis concerning the use of a link tension coefficient given through the nematic order parameter had been provided. The current work elaborates on the quantitative predictions of the tumbling-snake model to demonstrate its capacity to predict undershoots in the time-dependent shear viscosity. These predictions are shown to compare favorably with experimental rheological data for both polymer melts and solutions, help us to clarify the microscopic origin of the observed phenomena, and demonstrate in detail why a constant link tension coefficient has to be abandoned.

QSPR models for various physical properties of carbohydrates based on molecular mechanics and quantum chemical calculations.

PubMed

Dyekjaer, Jane Dannow; Jónsdóttir, Svava Osk

2004-01-22

Quantitative Structure-Property Relationships (QSPR) have been developed for a series of monosaccharides, including the physical properties of partial molar heat capacity, heat of solution, melting point, heat of fusion, glass-transition temperature, and solid state density. The models were based on molecular descriptors obtained from molecular mechanics and quantum chemical calculations, combined with other types of descriptors. Saccharides exhibit a large degree of conformational flexibility, therefore a methodology for selecting the energetically most favorable conformers has been developed, and was used for the development of the QSPR models. In most cases good correlations were obtained for monosaccharides. For five of the properties predictions were made for disaccharides, and the predicted values for the partial molar heat capacities were in excellent agreement with experimental values.

Modeling and predicting vegetation response of western USA grasslands, shrublands, and deserts to climate change (Chapter 1)

Treesearch

Megan M. Friggens; Marcus V. Warwell; Jeanne C. Chambers; Stanley G. Kitchen

2012-01-01

Experimental research and species distribution modeling predict large changes in the distributions of species and vegetation types in the Interior West due to climate change. Species’ responses will depend not only on their physiological tolerances but also on their phenology, establishment properties, biotic interactions, and capacity to evolve and migrate. Because...

Prediction of heat capacity of amine solutions using artificial neural network and thermodynamic models for CO2 capture processes

NASA Astrophysics Data System (ADS)

Afkhamipour, Morteza; Mofarahi, Masoud; Borhani, Tohid Nejad Ghaffar; Zanganeh, Masoud

2018-03-01

In this study, artificial neural network (ANN) and thermodynamic models were developed for prediction of the heat capacity ( C P ) of amine-based solvents. For ANN model, independent variables such as concentration, temperature, molecular weight and CO2 loading of amine were selected as the inputs of the model. The significance of the input variables of the ANN model on the C P values was investigated statistically by analyzing of correlation matrix. A thermodynamic model based on the Redlich-Kister equation was used to correlate the excess molar heat capacity ({C}_P^E) data as function of temperature. In addition, the effects of temperature and CO2 loading at different concentrations of conventional amines on the C P values were investigated. Both models were validated against experimental data and very good results were obtained between two mentioned models and experimental data of C P collected from various literatures. The AARD between ANN model results and experimental data of C P for 47 systems of amine-based solvents studied was 4.3%. For conventional amines, the AARD for ANN model and thermodynamic model in comparison with experimental data were 0.59% and 0.57%, respectively. The results showed that both ANN and Redlich-Kister models can be used as a practical tool for simulation and designing of CO2 removal processes by using amine solutions.

Conceptualizing agency: Folkpsychological and folkcommunicative perspectives on plants.

PubMed

Ojalehto, Bethany L; Medin, Douglas L; García, Salino G

2017-05-01

The present research addresses cultural variation in concepts of agency. Across two experiments, we investigate how Indigenous Ngöbe of Panama and US college students interpret and make inferences about nonhuman agency, focusing on plants as a critical test case. In Experiment 1, participants predicted goal-directed actions for plants and other nonhuman kinds and judged their capacities for intentional agency. Goal-directed action is pervasive among living kinds and as such we expected cultural agreement on these predictions. However, we expected that interpretation of the capacities involved would differ based on cultural folktheories. As expected, Ngöbe and US participants both inferred that plants would engage in goal-directed action but Ngöbe were more likely to attribute intentional agency capacities to plants. Experiment 2 extends these findings by investigating action predictions and capacity attributions linked to complex forms of plant social agency recently discovered in botanical sciences (communication, kin altruism). We hypothesized that the Ngöbe view of plants as active agents would productively guide inferences for plant social interaction. Indeed, Ngöbe were more likely than US participants to infer that plants can engage in social behaviors and they also attributed more social agency capacities to plants. We consolidate these findings by using bottom-up consensus modeling to show that these cultural differences reflect two distinct conceptual models of agency rather than variations on a single (universal) model. We consider these findings in light of current theories of domain-specificity and animism, and offer an alternative account based on a folktheory of communication that infers agency on the basis of relational interactions rather than having a mind. Copyright © 2017 Elsevier B.V. All rights reserved.

A Novel Approach to Model the Air-Side Heat Transfer in Microchannel Condensers

NASA Astrophysics Data System (ADS)

Martínez-Ballester, S.; Corberán, José-M.; Gonzálvez-Maciá, J.

2012-11-01

The work presents a model (Fin1D×3) for microchannel condensers and gas coolers. The paper focusses on the description of the novel approach employed to model the air-side heat transfer. The model applies a segment-by-segment discretization to the heat exchanger adding, in each segment, a specific bi-dimensional grid to the air flow and fin wall. Given this discretization, the fin theory is applied by using a continuous piecewise function for the fin wall temperature. It allows taking into account implicitly the heat conduction between tubes along the fin, and the unmixed air influence on the heat capacity. The model has been validated against experimental data resulting in predicted capacity errors within ± 5%. Differences on prediction results and computational cost were studied and compared with the previous authors' model (Fin2D) and with other simplified model. Simulation time of the proposed model was reduced one order of magnitude respect the Fin2D's time retaining its same accuracy.

Enhancing seasonal climate prediction capacity for the Pacific countries

NASA Astrophysics Data System (ADS)

Kuleshov, Y.; Jones, D.; Hendon, H.; Charles, A.; Cottrill, A.; Lim, E.-P.; Langford, S.; de Wit, R.; Shelton, K.

2012-04-01

Seasonal and inter-annual climate variability is a major factor in determining the vulnerability of many Pacific Island Countries to climate change and there is need to improve weekly to seasonal range climate prediction capabilities beyond what is currently available from statistical models. In the seasonal climate prediction project under the Australian Government's Pacific Adaptation Strategy Assistance Program (PASAP), we describe a comprehensive project to strengthen the climate prediction capacities in National Meteorological Services in 14 Pacific Island Countries and East Timor. The intent is particularly to reduce the vulnerability of current services to a changing climate, and improve the overall level of information available assist with managing climate variability. Statistical models cannot account for aspects of climate variability and change that are not represented in the historical record. In contrast, dynamical physics-based models implicitly include the effects of a changing climate whatever its character or cause and can predict outcomes not seen previously. The transition from a statistical to a dynamical prediction system provides more valuable and applicable climate information to a wide range of climate sensitive sectors throughout the countries of the Pacific region. In this project, we have developed seasonal climate outlooks which are based upon the current dynamical model POAMA (Predictive Ocean-Atmosphere Model for Australia) seasonal forecast system. At present, meteorological services of the Pacific Island Countries largely employ statistical models for seasonal outlooks. Outcomes of the PASAP project enhanced capabilities of the Pacific Island Countries in seasonal prediction providing National Meteorological Services with an additional tool to analyse meteorological variables such as sea surface temperatures, air temperature, pressure and rainfall using POAMA outputs and prepare more accurate seasonal climate outlooks.

Calibration of the 2D Hydrodynamic Model Floodos and Implications of Distributed Friction on Sediment Transport Capacity

NASA Astrophysics Data System (ADS)

Croissant, T.; Lague, D.; Davy, P.

2014-12-01

Numerical models of floodplain dynamics often use a simplified 1D description of flow hydraulics and sediment transport that cannot fully account for differential friction between vegetated banks and low friction in the main channel. Key parameters of such models are the friction coefficient and the description of the channel bathymetry which strongly influence predicted water depth and velocity, and therefore sediment transport capacity. In this study, we use a newly developed 2D hydrodynamic model, Floodos, whose efficiency is a major advantage for exploring channel morphodynamics from a flood event to millennial time scales. We evaluate the quality of Floodos predictions in the Whataroa river, New Zealand and assess the effect of a spatially distributed friction coefficient (SDFC) on long term sediment transport. Predictions from the model are compared to water depth data from a gauging station located on the Whataroa River in Southern Alps, New Zealand. The Digital Elevation Model (DEM) of the 2.5 km long studied reach is derived from a 2010 LiDAR acquisition with 2 m resolution and an interpolated bathymetry. The several large floods experienced by this river during 2010 allow us to access water depth for a wide range of possible river discharges and to retrieve the scaling between these two parameters. The high resolution DEM used has a non-negligible part of submerged bathymetry that airborne LiDAR was not able to capture. Bathymetry can be reconstructed by interpolation methods that introduce several uncertainties concerning water depth predictions. We address these uncertainties inherent to the interpolation using a simplified channel with a geometry (slope and width) similar to the Whataroa river. We then explore the effect of a SDFC on velocity pattern, water depth and sediment transport capacity and discuss its relevance on long term predictions of sediment transport and channel morphodynamics.

A core stochastic population projection model for Florida manatees (Trichechus manatus latirostris)

USGS Publications Warehouse

Runge, Michael C.; Sanders-Reed, Carol A.; Fonnesbeck, Christopher J.

2007-01-01

A stochastic, stage-based population model was developed to describe the life history and forecast the population dynamics of the Florida manatee (Trichechus manatus latirostris) in four separate regions of Florida. This population model includes annual variability in survival and reproductive rates, demographic stochasticity, effects of changes in warm-water capacity, and catastrophes. Further, the model explicitly accounts for uncertainty in parameter estimates. This model is meant to serve as a flexible tool for use in assessments relevant to management decision making, and was used in the State of Florida's recent biological status review. The parameter estimates and model structure described herein reflect our understanding of manatee demography at the time that this status review was completed. In the Northwest and Upper St. Johns regions, the model predicts that the populations will increase over time until warm-water capacity is reached, at which point growth will taper off. In the Atlantic region, the model predicts a stable or slightly increasing population over the next decade or so, and then a decrease as industrial warm-water capacity is lost. In the Southwest region, the model predicts a decline over time, driven by high annual mortality in the short-term and exacerbated by loss of industrial warm-water winter refuges over the next 40 years. Statewide, the likelihood of a 50% or greater decline in three manatee generations was 12%; the likelihood of a 20% or greater decline in two generations was 56%. These declines are largely driven by the anticipated loss of warm-water capacity, especially in the Atlantic and Southwest regions. The estimates of probability of extinction within 100 years were 11.9% for the Southwest region, 0.6% for the Northwest, 0.04% for the Atlantic, and <0.02% for the Upper St. Johns. The estimated probability that the statewide population will fall below 1000 animals within 100 years was 2.3%. Thus, while the estimated probability of extinction is low, the model predicts that current and emerging threats are likely to result in a long-term decline in the statewide population and a change in the regional distribution of manatees. Analyses of sensitivity and variance contribution highlight the importance of reducing uncertainty in some life-history parameters, particularly adult survival, temporal variance of adult survival, and long-term warm-water capacity. This core biological model is expected to evolve over time, as better information becomes available about manatees and their habitat, and as new assessment needs arise. We anticipate that this core model will be customized for other state and federal assessments in the near future.

Support vector regression-guided unravelling: antioxidant capacity and quantitative structure-activity relationship predict reduction and promotion effects of flavonoids on acrylamide formation

PubMed Central

Huang, Mengmeng; Wei, Yan; Wang, Jun; Zhang, Yu

2016-01-01

We used the support vector regression (SVR) approach to predict and unravel reduction/promotion effect of characteristic flavonoids on the acrylamide formation under a low-moisture Maillard reaction system. Results demonstrated the reduction/promotion effects by flavonoids at addition levels of 1–10000 μmol/L. The maximal inhibition rates (51.7%, 68.8% and 26.1%) and promote rates (57.7%, 178.8% and 27.5%) caused by flavones, flavonols and isoflavones were observed at addition levels of 100 μmol/L and 10000 μmol/L, respectively. The reduction/promotion effects were closely related to the change of trolox equivalent antioxidant capacity (ΔTEAC) and well predicted by triple ΔTEAC measurements via SVR models (R: 0.633–0.900). Flavonols exhibit stronger effects on the acrylamide formation than flavones and isoflavones as well as their O-glycosides derivatives, which may be attributed to the number and position of phenolic and 3-enolic hydroxyls. The reduction/promotion effects were well predicted by using optimized quantitative structure-activity relationship (QSAR) descriptors and SVR models (R: 0.926–0.994). Compared to artificial neural network and multi-linear regression models, SVR models exhibited better fitting performance for both TEAC-dependent and QSAR descriptor-dependent predicting work. These observations demonstrated that the SVR models are competent for predicting our understanding on the future use of natural antioxidants for decreasing the acrylamide formation. PMID:27586851

Support vector regression-guided unravelling: antioxidant capacity and quantitative structure-activity relationship predict reduction and promotion effects of flavonoids on acrylamide formation

NASA Astrophysics Data System (ADS)

Huang, Mengmeng; Wei, Yan; Wang, Jun; Zhang, Yu

2016-09-01

We used the support vector regression (SVR) approach to predict and unravel reduction/promotion effect of characteristic flavonoids on the acrylamide formation under a low-moisture Maillard reaction system. Results demonstrated the reduction/promotion effects by flavonoids at addition levels of 1-10000 μmol/L. The maximal inhibition rates (51.7%, 68.8% and 26.1%) and promote rates (57.7%, 178.8% and 27.5%) caused by flavones, flavonols and isoflavones were observed at addition levels of 100 μmol/L and 10000 μmol/L, respectively. The reduction/promotion effects were closely related to the change of trolox equivalent antioxidant capacity (ΔTEAC) and well predicted by triple ΔTEAC measurements via SVR models (R: 0.633-0.900). Flavonols exhibit stronger effects on the acrylamide formation than flavones and isoflavones as well as their O-glycosides derivatives, which may be attributed to the number and position of phenolic and 3-enolic hydroxyls. The reduction/promotion effects were well predicted by using optimized quantitative structure-activity relationship (QSAR) descriptors and SVR models (R: 0.926-0.994). Compared to artificial neural network and multi-linear regression models, SVR models exhibited better fitting performance for both TEAC-dependent and QSAR descriptor-dependent predicting work. These observations demonstrated that the SVR models are competent for predicting our understanding on the future use of natural antioxidants for decreasing the acrylamide formation.

Functional capacity evaluation of work performance among individuals with pelvic injuries following motor vehicle accidents.

PubMed

Ratzon, Navah Z; Ari Shevil, Eynat Ben; Froom, Paul; Friedman, Sharon; Amit, Yehuda

2013-01-01

Pelvic injuries following motor vehicle accidents (MVA) cause disability and affect work capabilities. This study evaluated functional, self-report, and medical-based factors that could predict work capacity as was reflected in a functional capacity evaluation (FCE) among persons who sustained a pelvic injury. It was hypothesized that self-reported functional status and bio-demographic variables would predict work capacity. Sixty-one community-dwelling adults previously hospitalized following a MVA induced pelvic injury. FCE for work performance was conducted using the Physical Work Performance Evaluation (PWPE). Additional data was collected through a demographics questionnaire and the Functional Status Questionnaire. All participants underwent an orthopedic medical examination of the hip and lower extremities. Most participants self-reported that their work capacity post-injury were lower than their job required. PWPE scores indicated below-range functional performance. Regression models predicted 23% to 51% of PWPE subtests. Participants' self-report of functioning (instrumental activities of daily living and work) and bio-demographic variables (gender and age) were better predictors of PWPE scores than factors originating from the medical examination. Results support the inclusion of FCE, in addition to self-report of functioning and medical examination, to evaluate work capacity among individuals' post-pelvic injury and interventions and discharge planning.

Modeling the degradation mechanisms of C6/LiFePO4 batteries

NASA Astrophysics Data System (ADS)

Li, Dongjiang; Danilov, Dmitri L.; Zwikirsch, Barbara; Fichtner, Maximilian; Yang, Yong; Eichel, Rüdiger-A.; Notten, Peter H. L.

2018-01-01

A fundamental electrochemical model is developed, describing the capacity fade of C6/LiFePO4 batteries as a function of calendar time and cycling conditions. At moderate temperatures the capacity losses are mainly attributed to Li immobilization in Solid-Electrolyte-Interface (SEI) layers at the anode surface. The SEI formation model presumes the availability of an outer and inner SEI layers. Electron tunneling through the inner SEI layer is regarded as the rate-determining step. The model also includes high temperature degradation. At elevated temperatures, iron dissolution from the positive electrode and the subsequent metal sedimentation on the negative electrode influence the capacity loss. The SEI formation on the metal-covered graphite surface is faster than the conventional SEI formation. The model predicts that capacity fade during storage is lower than during cycling due to the generation of SEI cracks induced by the volumetric changes during (dis)charging. The model has been validated by cycling and calendar aging experiments and shows that the capacity loss during storage depends on the storage time, the State-of-Charge (SoC), and temperature. The capacity losses during cycling depend on the cycling current, cycling time, temperature and cycle number. All these dependencies can be explained by the single model presented in this paper.

Spatial models reveal the microclimatic buffering capacity of old-growth forests

PubMed Central

Frey, Sarah J. K.; Hadley, Adam S.; Johnson, Sherri L.; Schulze, Mark; Jones, Julia A.; Betts, Matthew G.

2016-01-01

Climate change is predicted to cause widespread declines in biodiversity, but these predictions are derived from coarse-resolution climate models applied at global scales. Such models lack the capacity to incorporate microclimate variability, which is critical to biodiversity microrefugia. In forested montane regions, microclimate is thought to be influenced by combined effects of elevation, microtopography, and vegetation, but their relative effects at fine spatial scales are poorly known. We used boosted regression trees to model the spatial distribution of fine-scale, under-canopy air temperatures in mountainous terrain. Spatial models predicted observed independent test data well (r = 0.87). As expected, elevation strongly predicted temperatures, but vegetation and microtopography also exerted critical effects. Old-growth vegetation characteristics, measured using LiDAR (light detection and ranging), appeared to have an insulating effect; maximum spring monthly temperatures decreased by 2.5°C across the observed gradient in old-growth structure. These cooling effects across a gradient in forest structure are of similar magnitude to 50-year forecasts of the Intergovernmental Panel on Climate Change and therefore have the potential to mitigate climate warming at local scales. Management strategies to conserve old-growth characteristics and to curb current rates of primary forest loss could maintain microrefugia, enhancing biodiversity persistence in mountainous systems under climate warming. PMID:27152339

Spatial models reveal the microclimatic buffering capacity of old-growth forests.

PubMed

Frey, Sarah J K; Hadley, Adam S; Johnson, Sherri L; Schulze, Mark; Jones, Julia A; Betts, Matthew G

2016-04-01

Climate change is predicted to cause widespread declines in biodiversity, but these predictions are derived from coarse-resolution climate models applied at global scales. Such models lack the capacity to incorporate microclimate variability, which is critical to biodiversity microrefugia. In forested montane regions, microclimate is thought to be influenced by combined effects of elevation, microtopography, and vegetation, but their relative effects at fine spatial scales are poorly known. We used boosted regression trees to model the spatial distribution of fine-scale, under-canopy air temperatures in mountainous terrain. Spatial models predicted observed independent test data well (r = 0.87). As expected, elevation strongly predicted temperatures, but vegetation and microtopography also exerted critical effects. Old-growth vegetation characteristics, measured using LiDAR (light detection and ranging), appeared to have an insulating effect; maximum spring monthly temperatures decreased by 2.5°C across the observed gradient in old-growth structure. These cooling effects across a gradient in forest structure are of similar magnitude to 50-year forecasts of the Intergovernmental Panel on Climate Change and therefore have the potential to mitigate climate warming at local scales. Management strategies to conserve old-growth characteristics and to curb current rates of primary forest loss could maintain microrefugia, enhancing biodiversity persistence in mountainous systems under climate warming.

When High-Capacity Readers Slow Down and Low-Capacity Readers Speed Up: Working Memory and Locality Effects.

PubMed

Nicenboim, Bruno; Logačev, Pavel; Gattei, Carolina; Vasishth, Shravan

2016-01-01

We examined the effects of argument-head distance in SVO and SOV languages (Spanish and German), while taking into account readers' working memory capacity and controlling for expectation (Levy, 2008) and other factors. We predicted only locality effects, that is, a slowdown produced by increased dependency distance (Gibson, 2000; Lewis and Vasishth, 2005). Furthermore, we expected stronger locality effects for readers with low working memory capacity. Contrary to our predictions, low-capacity readers showed faster reading with increased distance, while high-capacity readers showed locality effects. We suggest that while the locality effects are compatible with memory-based explanations, the speedup of low-capacity readers can be explained by an increased probability of retrieval failure. We present a computational model based on ACT-R built under the previous assumptions, which is able to give a qualitative account for the present data and can be tested in future research. Our results suggest that in some cases, interpreting longer RTs as indexing increased processing difficulty and shorter RTs as facilitation may be too simplistic: The same increase in processing difficulty may lead to slowdowns in high-capacity readers and speedups in low-capacity ones. Ignoring individual level capacity differences when investigating locality effects may lead to misleading conclusions.

When High-Capacity Readers Slow Down and Low-Capacity Readers Speed Up: Working Memory and Locality Effects

PubMed Central

Nicenboim, Bruno; Logačev, Pavel; Gattei, Carolina; Vasishth, Shravan

2016-01-01

We examined the effects of argument-head distance in SVO and SOV languages (Spanish and German), while taking into account readers' working memory capacity and controlling for expectation (Levy, 2008) and other factors. We predicted only locality effects, that is, a slowdown produced by increased dependency distance (Gibson, 2000; Lewis and Vasishth, 2005). Furthermore, we expected stronger locality effects for readers with low working memory capacity. Contrary to our predictions, low-capacity readers showed faster reading with increased distance, while high-capacity readers showed locality effects. We suggest that while the locality effects are compatible with memory-based explanations, the speedup of low-capacity readers can be explained by an increased probability of retrieval failure. We present a computational model based on ACT-R built under the previous assumptions, which is able to give a qualitative account for the present data and can be tested in future research. Our results suggest that in some cases, interpreting longer RTs as indexing increased processing difficulty and shorter RTs as facilitation may be too simplistic: The same increase in processing difficulty may lead to slowdowns in high-capacity readers and speedups in low-capacity ones. Ignoring individual level capacity differences when investigating locality effects may lead to misleading conclusions. PMID:27014113

Effects of children's working memory capacity and processing speed on their sentence imitation performance.

PubMed

Poll, Gerard H; Miller, Carol A; Mainela-Arnold, Elina; Adams, Katharine Donnelly; Misra, Maya; Park, Ji Sook

2013-01-01

More limited working memory capacity and slower processing for language and cognitive tasks are characteristics of many children with language difficulties. Individual differences in processing speed have not consistently been found to predict language ability or severity of language impairment. There are conflicting views on whether working memory and processing speed are integrated or separable abilities. To evaluate four models for the relations of individual differences in children's processing speed and working memory capacity in sentence imitation. The models considered whether working memory and processing speed are integrated or separable, as well as the effect of the number of operations required per sentence. The role of working memory as a mediator of the effect of processing speed on sentence imitation was also evaluated. Forty-six children with varied language and reading abilities imitated sentences. Working memory was measured with the Competing Language Processing Task (CLPT), and processing speed was measured with a composite of truth-value judgment and rapid automatized naming tasks. Mixed-effects ordinal regression models evaluated the CLPT and processing speed as predictors of sentence imitation item scores. A single mediator model evaluated working memory as a mediator of the effect of processing speed on sentence imitation total scores. Working memory was a reliable predictor of sentence imitation accuracy, but processing speed predicted sentence imitation only as a component of a processing speed by number of operations interaction. Processing speed predicted working memory capacity, and there was evidence that working memory acted as a mediator of the effect of processing speed on sentence imitation accuracy. The findings support a refined view of working memory and processing speed as separable factors in children's sentence imitation performance. Processing speed does not independently explain sentence imitation accuracy for all sentence types, but contributes when the task requires more mental operations. Processing speed also has an indirect effect on sentence imitation by contributing to working memory capacity. © 2013 Royal College of Speech and Language Therapists.

How Many Protein Sequences Fold to a Given Structure? A Coevolutionary Analysis.

PubMed

Tian, Pengfei; Best, Robert B

2017-10-17

Quantifying the relationship between protein sequence and structure is key to understanding the protein universe. A fundamental measure of this relationship is the total number of amino acid sequences that can fold to a target protein structure, known as the "sequence capacity," which has been suggested as a proxy for how designable a given protein fold is. Although sequence capacity has been extensively studied using lattice models and theory, numerical estimates for real protein structures are currently lacking. In this work, we have quantitatively estimated the sequence capacity of 10 proteins with a variety of different structures using a statistical model based on residue-residue co-evolution to capture the variation of sequences from the same protein family. Remarkably, we find that even for the smallest protein folds, such as the WW domain, the number of foldable sequences is extremely large, exceeding the Avogadro constant. In agreement with earlier theoretical work, the calculated sequence capacity is positively correlated with the size of the protein, or better, the density of contacts. This allows the absolute sequence capacity of a given protein to be approximately predicted from its structure. On the other hand, the relative sequence capacity, i.e., normalized by the total number of possible sequences, is an extremely tiny number and is strongly anti-correlated with the protein length. Thus, although there may be more foldable sequences for larger proteins, it will be much harder to find them. Lastly, we have correlated the evolutionary age of proteins in the CATH database with their sequence capacity as predicted by our model. The results suggest a trade-off between the opposing requirements of high designability and the likelihood of a novel fold emerging by chance. Published by Elsevier Inc.

Representing pump-capacity relations in groundwater simulation models

USGS Publications Warehouse

Konikow, Leonard F.

2010-01-01

The yield (or discharge) of constant-speed pumps varies with the total dynamic head (or lift) against which the pump is discharging. The variation in yield over the operating range of the pump may be substantial. In groundwater simulations that are used for management evaluations or other purposes, where predictive accuracy depends on the reliability of future discharge estimates, model reliability may be enhanced by including the effects of head-capacity (or pump-capacity) relations on the discharge from the well. A relatively simple algorithm has been incorporated into the widely used MODFLOW groundwater flow model that allows a model user to specify head-capacity curves. The algorithm causes the model to automatically adjust the pumping rate each time step to account for the effect of drawdown in the cell and changing lift, and will shut the pump off if lift exceeds a critical value. The algorithm is available as part of a new multinode well package (MNW2) for MODFLOW.

Representing pump-capacity relations in groundwater simulati on models

USGS Publications Warehouse

Konikow, Leonard F.

2010-01-01

The yield (or discharge) of constant-speed pumps varies with the total dynamic head (or lift) against which the pump is discharging. The variation in yield over the operating range of the pump may be substantial. In groundwater simulations that are used for management evaluations or other purposes, where predictive accuracy depends on the reliability of future discharge estimates, model reliability may be enhanced by including the effects of head-capacity (or pump-capacity) relations on the discharge from the well. A relatively simple algorithm has been incorporated into the widely used MODFLOW groundwater flow model that allows a model user to specify head-capacity curves. The algorithm causes the model to automatically adjust the pumping rate each time step to account for the effect of drawdown in the cell and changing lift, and will shut the pump off if lift exceeds a critical value. The algorithm is available as part of a new multinode well package (MNW2) for MODFLOW. ?? 2009 National Ground Water Association.

PREDICTING THE ADSORPTION CAPACITY OF ACTIVATED CARBON FOR ORGANIC CONTAMINANTS FROM ADSORBENT AND ADSORBATE PROPERTIES

EPA Science Inventory

A quantitative structure-property relationship (QSPR) was developed and combined with the Polanyi-Dubinin-Manes model to predict adsorption isotherms of emerging contaminants on activated carbons with a wide range of physico-chemical properties. Affinity coefficients (β_l

An ideal observer analysis of visual working memory.

PubMed

Sims, Chris R; Jacobs, Robert A; Knill, David C

2012-10-01

Limits in visual working memory (VWM) strongly constrain human performance across many tasks. However, the nature of these limits is not well understood. In this article we develop an ideal observer analysis of human VWM by deriving the expected behavior of an optimally performing but limited-capacity memory system. This analysis is framed around rate-distortion theory, a branch of information theory that provides optimal bounds on the accuracy of information transmission subject to a fixed information capacity. The result of the ideal observer analysis is a theoretical framework that provides a task-independent and quantitative definition of visual memory capacity and yields novel predictions regarding human performance. These predictions are subsequently evaluated and confirmed in 2 empirical studies. Further, the framework is general enough to allow the specification and testing of alternative models of visual memory (e.g., how capacity is distributed across multiple items). We demonstrate that a simple model developed on the basis of the ideal observer analysis-one that allows variability in the number of stored memory representations but does not assume the presence of a fixed item limit-provides an excellent account of the empirical data and further offers a principled reinterpretation of existing models of VWM. PsycINFO Database Record (c) 2012 APA, all rights reserved.

An Ideal Observer Analysis of Visual Working Memory

PubMed Central

Sims, Chris R.; Jacobs, Robert A.; Knill, David C.

2013-01-01

Limits in visual working memory (VWM) strongly constrain human performance across many tasks. However, the nature of these limits is not well understood. In this paper we develop an ideal observer analysis of human visual working memory, by deriving the expected behavior of an optimally performing, but limited-capacity memory system. This analysis is framed around rate–distortion theory, a branch of information theory that provides optimal bounds on the accuracy of information transmission subject to a fixed information capacity. The result of the ideal observer analysis is a theoretical framework that provides a task-independent and quantitative definition of visual memory capacity and yields novel predictions regarding human performance. These predictions are subsequently evaluated and confirmed in two empirical studies. Further, the framework is general enough to allow the specification and testing of alternative models of visual memory (for example, how capacity is distributed across multiple items). We demonstrate that a simple model developed on the basis of the ideal observer analysis—one which allows variability in the number of stored memory representations, but does not assume the presence of a fixed item limit—provides an excellent account of the empirical data, and further offers a principled re-interpretation of existing models of visual working memory. PMID:22946744

Adaptive data rate capacity of meteor-burst communications

NASA Astrophysics Data System (ADS)

Larsen, J. D.; Melville, S. W.; Mawrey, R. S.

The use of adaptive data rates in the meteor-burst communications environment is investigated. Measured results obtained from a number of meteor links are presented and compared with previous theoretical predictions. The contribution of various meteor trail families to throughput capacity are also investigated. The results show that the use of adaptive data rates can significantly increase the throughput capacity of meteor-burst communication systems. The greatest rate of increase in throughput with increase in operating rate is found at low operating rates. This finding has been confirmed for a variety of links and days. Reasonable correspondence is obtained between the predicted modified overdense model and the observed results. Overdense trails, in particular two trail types within the overdense family, are shown to dominate adaptive data throughput.

Relation between aerobic capacity and walking ability in older adults with a lower-limb amputation.

PubMed

Wezenberg, Daphne; van der Woude, Lucas H; Faber, Willemijn X; de Haan, Arnold; Houdijk, Han

2013-09-01

To determine the relative aerobic load, walking speed, and walking economy of older adults with a lower-limb prosthesis, and to predict the effect of an increased aerobic capacity on their walking ability. Cross-sectional. Human motion laboratory at a rehabilitation center. Convenience sample of older adults (n=36) who underwent lower-limb amputation because of vascular deficiency or trauma and able-bodied controls (n=21). Not applicable. Peak aerobic capacity and oxygen consumption while walking were determined. The relative aerobic load and walking economy were assessed as a function of walking speed, and a data-based model was constructed to predict the effect of an increased aerobic capacity on walking ability. People with a vascular amputation walked at a substantially higher (45.2%) relative aerobic load than people with an amputation because of trauma. The preferred walking speed in both groups of amputees was slower than that of able-bodied controls and below their most economical walking speed. We predicted that a 10% increase in peak aerobic capacity could potentially result in a reduction in the relative aerobic load of 9.1%, an increase in walking speed of 17.3% and 13.9%, and an improvement in the walking economy of 6.8% and 2.9%, for people after a vascular or traumatic amputation, respectively. Current findings corroborate the notion that, especially in people with a vascular amputation, the peak aerobic capacity is an important determinant for walking ability. The data provide quantitative predictions on the effect of aerobic training; however, future research is needed to experimentally confirm these predictions. Copyright © 2013 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

Toward a computational framework for cognitive biology: Unifying approaches from cognitive neuroscience and comparative cognition

NASA Astrophysics Data System (ADS)

Fitch, W. Tecumseh

2014-09-01

Progress in understanding cognition requires a quantitative, theoretical framework, grounded in the other natural sciences and able to bridge between implementational, algorithmic and computational levels of explanation. I review recent results in neuroscience and cognitive biology that, when combined, provide key components of such an improved conceptual framework for contemporary cognitive science. Starting at the neuronal level, I first discuss the contemporary realization that single neurons are powerful tree-shaped computers, which implies a reorientation of computational models of learning and plasticity to a lower, cellular, level. I then turn to predictive systems theory (predictive coding and prediction-based learning) which provides a powerful formal framework for understanding brain function at a more global level. Although most formal models concerning predictive coding are framed in associationist terms, I argue that modern data necessitate a reinterpretation of such models in cognitive terms: as model-based predictive systems. Finally, I review the role of the theory of computation and formal language theory in the recent explosion of comparative biological research attempting to isolate and explore how different species differ in their cognitive capacities. Experiments to date strongly suggest that there is an important difference between humans and most other species, best characterized cognitively as a propensity by our species to infer tree structures from sequential data. Computationally, this capacity entails generative capacities above the regular (finite-state) level; implementationally, it requires some neural equivalent of a push-down stack. I dub this unusual human propensity "dendrophilia", and make a number of concrete suggestions about how such a system may be implemented in the human brain, about how and why it evolved, and what this implies for models of language acquisition. I conclude that, although much remains to be done, a neurally-grounded framework for theoretical cognitive science is within reach that can move beyond polarized debates and provide a more adequate theoretical future for cognitive biology.

Toward a computational framework for cognitive biology: unifying approaches from cognitive neuroscience and comparative cognition.

PubMed

Fitch, W Tecumseh

2014-09-01

Progress in understanding cognition requires a quantitative, theoretical framework, grounded in the other natural sciences and able to bridge between implementational, algorithmic and computational levels of explanation. I review recent results in neuroscience and cognitive biology that, when combined, provide key components of such an improved conceptual framework for contemporary cognitive science. Starting at the neuronal level, I first discuss the contemporary realization that single neurons are powerful tree-shaped computers, which implies a reorientation of computational models of learning and plasticity to a lower, cellular, level. I then turn to predictive systems theory (predictive coding and prediction-based learning) which provides a powerful formal framework for understanding brain function at a more global level. Although most formal models concerning predictive coding are framed in associationist terms, I argue that modern data necessitate a reinterpretation of such models in cognitive terms: as model-based predictive systems. Finally, I review the role of the theory of computation and formal language theory in the recent explosion of comparative biological research attempting to isolate and explore how different species differ in their cognitive capacities. Experiments to date strongly suggest that there is an important difference between humans and most other species, best characterized cognitively as a propensity by our species to infer tree structures from sequential data. Computationally, this capacity entails generative capacities above the regular (finite-state) level; implementationally, it requires some neural equivalent of a push-down stack. I dub this unusual human propensity "dendrophilia", and make a number of concrete suggestions about how such a system may be implemented in the human brain, about how and why it evolved, and what this implies for models of language acquisition. I conclude that, although much remains to be done, a neurally-grounded framework for theoretical cognitive science is within reach that can move beyond polarized debates and provide a more adequate theoretical future for cognitive biology. Copyright © 2014. Published by Elsevier B.V.

Supercomputers Of The Future

NASA Technical Reports Server (NTRS)

Peterson, Victor L.; Kim, John; Holst, Terry L.; Deiwert, George S.; Cooper, David M.; Watson, Andrew B.; Bailey, F. Ron

1992-01-01

Report evaluates supercomputer needs of five key disciplines: turbulence physics, aerodynamics, aerothermodynamics, chemistry, and mathematical modeling of human vision. Predicts these fields will require computer speed greater than 10(Sup 18) floating-point operations per second (FLOP's) and memory capacity greater than 10(Sup 15) words. Also, new parallel computer architectures and new structured numerical methods will make necessary speed and capacity available.

Optimal fiber design for large capacity long haul coherent transmission [Invited].

PubMed

Hasegawa, Takemi; Yamamoto, Yoshinori; Hirano, Masaaki

2017-01-23

Fiber figure of merit (FOM), derived from the GN-model theory and validated by several experiments, can predict improvement in OSNR or transmission distance using advanced fibers. We review the FOM theory and present design results of optimal fiber for large capacity long haul transmission, showing variation in design results according to system configuration.

The movement ecology and dynamics of plant communities in fragmented landscapes.

PubMed

Damschen, Ellen I; Brudvig, Lars A; Haddad, Nick M; Levey, Douglas J; Orrock, John L; Tewksbury, Joshua J

2008-12-09

A conceptual model of movement ecology has recently been advanced to explain all movement by considering the interaction of four elements: internal state, motion capacity, navigation capacities, and external factors. We modified this framework to generate predictions for species richness dynamics of fragmented plant communities and tested them in experimental landscapes across a 7-year time series. We found that two external factors, dispersal vectors and habitat features, affected species colonization and recolonization in habitat fragments and their effects varied and depended on motion capacity. Bird-dispersed species richness showed connectivity effects that reached an asymptote over time, but no edge effects, whereas wind-dispersed species richness showed steadily accumulating edge and connectivity effects, with no indication of an asymptote. Unassisted species also showed increasing differences caused by connectivity over time, whereas edges had no effect. Our limited use of proxies for movement ecology (e.g., dispersal mode as a proxy for motion capacity) resulted in moderate predictive power for communities and, in some cases, highlighted the importance of a more complete understanding of movement ecology for predicting how landscape conservation actions affect plant community dynamics.

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

Damschen, Ellen I.; Brudvig, Lars A.; Haddad, Nick M.

A conceptual model of movement ecology has recently been advanced to explain all movement by considering the interaction of four elements: internal state, motion capacity, navigation capacities,and external factors. We modified this framework togenerate predictions for species richness dynamics of fragmented plant communities and tested them in experimental landscapes across a 7-year time series. We found that two external factors, dispersal vectors and habitat features, affected species colonization and recolonization in habitat fragments and their effects varied and depended on motion capacity. Bird-dispersed species richness showed connectivity effects that reached an asymptote over time, but no edge effects, whereas wind-dispersedmore » species richness showed steadily accumulating edge and connectivity effects, with no indication of an asymptote. Unassisted species also showed increasing differences caused by connectivity over time,whereas edges had no effect. Our limited use of proxies for movement ecology (e.g., dispersal mode as a proxy for motion capacity) resulted in moderate predictive power for communities and, in some cases, highlighted the importance of a more complete understanding of movement ecology for predicting how landscape conservation actions affect plant community dynamics.« less

North American pulp & paper model (NAPAP)

Treesearch

Peter J. Ince; Joseph Buongiorno

2007-01-01

This chapter describes the development and structure of the NAPAP model and compares it to other forest sector models. The NAPAP model was based on PELPS and adapted to describe paper and paperboard product demand, pulpwood and recovered paper supply, and production capacity and technology, with spatially dynamic market equilibria. We describe how the model predicts...

Time-Series Approaches for Forecasting the Number of Hospital Daily Discharged Inpatients.

PubMed

Ting Zhu; Li Luo; Xinli Zhang; Yingkang Shi; Wenwu Shen

2017-03-01

For hospitals where decisions regarding acceptable rates of elective admissions are made in advance based on expected available bed capacity and emergency requests, accurate predictions of inpatient bed capacity are especially useful for capacity reservation purposes. As given, the remaining unoccupied beds at the end of each day, bed capacity of the next day can be obtained by examining the forecasts of the number of discharged patients during the next day. The features of fluctuations in daily discharges like trend, seasonal cycles, special-day effects, and autocorrelation complicate decision optimizing, while time-series models can capture these features well. This research compares three models: a model combining seasonal regression and ARIMA, a multiplicative seasonal ARIMA (MSARIMA) model, and a combinatorial model based on MSARIMA and weighted Markov Chain models in generating forecasts of daily discharges. The models are applied to three years of discharge data of an entire hospital. Several performance measures like the direction of the symmetry value, normalized mean squared error, and mean absolute percentage error are utilized to capture the under- and overprediction in model selection. The findings indicate that daily discharges can be forecast by using the proposed models. A number of important practical implications are discussed, such as the use of accurate forecasts in discharge planning, admission scheduling, and capacity reservation.

Projected impacts of climate change on environmental suitability for malaria transmission in West Africa.

PubMed

Yamana, Teresa K; Eltahir, Elfatih A B

2013-10-01

Climate change is expected to affect the distribution of environmental suitability for malaria transmission by altering temperature and rainfall patterns; however, the local and global impacts of climate change on malaria transmission are uncertain. We assessed the effect of climate change on malaria transmission in West Africa. We coupled a detailed mechanistic hydrology and entomology model with climate projections from general circulation models (GCMs) to predict changes in vectorial capacity, an indication of the risk of human malaria infections, resulting from changes in the availability of mosquito breeding sites and temperature-dependent development rates. Because there is strong disagreement in climate predictions from different GCMs, we focused on the GCM projections that produced the best and worst conditions for malaria transmission in each zone of the study area. Simulation-based estimates suggest that in the desert fringes of the Sahara, vectorial capacity would increase under the worst-case scenario, but not enough to sustain transmission. In the transitional zone of the Sahel, climate change is predicted to decrease vectorial capacity. In the wetter regions to the south, our estimates suggest an increase in vectorial capacity under all scenarios. However, because malaria is already highly endemic among human populations in these regions, we expect that changes in malaria incidence would be small. Our findings highlight the importance of rainfall in shaping the impact of climate change on malaria transmission in future climates. Even under the GCM predictions most conducive to malaria transmission, we do not expect to see a significant increase in malaria prevalence in this region.

A multi-objective framework to predict flows of ungauged rivers within regions of sparse hydrometeorologic observation

NASA Astrophysics Data System (ADS)

Alipour, M.; Kibler, K. M.

2017-12-01

Despite advances in flow prediction, managers of ungauged rivers located within broad regions of sparse hydrometeorologic observation still lack prescriptive methods robust to the data challenges of such regions. We propose a multi-objective streamflow prediction framework for regions of minimum observation to select models that balance runoff efficiency with choice of accurate parameter values. We supplement sparse observed data with uncertain or low-resolution information incorporated as `soft' a priori parameter estimates. The performance of the proposed framework is tested against traditional single-objective and constrained single-objective calibrations in two catchments in a remote area of southwestern China. We find that the multi-objective approach performs well with respect to runoff efficiency in both catchments (NSE = 0.74 and 0.72), within the range of efficiencies returned by other models (NSE = 0.67 - 0.78). However, soil moisture capacity estimated by the multi-objective model resonates with a priori estimates (parameter residuals of 61 cm versus 289 and 518 cm for maximum soil moisture capacity in one catchment, and 20 cm versus 246 and 475 cm in the other; parameter residuals of 0.48 versus 0.65 and 0.7 for soil moisture distribution shape factor in one catchment, and 0.91 versus 0.79 and 1.24 in the other). Thus, optimization to a multi-criteria objective function led to very different representations of soil moisture capacity as compared to models selected by single-objective calibration, without compromising runoff efficiency. These different soil moisture representations may translate into considerably different hydrological behaviors. The proposed approach thus offers a preliminary step towards greater process understanding in regions of severe data limitations. For instance, the multi-objective framework may be an adept tool to discern between models of similar efficiency to select models that provide the "right answers for the right reasons". Managers may feel more confident to utilize such models to predict flows in fully ungauged areas.

Working memory capacity and the spacing effect in cued recall.

PubMed

Delaney, Peter F; Godbole, Namrata R; Holden, Latasha R; Chang, Yoojin

2018-07-01

Spacing repetitions typically improves memory (the spacing effect). In three cued recall experiments, we explored the relationship between working memory capacity and the spacing effect. People with higher working memory capacity are more accurate on memory tasks that require retrieval relative to people with lower working memory capacity. The experiments used different retention intervals and lags between repetitions, but were otherwise similar. Working memory capacity and spacing of repetitions both improved memory in most of conditions, but they did not interact, suggesting additive effects. The results are consistent with the ACT-R model's predictions, and with a study-phase recognition process underpinning the spacing effect in cued recall.

Determination of quantitative retention-activity relationships between pharmacokinetic parameters and biological effectiveness fingerprints of Salvia miltiorrhiza constituents using biopartitioning and microemulsion high-performance liquid chromatography.

PubMed

Gao, Haoshi; Huang, Hongzhang; Zheng, Aini; Yu, Nuojun; Li, Ning

2017-11-01

In this study, we analyzed danshen (Salvia miltiorrhiza) constituents using biopartitioning and microemulsion high-performance liquid chromatography (MELC). The quantitative retention-activity relationships (QRARs) of the constituents were established to model their pharmacokinetic (PK) parameters and chromatographic retention data, and generate their biological effectiveness fingerprints. A high-performance liquid chromatography (HPLC) method was established to determine the abundance of the extracted danshen constituents, such as sodium danshensu, rosmarinic acid, salvianolic acid B, protocatechuic aldehyde, cryptotanshinone, and tanshinone IIA. And another HPLC protocol was established to determine the abundance of those constituents in rat plasma samples. An experimental model was built in Sprague Dawley (SD) rats, and calculated the corresponding PK parameterst with 3P97 software package. Thirty-five model drugs were selected to test the PK parameter prediction capacities of the various MELC systems and to optimize the chromatographic protocols. QRARs and generated PK fingerprints were established. The test included water/oil-soluble danshen constituents and the prediction capacity of the regression model was validated. The results showed that the model had good predictability. Copyright © 2017. Published by Elsevier B.V.

Neurocognitive predictors of financial capacity in traumatic brain injury.

PubMed

Martin, Roy C; Triebel, Kristen; Dreer, Laura E; Novack, Thomas A; Turner, Crystal; Marson, Daniel C

2012-01-01

To develop cognitive models of financial capacity (FC) in patients with traumatic brain injury (TBI). Longitudinal design. Inpatient brain injury rehabilitation unit. Twenty healthy controls, and 24 adults with moderate-to-severe TBI were assessed at baseline (30 days postinjury) and 6 months postinjury. The FC instrument (FCI) and a neuropsychological test battery. Univariate correlation and multiple regression procedures were employed to develop cognitive models of FCI performance in the TBI group, at baseline and 6-month time follow-up. Three cognitive predictor models of FC were developed. At baseline, measures of mental arithmetic/working memory and immediate verbal memory predicted baseline FCI performance (R = 0.72). At 6-month follow-up, measures of executive function and mental arithmetic/working memory predicted 6-month FCI performance (R = 0.79), and a third model found that these 2 measures at baseline predicted 6-month FCI performance (R = 0.71). Multiple cognitive functions are associated with initial impairment and partial recovery of FC in moderate-to-severe TBI patients. In particular, arithmetic, working memory, and executive function skills appear critical to recovery of FC in TBI. The study results represent an initial step toward developing a neurocognitive model of FC in patients with TBI.

PREDICTING THE ADSORPTION CAPACITY OF ACTIVATED CARBON FOR EMERGING ORGANIC CONTAMINANTS FROM FUNDAMENTAL ADSORBENT AND ADSORBATE PROPERTIES - PRESENTATION

EPA Science Inventory

A quantitative structure-property relationship (QSPR) was developed and combined with the Polanyi-Dubinin-Manes model to predict adsorption isotherms of emerging contaminants on activated carbons with a wide range of physico-chemical properties. Affinity coefficients (β_l

The role of postural control in the association between aerobic capacity and walking capacity in chronic stroke: a cross-sectional analysis.

PubMed

Outermans, Jacqueline C; van de Port, Ingrid; Kwakkel, Gert; Visser-Meily, Johanna M; Wittink, Harriet

2018-03-12

Reports on the association between aerobic capacity and walking capacity in people after stroke show disparate results. To determine (1) if the predictive validity of peak oxygen uptake (VO2peak) for walking capacity post stroke is different from that of maximal oxygen uptake (VO2max) and (2) if postural control, hemiplegic lower extremity muscle strength, age and gender distort the association between aerobic capacity and walking capacity. Cross-sectional study. General community in Utrecht, the Netherlands. Community-dwelling people more than three months after stroke. Measurement of aerobic capacity were performed with cardiopulmonary exercise testing (CPET) and differentiated between the achievement of VO2peak or VO2max. Measurement of walking capacity with the Six Minute Walk Test (6MWT), postural control with the Performance Oriented Mobility Assessment (POMA) and hemiplegic lower extremity muscle strength with the Motricity Index (MI-LE). Fifty-one out of 62 eligible participants, aged 64.7 (±12.5) years were included. Analysis of covariance (ANCOVA) showed a nonsignificant difference between the predictive validities of VO2max (N = 22, β = 0.56; 95%CI 0.12 - 0.97) and VO2peak (N = 29, β = 0.72; 95%CI 0.38 - 0.92). Multiple regression analysis of the pooled sample showed a significant decrease in the β value of VO2peak (21.6%) for the 6MWT when adding the POMA as a covariate in the association model. VO2peak remained significantly related to 6MWT after correcting for the POMA (β = 0.56 (95%CI 0.39 - 0.75)) CONCLUSIONS: The results suggest similar predictive validity of aerobic capacity for walking capacity in participants achieving VO2max compared to those only achieving VO2peak. Postural control confounds the association between aerobic capacity and walking capacity. Aerobic capacity remains a valid predictor of walking capacity. Aerobic capacity is an important factor associated with walking capacity after stroke. However, to understand this relationship, postural control needs to be measured. Both aerobic capacity and postural control may need to be addressed during interventions aiming to improve walking capacity after stroke.

Estimation of groundwater recharge to chalk and sandstone aquifers using simple soil models

NASA Astrophysics Data System (ADS)

Ragab, R.; Finch, J.; Harding, R.

1997-03-01

On the assumption that the water draining below the root zone is potentially available for groundwater recharge, two current UK methods for estimating annual groundwater recharge have been compared with a new soil model using data from four sites under permanent grass in the UK: two sites representative of the Chalk aquifer at Bridgest Farm (Hampshire) and Fleam Dyke (Cambridgeshire), and two sites on the Triassic sandstone at Bicton College (Devon) and Bacon Hall (Shropshire). A Four Root Layers Model (FRLM), the Penman-Grindley model and the UK Meteorological Office Rainfall and Evaporation Calculation System (MORECS) were used. The new soil model was run with potential evaporation as input both from the MORECS and from the Penman-Monteith equation. The models were run for the Chalk sites both with and without a bypass flow of 15% of rainfall. Bypass was not considered for the sandstone sites. The performance of the models was tested against neutron probes measurements of soil moisture deficits. In addition, the annual groundwater recharge estimated from the models was compared with the published values obtained from the 'zero flux plane' method. Generally, the Penman-Grindley model was more successful in predicting the time for soil to return to its field capacity than in predicting the magnitude of the soil moisture deficit. The annual groundwater recharge was predicted with reasonable accuracy. The MORECS relatively tended to overestimate the soil moisture deficits and to delay the time at which the soil returns to its field capacity. The consequences were underestimates of annual groundwater recharge, owing either to the higher values of potential evaporation calculated from the MORECS or tothe high available water capacity values associated with the soils under consideration. The new soil model (FRLM) predicts the soil moisture deficits successfully and hence is reliable in estimating the annual groundwater recharge. The model is capable of doing this with potential evaporation input calculated either from the MORECS or from the Penman-Monteith equation. The model also demonstrated that the inclusion of 15% of rainfall as bypass flow is viable for Chalk sites.

Validation assessment of risk tools to predict outcome after thrombolytic therapy for acute ischemic stroke.

PubMed

Van Hooff, Robbert-Jan; Nieboer, Koenraad; De Smedt, Ann; Moens, Maarten; De Deyn, Peter Paul; De Keyser, Jacques; Brouns, Raf

2014-10-01

We evaluated the reliability of eight clinical prediction models for symptomatic intracerebral hemorrhage (sICH) and long-term functional outcome in stroke patients treated with thrombolytics according to clinical practice. In a cohort of 169 patients, 60 patients (35.5%) received IV rtPA according to the European license criteria. The remaining patients received off-label IV rtPA and/or were treated with intra-arterial thrombolysis. We used receiver operator characteristic curves to analyze the discriminative capacity of the MSS score, the HAT score, the SITS SICH score, the SEDAN score and the GRASPS score for sICH according to the NINDS and the ECASSII criteria. Similarly, the discriminative capacity of the s-TPI, the iScore and the DRAGON score were assessed for the modified Rankin Scale (mRS) score at 3 months poststroke. An area under the curve (c-statistic) >0.8 was considered to reflect good discriminative capacity. The reliability of the best performing prediction model was further examined with calibration curves. Separate analyses were performed for patients meeting the European license criteria for IV rtPA and patients outside these criteria. For prediction of sICH c-statistics were 0.66-0.86 and the MMS yielded the best results. For functional outcome c-statistics ranged from 0.72 to 0.86 with s-TPI as best performer. The s-TPI had the lowest absolute error on the calibration curve for predicting excellent outcome (mRS 0-1) and catastrophic outcome (mRS 5-6). All eight clinical models for outcome prediction after thrombolysis for acute ischemic stroke showed fair predictive value in patients treated according daily practice. The s-TPI had the best discriminatory ability and was well calibrated in our study population. Copyright © 2014 Elsevier B.V. All rights reserved.

A Global Model for Bankruptcy Prediction

PubMed Central

Alaminos, David; del Castillo, Agustín; Fernández, Manuel Ángel

2016-01-01

The recent world financial crisis has increased the number of bankruptcies in numerous countries and has resulted in a new area of research which responds to the need to predict this phenomenon, not only at the level of individual countries, but also at a global level, offering explanations of the common characteristics shared by the affected companies. Nevertheless, few studies focus on the prediction of bankruptcies globally. In order to compensate for this lack of empirical literature, this study has used a methodological framework of logistic regression to construct predictive bankruptcy models for Asia, Europe and America, and other global models for the whole world. The objective is to construct a global model with a high capacity for predicting bankruptcy in any region of the world. The results obtained have allowed us to confirm the superiority of the global model in comparison to regional models over periods of up to three years prior to bankruptcy. PMID:27880810

Prioritizing CD4 Count Monitoring in Response to ART in Resource-Constrained Settings: A Retrospective Application of Prediction-Based Classification

PubMed Central

Liu, Yan; Li, Xiaohong; Johnson, Margaret; Smith, Collette; Kamarulzaman, Adeeba bte; Montaner, Julio; Mounzer, Karam; Saag, Michael; Cahn, Pedro; Cesar, Carina; Krolewiecki, Alejandro; Sanne, Ian; Montaner, Luis J.

2012-01-01

Background Global programs of anti-HIV treatment depend on sustained laboratory capacity to assess treatment initiation thresholds and treatment response over time. Currently, there is no valid alternative to CD4 count testing for monitoring immunologic responses to treatment, but laboratory cost and capacity limit access to CD4 testing in resource-constrained settings. Thus, methods to prioritize patients for CD4 count testing could improve treatment monitoring by optimizing resource allocation. Methods and Findings Using a prospective cohort of HIV-infected patients (n = 1,956) monitored upon antiretroviral therapy initiation in seven clinical sites with distinct geographical and socio-economic settings, we retrospectively apply a novel prediction-based classification (PBC) modeling method. The model uses repeatedly measured biomarkers (white blood cell count and lymphocyte percent) to predict CD4+ T cell outcome through first-stage modeling and subsequent classification based on clinically relevant thresholds (CD4+ T cell count of 200 or 350 cells/µl). The algorithm correctly classified 90% (cross-validation estimate = 91.5%, standard deviation [SD] = 4.5%) of CD4 count measurements <200 cells/µl in the first year of follow-up; if laboratory testing is applied only to patients predicted to be below the 200-cells/µl threshold, we estimate a potential savings of 54.3% (SD = 4.2%) in CD4 testing capacity. A capacity savings of 34% (SD = 3.9%) is predicted using a CD4 threshold of 350 cells/µl. Similar results were obtained over the 3 y of follow-up available (n = 619). Limitations include a need for future economic healthcare outcome analysis, a need for assessment of extensibility beyond the 3-y observation time, and the need to assign a false positive threshold. Conclusions Our results support the use of PBC modeling as a triage point at the laboratory, lessening the need for laboratory-based CD4+ T cell count testing; implementation of this tool could help optimize the use of laboratory resources, directing CD4 testing towards higher-risk patients. However, further prospective studies and economic analyses are needed to demonstrate that the PBC model can be effectively applied in clinical settings. Please see later in the article for the Editors' Summary PMID:22529752

Breast cancer screening services: trade-offs in quality, capacity, outreach, and centralization.

PubMed

Güneş, Evrim D; Chick, Stephen E; Akşin, O Zeynep

2004-11-01

This work combines and extends previous work on breast cancer screening models by explicitly incorporating, for the first time, aspects of the dynamics of health care states, program outreach, and the screening volume-quality relationship in a service system model to examine the effect of public health policy and service capacity decisions on public health outcomes. We consider the impact of increasing standards for minimum reading volume to improve quality, expanding outreach with or without decentralization of service facilities, and the potential of queueing due to stochastic effects and limited capacity. The results indicate a strong relation between screening quality and the cost of screening and treatment, and emphasize the importance of accounting for service dynamics when assessing the performance of health care interventions. For breast cancer screening, increasing outreach without improving quality and maintaining capacity results in less benefit than predicted by standard models.

A motor unit-based model of muscle fatigue

PubMed Central

2017-01-01

Muscle fatigue is a temporary decline in the force and power capacity of skeletal muscle resulting from muscle activity. Because control of muscle is realized at the level of the motor unit (MU), it seems important to consider the physiological properties of motor units when attempting to understand and predict muscle fatigue. Therefore, we developed a phenomenological model of motor unit fatigue as a tractable means to predict muscle fatigue for a variety of tasks and to illustrate the individual contractile responses of MUs whose collective action determines the trajectory of changes in muscle force capacity during prolonged activity. An existing MU population model was used to simulate MU firing rates and isometric muscle forces and, to that model, we added fatigue-related changes in MU force, contraction time, and firing rate associated with sustained voluntary contractions. The model accurately estimated endurance times for sustained isometric contractions across a wide range of target levels. In addition, simulations were run for situations that have little experimental precedent to demonstrate the potential utility of the model to predict motor unit fatigue for more complicated, real-world applications. Moreover, the model provided insight into the complex orchestration of MU force contributions during fatigue, that would be unattainable with current experimental approaches. PMID:28574981

The Cosmetics Europe strategy for animal-free genotoxicity testing: project status up-date.

PubMed

Pfuhler, S; Fautz, R; Ouedraogo, G; Latil, A; Kenny, J; Moore, C; Diembeck, W; Hewitt, N J; Reisinger, K; Barroso, J

2014-02-01

The Cosmetics Europe (formerly COLIPA) Genotoxicity Task Force has driven and funded three projects to help address the high rate of misleading positives in in vitro genotoxicity tests: The completed "False Positives" project optimized current mammalian cell assays and showed that the predictive capacity of the in vitro micronucleus assay was improved dramatically by selecting more relevant cells and more sensitive toxicity measures. The on-going "3D skin model" project has been developed and is now validating the use of human reconstructed skin (RS) models in combination with the micronucleus (MN) and Comet assays. These models better reflect the in use conditions of dermally applied products, such as cosmetics. Both assays have demonstrated good inter- and intra-laboratory reproducibility and are entering validation stages. The completed "Metabolism" project investigated enzyme capacities of human skin and RS models. The RS models were shown to have comparable metabolic capacity to native human skin, confirming their usefulness for testing of compounds with dermal exposure. The program has already helped to improve the initial test battery predictivity and the RS projects have provided sound support for their use as a follow-up test in the assessment of the genotoxic hazard of cosmetic ingredients in the absence of in vivo data. Copyright © 2013 Elsevier Ltd. All rights reserved.

Estimation of performance of a J-T refrigerators operating with nitrogen-hydrocarbon mixtures and a coiled tubes-in-tube heat exchanger

NASA Astrophysics Data System (ADS)

Satya Meher, R.; Venkatarathnam, G.

2018-06-01

The exergy efficiency of Joule-Thomson (J-T) refrigerators operating with mixtures (MRC systems) strongly depends on the choice of refrigerant mixture and the performance of the heat exchanger used. Helically coiled, multiple tubes-in-tube heat exchangers with an effectiveness of over 96% are widely used in these types of systems. All the current studies focus only on the different heat transfer correlations and the uncertainty in predicting performance of the heat exchanger alone. The main focus of this work is to estimate the uncertainty in cooling capacity when the homogenous model is used by comparing the theoretical and experimental studies. The comparisons have been extended to some two-phase models present in the literature as well. Experiments have been carried out on a J-T refrigerator at a fixed heat load of 10 W with different nitrogen-hydrocarbon mixtures in the evaporator temperature range of 100-120 K. Different heat transfer models have been used to predict the temperature profiles as well as the cooling capacity of the refrigerator. The results show that the homogenous two-phase flow model is probably the most suitable model for rating the cooling capacity of a J-T refrigerator operating with nitrogen-hydrocarbon mixtures.

Virtual milk for modelling and simulation of dairy processes.

PubMed

Munir, M T; Zhang, Y; Yu, W; Wilson, D I; Young, B R

2016-05-01

The modeling of dairy processing using a generic process simulator suffers from shortcomings, given that many simulators do not contain milk components in their component libraries. Recently, pseudo-milk components for a commercial process simulator were proposed for simulation and the current work extends this pseudo-milk concept by studying the effect of both total milk solids and temperature on key physical properties such as thermal conductivity, density, viscosity, and heat capacity. This paper also uses expanded fluid and power law models to predict milk viscosity over the temperature range from 4 to 75°C and develops a succinct regressed model for heat capacity as a function of temperature and fat composition. The pseudo-milk was validated by comparing the simulated and actual values of the physical properties of milk. The milk thermal conductivity, density, viscosity, and heat capacity showed differences of less than 2, 4, 3, and 1.5%, respectively, between the simulated results and actual values. This work extends the capabilities of the previously proposed pseudo-milk and of a process simulator to model dairy processes, processing different types of milk (e.g., whole milk, skim milk, and concentrated milk) with different intrinsic compositions, and to predict correct material and energy balances for dairy processes. Copyright © 2016 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Evapotranspiration from drained wetlands: drivers, modeling, storage functions, and restoration implications

NASA Astrophysics Data System (ADS)

Shukla, S.; Wu, C. L.; Shrestha, N.

2017-12-01

Abstract Evapotranspiration (ET) is a major component of wetland and watershed water budgets. The effect of wetland drainage on ET is not well understood. We tested whether the current understanding of insignificant effect of drainage on ET in the temperate region wetlands applies to those in the sub-tropics. Eddy covariance (EC) based ET measurements were made for two years at two previously drained and geographically close wetlands in the Everglades region of Florida. One wetland was significantly drained with 97% of its storage capacity lost. The other was a more functional wetland with 42% of storage capacity lost. Annual average ET at the significantly drained wetland was 836 mm, 34% less than the function wetland (1271 mm) and the difference was statistically significant (p = 0.001). Such differences in wetland ET in the same climatic region have not been observed. The difference in ET was mainly due to drainage driven differences in inundation and associated effects on net radiation (Rn) and local relative humidity. Two daily ET models, a regression (r2 = 0.80) and a Relevance Vector Machine (RVM) model (r2 = 0.84), were developed with the latter being more robust. These models, when used in conjunction with hydrologic models, improved ET predictions for drained wetlands. Predictions from an integrated model showed that more intensely drained wetlands at higher elevation should be targeted for restoration of downstream flows (flooding) because they have the ability to loose higher water volume through ET which increases available water storage capacity of wetlands. Daily ET models can predict changes in ET for improved evaluation of basin-scale effects of restoration programs and climate change scenarios.

Computational Embryology and Predictive Toxicology of Cleft Palate

EPA Science Inventory

Capacity to model and simulate key events in developmental toxicity using computational systems biology and biological knowledge steps closer to hazard identification across the vast landscape of untested environmental chemicals. In this context, we chose cleft palate as a model ...

Serum Methylarginines and Spirometry-Measured Lung Function in Older Adults

PubMed Central

McEvoy, Mark A.; Schofield, Peter W.; Smith, Wayne T.; Agho, Kingsley; Mangoni, Arduino A.; Soiza, Roy L.; Peel, Roseanne; Hancock, Stephen J.; Carru, Ciriaco; Zinellu, Angelo; Attia, John R.

2013-01-01

Rationale Methylarginines are endogenous nitric oxide synthase inhibitors that have been implicated in animal models of lung disease but have not previously been examined for their association with spirometric measures of lung function in humans. Objectives This study measured serum concentrations of asymmetric and symmetric dimethylarginine in a representative sample of older community-dwelling adults and determined their association with spirometric lung function measures. Methods Data on clinical, lifestyle, and demographic characteristics, methylated arginines, and L-arginine (measured using LC-MS/MS) were collected from a population-based sample of older Australian adults from the Hunter Community Study. The five key lung function measures included as outcomes were Forced Expiratory Volume in 1 second, Forced Vital Capacity, Forced Expiratory Volume in 1 second to Forced Vital Capacity ratio, Percent Predicted Forced Expiratory Volume in 1 second, and Percent Predicted Forced Vital Capacity. Measurements and Main Results In adjusted analyses there were statistically significant independent associations between a) higher asymmetric dimethylarginine, lower Forced Expiratory Volume in 1 second and lower Forced Vital Capacity; and b) lower L-arginine/asymmetric dimethylarginine ratio, lower Forced Expiratory Volume in 1 second, lower Percent Predicted Forced Expiratory Volume in 1 second and lower Percent Predicted Forced Vital Capacity. By contrast, no significant associations were observed between symmetric dimethylarginine and lung function. Conclusions After adjusting for clinical, demographic, biochemical, and pharmacological confounders, higher serum asymmetric dimethylarginine was independently associated with a reduction in key measures of lung function. Further research is needed to determine if methylarginines predict the decline in lung function. PMID:23690915

Bringing free will down to Earth: people's psychological concept of free will and its role in moral judgment.

PubMed

Monroe, Andrew E; Dillon, Kyle D; Malle, Bertram F

2014-07-01

Belief in free will is widespread, and this belief is supposed to undergird moral and legal judgment. Despite the importance of the free will concept, however, there remains widespread confusion regarding its definition and its connection to blame. We address this confusion by testing two prominent models of the folk concept of free will-a metaphysical model, in which free will involves a soul as an uncaused "first mover," and a psychological model, in which free will involves choice, alignment with desires, and lack of constraints. We test the predictions of these two models by creating agents that vary in their capacity for choice and the presence of a soul. In two studies, people's judgments of free will and blame for these agents show little to no basis in ascriptions of a soul but are powerfully predicted by ascriptions of choice capacity. These results support a psychological model of the folk concept of free will. Copyright © 2014 Elsevier Inc. All rights reserved.

Life Prediction Model for Grid-Connected Li-ion Battery Energy Storage System

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

Smith, Kandler A; Saxon, Aron R; Keyser, Matthew A

Lithium-ion (Li-ion) batteries are being deployed on the electrical grid for a variety of purposes, such as to smooth fluctuations in solar renewable power generation. The lifetime of these batteries will vary depending on their thermal environment and how they are charged and discharged. To optimal utilization of a battery over its lifetime requires characterization of its performance degradation under different storage and cycling conditions. Aging tests were conducted on commercial graphite/nickel-manganese-cobalt (NMC) Li-ion cells. A general lifetime prognostic model framework is applied to model changes in capacity and resistance as the battery degrades. Across 9 aging test conditions frommore » 0oC to 55oC, the model predicts capacity fade with 1.4% RMS error and resistance growth with 15% RMS error. The model, recast in state variable form with 8 states representing separate fade mechanisms, is used to extrapolate lifetime for example applications of the energy storage system integrated with renewable photovoltaic (PV) power generation.« less

Development of a predictive model for lead, cadmium and fluorine soil-water partition coefficients using sparse multiple linear regression analysis.

PubMed

Nakamura, Kengo; Yasutaka, Tetsuo; Kuwatani, Tatsu; Komai, Takeshi

2017-11-01

In this study, we applied sparse multiple linear regression (SMLR) analysis to clarify the relationships between soil properties and adsorption characteristics for a range of soils across Japan and identify easily-obtained physical and chemical soil properties that could be used to predict K and n values of cadmium, lead and fluorine. A model was first constructed that can easily predict the K and n values from nine soil parameters (pH, cation exchange capacity, specific surface area, total carbon, soil organic matter from loss on ignition and water holding capacity, the ratio of sand, silt and clay). The K and n values of cadmium, lead and fluorine of 17 soil samples were used to verify the SMLR models by the root mean square error values obtained from 512 combinations of soil parameters. The SMLR analysis indicated that fluorine adsorption to soil may be associated with organic matter, whereas cadmium or lead adsorption to soil is more likely to be influenced by soil pH, IL. We found that an accurate K value can be predicted from more than three soil parameters for most soils. Approximately 65% of the predicted values were between 33 and 300% of their measured values for the K value; 76% of the predicted values were within ±30% of their measured values for the n value. Our findings suggest that adsorption properties of lead, cadmium and fluorine to soil can be predicted from the soil physical and chemical properties using the presented models. Copyright © 2017 Elsevier Ltd. All rights reserved.

Sustainable Army Training Lands/Carrying Capacity: Training Use Distribution Model (TUDM)

DTIC Science & Technology

2002-05-01

management (Senft, Rittenhouse, and Woodmansee 1983; Van Manen and Pelton 1997). The underlying principle behind this type of modeling application...Texas Rangeland. Technical Manuscript EN-95/02 (USACERL February 1995). tr o Van Manen , F.T. and M.R. Pelton. 1997. “A GIS Model to Predict Black

The Mediating Role of Mind Wandering in the Relationship between Working Memory Capacity and Reading Comprehension

ERIC Educational Resources Information Center

McVay, Jennifer C.

2010-01-01

The primary goal of this study was to investigate the mediating role of mind wandering in the relationship between working memory capacity (WMC) and reading comprehension as predicted by the executive-attention theory of WMC (e.g., Kane & Engle, 2003). I used a latent-variable, structural-equation-model approach with three WMC span tasks, seven…

Photosynthetic capacity regulation is uncoupled from nutrient limitation

NASA Astrophysics Data System (ADS)

Smith, N. G.; Keenan, T. F.; Prentice, I. C.; Wang, H.

2017-12-01

Ecosystem and Earth system models need information on leaf-level photosynthetic capacity, but to date typically rely on empirical estimates and an assumed dependence on nitrogen supply. Recent evidence suggests that leaf nitrogen is actively controlled though plant responses to photosynthetic demand. Here, we propose and test a theory of demand-driven coordination of photosynthetic processes, and use it to assess the relative roles of nutrient supply and photosynthetic demand. The theory captured 63% of observed variability in a global dataset of Rubisco carboxylation capacity (Vcmax; 3,939 values at 219 sites), suggesting that environmentally regulated biophysical costs and light availability are the first-order drivers of photosynthetic capacity. Leaf nitrogen, on the other hand, was a weak secondary driver of Vcmax, explaining less than 6% of additional observed variability. We conclude that leaf nutrient allocation is primarily driven by demand. Our theory offers a simple, robust strategy for dynamically predicting leaf-level photosynthetic capacity in global models.

Economically sustainable scaling of photovoltaics to meet climate targets

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

Needleman, David Berney; Poindexter, Jeremy R.; Kurchin, Rachel C.

To meet climate targets, power generation capacity from photovoltaics (PV) in 2030 will have to be much greater than is predicted from either steady state growth using today's manufacturing capacity or industry roadmaps. Analysis of whether current technology can scale, in an economically sustainable way, to sufficient levels to meet these targets has not yet been undertaken, nor have tools to perform this analysis been presented. Here, we use bottom-up cost modeling to predict cumulative capacity as a function of technological and economic variables. We find that today's technology falls short in two ways: profits are too small relative tomore » upfront factory costs to grow manufacturing capacity rapidly enough to meet climate targets, and costs are too high to generate enough demand to meet climate targets. We show that decreasing the capital intensity (capex) of PV manufacturing to increase manufacturing capacity and effectively reducing cost (e.g., through higher efficiency) to increase demand are the most effective and least risky ways to address these barriers to scale. We also assess the effects of variations in demand due to hard-to-predict factors, like public policy, on the necessary reductions in cost.Lastly, we review examples of redundant technology pathways for crystalline silicon PV to achieve the necessary innovations in capex, performance, and price.« less

Economically sustainable scaling of photovoltaics to meet climate targets

DOE PAGES

Needleman, David Berney; Poindexter, Jeremy R.; Kurchin, Rachel C.; ...

2016-04-21

To meet climate targets, power generation capacity from photovoltaics (PV) in 2030 will have to be much greater than is predicted from either steady state growth using today's manufacturing capacity or industry roadmaps. Analysis of whether current technology can scale, in an economically sustainable way, to sufficient levels to meet these targets has not yet been undertaken, nor have tools to perform this analysis been presented. Here, we use bottom-up cost modeling to predict cumulative capacity as a function of technological and economic variables. We find that today's technology falls short in two ways: profits are too small relative tomore » upfront factory costs to grow manufacturing capacity rapidly enough to meet climate targets, and costs are too high to generate enough demand to meet climate targets. We show that decreasing the capital intensity (capex) of PV manufacturing to increase manufacturing capacity and effectively reducing cost (e.g., through higher efficiency) to increase demand are the most effective and least risky ways to address these barriers to scale. We also assess the effects of variations in demand due to hard-to-predict factors, like public policy, on the necessary reductions in cost.Lastly, we review examples of redundant technology pathways for crystalline silicon PV to achieve the necessary innovations in capex, performance, and price.« less

Copper bioavailability and toxicity to Mytilus galloprovincialis in Shelter Island Yacht Basin, San Diego, CA.

PubMed

Bosse, Casey; Rosen, Gunther; Colvin, Marienne; Earley, Patrick; Santore, Robert; Rivera-Duarte, Ignacio

2014-08-15

The bioavailability and toxicity of copper (Cu) in Shelter Island Yacht Basin (SIYB), San Diego, CA, USA, was assessed with simultaneous toxicological, chemical, and modeling approaches. Toxicological measurements included laboratory toxicity testing with Mytilus galloprovincialis (Mediterranean mussel) embryos added to both site water (ambient) and site water spiked with multiple Cu concentrations. Chemical assessment of ambient samples included total and dissolved Cu concentrations, and Cu complexation capacity measurements. Modeling was based on chemical speciation and predictions of bioavailability and toxicity using a marine Biotic Ligand Model (BLM). Cumulatively, these methods assessed the natural buffering capacity of Cu in SIYB during singular wet and dry season sampling events. Overall, the three approaches suggested negligible bioavailability, and isolated observed or predicted toxicity, despite an observed gradient of increasing Cu concentration, both horizontally and vertically within the water body, exceeding current water quality criteria for saltwater. Published by Elsevier Ltd.

Mechanisms of in vivo muscle fatigue in humans: investigating age‐related fatigue resistance with a computational model

PubMed Central

Callahan, Damien M.; Umberger, Brian R.

2016-01-01

Key points Muscle fatigue can be defined as the transient decrease in maximal force that occurs in response to muscle use. Fatigue develops because of a complex set of changes within the neuromuscular system that are difficult to evaluate simultaneously in humans.The skeletal muscle of older adults fatigues less than that of young adults during static contractions. The potential sources of this difference are multiple and intertwined.To evaluate the individual mechanisms of fatigue, we developed an integrative computational model based on neural, biochemical, morphological and physiological properties of human skeletal muscle.Our results indicate first that the model provides accurate predictions of fatigue and second that the age‐related resistance to fatigue is due largely to a lower reliance on glycolytic metabolism during contraction.This model should prove useful for generating hypotheses for future experimental studies into the mechanisms of muscle fatigue. Abstract During repeated or sustained muscle activation, force‐generating capacity becomes limited in a process referred to as fatigue. Multiple factors, including motor unit activation patterns, muscle fibre contractile properties and bioenergetic function, can impact force‐generating capacity and thus the potential to resist fatigue. Given that neuromuscular fatigue depends on interrelated factors, quantifying their independent effects on force‐generating capacity is not possible in vivo. Computational models can provide insight into complex systems in which multiple inputs determine discrete outputs. However, few computational models to date have investigated neuromuscular fatigue by incorporating the multiple levels of neuromuscular function known to impact human in vivo function. To address this limitation, we present a computational model that predicts neural activation, biomechanical forces, intracellular metabolic perturbations and, ultimately, fatigue during repeated isometric contractions. This model was compared with metabolic and contractile responses to repeated activation using values reported in the literature. Once validated in this way, the model was modified to reflect age‐related changes in neuromuscular function. Comparisons between initial and age‐modified simulations indicated that the age‐modified model predicted less fatigue during repeated isometric contractions, consistent with reports in the literature. Together, our simulations suggest that reduced glycolytic flux is the greatest contributor to the phenomenon of age‐related fatigue resistance. In contrast, oxidative resynthesis of phosphocreatine between intermittent contractions and inherent buffering capacity had minimal impact on predicted fatigue during isometric contractions. The insights gained from these simulations cannot be achieved through traditional in vivo or in vitro experimentation alone. PMID:26824934

Computational Model of Secondary Palate Fusion and Disruption

EPA Science Inventory

Morphogenetic events are driven by cell-generated physical forces and complex cellular dynamics. To improve our capacity to predict developmental effects from cellular alterations, we built a multi-cellular agent-based model in CompuCell3D that recapitulates the cellular networks...

The role of residence time in diagnostic models of global carbon storage capacity: model decomposition based on a traceable scheme.

PubMed

Yizhao, Chen; Jianyang, Xia; Zhengguo, Sun; Jianlong, Li; Yiqi, Luo; Chengcheng, Gang; Zhaoqi, Wang

2015-11-06

As a key factor that determines carbon storage capacity, residence time (τE) is not well constrained in terrestrial biosphere models. This factor is recognized as an important source of model uncertainty. In this study, to understand how τE influences terrestrial carbon storage prediction in diagnostic models, we introduced a model decomposition scheme in the Boreal Ecosystem Productivity Simulator (BEPS) and then compared it with a prognostic model. The result showed that τE ranged from 32.7 to 158.2 years. The baseline residence time (τ'E) was stable for each biome, ranging from 12 to 53.7 years for forest biomes and 4.2 to 5.3 years for non-forest biomes. The spatiotemporal variations in τE were mainly determined by the environmental scalar (ξ). By comparing models, we found that the BEPS uses a more detailed pool construction but rougher parameterization for carbon allocation and decomposition. With respect to ξ comparison, the global difference in the temperature scalar (ξt) averaged 0.045, whereas the moisture scalar (ξw) had a much larger variation, with an average of 0.312. We propose that further evaluations and improvements in τ'E and ξw predictions are essential to reduce the uncertainties in predicting carbon storage by the BEPS and similar diagnostic models.

The role of residence time in diagnostic models of global carbon storage capacity: model decomposition based on a traceable scheme

PubMed Central

Yizhao, Chen; Jianyang, Xia; Zhengguo, Sun; Jianlong, Li; Yiqi, Luo; Chengcheng, Gang; Zhaoqi, Wang

2015-01-01

As a key factor that determines carbon storage capacity, residence time (τE) is not well constrained in terrestrial biosphere models. This factor is recognized as an important source of model uncertainty. In this study, to understand how τE influences terrestrial carbon storage prediction in diagnostic models, we introduced a model decomposition scheme in the Boreal Ecosystem Productivity Simulator (BEPS) and then compared it with a prognostic model. The result showed that τE ranged from 32.7 to 158.2 years. The baseline residence time (τ′E) was stable for each biome, ranging from 12 to 53.7 years for forest biomes and 4.2 to 5.3 years for non-forest biomes. The spatiotemporal variations in τE were mainly determined by the environmental scalar (ξ). By comparing models, we found that the BEPS uses a more detailed pool construction but rougher parameterization for carbon allocation and decomposition. With respect to ξ comparison, the global difference in the temperature scalar (ξt) averaged 0.045, whereas the moisture scalar (ξw) had a much larger variation, with an average of 0.312. We propose that further evaluations and improvements in τ′E and ξw predictions are essential to reduce the uncertainties in predicting carbon storage by the BEPS and similar diagnostic models. PMID:26541245

Semi-empirical long-term cycle life model coupled with an electrolyte depletion function for large-format graphite/LiFePO4 lithium-ion batteries

NASA Astrophysics Data System (ADS)

Park, Joonam; Appiah, Williams Agyei; Byun, Seoungwoo; Jin, Dahee; Ryou, Myung-Hyun; Lee, Yong Min

2017-10-01

To overcome the limitation of simple empirical cycle life models based on only equivalent circuits, we attempt to couple a conventional empirical capacity loss model with Newman's porous composite electrode model, which contains both electrochemical reaction kinetics and material/charge balances. In addition, an electrolyte depletion function is newly introduced to simulate a sudden capacity drop at the end of cycling, which is frequently observed in real lithium-ion batteries (LIBs). When simulated electrochemical properties are compared with experimental data obtained with 20 Ah-level graphite/LiFePO4 LIB cells, our semi-empirical model is sufficiently accurate to predict a voltage profile having a low standard deviation of 0.0035 V, even at 5C. Additionally, our model can provide broad cycle life color maps under different c-rate and depth-of-discharge operating conditions. Thus, this semi-empirical model with an electrolyte depletion function will be a promising platform to predict long-term cycle lives of large-format LIB cells under various operating conditions.

Effect of surface hydroxyl groups on heat capacity of mesoporous silica

NASA Astrophysics Data System (ADS)

Marszewski, Michal; Butts, Danielle; Lan, Esther; Yan, Yan; King, Sophia C.; McNeil, Patricia E.; Galy, Tiphaine; Dunn, Bruce; Tolbert, Sarah H.; Hu, Yongjie; Pilon, Laurent

2018-05-01

This paper quantifies the effect of surface hydroxyl groups on the effective specific and volumetric heat capacities of mesoporous silica. To achieve a wide range of structural diversity, mesoporous silica samples were synthesized by various methods, including (i) polymer-templated nanoparticle-based powders, (ii) polymer-templated sol-gel powders, and (iii) ambigel silica samples dried by solvent exchange at room temperature. Their effective specific heat capacity, specific surface area, and porosity were measured using differential scanning calorimetry and low-temperature nitrogen adsorption-desorption measurements. The experimentally measured specific heat capacity was larger than the conventional weight-fraction-weighted specific heat capacity of the air and silica constituents. The difference was attributed to the presence of OH groups in the large internal surface area. A thermodynamic model was developed based on surface energy considerations to account for the effect of surface OH groups on the specific and volumetric heat capacity. The model predictions fell within the experimental uncertainty.

The use of laboratory-determined ion exchange parameters in the predictive modelling of field-scale major cation migration in groundwater over a 40-year period.

PubMed

Carlyle, Harriet F; Tellam, John H; Parker, Karen E

2004-01-01

An attempt has been made to estimate quantitatively cation concentration changes as estuary water invades a Triassic Sandstone aquifer in northwest England. Cation exchange capacities and selectivity coefficients for Na(+), K(+), Ca(2+), and Mg(2+) were measured in the laboratory using standard techniques. Selectivity coefficients were also determined using a method involving optimized back-calculation from flushing experiments, thus permitting better representation of field conditions; in all cases, the Gaines-Thomas/constant cation exchange capacity (CEC) model was found to be a reasonable, though not perfect, first description. The exchange parameters interpreted from the laboratory experiments were used in a one-dimensional reactive transport mixing cell model, and predictions compared with field pumping well data (Cl and hardness spanning a period of around 40 years, and full major ion analyses in approximately 1980). The concentration patterns predicted using Gaines-Thomas exchange with calcite equilibrium were similar to the observed patterns, but the concentrations of the divalent ions were significantly overestimated, as were 1980 sulphate concentrations, and 1980 alkalinity concentrations were underestimated. Including representation of sulphate reduction in the estuarine alluvium failed to replicate 1980 HCO(3) and pH values. However, by including partial CO(2) degassing following sulphate reduction, a process for which there is 34S and 18O evidence from a previous study, a good match for SO(4), HCO(3), and pH was attained. Using this modified estuary water and averaged values from the laboratory ion exchange parameter determinations, good predictions for the field cation data were obtained. It is concluded that the Gaines-Thomas/constant exchange capacity model with averaged parameter values can be used successfully in ion exchange predictions in this aquifer at a regional scale and over extended time scales, despite the numerous assumptions inherent in the approach; this has also been found to be the case in the few other published studies of regional ion exchanging flow.

The use of laboratory-determined ion exchange parameters in the predictive modelling of field-scale major cation migration in groundwater over a 40-year period

NASA Astrophysics Data System (ADS)

Carlyle, Harriet F.; Tellam, John H.; Parker, Karen E.

2004-01-01

An attempt has been made to estimate quantitatively cation concentration changes as estuary water invades a Triassic Sandstone aquifer in northwest England. Cation exchange capacities and selectivity coefficients for Na +, K +, Ca 2+, and Mg 2+ were measured in the laboratory using standard techniques. Selectivity coefficients were also determined using a method involving optimized back-calculation from flushing experiments, thus permitting better representation of field conditions; in all cases, the Gaines-Thomas/constant cation exchange capacity (CEC) model was found to be a reasonable, though not perfect, first description. The exchange parameters interpreted from the laboratory experiments were used in a one-dimensional reactive transport mixing cell model, and predictions compared with field pumping well data (Cl and hardness spanning a period of around 40 years, and full major ion analyses in ˜1980). The concentration patterns predicted using Gaines-Thomas exchange with calcite equilibrium were similar to the observed patterns, but the concentrations of the divalent ions were significantly overestimated, as were 1980 sulphate concentrations, and 1980 alkalinity concentrations were underestimated. Including representation of sulphate reduction in the estuarine alluvium failed to replicate 1980 HCO 3 and pH values. However, by including partial CO 2 degassing following sulphate reduction, a process for which there is 34S and 18O evidence from a previous study, a good match for SO 4, HCO 3, and pH was attained. Using this modified estuary water and averaged values from the laboratory ion exchange parameter determinations, good predictions for the field cation data were obtained. It is concluded that the Gaines-Thomas/constant exchange capacity model with averaged parameter values can be used successfully in ion exchange predictions in this aquifer at a regional scale and over extended time scales, despite the numerous assumptions inherent in the approach; this has also been found to be the case in the few other published studies of regional ion exchanging flow.

Mitigating effects of vaccination on influenza outbreaks given constraints in stockpile size and daily administration capacity

PubMed Central

2011-01-01

Background Influenza viruses are a major cause of morbidity and mortality worldwide. Vaccination remains a powerful tool for preventing or mitigating influenza outbreaks. Yet, vaccine supplies and daily administration capacities are limited, even in developed countries. Understanding how such constraints can alter the mitigating effects of vaccination is a crucial part of influenza preparedness plans. Mathematical models provide tools for government and medical officials to assess the impact of different vaccination strategies and plan accordingly. However, many existing models of vaccination employ several questionable assumptions, including a rate of vaccination proportional to the population at each point in time. Methods We present a SIR-like model that explicitly takes into account vaccine supply and the number of vaccines administered per day and places data-informed limits on these parameters. We refer to this as the non-proportional model of vaccination and compare it to the proportional scheme typically found in the literature. Results The proportional and non-proportional models behave similarly for a few different vaccination scenarios. However, there are parameter regimes involving the vaccination campaign duration and daily supply limit for which the non-proportional model predicts smaller epidemics that peak later, but may last longer, than those of the proportional model. We also use the non-proportional model to predict the mitigating effects of variably timed vaccination campaigns for different levels of vaccination coverage, using specific constraints on daily administration capacity. Conclusions The non-proportional model of vaccination is a theoretical improvement that provides more accurate predictions of the mitigating effects of vaccination on influenza outbreaks than the proportional model. In addition, parameters such as vaccine supply and daily administration limit can be easily adjusted to simulate conditions in developed and developing nations with a wide variety of financial and medical resources. Finally, the model can be used by government and medical officials to create customized pandemic preparedness plans based on the supply and administration constraints of specific communities. PMID:21806800

Mathematical Modeling of Intestinal Iron Absorption Using Genetic Programming

PubMed Central

Colins, Andrea; Gerdtzen, Ziomara P.; Nuñez, Marco T.; Salgado, J. Cristian

2017-01-01

Iron is a trace metal, key for the development of living organisms. Its absorption process is complex and highly regulated at the transcriptional, translational and systemic levels. Recently, the internalization of the DMT1 transporter has been proposed as an additional regulatory mechanism at the intestinal level, associated to the mucosal block phenomenon. The short-term effect of iron exposure in apical uptake and initial absorption rates was studied in Caco-2 cells at different apical iron concentrations, using both an experimental approach and a mathematical modeling framework. This is the first report of short-term studies for this system. A non-linear behavior in the apical uptake dynamics was observed, which does not follow the classic saturation dynamics of traditional biochemical models. We propose a method for developing mathematical models for complex systems, based on a genetic programming algorithm. The algorithm is aimed at obtaining models with a high predictive capacity, and considers an additional parameter fitting stage and an additional Jackknife stage for estimating the generalization error. We developed a model for the iron uptake system with a higher predictive capacity than classic biochemical models. This was observed both with the apical uptake dataset used for generating the model and with an independent initial rates dataset used to test the predictive capacity of the model. The model obtained is a function of time and the initial apical iron concentration, with a linear component that captures the global tendency of the system, and a non-linear component that can be associated to the movement of DMT1 transporters. The model presented in this paper allows the detailed analysis, interpretation of experimental data, and identification of key relevant components for this complex biological process. This general method holds great potential for application to the elucidation of biological mechanisms and their key components in other complex systems. PMID:28072870

Modeling of urban solid waste management system: The case of Dhaka city

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

Sufian, M.A.; Bala, B.K.

2007-07-01

This paper presents a system dynamics computer model to predict solid waste generation, collection capacity and electricity generation from solid waste and to assess the needs for waste management of the urban city of Dhaka, Bangladesh. Simulated results show that solid waste generation, collection capacity and electricity generation potential from solid waste increase with time. Population, uncleared waste, untreated waste, composite index and public concern are projected to increase with time for Dhaka city. Simulated results also show that increasing the budget for collection capacity alone does not improve environmental quality; rather an increased budget is required for both collectionmore » and treatment of solid wastes of Dhaka city. Finally, this model can be used as a computer laboratory for urban solid waste management (USWM) policy analysis.« less

The Role of Multimodel Combination in Improving Streamflow Prediction

NASA Astrophysics Data System (ADS)

Arumugam, S.; Li, W.

2008-12-01

Model errors are the inevitable part in any prediction exercise. One approach that is currently gaining attention to reduce model errors is by optimally combining multiple models to develop improved predictions. The rationale behind this approach primarily lies on the premise that optimal weights could be derived for each model so that the developed multimodel predictions will result in improved predictability. In this study, we present a new approach to combine multiple hydrological models by evaluating their predictability contingent on the predictor state. We combine two hydrological models, 'abcd' model and Variable Infiltration Capacity (VIC) model, with each model's parameter being estimated by two different objective functions to develop multimodel streamflow predictions. The performance of multimodel predictions is compared with individual model predictions using correlation, root mean square error and Nash-Sutcliffe coefficient. To quantify precisely under what conditions the multimodel predictions result in improved predictions, we evaluate the proposed algorithm by testing it against streamflow generated from a known model ('abcd' model or VIC model) with errors being homoscedastic or heteroscedastic. Results from the study show that streamflow simulated from individual models performed better than multimodels under almost no model error. Under increased model error, the multimodel consistently performed better than the single model prediction in terms of all performance measures. The study also evaluates the proposed algorithm for streamflow predictions in two humid river basins from NC as well as in two arid basins from Arizona. Through detailed validation in these four sites, the study shows that multimodel approach better predicts the observed streamflow in comparison to the single model predictions.

Prediction of battery storage ageing and solid electrolyte interphase property estimation using an electrochemical model

NASA Astrophysics Data System (ADS)

Ashwin, T. R.; Barai, A.; Uddin, K.; Somerville, L.; McGordon, A.; Marco, J.

2018-05-01

Ageing prediction is often complicated due to the interdependency of ageing mechanisms. Research has highlighted that storage ageing is not linear with time. Capacity loss due to storing the battery at constant temperature can shed more light on parametrising the properties of the Solid Electrolyte Interphase (SEI); the identification of which, using an electrochemical model, is systematically addressed in this work. A new methodology is proposed where any one of the available storage ageing datasets can be used to find the property of the SEI layer. A sensitivity study is performed with different molecular mass and densities which are key parameters in modelling the thickness of the SEI deposit. The conductivity is adjusted to fine tune the rate of capacity fade to match experimental results. A correlation is fitted for the side reaction variation to capture the storage ageing in the 0%-100% SoC range. The methodology presented in this paper can be used to predict the unknown properties of the SEI layer which is difficult to measure experimentally. The simulation and experimental results show that the storage ageing model shows good accuracy for the cases at 50% and 90% and an acceptable agreement at 20% SoC.

Establishing and evaluating the key functions of an interactive systems framework using an assets-getting to outcomes intervention.

PubMed

Chinman, Matthew; Acosta, Joie; Ebener, Patricia; Q Burkhart; Clifford, Michael; Corsello, Maryann; Duffey, Tim; Hunter, Sarah; Jones, Margaret; Lahti, Michel; Malone, Patrick S; Paddock, Susan; Phillips, Andrea; Savell, Susan; Scales, Peter C; Tellett-Royce, Nancy

2012-12-01

Community practitioners can face difficulty in achieving outcomes demonstrated by prevention science. Building a community practitioner's prevention capacity-the knowledge and skills needed to conduct critical prevention practices-could improve the quality of prevention and its outcomes. The purpose of this article is to: (1) describe how an intervention called Assets-Getting To Outcomes (AGTO) was used to establish the key functions of the ISF and present early lessons learned from that intervention's first 6 months and (2) examine whether there is an empirical relationship between practitioner capacity at the individual level and the performance of prevention at the program level-a relationship predicted by the ISF but untested. The article describes an operationalization of the ISF in the context of a five-year randomized controlled efficacy trial that combines two complementary models designed to build capacity: Getting To Outcomes (GTO) and Developmental Assets. The trial compares programs and individual practitioners from six community-based coalitions using AGTO with programs and practitioners from six similar coalitions that are not. In this article, we primarily focus on what the ISF calls innovation specific capacity and discuss how the combined AGTO innovation structures and uses feedback about its capacity-building activities, which can serve as a model for implementing the ISF. Focus group discussions used to gather lessons learned from the first 6 months of the AGTO intervention suggest that while the ISF may have been conceptualized as three distinct systems, in practice they are less distinct. Findings from the baseline wave of data collection of individual capacity and program performance suggest that practitioner capacity predicts, in part, performance of prevention programs. Empirically linking practitioner capacity and performance of prevention provides empirical support for both the ISF and AGTO.

Inhibitory effect on the uptake and diffusion of Cd(2+) onto soybean hull sorbent in Cd-Pb binary sorption systems.

PubMed

Módenes, Aparecido N; Espinoza-Quiñones, Fernando R; Colombo, Andréia; Geraldi, Claudinéia L; Trigueros, Daniela E G

2015-05-01

The uptake of Cd(2+) and Pb(2+) ions by a soybean hull (SH) biosorbent in single and binary systems has been investigated. Sorption tests regarding SH in natura and chemically treated were carried out testing a suitable value range of solution pH, sorption temperature and shaking velocity. Sorption capacity is improved at pH 4, 30 °C temperature and 100 rpm. When a strong base is applied, a related-to-untreated SH increasing of 20% in the sorption capacity of Pb(2+) ions was observed, but with poor results for Cd(2+) uptake. Additionally, a relatively strong decreasing in both sorption capacities of Pb(2+) and Cd(2+) ions was evidenced for all acidic treatments. Regarding untreated SH, kinetic sorption data of both metals were well-interpreted by a pseudo second-order model and a rate-limiting step on the basis of an intra-particle diffusion model was suggested to occur. An inhibitory effect of Pb(2+) diffusion over Cd(2+) one was observed, limiting to reach the obtained maximum sorption capacity in single system. Maximum adsorption capacities of 0.49 and 0.67mequivg(-1) for Cd(2+) and Pb(2+), respectively, were predicted by the Langmuir isotherm model that reproduced well the equilibrium sorption data for single systems. The inhibitory effect of one metal over the other one was verified in equilibrium sorption data for binary systems interpreted on the basis of a modified extended Langmuir isotherm model, predicting changes in metal affinity onto the SH surface. Finally, SH is an alternative biosorbent with a great potential for the wastewater treatment containing cadmium and lead ions. Copyright © 2015 Elsevier Ltd. All rights reserved.

A redefinition of the representation of mammary cells and enzyme activities in a lactating dairy cow model.

PubMed

Hanigan, M D; Rius, A G; Kolver, E S; Palliser, C C

2007-08-01

The Molly model predicts various aspects of digestion and metabolism in the cow, including nutrient partitioning between milk and body stores. It has been observed previously that the model underpredicts milk component yield responses to nutrition and consequently overpredicts body energy store responses. In Molly, mammary enzyme activity is represented as an aggregate of mammary cell numbers and activity per cell with minimal endocrine regulation. Work by others suggests that mammary cells can cycle between active and quiescent states in response to various stimuli. Simple models of milk production have demonstrated the utility of this representation when using the model to simulate variable milking and nutrient restriction. It was hypothesized that replacing the current representation of mammary cells and enzyme activity in Molly with a representation of active and quiescent cells and improving the representation of endocrine control of cell activity would improve predictions of milk component yield. The static representation of cell numbers was replaced with a representation of cell growth during gestation and early lactation periods and first-order cell death. Enzyme capacity for fat and protein synthesis was assumed to be proportional to cell numbers. Enzyme capacity for lactose synthesis was represented with the same equation form as for cell numbers. Data used for parameter estimation were collected as part of an extended lactation trial. Cows with North American or New Zealand genotypes were fed 0, 3, or 6 kg of concentrate dry matter daily during a 600-d lactation. The original model had root mean square prediction errors of 17.7, 22.3, and 19.8% for lactose, protein, and fat yield, respectively, as compared with values of 8.3, 9.4, and 11.7% for the revised model, respectively. The original model predicted body weight with an error of 19.7% vs. 5.7% for the revised model. Based on these observations, it was concluded that representing mammary synthetic capacity as a function of active cell numbers and revisions to endocrine control of cell activity was meritorious.

Sediment transport through self-adjusting, bedrock-walled waterfall plunge pools

NASA Astrophysics Data System (ADS)

Scheingross, Joel S.; Lamb, Michael P.

2016-05-01

Many waterfalls have deep plunge pools that are often partially or fully filled with sediment. Sediment fill may control plunge-pool bedrock erosion rates, partially determine habitat availability for aquatic organisms, and affect sediment routing and debris flow initiation. Currently, there exists no mechanistic model to describe sediment transport through waterfall plunge pools. Here we develop an analytical model to predict steady-state plunge-pool depth and sediment-transport capacity by combining existing jet theory with sediment transport mechanics. Our model predicts plunge-pool sediment-transport capacity increases with increasing river discharge, flow velocity, and waterfall drop height and decreases with increasing plunge-pool depth, radius, and grain size. We tested the model using flume experiments under varying waterfall and plunge-pool geometries, flow hydraulics, and sediment size. The model and experiments show that through morphodynamic feedbacks, plunge pools aggrade to reach shallower equilibrium pool depths in response to increases in imposed sediment supply. Our theory for steady-state pool depth matches the experiments with an R2 value of 0.8, with discrepancies likely due to model simplifications of the hydraulics and sediment transport. Analysis of 75 waterfalls suggests that the water depths in natural plunge pools are strongly influenced by upstream sediment supply, and our model provides a mass-conserving framework to predict sediment and water storage in waterfall plunge pools for sediment routing, habitat assessment, and bedrock erosion modeling.

Gaussian process regression for forecasting battery state of health

NASA Astrophysics Data System (ADS)

Richardson, Robert R.; Osborne, Michael A.; Howey, David A.

2017-07-01

Accurately predicting the future capacity and remaining useful life of batteries is necessary to ensure reliable system operation and to minimise maintenance costs. The complex nature of battery degradation has meant that mechanistic modelling of capacity fade has thus far remained intractable; however, with the advent of cloud-connected devices, data from cells in various applications is becoming increasingly available, and the feasibility of data-driven methods for battery prognostics is increasing. Here we propose Gaussian process (GP) regression for forecasting battery state of health, and highlight various advantages of GPs over other data-driven and mechanistic approaches. GPs are a type of Bayesian non-parametric method, and hence can model complex systems whilst handling uncertainty in a principled manner. Prior information can be exploited by GPs in a variety of ways: explicit mean functions can be used if the functional form of the underlying degradation model is available, and multiple-output GPs can effectively exploit correlations between data from different cells. We demonstrate the predictive capability of GPs for short-term and long-term (remaining useful life) forecasting on a selection of capacity vs. cycle datasets from lithium-ion cells.

EVALUATING REGIONAL PREDICTIVE CAPACITY OF A PROCESS-BASED MERCURY EXPOSURE MODEL, REGIONAL-MERCURY CYCLING MODEL (R-MCM), APPLIED TO 91 VERMONT AND NEW HAMPSHIRE LAKES AND PONDS, USA

EPA Science Inventory

Regulatory agencies must develop fish consumption advisories for many lakes and rivers with limited resources. Process-based mathematical models are potentially valuable tools for developing regional fish advisories. The Regional Mercury Cycling model (R-MCM) was specifically d...

Variations of leaf longevity in tropical moist forests predicted by a trait-driven carbon optimality model

DOE PAGES

Xu, Xiangtao; Medvigy, David; Wright, Stuart Joseph; ...

2017-07-04

Leaf longevity (LL) varies more than 20-fold in tropical evergreen forests, but it remains unclear how to capture these variations using predictive models. Current theories of LL that are based on carbon optimisation principles are challenging to quantitatively assess because of uncertainty across species in the ‘ageing rate:’ the rate at which leaf photosynthetic capacity declines with age. Here in this paper, we present a meta-analysis of 49 species across temperate and tropical biomes, demonstrating that the ageing rate of photosynthetic capacity is positively correlated with the mass-based carboxylation rate of mature leaves. We assess an improved trait-driven carbon optimalitymore » model with in situLL data for 105 species in two Panamanian forests. Additionally, we show that our model explains over 40% of the cross-species variation in LL under contrasting light environment. Collectively, our results reveal how variation in LL emerges from carbon optimisation constrained by both leaf structural traits and abiotic environment.« less

Variations of leaf longevity in tropical moist forests predicted by a trait-driven carbon optimality model

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

Xu, Xiangtao; Medvigy, David; Wright, Stuart Joseph

Leaf longevity (LL) varies more than 20-fold in tropical evergreen forests, but it remains unclear how to capture these variations using predictive models. Current theories of LL that are based on carbon optimisation principles are challenging to quantitatively assess because of uncertainty across species in the ‘ageing rate:’ the rate at which leaf photosynthetic capacity declines with age. Here in this paper, we present a meta-analysis of 49 species across temperate and tropical biomes, demonstrating that the ageing rate of photosynthetic capacity is positively correlated with the mass-based carboxylation rate of mature leaves. We assess an improved trait-driven carbon optimalitymore » model with in situLL data for 105 species in two Panamanian forests. Additionally, we show that our model explains over 40% of the cross-species variation in LL under contrasting light environment. Collectively, our results reveal how variation in LL emerges from carbon optimisation constrained by both leaf structural traits and abiotic environment.« less

Prediction of the partitioning behaviour of proteins in aqueous two-phase systems using only their amino acid composition.

PubMed

Salgado, J Cristian; Andrews, Barbara A; Ortuzar, Maria Fernanda; Asenjo, Juan A

2008-01-18

The prediction of the partition behaviour of proteins in aqueous two-phase systems (ATPS) using mathematical models based on their amino acid composition was investigated. The predictive models are based on the average surface hydrophobicity (ASH). The ASH was estimated by means of models that use the three-dimensional structure of proteins and by models that use only the amino acid composition of proteins. These models were evaluated for a set of 11 proteins with known experimental partition coefficient in four-phase systems: polyethylene glycol (PEG) 4000/phosphate, sulfate, citrate and dextran and considering three levels of NaCl concentration (0.0% w/w, 0.6% w/w and 8.8% w/w). The results indicate that such prediction is feasible even though the quality of the prediction depends strongly on the ATPS and its operational conditions such as the NaCl concentration. The ATPS 0 model which use the three-dimensional structure obtains similar results to those given by previous models based on variables measured in the laboratory. In addition it maintains the main characteristics of the hydrophobic resolution and intrinsic hydrophobicity reported before. Three mathematical models, ATPS I-III, based only on the amino acid composition were evaluated. The best results were obtained by the ATPS I model which assumes that all of the amino acids are completely exposed. The performance of the ATPS I model follows the behaviour reported previously, i.e. its correlation coefficients improve as the NaCl concentration increases in the system and, therefore, the effect of the protein hydrophobicity prevails over other effects such as charge or size. Its best predictive performance was obtained for the PEG/dextran system at high NaCl concentration. An increase in the predictive capacity of at least 54.4% with respect to the models which use the three-dimensional structure of the protein was obtained for that system. In addition, the ATPS I model exhibits high correlation coefficients in that system being higher than 0.88 on average. The ATPS I model exhibited correlation coefficients higher than 0.67 for the rest of the ATPS at high NaCl concentration. Finally, we tested our best model, the ATPS I model, on the prediction of the partition coefficient of the protein invertase. We found that the predictive capacities of the ATPS I model are better in PEG/dextran systems, where the relative error of the prediction with respect to the experimental value is 15.6%.

The movement ecology and dynamics of plant communities in fragmented landscapes

DOE PAGES

Damschen, Ellen I.; Brudvig, Lars A.; Haddad, Nick M.; ...

2008-12-05

A conceptual model of movement ecology has recently been advanced to explain all movement by considering the interaction of four elements: internal state, motion capacity, navigation capacities,and external factors. We modified this framework togenerate predictions for species richness dynamics of fragmented plant communities and tested them in experimental landscapes across a 7-year time series. We found that two external factors, dispersal vectors and habitat features, affected species colonization and recolonization in habitat fragments and their effects varied and depended on motion capacity. Bird-dispersed species richness showed connectivity effects that reached an asymptote over time, but no edge effects, whereas wind-dispersedmore » species richness showed steadily accumulating edge and connectivity effects, with no indication of an asymptote. Unassisted species also showed increasing differences caused by connectivity over time,whereas edges had no effect. Our limited use of proxies for movement ecology (e.g., dispersal mode as a proxy for motion capacity) resulted in moderate predictive power for communities and, in some cases, highlighted the importance of a more complete understanding of movement ecology for predicting how landscape conservation actions affect plant community dynamics.« less

Use of the HepaRG Cell Line to Assess Potential Human Hepatotoxicity of ToxCast™ Chemicals

EPA Science Inventory

The HepaRG cell line is a promising model system for predicting human hepatotoxicity in part because of the greater capacity to metabolize chemicals than other cell models. We hypothesized that this cell line would be a relevant model for toxicity testing of industrial chemicals....

Preventing patient absenteeism: validation of a predictive overbooking model.

PubMed

Reid, Mark W; Cohen, Samuel; Wang, Hank; Kaung, Aung; Patel, Anish; Tashjian, Vartan; Williams, Demetrius L; Martinez, Bibiana; Spiegel, Brennan M R

2015-12-01

To develop a model that identifies patients at high risk for missing scheduled appointments ("no-shows" and cancellations) and to project the impact of predictive overbooking in a gastrointestinal endoscopy clinic-an exemplar resource-intensive environment with a high no-show rate. We retrospectively developed an algorithm that uses electronic health record (EHR) data to identify patients who do not show up to their appointments. Next, we prospectively validated the algorithm at a Veterans Administration healthcare network clinic. We constructed a multivariable logistic regression model that assigned a no-show risk score optimized by receiver operating characteristic curve analysis. Based on these scores, we created a calendar of projected open slots to offer to patients and compared the daily performance of predictive overbooking with fixed overbooking and typical "1 patient, 1 slot" scheduling. Data from 1392 patients identified several predictors of no-show, including previous absenteeism, comorbid disease burden, and current diagnoses of mood and substance use disorders. The model correctly classified most patients during the development (area under the curve [AUC] = 0.80) and validation phases (AUC = 0.75). Prospective testing in 1197 patients found that predictive overbooking averaged 0.51 unused appointments per day versus 6.18 for typical booking (difference = -5.67; 95% CI, -6.48 to -4.87; P < .0001). Predictive overbooking could have increased service utilization from 62% to 97% of capacity, with only rare clinic overflows. Information from EHRs can accurately predict whether patients will no-show. This method can be used to overbook appointments, thereby maximizing service utilization while staying within clinic capacity.

An information capacity limitation of visual short-term memory.

PubMed

Sewell, David K; Lilburn, Simon D; Smith, Philip L

2014-12-01

Research suggests that visual short-term memory (VSTM) has both an item capacity, of around 4 items, and an information capacity. We characterize the information capacity limits of VSTM using a task in which observers discriminated the orientation of a single probed item in displays consisting of 1, 2, 3, or 4 orthogonally oriented Gabor patch stimuli that were presented in noise for 50 ms, 100 ms, 150 ms, or 200 ms. The observed capacity limitations are well described by a sample-size model, which predicts invariance of ∑(i)(d'(i))² for displays of different sizes and linearity of (d'(i))² for displays of different durations. Performance was the same for simultaneous and sequentially presented displays, which implicates VSTM as the locus of the observed invariance and rules out explanations that ascribe it to divided attention or stimulus encoding. The invariance of ∑(i)(d'(i))² is predicted by the competitive interaction theory of Smith and Sewell (2013), which attributes it to the normalization of VSTM traces strengths arising from competition among stimuli entering VSTM. PsycINFO Database Record (c) 2014 APA, all rights reserved.

The ability of in vitro antioxidant assays to predict the efficiency of a cod protein hydrolysate and brown seaweed extract to prevent oxidation in marine food model systems.

PubMed

Jónsdóttir, Rósa; Geirsdóttir, Margrét; Hamaguchi, Patricia Y; Jamnik, Polona; Kristinsson, Hordur G; Undeland, Ingrid

2016-04-01

The ability of different in vitro antioxidant assays to predict the efficiency of cod protein hydrolysate (CPH) and Fucus vesiculosus ethyl acetate extract (EA) towards lipid oxidation in haemoglobin-fortified washed cod mince and iron-containing cod liver oil emulsion was evaluated. The progression of oxidation was followed by sensory analysis, lipid hydroperoxides and thiobarbituric acid-reactive substances (TBARS) in both systems, as well as loss of redness and protein carbonyls in the cod system. The in vitro tests revealed high reducing capacity, high DPPH radical scavenging properties and a high oxygen radical absorbance capacity (ORAC) value of the EA which also inhibited lipid and protein oxidation in the cod model system. The CPH had a high metal chelating capacity and was efficient against oxidation in the cod liver oil emulsion. The results indicate that the F. vesiculosus extract has a potential as an excellent natural antioxidant against lipid oxidation in fish muscle foods while protein hydrolysates are more promising for fish oil emulsions. The usefulness of in vitro assays to predict the antioxidative properties of new natural ingredients in foods thus depends on the knowledge about the food systems, particularly the main pro-oxidants present. © 2015 Society of Chemical Industry.

Specific heat capacity of molten salt-based alumina nanofluid.

PubMed

Lu, Ming-Chang; Huang, Chien-Hsun

2013-06-21

There is no consensus on the effect of nanoparticle (NP) addition on the specific heat capacity (SHC) of fluids. In addition, the predictions from the existing model have a large discrepancy from the measured SHCs in nanofluids. We show that the SHC of the molten salt-based alumina nanofluid decreases with reducing particle size and increasing particle concentration. The NP size-dependent SHC is resulted from an augmentation of the nanolayer effect as particle size reduces. A model considering the nanolayer effect which supports the experimental results was proposed.

Specific heat capacity of molten salt-based alumina nanofluid

PubMed Central

2013-01-01

There is no consensus on the effect of nanoparticle (NP) addition on the specific heat capacity (SHC) of fluids. In addition, the predictions from the existing model have a large discrepancy from the measured SHCs in nanofluids. We show that the SHC of the molten salt-based alumina nanofluid decreases with reducing particle size and increasing particle concentration. The NP size-dependent SHC is resulted from an augmentation of the nanolayer effect as particle size reduces. A model considering the nanolayer effect which supports the experimental results was proposed. PMID:23800321

A Capacity Forecast Model for Volatile Data in Maintenance Logistics

NASA Astrophysics Data System (ADS)

Berkholz, Daniel

2009-05-01

Maintenance, repair and overhaul processes (MRO processes) are elaborate and complex. Rising demands on these after sales services require reliable production planning and control methods particularly for maintaining valuable capital goods. Downtimes lead to high costs and an inability to meet delivery due dates results in severe contract penalties. Predicting the required capacities for maintenance orders in advance is often difficult due to unknown part conditions unless the goods are actually inspected. This planning uncertainty results in extensive capital tie-up by rising stock levels within the whole MRO network. The article outlines an approach to planning capacities when maintenance data forecasting is volatile. It focuses on the development of prerequisites for a reliable capacity planning model. This enables a quick response to maintenance orders by employing appropriate measures. The information gained through the model is then systematically applied to forecast both personnel capacities and the demand for spare parts. The improved planning reliability can support MRO service providers in shortening delivery times and reducing stock levels in order to enhance the performance of their maintenance logistics.

Modeling Stationary Lithium-Ion Batteries for Optimization and Predictive Control

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

Baker, Kyri A; Shi, Ying; Christensen, Dane T

Accurately modeling stationary battery storage behavior is crucial to understand and predict its limitations in demand-side management scenarios. In this paper, a lithium-ion battery model was derived to estimate lifetime and state-of-charge for building-integrated use cases. The proposed battery model aims to balance speed and accuracy when modeling battery behavior for real-time predictive control and optimization. In order to achieve these goals, a mixed modeling approach was taken, which incorporates regression fits to experimental data and an equivalent circuit to model battery behavior. A comparison of the proposed battery model output to actual data from the manufacturer validates the modelingmore » approach taken in the paper. Additionally, a dynamic test case demonstrates the effects of using regression models to represent internal resistance and capacity fading.« less

Modeling Stationary Lithium-Ion Batteries for Optimization and Predictive Control: Preprint

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

Raszmann, Emma; Baker, Kyri; Shi, Ying

Accurately modeling stationary battery storage behavior is crucial to understand and predict its limitations in demand-side management scenarios. In this paper, a lithium-ion battery model was derived to estimate lifetime and state-of-charge for building-integrated use cases. The proposed battery model aims to balance speed and accuracy when modeling battery behavior for real-time predictive control and optimization. In order to achieve these goals, a mixed modeling approach was taken, which incorporates regression fits to experimental data and an equivalent circuit to model battery behavior. A comparison of the proposed battery model output to actual data from the manufacturer validates the modelingmore » approach taken in the paper. Additionally, a dynamic test case demonstrates the effects of using regression models to represent internal resistance and capacity fading.« less

A theoretical model to determine the capacity performance of shape-specific electrodes

NASA Astrophysics Data System (ADS)

Yue, Yuan; Liang, Hong

2018-06-01

A theory is proposed to explain and predict the electrochemical process during reaction between lithium ions and electrode materials. In the model, the process of reaction is proceeded into two steps, surface adsorption and diffusion of lithium ions. The surface adsorption is an instantaneous process for lithium ions to adsorb onto the surface sites of active materials. The diffusion of lithium ions into particles is determined by the charge-discharge condition. A formula to determine the maximum specific capacity of active materials at different charging rates (C-rates) is derived. The maximum specific capacity is correlated to characteristic parameters of materials and cycling - such as size, aspect ratio, surface area, and C-rate. Analysis indicates that larger particle size or greater aspect ratio of active materials and faster C-rates can reduce maximum specific capacity. This suggests that reducing particle size of active materials and slowing the charge-discharge speed can provide enhanced electrochemical performance of a battery cell. Furthermore, the model is validated by published experimental results. This model brings new understanding in quantification of electrochemical kinetics and capacity performance. It enables development of design strategies for novel electrodes and future generation of energy storage devices.

Economic Models for Projecting Industrial Capacity for Defense Production: A Review

DTIC Science & Technology

1983-02-01

macroeconomic forecast to establish the level of civilian final demand; all use the DoD Bridge Table to allocate budget category outlays to industries. Civilian...output table.’ 3. Macroeconomic Assumptions and the Prediction of Final Demand All input-output models require as a starting point a prediction of final... macroeconomic fore- cast of GNP and its components and (2) a methodology to transform these forecast values of consumption, investment, exports, etc. into

MODELING CST ION EXCHANGE FOR CESIUM REMOVAL FROM SCIX BATCHES 1 - 4

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

Smith, F.

2011-04-25

The objective of this work is, through modeling, to predict the performance of Crystalline Silicotitinate (CST) for the removal of cesium from Small Column Ion Exchange (SCIX) Batches 1-4 (as proposed in Revision 16 of the Liquid Waste System Plan). The scope of this task is specified in Technical Task Request (TTR) 'SCIX Feed Modeling', HLE-TTR-2011-003, which specified using the Zheng, Anthony, Miller (ZAM) code to predict CST isotherms for six given SCIX feed compositions and the VErsatile Reaction and SEparation simulator for Liquid Chromatography (VERSE-LC) code to predict ion-exchange column behavior. The six SCIX feed compositions provided in themore » TTR represent SCIX Batches 1-4 and Batches 1 and 2 without caustic addition. The study also investigated the sensitivity in column performance to: (1) Flow rates of 5, 10, and 20 gpm with 10 gpm as the nominal flow; and (2) Temperatures of 25, 35, and 45 C with 35 C as the nominal temperature. The isotherms and column predictions presented in this report reflect the expected performance of engineered CST IE-911. This form of CST was used in experiments conducted at the Savannah River National Laboratory (SRNL) that formed the basis for estimating model parameters (Hamm et al., 2002). As has been done previously, the engineered resin capacity is estimated to be 68% of the capacity of particulate CST without binder.« less

Purposes and methods of scoring earthquake forecasts

NASA Astrophysics Data System (ADS)

Zhuang, J.

2010-12-01

There are two kinds of purposes in the studies on earthquake prediction or forecasts: one is to give a systematic estimation of earthquake risks in some particular region and period in order to give advice to governments and enterprises for the use of reducing disasters, the other one is to search for reliable precursors that can be used to improve earthquake prediction or forecasts. For the first case, a complete score is necessary, while for the latter case, a partial score, which can be used to evaluate whether the forecasts or predictions have some advantages than a well know model, is necessary. This study reviews different scoring methods for evaluating the performance of earthquake prediction and forecasts. Especially, the gambling scoring method, which is developed recently, shows its capacity in finding good points in an earthquake prediction algorithm or model that are not in a reference model, even if its overall performance is no better than the reference model.

Machine learning and hurdle models for improving regional predictions of stream water acid neutralizing capacity

Treesearch

Nicholas A. Povak; Paul F. Hessburg; Keith M. Reynolds; Timothy J. Sullivan; Todd C. McDonnell; R. Brion Salter

2013-01-01

In many industrialized regions of the world, atmospherically deposited sulfur derived from industrial, nonpoint air pollution sources reduces stream water quality and results in acidic conditions that threaten aquatic resources. Accurate maps of predicted stream water acidity are an essential aid to managers who must identify acid-sensitive streams, potentially...

Working memory differences in long-distance dependency resolution

PubMed Central

Nicenboim, Bruno; Vasishth, Shravan; Gattei, Carolina; Sigman, Mariano; Kliegl, Reinhold

2015-01-01

There is a wealth of evidence showing that increasing the distance between an argument and its head leads to more processing effort, namely, locality effects; these are usually associated with constraints in working memory (DLT: Gibson, 2000; activation-based model: Lewis and Vasishth, 2005). In SOV languages, however, the opposite effect has been found: antilocality (see discussion in Levy et al., 2013). Antilocality effects can be explained by the expectation-based approach as proposed by Levy (2008) or by the activation-based model of sentence processing as proposed by Lewis and Vasishth (2005). We report an eye-tracking and a self-paced reading study with sentences in Spanish together with measures of individual differences to examine the distinction between expectation- and memory-based accounts, and within memory-based accounts the further distinction between DLT and the activation-based model. The experiments show that (i) antilocality effects as predicted by the expectation account appear only for high-capacity readers; (ii) increasing dependency length by interposing material that modifies the head of the dependency (the verb) produces stronger facilitation than increasing dependency length with material that does not modify the head; this is in agreement with the activation-based model but not with the expectation account; and (iii) a possible outcome of memory load on low-capacity readers is the increase in regressive saccades (locality effects as predicted by memory-based accounts) or, surprisingly, a speedup in the self-paced reading task; the latter consistent with good-enough parsing (Ferreira et al., 2002). In sum, the study suggests that individual differences in working memory capacity play a role in dependency resolution, and that some of the aspects of dependency resolution can be best explained with the activation-based model together with a prediction component. PMID:25852623

Working memory differences in long-distance dependency resolution.

PubMed

Nicenboim, Bruno; Vasishth, Shravan; Gattei, Carolina; Sigman, Mariano; Kliegl, Reinhold

2015-01-01

There is a wealth of evidence showing that increasing the distance between an argument and its head leads to more processing effort, namely, locality effects; these are usually associated with constraints in working memory (DLT: Gibson, 2000; activation-based model: Lewis and Vasishth, 2005). In SOV languages, however, the opposite effect has been found: antilocality (see discussion in Levy et al., 2013). Antilocality effects can be explained by the expectation-based approach as proposed by Levy (2008) or by the activation-based model of sentence processing as proposed by Lewis and Vasishth (2005). We report an eye-tracking and a self-paced reading study with sentences in Spanish together with measures of individual differences to examine the distinction between expectation- and memory-based accounts, and within memory-based accounts the further distinction between DLT and the activation-based model. The experiments show that (i) antilocality effects as predicted by the expectation account appear only for high-capacity readers; (ii) increasing dependency length by interposing material that modifies the head of the dependency (the verb) produces stronger facilitation than increasing dependency length with material that does not modify the head; this is in agreement with the activation-based model but not with the expectation account; and (iii) a possible outcome of memory load on low-capacity readers is the increase in regressive saccades (locality effects as predicted by memory-based accounts) or, surprisingly, a speedup in the self-paced reading task; the latter consistent with good-enough parsing (Ferreira et al., 2002). In sum, the study suggests that individual differences in working memory capacity play a role in dependency resolution, and that some of the aspects of dependency resolution can be best explained with the activation-based model together with a prediction component.

Nondestructive Characterization Techniques Used for Ceramic Matrix Composite Life Determination

NASA Technical Reports Server (NTRS)

Effinger, Michael; Koenig, John; Ellingson, Bill; Spohnholtz, Todd

2000-01-01

Recent results indicate that the specific damping capacity and resonant frequency measurements taken periodically during a component's lifetime is able to quantify the mechanical fatigue of CMCS. This gives hope for the potential of determining the actual and residual life of CMC materials using a combination of nondestructive techniques. If successful, then this new paradigm for life prediction of CMCs could revolutionize the approach for designing and servicing CMC components, thereby significantly reducing costs for design, development, health monitoring, and maintenance of CMC components and systems. The Nondestructive Characterization (NDC) life prediction approach would complement life prediction using micromechanics and continuum finite element models. This paper reports on the initial concept of NDC life prediction, a review of the C/SiC blisk damping data, and how changes in the specific damping capacity & ultrasonic elastic modulus data have established the concept as a possibility.

Comprehensive Modeling of Temperature-Dependent Degradation Mechanisms in Lithium Iron Phosphate Batteries

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

Smith, Kandler A; Schimpe, Michael; von Kuepach, Markus Edler

For reliable lifetime predictions of lithium-ion batteries, models for cell degradation are required. A comprehensive semi-empirical model based on a reduced set of internal cell parameters and physically justified degradation functions for the capacity loss is developed and presented for a commercial lithium iron phosphate/graphite cell. One calendar and several cycle aging effects are modeled separately. Emphasis is placed on the varying degradation at different temperatures. Degradation mechanisms for cycle aging at high and low temperatures as well as the increased cycling degradation at high state of charge are calculated separately.For parameterization, a lifetime test study is conducted including storagemore » and cycle tests. Additionally, the model is validated through a dynamic current profile based on real-world application in a stationary energy storage system revealing the accuracy. The model error for the cell capacity loss in the application-based tests is at the end of testing below 1 % of the original cell capacity.« less

Application of Artificial Neural Network to Predict the use of Runway at Juanda International Airport

NASA Astrophysics Data System (ADS)

Putra, J. C. P.; Safrilah

2017-06-01

Artificial neural network approaches are useful to solve many complicated problems. It solves a number of problems in various areas such as engineering, medicine, business, manufacturing, etc. This paper presents an application of artificial neural network to predict a runway capacity at Juanda International Airport. An artificial neural network model of backpropagation and multi-layer perceptron is adopted to this research to learning process of runway capacity at Juanda International Airport. The results indicate that the training data is successfully recognizing the certain pattern of runway use at Juanda International Airport. Whereas, testing data indicate vice versa. Finally, it can be concluded that the approach of uniformity data and network architecture is the critical part to determine the accuracy of prediction results.

Thermal acclimation of leaf photosynthetic traits in an evergreen woodland, consistent with the coordination hypothesis

NASA Astrophysics Data System (ADS)

Fürstenau Togashi, Henrique; Prentice, Iain Colin; Atkin, Owen K.; Macfarlane, Craig; Prober, Suzanne M.; Bloomfield, Keith J.; Evans, Bradley John

2018-06-01

Ecosystem models commonly assume that key photosynthetic traits, such as carboxylation capacity measured at a standard temperature, are constant in time. The temperature responses of modelled photosynthetic or respiratory rates then depend entirely on enzyme kinetics. Optimality considerations, however, suggest this assumption may be incorrect. The coordination hypothesis (that Rubisco- and electron-transport-limited rates of photosynthesis are co-limiting under typical daytime conditions) predicts, instead, that carboxylation (Vcmax) capacity should acclimate so that it increases somewhat with growth temperature but less steeply than its instantaneous response, implying that Vcmax when normalized to a standard temperature (e.g. 25 °C) should decline with growth temperature. With additional assumptions, similar predictions can be made for electron-transport capacity (Jmax) and mitochondrial respiration in the dark (Rdark). To explore these hypotheses, photosynthetic measurements were carried out on woody species during the warm and the cool seasons in the semi-arid Great Western Woodlands, Australia, under broadly similar light environments. A consistent proportionality between Vcmax and Jmax was found across species. Vcmax, Jmax and Rdark increased with temperature in most species, but their values standardized to 25 °C declined. The ci : ca ratio increased slightly with temperature. The leaf N : P ratio was lower in the warm season. The slopes of the relationships between log-transformed Vcmax and Jmax and temperature were close to values predicted by the coordination hypothesis but shallower than those predicted by enzyme kinetics.

Morphogenesis and Complexity of the Tumor Patterns

NASA Astrophysics Data System (ADS)

Izquierdo-Kulich, E.; Nieto-Villar, J. M.

A mechanism to describe the apoptosis process at mesoscopic level through p53 is proposed in this paper. A deterministic model given by three differential equations is deduced from the mesoscopic approach, which exhibits sustained oscillations caused by a supercritical Andronov-Hopf bifurcation. Taking as hypothesis that the p53 sustained oscillation is the fundamental mechanism for apoptosis regulation; the model predicts that it is necessary a strict control of p53 to stimulated it, which is an important consideration to established new therapy strategy to fight cancer. The mathematical modeling of tumor growth allows us to describe the most important regularities of these systems. A stochastic model, based on the most important processes that take place at the level of individual cells, is proposed to predict the dynamical behavior of the expected radius of the tumor and its fractal dimension. It was found that the tumor has a characteristic fractal dimension, which contains the necessary information to predict the tumor growth until it reaches a stationary state. The mathematical modeling of tumor growth is an approach to explain the complex nature of these systems. A model that describes tumor growth was obtained by using a mesoscopic formalism and fractal dimension. This model theoretically predicts the relation between the morphology of the cell pattern and the mitosis/apoptosis quotient that helps to predict tumor growth from tumoral cells fractal dimension. The relation between the tumor macroscopic morphology and the cell pattern morphology is also determined. This could explain why the interface fractal dimension decreases with the increase of the cell pattern fractal dimension and consequently with the increase of the mitosis/apoptosis relation. Indexes to characterize tumoral cell proliferation and invasion capacities are proposed and used to predict the growth of different types of tumors. These indexes also show that the proliferation capacity is directly proportional to the invasion capacity. The proposed model assumes: i) only interface cells proliferate and invade the host, and ii) the fractal dimension of tumoral cell patterns, can reproduce the Gompertzian growth law. A mathematical model was obtained to describe the relation between the tissue morphology of cervix carcinoma and both dynamic processes of mitosis and apoptosis, and an expression to quantify the tumor aggressiveness, which in this context is associated with the tumor growth rate. The proposed model was applied to Stage III cervix carcinoma in vivo studies. In this study we found that the apoptosis rate was significantly smaller in the tumor tissues and both the mitosis rate and aggressiveness index decrease with Stage III patient's age. These quantitative results correspond to observed behavior in clinical and genetics studies. Finally, the entropy production rate was determined for avascular tumor growth. The proposed formula relates the fractal dimension of the tumor contour with the quotient between mitosis and apoptosis rate, which can be used to characterize the degree of proliferation of tumor cells. The entropy production rate was determined for fourteen tumor cell lines as a physical function of cancer robustness. The entropy production rate is a hallmark that allows us the possibility of prognosis of tumor proliferation and invasion capacities, key factors to improve cancer therapy.

The architecture of dynamic reservoir in the echo state network

NASA Astrophysics Data System (ADS)

Cui, Hongyan; Liu, Xiang; Li, Lixiang

2012-09-01

Echo state network (ESN) has recently attracted increasing interests because of its superior capability in modeling nonlinear dynamic systems. In the conventional echo state network model, its dynamic reservoir (DR) has a random and sparse topology, which is far from the real biological neural networks from both structural and functional perspectives. We hereby propose three novel types of echo state networks with new dynamic reservoir topologies based on complex network theory, i.e., with a small-world topology, a scale-free topology, and a mixture of small-world and scale-free topologies, respectively. We then analyze the relationship between the dynamic reservoir structure and its prediction capability. We utilize two commonly used time series to evaluate the prediction performance of the three proposed echo state networks and compare them to the conventional model. We also use independent and identically distributed time series to analyze the short-term memory and prediction precision of these echo state networks. Furthermore, we study the ratio of scale-free topology and the small-world topology in the mixed-topology network, and examine its influence on the performance of the echo state networks. Our simulation results show that the proposed echo state network models have better prediction capabilities, a wider spectral radius, but retain almost the same short-term memory capacity as compared to the conventional echo state network model. We also find that the smaller the ratio of the scale-free topology over the small-world topology, the better the memory capacities.

FPL-PELPS : a price endogenous linear programming system for economic modeling, supplement to PELPS III, version 1.1.

Treesearch

Patricia K. Lebow; Henry Spelter; Peter J. Ince

2003-01-01

This report provides documentation and user information for FPL-PELPS, a personal computer price endogenous linear programming system for economic modeling. Originally developed to model the North American pulp and paper industry, FPL-PELPS follows its predecessors in allowing the modeling of any appropriate sector to predict consumption, production and capacity by...

Modeling opportunities and feasibility of siting wood-fired electrical generating facilities to facilitate landscape-scale fuel treatment with FIA BioSum.

Treesearch

Jeremy S. Fried; Glenn Christensen; Dale Weyermann; R. Jamie Barbour; Roger Fight; Bruce Hiserote; Guy Pinjuv

2005-01-01

Utilization of small diameter trees is viewed by many as the key to making landscape-scale fuel treatment financially feasible. But little capacity currently exists for utilizing such material and capacity of sufficient scale to have a significant impact on the economics of small diameter removals will only be added if predictable feedstocks can be assured. The FIA...

Developmental improvements in the resolution and capacity of visual working memory share a common source

PubMed Central

Simmering, Vanessa R.; Miller, Hilary E.

2016-01-01

The nature of visual working memory (VWM) representations is currently a source of debate between characterizations as slot-like versus a flexibly-divided pool of resources. Recently, a dynamic neural field model has been proposed as an alternative account that focuses more on the processes by which VWM representations are formed, maintained, and used in service of behavior. This dynamic model has explained developmental increases in VWM capacity and resolution through strengthening excitatory and inhibitory connections. Simulations of developmental improvements in VWM resolution suggest that one important change is the accuracy of comparisons between items held in memory and new inputs. Thus, the ability to detect changes is a critical component of developmental improvements in VWM performance across tasks, leading to the prediction that capacity and resolution should correlate during childhood. Comparing 5- to 8-year-old children’s performance across color discrimination and change detection tasks revealed the predicted correlation between estimates of VWM capacity and resolution, supporting the hypothesis that increasing connectivity underlies improvements in VWM during childhood. These results demonstrate the importance of formalizing the processes that support the use of VWM, rather than focusing solely on the nature of representations. We conclude by considering our results in the broader context of VWM development. PMID:27329264

Model for the Prediction of the Hydriding Thermodynamics of Pd-Rh-Co Ternary Alloys

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

Teter, D.F.; Thoma, D.J.

1999-03-01

A dilute solution model (with respect to the substitutional alloying elements) has been developed, which accurately predicts the hydride formation and decomposition thermodynamics and the storage capacities of dilute ternary Pd-Rh-Co alloys. The effect of varying the rhodium and cobalt compositions on the thermodynamics of hydride formation and decomposition and hydrogen capacity of several palladium-rhodium-cobalt ternary alloys has been investigated using pressure-composition (PC) isotherms. Alloying in the dilute regime (<10 at.%) causes the enthalpy for hydride formation to linearly decrease with increasing alloying content. Cobalt has a stronger effect on the reduction in enthalpy than rhodium for equivalent alloying amounts.more » Also, cobalt reduces the hydrogen storage capacity with increasing alloying content. The plateau thermodynamics are strongly linked to the lattice parameters of the alloys. A near-linear dependence of the enthalpy of hydride formation on the lattice parameter was observed for both the binary Pd-Rh and Pd-Co alloys, as well as for the ternary Pd-Rh-Co alloys. The Pd-5Rh-3Co (at. %) alloy was found to have similar plateau thermodynamics as a Pd-10Rh alloy, however, this ternary alloy had a diminished hydrogen storage capacity relative to Pd-10Rh.« less

Cognitive Niches: An Ecological Model of Strategy Selection

ERIC Educational Resources Information Center

Marewski, Julian N.; Schooler, Lael J.

2011-01-01

How do people select among different strategies to accomplish a given task? Across disciplines, the strategy selection problem represents a major challenge. We propose a quantitative model that predicts how selection emerges through the interplay among strategies, cognitive capacities, and the environment. This interplay carves out for each…

Why environmental scientists are becoming Bayesians

Treesearch

James S. Clark

2005-01-01

Advances in computational statistics provide a general framework for the high dimensional models typically needed for ecological inference and prediction. Hierarchical Bayes (HB) represents a modelling structure with capacity to exploit diverse sources of information, to accommodate influences that are unknown (or unknowable), and to draw inference on large numbers of...

Path Analysis Tests of Theoretical Models of Children's Memory Performance

ERIC Educational Resources Information Center

DeMarie, Darlene; Miller, Patricia H.; Ferron, John; Cunningham, Walter R.

2004-01-01

Path analysis was used to test theoretical models of relations among variables known to predict differences in children's memory--strategies, capacity, and metamemory. Children in kindergarten to fourth grade (chronological ages 5 to 11) performed different memory tasks. Several strategies (i.e., sorting, clustering, rehearsal, and self-testing)…

An Optimization-Based System Model of Disturbance-Generated Forest Biomass Utilization

ERIC Educational Resources Information Center

Curry, Guy L.; Coulson, Robert N.; Gan, Jianbang; Tchakerian, Maria D.; Smith, C. Tattersall

2008-01-01

Disturbance-generated biomass results from endogenous and exogenous natural and cultural disturbances that affect the health and productivity of forest ecosystems. These disturbances can create large quantities of plant biomass on predictable cycles. A systems analysis model has been developed to quantify aspects of system capacities (harvest,…

Establishing and Evaluating the Key Functions of an Interactive Systems Framework Using an Assets-Getting to Outcomes Intervention

PubMed Central

Chinman, Matthew; Acosta, Joie; Ebener, Patricia; Burkhart, Q; Clifford, Michael; Corsello, Maryann; Duffey, Tim; Hunter, Sarah; Jones, Margaret; Lahti, Michel; Malone, Patrick S.; Paddock, Susan; Phillips, Andrea; Savell, Susan; Scales, Peter C.; Tellett-Royce, Nancy

2012-01-01

Community practitioners can face difficulty in achieving outcomes demonstrated by prevention science. Building a community practitioner’s prevention capacity—the knowledge and skills needed to conduct critical prevention practices—could improve the quality of prevention and its outcomes. The purpose of this article is to: (1) describe how an intervention called Assets-Getting To Outcomes (AGTO) was used to establish the key functions of the ISF and present early lessons learned from that intervention’s first 6 months and (2) examine whether there is an empirical relationship between practitioner capacity at the individual level and the performance of prevention at the program level—a relationship predicted by the ISF but untested. The article describes an operationalization of the ISF in the context of a five-year randomized controlled efficacy trial that combines two complementary models designed to build capacity: Getting To Outcomes (GTO) and Developmental Assets. The trial compares programs and individual practitioners from six community-based coalitions using AGTO with programs and practitionersfrom six similar coalitions that are not. In this article, we primarily focus on what the ISF calls innovation specific capacity and discuss how the combined AGTO innovation structures and uses feedback about its capacity-building activities, which can serve as a model for implementing the ISF. Focus group discussions used to gather lessons learned from the first 6 months of the AGTO intervention suggest that while the ISF may have been conceptualized as three distinct systems, in practice they are less distinct. Findings from the baseline wave of data collection of individual capacity and program performance suggest that practitioner capacity predicts, in part, performance of prevention programs. Empirically linking practitioner capacity and performance of prevention provides empirical support for both the ISF and AGTO. PMID:22446975

Source and Message Factors in Persuasion: A Reply to Stiff's Critique of the Elaboration Likelihood Model.

ERIC Educational Resources Information Center

Petty, Richard E.; And Others

1987-01-01

Answers James Stiff's criticism of the Elaboration Likelihood Model (ELM) of persuasion. Corrects certain misperceptions of the ELM and criticizes Stiff's meta-analysis that compares ELM predictions with those derived from Kahneman's elastic capacity model. Argues that Stiff's presentation of the ELM and the conclusions he draws based on the data…

EVALUATING THE REGIONAL PREDICTIVE CAPACITY OF A PROCESS-BASED MERCURY EXPOSURE MODEL (R-MCM) FOR LAKES ACROSS VERMONT AND NEW HAMPSHIRE, USA

EPA Science Inventory

Regulatory agencies are confronted with a daunting task of developing fish consumption advisories for a large number of lakes and rivers with little resources. A feasible mechanism to develop region-wide fish advisories is by using a process-based mathematical model. One model of...

The contribution of attentional lapses to individual differences in visual working memory capacity.

PubMed

Adam, Kirsten C S; Mance, Irida; Fukuda, Keisuke; Vogel, Edward K

2015-08-01

Attentional control and working memory capacity are important cognitive abilities that substantially vary between individuals. Although much is known about how attentional control and working memory capacity relate to each other and to constructs like fluid intelligence, little is known about how trial-by-trial fluctuations in attentional engagement impact trial-by-trial working memory performance. Here, we employ a novel whole-report memory task that allowed us to distinguish between varying levels of attentional engagement in humans performing a working memory task. By characterizing low-performance trials, we can distinguish between models in which working memory performance failures are caused by either (1) complete lapses of attention or (2) variations in attentional control. We found that performance failures increase with set-size and strongly predict working memory capacity. Performance variability was best modeled by an attentional control model of attention, not a lapse model. We examined neural signatures of performance failures by measuring EEG activity while participants performed the whole-report task. The number of items correctly recalled in the memory task was predicted by frontal theta power, with decreased frontal theta power associated with poor performance on the task. In addition, we found that poor performance was not explained by failures of sensory encoding; the P1/N1 response and ocular artifact rates were equivalent for high- and low-performance trials. In all, we propose that attentional lapses alone cannot explain individual differences in working memory performance. Instead, we find that graded fluctuations in attentional control better explain the trial-by-trial differences in working memory that we observe.

The Contribution of Attentional Lapses to Individual Differences in Visual Working Memory Capacity

PubMed Central

Adam, Kirsten C. S.; Mance, Irida; Fukuda, Keisuke; Vogel, Edward K.

2015-01-01

Attentional control and working memory capacity are important cognitive abilities that substantially vary between individuals. Although much is known about how attentional control and working memory capacity relate to each other and to constructs like fluid intelligence, little is known about how trial-by-trial fluctuations in attentional engagement impact trial-by-trial working memory performance. Here, we employ a novel whole-report memory task that allowed us to distinguish between varying levels of attentional engagement in humans performing a working memory task. By characterizing low-performance trials, we can distinguish between models in which working memory performance failures are caused by either (1) complete lapses of attention or (2) variations in attentional control. We found that performance failures increase with set-size and strongly predict working memory capacity. Performance variability was best modeled by an attentional control model of attention, not a lapse model. We examined neural signatures of performance failures by measuring EEG activity while participants performed the whole-report task. The number of items correctly recalled in the memory task was predicted by frontal theta power, with decreased frontal theta power associated with poor performance on the task. In addition, we found that poor performance was not explained by failures of sensory encoding; the P1/N1 response and ocular artifact rates were equivalent for high- and low-performance trials. In all, we propose that attentional lapses alone cannot explain individual differences in working memory performance. Instead, we find that graded fluctuations in attentional control better explain the trial-by-trial differences in working memory that we observe. PMID:25811710

Integrated multiscale biomaterials experiment and modelling: a perspective

PubMed Central

Buehler, Markus J.; Genin, Guy M.

2016-01-01

Advances in multiscale models and computational power have enabled a broad toolset to predict how molecules, cells, tissues and organs behave and develop. A key theme in biological systems is the emergence of macroscale behaviour from collective behaviours across a range of length and timescales, and a key element of these models is therefore hierarchical simulation. However, this predictive capacity has far outstripped our ability to validate predictions experimentally, particularly when multiple hierarchical levels are involved. The state of the art represents careful integration of multiscale experiment and modelling, and yields not only validation, but also insights into deformation and relaxation mechanisms across scales. We present here a sampling of key results that highlight both challenges and opportunities for integrated multiscale experiment and modelling in biological systems. PMID:28981126

Assessment of Short Term Flood Operation Strategies Using Numerical Weather Prediction Data in YUVACΙK DAM Reservoir, Turkey

NASA Astrophysics Data System (ADS)

Uysal, G.; Yavuz, O.; Sensoy, A.; Sorman, A.; Akgun, T.; Gezgin, T.

2011-12-01

Yuvacik Dam Reservoir Basin, located in the Marmara region of Turkey with 248 km2 drainage area, has steep topography, mild and rainy climate thus induces high flood potential with fast flow response, especially to early spring and fall precipitation events. Moreover, the basin provides considerable snowmelt contribution to the streamflow during melt season since the elevation ranges between 80 - 1548 m. The long term strategies are based on supplying annual demand of 142 hm3 water despite a relatively small reservoir capacity of 51 hm3. This situation makes short term release decisions as the challenging task regarding the constrained downstream safe channel capacity especially in times of floods. Providing the demand of 1.5 million populated city of Kocaeli is the highest priority issue in terms of reservoir management but risk optimization is also required due to flood regulation. Although, the spillway capacity is 1560 m3/s, the maximum amount of water to be released is set as 100 m3/s by the regional water authority taking into consideration the downstream channel capacity which passes through industrial region of the city. The reservoir is a controlled one and it is possible to hold back the 15 hm3 additional water by keeping the gates closed. Flood regulation is set to achieve the maximum possible flood attenuation by using the full flood-control zone capacity in the reservoir before making releases in excess of the downstream safe-channel capacity. However, the operators still need to exceed flood regulation zones to take precautions for drought summer periods in order to supply water without any shortage that increases the risk in times of flood. Regarding to this circumstances, a hydrological model integrated reservoir modeling system, is applied to account for the physical behavior of the system. Hence, this reservoir modeling is carried out to analyze both previous decisions and also the future scenarios as a decision support tool for operators. In the first step, a hydrological model with an embedded snow module is used to establish a rainfall-runoff relationship to calculate the inflow into the dam reservoir. The basin is divided into four sub-basins, along with the three elevation zones for each subbasin. Hydro-meteorological data are collected via 11 automated stations in and around the basin and a semi-distributed rainfall-runoff model, HEC-HMS, is calibrated for sub-basins. Then, HEC-ResSim is used to create simulation alternatives of reservoir system according to user defined guide curves and rules based on internal and/or external variables. The decision support modeling scenarios are tested with Numerical Weather Prediction Mesoscale Model 5 (MM5) daily total precipitation and daily average temperature data. Predicted precipitation and temperature data are compared with ground observations to examine the consistency. Predicted inflows computed by HEC-HMS are used as main forcing inputs into HEC-ResSim for the short term operation of reservoir during the flood events.

A probabilistic approach to photovoltaic generator performance prediction

NASA Astrophysics Data System (ADS)

Khallat, M. A.; Rahman, S.

1986-09-01

A method for predicting the performance of a photovoltaic (PV) generator based on long term climatological data and expected cell performance is described. The equations for cell model formulation are provided. Use of the statistical model for characterizing the insolation level is discussed. The insolation data is fitted to appropriate probability distribution functions (Weibull, beta, normal). The probability distribution functions are utilized to evaluate the capacity factors of PV panels or arrays. An example is presented revealing the applicability of the procedure.

Verification of a VRF Heat Pump Computer Model in EnergyPlus

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

Nigusse, Bereket; Raustad, Richard

2013-06-15

This paper provides verification results of the EnergyPlus variable refrigerant flow (VRF) heat pump computer model using manufacturer's performance data. The paper provides an overview of the VRF model, presents the verification methodology, and discusses the results. The verification provides quantitative comparison of full and part-load performance to manufacturer's data in cooling-only and heating-only modes of operation. The VRF heat pump computer model uses dual range bi-quadratic performance curves to represent capacity and Energy Input Ratio (EIR) as a function of indoor and outdoor air temperatures, and dual range quadratic performance curves as a function of part-load-ratio for modeling part-loadmore » performance. These performance curves are generated directly from manufacturer's published performance data. The verification compared the simulation output directly to manufacturer's performance data, and found that the dual range equation fit VRF heat pump computer model predicts the manufacturer's performance data very well over a wide range of indoor and outdoor temperatures and part-load conditions. The predicted capacity and electric power deviations are comparbale to equation-fit HVAC computer models commonly used for packaged and split unitary HVAC equipment.« less

Colorimetric Analysis of Hibiscus Beverages and their Potential Antioxidant Properties.

PubMed

Camelo-Méndez, G A; Vanegas-Espinoza, P E; Escudero-Gilete, M L; Heredia, F J; Paredes-López, O; Del Villar-Martínez, A A

2018-05-25

In food industry, roselle beverages and their subproducts could be functional ingredients since they are an excellent source of bioactive compounds with improved performance due to their important anthocyanins content. The aim of this study was to analyze anthocyanin content and antioxidant properties of aqueous infusions elaborated with color contrasting Hibiscus materials and design a mathematical model in order to predict color-composition relationship. Color measurements of beverages from roselle (Negra, Sudan and Rosa) were made by transmission spectrophotometry, anthocyanins quantification was determined by HPLC, and antioxidant potential was evaluated by in vitro methods (ABTS and FRAP assays). Beverages prepared with particle size minor of 250 μm presented until 4- and 2- times more anthocyanins content and antioxidant capacity respectively, in comparison to beverages prepared with powders with particle size major of 750 μm. Positive correlations among pigments composition and color parameters were found (p < 0.05), showing that anthocyanins content, antioxidant capacity, C* ab and h ab values increased in relation with the smallest particle size of flours. Also, mathematical models were stablished to predict anthocyanin content (r ≥ 0.97) and antioxidant capacity (r ≥ 0.89) from color data; we propose equations for quick estimation of the antioxidant capacity in the Hibiscus beverages with high anthocyanin content. The obtained models could be an important tool to be used in food industry for pigment characterization or functional compounds with potential health benefits.

Prevalence and features of advanced asbestosis (ILO profusion scores above 2/2). International Labour Office.

PubMed

Kilburn, K H

2000-01-01

In this study, the author addressed the following question: Do workers with advanced asbestosis have a restrictive pulmonary physiology, and, alternately, do those who have restrictive physiological tests have advanced asbestosis? One group was identified by obvious radiographic measurements, and the other group was defined via physiologic measurements. Total lung capacity, vital capacity, and flows were measured in 12,856 men exposed to asbestos, of whom 3,445 had radiographic signs of asbestosis, as defined by the International Labour Office criteria. Radiographically advanced asbestosis-International Labour Office criteria profusion greater than 2/2 was present in 85 (2.5%) of men. An additional 52 men had physiologically restrictive disease. The author, who compared pulmonary flows and volumes of these two groups, used mean percentage predicted, adjusted for height, age, and duration of cigarette smoking. Men with radiographically advanced asbestosis had normal total lung capacity (i.e., 105.5% predicted), reduced forced vital capacities (i.e., 82.7% predicted), air trapping (i.e., residual volume/total lung capacity increased to 54.4%), and reduced flows (i.e., forced expiratory flow [FEF25-75] = 60.6% predicted, forced expiratory volume in 1 s = 78.0% predicted, and forced expiratory volume in 1 s/forced vital capacity = 65.5%). In contrast, men selected from the same exposed population for restrictive disease (i.e., reduced total lung capacity [72.6% predicted] and forced vital capacity [61.5% predicted]) also had airflow obstruction (i.e., forced expiratory volume in 1 s/forced vital capacity of 74.5% predicted) and air trapping (i.e., residual volume/total lung capacity of 46.7%). Only half of these men had asbestosis--and it was of minimal severity. In summary, advanced asbestosis was characterized by airway obstruction and air trapping, both of which reduced vital capacity but not total lung capacity; therefore, it was not a restrictive disease. In contrast, restrictive disease was rare and was associated with minimal asbestosis.

New strategy for determination of anthocyanins, polyphenols and antioxidant capacity of Brassica oleracea liquid extract using infrared spectroscopies and multivariate regression

NASA Astrophysics Data System (ADS)

de Oliveira, Isadora R. N.; Roque, Jussara V.; Maia, Mariza P.; Stringheta, Paulo C.; Teófilo, Reinaldo F.

2018-04-01

A new method was developed to determine the antioxidant properties of red cabbage extract (Brassica oleracea) by mid (MID) and near (NIR) infrared spectroscopies and partial least squares (PLS) regression. A 70% (v/v) ethanolic extract of red cabbage was concentrated to 9° Brix and further diluted (12 to 100%) in water. The dilutions were used as external standards for the building of PLS models. For the first time, this strategy was applied for building multivariate regression models. Reference analyses and spectral data were obtained from diluted extracts. The determinate properties were total and monomeric anthocyanins, total polyphenols and antioxidant capacity by ABTS (2,2-azino-bis(3-ethyl-benzothiazoline-6-sulfonate)) and DPPH (2,2-diphenyl-1-picrylhydrazyl) methods. Ordered predictors selection (OPS) and genetic algorithm (GA) were used for feature selection before PLS regression (PLS-1). In addition, a PLS-2 regression was applied to all properties simultaneously. PLS-1 models provided more predictive models than did PLS-2 regression. PLS-OPS and PLS-GA models presented excellent prediction results with a correlation coefficient higher than 0.98. However, the best models were obtained using PLS and variable selection with the OPS algorithm and the models based on NIR spectra were considered more predictive for all properties. Then, these models provided a simple, rapid and accurate method for determination of red cabbage extract antioxidant properties and its suitability for use in the food industry.

Project Evaluation: Validation of a Scale and Analysis of Its Predictive Capacity

ERIC Educational Resources Information Center

Fernandes Malaquias, Rodrigo; de Oliveira Malaquias, Fernanda Francielle

2014-01-01

The objective of this study was to validate a scale for assessment of academic projects. As a complement, we examined its predictive ability by comparing the scores of advised/corrected projects based on the model and the final scores awarded to the work by an examining panel (approximately 10 months after the project design). Results of…

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.

A combination of routine blood analytes predicts fitness decrement in elderly endurance athletes.

PubMed

Haslacher, Helmuth; Ratzinger, Franz; Perkmann, Thomas; Batmyagmar, Delgerdalai; Nistler, Sonja; Scherzer, Thomas M; Ponocny-Seliger, Elisabeth; Pilger, Alexander; Gerner, Marlene; Scheichenberger, Vanessa; Kundi, Michael; Endler, Georg; Wagner, Oswald F; Winker, Robert

2017-01-01

Endurance sports are enjoying greater popularity, particularly among new target groups such as the elderly. Predictors of future physical capacities providing a basis for training adaptations are in high demand. We therefore aimed to estimate the future physical performance of elderly marathoners (runners/bicyclists) using a set of easily accessible standard laboratory parameters. To this end, 47 elderly marathon athletes underwent physical examinations including bicycle ergometry and a blood draw at baseline and after a three-year follow-up period. In order to compile a statistical model containing baseline laboratory results allowing prediction of follow-up ergometry performance, the cohort was subgrouped into a model training (n = 25) and a test sample (n = 22). The model containing significant predictors in univariate analysis (alanine aminotransferase, urea, folic acid, myeloperoxidase and total cholesterol) presented with high statistical significance and excellent goodness of fit (R2 = 0.789, ROC-AUC = 0.951±0.050) in the model training sample and was validated in the test sample (ROC-AUC = 0.786±0.098). Our results suggest that standard laboratory parameters could be particularly useful for predicting future physical capacity in elderly marathoners. It hence merits further research whether these conclusions can be translated to other disciplines or age groups.

Activated carbon adsorption of quinolone antibiotics in water: Performance, mechanism, and modeling.

PubMed

Fu, Hao; Li, Xuebing; Wang, Jun; Lin, Pengfei; Chen, Chao; Zhang, Xiaojian; Suffet, I H Mel

2017-06-01

The extensive use of antibiotics has led to their presence in the aquatic environment, and introduces potential impacts on human and ecological health. The capability of powdered activated carbon (PAC) to remove six frequently used quinolone (QN) antibiotics during water treatment was evaluated to improve drinking water safety. The kinetics of QN adsorption by PAC was best described by a pseudo second-order equation, and the adsorption capacity was well described by the Freundlich isotherm equation. Isotherms measured at different pH showed that hydrophobic interaction, electrostatic interaction, and π-π dispersion force were the main mechanisms for adsorption of QNs by PAC. A pH-dependent isotherm model based on the Freundlich equation was developed to predict the adsorption capacity of QNs by PAC at different pH values. This model had excellent prediction capabilities under different laboratory scenarios. Small relative standard derivations (RSDs), i.e., 0.59%-0.92% for ciprofloxacin and 0.09%-3.89% for enrofloxacin, were observed for equilibrium concentrations above the 0.3mg/L level. The RSDs increased to 11.9% for ciprofloxacin and 32.1% for enrofloxacin at μg/L equilibrium levels, which is still acceptable. This model could be applied to predict the adsorption of other chemicals having different ionized forms. Copyright © 2016. Published by Elsevier B.V.

A combination of routine blood analytes predicts fitness decrement in elderly endurance athletes

PubMed Central

Ratzinger, Franz; Perkmann, Thomas; Batmyagmar, Delgerdalai; Nistler, Sonja; Scherzer, Thomas M.; Ponocny-Seliger, Elisabeth; Pilger, Alexander; Gerner, Marlene; Scheichenberger, Vanessa; Kundi, Michael; Endler, Georg; Wagner, Oswald F.; Winker, Robert

2017-01-01

Endurance sports are enjoying greater popularity, particularly among new target groups such as the elderly. Predictors of future physical capacities providing a basis for training adaptations are in high demand. We therefore aimed to estimate the future physical performance of elderly marathoners (runners/bicyclists) using a set of easily accessible standard laboratory parameters. To this end, 47 elderly marathon athletes underwent physical examinations including bicycle ergometry and a blood draw at baseline and after a three-year follow-up period. In order to compile a statistical model containing baseline laboratory results allowing prediction of follow-up ergometry performance, the cohort was subgrouped into a model training (n = 25) and a test sample (n = 22). The model containing significant predictors in univariate analysis (alanine aminotransferase, urea, folic acid, myeloperoxidase and total cholesterol) presented with high statistical significance and excellent goodness of fit (R2 = 0.789, ROC-AUC = 0.951±0.050) in the model training sample and was validated in the test sample (ROC-AUC = 0.786±0.098). Our results suggest that standard laboratory parameters could be particularly useful for predicting future physical capacity in elderly marathoners. It hence merits further research whether these conclusions can be translated to other disciplines or age groups. PMID:28475643

Aging and mind wandering during text comprehension.

PubMed

Krawietz, Sabine A; Tamplin, Andrea K; Radvansky, Gabriel A

2012-12-01

Mind wandering occurs when a person's stream of thought moves from the primary task to task-unrelated matters. Some theories of mind wandering suggest that it is caused by decreased attentional control associated with lower working memory (WM) capacity. Others suggest that it is caused by attention being directed toward internally generated thoughts and that it is associated with higher WM capacity. These ideas were assessed testing older adults because they have been argued to have reduced attentional control and lower WM capacity. The first account predicts that mind wandering should increase in older adults, while the second account predicts the opposite. Two experiments show that older adults exhibited a lower rate of mind wandering than younger adults. However, when using text interest as a covariate, the age difference in mind wandering disappeared. These results are further addressed in light of participants' current concerns and preserved situation model processing in cognitive aging. 2013 APA, all rights reserved

Memory self-efficacy predicts responsiveness to inductive reasoning training in older adults.

PubMed

Payne, Brennan R; Jackson, Joshua J; Hill, Patrick L; Gao, Xuefei; Roberts, Brent W; Stine-Morrow, Elizabeth A L

2012-01-01

In the current study, we assessed the relationship between memory self-efficacy at pretest and responsiveness to inductive reasoning training in a sample of older adults. Participants completed a measure of self-efficacy assessing beliefs about memory capacity. Participants were then randomly assigned to a waitlist control group or an inductive reasoning training intervention. Latent change score models were used to examine the moderators of change in inductive reasoning. Inductive reasoning showed clear improvements in the training group compared with the control. Within the training group, initial memory capacity beliefs significantly predicted change in inductive reasoning such that those with higher levels of capacity beliefs showed greater responsiveness to the intervention. Further analyses revealed that self-efficacy had effects on how trainees allocated time to the training materials over the course of the intervention. Results indicate that self-referential beliefs about cognitive potential may be an important factor contributing to plasticity in adulthood.

Toward a neurologic model of competency: Cognitive predictors of capacity to consent in Alzheimer's disease using three different legal standards.

PubMed

Marson, D C; Chatterjee, A; Ingram, K K; Harrell, L E

1996-03-01

To identify cognitive predictors of competency performance and status in Alzheimer's disease (AD) using three differentially stringent legal standards for capacity to consent. Univariate and multivariate analyses of independent neuropsychological test measures with three dependent measures of competency to consent to treatment. University medical center. 15 normal older controls and 29 patients with probably AD (15 mild and 14 moderate). Subjects were administered a batter of neuropsychological measures theoretically linked to competency function, as well as two clinical vignettes testing capacity to consent to medical treatment under five legal standards (LSs). The present study focused on three differentially stringent LSs: the capacity simply to "evidence a treatment of choice" (LS1), which is a minimal standard; the capacity to "appreciate the consequences" of a treatment of choice (LS3), a moderately stringent standard; and the capacity to "understand the treatment situation and choices" (LS5), the most stringent standard. Control subject and AD patient neuropsychological test scores were correlated with scores on the three LSs. The resulting univariate correlates were than analyzed using stepwise regression and discriminant function to identify key multivariate predictors of competency performance and status under each LS. No neuropsychological measures predicted control group performance on the LSs. For the AD group, a measure of simple auditory comprehension predicted LS1 performance (r(2)=0.44, p < 0.0001), a word fluency measure predicted LS3 performance (r(2)=0.58, p < 0.0001), and measures of conceptualization and confrontation naming together predicted LS5 performance (r(2)=0.81, p < 0.0001). Under discriminant function analysis, confrontation naming was the best single predictor of LS1 competency status for all subjects, correctly classifying 96% of cases (42/44). Measures of visumotor tracking and confrontation naming were the best single predictors, respectively, of competency status under LS3 (91% [39/43]) and LS5 (98% [43/44]). Multiple cognitive functions are associated with loss of competency in AD. Deficits in conceptualization, semantic memory, and probably verbal recall are associated with the declining capacity of mild AD patients to understand a treatment situation and choices (LS5); executive dysfunction with the declining capacity of mild to moderate AD patients to identify the consequences of treatment choice (LS3); and receptive aphasia and severe dysnomia with the declining capacity of advanced AD patients to evidence a simple treatment choice (LS1). The results offer insight into the relationship between different legal thresholds of competency and the progressive cognitive changes characteristic of AD, and represent an initial step toward a neurologic model of competency.

Modeling individual differences in working memory performance: a source activation account

PubMed Central

Daily, Larry Z.; Lovett, Marsha C.; Reder, Lynne M.

2008-01-01

Working memory resources are needed for processing and maintenance of information during cognitive tasks. Many models have been developed to capture the effects of limited working memory resources on performance. However, most of these models do not account for the finding that different individuals show different sensitivities to working memory demands, and none of the models predicts individual subjects' patterns of performance. We propose a computational model that accounts for differences in working memory capacity in terms of a quantity called source activation, which is used to maintain goal-relevant information in an available state. We apply this model to capture the working memory effects of individual subjects at a fine level of detail across two experiments. This, we argue, strengthens the interpretation of source activation as working memory capacity. PMID:19079561

Functional Capacity Evaluation & Disability

PubMed Central

Chen, Joseph J

2007-01-01

Function, Impairment, and Disability are words in which many physicians have little interest. Most physicians are trained to deal with structure and physiology and not function and disability. The purpose of this article is to address some of the common questions that many physicians have with the use of functional capacity evaluation and disability and also to provide a unifying model that can explain the medical and societal variables in predicting disability. We will first define the functional capacity evaluation (FCE) and explore the different types available as well as their uses. We will review several studies exploring the validity and reliability of the FCE on healthy and chronic pain patients. We will examine the few studies that look into whether an FCE is predictive of return to work and whether an FCE is predictive of disability. In the second half of this article, we will focus on the Assessment of Disability from the origins of the United States Social Security Administration to a bold new concept, the World Health Organization's International Classification of Function, Disability and Health. PMID:17907444

Development of an in Silico Model of DPPH• Free Radical Scavenging Capacity: Prediction of Antioxidant Activity of Coumarin Type Compounds.

PubMed

Goya Jorge, Elizabeth; Rayar, Anita Maria; Barigye, Stephen J; Jorge Rodríguez, María Elisa; Sylla-Iyarreta Veitía, Maité

2016-06-07

A quantitative structure-activity relationship (QSAR) study of the 2,2-diphenyl-l-picrylhydrazyl (DPPH•) radical scavenging ability of 1373 chemical compounds, using DRAGON molecular descriptors (MD) and the neural network technique, a technique based on the multilayer multilayer perceptron (MLP), was developed. The built model demonstrated a satisfactory performance for the training ( R 2 = 0.713 ) and test set ( Q ext 2 = 0.654 ) , respectively. To gain greater insight on the relevance of the MD contained in the MLP model, sensitivity and principal component analyses were performed. Moreover, structural and mechanistic interpretation was carried out to comprehend the relationship of the variables in the model with the modeled property. The constructed MLP model was employed to predict the radical scavenging ability for a group of coumarin-type compounds. Finally, in order to validate the model's predictions, an in vitro assay for one of the compounds (4-hydroxycoumarin) was performed, showing a satisfactory proximity between the experimental and predicted pIC50 values.

Pragmatic perspective on aerobic scope: peaking, plummeting, pejus and apportioning.

PubMed

Farrell, A P

2016-01-01

A major challenge for fish biologists in the 21st century is to predict the biotic effects of global climate change. With marked changes in biogeographic distribution already in evidence for a variety of aquatic animals, mechanistic explanations for these shifts are being sought, ones that then can be used as a foundation for predictive models of future climatic scenarios. One mechanistic explanation for the thermal performance of fishes that has gained some traction is the oxygen and capacity-limited thermal tolerance (OCLTT) hypothesis, which suggests that an aquatic organism's capacity to supply oxygen to tissues becomes limited when body temperature reaches extremes. Central to this hypothesis is an optimum temperature for absolute aerobic scope (AAS, loosely defined as the capacity to deliver oxygen to tissues beyond a basic need). On either side of this peak for AAS are pejus temperatures that define when AAS falls off and thereby reduces an animal's absolute capacity for activity. This article provides a brief perspective on the potential uses and limitations of some of the key physiological indicators related to aerobic scope in fishes. The intent is that practitioners who attempt predictive ecological applications can better recognize limitations and make better use of the OCLTT hypothesis and its underlying physiology. © 2015 The Fisheries Society of the British Isles.

Association of increased total antioxidant capacity and anovulation in nonobese infertile patients with clomiphene citrate-resistant polycystic ovary syndrome.

PubMed

Verit, Fatma Ferda; Erel, Ozcan; Kocyigit, Abdurrahim

2007-08-01

To investigate whether total antioxidant capacity (TAC) could predict the response to ovulation induction to clomiphene citrate (CC) in nonobese women with polycystic ovary syndrome. Prospective longitudinal follow-up study. Academic hospital. Fifty-five nonobese, oligomenorrheic women with polycystic ovary syndrome and normal indices of insulin sensitivity. None. Standard clinical examinations and ultrasonographic and endocrine screening, including FSH, LH, E(2), P, total T, sex hormone-binding globulin, DHEAS, and TAC were performed before initiation of CC medication. Within the total group, 27 (49%) of the patients did not ovulate at the end of follow-up. TAC, free androgen index, and ovarian volume were all significantly different in CC nonresponders from those in responders. Total antioxidant capacity was found to be the best predictor in univariate analysis (odds ratio, 171.55; 95% confidence interval, 10.61-2,772.93), and it had the highest area in the receiver operating characteristics analysis (0.91). In a multivariate prediction model, TAC, free androgen index, and ovarian volume showed good predictive power, with Hosmer-Lemeshow goodness of fit test of 0.80. Total antioxidant capacity was the strongest predictor of ovarian response during CC induction of ovulation in these patients. It can be concluded that TAC can be used as a routine screening test.

Large sensitivity in land carbon storage due to geographical and temporal variation in the thermal response of photosynthetic capacity.

PubMed

Mercado, Lina M; Medlyn, Belinda E; Huntingford, Chris; Oliver, Rebecca J; Clark, Douglas B; Sitch, Stephen; Zelazowski, Przemyslaw; Kattge, Jens; Harper, Anna B; Cox, Peter M

2018-06-01

Plant temperature responses vary geographically, reflecting thermally contrasting habitats and long-term species adaptations to their climate of origin. Plants also can acclimate to fast temporal changes in temperature regime to mitigate stress. Although plant photosynthetic responses are known to acclimate to temperature, many global models used to predict future vegetation and climate-carbon interactions do not include this process. We quantify the global and regional impacts of biogeographical variability and thermal acclimation of temperature response of photosynthetic capacity on the terrestrial carbon (C) cycle between 1860 and 2100 within a coupled climate-carbon cycle model, that emulates 22 global climate models. Results indicate that inclusion of biogeographical variation in photosynthetic temperature response is most important for present-day and future C uptake, with increasing importance of thermal acclimation under future warming. Accounting for both effects narrows the range of predictions of the simulated global land C storage in 2100 across climate projections (29% and 43% globally and in the tropics, respectively). Contrary to earlier studies, our results suggest that thermal acclimation of photosynthetic capacity makes tropical and temperate C less vulnerable to warming, but reduces the warming-induced C uptake in the boreal region under elevated CO 2 . © 2018 The Authors. New Phytologist © 2018 New Phytologist Trust.

Towards an Effective Health Interventions Design: An Extension of the Health Belief Model

PubMed Central

Orji, Rita; Vassileva, Julita; Mandryk, Regan

2012-01-01

Introduction The recent years have witnessed a continuous increase in lifestyle related health challenges around the world. As a result, researchers and health practitioners have focused on promoting healthy behavior using various behavior change interventions. The designs of most of these interventions are informed by health behavior models and theories adapted from various disciplines. Several health behavior theories have been used to inform health intervention designs, such as the Theory of Planned Behavior, the Transtheoretical Model, and the Health Belief Model (HBM). However, the Health Belief Model (HBM), developed in the 1950s to investigate why people fail to undertake preventive health measures, remains one of the most widely employed theories of health behavior. However, the effectiveness of this model is limited. The first limitation is the low predictive capacity (R2 < 0.21 on average) of existing HBM’s variables coupled with the small effect size of individual variables. The second is lack of clear rules of combination and relationship between the individual variables. In this paper, we propose a solution that aims at addressing these limitations as follows: (1) we extended the Health Belief Model by introducing four new variables: Self-identity, Perceived Importance, Consideration of Future Consequences, and Concern for Appearance as possible determinants of healthy behavior. (2) We exhaustively explored the relationships/interactions between the HBM variables and their effect size. (3) We tested the validity of both our proposed extended model and the original HBM on healthy eating behavior. Finally, we compared the predictive capacity of the original HBM model and our extended model. Methods: To achieve the objective of this paper, we conducted a quantitative study of 576 participants’ eating behavior. Data for this study were collected over a period of one year (from August 2011 to August 2012). The questionnaire consisted of validated scales assessing the HBM determinants – perceived benefit, barrier, susceptibility, severity, cue to action, and self-efficacy – using 7-point Likert scale. We also assessed other health determinants such as consideration of future consequences, self-identity, concern for appearance and perceived importance. To analyses our data, we employed factor analysis and Partial Least Square Structural Equation Model (PLS-SEM) to exhaustively explore the interaction/relationship between the determinants and healthy eating behavior. We tested for the validity of both our proposed extended model and the original HBM on healthy eating behavior. Finally, we compared the predictive capacity of the original HBM model and our extended model and investigated possible mediating effects. Results: The results show that the three newly added determinants are better predictors of healthy behavior. Our extended HBM model lead to approximately 78% increase (from 40 to 71%) in predictive capacity compared to the old model. This shows the suitability of our extended HBM for use in predicting healthy behavior and in informing health intervention design. The results from examining possible relationships between the determinants in our model lead to an interesting discovery of some mediating relationships between the HBM’s determinants, therefore, shedding light on some possible combinations of determinants that could be employed by intervention designers to increase the effectiveness of their design. Conclusion: Consideration of future consequences, self-identity, concern for appearance, perceived importance, self-efficacy, perceived susceptibility are significant determinants of healthy eating behavior that can be manipulated by healthy eating intervention design. Most importantly, the result from our model established the existence of some mediating relationships among the determinants. The knowledge of both the direct and indirect relationships sheds some light on the possible combination rules. PMID:23569653

Predictive modeling of mosquito abundance and dengue transmission in Kenya

NASA Astrophysics Data System (ADS)

Caldwell, J.; Krystosik, A.; Mutuku, F.; Ndenga, B.; LaBeaud, D.; Mordecai, E.

2017-12-01

Approximately 390 million people are exposed to dengue virus every year, and with no widely available treatments or vaccines, predictive models of disease risk are valuable tools for vector control and disease prevention. The aim of this study was to modify and improve climate-driven predictive models of dengue vector abundance (Aedes spp. mosquitoes) and viral transmission to people in Kenya. We simulated disease transmission using a temperature-driven mechanistic model and compared model predictions with vector trap data for larvae, pupae, and adult mosquitoes collected between 2014 and 2017 at four sites across urban and rural villages in Kenya. We tested predictive capacity of our models using four temperature measurements (minimum, maximum, range, and anomalies) across daily, weekly, and monthly time scales. Our results indicate seasonal temperature variation is a key driving factor of Aedes mosquito abundance and disease transmission. These models can help vector control programs target specific locations and times when vectors are likely to be present, and can be modified for other Aedes-transmitted diseases and arboviral endemic regions around the world.

Drivers of leaf carbon exchange capacity across biomes at the continental scale.

PubMed

Smith, Nicholas G; Dukes, Jeffrey S

2018-04-29

Realistic representations of plant carbon exchange processes are necessary to reliably simulate biosphere-atmosphere feedbacks. These processes are known to vary over time and space, though the drivers of the underlying rates are still widely debated in the literature. Here, we measured leaf carbon exchange in >500 individuals of 98 species from the Neotropics to high boreal biomes to determine the drivers of photosynthetic and dark respiration capacity. Covariate abiotic (long- and short-term climate) and biotic (plant type, plant size, ontogeny, water status) data were used to explore significant drivers of temperature-standardized leaf carbon exchange rates. Using model selection, we found the previous week's temperature and soil moisture at the time of measurement to be a better predictor of photosynthetic capacity than long-term climate, with the combination of high recent temperatures and low soil moisture tending to decrease photosynthetic capacity. Non-trees (annual and perennials) tended to have greater photosynthetic capacity than trees, and, within trees, adults tended to have greater photosynthetic capacity than juveniles, possibly as a result of differences in light availability. Dark respiration capacity was less responsive to the assessed drivers than photosynthetic capacity, with rates best predicted by multi-year average site temperature alone. Our results suggest that, across large spatial scales, photosynthetic capacity quickly adjusts to changing environmental conditions, namely light, temperature, and soil moisture. Respiratory capacity is more conservative and most responsive to longer-term conditions. Our results provide a framework for incorporating these processes into large-scale models and a data set to benchmark such models. © 2018 by the Ecological Society of America.

Effect of Phosphate-Buffered Solution Corrosion on the Ratcheting Fatigue Behavior of a Duplex Mg-Li-Al Alloy

NASA Astrophysics Data System (ADS)

Yuan, Xin; Yu, Dunji; Gao, Li-Lan; Gao, Hong

2016-05-01

This work reports the uniaxial ratcheting and fatigue behavior of a duplex Mg-Li-Al alloy under the influence of phosphate-buffered solution corrosion. Microstructural observations reveal pitting and filament corrosion defects, which impair the load-bearing capacity of the alloy and cause stress concentration, thus leading to an accelerated accumulation of ratcheting strain and shortened fatigue life under the same nominal loading conditions. Comparing Smith model, Smith-Watson-Topper model, and Paul-Sivaprasad-Dhar model, a ratcheting fatigue life prediction model based on the Broberg damage rule and the Paul-Sivaprasad-Dhar model was proposed, and the model yielded a superior prediction for the studied magnesium alloy.

Towards robust quantification and reduction of uncertainty in hydrologic predictions: Integration of particle Markov chain Monte Carlo and factorial polynomial chaos expansion

NASA Astrophysics Data System (ADS)

Wang, S.; Huang, G. H.; Baetz, B. W.; Ancell, B. C.

2017-05-01

The particle filtering techniques have been receiving increasing attention from the hydrologic community due to its ability to properly estimate model parameters and states of nonlinear and non-Gaussian systems. To facilitate a robust quantification of uncertainty in hydrologic predictions, it is necessary to explicitly examine the forward propagation and evolution of parameter uncertainties and their interactions that affect the predictive performance. This paper presents a unified probabilistic framework that merges the strengths of particle Markov chain Monte Carlo (PMCMC) and factorial polynomial chaos expansion (FPCE) algorithms to robustly quantify and reduce uncertainties in hydrologic predictions. A Gaussian anamorphosis technique is used to establish a seamless bridge between the data assimilation using the PMCMC and the uncertainty propagation using the FPCE through a straightforward transformation of posterior distributions of model parameters. The unified probabilistic framework is applied to the Xiangxi River watershed of the Three Gorges Reservoir (TGR) region in China to demonstrate its validity and applicability. Results reveal that the degree of spatial variability of soil moisture capacity is the most identifiable model parameter with the fastest convergence through the streamflow assimilation process. The potential interaction between the spatial variability in soil moisture conditions and the maximum soil moisture capacity has the most significant effect on the performance of streamflow predictions. In addition, parameter sensitivities and interactions vary in magnitude and direction over time due to temporal and spatial dynamics of hydrologic processes.

Choice of reserve capacity by hospitals: a problem for prospective payment.

PubMed

Widmer, Philippe K; Trottmann, Maria; Zweifel, Peter

2018-06-01

This contribution analyzes the impact of prospective payment on hospital decisions with regard to reserve capacity, using Swiss hospital data covering the years 2004-2009. This data set is unique because it permits distinguishing of institutional characteristics (e.g., ownership status) from the mode of payment as determinants of hospital efficiency, due to the fact that some Swiss cantons introduced prospective payment early while others waited for federal legislation to be enacted in 2012. Since a hospital's choice of reserve capacity depends also on the risk preferences of management while affecting the cost function, heterogeneity is predicted even in the presence of identical technology and factor prices. For estimating hospitals' marginal costs, we employ the flexible representation of risk preferences by Pope and Chavas [Am J Agric Econ 76, 196-204 (1994)]. Production uncertainty is measured as the difference between actual admissions and admissions predicted by an autoregressive moving average model. Its effect on hospital cost is analyzed using a multilevel stochastic cost frontier model with random coefficients reflecting unobserved differences in technology. Public hospitals are found to opt for a higher probability of meeting unexpected demand, as predicted. Their operating cost is 1.1% higher than for private hospitals and even 1.9% higher than for teaching hospitals, creating an incentive to turn away patients or to keep them waiting for treatment.

Body composition, muscle capacity, and physical function in older adults: an integrated conceptual model.

PubMed

Brady, Anne O; Straight, Chad R; Evans, Ellen M

2014-07-01

The aging process leads to adverse changes in body composition (increases in fat mass and decreases in skeletal muscle mass), declines in physical function (PF), and ultimately increased risk for disability and loss of independence. Specific components of body composition or muscle capacity (strength and power) may be useful in predicting PF; however, findings have been mixed regarding the most salient predictor of PF. The development of a conceptual model potentially aids in understanding the interrelated factors contributing to PF with the factors of interest being physical activity, body composition, and muscle capacity. This article also highlights sex differences in these domains. Finally, factors known to affect PF, such as sleep, depression, fatigue, and self-efficacy, are discussed. Development of a comprehensive conceptual model is needed to better characterize the most salient factors contributing to PF and to subsequently inform the development of interventions to reduce physical disability in older adults.

Prognostic model for survival in patients with early stage cervical cancer.

PubMed

Biewenga, Petra; van der Velden, Jacobus; Mol, Ben Willem J; Stalpers, Lukas J A; Schilthuis, Marten S; van der Steeg, Jan Willem; Burger, Matthé P M; Buist, Marrije R

2011-02-15

In the management of early stage cervical cancer, knowledge about the prognosis is critical. Although many factors have an impact on survival, their relative importance remains controversial. This study aims to develop a prognostic model for survival in early stage cervical cancer patients and to reconsider grounds for adjuvant treatment. A multivariate Cox regression model was used to identify the prognostic weight of clinical and histological factors for disease-specific survival (DSS) in 710 consecutive patients who had surgery for early stage cervical cancer (FIGO [International Federation of Gynecology and Obstetrics] stage IA2-IIA). Prognostic scores were derived by converting the regression coefficients for each prognostic marker and used in a score chart. The discriminative capacity was expressed as the area under the curve (AUC) of the receiver operating characteristic. The 5-year DSS was 92%. Tumor diameter, histological type, lymph node metastasis, depth of stromal invasion, lymph vascular space invasion, and parametrial extension were independently associated with DSS and were included in a Cox regression model. This prognostic model, corrected for the 9% overfit shown by internal validation, showed a fair discriminative capacity (AUC, 0.73). The derived score chart predicting 5-year DSS showed a good discriminative capacity (AUC, 0.85). In patients with early stage cervical cancer, DSS can be predicted with a statistical model. Models, such as that presented here, should be used in clinical trials on the effects of adjuvant treatments in high-risk early cervical cancer patients, both to stratify and to include patients. Copyright © 2010 American Cancer Society.

Near infrared spectroscopy for prediction of antioxidant compounds in the honey.

PubMed

Escuredo, Olga; Seijo, M Carmen; Salvador, Javier; González-Martín, M Inmaculada

2013-12-15

The selection of antioxidant variables in honey is first time considered applying the near infrared (NIR) spectroscopic technique. A total of 60 honey samples were used to develop the calibration models using the modified partial least squares (MPLS) regression method and 15 samples were used for external validation. Calibration models on honey matrix for the estimation of phenols, flavonoids, vitamin C, antioxidant capacity (DPPH), oxidation index and copper using near infrared (NIR) spectroscopy has been satisfactorily obtained. These models were optimised by cross-validation, and the best model was evaluated according to multiple correlation coefficient (RSQ), standard error of cross-validation (SECV), ratio performance deviation (RPD) and root mean standard error (RMSE) in the prediction set. The result of these statistics suggested that the equations developed could be used for rapid determination of antioxidant compounds in honey. This work shows that near infrared spectroscopy can be considered as rapid tool for the nondestructive measurement of antioxidant constitutes as phenols, flavonoids, vitamin C and copper and also the antioxidant capacity in the honey. Copyright © 2013 Elsevier Ltd. All rights reserved.

Simulated front crawl swimming performance related to critical speed and critical power.

PubMed

Toussaint, H M; Wakayoshi, K; Hollander, A P; Ogita, F

1998-01-01

Competitive pool swimming events range in distance from 50 to 1500 m. Given the difference in performance times (+/- 23-1000 s), the contribution of the aerobic and anaerobic energy systems changes considerably with race distance. In training practice the regression line between swimming distance and time (Distance = critical velocity x time + anaerobic swimming capacity) is used to determine the individual capacity of the aerobic and anaerobic metabolic pathways. Although there is confidence that critical velocity and anaerobic swimming capacity are fitness measures that separate aerobic and anaerobic components, a firm theoretical basis for the interpretation of these results does not exist. The purpose of this study was to evaluate the critical power concept and anaerobic swimming capacity as measures of the aerobic and anaerobic capacity using a modeling approach. A systems model was developed that relates the mechanics and energetics involved in front crawl swimming performance. From actual swimming flume measurements, the time dependent aerobic and anaerobic energy release was modeled. Data derived from the literature were used to relate the energy cost of front crawl swimming to swimming velocity. A balance should exist between the energy cost to swim a distance in a certain time and the concomitant aerobic and anaerobic energy release. The ensuing model was used to predict performance times over a range of distances (50-1500 m) and to calculate the regression line between swimming distance and time. Using a sensitivity analysis, it was demonstrated that the critical velocity is indicative for the capacity of the aerobic energy system. Estimates of the anaerobic swimming capacity, however, were influenced by variations in both anaerobic and aerobic energy release. Therefore, it was concluded that the anaerobic swimming capacity does not provide a reliable estimate of the anaerobic capacity.

The evolution of root zone moisture storage capacities after deforestation: a step towards hydrological predictions under change?

NASA Astrophysics Data System (ADS)

Nijzink, Remko C.; Hutton, Christopher; Pechlivanidis, Ilias; Capell, René; Arheimer, Berit; Freer, Jim; Han, Dawei; Wagener, Thorsten; McGuire, Kevin; Savenije, Hubert; Hrachowitz, Markus

2017-04-01

The moisture storage available to vegetation is a key parameter in the hydrological functioning of ecosystems. This parameter, the root zone storage capacity, determines the partitioning between runoff and transpiration, but is impossible to observe at the catchment scale. In this research, data from the experimental forests of HJ Andrews (Oregon, USA) and Hubbard Brook (New Hampshire, USA) was used to test the hypotheses that: (1) the root zone storage capacity significantly changes after deforestation, (2) changes in the root zone storage capacity can to a large extent explain post-treatment changes to the hydrological regimes and that (3) a time-dynamic formulation of the root zone storage can improve the performance of a hydrological model. At first, root zone storage capacities were estimated based on a simple, water-balance based method. Briefly, the maximum difference between cumulative rainfall and estimated transpiration was determined, which could be considered a proxy for root zone storage capacity. These values were compared with root zone storage capacities obtained from four conceptual models (HYPE, HYMOD, FLEX, TUW), calibrated for consecutive 2-year windows. Both methods showed a sharp decline in root zone storage capacity after deforestation, which was followed by a gradual recovery signal. It was found in a trend analysis that these recovery periods took between 5 and 13 years for the different catchments. Eventually, one of the models was adjusted to allow for a time-dynamic formulation of root zone storage capacity. This adjusted model showed improvements in model performance as evaluated by 28 hydrological signatures, such as rising limb density or peak flows. Thus, this research clearly shows the time-dynamic character of a crucial parameter, which is often considered to remain constant in time. Root zone storage capacities are strongly affected by deforestation, leading to changes in hydrological regimes, and time-dynamic formulations of root zone storage are therefore necessary in systems under change.

Dynamic Capacity and Surface Fatigue Life for Spur and Helical Gears

NASA Technical Reports Server (NTRS)

Coy, J. J.; Townsend, D. P.; Zaretsky, E. V.

1975-01-01

A mathematical model for surface fatigue life of gear, pinion, or entire meshing gear train is given. The theory is based on a previous statistical approach for rolling-element bearings. Equations are presented which give the dynamic capacity of the gear set. The dynamic capacity is the transmitted tangential load which gives a 90 percent probability of survival of the gear set for one million pinion revolutions. The analytical results are compared with test data for a set of AISI 9310 spur gears operating at a maximum Hertz stress of 1.71 billion N/sq m and 10,000 rpm. The theoretical life predictions are shown to be good when material constants obtained from rolling-element bearing tests were used in the gear life model.

A high-resolution model of the planetary boundary layer - Sensitivity tests and comparisons with SESAME-79 data

NASA Technical Reports Server (NTRS)

Zhang, D.; Anthes, R. A.

1982-01-01

A one-dimensional, planetary boundary layer (PBL) model is presented and verified using April 10, 1979 SESAME data. The model contains two modules to account for two different regimes of turbulent mixing. Separate parameterizations are made for stable and unstable conditions, with a predictive slab model for surface temperature. Atmospheric variables in the surface layer are calculated with a prognostic model, with moisture included in the coupled surface/PBL modeling. Sensitivity tests are performed for factors such as moisture availability, albedo, surface roughness, and thermal capacity, and a 24 hr simulation is summarized for day and night conditions. The comparison with the SESAME data comprises three hour intervals, using a time-dependent geostrophic wind. Close correlations were found with daytime conditions, but not in nighttime thermal structure, while the turbulence was faithfully predicted. Both geostrophic flow and surface characteristics were shown to have significant effects on the model predictions

Prediction models for transfer of arsenic from soil to corn grain (Zea mays L.).

PubMed

Yang, Hua; Li, Zhaojun; Long, Jian; Liang, Yongchao; Xue, Jianming; Davis, Murray; He, Wenxiang

2016-04-01

In this study, the transfer of arsenic (As) from soil to corn grain was investigated in 18 soils collected from throughout China. The soils were treated with three concentrations of As and the transfer characteristics were investigated in the corn grain cultivar Zhengdan 958 in a greenhouse experiment. Through stepwise multiple-linear regression analysis, prediction models were developed combining the As bioconcentration factor (BCF) of Zhengdan 958 and soil pH, organic matter (OM) content, and cation exchange capacity (CEC). The possibility of applying the Zhengdan 958 model to other cultivars was tested through a cross-cultivar extrapolation approach. The results showed that the As concentration in corn grain was positively correlated with soil pH. When the prediction model was applied to non-model cultivars, the ratio ranges between the predicted and measured BCF values were within a twofold interval between predicted and measured values. The ratios were close to a 1:1 relationship between predicted and measured values. It was also found that the prediction model (Log [BCF]=0.064 pH-2.297) could effectively reduce the measured BCF variability for all non-model corn cultivars. The novel model is firstly developed for As concentration in crop grain from soil, which will be very useful for understanding the As risk in soil environment.

Neither Single nor a Combination of Routine Laboratory Parameters can Discriminate between Gram-positive and Gram-negative Bacteremia

PubMed Central

Ratzinger, Franz; Dedeyan, Michel; Rammerstorfer, Matthias; Perkmann, Thomas; Burgmann, Heinz; Makristathis, Athanasios; Dorffner, Georg; Loetsch, Felix; Blacky, Alexander; Ramharter, Michael

2015-01-01

Adequate early empiric antibiotic therapy is pivotal for the outcome of patients with bloodstream infections. In clinical practice the use of surrogate laboratory parameters is frequently proposed to predict underlying bacterial pathogens; however there is no clear evidence for this assumption. In this study, we investigated the discriminatory capacity of predictive models consisting of routinely available laboratory parameters to predict the presence of Gram-positive or Gram-negative bacteremia. Major machine learning algorithms were screened for their capacity to maximize the area under the receiver operating characteristic curve (ROC-AUC) for discriminating between Gram-positive and Gram-negative cases. Data from 23,765 patients with clinically suspected bacteremia were screened and 1,180 bacteremic patients were included in the study. A relative predominance of Gram-negative bacteremia (54.0%), which was more pronounced in females (59.1%), was observed. The final model achieved 0.675 ROC-AUC resulting in 44.57% sensitivity and 79.75% specificity. Various parameters presented a significant difference between both genders. In gender-specific models, the discriminatory potency was slightly improved. The results of this study do not support the use of surrogate laboratory parameters for predicting classes of causative pathogens. In this patient cohort, gender-specific differences in various laboratory parameters were observed, indicating differences in the host response between genders. PMID:26522966

Teacher' Interpersonal Self-Efficacy: Evaluation and Predictive Capacity of Teacher Burnout

ERIC Educational Resources Information Center

García-Ros, Rafael; Fuentes, María C.; Fernández, Basilio

2015-01-01

Introduction: This study analyzed the predictive capacity and incremental validity of teachers' interpersonal self-efficacy on their levels of burnout. First, it presents the validation process of a Spanish adaptation of the Teacher Interpersonal Self-Efficacy Scale--TISES--(Browers & Tomic, 1999, 2001). Second, the predictive capacity of…

Quantitative Structure – Property Relationship Modeling of Remote Liposome Loading Of Drugs

PubMed Central

Cern, Ahuva; Golbraikh, Alexander; Sedykh, Aleck; Tropsha, Alexander; Barenholz, Yechezkel; Goldblum, Amiram

2012-01-01

Remote loading of liposomes by trans-membrane gradients is used to achieve therapeutically efficacious intra-liposome concentrations of drugs. We have developed Quantitative Structure Property Relationship (QSPR) models of remote liposome loading for a dataset including 60 drugs studied in 366 loading experiments internally or elsewhere. Both experimental conditions and computed chemical descriptors were employed as independent variables to predict the initial drug/lipid ratio (D/L) required to achieve high loading efficiency. Both binary (to distinguish high vs. low initial D/L) and continuous (to predict real D/L values) models were generated using advanced machine learning approaches and five-fold external validation. The external prediction accuracy for binary models was as high as 91–96%; for continuous models the mean coefficient R2 for regression between predicted versus observed values was 0.76–0.79. We conclude that QSPR models can be used to identify candidate drugs expected to have high remote loading capacity while simultaneously optimizing the design of formulation experiments. PMID:22154932

Emphysema Distribution and Diffusion Capacity Predict Emphysema Progression in Human Immunodeficiency Virus Infection

PubMed Central

Leung, Janice M; Malagoli, Andrea; Santoro, Antonella; Besutti, Giulia; Ligabue, Guido; Scaglioni, Riccardo; Dai, Darlene; Hague, Cameron; Leipsic, Jonathon; Sin, Don D.; Man, SF Paul; Guaraldi, Giovanni

2016-01-01

Background Chronic obstructive pulmonary disease (COPD) and emphysema are common amongst patients with human immunodeficiency virus (HIV). We sought to determine the clinical factors that are associated with emphysema progression in HIV. Methods 345 HIV-infected patients enrolled in an outpatient HIV metabolic clinic with ≥2 chest computed tomography scans made up the study cohort. Images were qualitatively scored for emphysema based on percentage involvement of the lung. Emphysema progression was defined as any increase in emphysema score over the study period. Univariate analyses of clinical, respiratory, and laboratory data, as well as multivariable logistic regression models, were performed to determine clinical features significantly associated with emphysema progression. Results 17.4% of the cohort were emphysema progressors. Emphysema progression was most strongly associated with having a low baseline diffusion capacity of carbon monoxide (DLCO) and having combination centrilobular and paraseptal emphysema distribution. In adjusted models, the odds ratio (OR) for emphysema progression for every 10% increase in DLCO percent predicted was 0.58 (95% confidence interval [CI] 0.41–0.81). The equivalent OR (95% CI) for centrilobular and paraseptal emphysema distribution was 10.60 (2.93–48.98). Together, these variables had an area under the curve (AUC) statistic of 0.85 for predicting emphysema progression. This was an improvement over the performance of spirometry (forced expiratory volume in 1 second to forced vital capacity ratio), which predicted emphysema progression with an AUC of only 0.65. Conclusion Combined paraseptal and centrilobular emphysema distribution and low DLCO could identify HIV patients who may experience emphysema progression. PMID:27902753

Modeling the Capacity of Riverscapes to Support Dam-Building Beaver

NASA Astrophysics Data System (ADS)

Macfarlane, W.; Wheaton, J. M.

2012-12-01

Beaver (Castor canadensis) dam-building activities lead to a cascade of aquatic and riparian effects that increase the complexity of streams. As a result, beaver are increasingly being used as a critical component of passive stream and riparian restoration strategies. We developed the spatially-explicit Beaver Assessment and Restoration Tool (BRAT) to assess the capacity of the landscape in and around streams and rivers to support dam-building activity for beaver. Capacity was assessed in terms of readily available nation-wide GIS datasets to assess key habitat capacity indicators: water availability, relative abundance of preferred food/building materials and stream power. Beaver capacity was further refined by: 1) ungulate grazing capacity 2) proximity to human conflicts (e.g., irrigation diversions, settlements) 3) conservation/management objectives (endangered fish habitat) and 4) projected benefits related to beaver re-introductions (e.g., repair incisions). Fuzzy inference systems were used to assess the relative importance of these inputs which allowed explicit incorporation of uncertainty resulting from categorical ambiguity of inputs into the capacity model. Results indicate that beaver capacity varies widely within the study area, but follows predictable spatial patterns that correspond to distinct River Styles and landscape units. We present a case study application and verification/validation data from the Escalante River Watershed in southern Utah, and show how the models can be used to help resource managers develop and implement restoration and conservation strategies employing beaver that will have the greatest potential to yield increases in biodiversity and ecosystem services.

Research on prediction of agricultural machinery total power based on grey model optimized by genetic algorithm

NASA Astrophysics Data System (ADS)

Xie, Yan; Li, Mu; Zhou, Jin; Zheng, Chang-zheng

2009-07-01

Agricultural machinery total power is an important index to reflex and evaluate the level of agricultural mechanization. It is the power source of agricultural production, and is the main factors to enhance the comprehensive agricultural production capacity expand production scale and increase the income of the farmers. Its demand is affected by natural, economic, technological and social and other "grey" factors. Therefore, grey system theory can be used to analyze the development of agricultural machinery total power. A method based on genetic algorithm optimizing grey modeling process is introduced in this paper. This method makes full use of the advantages of the grey prediction model and characteristics of genetic algorithm to find global optimization. So the prediction model is more accurate. According to data from a province, the GM (1, 1) model for predicting agricultural machinery total power was given based on the grey system theories and genetic algorithm. The result indicates that the model can be used as agricultural machinery total power an effective tool for prediction.

Application of physicochemical properties and process parameters in the development of a neural network model for prediction of tablet characteristics.

PubMed

Sovány, Tamás; Papós, Kitti; Kása, Péter; Ilič, Ilija; Srčič, Stane; Pintye-Hódi, Klára

2013-06-01

The importance of in silico modeling in the pharmaceutical industry is continuously increasing. The aim of the present study was the development of a neural network model for prediction of the postcompressional properties of scored tablets based on the application of existing data sets from our previous studies. Some important process parameters and physicochemical characteristics of the powder mixtures were used as training factors to achieve the best applicability in a wide range of possible compositions. The results demonstrated that, after some pre-processing of the factors, an appropriate prediction performance could be achieved. However, because of the poor extrapolation capacity, broadening of the training data range appears necessary.

Predictive equations for lung volumes from computed tomography for size matching in pulmonary transplantation.

PubMed

Konheim, Jeremy A; Kon, Zachary N; Pasrija, Chetan; Luo, Qingyang; Sanchez, Pablo G; Garcia, Jose P; Griffith, Bartley P; Jeudy, Jean

2016-04-01

Size matching for lung transplantation is widely accomplished using height comparisons between donors and recipients. This gross approximation allows for wide variation in lung size and, potentially, size mismatch. Three-dimensional computed tomography (3D-CT) volumetry comparisons could offer more accurate size matching. Although recipient CT scans are universally available, donor CT scans are rarely performed. Therefore, predicted donor lung volumes could be used for comparison to measured recipient lung volumes, but no such predictive equations exist. We aimed to use 3D-CT volumetry measurements from a normal patient population to generate equations for predicted total lung volume (pTLV), predicted right lung volume (pRLV), and predicted left lung volume (pLLV), for size-matching purposes. Chest CT scans of 400 normal patients were retrospectively evaluated. 3D-CT volumetry was performed to measure total lung volume, right lung volume, and left lung volume of each patient, and predictive equations were generated. The fitted model was tested in a separate group of 100 patients. The model was externally validated by comparison of total lung volume with total lung capacity from pulmonary function tests in a subset of those patients. Age, gender, height, and race were independent predictors of lung volume. In the test group, there were strong linear correlations between predicted and actual lung volumes measured by 3D-CT volumetry for pTLV (r = 0.72), pRLV (r = 0.72), and pLLV (r = 0.69). A strong linear correlation was also observed when comparing pTLV and total lung capacity (r = 0.82). We successfully created a predictive model for pTLV, pRLV, and pLLV. These may serve as reference standards and predict donor lung volume for size matching in lung transplantation. Copyright © 2016 The American Association for Thoracic Surgery. Published by Elsevier Inc. All rights reserved.

Product toxicity and cometabolic competitive inhibition modeling of chloroform and trichloroethylene transformation by methanotrophic resting cells.

PubMed Central

Alvarez-Cohen, L; McCarty, P L

1991-01-01

The rate and capacity for chloroform (CF) and trichloroethylene (TCE) transformation by a mixed methanotrophic culture of resting cells (no exogenous energy source) and formate-fed cells were measured. As reported previously for TCE, formate addition resulted in an increased CF transformation rate (0.35 day-1 for resting cells and 1.5 day-1 for formate-fed cells) and transformation capacity (0.0065 mg of CF per mg of cells for resting cells and 0.015 mg of CF per mg of cells for formate-fed cells), suggesting that depletion of energy stores affects transformation behavior. The observed finite transformation capacity, even with an exogenous energy source, suggests that toxicity was also a factor. CF transformation capacity was significantly lower than that for TCE, suggesting a greater toxicity from CF transformation. The toxicity of CF, TCE, and their transformation products to whole cells was evaluated by comparing the formate oxidation activity of acetylene-treated cells to that of non-acetylene-treated cells with and without prior exposure to CF or TCE. Acetylene arrests the activity of methane monooxygenase in CF and TCE oxidation without halting cell activity toward formate. Significantly diminished formate oxidation by cells exposed to either CR or TCE without acetylene compared with that with acetylene suggests that the solvents themselves were not toxic under the experimental conditions but their transformation products were. The concurrent transformation of CF and TCE by resting cells was measured, and results were compared with predictions from a competitive-inhibition cometabolic transformation model. The reasonable fit between model predictions and experimental observations was supportive of model assumptions. PMID:1905516

Strategies for soil quality assessment using VNIR gyperspectral spectroscopy in a western Kenya Chronosequence

USGS Publications Warehouse

Kinoshita, Rintaro; Moebius-Clune, Bianca N.; van Es, Harold M.; Hively, W. Dean; Bilgilis, A. Volkan

2012-01-01

Visible and near-infrared reflectance spectroscopy (VNIRS) is a rapid and nondestructive method that can predict multiple soil properties simultaneously, but its application in multidimensional soil quality (SQ) assessment in the tropics still needs to be further assessed. In this study, VNIRS (350–2500 nm) was employed to analyze 227 air-dried soil samples of Ultisols from a soil chronosequence in western Kenya and assess 16 SQ indicators. Partial least squares regression (PLSR) was validated using the full-site cross-validation method by grouping samples from each farm or forest site. Most suitable models successfully predicted SQ indicators (R2 ≥ 0.80; ratio of performance to deviation [RPD] ≥ 2.00) including soil organic matter (OMLOI), active C, Ca, cation exchange capacity (CEC), and clay. Moderately-well predicted indicators (0.50 ≤ R2 pwp), and field capacity (Θfc). Poorly predicted indicators (R2 < 0.50; RPD < 1.40) were EC, S, P, available water capacity (AWC), K, Zn, and penetration resistance. Combining VNIRS with selected field- and laboratory-measured SQ indicator values increased predictability. Furthermore, VNIRS showed moderate to substantial agreement in predicting interpretive SQ scores and a composite soil quality index (CSQI) especially when combined with directly measured SQ indicator values. In conclusion, VNIRS has good potential for low cost, rapid assessment of physical and biological SQ indicators but conventional soil chemical tests may need to be retained to provide comprehensive SQ assessments.

Pulmonary function levels as predictors of mortality in a national sample of US adults.

PubMed

Neas, L M; Schwartz, J

1998-06-01

Single breath pulmonary diffusing capacity for carbon monoxide (DL(CO)) was examined as a predictor of all-cause mortality among 4,333 subjects who were aged 25-74 years at baseline in the First National Health and Nutrition Examination Survey (NHANES I) conducted from 1971 to 1975. The relation of the percentage of predicted DL(CO) to all-cause mortality was examined in a Cox proportional hazard model that included age, sex, race, current smoking status, systolic blood pressure, serum cholesterol, alcohol consumption, body mass index, percentage of predicted forced vital capacity (FVC), and the ratio of forced expiratory volume at 1 second (FEV1) to FVC. Mortality had a linear association with the percentage of predicted FVC (rate ratio (RR) = 1.12, 95% confidence interval (CI) 1.08-1.17, for a 10% decrement) and a significantly nonlinear association with the percentage of predicted DL(CO) with an adverse effect that was clearly evident for levels below 85% of those predicted (RR = 1.24, 95% CI 1.12-1.37 for a 10% decrement). The relative hazard for the percentage of predicted DL(CO) below 85% was not modified by sex, smoking status, or exclusion of subjects with clinical respiratory disease on the initial examination. This association with the percentage of predicted DL(CO) was present among 3,005 subjects with FEV1 levels above 90% of those predicted. Thus, pulmonary diffusing capacity below 85% of predicted levels is a significant predictor of the all-cause mortality rate within the general US population independent of standard spirometry measures and even in the absence of apparent clinical respiratory disease.

Effect of heteroscedasticity treatment in residual error models on model calibration and prediction uncertainty estimation

NASA Astrophysics Data System (ADS)

Sun, Ruochen; Yuan, Huiling; Liu, Xiaoli

2017-11-01

The heteroscedasticity treatment in residual error models directly impacts the model calibration and prediction uncertainty estimation. This study compares three methods to deal with the heteroscedasticity, including the explicit linear modeling (LM) method and nonlinear modeling (NL) method using hyperbolic tangent function, as well as the implicit Box-Cox transformation (BC). Then a combined approach (CA) combining the advantages of both LM and BC methods has been proposed. In conjunction with the first order autoregressive model and the skew exponential power (SEP) distribution, four residual error models are generated, namely LM-SEP, NL-SEP, BC-SEP and CA-SEP, and their corresponding likelihood functions are applied to the Variable Infiltration Capacity (VIC) hydrologic model over the Huaihe River basin, China. Results show that the LM-SEP yields the poorest streamflow predictions with the widest uncertainty band and unrealistic negative flows. The NL and BC methods can better deal with the heteroscedasticity and hence their corresponding predictive performances are improved, yet the negative flows cannot be avoided. The CA-SEP produces the most accurate predictions with the highest reliability and effectively avoids the negative flows, because the CA approach is capable of addressing the complicated heteroscedasticity over the study basin.

Multiscale Modeling of Angiogenesis and Predictive Capacity

NASA Astrophysics Data System (ADS)

Pillay, Samara; Byrne, Helen; Maini, Philip

Tumors induce the growth of new blood vessels from existing vasculature through angiogenesis. Using an agent-based approach, we model the behavior of individual endothelial cells during angiogenesis. We incorporate crowding effects through volume exclusion, motility of cells through biased random walks, and include birth and death-like processes. We use the transition probabilities associated with the discrete model and a discrete conservation equation for cell occupancy to determine collective cell behavior, in terms of partial differential equations (PDEs). We derive three PDE models incorporating single, multi-species and no volume exclusion. By fitting the parameters in our PDE models and other well-established continuum models to agent-based simulations during a specific time period, and then comparing the outputs from the PDE models and agent-based model at later times, we aim to determine how well the PDE models predict the future behavior of the agent-based model. We also determine whether predictions differ across PDE models and the significance of those differences. This may impact drug development strategies based on PDE models.

Numerical weather prediction model tuning via ensemble prediction system

NASA Astrophysics Data System (ADS)

Jarvinen, H.; Laine, M.; Ollinaho, P.; Solonen, A.; Haario, H.

2011-12-01

This paper discusses a novel approach to tune predictive skill of numerical weather prediction (NWP) models. NWP models contain tunable parameters which appear in parameterizations schemes of sub-grid scale physical processes. Currently, numerical values of these parameters are specified manually. In a recent dual manuscript (QJRMS, revised) we developed a new concept and method for on-line estimation of the NWP model parameters. The EPPES ("Ensemble prediction and parameter estimation system") method requires only minimal changes to the existing operational ensemble prediction infra-structure and it seems very cost-effective because practically no new computations are introduced. The approach provides an algorithmic decision making tool for model parameter optimization in operational NWP. In EPPES, statistical inference about the NWP model tunable parameters is made by (i) generating each member of the ensemble of predictions using different model parameter values, drawn from a proposal distribution, and (ii) feeding-back the relative merits of the parameter values to the proposal distribution, based on evaluation of a suitable likelihood function against verifying observations. In the presentation, the method is first illustrated in low-order numerical tests using a stochastic version of the Lorenz-95 model which effectively emulates the principal features of ensemble prediction systems. The EPPES method correctly detects the unknown and wrongly specified parameters values, and leads to an improved forecast skill. Second, results with an atmospheric general circulation model based ensemble prediction system show that the NWP model tuning capacity of EPPES scales up to realistic models and ensemble prediction systems. Finally, a global top-end NWP model tuning exercise with preliminary results is published.

Recent tests of the equilibrium-point hypothesis (lambda model).

PubMed

Feldman, A G; Ostry, D J; Levin, M F; Gribble, P L; Mitnitski, A B

1998-07-01

The lambda model of the equilibrium-point hypothesis (Feldman & Levin, 1995) is an approach to motor control which, like physics, is based on a logical system coordinating empirical data. The model has gone through an interesting period. On one hand, several nontrivial predictions of the model have been successfully verified in recent studies. In addition, the explanatory and predictive capacity of the model has been enhanced by its extension to multimuscle and multijoint systems. On the other hand, claims have recently appeared suggesting that the model should be abandoned. The present paper focuses on these claims and concludes that they are unfounded. Much of the experimental data that have been used to reject the model are actually consistent with it.

System capacity and economic modeling computer tool for satellite mobile communications systems

NASA Technical Reports Server (NTRS)

Wiedeman, Robert A.; Wen, Doong; Mccracken, Albert G.

1988-01-01

A unique computer modeling tool that combines an engineering tool with a financial analysis program is described. The resulting combination yields a flexible economic model that can predict the cost effectiveness of various mobile systems. Cost modeling is necessary in order to ascertain if a given system with a finite satellite resource is capable of supporting itself financially and to determine what services can be supported. Personal computer techniques using Lotus 123 are used for the model in order to provide as universal an application as possible such that the model can be used and modified to fit many situations and conditions. The output of the engineering portion of the model consists of a channel capacity analysis and link calculations for several qualities of service using up to 16 types of earth terminal configurations. The outputs of the financial model are a revenue analysis, an income statement, and a cost model validation section.

Effect of the composition of extra virgin olive oils on the differentiation and antioxidant capacities of twelve monovarietals.

PubMed

Fuentes, Edwar; Paucar, Fiorela; Tapia, Francisco; Ortiz, Jaime; Jimenez, Paula; Romero, Nalda

2018-03-15

The effect of the composition of twelve varieties of extra virgin olive oils (EVOOs) on their differentiation based in agronomic criteria and on the antioxidant capacity was studied. Principal component analysis permitted an overview of the samples and their compositions, showing evidence of grouping and correlation between antioxidant capacity, oleuropein and ligstroside derivatives (OLD) and specific extinction at 270. Oleic and linoleic acids, 3,4-DHPEA-EA and p-HPEA-EDA (OLD), unsaturated/saturated ratio and induction time (IT) allowed the correct classification of samples according to year of harvest, ripening stage and variety. The antioxidant capacity of EVOOs was satisfactory predicted through a partial least square model based on ΔK, hydroxytyrosol, pinoresinol, oleuropein derivate and IT. Validation of the model gave a correlation R>0.83 and an error of 7% for independent samples. This model could be a useful tool for the olive industry to highlight the nutritional quality of EVOOs and improve their marketing. Copyright © 2017 Elsevier Ltd. All rights reserved.

Reducing hydrologic model uncertainty in monthly streamflow predictions using multimodel combination

NASA Astrophysics Data System (ADS)

Li, Weihua; Sankarasubramanian, A.

2012-12-01

Model errors are inevitable in any prediction exercise. One approach that is currently gaining attention in reducing model errors is by combining multiple models to develop improved predictions. The rationale behind this approach primarily lies on the premise that optimal weights could be derived for each model so that the developed multimodel predictions will result in improved predictions. A new dynamic approach (MM-1) to combine multiple hydrological models by evaluating their performance/skill contingent on the predictor state is proposed. We combine two hydrological models, "abcd" model and variable infiltration capacity (VIC) model, to develop multimodel streamflow predictions. To quantify precisely under what conditions the multimodel combination results in improved predictions, we compare multimodel scheme MM-1 with optimal model combination scheme (MM-O) by employing them in predicting the streamflow generated from a known hydrologic model (abcd model orVICmodel) with heteroscedastic error variance as well as from a hydrologic model that exhibits different structure than that of the candidate models (i.e., "abcd" model or VIC model). Results from the study show that streamflow estimated from single models performed better than multimodels under almost no measurement error. However, under increased measurement errors and model structural misspecification, both multimodel schemes (MM-1 and MM-O) consistently performed better than the single model prediction. Overall, MM-1 performs better than MM-O in predicting the monthly flow values as well as in predicting extreme monthly flows. Comparison of the weights obtained from each candidate model reveals that as measurement errors increase, MM-1 assigns weights equally for all the models, whereas MM-O assigns higher weights for always the best-performing candidate model under the calibration period. Applying the multimodel algorithms for predicting streamflows over four different sites revealed that MM-1 performs better than all single models and optimal model combination scheme, MM-O, in predicting the monthly flows as well as the flows during wetter months.

Cognitive functioning in toddlerhood: The role of gestational age, attention capacities, and maternal stimulation.

PubMed

de Jong, Marjanneke; Verhoeven, Marjolein; Hooge, Ignace T C; Maingay-Visser, Arnoldina P G F; Spanjerberg, Louise; van Baar, Anneloes L

2018-04-01

Why do many preterm children show delays in development? An integrated model of biological risk, children's capacities, and maternal stimulation was investigated in relation to cognitive functioning at toddler age. Participants were 200 Dutch children (gestational age = 32-41 weeks); 51% boys, 96% Dutch nationality, 71.5% highly educated mothers. At 18 months, attention capacities were measured using eye-tracking, and maternal attention-directing behavior was observed. Cognitive functioning was measured at 24 months using the Bayley-III-NL. Cognitive functioning was directly predicted by children's attention capacities and maternal attention-maintaining behavior. Gestational age was indirectly related to cognitive functioning through children's attention capacities and through maternal attention-redirecting behavior. In this way, a combination of gestational age, children's attention capacities, and maternal stimulation was associated with early cognitive development. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Early warnings of the potential for malaria transmission in Rural Africa using the Hydrology, Entomology and Malaria Transmission Simulator (HYDREMATS)

NASA Astrophysics Data System (ADS)

Yamana, T. K.; Eltahir, E. A.

2010-12-01

Early warnings of malaria transmission allow health officials to better prepare for future epidemics. Monitoring rainfall is recognized as an important part of malaria early warning systems, as outlined by the Roll Back Malaria Initiative. The Hydrology, Entomology and Malaria Simulator (HYDREMATS) is a mechanistic model that relates rainfall to malaria transmission, and could be used to provide early warnings of malaria epidemics. HYDREMATS is used to make predictions of mosquito populations and vectorial capacity for 2005, 2006, and 2007 in Banizoumbou village in western Niger. HYDREMATS is forced by observed rainfall, followed by a rainfall prediction based on the seasonal mean rainfall for a period two or four weeks into the future. Predictions made using this method provided reasonable estimates of mosquito populations and vectorial capacity, two to four weeks in advance. The predictions were significantly improved compared to those made when HYDREMATS was forced with seasonal mean rainfall alone.

Stata Modules for Calculating Novel Predictive Performance Indices for Logistic Models.

PubMed

Barkhordari, Mahnaz; Padyab, Mojgan; Hadaegh, Farzad; Azizi, Fereidoun; Bozorgmanesh, Mohammadreza

2016-01-01

Prediction is a fundamental part of prevention of cardiovascular diseases (CVD). The development of prediction algorithms based on the multivariate regression models loomed several decades ago. Parallel with predictive models development, biomarker researches emerged in an impressively great scale. The key question is how best to assess and quantify the improvement in risk prediction offered by new biomarkers or more basically how to assess the performance of a risk prediction model. Discrimination, calibration, and added predictive value have been recently suggested to be used while comparing the predictive performances of the predictive models' with and without novel biomarkers. Lack of user-friendly statistical software has restricted implementation of novel model assessment methods while examining novel biomarkers. We intended, thus, to develop a user-friendly software that could be used by researchers with few programming skills. We have written a Stata command that is intended to help researchers obtain cut point-free and cut point-based net reclassification improvement index and (NRI) and relative and absolute Integrated discriminatory improvement index (IDI) for logistic-based regression analyses.We applied the commands to a real data on women participating the Tehran lipid and glucose study (TLGS) to examine if information of a family history of premature CVD, waist circumference, and fasting plasma glucose can improve predictive performance of the Framingham's "general CVD risk" algorithm. The command is addpred for logistic regression models. The Stata package provided herein can encourage the use of novel methods in examining predictive capacity of ever-emerging plethora of novel biomarkers.

Morphological features of IFN-γ–stimulated mesenchymal stromal cells predict overall immunosuppressive capacity

PubMed Central

Klinker, Matthew W.; Marklein, Ross A.; Lo Surdo, Jessica L.; Wei, Cheng-Hong

2017-01-01

Human mesenchymal stromal cell (MSC) lines can vary significantly in their functional characteristics, and the effectiveness of MSC-based therapeutics may be realized by finding predictive features associated with MSC function. To identify features associated with immunosuppressive capacity in MSCs, we developed a robust in vitro assay that uses principal-component analysis to integrate multidimensional flow cytometry data into a single measurement of MSC-mediated inhibition of T-cell activation. We used this assay to correlate single-cell morphological data with overall immunosuppressive capacity in a cohort of MSC lines derived from different donors and manufacturing conditions. MSC morphology after IFN-γ stimulation significantly correlated with immunosuppressive capacity and accurately predicted the immunosuppressive capacity of MSC lines in a validation cohort. IFN-γ enhanced the immunosuppressive capacity of all MSC lines, and morphology predicted the magnitude of IFN-γ–enhanced immunosuppressive activity. Together, these data identify MSC morphology as a predictive feature of MSC immunosuppressive function. PMID:28283659

Predicting the scanning branches of hysteretic soil water-retention capacity with use of the method of mathematical modeling

NASA Astrophysics Data System (ADS)

Terleev, V.; Ginevsky, R.; Lazarev, V.; Nikonorov, A.; Togo, I.; Topaj, A.; Moiseev, K.; Abakumov, E.; Melnichuk, A.; Dunaieva, I.

2017-10-01

A mathematical model of the hysteresis of the water-retention capacity of the soil is proposed. The parameters of the model are interpreted within the framework of physical concepts of the structure and capillary properties of soil pores. On the basis of the model, a computer program with an interface that allows for dialogue with the user is developed. The program has some of options: visualization of experimental data; identification of the model parameters with use of measured data by means of an optimizing algorithm; graphical presentation of the hysteresis loop with application of the assigned parameters. Using the program, computational experiments were carried out, which consisted in verifying the identifiability of the model parameters from data on the main branches, and also in testing the ability to predict the scanning branches of the hysteresis loop. For the experiments, literature data on two sandy soils were used. The absence of an “artificial pump effect” is proved. A sufficiently high accuracy of the prediction of the scanning branches of the hysteresis loop has been achieved in comparison with the three models of the precursors. The practical importance of the proposed model and computer program, which is developed on its basis, is to ensure the calculation of precision irrigation rates. The application of such rates in irrigation farming will help to prevent excess moisture from flowing beyond the root layer of the soil and, thus, minimize the unproductive loss of irrigation water and agrochemicals, as well as reduce the risk of groundwater contamination and natural water eutrophication.

Modeling impacts of management on carbon sequestration and trace gas emissions in forested wetland ecosystems

Treesearch

Changsheng Li; Jianbo Cui

2004-01-01

A process- based model, Wetland-DNDC, was modified to enhance its capacity to predict the impacts of management practices on carbon sequestration in and trace gas emissions from forested wetland ecosystems. The modifications included parameterization of management practices fe.g., forest harvest, chopping, burning, water management, fertilization, and tree planting),...

Modeling Reader and Text Interactions during Narrative Comprehension: A Test of the Lexical Quality Hypothesis

ERIC Educational Resources Information Center

Hamilton, Stephen T.; Freed, Erin M.; Long, Debra L.

2013-01-01

The goal of this study was to examine predictions derived from the Lexical Quality Hypothesis regarding relations among word decoding, working-memory capacity, and the ability to integrate new concepts into a developing discourse representation. Hierarchical Linear Modeling was used to quantify the effects of three text properties (length,…

Development and validation of a physiology-based model for the prediction of pharmacokinetics/toxicokinetics in rabbits

PubMed Central

Hermes, Helen E.; Teutonico, Donato; Preuss, Thomas G.; Schneckener, Sebastian

2018-01-01

The environmental fates of pharmaceuticals and the effects of crop protection products on non-target species are subjects that are undergoing intense review. Since measuring the concentrations and effects of xenobiotics on all affected species under all conceivable scenarios is not feasible, standard laboratory animals such as rabbits are tested, and the observed adverse effects are translated to focal species for environmental risk assessments. In that respect, mathematical modelling is becoming increasingly important for evaluating the consequences of pesticides in untested scenarios. In particular, physiologically based pharmacokinetic/toxicokinetic (PBPK/TK) modelling is a well-established methodology used to predict tissue concentrations based on the absorption, distribution, metabolism and excretion of drugs and toxicants. In the present work, a rabbit PBPK/TK model is developed and evaluated with data available from the literature. The model predictions include scenarios of both intravenous (i.v.) and oral (p.o.) administration of small and large compounds. The presented rabbit PBPK/TK model predicts the pharmacokinetics (Cmax, AUC) of the tested compounds with an average 1.7-fold error. This result indicates a good predictive capacity of the model, which enables its use for risk assessment modelling and simulations. PMID:29561908

Cloud-Based Numerical Weather Prediction for Near Real-Time Forecasting and Disaster Response

NASA Technical Reports Server (NTRS)

Molthan, Andrew; Case, Jonathan; Venners, Jason; Schroeder, Richard; Checchi, Milton; Zavodsky, Bradley; Limaye, Ashutosh; O'Brien, Raymond

2015-01-01

The use of cloud computing resources continues to grow within the public and private sector components of the weather enterprise as users become more familiar with cloud-computing concepts, and competition among service providers continues to reduce costs and other barriers to entry. Cloud resources can also provide capabilities similar to high-performance computing environments, supporting multi-node systems required for near real-time, regional weather predictions. Referred to as "Infrastructure as a Service", or IaaS, the use of cloud-based computing hardware in an on-demand payment system allows for rapid deployment of a modeling system in environments lacking access to a large, supercomputing infrastructure. Use of IaaS capabilities to support regional weather prediction may be of particular interest to developing countries that have not yet established large supercomputing resources, but would otherwise benefit from a regional weather forecasting capability. Recently, collaborators from NASA Marshall Space Flight Center and Ames Research Center have developed a scripted, on-demand capability for launching the NOAA/NWS Science and Training Resource Center (STRC) Environmental Modeling System (EMS), which includes pre-compiled binaries of the latest version of the Weather Research and Forecasting (WRF) model. The WRF-EMS provides scripting for downloading appropriate initial and boundary conditions from global models, along with higher-resolution vegetation, land surface, and sea surface temperature data sets provided by the NASA Short-term Prediction Research and Transition (SPoRT) Center. This presentation will provide an overview of the modeling system capabilities and benchmarks performed on the Amazon Elastic Compute Cloud (EC2) environment. In addition, the presentation will discuss future opportunities to deploy the system in support of weather prediction in developing countries supported by NASA's SERVIR Project, which provides capacity building activities in environmental monitoring and prediction across a growing number of regional hubs throughout the world. Capacity-building applications that extend numerical weather prediction to developing countries are intended to provide near real-time applications to benefit public health, safety, and economic interests, but may have a greater impact during disaster events by providing a source for local predictions of weather-related hazards, or impacts that local weather events may have during the recovery phase.

Determining the Utility Value of Water-Supply Interconnections.

ERIC Educational Resources Information Center

Hardman, James L.; Cheremisinoff, Paul N.

1979-01-01

This article is the third in a series which discusses a mathematical methodology for evaluating interconnections of water supply systems. The model can be used to analyze the carrying capacity of proposed links or predict the impact of abandoning interconnections. (AS)

Analysis of the heat capacity for pure CH4 and CH4/CCl4 on graphite near the melting point and calculation of the T-X phase diagram for (CH3)CCl3 + CCl4

NASA Astrophysics Data System (ADS)

Yurtseven, Hamit; Yılmaz, Aygül

2016-06-01

We study the temperature dependence of the heat capacity Cp for the pure CH4 and the coadsorbed CH4/CCl4 on graphite near the melting point. The heat capacity peaks are analyzed using the experimental data from the literature by means of the power-law formula. The critical exponents for the heat capacity are deduced below and above the melting point for CH4 (Tm = 104.8 K) and CH4/CCl4 (Tm = 99.2 K). Our exponent values are larger as compared with the predicted values of some theoretical models exhibiting second order transition. Our analyses indicate that the pure methane shows a nearly second order (weak discontinuity in the heat capacity peak), whereas the transition in coadsorbed CH4/CCl4 is of first order (apparent discontinuity in Cp). We also study the T - X phase diagram of a two-component system of CH3CCl3+CCl4 using the Landau phenomenological model. Phase lines of the R+L (rhombohedral+liquid) and FCC+L (face-centred cubic + liquid) are calculated using the observed T - X phase diagram of this binary mixture. Our results show that the Landau mean field theory describes the observed behavior of CH3CCl3+CCl4 adequately. From the calculated T - X phase diagram, critical behavior of some thermodynamic quantities can be predicted at various temperatures and concentrations (CCl4) for a binary mixture of CH3CCl3+CCl4.

Assessment of wetland productive capacity from a remote-sensing-based model - A NASA/NMFS joint research project

NASA Technical Reports Server (NTRS)

Butera, M. K.; Frick, A. L.; Browder, J.

1983-01-01

NASA and the U.S. National Marine Fisheries Service have undertaken the development of Landsat Thematic Mapper (TM) technology for the evaluation of the usefulness of wetlands to estuarine fish and shellfish production. Toward this end, a remote sensing-based Productive Capacity model has been developed which characterizes the biological and hydrographic features of a Gulf Coast Marsh to predict detrital export. Regression analyses of TM simulator data for wetland plant production estimation are noted to more accurately estimate the percent of total vegetative cover than biomass, indicating that a nonlinear relationship may be involved.

A simple method to predict regional fish abundance: an example in the McKenzie River Basin, Oregon

Treesearch

D.J. McGarvey; J.M. Johnston

2011-01-01

Regional assessments of fisheries resources are increasingly called for, but tools with which to perform them are limited. We present a simple method that can be used to estimate regional carrying capacity and apply it to the McKenzie River Basin, Oregon. First, we use a macroecological model to predict trout densities within small, medium, and large streams in the...

Logistic Regression Analyses for Predicting Clinically Important Differences in Motor Capacity, Motor Performance, and Functional Independence after Constraint-Induced Therapy in Children with Cerebral Palsy

ERIC Educational Resources Information Center

Wang, Tien-ni; Wu, Ching-yi; Chen, Chia-ling; Shieh, Jeng-yi; Lu, Lu; Lin, Keh-chung

2013-01-01

Given the growing evidence for the effects of constraint-induced therapy (CIT) in children with cerebral palsy (CP), there is a need for investigating the characteristics of potential participants who may benefit most from this intervention. This study aimed to establish predictive models for the effects of pediatric CIT on motor and functional…

Metabolic states following accumulation of intracellular aggregates: implications for neurodegenerative diseases.

PubMed

Vazquez, Alexei

2013-01-01

The formation of intracellular aggregates is a common etiology of several neurodegenerative diseases. Mitochondrial defects and oxidative stress has been pointed as the major mechanistic links between the accumulation of intracellular aggregates and cell death. In this work we propose a "metabolic cell death by overcrowding" as an alternative hypothesis. Using a model of neuron metabolism, we predict that as the concentration of protein aggregates increases the neurons transit through three different metabolic phases. The first phase (0-6 mM) corresponds with the normal neuron state, where the neuronal activity is sustained by the oxidative phosphorylation of lactate. The second phase (6-8.6 mM) is characterized by a mixed utilization of lactate and glucose as energy substrates and a switch from ammonia uptake to ammonia release by neurons. In the third phase (8.6-9.3 mM) neurons are predicted to support their energy demands from glycolysis and an alternative pathway for energy generation, involving reactions from serine synthesis, one carbon metabolism and the glycine cleavage system. The model also predicts a decrease in the maximum neuronal capacity for energy generation with increasing the concentration of protein aggregates. Ultimately this maximum capacity becomes zero when the protein aggregates reach a concentration of about 9.3 mM, predicting the cessation of neuronal activity.

Cognitive niches: an ecological model of strategy selection.

PubMed

Marewski, Julian N; Schooler, Lael J

2011-07-01

How do people select among different strategies to accomplish a given task? Across disciplines, the strategy selection problem represents a major challenge. We propose a quantitative model that predicts how selection emerges through the interplay among strategies, cognitive capacities, and the environment. This interplay carves out for each strategy a cognitive niche, that is, a limited number of situations in which the strategy can be applied, simplifying strategy selection. To illustrate our proposal, we consider selection in the context of 2 theories: the simple heuristics framework and the ACT-R (adaptive control of thought-rational) architecture of cognition. From the heuristics framework, we adopt the thesis that people make decisions by selecting from a repertoire of simple decision strategies that exploit regularities in the environment and draw on cognitive capacities, such as memory and time perception. ACT-R provides a quantitative theory of how these capacities adapt to the environment. In 14 simulations and 10 experiments, we consider the choice between strategies that operate on the accessibility of memories and those that depend on elaborate knowledge about the world. Based on Internet statistics, our model quantitatively predicts people's familiarity with and knowledge of real-world objects, the distributional characteristics of the associated speed of memory retrieval, and the cognitive niches of classic decision strategies, including those of the fluency, recognition, integration, lexicographic, and sequential-sampling heuristics. In doing so, the model specifies when people will be able to apply different strategies and how accurate, fast, and effortless people's decisions will be.

Use of comorbidity measures to predict the risk of death in Brazilian in-patients.

PubMed

Martins, Monica

2010-06-01

To assess the use of comorbidity measures to predict the risk of death in Brazilian in-patients. Data from the Sistema de Informações Hospitalares do Sistema Unico de Saúde (Unified Health System Hospital Information System) were used, which enables only one secondary diagnosis to be recorded. A total of 1,607,697 hospitalizations were selected, all of which occurred in Brazil, between 2003 and 2004, and whose main diagnoses were: ischemic heart disease, congestive cardiac failure, stroke and pneumonia. Charlson Index and Elixhauser comorbidities were the comorbidity measures used. In addition, the simple record of a certain secondary diagnosis was also used. Logistic regression was applied to assess the impact of comorbidity measures on the estimate of risk of death. The baseline model included the following variables: age, sex and main diagnosis. Models to predict death were assessed, based on C-statistic and Hosmer-Lemeshow test. Hospital mortality rate was 10.4% and mean length of stay was 5.7 days. The majority (52%) of hospitalizations occurred among men and mean age was 62.6 years. Of all hospitalizations, 5.4% included a recorded secondary diagnosis, although the odds ratio between death and presence of comorbidity was 1.93. The baseline model showed a discriminatory capacity (C-statistic) of 0.685. The improvement in the models, attributed to the introduction of comorbidity indices, was poor, equivalent to zero when C-statistic with only two digits was considered. Although the introduction of three comorbidity measures in distinct models to predict death improved the predictive capacity of the baseline model, the values obtained are still considered insufficient. The accuracy of this type of measure is influenced by the completeness of the source of information. In this sense, high underreporting of secondary diagnosis, in addition to the well-known lack of space to note down this type of information in the Sistema de Informações Hospitalares, are the main explanatory factors for the results found.

Rationale of the Spanish FRAX model in decision-making for predicting osteoporotic fractures: an update of FRIDEX cohort of Spanish women.

PubMed

Azagra, Rafael; Zwart, Marta; Encabo, Gloria; Aguyé, Amada; Martin-Sánchez, Juan Carlos; Puchol-Ruiz, Nuria; Gabriel-Escoda, Paula; Ortiz-Alinque, Sergio; Gené, Emilio; Iglesias, Milagros; Moriña, David; Diaz-Herrera, Miguel Angel; Utzet, Mireia; Manresa, Josep Maria

2016-06-17

The FRAX® tool estimates the risk of a fragility fracture among the population and many countries have been evaluating its performance among their populations since its creation in 2007. The purpose of this study is to update the first FRIDEX cohort analysis comparing FRAX with the bone mineral density (BMD) model, and its predictive abilities. The discriminatory ability of the FRAX was assessed using the 'area under curve' of the receiver operating characteristic (AUC-ROC). Predictive ability was assessed by comparing estimated risk fractures with incidence fractures after a 10-year follow up period. One thousand three hundred eight women ≥ 40 and ≤ 90 years followed up during a 10-year period. The AUC for major osteoporotic fractures using FRAX without DXA was 0.686 (95 % CI 0.630-0.742) and using FN T-score of DXA 0.714 (95 % CI 0.661-0.767). Using only the traditional parameters of DXA (FN T-score), the AUC was 0.706 (95 % CI 0.652-0.760). The AUC for hip osteoporotic fracture was 0.883 (95 % CI 0.827-0.938), 0.857 (95 % CI 0.773-0.941), and 0.814 (95 % CI 0.712-0.916) respectively. For major osteoporotic fractures, the overall predictive value using the ratio Observed fractures/Expected fractures calculated with FRAX without T-score of DXA was 2.29 and for hip fractures 2.28 and with the inclusion of the T-score 2.01 and 1.83 respectively. However, for hip fracture in women < 65 years was 1.53 and 1.24 respectively. The FRAX tool has been found to show a good discriminatory capacity for detecting women at high risk of fragility fracture, and is better for hip fracture than major fracture. The test of sensibility shows that it is, at least, not inferior than when using BMD model alone. The predictive capacity of FRAX tool needs some adjustment. This capacity is better for hip fracture prediction and better for women < 65 years. Further studies in Catalonia and other regions of Spain are needed to fine tune the FRAX tool's predictive capability.

Prognostics of lithium-ion batteries based on Dempster-Shafer theory and the Bayesian Monte Carlo method

NASA Astrophysics Data System (ADS)

He, Wei; Williard, Nicholas; Osterman, Michael; Pecht, Michael

A new method for state of health (SOH) and remaining useful life (RUL) estimations for lithium-ion batteries using Dempster-Shafer theory (DST) and the Bayesian Monte Carlo (BMC) method is proposed. In this work, an empirical model based on the physical degradation behavior of lithium-ion batteries is developed. Model parameters are initialized by combining sets of training data based on DST. BMC is then used to update the model parameters and predict the RUL based on available data through battery capacity monitoring. As more data become available, the accuracy of the model in predicting RUL improves. Two case studies demonstrating this approach are presented.

Exploring application of the Interpersonal-Psychological Theory of Suicidal Behaviour to self-injurious behaviour among women prisoners: Proposing a new model of understanding.

PubMed

Ireland, Jane L; York, Charlotte

2012-01-01

The current study examines the application of capacity, psychological distress, coping and personality to an understanding of self-injurious behaviour, with a specific focus on testing the Interpersonal-Psychological Theory of Suicidal Behaviour (IPTSB). One hundred and ninety women prisoners took part, completing a history questionnaire and measures of personality, coping styles and psychological distress. It was expected that self-injurious behaviour would be predicted by higher levels of emotional functioning difficulties, by an increased capacity to engage in such behaviours, by previous self-injurious behaviour, decreased levels of emotional stability and increased levels of emotional coping behaviour. Results supported the capacity component of the IPTSB, indicating that an increased history of self-injurious behaviour and of engagement in reckless behaviour were particular predictors. Increased psychological distress in some domains was also a predictor although the exact domain varied across the type of self-injurious engagement Increased levels of extraversion and decreased emotional coping predicted increased self-injurious engagement, although emotional coping only related to threats and cognition. The results point to the applicability of Interpersonal-Psychological Theory to understanding self-injurious behaviour and the importance of developing a revised model. The paper presents this in the form of the Integrated Model of Self-Injurious Activity. Copyright © 2011 Elsevier Ltd. All rights reserved.

Modelling equilibrium adsorption of single, binary, and ternary combinations of Cu, Pb, and Zn onto granular activated carbon.

PubMed

Loganathan, Paripurnanda; Shim, Wang Geun; Sounthararajah, Danious Pratheep; Kalaruban, Mahatheva; Nur, Tanjina; Vigneswaran, Saravanamuthu

2018-03-30

Elevated concentrations of heavy metals in water can be toxic to humans, animals, and aquatic organisms. A study was conducted on the removal of Cu, Pb, and Zn by a commonly used water treatment adsorbent, granular activated carbon (GAC), from three single, three binary (Cu-Pb, Cu-Zn, Pb-Zn), and one ternary (Cu-Pb-Zn) combination of metals. It also investigated seven mathematical models on their suitability to predict the metals adsorption capacities. Adsorption of Cu, Pb, and Zn increased with pH with an abrupt increase in adsorption at around pH 5.5, 4.5, and 6.0, respectively. At all pHs tested (2.5-7.0), the adsorption capacity followed the order Pb > Cu > Zn. The Langmuir and Sips models fitted better than the Freundlich model to the data in the single-metal system at pH 5. The Langmuir maximum adsorption capacities of Pb, Cu, and Zn (mmol/g) obtained from the model's fits were 0.142, 0.094, and 0.058, respectively. The adsorption capacities (mmol/g) for these metals at 0.01 mmol/L equilibrium liquid concentration were 0.130, 0.085, and 0.040, respectively. Ideal Adsorbed Solution (IAS)-Langmuir and IAS-Sips models fitted well to the binary and ternary metals adsorption data, whereas the Extended Langmuir and Extended Sips models' fits to the data were poor. The selectivity of adsorption followed the same order as the metals' capacities and affinities of adsorption in the single-metal systems.

Sediment Transport Capacity of Turbidity Currents: from Microscale to Geological Scale.

NASA Astrophysics Data System (ADS)

Eggenhuisen, J. T.; Tilston, M.; Cartigny, M.; Pohl, F.; de Leeuw, J.; van der Grind, G. J.

2016-12-01

A big question in sedimentology concerns the magnitude of fluxes of sediment particles, solute matter and dissolved gasses from shallow marine waters to deep basins by turbidity current flow. Here we establish sediment transport capacity of turbidity current flow on three levels. The most elementary level is set by the maximum amount of sediment that can be contained at the base of turbidity currents without causing complete extinction of boundary layer turbulence. The second level concerns the capacity in a vertical column within turbidity currents. The third level involves the amount of sediment that can be transported in turbidite systems on geological timescales. The capacity parameter Γ compares turbulent forces near the boundary of a turbulent suspension to gravity and buoyancy forces acting on suspended particles. The condition of Γ>1 coincides with complete suppression of coherent boundary layer turbulence in Direct Numerical Simulations of sediment-laden turbulent flow. Γ=1 coincides with the upper limit of observed suspended particle concentrations in flume and field measurements. Γ is grainsize independent, yet capacity of the full vertical structure of turbidity currents becomes grainsize dependent. This is due to the appearance of grainsize dependent vertical motions within turbulence as a primary control on the shape of the vertical concentration profile. We illustrate this dependence with experiments and theory and conclude that capacity depends on the competence of prevailing turbulence to suspend particle sizes. The concepts of capacity and competence are thus tangled. Finally, the capacity of turbidity current flow structure is coupled to geological constraints on recurrence times, channel and lobe life cycles, and allogenic forcing on system activity to arrive at system scale sediment transport capacity. We demonstrate a simple model that uses the fundamental process insight described above to estimate geological sediment budgets from architectural information. These predictions are tied to existing S2S analyses to constrain submarine channel and fan dimensions in ancient and subsurface systems. Predictions of sediment budgets in deep marine systems rely on integration of fundamental issues in turbulent particle suspension into geological models of turbidite systems.

Statistical analysis for understanding and predicting battery degradations in real-life electric vehicle use

NASA Astrophysics Data System (ADS)

Barré, Anthony; Suard, Frédéric; Gérard, Mathias; Montaru, Maxime; Riu, Delphine

2014-01-01

This paper describes the statistical analysis of recorded data parameters of electrical battery ageing during electric vehicle use. These data permit traditional battery ageing investigation based on the evolution of the capacity fade and resistance raise. The measured variables are examined in order to explain the correlation between battery ageing and operating conditions during experiments. Such study enables us to identify the main ageing factors. Then, detailed statistical dependency explorations present the responsible factors on battery ageing phenomena. Predictive battery ageing models are built from this approach. Thereby results demonstrate and quantify a relationship between variables and battery ageing global observations, and also allow accurate battery ageing diagnosis through predictive models.

Single well productivity prediction of carbonate reservoir

NASA Astrophysics Data System (ADS)

Le, Xu

2018-06-01

It is very important to predict the single-well productivity for the development of oilfields. The fracture structure of carbonate fractured-cavity reservoirs is complex, and the change of single-well productivity is inconsistent with that of sandstone reservoir. Therefore, the establishment of carbonate oil well productivity It is very important. Based on reservoir reality, three different methods for predicting the productivity of carbonate reservoirs have been established based on different types of reservoirs. (1) To qualitatively analyze the single-well capacity relations corresponding to different reservoir types, predict the production capacity according to the different wells encountered by single well; (2) Predict the productivity of carbonate reservoir wells by using numerical simulation technology; (3) According to the historical production data of oil well, fit the relevant capacity formula and make single-well productivity prediction; (4) Predict the production capacity by using oil well productivity formula of carbonate reservoir.

Evaluation of free flow speeds on interrupted flow facilities.

DOT National Transportation Integrated Search

2013-05-01

The efficacy of the Florida Department of Transportation (FDOT) simple model of predicting segment free flow speed by adding 5 miles per hour (mph) to the posted speed limit was compared to the performance of the new 2010 Highway Capacity Manual (HCM...

Decreased integration and information capacity in stroke measured by whole brain models of resting state activity.

PubMed

Adhikari, Mohit H; Hacker, Carl D; Siegel, Josh S; Griffa, Alessandra; Hagmann, Patric; Deco, Gustavo; Corbetta, Maurizio

2017-04-01

While several studies have shown that focal lesions affect the communication between structurally normal regions of the brain, and that these changes may correlate with behavioural deficits, their impact on brain's information processing capacity is currently unknown. Here we test the hypothesis that focal lesions decrease the brain's information processing capacity, of which changes in functional connectivity may be a measurable correlate. To measure processing capacity, we turned to whole brain computational modelling to estimate the integration and segregation of information in brain networks. First, we measured functional connectivity between different brain areas with resting state functional magnetic resonance imaging in healthy subjects (n = 26), and subjects who had suffered a cortical stroke (n = 36). We then used a whole-brain network model that coupled average excitatory activities of local regions via anatomical connectivity. Model parameters were optimized in each healthy or stroke participant to maximize correlation between model and empirical functional connectivity, so that the model's effective connectivity was a veridical representation of healthy or lesioned brain networks. Subsequently, we calculated two model-based measures: 'integration', a graph theoretical measure obtained from functional connectivity, which measures the connectedness of brain networks, and 'information capacity', an information theoretical measure that cannot be obtained empirically, representative of the segregative ability of brain networks to encode distinct stimuli. We found that both measures were decreased in stroke patients, as compared to healthy controls, particularly at the level of resting-state networks. Furthermore, we found that these measures, especially information capacity, correlate with measures of behavioural impairment and the segregation of resting-state networks empirically measured. This study shows that focal lesions affect the brain's ability to represent stimuli and task states, and that information capacity measured through whole brain models is a theory-driven measure of processing capacity that could be used as a biomarker of injury for outcome prediction or target for rehabilitation intervention. © The Author (2017). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Assessing Community Coalition Capacity and its Association with Underage Drinking Prevention Effectiveness in the Context of the SPF SIG.

PubMed

Flewelling, Robert L; Hanley, Sean M

2016-10-01

Community coalitions are a prominent organizational structure through which community-based substance abuse prevention efforts are implemented. There is little empirical evidence, however, regarding the association between coalition attributes and success in achieving community-level reductions in substance abuse behaviors. In this study, we assessed the relationship between coalition capacity, based on coalition coordinator responses to 16 survey items, and reductions in underage drinking prevalence rates. The coalitions were funded through the federally sponsored Strategic Prevention Framework State Incentive Grant (SPF SIG). We first examined whether coalition capacity increased over the life of the projects. Mean capacity scores increased for all 16 capacity items examined (N = 318 coalitions), the majority of which were statistically significant. Analysis of the associations between capacity and reductions in underage drinking was limited to coalitions that targeted underage drinking and provided usable outcome measures based on student survey data for either past 30-day alcohol use (N = 129) or binge drinking (N = 100). Bivariate associations between the capacity items and prevalence reductions for each outcome were consistently positive, although many were not statistically significant. Composite measures of correlated items were then created to represent six different capacity constructs, and included in multivariate models to predict reductions in the targeted outcomes. Constructs that significantly predicted reductions in one or both outcome measures included internal organization and structure, community connections and outreach, and funding from multiple sources. The findings provide support for the expectation that high functioning community coalitions can be effective agents for producing desirable community-level changes in targeted substance abuse behaviors.

An Empirical Approach to Predicting Effects of Climate Change on Stream Water Chemistry

NASA Astrophysics Data System (ADS)

Olson, J. R.; Hawkins, C. P.

2014-12-01

Climate change may affect stream solute concentrations by three mechanisms: dilution associated with increased precipitation, evaporative concentration associated with increased temperature, and changes in solute inputs associated with changes in climate-driven weathering. We developed empirical models predicting base-flow water chemistry from watershed geology, soils, and climate for 1975 individual stream sites across the conterminous USA. We then predicted future solute concentrations (2065 and 2099) by applying down-scaled global climate model predictions to these models. The electrical conductivity model (EC, model R2 = 0.78) predicted mean increases in EC of 19 μS/cm by 2065 and 40 μS/cm by 2099. However predicted responses for individual streams ranged from a 43% decrease to a 4x increase. Streams with the greatest predicted decreases occurred in the southern Rocky Mountains and Mid-West, whereas southern California and Sierra Nevada streams showed the greatest increases. Generally, streams in dry areas underlain by non-calcareous rocks were predicted to be the most vulnerable to increases in EC associated with climate change. Predicted changes in other water chemistry parameters (e.g., Acid Neutralization Capacity (ANC), SO4, and Ca) were similar to EC, although the magnitude of ANC and SO4 change was greater. Predicted changes in ANC and SO4 are in general agreement with those changes already observed in seven locations with long term records.

The Role of Mathematical Homework and Prior Knowledge on the Relationship between Students' Mathematical Performance, Cognitive Style and Working Memory Capacity

ERIC Educational Resources Information Center

Mousavi, Shima; Radmehr, Farzad; Alamolhodaei, Hasan

2012-01-01

Introduction: The main objective of this study is (a) to investigate whether cognitive styles and working memory capacity could predict mathematical performance and which variable is relatively most important in predicting mathematical performance and b) to explore whether cognitive styles and working memory capacity could predict mathematical…

Comprehensive Modeling of Temperature-Dependent Degradation Mechanisms in Lithium Iron Phosphate Batteries

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

Schimpe, Michael; von Kuepach, M. E.; Naumann, M.

For reliable lifetime predictions of lithium-ion batteries, models for cell degradation are required. A comprehensive semi-empirical model based on a reduced set of internal cell parameters and physically justified degradation functions for the capacity loss is developed and presented for a commercial lithium iron phosphate/graphite cell. One calendar and several cycle aging effects are modeled separately. Emphasis is placed on the varying degradation at different temperatures. Degradation mechanisms for cycle aging at high and low temperatures as well as the increased cycling degradation at high state of charge are calculated separately. For parameterization, a lifetime test study is conducted includingmore » storage and cycle tests. Additionally, the model is validated through a dynamic current profile based on real-world application in a stationary energy storage system revealing the accuracy. Tests for validation are continued for up to 114 days after the longest parametrization tests. In conclusion, the model error for the cell capacity loss in the application-based tests is at the end of testing below 1% of the original cell capacity and the maximum relative model error is below 21%.« less

Comprehensive Modeling of Temperature-Dependent Degradation Mechanisms in Lithium Iron Phosphate Batteries

DOE PAGES

Schimpe, Michael; von Kuepach, M. E.; Naumann, M.; ...

2018-01-12

For reliable lifetime predictions of lithium-ion batteries, models for cell degradation are required. A comprehensive semi-empirical model based on a reduced set of internal cell parameters and physically justified degradation functions for the capacity loss is developed and presented for a commercial lithium iron phosphate/graphite cell. One calendar and several cycle aging effects are modeled separately. Emphasis is placed on the varying degradation at different temperatures. Degradation mechanisms for cycle aging at high and low temperatures as well as the increased cycling degradation at high state of charge are calculated separately. For parameterization, a lifetime test study is conducted includingmore » storage and cycle tests. Additionally, the model is validated through a dynamic current profile based on real-world application in a stationary energy storage system revealing the accuracy. Tests for validation are continued for up to 114 days after the longest parametrization tests. In conclusion, the model error for the cell capacity loss in the application-based tests is at the end of testing below 1% of the original cell capacity and the maximum relative model error is below 21%.« less

Development of a CSP plant energy yield calculation tool applying predictive models to analyze plant performance sensitivities

NASA Astrophysics Data System (ADS)

Haack, Lukas; Peniche, Ricardo; Sommer, Lutz; Kather, Alfons

2017-06-01

At early project stages, the main CSP plant design parameters such as turbine capacity, solar field size, and thermal storage capacity are varied during the techno-economic optimization to determine most suitable plant configurations. In general, a typical meteorological year with at least hourly time resolution is used to analyze each plant configuration. Different software tools are available to simulate the annual energy yield. Software tools offering a thermodynamic modeling approach of the power block and the CSP thermal cycle, such as EBSILONProfessional®, allow a flexible definition of plant topologies. In EBSILON, the thermodynamic equilibrium for each time step is calculated iteratively (quasi steady state), which requires approximately 45 minutes to process one year with hourly time resolution. For better presentation of gradients, 10 min time resolution is recommended, which increases processing time by a factor of 5. Therefore, analyzing a large number of plant sensitivities, as required during the techno-economic optimization procedure, the detailed thermodynamic simulation approach becomes impracticable. Suntrace has developed an in-house CSP-Simulation tool (CSPsim), based on EBSILON and applying predictive models, to approximate the CSP plant performance for central receiver and parabolic trough technology. CSPsim significantly increases the speed of energy yield calculations by factor ≥ 35 and has automated the simulation run of all predefined design configurations in sequential order during the optimization procedure. To develop the predictive models, multiple linear regression techniques and Design of Experiment methods are applied. The annual energy yield and derived LCOE calculated by the predictive model deviates less than ±1.5 % from the thermodynamic simulation in EBSILON and effectively identifies the optimal range of main design parameters for further, more specific analysis.

Acclimation of photosynthetic parameters is not the icing on the cake. It is the cake.

NASA Astrophysics Data System (ADS)

Prentice, Iain Colin; Wang, Han; Togashi, Henrique; Keenan, Trevor; Davis, Tyler; Wright, Ian

2015-04-01

Photosynthesis and transpiration are tightly coupled through stomatal behaviour and therefore it is impossible to understand and parsimoniously model one without also considering the other. The ratio of leaf-internal to ambient carbon dioxide concentration (ci:ca ratio) is a measure of the "exchange rate" between water and carbon. We have shown that it is possible to predict the observed dependencies of ci:ca on environmental factors (temperature, vapour pressure deficit and atmospheric pressure) based on the "least-cost hypothesis", which states that plants minimize the sum of the unit costs (respiration per unit assimilation) of maintaining the capacities for carbon fixation (Vcmax) and water transport. Moreover, with the help of the "co-ordination hypothesis" (the long-accepted idea that Rubisco capacity and electron transport tend to co-limit photosynthesis) it is possible to predict not only how ci:ca should vary, but also how Vcmax and electron transport capacity (Jmax) should vary, in space and time. We will present empirical support for this idea based on both ecophysiological measurements at the leaf scale, and analysis of carbon dioxide flux measurements at the ecosystem scale. We conclude that acclimation of photosynthetic parameters is pervasive. This is fundamental because it predicts a quite different set of environmental responses than those that are usually applied in models that incorrectly assume constancy of parameter values with time and within plant functional types (PFTs). In addition, acclimation actually simplifies modelling because it describes universal relationships that apply across all PFTs with the C3 photosynthetic pathway, and it removes the need to specify parameters such as Vcmax and Jmax as if they were properties of PFTs.

Toward a theory of the general-anesthetic-induced phase transition of the cerebral cortex. I. A thermodynamics analogy

NASA Astrophysics Data System (ADS)

Steyn-Ross, Moira L.; Steyn-Ross, D. A.; Sleigh, J. W.; Wilcocks, Lara C.

2001-07-01

In a recent paper the authors developed a stochastic model for the response of the cerebral cortex to a general anesthetic agent. The model predicted that there would be an anesthetic-induced phase change at the point of transition into unconsciousness, manifested as a divergence in the electroencephalogram spectral power, and a change in spectral energy distribution from being relatively broadband in the conscious state to being strongly biased towards much lower frequencies in the unconscious state. Both predictions have been verified in recent clinical measurements. In the present paper we extend the model by calculating the equilibrium distribution function for the cortex, allowing us to establish a correspondence between the cortical phase transition and the more familiar thermodynamic phase transitions. This correspondence is achieved by first identifying a cortical free energy function, then by postulating that there exists an inverse relationship between an anesthetic effect and a quantity we define as cortical excitability, which plays a role analogous to temperature in thermodynamic phase transitions. We follow standard thermodynamic theory to compute a cortical entropy and a cortical ``heat capacity,'' and we investigate how these will vary with anesthetic concentration. The significant result is the prediction that the entropy will decrease discontinuously at the moment of induction into unconsciousness, concomitant with a release of ``latent heat'' which should manifest as a divergence in the analogous heat capacity. There is clear clinical evidence of heat capacity divergence in historical anesthetic-effect measurements performed in 1977 by Stullken et al. [Anesthesiology 46, 28 (1977)]. The discontinuous step change in cortical entropy suggests that the cortical phase transition is analogous to a first-order thermodynamic transition in which the comatose-quiescent state is strongly ordered, while the active cortical state is relatively disordered.

Population models of burrowing mayfly recolonization in Western Lake Erie

USGS Publications Warehouse

Madenjian, C.P.; Schloesser, D.W.; Krieger, K.A.

1998-01-01

Burrowing mayflies, Hexagenia spp. (H. limbata and H. rigida), began recolonizing western Lake Erie during the 1990s. Survey data for mayfly nymph densities indicated that the population experienced exponential growth between 1991 and 1997. To predict the time to full recovery of the mayfly population, we fitted logistic models, ranging in carrying capacity from 600 to 2000 nymphs/m2, to these survey data. Based on the fitted logistic curves, we forecast that the mayfly population in western Lake Erie would achieve full recovery between years 1998 and 2000, depending on the carrying capacity of the western basin. Additionally, we estimated the mortality rate of nymphs in western Lake Erie during 1994 and then applied an age-based matrix model to the mayfly population. The results of the matrix population modeling corroborated the exponential growth model application in that both methods yielded an estimate of the population growth rate, r, in excess of 0.8 yr-1. This was the first evidence that mayfly populations are capable of recolonizing large aquatic ecosystems at rates comparable with those observed in much smaller lentic ecosystems. Our model predictions should prove valuable to managers of power plant facilities along the western basin in planning for mayfly emergences and to managers of the yellow perch (Perca flavescens) fishery in western Lake Erie.

The comparative capacity of the Minnesota Multiphasic Personality Inventory-2 (MMPI-2) and MMPI-2 Restructured Form (MMPI-2-RF) validity scales to detect suspected malingering in a disability claimant sample.

PubMed

Chmielewski, Michael; Zhu, Jiani; Burchett, Danielle; Bury, Alison S; Bagby, R Michael

2017-02-01

The current study expands on past research examining the comparative capacity of the Minnesota Multiphasic Personality Inventory-2 (MMPI-2; Butcher et al., 2001) and MMPI-2 Restructured Form (MMPI-2-RF; Ben-Porath & Tellegen, 2008/2011) overreporting validity scales to detect suspected malingering, as assessed by the Miller Forensic Assessment of Symptoms Test (M-FAST; Miller, 2001), in a sample of public insurance disability claimants (N = 742) who were considered to have potential incentives to malinger. Results provide support for the capacity of both the MMPI-2 and the MMPI-2-RF overreporting validity scales to predict suspected malingering of psychopathology. The MMPI-2-RF overreporting validity scales proved to be modestly better predictors of suspected psychopathology malingering-compared with the MMPI-2 overreporting scales-in dimensional predictive models and categorical classification accuracy analyses. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Memory Self-Efficacy Predicts Responsiveness to Inductive Reasoning Training in Older Adults

PubMed Central

Jackson, Joshua J.; Hill, Patrick L.; Gao, Xuefei; Roberts, Brent W.; Stine-Morrow, Elizabeth A. L.

2012-01-01

Objectives. In the current study, we assessed the relationship between memory self-efficacy at pretest and responsiveness to inductive reasoning training in a sample of older adults. Methods. Participants completed a measure of self-efficacy assessing beliefs about memory capacity. Participants were then randomly assigned to a waitlist control group or an inductive reasoning training intervention. Latent change score models were used to examine the moderators of change in inductive reasoning. Results. Inductive reasoning showed clear improvements in the training group compared with the control. Within the training group, initial memory capacity beliefs significantly predicted change in inductive reasoning such that those with higher levels of capacity beliefs showed greater responsiveness to the intervention. Further analyses revealed that self-efficacy had effects on how trainees allocated time to the training materials over the course of the intervention. Discussion. Results indicate that self-referential beliefs about cognitive potential may be an important factor contributing to plasticity in adulthood. PMID:21743037

Three-dimensional thermal finite element modeling of lithium-ion battery in thermal abuse application

NASA Astrophysics Data System (ADS)

Guo, Guifang; Long, Bo; Cheng, Bo; Zhou, Shiqiong; Xu, Peng; Cao, Binggang

In order to better understand the thermal abuse behavior of high capacities and large power lithium-ion batteries for electric vehicle application, a three-dimensional thermal model has been developed for analyzing the temperature distribution under abuse conditions. The model takes into account the effects of heat generation, internal conduction and convection, and external heat dissipation to predict the temperature distribution in a battery. Three-dimensional model also considers the geometrical features to simulate oven test, which are significant in larger cells for electric vehicle application. The model predictions are compared to oven test results for VLP 50/62/100S-Fe (3.2 V/55 Ah) LiFePO 4/graphite cells and shown to be in great agreement.

Biomechanical influences on balance recovery by stepping.

PubMed

Hsiao, E T; Robinovitch, S N

1999-10-01

Stepping represents a common means for balance recovery after a perturbation to upright posture. Yet little is known regarding the biomechanical factors which determine whether a step succeeds in preventing a fall. In the present study, we developed a simple pendulum-spring model of balance recovery by stepping, and used this to assess how step length and step contact time influence the effort (leg contact force) and feasibility of balance recovery by stepping. We then compared model predictions of step characteristics which minimize leg contact force to experimentally observed values over a range of perturbation strengths. At all perturbation levels, experimentally observed step execution times were higher than optimal, and step lengths were smaller than optimal. However, the predicted increase in leg contact force associated with these deviations was substantial only for large perturbations. Furthermore, increases in the strength of the perturbation caused subjects to take larger, quicker steps, which reduced their predicted leg contact force. We interpret these data to reflect young subjects' desire to minimize recovery effort, subject to neuromuscular constraints on step execution time and step length. Finally, our model predicts that successful balance recovery by stepping is governed by a coupling between step length, step execution time, and leg strength, so that the feasibility of balance recovery decreases unless declines in one capacity are offset by enhancements in the others. This suggests that one's risk for falls may be affected more by small but diffuse neuromuscular impairments than by larger impairment in a single motor capacity.

Comparing simple and complex approaches to simulate the impacts of soil water repellency on runoff and erosion in burnt Mediterranean forest slopes

NASA Astrophysics Data System (ADS)

Nunes, João Pedro; Catarina Simões Vieira, Diana; Keizer, Jan Jacob

2017-04-01

Fires impact soil hydrological properties, enhancing soil water repellency and therefore increasing the potential for surface runoff generation and soil erosion. In consequence, the successful application of hydrological models to post-fire conditions requires the appropriate simulation of the effects of soil water repellency on soil hydrology. This work compared three approaches to model soil water repellency impacts on soil hydrology in burnt eucalypt and pine forest slopes in central Portugal: 1) Daily approach, simulating repellency as a function of soil moisture, and influencing the maximum soil available water holding capacity. It is based on the Thornthwaite-Mather soil water modelling approach, and is parameterized with the soil's wilting point and field capacity, and a parameter relating soil water repellency with water holding capacity. It was tested with soil moisture data from burnt and unburnt hillslopes. This approach was able to simulate post-fire soil moisture patterns, which the model without repellency was unable to do. However, model parameters were different between the burnt and unburnt slopes, indicating that more research is needed to derive standardized parameters from commonly measured soil and vegetation properties. 2) Seasonal approach, pre-determining repellency at the seasonal scale (3 months) in four classes (from none to extreme). It is based on the Morgan-Morgan-Finney (MMF) runoff and erosion model, applied at the seasonal scale and is parameterized with a parameter relating repellency class with field capacity. It was tested with runoff and erosion data from several experimental plots, and led to important improvements on runoff prediction over an approach with constant field capacity for all seasons (calibrated for repellency effects), but only slight improvements in erosion predictions. In contrast with the daily approach, the parameters could be reproduced between different sites 3) Constant approach, specifying values for soil water repellency for the three years after the fire, and keeping them constant throughout the year. It is based on a daily Curve Number (CN) approach, and was incorporated directly in the Soil and Water Assessment Tool (SWAT) model and tested with erosion data from a burnt hillslope. This approach was able to successfully reproduce soil erosion. The results indicate that simplified approaches can be used to adapt existing models for post-fire simulation, taking repellency into account. Taking into account the seasonality of repellency seems more important to simulate surface runoff than erosion, possibly since simulating the larger runoff rates correctly is sufficient for erosion simulation. The constant approach can be applied directly in the parameterization of existing runoff and erosion models for soil loss and sediment yield prediction, while the seasonal approach can readily be developed as a next step, with further work being needed to assess if the approach and associated parameters can be applied in multiple post-fire environments.

The Effect of Planning Sub-Processes on L2 Writing Fluency, Grammatical Complexity, and Lexical Complexity

ERIC Educational Resources Information Center

Johnson, Mark D.; Mercado, Leonardo; Acevedo, Anthony

2012-01-01

This study contributes to L2 writing research which seeks to tie predictions of the Limited Attentional Capacity Model (Skehan, 1998; Skehan & Foster, 2001) and Cognition Hypothesis (Robinson, 2001, 2005, 2011a, 2011b) to models of working memory in L1 writing (Kellogg, 1996). The study uses a quasi-experimental research design to investigate…

Improving Models of Photosynthetic Thermal Acclimation: Which Parameters are Most Important and How Many Should Be Modified?

NASA Astrophysics Data System (ADS)

Stinziano, J. R.; Way, D.; Bauerle, W.

2017-12-01

Photosynthetic temperature acclimation could strongly affect coupled vegetation-atmosphere feedbacks in the global carbon cycle, especially as the climate warms. Thermal acclimation of photosynthesis can be modelled as changes in the parameters describing the direct effect of temperature on photosynthetic capacity (activation energy, Ea; deactivation energy, Hd; entropy parameter, ΔS) or the basal value of photosynthetic capacity (i.e. photosynthetic capacity measured at 25 °C), however the impact of acclimating these parameters (individually or in combination) on vegetative carbon gain is relatively unexplored. Here we compare the ability of 66 photosynthetic temperature acclimation scenarios to improve predictions of a spatially explicit canopy carbon flux model, MAESTRA, for eddy covariance data from a loblolly pine forest. We show that: 1) incorporating seasonal temperature acclimation of basal photosynthetic capacity improves the model's ability to capture seasonal changes in carbon fluxes; 2) multifactor scenarios of photosynthetic temperature acclimation provide minimal (if any) improvement in model performance over single factor acclimation scenarios; 3) acclimation of enzyme activation energies should be restricted to the temperature ranges of the data from which the equations are derived; and 4) model performance is strongly affected by the choice of deactivation energy. We suggest that a renewed effort be made into understanding the thermal acclimation of enzyme activation and deactivation energies across broad temperature ranges to better understand the mechanisms underlying thermal photosynthetic acclimation.

A global scale mechanistic model of photosynthetic capacity (LUNA V1.0)

DOE PAGES

Ali, Ashehad A.; Xu, Chonggang; Rogers, Alistair; ...

2016-02-12

Although plant photosynthetic capacity as determined by the maximum carboxylation rate (i.e., V c,max25) and the maximum electron transport rate (i.e., J max25) at a reference temperature (generally 25 °C) is known to vary considerably in space and time in response to environmental conditions, it is typically parameterized in Earth system models (ESMs) with tabulated values associated with plant functional types. In this study, we have developed a mechanistic model of leaf utilization of nitrogen for assimilation (LUNA) to predict photosynthetic capacity at the global scale under different environmental conditions. We adopt an optimality hypothesis to nitrogen allocation among lightmore » capture, electron transport, carboxylation and respiration. The LUNA model is able to reasonably capture the measured spatial and temporal patterns of photosynthetic capacity as it explains ~55 % of the global variation in observed values of V c,max25 and ~65 % of the variation in the observed values of J max25. Model simulations with LUNA under current and future climate conditions demonstrate that modeled values of V c,max25 are most affected in high-latitude regions under future climates. In conclusion, ESMs that relate the values of V c,max25 or J max25 to plant functional types only are likely to substantially overestimate future global photosynthesis.« less

USING ISOTHERMS TO PREDICT GAC'S CAPACITY FOR SYNTHETIC ORGANICS

EPA Science Inventory

This investigation involved operating a pilot granular activated carbon (GAC) plant to obtain capacity data under typical field conditions, determining isotherms for selected synthetic organic chemicals, and comparing the capacity predicted by the isotherm data with the pilot-pla...

Predicting acidification recovery at the Hubbard Brook Experimental Forest, New Hampshire: evaluation of four models.

PubMed

Tominaga, Koji; Aherne, Julian; Watmough, Shaun A; Alveteg, Mattias; Cosby, Bernard J; Driscoll, Charles T; Posch, Maximilian; Pourmokhtarian, Afshin

2010-12-01

The performance and prediction uncertainty (owing to parameter and structural uncertainties) of four dynamic watershed acidification models (MAGIC, PnET-BGC, SAFE, and VSD) were assessed by systematically applying them to data from the Hubbard Brook Experimental Forest (HBEF), New Hampshire, where long-term records of precipitation and stream chemistry were available. In order to facilitate systematic evaluation, Monte Carlo simulation was used to randomly generate common model input data sets (n = 10,000) from parameter distributions; input data were subsequently translated among models to retain consistency. The model simulations were objectively calibrated against observed data (streamwater: 1963-2004, soil: 1983). The ensemble of calibrated models was used to assess future response of soil and stream chemistry to reduced sulfur deposition at the HBEF. Although both hindcast (1850-1962) and forecast (2005-2100) predictions were qualitatively similar across the four models, the temporal pattern of key indicators of acidification recovery (stream acid neutralizing capacity and soil base saturation) differed substantially. The range in predictions resulted from differences in model structure and their associated posterior parameter distributions. These differences can be accommodated by employing multiple models (ensemble analysis) but have implications for individual model applications.

Aerobic power and lean mass are indicators of competitive sprint performance among elite female cross-country skiers.

PubMed

Carlsson, Tomas; Tonkonogi, Michail; Carlsson, Magnus

2016-01-01

The purpose of this study was to establish the optimal allometric models to predict International Ski Federation's ski-ranking points for sprint competitions (FISsprint) among elite female cross-country skiers based on maximal oxygen uptake ( [Formula: see text]) and lean mass (LM). Ten elite female cross-country skiers (age: 24.5±2.8 years [mean ± SD]) completed a treadmill roller-skiing test to determine [Formula: see text] (ie, aerobic power) using the diagonal stride technique, whereas LM (ie, a surrogate indicator of anaerobic capacity) was determined by dual-emission X-ray anthropometry. The subjects' FISsprint were used as competitive performance measures. Power function modeling was used to predict the skiers' FISsprint based on [Formula: see text], LM, and body mass. The subjects' test and performance data were as follows: [Formula: see text], 4.0±0.3 L min -1 ; LM, 48.9±4.4 kg; body mass, 64.0±5.2 kg; and FISsprint, 116.4±59.6 points. The following power function models were established for the prediction of FISsprint: [Formula: see text] and 6.95 × 10 10 · LM -5.25 ; these models explained 66% ( P =0.0043) and 52% ( P =0.019), respectively, of the variance in the FISsprint. Body mass failed to contribute to both models; hence, the models are based on [Formula: see text] and LM expressed absolutely. The results demonstrate that the physiological variables that reflect aerobic power and anaerobic capacity are important indicators of competitive sprint performance among elite female skiers. To accurately indicate performance capability among elite female skiers, the presented power function models should be used. Skiers whose [Formula: see text] differs by 1% will differ in their FISsprint by 5.8%, whereas the corresponding 1% difference in LM is related to an FISsprint difference of 5.1%, where both differences are in favor of the skier with higher [Formula: see text] or LM. It is recommended that coaches use the absolute expression of these variables to monitor skiers' performance-related training adaptations linked to changes in aerobic power and anaerobic capacity.

Aerobic capacity over 16 years in patients with rheumatoid arthritis: Relationship to disease activity and risk factors for cardiovascular disease

PubMed Central

Sundström, Björn; Innala, Lena; Rantapää-Dahlqvist, Solbritt; Wållberg-Jonsson, Solveig

2017-01-01

The aim of this study was to analyse the change in aerobic capacity from disease onset of rheumatoid arthritis (RA) over 16.2 years, and its associations with disease activity and cardiovascular risk factors. Twenty-five patients (20 f/5 m), diagnosed with RA 1995-2002 were tested at disease onset and after mean 16.2 years. Parameters measured were: sub-maximal ergometer test for aerobic capacity, functional ability, self-efficacy, ESR, CRP and DAS28. At follow-up, cardiovascular risk factors were assessed as blood lipids, glucose concentrations, waist circumference, body mass index (BMI), body composition, pulse wave analysis and carotid intima-media thickness. Aerobic capacity [median (IQR)] was 32.3 (27.9-42.1) ml O2/kg x min at disease onset, and 33.2 (28.4-38.9) at follow-up (p>0.05). Baseline aerobic capacity was associated with follow-up values of: BMI (rs = -.401, p = .047), waist circumference (rs = -.498, p = .011), peripheral pulse pressure (rs = -.415, p = .039) self-efficacy (rs = .420, p = .037) and aerobic capacity (rs = .557, p = .004). In multiple regression models adjusted for baseline aerobic capacity, disease activity at baseline and over time predicted aerobic capacity at follow-up (AUC DAS28, 0-24 months; β = -.14, p = .004). At follow-up, aerobic capacity was inversely associated with blood glucose levels (rs = -.508, p = .016), BMI (rs = -.434, p = .030), body fat% (rs = -.419, p = .037), aortic pulse pressure (rs = -.405, p = .044), resting heart rate (rs = -.424, p = .034) and self-efficacy (rs = .464, p = .020) at follow-up. We conclude that the aerobic capacity was maintained over 16 years. High baseline aerobic capacity associated with favourable measures of cardiovascular risk factors at follow-up. Higher disease activity in early stages of RA predicted lower aerobic capacity after 16.2 years. PMID:29272303

Aerobic capacity over 16 years in patients with rheumatoid arthritis: Relationship to disease activity and risk factors for cardiovascular disease.

PubMed

Hörnberg, Kristina; Sundström, Björn; Innala, Lena; Rantapää-Dahlqvist, Solbritt; Wållberg-Jonsson, Solveig

2017-01-01

The aim of this study was to analyse the change in aerobic capacity from disease onset of rheumatoid arthritis (RA) over 16.2 years, and its associations with disease activity and cardiovascular risk factors. Twenty-five patients (20 f/5 m), diagnosed with RA 1995-2002 were tested at disease onset and after mean 16.2 years. Parameters measured were: sub-maximal ergometer test for aerobic capacity, functional ability, self-efficacy, ESR, CRP and DAS28. At follow-up, cardiovascular risk factors were assessed as blood lipids, glucose concentrations, waist circumference, body mass index (BMI), body composition, pulse wave analysis and carotid intima-media thickness. Aerobic capacity [median (IQR)] was 32.3 (27.9-42.1) ml O2/kg x min at disease onset, and 33.2 (28.4-38.9) at follow-up (p>0.05). Baseline aerobic capacity was associated with follow-up values of: BMI (rs = -.401, p = .047), waist circumference (rs = -.498, p = .011), peripheral pulse pressure (rs = -.415, p = .039) self-efficacy (rs = .420, p = .037) and aerobic capacity (rs = .557, p = .004). In multiple regression models adjusted for baseline aerobic capacity, disease activity at baseline and over time predicted aerobic capacity at follow-up (AUC DAS28, 0-24 months; β = -.14, p = .004). At follow-up, aerobic capacity was inversely associated with blood glucose levels (rs = -.508, p = .016), BMI (rs = -.434, p = .030), body fat% (rs = -.419, p = .037), aortic pulse pressure (rs = -.405, p = .044), resting heart rate (rs = -.424, p = .034) and self-efficacy (rs = .464, p = .020) at follow-up. We conclude that the aerobic capacity was maintained over 16 years. High baseline aerobic capacity associated with favourable measures of cardiovascular risk factors at follow-up. Higher disease activity in early stages of RA predicted lower aerobic capacity after 16.2 years.

Can we predict 4-year graduation in podiatric medical school using admission data?

PubMed

Sesodia, Sanjay; Molnar, David; Shaw, Graham P

2012-01-01

This study examined the predictive ability of educational background and demographic variables, available at the admission stage, to identify applicants who will graduate in 4 years from podiatric medical school. A logistic regression model was used to identify two predictors of 4-year graduation: age at matriculation and total Medical College Admission Test score. The model was cross-validated using a second independent sample from the same population. Cross-validation gives greater confidence that the results could be more generally applied. Total Medical College Admission Test score was the strongest predictor of 4-year graduation, with age at matriculation being a statistically significant but weaker predictor. Despite the model's capacity to predict 4-year graduation better than random assignment, a sufficient amount of error in prediction remained, suggesting that important predictors are missing from the model. Furthermore, the high rate of false-positives makes it inappropriate to use age and Medical College Admission Test score as admission screens in an attempt to eliminate attrition by not accepting at-risk students.

Thematic and spatial resolutions affect model-based predictions of tree species distribution.

PubMed

Liang, Yu; He, Hong S; Fraser, Jacob S; Wu, ZhiWei

2013-01-01

Subjective decisions of thematic and spatial resolutions in characterizing environmental heterogeneity may affect the characterizations of spatial pattern and the simulation of occurrence and rate of ecological processes, and in turn, model-based tree species distribution. Thus, this study quantified the importance of thematic and spatial resolutions, and their interaction in predictions of tree species distribution (quantified by species abundance). We investigated how model-predicted species abundances changed and whether tree species with different ecological traits (e.g., seed dispersal distance, competitive capacity) had different responses to varying thematic and spatial resolutions. We used the LANDIS forest landscape model to predict tree species distribution at the landscape scale and designed a series of scenarios with different thematic (different numbers of land types) and spatial resolutions combinations, and then statistically examined the differences of species abundance among these scenarios. Results showed that both thematic and spatial resolutions affected model-based predictions of species distribution, but thematic resolution had a greater effect. Species ecological traits affected the predictions. For species with moderate dispersal distance and relatively abundant seed sources, predicted abundance increased as thematic resolution increased. However, for species with long seeding distance or high shade tolerance, thematic resolution had an inverse effect on predicted abundance. When seed sources and dispersal distance were not limiting, the predicted species abundance increased with spatial resolution and vice versa. Results from this study may provide insights into the choice of thematic and spatial resolutions for model-based predictions of tree species distribution.

Thematic and Spatial Resolutions Affect Model-Based Predictions of Tree Species Distribution

PubMed Central

Liang, Yu; He, Hong S.; Fraser, Jacob S.; Wu, ZhiWei

2013-01-01

Subjective decisions of thematic and spatial resolutions in characterizing environmental heterogeneity may affect the characterizations of spatial pattern and the simulation of occurrence and rate of ecological processes, and in turn, model-based tree species distribution. Thus, this study quantified the importance of thematic and spatial resolutions, and their interaction in predictions of tree species distribution (quantified by species abundance). We investigated how model-predicted species abundances changed and whether tree species with different ecological traits (e.g., seed dispersal distance, competitive capacity) had different responses to varying thematic and spatial resolutions. We used the LANDIS forest landscape model to predict tree species distribution at the landscape scale and designed a series of scenarios with different thematic (different numbers of land types) and spatial resolutions combinations, and then statistically examined the differences of species abundance among these scenarios. Results showed that both thematic and spatial resolutions affected model-based predictions of species distribution, but thematic resolution had a greater effect. Species ecological traits affected the predictions. For species with moderate dispersal distance and relatively abundant seed sources, predicted abundance increased as thematic resolution increased. However, for species with long seeding distance or high shade tolerance, thematic resolution had an inverse effect on predicted abundance. When seed sources and dispersal distance were not limiting, the predicted species abundance increased with spatial resolution and vice versa. Results from this study may provide insights into the choice of thematic and spatial resolutions for model-based predictions of tree species distribution. PMID:23861828

Quantitative structure-property relationship modeling of remote liposome loading of drugs.

PubMed

Cern, Ahuva; Golbraikh, Alexander; Sedykh, Aleck; Tropsha, Alexander; Barenholz, Yechezkel; Goldblum, Amiram

2012-06-10

Remote loading of liposomes by trans-membrane gradients is used to achieve therapeutically efficacious intra-liposome concentrations of drugs. We have developed Quantitative Structure Property Relationship (QSPR) models of remote liposome loading for a data set including 60 drugs studied in 366 loading experiments internally or elsewhere. Both experimental conditions and computed chemical descriptors were employed as independent variables to predict the initial drug/lipid ratio (D/L) required to achieve high loading efficiency. Both binary (to distinguish high vs. low initial D/L) and continuous (to predict real D/L values) models were generated using advanced machine learning approaches and 5-fold external validation. The external prediction accuracy for binary models was as high as 91-96%; for continuous models the mean coefficient R(2) for regression between predicted versus observed values was 0.76-0.79. We conclude that QSPR models can be used to identify candidate drugs expected to have high remote loading capacity while simultaneously optimizing the design of formulation experiments. Copyright © 2011 Elsevier B.V. All rights reserved.

Antioxidant Compound Extraction from Maqui (Aristotelia chilensis [Mol] Stuntz) Berries: Optimization by Response Surface Methodology

PubMed Central

Quispe-Fuentes, Issis; Vega-Gálvez, Antonio; Campos-Requena, Víctor H.

2017-01-01

The optimum conditions for the antioxidant extraction from maqui berry were determined using a response surface methodology. A three level D-optimal design was used to investigate the effects of three independent variables namely, solvent type (methanol, acetone and ethanol), solvent concentration and extraction time over total antioxidant capacity by using the oxygen radical absorbance capacity (ORAC) method. The D-optimal design considered 42 experiments including 10 central point replicates. A second-order polynomial model showed that more than 89% of the variation is explained with a satisfactory prediction (78%). ORAC values are higher when acetone was used as a solvent at lower concentrations, and the extraction time range studied showed no significant influence on ORAC values. The optimal conditions for antioxidant extraction obtained were 29% of acetone for 159 min under agitation. From the results obtained it can be concluded that the given predictive model describes an antioxidant extraction process from maqui berry.

Runoff as a factor in USLE/RUSLE technology

NASA Astrophysics Data System (ADS)

Kinnell, Peter

2014-05-01

Modelling erosion for prediction purposes started with the development of the Universal Soil Loss Equation the focus of which was the prediction of long term (~20) average annul soil loss from field sized areas. That purpose has been maintained in the subsequent revision RUSLE, the most widely used erosion prediction model in the world. The lack of ability to predict short term soil loss saw the development of so-called process based models like WEPP and EUROSEM which focussed on predicting event erosion but failed to improve the prediction of long term erosion where the RUSLE worked well. One of the features of erosion recognised in the so-called process based modes is the fact that runoff is a primary factor in rainfall erosion and some modifications of USLE/RUSLE model have been proposed have included runoff as in independent factor in determining event erosivity. However, these models have ignored fundamental mathematical rules. The USLE-M which replaces the EI30 index by the product of the runoff ratio and EI30 was developed from the concept that soil loss is the product of runoff and sediment concentration and operates in a way that obeys the mathematical rules upon which the USLE/RUSLE model was based. In accounts for event soil loss better that the EI30 index where runoff values are known or predicted adequately. RUSLE2 now includes a capacity to model runoff driven erosion.

Support vector machine in crash prediction at the level of traffic analysis zones: Assessing the spatial proximity effects.

PubMed

Dong, Ni; Huang, Helai; Zheng, Liang

2015-09-01

In zone-level crash prediction, accounting for spatial dependence has become an extensively studied topic. This study proposes Support Vector Machine (SVM) model to address complex, large and multi-dimensional spatial data in crash prediction. Correlation-based Feature Selector (CFS) was applied to evaluate candidate factors possibly related to zonal crash frequency in handling high-dimension spatial data. To demonstrate the proposed approaches and to compare them with the Bayesian spatial model with conditional autoregressive prior (i.e., CAR), a dataset in Hillsborough county of Florida was employed. The results showed that SVM models accounting for spatial proximity outperform the non-spatial model in terms of model fitting and predictive performance, which indicates the reasonableness of considering cross-zonal spatial correlations. The best model predictive capability, relatively, is associated with the model considering proximity of the centroid distance by choosing the RBF kernel and setting the 10% of the whole dataset as the testing data, which further exhibits SVM models' capacity for addressing comparatively complex spatial data in regional crash prediction modeling. Moreover, SVM models exhibit the better goodness-of-fit compared with CAR models when utilizing the whole dataset as the samples. A sensitivity analysis of the centroid-distance-based spatial SVM models was conducted to capture the impacts of explanatory variables on the mean predicted probabilities for crash occurrence. While the results conform to the coefficient estimation in the CAR models, which supports the employment of the SVM model as an alternative in regional safety modeling. Copyright © 2015 Elsevier Ltd. All rights reserved.

Prediction of equibiaxial loading stress in collagen-based extracellular matrix using a three-dimensional unit cell model.

PubMed

Susilo, Monica E; Bell, Brett J; Roeder, Blayne A; Voytik-Harbin, Sherry L; Kokini, Klod; Nauman, Eric A

2013-03-01

Mechanical signals are important factors in determining cell fate. Therefore, insights as to how mechanical signals are transferred between the cell and its surrounding three-dimensional collagen fibril network will provide a basis for designing the optimum extracellular matrix (ECM) microenvironment for tissue regeneration. Previously we described a cellular solid model to predict fibril microstructure-mechanical relationships of reconstituted collagen matrices due to unidirectional loads (Acta Biomater 2010;6:1471-86). The model consisted of representative volume elements made up of an interconnected network of flexible struts. The present study extends this work by adapting the model to account for microstructural anisotropy of the collagen fibrils and a biaxial loading environment. The model was calibrated based on uniaxial tensile data and used to predict the equibiaxial tensile stress-stretch relationship. Modifications to the model significantly improved its predictive capacity for equibiaxial loading data. With a comparable fibril length (model 5.9-8μm, measured 7.5μm) and appropriate fibril anisotropy the anisotropic model provides a better representation of the collagen fibril microstructure. Such models are important tools for tissue engineering because they facilitate prediction of microstructure-mechanical relationships for collagen matrices over a wide range of microstructures and provide a framework for predicting cell-ECM interactions. Copyright © 2012 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Improving Flash Flood Prediction in Multiple Environments

NASA Astrophysics Data System (ADS)

Broxton, P. D.; Troch, P. A.; Schaffner, M.; Unkrich, C.; Goodrich, D.; Wagener, T.; Yatheendradas, S.

2009-12-01

Flash flooding is a major concern in many fast responding headwater catchments . There are many efforts to model and to predict these flood events, though it is not currently possible to adequately predict the nature of flash flood events with a single model, and furthermore, many of these efforts do not even consider snow, which can, by itself, or in combination with rainfall events, cause destructive floods. The current research is aimed at broadening the applicability of flash flood modeling. Specifically, we will take a state of the art flash flood model that is designed to work with warm season precipitation in arid environments, the KINematic runoff and EROSion model (KINEROS2), and combine it with a continuous subsurface flow model and an energy balance snow model. This should improve its predictive capacity in humid environments where lateral subsurface flow significantly contributes to streamflow, and it will make possible the prediction of flooding events that involve rain-on-snow or rapid snowmelt. By modeling changes in the hydrologic state of a catchment before a flood begins, we can also better understand the factors or combination of factors that are necessary to produce large floods. Broadening the applicability of an already state of the art flash flood model, such as KINEROS2, is logical because flash floods can occur in all types of environments, and it may lead to better predictions, which are necessary to preserve life and property.

On calculation of a steam-water flow in a geothermal well

NASA Astrophysics Data System (ADS)

Shulyupin, A. N.; Chermoshentseva, A. A.

2013-08-01

Approaches to calculation of a steam-water flow in a geothermal well are considered. For hydraulic applications, a WELL-4 model of a steam-water well is developed. Data obtained using this model are compared with experimental data and also with calculations by similar models including the well-known HOLA model. The capacity of the A-2 well in the Mutnovskoe flash-steam field (Kamchatka half-island, Russia) after planned reconstruction is predicted.

Urban pluvial flood prediction: a case study evaluating radar rainfall nowcasts and numerical weather prediction models as model inputs.

PubMed

Thorndahl, Søren; Nielsen, Jesper Ellerbæk; Jensen, David Getreuer

2016-12-01

Flooding produced by high-intensive local rainfall and drainage system capacity exceedance can have severe impacts in cities. In order to prepare cities for these types of flood events - especially in the future climate - it is valuable to be able to simulate these events numerically, both historically and in real-time. There is a rather untested potential in real-time prediction of urban floods. In this paper, radar data observations with different spatial and temporal resolution, radar nowcasts of 0-2 h leadtime, and numerical weather models with leadtimes up to 24 h are used as inputs to an integrated flood and drainage systems model in order to investigate the relative difference between different inputs in predicting future floods. The system is tested on the small town of Lystrup in Denmark, which was flooded in 2012 and 2014. Results show it is possible to generate detailed flood maps in real-time with high resolution radar rainfall data, but rather limited forecast performance in predicting floods with leadtimes more than half an hour.

Probability based remaining capacity estimation using data-driven and neural network model

NASA Astrophysics Data System (ADS)

Wang, Yujie; Yang, Duo; Zhang, Xu; Chen, Zonghai

2016-05-01

Since large numbers of lithium-ion batteries are composed in pack and the batteries are complex electrochemical devices, their monitoring and safety concerns are key issues for the applications of battery technology. An accurate estimation of battery remaining capacity is crucial for optimization of the vehicle control, preventing battery from over-charging and over-discharging and ensuring the safety during its service life. The remaining capacity estimation of a battery includes the estimation of state-of-charge (SOC) and state-of-energy (SOE). In this work, a probability based adaptive estimator is presented to obtain accurate and reliable estimation results for both SOC and SOE. For the SOC estimation, an n ordered RC equivalent circuit model is employed by combining an electrochemical model to obtain more accurate voltage prediction results. For the SOE estimation, a sliding window neural network model is proposed to investigate the relationship between the terminal voltage and the model inputs. To verify the accuracy and robustness of the proposed model and estimation algorithm, experiments under different dynamic operation current profiles are performed on the commercial 1665130-type lithium-ion batteries. The results illustrate that accurate and robust estimation can be obtained by the proposed method.

Cardio-vascular safety beyond hERG: in silico modelling of a guinea pig right atrium assay

NASA Astrophysics Data System (ADS)

Fenu, Luca A.; Teisman, Ard; De Buck, Stefan S.; Sinha, Vikash K.; Gilissen, Ron A. H. J.; Nijsen, Marjoleen J. M. A.; Mackie, Claire E.; Sanderson, Wendy E.

2009-12-01

As chemists can easily produce large numbers of new potential drug candidates, there is growing demand for high capacity models that can help in driving the chemistry towards efficacious and safe candidates before progressing towards more complex models. Traditionally, the cardiovascular (CV) safety domain plays an important role in this process, as many preclinical CV biomarkers seem to have high prognostic value for the clinical outcome. Throughout the industry, traditional ion channel binding data are generated to drive the early selection process. Although this assay can generate data at high capacity, it has the disadvantage of producing high numbers of false negatives. Therefore, our company applies the isolated guinea pig right atrium (GPRA) assay early-on in discovery. This functional multi-channel/multi-receptor model seems much more predictive in identifying potential CV liabilities. Unfortunately however, its capacity is limited, and there is no room for full automation. We assessed the correlation between ion channel binding and the GPRA's Rate of Contraction (RC), Contractile Force (CF), and effective refractory frequency (ERF) measures assay using over six thousand different data points. Furthermore, the existing experimental knowledge base was used to develop a set of in silico classification models attempting to mimic the GPRA inhibitory activity. The Naïve Bayesian classifier was used to built several models, using the ion channel binding data or in silico computed properties and structural fingerprints as descriptors. The models were validated on an independent and diverse test set of 200 reference compounds. Performances were assessed on the bases of their overall accuracy, sensitivity and specificity in detecting both active and inactive molecules. Our data show that all in silico models are highly predictive of actual GPRA data, at a level equivalent or superior to the ion channel binding assays. Furthermore, the models were interpreted in terms of the descriptors used to highlight the undesirable areas in the explored chemical space, specifically regions of low polarity, high lipophilicity and high molecular weight. In conclusion, we developed a predictive in silico model of a complex physiological assay based on a large and high quality set of experimental data. This model allows high throughput in silico safety screening based on chemical structure within a given chemical space.

Computational network model prediction of hemodynamic alterations due to arteriolar remodeling in interval sprint trained skeletal muscle.

PubMed

Binder, Kyle W; Murfee, Walter L; Song, Ji; Laughlin, M Harold; Price, Richard J

2007-01-01

Exercise training is known to enhance skeletal muscle blood flow capacity, with high-intensity interval sprint training (IST) primarily affecting muscles with a high proportion of fast twitch glycolytic fibers. The objective of this study was to determine the relative contributions of new arteriole formation and lumenal arteriolar remodeling to enhanced flow capacity and the impact of these adaptations on local microvascular hemodynamics deep within the muscle. The authors studied arteriolar adaptation in the white/mixed-fiber portion of gastrocnemius muscles of IST (6 bouts of running/day; 2.5 min/bout; 60 m/min speed; 15% grade; 4.5 min rest between bouts; 5 training days/wk; 10 wks total) and sedentary (SED) control rats using whole-muscle Microfil casts. Dimensional and topological data were then used to construct a series of computational hemodynamic network models that incorporated physiological red blood cell distributions and hematocrit and diameter dependent apparent viscosities. In comparison to SED controls, IST elicited a significant increase in arterioles/order in the 3A through 6A generations. Predicted IST and SED flows through the 2A generation agreed closely with in vivo measurements made in a previous study, illustrating the accuracy of the model. IST shifted the bulk of the pressure drop across the network from the 3As to the 4As and 5As, and flow capacity increased from 0.7 mL/min in SED to 1.5 mL/min in IST when a driving pressure of 80 mmHg was applied. The primary adaptation to IST is an increase in arterioles in the 3A through 6A generations, which, in turn, creates an approximate doubling of flow capacity and a deeper penetration of high pressure into the arteriolar network.

A genetic predisposition score for muscular endophenotypes predicts the increase in aerobic power after training: the CAREGENE study.

PubMed

Thomaes, Tom; Thomis, Martine; Onkelinx, Steven; Fagard, Robert; Matthijs, Gert; Buys, Roselien; Schepers, Dirk; Cornelissen, Véronique; Vanhees, Luc

2011-10-03

It is widely accepted that genetic variability might explain a large part of the observed heterogeneity in aerobic capacity and its response to training. Significant associations between polymorphisms of different genes with muscular strength, anaerobic phenotypes and body composition have been reported. Muscular endophenotypes are positively correlated with aerobic capacity, therefore, we tested the association of polymorphisms in twelve muscular related genes on aerobic capacity and its response to endurance training. 935 Coronary artery disease patients (CAD) who performed an incremental exercise test until exhaustion at baseline and after three months of training were included. Polymorphisms of the genes were detected using the invader assay. Genotype-phenotype association analyses were performed using ANCOVA. Different models for a genetic predisposition score (GPS) were constructed based on literature and own data and were related to baseline and response VO(2) scores. Carriers of the minor allele in the R23K polymorphism of the glucocorticoid receptor gene (GR) and the ciliary neurotrophic factor gene (CNTF) had a significantly higher increase in peakVO(2) after training (p < 0.05). Carriers of the minor allele (C34T) in the adenosine monophosphate deaminase (AMPD1) gene had a significantly lower relative increase (p < 0.05) in peakVO(2). GPS of data driven models were significantly associated with the increase in peakVO(2) after training. In CAD patients, suggestive associations were found in the GR, CNTF and the AMPD1 gene with an improved change in aerobic capacity after three months of training. Additionally data driven models with a genetic predisposition score (GPS) showed a significant predictive value for the increase in peakVO(2).

A genetic predisposition score for muscular endophenotypes predicts the increase in aerobic power after training: the CAREGENE study

PubMed Central

2011-01-01

Background It is widely accepted that genetic variability might explain a large part of the observed heterogeneity in aerobic capacity and its response to training. Significant associations between polymorphisms of different genes with muscular strength, anaerobic phenotypes and body composition have been reported. Muscular endophenotypes are positively correlated with aerobic capacity, therefore, we tested the association of polymorphisms in twelve muscular related genes on aerobic capacity and its response to endurance training. Methods 935 Coronary artery disease patients (CAD) who performed an incremental exercise test until exhaustion at baseline and after three months of training were included. Polymorphisms of the genes were detected using the invader assay. Genotype-phenotype association analyses were performed using ANCOVA. Different models for a genetic predisposition score (GPS) were constructed based on literature and own data and were related to baseline and response VO2 scores. Results Carriers of the minor allele in the R23K polymorphism of the glucocorticoid receptor gene (GR) and the ciliary neurotrophic factor gene (CNTF) had a significantly higher increase in peakVO2 after training (p < 0.05). Carriers of the minor allele (C34T) in the adenosine monophosphate deaminase (AMPD1) gene had a significantly lower relative increase (p < 0.05) in peakVO2. GPS of data driven models were significantly associated with the increase in peakVO2 after training. Conclusions In CAD patients, suggestive associations were found in the GR, CNTF and the AMPD1 gene with an improved change in aerobic capacity after three months of training. Additionally data driven models with a genetic predisposition score (GPS) showed a significant predictive value for the increase in peakVO2. PMID:21967077

Body composition indices of a load-capacity model: gender- and BMI-specific reference curves.

PubMed

Siervo, Mario; Prado, Carla M; Mire, Emily; Broyles, Stephanie; Wells, Jonathan C K; Heymsfield, Steven; Katzmarzyk, Peter T

2015-05-01

Fat mass (FM) and fat-free mass (FFM) are frequently measured to define body composition phenotypes. The load-capacity model integrates the effects of both FM and FFM to improve disease-risk prediction. We aimed to derive age-, gender- and BMI-specific reference curves of load-capacity model indices in an adult population (≥18 years). Cross-sectional study. Dual-energy X-ray absorptiometry was used to measure FM, FFM, appendicular skeletal muscle mass (ASM) and truncal fat mass (TrFM). Two metabolic load-capacity indices were calculated: ratio of FM (kg) to FFM (kg) and ratio of TrFM (kg) to ASM (kg). Age-standardised reference curves, stratified by gender and BMI (<25.0 kg/m2, 25.0-29.9 kg/m2, ≥30.0 kg/m2), were constructed using an LMS approach. Percentiles of the reference curves were 5th, 15th, 25th, 50th, 75th, 85th and 95th. Secondary analysis of data from the 1999-2004 National Health and Nutrition Examination Survey (NHANES). The population included 6580 females and 6656 males. The unweighted proportions of obesity in males and females were 25.5 % and 34.7 %, respectively. The average values of both FM:FFM and TrFM:ASM were greater in female and obese subjects. Gender and BMI influenced the shape of the association of age with FM:FFM and TrFM:ASM, as a curvilinear relationship was observed in female and obese subjects. Menopause appeared to modify the steepness of the reference curves of both indices. This is a novel risk-stratification approach integrating the effects of high adiposity and low muscle mass which may be particularly useful to identify cases of sarcopenic obesity and improve disease-risk prediction.

Modelling the sulfate capacity of simulated radioactive waste borosilicate glasses

DOE PAGES

Bingham, Paul A.; Vaishnav, Shuchi; Forder, Sue D.; ...

2016-11-10

In this paper, the capacity of simulated high-level radioactive waste borosilicate glasses to incorporate sulfate has been studied as a function of glass composition. Combined Raman, 57Fe Mössbauer and literature evidence supports the attribution of coordination numbers and oxidation states of constituent cations for the purposes of modelling, and results confirm the validity of correlating sulfate incorporation in multicomponent borosilicate radioactive waste glasses with different models. A strong compositional dependency is observed and this can be described by an inverse linear relationship between incorporated sulfate (mol% SO 4 2-) and total cation field strength index of the glass, Σ(z/a 2),more » with a high goodness-of-fit (R 2 ≈ 0.950). Similar relationships are also obtained if theoretical optical basicity, Λ th (R 2 ≈ 0.930) or non-bridging oxygen per tetrahedron ratio, NBO/T (R 2 ≈ 0.919), are used. Finally, results support the application of these models, and in particular Σ(z/a 2), as predictive tools to aid the development of new glass compositions with enhanced sulfate capacities.« less

Modelling the sulfate capacity of simulated radioactive waste borosilicate glasses

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

Bingham, P. A.; Vaishnav, S.; Forder, S. D.

2017-02-01

The capacity of simulated high-level radioactive waste borosilicate glasses to incorporate sulfate has been studied as a function of glass composition. Combined Raman, 57Fe Mössbauer and literature evidence supports the attribution of coordination numbers and oxidation states of constituent cations for the purposes of modelling, and results confirm the validity of correlating sulfate incorporation in multicomponent borosilicate radioactive waste glasses with different models. A strong compositional dependency is observed and this can be described by an inverse linear relationship between incorporated sulfate (mol% SO 4 2-) and total cation field strength index of the glass, Σ(z/a 2), with a highmore » goodness-of-fit (R 2 ≈ 0.950). Similar relationships are also obtained if theoretical optical basicity, Λ th (R 2 ≈ 0.930) or non-bridging oxygen per tetrahedron ratio, NBO/T (R 2 ≈ 0.919), are used. Results support the application of these models, and in particular Σ(z/a 2), as predictive tools to aid the development of new glass compositions with enhanced sulfate capacities.« less

Dynamics of social contagions with local trend imitation.

PubMed

Zhu, Xuzhen; Wang, Wei; Cai, Shimin; Stanley, H Eugene

2018-05-09

Research on social contagion dynamics has not yet included a theoretical analysis of the ubiquitous local trend imitation (LTI) characteristic. We propose a social contagion model with a tent-like adoption probability to investigate the effect of this LTI characteristic on behavior spreading. We also propose a generalized edge-based compartmental theory to describe the proposed model. Through extensive numerical simulations and theoretical analyses, we find a crossover in the phase transition: when the LTI capacity is strong, the growth of the final adoption size exhibits a second-order phase transition. When the LTI capacity is weak, we see a first-order phase transition. For a given behavioral information transmission probability, there is an optimal LTI capacity that maximizes the final adoption size. Finally we find that the above phenomena are not qualitatively affected by the heterogeneous degree distribution. Our suggested theoretical predictions agree with the simulation results.

Deliberative and spontaneous cognitive processes associated with HIV risk behavior

PubMed Central

Ames, Susan L.; Stacy, Alan W.

2012-01-01

Dual process models of decision-making suggest that behavior is mediated by a spontaneous behavior selection process or by a more deliberative evaluation of behavioral options. We examined whether the deliberative system moderates the influence of spontaneous cognition on HIV-risk behaviors. A measure of spontaneous sex-related associations (word association), a measure of deliberative working memory capacity (operation span), and two measures of sexual behavior (condom use and multiple partners) were assessed in a cross-sectional study among 490 adult drug offenders. Significant effects were observed among men but not among women in two latent interaction models. In a novel finding, the accessibility of spontaneous safe sex-related associations was significantly more predictive of condom use among men with higher working memory capacity than among men with lower capacity. These results have implications for the design of interventions to promote safe sex practices. PMID:22331437

Performance and driveline analyses of engine capacity in range extender engine hybrid vehicle

NASA Astrophysics Data System (ADS)

Praptijanto, Achmad; Santoso, Widodo Budi; Nur, Arifin; Wahono, Bambang; Putrasari, Yanuandri

2017-01-01

In this study, range extender engine designed should be able to meet the power needs of a power generator of hybrid electrical vehicle that has a minimum of 18 kW. Using this baseline model, the following range extenders will be compared between conventional SI piston engine (Baseline, BsL), engine capacity 1998 cm3, and efficiency-oriented SI piston with engine capacity 999 cm3 and 499 cm3 with 86 mm bore and stroke square gasoline engine in the performance, emission prediction of range extender engine, standard of charge by using engine and vehicle simulation software tools. In AVL Boost simulation software, range extender engine simulated from 1000 to 6000 rpm engine loads. The highest peak engine power brake reached up to 38 kW at 4500 rpm. On the other hand the highest torque achieved in 100 Nm at 3500 rpm. After that using AVL cruise simulation software, the model of range extended electric vehicle in series configuration with main components such as internal combustion engine, generator, electric motor, battery and the arthemis model rural road cycle was used to simulate the vehicle model. The simulation results show that engine with engine capacity 999 cm3 reported the economical performances of the engine and the emission and the control of engine cycle parameters.

How do subvocal rehearsal and general attentional resources contribute to verbal short-term memory span?

PubMed Central

Morra, Sergio

2015-01-01

Whether rehearsal has a causal role in verbal STM has been controversial in the literature. Recent theories of working memory emphasize a role of attentional resources, but leave unclear how they contribute to verbal STM. Two experiments (with 49 and 102 adult participants, respectively) followed up previous studies with children, aiming to clarify the contributions of attentional capacity and rehearsal to verbal STM. Word length and presentation modality were manipulated. Experiment 1 focused on order errors, Experiment 2 on predicting individual differences in span from attentional capacity and articulation rate. Structural equation modeling showed clearly a major role of attentional capacity as a predictor of verbal STM span; but was inconclusive on whether rehearsal efficiency is an additional cause or a consequence of verbal STM. The effects of word length and modality on STM were replicated; a significant interaction was also found, showing a larger modality effect for long than short words, which replicates a previous finding on children. Item errors occurred more often with long words and correlated negatively with articulation rate. This set of findings seems to point to a role of rehearsal in maintaining item information. The probability of order errors per position increased linearly with list length. A revised version of a neo-Piagetian model was fit to the data of Experiment 2. That model was based on two parameters: attentional capacity (independently measured) and a free parameter representing loss of partly-activated information. The model could partly account for the results, but underestimated STM performance of the participants with smaller attentional capacity. It is concluded that modeling of verbal STM should consider individual and developmental differences in attentional capacity, rehearsal rate, and (perhaps) order representation. PMID:25798114

How do subvocal rehearsal and general attentional resources contribute to verbal short-term memory span?

PubMed

Morra, Sergio

2015-01-01

Whether rehearsal has a causal role in verbal STM has been controversial in the literature. Recent theories of working memory emphasize a role of attentional resources, but leave unclear how they contribute to verbal STM. Two experiments (with 49 and 102 adult participants, respectively) followed up previous studies with children, aiming to clarify the contributions of attentional capacity and rehearsal to verbal STM. Word length and presentation modality were manipulated. Experiment 1 focused on order errors, Experiment 2 on predicting individual differences in span from attentional capacity and articulation rate. Structural equation modeling showed clearly a major role of attentional capacity as a predictor of verbal STM span; but was inconclusive on whether rehearsal efficiency is an additional cause or a consequence of verbal STM. The effects of word length and modality on STM were replicated; a significant interaction was also found, showing a larger modality effect for long than short words, which replicates a previous finding on children. Item errors occurred more often with long words and correlated negatively with articulation rate. This set of findings seems to point to a role of rehearsal in maintaining item information. The probability of order errors per position increased linearly with list length. A revised version of a neo-Piagetian model was fit to the data of Experiment 2. That model was based on two parameters: attentional capacity (independently measured) and a free parameter representing loss of partly-activated information. The model could partly account for the results, but underestimated STM performance of the participants with smaller attentional capacity. It is concluded that modeling of verbal STM should consider individual and developmental differences in attentional capacity, rehearsal rate, and (perhaps) order representation.

Evapotranspiration sensitivity to air temperature across a snow-influenced watershed: Space-for-time substitution versus integrated watershed modeling

NASA Astrophysics Data System (ADS)

Jepsen, S. M.; Harmon, T. C.; Ficklin, D. L.; Molotch, N. P.; Guan, B.

2018-01-01

Changes in long-term, montane actual evapotranspiration (ET) in response to climate change could impact future water supplies and forest species composition. For scenarios of atmospheric warming, predicted changes in long-term ET tend to differ between studies using space-for-time substitution (STS) models and integrated watershed models, and the influence of spatially varying factors on these differences is unclear. To examine this, we compared warming-induced (+2 to +6 °C) changes in ET simulated by an STS model and an integrated watershed model across zones of elevation, substrate available water capacity, and slope in the snow-influenced upper San Joaquin River watershed, Sierra Nevada, USA. We used the Soil Water and Assessment Tool (SWAT) for the watershed modeling and a Budyko-type relationship for the STS modeling. Spatially averaged increases in ET from the STS model increasingly surpassed those from the SWAT model in the higher elevation zones of the watershed, resulting in 2.3-2.6 times greater values from the STS model at the watershed scale. In sparse, deep colluvium or glacial soils on gentle slopes, the SWAT model produced ET increases exceeding those from the STS model. However, watershed areas associated with these conditions were too localized for SWAT to produce spatially averaged ET-gains comparable to the STS model. The SWAT model results nevertheless demonstrate that such soils on high-elevation, gentle slopes will form ET "hot spots" exhibiting disproportionately large increases in ET, and concomitant reductions in runoff yield, in response to warming. Predicted ET responses to warming from STS models and integrated watershed models may, in general, substantially differ (e.g., factor of 2-3) for snow-influenced watersheds exhibiting an elevational gradient in substrate water holding capacity and slope. Long-term water supplies in these settings may therefore be more resilient to warming than STS model predictions would suggest.

Predicting survival of de novo metastatic breast cancer in Asian women: systematic review and validation study.

PubMed

Miao, Hui; Hartman, Mikael; Bhoo-Pathy, Nirmala; Lee, Soo-Chin; Taib, Nur Aishah; Tan, Ern-Yu; Chan, Patrick; Moons, Karel G M; Wong, Hoong-Seam; Goh, Jeremy; Rahim, Siti Mastura; Yip, Cheng-Har; Verkooijen, Helena M

2014-01-01

In Asia, up to 25% of breast cancer patients present with distant metastases at diagnosis. Given the heterogeneous survival probabilities of de novo metastatic breast cancer, individual outcome prediction is challenging. The aim of the study is to identify existing prognostic models for patients with de novo metastatic breast cancer and validate them in Asia. We performed a systematic review to identify prediction models for metastatic breast cancer. Models were validated in 642 women with de novo metastatic breast cancer registered between 2000 and 2010 in the Singapore Malaysia Hospital Based Breast Cancer Registry. Survival curves for low, intermediate and high-risk groups according to each prognostic score were compared by log-rank test and discrimination of the models was assessed by concordance statistic (C-statistic). We identified 16 prediction models, seven of which were for patients with brain metastases only. Performance status, estrogen receptor status, metastatic site(s) and disease-free interval were the most common predictors. We were able to validate nine prediction models. The capacity of the models to discriminate between poor and good survivors varied from poor to fair with C-statistics ranging from 0.50 (95% CI, 0.48-0.53) to 0.63 (95% CI, 0.60-0.66). The discriminatory performance of existing prediction models for de novo metastatic breast cancer in Asia is modest. Development of an Asian-specific prediction model is needed to improve prognostication and guide decision making.

High capacity reversible watermarking for audio by histogram shifting and predicted error expansion.

PubMed

Wang, Fei; Xie, Zhaoxin; Chen, Zuo

2014-01-01

Being reversible, the watermarking information embedded in audio signals can be extracted while the original audio data can achieve lossless recovery. Currently, the few reversible audio watermarking algorithms are confronted with following problems: relatively low SNR (signal-to-noise) of embedded audio; a large amount of auxiliary embedded location information; and the absence of accurate capacity control capability. In this paper, we present a novel reversible audio watermarking scheme based on improved prediction error expansion and histogram shifting. First, we use differential evolution algorithm to optimize prediction coefficients and then apply prediction error expansion to output stego data. Second, in order to reduce location map bits length, we introduced histogram shifting scheme. Meanwhile, the prediction error modification threshold according to a given embedding capacity can be computed by our proposed scheme. Experiments show that this algorithm improves the SNR of embedded audio signals and embedding capacity, drastically reduces location map bits length, and enhances capacity control capability.

Round-robin analysis of the behavior of a 1:6-scale reinforced concrete containment model pressurized to failure: Posttest evaluations

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

Clauss, D.B.

A 1:6-scale model of a reinforced concrete containment building was pressurized incrementally to failure at a remote site at Sandia National Laboratories. The response of the model was recorded with more than 1000 channels of data (primarily strain and displacement measurements) at 37 discrete pressure levels. The primary objective of this test was to generate data that could be used to validate methods for predicting the performance of containment buildings subject to loads beyond their design basis. Extensive analyses were conducted before the test to predict the behavior of the model. Ten organizations in Europe and the US conducted independentmore » analyses of the model and contributed to a report on the pretest predictions. Predictions included structural response at certain predetermined locations in the model as well as capacity and failure mode. This report discusses comparisons between the pretest predictions and the experimental results. Posttest evaluations that were conducted to provide additional insight into the model behavior are also described. The significance of the analysis and testing of the 1:6-scale model to performance evaluations of actual containments subject to beyond design basis loads is also discussed. 70 refs., 428 figs., 24 tabs.« less

Evaluation of computer-based computer tomography stratification against outcome models in connective tissue disease-related interstitial lung disease: a patient outcome study.

PubMed

Jacob, Joseph; Bartholmai, Brian J; Rajagopalan, Srinivasan; Brun, Anne Laure; Egashira, Ryoko; Karwoski, Ronald; Kokosi, Maria; Wells, Athol U; Hansell, David M

2016-11-23

To evaluate computer-based computer tomography (CT) analysis (CALIPER) against visual CT scoring and pulmonary function tests (PFTs) when predicting mortality in patients with connective tissue disease-related interstitial lung disease (CTD-ILD). To identify outcome differences between distinct CTD-ILD groups derived following automated stratification of CALIPER variables. A total of 203 consecutive patients with assorted CTD-ILDs had CT parenchymal patterns evaluated by CALIPER and visual CT scoring: honeycombing, reticular pattern, ground glass opacities, pulmonary vessel volume, emphysema, and traction bronchiectasis. CT scores were evaluated against pulmonary function tests: forced vital capacity, diffusing capacity for carbon monoxide, carbon monoxide transfer coefficient, and composite physiologic index for mortality analysis. Automated stratification of CALIPER-CT variables was evaluated in place of and alongside forced vital capacity and diffusing capacity for carbon monoxide in the ILD gender, age physiology (ILD-GAP) model using receiver operating characteristic curve analysis. Cox regression analyses identified four independent predictors of mortality: patient age (P < 0.0001), smoking history (P = 0.0003), carbon monoxide transfer coefficient (P = 0.003), and pulmonary vessel volume (P < 0.0001). Automated stratification of CALIPER variables identified three morphologically distinct groups which were stronger predictors of mortality than all CT and functional indices. The Stratified-CT model substituted automated stratified groups for functional indices in the ILD-GAP model and maintained model strength (area under curve (AUC) = 0.74, P < 0.0001), ILD-GAP (AUC = 0.72, P < 0.0001). Combining automated stratified groups with the ILD-GAP model (stratified CT-GAP model) strengthened predictions of 1- and 2-year mortality: ILD-GAP (AUC = 0.87 and 0.86, respectively); stratified CT-GAP (AUC = 0.89 and 0.88, respectively). CALIPER-derived pulmonary vessel volume is an independent predictor of mortality across all CTD-ILD patients. Furthermore, automated stratification of CALIPER CT variables represents a novel method of prognostication at least as robust as PFTs in CTD-ILD patients.

Non-Contact Analysis of the Adsorptive Ink Capacity of Nano Silica Pigments on a Printing Coating Base

PubMed Central

Jiang, Bo; Huang, Yu Dong

2014-01-01

Near infrared spectra combined with partial least squares were proposed as a means of non-contact analysis of the adsorptive ink capacity of recording coating materials in ink jet printing. First, the recording coating materials were prepared based on nano silica pigments. 80 samples of the recording coating materials were selected to develop the calibration of adsorptive ink capacity against ink adsorption (g/m2). The model developed predicted samples in the validation set with r2  = 0.80 and SEP  = 1.108, analytical results showed that near infrared spectra had significant potential for the adsorption of ink capacity on the recording coating. The influence of factors such as recording coating thickness, mass ratio silica: binder-polyvinyl alcohol and the solution concentration on the adsorptive ink capacity were studied. With the help of the near infrared spectra, the adsorptive ink capacity of a recording coating material can be rapidly controlled. PMID:25329464

Non-contact analysis of the adsorptive ink capacity of nano silica pigments on a printing coating base.

PubMed

Jiang, Bo; Huang, Yu Dong

2014-01-01

Near infrared spectra combined with partial least squares were proposed as a means of non-contact analysis of the adsorptive ink capacity of recording coating materials in ink jet printing. First, the recording coating materials were prepared based on nano silica pigments. 80 samples of the recording coating materials were selected to develop the calibration of adsorptive ink capacity against ink adsorption (g/m2). The model developed predicted samples in the validation set with r2  = 0.80 and SEP = 1.108, analytical results showed that near infrared spectra had significant potential for the adsorption of ink capacity on the recording coating. The influence of factors such as recording coating thickness, mass ratio silica: binder-polyvinyl alcohol and the solution concentration on the adsorptive ink capacity were studied. With the help of the near infrared spectra, the adsorptive ink capacity of a recording coating material can be rapidly controlled.

Facial expression judgments support a socio-relational model, rather than a negativity bias model of political psychology.

PubMed

Vigil, Jacob M; Strenth, Chance

2014-06-01

Self-reported opinions and judgments may be more rooted in expressive biases than in cognitive processing biases, and ultimately operate within a broader behavioral style for advertising the capacity - versus the trustworthiness - dimension of human reciprocity potential. Our analyses of facial expression judgments of likely voters are consistent with this thesis, and directly contradict one major prediction from the authors' "negativity-bias" model.

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

Bodvarsson, G.S.; Pruess, K.; Stefansson, V.

A detailed three-dimensional well-by-well model of the East Olkaria geothermal field in Kenya has been developed. The model matches reasonably well the flow rate and enthalpy data from all wells, as well as the overall pressure decline in the reservoir. The model is used to predict the generating capacity of the field, well decline, enthalpy behavior, the number of make-up wells needed and the effects of injection on well performance and overall reservoir depletion. 26 refs., 10 figs.

Study on LOC for modern facility agriculture automatic walking equipment LiFePO4 battery

NASA Astrophysics Data System (ADS)

Liu, Xuepeng; Zhao, Dongmei

2017-08-01

LiFePO4 battery LOC (life Of Charge) is the assessment of the ability to work within a cycle of battery charge and discharge period, which likes the miles for vehicle. LOC is related with battery capacity, working condition and stress. LOC consists of the model of the battery's SOC online prediction model, the analysis of RBSOC and the LOC model of multi-condition and multi-stress.

Predictive models and spatial variations of vital capacity in healthy people from 6 to 84 years old in China based on geographical factors.

PubMed

He, Jinwei; Ge, Miao; Wang, Congxia; Jiang, Naigui; Zhang, Mingxin; Yun, Pujun

2014-07-01

The aim of this study was to provide a scientific basic for a unified standard of the reference value of vital capacity (VC) of healthy subjects from 6 and 84 years old in China. The normal reference value of VC was correlated to seven geographical factors, including altitude (X1), annual duration of sunshine (X2), annual mean air temperature (X3), annual mean relative humidity (X4), annual precipitation amount (X5), annual air temperature range (X6) and annual mean wind speed (X7). Predictive models were established by five different linear and nonlinear methods. The best models were selected by t-test. The geographical distribution map of VC in different age groups can be interpolated by Kriging's method using ArcGIS software. It was found that the correlation of VC and geographical factors in China was quite significant, especially for both males and females aged from 6 to 45. The best models were built for different age groups. The geographical distribution map shows the spatial variations of VC in China precisely. The VC of healthy subjects can be simulated by the best model or acquired from the geographical distribution map provided the geographical factors for that city or county of China are known.

A hybrid predictive model for acoustic noise in urban areas based on time series analysis and artificial neural network

NASA Astrophysics Data System (ADS)

Guarnaccia, Claudio; Quartieri, Joseph; Tepedino, Carmine

2017-06-01

The dangerous effect of noise on human health is well known. Both the auditory and non-auditory effects are largely documented in literature, and represent an important hazard in human activities. Particular care is devoted to road traffic noise, since it is growing according to the growth of residential, industrial and commercial areas. For these reasons, it is important to develop effective models able to predict the noise in a certain area. In this paper, a hybrid predictive model is presented. The model is based on the mixing of two different approach: the Time Series Analysis (TSA) and the Artificial Neural Network (ANN). The TSA model is based on the evaluation of trend and seasonality in the data, while the ANN model is based on the capacity of the network to "learn" the behavior of the data. The mixed approach will consist in the evaluation of noise levels by means of TSA and, once the differences (residuals) between TSA estimations and observed data have been calculated, in the training of a ANN on the residuals. This hybrid model will exploit interesting features and results, with a significant variation related to the number of steps forward in the prediction. It will be shown that the best results, in terms of prediction, are achieved predicting one step ahead in the future. Anyway, a 7 days prediction can be performed, with a slightly greater error, but offering a larger range of prediction, with respect to the single day ahead predictive model.

DEVELOPMENT OF AQUATIC MODELS FOR TESTING THE RELATIONSHIP BETWEEN GENETIC DIVERSITY AND POPULATION EXTINCTION RISK

EPA Science Inventory

The relationship between population adaptive potential and extinction risk in a changing environment is not well understood. Although the expectation is that genetic diversity is directly related to the capacity of populations to adapt, the statistical and predictive aspects of ...

Capacity planning for maternal-fetal medicine using discrete event simulation.

PubMed

Ferraro, Nicole M; Reamer, Courtney B; Reynolds, Thomas A; Howell, Lori J; Moldenhauer, Julie S; Day, Theodore Eugene

2015-07-01

Maternal-fetal medicine is a rapidly growing field requiring collaboration from many subspecialties. We provide an evidence-based estimate of capacity needs for our clinic, as well as demonstrate how simulation can aid in capacity planning in similar environments. A Discrete Event Simulation of the Center for Fetal Diagnosis and Treatment and Special Delivery Unit at The Children's Hospital of Philadelphia was designed and validated. This model was then used to determine the time until demand overwhelms inpatient bed availability under increasing capacity. No significant deviation was found between historical inpatient censuses and simulated censuses for the validation phase (p = 0.889). Prospectively increasing capacity was found to delay time to balk (the inability of the center to provide bed space for a patient in need of admission). With current capacity, the model predicts mean time to balk of 276 days. Adding three beds delays mean time to first balk to 762 days; an additional six beds to 1,335 days. Providing sufficient access is a patient safety issue, and good planning is crucial for targeting infrastructure investments appropriately. Computer-simulated analysis can provide an evidence base for both medical and administrative decision making in a complex clinical environment. Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.

A simplified model of a mechanical cooling tower with both a fill pack and a coil

NASA Astrophysics Data System (ADS)

Van Riet, Freek; Steenackers, Gunther; Verhaert, Ivan

2017-11-01

Cooling accounts for a large amount of the global primary energy consumption in buildings and industrial processes. A substantial part of this cooling demand is produced by mechanical cooling towers. Simulations benefit the sizing and integration of cooling towers in overall cooling networks. However, for these simulations fast-to-calculate and easy-to-parametrize models are required. In this paper, a new model is developed for a mechanical draught cooling tower with both a cooling coil and a fill pack. The model needs manufacturers' performance data at only three operational states (at varying air and water flow rates) to be parametrized. The model predicts the cooled, outgoing water temperature. These predictions were compared with experimental data for a wide range of operational states. The model was able to predict the temperature with a maximum absolute error of 0.59°C. The relative error of cooling capacity was mostly between ±5%.

A signal detection model predicts the effects of set size on visual search accuracy for feature, conjunction, triple conjunction, and disjunction displays

NASA Technical Reports Server (NTRS)

Eckstein, M. P.; Thomas, J. P.; Palmer, J.; Shimozaki, S. S.

2000-01-01

Recently, quantitative models based on signal detection theory have been successfully applied to the prediction of human accuracy in visual search for a target that differs from distractors along a single attribute (feature search). The present paper extends these models for visual search accuracy to multidimensional search displays in which the target differs from the distractors along more than one feature dimension (conjunction, disjunction, and triple conjunction displays). The model assumes that each element in the display elicits a noisy representation for each of the relevant feature dimensions. The observer combines the representations across feature dimensions to obtain a single decision variable, and the stimulus with the maximum value determines the response. The model accurately predicts human experimental data on visual search accuracy in conjunctions and disjunctions of contrast and orientation. The model accounts for performance degradation without resorting to a limited-capacity spatially localized and temporally serial mechanism by which to bind information across feature dimensions.

Phylogenetic Analysis Supports the Aerobic-Capacity Model for the Evolution of Endothermy.

PubMed

Nespolo, Roberto F; Solano-Iguaran, Jaiber J; Bozinovic, Francisco

2017-01-01

The evolution of endothermy is a controversial topic in evolutionary biology, although several hypotheses have been proposed to explain it. To a great extent, the debate has centered on the aerobic-capacity model (AC model), an adaptive hypothesis involving maximum and resting rates of metabolism (MMR and RMR, respectively; hereafter "metabolic traits"). The AC model posits that MMR, a proxy of aerobic capacity and sustained activity, is the target of directional selection and that RMR is also influenced as a correlated response. Associated with this reasoning are the assumptions that (1) factorial aerobic scope (FAS; MMR/RMR) and net aerobic scope (NAS; MMR - RMR), two commonly used indexes of aerobic capacity, show different evolutionary optima and (2) the functional link between MMR and RMR is a basic design feature of vertebrates. To test these assumptions, we performed a comparative phylogenetic analysis in 176 vertebrate species, ranging from fish and amphibians to birds and mammals. Using disparity-through-time analysis, we also explored trait diversification and fitted different evolutionary models to study the evolution of metabolic traits. As predicted, we found (1) a positive phylogenetic correlation between RMR and MMR, (2) diversification of metabolic traits exceeding that of random-walk expectations, (3) that a model assuming selection fits the data better than alternative models, and (4) that a single evolutionary optimum best fits FAS data, whereas a model involving two optima (one for ectotherms and another for endotherms) is the best explanatory model for NAS. These results support the AC model and give novel information concerning the mode and tempo of physiological evolution of vertebrates.

Development of an automated energy audit protocol for office buildings

NASA Astrophysics Data System (ADS)

Deb, Chirag

This study aims to enhance the building energy audit process, and bring about reduction in time and cost requirements in the conduction of a full physical audit. For this, a total of 5 Energy Service Companies in Singapore have collaborated and provided energy audit reports for 62 office buildings. Several statistical techniques are adopted to analyse these reports. These techniques comprise cluster analysis and development of prediction models to predict energy savings for buildings. The cluster analysis shows that there are 3 clusters of buildings experiencing different levels of energy savings. To understand the effect of building variables on the change in EUI, a robust iterative process for selecting the appropriate variables is developed. The results show that the 4 variables of GFA, non-air-conditioning energy consumption, average chiller plant efficiency and installed capacity of chillers should be taken for clustering. This analysis is extended to the development of prediction models using linear regression and artificial neural networks (ANN). An exhaustive variable selection algorithm is developed to select the input variables for the two energy saving prediction models. The results show that the ANN prediction model can predict the energy saving potential of a given building with an accuracy of +/-14.8%.

[The role of the Aedes aegypti vector in the epidemiology of dengue in Mexico].

PubMed

Fernández-Salas, I; Flores-Leal, A

1995-01-01

The role of Aedes aegypti (Lineo) in the epidemiology of dengue fever in Mexico is herein discussed based on the vectorial capacity model. Comments on the advantages and disadvantages of each model component at the time of field determinations are also presented. Emphasis is made on the impact of sampling and method bias on the results of vectorial capacity studies. The paper also addresses the need to increase vector biology knowledge as an input for epidemiological work to explain and predict dengue fever outbreaks. Comments on potential entomological variables not considered by the quantitative model are included. Finally, we elaborate on the introduction of Aedes albopictus (Skuse) in Mexico as a new risk factor and on its implications for the understanding of dengue fever transmission in Mexico.

Ultrasound waiting lists: rational queue or extended capacity?

PubMed

Brasted, Christopher

2008-06-01

The features and issues regarding clinical waiting lists in general and general ultrasound waiting lists in particular are reviewed, and operational aspects of providing a general ultrasound service are also discussed. A case study is presented describing a service improvement intervention in a UK NHS hospital's ultrasound department, from which arises requirements for a predictive planning model for an ultrasound waiting list. In the course of this, it becomes apparent that a booking system is a more appropriate way of describing the waiting list than a conventional queue. Distinctive features are identified from the literature and the case study as the basis for a predictive model, and a discrete event simulation model is presented which incorporates the distinctive features.

Genetic determinants of freckle occurrence in the Spanish population: Towards ephelides prediction from human DNA samples.

PubMed

Hernando, Barbara; Ibañez, Maria Victoria; Deserio-Cuesta, Julio Alberto; Soria-Navarro, Raquel; Vilar-Sastre, Inca; Martinez-Cadenas, Conrado

2018-03-01

Prediction of human pigmentation traits, one of the most differentiable externally visible characteristics among individuals, from biological samples represents a useful tool in the field of forensic DNA phenotyping. In spite of freckling being a relatively common pigmentation characteristic in Europeans, little is known about the genetic basis of this largely genetically determined phenotype in southern European populations. In this work, we explored the predictive capacity of eight freckle and sunlight sensitivity-related genes in 458 individuals (266 non-freckled controls and 192 freckled cases) from Spain. Four loci were associated with freckling (MC1R, IRF4, ASIP and BNC2), and female sex was also found to be a predictive factor for having a freckling phenotype in our population. After identifying the most informative genetic variants responsible for human ephelides occurrence in our sample set, we developed a DNA-based freckle prediction model using a multivariate regression approach. Once developed, the capabilities of the prediction model were tested by a repeated 10-fold cross-validation approach. The proportion of correctly predicted individuals using the DNA-based freckle prediction model was 74.13%. The implementation of sex into the DNA-based freckle prediction model slightly improved the overall prediction accuracy by 2.19% (76.32%). Further evaluation of the newly-generated prediction model was performed by assessing the model's performance in a new cohort of 212 Spanish individuals, reaching a classification success rate of 74.61%. Validation of this prediction model may be carried out in larger populations, including samples from different European populations. Further research to validate and improve this newly-generated freckle prediction model will be needed before its forensic application. Together with DNA tests already validated for eye and hair colour prediction, this freckle prediction model may lead to a substantially more detailed physical description of unknown individuals from DNA found at the crime scene. Copyright © 2017 Elsevier B.V. All rights reserved.

Stata Modules for Calculating Novel Predictive Performance Indices for Logistic Models

PubMed Central

Barkhordari, Mahnaz; Padyab, Mojgan; Hadaegh, Farzad; Azizi, Fereidoun; Bozorgmanesh, Mohammadreza

2016-01-01

Background Prediction is a fundamental part of prevention of cardiovascular diseases (CVD). The development of prediction algorithms based on the multivariate regression models loomed several decades ago. Parallel with predictive models development, biomarker researches emerged in an impressively great scale. The key question is how best to assess and quantify the improvement in risk prediction offered by new biomarkers or more basically how to assess the performance of a risk prediction model. Discrimination, calibration, and added predictive value have been recently suggested to be used while comparing the predictive performances of the predictive models’ with and without novel biomarkers. Objectives Lack of user-friendly statistical software has restricted implementation of novel model assessment methods while examining novel biomarkers. We intended, thus, to develop a user-friendly software that could be used by researchers with few programming skills. Materials and Methods We have written a Stata command that is intended to help researchers obtain cut point-free and cut point-based net reclassification improvement index and (NRI) and relative and absolute Integrated discriminatory improvement index (IDI) for logistic-based regression analyses.We applied the commands to a real data on women participating the Tehran lipid and glucose study (TLGS) to examine if information of a family history of premature CVD, waist circumference, and fasting plasma glucose can improve predictive performance of the Framingham’s “general CVD risk” algorithm. Results The command is addpred for logistic regression models. Conclusions The Stata package provided herein can encourage the use of novel methods in examining predictive capacity of ever-emerging plethora of novel biomarkers. PMID:27279830

Implementation of perturbed-chain statistical associating fluid theory (PC-SAFT), generalized (G)SAFT+cubic, and cubic-plus-association (CPA) for modeling thermophysical properties of selected 1-alkyl-3-methylimidazolium ionic liquids in a wide pressure range.

PubMed

Polishuk, Ilya

2013-03-14

This study is the first comparative investigation of predicting the isochoric and the isobaric heat capacities, the isothermal and the isentropic compressibilities, the isobaric thermal expansibilities, the thermal pressure coefficients, and the sound velocities of ionic liquids by statistical associating fluid theory (SAFT) equation of state (EoS) models and cubic-plus-association (CPA). It is demonstrated that, taking into account the high uncertainty of the literature data (excluding sound velocities), the generalized for heavy compounds version of SAFT+Cubic (GSAFT+Cubic) appears as a robust estimator of the auxiliary thermodynamic properties under consideration. In the case of the ionic liquids the performance of PC-SAFT seems to be less accurate in comparison to ordinary compounds. In particular, PC-SAFT substantially overestimates heat capacities and underestimates the temperature and pressure dependencies of sound velocities and compressibilities. An undesired phenomenon of predicting high fictitious critical temperatures of ionic liquids by PC-SAFT should be noticed as well. CPA is the less accurate estimator of the liquid phase properties, but it is advantageous in modeling vapor pressures and vaporization enthalpies of ionic liquids. At the same time, the preliminary results indicate that the inaccuracies in predicting the deep vacuum vapor pressures of ionic liquids do not influence modeling of phase equilibria in their mixtures at much higher pressures.

Acidification and Prognosis for Future Recovery of Acid-Sensitive Streams in the Southern Blue Ridge Province

Treesearch

Timothy Sullivan; Bernard Cosby; William Jackson; Kai Snyder; Alan Herlihy

2011-01-01

This study applied the Model of Acidification of Groundwater in Catchments (MAGIC) to estimate the sensitivity of 66 watersheds in the Southern Blue Ridge Province of the Southern Appalachian Mountains, United States, to changes in atmospheric sulfur (S) deposition. MAGIC predicted that stream acid neutralizing capacity (ANC) values were above 20 μeq/L in all modeled...

Comparative evaluation of adsorption kinetics of diclofenac and isoproturon by activated carbon.

PubMed

Torrellas, Silvia A; Rodriguez, Araceli R; Escudero, Gabriel O; Martín, José María G; Rodriguez, Juan G

2015-01-01

Adsorption mechanism of diclofenac and isoproturon onto activated carbon has been proposed using Langmuir and Freundlich isotherms. Adsorption capacity and optimum adsorption isotherms were predicted by nonlinear regression method. Different kinetic equations, pseudo-first-order, pseudo-second-order, intraparticle diffusion model and Bangham kinetic model, were applied to study the adsorption kinetics of emerging contaminants on activated carbon in two aqueous matrices.

Multi-scale Mexican spotted owl (Strix occidentalis lucida) nest/roost habitat selection in Arizona and a comparison with single-scale modeling results

Treesearch

Brad C. Timm; Kevin McGarigal; Samuel A. Cushman; Joseph L. Ganey

2016-01-01

Efficacy of future habitat selection studies will benefit by taking a multi-scale approach. In addition to potentially providing increased explanatory power and predictive capacity, multi-scale habitat models enhance our understanding of the scales at which species respond to their environment, which is critical knowledge required to implement effective...

Controls on sediment cover in bedrock-alluvial channels of the Henry Mountains, Utah

NASA Astrophysics Data System (ADS)

Hodge, R. A.; Yager, E.; Johnson, J. P.; Tranmer, A.

2017-12-01

The location and extent of sediment cover in bedrock-alluvial channels influences sediment transport rates, channel incision and instream ecology. However, factors affecting sediment cover and how it responds to changes in relative sediment supply have rarely been quantitatively evaluated in field settings. Using field surveys and SFM analysis of channel reach topography, we quantified sediment cover and channel properties including slope, width, grain size distributions, and bedrock and alluvial roughness in North Wash and Chelada Creek in the Henry Mountains, Utah. Along reaches where upstream sediment supply does not appear to be restricted, we find that the fraction of local bedrock exposure increases as a function of local relative transport capacity . In a downstream section of Chelada Creek, decadal-scale sediment supply has been restricted by an upstream culvert that has caused a backwater effect and corresponding upstream deposition. In this section, alluvial cover is uncorrelated with local stream power. To test the impact of relative sediment supply on sediment cover, a 1D sediment transport model was used to predict the equilibrium sediment cover in Chelada Creek under varying flow and sediment supply conditions. Sediment transport in each model section was predicted using the partial cover model of Johnson (2015), which accounts for differences in bedrock and alluvial roughness on critical shear stress and flow resistance. Model runs in which sediment supply was approximately equal to mean transport capacity produced a pattern of sediment cover which best matched the field observations upstream of the culvert. However, runs where sediment supply was under-capacity produced the pattern most similar to field observations downstream of the culvert, consistent with our field-based interpretations. Model results were insensitive to initial sediment cover, and equilibrium was relatively quickly reached, suggesting that the channel is responsive to changes in imposed conditions. Overall, our results suggest that alluvial cover fractions may be predictable at spatial scales relevant for landscape evolution modelling, but that local bed roughness and thresholds in relative sediment supply may need to be accounted for.

Know the risk, take the win: how executive functions and probability processing influence advantageous decision making under risk conditions.

PubMed

Brand, Matthias; Schiebener, Johannes; Pertl, Marie-Theres; Delazer, Margarete

2014-01-01

Recent models on decision making under risk conditions have suggested that numerical abilities are important ingredients of advantageous decision-making performance, but empirical evidence is still limited. The results of our first study show that logical reasoning and basic mental calculation capacities predict ratio processing and that ratio processing predicts decision making under risk. In the second study, logical reasoning together with executive functions predicted probability processing (numeracy and probability knowledge), and probability processing predicted decision making under risk. These findings suggest that increasing an individual's understanding of ratios and probabilities should lead to more advantageous decisions under risk conditions.

Brain regions engaged by part- and whole-task performance in a video game: a model-based test of the decomposition hypothesis.

PubMed

Anderson, John R; Bothell, Daniel; Fincham, Jon M; Anderson, Abraham R; Poole, Ben; Qin, Yulin

2011-12-01

Part- and whole-task conditions were created by manipulating the presence of certain components of the Space Fortress video game. A cognitive model was created for two-part games that could be combined into a model that performed the whole game. The model generated predictions both for behavioral patterns and activation patterns in various brain regions. The activation predictions concerned both tonic activation that was constant in these regions during performance of the game and phasic activation that occurred when there was resource competition. The model's predictions were confirmed about how tonic and phasic activation in different regions would vary with condition. These results support the Decomposition Hypothesis that the execution of a complex task can be decomposed into a set of information-processing components and that these components combine unchanged in different task conditions. In addition, individual differences in learning gains were predicted by individual differences in phasic activation in those regions that displayed highest tonic activity. This individual difference pattern suggests that the rate of learning of a complex skill is determined by capacity limits.

Planning Staff and Space Capacity Requirements during Wartime.

PubMed

Kepner, Elisa B; Spencer, Rachel

2016-01-01

Determining staff and space requirements for military medical centers can be challenging. Changing patient populations change the caseload requirements. Deployment and assignment rotations change the experience and education of clinicians and support staff, thereby changing the caseload capacity of a facility. During wartime, planning becomes increasingly more complex. What will the patient mix and caseload volume be by location? What type of clinicians will be available and when? How many beds are needed at each facility to meet caseload demand and match clinician supply? As soon as these factors are known, operations are likely to change and planning factors quickly become inaccurate. Soon, more beds or staff are needed in certain locations to meet caseload demand while other locations retain underutilized staff, waiting for additional caseload fluctuations. This type of complexity challenges the best commanders. As in so many other industries, supply and demand principles apply to military health, but very little is stable about military health capacity planning. Planning analysts build complex statistical forecasting models to predict caseload based on historical patterns. These capacity planning techniques work best in stable repeatable processes where caseload and staffing resources remain constant over a long period of time. Variability must be simplified to predict complex operations. This is counterintuitive to the majority of capacity planners who believe more data drives better answers. When the best predictor of future needs is not historical patterns, traditional capacity planning does not work. Rather, simplified estimation techniques coupled with frequent calibration adjustments to account for environmental changes will create the most accurate and most useful capacity planning and management system. The method presented in this article outlines the capacity planning approach used to actively manage hospital staff and space during Operations Iraqi Freedom and Enduring Freedom.

Machine learning study for the prediction of transdermal peptide

NASA Astrophysics Data System (ADS)

Jung, Eunkyoung; Choi, Seung-Hoon; Lee, Nam Kyung; Kang, Sang-Kee; Choi, Yun-Jaie; Shin, Jae-Min; Choi, Kihang; Jung, Dong Hyun

2011-04-01

In order to develop a computational method to rapidly evaluate transdermal peptides, we report approaches for predicting the transdermal activity of peptides on the basis of peptide sequence information using Artificial Neural Network (ANN), Partial Least Squares (PLS) and Support Vector Machine (SVM). We identified 269 transdermal peptides by the phage display technique and use them as the positive controls to develop and test machine learning models. Combinations of three descriptors with neural network architectures, the number of latent variables and the kernel functions are tried in training to make appropriate predictions. The capacity of models is evaluated by means of statistical indicators including sensitivity, specificity, and the area under the receiver operating characteristic curve (ROC score). In the ROC score-based comparison, three methods proved capable of providing a reasonable prediction of transdermal peptide. The best result is obtained by SVM model with a radial basis function and VHSE descriptors. The results indicate that it is possible to discriminate between transdermal peptides and random sequences using our models. We anticipate that our models will be applicable to prediction of transdermal peptide for large peptide database for facilitating efficient transdermal drug delivery through intact skin.

NASA AVOSS Fast-Time Wake Prediction Models: User's Guide

NASA Technical Reports Server (NTRS)

Ahmad, Nash'at N.; VanValkenburg, Randal L.; Pruis, Matthew

2014-01-01

The National Aeronautics and Space Administration (NASA) is developing and testing fast-time wake transport and decay models to safely enhance the capacity of the National Airspace System (NAS). The fast-time wake models are empirical algorithms used for real-time predictions of wake transport and decay based on aircraft parameters and ambient weather conditions. The aircraft dependent parameters include the initial vortex descent velocity and the vortex pair separation distance. The atmospheric initial conditions include vertical profiles of temperature or potential temperature, eddy dissipation rate, and crosswind. The current distribution includes the latest versions of the APA (3.4) and the TDP (2.1) models. This User's Guide provides detailed information on the model inputs, file formats, and the model output. An example of a model run and a brief description of the Memphis 1995 Wake Vortex Dataset is also provided.

Prediction of heat capacities of solid inorganic salts from group contributions

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

Mostafa, A.T.M.G.; Eakman, J.M.; Yarbro, S.L.

1997-01-01

A group contribution technique is proposed to predict the coefficients in the heat capacity correlation, C{sub p} = a + bT + c/T{sup 2} + dT{sup 2}, for solid inorganic salts. The results from this work are compared with fits to experimental data from the literature. It is shown to give good predictions for both simple and complex solid inorganic salts. Literature heat capacities for a large number (664) of solid inorganic salts covering a broad range of cations (129), anions (17) and ligands (2) have been used in regressions to obtain group contributions for the parameters in the heatmore » capacity temperature function. A mean error of 3.18% is found when predicted values are compared with literature values for heat capacity at 298{degrees} K. Estimates of the error standard deviation from the regression for each additivity constant are also determined.« less

[The Prognostic Validity of the Functional Capacity Evaluation ELA in Work-Related Medical Rehabilitation].

PubMed

Bühne, David; Alles, Torsten; Hetzel, Christian; Froböse, Ingo

2018-04-01

The aim of the study was to determine the ability of FCE (Functional Capacity Evaluation) to predict sustained return-to-work (RTW). A multicentric prospective cohort study was conducted in cooperation with 4 outpatient rehabilitation clinics. The sample consisted of 198 patients. Sustained RTW was defined as a combination of employment at 3-month follow-up with a low level of sick leave (dependent variable 1) resp. with a moderate or better rating of the current work ability with respect to the physical demands at work (dependent variable 2). Based on questionnaires and FCE information, logistic regression models were calculated to predict sustained RTW. The FCE-information at discharge predicted sustained RTW after adjusting for assessors (Odds Ratio - OR=17.2 [95% CI: 6.2-57.8] resp. OR 12.8 [95% CI: 5.1-32.1]) as well as after adjusting for additional RTW predictors (OR 14.6 [95% CI: 4.8-44.9] resp. OR 10.1 [95% CI: 3.5-29.4]). Concerning dependent variable 1 and the FCE-information at admission there was a gain of information towards a model based on patient self-reports (OR 2.6 [95% CI: 1.1-6.0]). The study supports the predictive validity of crude and adjusted FCE-information. The gain of information towards patient self-reports is unclear. © Georg Thieme Verlag KG Stuttgart · New York.

Modeling the light-induced electric potential difference (ΔΨ), the pH difference (ΔpH) and the proton motive force across the thylakoid membrane in C3 leaves.

PubMed

Lyu, Hui; Lazár, Dušan

2017-01-21

A model was constructed which includes electron transport (linear and cyclic and Mehler type reaction) coupled to proton translocation, counter ion movement, ATP synthesis, and Calvin-Benson cycle. The focus is on modeling of the light-induced total electric potential difference (ΔΨ) which in this model originates from the bulk phase electric potential difference (ΔΨ b ), the localized electric potential difference (ΔΨ c ), as well as the surface electric potential difference (ΔΨ s ). The measured dual wavelength transmittance signal (ΔA515-560nm, electrochromic shift) was used as a proxy for experimental ΔΨ. The predictions for theoretical ΔΨ vary with assumed contribution of ΔΨ s , which might imply that the measured ΔA515-560nm trace on a long time scale reflects the interplay of the ΔΨ components. Simulations also show that partitioning of proton motive force (pmf) to ΔΨ b and ΔpH components is sensitive to the stoichiometric ratio of H + /ATP, energy barrier for ATP synthesis, ionic strength, buffer capacity and light intensity. Our model shows that high buffer capacity promotes the establishment of ΔΨ b , while the formation of pH i minimum is not 'dissipated' but 'postponed' until it reaches the same level as that for low buffer capacity. Under physiologically optimal conditions, the output of the model shows that at steady state in light, the ΔpH component is the main contributor to pmf to drive ATP synthesis while a low ΔΨ b persists energizing the membrane. Our model predicts 11mV as the resting electric potential difference across the thylakoid membrane in dark. We suggest that the model presented in this work can be integrated as a module into a more comprehensive model of oxygenic photosynthesis. Copyright © 2016 Elsevier Ltd. All rights reserved.

Bend-scale geomorphic classification and assessment of the Lower Missouri River from Sioux City, Iowa, to the Mississippi River for application to pallid sturgeon management

USGS Publications Warehouse

Jacobson, Robert B.; Colvin, Michael E.; Bulliner, Edward A.; Pickard, Darcy; Elliott, Caroline M.

2018-06-07

Management actions intended to increase growth and survival of pallid sturgeon (Scaphirhynchus albus) age-0 larvae on the Lower Missouri River require a comprehensive understanding of the geomorphic habitat template of the river. The study described here had two objectives relating to where channel-reconfiguration projects should be located to optimize effectiveness. The first objective was to develop a bend-scale (that is, at the scale of individual bends, defined as “cross-over to cross-over”) geomorphic classification of the Lower Missouri River to help in the design of monitoring and evaluation of such projects. The second objective was to explore whether geomorphic variables could provide insight into varying capacities of bends to intercept drifting larvae. The bend-scale classification was based on geomorphic and engineering variables for 257 bends from Sioux City, Iowa, to the confluence with the Mississippi River near St. Louis, Missouri. We used k-means clustering to identify groupings of bends that shared the same characteristics. Separate 3-, 4-, and 6-cluster classifications were developed and mapped. The three classifications are nested in a hierarchical structure. We also explored capacities of bends to intercept larvae through evaluation of linear models that predicted persistent sand area or catch per unit effort (CPUE) of age-0 sturgeon as a function of the same geomorphic variables used in the classification. All highly ranked models that predict persistent sand area contained mean channel width and standard deviation of channel width as significant variables. Some top-ranked models also included contributions of channel sinuosity and density of navigation structures. The sand-area prediction models have r-squared values of 0.648–0.674. In contrast, the highest-ranking CPUE models have r-squared values of 0.011–0.170, indicating much more uncertainty for the biological response variable. Whereas the persistent sand model documents that physical processes of transport and accumulation are systematic and predictable, the poor performance of the CPUE models indicate that additional processes will need to be considered to predict biological transport and accumulation.

Uncertainty analysis of the nonideal competitive adsorption-donnan model: effects of dissolved organic matter variability on predicted metal speciation in soil solution.

PubMed

Groenenberg, Jan E; Koopmans, Gerwin F; Comans, Rob N J

2010-02-15

Ion binding models such as the nonideal competitive adsorption-Donnan model (NICA-Donnan) and model VI successfully describe laboratory data of proton and metal binding to purified humic substances (HS). In this study model performance was tested in more complex natural systems. The speciation predicted with the NICA-Donnan model and the associated uncertainty were compared with independent measurements in soil solution extracts, including the free metal ion activity and fulvic (FA) and humic acid (HA) fractions of dissolved organic matter (DOM). Potentially important sources of uncertainty are the DOM composition and the variation in binding properties of HS. HS fractions of DOM in soil solution extracts varied between 14 and 63% and consisted mainly of FA. Moreover, binding parameters optimized for individual FA samples show substantial variation. Monte Carlo simulations show that uncertainties in predicted metal speciation, for metals with a high affinity for FA (Cu, Pb), are largely due to the natural variation in binding properties (i.e., the affinity) of FA. Predictions for metals with a lower affinity (Cd) are more prone to uncertainties in the fraction FA in DOM and the maximum site density (i.e., the capacity) of the FA. Based on these findings, suggestions are provided to reduce uncertainties in model predictions.

Impacts of past and future climate change on wind energy resources in the United States

NASA Astrophysics Data System (ADS)

McCaa, J. R.; Wood, A.; Eichelberger, S.; Westrick, K.

2009-12-01

The links between climate change and trends in wind energy resources have important potential implications for the wind energy industry, and have received significant attention in recent studies. We have conducted two studies that provide insights into the potential for climate change to affect future wind power production. In one experiment, we projected changes in power capacity for a hypothetical wind farm located near Kennewick, Washington, due to greenhouse gas-induced climate change, estimated using a set of regional climate model simulations. Our results show that the annual wind farm power capacity is projected to decrease 1.3% by 2050. In a wider study focusing on wind speed instead of power, we analyzed projected changes in wind speed from 14 different climate simulations that were performed in support of the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR4). Our results show that the predicted ensemble mean changes in annual mean wind speeds are expected to be modest. However, seasonal changes and changes predicted by individual models are large enough to affect the profitability of existing and future wind projects. The majority of the model simulations reveal that near-surface wind speed values are expected to shift poleward in response to the IPCC A2 emission scenario, particularly during the winter season. In the United States, most models agree that the mean annual wind speed values will increase in a region extending from the Great Lakes southward across the Midwest and into Texas. Decreased values, though, are predicted across most of the western United States. However, these predicted changes have a strong seasonal dependence, with wind speed increases over most of the United States during the winter and decreases over the northern United States during the summer.

Slags in a Large Variation Range of Oxygen Potential Based on the Ion and Molecule Coexistence Theory

NASA Astrophysics Data System (ADS)

Yang, Xue-Min; Li, Jin-Yan; Zhang, Meng; Chai, Guo-Min; Zhang, Jian

2014-12-01

A thermodynamic model for predicting sulfide capacity of CaO-FeO-Fe2O3-Al2O3-P2O5 slags in a large variation range of oxygen potential corresponding to mass percentage of FetO from 1.88 to 55.50 pct, i.e., IMCT- model, has been developed by coupling with the deduced desulfurization mechanism of the slags based on the ion and molecule coexistence theory (IMCT). The developed IMCT- model has been verified through comparing the determined sulfide capacity after Ban-ya et al.[20] with the calculated by the developed IMCT- model and the calculated by the reported sulfide capacity models such as the KTH model. Mass percentage of FetO as 6.75 pct corresponding to the mass action concentration of FetO as 0.0637 or oxygen partial as 2.27 × 10-6 Pa is the criterion for distinguishing reducing and oxidizing zones for the slags. Sulfide capacity of the slags in reducing zone is controlled by reaction ability of CaO regardless of slag oxidization ability. However, sulfide capacity of the slags in oxidizing zone shows an obvious increase tendency with the increasing of slag oxidization ability. Sulfide capacity of the slags in reducing zone keeps almost constant with variation of the simplified complex basicity (pct CaO)/((pct Al2O3) + (pct P2O5)), or optical basicity, or the mass action concentration ratios of N FeO/ N CaO, , , and . Sulfide capacity of the slags in oxidizing zone shows an obvious increase with the increasing of the simplified complex basicity (pct CaO)/((pct Al2O3) + (pct P2O5)) or optical basicity, or the aforementioned mass action concentration ratios. Thus, the aforementioned mass action concentration ratios and the corresponding mass percentage ratios of various iron oxides to basic oxide CaO are recommended to represent the comprehensive effect of various iron oxides and basic oxide CaO on sulfide capacity of the slags.

Modeling and Performance Simulation of the Mass Storage Network Environment

NASA Technical Reports Server (NTRS)

Kim, Chan M.; Sang, Janche

2000-01-01

This paper describes the application of modeling and simulation in evaluating and predicting the performance of the mass storage network environment. Network traffic is generated to mimic the realistic pattern of file transfer, electronic mail, and web browsing. The behavior and performance of the mass storage network and a typical client-server Local Area Network (LAN) are investigated by modeling and simulation. Performance characteristics in throughput and delay demonstrate the important role of modeling and simulation in network engineering and capacity planning.

New approaches in the indirect quantification of thermal rock properties in sedimentary basins: the well-log perspective

NASA Astrophysics Data System (ADS)

Fuchs, Sven; Balling, Niels; Förster, Andrea

2016-04-01

Numerical temperature models generated for geodynamic studies as well as for geothermal energy solutions heavily depend on rock thermal properties. Best practice for the determination of those parameters is the measurement of rock samples in the laboratory. Given the necessity to enlarge databases of subsurface rock parameters beyond drill core measurements an approach for the indirect determination of these parameters is developed, for rocks as well a for geological formations. We present new and universally applicable prediction equations for thermal conductivity, thermal diffusivity and specific heat capacity in sedimentary rocks derived from data provided by standard geophysical well logs. The approach is based on a data set of synthetic sedimentary rocks (clastic rocks, carbonates and evaporates) composed of mineral assemblages with variable contents of 15 major rock-forming minerals and porosities varying between 0 and 30%. Petrophysical properties are assigned to both the rock-forming minerals and the pore-filling fluids. Using multivariate statistics, relationships then were explored between each thermal property and well-logged petrophysical parameters (density, sonic interval transit time, hydrogen index, volume fraction of shale and photoelectric absorption index) on a regression sub set of data (70% of data) (Fuchs et al., 2015). Prediction quality was quantified on the remaining test sub set (30% of data). The combination of three to five well-log parameters results in predictions on the order of <15% for thermal conductivity and thermal diffusivity, and of <10% for specific heat capacity. Comparison of predicted and benchmark laboratory thermal conductivity from deep boreholes of the Norwegian-Danish Basin, the North German Basin, and the Molasse Basin results in 3 to 5% larger uncertainties with regard to the test data set. With regard to temperature models, the use of calculated TC borehole profiles approximate measured temperature logs with an error of <3°C along a 4 km deep profile. A benchmark comparison for thermal diffusivity and specific heat capacity is pending. Fuchs, Sven; Balling, Niels; Förster, Andrea (2015): Calculation of thermal conductivity, thermal diffusivity and specific heat capacity of sedimentary rocks using petrophysical well logs, Geophysical Journal International 203, 1977-2000, doi: 10.1093/gji/ggv403

Body configuration at first stepping-foot contact predicts backward balance recovery capacity in people with chronic stroke.

PubMed

de Kam, Digna; Roelofs, Jolanda M B; Geurts, Alexander C H; Weerdesteyn, Vivian

2018-01-01

To determine the predictive value of leg and trunk inclination angles at stepping-foot contact for the capacity to recover from a backward balance perturbation with a single step in people after stroke. Twenty-four chronic stroke survivors and 21 healthy controls were included in a cross-sectional study. We studied reactive stepping responses by subjecting participants to multidirectional stance perturbations at different intensities on a translating platform. In this paper we focus on backward perturbations. Participants were instructed to recover from the perturbations with maximally one step. A trial was classified as 'success' if balance was restored according to this instruction. We recorded full-body kinematics and computed: 1) body configuration parameters at first stepping-foot contact (leg and trunk inclination angles) and 2) spatiotemporal step parameters (step onset, step length, step duration and step velocity). We identified predictors of balance recovery capacity using a stepwise logistic regression. Perturbation intensity was also included as a predictor. The model with spatiotemporal parameters (perturbation intensity, step length and step duration) could correctly classify 85% of the trials as success or fail (Nagelkerke R2 = 0.61). In the body configuration model (Nagelkerke R2 = 0.71), perturbation intensity and leg and trunk angles correctly classified the outcome of 86% of the recovery attempts. The goodness of fit was significantly higher for the body configuration model compared to the model with spatiotemporal variables (p<0.01). Participant group and stepping leg (paretic or non-paretic) did not significantly improve the explained variance of the final body configuration model. Body configuration at stepping-foot contact is a valid and clinically feasible indicator of backward fall risk in stroke survivors, given its potential to be derived from a single sagittal screenshot.

Quantitative EEG and functional outcome following acute ischemic stroke.

PubMed

Bentes, Carla; Peralta, Ana Rita; Viana, Pedro; Martins, Hugo; Morgado, Carlos; Casimiro, Carlos; Franco, Ana Catarina; Fonseca, Ana Catarina; Geraldes, Ruth; Canhão, Patrícia; Pinho E Melo, Teresa; Paiva, Teresa; Ferro, José M

2018-06-18

To identify the most accurate quantitative electroencephalographic (qEEG) predictor(s) of unfavorable post-ischemic stroke outcome, and its discriminative capacity compared to already known demographic, clinical and imaging prognostic markers. Prospective cohort of 151 consecutive anterior circulation ischemic stroke patients followed for 12 months. EEG was recorded within 72 h and at discharge or 7 days post-stroke. QEEG (global band power, symmetry, affected/unaffected hemisphere and time changes) indices were calculated from mean Fast Fourier Transform and analyzed as predictors of unfavorable outcome (mRS ≥ 3), at discharge and 12 months poststroke, before and after adjustment for age, admission NIHSS and ASPECTS. Higher delta, lower alpha and beta relative powers (RP) predicted outcome. Indices with higher discriminative capacity were delta-theta to alpha-beta ratio (DTABR) and alpha RP. Outcome models including either of these and other clinical/imaging stroke outcome predictors were superior to models without qEEG data. In models with qEEG indices, infarct size was not a significant outcome predictor. DTAABR and alpha RP are the best qEEG indices and superior to ASPECTS in post-stroke outcome prediction. They improve the discriminative capacity of already known clinical and imaging stroke outcome predictors, both at discharge and 12 months after stroke. qEEG indices are independent predictors of stroke outcome. Copyright © 2018 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

Prognostic nomogram for previously untreated adult patients with acute myeloid leukemia

PubMed Central

Zheng, Zhuojun; Li, Xiaodong; Zhu, Yuandong; Gu, Weiying; Xie, Xiaobao; Jiang, Jingting

2016-01-01

This study was designed to perform an acceptable prognostic nomogram for acute myeloid leukemia. The clinical data from 311 patients from our institution and 165 patients generated with Cancer Genome Atlas Research Network were reviewed. A prognostic nomogram was designed according to the Cox's proportional hazard model to predict overall survival (OS). To compare the capacity of the nomogram with that of the current prognostic system, the concordance index (C-index) was used to validate the accuracy as well as the calibration curve. The nomogram included 6 valuable variables: age, risk stratifications based on cytogenetic abnormalities, status of FLT3-ITD mutation, status of NPM1 mutation, expression of CD34, and expression of HLA-DR. The C-indexes were 0.71 and 0.68 in the primary and validation cohort respectively, which were superior to the predictive capacity of the current prognostic systems in both cohorts. The nomogram allowed both patients with acute myeloid leukemia and physicians to make prediction of OS individually prior to treatment. PMID:27689396

Modeling the expenditure and reconstitution of work capacity above critical power.

PubMed

Skiba, Philip Friere; Chidnok, Weerapong; Vanhatalo, Anni; Jones, Andrew M

2012-08-01

The critical power (CP) model includes two constants: the CP and the W' [P = (W' / t) + CP]. The W' is the finite work capacity available above CP. Power output above CP results in depletion of the W' complete depletion of the W' results in exhaustion. Monitoring the W' may be valuable to athletes during training and competition. Our purpose was to develop a function describing the dynamic state of the W' during intermittent exercise. After determination of V˙O(2max), CP, and W', seven subjects completed four separate exercise tests on a cycle ergometer on different days. Each protocol comprised a set of intervals: 60 s at a severe power output, followed by 30-s recovery at a lower prescribed power output. The intervals were repeated until exhaustion. These data were entered into a continuous equation predicting balance of W' remaining, assuming exponential reconstitution of the W'. The time constant was varied by an iterative process until the remaining modeled W' = 0 at the point of exhaustion. The time constants of W' recharge were negatively correlated with the difference between sub-CP recovery power and CP. The relationship was best fit by an exponential (r = 0.77). The model-predicted W' balance correlated with the temporal course of the rise in V˙O(2) (r = 0.82-0.96). The model accurately predicted exhaustion of the W' in a competitive cyclist during a road race. We have developed a function to track the dynamic state of the W' during intermittent exercise. This may have important implications for the planning and real-time monitoring of athletic performance.

Amino acid substitutions affecting protein dynamics in eglin C do not affect heat capacity change upon unfolding.

PubMed

Gribenko, Alexey V; Keiffer, Timothy R; Makhatadze, George I

2006-08-01

The heat capacity change upon unfolding (deltaC(p)) is a thermodynamic parameter that defines the temperature dependence of the thermodynamic stability of proteins; however, physical basis of the heat capacity change is not completely understood. Although empirical surface area-based calculations can predict heat capacity changes reasonably well, accumulating evidence suggests that changes in hydration of those surfaces is not the only parameter contributing to the observed heat capacity changes upon unfolding. Because packing density in the protein interior is similar to that observed in organic crystals, we hypothesized that changes in protein dynamics resulting in increased rigidity of the protein structure might contribute to the observed heat capacity change upon unfolding. Using differential scanning calorimetry we characterized the thermodynamic behavior of a serine protease inhibitor eglin C and two eglin C variants with altered native state dynamics, as determined by NMR. We found no evidence of changes in deltaC(p) in either of the variants, suggesting that changes in rigidity do not contribute to the heat capacity change upon unfolding in this model system. Copyright 2006 Wiley-Liss, Inc.

The Role of Working Memory in Metaphor Production and Comprehension

ERIC Educational Resources Information Center

Chiappe, Dan L.; Chiappe, Penny

2007-01-01

The following tested Kintsch's [Kintsch, W. (2000). "Metaphor comprehension: a computational theory." "Psychonomic Bulletin & Review," 7, 257-266 and Kintsch, W. (2001). "Predication." "Cognitive Science," 25, 173-202] Predication Model, which predicts that working memory capacity is an important factor in metaphor processing. In support of his…

Distillation time as tool for improved antimalarial activity and differential oil composition of cumin seed oil

USDA-ARS?s Scientific Manuscript database

A steam distillation extraction kinetics experiment was conducted to estimate essential oil yield, composition, antimalarial, and antioxidant capacity of cumin (Cuminum cyminum L.) seed (fruits). Furthermore, regression models were developed to predict essential oil yield and composition for a given...

Aftershock collapse vulnerability assessment of reinforced concrete frame structures

USGS Publications Warehouse

Raghunandan, Meera; Liel, Abbie B.; Luco, Nicolas

2015-01-01

In a seismically active region, structures may be subjected to multiple earthquakes, due to mainshock–aftershock phenomena or other sequences, leaving no time for repair or retrofit between the events. This study quantifies the aftershock vulnerability of four modern ductile reinforced concrete (RC) framed buildings in California by conducting incremental dynamic analysis of nonlinear MDOF analytical models. Based on the nonlinear dynamic analysis results, collapse and damage fragility curves are generated for intact and damaged buildings. If the building is not severely damaged in the mainshock, its collapse capacity is unaffected in the aftershock. However, if the building is extensively damaged in the mainshock, there is a significant reduction in its collapse capacity in the aftershock. For example, if an RC frame experiences 4% or more interstory drift in the mainshock, the median capacity to resist aftershock shaking is reduced by about 40%. The study also evaluates the effectiveness of different measures of physical damage observed in the mainshock-damaged buildings for predicting the reduction in collapse capacity of the damaged building in subsequent aftershocks. These physical damage indicators for the building are chosen such that they quantify the qualitative red tagging (unsafe for occupation) criteria employed in post-earthquake evaluation of RC frames. The results indicated that damage indicators related to the drift experienced by the damaged building best predicted the reduced aftershock collapse capacities for these ductile structures.

Structure and thermal properties of salicylate-based-protic ionic liquids as new heat storage media. COSMO-RS structure characterization and modeling of heat capacities.

PubMed

Jacquemin, Johan; Feder-Kubis, Joanna; Zorębski, Michał; Grzybowska, Katarzyna; Chorążewski, Mirosław; Hensel-Bielówka, Stella; Zorębski, Edward; Paluch, Marian; Dzida, Marzena

2014-02-28

During this research, we present a study on the thermal properties, such as the melting, cold crystallization, and glass transition temperatures as well as heat capacities from 293.15 K to 323.15 K of nine in-house synthesized protic ionic liquids based on the 3-(alkoxymethyl)-1H-imidazol-3-ium salicylate ([H-Im-C1OC(n)][Sal]) with n = 3-11. The 3D structures, surface charge distributions and COSMO volumes of all investigated ions are obtained by combining DFT calculations and the COSMO-RS methodology. The heat capacity data sets as a function of temperature of the 3-(alkoxymethyl)-1H-imidazol-3-ium salicylate are then predicted using the methodology originally proposed in the case of ionic liquids by Ge et al. 3-(Alkoxymethyl)-1H-imidazol-3-ium salicylate based ionic liquids present specific heat capacities higher in many cases than other ionic liquids that make them suitable as heat storage media and in heat transfer processes. It was found experimentally that the heat capacity increases linearly with increasing alkyl chain length of the alkoxymethyl group of 3-(alkoxymethyl)-1H-imidazol-3-ium salicylate as was expected and predicted using the Ge et al. method with an overall relative absolute deviation close to 3.2% for temperatures up to 323.15 K.

A multivariate model for predicting segmental body composition.

PubMed

Tian, Simiao; Mioche, Laurence; Denis, Jean-Baptiste; Morio, Béatrice

2013-12-01

The aims of the present study were to propose a multivariate model for predicting simultaneously body, trunk and appendicular fat and lean masses from easily measured variables and to compare its predictive capacity with that of the available univariate models that predict body fat percentage (BF%). The dual-energy X-ray absorptiometry (DXA) dataset (52% men and 48% women) with White, Black and Hispanic ethnicities (1999-2004, National Health and Nutrition Examination Survey) was randomly divided into three sub-datasets: a training dataset (TRD), a test dataset (TED); a validation dataset (VAD), comprising 3835, 1917 and 1917 subjects. For each sex, several multivariate prediction models were fitted from the TRD using age, weight, height and possibly waist circumference. The most accurate model was selected from the TED and then applied to the VAD and a French DXA dataset (French DB) (526 men and 529 women) to assess the prediction accuracy in comparison with that of five published univariate models, for which adjusted formulas were re-estimated using the TRD. Waist circumference was found to improve the prediction accuracy, especially in men. For BF%, the standard error of prediction (SEP) values were 3.26 (3.75) % for men and 3.47 (3.95)% for women in the VAD (French DB), as good as those of the adjusted univariate models. Moreover, the SEP values for the prediction of body and appendicular lean masses ranged from 1.39 to 2.75 kg for both the sexes. The prediction accuracy was best for age < 65 years, BMI < 30 kg/m2 and the Hispanic ethnicity. The application of our multivariate model to large populations could be useful to address various public health issues.

Inferring transit time distributions from atmospheric tracer data: Assessment of the predictive capacities of Lumped Parameter Models on a 3D crystalline aquifer model

NASA Astrophysics Data System (ADS)

Marçais, J.; de Dreuzy, J.-R.; Ginn, T. R.; Rousseau-Gueutin, P.; Leray, S.

2015-06-01

While central in groundwater resources and contaminant fate, Transit Time Distributions (TTDs) are never directly accessible from field measurements but always deduced from a combination of tracer data and more or less involved models. We evaluate the predictive capabilities of approximate distributions (Lumped Parameter Models abbreviated as LPMs) instead of fully developed aquifer models. We develop a generic assessment methodology based on synthetic aquifer models to establish references for observable quantities as tracer concentrations and prediction targets as groundwater renewal times. Candidate LPMs are calibrated on the observable tracer concentrations and used to infer renewal time predictions, which are compared with the reference ones. This methodology is applied to the produced crystalline aquifer of Plœmeur (Brittany, France) where flows leak through a micaschists aquitard to reach a sloping aquifer where they radially converge to the producing well, issuing broad rather than multi-modal TTDs. One, two and three parameters LPMs were calibrated to a corresponding number of simulated reference anthropogenic tracer concentrations (CFC-11, 85Kr and SF6). Extensive statistical analysis over the aquifer shows that a good fit of the anthropogenic tracer concentrations is neither a necessary nor a sufficient condition to reach acceptable predictive capability. Prediction accuracy is however strongly conditioned by the use of a priori relevant LPMs. Only adequate LPM shapes yield unbiased estimations. In the case of Plœmeur, relevant LPMs should have two parameters to capture the mean and the standard deviation of the residence times and cover the first few decades [0; 50 years]. Inverse Gaussian and shifted exponential performed equally well for the wide variety of the reference TTDs from strongly peaked in recharge zones where flows are diverging to broadly distributed in more converging zones. When using two sufficiently different atmospheric tracers like CFC-11 and 85Kr, groundwater renewal time predictions are accurate at 1-5 years for estimating mean transit times of some decades (10-50 years). 1-parameter LPMs calibrated on a single atmospheric tracer lead to substantially larger errors of the order of 10 years, while 3-parameter LPMs calibrated with a third atmospheric tracers (SF6) do not improve the prediction capabilities. Based on a specific site, this study highlights the high predictive capacities of two atmospheric tracers on the same time range with sufficiently different atmospheric concentration chronicles.

A gradient of nutrient enrichment reveals nonlinear impacts of fertilization on Arctic plant diversity and ecosystem function.

PubMed

Prager, Case M; Naeem, Shahid; Boelman, Natalie T; Eitel, Jan U H; Greaves, Heather E; Heskel, Mary A; Magney, Troy S; Menge, Duncan N L; Vierling, Lee A; Griffin, Kevin L

2017-04-01

Rapid environmental change at high latitudes is predicted to greatly alter the diversity, structure, and function of plant communities, resulting in changes in the pools and fluxes of nutrients. In Arctic tundra, increased nitrogen (N) and phosphorus (P) availability accompanying warming is known to impact plant diversity and ecosystem function; however, to date, most studies examining Arctic nutrient enrichment focus on the impact of relatively large (>25x estimated naturally occurring N enrichment) doses of nutrients on plant community composition and net primary productivity. To understand the impacts of Arctic nutrient enrichment, we examined plant community composition and the capacity for ecosystem function (net ecosystem exchange, ecosystem respiration, and gross primary production) across a gradient of experimental N and P addition expected to more closely approximate warming-induced fertilization. In addition, we compared our measured ecosystem CO 2 flux data to a widely used Arctic ecosystem exchange model to investigate the ability to predict the capacity for CO 2 exchange with nutrient addition. We observed declines in abundance-weighted plant diversity at low levels of nutrient enrichment, but species richness and the capacity for ecosystem carbon uptake did not change until the highest level of fertilization. When we compared our measured data to the model, we found that the model explained roughly 30%-50% of the variance in the observed data, depending on the flux variable, and the relationship weakened at high levels of enrichment. Our results suggest that while a relatively small amount of nutrient enrichment impacts plant diversity, only relatively large levels of fertilization-over an order of magnitude or more than warming-induced rates-significantly alter the capacity for tundra CO 2 exchange. Overall, our findings highlight the value of measuring and modeling the impacts of a nutrient enrichment gradient, as warming-related nutrient availability may impact ecosystems differently than single-level fertilization experiments.

Sorption of chlorophenols from aqueous solution by granular activated carbon, filter coal, pine and hardwood.

PubMed

Hossain, G S M; McLaughlan, R G

2012-09-01

Wood and coal, as low-cost sorbents, have been evaluated as an alternative to commercial granular activated carbon (GAC) for chlorophenol removal. Kinetic experiments indicated that filter coal had a significantly lower rate of uptake (approximately 10% of final uptake was achieved after three hours) than the other sorbents, owing to intra-particle diffusion limitations. The data fitted a pseudo-second-order model. Sorption capacity data showed that GAC had a high sorption capacity (294-467 mg g(-1)) compared with other sorbents (3.2-7.5 mg(g-1)). However, wood and coal had a greater sorption capacity per unit surface area than GAC. Sorption equilibrium data was best predicted using a Freundlich adsorption model. The sorption capacity for all sorbents was 2-chlorophenol < 4-chlorophenol < 2, 4-dichlorophenol, which correlates well with solute hydrophobicity, although the relative differences were much less for coal than the other sorbents. The results showed that pine, hardwood and filter coal can be used as sorbent materials for the removal of chlorophenol from water; however, kinetic considerations may limit the application of filter coal.

Cognitive performance predicts treatment decisional abilities in mild to moderate dementia.

PubMed

Gurrera, R J; Moye, J; Karel, M J; Azar, A R; Armesto, J C

2006-05-09

To examine the contribution of neuropsychological test performance to treatment decision-making capacity in community volunteers with mild to moderate dementia. The authors recruited volunteers (44 men, 44 women) with mild to moderate dementia from the community. Subjects completed a battery of 11 neuropsychological tests that assessed auditory and visual attention, logical memory, language, and executive function. To measure decision making capacity, the authors administered the Capacity to Consent to Treatment Interview, the Hopemont Capacity Assessment Interview, and the MacCarthur Competence Assessment Tool--Treatment. Each of these instruments individually scores four decisional abilities serving capacity: understanding, appreciation, reasoning, and expression of choice. The authors used principal components analysis to generate component scores for each ability across instruments, and to extract principal components for neuropsychological performance. Multiple linear regression analyses demonstrated that neuropsychological performance significantly predicted all four abilities. Specifically, it predicted 77.8% of the common variance for understanding, 39.4% for reasoning, 24.6% for appreciation, and 10.2% for expression of choice. Except for reasoning and appreciation, neuropsychological predictor (beta) profiles were unique for each ability. Neuropsychological performance substantially and differentially predicted capacity for treatment decisions in individuals with mild to moderate dementia. Relationships between elemental cognitive function and decisional capacity may differ in individuals whose decisional capacity is impaired by other disorders, such as mental illness.

A comprehensive adsorption study and modeling of antibiotics as a pharmaceutical waste by graphene oxide nanosheets.

PubMed

Rostamian, Rahele; Behnejad, Hassan

2018-01-01

The adsorption behavior of tetracycline (TCN), doxycycline (DCN) as the most common antibiotics in veterinary and ciprofloxacin (CPN) onto graphene oxide nanosheets (GOS) in aqueous solution was evaluated. The four factors influencing the adsorption of antibiotics (initial concentration, pH, temperature and contact time) were studied. The results showed that initial pH ∼ 6 to 7 and contact time ∼ 100 - 200min are optimum for each drug. The monolayer adsorption capacity was reduced with the increasing temperature from 25°C to 45°C. Non-linear regressions were carried out in order to define the best fit model for every system. To do this, eight error functions were applied to predict the optimum model. Among various models, Hill and Toth isotherm models represented the equilibrium adsorption data of antibiotics while the kinetic data were well fitted by pseudo second-order (PSO) kinetic model (DCN and TCN) and Elovich (CPN) models. The maximum adsorption capacity (q max ) is found to be in the following order: CPN > DCN > TCN, obtained from sips equation at the same temperature. The GOS shows highest adsorption capacity towards CPN up to 173.4mgg -1 . The study showed that GOS can be removed more efficiently from water solution. Copyright © 2017 Elsevier Inc. All rights reserved.

Forecasting of natural gas consumption with neural network and neuro fuzzy system

NASA Astrophysics Data System (ADS)

Kaynar, Oguz; Yilmaz, Isik; Demirkoparan, Ferhan

2010-05-01

The prediction of natural gas consumption is crucial for Turkey which follows foreign-dependent policy in point of providing natural gas and whose stock capacity is only 5% of internal total consumption. Prediction accuracy of demand is one of the elements which has an influence on sectored investments and agreements about obtaining natural gas, so on development of sector. In recent years, new techniques, such as artificial neural networks and fuzzy inference systems, have been widely used in natural gas consumption prediction in addition to classical time series analysis. In this study, weekly natural gas consumption of Turkey has been predicted by means of three different approaches. The first one is Autoregressive Integrated Moving Average (ARIMA), which is classical time series analysis method. The second approach is the Artificial Neural Network. Two different ANN models, which are Multi Layer Perceptron (MLP) and Radial Basis Function Network (RBFN), are employed to predict natural gas consumption. The last is Adaptive Neuro Fuzzy Inference System (ANFIS), which combines ANN and Fuzzy Inference System. Different prediction models have been constructed and one model, which has the best forecasting performance, is determined for each method. Then predictions are made by using these models and results are compared. Keywords: ANN, ANFIS, ARIMA, Natural Gas, Forecasting

A nanomaterial release model for waste shredding using a Bayesian belief network

NASA Astrophysics Data System (ADS)

Shandilya, Neeraj; Ligthart, Tom; van Voorde, Imelda; Stahlmecke, Burkhard; Clavaguera, Simon; Philippot, Cecile; Ding, Yaobo; Goede, Henk

2018-02-01

The shredding of waste of electrical and electronic equipment (WEEE) and other products, incorporated with nanomaterials, can lead to a substantial release of nanomaterials. Considering the uncertainty, complexity, and scarcity of experimental data on release, we present the development of a Bayesian belief network (BBN) model. This baseline model aims to give a first prediction of the release of nanomaterials (excluding nanofibers) during their mechanical shredding. With a focus on the description of the model development methodology, we characterize nanomaterial release in terms of number, size, mass, and composition of released particles. Through a sensitivity analysis of the model, we find the material-specific parameters like affinity of nanomaterials to the matrix of the composite and their state of dispersion inside the matrix to reduce the nanomaterial release up to 50%. The shredder-specific parameters like number of shafts in a shredder and input and output size of the material for shredding could minimize it up to 98%. The comparison with two experimental test cases shows promising outcome on the prediction capacity of the model. As additional experimental data on nanomaterial release becomes available, the model is able to further adapt and update risk forecasts. When adapting the model with additional expert beliefs, experts should be selected using criteria, e.g., substantial contribution to nanomaterial and/or particulate matter release-related scientific literature, the capacity and willingness to contribute to further development of the BBN model, and openness to accepting deviating opinions. [Figure not available: see fulltext.

Facebook Displays as Predictors of Binge Drinking: From the Virtual to the Visceral

PubMed Central

D'Angelo, Jonathan; Kerr, Bradley; Moreno, Megan A

2015-01-01

Given the prevalence of social media, a nascent but important area of research is the effect of social media posting on one's own self. It is possible that an individual's social media posts may have predictive capacity, especially in relation to health behavior. Researchers have long utilized concepts from the Theory of Reasoned Action (TRA) to predict health behaviors. The theory does not account for social media, which may influence or predict health behaviors. The purpose of this study was to test a model including Facebook alcohol displays and constructs from the TRA to predict binge drinking. Incoming college freshmen from two schools (312 participants between the ages of 18 and 19) were interviewed prior to (T1) and one year into college (T2), and their Facebook profiles were evaluated for displayed alcohol content. Path modeling was used to evaluate direct and indirect paths predicting binge drinking. Path analysis suggested that Facebook alcohol displays at T1 directly predict binge drinking at T2, while alcohol attitude both directly and indirectly predicts binge drinking. Based on these results, a preliminary model of social media presentation and action is discussed. PMID:26412923

Facebook Displays as Predictors of Binge Drinking: From the Virtual to the Visceral.

PubMed

D'Angelo, Jonathan; Kerr, Bradley; Moreno, Megan A

2014-01-01

Given the prevalence of social media, a nascent but important area of research is the effect of social media posting on one's own self. It is possible that an individual's social media posts may have predictive capacity, especially in relation to health behavior. Researchers have long utilized concepts from the Theory of Reasoned Action (TRA) to predict health behaviors. The theory does not account for social media, which may influence or predict health behaviors. The purpose of this study was to test a model including Facebook alcohol displays and constructs from the TRA to predict binge drinking. Incoming college freshmen from two schools (312 participants between the ages of 18 and 19) were interviewed prior to (T1) and one year into college (T2), and their Facebook profiles were evaluated for displayed alcohol content. Path modeling was used to evaluate direct and indirect paths predicting binge drinking. Path analysis suggested that Facebook alcohol displays at T1 directly predict binge drinking at T2, while alcohol attitude both directly and indirectly predicts binge drinking. Based on these results, a preliminary model of social media presentation and action is discussed.

A mathematical model of a lithium/thionyl chloride primary cell

NASA Technical Reports Server (NTRS)

Evans, T. I.; Nguyen, T. V.; White, R. E.

1987-01-01

A 1-D mathematical model for the lithium/thionyl chloride primary cell was developed to investigate methods of improving its performance and safety. The model includes many of the components of a typical lithium/thionyl chloride cell such as the porous lithium chloride film which forms on the lithium anode surface. The governing equations are formulated from fundamental conservation laws using porous electrode theory and concentrated solution theory. The model is used to predict 1-D, time dependent profiles of concentration, porosity, current, and potential as well as cell temperature and voltage. When a certain discharge rate is required, the model can be used to determine the design criteria and operating variables which yield high cell capacities. Model predictions can be used to establish operational and design limits within which the thermal runaway problem, inherent in these cells, can be avoided.

Balance Confidence: A Predictor of Perceived Physical Function, Perceived Mobility, and Perceived Recovery 1 Year After Inpatient Stroke Rehabilitation.

PubMed

Torkia, Caryne; Best, Krista L; Miller, William C; Eng, Janice J

2016-07-01

To estimate the effect of balance confidence measured at 1 month poststroke rehabilitation on perceived physical function, mobility, and stroke recovery 12 months later. Longitudinal study (secondary analysis). Multisite, community-based. Community-dwelling individuals (N=69) with stroke living in a home setting. Not applicable. Activities-specific Balance Confidence scale; physical function and mobility subscales of the Stroke Impact Scale 3.0; and a single item from the Stroke Impact Scale for perceived recovery. Balance confidence at 1 month postdischarge from inpatient rehabilitation predicts perceived physical function (model 1), mobility (model 2), and recovery (model 3) 12 months later after adjusting for important covariates. The covariates included in model 1 were age, sex, basic mobility, and depression. The covariates selected for model 2 were age, sex, balance capacity, and anxiety, and the covariates in model 3 were age, sex, walking capacity, and social support. The amount of variance in perceived physical function, perceived mobility, and perceived recovery that balance confidence accounted for was 12%, 9%, and 10%, respectively. After discharge from inpatient rehabilitation poststroke, balance confidence predicts individuals' perceived physical function, mobility, and recovery 12 months later. There is a need to address balance confidence at discharge from inpatient stroke rehabilitation. Copyright © 2016 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

A reexamination of age-related variation in body weight and morphometry of Maryland nutria

USGS Publications Warehouse

Sherfy, M.H.; Mollett, T.A.; McGowan, K.R.; Daugherty, S.L.

2006-01-01

Age-related variation in morphometry has been documented for many species. Knowledge of growth patterns can be useful for modeling energetics, detecting physiological influences on populations, and predicting age. These benefits have shown value in understanding population dynamics of invasive species, particularly in developing efficient control and eradication programs. However, development and evaluation of descriptive and predictive models is a critical initial step in this process. Accordingly, we used data from necropsies of 1,544 nutria (Myocastor coypus) collected in Maryland, USA, to evaluate the accuracy of previously published models for prediction of nutria age from body weight. Published models underestimated body weights of our animals, especially for ages <3. We used cross-validation procedures to develop and evaluate models for describing nutria growth patterns and for predicting nutria age. We derived models from a randomly selected model-building data set (n = 192-193 M, 217-222 F) and evaluated them with the remaining animals (n = 487-488 M, 642-647 F). We used nonlinear regression to develop Gompertz growth-curve models relating morphometric variables to age. Predicted values of morphometric variables fell within the 95% confidence limits of their true values for most age classes. We also developed predictive models for estimating nutria age from morphometry, using linear regression of log-transformed age on morphometric variables. The evaluation data set corresponded with 95% prediction intervals from the new models. Predictive models for body weight and length provided greater accuracy and less bias than models for foot length and axillary girth. Our growth models accurately described age-related variation in nutria morphometry, and our predictive models provided accurate estimates of ages from morphometry that will be useful for live-captured individuals. Our models offer better accuracy and precision than previously published models, providing a capacity for modeling energetics and growth patterns of Maryland nutria as well as an empirical basis for determining population age structure from live-captured animals.

Prediction of pilot reserve attention capacity during air-to-air target tracking

NASA Technical Reports Server (NTRS)

Onstott, E. D.; Faulkner, W. H.

1977-01-01

Reserve attention capacity of a pilot was calculated using a pilot model that allocates exclusive model attention according to the ranking of task urgency functions whose variables are tracking error and error rate. The modeled task consisted of tracking a maneuvering target aircraft both vertically and horizontally, and when possible, performing a diverting side task which was simulated by the precise positioning of an electrical stylus and modeled as a task of constant urgency in the attention allocation algorithm. The urgency of the single loop vertical task is simply the magnitude of the vertical tracking error, while the multiloop horizontal task requires a nonlinear urgency measure of error and error rate terms. Comparison of model results with flight simulation data verified the computed model statistics of tracking error of both axes, lateral and longitudinal stick amplitude and rate, and side task episodes. Full data for the simulation tracking statistics as well as the explicit equations and structure of the urgency function multiaxis pilot model are presented.

Terminal Area Productivity Airport Wind Analysis and Chicago O'Hare Model Description

NASA Technical Reports Server (NTRS)

Hemm, Robert; Shapiro, Gerald

1998-01-01

This paper describes two results from a continuing effort to provide accurate cost-benefit analyses of the NASA Terminal Area Productivity (TAP) program technologies. Previous tasks have developed airport capacity and delay models and completed preliminary cost benefit estimates for TAP technologies at 10 U.S. airports. This task covers two improvements to the capacity and delay models. The first improvement is the completion of a detailed model set for the Chicago O'Hare (ORD) airport. Previous analyses used a more general model to estimate the benefits for ORD. This paper contains a description of the model details with results corresponding to current conditions. The second improvement is the development of specific wind speed and direction criteria for use in the delay models to predict when the Aircraft Vortex Spacing System (AVOSS) will allow use of reduced landing separations. This paper includes a description of the criteria and an estimate of AVOSS utility for 10 airports based on analysis of 35 years of weather data.

[Spectral reflectance characteristics and modeling of typical Takyr Solonetzs water content].

PubMed

Zhang, Jun-hua; Jia, Ke-li

2015-03-01

Based on the analysis of the spectral reflectance of the typical Takyr Solonetzs soil in Ningxia, the relationship of soil water content and spectral reflectance was determined, and a quantitative model for the prediction of soil water content was constructed. The results showed that soil spectral reflectance decreased with the increasing soil water content when it was below the water holding capacity but increased with the increasing soil water content when it was higher than the water holding capacity. Soil water content presented significantly negative correlation with original reflectance (r), smooth reflectance (R), logarithm of reflectance (IgR), and positive correlation with the reciprocal of R and logarithm of reciprocal [lg (1/R)]. The correlation coefficient of soil water content and R in the whole wavelength was 0.0013, 0.0397 higher than r and lgR, respectively. Average correlation coefficient of soil water content with 1/R and [lg (1/R)] at the wavelength of 950-1000 nm was 0.2350 higher than that of 400-950 nm. The relationships of soil water content with the first derivate differential (R') , the first derivate differential of logarithm (lgR)' and the first derivate differential of logarithm of reciprocal [lg(1/R)]' were unstable. Base on the coefficients of r, lg(1/R), R' and (lgR)', different regression models were established to predict soil water content, and the coefficients of determination were 0.7610, 0.8184, 0.8524 and 0.8255, respectively. The determination coefficient for power function model of R'. reached 0.9447, while the fitting degree between the predicted value based on this model and on-site measured value was 0.8279. The model of R' had the highest fitted accuracy, while that of r had the lowest one. The results could provide a scientific basis for soil water content prediction and field irrigation in the Takyr Solonetzs region.

Survival Regression Modeling Strategies in CVD Prediction.

PubMed

Barkhordari, Mahnaz; Padyab, Mojgan; Sardarinia, Mahsa; Hadaegh, Farzad; Azizi, Fereidoun; Bozorgmanesh, Mohammadreza

2016-04-01

A fundamental part of prevention is prediction. Potential predictors are the sine qua non of prediction models. However, whether incorporating novel predictors to prediction models could be directly translated to added predictive value remains an area of dispute. The difference between the predictive power of a predictive model with (enhanced model) and without (baseline model) a certain predictor is generally regarded as an indicator of the predictive value added by that predictor. Indices such as discrimination and calibration have long been used in this regard. Recently, the use of added predictive value has been suggested while comparing the predictive performances of the predictive models with and without novel biomarkers. User-friendly statistical software capable of implementing novel statistical procedures is conspicuously lacking. This shortcoming has restricted implementation of such novel model assessment methods. We aimed to construct Stata commands to help researchers obtain the aforementioned statistical indices. We have written Stata commands that are intended to help researchers obtain the following. 1, Nam-D'Agostino X 2 goodness of fit test; 2, Cut point-free and cut point-based net reclassification improvement index (NRI), relative absolute integrated discriminatory improvement index (IDI), and survival-based regression analyses. We applied the commands to real data on women participating in the Tehran lipid and glucose study (TLGS) to examine if information relating to a family history of premature cardiovascular disease (CVD), waist circumference, and fasting plasma glucose can improve predictive performance of Framingham's general CVD risk algorithm. The command is adpredsurv for survival models. Herein we have described the Stata package "adpredsurv" for calculation of the Nam-D'Agostino X 2 goodness of fit test as well as cut point-free and cut point-based NRI, relative and absolute IDI, and survival-based regression analyses. We hope this work encourages the use of novel methods in examining predictive capacity of the emerging plethora of novel biomarkers.

Can next-generation soil data products improve soil moisture modelling at the continental scale? An assessment using a new microclimate package for the R programming environment

NASA Astrophysics Data System (ADS)

Kearney, Michael R.; Maino, James L.

2018-06-01

Accurate models of soil moisture are vital for solving core problems in meteorology, hydrology, agriculture and ecology. The capacity for soil moisture modelling is growing rapidly with the development of high-resolution, continent-scale gridded weather and soil data together with advances in modelling methods. In particular, the GlobalSoilMap.net initiative represents next-generation, depth-specific gridded soil products that may substantially increase soil moisture modelling capacity. Here we present an implementation of Campbell's infiltration and redistribution model within the NicheMapR microclimate modelling package for the R environment, and use it to assess the predictive power provided by the GlobalSoilMap.net product Soil and Landscape Grid of Australia (SLGA, ∼100 m) as well as the coarser resolution global product SoilGrids (SG, ∼250 m). Predictions were tested in detail against 3 years of root-zone (3-75 cm) soil moisture observation data from 35 monitoring sites within the OzNet project in Australia, with additional tests of the finalised modelling approach against cosmic-ray neutron (CosmOz, 0-50 cm, 9 sites from 2011 to 2017) and satellite (ASCAT, 0-2 cm, continent-wide from 2007 to 2009) observations. The model was forced by daily 0.05° (∼5 km) gridded meteorological data. The NicheMapR system predicted soil moisture to within experimental error for all data sets. Using the SLGA or the SG soil database, the OzNet soil moisture could be predicted with a root mean square error (rmse) of ∼0.075 m3 m-3 and a correlation coefficient (r) of 0.65 consistently through the soil profile without any parameter tuning. Soil moisture predictions based on the SLGA and SG datasets were ≈ 17% closer to the observations than when using a chloropleth-derived soil data set (Digital Atlas of Australian Soils), with the greatest improvements occurring for deeper layers. The CosmOz observations were predicted with similar accuracy (r = 0.76 and rmse of ∼0.085 m3 m-3). Comparisons at the continental scale to 0-2 cm satellite data (ASCAT) showed that the SLGA/SG datasets increased model fit over simulations using the DAAS soil properties (r ∼ 0.63 &rmse 15% vs. r 0.48 &rmse 18%, respectively). Overall, our results demonstrate the advantages of using GlobalSoilMap.net products in combination with gridded weather data for modelling soil moisture at fine spatial and temporal resolution at the continental scale.

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.

Determination of Complex Microcalorimeter Parameters with Impedance Measurements

NASA Technical Reports Server (NTRS)

Saab, T.; Bandler, S. R.; Chervenak, J.; Figueroa-Feliciano, E.; Finkbeiner, F.; Iyomoto, N.; Kelley, R.; Kilbourne, C. A.; Lindeman, M. A.; Porter, F. S.;

An Improved Formulation of Hybrid Model Predictive Control With Application to Production-Inventory Systems.

PubMed

Nandola, Naresh N; Rivera, Daniel E

2013-01-01

We consider an improved model predictive control (MPC) formulation for linear hybrid systems described by mixed logical dynamical (MLD) models. The algorithm relies on a multiple-degree-of-freedom parametrization that enables the user to adjust the speed of setpoint tracking, measured disturbance rejection and unmeasured disturbance rejection independently in the closed-loop system. Consequently, controller tuning is more flexible and intuitive than relying on objective function weights (such as move suppression) traditionally used in MPC schemes. The controller formulation is motivated by the needs of non-traditional control applications that are suitably described by hybrid production-inventory systems. Two applications are considered in this paper: adaptive, time-varying interventions in behavioral health, and inventory management in supply chains under conditions of limited capacity. In the adaptive intervention application, a hypothetical intervention inspired by the Fast Track program, a real-life preventive intervention for reducing conduct disorder in at-risk children, is examined. In the inventory management application, the ability of the algorithm to judiciously alter production capacity under conditions of varying demand is presented. These case studies demonstrate that MPC for hybrid systems can be tuned for desired performance under demanding conditions involving noise and uncertainty.

An Improved Formulation of Hybrid Model Predictive Control With Application to Production-Inventory Systems

PubMed Central

Nandola, Naresh N.; Rivera, Daniel E.

2013-01-01

We consider an improved model predictive control (MPC) formulation for linear hybrid systems described by mixed logical dynamical (MLD) models. The algorithm relies on a multiple-degree-of-freedom parametrization that enables the user to adjust the speed of setpoint tracking, measured disturbance rejection and unmeasured disturbance rejection independently in the closed-loop system. Consequently, controller tuning is more flexible and intuitive than relying on objective function weights (such as move suppression) traditionally used in MPC schemes. The controller formulation is motivated by the needs of non-traditional control applications that are suitably described by hybrid production-inventory systems. Two applications are considered in this paper: adaptive, time-varying interventions in behavioral health, and inventory management in supply chains under conditions of limited capacity. In the adaptive intervention application, a hypothetical intervention inspired by the Fast Track program, a real-life preventive intervention for reducing conduct disorder in at-risk children, is examined. In the inventory management application, the ability of the algorithm to judiciously alter production capacity under conditions of varying demand is presented. These case studies demonstrate that MPC for hybrid systems can be tuned for desired performance under demanding conditions involving noise and uncertainty. PMID:24348004

Improved Performance and Safety for High Energy Batteries Through Use of Hazard Anticipation and Capacity Prediction

NASA Technical Reports Server (NTRS)

Atwater, Terrill

1993-01-01

Prediction of the capacity remaining in used high rate, high energy batteries is important information to the user. Knowledge of the capacity remaining in used batteries results in better utilization. This translates into improved readiness and cost savings due to complete, efficient use. High rate batteries, due to their chemical nature, are highly sensitive to misuse (i.e., over discharge or very high rate discharge). Battery failure due to misuse or manufacturing defects could be disastrous. Since high rate, high energy batteries are expensive and energetic, a reliable method of predicting both failures and remaining energy has been actively sought. Due to concerns over safety, the behavior of lithium/sulphur dioxide cells at different temperatures and current drains was examined. The main thrust of this effort was to determine failure conditions for incorporation in hazard anticipation circuitry. In addition, capacity prediction formulas have been developed from test data. A process that performs continuous, real-time hazard anticipation and capacity prediction was developed. The introduction of this process into microchip technology will enable the production of reliable, safe, and efficient high energy batteries.

Determination of main fruits in adulterated nectars by ATR-FTIR spectroscopy combined with multivariate calibration and variable selection methods.

PubMed

Miaw, Carolina Sheng Whei; Assis, Camila; Silva, Alessandro Rangel Carolino Sales; Cunha, Maria Luísa; Sena, Marcelo Martins; de Souza, Scheilla Vitorino Carvalho

2018-07-15

Grape, orange, peach and passion fruit nectars were formulated and adulterated by dilution with syrup, apple and cashew juices at 10 levels for each adulterant. Attenuated total reflectance Fourier transform mid infrared (ATR-FTIR) spectra were obtained. Partial least squares (PLS) multivariate calibration models allied to different variable selection methods, such as interval partial least squares (iPLS), ordered predictors selection (OPS) and genetic algorithm (GA), were used to quantify the main fruits. PLS improved by iPLS-OPS variable selection showed the highest predictive capacity to quantify the main fruit contents. The selected variables in the final models varied from 72 to 100; the root mean square errors of prediction were estimated from 0.5 to 2.6%; the correlation coefficients of prediction ranged from 0.948 to 0.990; and, the mean relative errors of prediction varied from 3.0 to 6.7%. All of the developed models were validated. Copyright © 2018 Elsevier Ltd. All rights reserved.

Risk Factors for Addiction and Their Association with Model-Based Behavioral Control.

PubMed

Reiter, Andrea M F; Deserno, Lorenz; Wilbertz, Tilmann; Heinze, Hans-Jochen; Schlagenhauf, Florian

2016-01-01

Addiction shows familial aggregation and previous endophenotype research suggests that healthy relatives of addicted individuals share altered behavioral and cognitive characteristics with individuals suffering from addiction. In this study we asked whether impairments in behavioral control proposed for addiction, namely a shift from goal-directed, model-based toward habitual, model-free control, extends toward an unaffected sample (n = 20) of adult children of alcohol-dependent fathers as compared to a sample without any personal or family history of alcohol addiction (n = 17). Using a sequential decision-making task designed to investigate model-free and model-based control combined with a computational modeling analysis, we did not find any evidence for altered behavioral control in individuals with a positive family history of alcohol addiction. Independent of family history of alcohol dependence, we however observed that the interaction of two different risk factors of addiction, namely impulsivity and cognitive capacities, predicts the balance of model-free and model-based behavioral control. Post-hoc tests showed a positive association of model-based behavior with cognitive capacity in the lower, but not in the higher impulsive group of the original sample. In an independent sample of particularly high- vs. low-impulsive individuals, we confirmed the interaction effect of cognitive capacities and high vs. low impulsivity on model-based control. In the confirmation sample, a positive association of omega with cognitive capacity was observed in highly impulsive individuals, but not in low impulsive individuals. Due to the moderate sample size of the study, further investigation of the association of risk factors for addiction with model-based behavior in larger sample sizes is warranted.

Reef-coral refugia in a rapidly changing ocean.

PubMed

Cacciapaglia, Chris; van Woesik, Robert

2015-06-01

This study sought to identify climate-change thermal-stress refugia for reef corals in the Indian and Pacific Oceans. A species distribution modeling approach was used to identify refugia for 12 coral species that differed considerably in their local response to thermal stress. We hypothesized that the local response of coral species to thermal stress might be similarly reflected as a regional response to climate change. We assessed the contemporary geographic range of each species and determined their temperature and irradiance preferences using a k-fold algorithm to randomly select training and evaluation sites. That information was applied to downscaled outputs of global climate models to predict where each species is likely to exist by the year 2100. Our model was run with and without a 1°C capacity to adapt to the rising ocean temperature. The results show a positive exponential relationship between the current area of habitat that coral species occupy and the predicted area of habitat that they will occupy by 2100. There was considerable decoupling between scales of response, however, and with further ocean warming some 'winners' at local scales will likely become 'losers' at regional scales. We predicted that nine of the 12 species examined will lose 24-50% of their current habitat. Most reductions are predicted to occur between the latitudes 5-15°, in both hemispheres. Yet when we modeled a 1°C capacity to adapt, two ubiquitous species, Acropora hyacinthus and Acropora digitifera, were predicted to retain much of their current habitat. By contrast, the thermally tolerant Porites lobata is expected to increase its current distribution by 14%, particularly southward along the east and west coasts of Australia. Five areas were identified as Indian Ocean refugia, and seven areas were identified as Pacific Ocean refugia for reef corals under climate change. All 12 of these reef-coral refugia deserve high-conservation status. © 2015 John Wiley & Sons Ltd.

Developing strategies to initialize landscape-scale vegetation maps from FIA data to enhance resolution of individual species-size cohort representation in the landscape disturbance model SIMPPLLE

Treesearch

Jacob John Muller

2014-01-01

The ability of forest resource managers to understand and anticipate landscape-scale change in composition and structure relies upon an adequate characterization of the current forest composition and structure of various patches (or stands), along with the capacity of forest landscape models (FLMs) to predict patterns of growth, succession, and disturbance at multiple...

Probabilistic, sediment-geochemical parameterisation of the groundwater compartment of the Netherlands for spatially distributed, reactive transport modelling

NASA Astrophysics Data System (ADS)

Janssen, Gijs; Gunnink, Jan; van Vliet, Marielle; Goldberg, Tanya; Griffioen, Jasper

2017-04-01

Pollution of groundwater aquifers with contaminants as nitrate is a common problem. Reactive transport models are useful to predict the fate of such contaminants and to characterise the efficiency of mitigating or preventive measures. Parameterisation of a groundwater transport model on reaction capacity is a necessary step during building the model. Two Dutch, national programs are combined to establish a methodology for building a probabilistic model on reaction capacity of the groundwater compartment at the national scale: the Geological Survey program and the NHI Netherlands Hydrological Instrument program. Reaction capacity is considered as a series of geochemical characteristics that control acid/base condition, redox condition and sorption capacity. Five primary reaction capacity variables are characterised: 1. pyrite, 2. non-pyrite, reactive iron (oxides, siderite and glauconite), 3. clay fraction, 4. organic matter and 5. Ca-carbonate. Important reaction capacity variables that are determined by more than one solid compound are also deduced: 1. potential reduction capacity (PRC) by pyrite and organic matter, 2. cation-exchange capacity (CEC) by organic matter and clay content, 3. carbonate buffering upon pyrite oxidation (CPBO) by carbonate and pyrite. Statistical properties of these variables are established based on c. 16,000 sediment geochemical analyses. The first tens of meters are characterised based on 25 regions using combinations of lithological class and geological formation as strata. Because of both less data and more geochemical uniformity, the deeper subsurface is characterised in a similar way based on 3 regions. The statistical data is used as input in an algoritm that probabilistically calculates the reaction capacity per grid cell. First, the cumulative frequency distribution (cfd) functions are calculated from the statistical data for the geochemical strata. Second, all voxel cells are classified into the geochemical strata. Third, the cfd functions are used to put random reaction capacity variables into the hydrological voxel model. Here, the distribution can be conditioned on two variables. Two important variables are clay content and depth. The first is valid because more dense data is available for clay content than for geochemical variables as pyrite and probabilistic, lithological models are also built at TNO Geological Survey. The second is important to account for locally different depths at which the redox cline between NO3-rich and Fe(II)-rich groundwater occurs within the first tens of meters of the subsurface. An extensive data-set of groundwater quality analyses is used to derive criteria for depth variability of the redox cline. The result is a unique algoritm in order to obtain heterogeneous geochemical reaction capacity models of the entire groundwater compartment of the Netherlands.

The evolution of root-zone moisture capacities after deforestation: a step towards hydrological predictions under change?

NASA Astrophysics Data System (ADS)

Nijzink, Remko; Hutton, Christopher; Pechlivanidis, Ilias; Capell, René; Arheimer, Berit; Freer, Jim; Han, Dawei; Wagener, Thorsten; McGuire, Kevin; Savenije, Hubert; Hrachowitz, Markus

2016-12-01

The core component of many hydrological systems, the moisture storage capacity available to vegetation, is impossible to observe directly at the catchment scale and is typically treated as a calibration parameter or obtained from a priori available soil characteristics combined with estimates of rooting depth. Often this parameter is considered to remain constant in time. Using long-term data (30-40 years) from three experimental catchments that underwent significant land cover change, we tested the hypotheses that: (1) the root-zone storage capacity significantly changes after deforestation, (2) changes in the root-zone storage capacity can to a large extent explain post-treatment changes to the hydrological regimes and that (3) a time-dynamic formulation of the root-zone storage can improve the performance of a hydrological model.A recently introduced method to estimate catchment-scale root-zone storage capacities based on climate data (i.e. observed rainfall and an estimate of transpiration) was used to reproduce the temporal evolution of root-zone storage capacity under change. Briefly, the maximum deficit that arises from the difference between cumulative daily precipitation and transpiration can be considered as a proxy for root-zone storage capacity. This value was compared to the value obtained from four different conceptual hydrological models that were calibrated for consecutive 2-year windows.It was found that water-balance-derived root-zone storage capacities were similar to the values obtained from calibration of the hydrological models. A sharp decline in root-zone storage capacity was observed after deforestation, followed by a gradual recovery, for two of the three catchments. Trend analysis suggested hydrological recovery periods between 5 and 13 years after deforestation. In a proof-of-concept analysis, one of the hydrological models was adapted to allow dynamically changing root-zone storage capacities, following the observed changes due to deforestation. Although the overall performance of the modified model did not considerably change, in 51 % of all the evaluated hydrological signatures, considering all three catchments, improvements were observed when adding a time-variant representation of the root-zone storage to the model.In summary, it is shown that root-zone moisture storage capacities can be highly affected by deforestation and climatic influences and that a simple method exclusively based on climate data can not only provide robust, catchment-scale estimates of this critical parameter, but also reflect its time-dynamic behaviour after deforestation.

Characterization of recent and minimally passaged Brazilian dengue viruses inducing robust infection in rhesus macaques.

PubMed

Borges, Maria Beatriz; Marchevsky, Renato Sergio; Mendes, Ygara S; Mendes, Luiz Gustavo; Duarte, Ana Claudia; Cruz, Michael; de Filippis, Ana Maria Bispo; Vasconcelos, Pedro Fernando C; Freire, Marcos; Homma, Akira; Mossman, Sally; Lepine, Edith; Vanloubbeeck, Yannick; Lorin, Clarisse; Malice, Marie-Pierre; Caride, Elena; Warter, Lucile

2018-01-01

The macaque is widely accepted as a suitable model for preclinical characterization of dengue vaccine candidates. However, the only vaccine for which both preclinical and clinical efficacy results were reported so far showed efficacy levels that were substantially different between macaques and humans. We hypothesized that this model's predictive capacity may be improved using recent and minimally passaged dengue virus isolates, and by assessing vaccine efficacy by characterizing not only the post-dengue virus challenge viremia/RNAemia but also the associated-cytokine profile. Ten recent and minimally passaged Brazilian clinical isolates from the four dengue virus serotypes were tested for their infectivity in rhesus macaques. For the strains showing robust replication capacity, the associated-changes in soluble mediator levels, and the elicited dengue virus-neutralizing antibody responses, were also characterized. Three isolates from dengue virus serotypes 1, 2 and 4 induced viremia of high magnitude and longer duration relative to previously reported viremia kinetics in this model, and robust dengue virus-neutralizing antibody responses. Consistent with observations in humans, increased MCP-1, IFN-γ and VEGF-A levels, and transiently decreased IL-8 levels were detected after infection with the selected isolates. These results may contribute to establishing a dengue macaque model showing a higher predictability for vaccine efficacy in humans.

Predicting Survival of De Novo Metastatic Breast Cancer in Asian Women: Systematic Review and Validation Study

PubMed Central

Miao, Hui; Hartman, Mikael; Bhoo-Pathy, Nirmala; Lee, Soo-Chin; Taib, Nur Aishah; Tan, Ern-Yu; Chan, Patrick; Moons, Karel G. M.; Wong, Hoong-Seam; Goh, Jeremy; Rahim, Siti Mastura; Yip, Cheng-Har; Verkooijen, Helena M.

2014-01-01

Background In Asia, up to 25% of breast cancer patients present with distant metastases at diagnosis. Given the heterogeneous survival probabilities of de novo metastatic breast cancer, individual outcome prediction is challenging. The aim of the study is to identify existing prognostic models for patients with de novo metastatic breast cancer and validate them in Asia. Materials and Methods We performed a systematic review to identify prediction models for metastatic breast cancer. Models were validated in 642 women with de novo metastatic breast cancer registered between 2000 and 2010 in the Singapore Malaysia Hospital Based Breast Cancer Registry. Survival curves for low, intermediate and high-risk groups according to each prognostic score were compared by log-rank test and discrimination of the models was assessed by concordance statistic (C-statistic). Results We identified 16 prediction models, seven of which were for patients with brain metastases only. Performance status, estrogen receptor status, metastatic site(s) and disease-free interval were the most common predictors. We were able to validate nine prediction models. The capacity of the models to discriminate between poor and good survivors varied from poor to fair with C-statistics ranging from 0.50 (95% CI, 0.48–0.53) to 0.63 (95% CI, 0.60–0.66). Conclusion The discriminatory performance of existing prediction models for de novo metastatic breast cancer in Asia is modest. Development of an Asian-specific prediction model is needed to improve prognostication and guide decision making. PMID:24695692

Mixed polyanion glass cathodes: Glass-state conversion reactions

DOE PAGES

Kercher, Andrew K.; Kolopus, James A.; Carroll, Kyler; ...

2015-11-10

Mixed polyanion (MP) glasses can undergo glass-state conversion (GSC) reactions to provide an alternate class of high-capacity cathode materials. GSC reactions have been demonstrated in phosphate/vanadate glasses with Ag, Co, Cu, Fe, and Ni cations. These MP glasses provided high capacity and good high power performance, but suffer from moderate voltages, large voltage hysteresis, and significant capacity fade with cycling. Details of the GSC reaction have been revealed by x-ray absorption spectroscopy, electron microscopy, and energy dispersive x-ray spectroscopy of ex situ cathodes at key states of charge. Using the Open Quantum Materials Database (OQMD), a computational thermodynamic model hasmore » been developed to predict the near-equilibrium voltages of glass-state conversion reactions in MP glasses.« less

Controlling the stream of thought: working memory capacity predicts adjustment of mind-wandering to situational demands.

PubMed

Rummel, Jan; Boywitt, C Dennis

2014-10-01

Although engaging in task-unrelated thoughts can be enjoyable and functional under certain circumstances, allowing one's mind to wander off-task will come at a cost to performance in many situations. Given that task-unrelated thoughts need to be blocked out when the current task requires full attention, it has been argued that cognitive control is necessary to prevent mind-wandering from becoming maladaptive. Extending this idea, we exposed participants to tasks of different demands and assessed mind-wandering via thought probes. Employing a latent-change model, we found mind-wandering to be adjusted to current task demands. As hypothesized, the degree of adjustment was predicted by working memory capacity, indicating that participants with higher working memory capacity were more flexible in their coordination of on- and off-task thoughts. Notably, the better the adjustment, the smaller performance decrements due to increased task demands were. On the basis of these findings, we argue that cognitive control does not simply allow blocking out task-unrelated thoughts but, rather, allows one to flexibly adjust mind-wandering to situational demands.

The role of thermal and lubricant boundary layers in the transient thermal analysis of spur gears

NASA Technical Reports Server (NTRS)

El-Bayoumy, L. E.; Akin, L. S.; Townsend, D. P.; Choy, F. C.

1989-01-01

An improved convection heat-transfer model has been developed for the prediction of the transient tooth surface temperature of spur gears. The dissipative quality of the lubricating fluid is shown to be limited to the capacity extent of the thermal boundary layer. This phenomenon can be of significance in the determination of the thermal limit of gears accelerating to the point where gear scoring occurs. Steady-state temperature prediction is improved considerably through the use of a variable integration time step that substantially reduces computer time. Computer-generated plots of temperature contours enable the user to animate the propagation of the thermal wave as the gears come into and out of contact, thus contributing to better understanding of this complex problem. This model has a much better capability at predicting gear-tooth temperatures than previous models.

Prediction of anticancer activity of diterpenes isolated from the paraiban flora through a PLS model and molecular surfaces.

PubMed

Scotti, Luciana; Scotti, Marcus T; Ishiki, Hamilton; Junior, Francisco J B M; dos, Santos Paula F; Tavares, Josean F; da Silva, Marcelo S

2014-05-01

The aim of this work was to predict the anticancer potential of 3 atisane, and 3 trachylobane diterpene compounds extracted from the roots of Xylopia langsdorffiana. The prediction of anticancer activity as expressed against PC-3 tumor cells was made using a PLS model built with 26 diterpenes in the training set. Significant statistical measures were obtained. The six investigated diterpenes were applied to the model and their activities against PC-3 cells were calculated. All the diterpenes were active, with atisane diterpenes showing the higher pICso values. In human prostate carcinoma PC-3 cells, the apoptosis mechanism is related to an inhibition of IKK/NF-KB. Antioxidant potential implies a greater electronic molecular atmosphere (increased donor electron capacity), which can reduce radical reactivity, and facilitate post donation charge accommodation. Molecular surfaces indicated a much greater electronic cloud over atisane diterpenes.

Modeling Weather Impact on Ground Delay Programs

NASA Technical Reports Server (NTRS)

Wang, Yao; Kulkarni, Deepak

2011-01-01

Scheduled arriving aircraft demand may exceed airport arrival capacity when there is abnormal weather at an airport. In such situations, Federal Aviation Administration (FAA) institutes ground-delay programs (GDP) to delay flights before they depart from their originating airports. Efficient GDP planning depends on the accuracy of prediction of airport capacity and demand in the presence of uncertainties in weather forecast. This paper presents a study of the impact of dynamic airport surface weather on GDPs. Using the National Traffic Management Log, effect of weather conditions on the characteristics of GDP events at selected busy airports is investigated. Two machine learning methods are used to generate models that map the airport operational conditions and weather information to issued GDP parameters and results of validation tests are described.

Transient finite element analysis of electric double layer using Nernst-Planck-Poisson equations with a modified Stern layer.

PubMed

Lim, Jongil; Whitcomb, John; Boyd, James; Varghese, Julian

2007-01-01

A finite element implementation of the transient nonlinear Nernst-Planck-Poisson (NPP) and Nernst-Planck-Poisson-modified Stern (NPPMS) models is presented. The NPPMS model uses multipoint constraints to account for finite ion size, resulting in realistic ion concentrations even at high surface potential. The Poisson-Boltzmann equation is used to provide a limited check of the transient models for low surface potential and dilute bulk solutions. The effects of the surface potential and bulk molarity on the electric potential and ion concentrations as functions of space and time are studied. The ability of the models to predict realistic energy storage capacity is investigated. The predicted energy is much more sensitive to surface potential than to bulk solution molarity.

Regional-scale estimates of surface moisture availability and thermal inertia using remote thermal measurements

NASA Technical Reports Server (NTRS)

Carlson, T. N.

1986-01-01

A review is presented of numerical models which were developed to interpret thermal IR data and to identify the governing parameters and surface energy fluxes recorded in the images. Analytic, predictive, diagnostic and empirical models are described. The limitations of each type of modeling approach are explored in terms of the error sources and inherent constraints due to theoretical or measurement limitations. Sample results of regional-scale soil moisture or evaporation patterns derived from the Heat Capacity Mapping Mission and GOES satellite data through application of the predictive model devised by Carlson (1981) are discussed. The analysis indicates that pattern recognition will probably be highest when data are collected over flat, arid, sparsely vegetated terrain. The soil moisture data then obtained may be accurate to within 10-20 percent.

Granular activated carbon adsorption of MIB in the presence of dissolved organic matter.

PubMed

Summers, R Scott; Kim, Soo Myung; Shimabuku, Kyle; Chae, Seon-Ha; Corwin, Christopher J

2013-06-15

Based on the results of over twenty laboratory granular activated carbon (GAC) column runs, models were developed and utilized for the prediction of 2-methylisoborneol (MIB) breakthrough behavior at parts per trillion levels and verified with pilot-scale data. The influent MIB concentration was found not to impact the concentration normalized breakthrough. Increasing influent background dissolved organic matter (DOM) concentration was found to systematically decrease the GAC adsorption capacity for MIB. A series of empirical models were developed that related the throughput in bed volumes for a range of MIB breakthrough targets to the influent DOM concentration. The proportional diffusivity (PD) designed rapid small-scale column test (RSSCT) could be directly used to scale-up MIB breakthrough performance below 15% breakthrough. The empirical model to predict the throughput to 50% breakthrough based on the influent DOM concentration served as input to the pore diffusion model (PDM) and well-predicted the MIB breakthrough performance below a 50% breakthrough. The PDM predictions of throughput to 10% breakthrough well simulated the PD-RSSCT and pilot-scale 10% MIB breakthrough. Copyright © 2013 Elsevier Ltd. All rights reserved.

Historical Maps from Modern Images: Using Remote Sensing to Model and Map Century-Long Vegetation Change in a Fire-Prone Region

PubMed Central

Callister, Kate E.; Griffioen, Peter A.; Avitabile, Sarah C.; Haslem, Angie; Kelly, Luke T.; Kenny, Sally A.; Nimmo, Dale G.; Farnsworth, Lisa M.; Taylor, Rick S.; Watson, Simon J.; Bennett, Andrew F.; Clarke, Michael F.

2016-01-01

Understanding the age structure of vegetation is important for effective land management, especially in fire-prone landscapes where the effects of fire can persist for decades and centuries. In many parts of the world, such information is limited due to an inability to map disturbance histories before the availability of satellite images (~1972). Here, we describe a method for creating a spatial model of the age structure of canopy species that established pre-1972. We built predictive neural network models based on remotely sensed data and ecological field survey data. These models determined the relationship between sites of known fire age and remotely sensed data. The predictive model was applied across a 104,000 km2 study region in semi-arid Australia to create a spatial model of vegetation age structure, which is primarily the result of stand-replacing fires which occurred before 1972. An assessment of the predictive capacity of the model using independent validation data showed a significant correlation (rs = 0.64) between predicted and known age at test sites. Application of the model provides valuable insights into the distribution of vegetation age-classes and fire history in the study region. This is a relatively straightforward method which uses widely available data sources that can be applied in other regions to predict age-class distribution beyond the limits imposed by satellite imagery. PMID:27029046

Electricity Consumption in the Industrial Sector of Jordan: Application of Multivariate Linear Regression and Adaptive Neuro-Fuzzy Techniques

NASA Astrophysics Data System (ADS)

Samhouri, M.; Al-Ghandoor, A.; Fouad, R. H.

2009-08-01

In this study two techniques, for modeling electricity consumption of the Jordanian industrial sector, are presented: (i) multivariate linear regression and (ii) neuro-fuzzy models. Electricity consumption is modeled as function of different variables such as number of establishments, number of employees, electricity tariff, prevailing fuel prices, production outputs, capacity utilizations, and structural effects. It was found that industrial production and capacity utilization are the most important variables that have significant effect on future electrical power demand. The results showed that both the multivariate linear regression and neuro-fuzzy models are generally comparable and can be used adequately to simulate industrial electricity consumption. However, comparison that is based on the square root average squared error of data suggests that the neuro-fuzzy model performs slightly better for future prediction of electricity consumption than the multivariate linear regression model. Such results are in full agreement with similar work, using different methods, for other countries.

Monitoring Crop Yield in USA Using a Satellite-Based Climate-Variability Impact Index

NASA Technical Reports Server (NTRS)

Zhang, Ping; Anderson, Bruce; Tan, Bin; Barlow, Mathew; Myneni, Ranga

2011-01-01

A quantitative index is applied to monitor crop growth and predict agricultural yield in continental USA. The Climate-Variability Impact Index (CVII), defined as the monthly contribution to overall anomalies in growth during a given year, is derived from 1-km MODIS Leaf Area Index. The growing-season integrated CVII can provide an estimate of the fractional change in overall growth during a given year. In turn these estimates can provide fine-scale and aggregated information on yield for various crops. Trained from historical records of crop production, a statistical model is used to produce crop yield during the growing season based upon the strong positive relationship between crop yield and the CVII. By examining the model prediction as a function of time, it is possible to determine when the in-season predictive capability plateaus and which months provide the greatest predictive capacity.

A Three-Threshold Learning Rule Approaches the Maximal Capacity of Recurrent Neural Networks

PubMed Central

Alemi, Alireza; Baldassi, Carlo; Brunel, Nicolas; Zecchina, Riccardo

2015-01-01

Understanding the theoretical foundations of how memories are encoded and retrieved in neural populations is a central challenge in neuroscience. A popular theoretical scenario for modeling memory function is the attractor neural network scenario, whose prototype is the Hopfield model. The model simplicity and the locality of the synaptic update rules come at the cost of a poor storage capacity, compared with the capacity achieved with perceptron learning algorithms. Here, by transforming the perceptron learning rule, we present an online learning rule for a recurrent neural network that achieves near-maximal storage capacity without an explicit supervisory error signal, relying only upon locally accessible information. The fully-connected network consists of excitatory binary neurons with plastic recurrent connections and non-plastic inhibitory feedback stabilizing the network dynamics; the memory patterns to be memorized are presented online as strong afferent currents, producing a bimodal distribution for the neuron synaptic inputs. Synapses corresponding to active inputs are modified as a function of the value of the local fields with respect to three thresholds. Above the highest threshold, and below the lowest threshold, no plasticity occurs. In between these two thresholds, potentiation/depression occurs when the local field is above/below an intermediate threshold. We simulated and analyzed a network of binary neurons implementing this rule and measured its storage capacity for different sizes of the basins of attraction. The storage capacity obtained through numerical simulations is shown to be close to the value predicted by analytical calculations. We also measured the dependence of capacity on the strength of external inputs. Finally, we quantified the statistics of the resulting synaptic connectivity matrix, and found that both the fraction of zero weight synapses and the degree of symmetry of the weight matrix increase with the number of stored patterns. PMID:26291608

A Three-Threshold Learning Rule Approaches the Maximal Capacity of Recurrent Neural Networks.

PubMed

Alemi, Alireza; Baldassi, Carlo; Brunel, Nicolas; Zecchina, Riccardo

2015-08-01

Understanding the theoretical foundations of how memories are encoded and retrieved in neural populations is a central challenge in neuroscience. A popular theoretical scenario for modeling memory function is the attractor neural network scenario, whose prototype is the Hopfield model. The model simplicity and the locality of the synaptic update rules come at the cost of a poor storage capacity, compared with the capacity achieved with perceptron learning algorithms. Here, by transforming the perceptron learning rule, we present an online learning rule for a recurrent neural network that achieves near-maximal storage capacity without an explicit supervisory error signal, relying only upon locally accessible information. The fully-connected network consists of excitatory binary neurons with plastic recurrent connections and non-plastic inhibitory feedback stabilizing the network dynamics; the memory patterns to be memorized are presented online as strong afferent currents, producing a bimodal distribution for the neuron synaptic inputs. Synapses corresponding to active inputs are modified as a function of the value of the local fields with respect to three thresholds. Above the highest threshold, and below the lowest threshold, no plasticity occurs. In between these two thresholds, potentiation/depression occurs when the local field is above/below an intermediate threshold. We simulated and analyzed a network of binary neurons implementing this rule and measured its storage capacity for different sizes of the basins of attraction. The storage capacity obtained through numerical simulations is shown to be close to the value predicted by analytical calculations. We also measured the dependence of capacity on the strength of external inputs. Finally, we quantified the statistics of the resulting synaptic connectivity matrix, and found that both the fraction of zero weight synapses and the degree of symmetry of the weight matrix increase with the number of stored patterns.

Cholesterol efflux capacity is an independent predictor of all-cause and cardiovascular mortality in patients with coronary artery disease: A prospective cohort study.

PubMed

Liu, Chaoqun; Zhang, Yuan; Ding, Ding; Li, Xinrui; Yang, Yunou; Li, Qing; Zheng, Yuanzhu; Wang, Dongliang; Ling, Wenhua

2016-06-01

Although diminished cholesterol efflux capacity is positively related with prevalent coronary artery disease, its prognostic value for incident cardiovascular events remains a topic of debate. This work aims to investigate the association between cholesterol efflux capacity and all-cause and cardiovascular mortality in patients with coronary artery disease. We measured cholesterol efflux capacity at baseline in 1737 patients with coronary artery disease from the Guangdong Coronary Artery Disease Cohort. During 6645 person-years of follow-up, 166 deaths were registered, 122 of which were caused by cardiovascular diseases. After multivariate adjustment for factors related to cardiovascular diseases, the hazard ratios of cholesterol efflux capacity in the fourth quartile compared with those in the bottom quartile were 0.24 (95% confidence intervals 0.13-0.44) for all-cause mortality (P < 0.001), and 0.17 (95% confidence intervals 0.08-0.39) for cardiovascular mortality (P < 0.001). Adding cholesterol efflux capacity to a model containing traditional cardiovascular risk factors significantly increases its discriminatory power and predictive ability for all-cause (area under receiver operating characteristic curve 0.79 versus 0.76, P = 0.001; net reclassification improvement 14.5%, P = 0.001; integrated discrimination improvement 0.016, P < 0.001) and cardiovascular (area under receiver operating characteristic curve 0.81 versus 0.78, P = 0.001; net reclassification improvement 18.4%, P < 0.001; integrated discrimination improvement 0.015, P < 0.001) death, respectively. Cholesterol efflux capacity may serve as an independent measure for predicting all-cause and cardiovascular mortality in patients with coronary artery disease. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Perceptual tools for quality-aware video networks

NASA Astrophysics Data System (ADS)

Bovik, A. C.

2014-01-01

Monitoring and controlling the quality of the viewing experience of videos transmitted over increasingly congested networks (especially wireless networks) is a pressing problem owing to rapid advances in video-centric mobile communication and display devices that are straining the capacity of the network infrastructure. New developments in automatic perceptual video quality models offer tools that have the potential to be used to perceptually optimize wireless video, leading to more efficient video data delivery and better received quality. In this talk I will review key perceptual principles that are, or could be used to create effective video quality prediction models, and leading quality prediction models that utilize these principles. The goal is to be able to monitor and perceptually optimize video networks by making them "quality-aware."

Working memory constraints on the processing of syntactic ambiguity.

PubMed

MacDonald, M C; Just, M A; Carpenter, P A

1992-01-01

We propose a model that explains how the working-memory capacity of a comprehender can constrain syntactic parsing and thereby affect the processing of syntactic ambiguities. The model's predictions are examined in four experiments that measure the reading times for two constructions that contain a temporary syntactic ambiguity. An example of the syntactic ambiguity is The soldiers warned about the dangers . . . ; the verb warned may either be the main verb, in which case soldiers is the agent; or the verb warned may introduce a relative clause, in which case soldiers is the patient of warned rather than the agent, as in The soldiers warned about the dangers conducted the midnight raid. The model proposes that both alternative interpretations of warned are initially activated. However, the duration for which both interpretations are maintained depends, in part, on the reader's working-memory capacity, which can be assessed by the Reading Span task (Daneman & Carpenter, 1980). The word-by-word reading times indicate that all subjects do additional processing after encountering an ambiguity, suggesting that they generate both representations. Furthermore, readers with larger working-memory capacities maintain both representations for some period of time (several words), whereas readers with smaller working-memory capacities revert to maintaining only the more likely representation.

Streamflow simulation for continental-scale river basins

NASA Astrophysics Data System (ADS)

Nijssen, Bart; Lettenmaier, Dennis P.; Liang, Xu; Wetzel, Suzanne W.; Wood, Eric F.

1997-04-01

A grid network version of the two-layer variable infiltration capacity (VIC-2L) macroscale hydrologic model is described. VIC-2L is a hydrologically based soil- vegetation-atmosphere transfer scheme designed to represent the land surface in numerical weather prediction and climate models. The grid network scheme allows streamflow to be predicted for large continental rivers. Off-line (observed and estimated surface meteorological and radiative forcings) applications of the model to the Columbia River (1° latitude-longitude spatial resolution) and Delaware River (0.5° resolution) are described. The model performed quite well in both applications, reproducing the seasonal hydrograph and annual flow volumes to within a few percent. Difficulties in reproducing observed streamflow in the arid portion of the Snake River basin are attributed to groundwater-surface water interactions, which are not modeled by VIC-2L.

Implementing Scientific-Based Research: Learning from the History of the Reading First Program

ERIC Educational Resources Information Center

Mohammed, Shereeza; Walker, David A.; Conderman, Greg; Pasapia, John

2016-01-01

The No Child Left Behind Act requires that educators at all grade levels use Scientifically Based Research (SBR) instructional practices. Researchers constructed a literature-based model to examine the predictive relationship between a state's comprehensiveness of planned implementation and its capacity to implement the plan. An approach comprised…

Determinants of Literacy Proficiency: A Lifelong-Lifewide Learning Perspective

ERIC Educational Resources Information Center

Desjardins, Richard

2003-01-01

The aim of this article is to investigate the predictive capacity of major determinants of literacy proficiency that are associated with a variety of contexts including school, home, work, community and leisure. An identical structural model based on previous research is fitted to data for 18 countries. The results show that even after accounting…

Empirically Derived Optimal Growth Equations For Hardwoods and Softwoods in Arkansas

Treesearch

Don C. Bragg

2002-01-01

Accurate growth projections are critical to reliable forest models, and ecologically based simulators can improve siivicultural predictions because of their sensitivity to change and their capacity to produce long-term forecasts. Potential relative increment (PRI) optimal diameter growth equations for loblolly pine, shortleaf pine, sweetgum, and white oak were fit to...

Thermal-Interaction Matrix For Resistive Test Structure

NASA Technical Reports Server (NTRS)

Buehler, Martin G.; Dhiman, Jaipal K.; Zamani, Nasser

1990-01-01

Linear mathematical model predicts increase in temperature in each segment of 15-segment resistive structure used to test electromigration. Assumption of linearity based on fact: equations that govern flow of heat are linear and coefficients in equations (heat conductivities and capacities) depend only weakly on temperature and considered constant over limited range of temperature.

SABER on Orbit Performance Evaluation and Lessons Learned

NASA Astrophysics Data System (ADS)

Jensen, Scott M.; Batty, J. Clair

2004-06-01

The Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) instrument, launched into orbit December 7, 2001, utilized a miniature pulse-tube cryocooler to maintain the SABER focal plane assembly (FPA) at 75 K. The limited cooling capacity of the cryocooler necessitated the development of a new never before flown Fiber Support Technology (FiST) for supporting and thermally isolating the FPA. A very precise predictive thermal modeling effort to ensure successful operation was also needed due to the very small capacity margin of the cryocooler. A high performance thermal link that minimized the temperature difference between the FPA and the cryocooler cold block and also the mechanical dynamic loading on the fragile pulse tube was developed and space qualified. This paper presents a comparison of the thermal modeling predictions with on orbit measurements, and discusses the lessons learned concerning long term performance issues of thermal isolation systems which utilize cryocoolers for cooling focal plane assemblies (FPA's). The effect of ice deposition on the thermal blankets and other FPA cooled structures, as well as the lessons learned in dealing with this ice deposition, will also be presented.

Highly macroscopically degenerated single-point ground states as source of specific heat capacity anomalies in magnetic frustrated systems

NASA Astrophysics Data System (ADS)

Jurčišinová, E.; Jurčišin, M.

2018-04-01

Anomalies of the specific heat capacity are investigated in the framework of the exactly solvable antiferromagnetic spin- 1 / 2 Ising model in the external magnetic field on the geometrically frustrated tetrahedron recursive lattice. It is shown that the Schottky-type anomaly in the behavior of the specific heat capacity is related to the existence of unique highly macroscopically degenerated single-point ground states which are formed on the borders between neighboring plateau-like ground states. It is also shown that the very existence of these single-point ground states with large residual entropies predicts the appearance of another anomaly in the behavior of the specific heat capacity for low temperatures, namely, the field-induced double-peak structure, which exists, and should be observed experimentally, along with the Schottky-type anomaly in various frustrated magnetic system.

Cognitive performance predicts treatment decisional abilities in mild to moderate dementia

PubMed Central

Gurrera, R.J.; Moye, J.; Karel, M.J.; Azar, A.R.; Armesto, J.C.

2016-01-01

Objective To examine the contribution of neuropsychological test performance to treatment decision-making capacity in community volunteers with mild to moderate dementia. Methods The authors recruited volunteers (44 men, 44 women) with mild to moderate dementia from the community. Subjects completed a battery of 11 neuropsychological tests that assessed auditory and visual attention, logical memory, language, and executive function. To measure decision making capacity, the authors administered the Capacity to Consent to Treatment Interview, the Hopemont Capacity Assessment Interview, and the MacCarthur Competence Assessment Tool—Treatment. Each of these instruments individually scores four decisional abilities serving capacity: understanding, appreciation, reasoning, and expression of choice. The authors used principal components analysis to generate component scores for each ability across instruments, and to extract principal components for neuropsychological performance. Results Multiple linear regression analyses demonstrated that neuropsychological performance significantly predicted all four abilities. Specifically, it predicted 77.8% of the common variance for understanding, 39.4% for reasoning, 24.6% for appreciation, and 10.2% for expression of choice. Except for reasoning and appreciation, neuropsychological predictor (β) profiles were unique for each ability. Conclusions Neuropsychological performance substantially and differentially predicted capacity for treatment decisions in individuals with mild to moderate dementia. Relationships between elemental cognitive function and decisional capacity may differ in individuals whose decisional capacity is impaired by other disorders, such as mental illness. PMID:16682669

Evaluation of predictive capacities of biomarkers based on research synthesis.

PubMed

Hattori, Satoshi; Zhou, Xiao-Hua

2016-11-10

The objective of diagnostic studies or prognostic studies is to evaluate and compare predictive capacities of biomarkers. Suppose we are interested in evaluation and comparison of predictive capacities of continuous biomarkers for a binary outcome based on research synthesis. In analysis of each study, subjects are often classified into two groups of the high-expression and low-expression groups according to a cut-off value, and statistical analysis is based on a 2 × 2 table defined by the response and the high expression or low expression of the biomarker. Because the cut-off is study specific, it is difficult to interpret a combined summary measure such as an odds ratio based on the standard meta-analysis techniques. The summary receiver operating characteristic curve is a useful method for meta-analysis of diagnostic studies in the presence of heterogeneity of cut-off values to examine discriminative capacities of biomarkers. We develop a method to estimate positive or negative predictive curves, which are alternative to the receiver operating characteristic curve based on information reported in published papers of each study. These predictive curves provide a useful graphical presentation of pairs of positive and negative predictive values and allow us to compare predictive capacities of biomarkers of different scales in the presence of heterogeneity in cut-off values among studies. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Forecasting high-priority infectious disease surveillance regions: a socioeconomic model.

PubMed

Chan, Emily H; Scales, David A; Brewer, Timothy F; Madoff, Lawrence C; Pollack, Marjorie P; Hoen, Anne G; Choden, Tenzin; Brownstein, John S

2013-02-01

Few researchers have assessed the relationships between socioeconomic inequality and infectious disease outbreaks at the population level globally. We use a socioeconomic model to forecast national annual rates of infectious disease outbreaks. We constructed a multivariate mixed-effects Poisson model of the number of times a given country was the origin of an outbreak in a given year. The dataset included 389 outbreaks of international concern reported in the World Health Organization's Disease Outbreak News from 1996 to 2008. The initial full model included 9 socioeconomic variables related to education, poverty, population health, urbanization, health infrastructure, gender equality, communication, transportation, and democracy, and 1 composite index. Population, latitude, and elevation were included as potential confounders. The initial model was pared down to a final model by a backwards elimination procedure. The dependent and independent variables were lagged by 2 years to allow for forecasting future rates. Among the socioeconomic variables tested, the final model included child measles immunization rate and telephone line density. The Democratic Republic of Congo, China, and Brazil were predicted to be at the highest risk for outbreaks in 2010, and Colombia and Indonesia were predicted to have the highest percentage of increase in their risk compared to their average over 1996-2008. Understanding socioeconomic factors could help improve the understanding of outbreak risk. The inclusion of the measles immunization variable suggests that there is a fundamental basis in ensuring adequate public health capacity. Increased vigilance and expanding public health capacity should be prioritized in the projected high-risk regions.

Data Mining for Understanding and Impriving Decision-Making Affecting Ground Delay Programs

NASA Technical Reports Server (NTRS)

Kulkarni, Deepak; Wang, Yao Xun; Sridhar, Banavar

2013-01-01

The continuous growth in the demand for air transportation results in an imbalance between airspace capacity and traffic demand. The airspace capacity of a region depends on the ability of the system to maintain safe separation between aircraft in the region. In addition to growing demand, the airspace capacity is severely limited by convective weather. During such conditions, traffic managers at the FAA's Air Traffic Control System Command Center (ATCSCC) and dispatchers at various Airlines' Operations Center (AOC) collaborate to mitigate the demand-capacity imbalance caused by weather. The end result is the implementation of a set of Traffic Flow Management (TFM) initiatives such as ground delay programs, reroute advisories, flow metering, and ground stops. Data Mining is the automated process of analyzing large sets of data and then extracting patterns in the data. Data mining tools are capable of predicting behaviors and future trends, allowing an organization to benefit from past experience in making knowledge-driven decisions. The work reported in this paper is focused on ground delay programs. Data mining algorithms have the potential to develop associations between weather patterns and the corresponding ground delay program responses. If successful, they can be used to improve and standardize TFM decision resulting in better predictability of traffic flows on days with reliable weather forecasts. The approach here seeks to develop a set of data mining and machine learning models and apply them to historical archives of weather observations and forecasts and TFM initiatives to determine the extent to which the theory can predict and explain the observed traffic flow behaviors.

Short-term predictive capacity of two different triage systems in patients with acute heart failure: TRICA-EAHFE study.

PubMed

Miró, Òscar; Tost, Josep; Herrero, Pablo; Jacob, Javier; Martín-Sánchez, Francisco Javier; Gil, Víctor; Fernández-Pérez, Cristina; Escoda, Rosa; Llorens, Pere

2016-12-01

To evaluate whether prioritization of patients with acute heart failure (AHF) in the Andorran Triage Model/Spanish Triage System (MAT/SET) and the Manchester Triage System (MTS) also allows the identification of different profiles of outcome and prognosis and determine whether either system has a better predictive capacity of outcomes. Patients with AHF included in the Spanish EAHFE registry from hospitals using the MAT/SET or MTS were selected and divided according to the triage system used. Outcome variables included hospital admission, length of stay, death during admission, 3, 7, and 30-day all-cause mortality, and emergency department (ED) reconsultation at 30 days. The results were compared according to the level of priority and the triage system used. We included 3837 patients (MAT/SET=2474; MTS=1363) classified as follows: 4.0% level 1; 34.7% level 2; 55.1% level 3; and 6.3% levels 4-5. Both systems associated greater priority with higher rates of admission and mortality; the MTS associated greater priority with greater ED reconsultation and the MAT/SET found greater priority to be associated with less ED reconsultation. The discriminative capacity of the two scales for adverse outcomes was statistically significant, albeit poor, for almost all the outcome events and it was of scarce clinical relevance (Area under the curve of the receiver operating characteristic between 0.458 and 0.661). The prediction of the outcome of patients with AHF determined with the MAT/SET or MTS showed scarce differences between the two systems, and their discriminative capacity does not seem to be clinically relevant.

Precipitation-mediated responses of soil acid buffering capacity to long-term nitrogen addition in a semi-arid grassland

NASA Astrophysics Data System (ADS)

Cai, Jiangping; Luo, Wentao; Liu, Heyong; Feng, Xue; Zhang, Yongyong; Wang, Ruzhen; Xu, Zhuwen; Zhang, Yuge; Jiang, Yong

2017-12-01

Atmospheric nitrogen (N) deposition can result in soil acidification and reduce soil acid buffering capacity. However, it remains poorly understood how changes in precipitation regimes with elevated atmospheric N deposition affect soil acidification processes in a water-limited grassland. Here, we conducted a 9-year split-plot experiment with water addition as the main factor and N addition as the second factor. Results showed that soil acid buffering capacity significantly decreased with increased N inputs, mainly due to the decline of soil effective cation exchange capacity (ECEC) and exchangeable basic cations (especially Ca2+), indicating an acceleration of soil acidification status in this steppes. Significant interactive N and water effects were detected on the soil acid buffering capacity. Water addition enhanced the soil ECEC and exchangeable base cations and thus alleviated the decrease of soil acid buffering capacity under N addition. Our findings suggested that precipitation can mitigate the impact of increased N deposition on soil acidification in semi-arid grasslands. This knowledge should be used to improve models predicting soil acidification processes in terrestrial ecosystems under changing environmental conditions.

A comparative analysis of predictive models of morbidity in intensive care unit after cardiac surgery - part II: an illustrative example.

PubMed

Cevenini, Gabriele; Barbini, Emanuela; Scolletta, Sabino; Biagioli, Bonizella; Giomarelli, Pierpaolo; Barbini, Paolo

2007-11-22

Popular predictive models for estimating morbidity probability after heart surgery are compared critically in a unitary framework. The study is divided into two parts. In the first part modelling techniques and intrinsic strengths and weaknesses of different approaches were discussed from a theoretical point of view. In this second part the performances of the same models are evaluated in an illustrative example. Eight models were developed: Bayes linear and quadratic models, k-nearest neighbour model, logistic regression model, Higgins and direct scoring systems and two feed-forward artificial neural networks with one and two layers. Cardiovascular, respiratory, neurological, renal, infectious and hemorrhagic complications were defined as morbidity. Training and testing sets each of 545 cases were used. The optimal set of predictors was chosen among a collection of 78 preoperative, intraoperative and postoperative variables by a stepwise procedure. Discrimination and calibration were evaluated by the area under the receiver operating characteristic curve and Hosmer-Lemeshow goodness-of-fit test, respectively. Scoring systems and the logistic regression model required the largest set of predictors, while Bayesian and k-nearest neighbour models were much more parsimonious. In testing data, all models showed acceptable discrimination capacities, however the Bayes quadratic model, using only three predictors, provided the best performance. All models showed satisfactory generalization ability: again the Bayes quadratic model exhibited the best generalization, while artificial neural networks and scoring systems gave the worst results. Finally, poor calibration was obtained when using scoring systems, k-nearest neighbour model and artificial neural networks, while Bayes (after recalibration) and logistic regression models gave adequate results. Although all the predictive models showed acceptable discrimination performance in the example considered, the Bayes and logistic regression models seemed better than the others, because they also had good generalization and calibration. The Bayes quadratic model seemed to be a convincing alternative to the much more usual Bayes linear and logistic regression models. It showed its capacity to identify a minimum core of predictors generally recognized as essential to pragmatically evaluate the risk of developing morbidity after heart surgery.

High Aerobic Capacity Mitigates Changes in the Plasma Metabolomic Profile Associated with Aging.

PubMed

Falegan, Oluyemi S; Vogel, Hans J; Hittel, Dustin S; Koch, Lauren G; Britton, Steven L; Hepple, Russ T; Shearer, Jane

2017-02-03

Advancing age is associated with declines in maximal oxygen consumption. Declines in aerobic capacity not only contribute to the aging process but also are an independent risk factor for morbidity, cardiovascular disease, and all-cause mortality. Although statistically convincing, the relationships between aerobic capacity, aging, and disease risk remain largely unresolved. To this end, we employed sensitive, system-based metabolomics approach to determine whether enhanced aerobic capacity could mitigate some of the changes seen in the plasma metabolomic profile associated with aging. Metabolomic profiles of plasma samples obtained from young (13 month) and old (26 month) rats bred for low (LCR) or high (HCR) running capacity using proton nuclear magnetic resonance spectroscopy ( 1 H NMR) were examined. Results demonstrated strong profile separation in old and low aerobic capacity rats, whereas young and high aerobic capacity rat models were less predictive. Significantly differential metabolites between the groups include taurine, acetone, valine, and trimethylamine-N-oxide among other metabolites, specifically citrate, succinate, isovalerate, and proline, were differentially increased in older HCR animals compared with their younger counterparts. When interactions between age and aerobic capacity were examined, results demonstrated that enhanced aerobic capacity could mitigate some but not all age-associated alterations in the metabolomic profile.

Modeling the eco-physiology of the purple mauve stinger, Pelagia noctiluca using Dynamic Energy Budget theory

NASA Astrophysics Data System (ADS)

Augustine, Starrlight; Rosa, Sara; Kooijman, Sebastiaan A. L. M.; Carlotti, François; Poggiale, Jean-Christophe

2014-11-01

Parameters for the standard Dynamic Energy Budget (DEB) model were estimated for the purple mauve stinger, Pelagia noctiluca, using literature data. Overall, the model predictions are in good agreement with data covering the full life-cycle. The parameter set we obtain suggests that P. noctiluca is well adapted to survive long periods of starvation since the predicted maximum reserve capacity is extremely high. Moreover we predict that the reproductive output of larger individuals is relatively insensitive to changes in food level while wet mass and length are. Furthermore, the parameters imply that even if food were scarce (ingestion levels only 14% of the maximum for a given size) an individual would still mature and be able to reproduce. We present detailed model predictions for embryo development and discuss the developmental energetics of the species such as the fact that the metabolism of ephyrae accelerates for several days after birth. Finally we explore a number of concrete testable model predictions which will help to guide future research. The application of DEB theory to the collected data allowed us to conclude that P. noctiluca combines maximizing allocation to reproduction with rather extreme capabilities to survive starvation. The combination of these properties might explain why P. noctiluca is a rapidly growing concern to fisheries and tourism.

Preserving privacy whilst maintaining robust epidemiological predictions.

PubMed

Werkman, Marleen; Tildesley, Michael J; Brooks-Pollock, Ellen; Keeling, Matt J

2016-12-01

Mathematical models are invaluable tools for quantifying potential epidemics and devising optimal control strategies in case of an outbreak. State-of-the-art models increasingly require detailed individual farm-based and sensitive data, which may not be available due to either lack of capacity for data collection or privacy concerns. However, in many situations, aggregated data are available for use. In this study, we systematically investigate the accuracy of predictions made by mathematical models initialised with varying data aggregations, using the UK 2001 Foot-and-Mouth Disease Epidemic as a case study. We consider the scenario when the only data available are aggregated into spatial grid cells, and develop a metapopulation model where individual farms in a single subpopulation are assumed to behave uniformly and transmit randomly. We also adapt this standard metapopulation model to capture heterogeneity in farm size and composition, using farm census data. Our results show that homogeneous models based on aggregated data overestimate final epidemic size but can perform well for predicting spatial spread. Recognising heterogeneity in farm sizes improves predictions of the final epidemic size, identifying risk areas, determining the likelihood of epidemic take-off and identifying the optimal control strategy. In conclusion, in cases where individual farm-based data are not available, models can still generate meaningful predictions, although care must be taken in their interpretation and use. Copyright © 2016. Published by Elsevier B.V.

When high working memory capacity is and is not beneficial for predicting nonlinear processes.

PubMed

Fischer, Helen; Holt, Daniel V

2017-04-01

Predicting the development of dynamic processes is vital in many areas of life. Previous findings are inconclusive as to whether higher working memory capacity (WMC) is always associated with using more accurate prediction strategies, or whether higher WMC can also be associated with using overly complex strategies that do not improve accuracy. In this study, participants predicted a range of systematically varied nonlinear processes based on exponential functions where prediction accuracy could or could not be enhanced using well-calibrated rules. Results indicate that higher WMC participants seem to rely more on well-calibrated strategies, leading to more accurate predictions for processes with highly nonlinear trajectories in the prediction region. Predictions of lower WMC participants, in contrast, point toward an increased use of simple exemplar-based prediction strategies, which perform just as well as more complex strategies when the prediction region is approximately linear. These results imply that with respect to predicting dynamic processes, working memory capacity limits are not generally a strength or a weakness, but that this depends on the process to be predicted.

Cognitive distance, absorptive capacity and group rationality: a simulation study.

PubMed

Curşeu, Petru Lucian; Krehel, Oleh; Evers, Joep H M; Muntean, Adrian

2014-01-01

We report the results of a simulation study in which we explore the joint effect of group absorptive capacity (as the average individual rationality of the group members) and cognitive distance (as the distance between the most rational group member and the rest of the group) on the emergence of collective rationality in groups. We start from empirical results reported in the literature on group rationality as collective group level competence and use data on real-life groups of four and five to validate a mathematical model. We then use this mathematical model to predict group level scores from a variety of possible group configurations (varying both in cognitive distance and average individual rationality). Our results show that both group competence and cognitive distance are necessary conditions for emergent group rationality. Group configurations, in which the groups become more rational than the most rational group member, are groups scoring low on cognitive distance and scoring high on absorptive capacity.

Cognitive Distance, Absorptive Capacity and Group Rationality: A Simulation Study

PubMed Central

Curşeu, Petru Lucian; Krehel, Oleh; Evers, Joep H. M.; Muntean, Adrian

2014-01-01

We report the results of a simulation study in which we explore the joint effect of group absorptive capacity (as the average individual rationality of the group members) and cognitive distance (as the distance between the most rational group member and the rest of the group) on the emergence of collective rationality in groups. We start from empirical results reported in the literature on group rationality as collective group level competence and use data on real-life groups of four and five to validate a mathematical model. We then use this mathematical model to predict group level scores from a variety of possible group configurations (varying both in cognitive distance and average individual rationality). Our results show that both group competence and cognitive distance are necessary conditions for emergent group rationality. Group configurations, in which the groups become more rational than the most rational group member, are groups scoring low on cognitive distance and scoring high on absorptive capacity. PMID:25314132

Highly spatially- and seasonally-resolved predictive contamination maps for persistent organic pollutants: development and validation.

PubMed

Ballabio, Cristiano; Guazzoni, Niccoló; Comolli, Roberto; Tremolada, Paolo

2013-08-01

A reliable spatial assessment of the POPs contamination in soils is essential for burden studies and flux evaluations. Soil characteristics and properties vary enormously even within small spatial scale and over time; therefore soil capacity of accumulating POPs varies greatly. In order to include this very high spatial and temporal variability, models can be used for assessing soil accumulation capacity in a specific time and space and, from it, the spatial distribution and temporal trends of POPs concentrations. In this work, predictive contamination maps of the accumulation capacity of soils were developed at a space resolution of 1×1m with a time frame of one day, in a study area located in the central Alps. Physical algorithms for temperature and organic carbon estimation along the soil profile and across the year were fitted to estimate the horizontal, vertical and seasonal distribution of the contamination potential for PCBs in soil (Ksa maps). The resulting maps were cross-validated with an independent set of PCB contamination data, showing very good agreement (e.g. for CB-153, R(2)=0.80, p-value≤2.2·10(-06)). Slopes of the regression between predicted Ksa and experimental concentrations were used to map the soil contamination for the whole area, taking into account soil characteristics and temperature conditions. These maps offer the opportunity to evaluate burden (concentration maps) and fluxes (emission maps) with highly resolved temporal and spatial detail. In addition, in order to explain the observed low autumn PCB concentrations in soil related to the high Ksa values of this period, a dynamic model of seasonal variation of soil concentrations was developed basing on rate parameters fitted on measured concentrations. The model was able to describe, at least partially, the observed different behavior between the quite rapid discharge phase in summer and the slow recharge phase in autumn. Copyright © 2013 Elsevier B.V. All rights reserved.

A Critical Assessment of Combined Ligand-based and Structure-based Approaches to hERG Channel Blocker Modeling

PubMed Central

Du-Cuny, Lei; Chen, Lu; Zhang, Shuxing

2014-01-01

Blockade of hERG channel prolongs the duration of the cardiac action potential and is a common reason for drug failure in preclinical safety trials. Therefore, it is of great importance to develop robust in silico tools to predict potential hERG blockers in the early stages of drug discovery and development. Herein we described comprehensive approaches to assess the discrimination of hERG-active and -inactive compounds by combining QSAR modeling, pharmacophore analysis, and molecular docking. Our consensus models demonstrated high predictive capacity and improved enrichment, and they could correctly classify 91.8% of 147 hERG blockers from 351 inactives. To further enhance our modeling effort, hERG homology models were constructed and molecular docking studies were conducted, resulting in high correlations (R2=0.81) between predicted and experimental binding affinities. We expect our unique models can be applied to efficient screening for hERG blockades, and our extensive understanding of the hERG-inhibitor interactions will facilitate the rational design of drugs devoid of hERG channel activity and hence with reduced cardiac toxicities. PMID:21902220

Predicting mutant selection in competition experiments with ciprofloxacin-exposed Escherichia coli.

PubMed

Khan, David D; Lagerbäck, Pernilla; Malmberg, Christer; Kristoffersson, Anders N; Wistrand-Yuen, Erik; Sha, Cao; Cars, Otto; Andersson, Dan I; Hughes, Diarmaid; Nielsen, Elisabet I; Friberg, Lena E

2018-03-01

Predicting competition between antibiotic-susceptible wild-type (WT) and less susceptible mutant (MT) bacteria is valuable for understanding how drug concentrations influence the emergence of resistance. Pharmacokinetic/pharmacodynamic (PK/PD) models predicting the rate and extent of takeover of resistant bacteria during different antibiotic pressures can thus be a valuable tool in improving treatment regimens. The aim of this study was to evaluate a previously developed mechanism-based PK/PD model for its ability to predict in vitro mixed-population experiments with competition between Escherichia coli (E. coli) WT and three well-defined E. coli resistant MTs when exposed to ciprofloxacin. Model predictions for each bacterial strain and ciprofloxacin concentration were made for in vitro static and dynamic time-kill experiments measuring CFU (colony forming units)/mL up to 24 h with concentrations close to or below the minimum inhibitory concentration (MIC), as well as for serial passage experiments with concentrations well below the MIC measuring ratios between the two strains with flow cytometry. The model was found to reasonably well predict the initial bacterial growth and killing of most static and dynamic time-kill competition experiments without need for parameter re-estimation. With parameter re-estimation of growth rates, an adequate fit was also obtained for the 6-day serial passage competition experiments. No bacterial interaction in growth was observed. This study demonstrates the predictive capacity of a PK/PD model and further supports the application of PK/PD modelling for prediction of bacterial kill in different settings, including resistance selection. Copyright © 2017 Elsevier B.V. and International Society of Chemotherapy. All rights reserved.

A unified computational model of the development of object unity, object permanence, and occluded object trajectory perception.

PubMed

Franz, A; Triesch, J

2010-12-01

The perception of the unity of objects, their permanence when out of sight, and the ability to perceive continuous object trajectories even during occlusion belong to the first and most important capacities that infants have to acquire. Despite much research a unified model of the development of these abilities is still missing. Here we make an attempt to provide such a unified model. We present a recurrent artificial neural network that learns to predict the motion of stimuli occluding each other and that develops representations of occluded object parts. It represents completely occluded, moving objects for several time steps and successfully predicts their reappearance after occlusion. This framework allows us to account for a broad range of experimental data. Specifically, the model explains how the perception of object unity develops, the role of the width of the occluders, and it also accounts for differences between data for moving and stationary stimuli. We demonstrate that these abilities can be acquired by learning to predict the sensory input. The model makes specific predictions and provides a unifying framework that has the potential to be extended to other visual event categories. Copyright © 2010 Elsevier Inc. All rights reserved.

Ventral striatal prediction error signaling is associated with dopamine synthesis capacity and fluid intelligence

PubMed Central

Schlagenhauf, Florian; Rapp, Michael A.; Huys, Quentin J. M.; Beck, Anne; Wüstenberg, Torsten; Deserno, Lorenz; Buchholz, Hans-Georg; Kalbitzer, Jan; Buchert, Ralph; Kienast, Thorsten; Cumming, Paul; Plotkin, Michail; Kumakura, Yoshitaka; Grace, Anthony A.; Dolan, Raymond J.; Heinz, Andreas

2013-01-01

Fluid intelligence represents the capacity for flexible problem solving and rapid behavioral adaptation. Rewards drive flexible behavioral adaptation, in part via a teaching signal expressed as reward prediction errors in the ventral striatum, which has been associated with phasic dopamine release in animal studies. We examined a sample of 28 healthy male adults using multimodal imaging and biological parametric mapping with 1) functional magnetic resonance imaging during a reversal learning task and 2) in a subsample of 17 subjects also with positron emission tomography using 6-[18F]fluoro-L-DOPA to assess dopamine synthesis capacity. Fluid intelligence was measured using a battery of nine standard neuropsychological tests. Ventral striatal BOLD correlates of reward prediction errors were positively correlated with fluid intelligence and, in the right ventral striatum, also inversely correlated with dopamine synthesis capacity (FDOPA Kinapp). When exploring aspects of fluid intelligence, we observed that prediction error signaling correlates with complex attention and reasoning. These findings indicate that individual differences in the capacity for flexible problem solving may be driven by ventral striatal activation during reward-related learning, which in turn proved to be inversely associated with ventral striatal dopamine synthesis capacity. PMID:22344813

Characterization of Thermo-Physical Properties of EVA/ATH: Application to Gasification Experiments and Pyrolysis Modeling.

PubMed

Girardin, Bertrand; Fontaine, Gaëlle; Duquesne, Sophie; Försth, Michael; Bourbigot, Serge

2015-11-20

The pyrolysis of solid polymeric materials is a complex process that involves both chemical and physical phenomena such as phase transitions, chemical reactions, heat transfer, and mass transport of gaseous components. For modeling purposes, it is important to characterize and to quantify the properties driving those phenomena, especially in the case of flame-retarded materials. In this study, protocols have been developed to characterize the thermal conductivity and the heat capacity of an ethylene-vinyl acetate copolymer (EVA) flame retarded with aluminum tri-hydroxide (ATH). These properties were measured for the various species identified across the decomposition of the material. Namely, the thermal conductivity was found to decrease as a function of temperature before decomposition whereas the ceramic residue obtained after the decomposition at the steady state exhibits a thermal conductivity as low as 0.2 W/m/K. The heat capacity of the material was also investigated using both isothermal modulated Differential Scanning Calorimetry (DSC) and the standard method (ASTM E1269). It was shown that the final residue exhibits a similar behavior to alumina, which is consistent with the decomposition pathway of EVA/ATH. Besides, the two experimental approaches give similar results over the whole range of temperatures. Moreover, the optical properties before decomposition and the heat capacity of the decomposition gases were also analyzed. Those properties were then used as input data for a pyrolysis model in order to predict gasification experiments. Mass losses of gasification experiments were well predicted, thus validating the characterization of the thermo-physical properties of the material.

Characterization of Thermo-Physical Properties of EVA/ATH: Application to Gasification Experiments and Pyrolysis Modeling

PubMed Central

Girardin, Bertrand; Fontaine, Gaëlle; Duquesne, Sophie; Försth, Michael; Bourbigot, Serge

2015-01-01

The pyrolysis of solid polymeric materials is a complex process that involves both chemical and physical phenomena such as phase transitions, chemical reactions, heat transfer, and mass transport of gaseous components. For modeling purposes, it is important to characterize and to quantify the properties driving those phenomena, especially in the case of flame-retarded materials. In this study, protocols have been developed to characterize the thermal conductivity and the heat capacity of an ethylene-vinyl acetate copolymer (EVA) flame retarded with aluminum tri-hydroxide (ATH). These properties were measured for the various species identified across the decomposition of the material. Namely, the thermal conductivity was found to decrease as a function of temperature before decomposition whereas the ceramic residue obtained after the decomposition at the steady state exhibits a thermal conductivity as low as 0.2 W/m/K. The heat capacity of the material was also investigated using both isothermal modulated Differential Scanning Calorimetry (DSC) and the standard method (ASTM E1269). It was shown that the final residue exhibits a similar behavior to alumina, which is consistent with the decomposition pathway of EVA/ATH. Besides, the two experimental approaches give similar results over the whole range of temperatures. Moreover, the optical properties before decomposition and the heat capacity of the decomposition gases were also analyzed. Those properties were then used as input data for a pyrolysis model in order to predict gasification experiments. Mass losses of gasification experiments were well predicted, thus validating the characterization of the thermo-physical properties of the material. PMID:28793682

Management of marine cage aquaculture. Environmental carrying capacity method based on dry feed conversion rate.

PubMed

Cai, Huiwen; Sun, Yinglan

2007-11-01

Marine cage aquaculture produces a large amount of waste that is released directly into the environment. To effectively manage the mariculture environment, it is important to determine the carrying capacity of an aquaculture area. In many Asian countries trash fish is dominantly used in marine cage aquaculture, which contains more water than pellet feed. The traditional nutrient loading analysis is for pellet feed not for trash fish feed. So, a more critical analysis is necessary in trash fish feed culturing areas. Corresponding to FCR (feed conversion rate), dry feed conversion rate (DFCR) was used to analyze the nutrient loadings from marine cage aquaculture where trash fish is used. Based on the hydrodynamic model and the mass transport model in Xiangshan Harbor, the relationship between the water quality and the waste discharged from cage aquaculture has been determined. The environmental carrying capacity of the aquaculture sea area was calculated by applying the models noted above. Nitrogen and phosphorus are the water quality parameters considered in this study. The simulated results show that the maximum nitrogen and phosphorus concentrations were 0.216 mg/L and 0.039 mg/L, respectively. In most of the sea area, the nutrient concentrations were higher than the water quality standard. The calculated environmental carrying capacity of nitrogen and phosphorus in Xiangshan Harbor were 1,107.37 t/yr and 134.35 t/yr, respectively. The waste generated from cage culturing in 2000 has already exceeded the environmental carrying capacity. Unconsumed feed has been identified as the most important origin of all pollutants in cage culturing systems. It suggests the importance of increasing the feed utilization and improving the feed composition on the basis of nutrient requirement. For the sustainable development of the aquaculture industry, it is an effective management measure to keep the stocking density and pollution loadings below the environmental carrying capacity. The DFCR-based nutrient loadings analysis indicates, in trash fish feed culturing areas, that it is more critical and has been proved to be a valuable loading calculation method. The modeling approach for Xiangshan Harbor presented in this paper is a cost-effective method for assessing the environmental impact and determining the capacity. Carrying capacity information can give scientific suggestions for the sustainable management of aquaculture environments. It has been proved that numerical models were convenient tools to predict the environmental carrying capacity. The development of models coupled with dynamic and aquaculture ecology is a requirement of further research. Such models can also be useful in monitoring the ecological impacts caused by mariculture activities.

Failure of self-consistency in the discrete resource model of visual working memory.

PubMed

Bays, Paul M

2018-06-03

The discrete resource model of working memory proposes that each individual has a fixed upper limit on the number of items they can store at one time, due to division of memory into a few independent "slots". According to this model, responses on short-term memory tasks consist of a mixture of noisy recall (when the tested item is in memory) and random guessing (when the item is not in memory). This provides two opportunities to estimate capacity for each observer: first, based on their frequency of random guesses, and second, based on the set size at which the variability of stored items reaches a plateau. The discrete resource model makes the simple prediction that these two estimates will coincide. Data from eight published visual working memory experiments provide strong evidence against such a correspondence. These results present a challenge for discrete models of working memory that impose a fixed capacity limit. Copyright © 2018 The Author. Published by Elsevier Inc. All rights reserved.

Product unit neural network models for predicting the growth limits of Listeria monocytogenes.

PubMed

Valero, A; Hervás, C; García-Gimeno, R M; Zurera, G

2007-08-01

A new approach to predict the growth/no growth interface of Listeria monocytogenes as a function of storage temperature, pH, citric acid (CA) and ascorbic acid (AA) is presented. A linear logistic regression procedure was performed and a non-linear model was obtained by adding new variables by means of a Neural Network model based on Product Units (PUNN). The classification efficiency of the training data set and the generalization data of the new Logistic Regression PUNN model (LRPU) were compared with Linear Logistic Regression (LLR) and Polynomial Logistic Regression (PLR) models. 92% of the total cases from the LRPU model were correctly classified, an improvement on the percentage obtained using the PLR model (90%) and significantly higher than the results obtained with the LLR model, 80%. On the other hand predictions of LRPU were closer to data observed which permits to design proper formulations in minimally processed foods. This novel methodology can be applied to predictive microbiology for describing growth/no growth interface of food-borne microorganisms such as L. monocytogenes. The optimal balance is trying to find models with an acceptable interpretation capacity and with good ability to fit the data on the boundaries of variable range. The results obtained conclude that these kinds of models might well be very a valuable tool for mathematical modeling.

A Field Study on Simulation of CO 2 Injection and ECBM Production and Prediction of CO 2 Storage Capacity in Unmineable Coal Seam

DOE PAGES

He, Qin; Mohaghegh, Shahab D.; Gholami, Vida

2013-01-01

CO 2 sequestration into a coal seam project was studied and a numerical model was developed in this paper to simulate the primary and secondary coal bed methane production (CBM/ECBM) and carbon dioxide (CO 2 ) injection. The key geological and reservoir parameters, which are germane to driving enhanced coal bed methane (ECBM) and CO 2 sequestration processes, including cleat permeability, cleat porosity, CH 4 adsorption time, CO 2 adsorption time, CH 4 Langmuir isotherm, CO 2 Langmuir isotherm, and Palmer and Mansoori parameters, have been analyzed within a reasonable range. The model simulation results showed good matches for bothmore » CBM/ECBM production and CO 2 injection compared with the field data. The history-matched model was used to estimate the total CO 2 sequestration capacity in the field. The model forecast showed that the total CO 2 injection capacity in the coal seam could be 22,817 tons, which is in agreement with the initial estimations based on the Langmuir isotherm experiment. Total CO 2 injected in the first three years was 2,600 tons, which according to the model has increased methane recovery (due to ECBM) by 6,700 scf/d.« less

Watchful Waiting for Cases of Pediatric Otitis Media: Modeling Parental Response to Physician Advice.

PubMed

MacGeorge, Erina L; Smith, Rachel A; Caldes, Emily P; Hackman, Nicole M

2016-08-01

Watchful waiting (WW) can reduce unnecessary antibiotic use in the treatment of pediatric otitis media (ear infection), but its utility is impaired by underutilization and noncompliance. Guided by advice response theory, the current study proposes advantage and capacity as factors that predict how caregivers evaluate and respond affectively to WW. Parents (N = 373) of at least 1 child age 5 years or younger completed questionnaires that assessed responses to hypothetical WW advice for their youngest child. Perceptions of advantage from WW and the capacity to monitor and manage symptoms predicted advice quality, physician trust, and future compliance both directly and indirectly through negative affect. The findings suggest the elaboration of advice response theory to include more aspects of advice content evaluation (e.g., advantage) and the influence of negative affect. The study also provides practical guidance for physicians seeking to improve caregiver reception of WW advice.

An MRI-based classification scheme to predict passive access of 5 to 50-nm large nanoparticles to tumors

PubMed Central

Karageorgis, Anastassia; Dufort, Sandrine; Sancey, Lucie; Henry, Maxime; Hirsjärvi, Samuli; Passirani, Catherine; Benoit, Jean-Pierre; Gravier, Julien; Texier, Isabelle; Montigon, Olivier; Benmerad, Mériem; Siroux, Valérie; Barbier, Emmanuel L.; Coll, Jean-Luc

2016-01-01

Nanoparticles are useful tools in oncology because of their capacity to passively accumulate in tumors in particular via the enhanced permeability and retention (EPR) effect. However, the importance and reliability of this effect remains controversial and quite often unpredictable. In this preclinical study, we used optical imaging to detect the accumulation of three types of fluorescent nanoparticles in eight different subcutaneous and orthotopic tumor models, and dynamic contrast-enhanced and vessel size index Magnetic Resonance Imaging (MRI) to measure the functional parameters of these tumors. The results demonstrate that the permeability and blood volume fraction determined by MRI are useful parameters for predicting the capacity of a tumor to accumulate nanoparticles. Translated to a clinical situation, this strategy could help anticipate the EPR effect of a particular tumor and thus its accessibility to nanomedicines. PMID:26892874

An MRI-based classification scheme to predict passive access of 5 to 50-nm large nanoparticles to tumors.

PubMed

Karageorgis, Anastassia; Dufort, Sandrine; Sancey, Lucie; Henry, Maxime; Hirsjärvi, Samuli; Passirani, Catherine; Benoit, Jean-Pierre; Gravier, Julien; Texier, Isabelle; Montigon, Olivier; Benmerad, Mériem; Siroux, Valérie; Barbier, Emmanuel L; Coll, Jean-Luc

2016-02-19

Nanoparticles are useful tools in oncology because of their capacity to passively accumulate in tumors in particular via the enhanced permeability and retention (EPR) effect. However, the importance and reliability of this effect remains controversial and quite often unpredictable. In this preclinical study, we used optical imaging to detect the accumulation of three types of fluorescent nanoparticles in eight different subcutaneous and orthotopic tumor models, and dynamic contrast-enhanced and vessel size index Magnetic Resonance Imaging (MRI) to measure the functional parameters of these tumors. The results demonstrate that the permeability and blood volume fraction determined by MRI are useful parameters for predicting the capacity of a tumor to accumulate nanoparticles. Translated to a clinical situation, this strategy could help anticipate the EPR effect of a particular tumor and thus its accessibility to nanomedicines.

Soil carbon sequestration potential of permanent pasture and continuous cropping soils in New Zealand.

PubMed

McNally, Sam R; Beare, Mike H; Curtin, Denis; Meenken, Esther D; Kelliher, Francis M; Calvelo Pereira, Roberto; Shen, Qinhua; Baldock, Jeff

2017-11-01

Understanding soil organic carbon (SOC) sequestration is important to develop strategies to increase the SOC stock and, thereby, offset some of the increases in atmospheric carbon dioxide. Although the capacity of soils to store SOC in a stable form is commonly attributed to the fine (clay + fine silt) fraction, the properties of the fine fraction that determine the SOC stabilization capacity are poorly known. The aim of this study was to develop an improved model to estimate the SOC stabilization capacity of Allophanic (Andisols) and non-Allophanic topsoils (0-15 cm) and, as a case study, to apply the model to predict the sequestration potential of pastoral soils across New Zealand. A quantile (90th) regression model, based on the specific surface area and extractable aluminium (pyrophosphate) content of soils, provided the best prediction of the upper limit of fine fraction carbon (FFC) (i.e. the stabilization capacity), but with different coefficients for Allophanic and non-Allophanic soils. The carbon (C) saturation deficit was estimated as the difference between the stabilization capacity of individual soils and their current C concentration. For long-term pastures, the mean saturation deficit of Allophanic soils (20.3 mg C g -1 ) was greater than that of non-Allophanic soils (16.3 mg C g -1 ). The saturation deficit of cropped soils was 1.14-1.89 times that of pasture soils. The sequestration potential of pasture soils ranged from 10 t C ha -1 (Ultic soils) to 42 t C ha -1 (Melanic soils). Although meeting the estimated national soil C sequestration potential (124 Mt C) is unrealistic, improved management practices targeted to those soils with the greatest sequestration potential could contribute significantly to off-setting New Zealand's greenhouse gas emissions. As the first national-scale estimate of SOC sequestration potential that encompasses both Allophanic and non-Allophanic soils, this serves as an informative case study for the international community. © 2017 John Wiley & Sons Ltd.

Breast density and parenchymal texture measures as potential risk factors for estrogen-receptor positive breast cancer

NASA Astrophysics Data System (ADS)

Keller, Brad M.; Chen, Jinbo; Conant, Emily F.; Kontos, Despina

2014-03-01

Accurate assessment of a woman's risk to develop specific subtypes of breast cancer is critical for appropriate utilization of chemopreventative measures, such as with tamoxifen in preventing estrogen-receptor positive breast cancer. In this context, we investigate quantitative measures of breast density and parenchymal texture, measures of glandular tissue content and tissue structure, as risk factors for estrogen-receptor positive (ER+) breast cancer. Mediolateral oblique (MLO) view digital mammograms of the contralateral breast from 106 women with unilateral invasive breast cancer were retrospectively analyzed. Breast density and parenchymal texture were analyzed via fully-automated software. Logistic regression with feature selection and was performed to predict ER+ versus ER- cancer status. A combined model considering all imaging measures extracted was compared to baseline models consisting of density-alone and texture-alone features. Area under the curve (AUC) of the receiver operating characteristic (ROC) and Delong's test were used to compare the models' discriminatory capacity for receptor status. The density-alone model had a discriminatory capacity of 0.62 AUC (p=0.05). The texture-alone model had a higher discriminatory capacity of 0.70 AUC (p=0.001), which was not significantly different compared to the density-alone model (p=0.37). In contrast the combined density-texture logistic regression model had a discriminatory capacity of 0.82 AUC (p<0.001), which was statistically significantly higher than both the density-alone (p<0.001) and texture-alone regression models (p=0.04). The combination of breast density and texture measures may have the potential to identify women specifically at risk for estrogen-receptor positive breast cancer and could be useful in triaging women into appropriate risk-reduction strategies.

Idiopathic Pulmonary Fibrosis: Data-driven Textural Analysis of Extent of Fibrosis at Baseline and 15-Month Follow-up.

PubMed

Humphries, Stephen M; Yagihashi, Kunihiro; Huckleberry, Jason; Rho, Byung-Hak; Schroeder, Joyce D; Strand, Matthew; Schwarz, Marvin I; Flaherty, Kevin R; Kazerooni, Ella A; van Beek, Edwin J R; Lynch, David A

2017-10-01

Purpose To evaluate associations between pulmonary function and both quantitative analysis and visual assessment of thin-section computed tomography (CT) images at baseline and at 15-month follow-up in subjects with idiopathic pulmonary fibrosis (IPF). Materials and Methods This retrospective analysis of preexisting anonymized data, collected prospectively between 2007 and 2013 in a HIPAA-compliant study, was exempt from additional institutional review board approval. The extent of lung fibrosis at baseline inspiratory chest CT in 280 subjects enrolled in the IPF Network was evaluated. Visual analysis was performed by using a semiquantitative scoring system. Computer-based quantitative analysis included CT histogram-based measurements and a data-driven textural analysis (DTA). Follow-up CT images in 72 of these subjects were also analyzed. Univariate comparisons were performed by using Spearman rank correlation. Multivariate and longitudinal analyses were performed by using a linear mixed model approach, in which models were compared by using asymptotic χ 2 tests. Results At baseline, all CT-derived measures showed moderate significant correlation (P < .001) with pulmonary function. At follow-up CT, changes in DTA scores showed significant correlation with changes in both forced vital capacity percentage predicted (ρ = -0.41, P < .001) and diffusing capacity for carbon monoxide percentage predicted (ρ = -0.40, P < .001). Asymptotic χ 2 tests showed that inclusion of DTA score significantly improved fit of both baseline and longitudinal linear mixed models in the prediction of pulmonary function (P < .001 for both). Conclusion When compared with semiquantitative visual assessment and CT histogram-based measurements, DTA score provides additional information that can be used to predict diminished function. Automatic quantification of lung fibrosis at CT yields an index of severity that correlates with visual assessment and functional change in subjects with IPF. © RSNA, 2017.

Models for Train Passenger Forecasting of Java and Sumatra

NASA Astrophysics Data System (ADS)

Sartono

2017-04-01

People tend to take public transportation to avoid high traffic, especially in Java. In Jakarta, the number of railway passengers is over than the capacity of the train at peak time. This is an opportunity as well as a challenge. If it is managed well then the company can get high profit. Otherwise, it may lead to disaster. This article discusses models for the train passengers, hence, finding the reasonable models to make a prediction overtimes. The Box-Jenkins method is occupied to develop a basic model. Then, this model is compared to models obtained using exponential smoothing method and regression method. The result shows that Holt-Winters model is better to predict for one-month, three-month, and six-month ahead for the passenger in Java. In addition, SARIMA(1,1,0)(2,0,0) is more accurate for nine-month and twelve-month oversee. On the other hand, for Sumatra passenger forecasting, SARIMA(1,1,1)(0,0,2) gives a better approximation for one-month ahead, and ARIMA model is best for three-month ahead prediction. The rest, Trend Seasonal and Liner Model has the least of RMSE to forecast for six-month, nine-month, and twelve-month ahead.

Nonstarch polysaccharides in wheat flour wire-cut cookie making.

PubMed

Guttieri, Mary J; Souza, Edward J; Sneller, Clay

2008-11-26

Nonstarch polysaccharides in wheat flour have significant capacity to affect the processing quality of wheat flour dough and the finished quality of wheat flour products. Most research has focused on the effects of arabinoxylans (AX) in bread making. This study found that water-extractable AX and arabinogalactan peptides can predict variation in pastry wheat quality as captured by the wire-cut cookie model system. The sum of water-extractable AX plus arabinogalactan was highly predictive of cookie spread factor. The combination of cookie spread factor and the ratio of water-extractable arabinose to xylose predicted peak force of the three-point bend test of cookie texture.

Ability of regional hospitals to meet projected avian flu pandemic surge capacity requirements.

PubMed

Ten Eyck, Raymond P

2008-01-01

Hospital surge capacity is a crucial part of community disaster preparedness planning, which focuses on the requirements for additional beds, equipment, personnel, and special capabilities. The scope and urgency of these requirements must be balanced with a practical approach addressing cost and space concerns. Renewed concerns for infectious disease threats, particularly from a potential avian flu pandemic perspective, have emphasized the need to be prepared for a prolonged surge that could last six to eight weeks. The surge capacity that realistically would be generated by the cumulative Greater Dayton Area Hospital Association (GDAHA) plan is sufficient to meet the demands of an avian influenza pandemic as predicted by the [US] Centers for Disease Control and Prevention (CDC) models. Using a standardized data form, surge response plans for each hospital in the GDAHA were assessed. The cumulative results were compared to the demand projected for an avian influenza pandemic using the CDC's FluAid and FluSurge models. The cumulative GDAHA capacity is sufficient to meet the projected demand for bed space, intensive care unit beds, ventilators, morgue space, and initial personal protective equipment (PPE) use. There is a shortage of negative pressure rooms, some basic equipment, and neuraminidase inhibitors. Many facilities lack a complete set of written surge policies, including screening plans to segregate contaminated patients and staff prior to entering the hospital. Few hospitals have agreements with nursing homes or home healthcare agencies to provide care for patients discharged in order to clear surge beds. If some of the assumptions in the CDC's models are changed to match the morbidity and mortality rates reported from the 1918 pandemic, the surge capacity of GDAHA facilities would not meet the projected demand. The GDAHA hospitals should test their regional distributors' ability to resupply PPE for multiple facilities simultaneously. Facilities should retrofit current air exchange systems to increase the number of potential negative pressure rooms and include such designs in all future construction. Neuraminidase inhibitor supplies should be increased to provide treatment for healthcare workers exposed in the course of their duties. Each hospital should have a complete set of policies to address the special considerations for a prolonged surge. Additional capacity is required to meet the predicted demands of a threat similar to the 1918 pandemic.

Neural Network Prediction of ICU Length of Stay Following Cardiac Surgery Based on Pre-Incision Variables

PubMed Central

Pothula, Venu M.; Yuan, Stanley C.; Maerz, David A.; Montes, Lucresia; Oleszkiewicz, Stephen M.; Yusupov, Albert; Perline, Richard

2015-01-01

Background Advanced predictive analytical techniques are being increasingly applied to clinical risk assessment. This study compared a neural network model to several other models in predicting the length of stay (LOS) in the cardiac surgical intensive care unit (ICU) based on pre-incision patient characteristics. Methods Thirty six variables collected from 185 cardiac surgical patients were analyzed for contribution to ICU LOS. The Automatic Linear Modeling (ALM) module of IBM-SPSS software identified 8 factors with statistically significant associations with ICU LOS; these factors were also analyzed with the Artificial Neural Network (ANN) module of the same software. The weighted contributions of each factor (“trained” data) were then applied to data for a “new” patient to predict ICU LOS for that individual. Results Factors identified in the ALM model were: use of an intra-aortic balloon pump; O2 delivery index; age; use of positive cardiac inotropic agents; hematocrit; serum creatinine ≥ 1.3 mg/deciliter; gender; arterial pCO2. The r2 value for ALM prediction of ICU LOS in the initial (training) model was 0.356, p <0.0001. Cross validation in prediction of a “new” patient yielded r2 = 0.200, p <0.0001. The same 8 factors analyzed with ANN yielded a training prediction r2 of 0.535 (p <0.0001) and a cross validation prediction r2 of 0.410, p <0.0001. Two additional predictive algorithms were studied, but they had lower prediction accuracies. Our validated neural network model identified the upper quartile of ICU LOS with an odds ratio of 9.8(p <0.0001). Conclusions ANN demonstrated a 2-fold greater accuracy than ALM in prediction of observed ICU LOS. This greater accuracy would be presumed to result from the capacity of ANN to capture nonlinear effects and higher order interactions. Predictive modeling may be of value in early anticipation of risks of post-operative morbidity and utilization of ICU facilities. PMID:26710254

Study of stiffness and bearing capacity degradation of reinforced concrete beams under constant-amplitude fatigue

PubMed Central

Zhou, Jianting; Yan, Lei

2018-01-01

For a reinforced concrete beam subjected to fatigue loads, the structural stiffness and bearing capacity will gradually undergo irreversible degeneration, leading to damage. Moreover, there is an inherent relationship between the stiffness and bearing capacity degradation and fatigue damage. In this study, a series of fatigue tests are performed to examine the degradation law of the stiffness and bearing capacity. The results pertaining to the stiffness show that the stiffness degradation of a reinforced concrete beam exhibits a very clear monotonic decreasing "S" curve, i.e., the stiffness of the beam decreases significantly at the start of the fatigue loading, it undergoes a linear decline phase in the middle for a long loading period, and before the failure, the bearing capacity decreases drastically again. The relationship between the residual stiffness and residual bearing capacity is determined based on the assumption that the residual stiffness and residual bearing capacity depend on the same damage state, and then, the bearing capacity degradation model of the reinforced concrete beam is established based on the fatigue stiffness. Through the established model and under the premise of the known residual stiffness degradation law, the degradation law of the bearing capacity is determined by using at least one residual bearing capacity test data, for which the parameters of the stiffness degradation function are considered as material constants. The results of the bearing capacity show that the bearing capacity degradation of the reinforced concrete beam also exhibits a very clear monotonic decreasing "S" curve, which is consistent with the stiffness degradation process and in good agreement with the experiment. In this study, the stiffness and bearing capacity degradation expressions are used to quantitatively describe their occurrence in reinforced concrete beams. In particular, the expression of the bearing capacity degradation can mitigate numerous destructive tests and save cost. The stiffness and bearing capacity degradation expressions for a reinforced concrete beam can be used to predict the deformation and bearing capacity of a structure during the service process and determine the structural fatigue damage and degree of degradation. PMID:29522572

Study of stiffness and bearing capacity degradation of reinforced concrete beams under constant-amplitude fatigue.

PubMed

Liu, Fangping; Zhou, Jianting; Yan, Lei

2018-01-01

For a reinforced concrete beam subjected to fatigue loads, the structural stiffness and bearing capacity will gradually undergo irreversible degeneration, leading to damage. Moreover, there is an inherent relationship between the stiffness and bearing capacity degradation and fatigue damage. In this study, a series of fatigue tests are performed to examine the degradation law of the stiffness and bearing capacity. The results pertaining to the stiffness show that the stiffness degradation of a reinforced concrete beam exhibits a very clear monotonic decreasing "S" curve, i.e., the stiffness of the beam decreases significantly at the start of the fatigue loading, it undergoes a linear decline phase in the middle for a long loading period, and before the failure, the bearing capacity decreases drastically again. The relationship between the residual stiffness and residual bearing capacity is determined based on the assumption that the residual stiffness and residual bearing capacity depend on the same damage state, and then, the bearing capacity degradation model of the reinforced concrete beam is established based on the fatigue stiffness. Through the established model and under the premise of the known residual stiffness degradation law, the degradation law of the bearing capacity is determined by using at least one residual bearing capacity test data, for which the parameters of the stiffness degradation function are considered as material constants. The results of the bearing capacity show that the bearing capacity degradation of the reinforced concrete beam also exhibits a very clear monotonic decreasing "S" curve, which is consistent with the stiffness degradation process and in good agreement with the experiment. In this study, the stiffness and bearing capacity degradation expressions are used to quantitatively describe their occurrence in reinforced concrete beams. In particular, the expression of the bearing capacity degradation can mitigate numerous destructive tests and save cost. The stiffness and bearing capacity degradation expressions for a reinforced concrete beam can be used to predict the deformation and bearing capacity of a structure during the service process and determine the structural fatigue damage and degree of degradation.

Neuropsychological Correlates of Capacity Determinations in Alzheimer’s Disease: Implications for Assessment

PubMed Central

Palmer, Barton W.; Ryan, Kerry A.; Kim, H. Myra; Karlawish, Jason H.; Appelbaum, Paul S.; Kim, Scott Y. H.

2011-01-01

Objectives To explore the neuropsychological correlates of the capacity to consent to research and to appoint a research proxy among persons with Alzheimer’s disease. Design, Setting, and Participants Interview study of 77 persons with Alzheimer’s disease recruited through an Alzheimer’s disease research center and a memory disorder clinic. Measurements The capacity to consent to two research scenarios (a drug randomized clinical trial and a neurosurgical clinical trial) and the capacity to appoint a research proxy were determined by five experienced consultation psychiatrists who rendered categorical judgments based on videotaped interviews of the MacArthur Competence Assessment Tool-Clinical Research (MacCAT-CR) and the Capacity to Appoint a Proxy Assessment (CAPA). Mattis Dementia Rating Scale-2 (DRS-2) was used to assess neuropsychological functioning. Results The capacity to appoint a proxy and to consent to the drug randomized clinical trial, as determined by a majority or greater opinion of the 5-psychiatrist panel, were predicted by Conceptualization and Initiation/Perseveration subscales whereas the capacity to consent to a neurosurgical randomized clinical trial was predicted by the Memory subscale. Furthermore, the more lenient individual psychiatrists’ judgments were predicted by the Conceptualization subscale whereas the stricter psychiatrists’ judgments were predicted by the Memory subscale. Conclusions How experienced psychiatrists view Alzheimer’s patients’ capacity for consenting to research and for appointing a proxy may be related to the patients’ conceptualization and memory functioning. More explicit and standardized guidance on the role of short term memory in capacity determinations may be useful. PMID:23498384

Tunable gas adsorption in graphene oxide framework

NASA Astrophysics Data System (ADS)

Razmkhah, Mohammad; Moosavi, Fatemeh; Taghi Hamed Mosavian, Mohammad; Ahmadpour, Ali

2018-06-01

Effect of length of linker inter-space was studied on the adsorption capacity of CO2 by graphene oxide framework (GOF). Effect of linker inter-space of 14, 11, and 8 Å was studied here. The linker inter-space of 11 Å showed the highest CO2 adsorption capacity. A dual-site Langmuir model was observed for adsorption of CO2 and CH4 into the GOF. According to radial distribution function (RDF), facial and central atoms of linker are the dual-site predicted by Langmuir model. Two distinguishable sites of adsorption and parallel orientation of CO2 are the main reasons of high adsorption capacity in 11 Å linker inter-space. Gas-adsorbent affinity obtains the orientation of CO2 near the linker. The affinity in the 11 Å linker inter-space is the highest. Thus, it forces the CO2 to lay parallel and orient more localized than the other GOFs. In addition, CH4 resulted higher working capacity than CO2 in 14 Å. This occurs because of the change in gas-adsorbent affinity by changing pressure. An entrance adsorption occurs out of the pore of the GOF. This adsorption is not as stable as deep adsorption.

Ecology and the ratchet of events: climate variability, niche dimensions, and species distributions

USGS Publications Warehouse

Jackson, Stephen T.; Betancourt, Julio L.; Booth, Robert K.; Gray, Stephen T.

2009-01-01

Climate change in the coming centuries will be characterized by interannual, decadal, and multidecadal fluctuations superimposed on anthropogenic trends. Predicting ecological and biogeographic responses to these changes constitutes an immense challenge for ecologists. Perspectives from climatic and ecological history indicate that responses will be laden with contingencies, resulting from episodic climatic events interacting with demographic and colonization events. This effect is compounded by the dependency of environmental sensitivity upon life-stage for many species. Climate variables often used in empirical niche models may become decoupled from the proximal variables that directly influence individuals and populations. Greater predictive capacity, and more-fundamental ecological and biogeographic understanding, will come from integration of correlational niche modeling with mechanistic niche modeling, dynamic ecological modeling, targeted experiments, and systematic observations of past and present patterns and dynamics.

Ecology and the ratchet of events: Climate variability, niche dimensions, and species distributions

USGS Publications Warehouse

Jackson, S.T.; Betancourt, J.L.; Booth, R.K.; Gray, S.T.

2009-01-01

Climate change in the coming centuries will be characterized by interannual, decadal, and multidecadal fluctuations superimposed on anthropogenic trends. Predicting ecological and biogeographic responses to these changes constitutes an immense challenge for ecologists. Perspectives from climatic and ecological history indicate that responses will be laden with contingencies, resulting from episodic climatic events interacting with demographic and colonization events. This effect is compounded by the dependency of environmental sensitivity upon life-stage for many species. Climate variables often used in empirical niche models may become decoupled from the proximal variables that directly influence individuals and populations. Greater predictive capacity, and morefundamental ecological and biogeographic understanding, will come from integration of correlational niche modeling with mechanistic niche modeling, dynamic ecological modeling, targeted experiments, and systematic observations of past and present patterns and dynamics.

Managing time-substitutable electricity usage using dynamic controls

DOEpatents

Ghosh, Soumyadip; Hosking, Jonathan R.; Natarajan, Ramesh; Subramaniam, Shivaram; Zhang, Xiaoxuan

2017-02-07

A predictive-control approach allows an electricity provider to monitor and proactively manage peak and off-peak residential intra-day electricity usage in an emerging smart energy grid using time-dependent dynamic pricing incentives. The daily load is modeled as time-shifted, but cost-differentiated and substitutable, copies of the continuously-consumed electricity resource, and a consumer-choice prediction model is constructed to forecast the corresponding intra-day shares of total daily load according to this model. This is embedded within an optimization framework for managing the daily electricity usage. A series of transformations are employed, including the reformulation-linearization technique (RLT) to obtain a Mixed-Integer Programming (MIP) model representation of the resulting nonlinear optimization problem. In addition, various regulatory and pricing constraints are incorporated in conjunction with the specified profit and capacity utilization objectives.