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Sample records for affect model predictions

  1. Sensitivity Analysis of Corrosion Rate Prediction Models Utilized for Reinforced Concrete Affected by Chloride

    NASA Astrophysics Data System (ADS)

    Siamphukdee, Kanjana; Collins, Frank; Zou, Roger

    2013-06-01

    Chloride-induced reinforcement corrosion is one of the major causes of premature deterioration in reinforced concrete (RC) structures. Given the high maintenance and replacement costs, accurate modeling of RC deterioration is indispensable for ensuring the optimal allocation of limited economic resources. Since corrosion rate is one of the major factors influencing the rate of deterioration, many predictive models exist. However, because the existing models use very different sets of input parameters, the choice of model for RC deterioration is made difficult. Although the factors affecting corrosion rate are frequently reported in the literature, there is no published quantitative study on the sensitivity of predicted corrosion rate to the various input parameters. This paper presents the results of the sensitivity analysis of the input parameters for nine selected corrosion rate prediction models. Three different methods of analysis are used to determine and compare the sensitivity of corrosion rate to various input parameters: (i) univariate regression analysis, (ii) multivariate regression analysis, and (iii) sensitivity index. The results from the analysis have quantitatively verified that the corrosion rate of steel reinforcement bars in RC structures is highly sensitive to corrosion duration time, concrete resistivity, and concrete chloride content. These important findings establish that future empirical models for predicting corrosion rate of RC should carefully consider and incorporate these input parameters.

  2. Statistical model selection for better prediction and discovering science mechanisms that affect reliability

    SciTech Connect

    Anderson-Cook, Christine M.; Morzinski, Jerome; Blecker, Kenneth D.

    2015-08-19

    Understanding the impact of production, environmental exposure and age characteristics on the reliability of a population is frequently based on underlying science and empirical assessment. When there is incomplete science to prescribe which inputs should be included in a model of reliability to predict future trends, statistical model/variable selection techniques can be leveraged on a stockpile or population of units to improve reliability predictions as well as suggest new mechanisms affecting reliability to explore. We describe a five-step process for exploring relationships between available summaries of age, usage and environmental exposure and reliability. The process involves first identifying potential candidate inputs, then second organizing data for the analysis. Third, a variety of models with different combinations of the inputs are estimated, and fourth, flexible metrics are used to compare them. As a result, plots of the predicted relationships are examined to distill leading model contenders into a prioritized list for subject matter experts to understand and compare. The complexity of the model, quality of prediction and cost of future data collection are all factors to be considered by the subject matter experts when selecting a final model.

  3. Statistical model selection for better prediction and discovering science mechanisms that affect reliability

    DOE PAGES

    Anderson-Cook, Christine M.; Morzinski, Jerome; Blecker, Kenneth D.

    2015-08-19

    Understanding the impact of production, environmental exposure and age characteristics on the reliability of a population is frequently based on underlying science and empirical assessment. When there is incomplete science to prescribe which inputs should be included in a model of reliability to predict future trends, statistical model/variable selection techniques can be leveraged on a stockpile or population of units to improve reliability predictions as well as suggest new mechanisms affecting reliability to explore. We describe a five-step process for exploring relationships between available summaries of age, usage and environmental exposure and reliability. The process involves first identifying potential candidatemore » inputs, then second organizing data for the analysis. Third, a variety of models with different combinations of the inputs are estimated, and fourth, flexible metrics are used to compare them. As a result, plots of the predicted relationships are examined to distill leading model contenders into a prioritized list for subject matter experts to understand and compare. The complexity of the model, quality of prediction and cost of future data collection are all factors to be considered by the subject matter experts when selecting a final model.« less

  4. Predicting nitrogen loading with land-cover composition: how can watershed size affect model performance?

    PubMed

    Zhang, Tao; Yang, Xiaojun

    2013-01-01

    Watershed-wide land-cover proportions can be used to predict the in-stream non-point source pollutant loadings through regression modeling. However, the model performance can vary greatly across different study sites and among various watersheds. Existing literature has shown that this type of regression modeling tends to perform better for large watersheds than for small ones, and that such a performance variation has been largely linked with different interwatershed landscape heterogeneity levels. The purpose of this study is to further examine the previously mentioned empirical observation based on a set of watersheds in the northern part of Georgia (USA) to explore the underlying causes of the variation in model performance. Through the combined use of the neutral landscape modeling approach and a spatially explicit nutrient loading model, we tested whether the regression model performance variation over the watershed groups ranging in size is due to the different watershed landscape heterogeneity levels. We adopted three neutral landscape modeling criteria that were tied with different similarity levels in watershed landscape properties and used the nutrient loading model to estimate the nitrogen loads for these neutral watersheds. Then we compared the regression model performance for the real and neutral landscape scenarios, respectively. We found that watershed size can affect the regression model performance both directly and indirectly. Along with the indirect effect through interwatershed heterogeneity, watershed size can directly affect the model performance over the watersheds varying in size. We also found that the regression model performance can be more significantly affected by other physiographic properties shaping nitrogen delivery effectiveness than the watershed land-cover heterogeneity. This study contrasts with many existing studies because it goes beyond hypothesis formulation based on empirical observations and into hypothesis testing to

  5. Changing head model extent affects finite element predictions of transcranial direct current stimulation distributions

    NASA Astrophysics Data System (ADS)

    Indahlastari, Aprinda; Chauhan, Munish; Schwartz, Benjamin; Sadleir, Rosalind J.

    2016-12-01

    Objective. In this study, we determined efficient head model sizes relative to predicted current densities in transcranial direct current stimulation (tDCS). Approach. Efficiency measures were defined based on a finite element (FE) simulations performed using nine human head models derived from a single MRI data set, having extents varying from 60%-100% of the original axial range. Eleven tissue types, including anisotropic white matter, and three electrode montages (T7-T8, F3-right supraorbital, Cz-Oz) were used in the models. Main results. Reducing head volume extent from 100% to 60%, that is, varying the model’s axial range from between the apex and C3 vertebra to one encompassing only apex to the superior cerebellum, was found to decrease the total modeling time by up to half. Differences between current density predictions in each model were quantified by using a relative difference measure (RDM). Our simulation results showed that {RDM} was the least affected (a maximum of 10% error) for head volumes modeled from the apex to the base of the skull (60%-75% volume). Significance. This finding suggested that the bone could act as a bioelectricity boundary and thus performing FE simulations of tDCS on the human head with models extending beyond the inferior skull may not be necessary in most cases to obtain reasonable precision in current density results.

  6. Review of uncertainty sources affecting the long-term predictions of space debris evolutionary models

    NASA Astrophysics Data System (ADS)

    Dolado-Perez, J. C.; Pardini, Carmen; Anselmo, Luciano

    2015-08-01

    Since the launch of Sputnik-I in 1957, the amount of space debris in Earth's orbit has increased continuously. Historically, besides abandoned intact objects (spacecraft and orbital stages), the primary sources of space debris in Earth's orbit were (i) accidental and intentional break-ups which produced long-lasting debris and (ii) debris released intentionally during the operation of launch vehicle orbital stages and spacecraft. In the future, fragments generated by collisions are expected to become a significant source as well. In this context, and from a purely mathematical point of view, the orbital debris population in Low Earth Orbit (LEO) should be intrinsically unstable, due to the physics of mutual collisions and the relative ineffectiveness of natural sink mechanisms above~700 km. Therefore, the real question should not be "if", but "when" the exponential growth of the space debris population is supposed to start. From a practical point of view, and in order to answer the previous question, since the end of the 1980's several sophisticated long-term debris evolutionary models have been developed. Unfortunately, the predictions performed with such models, in particular beyond a few decades, are affected by considerable uncertainty. Such uncertainty comes from a relative important number of variables that being either under the partial control or completely out of the control of modellers, introduce a variability on the long-term simulation of the space debris population which cannot be captured with standard Monte Carlo statistics. The objective of this paper is to present and discuss many of the uncertainty sources affecting the long-term predictions done with evolutionary models, in order to serve as a roadmap for the uncertainty and the statistical robustness analysis of the long-term evolution of the space debris population.

  7. Numerical simulation of friction stir welding (FSW): Prediction of the heat affect zone using a softening model

    NASA Astrophysics Data System (ADS)

    Paulo, R. M. F.; Carlone, P.; Valente, R. A. F.; Teixeira-Dias, F.; Palazzo, G. S.

    2016-10-01

    In this work a numerical model is proposed to simulate Friction Stir Welding (FSW) process in AA2024-T3 plates. This model included a softening model that account for the temperature history and the hardness distribution on a welded plate can thus be predicted. The validation of the model was performed using experimental measurements of the hardness in the plate cross-section. There is an acceptable prediction of the material softening in the Heat Affected Zone (HAZ) using the adopted model.

  8. QCT/FEA predictions of femoral stiffness are strongly affected by boundary condition modeling

    PubMed Central

    Rossman, Timothy; Kushvaha, Vinod; Dragomir-Daescu, Dan

    2015-01-01

    Quantitative computed tomography-based finite element models of proximal femora must be validated with cadaveric experiments before using them to assess fracture risk in osteoporotic patients. During validation it is essential to carefully assess whether the boundary condition modeling matches the experimental conditions. This study evaluated proximal femur stiffness results predicted by six different boundary condition methods on a sample of 30 cadaveric femora and compared the predictions with experimental data. The average stiffness varied by 280% among the six boundary conditions. Compared with experimental data the predictions ranged from overestimating the average stiffness by 65% to underestimating it by 41%. In addition we found that the boundary condition that distributed the load to the contact surfaces similar to the expected contact mechanics predictions had the best agreement with experimental stiffness. We concluded that boundary conditions modeling introduced large variations in proximal femora stiffness predictions. PMID:25804260

  9. How do the strength and type of ENSO affect SST predictability in coupled models

    PubMed Central

    Sohn, Soo-Jin; Tam, Chi-Yung; Jeong, Hye-In

    2016-01-01

    The effects of amplitude and type of the El Niño-Southern Oscillation (ENSO) on sea surface temperature (SST) predictability on a global scale were investigated, by examining historical climate forecasts for the period 1982–2006 from air-sea coupled seasonal prediction systems. Unlike in previous studies, SST predictability was evaluated in different phases of ENSO and for episodes with various strengths. Our results reveal that the seasonal mean Niño 3.4 index is well predicted in a multi-model ensemble (MME), even for four-month lead predictions. However, coupled models have particularly low skill in predicting the global SST pattern during weak ENSO events. During weak El Niño events, which are also El Niño Modoki in this period, a number of models fail to reproduce the associated tri-pole SST pattern over the tropical Pacific. During weak La Niña periods, SST signals in the MME tend to be less persistent than observations. Therefore, a good ENSO forecast does not guarantee a good SST prediction from a global perspective. The strength and type of ENSO need to be considered when inferring global SST and other climate impacts from model-predicted ENSO information. PMID:27650415

  10. Factors Affecting Retention Behavior: A Model To Predict At-Risk Students. AIR 1997 Annual Forum Paper.

    ERIC Educational Resources Information Center

    Sadler, William E.; Cohen, Frederic L.; Kockesen, Levent

    This paper describes a methodology used in an on-going retention study at New York University (NYU) to identify a series of easily measured factors affecting student departure decisions. Three logistic regression models for predicting student retention were developed, each containing data available at three distinct times during the first…

  11. How would peak rainfall intensity affect runoff predictions using conceptual water balance models?

    NASA Astrophysics Data System (ADS)

    Yu, B.

    2015-06-01

    Most hydrological models use continuous daily precipitation and potential evapotranspiration for streamflow estimation. With the projected increase in mean surface temperature, hydrological processes are set to intensify irrespective of the underlying changes to the mean precipitation. The effect of an increase in rainfall intensity on the long-term water balance is, however, not adequately accounted for in the commonly used hydrological models. This study follows from a previous comparative analysis of a non-stationary daily series of stream flow of a forested watershed (River Rimbaud) in the French Alps (area = 1.478 km2) (1966-2006). Non-stationarity in the recorded stream flow occurred as a result of a severe wild fire in 1990. Two daily models (AWBM and SimHyd) were initially calibrated for each of three distinct phases in relation to the well documented land disturbance. At the daily and monthly time scales, both models performed satisfactorily with the Nash-Sutcliffe coefficient of efficiency (NSE) varying from 0.77 to 0.92. When aggregated to the annual time scale, both models underestimated the flow by about 22% with a reduced NSE at about 0.71. Exploratory data analysis was undertaken to relate daily peak hourly rainfall intensity to the discrepancy between the observed and modelled daily runoff amount. Preliminary results show that the effect of peak hourly rainfall intensity on runoff prediction is insignificant, and model performance is unlikely to improve when peak daily precipitation is included. Trend analysis indicated that the large decrease of precipitation when daily precipitation amount exceeded 10-20 mm may have contributed greatly to the decrease in stream flow of this forested watershed.

  12. Motor Execution Affects Action Prediction

    ERIC Educational Resources Information Center

    Springer, Anne; Brandstadter, Simone; Liepelt, Roman; Birngruber, Teresa; Giese, Martin; Mechsner, Franz; Prinz, Wolfgang

    2011-01-01

    Previous studies provided evidence of the claim that the prediction of occluded action involves real-time simulation. We report two experiments that aimed to study how real-time simulation is affected by simultaneous action execution under conditions of full, partial or no overlap between observed and executed actions. This overlap was analysed by…

  13. A mathematical model to predict the size of the pellets formed in freeze pelletization techniques: parameters affecting pellet size.

    PubMed

    Cheboyina, Sreekhar; O'Haver, John; Wyandt, Christy M

    2006-01-01

    A mathematical model was developed based on the theory of drop formation to predict the size of the pellets formed in the freeze pelletization process. Further the model was validated by studying the effect of various parameters on the pellet size such as viscosity of the pellet forming and column liquids, surface/interfacial tension, density difference between pellet forming and column liquids; size, shape, and material of construction of the needle tips and temperatures maintained in the columns. In this study, pellets were prepared from different matrices including polyethylene glycols and waxes. The column liquids studied were silicone oils and aqueous glycerol solutions. The surface/interfacial tension, density difference between pellet forming and column liquids and needle tip size were found to be the most important factors affecting pellet size. The viscosity of the column liquid was not found to significantly affect the size of the pellets. The size of the pellets was also not affected by the pellet forming liquids of low viscosities. An increase in the initial column temperature slightly decreased the pellet size. The mathematical model developed was found to successfully predict the size of the pellets with an average error of 3.32% for different matrices that were studied.

  14. PREDICTIVE MODELS

    SciTech Connect

    Ray, R.M. )

    1986-12-01

    PREDICTIVE MODELS is a collection of five models - CFPM, CO2PM, ICPM, PFPM, and SFPM - used in the 1982-1984 National Petroleum Council study of enhanced oil recovery (EOR) potential. Each pertains to a specific EOR process designed to squeeze additional oil from aging or spent oil fields. The processes are: 1) chemical flooding, where soap-like surfactants are injected into the reservoir to wash out the oil; 2) carbon dioxide miscible flooding, where carbon dioxide mixes with the lighter hydrocarbons making the oil easier to displace; 3) in-situ combustion, which uses the heat from burning some of the underground oil to thin the product; 4) polymer flooding, where thick, cohesive material is pumped into a reservoir to push the oil through the underground rock; and 5) steamflood, where pressurized steam is injected underground to thin the oil. CFPM, the Chemical Flood Predictive Model, models micellar (surfactant)-polymer floods in reservoirs, which have been previously waterflooded to residual oil saturation. Thus, only true tertiary floods are considered. An option allows a rough estimate of oil recovery by caustic or caustic-polymer processes. CO2PM, the Carbon Dioxide miscible flooding Predictive Model, is applicable to both secondary (mobile oil) and tertiary (residual oil) floods, and to either continuous CO2 injection or water-alternating gas processes. ICPM, the In-situ Combustion Predictive Model, computes the recovery and profitability of an in-situ combustion project from generalized performance predictive algorithms. PFPM, the Polymer Flood Predictive Model, is switch-selectable for either polymer or waterflooding, and an option allows the calculation of the incremental oil recovery and economics of polymer relative to waterflooding. SFPM, the Steamflood Predictive Model, is applicable to the steam drive process, but not to cyclic steam injection (steam soak) processes.

  15. Operation ARIES!: Methods, Mystery, and Mixed Models: Discourse Features Predict Affect in a Serious Game

    ERIC Educational Resources Information Center

    Forsyth, Carol M.; Graesser, Arthur C.; Pavlik, Philip, Jr.; Cai, Zhiqiang; Butler, Heather; Halpern, Diane; Millis, Keith

    2013-01-01

    Operation ARIES! is an Intelligent Tutoring System that is designed to teach scientific methodology in a game-like atmosphere. A fundamental goal of this serious game is to engage students during learning through natural language tutorial conversations. A tight integration of cognition, discourse, motivation, and affect is desired to meet this…

  16. Fascicular perineurium thickness, size, and position affect model predictions of neural excitation.

    PubMed

    Grinberg, Yanina; Schiefer, Matthew A; Tyler, Dustin J; Gustafson, Kenneth J

    2008-12-01

    The number of applications using neural prosthetic interfaces is expanding. Computer models are a valuable tool to evaluate stimulation techniques and electrode designs. Although our understanding of neural anatomy has improved, its impact on the effects of neural stimulation is not well understood. This study evaluated the effects of fascicle perineurial thickness, diameter, and position on axonal excitation thresholds and population recruitment using finite element models and NEURON simulations. The perineurial thickness of human fascicles was found to be 3.0% +/- 1.0% of the fascicle diameter. Increased perineurial thickness and fascicle diameter increased activation thresholds. The presence of a large neighboring fascicle caused a significant change in activation of a smaller target fascicle by as much as 80% +/- 11% of the total axon population. Smaller fascicles were recruited at lower amplitudes than neighboring larger fascicles. These effects were further illustrated in a realistic model of a human femoral nerve surrounded by a nerve cuff electrode. The data suggest that fascicular selectivity is strongly dependent upon the anatomy of the nerve being stimulated. Therefore, accurate representations of nerve anatomy are required to develop more accurate computer models to evaluate and optimize nerve electrode designs for neural prosthesis applications.

  17. How does spatial variability of climate affect catchment streamflow predictions?

    EPA Science Inventory

    Spatial variability of climate can negatively affect catchment streamflow predictions if it is not explicitly accounted for in hydrologic models. In this paper, we examine the changes in streamflow predictability when a hydrologic model is run with spatially variable (distribute...

  18. Early Adolescent Affect Predicts Later Life Outcomes

    PubMed Central

    Kansky, Jessica; Allen, Joseph P.; Diener, Ed

    2016-01-01

    Background Subjective well-being as a predictor for later behavior and health has highlighted its relationship to health, work performance, and social relationships. However, the majority of such studies neglect the developmental nature of well-being in contributing to important changes across the transition to adulthood. Methods To examine the potential role of subjective well-being as a long-term predictor of critical life outcomes, we examined indicators of positive and negative affect at age 14 as a predictor of relationship, adjustment, self worth, and career outcomes a decade later at ages 23 to 25, controlling for family income and gender. We utilized multi-informant methods including reports from the target participant, close friends, and romantic partners in a demographically diverse community sample of 184 participants. Results Early adolescent positive affect predicted less relationship problems (less self-reported and partner-reported conflict, greater friendship attachment as rated by close peers), healthy adjustment to adulthood (lower levels of depression, anxiety, and loneliness). It also predicted positive work functioning (higher levels of career satisfaction and job competence) and increased self-worth. Negative affect did not significantly predict any of these important life outcomes. In addition to predicting desirable mean levels of later outcomes, early positive affect predicted beneficial changes across time in many outcomes. Conclusions The findings extend early research on the beneficial outcomes of subjective well-being by having an earlier assessment of well-being, including informant reports in measuring a large variety of outcome variables, and by extending the findings to a lower socioeconomic group of a diverse and younger sample. The results highlight the importance of considering positive affect as an important component of subjective well-being distinct from negative affect. PMID:27075545

  19. Sea Level Affecting Marshes Model (SLAMM) ‐ New functionality for predicting changes in distribution of submerged aquatic vegetation in response to sea level rise

    USGS Publications Warehouse

    Lee II, Henry; Reusser, Deborah A.; Frazier, Melanie R; McCoy, Lee M; Clinton, Patrick J.; Clough, Jonathan S.

    2014-01-01

    The “Sea‐Level Affecting Marshes Model” (SLAMM) is a moderate resolution model used to predict the effects of sea level rise on marsh habitats (Craft et al. 2009). SLAMM has been used extensively on both the west coast (e.g., Glick et al., 2007) and east coast (e.g., Geselbracht et al., 2011) of the United States to evaluate potential changes in the distribution and extent of tidal marsh habitats. However, a limitation of the current version of SLAMM, (Version 6.2) is that it lacks the ability to model distribution changes in seagrass habitat resulting from sea level rise. Because of the ecological importance of SAV habitats, U.S. EPA, USGS, and USDA partnered with Warren Pinnacle Consulting to enhance the SLAMM modeling software to include new functionality in order to predict changes in Zostera marina distribution within Pacific Northwest estuaries in response to sea level rise. Specifically, the objective was to develop a SAV model that used generally available GIS data and parameters that were predictive and that could be customized for other estuaries that have GIS layers of existing SAV distribution. This report describes the procedure used to develop the SAV model for the Yaquina Bay Estuary, Oregon, appends a statistical script based on the open source R software to generate a similar SAV model for other estuaries that have data layers of existing SAV, and describes how to incorporate the model coefficients from the site‐specific SAV model into SLAMM to predict the effects of sea level rise on Zostera marina distributions. To demonstrate the applicability of the R tools, we utilize them to develop model coefficients for Willapa Bay, Washington using site‐specific SAV data.

  20. Predictive modeling of complications.

    PubMed

    Osorio, Joseph A; Scheer, Justin K; Ames, Christopher P

    2016-09-01

    Predictive analytic algorithms are designed to identify patterns in the data that allow for accurate predictions without the need for a hypothesis. Therefore, predictive modeling can provide detailed and patient-specific information that can be readily applied when discussing the risks of surgery with a patient. There are few studies using predictive modeling techniques in the adult spine surgery literature. These types of studies represent the beginning of the use of predictive analytics in spine surgery outcomes. We will discuss the advancements in the field of spine surgery with respect to predictive analytics, the controversies surrounding the technique, and the future directions.

  1. How will predicted land-use change affect waterfowl spring stopover ecology? Inferences from an individual-based model

    USGS Publications Warehouse

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

    2016-01-01

    Habitat loss, habitat fragmentation, overexploitation and climate change pose familiar and new challenges to conserving natural populations throughout the world. One approach conservation planners may use to evaluate the effects of these challenges on wildlife populations is scenario planning.We developed an individual-based model to evaluate the effects of future land use and land cover changes on spring-migrating dabbling ducks in North America. We assessed the effects of three Intergovernmental Panel on Climate Change emission scenarios (A1B, A2 and B1) on dabbling duck stopover duration, movement distances and mortality. We specifically focused on migration stopover duration because previous research has demonstrated that individuals arriving earlier on the nesting grounds exhibit increased reproductive fitness.Compared to present conditions, all three scenarios increased stopover duration and movement distances of agent ducks.Although all three scenarios presented migrating ducks with increased amounts of wetland habitat, scenarios also contained substantially less cropland, which decreased overall carrying capacity of the study area.Synthesis and applications. Land-use change may increase waterfowl spring migration stopover duration in the midcontinent region of North America due to reduced landscape energetic carrying capacity. Climate change will alter spatial patterns of crop distributions with corn and rice production areas shifting to different regions. Thus, conservation planners will have to address population-level energetic implications of shifting agricultural food resources and increased uncertainty in yearly precipitation patterns within the next 50 years.

  2. Wind power prediction models

    NASA Technical Reports Server (NTRS)

    Levy, R.; Mcginness, H.

    1976-01-01

    Investigations were performed to predict the power available from the wind at the Goldstone, California, antenna site complex. The background for power prediction was derived from a statistical evaluation of available wind speed data records at this location and at nearby locations similarly situated within the Mojave desert. In addition to a model for power prediction over relatively long periods of time, an interim simulation model that produces sample wind speeds is described. The interim model furnishes uncorrelated sample speeds at hourly intervals that reproduce the statistical wind distribution at Goldstone. A stochastic simulation model to provide speed samples representative of both the statistical speed distributions and correlations is also discussed.

  3. Model Valid Prediction Period

    NASA Astrophysics Data System (ADS)

    Chu, P. C.

    2002-12-01

    A new concept, valid prediction period (VPP), is presented here to evaluate model predictability. VPP is defined as the time period when the prediction error first exceeds a pre-determined criterion (i.e., the tolerance level). It depends not only on the instantaneous error growth, but also on the noise level, the initial error, and tolerance level. The model predictability skill is then represented by a single scalar, VPP. The longer the VPP, the higher the model predictability skill is. A theoretical framework on the base of the backward Fokker-Planck equation is developed to determine the probability density function (pdf) of VPP. Verification of a Gulf of Mexico nowcast/forecast model is used as an example to demonstrate the usefulness of VPP. Power law scaling is found in the mean square error of displacement between drifting buoy and model trajectories (both at 50 m depth). The pdf of VPP is asymmetric with a long and broad tail on the higher value side, which suggests long-term predictability. The calculations demonstrate that the long-term (extreme long such as 50-60 day) predictability is not an "outlier" and shares the same statistical properties as the short-term predictions. References Chu P. C., L. M. Ivanov, and C.W. Fan, Backward Fokker-Plank equation for determining model predictability with unknown initial error distribution. J. Geophys. Res., in press, 2002. Chu P.C., L.M.Ivanov, T.M. Margolina, and O.V.Melnichenko, 2002b: On probabilistic stability of an atmospheric model to various amplitude perturbations. J. Atmos. Sci., in press Chu P.C., L.M. Ivanov, L. Kantha, O.V. Melnichenko and Y.A. Poberezhny, 2002c: The long-term correlations and power decay law in model prediction skill. Geophys. Res. Let., in press.

  4. Predicting individual affect of health interventions to reduce HPV prevalence.

    PubMed

    Corley, Courtney D; Mihalcea, Rada; Mikler, Armin R; Sanfilippo, Antonio P

    2011-01-01

    Recently, human papilloma virus (HPV) has been implicated to cause several throat and oral cancers and HPV is established to cause most cervical cancers. A human papilloma virus vaccine has been proven successful to reduce infection incidence in FDA clinical trials, and it is currently available in the USA. Current intervention policy targets adolescent females for vaccination; however, the expansion of suggested guidelines may extend to other age groups and males as well. This research takes a first step toward automatically predicting personal beliefs, regarding health intervention, on the spread of disease. Using linguistic or statistical approaches, sentiment analysis determines a text's affective content. Self-reported HPV vaccination beliefs published in web and social media are analyzed for affect polarity and leveraged as knowledge inputs to epidemic models. With this in mind, we have developed a discrete-time model to facilitate predicting impact on the reduction of HPV prevalence due to arbitrary age- and gender-targeted vaccination schemes.

  5. Predicting Individual Affect of Health Interventions to Reduce HPV Prevalence

    SciTech Connect

    Corley, Courtney D.; Mihalcea, Rada; Mikler, Armin R.; Sanfilippo, Antonio P.

    2011-04-01

    Recently, human papilloma virus has been implicated to cause several throat and oral cancers and hpv is established to cause most cervical cancers. A human papilloma virus vaccine has been proven successful to reduce infection incidence in FDA clinical trials and it is currently available in the United States. Current intervention policy targets adolescent females for vaccination; however, the expansion of suggested guidelines may extend to other age groups and males as well. This research takes a first step towards automatically predicting personal beliefs, regarding health intervention, on the spread of disease. Using linguistic or statistical approaches, sentiment analysis determines a texts affective content. Self-reported HPV vaccination beliefs published in web and social media are analyzed for affect polarity and leveraged as knowledge inputs to epidemic models. With this in mind, we have developed a discrete-time model to facilitate predicting impact on the reduction of HPV prevalence due to arbitrary age and gender targeted vaccination schemes.

  6. Principles of Predictive Modeling

    NASA Astrophysics Data System (ADS)

    Delignette-Muller, Marie Laure

    Mathematical models were first used in food microbiology in the early 20th century to describe the thermal destruction of pathogens in food, but the concept of predictive microbiology really emerged in the 1980 s. This concept was first developed and extensively discussed by McMeekin and his colleagues at the University of Tasmania (Ratkowsky, Olley, McMeekin, & Ball, 1982; McMeekin, Olley, Ross, & Ratkowsky, 1993; McMeekin, Olley, Ratkowsky, & Ross, 2002). Now predictive microbiology or predictive modeling in foods may be considered as a subdiscipline of food microbiology, with its international meetings (5th conference on “Predictive Modelling in Foods” in 2007) gathering a scientific community from all over the world.

  7. Predictive models in urology.

    PubMed

    Cestari, Andrea

    2013-01-01

    Predictive modeling is emerging as an important knowledge-based technology in healthcare. The interest in the use of predictive modeling reflects advances on different fronts such as the availability of health information from increasingly complex databases and electronic health records, a better understanding of causal or statistical predictors of health, disease processes and multifactorial models of ill-health and developments in nonlinear computer models using artificial intelligence or neural networks. These new computer-based forms of modeling are increasingly able to establish technical credibility in clinical contexts. The current state of knowledge is still quite young in understanding the likely future direction of how this so-called 'machine intelligence' will evolve and therefore how current relatively sophisticated predictive models will evolve in response to improvements in technology, which is advancing along a wide front. Predictive models in urology are gaining progressive popularity not only for academic and scientific purposes but also into the clinical practice with the introduction of several nomograms dealing with the main fields of onco-urology.

  8. Atmospheric prediction model survey

    NASA Technical Reports Server (NTRS)

    Wellck, R. E.

    1976-01-01

    As part of the SEASAT Satellite program of NASA, a survey of representative primitive equation atmospheric prediction models that exist in the world today was written for the Jet Propulsion Laboratory. Seventeen models developed by eleven different operational and research centers throughout the world are included in the survey. The surveys are tutorial in nature describing the features of the various models in a systematic manner.

  9. Modeling and Prediction Overview

    SciTech Connect

    Ermak, D L

    2002-10-18

    Effective preparation for and response to the release of toxic materials into the atmosphere hinges on accurate predictions of the dispersion pathway, concentration, and ultimate fate of the chemical or biological agent. Of particular interest is the threat to civilian populations within major urban areas, which are likely targets for potential attacks. The goals of the CBNP Modeling and Prediction area are: (1) Development of a suite of validated, multi-scale, atmospheric transport and fate modeling capabilities for chemical and biological agent releases within the complex urban environment; (2) Integration of these models and related user tools into operational emergency response systems. Existing transport and fate models are being adapted to treat the complex atmospheric flows within and around structures (e.g., buildings, subway systems, urban areas) and over terrain. Relevant source terms and the chemical and physical behavior of gas- and particle-phase species (e.g., losses due to deposition, bio-agent viability, degradation) are also being developed and incorporated into the models. Model validation is performed using both laboratory and field data. CBNP is producing and testing a suite of models with differing levels of complexity and fidelity to address the full range of user needs and applications. Lumped-parameter transport models are being developed for subway systems and building interiors, supplemented by the use of computational fluid dynamics (CFD) models to describe the circulation within large, open spaces such as auditoriums. Both sophisticated CFD transport models and simpler fast-response models are under development to treat the complex flow around individual structures and arrays of buildings. Urban parameterizations are being incorporated into regional-scale weather forecast, meteorological data assimilation, and dispersion models for problems involving larger-scale urban and suburban areas. Source term and dose response models are being

  10. Daily affective experiences predict objective sleep outcomes among adolescents.

    PubMed

    Tavernier, Royette; Choo, Sungsub B; Grant, Kathryn; Adam, Emma K

    2016-02-01

    Adolescence is a sensitive period for changes in both sleep and affect. Although past research has assessed the association between affect and sleep among adolescents, few studies have examined both trait (typical) and day-to-day changes in affect, and fewer still have specifically examined negative social evaluative emotions (e.g. embarrassment) in relation to sleep. Both between- and within-person variations in daily affect were examined in relation to four objectively-measured sleep outcomes (sleep hours; sleep latency; sleep efficiency; and length of wake bouts) among adolescents. Participants (N = 77 high-school students; 42.9% female; M = 14.37 years) wore an actiwatch and completed daily-diaries for 3 days. The results of hierarchical linear models (controlling for age, gender, race, ethnicity, parental employment status, income, puberty and caffeine) indicated that negative social evaluative emotions and high-arousal affective experiences generally predicted poor sleep outcomes, whereas low-arousal affective experiences were associated with good sleep outcomes. Specifically, at the person level, adolescents reporting higher negative social evaluative emotions had shorter average sleep hours, and those experiencing higher anxiety–nervousness had longer wake bouts. In addition, individuals experiencing more dysphoria (sad, depressed, lonely) had longer average sleep hours and shorter wake bouts, while those experiencing more calmness had shorter sleep latencies. At the within-person level, individuals had longer sleep latencies following days that they had experienced high-arousal positive affect (e.g. excitement), and had longer wake bouts following days they had experienced more negative social evaluative emotions. The results highlight the detrimental effects of negative social evaluative emotions and high-arousal affective states for adolescent sleep.

  11. Daily Affective Experiences Predict Objective Sleep Outcomes among Adolescents

    PubMed Central

    Tavernier, Royette; Choo, Sungsub B; Grant, Kathryn; Adam, Emma K

    2015-01-01

    Summary Adolescence is a sensitive period for changes in both sleep and affect. Although past research has assessed the association between affect and sleep among adolescents, few studies have examined both trait (typical) and day-to-day changes in affect, and fewer still have specifically examined negative social evaluative emotions (NSEE; e.g., embarrassment) in relation to sleep. We examined both between- and within-person variations in daily affect in relation to four objectively-measured sleep outcomes (sleep hours, sleep latency, sleep efficiency, and length of wake bouts) among adolescents. Participants (N = 77 high school students, 42.9% female; M = 14.37 years) wore an actiwatch and completed daily diaries for 3 days. Results of hierarchical linear models (controlling for age, gender, race, ethnicity, parental employment status, income, puberty, and caffeine) indicated that NSEE and high arousal affective experiences generally predicted poor sleep outcomes, whereas low arousal affective experiences were associated with good sleep outcomes. Specifically, at the person level, adolescents reporting higher NSEE had shorter average sleep hours, and those experiencing higher anxiety-nervousness had longer wake bouts. In addition, individuals experiencing more dysphoria (sad, depressed, lonely) had longer average sleep hours and shorter wake bouts, while those experiencing more calmness had shorter sleep latencies. At the within person level, individuals had longer sleep latencies following days that they had experienced high arousal positive affect (e.g., excitement) and had longer wake bouts following days they had experienced more NSEE. Results highlight the detrimental effects of NSEE and high arousal affective states for adolescent sleep. PMID:26365539

  12. Models of Affective Decision Making

    PubMed Central

    Charpentier, Caroline J.; De Neve, Jan-Emmanuel; Li, Xinyi; Roiser, Jonathan P.; Sharot, Tali

    2016-01-01

    Intuitively, how you feel about potential outcomes will determine your decisions. Indeed, an implicit assumption in one of the most influential theories in psychology, prospect theory, is that feelings govern choice. Surprisingly, however, very little is known about the rules by which feelings are transformed into decisions. Here, we specified a computational model that used feelings to predict choices. We found that this model predicted choice better than existing value-based models, showing a unique contribution of feelings to decisions, over and above value. Similar to the value function in prospect theory, our feeling function showed diminished sensitivity to outcomes as value increased. However, loss aversion in choice was explained by an asymmetry in how feelings about losses and gains were weighted when making a decision, not by an asymmetry in the feelings themselves. The results provide new insights into how feelings are utilized to reach a decision. PMID:27071751

  13. Predictive Surface Complexation Modeling

    SciTech Connect

    Sverjensky, Dimitri A.

    2016-11-29

    Surface complexation plays an important role in the equilibria and kinetics of processes controlling the compositions of soilwaters and groundwaters, the fate of contaminants in groundwaters, and the subsurface storage of CO2 and nuclear waste. Over the last several decades, many dozens of individual experimental studies have addressed aspects of surface complexation that have contributed to an increased understanding of its role in natural systems. However, there has been no previous attempt to develop a model of surface complexation that can be used to link all the experimental studies in order to place them on a predictive basis. Overall, my research has successfully integrated the results of the work of many experimentalists published over several decades. For the first time in studies of the geochemistry of the mineral-water interface, a practical predictive capability for modeling has become available. The predictive correlations developed in my research now enable extrapolations of experimental studies to provide estimates of surface chemistry for systems not yet studied experimentally and for natural and anthropogenically perturbed systems.

  14. Is the future blue-green? A review of the current model predictions of how climate change could affect pelagic freshwater cyanobacteria.

    PubMed

    Elliott, J Alex

    2012-04-01

    There is increasing evidence that recent changes in climate have had an effect on lake phytoplankton communities and it has been suggested that it is likely that Cyanobacteria will increase in relative abundance under the predicted future climate. However, testing such a qualitative prediction is challenging and usually requires some form of numerical computer model. Therefore, the lake modelling literature was reviewed for studies that examined the impact of climate change upon Cyanobacteria. These studies, taken collectively, generally show an increase in relative Cyanobacteria abundance with increasing water temperature, decreased flushing rate and increased nutrient loads. Furthermore, they suggest that whilst the direct effects of climate change on the lakes can change the timing of bloom events and Cyanobacteria abundance, the amount of phytoplankton biomass produced over a year is not enhanced directly by these changes. Also, warmer waters in the spring increased nutrient consumption by the phytoplankton community which in some lakes caused nitrogen limitation later in the year to the advantage of some nitrogen-fixing Cyanobacteria. Finally, it is also possible that an increase in Cyanobacteria dominance of the phytoplankton biomass will lead to poorer energy flow to higher trophic levels due to their relatively poor edibility for zooplankton.

  15. Predicting when climate-driven phenotypic change affects population dynamics.

    PubMed

    McLean, Nina; Lawson, Callum R; Leech, Dave I; van de Pol, Martijn

    2016-06-01

    Species' responses to climate change are variable and diverse, yet our understanding of how different responses (e.g. physiological, behavioural, demographic) relate and how they affect the parameters most relevant for conservation (e.g. population persistence) is lacking. Despite this, studies that observe changes in one type of response typically assume that effects on population dynamics will occur, perhaps fallaciously. We use a hierarchical framework to explain and test when impacts of climate on traits (e.g. phenology) affect demographic rates (e.g. reproduction) and in turn population dynamics. Using this conceptual framework, we distinguish four mechanisms that can prevent lower-level responses from impacting population dynamics. Testable hypotheses were identified from the literature that suggest life-history and ecological characteristics which could predict when these mechanisms are likely to be important. A quantitative example on birds illustrates how, even with limited data and without fully-parameterized population models, new insights can be gained; differences among species in the impacts of climate-driven phenological changes on population growth were not explained by the number of broods or density dependence. Our approach helps to predict the types of species in which climate sensitivities of phenotypic traits have strong demographic and population consequences, which is crucial for conservation prioritization of data-deficient species.

  16. Predicting Vaccination Intention and Benefit and Risk Perceptions: The Incorporation of Affect, Trust, and Television Influence in a Dual-Mode Model.

    PubMed

    Chen, Nien-Tsu Nancy

    2015-07-01

    Major health behavior change models tend to consider health decisions as primarily resulting from a systematic appraisal of relevant beliefs, such as the perceived benefits and risks of a pharmacological intervention. Drawing on research from the disciplines of risk management, communication, and psychology, this study proposed the inclusion of a heuristic route in established theory and tested the direction of influence between heuristic and systematic process variables. Affect and social trust were included as key heuristics in the proposed dual-mode framework of health decision making. Furthermore, exposure to health-related coverage on television was considered potentially influential over both heuristic and systematic process variables. To test this framework, data were collected from a national probability sample of 584 adults in the United States in 2012 regarding their decision to vaccinate against a hypothetical avian flu. The results provided some support for the bidirectional influence between heuristic and systematic processing. Affect toward flu vaccination and trust in the Food and Drug Administration were found to be powerful predictors of vaccination intention, enhancing intention both directly and indirectly via certain systematic process variables. The direction of influence between perceived susceptibility and severity, on the one hand, and affect, on the other, is less clear, suggesting the need for further research. Contrary to the opinion of media critics, exposure to televised health coverage was negatively associated with the perceived risks of vaccination. Results from this study carry theoretical and practical implications, and applying this model to the acceptance of different health interventions constitutes an area for future inquiries.

  17. The application of predictive modelling for determining bio-environmental factors affecting the distribution of blackflies (Diptera: Simuliidae) in the Gilgel Gibe watershed in Southwest Ethiopia.

    PubMed

    Ambelu, Argaw; Mekonen, Seblework; Koch, Magaly; Addis, Taffere; Boets, Pieter; Everaert, Gert; Goethals, Peter

    2014-01-01

    Blackflies are important macroinvertebrate groups from a public health as well as ecological point of view. Determining the biological and environmental factors favouring or inhibiting the existence of blackflies could facilitate biomonitoring of rivers as well as control of disease vectors. The combined use of different predictive modelling techniques is known to improve identification of presence/absence and abundance of taxa in a given habitat. This approach enables better identification of the suitable habitat conditions or environmental constraints of a given taxon. Simuliidae larvae are important biological indicators as they are abundant in tropical aquatic ecosystems. Some of the blackfly groups are also important disease vectors in poor tropical countries. Our investigations aim to establish a combination of models able to identify the environmental factors and macroinvertebrate organisms that are favourable or inhibiting blackfly larvae existence in aquatic ecosystems. The models developed using macroinvertebrate predictors showed better performance than those based on environmental predictors. The identified environmental and macroinvertebrate parameters can be used to determine the distribution of blackflies, which in turn can help control river blindness in endemic tropical places. Through a combination of modelling techniques, a reliable method has been developed that explains environmental and biological relationships with the target organism, and, thus, can serve as a decision support tool for ecological management strategies.

  18. The Application of Predictive Modelling for Determining Bio-Environmental Factors Affecting the Distribution of Blackflies (Diptera: Simuliidae) in the Gilgel Gibe Watershed in Southwest Ethiopia

    PubMed Central

    Ambelu, Argaw; Mekonen, Seblework; Koch, Magaly; Addis, Taffere; Boets, Pieter; Everaert, Gert; Goethals, Peter

    2014-01-01

    Blackflies are important macroinvertebrate groups from a public health as well as ecological point of view. Determining the biological and environmental factors favouring or inhibiting the existence of blackflies could facilitate biomonitoring of rivers as well as control of disease vectors. The combined use of different predictive modelling techniques is known to improve identification of presence/absence and abundance of taxa in a given habitat. This approach enables better identification of the suitable habitat conditions or environmental constraints of a given taxon. Simuliidae larvae are important biological indicators as they are abundant in tropical aquatic ecosystems. Some of the blackfly groups are also important disease vectors in poor tropical countries. Our investigations aim to establish a combination of models able to identify the environmental factors and macroinvertebrate organisms that are favourable or inhibiting blackfly larvae existence in aquatic ecosystems. The models developed using macroinvertebrate predictors showed better performance than those based on environmental predictors. The identified environmental and macroinvertebrate parameters can be used to determine the distribution of blackflies, which in turn can help control river blindness in endemic tropical places. Through a combination of modelling techniques, a reliable method has been developed that explains environmental and biological relationships with the target organism, and, thus, can serve as a decision support tool for ecological management strategies. PMID:25372843

  19. Using Historic Models of Cn2 to predict r0 and regimes affected by atmospheric turbulence for horizontal, slant and topological paths

    SciTech Connect

    Lawson, J K; Carrano, C J

    2006-06-20

    Image data collected near the ground, in the boundary layer, or from low altitude planes must contend with the detrimental effects of atmospheric turbulence on the image quality. So it is useful to predict operating regimes (wavelength, height of target, height of detector, total path distance, day vs. night viewing, etc.) where atmospheric turbulence is expected to play a significant role in image degradation. In these regimes, image enhancement techniques such as speckle processing, deconvolution and Wiener filtering methods can be utilized to recover near instrument-limited resolution in degraded images. We conducted a literature survey of various boundary layer and lower troposphere models for the structure coefficient of the index of refraction (C{sub n}{sup 2}). Using these models, we constructed a spreadsheet tool to estimate the Fried parameter (r{sub 0}) for different scenarios, including slant and horizontal path trajectories. We also created a tool for scenarios where the height along the path crudely accounted for the topology of the path. This would be of particular interest in mountain-based viewing platforms surveying ground targets. The tools that we developed utilized Visual Basic{reg_sign} programming in an Excel{reg_sign} spreadsheet environment for accessibility and ease of use. In this paper, we will discuss the C{sub n}{sup 2} profile models used, describe the tools developed and compare the results obtained for the Fried parameter with those estimated from experimental data.

  20. Extracting falsifiable predictions from sloppy models.

    PubMed

    Gutenkunst, Ryan N; Casey, Fergal P; Waterfall, Joshua J; Myers, Christopher R; Sethna, James P

    2007-12-01

    Successful predictions are among the most compelling validations of any model. Extracting falsifiable predictions from nonlinear multiparameter models is complicated by the fact that such models are commonly sloppy, possessing sensitivities to different parameter combinations that range over many decades. Here we discuss how sloppiness affects the sorts of data that best constrain model predictions, makes linear uncertainty approximations dangerous, and introduces computational difficulties in Monte-Carlo uncertainty analysis. We also present a useful test problem and suggest refinements to the standards by which models are communicated.

  1. Do affective attitudes predict organ donor registration? A prospective study.

    PubMed

    Shepherd, Lee; O'Carroll, Ronan E

    2014-10-01

    This study assessed whether people's affective attitudes predicted organ donor registration at a later time. People who were not registered as an organ donor prior to completing the study (N = 150) first rated their affective attitudes towards organ donation. We then measured whether they clicked on a hyperlink to register as an organ donor. Believing that the body should be kept whole for burial (bodily integrity) was the only affective attitude to predict this organ donation behaviour. Future campaigns should target this concern in order to increase organ donor registration and the availability of donor organs.

  2. The role of personal self-regulation and regulatory teaching to predict motivational-affective variables, achievement, and satisfaction: a structural model

    PubMed Central

    De la Fuente, Jesus; Zapata, Lucía; Martínez-Vicente, Jose M.; Sander, Paul; Cardelle-Elawar, María

    2014-01-01

    The present investigation examines how personal self-regulation (presage variable) and regulatory teaching (process variable of teaching) relate to learning approaches, strategies for coping with stress, and self-regulated learning (process variables of learning) and, finally, how they relate to performance and satisfaction with the learning process (product variables). The objective was to clarify the associative and predictive relations between these variables, as contextualized in two different models that use the presage-process-product paradigm (the Biggs and DEDEPRO models). A total of 1101 university students participated in the study. The design was cross-sectional and retrospective with attributional (or selection) variables, using correlations and structural analysis. The results provide consistent and significant empirical evidence for the relationships hypothesized, incorporating variables that are part of and influence the teaching–learning process in Higher Education. Findings confirm the importance of interactive relationships within the teaching–learning process, where personal self-regulation is assumed to take place in connection with regulatory teaching. Variables that are involved in the relationships validated here reinforce the idea that both personal factors and teaching and learning factors should be taken into consideration when dealing with a formal teaching–learning context at university. PMID:25964764

  3. The role of personal self-regulation and regulatory teaching to predict motivational-affective variables, achievement, and satisfaction: a structural model.

    PubMed

    De la Fuente, Jesus; Zapata, Lucía; Martínez-Vicente, Jose M; Sander, Paul; Cardelle-Elawar, María

    2015-01-01

    The present investigation examines how personal self-regulation (presage variable) and regulatory teaching (process variable of teaching) relate to learning approaches, strategies for coping with stress, and self-regulated learning (process variables of learning) and, finally, how they relate to performance and satisfaction with the learning process (product variables). The objective was to clarify the associative and predictive relations between these variables, as contextualized in two different models that use the presage-process-product paradigm (the Biggs and DEDEPRO models). A total of 1101 university students participated in the study. The design was cross-sectional and retrospective with attributional (or selection) variables, using correlations and structural analysis. The results provide consistent and significant empirical evidence for the relationships hypothesized, incorporating variables that are part of and influence the teaching-learning process in Higher Education. Findings confirm the importance of interactive relationships within the teaching-learning process, where personal self-regulation is assumed to take place in connection with regulatory teaching. Variables that are involved in the relationships validated here reinforce the idea that both personal factors and teaching and learning factors should be taken into consideration when dealing with a formal teaching-learning context at university.

  4. Predicting Affective Information – An Evaluation of Repetition Suppression Effects

    PubMed Central

    Trapp, Sabrina; Kotz, Sonja A.

    2016-01-01

    Both theoretical proposals and empirical studies suggest that the brain interprets sensory input based on expectations to mitigate computational burden. However, as social beings, much of sensory input is affectively loaded – e.g., the smile of a partner, the critical voice of a boss, or the welcoming gesture of a friend. Given that affective information is highly complex and often ambiguous, building up expectations of upcoming affective sensory input may greatly contribute to its rapid and efficient processing. This review points to the role of affective information in the context of the ‘predictive brain’. It particularly focuses on repetition suppression (RS) effects that have recently been linked to prediction processes. The findings are interpreted as evidence for more pronounced prediction processes with affective material. Importantly, it is argued that bottom-up attention inflates the neural RS effect, and because affective stimuli tend to attract more bottom-up attention, it thereby particularly overshadows the magnitude of RS effects for this information. Finally, anxiety disorders, such as social phobia, are briefly discussed as manifestations of modulations in affective prediction. PMID:27667980

  5. Prediction models in cancer care.

    PubMed

    Vickers, Andrew J

    2011-01-01

    Prediction is ubiquitous across the spectrum of cancer care from screening to hospice. Indeed, oncology is often primarily a prediction problem; many of the early stage cancers cause no symptoms, and treatment is recommended because of a prediction that tumor progression would ultimately threaten a patient's quality of life or survival. Recent years have seen attempts to formalize risk prediction in cancer care. In place of qualitative and implicit prediction algorithms, such as cancer stage, researchers have developed statistical prediction tools that provide a quantitative estimate of the probability of a specific event for an individual patient. Prediction models generally have greater accuracy than reliance on stage or risk groupings, can incorporate novel predictors such as genomic data, and can be used more rationally to make treatment decisions. Several prediction models are now widely used in clinical practice, including the Gail model for breast cancer incidence or the Adjuvant! Online prediction model for breast cancer recurrence. Given the burgeoning complexity of diagnostic and prognostic information, there is simply no realistic alternative to incorporating multiple variables into a single prediction model. As such, the question should not be whether but how prediction models should be used to aid decision-making. Key issues will be integration of models into the electronic health record and more careful evaluation of models, particularly with respect to their effects on clinical outcomes.

  6. Early prediction of student goals and affect in narrative-centered learning environments

    NASA Astrophysics Data System (ADS)

    Lee, Sunyoung

    Recent years have seen a growing recognition of the role of goal and affect recognition in intelligent tutoring systems. Goal recognition is the task of inferring users' goals from a sequence of observations of their actions. Because of the uncertainty inherent in every facet of human computer interaction, goal recognition is challenging, particularly in contexts in which users can perform many actions in any order, as is the case with intelligent tutoring systems. Affect recognition is the task of identifying the emotional state of a user from a variety of physical cues, which are produced in response to affective changes in the individual. Accurately recognizing student goals and affect states could contribute to more effective and motivating interactions in intelligent tutoring systems. By exploiting knowledge of student goals and affect states, intelligent tutoring systems can dynamically modify their behavior to better support individual students. To create effective interactions in intelligent tutoring systems, goal and affect recognition models should satisfy two key requirements. First, because incorrectly predicted goals and affect states could significantly diminish the effectiveness of interactive systems, goal and affect recognition models should provide accurate predictions of user goals and affect states. When observations of users' activities become available, recognizers should make accurate early" predictions. Second, goal and affect recognition models should be highly efficient so they can operate in real time. To address key issues, we present an inductive approach to recognizing student goals and affect states in intelligent tutoring systems by learning goals and affect recognition models. Our work focuses on goal and affect recognition in an important new class of intelligent tutoring systems, narrative-centered learning environments. We report the results of empirical studies of induced recognition models from observations of students

  7. [Fire behavior of Mongolian oak leaves fuel bed under no-wind and zero-slope conditions. II. Analysis of the factors affecting flame length and residence time and related prediction models].

    PubMed

    Zhang, Ji-Li; Liu, Bo-Fei; Di, Xue-Ying; Chu, Teng-Fei; Jin, Sen

    2012-11-01

    Taking fuel moisture content, fuel loading, and fuel bed depth as controlling factors, the fuel beds of Mongolian oak leaves in Maoershan region of Northeast China in field were simulated, and a total of one hundred experimental burnings under no-wind and zero-slope conditions were conducted in laboratory, with the effects of the fuel moisture content, fuel loading, and fuel bed depth on the flame length and its residence time analyzed and the multivariate linear prediction models constructed. The results indicated that fuel moisture content had a significant negative liner correlation with flame length, but less correlation with flame residence time. Both the fuel loading and the fuel bed depth were significantly positively correlated with flame length and its residence time. The interactions of fuel bed depth with fuel moisture content and fuel loading had significant effects on the flame length, while the interactions of fuel moisture content with fuel loading and fuel bed depth affected the flame residence time significantly. The prediction model of flame length had better prediction effect, which could explain 83.3% of variance, with a mean absolute error of 7.8 cm and a mean relative error of 16.2%, while the prediction model of flame residence time was not good enough, which could only explain 54% of variance, with a mean absolute error of 9.2 s and a mean relative error of 18.6%.

  8. Predictability affects the perception of audiovisual synchrony in complex sequences.

    PubMed

    Cook, Laura A; Van Valkenburg, David L; Badcock, David R

    2011-10-01

    The ability to make accurate audiovisual synchrony judgments is affected by the "complexity" of the stimuli: We are much better at making judgments when matching single beeps or flashes as opposed to video recordings of speech or music. In the present study, we investigated whether the predictability of sequences affects whether participants report that auditory and visual sequences appear to be temporally coincident. When we reduced their ability to predict both the next pitch in the sequence and the temporal pattern, we found that participants were increasingly likely to report that the audiovisual sequences were synchronous. However, when we manipulated pitch and temporal predictability independently, the same effect did not occur. By altering the temporal density (items per second) of the sequences, we further determined that the predictability effect occurred only in temporally dense sequences: If the sequences were slow, participants' responses did not change as a function of predictability. We propose that reduced predictability affects synchrony judgments by reducing the effective pitch and temporal acuity in perception of the sequences.

  9. Acculturation Predicts Negative Affect and Shortened Telomere Length.

    PubMed

    Ruiz, R Jeanne; Trzeciakowski, Jerome; Moore, Tiffany; Ayers, Kimberly S; Pickler, Rita H

    2016-10-12

    Chronic stress may accelerate cellular aging. Telomeres, protective "caps" at the end of chromosomes, modulate cellular aging and may be good biomarkers for the effects of chronic stress, including that associated with acculturation. The purpose of this analysis was to examine telomere length (TL) in acculturating Hispanic Mexican American women and to determine the associations among TL, acculturation, and psychological factors. As part of a larger cross-sectional study of 516 pregnant Hispanic Mexican American women, we analyzed DNA in blood samples (N = 56) collected at 22-24 weeks gestation for TL as an exploratory measure using monochrome multiplex quantitative telomere polymerase chain reaction (PCR). We measured acculturation with the Acculturation Rating Scale for Mexican Americans, depression with the Beck Depression Inventory, discrimination with the Experiences of Discrimination Scale, and stress with the Perceived Stress Scale. TL was negatively moderately correlated with two variables of acculturation: Anglo orientation and greater acculturation-level scores. We combined these scores for a latent variable, acculturation, and we combined depression, stress, and discrimination scores in another latent variable, "negative affectivity." Acculturation and negative affectivity were bidirectionally correlated. Acculturation significantly negatively predicted TL. Using structural equation modeling, we found the model had an excellent fit with the root mean square error of approximation estimate = .0001, comparative fit index = 1.0, Tucker-Lewis index = 1.0, and standardized root mean square residual = .05. The negative effects of acculturation on the health of Hispanic women have been previously demonstrated. Findings from this analysis suggest a link between acculturation and TL, which may indicate accelerated cellular aging associated with overall poor health outcomes.

  10. Regional brain activity and strenuous exercise: predicting affective responses using EEG asymmetry.

    PubMed

    Hall, Eric E; Ekkekakis, Panteleimon; Petruzzello, Steven J

    2007-05-01

    Previous research using the model proposed by Davidson has shown that resting frontal electroencephalographic (EEG) asymmetry can predict affective responses to aerobic exercise at moderate intensities. Specifically, greater relative left frontal activity has been shown to predict positive affect (i.e., energy) following exercise. The purpose of this study was to determine if resting frontal EEG asymmetry would predict affective responses following strenuous exercise. Thirty participants (13 women, 17 men) completed a maximal graded exercise test on a treadmill. EEG was recorded prior to exercise. Affect was measured by the Activation Deactivation Adjective Check List prior to the graded exercise test, immediately following, 10 and 20-min following exercise. Greater relative left frontal activity predicted tiredness and calmness during recovery from exercise, but not tension or energy. Tiredness and calmness following exercise covaried, suggesting that tiredness following exercise might not have been linked with displeasure. These findings offer further support for the link between EEG asymmetry and affective responses to exercise.

  11. 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

  12. Negative Affective Spillover from Daily Events Predicts Early Response to Cognitive Therapy for Depression

    ERIC Educational Resources Information Center

    Cohen, Lawrence H.; Gunthert, Kathleen C.; Butler, Andrew C.; Parrish, Brendt P.; Wenze, Susan J.; Beck, Judith S.

    2008-01-01

    This study evaluated the predictive role of depressed outpatients' (N = 62) affective reactivity to daily stressors in their rates of improvement in cognitive therapy (CT). For 1 week before treatment, patients completed nightly electronic diaries that assessed daily stressors and negative affect (NA). The authors used multilevel modeling to…

  13. Affect as Information in Persuasion: A Model of Affect Identification and Discounting

    PubMed Central

    Albarracín, Dolores; Kumkale, G. Tarcan

    2016-01-01

    Three studies examined the implications of a model of affect as information in persuasion. According to this model, extraneous affect may have an influence when message recipients exert moderate amounts of thought, because they identify their affective reactions as potential criteria but fail to discount them as irrelevant. However, message recipients may not use affect as information when they deem affect irrelevant or when they do not identify their affective reactions at all. Consistent with this curvilinear prediction, recipients of a message that either favored or opposed comprehensive exams used affect as a basis for attitudes in situations that elicited moderate thought. Affect, however, had no influence on attitudes in conditions that elicited either large or small amounts of thought. PMID:12635909

  14. Temporal Prediction Errors Affect Short-Term Memory Scanning Response Time.

    PubMed

    Limongi, Roberto; Silva, Angélica M

    2016-11-01

    The Sternberg short-term memory scanning task has been used to unveil cognitive operations involved in time perception. Participants produce time intervals during the task, and the researcher explores how task performance affects interval production - where time estimation error is the dependent variable of interest. The perspective of predictive behavior regards time estimation error as a temporal prediction error (PE), an independent variable that controls cognition, behavior, and learning. Based on this perspective, we investigated whether temporal PEs affect short-term memory scanning. Participants performed temporal predictions while they maintained information in memory. Model inference revealed that PEs affected memory scanning response time independently of the memory-set size effect. We discuss the results within the context of formal and mechanistic models of short-term memory scanning and predictive coding, a Bayes-based theory of brain function. We state the hypothesis that our finding could be associated with weak frontostriatal connections and weak striatal activity.

  15. Social anxiety and the accuracy of predicted affect.

    PubMed

    Martin, Shannon M; Quirk, Stuart W

    2015-01-01

    Social anxiety is theorised to arise from sustained over-activation of a mammalian evolved system for detecting and responding to social threat with corresponding diminished opportunities for attaining the pleasure of safe attachments. Emotional forecasting data from two holidays were used to test the hypothesis that greater social anxiety would be associated with decreased expectations of positive affect (PA) and greater anticipated negative affect (NA) on a holiday marked by group celebration (St. Patrick's Day) while being associated with greater predicted PA for daters on a romantic holiday (Valentine's Day). Participants completed symptom reports, made affective forecasts and provided multiple affect reports throughout each holiday. Higher levels of social anxiety were associated with greater anticipated PA for Valentine's Day daters, but lower experienced PA on the holiday; this was not found for trait anxiety and depression. Alternatively, trait anxiety, depression and social anxiety were associated with less predicted PA for St. Patrick's Day, greater anticipated NA and diminished experienced PA/greater NA during the holiday. Results are discussed in light of perceived hope for rewarding safe emotional contact for those daters in contrast to the greater possibility for social threat associated with group celebration typical of St. Patrick's Day.

  16. Melanoma Risk Prediction Models

    Cancer.gov

    Developing statistical models that estimate the probability of developing melanoma cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  17. Predictive Models and Computational Embryology

    EPA Science Inventory

    EPA’s ‘virtual embryo’ project is building an integrative systems biology framework for predictive models of developmental toxicity. One schema involves a knowledge-driven adverse outcome pathway (AOP) framework utilizing information from public databases, standardized ontologies...

  18. Proton Fluence Prediction Models

    NASA Technical Reports Server (NTRS)

    Feynman, Joan

    1996-01-01

    Many spacecraft anomalies are caused by positively charged high energy particles impinging on the vehicle and its component parts. Here we review the current knowledge of the interplanetary particle environment in the energy ranges that are most important for these effects, 10 to 100 MeV/amu. The emphasis is on the particle environment at 1 AU. State-of-the-art engineering models are briefly described along with comments on the future work required in this field.

  19. Predictive Modeling in Race Walking

    PubMed Central

    Wiktorowicz, Krzysztof; Przednowek, Krzysztof; Lassota, Lesław; Krzeszowski, Tomasz

    2015-01-01

    This paper presents the use of linear and nonlinear multivariable models as tools to support training process of race walkers. These models are calculated using data collected from race walkers' training events and they are used to predict the result over a 3 km race based on training loads. The material consists of 122 training plans for 21 athletes. In order to choose the best model leave-one-out cross-validation method is used. The main contribution of the paper is to propose the nonlinear modifications for linear models in order to achieve smaller prediction error. It is shown that the best model is a modified LASSO regression with quadratic terms in the nonlinear part. This model has the smallest prediction error and simplified structure by eliminating some of the predictors. PMID:26339230

  20. Predictive models of battle dynamics

    NASA Astrophysics Data System (ADS)

    Jelinek, Jan

    2001-09-01

    The application of control and game theories to improve battle planning and execution requires models, which allow military strategists and commanders to reliably predict the expected outcomes of various alternatives over a long horizon into the future. We have developed probabilistic battle dynamics models, whose building blocks in the form of Markov chains are derived from the first principles, and applied them successfully in the design of the Model Predictive Task Commander package. This paper introduces basic concepts of our modeling approach and explains the probability distributions needed to compute the transition probabilities of the Markov chains.

  1. Risk terrain modeling predicts child maltreatment.

    PubMed

    Daley, Dyann; Bachmann, Michael; Bachmann, Brittany A; Pedigo, Christian; Bui, Minh-Thuy; Coffman, Jamye

    2016-12-01

    As indicated by research on the long-term effects of adverse childhood experiences (ACEs), maltreatment has far-reaching consequences for affected children. Effective prevention measures have been elusive, partly due to difficulty in identifying vulnerable children before they are harmed. This study employs Risk Terrain Modeling (RTM), an analysis of the cumulative effect of environmental factors thought to be conducive for child maltreatment, to create a highly accurate prediction model for future substantiated child maltreatment cases in the City of Fort Worth, Texas. The model is superior to commonly used hotspot predictions and more beneficial in aiding prevention efforts in a number of ways: 1) it identifies the highest risk areas for future instances of child maltreatment with improved precision and accuracy; 2) it aids the prioritization of risk-mitigating efforts by informing about the relative importance of the most significant contributing risk factors; 3) since predictions are modeled as a function of easily obtainable data, practitioners do not have to undergo the difficult process of obtaining official child maltreatment data to apply it; 4) the inclusion of a multitude of environmental risk factors creates a more robust model with higher predictive validity; and, 5) the model does not rely on a retrospective examination of past instances of child maltreatment, but adapts predictions to changing environmental conditions. The present study introduces and examines the predictive power of this new tool to aid prevention efforts seeking to improve the safety, health, and wellbeing of vulnerable children.

  2. Model aids cuttings transport prediction

    SciTech Connect

    Gavignet, A.A. ); Sobey, I.J. )

    1989-09-01

    Drilling of highly deviated wells can be complicated by the formation of a thick bed of cuttings at low flow rates. The model proposed in this paper shows what mechanisms control the thickness of such a bed, and the model predictions are compared with experimental results.

  3. In silico prediction of splice-affecting nucleotide variants.

    PubMed

    Houdayer, Claude

    2011-01-01

    It appears that all types of genomic nucleotide variations can be deleterious by affecting normal pre-mRNA splicing via disruption/creation of splice site consensus sequences. As it is neither pertinent nor realistic to perform functional testing for all of these variants, it is important to identify those that could lead to a splice defect in order to restrict experimental transcript analyses to the most appropriate cases. In silico tools designed to provide this type of prediction are available. In this chapter, we present in silico splice tools integrated in the Alamut (Interactive Biosoftware) application and detail their use in routine diagnostic applications. At this time, in silico predictions are useful for variants that decrease the strength of wild-type splice sites or create a cryptic splice site. Importantly, in silico predictions are not sufficient to classify variants as neutral or deleterious: they should be used as part of the decision-making process to detect potential candidates for splicing anomalies, prompting molecular geneticists to carry out transcript analyses in a limited and pertinent number of cases which could be managed in routine settings.

  4. Hydrometeorological model for streamflow prediction

    USGS Publications Warehouse

    Tangborn, Wendell V.

    1979-01-01

    The hydrometeorological model described in this manual was developed to predict seasonal streamflow from water in storage in a basin using streamflow and precipitation data. The model, as described, applies specifically to the Skokomish, Nisqually, and Cowlitz Rivers, in Washington State, and more generally to streams in other regions that derive seasonal runoff from melting snow. Thus the techniques demonstrated for these three drainage basins can be used as a guide for applying this method to other streams. Input to the computer program consists of daily averages of gaged runoff of these streams, and daily values of precipitation collected at Longmire, Kid Valley, and Cushman Dam. Predictions are based on estimates of the absolute storage of water, predominately as snow: storage is approximately equal to basin precipitation less observed runoff. A pre-forecast test season is used to revise the storage estimate and improve the prediction accuracy. To obtain maximum prediction accuracy for operational applications with this model , a systematic evaluation of several hydrologic and meteorologic variables is first necessary. Six input options to the computer program that control prediction accuracy are developed and demonstrated. Predictions of streamflow can be made at any time and for any length of season, although accuracy is usually poor for early-season predictions (before December 1) or for short seasons (less than 15 days). The coefficient of prediction (CP), the chief measure of accuracy used in this manual, approaches zero during the late autumn and early winter seasons and reaches a maximum of about 0.85 during the spring snowmelt season. (Kosco-USGS)

  5. Predictive models of forest dynamics.

    PubMed

    Purves, Drew; Pacala, Stephen

    2008-06-13

    Dynamic global vegetation models (DGVMs) have shown that forest dynamics could dramatically alter the response of the global climate system to increased atmospheric carbon dioxide over the next century. But there is little agreement between different DGVMs, making forest dynamics one of the greatest sources of uncertainty in predicting future climate. DGVM predictions could be strengthened by integrating the ecological realities of biodiversity and height-structured competition for light, facilitated by recent advances in the mathematics of forest modeling, ecological understanding of diverse forest communities, and the availability of forest inventory data.

  6. Neural Affective Mechanisms Predict Market-Level Microlending.

    PubMed

    Genevsky, Alexander; Knutson, Brian

    2015-09-01

    Humans sometimes share with others whom they may never meet or know, in violation of the dictates of pure self-interest. Research has not established which neuropsychological mechanisms support lending decisions, nor whether their influence extends to markets involving significant financial incentives. In two studies, we found that neural affective mechanisms influence the success of requests for microloans. In a large Internet database of microloan requests (N = 13,500), we found that positive affective features of photographs promoted the success of those requests. We then established that neural activity (i.e., in the nucleus accumbens) and self-reported positive arousal in a neuroimaging sample (N = 28) predicted the success of loan requests on the Internet, above and beyond the effects of the neuroimaging sample's own choices (i.e., to lend or not). These findings suggest that elicitation of positive arousal can promote the success of loan requests, both in the laboratory and on the Internet. They also highlight affective neuroscience's potential to probe neuropsychological mechanisms that drive microlending, enhance the effectiveness of loan requests, and forecast market-level behavior.

  7. What do saliency models predict?

    PubMed Central

    Koehler, Kathryn; Guo, Fei; Zhang, Sheng; Eckstein, Miguel P.

    2014-01-01

    Saliency models have been frequently used to predict eye movements made during image viewing without a specified task (free viewing). Use of a single image set to systematically compare free viewing to other tasks has never been performed. We investigated the effect of task differences on the ability of three models of saliency to predict the performance of humans viewing a novel database of 800 natural images. We introduced a novel task where 100 observers made explicit perceptual judgments about the most salient image region. Other groups of observers performed a free viewing task, saliency search task, or cued object search task. Behavior on the popular free viewing task was not best predicted by standard saliency models. Instead, the models most accurately predicted the explicit saliency selections and eye movements made while performing saliency judgments. Observers' fixations varied similarly across images for the saliency and free viewing tasks, suggesting that these two tasks are related. The variability of observers' eye movements was modulated by the task (lowest for the object search task and greatest for the free viewing and saliency search tasks) as well as the clutter content of the images. Eye movement variability in saliency search and free viewing might be also limited by inherent variation of what observers consider salient. Our results contribute to understanding the tasks and behavioral measures for which saliency models are best suited as predictors of human behavior, the relationship across various perceptual tasks, and the factors contributing to observer variability in fixational eye movements. PMID:24618107

  8. PREDICTIVE MODELS. Enhanced Oil Recovery Model

    SciTech Connect

    Ray, R.M.

    1992-02-26

    PREDICTIVE MODELS is a collection of five models - CFPM, CO2PM, ICPM, PFPM, and SFPM - used in the 1982-1984 National Petroleum Council study of enhanced oil recovery (EOR) potential. Each pertains to a specific EOR process designed to squeeze additional oil from aging or spent oil fields. The processes are: 1 chemical flooding, where soap-like surfactants are injected into the reservoir to wash out the oil; 2 carbon dioxide miscible flooding, where carbon dioxide mixes with the lighter hydrocarbons making the oil easier to displace; 3 in-situ combustion, which uses the heat from burning some of the underground oil to thin the product; 4 polymer flooding, where thick, cohesive material is pumped into a reservoir to push the oil through the underground rock; and 5 steamflood, where pressurized steam is injected underground to thin the oil. CFPM, the Chemical Flood Predictive Model, models micellar (surfactant)-polymer floods in reservoirs, which have been previously waterflooded to residual oil saturation. Thus, only true tertiary floods are considered. An option allows a rough estimate of oil recovery by caustic or caustic-polymer processes. CO2PM, the Carbon Dioxide miscible flooding Predictive Model, is applicable to both secondary (mobile oil) and tertiary (residual oil) floods, and to either continuous CO2 injection or water-alternating gas processes. ICPM, the In-situ Combustion Predictive Model, computes the recovery and profitability of an in-situ combustion project from generalized performance predictive algorithms. PFPM, the Polymer Flood Predictive Model, is switch-selectable for either polymer or waterflooding, and an option allows the calculation of the incremental oil recovery and economics of polymer relative to waterflooding. SFPM, the Steamflood Predictive Model, is applicable to the steam drive process, but not to cyclic steam injection (steam soak) processes.

  9. Environmental Factors Affecting Asthma and Allergies: Predicting and Simulating Downwind Exposure to Airborne Pollen

    NASA Technical Reports Server (NTRS)

    Luvall, Jeffrey; Estes, Sue; Sprigg, William A.; Nickovic, Slobodan; Huete, Alfredo; Solano, Ramon; Ratana, Piyachat; Jiang, Zhangyan; Flowers, Len; Zelicoff, Alan

    2009-01-01

    This slide presentation reviews the environmental factors that affect asthma and allergies and work to predict and simulate the downwind exposure to airborne pollen. Using a modification of Dust REgional Atmosphere Model (DREAM) that incorporates phenology (i.e. PREAM) the aim was to predict concentrations of pollen in time and space. The strategy for using the model to simulate downwind pollen dispersal, and evaluate the results. Using MODerate-resolution Imaging Spectroradiometer (MODIS), to get seasonal sampling of Juniper, the pollen chosen for the study, land cover on a near daily basis. The results of the model are reviewed.

  10. Predictive Models of Liver Cancer

    EPA Science Inventory

    Predictive models of chemical-induced liver cancer face the challenge of bridging causative molecular mechanisms to adverse clinical outcomes. The latent sequence of intervening events from chemical insult to toxicity are poorly understood because they span multiple levels of bio...

  11. Calibrated predictions for multivariate competing risks models.

    PubMed

    Gorfine, Malka; Hsu, Li; Zucker, David M; Parmigiani, Giovanni

    2014-04-01

    Prediction models for time-to-event data play a prominent role in assessing the individual risk of a disease, such as cancer. Accurate disease prediction models provide an efficient tool for identifying individuals at high risk, and provide the groundwork for estimating the population burden and cost of disease and for developing patient care guidelines. We focus on risk prediction of a disease in which family history is an important risk factor that reflects inherited genetic susceptibility, shared environment, and common behavior patterns. In this work family history is accommodated using frailty models, with the main novel feature being allowing for competing risks, such as other diseases or mortality. We show through a simulation study that naively treating competing risks as independent right censoring events results in non-calibrated predictions, with the expected number of events overestimated. Discrimination performance is not affected by ignoring competing risks. Our proposed prediction methodologies correctly account for competing events, are very well calibrated, and easy to implement.

  12. Community history affects the predictability of microbial ecosystem development.

    PubMed

    Pagaling, Eulyn; Strathdee, Fiona; Spears, Bryan M; Cates, Michael E; Allen, Rosalind J; Free, Andrew

    2014-01-01

    Microbial communities mediate crucial biogeochemical, biomedical and biotechnological processes, yet our understanding of their assembly, and our ability to control its outcome, remain poor. Existing evidence presents conflicting views on whether microbial ecosystem assembly is predictable, or inherently unpredictable. We address this issue using a well-controlled laboratory model system, in which source microbial communities colonize a pristine environment to form complex, nutrient-cycling ecosystems. When the source communities colonize a novel environment, final community composition and function (as measured by redox potential) are unpredictable, although a signature of the community's previous history is maintained. However, when the source communities are pre-conditioned to their new habitat, community development is more reproducible. This situation contrasts with some studies of communities of macro-organisms, where strong selection under novel environmental conditions leads to reproducible community structure, whereas communities under weaker selection show more variability. Our results suggest that the microbial rare biosphere may have an important role in the predictability of microbial community development, and that pre-conditioning may help to reduce unpredictability in the design of microbial communities for biotechnological applications.

  13. Community history affects the predictability of microbial ecosystem development

    PubMed Central

    Pagaling, Eulyn; Strathdee, Fiona; Spears, Bryan M; Cates, Michael E; Allen, Rosalind J; Free, Andrew

    2014-01-01

    Microbial communities mediate crucial biogeochemical, biomedical and biotechnological processes, yet our understanding of their assembly, and our ability to control its outcome, remain poor. Existing evidence presents conflicting views on whether microbial ecosystem assembly is predictable, or inherently unpredictable. We address this issue using a well-controlled laboratory model system, in which source microbial communities colonize a pristine environment to form complex, nutrient-cycling ecosystems. When the source communities colonize a novel environment, final community composition and function (as measured by redox potential) are unpredictable, although a signature of the community's previous history is maintained. However, when the source communities are pre-conditioned to their new habitat, community development is more reproducible. This situation contrasts with some studies of communities of macro-organisms, where strong selection under novel environmental conditions leads to reproducible community structure, whereas communities under weaker selection show more variability. Our results suggest that the microbial rare biosphere may have an important role in the predictability of microbial community development, and that pre-conditioning may help to reduce unpredictability in the design of microbial communities for biotechnological applications. PMID:23985743

  14. Neutral models as a way to evaluate the Sea Level Affecting Marshes Model (SLAMM)

    EPA Science Inventory

    A commonly used landscape model to simulate wetland change – the Sea Level Affecting Marshes Model(SLAMM) – has rarely been explicitly assessed for its prediction accuracy. Here, we evaluated this model using recently proposed neutral models – including the random constraint matc...

  15. Mars solar conjunction prediction modeling

    NASA Astrophysics Data System (ADS)

    Srivastava, Vineet K.; Kumar, Jai; Kulshrestha, Shivali; Kushvah, Badam Singh

    2016-01-01

    During the Mars solar conjunction, telecommunication and tracking between the spacecraft and the Earth degrades significantly. The radio signal degradation depends on the angular separation between the Sun, Earth and probe (SEP), the signal frequency band and the solar activity. All radiometric tracking data types display increased noise and signatures for smaller SEP angles. Due to scintillation, telemetry frame errors increase significantly when solar elongation becomes small enough. This degradation in telemetry data return starts at solar elongation angles of around 5° at S-band, around 2° at X-band and about 1° at Ka-band. This paper presents a mathematical model for predicting Mars superior solar conjunction for any Mars orbiting spacecraft. The described model is simulated for the Mars Orbiter Mission which experienced Mars solar conjunction during May-July 2015. Such a model may be useful to flight projects and design engineers in the planning of Mars solar conjunction operational scenarios.

  16. Prior task experience affects temporal prediction and estimation

    PubMed Central

    Tobin, Simon; Grondin, Simon

    2015-01-01

    It has been shown that prior experience with a task improves temporal prediction, even when the amount of prior experience with the task is often limited. The present study targeted the role of extensive training on temporal prediction. Expert and intermediate runners had to predict the time of a 5 km running competition. Furthermore, after the race’s completion, participants had to estimate their running time so that it could be compared with the predicted time. Results show that expert runners were more accurate than intermediate runners for both predicting and estimating their running time. Furthermore, only expert runners had an estimation that was more accurate than their initial prediction. The results confirm the role of prior task experience in both temporal prediction and estimation. PMID:26217261

  17. Prior task experience affects temporal prediction and estimation.

    PubMed

    Tobin, Simon; Grondin, Simon

    2015-01-01

    It has been shown that prior experience with a task improves temporal prediction, even when the amount of prior experience with the task is often limited. The present study targeted the role of extensive training on temporal prediction. Expert and intermediate runners had to predict the time of a 5 km running competition. Furthermore, after the race's completion, participants had to estimate their running time so that it could be compared with the predicted time. Results show that expert runners were more accurate than intermediate runners for both predicting and estimating their running time. Furthermore, only expert runners had an estimation that was more accurate than their initial prediction. The results confirm the role of prior task experience in both temporal prediction and estimation.

  18. Affective forecasting in an orangutan: predicting the hedonic outcome of novel juice mixes.

    PubMed

    Sauciuc, Gabriela-Alina; Persson, Tomas; Bååth, Rasmus; Bobrowicz, Katarzyna; Osvath, Mathias

    2016-11-01

    Affective forecasting is an ability that allows the prediction of the hedonic outcome of never-before experienced situations, by mentally recombining elements of prior experiences into possible scenarios, and pre-experiencing what these might feel like. It has been hypothesised that this ability is uniquely human. For example, given prior experience with the ingredients, but in the absence of direct experience with the mixture, only humans are said to be able to predict that lemonade tastes better with sugar than without it. Non-human animals, on the other hand, are claimed to be confined to predicting-exclusively and inflexibly-the outcome of previously experienced situations. Relying on gustatory stimuli, we devised a non-verbal method for assessing affective forecasting and tested comparatively one Sumatran orangutan and ten human participants. Administered as binary choices, the test required the participants to mentally construct novel juice blends from familiar ingredients and to make hedonic predictions concerning the ensuing mixes. The orangutan's performance was within the range of that shown by the humans. Both species made consistent choices that reflected independently measured taste preferences for the stimuli. Statistical models fitted to the data confirmed the predictive accuracy of such a relationship. The orangutan, just like humans, thus seems to have been able to make hedonic predictions concerning never-before experienced events.

  19. Climate Modeling and Prediction at NSIPP

    NASA Technical Reports Server (NTRS)

    Suarez, Max; Einaudi, Franco (Technical Monitor)

    2001-01-01

    The talk will review modeling and prediction efforts undertaken as part of NASA's Seasonal to Interannual Prediction Project (NSIPP). The focus will be on atmospheric model results, including its use for experimental seasonal prediction and the diagnostic analysis of climate anomalies. The model's performance in coupled experiments with land and atmosphere models will also be discussed.

  20. Predictive Modeling of Cardiac Ischemia

    NASA Technical Reports Server (NTRS)

    Anderson, Gary T.

    1996-01-01

    The goal of the Contextual Alarms Management System (CALMS) project is to develop sophisticated models to predict the onset of clinical cardiac ischemia before it occurs. The system will continuously monitor cardiac patients and set off an alarm when they appear about to suffer an ischemic episode. The models take as inputs information from patient history and combine it with continuously updated information extracted from blood pressure, oxygen saturation and ECG lines. Expert system, statistical, neural network and rough set methodologies are then used to forecast the onset of clinical ischemia before it transpires, thus allowing early intervention aimed at preventing morbid complications from occurring. The models will differ from previous attempts by including combinations of continuous and discrete inputs. A commercial medical instrumentation and software company has invested funds in the project with a goal of commercialization of the technology. The end product will be a system that analyzes physiologic parameters and produces an alarm when myocardial ischemia is present. If proven feasible, a CALMS-based system will be added to existing heart monitoring hardware.

  1. Predictive Modeling of Tokamak Configurations*

    NASA Astrophysics Data System (ADS)

    Casper, T. A.; Lodestro, L. L.; Pearlstein, L. D.; Bulmer, R. H.; Jong, R. A.; Kaiser, T. B.; Moller, J. M.

    2001-10-01

    The Corsica code provides comprehensive toroidal plasma simulation and design capabilities with current applications [1] to tokamak, reversed field pinch (RFP) and spheromak configurations. It calculates fixed and free boundary equilibria coupled to Ohm's law, sources, transport models and MHD stability modules. We are exploring operations scenarios for both the DIII-D and KSTAR tokamaks. We will present simulations of the effects of electron cyclotron heating (ECH) and current drive (ECCD) relevant to the Quiescent Double Barrier (QDB) regime on DIII-D exploring long pulse operation issues. KSTAR simulations using ECH/ECCD in negative central shear configurations explore evolution to steady state while shape evolution studies during current ramp up using a hyper-resistivity model investigate startup scenarios and limitations. Studies of high bootstrap fraction operation stimulated by recent ECH/ECCD experiments on DIIID will also be presented. [1] Pearlstein, L.D., et al, Predictive Modeling of Axisymmetric Toroidal Configurations, 28th EPS Conference on Controlled Fusion and Plasma Physics, Madeira, Portugal, June 18-22, 2001. * Work performed under the auspices of the U.S. Department of Energy by the University of California, Lawrence Livermore National Laboratory under contract No. W-7405-Eng-48.

  2. Referent Predictability Is Affected by Syntactic Structure: Evidence from Chinese

    ERIC Educational Resources Information Center

    Cheng, Wei; Almor, Amit

    2017-01-01

    This paper examines the effect of syntactic structures on referent predictability. Focusing on stimulus-experiencer (SE) verbs, we conducted two sentence-completion experiments in Chinese by contrasting SE verbs in three structures (active canonical, active "ba," and passive). The results showed that although verb semantics and discourse…

  3. Predicting Emotional Responses to Horror Films from Cue-Specific Affect.

    ERIC Educational Resources Information Center

    Neuendorf, Kimberly A.; Sparks, Glenn G.

    1988-01-01

    Assesses individuals' fear and enjoyment reactions to horror films, applying theories of cognition and affect that predict emotional responses to a stimulus on the basis of prior affect toward specific cues included in that stimulus. (MM)

  4. Exploration of SNP variants affecting hair colour prediction in Europeans.

    PubMed

    Söchtig, Jens; Phillips, Chris; Maroñas, Olalla; Gómez-Tato, Antonio; Cruz, Raquel; Alvarez-Dios, Jose; de Cal, María-Ángeles Casares; Ruiz, Yarimar; Reich, Kristian; Fondevila, Manuel; Carracedo, Ángel; Lareu, María V

    2015-09-01

    DNA profiling is a key tool for forensic analysis; however, current methods identify a suspect either by direct comparison or from DNA database searches. In cases with unidentified suspects, prediction of visible physical traits e.g. pigmentation or hair distribution of the DNA donors can provide important probative information. This study aimed to explore single nucleotide polymorphism (SNP) variants for their effect on hair colour prediction. A discovery panel of 63 SNPs consisting of already established hair colour markers from the HIrisPlex hair colour phenotyping assay as well as additional markers for which associations to human pigmentation traits were previously identified was used to develop multiplex assays based on SNaPshot single-base extension technology. A genotyping study was performed on a range of European populations (n = 605). Hair colour phenotyping was accomplished by matching donor's hair to a graded colour category system of reference shades and photography. Since multiple SNPs in combination contribute in varying degrees to hair colour predictability in Europeans, we aimed to compile a compact marker set that could provide a reliable hair colour inference from the fewest SNPs. The predictive approach developed uses a naïve Bayes classifier to provide hair colour assignment probabilities for the SNP profiles of the key SNPs and was embedded into the Snipper online SNP classifier ( http://mathgene.usc.es/snipper/ ). Results indicate that red, blond, brown and black hair colours are predictable with informative probabilities in a high proportion of cases. Our study resulted in the identification of 12 most strongly associated SNPs to hair pigmentation variation in six genes.

  5. Early negative affect predicts anxiety, not autism, in preschool boys with fragile X syndrome.

    PubMed

    Tonnsen, Bridgette L; Malone, Patrick S; Hatton, Deborah D; Roberts, Jane E

    2013-02-01

    Children with fragile X syndrome (FXS) face high risk for anxiety disorders, yet no studies have explored FXS as a high-risk sample for investigating early manifestations of anxiety outcomes. Negative affect is one of the most salient predictors of problem behaviors and has been associated with both anxiety and autistic outcomes in clinical and non-clinical pediatric samples. In light of the high comorbidity between autism and anxiety within FXS, the present study investigates the relationship between longitudinal trajectories of negative affect (between 8 and 71 months) and severity of anxiety and autistic outcomes in young males with FXS (n = 25). Multilevel models indicated associations between elevated anxiety and higher fear and sadness, lower soothability, and steeper longitudinal increases in approach. Autistic outcomes were unrelated to negative affect. These findings suggest early negative affect differentially predicts anxiety, not autistic symptoms, within FXS. Future research is warranted to determine the specificity of the relationship between negative affect and anxiety, as well as to explore potential moderators. Characterizing the relationship between early negative affect and anxiety within FXS may inform etiology and treatment considerations specific to children with FXS, as well as lend insight into precursors of anxiety disorders in other clinical groups and community samples.

  6. Predictive Model Assessment for Count Data

    DTIC Science & Technology

    2007-09-05

    critique count regression models for patent data, and assess the predictive performance of Bayesian age-period-cohort models for larynx cancer counts...the predictive performance of Bayesian age-period-cohort models for larynx cancer counts in Germany. We consider a recent suggestion by Baker and...Figure 5. Boxplots for various scores for patent data count regressions. 11 Table 1 Four predictive models for larynx cancer counts in Germany, 1998–2002

  7. The interaction of borderline personality disorder symptoms and relationship satisfaction in predicting affect.

    PubMed

    Kuhlken, Katherine; Robertson, Christopher; Benson, Jessica; Nelson-Gray, Rosemery

    2014-01-01

    Previous research has suggested that stable, marital relationships may have overall prognostic significance for individuals with borderline personality disorder (BPD); however, research focused on the impact of nonmarital, and perhaps short-term, romantic relationships is lacking. Thus, the primary goal of this study was to examine the impact of the interaction of BPD symptoms and relationship satisfaction on state negative affect in college undergraduates. It was predicted that individuals who scored higher on measures of BPD symptoms and who were in a satisfying romantic relationship would report less negative affect than comparable individuals in a less satisfying romantic relationship. Questionnaires assessing BPD symptoms, relationship satisfaction, and negative affect were administered to 111 women, the majority of whom then completed daily measures of relationship satisfaction and negative affect over a 2-week follow-up period. Hierarchical multiple regression and hierarchical linear modeling were used to test the hypotheses. The interaction of BPD symptoms with relationship satisfaction was found to significantly predict anger, as measured at one time point, suggesting that satisfying romantic relationships may be a protective factor for individuals scoring high on measures of BPD symptoms with regard to anger.

  8. Objective calibration of numerical weather prediction models

    NASA Astrophysics Data System (ADS)

    Voudouri, A.; Khain, P.; Carmona, I.; Bellprat, O.; Grazzini, F.; Avgoustoglou, E.; Bettems, J. M.; Kaufmann, P.

    2017-07-01

    Numerical weather prediction (NWP) and climate models use parameterization schemes for physical processes, which often include free or poorly confined parameters. Model developers normally calibrate the values of these parameters subjectively to improve the agreement of forecasts with available observations, a procedure referred as expert tuning. A practicable objective multi-variate calibration method build on a quadratic meta-model (MM), that has been applied for a regional climate model (RCM) has shown to be at least as good as expert tuning. Based on these results, an approach to implement the methodology to an NWP model is presented in this study. Challenges in transferring the methodology from RCM to NWP are not only restricted to the use of higher resolution and different time scales. The sensitivity of the NWP model quality with respect to the model parameter space has to be clarified, as well as optimize the overall procedure, in terms of required amount of computing resources for the calibration of an NWP model. Three free model parameters affecting mainly turbulence parameterization schemes were originally selected with respect to their influence on the variables associated to daily forecasts such as daily minimum and maximum 2 m temperature as well as 24 h accumulated precipitation. Preliminary results indicate that it is both affordable in terms of computer resources and meaningful in terms of improved forecast quality. In addition, the proposed methodology has the advantage of being a replicable procedure that can be applied when an updated model version is launched and/or customize the same model implementation over different climatological areas.

  9. Affect, Reason, and Persuasion: Advertising Strategies That Predict Affective and Analytic-Cognitive Responses.

    ERIC Educational Resources Information Center

    Chaudhuri, Arjun; Buck, Ross

    1995-01-01

    Develops and tests hypotheses concerning the relationship of specific advertising strategies to affective and analytic cognitive responses of the audience. Analyses undergraduate students' responses to 240 advertisements. Demonstrates that advertising strategy variables accounted substantially for the variance in affective and analytic cognition.…

  10. Predicting performance expectations from affective impressions: linking affect control theory and status characteristics theory.

    PubMed

    Dippong, Joseph; Kalkhoff, Will

    2015-03-01

    Affect control theory (ACT) and status characteristics theory (SCT) offer separate and distinct explanations for how individuals interpret and process status- and power-relevant information about interaction partners. Existing research within affect control theory offers evidence that status and power are related to the affective impressions that individuals form of others along the dimensions of evaluation and potency, respectively. Alternately, status characteristics theory suggests that status and power influence interaction through the mediating cognitive construct of performance expectations. Although both theories have amassed an impressive amount of empirical support, research has yet to articulate theoretical and empirical connections between affective impressions and performance expectations. The purpose of our study is to address this gap. Elaborating a link between ACT and SCT in terms of their central concepts can serve as a stepping stone to improving the explanatory capacity of both theories, while providing a potential bridge by which they can be employed jointly.

  11. Prediction of laser cutting heat affected zone by extreme learning machine

    NASA Astrophysics Data System (ADS)

    Anicic, Obrad; Jović, Srđan; Skrijelj, Hivzo; Nedić, Bogdan

    2017-01-01

    Heat affected zone (HAZ) of the laser cutting process may be developed based on combination of different factors. In this investigation the HAZ forecasting, based on the different laser cutting parameters, was analyzed. The main goal was to predict the HAZ according to three inputs. The purpose of this research was to develop and apply the Extreme Learning Machine (ELM) to predict the HAZ. The ELM results were compared with genetic programming (GP) and artificial neural network (ANN). The reliability of the computational models were accessed based on simulation results and by using several statistical indicators. Based upon simulation results, it was demonstrated that ELM can be utilized effectively in applications of HAZ forecasting.

  12. Affect and non-uniform characteristics of predictive processing in musical behaviour.

    PubMed

    Schaefer, Rebecca S; Overy, Katie; Nelson, Peter

    2013-06-01

    The important roles of prediction and prior experience are well established in music research and fit well with Clark's concept of unified perception, cognition, and action arising from hierarchical, bidirectional predictive processing. However, in order to fully account for human musical intelligence, Clark needs to further consider the powerful and variable role of affect in relation to prediction error.

  13. Future missions studies: Combining Schatten's solar activity prediction model with a chaotic prediction model

    NASA Technical Reports Server (NTRS)

    Ashrafi, S.

    1991-01-01

    K. Schatten (1991) recently developed a method for combining his prediction model with our chaotic model. The philosophy behind this combined model and his method of combination is explained. Because the Schatten solar prediction model (KS) uses a dynamo to mimic solar dynamics, accurate prediction is limited to long-term solar behavior (10 to 20 years). The Chaotic prediction model (SA) uses the recently developed techniques of nonlinear dynamics to predict solar activity. It can be used to predict activity only up to the horizon. In theory, the chaotic prediction should be several orders of magnitude better than statistical predictions up to that horizon; beyond the horizon, chaotic predictions would theoretically be just as good as statistical predictions. Therefore, chaos theory puts a fundamental limit on predictability.

  14. Models Predicting Success of Infertility Treatment: A Systematic Review

    PubMed Central

    Zarinara, Alireza; Zeraati, Hojjat; Kamali, Koorosh; Mohammad, Kazem; Shahnazari, Parisa; Akhondi, Mohammad Mehdi

    2016-01-01

    Background: Infertile couples are faced with problems that affect their marital life. Infertility treatment is expensive and time consuming and occasionally isn’t simply possible. Prediction models for infertility treatment have been proposed and prediction of treatment success is a new field in infertility treatment. Because prediction of treatment success is a new need for infertile couples, this paper reviewed previous studies for catching a general concept in applicability of the models. Methods: This study was conducted as a systematic review at Avicenna Research Institute in 2015. Six data bases were searched based on WHO definitions and MESH key words. Papers about prediction models in infertility were evaluated. Results: Eighty one papers were eligible for the study. Papers covered years after 1986 and studies were designed retrospectively and prospectively. IVF prediction models have more shares in papers. Most common predictors were age, duration of infertility, ovarian and tubal problems. Conclusion: Prediction model can be clinically applied if the model can be statistically evaluated and has a good validation for treatment success. To achieve better results, the physician and the couples’ needs estimation for treatment success rate were based on history, the examination and clinical tests. Models must be checked for theoretical approach and appropriate validation. The privileges for applying the prediction models are the decrease in the cost and time, avoiding painful treatment of patients, assessment of treatment approach for physicians and decision making for health managers. The selection of the approach for designing and using these models is inevitable. PMID:27141461

  15. Potential animal models of seasonal affective disorder.

    PubMed

    Workman, Joanna L; Nelson, Randy J

    2011-01-01

    Seasonal affective disorder (SAD) is characterized by depressive episodes during winter that are alleviated during summer and by morning bright light treatment. Currently, there is no animal model of SAD. However, it may be possible to use rodents that respond to day length (photoperiod) to understand how photoperiod can shape the brain and behavior in humans. As nights lengthen in the autumn, the duration of the nightly elevation of melatonin increase; seasonally breeding animals use this information to orchestrate seasonal changes in physiology and behavior. SAD may originate from the extended duration of nightly melatonin secretion during fall and winter. These similarities between humans and rodents in melatonin secretion allows for comparisons with rodents that express more depressive-like responses when exposed to short day lengths. For instance, Siberian hamsters, fat sand rats, Nile grass rats, and Wistar rats display a depressive-like phenotype when exposed to short days. Current research in depression and animal models of depression suggests that hippocampal plasticity may underlie the symptoms of depression and depressive-like behaviors, respectively. It is also possible that day length induces structural changes in human brains. Many seasonally breeding rodents undergo changes in whole brain and hippocampal volume in short days. Based on strict validity criteria, there is no animal model of SAD, but rodents that respond to reduced day lengths may be useful to approximate the neurobiological phenomena that occur in people with SAD, leading to greater understanding of the etiology of the disorder as well as novel therapeutic interventions.

  16. Product component genealogy modeling and field-failure prediction

    SciTech Connect

    King, Caleb; Hong, Yili; Meeker, William Q.

    2016-04-13

    Many industrial products consist of multiple components that are necessary for system operation. There is an abundance of literature on modeling the lifetime of such components through competing risks models. During the life-cycle of a product, it is common for there to be incremental design changes to improve reliability, to reduce costs, or due to changes in availability of certain part numbers. These changes can affect product reliability but are often ignored in system lifetime modeling. By incorporating this information about changes in part numbers over time (information that is readily available in most production databases), better accuracy can be achieved in predicting time to failure, thus yielding more accurate field-failure predictions. This paper presents methods for estimating parameters and predictions for this generational model and a comparison with existing methods through the use of simulation. Our results indicate that the generational model has important practical advantages and outperforms the existing methods in predicting field failures.

  17. [Lightning-caused fire, its affecting factors and prediction: a review].

    PubMed

    Zhang, Ji-Li; Bi, Wu; Wang, Xiao-Hong; Wang, Zi-Bo; Li, Di-Fei

    2013-09-01

    Lightning-caused fire is the most important natural fire source. Its induced forest fire brings enormous losses to human beings and ecological environment. Many countries have paid great attention to the prediction of lightning-caused fire. From the viewpoint of the main factors affecting the formation of lightning-caused fire, this paper emphatically analyzed the effects and action mechanisms of cloud-to-ground lightning, fuel, meteorology, and terrain on the formation and development process of lightning-caused fire, and, on the basis of this, summarized and reviewed the logistic model, K-function, and other mathematical methods widely used in prediction research of lightning-caused fire. The prediction methods and processes of lightning-caused fire in America and Canada were also introduced. The insufficiencies and their possible solutions for the present researches as well as the directions of further studies were proposed, aimed to provide necessary theoretical basis and literature reference for the prediction of lightning-caused fire in China.

  18. Mesoscale Wind Predictions for Wave Model Evaluation

    DTIC Science & Technology

    2016-06-07

    SEP 1999 2. REPORT TYPE 3. DATES COVERED 00-00-1999 to 00-00-1999 4. TITLE AND SUBTITLE Mesoscale Wind Predictions for Wave Model Evaluation...unclassified c. THIS PAGE unclassified Standard Form 298 (Rev. 8-98) Prescribed by ANSI Std Z39-18 1 Mesoscale Wind Predictions for Wave Model...resolution (< 10 km) atmospheric wind and surface stress fields produced by an atmospheric mesoscale data assimilation system to the numerical prediction of

  19. Childhood asthma prediction models: a systematic review.

    PubMed

    Smit, Henriette A; Pinart, Mariona; Antó, Josep M; Keil, Thomas; Bousquet, Jean; Carlsen, Kai H; Moons, Karel G M; Hooft, Lotty; Carlsen, Karin C Lødrup

    2015-12-01

    Early identification of children at risk of developing asthma at school age is crucial, but the usefulness of childhood asthma prediction models in clinical practice is still unclear. We systematically reviewed all existing prediction models to identify preschool children with asthma-like symptoms at risk of developing asthma at school age. Studies were included if they developed a new prediction model or updated an existing model in children aged 4 years or younger with asthma-like symptoms, with assessment of asthma done between 6 and 12 years of age. 12 prediction models were identified in four types of cohorts of preschool children: those with health-care visits, those with parent-reported symptoms, those at high risk of asthma, or children in the general population. Four basic models included non-invasive, easy-to-obtain predictors only, notably family history, allergic disease comorbidities or precursors of asthma, and severity of early symptoms. Eight extended models included additional clinical tests, mostly specific IgE determination. Some models could better predict asthma development and other models could better rule out asthma development, but the predictive performance of no single model stood out in both aspects simultaneously. This finding suggests that there is a large proportion of preschool children with wheeze for which prediction of asthma development is difficult.

  20. Does trait affectivity predict work-to-family conflict and enrichment beyond job characteristics?

    PubMed

    Tement, Sara; Korunka, Christian

    2013-01-01

    The present study examines whether negative and positive affectivity (NA and PA, respectively) predict different forms of work-to-family conflict (WFC-time, WFC-strain, WFC-behavior) and enrichment (WFE-development, WFE-affect, WFE-capital) beyond job characteristics (workload, autonomy, variety, workplace support). Furthermore, interactions between job characteristics and trait affectivity while predicting WFC and WFE were examined. Using a large sample of Slovenian employees (N = 738), NA and PA were found to explain variance in WFC as well as in WFE above and beyond job characteristics. More precisely, NA significantly predicted WFC, whereas PA significantly predicted WFE. In addition, several interactive effects were found to predict forms of WFC and WFE. These results highlight the importance of trait affectivity in work-family research. They provide further support for the crucial impact of job characteristics as well.

  1. Evaluating the Predictive Value of Growth Prediction Models

    ERIC Educational Resources Information Center

    Murphy, Daniel L.; Gaertner, Matthew N.

    2014-01-01

    This study evaluates four growth prediction models--projection, student growth percentile, trajectory, and transition table--commonly used to forecast (and give schools credit for) middle school students' future proficiency. Analyses focused on vertically scaled summative mathematics assessments, and two performance standards conditions (high…

  2. Hybrid approaches to physiologic modeling and prediction

    NASA Astrophysics Data System (ADS)

    Olengü, Nicholas O.; Reifman, Jaques

    2005-05-01

    This paper explores how the accuracy of a first-principles physiological model can be enhanced by integrating data-driven, "black-box" models with the original model to form a "hybrid" model system. Both linear (autoregressive) and nonlinear (neural network) data-driven techniques are separately combined with a first-principles model to predict human body core temperature. Rectal core temperature data from nine volunteers, subject to four 30/10-minute cycles of moderate exercise/rest regimen in both CONTROL and HUMID environmental conditions, are used to develop and test the approach. The results show significant improvements in prediction accuracy, with average improvements of up to 30% for prediction horizons of 20 minutes. The models developed from one subject's data are also used in the prediction of another subject's core temperature. Initial results for this approach for a 20-minute horizon show no significant improvement over the first-principles model by itself.

  3. Incorporating uncertainty in predictive species distribution modelling.

    PubMed

    Beale, Colin M; Lennon, Jack J

    2012-01-19

    Motivated by the need to solve ecological problems (climate change, habitat fragmentation and biological invasions), there has been increasing interest in species distribution models (SDMs). Predictions from these models inform conservation policy, invasive species management and disease-control measures. However, predictions are subject to uncertainty, the degree and source of which is often unrecognized. Here, we review the SDM literature in the context of uncertainty, focusing on three main classes of SDM: niche-based models, demographic models and process-based models. We identify sources of uncertainty for each class and discuss how uncertainty can be minimized or included in the modelling process to give realistic measures of confidence around predictions. Because this has typically not been performed, we conclude that uncertainty in SDMs has often been underestimated and a false precision assigned to predictions of geographical distribution. We identify areas where development of new statistical tools will improve predictions from distribution models, notably the development of hierarchical models that link different types of distribution model and their attendant uncertainties across spatial scales. Finally, we discuss the need to develop more defensible methods for assessing predictive performance, quantifying model goodness-of-fit and for assessing the significance of model covariates.

  4. Modelling cognitive affective biases in major depressive disorder using rodents.

    PubMed

    Hales, Claire A; Stuart, Sarah A; Anderson, Michael H; Robinson, Emma S J

    2014-10-01

    Major depressive disorder (MDD) affects more than 10% of the population, although our understanding of the underlying aetiology of the disease and how antidepressant drugs act to remediate symptoms is limited. Major obstacles include the lack of availability of good animal models that replicate aspects of the phenotype and tests to assay depression-like behaviour in non-human species. To date, research in rodents has been dominated by two types of assays designed to test for depression-like behaviour: behavioural despair tests, such as the forced swim test, and measures of anhedonia, such as the sucrose preference test. These tests have shown relatively good predictive validity in terms of antidepressant efficacy, but have limited translational validity. Recent developments in clinical research have revealed that cognitive affective biases (CABs) are a key feature of MDD. Through the development of neuropsychological tests to provide objective measures of CAB in humans, we have the opportunity to use 'reverse translation' to develop and evaluate whether similar methods are suitable for research into MDD using animals. The first example of this approach was reported in 2004 where rodents in a putative negative affective state were shown to exhibit pessimistic choices in a judgement bias task. Subsequent work in both judgement bias tests and a novel affective bias task suggest that these types of assay may provide translational methods for studying MDD using animals. This review considers recent work in this area and the pharmacological and translational validity of these new animal models of CABs.

  5. Model predictions and trend analysis

    NASA Technical Reports Server (NTRS)

    1982-01-01

    Individual perturbations in atmospheric models are discussed. These are hypothetical perturbations determined by model computation in which it is assumed that one particular input or set of inputs to the model is changed while all others are held constant. The best estimates of past time dependent variations of globally averaged total ozone, and upper tropospheric and stratospheric ozone were determined along with geographical differences in the variations.

  6. Relative importance of parameters affecting wind speed prediction using artificial neural networks

    NASA Astrophysics Data System (ADS)

    Ghorbani, M. A.; Khatibi, R.; Hosseini, B.; Bilgili, M.

    2013-10-01

    In traditional artificial neural networks (ANN) models, the relative importance of the individual meteorological input variables is often overlooked. A case study is presented in this paper to model monthly wind speed values using meteorological data (air pressure, air temperature, relative humidity, and precipitation), where the study also includes an estimate of the relative importance of these variables. Recorded monthly mean data are available at a gauging site in Tabriz, Azerbaijan, Iran, for the period from 2000 to 2005, gauged in the city at the outskirt of alluvial funneling mountains with an established microclimatic conditions and a diurnal wind regime. This provides a sufficiently severe test for the ANN model with a good predictive capability of 1 year of lead time but without any direct approach to refer the predicted results to local microclimatic conditions. A method is used in this paper to calculate the relative importance of each meteorological input parameters affecting wind speed, showing that air pressure and precipitation are the most and least influential parameters with approximate values of 40 and 10 %, respectively. This gained knowledge corresponds to the local knowledge of the microclimatic and geomorphologic conditions surrounding Tabriz.

  7. To branch out or stay focused? Affective shifts differentially predict organizational citizenship behavior and task performance.

    PubMed

    Yang, Liu-Qin; Simon, Lauren S; Wang, Lei; Zheng, Xiaoming

    2016-06-01

    We draw from personality systems interaction (PSI) theory (Kuhl, 2000) and regulatory focus theory (Higgins, 1997) to examine how dynamic positive and negative affective processes interact to predict both task and contextual performance. Using a twice-daily diary design over the course of a 3-week period, results from multilevel regression analysis revealed that distinct patterns of change in positive and negative affect optimally predicted contextual and task performance among a sample of 71 employees at a medium-sized technology company. Specifically, within persons, increases (upshifts) in positive affect over the course of a workday better predicted the subsequent day's organizational citizenship behavior (OCB) when such increases were coupled with decreases (downshifts) in negative affect. The optimal pattern of change in positive and negative affect differed, however, in predicting task performance. That is, upshifts in positive affect over the course of the workday better predicted the subsequent day's task performance when such upshifts were accompanied by upshifts in negative affect. The contribution of our findings to PSI theory and the broader affective and motivation regulation literatures, along with practical implications, are discussed. (PsycINFO Database Record

  8. Woodpecker cavity aeration: a predictive model.

    PubMed

    Ar, Amos; Barnea, Anat; Yom-Tov, Yoram; Mersten-Katz, Cynthia

    2004-12-15

    We studied characteristics of the Syrian woodpecker (Dendrocopos syriacus) cavities in the field and a laboratory model, and rates of gas exchange in the laboratory. Night temperature of occupied cavities is 4.3 degrees C higher than empty ones, representing energy savings of approximately 24%. Oxygen conductance (GNO2) of an empty cavity is 7.1 ml[STPD] (Torr h)(-1), and is affected by winds at velocities up to 0.8 m/s. Day and night body temperatures were 42.0 and 40.1 degrees C, respectively. Steady-state O2 consumption rates (MO2) were 3.49 +/- 0.49 and 2.53 +/- 0.26 ml[STPD] (g h)(-1) during day and night respectively -- higher than predicted by allometry. A mathematical model describing PO2 in a cavity, taking into consideration MO2, GNO2, heat convection and wind speed, from the moment birds inhabit it, was developed. It shows that on the average, one woodpecker staying in its cavity at night does not encounter hypoxic conditions. However, in nest cavities with below the average GNO2, with more inhabitants (e.g. during the breeding season), hypoxia may become a problem.

  9. Enhancing OPC model stability and predictability using SEM image contours

    NASA Astrophysics Data System (ADS)

    Habib, Mohamed Serag El-Din

    2008-10-01

    The process model is a major factor affecting the quality of the Model Based Optical Proximity Correction (OPC). Better process model directly leads to better OPC, hence better yield and more profit. While the traditional way in calibrating these process models is using CD measurements at sample locations in the test chip, however, the use of Scanning Electron Microscope (SEM) image contours for process model calibration and optimization has been recently introduced in trial to build more predictable models. In this study, we characterize the traditional flow models versus the contour calibrated models and study the effect of using different combinations and weighting schemes on the quality of the resulting process models, its stability and its ability to correctly predict the process.

  10. The circumplex model of affect: An integrative approach to affective neuroscience, cognitive development, and psychopathology

    PubMed Central

    Posner, Jonathan; Russell, James A.; Peterson, Bradley S.

    2008-01-01

    The circumplex model of affect proposes that all affective states arise from cognitive interpretations of core neural sensations that are the product of two independent neurophysiological systems. This model stands in contrast to theories of basic emotions, which posit that a discrete and independent neural system subserves every emotion. We propose that basic emotion theories no longer explain adequately the vast number of empirical observations from studies in affective neuroscience, and we suggest that a conceptual shift is needed in the empirical approaches taken to the study of emotion and affective psychopathologies. The circumplex model of affect is more consistent with many recent findings from behavioral, cognitive neuroscience, neuroimaging, and developmental studies of affect. Moreover, the model offers new theoretical and empirical approaches to studying the development of affective disorders as well as the genetic and cognitive underpinnings of affective processing within the central nervous system. PMID:16262989

  11. Breast Cancer Risk Prediction Models

    Cancer.gov

    Developing statistical models that estimate the probability of developing breast cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  12. Esophageal Cancer Risk Prediction Models

    Cancer.gov

    Developing statistical models that estimate the probability of developing esophageal cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  13. Colorectal Cancer Risk Prediction Models

    Cancer.gov

    Developing statistical models that estimate the probability of developing colorectal cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  14. Prostate Cancer Risk Prediction Models

    Cancer.gov

    Developing statistical models that estimate the probability of developing prostate cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  15. Pancreatic Cancer Risk Prediction Models

    Cancer.gov

    Developing statistical models that estimate the probability of developing pancreatic cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  16. Lung Cancer Risk Prediction Models

    Cancer.gov

    Developing statistical models that estimate the probability of developing lung cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  17. Testicular Cancer Risk Prediction Models

    Cancer.gov

    Developing statistical models that estimate the probability of testicular cervical cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  18. Ovarian Cancer Risk Prediction Models

    Cancer.gov

    Developing statistical models that estimate the probability of developing ovarian cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  19. Cervical Cancer Risk Prediction Models

    Cancer.gov

    Developing statistical models that estimate the probability of developing cervical cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  20. Bladder Cancer Risk Prediction Models

    Cancer.gov

    Developing statistical models that estimate the probability of developing bladder cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  1. Predictive Modeling in Adult Education

    ERIC Educational Resources Information Center

    Lindner, Charles L.

    2011-01-01

    The current economic crisis, a growing workforce, the increasing lifespan of workers, and demanding, complex jobs have made organizations highly selective in employee recruitment and retention. It is therefore important, to the adult educator, to develop models of learning that better prepare adult learners for the workplace. The purpose of…

  2. Liver Cancer Risk Prediction Models

    Cancer.gov

    Developing statistical models that estimate the probability of developing liver cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  3. Affect in the "Communicative" Classroom: A Model.

    ERIC Educational Resources Information Center

    Acton, William

    Recent research on affective variables and classroom second language learning suggests that: (1) affective variables are context-sensitive in at least two ways; (2) attitudes are contagious, and the general attitude of students can be influenced from various directions; (3) research in pragmatics, discourse analysis, and communicative functions…

  4. Posterior Predictive Model Checking in Bayesian Networks

    ERIC Educational Resources Information Center

    Crawford, Aaron

    2014-01-01

    This simulation study compared the utility of various discrepancy measures within a posterior predictive model checking (PPMC) framework for detecting different types of data-model misfit in multidimensional Bayesian network (BN) models. The investigated conditions were motivated by an applied research program utilizing an operational complex…

  5. A Hierarchical Latent Stochastic Differential Equation Model for Affective Dynamics

    ERIC Educational Resources Information Center

    Oravecz, Zita; Tuerlinckx, Francis; Vandekerckhove, Joachim

    2011-01-01

    In this article a continuous-time stochastic model (the Ornstein-Uhlenbeck process) is presented to model the perpetually altering states of the core affect, which is a 2-dimensional concept underlying all our affective experiences. The process model that we propose can account for the temporal changes in core affect on the latent level. The key…

  6. Predictive modelling of boiler fouling

    SciTech Connect

    Not Available

    1990-02-01

    The primary objective of this work is the development of a comprehensive numerical model describing the time evolution of fouling under realistic heat exchanger conditions. As fouling is a complex interaction of gas flow, mineral transport and adhesion mechanisms, understanding and subsequently improved controlling of fouling achieved via appropriate manipulation of the various coupled, nonlinear processes in a complex fluid mechanics environment will undoubtedly help reduce the substantial operating costs incurred by the utilities annually, as well as afford greater flexibility in coal selection and reduce the emission of various pollutants. In a more specialized context the numerical model to be developed as part of this activity will be used as a tool to address the interaction of the various mechanisms controlling deposit development in specific regimes or correlative relationships. These should prove of direct use to the coal burning industry.

  7. Predictive modelling of boiler fouling

    SciTech Connect

    Not Available

    1991-01-01

    The primary objective of this work is the development of a comprehensive numerical model describing the time evolution of fouling under realistic heat exchanger conditions. As fouling is a complex interaction of gas flow, mineral transport and adhesion mechanisms, understanding and subsequently improved controlling of fouling achieved via appropriate manipulation of the various coupled, nonlinear processes in a complex fluid mechanics environment will undoubtedly help reduce the substantial operating costs incurred by the utilities annually, as well as afford greater flexibility in coal selection and reduce the emission of various pollutants. In a more specialized context, the numerical model to be developed as part of this activity will be used as a tool to address the interaction of the various mechanisms controlling deposit development in specific regimes or correlative relationships. These should prove of direct use to the coal burning industry.

  8. Predictive Models and Computational Toxicology (II IBAMTOX)

    EPA Science Inventory

    EPA’s ‘virtual embryo’ project is building an integrative systems biology framework for predictive models of developmental toxicity. One schema involves a knowledge-driven adverse outcome pathway (AOP) framework utilizing information from public databases, standardized ontologies...

  9. A Predictive Model of Group Panic Behavior.

    ERIC Educational Resources Information Center

    Weinberg, Sanford B.

    1978-01-01

    Reports results of a study which tested the following model to predict group panic behavior: that panic reactions are characterized by the exercise of inappropriate leadership behaviors in situations of high stress. (PD)

  10. Irma multisensor predictive signature model

    NASA Astrophysics Data System (ADS)

    Watson, John S.; Flynn, David S.; Wellfare, Michael R.; Richards, Mike; Prestwood, Lee

    1995-06-01

    The Irma synthetic signature model was one of the first high resolution synthetic infrared (IR) target and background signature models to be developed for tactical air-to-surface weapon scenarios. Originally developed in 1980 by the Armament Directorate of the Air Force Wright Laboratory (WL/MN), the Irma model was used exclusively to generate IR scenes for smart weapons research and development. In 1988, a number of significant upgrades to Irma were initiated including the addition of a laser channel. This two channel version, Irma 3.0, was released to the user community in 1990. In 1992, an improved scene generator was incorporated into the Irma model which supported correlated frame-to-frame imagery. This and other improvements were released in Irma 2.2. Recently, Irma 3.2, a passive IR/millimeter wave (MMW) code, was completed. Currently, upgrades are underway to include an active MMW channel. Designated Irma 4.0, this code will serve as a cornerstone of sensor fusion research in the laboratory from 6.1 concept development to 6.3 technology demonstration programs for precision guided munitions. Several significant milestones have been reached in this development process and are demonstrated. The Irma 4.0 software design has been developed and interim results are available. Irma is being developed to facilitate multi-sensor smart weapons research and development. It is currently in distribution to over 80 agencies within the U.S. Air Force, U.S. Army, U.S. Navy, ARPA, NASA, Department of Transportation, academia, and industry.

  11. Variability in affective activation predicts non-suicidal self-injury in eating disorders.

    PubMed

    Vansteelandt, Kristof; Claes, Laurence; Muehlenkamp, Jennifer; De Cuyper, Kathleen; Lemmens, Jos; Probst, Michel; Vanderlinden, Johan; Pieters, Guido

    2013-03-01

    We examined whether affective variability can predict non-suicidal self-injury (NSSI) in eating disorders. Affect was represented by valence (positive versus negative) and activation (high versus low). Twenty-one patients with anorexia nervosa-restricting type, 18 patients with anorexia nervosa-binge-purging type and 20 patients with bulimia nervosa reported their momentary affect at nine random times a day during a one week period using a hand-held computer. Affective variability was calculated as the within-person standard deviation of valence and activation over time. Results indicate that patients displaying greater variability in activation and using selective serotonin reuptake inhibitors have a higher probability to engage in lifetime NSSI after adjustment for depression and borderline personality disorder. Neither variability of valence nor mean level of valence and activation had any predictive association with engaging in NSSI. It is suggested that the treatment of NSSI should focus on affect stabilization rather than reducing negative affect.

  12. Sensitivity analysis of uncertainty in model prediction.

    PubMed

    Russi, Trent; Packard, Andrew; Feeley, Ryan; Frenklach, Michael

    2008-03-27

    Data Collaboration is a framework designed to make inferences from experimental observations in the context of an underlying model. In the prior studies, the methodology was applied to prediction on chemical kinetics models, consistency of a reaction system, and discrimination among competing reaction models. The present work advances Data Collaboration by developing sensitivity analysis of uncertainty in model prediction with respect to uncertainty in experimental observations and model parameters. Evaluation of sensitivity coefficients is performed alongside the solution of the general optimization ansatz of Data Collaboration. The obtained sensitivity coefficients allow one to determine which experiment/parameter uncertainty contributes the most to the uncertainty in model prediction, rank such effects, consider new or even hypothetical experiments to perform, and combine the uncertainty analysis with the cost of uncertainty reduction, thereby providing guidance in selecting an experimental/theoretical strategy for community action.

  13. Irma multisensor predictive signature model

    NASA Astrophysics Data System (ADS)

    Watson, John S.; Wellfare, Michael R.; Chenault, David B.; Talele, Sunjay E.; Blume, Bradley T.; Richards, Mike; Prestwood, Lee

    1997-06-01

    Development of target acquisition and target recognition algorithms in highly cluttered backgrounds in a variety of battlefield conditions demands a flexible, high fidelity capability for synthetic image generation. Cost effective smart weapons research and testing also requires extensive scene generation capability. The Irma software package addresses this need through a first principles, phenomenology based scene generator that enhances research into new algorithms, novel sensors, and sensor fusion approaches. Irma was one of the first high resolution synthetic infrared target and background signature models developed for tactical air-to-surface weapon scenarios. Originally developed in 1980 by the Armament Directorate of the Air Force Wright Laboratory, the Irma model was used exclusively to generate IR scenes for smart weapons research and development. in 1987, Nichols Research Corporation took over the maintenance of Irma and has since added substantial capabilities. The development of Irma has culminated in a program that includes not only passive visible, IR, and millimeter wave (MMW) channels but also active MMW and ladar channels. Each of these channels is co-registered providing the capability to develop algorithms for multi-band sensor fusion concepts and associated algorithms. In this paper, the capabilities of the latest release of Irma, Irma 4.0, will be described. A brief description of the elements of the software that are common to all channels will be provided. Each channel will be described briefly including a summary of the phenomenological effects and the sensor effects modeled in the software. Examples of Irma multi- channel imagery will be presented.

  14. Modelling cognitive affective biases in major depressive disorder using rodents

    PubMed Central

    Hales, Claire A; Stuart, Sarah A; Anderson, Michael H; Robinson, Emma S J

    2014-01-01

    Major depressive disorder (MDD) affects more than 10% of the population, although our understanding of the underlying aetiology of the disease and how antidepressant drugs act to remediate symptoms is limited. Major obstacles include the lack of availability of good animal models that replicate aspects of the phenotype and tests to assay depression-like behaviour in non-human species. To date, research in rodents has been dominated by two types of assays designed to test for depression-like behaviour: behavioural despair tests, such as the forced swim test, and measures of anhedonia, such as the sucrose preference test. These tests have shown relatively good predictive validity in terms of antidepressant efficacy, but have limited translational validity. Recent developments in clinical research have revealed that cognitive affective biases (CABs) are a key feature of MDD. Through the development of neuropsychological tests to provide objective measures of CAB in humans, we have the opportunity to use ‘reverse translation’ to develop and evaluate whether similar methods are suitable for research into MDD using animals. The first example of this approach was reported in 2004 where rodents in a putative negative affective state were shown to exhibit pessimistic choices in a judgement bias task. Subsequent work in both judgement bias tests and a novel affective bias task suggest that these types of assay may provide translational methods for studying MDD using animals. This review considers recent work in this area and the pharmacological and translational validity of these new animal models of CABs. Linked Articles This article is part of a themed section on Animal Models in Psychiatry Research. To view the other articles in this section visit http://dx.doi.org/10.1111/bph.2014.171.issue-20 PMID:24467454

  15. Model predictive control: A new approach

    NASA Astrophysics Data System (ADS)

    Nagy, Endre

    2017-01-01

    New methods are proposed in this paper for solution of the model predictive control problem. Nonlinear state space design techniques are also treated. For nonlinear state prediction (state evolution computation) a new predictor given with an operator is introduced and tested. Settling the model predictive control problem may be obtained through application of the principle "direct stochastic optimum tracking" with a simple algorithm, which can be derived from a previously developed optimization procedure. The final result is obtained through iterations. Two examples show the applicability and advantages of the method.

  16. Survival Regression Modeling Strategies in CVD Prediction

    PubMed Central

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

    2016-01-01

    Background 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. Objectives 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. Materials and Methods We have written Stata commands that are intended to help researchers obtain the following. 1, Nam-D’Agostino X2 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. Results The command is adpredsurv for survival models. Conclusions Herein we have described the Stata package “adpredsurv” for calculation of the Nam-D’Agostino X2 goodness of fit test as well as cut point-free and cut point-based NRI, relative

  17. Happiness as a motivator: positive affect predicts primary control striving for career and educational goals.

    PubMed

    Haase, Claudia M; Poulin, Michael J; Heckhausen, Jutta

    2012-08-01

    What motivates individuals to invest time and effort and overcome obstacles (i.e., strive for primary control) when pursuing important goals? We propose that positive affect predicts primary control striving for career and educational goals, and we explore the mediating role of control beliefs. In Study 1, positive affect predicted primary control striving for career goals in a two-wave longitudinal study of a U.S. sample. In Study 2, positive affect predicted primary control striving for career and educational goals and objective career outcomes in a six-wave longitudinal study of a German sample. Control beliefs partially mediated the longitudinal associations with primary control striving. Thus, when individuals experience positive affect, they become more motivated to invest time and effort, and overcome obstacles when pursuing their goals, in part because they believe they have more control over attaining their goals.

  18. Thermal barrier coating life prediction model

    NASA Technical Reports Server (NTRS)

    Pilsner, B. H.; Hillery, R. V.; Mcknight, R. L.; Cook, T. S.; Kim, K. S.; Duderstadt, E. C.

    1986-01-01

    The objectives of this program are to determine the predominant modes of degradation of a plasma sprayed thermal barrier coating system, and then to develop and verify life prediction models accounting for these degradation modes. The program is divided into two phases, each consisting of several tasks. The work in Phase 1 is aimed at identifying the relative importance of the various failure modes, and developing and verifying life prediction model(s) for the predominant model for a thermal barrier coating system. Two possible predominant failure mechanisms being evaluated are bond coat oxidation and bond coat creep. The work in Phase 2 will develop design-capable, causal, life prediction models for thermomechanical and thermochemical failure modes, and for the exceptional conditions of foreign object damage and erosion.

  19. Interpretable Deep Models for ICU Outcome Prediction

    PubMed Central

    Che, Zhengping; Purushotham, Sanjay; Khemani, Robinder; Liu, Yan

    2016-01-01

    Exponential surge in health care data, such as longitudinal data from electronic health records (EHR), sensor data from intensive care unit (ICU), etc., is providing new opportunities to discover meaningful data-driven characteristics and patterns ofdiseases. Recently, deep learning models have been employedfor many computational phenotyping and healthcare prediction tasks to achieve state-of-the-art performance. However, deep models lack interpretability which is crucial for wide adoption in medical research and clinical decision-making. In this paper, we introduce a simple yet powerful knowledge-distillation approach called interpretable mimic learning, which uses gradient boosting trees to learn interpretable models and at the same time achieves strong prediction performance as deep learning models. Experiment results on Pediatric ICU dataset for acute lung injury (ALI) show that our proposed method not only outperforms state-of-the-art approaches for morality and ventilator free days prediction tasks but can also provide interpretable models to clinicians. PMID:28269832

  20. Reactions to Stigmas among Canadian Students: Testing an Attribution-Affect-Help Judgment Model.

    ERIC Educational Resources Information Center

    Menec, Verena H.; Perry, Raymond P.

    1998-01-01

    Tests Weiner's (Bernard) attribution-affect-help judgment model in the context of nine stigmas and ascribed each to either a controllable or uncontrollable factor. Finds that higher controllability was linked to greater anger and less pity, greater pity was predictive of a greater willingness to help, and anger did not predict help judgments. (CMK)

  1. Economic decision making and the application of nonparametric prediction models

    USGS Publications Warehouse

    Attanasi, E.D.; Coburn, T.C.; Freeman, P.A.

    2007-01-01

    Sustained increases in energy prices have focused attention on gas resources in low permeability shale or in coals that were previously considered economically marginal. Daily well deliverability is often relatively small, although the estimates of the total volumes of recoverable resources in these settings are large. Planning and development decisions for extraction of such resources must be area-wide because profitable extraction requires optimization of scale economies to minimize costs and reduce risk. For an individual firm the decision to enter such plays depends on reconnaissance level estimates of regional recoverable resources and on cost estimates to develop untested areas. This paper shows how simple nonparametric local regression models, used to predict technically recoverable resources at untested sites, can be combined with economic models to compute regional scale cost functions. The context of the worked example is the Devonian Antrim shale gas play, Michigan Basin. One finding relates to selection of the resource prediction model to be used with economic models. Models which can best predict aggregate volume over larger areas (many hundreds of sites) may lose granularity in the distribution of predicted volumes at individual sites. This loss of detail affects the representation of economic cost functions and may affect economic decisions. Second, because some analysts consider unconventional resources to be ubiquitous, the selection and order of specific drilling sites may, in practice, be determined by extraneous factors. The paper also shows that when these simple prediction models are used to strategically order drilling prospects, the gain in gas volume over volumes associated with simple random site selection amounts to 15 to 20 percent. It also discusses why the observed benefit of updating predictions from results of new drilling, as opposed to following static predictions, is somewhat smaller. Copyright 2007, Society of Petroleum Engineers.

  2. Trait Reappraisal Predicts Affective Reactivity to Daily Positive and Negative Events

    PubMed Central

    Gunaydin, Gul; Selcuk, Emre; Ong, Anthony D.

    2016-01-01

    Past research on emotion regulation has provided evidence that cognitive reappraisal predicts reactivity to affective stimuli and challenge tests in laboratory settings. However, little is known about how trait reappraisal might contribute to affective reactivity to everyday positive and negative events. Using a large, life-span sample of adults (N = 1755), the present study addressed this important gap in the literature. Respondents completed a measure of trait reappraisal and reported on their daily experiences of positive and negative events and positive and negative affect for eight consecutive days. Results showed that trait reappraisal predicted lower increases in negative affect in response to daily negative events and lower increases in positive affect in response to daily positive events. These findings advance our understanding of the role of reappraisal in emotion regulation by showing how individual differences in the use of this strategy relate to emotional reactions to both positive and negative events outside the laboratory. PMID:27445954

  3. Beyond sensation seeking: affect regulation as a framework for predicting risk-taking behaviors in high-risk sport.

    PubMed

    Castanier, Carole; Le Scanff, Christine; Woodman, Tim

    2010-10-01

    Sensation seeking has been widely studied when investigating individual differences in the propensity for taking risks. However, risk taking can serve many different goals beyond the simple management of physiological arousal. The present study is an investigation of affect self-regulation as a predictor of risk-taking behaviors in high-risk sport. Risk-taking behaviors, negative affectivity, escape self-awareness strategy, and sensation seeking data were obtained from 265 high-risk sportsmen. Moderated hierarchical regression analysis revealed significant main and interaction effects of negative affectivity and escape self-awareness strategy in predicting risk-taking behaviors: high-risk sportsmen's negative affectivity leads them to adopt risk-taking behaviors only if they also use escape self-awareness strategy. Furthermore, the affective model remained significant when controlling for sensation seeking. The present study contributes to an in-depth understanding of risk taking in high-risk sport.

  4. Prediction of PARP Inhibition with Proteochemometric Modelling and Conformal Prediction.

    PubMed

    Cortés-Ciriano, Isidro; Bender, Andreas; Malliavin, Thérèse

    2015-06-01

    Poly(ADP-ribose) polymerases (PARPs) play a key role in DNA damage repair. PARP inhibitors act as chemo- and radio- sensitizers and thus potentiate the cytotoxicity of DNA damaging agents. Although PARP inhibitors are currently investigated as chemotherapeutic agents, their cross-reactivity with other members of the PARP family remains unclear. Here, we apply Proteochemometric Modelling (PCM) to model the activity of 181 compounds on 12 human PARPs. We demonstrate that PCM (R0 (2) test =0.65-0.69; RMSEtest =0.95-1.01 °C) displays higher performance on the test set (interpolation) than Family QSAR and Family QSAM (Tukey's HSD, α 0.05), and outperforms Inductive Transfer knowledge among targets (Tukey's HSD, α 0.05). We benchmark the predictive signal of 8 amino acid and 11 full-protein sequence descriptors, obtaining that all of them (except for SOCN) perform at the same level of statistical significance (Tukey's HSD, α 0.05). The extrapolation power of PCM to new compounds (RMSE=1.02±0.80 °C) and targets (RMSE=1.03±0.50 °C) is comparable to interpolation, although the extrapolation ability is not uniform across the chemical and the target space. For this reason, we also provide confidence intervals calculated with conformal prediction. In addition, we present the R package conformal, which permits the calculation of confidence intervals for regression and classification caret models.

  5. Mathematical model for predicting human vertebral fracture

    NASA Technical Reports Server (NTRS)

    Benedict, J. V.

    1973-01-01

    Mathematical model has been constructed to predict dynamic response of tapered, curved beam columns in as much as human spine closely resembles this form. Model takes into consideration effects of impact force, mass distribution, and material properties. Solutions were verified by dynamic tests on curved, tapered, elastic polyethylene beam.

  6. Thermal barrier coating life prediction model development

    NASA Technical Reports Server (NTRS)

    Meier, Susan M.; Nissley, David M.; Sheffler, Keith D.; Cruse, Thomas A.

    1991-01-01

    A thermal barrier coated (TBC) turbine component design system, including an accurate TBC life prediction model, is needed to realize the full potential of available TBC engine performance and/or durability benefits. The objective of this work, which was sponsored in part by NASA, was to generate a life prediction model for electron beam - physical vapor deposited (EB-PVD) zirconia TBC. Specific results include EB-PVD zirconia mechanical and physical properties, coating adherence strength measurements, interfacial oxide growth characteristics, quantitative cyclic thermal spallation life data, and a spallation life model.

  7. The R-γ transition prediction model

    NASA Astrophysics Data System (ADS)

    Goldberg, Uriel C.; Batten, Paul; Peroomian, Oshin; Chakravarthy, Sukumar

    2015-01-01

    The Rt turbulence closure (Goldberg 2003) is coupled with an intermittency transport equation, γ, to enable prediction of laminar-to-turbulent flow by-pass transition. The model is not correlation-based and is completely topography-parameter-free, thus ready for use in parallelized Computational Fluid Dynamics (CFD) solvers based on unstructured book-keeping. Several examples compare the R-γ model's performance with experimental data and with predictions by the Langtry-Menter γ-Reθ transition closure (2009). Like the latter, the R-γ model is very sensitive to freestream turbulence levels, limiting its utility for engineering purposes.

  8. Pons to Posterior Cingulate Functional Projections Predict Affective Processing Changes in the Elderly Following Eight Weeks of Meditation Training.

    PubMed

    Shao, Robin; Keuper, Kati; Geng, Xiujuan; Lee, Tatia M C

    2016-08-01

    Evidence indicates meditation facilitates affective regulation and reduces negative affect. It also influences resting-state functional connectivity between affective networks and the posterior cingulate (PCC)/precuneus, regions critically implicated in self-referential processing. However, no longitudinal study employing active control group has examined the effect of meditation training on affective processing, PCC/precuneus connectivity, and their association. Here, we report that eight-week meditation, but not relaxation, training 'neutralized' affective processing of positive and negative stimuli in healthy elderly participants. Additionally, meditation versus relaxation training increased the positive connectivity between the PCC/precuneus and the pons, the direction of which was largely directed from the pons to the PCC/precuneus, as revealed by dynamic causal modeling. Further, changes in connectivity between the PCC/precuneus and pons predicted changes in affective processing after meditation training. These findings indicate meditation promotes self-referential affective regulation based on increased regulatory influence of the pons on PCC/precuneus, which new affective-processing strategy is employed across both resting state and when evaluating affective stimuli. Such insights have clinical implications on interventions on elderly individuals with affective disorders.

  9. Thermal barrier coating life prediction model development

    NASA Technical Reports Server (NTRS)

    Hillery, R. V.; Pilsner, B. H.; Mcknight, R. L.; Cook, T. S.; Hartle, M. S.

    1988-01-01

    This report describes work performed to determine the predominat modes of degradation of a plasma sprayed thermal barrier coating system and to develop and verify life prediction models accounting for these degradation modes. The primary TBC system consisted of a low pressure plasma sprayed NiCrAlY bond coat, an air plasma sprayed ZrO2-Y2O3 top coat, and a Rene' 80 substrate. The work was divided into 3 technical tasks. The primary failure mode to be addressed was loss of the zirconia layer through spalling. Experiments showed that oxidation of the bond coat is a significant contributor to coating failure. It was evident from the test results that the species of oxide scale initially formed on the bond coat plays a role in coating degradation and failure. It was also shown that elevated temperature creep of the bond coat plays a role in coating failure. An empirical model was developed for predicting the test life of specimens with selected coating, specimen, and test condition variations. In the second task, a coating life prediction model was developed based on the data from Task 1 experiments, results from thermomechanical experiments performed as part of Task 2, and finite element analyses of the TBC system during thermal cycles. The third and final task attempted to verify the validity of the model developed in Task 2. This was done by using the model to predict the test lives of several coating variations and specimen geometries, then comparing these predicted lives to experimentally determined test lives. It was found that the model correctly predicts trends, but that additional refinement is needed to accurately predict coating life.

  10. Are animal models predictive for humans?

    PubMed Central

    2009-01-01

    It is one of the central aims of the philosophy of science to elucidate the meanings of scientific terms and also to think critically about their application. The focus of this essay is the scientific term predict and whether there is credible evidence that animal models, especially in toxicology and pathophysiology, can be used to predict human outcomes. Whether animals can be used to predict human response to drugs and other chemicals is apparently a contentious issue. However, when one empirically analyzes animal models using scientific tools they fall far short of being able to predict human responses. This is not surprising considering what we have learned from fields such evolutionary and developmental biology, gene regulation and expression, epigenetics, complexity theory, and comparative genomics. PMID:19146696

  11. Prediction oriented QSAR modelling of EGFR inhibition.

    PubMed

    Szántai-Kis, C; Kövesdi, I; Eros, D; Bánhegyi, P; Ullrich, A; Kéri, G; Orfi, L

    2006-01-01

    Epidermal Growth Factor Receptor (EGFR) is a high priority target in anticancer drug research. Thousands of very effective EGFR inhibitors have been developed in the last decade. The known inhibitors are originated from a very diverse chemical space but--without exception--all of them act at the Adenosine TriPhosphate (ATP) binding site of the enzyme. We have collected all of the diverse inhibitor structures and the relevant biological data obtained from comparable assays and built prediction oriented Quantitative Structure-Activity Relationship (QSAR) which models the ATP binding pocket's interactive surface from the ligand side. We describe a QSAR method with automatic Variable Subset Selection (VSS) by Genetic Algorithm (GA) and goodness-of-prediction driven QSAR model building, resulting an externally validated EGFR inhibitory model built from pIC50 values of a diverse structural set of 623 EGFR inhibitors. Repeated Trainings/Evaluations (RTE) were used to obtain model fitness values and the effectiveness of VSS is amplified by using predictive ability scores of descriptors. Numerous models were generated by different methods and viable models were collected. Then, intensive RTE were applied to identify ultimate models for external validations. Finally, suitable models were validated by statistical tests. Since we use calculated molecular descriptors in the modeling, these models are suitable for virtual screening for obtaining novel potential EGFR inhibitors.

  12. A High Precision Prediction Model Using Hybrid Grey Dynamic Model

    ERIC Educational Resources Information Center

    Li, Guo-Dong; Yamaguchi, Daisuke; Nagai, Masatake; Masuda, Shiro

    2008-01-01

    In this paper, we propose a new prediction analysis model which combines the first order one variable Grey differential equation Model (abbreviated as GM(1,1) model) from grey system theory and time series Autoregressive Integrated Moving Average (ARIMA) model from statistics theory. We abbreviate the combined GM(1,1) ARIMA model as ARGM(1,1)…

  13. How processing digital elevation models can affect simulated water budgets.

    PubMed

    Kuniansky, Eve L; Lowery, Mark A; Campbell, Bruce G

    2009-01-01

    For regional models, the shallow water table surface is often used as a source/sink boundary condition, as model grid scale precludes simulation of the water table aquifer. This approach is appropriate when the water table surface is relatively stationary. Since water table surface maps are not readily available, the elevation of the water table used in model cells is estimated via a two-step process. First, a regression equation is developed using existing land and water table elevations from wells in the area. This equation is then used to predict the water table surface for each model cell using land surface elevation available from digital elevation models (DEM). Two methods of processing DEM for estimating the land surface for each cell are commonly used (value nearest the cell centroid or mean value in the cell). This article demonstrates how these two methods of DEM processing can affect the simulated water budget. For the example presented, approximately 20% more total flow through the aquifer system is simulated if the centroid value rather than the mean value is used. This is due to the one-third greater average ground water gradients associated with the centroid value than the mean value. The results will vary depending on the particular model area topography and cell size. The use of the mean DEM value in each model cell will result in a more conservative water budget and is more appropriate because the model cell water table value should be representative of the entire cell area, not the centroid of the model cell.

  14. Sensory Hypersensitivity Predicts Reduced Sleeping Quality in Patients With Major Affective Disorders.

    PubMed

    Engel-Yeger, Batya; Gonda, Xenia; Walker, Muffy; Rihmer, Zoltan; Pompili, Maurizio; Amore, Mario; Serafini, Gianluca

    2017-01-01

    The goal of this study was to examine the sensory profile (expressed as hypersensitivity or hyposensitivity) of patients with major affective disorders and its relative contribution to the prediction of sleep quality while considering affective temperaments and depression, which may impact sleep quality. We recruited 176 participants (mean age, 47.3 y), of whom 56.8% had a diagnosis of unipolar major depressive disorder and 43.2% a diagnosis of bipolar disorder. Reduced sleep quality was evaluated using the Pittsburgh Sleep Quality Index. Affective temperaments were assessed using the Temperament Evaluation of Memphis, Pisa, Paris and San Diego. Sensory hypersensitivity, assessed using the Adolescent/Adult Sensory Profile, significantly distinguished between poor and good sleepers. Sleep quality was mainly predicted by the Beck Depression Inventory-II total score and anxious temperament. Sensory hypersensitivity contributed to this prediction mainly with regard to sleep efficiency and related daytime dysfunction.

  15. Numerical Weather Prediction and Earth System Prediction to Better Understand Sea Level Rise/Coastal Issues as They Affect Readiness

    DTIC Science & Technology

    2011-11-01

    National Unified Operational Prediction Capability NUOPC 19 Cancelled  Sor&es  during  OIF   Dust   Storm   (Based  on  LCDR  Jake...Global Atmospheric Prediction System (NOGAPS) •  Coupled Ocean Atmosphere Mesoscale Prediction System ( COAMPS ) •  Navy Coastal Ocean Model (NCOM... Storms •  Arctic •  Sea Level •  Perma frost •  Military preparedness –  Equipment –  Facilities –  Training National Unified Operational

  16. Plasma Stabilization Based on Model Predictive Control

    NASA Astrophysics Data System (ADS)

    Sotnikova, Margarita

    The nonlinear model predictive control algorithms for plasma current and shape stabilization are proposed. Such algorithms are quite suitable for the situations when the plant to be controlled has essentially nonlinear dynamics. Besides that, predictive model based control algorithms allow to take into account a lot of requirements and constraints involved both on the controlled and manipulated variables. The significant drawback of the algorithms is that they require a lot of time to compute control input at each sampling instant. In this paper the model predictive control algorithms are demonstrated by the example of plasma vertical stabilization for ITER-FEAT tokamak. The tuning of parameters of algorithms is performed in order to decrease computational load.

  17. Predictive model for segmented poly(urea)

    NASA Astrophysics Data System (ADS)

    Gould, P. J.; Cornish, R.; Frankl, P.; Lewtas, I.

    2012-08-01

    Segmented poly(urea) has been shown to be of significant benefit in protecting vehicles from blast and impact and there have been several experimental studies to determine the mechanisms by which this protective function might occur. One suggested route is by mechanical activation of the glass transition. In order to enable design of protective structures using this material a constitutive model and equation of state are needed for numerical simulation hydrocodes. Determination of such a predictive model may also help elucidate the beneficial mechanisms that occur in polyurea during high rate loading. The tool deployed to do this has been Group Interaction Modelling (GIM) - a mean field technique that has been shown to predict the mechanical and physical properties of polymers from their structure alone. The structure of polyurea has been used to characterise the parameters in the GIM scheme without recourse to experimental data and the equation of state and constitutive model predicts response over a wide range of temperatures and strain rates. The shock Hugoniot has been predicted and validated against existing data. Mechanical response in tensile tests has also been predicted and validated.

  18. Multi-Model Ensemble Wake Vortex Prediction

    NASA Technical Reports Server (NTRS)

    Koerner, Stephan; Holzaepfel, Frank; Ahmad, Nash'at N.

    2015-01-01

    Several multi-model ensemble methods are investigated for predicting wake vortex transport and decay. This study is a joint effort between National Aeronautics and Space Administration and Deutsches Zentrum fuer Luft- und Raumfahrt to develop a multi-model ensemble capability using their wake models. An overview of different multi-model ensemble methods and their feasibility for wake applications is presented. The methods include Reliability Ensemble Averaging, Bayesian Model Averaging, and Monte Carlo Simulations. The methodologies are evaluated using data from wake vortex field experiments.

  19. Thermal barrier coating life prediction model development

    NASA Technical Reports Server (NTRS)

    Cruse, T. A.; Stewart, S. E.; Ortiz, M.

    1988-01-01

    A life prediction model for correlating the spallation life of ceramic thermal barrier coatings is developed which includes both cyclic and time-dependent damage. The cyclic damage is related to the calculated cyclic inelastic strain range, while the time-dependent damage is related to the oxidation kinetics at the bond-ceramic interface. The cyclic inelastic strain range is calculated using a modified form of the Walker viscoplastic material model; calculation of the oxidation kinetics is based on traditional oxidation algorithms using experimentally determined parameters. The correlation between the actual and predicted spallation lives is within a factor of 3.

  20. Negative Social Relationships Predict Posttraumatic Stress Symptoms Among War-Affected Children Via Posttraumatic Cognitions.

    PubMed

    Palosaari, Esa; Punamäki, Raija-Leena; Peltonen, Kirsi; Diab, Marwan; Qouta, Samir R

    2016-07-01

    Post traumatic cognitions (PTCs) are important determinants of post traumatic stress symptoms (PTS symptoms). We tested whether risk factors of PTS symptoms (trauma, demographics, social and family-related factors) predict PTCs and whether PTCs mediate the association between risk factors and PTS symptoms among war-affected children. The participants were 240 Palestinian children 10-12 years old, half boys and half girls, and their parents. Children reported about psychological maltreatment, sibling and peer relations, war trauma, PTCs, PTS symptoms, and depression. Parents reported about their socioeconomic status and their own PTS symptoms. The associations between the variables were estimated in structural equation models. In models which included all the variables, PTCs were predicted by and mediated the effects of psychological maltreatment, war trauma, sibling conflict, and peer unpopularity on PTS symptoms. Other predictors had statistically non-significant effects. Psychological maltreatment had the largest indirect effect (b* = 0.29, p = 0.002) and the indirect effects of war trauma (b* = 0.10, p = 0.045), sibling conflict (b* = 0.10, p = 0.045), and peer unpopularity (b* = 0.10, p = 0.094) were lower and about the same size. Age-salient social relationships are potentially important in the development of both PTCs and PTS symptoms among preadolescents. Furthermore, PTCs mediate the effects of the risk factors of PTS symptoms. The causality of the associations among the variables is not established but it could be studied in the future with interventions which improve the negative aspects of traumatized children's important social relationships.

  1. Constructing and Validating a Decadal Prediction Model

    NASA Astrophysics Data System (ADS)

    Foss, I.; Woolf, D. K.; Gagnon, A. S.; Merchant, C. J.

    2010-05-01

    For the purpose of identifying potential sources of predictability of Scottish mean air temperature (SMAT), a redundancy analysis (RA) was accomplished to quantitatively assess the predictability of SMAT from North Atlantic SSTs as well as the temporal consistency of this predictability. The RA was performed between the main principal components of North Atlantic SST anomalies and SMAT anomalies for two time periods: 1890-1960 and 1960-2006. The RA models developed using data from the 1890-1960 period were validated using the 1960-2006 period; in a similar way the model developed based on the 1960-2006 period was validated using data from the 1890-1960 period. The results indicate the potential to forecast decadal trends in SMAT for all seasons in 1960-2006 time period and all seasons with the exception of winter for the period 1890-1960 with the best predictability achieved in summer. The statistical models show the best performance when SST anomalies in the European shelf seas (45°N-65°N, 20W-20E) rather than those for the SSTs over the entire North Atlantic (30°N-75°N, 80°W-30°E) were used as a predictor. The results of the RA demonstrated that similar SSTs modes were responsible for predictions in the first and second half of the 20th century, establishing temporal consistency, though with stronger influence in the more recent half. The SST pattern responsible for explaining the largest amount of variance in SMAT was stronger in the second half of the 20th century and showed increasing influence from the area of the North Sea, possibly due to faster sea-surface warming in that region in comparison with the open North Atlantic. The Wavelet Transform (WT), Cross Wavelet Transform (XWT) and Wavelet Coherence (WTC) techniques were used to analyse RA-model-based forecasts of SMAT in the time-frequency domain. Wavelet-based techniques applied to the predicted and observed time series revealed a good performance of RA models to predict the frequency variability

  2. Observation simulation experiments with regional prediction models

    NASA Technical Reports Server (NTRS)

    Diak, George; Perkey, Donald J.; Kalb, Michael; Robertson, Franklin R.; Jedlovec, Gary

    1990-01-01

    Research efforts in FY 1990 included studies employing regional scale numerical models as aids in evaluating potential contributions of specific satellite observing systems (current and future) to numerical prediction. One study involves Observing System Simulation Experiments (OSSEs) which mimic operational initialization/forecast cycles but incorporate simulated Advanced Microwave Sounding Unit (AMSU) radiances as input data. The objective of this and related studies is to anticipate the potential value of data from these satellite systems, and develop applications of remotely sensed data for the benefit of short range forecasts. Techniques are also being used that rely on numerical model-based synthetic satellite radiances to interpret the information content of various types of remotely sensed image and sounding products. With this approach, evolution of simulated channel radiance image features can be directly interpreted in terms of the atmospheric dynamical processes depicted by a model. Progress is being made in a study using the internal consistency of a regional prediction model to simplify the assessment of forced diabatic heating and moisture initialization in reducing model spinup times. Techniques for model initialization are being examined, with focus on implications for potential applications of remote microwave observations, including AMSU and Special Sensor Microwave Imager (SSM/I), in shortening model spinup time for regional prediction.

  3. Combining Modeling and Gaming for Predictive Analytics

    SciTech Connect

    Riensche, Roderick M.; Whitney, Paul D.

    2012-08-22

    Many of our most significant challenges involve people. While human behavior has long been studied, there are recent advances in computational modeling of human behavior. With advances in computational capabilities come increases in the volume and complexity of data that humans must understand in order to make sense of and capitalize on these modeling advances. Ultimately, models represent an encapsulation of human knowledge. One inherent challenge in modeling is efficient and accurate transfer of knowledge from humans to models, and subsequent retrieval. The simulated real-world environment of games presents one avenue for these knowledge transfers. In this paper we describe our approach of combining modeling and gaming disciplines to develop predictive capabilities, using formal models to inform game development, and using games to provide data for modeling.

  4. Bayesian Genomic Prediction with Genotype × Environment Interaction Kernel Models

    PubMed Central

    Cuevas, Jaime; Crossa, José; Montesinos-López, Osval A.; Burgueño, Juan; Pérez-Rodríguez, Paulino; de los Campos, Gustavo

    2016-01-01

    The phenomenon of genotype × environment (G × E) interaction in plant breeding decreases selection accuracy, thereby negatively affecting genetic gains. Several genomic prediction models incorporating G × E have been recently developed and used in genomic selection of plant breeding programs. Genomic prediction models for assessing multi-environment G × E interaction are extensions of a single-environment model, and have advantages and limitations. In this study, we propose two multi-environment Bayesian genomic models: the first model considers genetic effects (u) that can be assessed by the Kronecker product of variance–covariance matrices of genetic correlations between environments and genomic kernels through markers under two linear kernel methods, linear (genomic best linear unbiased predictors, GBLUP) and Gaussian (Gaussian kernel, GK). The other model has the same genetic component as the first model (u) plus an extra component, f, that captures random effects between environments that were not captured by the random effects u. We used five CIMMYT data sets (one maize and four wheat) that were previously used in different studies. Results show that models with G × E always have superior prediction ability than single-environment models, and the higher prediction ability of multi-environment models with u and f over the multi-environment model with only u occurred 85% of the time with GBLUP and 45% of the time with GK across the five data sets. The latter result indicated that including the random effect f is still beneficial for increasing prediction ability after adjusting by the random effect u. PMID:27793970

  5. Advancements in predictive plasma formation modeling

    NASA Astrophysics Data System (ADS)

    Purvis, Michael A.; Schafgans, Alexander; Brown, Daniel J. W.; Fomenkov, Igor; Rafac, Rob; Brown, Josh; Tao, Yezheng; Rokitski, Slava; Abraham, Mathew; Vargas, Mike; Rich, Spencer; Taylor, Ted; Brandt, David; Pirati, Alberto; Fisher, Aaron; Scott, Howard; Koniges, Alice; Eder, David; Wilks, Scott; Link, Anthony; Langer, Steven

    2016-03-01

    We present highlights from plasma simulations performed in collaboration with Lawrence Livermore National Labs. This modeling is performed to advance the rate of learning about optimal EUV generation for laser produced plasmas and to provide insights where experimental results are not currently available. The goal is to identify key physical processes necessary for an accurate and predictive model capable of simulating a wide range of conditions. This modeling will help to drive source performance scaling in support of the EUV Lithography roadmap. The model simulates pre-pulse laser interaction with the tin droplet and follows the droplet expansion into the main pulse target zone. Next, the interaction of the expanded droplet with the main laser pulse is simulated. We demonstrate the predictive nature of the code and provide comparison with experimental results.

  6. Model Predictive Control of Sewer Networks

    NASA Astrophysics Data System (ADS)

    Pedersen, Einar B.; Herbertsson, Hannes R.; Niemann, Henrik; Poulsen, Niels K.; Falk, Anne K. V.

    2017-01-01

    The developments in solutions for management of urban drainage are of vital importance, as the amount of sewer water from urban areas continues to increase due to the increase of the world’s population and the change in the climate conditions. How a sewer network is structured, monitored and controlled have thus become essential factors for effcient performance of waste water treatment plants. This paper examines methods for simplified modelling and controlling a sewer network. A practical approach to the problem is used by analysing simplified design model, which is based on the Barcelona benchmark model. Due to the inherent constraints the applied approach is based on Model Predictive Control.

  7. DKIST Polarization Modeling and Performance Predictions

    NASA Astrophysics Data System (ADS)

    Harrington, David

    2016-05-01

    Calibrating the Mueller matrices of large aperture telescopes and associated coude instrumentation requires astronomical sources and several modeling assumptions to predict the behavior of the system polarization with field of view, altitude, azimuth and wavelength. The Daniel K Inouye Solar Telescope (DKIST) polarimetric instrumentation requires very high accuracy calibration of a complex coude path with an off-axis f/2 primary mirror, time dependent optical configurations and substantial field of view. Polarization predictions across a diversity of optical configurations, tracking scenarios, slit geometries and vendor coating formulations are critical to both construction and contined operations efforts. Recent daytime sky based polarization calibrations of the 4m AEOS telescope and HiVIS spectropolarimeter on Haleakala have provided system Mueller matrices over full telescope articulation for a 15-reflection coude system. AEOS and HiVIS are a DKIST analog with a many-fold coude optical feed and similar mirror coatings creating 100% polarization cross-talk with altitude, azimuth and wavelength. Polarization modeling predictions using Zemax have successfully matched the altitude-azimuth-wavelength dependence on HiVIS with the few percent amplitude limitations of several instrument artifacts. Polarization predictions for coude beam paths depend greatly on modeling the angle-of-incidence dependences in powered optics and the mirror coating formulations. A 6 month HiVIS daytime sky calibration plan has been analyzed for accuracy under a wide range of sky conditions and data analysis algorithms. Predictions of polarimetric performance for the DKIST first-light instrumentation suite have been created under a range of configurations. These new modeling tools and polarization predictions have substantial impact for the design, fabrication and calibration process in the presence of manufacturing issues, science use-case requirements and ultimate system calibration

  8. Predictive Modeling of the CDRA 4BMS

    NASA Technical Reports Server (NTRS)

    Coker, Robert; Knox, James

    2016-01-01

    Fully predictive models of the Four Bed Molecular Sieve of the Carbon Dioxide Removal Assembly on the International Space Station are being developed. This virtual laboratory will be used to help reduce mass, power, and volume requirements for future missions. In this paper we describe current and planned modeling developments in the area of carbon dioxide removal to support future crewed Mars missions as well as the resolution of anomalies observed in the ISS CDRA.

  9. Predictive analytics can support the ACO model.

    PubMed

    Bradley, Paul

    2012-04-01

    Predictive analytics can be used to rapidly spot hard-to-identify opportunities to better manage care--a key tool in accountable care. When considering analytics models, healthcare providers should: Make value-based care a priority and act on information from analytics models. Create a road map that includes achievable steps, rather than major endeavors. Set long-term expectations and recognize that the effectiveness of an analytics program takes time, unlike revenue cycle initiatives that may show a quick return.

  10. Predictive performance models and multiple task performance

    NASA Technical Reports Server (NTRS)

    Wickens, Christopher D.; Larish, Inge; Contorer, Aaron

    1989-01-01

    Five models that predict how performance of multiple tasks will interact in complex task scenarios are discussed. The models are shown in terms of the assumptions they make about human operator divided attention. The different assumptions about attention are then empirically validated in a multitask helicopter flight simulation. It is concluded from this simulation that the most important assumption relates to the coding of demand level of different component tasks.

  11. A Robustly Stabilizing Model Predictive Control Algorithm

    NASA Technical Reports Server (NTRS)

    Ackmece, A. Behcet; Carson, John M., III

    2007-01-01

    A model predictive control (MPC) algorithm that differs from prior MPC algorithms has been developed for controlling an uncertain nonlinear system. This algorithm guarantees the resolvability of an associated finite-horizon optimal-control problem in a receding-horizon implementation.

  12. Thermal barrier coating life prediction model development

    NASA Technical Reports Server (NTRS)

    Demasi, J. T.; Sheffler, K. D.

    1985-01-01

    The objective is to develop an integrated life prediction model accounting for all potential life-limiting thermal barrier coating (TBC) degradation and failure modes, including spallation resulting from cyclic thermal stress, oxidation degradation, hot corrosion, erosion and foreign object damage.

  13. Nearshore Operational Model for Rip Current Predictions

    NASA Astrophysics Data System (ADS)

    Sembiring, L. E.; Van Dongeren, A. R.; Van Ormondt, M.; Winter, G.; Roelvink, J.

    2012-12-01

    A coastal operational model system can serve as a tool in order to monitor and predict coastal hazards, and to acquire up-to-date information on coastal state indicators. The objective of this research is to develop a nearshore operational model system for the Dutch coast focusing on swimmer safety. For that purpose, an operational model system has been built which can predict conditions up to 48 hours ahead. The model system consists of three different nested model domain covering The North Sea, The Dutch coastline, and one local model which is the area of interest. Three different process-based models are used to simulate physical processes within the system: SWAN to simulate wave propagation, Delft3D-Flow for hydraulics flow simulation, and XBeach for the nearshore models. The SWAN model is forced by wind fields from operational HiRLAM, as well as two dimensional wave spectral data from WaveWatch 3 Global as the ocean boundaries. The Delft3D Flow model is forced by assigning the boundaries with tidal constants for several important astronomical components as well as HiRLAM wind fields. For the local XBeach model, up-to-date bathymetry will be obtained by assimilating model computation and Argus video data observation. A hindcast is carried out on the Continental Shelf Model, covering the North Sea and nearby Atlantic Ocean, for the year 2009. Model skills are represented by several statistical measures such as rms error and bias. In general the results show that the model system exhibits a good agreement with field data. For SWAN results, integral significant wave heights are predicted well by the model for all wave buoys considered, with rms errors ranging from 0.16 m for the month of May with observed mean significant wave height of 1.08 m, up to rms error of 0.39 m for the month of November, with observed mean significant wave height of 1.91 m. However, it is found that the wave model slightly underestimates the observation for the period of June, especially

  14. Situational Motivation and Perceived Intensity: Their Interaction in Predicting Changes in Positive Affect from Physical Activity

    PubMed Central

    Guérin, Eva; Fortier, Michelle S.

    2012-01-01

    There is evidence that affective experiences surrounding physical activity can contribute to the proper self-regulation of an active lifestyle. Motivation toward physical activity, as portrayed by self-determination theory, has been linked to positive affect, as has the intensity of physical activity, especially of a preferred nature. The purpose of this experimental study was to examine the interaction between situational motivation and intensity [i.e., ratings of perceived exertion (RPE)] in predicting changes in positive affect following an acute bout of preferred physical activity, namely, running. Fourty-one female runners engaged in a 30-minute self-paced treadmill run in a laboratory context. Situational motivation for running, pre- and post-running positive affect, and RPE were assessed via validated self-report questionnaires. Hierarchical regression analyses revealed a significant interaction effect between RPE and introjection (P < .05) but not between RPE and identified regulation or intrinsic motivation. At low levels of introjection, the influence of RPE on the change in positive affect was considerable, with higher RPE ratings being associated with greater increases in positive affect. The implications of the findings in light of SDT principles as well as the potential contingencies between the regulations and RPE in predicting positive affect among women are discussed. PMID:22778914

  15. PREDICTIVE MODELING OF CHOLERA OUTBREAKS IN BANGLADESH

    PubMed Central

    Koepke, Amanda A.; Longini, Ira M.; Halloran, M. Elizabeth; Wakefield, Jon; Minin, Vladimir N.

    2016-01-01

    Despite seasonal cholera outbreaks in Bangladesh, little is known about the relationship between environmental conditions and cholera cases. We seek to develop a predictive model for cholera outbreaks in Bangladesh based on environmental predictors. To do this, we estimate the contribution of environmental variables, such as water depth and water temperature, to cholera outbreaks in the context of a disease transmission model. We implement a method which simultaneously accounts for disease dynamics and environmental variables in a Susceptible-Infected-Recovered-Susceptible (SIRS) model. The entire system is treated as a continuous-time hidden Markov model, where the hidden Markov states are the numbers of people who are susceptible, infected, or recovered at each time point, and the observed states are the numbers of cholera cases reported. We use a Bayesian framework to fit this hidden SIRS model, implementing particle Markov chain Monte Carlo methods to sample from the posterior distribution of the environmental and transmission parameters given the observed data. We test this method using both simulation and data from Mathbaria, Bangladesh. Parameter estimates are used to make short-term predictions that capture the formation and decline of epidemic peaks. We demonstrate that our model can successfully predict an increase in the number of infected individuals in the population weeks before the observed number of cholera cases increases, which could allow for early notification of an epidemic and timely allocation of resources. PMID:27746850

  16. Steps/day ability to predict anthropometric changes is not affected by its plausibility

    Technology Transfer Automated Retrieval System (TEKTRAN)

    We evaluated whether treating steps/day data for implausible values (<500 or >30,000) affected the ability of these data to predict intervention-induced anthropometric (waist circumference, body mass index, percent body fat, and fat mass) changes. Data were from 269 African American participants wh...

  17. Marijuana and tobacco exposure predict affect-regulation expectancies in dual users.

    PubMed

    Martens, K M; Gilbert, David G

    2008-11-01

    In order to better compare affect-related expectancies for tobacco and marijuana smoking, associations of marijuana and tobacco exposure to negative affect reduction (NAR), positive affect enhancement (PAE), and related smoking outcome expectancies were assessed in young individuals who reported smoking both marijuana and tobacco on a regular basis (dual users). More frequent smoking of a given substance was associated with expectations of greater NAR and PAE by that substance while duration of exposure did not reliably predict NAR or PAE drug expectancies. Contrary to expectations, individuals anticipating greater NAR and/or PAE for one substance did not exhibit corresponding expectancies for the other drug. These findings suggest that exposure duration may be less important than current usage levels in influencing affect expectancies and that the affect-related expectancies for tobacco and marijuana are largely independent of each other.

  18. A stepwise model to predict monthly streamflow

    NASA Astrophysics Data System (ADS)

    Mahmood Al-Juboori, Anas; Guven, Aytac

    2016-12-01

    In this study, a stepwise model empowered with genetic programming is developed to predict the monthly flows of Hurman River in Turkey and Diyalah and Lesser Zab Rivers in Iraq. The model divides the monthly flow data to twelve intervals representing the number of months in a year. The flow of a month, t is considered as a function of the antecedent month's flow (t - 1) and it is predicted by multiplying the antecedent monthly flow by a constant value called K. The optimum value of K is obtained by a stepwise procedure which employs Gene Expression Programming (GEP) and Nonlinear Generalized Reduced Gradient Optimization (NGRGO) as alternative to traditional nonlinear regression technique. The degree of determination and root mean squared error are used to evaluate the performance of the proposed models. The results of the proposed model are compared with the conventional Markovian and Auto Regressive Integrated Moving Average (ARIMA) models based on observed monthly flow data. The comparison results based on five different statistic measures show that the proposed stepwise model performed better than Markovian model and ARIMA model. The R2 values of the proposed model range between 0.81 and 0.92 for the three rivers in this study.

  19. Disease prediction models and operational readiness.

    PubMed

    Corley, Courtney D; Pullum, Laura L; Hartley, David M; Benedum, Corey; Noonan, Christine; Rabinowitz, Peter M; Lancaster, Mary J

    2014-01-01

    The objective of this manuscript is to present a systematic review of biosurveillance models that operate on select agents and can forecast the occurrence of a disease event. We define a disease event to be a biological event with focus on the One Health paradigm. These events are characterized by evidence of infection and or disease condition. We reviewed models that attempted to predict a disease event, not merely its transmission dynamics and we considered models involving pathogens of concern as determined by the US National Select Agent Registry (as of June 2011). We searched commercial and government databases and harvested Google search results for eligible models, using terms and phrases provided by public health analysts relating to biosurveillance, remote sensing, risk assessments, spatial epidemiology, and ecological niche modeling. After removal of duplications and extraneous material, a core collection of 6,524 items was established, and these publications along with their abstracts are presented in a semantic wiki at http://BioCat.pnnl.gov. As a result, we systematically reviewed 44 papers, and the results are presented in this analysis. We identified 44 models, classified as one or more of the following: event prediction (4), spatial (26), ecological niche (28), diagnostic or clinical (6), spread or response (9), and reviews (3). The model parameters (e.g., etiology, climatic, spatial, cultural) and data sources (e.g., remote sensing, non-governmental organizations, expert opinion, epidemiological) were recorded and reviewed. A component of this review is the identification of verification and validation (V&V) methods applied to each model, if any V&V method was reported. All models were classified as either having undergone Some Verification or Validation method, or No Verification or Validation. We close by outlining an initial set of operational readiness level guidelines for disease prediction models based upon established Technology Readiness

  20. Disease Prediction Models and Operational Readiness

    PubMed Central

    Corley, Courtney D.; Pullum, Laura L.; Hartley, David M.; Benedum, Corey; Noonan, Christine; Rabinowitz, Peter M.; Lancaster, Mary J.

    2014-01-01

    The objective of this manuscript is to present a systematic review of biosurveillance models that operate on select agents and can forecast the occurrence of a disease event. We define a disease event to be a biological event with focus on the One Health paradigm. These events are characterized by evidence of infection and or disease condition. We reviewed models that attempted to predict a disease event, not merely its transmission dynamics and we considered models involving pathogens of concern as determined by the US National Select Agent Registry (as of June 2011). We searched commercial and government databases and harvested Google search results for eligible models, using terms and phrases provided by public health analysts relating to biosurveillance, remote sensing, risk assessments, spatial epidemiology, and ecological niche modeling. After removal of duplications and extraneous material, a core collection of 6,524 items was established, and these publications along with their abstracts are presented in a semantic wiki at http://BioCat.pnnl.gov. As a result, we systematically reviewed 44 papers, and the results are presented in this analysis. We identified 44 models, classified as one or more of the following: event prediction (4), spatial (26), ecological niche (28), diagnostic or clinical (6), spread or response (9), and reviews (3). The model parameters (e.g., etiology, climatic, spatial, cultural) and data sources (e.g., remote sensing, non-governmental organizations, expert opinion, epidemiological) were recorded and reviewed. A component of this review is the identification of verification and validation (V&V) methods applied to each model, if any V&V method was reported. All models were classified as either having undergone Some Verification or Validation method, or No Verification or Validation. We close by outlining an initial set of operational readiness level guidelines for disease prediction models based upon established Technology Readiness

  1. Can contaminant transport models predict breakthrough?

    USGS Publications Warehouse

    Peng, Wei-Shyuan; Hampton, Duane R.; Konikow, Leonard F.; Kambham, Kiran; Benegar, Jeffery J.

    2000-01-01

    A solute breakthrough curve measured during a two-well tracer test was successfully predicted in 1986 using specialized contaminant transport models. Water was injected into a confined, unconsolidated sand aquifer and pumped out 125 feet (38.3 m) away at the same steady rate. The injected water was spiked with bromide for over three days; the outflow concentration was monitored for a month. Based on previous tests, the horizontal hydraulic conductivity of the thick aquifer varied by a factor of seven among 12 layers. Assuming stratified flow with small dispersivities, two research groups accurately predicted breakthrough with three-dimensional (12-layer) models using curvilinear elements following the arc-shaped flowlines in this test. Can contaminant transport models commonly used in industry, that use rectangular blocks, also reproduce this breakthrough curve? The two-well test was simulated with four MODFLOW-based models, MT3D (FD and HMOC options), MODFLOWT, MOC3D, and MODFLOW-SURFACT. Using the same 12 layers and small dispersivity used in the successful 1986 simulations, these models fit almost as accurately as the models using curvilinear blocks. Subtle variations in the curves illustrate differences among the codes. Sensitivities of the results to number and size of grid blocks, number of layers, boundary conditions, and values of dispersivity and porosity are briefly presented. The fit between calculated and measured breakthrough curves degenerated as the number of layers and/or grid blocks decreased, reflecting a loss of model predictive power as the level of characterization lessened. Therefore, the breakthrough curve for most field sites can be predicted only qualitatively due to limited characterization of the hydrogeology and contaminant source strength.

  2. Developmental trends in alcohol use initiation and escalation from early- to middle-adolescence: Prediction by urgency and trait affect

    PubMed Central

    Spillane, Nichea S.; Merrill, Jennifer E.; Jackson, Kristina M.

    2016-01-01

    Studies on adolescent drinking have not always been able to distinguish between initiation and escalation of drinking, because many studies include samples in which initiation has already occurred; hence initiation and escalation are often confounded. The present study draws from a dual-process theoretical framework to investigate: if changes in the likelihood of drinking initiation and escalation are predicted by a tendency towards rash action when experiencing positive and negative emotions (positive and negative urgency); and whether trait positive and negative affect moderate such effects. Alcohol naïve adolescents (n=944; age: M=12.16, SD=.96; 52% female) completed 6 semi-annual assessments of trait urgency and affect (wave-1) and alcohol use (waves 2–6). A two-part random-effects model was used to estimate changes in the likelihood of any alcohol use vs. escalation in the volume of use amongst initiators. Main effects suggest a significant association between positive affect and change in level of alcohol use amongst initiators, such that lower positive affect predicted increased alcohol involvement. This main effect was qualified by a significant interaction between positive urgency and positive affect predicting changes in the escalation of drinking, such that the effect of positive urgency was augmented for those high on trait positive affect, though only at extremely high levels of positive affect. Results suggest risk factors in the development of drinking depend on whether initiation or escalation is investigated. A more nuanced understanding of the early developmental phases of alcohol involvement can inform prevention and intervention efforts. PMID:27031086

  3. Developmental trends in alcohol use initiation and escalation from early to middle adolescence: Prediction by urgency and trait affect.

    PubMed

    Lopez-Vergara, Hector I; Spillane, Nichea S; Merrill, Jennifer E; Jackson, Kristina M

    2016-08-01

    Studies on adolescent drinking have not always been able to distinguish between initiation and escalation of drinking, because many studies include samples in which initiation has already occurred; hence, initiation and escalation are often confounded. The present study draws from a dual-process theoretical framework to investigate: if changes in the likelihood of drinking initiation and escalation are predicted by a tendency toward rash action when experiencing positive and negative emotions (positive and negative urgency) and whether trait positive and negative affect moderate such effects. Alcohol naïve adolescents (n = 944; age M = 12.16, SD = .96; 52% female) completed 6 semiannual assessments of trait urgency and affect (Wave 1) and alcohol use (Waves 2-6). A 2-part random-effects model was used to estimate changes in the likelihood of any alcohol use versus escalation in the volume of use among initiators. Main effects suggest a significant association between positive affect and change in level of alcohol use among initiators, such that lower positive affect predicted increased alcohol involvement. This main effect was qualified by a significant interaction between positive urgency and positive affect predicting changes in the escalation of drinking, such that the effect of positive urgency was augmented for those high on trait positive affect, though only at extremely high levels of positive affect. Results suggest risk factors in the development of drinking depend on whether initiation or escalation is investigated. A more nuanced understanding of the early developmental phases of alcohol involvement can inform prevention and intervention efforts. (PsycINFO Database Record

  4. Toddler Inhibitory Control, Bold Response to Novelty, and Positive Affect Predict Externalizing Symptoms in Kindergarten

    PubMed Central

    Buss, Kristin A.; Kiel, Elizabeth J.; Morales, Santiago; Robinson, Emily

    2013-01-01

    Poor inhibitory control and bold-approach have been found to predict the development of externalizing behavior problems in young children. Less research has examined how positive affect may influence the development of externalizing behavior in the context of low inhibitory control and high approach. We used a multimethod approach to examine how observed toddler inhibitory control, bold-approach, and positive affect predicted externalizing outcomes (observed, adult- and self-reported) in additive and interactive ways at the beginning of kindergarten. 24-month-olds (N = 110) participated in a laboratory visit and 84 were followed up in kindergarten for externalizing behaviors. Overall, children who were low in inhibitory control, high in bold-approach, and low in positive affect at 24-months of age were at greater risk for externalizing behaviors during kindergarten. PMID:25018589

  5. A predictive model for artificial mechanical cochlea

    NASA Astrophysics Data System (ADS)

    Ahmed, Riaz U.; Adiba, Afifa; Banerjee, Sourav

    2015-03-01

    To recover the hearing deficiency, cochlea implantation is essential if the inner ear is damaged. Existing implantable cochlea is an electronic device, usually placed outside the ear to receive sound from environment, convert into electric impulses and send to auditory nerve bypassing the damaged cochlea. However, due to growing demand researchers are interested in fabricating artificial mechanical cochlea to overcome the limitations of electronic cochlea. Only a hand full number of research have been published in last couple of years showing fabrication of basilar membrane mimicking the cochlear operations. Basilar membrane plays the most important role in a human cochlea by responding only on sonic frequencies using its varying material properties from basal to apical end. Only few sonic frequencies have been sensed with the proposed models; however no process was discussed on how the frequency selectivity of the models can be improved to sense the entire sonic frequency range. Thus, we argue that a predictive model is the missing-link and is the utmost necessity. Hence, in this study, we intend to develop a multi-scale predictive model for basilar membrane such that sensing potential of the artificial cochlea can be designed and tuned predictively by altering the model parameters.

  6. Genetic models of homosexuality: generating testable predictions.

    PubMed

    Gavrilets, Sergey; Rice, William R

    2006-12-22

    Homosexuality is a common occurrence in humans and other species, yet its genetic and evolutionary basis is poorly understood. Here, we formulate and study a series of simple mathematical models for the purpose of predicting empirical patterns that can be used to determine the form of selection that leads to polymorphism of genes influencing homosexuality. Specifically, we develop theory to make contrasting predictions about the genetic characteristics of genes influencing homosexuality including: (i) chromosomal location, (ii) dominance among segregating alleles and (iii) effect sizes that distinguish between the two major models for their polymorphism: the overdominance and sexual antagonism models. We conclude that the measurement of the genetic characteristics of quantitative trait loci (QTLs) found in genomic screens for genes influencing homosexuality can be highly informative in resolving the form of natural selection maintaining their polymorphism.

  7. Genetic models of homosexuality: generating testable predictions

    PubMed Central

    Gavrilets, Sergey; Rice, William R

    2006-01-01

    Homosexuality is a common occurrence in humans and other species, yet its genetic and evolutionary basis is poorly understood. Here, we formulate and study a series of simple mathematical models for the purpose of predicting empirical patterns that can be used to determine the form of selection that leads to polymorphism of genes influencing homosexuality. Specifically, we develop theory to make contrasting predictions about the genetic characteristics of genes influencing homosexuality including: (i) chromosomal location, (ii) dominance among segregating alleles and (iii) effect sizes that distinguish between the two major models for their polymorphism: the overdominance and sexual antagonism models. We conclude that the measurement of the genetic characteristics of quantitative trait loci (QTLs) found in genomic screens for genes influencing homosexuality can be highly informative in resolving the form of natural selection maintaining their polymorphism. PMID:17015344

  8. Robust model predictive control of Wiener systems

    NASA Astrophysics Data System (ADS)

    Biagiola, S. I.; Figueroa, J. L.

    2011-03-01

    Block-oriented models (BOMs) have shown to be appealing and efficient as nonlinear representations for many applications. They are at the same time valid and simple models in a more extensive region than time-invariant linear models. In this work, Wiener models are considered. They are one of the most diffused BOMs, and their structure consists in a linear dynamics in cascade with a nonlinear static block. Particularly, the problem of control of these systems in the presence of uncertainty is treated. The proposed methodology makes use of a robust identification procedure in order to obtain a robust model to represent the uncertain system. This model is then employed to design a model predictive controller. The mathematical problem involved in the controller design is formulated in the context of the existing linear matrix inequalities (LMI) theory. The main feature of this approach is that it takes advantage of the static nature of the nonlinearity, which allows to solve the control problem by focusing only in the linear dynamics. This formulation results in a simplified design procedure, because the original nonlinear model predictive control (MPC) problem turns into a linear one.

  9. A statistical model for predicting muscle performance

    NASA Astrophysics Data System (ADS)

    Byerly, Diane Leslie De Caix

    The objective of these studies was to develop a capability for predicting muscle performance and fatigue to be utilized for both space- and ground-based applications. To develop this predictive model, healthy test subjects performed a defined, repetitive dynamic exercise to failure using a Lordex spinal machine. Throughout the exercise, surface electromyography (SEMG) data were collected from the erector spinae using a Mega Electronics ME3000 muscle tester and surface electrodes placed on both sides of the back muscle. These data were analyzed using a 5th order Autoregressive (AR) model and statistical regression analysis. It was determined that an AR derived parameter, the mean average magnitude of AR poles, significantly correlated with the maximum number of repetitions (designated Rmax) that a test subject was able to perform. Using the mean average magnitude of AR poles, a test subject's performance to failure could be predicted as early as the sixth repetition of the exercise. This predictive model has the potential to provide a basis for improving post-space flight recovery, monitoring muscle atrophy in astronauts and assessing the effectiveness of countermeasures, monitoring astronaut performance and fatigue during Extravehicular Activity (EVA) operations, providing pre-flight assessment of the ability of an EVA crewmember to perform a given task, improving the design of training protocols and simulations for strenuous International Space Station assembly EVA, and enabling EVA work task sequences to be planned enhancing astronaut performance and safety. Potential ground-based, medical applications of the predictive model include monitoring muscle deterioration and performance resulting from illness, establishing safety guidelines in the industry for repetitive tasks, monitoring the stages of rehabilitation for muscle-related injuries sustained in sports and accidents, and enhancing athletic performance through improved training protocols while reducing

  10. Prediction failure of a wolf landscape model

    USGS Publications Warehouse

    Mech, L.D.

    2006-01-01

    I compared 101 wolf (Canis lupus) pack territories formed in Wisconsin during 1993-2004 to the logistic regression predictive model of Mladenoff et al. (1995, 1997, 1999). Of these, 60% were located in putative habitat suitabilities 50% remained unoccupied by known packs after 24 years of recolonization. This model was a poor predictor of wolf re-colonizing locations in Wisconsin, apparently because it failed to consider the adaptability of wolves. Such models should be used cautiously in wolf-management or restoration plans.

  11. Modeling Benthic Sediment Processes to Predict Water ...

    EPA Pesticide Factsheets

    The benthic sediment acts as a huge reservoir of particulate and dissolved material (within interstitial water) which can contribute to loading of contaminants and nutrients to the water column. A benthic sediment model is presented in this report to predict spatial and temporal benthic fluxes of nutrients and chemicals in Narragansett Bay. A benthic sediment model is presented in this report to identify benthic flux into the water column in Narragansett Bay. Benthic flux is essential to properly model water quality and ecology in estuarine and coastal systems.

  12. SNP2TFBS – a database of regulatory SNPs affecting predicted transcription factor binding site affinity

    PubMed Central

    Kumar, Sunil; Ambrosini, Giovanna; Bucher, Philipp

    2017-01-01

    SNP2TFBS is a computational resource intended to support researchers investigating the molecular mechanisms underlying regulatory variation in the human genome. The database essentially consists of a collection of text files providing specific annotations for human single nucleotide polymorphisms (SNPs), namely whether they are predicted to abolish, create or change the affinity of one or several transcription factor (TF) binding sites. A SNP's effect on TF binding is estimated based on a position weight matrix (PWM) model for the binding specificity of the corresponding factor. These data files are regenerated at regular intervals by an automatic procedure that takes as input a reference genome, a comprehensive SNP catalogue and a collection of PWMs. SNP2TFBS is also accessible over a web interface, enabling users to view the information provided for an individual SNP, to extract SNPs based on various search criteria, to annotate uploaded sets of SNPs or to display statistics about the frequencies of binding sites affected by selected SNPs. Homepage: http://ccg.vital-it.ch/snp2tfbs/. PMID:27899579

  13. SNP2TFBS - a database of regulatory SNPs affecting predicted transcription factor binding site affinity.

    PubMed

    Kumar, Sunil; Ambrosini, Giovanna; Bucher, Philipp

    2017-01-04

    SNP2TFBS is a computational resource intended to support researchers investigating the molecular mechanisms underlying regulatory variation in the human genome. The database essentially consists of a collection of text files providing specific annotations for human single nucleotide polymorphisms (SNPs), namely whether they are predicted to abolish, create or change the affinity of one or several transcription factor (TF) binding sites. A SNP's effect on TF binding is estimated based on a position weight matrix (PWM) model for the binding specificity of the corresponding factor. These data files are regenerated at regular intervals by an automatic procedure that takes as input a reference genome, a comprehensive SNP catalogue and a collection of PWMs. SNP2TFBS is also accessible over a web interface, enabling users to view the information provided for an individual SNP, to extract SNPs based on various search criteria, to annotate uploaded sets of SNPs or to display statistics about the frequencies of binding sites affected by selected SNPs. Homepage: http://ccg.vital-it.ch/snp2tfbs/.

  14. Product component genealogy modeling and field-failure prediction

    DOE PAGES

    King, Caleb; Hong, Yili; Meeker, William Q.

    2016-04-13

    Many industrial products consist of multiple components that are necessary for system operation. There is an abundance of literature on modeling the lifetime of such components through competing risks models. During the life-cycle of a product, it is common for there to be incremental design changes to improve reliability, to reduce costs, or due to changes in availability of certain part numbers. These changes can affect product reliability but are often ignored in system lifetime modeling. By incorporating this information about changes in part numbers over time (information that is readily available in most production databases), better accuracy can bemore » achieved in predicting time to failure, thus yielding more accurate field-failure predictions. This paper presents methods for estimating parameters and predictions for this generational model and a comparison with existing methods through the use of simulation. Our results indicate that the generational model has important practical advantages and outperforms the existing methods in predicting field failures.« less

  15. Predictive Models for Carcinogenicity and Mutagenicity ...

    EPA Pesticide Factsheets

    Mutagenicity and carcinogenicity are endpoints of major environmental and regulatory concern. These endpoints are also important targets for development of alternative methods for screening and prediction due to the large number of chemicals of potential concern and the tremendous cost (in time, money, animals) of rodent carcinogenicity bioassays. Both mutagenicity and carcinogenicity involve complex, cellular processes that are only partially understood. Advances in technologies and generation of new data will permit a much deeper understanding. In silico methods for predicting mutagenicity and rodent carcinogenicity based on chemical structural features, along with current mutagenicity and carcinogenicity data sets, have performed well for local prediction (i.e., within specific chemical classes), but are less successful for global prediction (i.e., for a broad range of chemicals). The predictivity of in silico methods can be improved by improving the quality of the data base and endpoints used for modelling. In particular, in vitro assays for clastogenicity need to be improved to reduce false positives (relative to rodent carcinogenicity) and to detect compounds that do not interact directly with DNA or have epigenetic activities. New assays emerging to complement or replace some of the standard assays include VitotoxTM, GreenScreenGC, and RadarScreen. The needs of industry and regulators to assess thousands of compounds necessitate the development of high-t

  16. Seasonal Predictability in a Model Atmosphere.

    NASA Astrophysics Data System (ADS)

    Lin, Hai

    2001-07-01

    The predictability of atmospheric mean-seasonal conditions in the absence of externally varying forcing is examined. A perfect-model approach is adopted, in which a global T21 three-level quasigeostrophic atmospheric model is integrated over 21 000 days to obtain a reference atmospheric orbit. The model is driven by a time-independent forcing, so that the only source of time variability is the internal dynamics. The forcing is set to perpetual winter conditions in the Northern Hemisphere (NH) and perpetual summer in the Southern Hemisphere.A significant temporal variability in the NH 90-day mean states is observed. The component of that variability associated with the higher-frequency motions, or climate noise, is estimated using a method developed by Madden. In the polar region, and to a lesser extent in the midlatitudes, the temporal variance of the winter means is significantly greater than the climate noise, suggesting some potential predictability in those regions.Forecast experiments are performed to see whether the presence of variance in the 90-day mean states that is in excess of the climate noise leads to some skill in the prediction of these states. Ensemble forecast experiments with nine members starting from slightly different initial conditions are performed for 200 different 90-day means along the reference atmospheric orbit. The serial correlation between the ensemble means and the reference orbit shows that there is skill in the 90-day mean predictions. The skill is concentrated in those regions of the NH that have the largest variance in excess of the climate noise. An EOF analysis shows that nearly all the predictive skill in the seasonal means is associated with one mode of variability with a strong axisymmetric component.

  17. Model atmospheres, predicted spectra, and colors

    NASA Technical Reports Server (NTRS)

    1982-01-01

    Theoretical models of stellar atmospheres and the process of forming a spectrum are reviewed with particular reference to the spectra of B stars. In the case of classical models the stellar atmosphere is though to consist of plane parallel layers of gas in which radiative and hydrostatic equilibrium exists. No radiative energy is lost or gained in the model atmosphere, but the detailed shape of the spectrum is changed as a result of the interactions with the ionized gas. Predicted line spectra using statistical equilibrium local thermodynamic equilibrium (LTE), and non-LTE physics are compared and the determination of abundances is discussed. The limitations of classical modeling are examined. Models developed to demonstrate what motions in the upper atmosphere will do to the spectrum and to explore the effects of using geometries different from plane parallel layer are reviewed. In particular the problem of radiative transfer is addressed.

  18. Economic decision making and the application of nonparametric prediction models

    USGS Publications Warehouse

    Attanasi, E.D.; Coburn, T.C.; Freeman, P.A.

    2008-01-01

    Sustained increases in energy prices have focused attention on gas resources in low-permeability shale or in coals that were previously considered economically marginal. Daily well deliverability is often relatively small, although the estimates of the total volumes of recoverable resources in these settings are often large. Planning and development decisions for extraction of such resources must be areawide because profitable extraction requires optimization of scale economies to minimize costs and reduce risk. For an individual firm, the decision to enter such plays depends on reconnaissance-level estimates of regional recoverable resources and on cost estimates to develop untested areas. This paper shows how simple nonparametric local regression models, used to predict technically recoverable resources at untested sites, can be combined with economic models to compute regional-scale cost functions. The context of the worked example is the Devonian Antrim-shale gas play in the Michigan basin. One finding relates to selection of the resource prediction model to be used with economic models. Models chosen because they can best predict aggregate volume over larger areas (many hundreds of sites) smooth out granularity in the distribution of predicted volumes at individual sites. This loss of detail affects the representation of economic cost functions and may affect economic decisions. Second, because some analysts consider unconventional resources to be ubiquitous, the selection and order of specific drilling sites may, in practice, be determined arbitrarily by extraneous factors. The analysis shows a 15-20% gain in gas volume when these simple models are applied to order drilling prospects strategically rather than to choose drilling locations randomly. Copyright ?? 2008 Society of Petroleum Engineers.

  19. Validated predictive modelling of the environmental resistome

    PubMed Central

    Amos, Gregory CA; Gozzard, Emma; Carter, Charlotte E; Mead, Andrew; Bowes, Mike J; Hawkey, Peter M; Zhang, Lihong; Singer, Andrew C; Gaze, William H; Wellington, Elizabeth M H

    2015-01-01

    Multi-drug-resistant bacteria pose a significant threat to public health. The role of the environment in the overall rise in antibiotic-resistant infections and risk to humans is largely unknown. This study aimed to evaluate drivers of antibiotic-resistance levels across the River Thames catchment, model key biotic, spatial and chemical variables and produce predictive models for future risk assessment. Sediment samples from 13 sites across the River Thames basin were taken at four time points across 2011 and 2012. Samples were analysed for class 1 integron prevalence and enumeration of third-generation cephalosporin-resistant bacteria. Class 1 integron prevalence was validated as a molecular marker of antibiotic resistance; levels of resistance showed significant geospatial and temporal variation. The main explanatory variables of resistance levels at each sample site were the number, proximity, size and type of surrounding wastewater-treatment plants. Model 1 revealed treatment plants accounted for 49.5% of the variance in resistance levels. Other contributing factors were extent of different surrounding land cover types (for example, Neutral Grassland), temporal patterns and prior rainfall; when modelling all variables the resulting model (Model 2) could explain 82.9% of variations in resistance levels in the whole catchment. Chemical analyses correlated with key indicators of treatment plant effluent and a model (Model 3) was generated based on water quality parameters (contaminant and macro- and micro-nutrient levels). Model 2 was beta tested on independent sites and explained over 78% of the variation in integron prevalence showing a significant predictive ability. We believe all models in this study are highly useful tools for informing and prioritising mitigation strategies to reduce the environmental resistome. PMID:25679532

  20. Disease Prediction Models and Operational Readiness

    SciTech Connect

    Corley, Courtney D.; Pullum, Laura L.; Hartley, David M.; Benedum, Corey M.; Noonan, Christine F.; Rabinowitz, Peter M.; Lancaster, Mary J.

    2014-03-19

    INTRODUCTION: The objective of this manuscript is to present a systematic review of biosurveillance models that operate on select agents and can forecast the occurrence of a disease event. One of the primary goals of this research was to characterize the viability of biosurveillance models to provide operationally relevant information for decision makers to identify areas for future research. Two critical characteristics differentiate this work from other infectious disease modeling reviews. First, we reviewed models that attempted to predict the disease event, not merely its transmission dynamics. Second, we considered models involving pathogens of concern as determined by the US National Select Agent Registry (as of June 2011). Methods: We searched dozens of commercial and government databases and harvested Google search results for eligible models utilizing terms and phrases provided by public health analysts relating to biosurveillance, remote sensing, risk assessments, spatial epidemiology, and ecological niche-modeling, The publication date of search results returned are bound by the dates of coverage of each database and the date in which the search was performed, however all searching was completed by December 31, 2010. This returned 13,767 webpages and 12,152 citations. After de-duplication and removal of extraneous material, a core collection of 6,503 items was established and these publications along with their abstracts are presented in a semantic wiki at http://BioCat.pnnl.gov. Next, PNNL’s IN-SPIRE visual analytics software was used to cross-correlate these publications with the definition for a biosurveillance model resulting in the selection of 54 documents that matched the criteria resulting Ten of these documents, However, dealt purely with disease spread models, inactivation of bacteria, or the modeling of human immune system responses to pathogens rather than predicting disease events. As a result, we systematically reviewed 44 papers and the

  1. Predictive Modeling in Actinide Chemistry and Catalysis

    SciTech Connect

    Yang, Ping

    2016-05-16

    These are slides from a presentation on predictive modeling in actinide chemistry and catalysis. The following topics are covered in these slides: Structures, bonding, and reactivity (bonding can be quantified by optical probes and theory, and electronic structures and reaction mechanisms of actinide complexes); Magnetic resonance properties (transition metal catalysts with multi-nuclear centers, and NMR/EPR parameters); Moving to more complex systems (surface chemistry of nanomaterials, and interactions of ligands with nanoparticles); Path forward and conclusions.

  2. Predicting acute affective symptoms after deep brain stimulation surgery in Parkinson's disease.

    PubMed

    Schneider, Frank; Reske, Martina; Finkelmeyer, Andreas; Wojtecki, Lars; Timmermann, Lars; Brosig, Timo; Backes, Volker; Amir-Manavi, Atoosa; Sturm, Volker; Habel, Ute; Schnitzler, Alfons

    2010-01-01

    The current study aimed to investigate predictive markers for acute symptoms of depression and mania following deep brain stimulation (DBS) surgery of the subthalamic nucleus for the treatment of motor symptoms in Parkinson's disease (PD). Fourteen patients with PD (7 males) were included in a prospective longitudinal study. Neuropsychological tests, psychopathology scales and tests of motor functions were administered at several time points prior to and after neurosurgery. Pre-existing psychopathological and motor symptoms predicted postoperative affective side effects of DBS surgery. As these can easily be assessed, they should be considered along with other selection criteria for DBS surgery.

  3. Prediction of symptoms of emotional distress by mood regulation expectancies and affective traits.

    PubMed

    Catanzaro, Salvatore J; Backenstrass, Matthias; Miller, Steven A; Mearns, Jack; Pfeiffer, Nils; Brendalen, Sherry

    2014-12-01

    Three studies examined negative mood regulation expectancies (NMRE) and affective traits as independent predictors of self-reported symptoms of emotional distress. NMRE represent individuals' beliefs that they can alleviate unpleasant emotional states. Stronger NMRE are associated with more adaptive coping, more positive cognition during negative moods, more effective responses under stress and less emotional distress. Affective traits represent long-term tendencies toward particular affective experiences; they confer risk for specific symptoms of emotional distress. In Study 1, NMRE, trait negative affect (TNA) and trait positive affect (TPA) were all independently associated with depression among students and staff of a German university. In Study 2, in prospective analyses among U.S. college students traits exhibited hypothesised relationships with anxiety and depressive symptoms, and NMRE uniquely predicted anhedonic depression. Study 3 revealed independent prediction of change in symptoms over time by NMRE among U.S. college students, whereas traits were not associated with change in distress, anxiety and depression symptoms. Results suggest independent roles for NMRE and traits in the development of depression and anxiety symptoms and highlight the importance of NMRE as a potential target of therapeutic intervention in the process of symptom change.

  4. Predicting the accuracy of facial affect recognition: the interaction of child maltreatment and intellectual functioning.

    PubMed

    Shenk, Chad E; Putnam, Frank W; Noll, Jennie G

    2013-02-01

    Previous research demonstrates that both child maltreatment and intellectual performance contribute uniquely to the accurate identification of facial affect by children and adolescents. The purpose of this study was to extend this research by examining whether child maltreatment affects the accuracy of facial recognition differently at varying levels of intellectual functioning. A sample of maltreated (n=50) and nonmaltreated (n=56) adolescent females, 14 to 19 years of age, was recruited to participate in this study. Participants completed demographic and study-related questionnaires and interviews to control for potential psychological and psychiatric confounds such as symptoms of posttraumatic stress disorder, negative affect, and difficulties in emotion regulation. Participants also completed an experimental paradigm that recorded responses to facial affect displays starting in a neutral expression and changing into a full expression of one of six emotions: happiness, sadness, anger, disgust, fear, or surprise. Hierarchical multiple regression assessed the incremental advantage of evaluating the interaction between child maltreatment and intellectual functioning. Results indicated that the interaction term accounted for a significant amount of additional variance in the accurate identification of facial affect after controlling for relevant covariates and main effects. Specifically, maltreated females with lower levels of intellectual functioning were least accurate in identifying facial affect displays, whereas those with higher levels of intellectual functioning performed as well as nonmaltreated females. These results suggest that maltreatment and intellectual functioning interact to predict the recognition of facial affect, with potential long-term consequences for the interpersonal functioning of maltreated females.

  5. Conflict adaptation is predicted by the cognitive, but not the affective alexithymia dimension

    PubMed Central

    de Galan, Michiel; Sellaro, Roberta; Colzato, Lorenza S.; Hommel, Bernhard

    2014-01-01

    Stimulus-induced response conflict (e.g., in Simon or Stroop tasks) is often reduced after conflict trials—the Gratton effect. It is generally assumed that this effect is due to a strengthening of the representation of the current intention or goal, which in turn increases the degree of stimulus and/or response control. Recent evidence suggests that the motivational signal driving the Gratton effect might be affective in nature. If so, individual differences in either the strength of affective signals and/or the ability to interpret such signals might explain individual differences in cognitive-control adjustments as reflected in the Gratton effect. We tested this hypothesis by relating individual sizes of the Gratton effect in a Simon task to scores on the affective and the cognitive dimension of the Bermond/Vorst Alexithymia Questionnaire (BVAQ)—which we assumed to assess individual differences in affective-signal strength and ability to interpret affective signals, respectively. Results show that the cognitive, but not the affective dimension predicted control adjustment, while the accuracy of heartbeat detection was only (and only weakly) related to online control. This suggests that the motivation to fine-tune one's cognitive-control operations is mediated by, and may depend on one's ability to interpret one's own affective signals. PMID:25101033

  6. Probabilistic prediction models for aggregate quarry siting

    USGS Publications Warehouse

    Robinson, G.R.; Larkins, P.M.

    2007-01-01

    Weights-of-evidence (WofE) and logistic regression techniques were used in a GIS framework to predict the spatial likelihood (prospectivity) of crushed-stone aggregate quarry development. The joint conditional probability models, based on geology, transportation network, and population density variables, were defined using quarry location and time of development data for the New England States, North Carolina, and South Carolina, USA. The Quarry Operation models describe the distribution of active aggregate quarries, independent of the date of opening. The New Quarry models describe the distribution of aggregate quarries when they open. Because of the small number of new quarries developed in the study areas during the last decade, independent New Quarry models have low parameter estimate reliability. The performance of parameter estimates derived for Quarry Operation models, defined by a larger number of active quarries in the study areas, were tested and evaluated to predict the spatial likelihood of new quarry development. Population density conditions at the time of new quarry development were used to modify the population density variable in the Quarry Operation models to apply to new quarry development sites. The Quarry Operation parameters derived for the New England study area, Carolina study area, and the combined New England and Carolina study areas were all similar in magnitude and relative strength. The Quarry Operation model parameters, using the modified population density variables, were found to be a good predictor of new quarry locations. Both the aggregate industry and the land management community can use the model approach to target areas for more detailed site evaluation for quarry location. The models can be revised easily to reflect actual or anticipated changes in transportation and population features. ?? International Association for Mathematical Geology 2007.

  7. Predictive Modeling of the CDRA 4BMS

    NASA Technical Reports Server (NTRS)

    Coker, Robert F.; Knox, James C.

    2016-01-01

    As part of NASA's Advanced Exploration Systems (AES) program and the Life Support Systems Project (LSSP), fully predictive models of the Four Bed Molecular Sieve (4BMS) of the Carbon Dioxide Removal Assembly (CDRA) on the International Space Station (ISS) are being developed. This virtual laboratory will be used to help reduce mass, power, and volume requirements for future missions. In this paper we describe current and planned modeling developments in the area of carbon dioxide removal to support future crewed Mars missions as well as the resolution of anomalies observed in the ISS CDRA.

  8. Analysis of algae growth mechanism and water bloom prediction under the effect of multi-affecting factor.

    PubMed

    Wang, Li; Wang, Xiaoyi; Jin, Xuebo; Xu, Jiping; Zhang, Huiyan; Yu, Jiabin; Sun, Qian; Gao, Chong; Wang, Lingbin

    2017-03-01

    The formation process of algae is described inaccurately and water blooms are predicted with a low precision by current methods. In this paper, chemical mechanism of algae growth is analyzed, and a correlation analysis of chlorophyll-a and algal density is conducted by chemical measurement. Taking into account the influence of multi-factors on algae growth and water blooms, the comprehensive prediction method combined with multivariate time series and intelligent model is put forward in this paper. Firstly, through the process of photosynthesis, the main factors that affect the reproduction of the algae are analyzed. A compensation prediction method of multivariate time series analysis based on neural network and Support Vector Machine has been put forward which is combined with Kernel Principal Component Analysis to deal with dimension reduction of the influence factors of blooms. Then, Genetic Algorithm is applied to improve the generalization ability of the BP network and Least Squares Support Vector Machine. Experimental results show that this method could better compensate the prediction model of multivariate time series analysis which is an effective way to improve the description accuracy of algae growth and prediction precision of water blooms.

  9. Constructing predictive models of human running

    PubMed Central

    Maus, Horst-Moritz; Revzen, Shai; Guckenheimer, John; Ludwig, Christian; Reger, Johann; Seyfarth, Andre

    2015-01-01

    Running is an essential mode of human locomotion, during which ballistic aerial phases alternate with phases when a single foot contacts the ground. The spring-loaded inverted pendulum (SLIP) provides a starting point for modelling running, and generates ground reaction forces that resemble those of the centre of mass (CoM) of a human runner. Here, we show that while SLIP reproduces within-step kinematics of the CoM in three dimensions, it fails to reproduce stability and predict future motions. We construct SLIP control models using data-driven Floquet analysis, and show how these models may be used to obtain predictive models of human running with six additional states comprising the position and velocity of the swing-leg ankle. Our methods are general, and may be applied to any rhythmic physical system. We provide an approach for identifying an event-driven linear controller that approximates an observed stabilization strategy, and for producing a reduced-state model which closely recovers the observed dynamics. PMID:25505131

  10. A predictive geologic model of radon occurrence

    SciTech Connect

    Gregg, L.T. )

    1990-01-01

    Earlier work by LeGrand on predictive geologic models for radon focused on hydrogeologic aspects of radon transport from a given uranium/radium source in a fractured crystalline rock aquifer, and included submodels for bedrock lithology (uranium concentration), topographic slope, and water-table behavior and characteristics. LeGrand's basic geologic model has been modified and extended into a submodel for crystalline rocks (Blue Ridge and Piedmont Provinces) and a submodel for sedimentary rocks (Valley and Ridge and Coastal Plain Provinces). Each submodel assigns a ranking of 1 to 15 to the bedrock type, based on (a) known or supposed uranium/thorium content, (b) petrography/lithology, and (c) structural features such as faults, shear or breccia zones, diabase dikes, and jointing/fracturing. The bedrock ranking is coupled with a generalized soil/saprolite model which ranks soil/saprolite type and thickness from 1 to 10. A given site is thus assessed a ranking of 1 to 150 as a guide to its potential for high radon occurrence in the upper meter or so of soil. Field trials of the model are underway, comparing model predictions with measured soil-gas concentrations of radon.

  11. Statistical Seasonal Sea Surface based Prediction Model

    NASA Astrophysics Data System (ADS)

    Suarez, Roberto; Rodriguez-Fonseca, Belen; Diouf, Ibrahima

    2014-05-01

    The interannual variability of the sea surface temperature (SST) plays a key role in the strongly seasonal rainfall regime on the West African region. The predictability of the seasonal cycle of rainfall is a field widely discussed by the scientific community, with results that fail to be satisfactory due to the difficulty of dynamical models to reproduce the behavior of the Inter Tropical Convergence Zone (ITCZ). To tackle this problem, a statistical model based on oceanic predictors has been developed at the Universidad Complutense of Madrid (UCM) with the aim to complement and enhance the predictability of the West African Monsoon (WAM) as an alternative to the coupled models. The model, called S4CAST (SST-based Statistical Seasonal Forecast) is based on discriminant analysis techniques, specifically the Maximum Covariance Analysis (MCA) and Canonical Correlation Analysis (CCA). Beyond the application of the model to the prediciton of rainfall in West Africa, its use extends to a range of different oceanic, atmospheric and helth related parameters influenced by the temperature of the sea surface as a defining factor of variability.

  12. New model accurately predicts reformate composition

    SciTech Connect

    Ancheyta-Juarez, J.; Aguilar-Rodriguez, E. )

    1994-01-31

    Although naphtha reforming is a well-known process, the evolution of catalyst formulation, as well as new trends in gasoline specifications, have led to rapid evolution of the process, including: reactor design, regeneration mode, and operating conditions. Mathematical modeling of the reforming process is an increasingly important tool. It is fundamental to the proper design of new reactors and revamp of existing ones. Modeling can be used to optimize operating conditions, analyze the effects of process variables, and enhance unit performance. Instituto Mexicano del Petroleo has developed a model of the catalytic reforming process that accurately predicts reformate composition at the higher-severity conditions at which new reformers are being designed. The new AA model is more accurate than previous proposals because it takes into account the effects of temperature and pressure on the rate constants of each chemical reaction.

  13. Applications of predictive environmental strain models.

    PubMed

    Reardon, M J; Gonzalez, R R; Pandolf, K B

    1997-02-01

    Researchers at the U.S. Army Research Institute of Environmental Medicine have developed and validated numerical models capable of predicting the extent of physiologic strain and adverse terrain and weather-related medical consequences of military operations in harsh environments. A descriptive historical account is provided that details how physiologic models for hot and cold weather exposure have been integrated into portable field advisory devices, computer-based meteorologic planning software, and combat-oriented simulation systems. It is important that medical officers be aware of the existence of these types of decision support tools so that they can assure that outputs are interpreted in a balanced and medically realistic manner. Additionally, these modeling applications may facilitate timely preventive medicine planning and efficient dissemination of appropriate measures to prevent weather- and altitude-related illnesses and performance decrements. Such environmental response modeling applications may therefore be utilized to support deployment preventive medicine planning by field medical officers.

  14. Predictive Computational Modeling of Chromatin Folding

    NASA Astrophysics Data System (ADS)

    di Pierro, Miichele; Zhang, Bin; Wolynes, Peter J.; Onuchic, Jose N.

    In vivo, the human genome folds into well-determined and conserved three-dimensional structures. The mechanism driving the folding process remains unknown. We report a theoretical model (MiChroM) for chromatin derived by using the maximum entropy principle. The proposed model allows Molecular Dynamics simulations of the genome using as input the classification of loci into chromatin types and the presence of binding sites of loop forming protein CTCF. The model was trained to reproduce the Hi-C map of chromosome 10 of human lymphoblastoid cells. With no additional tuning the model was able to predict accurately the Hi-C maps of chromosomes 1-22 for the same cell line. Simulations show unknotted chromosomes, phase separation of chromatin types and a preference of chromatin of type A to sit at the periphery of the chromosomes.

  15. Predictive mathematical models of cancer signalling pathways.

    PubMed

    Bachmann, J; Raue, A; Schilling, M; Becker, V; Timmer, J; Klingmüller, U

    2012-02-01

    Complex intracellular signalling networks integrate extracellular signals and convert them into cellular responses. In cancer cells, the tightly regulated and fine-tuned dynamics of information processing in signalling networks is altered, leading to uncontrolled cell proliferation, survival and migration. Systems biology combines mathematical modelling with comprehensive, quantitative, time-resolved data and is most advanced in addressing dynamic properties of intracellular signalling networks. Here, we introduce different modelling approaches and their application to medical systems biology, focusing on the identifiability of parameters in ordinary differential equation models and their importance in network modelling to predict cellular decisions. Two related examples are given, which include processing of ligand-encoded information and dual feedback regulation in erythropoietin (Epo) receptor signalling. Finally, we review the current understanding of how systems biology could foster the development of new treatment strategies in the context of lung cancer and anaemia.

  16. Predicted and experienced affective responses to the outcome of the 2008 U.S. presidential election.

    PubMed

    Kitchens, Michael B; Corser, Grant C; Gohm, Carol L; VonWaldner, Kristen L; Foreman, Elizabeth L

    2010-12-01

    People typically have intense feelings about politics. Therefore, it was no surprise that the campaign and eventual election of Barack Obama were highly anticipated and emotionally charged events, making it and the emotion experienced afterward a useful situation in which to replicate prior research showing that people typically overestimate the intensity and duration of their future affective states. Consequently, it was expected that Obama supporters and McCain supporters might overestimate the intensity of their affective responses to the outcome of the election. Data showed that while McCain supporters underestimated how happy they would be following the election, Obama supporters accurately predicted how happy they would be following the election. These data provide descriptive information on the accuracy of people's predicted reactions to the 2008 U.S. presidential election. The findings are discussed in the context of the broad literature and this specific and unique event.

  17. Affective and instrumental communication in primary care interactions: predicting the satisfaction of nursing staff and patients.

    PubMed

    Haskard, Kelly B; DiMatteo, M Robin; Heritage, John

    2009-01-01

    Verbal and nonverbal communication between nursing staff and patients has received scant research attention. This study examined patients' and nursing staff members' global affective and instrumental communication, mutual influence, and relationship to postvisit satisfaction. This study employed ratings of videotaped primary care visits of 81 nursing staff members with 235 patients, and assessed communication in 2 channels: nonverbal visual and speech including vocal tone. Communication channel differences and prediction of patient satisfaction were examined. The visual and vocal communication of nursing staff members and patients robustly predicted each other's satisfaction and reflected their own satisfaction with the dyadic visit. Affect was communicated more clearly through the speech with vocal tone channel, whereas instrumental communication was stronger in visual nonverbal behavior. Patients' and nursing staff members' behaviors of pleasantness and involvement frequently co-occurred.

  18. Progress towards a PETN Lifetime Prediction Model

    SciTech Connect

    Burnham, A K; Overturf III, G E; Gee, R; Lewis, P; Qiu, R; Phillips, D; Weeks, B; Pitchimani, R; Maiti, A; Zepeda-Ruiz, L; Hrousis, C

    2006-09-11

    Dinegar (1) showed that decreases in PETN surface area causes EBW detonator function times to increase. Thermal aging causes PETN to agglomerate, shrink, and densify indicating a ''sintering'' process. It has long been a concern that the formation of a gap between the PETN and the bridgewire may lead to EBW detonator failure. These concerns have led us to develop a model to predict the rate of coarsening that occurs with age for thermally driven PETN powder (50% TMD). To understand PETN contributions to detonator aging we need three things: (1) Curves describing function time dependence on specific surface area, density, and gap. (2) A measurement of the critical gap distance for no fire as a function of density and surface area for various wire configurations. (3) A model describing how specific surface area, density and gap change with time and temperature. We've had good success modeling high temperature surface area reduction and function time increase using a phenomenological deceleratory kinetic model based on a distribution of parallel nth-order reactions having evenly spaced activation energies where weighing factors of the reactions follows a Gaussian distribution about the reaction with the mean activation energy (Figure 1). Unfortunately, the mean activation energy derived from this approach is high (typically {approx}75 kcal/mol) so that negligible sintering is predicted for temperatures below 40 C. To make more reliable predictions, we've established a three-part effort to understand PETN mobility. First, we've measured the rates of step movement and pit nucleation as a function of temperature from 30 to 50 C for single crystals. Second, we've measured the evaporation rate from single crystals and powders from 105 to 135 C to obtain an activation energy for evaporation. Third, we've pursued mechanistic kinetic modeling of surface mobility, evaporation, and ripening.

  19. Predictive Models and Tools for Assessing Chemicals under the Toxic Substances Control Act (TSCA)

    EPA Pesticide Factsheets

    EPA has developed databases and predictive models to help evaluate the hazard, exposure, and risk of chemicals released to the environment and how workers, the general public, and the environment may be exposed to and affected by them.

  20. Predictive modeling by the cerebellum improves proprioception.

    PubMed

    Bhanpuri, Nasir H; Okamura, Allison M; Bastian, Amy J

    2013-09-04

    Because sensation is delayed, real-time movement control requires not just sensing, but also predicting limb position, a function hypothesized for the cerebellum. Such cerebellar predictions could contribute to perception of limb position (i.e., proprioception), particularly when a person actively moves the limb. Here we show that human cerebellar patients have proprioceptive deficits compared with controls during active movement, but not when the arm is moved passively. Furthermore, when healthy subjects move in a force field with unpredictable dynamics, they have active proprioceptive deficits similar to cerebellar patients. Therefore, muscle activity alone is likely insufficient to enhance proprioception and predictability (i.e., an internal model of the body and environment) is important for active movement to benefit proprioception. We conclude that cerebellar patients have an active proprioceptive deficit consistent with disrupted movement prediction rather than an inability to generally enhance peripheral proprioceptive signals during action and suggest that active proprioceptive deficits should be considered a fundamental cerebellar impairment of clinical importance.

  1. Accuracy of travel time distribution (TTD) models as affected by TTD complexity, observation errors, and model and tracer selection

    USGS Publications Warehouse

    Green, Christopher T.; Zhang, Yong; Jurgens, Bryant C.; Starn, J. Jeffrey; Landon, Matthew K.

    2014-01-01

    Analytical models of the travel time distribution (TTD) from a source area to a sample location are often used to estimate groundwater ages and solute concentration trends. The accuracies of these models are not well known for geologically complex aquifers. In this study, synthetic datasets were used to quantify the accuracy of four analytical TTD models as affected by TTD complexity, observation errors, model selection, and tracer selection. Synthetic TTDs and tracer data were generated from existing numerical models with complex hydrofacies distributions for one public-supply well and 14 monitoring wells in the Central Valley, California. Analytical TTD models were calibrated to synthetic tracer data, and prediction errors were determined for estimates of TTDs and conservative tracer (NO3−) concentrations. Analytical models included a new, scale-dependent dispersivity model (SDM) for two-dimensional transport from the watertable to a well, and three other established analytical models. The relative influence of the error sources (TTD complexity, observation error, model selection, and tracer selection) depended on the type of prediction. Geological complexity gave rise to complex TTDs in monitoring wells that strongly affected errors of the estimated TTDs. However, prediction errors for NO3− and median age depended more on tracer concentration errors. The SDM tended to give the most accurate estimates of the vertical velocity and other predictions, although TTD model selection had minor effects overall. Adding tracers improved predictions if the new tracers had different input histories. Studies using TTD models should focus on the factors that most strongly affect the desired predictions.

  2. Modelling Rho GTPase biochemistry to predict collective cell migration

    NASA Astrophysics Data System (ADS)

    Merchant, Brian; Feng, James

    The collective migration of cells, due to individual cell polarization and intercellular contact inhibition of locomotion, features prominently in embryogenesis and metastatic cancers. Existing methods for modelling collectively migrating cells tend to rely either on highly abstracted agent-based models, or on continuum approximations of the group. Both of these frameworks represent intercellular interactions such as contact inhibition of locomotion as hard-coded rules defining model cells. In contrast, we present a vertex-dynamics framework which predicts polarization and contact inhibition of locomotion naturally from an underlying model of Rho GTPase biochemistry and cortical mechanics. We simulate the interaction between many such model cells, and study how modulating Rho GTPases affects migratory characteristics of the group, in the context of long-distance collective migration of neural crest cells during embryogenesis.

  3. Thermal barrier coating life prediction model development

    NASA Technical Reports Server (NTRS)

    Demasi, J. T.

    1986-01-01

    A methodology is established to predict thermal barrier coating life in a environment similar to that experienced by gas turbine airfoils. Experiments were conducted to determine failure modes of the thermal barrier coating. Analytical studies were employed to derive a life prediction model. A review of experimental and flight service components as well as laboratory post evaluations indicates that the predominant mode of TBC failure involves thermomechanical spallation of the ceramic coating layer. This ceramic spallation involves the formation of a dominant crack in the ceramic coating parallel to and closely adjacent to the topologically complex metal ceramic interface. This mechanical failure mode clearly is influenced by thermal exposure effects as shown in experiments conducted to study thermal pre-exposure and thermal cycle-rate effects. The preliminary life prediction model developed focuses on the two major damage modes identified in the critical experiments tasks. The first of these involves a mechanical driving force, resulting from cyclic strains and stresses caused by thermally induced and externally imposed mechanical loads. The second is an environmental driving force based on experimental results, and is believed to be related to bond coat oxidation. It is also believed that the growth of this oxide scale influences the intensity of the mechanical driving force.

  4. Entity Network Prediction Using Multitype Topic Models

    NASA Astrophysics Data System (ADS)

    Shiozaki, Hitohiro; Eguchi, Koji; Ohkawa, Takenao

    Conveying information about who, what, when and where is a primary purpose of some genres of documents, typically news articles. Statistical models that capture dependencies between named entities and topics can play an important role. Although some relationships between who and where should be mentioned in such a document, no statistical topic models explicitly address in handling such information the textual interactions between a who-entity and a where-entity. This paper presents a statistical model that directly captures the dependencies between an arbitrary number of word types, such as who-entities, where-entities and topics, mentioned in each document. We show that this multitype topic model performs better at making predictions on entity networks, in which each vertex represents an entity and each edge weight represents how a pair of entities at the incident vertices is closely related, through our experiments on predictions of who-entities and links between them. We also demonstrate the scale-free property in the weighted networks of entities extracted from written mentions.

  5. Thermal barrier coating life prediction model

    NASA Technical Reports Server (NTRS)

    Hillery, R. V.; Pilsner, B. H.; Cook, T. S.; Kim, K. S.

    1986-01-01

    This is the second annual report of the first 3-year phase of a 2-phase, 5-year program. The objectives of the first phase are to determine the predominant modes of degradation of a plasma sprayed thermal barrier coating system and to develop and verify life prediction models accounting for these degradation modes. The primary TBC system consists of an air plasma sprayed ZrO-Y2O3 top coat, a low pressure plasma sprayed NiCrAlY bond coat, and a Rene' 80 substrate. Task I was to evaluate TBC failure mechanisms. Both bond coat oxidation and bond coat creep have been identified as contributors to TBC failure. Key property determinations have also been made for the bond coat and the top coat, including tensile strength, Poisson's ratio, dynamic modulus, and coefficient of thermal expansion. Task II is to develop TBC life prediction models for the predominant failure modes. These models will be developed based on the results of thermmechanical experiments and finite element analysis. The thermomechanical experiments have been defined and testing initiated. Finite element models have also been developed to handle TBCs and are being utilized to evaluate different TBC failure regimes.

  6. Prediction of Chemical Function: Model Development and ...

    EPA Pesticide Factsheets

    The United States Environmental Protection Agency’s Exposure Forecaster (ExpoCast) project is developing both statistical and mechanism-based computational models for predicting exposures to thousands of chemicals, including those in consumer products. The high-throughput (HT) screening-level exposures developed under ExpoCast can be combined with HT screening (HTS) bioactivity data for the risk-based prioritization of chemicals for further evaluation. The functional role (e.g. solvent, plasticizer, fragrance) that a chemical performs can drive both the types of products in which it is found and the concentration in which it is present and therefore impacting exposure potential. However, critical chemical use information (including functional role) is lacking for the majority of commercial chemicals for which exposure estimates are needed. A suite of machine-learning based models for classifying chemicals in terms of their likely functional roles in products based on structure were developed. This effort required collection, curation, and harmonization of publically-available data sources of chemical functional use information from government and industry bodies. Physicochemical and structure descriptor data were generated for chemicals with function data. Machine-learning classifier models for function were then built in a cross-validated manner from the descriptor/function data using the method of random forests. The models were applied to: 1) predict chemi

  7. Modeling and Prediction of Krueger Device Noise

    NASA Technical Reports Server (NTRS)

    Guo, Yueping; Burley, Casey L.; Thomas, Russell H.

    2016-01-01

    This paper presents the development of a noise prediction model for aircraft Krueger flap devices that are considered as alternatives to leading edge slotted slats. The prediction model decomposes the total Krueger noise into four components, generated by the unsteady flows, respectively, in the cove under the pressure side surface of the Krueger, in the gap between the Krueger trailing edge and the main wing, around the brackets supporting the Krueger device, and around the cavity on the lower side of the main wing. For each noise component, the modeling follows a physics-based approach that aims at capturing the dominant noise-generating features in the flow and developing correlations between the noise and the flow parameters that control the noise generation processes. The far field noise is modeled using each of the four noise component's respective spectral functions, far field directivities, Mach number dependencies, component amplitudes, and other parametric trends. Preliminary validations are carried out by using small scale experimental data, and two applications are discussed; one for conventional aircraft and the other for advanced configurations. The former focuses on the parametric trends of Krueger noise on design parameters, while the latter reveals its importance in relation to other airframe noise components.

  8. Gamma-ray Pulsars: Models and Predictions

    NASA Technical Reports Server (NTRS)

    Harding Alice K.; White, Nicholas E. (Technical Monitor)

    2000-01-01

    Pulsed emission from gamma-ray pulsars originates inside the magnetosphere, from radiation by charged particles accelerated near the magnetic poles or in the outer gaps. In polar cap models, the high energy spectrum is cut off by magnetic pair production above an energy that is, dependent on the local magnetic field strength. While most young pulsars with surface fields in the range B = 10(exp 12) - 10(exp 13) G are expected to have high energy cutoffs around several GeV, the gamma-ray spectra of old pulsars having lower surface fields may extend to 50 GeV. Although the gamma-ray emission of older pulsars is weaker, detecting pulsed emission at high energies from nearby sources would be an important confirmation of polar cap models. Outer gap models predict more gradual high-energy turnovers of the primary curvature emission around 10 GeV, but also predict an inverse Compton component extending to TeV energies. Detection of pulsed TeV emission, which would not survive attenuation at the polar caps, is thus an important test of outer gap models. Next-generation gamma-ray telescopes sensitive to GeV-TeV emission will provide critical tests of pulsar acceleration and emission mechanisms.

  9. Investigation of models for relating roundabout safety to predicted speed.

    PubMed

    Chen, Yongsheng; Persaud, Bhagwant; Sacchi, Emanuele; Bassani, Marco

    2013-01-01

    Despite widespread recognition of operating speed as a key safety-related variable for roundabouts, there is no consensus on the best models for capturing the relationship between crashes and speed, or, for that matter, on how speed can be estimated in situations where it cannot be observed (such as when a roundabout is being designed or redesigned). This paper uses US and Italian roundabout approach-level data to investigate models relating safety to various measures of predicted speed. This is an indirect approach for developing safety models for estimating the effects of design features, the premise being that these features can better predict speed, which, in turn, can be used as a predictor of crash frequency. After exploring various possibilities, the approach average speed (AAS) - defined as the average of entry, upstream circulating and exiting speeds in this study - was found to be the speed measure that best predicts safety. US data were used to develop a Bayesian Poisson-gamma safety model based on predicted AAS with random coefficients and varying dispersion parameter. This model structure was not appropriate for the Italian data used to examine whether the approach could be generalized to data for another country. For that data, a zero-inflated Poisson (ZIP) model was found to be suitable. Notwithstanding the heterogeneity of the model structure, the investigation suggests that the indirect approach for evaluating the safety of a roundabout is a sound one in that it can preserve model parsimony while capturing the effects of design changes that affect safety.

  10. The role of affective experience in work motivation: Test of a conceptual model

    PubMed Central

    SEO, MYEONG-GU; BARTUNEK, JEAN M.; BARRETT, LISA FELDMAN

    2011-01-01

    Summary The purpose of this paper was to contribute to understanding of the crucial role of emotion in work motivation by testing a conceptual model developed by Seo, Barrett, and Bartunek (2004) that predicted the impacts of core affect on three behavioral outcomes of work motivation, generative-defensive orientation, effort, and persistence. We tested the model using an Internet-based investment simulation combined with an experience sampling procedure. Consistent with the predictions of the model, pleasantness was positively related to all three of the predicted indices. For the most part, these effects occurred indirectly via its relationships with expectancy, valence, and progress judgment components. Also as predicted by the model, activation was directly and positively related to effort. PMID:21785527

  11. Addiction Motivation Reformulated: An Affective Processing Model of Negative Reinforcement

    ERIC Educational Resources Information Center

    Baker, Timothy B.; Piper, Megan E.; McCarthy, Danielle E.; Majeskie, Matthew R.; Fiore, Michael C.

    2004-01-01

    This article offers a reformulation of the negative reinforcement model of drug addiction and proposes that the escape and avoidance of negative affect is the prepotent motive for addictive drug use. The authors posit that negative affect is the motivational core of the withdrawal syndrome and argue that, through repeated cycles of drug use and…

  12. A personalized biomechanical model for respiratory motion prediction.

    PubMed

    Fuerst, B; Mansi, T; Zhang, Jianwen; Khurd, P; Declerck, J; Boettger, T; Navab, Nassir; Bayouth, J; Comaniciu, Dorin; Kamen, A

    2012-01-01

    Time-resolved imaging of the thorax or abdominal area is affected by respiratory motion. Nowadays, one-dimensional respiratory surrogates are used to estimate the current state of the lung during its cycle, but with rather poor results. This paper presents a framework to predict the 3D lung motion based on a patient-specific finite element model of respiratory mechanics estimated from two CT images at end of inspiration (EI) and end of expiration (EE). We first segment the lung, thorax and sub-diaphragm organs automatically using a machine-learning algorithm. Then, a biomechanical model of the lung, thorax and sub-diaphragm is employed to compute the 3D respiratory motion. Our model is driven by thoracic pressures, estimated automatically from the EE and EI images using a trust-region approach. Finally, lung motion is predicted by modulating the thoracic pressures. The effectiveness of our approach is evaluated by predicting lung deformation during exhale on five DIR-Lab datasets. Several personalization strategies are tested, showing that an average error of 3.88 +/- 1.54 mm in predicted landmark positions can be achieved. Since our approach is generative, it may constitute a 3D surrogate information for more accurate medical image reconstruction and patient respiratory analysis.

  13. Monoamine Oxidase A (MAOA) Genotype Predicts Greater Aggression Through Impulsive Reactivity to Negative Affect

    PubMed Central

    Chester, David S.; DeWall, C. Nathan; Derefinko, Karen J.; Estus, Steven; Peters, Jessica R.; Lynam, Donald R.; Jiang, Yang

    2015-01-01

    Low functioning MAOA genotypes have been reliably linked to increased reactive aggression, yet the psychological mechanisms of this effect remain largely unknown. The low functioning MAOA genotype’s established link to diminished inhibition and greater reactivity to conditions of negative affect suggest that negative urgency, the tendency to act impulsively in the context of negative affect, may fill this mediating role. Such MAOA carriers may have higher negative urgency, which may in turn predict greater aggressive responses to provocation. To test these hypotheses, 277 female and male participants were genotyped for an MAOA SNP yet to be linked to aggression (rs1465108), and then reported their negative urgency and past aggressive behavior. We replicated the effect of the low functioning MAOA genotype on heightened aggression, which was mediated by greater negative urgency. These results suggest that disrupted serotonergic systems predispose individuals towards aggressive behavior by increasing impulsive reactivity to negative affect. PMID:25637908

  14. Monoamine oxidase A (MAOA) genotype predicts greater aggression through impulsive reactivity to negative affect.

    PubMed

    Chester, David S; DeWall, C Nathan; Derefinko, Karen J; Estus, Steven; Peters, Jessica R; Lynam, Donald R; Jiang, Yang

    2015-04-15

    Low functioning MAOA genotypes have been reliably linked to increased reactive aggression, yet the psychological mechanisms of this effect remain largely unknown. The low functioning MAOA genotype's established link to diminished inhibition and greater reactivity to conditions of negative affect suggest that negative urgency, the tendency to act impulsively in the context of negative affect, may fill this mediating role. Such MAOA carriers may have higher negative urgency, which may in turn predict greater aggressive responses to provocation. To test these hypotheses, 277 female and male participants were genotyped for an MAOA SNP yet to be linked to aggression (rs1465108), and then reported their negative urgency and past aggressive behavior. We replicated the effect of the low functioning MAOA genotype on heightened aggression, which was mediated by greater negative urgency. These results suggest that disrupted serotonergic systems predispose individuals towards aggressive behavior by increasing impulsive reactivity to negative affect.

  15. On the dynamic covariation between interpersonal behavior and affect: prediction from neuroticism, extraversion, and agreeableness.

    PubMed

    Côté, S; Moskowitz, D S

    1998-10-01

    It was posited that the traits of Neuroticism, Extraversion, and Agreeableness are predictors of dynamic intraindividual processes involving interpersonal behavior and affect. Hypotheses derived from the behavioral concordance model that individuals with high scores on a trait would experience more positively valenced affect when engaging in behavior concordant with that trait than individuals with low scores on the trait were tested. Participants completed a questionnaire measure of the traits and reported on behavior and affect during interpersonal interactions using event-contingent sampling forms approximately 6 times a day for 20 days. Trait scores were related to indexes of the association between each dimension of interpersonal behavior and affect calculated for each individual. Previous findings concerning the trait of Agreeableness were replicated, and results strongly supported the behavioral concordance model for the trait of Neuroticism. Thus, at least some traits can provide information about intraindividual processes that vary over time.

  16. Lagrangian predictability characteristics of an Ocean Model

    NASA Astrophysics Data System (ADS)

    Lacorata, Guglielmo; Palatella, Luigi; Santoleri, Rosalia

    2014-11-01

    The Mediterranean Forecasting System (MFS) Ocean Model, provided by INGV, has been chosen as case study to analyze Lagrangian trajectory predictability by means of a dynamical systems approach. To this regard, numerical trajectories are tested against a large amount of Mediterranean drifter data, used as sample of the actual tracer dynamics across the sea. The separation rate of a trajectory pair is measured by computing the Finite-Scale Lyapunov Exponent (FSLE) of first and second kind. An additional kinematic Lagrangian model (KLM), suitably treated to avoid "sweeping"-related problems, has been nested into the MFS in order to recover, in a statistical sense, the velocity field contributions to pair particle dispersion, at mesoscale level, smoothed out by finite resolution effects. Some of the results emerging from this work are: (a) drifter pair dispersion displays Richardson's turbulent diffusion inside the [10-100] km range, while numerical simulations of MFS alone (i.e., without subgrid model) indicate exponential separation; (b) adding the subgrid model, model pair dispersion gets very close to observed data, indicating that KLM is effective in filling the energy "mesoscale gap" present in MFS velocity fields; (c) there exists a threshold size beyond which pair dispersion becomes weakly sensitive to the difference between model and "real" dynamics; (d) the whole methodology here presented can be used to quantify model errors and validate numerical current fields, as far as forecasts of Lagrangian dispersion are concerned.

  17. Model predictive control of MSMPR crystallizers

    NASA Astrophysics Data System (ADS)

    Moldoványi, Nóra; Lakatos, Béla G.; Szeifert, Ferenc

    2005-02-01

    A multi-input-multi-output (MIMO) control problem of isothermal continuous crystallizers is addressed in order to create an adequate model-based control system. The moment equation model of mixed suspension, mixed product removal (MSMPR) crystallizers that forms a dynamical system is used, the state of which is represented by the vector of six variables: the first four leading moments of the crystal size, solute concentration and solvent concentration. Hence, the time evolution of the system occurs in a bounded region of the six-dimensional phase space. The controlled variables are the mean size of the grain; the crystal size-distribution and the manipulated variables are the input concentration of the solute and the flow rate. The controllability and observability as well as the coupling between the inputs and the outputs was analyzed by simulation using the linearized model. It is shown that the crystallizer is a nonlinear MIMO system with strong coupling between the state variables. Considering the possibilities of the model reduction, a third-order model was found quite adequate for the model estimation in model predictive control (MPC). The mean crystal size and the variance of the size distribution can be nearly separately controlled by the residence time and the inlet solute concentration, respectively. By seeding, the controllability of the crystallizer increases significantly, and the overshoots and the oscillations become smaller. The results of the controlling study have shown that the linear MPC is an adaptable and feasible controller of continuous crystallizers.

  18. An Anisotropic Hardening Model for Springback Prediction

    SciTech Connect

    Zeng, Danielle; Xia, Z. Cedric

    2005-08-05

    As more Advanced High-Strength Steels (AHSS) are heavily used for automotive body structures and closures panels, accurate springback prediction for these components becomes more challenging because of their rapid hardening characteristics and ability to sustain even higher stresses. In this paper, a modified Mroz hardening model is proposed to capture realistic Bauschinger effect at reverse loading, such as when material passes through die radii or drawbead during sheet metal forming process. This model accounts for material anisotropic yield surface and nonlinear isotropic/kinematic hardening behavior. Material tension/compression test data are used to accurately represent Bauschinger effect. The effectiveness of the model is demonstrated by comparison of numerical and experimental springback results for a DP600 straight U-channel test.

  19. Prediction models from CAD models of 3D objects

    NASA Astrophysics Data System (ADS)

    Camps, Octavia I.

    1992-11-01

    In this paper we present a probabilistic prediction based approach for CAD-based object recognition. Given a CAD model of an object, the PREMIO system combines techniques of analytic graphics and physical models of lights and sensors to predict how features of the object will appear in images. In nearly 4,000 experiments on analytically-generated and real images, we show that in a semi-controlled environment, predicting the detectability of features of the image can successfully guide a search procedure to make informed choices of model and image features in its search for correspondences that can be used to hypothesize the pose of the object. Furthermore, we provide a rigorous experimental protocol that can be used to determine the optimal number of correspondences to seek so that the probability of failing to find a pose and of finding an inaccurate pose are minimized.

  20. Visual Performance Prediction Using Schematic Eye Models

    NASA Astrophysics Data System (ADS)

    Schwiegerling, James Theodore

    The goal of visual modeling is to predict the visual performance or a change in performance of an individual from a model of the human visual system. In designing a model of the human visual system, two distinct functions are considered. The first is the production of an image incident on the retina by the optical system of the eye, and the second is the conversion of this image into a perceived image by the retina and brain. The eye optics are evaluated using raytracing techniques familiar to the optical engineer. The effect of the retinal and brain function are combined with the raytracing results by analyzing the modulation of the retinal image. Each of these processes is important far evaluating the performance of the entire visual system. Techniques for converting the abstract system performance measures used by optical engineers into clinically -applicable measures such as visual acuity and contrast sensitivity are developed in this dissertation. Furthermore, a methodology for applying videokeratoscopic height data to the visual model is outlined. These tools are useful in modeling the visual effects of corrective lenses, ocular maladies and refractive surgeries. The modeling techniques are applied to examples of soft contact lenses, keratoconus, radial keratotomy, photorefractive keratectomy and automated lamellar keratoplasty. The modeling tools developed in this dissertation are meant to be general and modular. As improvements to the measurements of the properties and functionality of the various visual components are made, the new information can be incorporated into the visual system model. Furthermore, the examples discussed here represent only a small subset of the applications of the visual model. Additional ocular maladies and emerging refractive surgeries can be modeled as well.

  1. Predictive models of malignant transudative pleural effusions

    PubMed Central

    Gude, Francisco; Toubes, María E.; Lama, Adriana; Suárez-Antelo, Juan; San-José, Esther; González-Barcala, Francisco Javier; Golpe, Antonio; Álvarez-Dobaño, José M.; Rábade, Carlos; Rodríguez-Núñez, Nuria; Díaz-Louzao, Carla; Valdés, Luis

    2017-01-01

    Background There are no firm recommendations when cytology should be performed in pleural transudates, since some malignant pleural effusions (MPEs) behave biochemically as transudates. The objective was to assess when would be justified to perform cytology on pleural transudates. Methods Consecutive patients with transudative pleural effusion (PE) were enrolled and divided in two groups: malignant and non-MPE. Logistic regression analysis was used to estimate the probability of malignancy. Two prognostic models were considered: (I) clinical-radiological variables; and (II) combination of clinical-radiological and analytical variables. Calibration and discrimination [receiver operating characteristics (ROC) curves and area under the curve (AUC)] were performed. Results A total of 281 pleural transudates were included: 26 malignant and 255 non-malignant. The AUC obtained with Model 1 (left PE, radiological images compatible with malignancy, absence of dyspnea, and serosanguinous appearance of the fluid), and Model 2 (the variables of Model 1 plus CEA) were 0.973 and 0.995, respectively. Although no false negatives are found in Models 1 and 2 to probabilities of 11% and 14%, respectively, by applying bootstrapping techniques to not find false negatives in 95% of other possible samples would require lowering the cut-off points for the aforementioned probabilities to 3% (Model 1) and 4% (Model 2), respectively. The false positive results are 32 (Model 1) and 18 (Model 2), with no false negatives. Conclusions The applied models have a high discriminative ability to predict when a transudative PE may be of neoplastic origin, being superior to adding an analytical variable to the clinic-radiological variables. PMID:28203412

  2. Predictive modelling of ferroelectric tunnel junctions

    NASA Astrophysics Data System (ADS)

    Velev, Julian P.; Burton, John D.; Zhuravlev, Mikhail Ye; Tsymbal, Evgeny Y.

    2016-05-01

    Ferroelectric tunnel junctions combine the phenomena of quantum-mechanical tunnelling and switchable spontaneous polarisation of a nanometre-thick ferroelectric film into novel device functionality. Switching the ferroelectric barrier polarisation direction produces a sizable change in resistance of the junction—a phenomenon known as the tunnelling electroresistance effect. From a fundamental perspective, ferroelectric tunnel junctions and their version with ferromagnetic electrodes, i.e., multiferroic tunnel junctions, are testbeds for studying the underlying mechanisms of tunnelling electroresistance as well as the interplay between electric and magnetic degrees of freedom and their effect on transport. From a practical perspective, ferroelectric tunnel junctions hold promise for disruptive device applications. In a very short time, they have traversed the path from basic model predictions to prototypes for novel non-volatile ferroelectric random access memories with non-destructive readout. This remarkable progress is to a large extent driven by a productive cycle of predictive modelling and innovative experimental effort. In this review article, we outline the development of the ferroelectric tunnel junction concept and the role of theoretical modelling in guiding experimental work. We discuss a wide range of physical phenomena that control the functional properties of ferroelectric tunnel junctions and summarise the state-of-the-art achievements in the field.

  3. Thermal barrier coating life prediction model development

    NASA Technical Reports Server (NTRS)

    Demasi, J. T.; Sheffler, K. D.

    1986-01-01

    The objective of this program is to establish a methodology to predict Thermal Barrier Coating (TBC) life on gas turbine engine components. The approach involves experimental life measurement coupled with analytical modeling of relevant degradation modes. The coating being studied is a flight qualified two layer system, designated PWA 264, consisting of a nominal ten mil layer of seven percent yttria partially stabilized zirconia plasma deposited over a nominal five mil layer of low pressure plasma deposited NiCoCrAlY. Thermal barrier coating degradation modes being investigated include: thermomechanical fatigue, oxidation, erosion, hot corrosion, and foreign object damage.

  4. Critical conceptualism in environmental modeling and prediction.

    PubMed

    Christakos, G

    2003-10-15

    Many important problems in environmental science and engineering are of a conceptual nature. Research and development, however, often becomes so preoccupied with technical issues, which are themselves fascinating, that it neglects essential methodological elements of conceptual reasoning and theoretical inquiry. This work suggests that valuable insight into environmental modeling can be gained by means of critical conceptualism which focuses on the software of human reason and, in practical terms, leads to a powerful methodological framework of space-time modeling and prediction. A knowledge synthesis system develops the rational means for the epistemic integration of various physical knowledge bases relevant to the natural system of interest in order to obtain a realistic representation of the system, provide a rigorous assessment of the uncertainty sources, generate meaningful predictions of environmental processes in space-time, and produce science-based decisions. No restriction is imposed on the shape of the distribution model or the form of the predictor (non-Gaussian distributions, multiple-point statistics, and nonlinear models are automatically incorporated). The scientific reasoning structure underlying knowledge synthesis involves teleologic criteria and stochastic logic principles which have important advantages over the reasoning method of conventional space-time techniques. Insight is gained in terms of real world applications, including the following: the study of global ozone patterns in the atmosphere using data sets generated by instruments on board the Nimbus 7 satellite and secondary information in terms of total ozone-tropopause pressure models; the mapping of arsenic concentrations in the Bangladesh drinking water by assimilating hard and soft data from an extensive network of monitoring wells; and the dynamic imaging of probability distributions of pollutants across the Kalamazoo river.

  5. Using item response theory to investigate the structure of anticipated affect: do self-reports about future affective reactions conform to typical or maximal models?

    PubMed Central

    Zampetakis, Leonidas A.; Lerakis, Manolis; Kafetsios, Konstantinos; Moustakis, Vassilis

    2015-01-01

    In the present research, we used item response theory (IRT) to examine whether effective predictions (anticipated affect) conforms to a typical (i.e., what people usually do) or a maximal behavior process (i.e., what people can do). The former, correspond to non-monotonic ideal point IRT models, whereas the latter correspond to monotonic dominance IRT models. A convenience, cross-sectional student sample (N = 1624) was used. Participants were asked to report on anticipated positive and negative affect around a hypothetical event (emotions surrounding the start of a new business). We carried out analysis comparing graded response model (GRM), a dominance IRT model, against generalized graded unfolding model, an unfolding IRT model. We found that the GRM provided a better fit to the data. Findings suggest that the self-report responses to anticipated affect conform to dominance response process (i.e., maximal behavior). The paper also discusses implications for a growing literature on anticipated affect. PMID:26441806

  6. Predictive modelling of boiler fouling. Final report.

    SciTech Connect

    Chatwani, A

    1990-12-31

    A spectral element method embodying Large Eddy Simulation based on Re- Normalization Group theory for simulating Sub Grid Scale viscosity was chosen for this work. This method is embodied in a computer code called NEKTON. NEKTON solves the unsteady, 2D or 3D,incompressible Navier Stokes equations by a spectral element method. The code was later extended to include the variable density and multiple reactive species effects at low Mach numbers, and to compute transport of large particles governed by inertia. Transport of small particles is computed by treating them as trace species. Code computations were performed for a number of test conditions typical of flow past a deep tube bank in a boiler. Results indicate qualitatively correct behavior. Predictions of deposition rates and deposit shape evolution also show correct qualitative behavior. These simulations are the first attempts to compute flow field results at realistic flow Reynolds numbers of the order of 10{sup 4}. Code validation was not done; comparison with experiment also could not be made as many phenomenological model parameters, e.g., sticking or erosion probabilities and their dependence on experimental conditions were not known. The predictions however demonstrate the capability to predict fouling from first principles. Further work is needed: use of large or massively parallel machine; code validation; parametric studies, etc.

  7. Thermal barrier coating life prediction model development, phase 1

    NASA Technical Reports Server (NTRS)

    Demasi, Jeanine T.; Ortiz, Milton

    1989-01-01

    The objective of this program was to establish a methodology to predict thermal barrier coating (TBC) life on gas turbine engine components. The approach involved experimental life measurement coupled with analytical modeling of relevant degradation modes. Evaluation of experimental and flight service components indicate the predominant failure mode to be thermomechanical spallation of the ceramic coating layer resulting from propagation of a dominant near interface crack. Examination of fractionally exposed specimens indicated that dominant crack formation results from progressive structural damage in the form of subcritical microcrack link-up. Tests conducted to isolate important life drivers have shown MCrAlY oxidation to significantly affect the rate of damage accumulation. Mechanical property testing has shown the plasma deposited ceramic to exhibit a non-linear stress-strain response, creep and fatigue. The fatigue based life prediction model developed accounts for the unusual ceramic behavior and also incorporates an experimentally determined oxide rate model. The model predicts the growth of this oxide scale to influence the intensity of the mechanic driving force, resulting from cyclic strains and stresses caused by thermally induced and externally imposed mechanical loads.

  8. Comparison of IRI model predictions with low latitude ionospheric observations

    NASA Astrophysics Data System (ADS)

    Bittencourt, J. A.; Chryssafidis, M.

    During a period of high solar activity (1979/1980), IRI-predicted electron density profiles were compared with measurements made at Fortaleza (Brazil), 2 degrees off the dip equator. A few discrepancies were found. They are attributed mainly to dynamical effects associated with low latitude E x B electromagnetic plasma drifts and thermospheric neutral winds that are not correctly reproduced in the CCIR numerical maps and in the IRI profile shapes as well. In particular, the dependence on the magnetic declination angle, which strongly affects the electrodynamical plasma motions at low latitudes, is not satisfactorily considered in the models.

  9. Concrete modelling for expertise of structures affected by alkali aggregate reaction

    SciTech Connect

    Grimal, E.; Sellier, A.; Multon, S.; Le Pape, Y.; Bourdarot, E.

    2010-04-15

    Alkali aggregate reaction (AAR) affects numerous civil engineering structures and causes irreversible expansion and cracking. In order to control the safety level and the maintenance cost of its hydraulic dams, Electricite de France (EDF) must reach better comprehension and better prediction of the expansion phenomena. For this purpose, EDF has developed a numerical model based on the finite element method in order to assess the mechanical behaviour of damaged structures. The model takes the following phenomena into account: concrete creep, the stress induced by the formation of AAR gel and the mechanical damage. A rheological model was developed to assess the coupling between the different phenomena (creep, AAR and anisotropic damage). Experimental results were used to test the model. The results show the capability of the model to predict the experimental behaviour of beams subjected to AAR. In order to obtain such prediction, it is necessary to take all the phenomena occurring in the concrete into consideration.

  10. Model predictive formation control of helicopter systems

    NASA Astrophysics Data System (ADS)

    Saffarian, Mehdi

    In this thesis, a robust formation control framework for formation control of a group of helicopters is proposed and designed. The dynamic model of the helicopter has been developed and verified through simulations. The control framework is constructed using two main control schemes for navigation of a helicopter group in three-dimensional (3D) environments. Two schemes are designed to maintain the position of one helicopter with respect to one or two other neighboring members, respectively. The developed parameters can uniquely define the position of the helicopters with respect to each other and can be used for any other aerial and under water vehicles such as airplanes, spacecrafts and submarines. Also, since this approach is modular, it is possible to use it for desired number and form of the group helicopters. Using the defined control parameters, two decentralized controllers are designed based on Nonlinear Model Predictive Control (NMPC) algorithm technique. The framework performance has been tested through simulation of different formation scenarios.

  11. Predictive Capability Maturity Model for computational modeling and simulation.

    SciTech Connect

    Oberkampf, William Louis; Trucano, Timothy Guy; Pilch, Martin M.

    2007-10-01

    The Predictive Capability Maturity Model (PCMM) is a new model that can be used to assess the level of maturity of computational modeling and simulation (M&S) efforts. The development of the model is based on both the authors experience and their analysis of similar investigations in the past. The perspective taken in this report is one of judging the usefulness of a predictive capability that relies on the numerical solution to partial differential equations to better inform and improve decision making. The review of past investigations, such as the Software Engineering Institute's Capability Maturity Model Integration and the National Aeronautics and Space Administration and Department of Defense Technology Readiness Levels, indicates that a more restricted, more interpretable method is needed to assess the maturity of an M&S effort. The PCMM addresses six contributing elements to M&S: (1) representation and geometric fidelity, (2) physics and material model fidelity, (3) code verification, (4) solution verification, (5) model validation, and (6) uncertainty quantification and sensitivity analysis. For each of these elements, attributes are identified that characterize four increasing levels of maturity. Importantly, the PCMM is a structured method for assessing the maturity of an M&S effort that is directed toward an engineering application of interest. The PCMM does not assess whether the M&S effort, the accuracy of the predictions, or the performance of the engineering system satisfies or does not satisfy specified application requirements.

  12. How the choice of safety performance function affects the identification of important crash prediction variables.

    PubMed

    Wang, Ketong; Simandl, Jenna K; Porter, Michael D; Graettinger, Andrew J; Smith, Randy K

    2016-03-01

    Across the nation, researchers and transportation engineers are developing safety performance functions (SPFs) to predict crash rates and develop crash modification factors to improve traffic safety at roadway segments and intersections. Generalized linear models (GLMs), such as Poisson or negative binomial regression, are most commonly used to develop SPFs with annual average daily traffic as the primary roadway characteristic to predict crashes. However, while more complex to interpret, data mining models such as boosted regression trees have improved upon GLMs crash prediction performance due to their ability to handle more data characteristics, accommodate non-linearities, and include interaction effects between the characteristics. An intersection data inventory of 36 safety relevant parameters for three- and four-legged non-signalized intersections along state routes in Alabama was used to study the importance of intersection characteristics on crash rate and the interaction effects between key characteristics. Four different SPFs were investigated and compared: Poisson regression, negative binomial regression, regularized generalized linear model, and boosted regression trees. The models did not agree on which intersection characteristics were most related to the crash rate. The boosted regression tree model significantly outperformed the other models and identified several intersection characteristics as having strong interaction effects.

  13. A predictive fitness model for influenza

    NASA Astrophysics Data System (ADS)

    Łuksza, Marta; Lässig, Michael

    2014-03-01

    The seasonal human influenza A/H3N2 virus undergoes rapid evolution, which produces significant year-to-year sequence turnover in the population of circulating strains. Adaptive mutations respond to human immune challenge and occur primarily in antigenic epitopes, the antibody-binding domains of the viral surface protein haemagglutinin. Here we develop a fitness model for haemagglutinin that predicts the evolution of the viral population from one year to the next. Two factors are shown to determine the fitness of a strain: adaptive epitope changes and deleterious mutations outside the epitopes. We infer both fitness components for the strains circulating in a given year, using population-genetic data of all previous strains. From fitness and frequency of each strain, we predict the frequency of its descendent strains in the following year. This fitness model maps the adaptive history of influenza A and suggests a principled method for vaccine selection. Our results call for a more comprehensive epidemiology of influenza and other fast-evolving pathogens that integrates antigenic phenotypes with other viral functions coupled by genetic linkage.

  14. Thermal barrier coating life prediction model development

    NASA Technical Reports Server (NTRS)

    Sheffler, K. D.; Demasi, J. T.

    1985-01-01

    A methodology was established to predict thermal barrier coating life in an environment simulative of that experienced by gas turbine airfoils. Specifically, work is being conducted to determine failure modes of thermal barrier coatings in the aircraft engine environment. Analytical studies coupled with appropriate physical and mechanical property determinations are being employed to derive coating life prediction model(s) on the important failure mode(s). An initial review of experimental and flight service components indicates that the predominant mode of TBC failure involves thermomechanical spallation of the ceramic coating layer. This ceramic spallation involves the formation of a dominant crack in the ceramic coating parallel to and closely adjacent to the metal-ceramic interface. Initial results from a laboratory test program designed to study the influence of various driving forces such as temperature, thermal cycle frequency, environment, and coating thickness, on ceramic coating spalling life suggest that bond coat oxidation damage at the metal-ceramic interface contributes significantly to thermomechanical cracking in the ceramic layer. Low cycle rate furnace testing in air and in argon clearly shows a dramatic increase of spalling life in the non-oxidizing environments.

  15. A predictive fitness model for influenza.

    PubMed

    Luksza, Marta; Lässig, Michael

    2014-03-06

    The seasonal human influenza A/H3N2 virus undergoes rapid evolution, which produces significant year-to-year sequence turnover in the population of circulating strains. Adaptive mutations respond to human immune challenge and occur primarily in antigenic epitopes, the antibody-binding domains of the viral surface protein haemagglutinin. Here we develop a fitness model for haemagglutinin that predicts the evolution of the viral population from one year to the next. Two factors are shown to determine the fitness of a strain: adaptive epitope changes and deleterious mutations outside the epitopes. We infer both fitness components for the strains circulating in a given year, using population-genetic data of all previous strains. From fitness and frequency of each strain, we predict the frequency of its descendent strains in the following year. This fitness model maps the adaptive history of influenza A and suggests a principled method for vaccine selection. Our results call for a more comprehensive epidemiology of influenza and other fast-evolving pathogens that integrates antigenic phenotypes with other viral functions coupled by genetic linkage.

  16. Thermal barrier coating life prediction model development

    NASA Technical Reports Server (NTRS)

    Demasi, J.; Sheffler, K.

    1984-01-01

    The objective of this program is to develop an integrated life prediction model accounting for all potential life-limiting Thermal Barrier Coating (TBC) degradation and failure modes including spallation resulting from cyclic thermal stress, oxidative degradation, hot corrosion, erosion, and foreign object damage (FOD). The mechanisms and relative importance of the various degradation and failure modes will be determined, and the methodology to predict predominant mode failure life in turbine airfoil application will be developed and verified. An empirically based correlative model relating coating life to parametrically expressed driving forces such as temperature and stress will be employed. The two-layer TBC system being investigated, designated PWA264, currently is in commercial aircraft revenue service. It consists of an inner low pressure chamber plasma-sprayed NiCoCrAlY metallic bond coat underlayer (4 to 6 mils) and an outer air plasma-sprayed 7 w/o Y2O3-ZrO2 (8 to 12 mils) ceramic top layer.

  17. Effect on Prediction when Modeling Covariates in Bayesian Nonparametric Models.

    PubMed

    Cruz-Marcelo, Alejandro; Rosner, Gary L; Müller, Peter; Stewart, Clinton F

    2013-04-01

    In biomedical research, it is often of interest to characterize biologic processes giving rise to observations and to make predictions of future observations. Bayesian nonparametric methods provide a means for carrying out Bayesian inference making as few assumptions about restrictive parametric models as possible. There are several proposals in the literature for extending Bayesian nonparametric models to include dependence on covariates. Limited attention, however, has been directed to the following two aspects. In this article, we examine the effect on fitting and predictive performance of incorporating covariates in a class of Bayesian nonparametric models by one of two primary ways: either in the weights or in the locations of a discrete random probability measure. We show that different strategies for incorporating continuous covariates in Bayesian nonparametric models can result in big differences when used for prediction, even though they lead to otherwise similar posterior inferences. When one needs the predictive density, as in optimal design, and this density is a mixture, it is better to make the weights depend on the covariates. We demonstrate these points via a simulated data example and in an application in which one wants to determine the optimal dose of an anticancer drug used in pediatric oncology.

  18. Specific predictive power of automatic spider-related affective associations for controllable and uncontrollable fear responses toward spiders.

    PubMed

    Huijding, Jorg; de Jong, Peter J

    2006-02-01

    This study examined the predictive power of automatically activated spider-related affective associations for automatic and controllable fear responses. The Extrinsic Affective Simon Task (EAST; De Houwer, 2003) was used to indirectly assess automatic spider fear-related associations. The EAST and the Fear of Spiders Questionnaire (FSQ) were used to predict fear responses in 48 female students from Maastricht University with varying levels of spider fear. Results showed that: (i) the EAST best predicted automatic fear responses, whereas (ii) the FSQ best predicted strategic avoidance behavior. These results suggest that indirect measures of automatic associations may have specific predictive power for automatic fear responses.

  19. How Nonrecidivism Affects Predictive Accuracy: Evidence from a Cross-Validation of the Ontario Domestic Assault Risk Assessment (ODARA)

    ERIC Educational Resources Information Center

    Hilton, N. Zoe; Harris, Grant T.

    2009-01-01

    Prediction effect sizes such as ROC area are important for demonstrating a risk assessment's generalizability and utility. How a study defines recidivism might affect predictive accuracy. Nonrecidivism is problematic when predicting specialized violence (e.g., domestic violence). The present study cross-validates the ability of the Ontario…

  20. Two criteria for evaluating risk prediction models.

    PubMed

    Pfeiffer, R M; Gail, M H

    2011-09-01

    We propose and study two criteria to assess the usefulness of models that predict risk of disease incidence for screening and prevention, or the usefulness of prognostic models for management following disease diagnosis. The first criterion, the proportion of cases followed PCF (q), is the proportion of individuals who will develop disease who are included in the proportion q of individuals in the population at highest risk. The second criterion is the proportion needed to follow-up, PNF (p), namely the proportion of the general population at highest risk that one needs to follow in order that a proportion p of those destined to become cases will be followed. PCF (q) assesses the effectiveness of a program that follows 100q% of the population at highest risk. PNF (p) assess the feasibility of covering 100p% of cases by indicating how much of the population at highest risk must be followed. We show the relationship of those two criteria to the Lorenz curve and its inverse, and present distribution theory for estimates of PCF and PNF. We develop new methods, based on influence functions, for inference for a single risk model, and also for comparing the PCFs and PNFs of two risk models, both of which were evaluated in the same validation data.

  1. Affective Self-Regulation Trajectories During Secondary School Predict Substance Use Among Urban Minority Young Adults

    PubMed Central

    Griffin, Kenneth W.; Lowe, Sarah R.; Acevedo, Bianca P.; Botvin, Gilbert J.

    2015-01-01

    This study explored the relationship between trajectories of affective self-regulation skills during secondary school and young adult substance use in a large multi-ethnic, urban sample (N = 995). During secondary school, participants completed a measure of cognitive and behavioral skills used to control negative, unpleasant emotions or perceived stress. As young adults, participants reported on the frequency and quantity of their alcohol, cigarette, and marijuana use in a telephone interview. Controlling for demographic variables, self-regulation did not significantly change over adolescence, although there was significant variation in participants’ rates of growth and decline. Lower seventh grade self-regulation and less steep increases in self-regulation were predictive of higher young adult substance use. Male participants had significantly lower initial self-regulation and higher young adult substance use. The results suggest that interventions that build affective self-regulation skills in adolescence may decrease the risk of young adult substance use. PMID:26549966

  2. Phenotypic plasticity is not affected by experimental evolution in constant, predictable or unpredictable fluctuating thermal environments.

    PubMed

    Manenti, T; Loeschcke, V; Moghadam, N N; Sørensen, J G

    2015-11-01

    The selective past of populations is presumed to affect the levels of phenotypic plasticity. Experimental evolution at constant temperatures is generally expected to lead to a decreased level of plasticity due to presumed costs associated with phenotypic plasticity when not needed. In this study, we investigated the effect of experimental evolution in constant, predictable and unpredictable daily fluctuating temperature regimes on the levels of phenotype plasticity in several life history and stress resistance traits in Drosophila simulans. Contrary to the expectation, evolution in the different regimes did not affect the levels of plasticity in any of the traits investigated even though the populations from the different thermal regimes had evolved different stress resistance and fitness trait means. Although costs associated with phenotypic plasticity are known, our results suggest that the maintenance of phenotypic plasticity might come at low and negligible costs, and thus, the potential of phenotypic plasticity to evolve in populations exposed to different environmental conditions might be limited.

  3. Developing Models for Predictive Climate Science

    SciTech Connect

    Drake, John B; Jones, Philip W

    2007-01-01

    The Community Climate System Model results from a multi-agency collaboration designed to construct cutting-edge climate science simulation models for a broad research community. Predictive climate simulations are currently being prepared for the petascale computers of the near future. Modeling capabilities are continuously being improved in order to provide better answers to critical questions about Earth's climate. Climate change and its implications are front page news in today's world. Could global warming be responsible for the July 2006 heat waves in Europe and the United States? Should more resources be devoted to preparing for an increase in the frequency of strong tropical storms and hurricanes like Katrina? Will coastal cities be flooded due to a rise in sea level? The National Climatic Data Center (NCDC), which archives all weather data for the nation, reports that global surface temperatures have increased over the last century, and that the rate of increase is three times greater since 1976. Will temperatures continue to climb at this rate, will they decline again, or will the rate of increase become even steeper? To address such a flurry of questions, scientists must adopt a systematic approach and develop a predictive framework. With responsibility for advising on energy and technology strategies, the DOE is dedicated to advancing climate research in order to elucidate the causes of climate change, including the role of carbon loading from fossil fuel use. Thus, climate science--which by nature involves advanced computing technology and methods--has been the focus of a number of DOE's SciDAC research projects. Dr. John Drake (ORNL) and Dr. Philip Jones (LANL) served as principal investigators on the SciDAC project, 'Collaborative Design and Development of the Community Climate System Model for Terascale Computers.' The Community Climate System Model (CCSM) is a fully-coupled global system that provides state-of-the-art computer simulations of the

  4. Dispositional Mindfulness Predicts Adaptive Affective Responses to Health Messages and Increased Exercise Motivation.

    PubMed

    Kang, Yoona; O'Donnell, Matthew Brook; Strecher, Victor J; Falk, Emily B

    2017-04-01

    Feelings can shape how people respond to persuasive messages. In health communication, adaptive affective responses to potentially threating messages constitute one key to intervention success. The current study tested dispositional mindfulness, characterized by awareness of the present moment, as a predictor of adaptive affective responses to potentially threatening health messages and desirable subsequent health outcomes. Both general and discrete negative affective states (i.e., shame) were examined in relation to mindfulness and intervention success. Individuals (n=67) who reported less than 195 weekly minutes of exercise were recruited. At baseline, participants' dispositional mindfulness and exercise outcomes were assessed, including self-reported exercise motivation and physical activity. A week later, all participants were presented with potentially threatening and self-relevant health messages encouraging physical activity and discouraging sedentary lifestyle, and their subsequent affective response and exercise motivation were assessed. Approximately one month later, changes in exercise motivation and physical activity were assessed again. In addition, participants' level of daily physical activity was monitored by a wrist worn accelerometer throughout the entire duration of the study. Higher dispositional mindfulness predicted greater increases in exercise motivation one month after the intervention. Importantly, this effect was fully mediated by lower negative affect and shame specifically, in response to potentially threatening health messages among highly mindful individuals. Baseline mindfulness was also associated with increased self-reported vigorous activity, but not with daily physical activity as assessed by accelerometers. These findings suggest potential benefits of considering mindfulness as an active individual difference variable in theories of affective processing and health communication.

  5. Mindfulness predicts lower affective volatility among African Americans during smoking cessation.

    PubMed

    Adams, Claire E; Chen, Minxing; Guo, Lin; Lam, Cho Y; Stewart, Diana W; Correa-Fernández, Virmarie; Cano, Miguel A; Heppner, Whitney L; Vidrine, Jennifer Irvin; Li, Yisheng; Ahluwalia, Jasjit S; Cinciripini, Paul M; Wetter, David W

    2014-06-01

    Recent research suggests that mindfulness benefits emotion regulation and smoking cessation. However, the mechanisms by which mindfulness affects emotional and behavioral functioning are unclear. One potential mechanism, lower affective volatility, has not been empirically tested during smoking cessation. This study examined longitudinal associations among mindfulness and emotional responding over the course of smoking cessation treatment among predominantly low-socioeconomic status (SES) African American smokers, who are at high risk for relapse to smoking and tobacco-related health disparities. Participants (N = 399, 51% female, mean age = 42, 48% with annual income <$10,000) completed a baseline measure of trait mindfulness. Negative affect, positive affect, and depressive symptoms were assessed at five time points during smoking cessation treatment (up to 31 days postquit). Volatility indices were calculated to quantify within-person instability of emotional symptoms over time. Over and above demographic characteristics, nicotine dependence, and abstinence status, greater baseline trait mindfulness predicted lower volatility of negative affect and depressive symptoms surrounding the quit attempt and up to 1 month postquit, ps < 0.05. Although volatility did not mediate the association between greater mindfulness and smoking cessation, these results are the first to show that mindfulness is linked to lower affective volatility (or greater stability) of negative emotions during the course of smoking cessation. The present study suggests that mindfulness is linked to greater emotional stability and augments the study of mindfulness in diverse populations. Future studies should examine the effects of mindfulness-based interventions on volatility and whether lower volatility explains effects of mindfulness-based treatments on smoking cessation.

  6. Estimating the magnitude of prediction uncertainties for the APLE model

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Models are often used to predict phosphorus (P) loss from agricultural fields. While it is commonly recognized that model predictions are inherently uncertain, few studies have addressed prediction uncertainties using P loss models. In this study, we conduct an uncertainty analysis for the Annual P ...

  7. Infiltration under snow cover: Modeling approaches and predictive uncertainty

    NASA Astrophysics Data System (ADS)

    Meeks, Jessica; Moeck, Christian; Brunner, Philip; Hunkeler, Daniel

    2017-03-01

    Groundwater recharge from snowmelt represents a temporal redistribution of precipitation. This is extremely important because the rate and timing of snowpack drainage has substantial consequences to aquifer recharge patterns, which in turn affect groundwater availability throughout the rest of the year. The modeling methods developed to estimate drainage from a snowpack, which typically rely on temporally-dense point-measurements or temporally-limited spatially-dispersed calibration data, range in complexity from the simple degree-day method to more complex and physically-based energy balance approaches. While the gamut of snowmelt models are routinely used to aid in water resource management, a comparison of snowmelt models' predictive uncertainties had previously not been done. Therefore, we established a snowmelt model calibration dataset that is both temporally dense and represents the integrated snowmelt infiltration signal for the Vers Chez le Brandt research catchment, which functions as a rather unique natural lysimeter. We then evaluated the uncertainty associated with the degree-day, a modified degree-day and energy balance snowmelt model predictions using the null-space Monte Carlo approach. All three melt models underestimate total snowpack drainage, underestimate the rate of early and midwinter drainage and overestimate spring snowmelt rates. The actual rate of snowpack water loss is more constant over the course of the entire winter season than the snowmelt models would imply, indicating that mid-winter melt can contribute as significantly as springtime snowmelt to groundwater recharge in low alpine settings. Further, actual groundwater recharge could be between 2 and 31% greater than snowmelt models suggest, over the total winter season. This study shows that snowmelt model predictions can have considerable uncertainty, which may be reduced by the inclusion of more data that allows for the use of more complex approaches such as the energy balance

  8. Heuristic Modeling for TRMM Lifetime Predictions

    NASA Technical Reports Server (NTRS)

    Jordan, P. S.; Sharer, P. J.; DeFazio, R. L.

    1996-01-01

    Analysis time for computing the expected mission lifetimes of proposed frequently maneuvering, tightly altitude constrained, Earth orbiting spacecraft have been significantly reduced by means of a heuristic modeling method implemented in a commercial-off-the-shelf spreadsheet product (QuattroPro) running on a personal computer (PC). The method uses a look-up table to estimate the maneuver frequency per month as a function of the spacecraft ballistic coefficient and the solar flux index, then computes the associated fuel use by a simple engine model. Maneuver frequency data points are produced by means of a single 1-month run of traditional mission analysis software for each of the 12 to 25 data points required for the table. As the data point computations are required only a mission design start-up and on the occasion of significant mission redesigns, the dependence on time consuming traditional modeling methods is dramatically reduced. Results to date have agreed with traditional methods to within 1 to 1.5 percent. The spreadsheet approach is applicable to a wide variety of Earth orbiting spacecraft with tight altitude constraints. It will be particularly useful to such missions as the Tropical Rainfall Measurement Mission scheduled for launch in 1997, whose mission lifetime calculations are heavily dependent on frequently revised solar flux predictions.

  9. Prediction of catastrophes: an experimental model.

    PubMed

    Peters, Randall D; Le Berre, Martine; Pomeau, Yves

    2012-08-01

    Catastrophes of all kinds can be roughly defined as short-duration, large-amplitude events following and followed by long periods of "ripening." Major earthquakes surely belong to the class of "catastrophic" events. Because of the space-time scales involved, an experimental approach is often difficult, not to say impossible, however desirable it could be. Described in this article is a "laboratory" setup that yields data of a type that is amenable to theoretical methods of prediction. Observations are made of a critical slowing down in the noisy signal of a solder wire creeping under constant stress. This effect is shown to be a fair signal of the forthcoming catastrophe in two separate dynamical models. The first is an "abstract" model in which a time-dependent quantity drifts slowly but makes quick jumps from time to time. The second is a realistic physical model for the collective motion of dislocations (the Ananthakrishna set of equations for unstable creep). Hope thus exists that similar changes in the response to noise could forewarn catastrophes in other situations, where such precursor effects should manifest early enough.

  10. Predictive modeling of low solubility semiconductor alloys

    NASA Astrophysics Data System (ADS)

    Rodriguez, Garrett V.; Millunchick, Joanna M.

    2016-09-01

    GaAsBi is of great interest for applications in high efficiency optoelectronic devices due to its highly tunable bandgap. However, the experimental growth of high Bi content films has proven difficult. Here, we model GaAsBi film growth using a kinetic Monte Carlo simulation that explicitly takes cation and anion reactions into account. The unique behavior of Bi droplets is explored, and a sharp decrease in Bi content upon Bi droplet formation is demonstrated. The high mobility of simulated Bi droplets on GaAsBi surfaces is shown to produce phase separated Ga-Bi droplets as well as depressions on the film surface. A phase diagram for a range of growth rates that predicts both Bi content and droplet formation is presented to guide the experimental growth of high Bi content GaAsBi films.

  11. How does a lower predictability of lane changes affect performance in the Lane Change Task?

    PubMed

    Petzoldt, Tibor; Krems, Josef F

    2014-07-01

    The Lane Change Task (LCT) is an established method to assess driver distraction caused by secondary tasks. In the LCT ISO standard, "course following and maneuvering" and "event detection" are mentioned as central task properties. Especially event detection seems to be a reasonable feature, as research suggests that distraction has profound effects on drivers' reactions to sudden, unexpected events. However, closer inspection of the LCT reveals that the events to be detected (lane change signs) and the required response are highly predictable. To investigate how the LCT's distraction assessment of secondary tasks might change if lane change events and responses were less predictable, we implemented three different versions of the LCT - an "original" one, a second one with lowered predictability of event position, and a third one with lowered predictability of event position and response. We tested each of these implementations with the same set of visual and cognitive secondary tasks of varying demand. The results showed that a decrease in predictability resulted in overall degraded performance in the LCT when using the basic lane change model for analysis. However, all secondary task conditions suffered equally. No differential effects were found. We conclude that although an ISO conforming implementation of the LCT might not be excessively valid regarding its depiction of safety relevant events, the results obtained are nevertheless comparable to what would be found in settings of higher validity.

  12. Implementing a predictive modeling program, part II: Use of motivational interviewing in a predictive modeling program.

    PubMed

    Calhoun, Jean; Admire, Kaye S

    2005-01-01

    This is the second article of a two-part series about issues encountered in implementing a predictive modeling program. Part I looked at how to effectively implement a program and discussed helpful hints and lessons learned for case managers who are required to change their approach to patients. In Part II, we discuss the readiness to change model, examine the spirit of motivational interviewing and related techniques, and explore how motivational interviewing is different from more traditional interviewing and assessment methods.

  13. Poisson Mixture Regression Models for Heart Disease Prediction.

    PubMed

    Mufudza, Chipo; Erol, Hamza

    2016-01-01

    Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regression models. Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value. Furthermore, a Zero Inflated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available. It is deduced that heart disease prediction can be effectively done by identifying the major risks componentwise using Poisson mixture regression model.

  14. Evaluation of demographic factors affecting predictability of the sacro-femoral-pubic angle in healthy adolescents.

    PubMed

    Liu, Zhen; Bao, Hongda; Qiu, Yong; Qiao, Jun; Xu, Leilei; Zhu, Feng; Qian, Bangping; Zhu, Zezhang

    2015-02-01

    The objectives of the present study are to evaluate the correlation between pelvic tilt (PT) and the sacro-femoral-pubic (SFP) angle in Asian healthy adolescents, to provide the normal value of SFP angle as reference data in Asian adolescents, and to clarify whether the predictability of PT could be affected by gender and ontogenesis. In all, 100 girls with a mean age of 12.66 years (range 8-18 years) and 70 boys with a mean age of 13.35 years (range 8-18 years) were recruited in this retrospective study. SFP angles and PT were obtained on long-cassette standing upright radiographs. The subjects were grouped based on age. Independent-sample t-tests were performed to compare age, SFP angle, and PT between genders. In all age groups, the relationship between SFP angle and PT was analyzed by Pearson's correlation analysis and linear regression analysis, respectively. Reliability analysis showed high intra- and inter-observer agreements in PT and SFP, with an intra-class correlation coefficient (ICC) > 0.8. SFP angle averaged 71.64° ± 4.91 in all the normal subjects, of which the mean PT was 72.03°± 4.94 in the female group and 71.09°± 4.83 in the male group. SFP and PT were strongly correlated in all the age groups according to Pearson's correlation analysis. The overall coefficient was 0.679 in girls and 0.584 in boys. The present study is the first to describe the normal value of SFP angle in healthy Asian adolescents to serve as a reference data. In all age groups, SFP angles can be used to predict PT when lateral radiographs do not permit assessment of PT. The predictability of SFP angle for PT was not affected by gender or maturation status.

  15. Spirituality and religiousness as predictive factors of outcome in schizophrenia and schizo-affective disorders.

    PubMed

    Mohr, Sylvia; Perroud, Nader; Gillieron, Christiane; Brandt, Pierre-Yves; Rieben, Isabelle; Borras, Laurence; Huguelet, Philippe

    2011-04-30

    Spirituality and religiousness have been shown to be highly prevalent in patients with schizophrenia. This study assesses the predictive value of helpful vs. harmful use of religion to cope with schizophrenia or schizo-affective disorder at 3 years. From an initial cohort of 115 outpatients, 80% were reassessed for positive, negative and general symptoms, clinical global impression, social adaptation and quality of life. For patients with helpful religion at baseline, the importance of spirituality was predictive of fewer negative symptoms, better clinical global impression, social functioning and quality of life. The frequencies of religious practices in community and support from religious community had no effect on outcome. For patients with harmful religion at baseline, no relationships were elicited. This result may be due to sample size. Indeed, helpful spiritual/religious coping concerns 83% of patients, whereas harmful spiritual/religious coping concerns only 14% of patients. Our study shows that helpful use of spirituality is predictive of a better outcome. Spirituality may facilitate recovery by providing resources for coping with symptoms. In some cases, however, spirituality and religiousness are a source of suffering. Helpful vs. harmful spiritual/religious coping appears to be of clinical significance.

  16. The Neurodynamics of Affect in the Laboratory Predicts Persistence of Real-World Emotional Responses.

    PubMed

    Heller, Aaron S; Fox, Andrew S; Wing, Erik K; McQuisition, Kaitlyn M; Vack, Nathan J; Davidson, Richard J

    2015-07-22

    Failure to sustain positive affect over time is a hallmark of depression and other psychopathologies, but the mechanisms supporting the ability to sustain positive emotional responses are poorly understood. Here, we investigated the neural correlates associated with the persistence of positive affect in the real world by conducting two experiments in humans: an fMRI task of reward responses and an experience-sampling task measuring emotional responses to a reward obtained in the field. The magnitude of DLPFC engagement to rewards administered in the laboratory predicted reactivity of real-world positive emotion following a reward administered in the field. Sustained ventral striatum engagement in the laboratory positively predicted the duration of real-world positive emotional responses. These results suggest that common pathways are associated with the unfolding of neural processes over seconds and with the dynamics of emotions experienced over minutes. Examining such dynamics may facilitate a better understanding of the brain-behavior associations underlying emotion. Significance statement: How real-world emotion, experienced over seconds, minutes, and hours, is instantiated in the brain over the course of milliseconds and seconds is unknown. We combined a novel, real-world experience-sampling task with fMRI to examine how individual differences in real-world emotion, experienced over minutes and hours, is subserved by affective neurodynamics of brain activity over the course of seconds. When winning money in the real world, individuals sustaining positive emotion the longest were those with the most prolonged ventral striatal activity. These results suggest that common pathways are associated with the unfolding of neural processes over seconds and with the dynamics of emotions experienced over minutes. Examining such dynamics may facilitate a better understanding of the brain-behavior associations underlying emotion.

  17. The Neurodynamics of Affect in the Laboratory Predicts Persistence of Real-World Emotional Responses

    PubMed Central

    Fox, Andrew S.; Wing, Erik K.; McQuisition, Kaitlyn M.; Vack, Nathan J.; Davidson, Richard J.

    2015-01-01

    Failure to sustain positive affect over time is a hallmark of depression and other psychopathologies, but the mechanisms supporting the ability to sustain positive emotional responses are poorly understood. Here, we investigated the neural correlates associated with the persistence of positive affect in the real world by conducting two experiments in humans: an fMRI task of reward responses and an experience-sampling task measuring emotional responses to a reward obtained in the field. The magnitude of DLPFC engagement to rewards administered in the laboratory predicted reactivity of real-world positive emotion following a reward administered in the field. Sustained ventral striatum engagement in the laboratory positively predicted the duration of real-world positive emotional responses. These results suggest that common pathways are associated with the unfolding of neural processes over seconds and with the dynamics of emotions experienced over minutes. Examining such dynamics may facilitate a better understanding of the brain-behavior associations underlying emotion. SIGNIFICANCE STATEMENT How real-world emotion, experienced over seconds, minutes, and hours, is instantiated in the brain over the course of milliseconds and seconds is unknown. We combined a novel, real-world experience-sampling task with fMRI to examine how individual differences in real-world emotion, experienced over minutes and hours, is subserved by affective neurodynamics of brain activity over the course of seconds. When winning money in the real world, individuals sustaining positive emotion the longest were those with the most prolonged ventral striatal activity. These results suggest that common pathways are associated with the unfolding of neural processes over seconds and with the dynamics of emotions experienced over minutes. Examining such dynamics may facilitate a better understanding of the brain-behavior associations underlying emotion. PMID:26203145

  18. Model predictive control of a wind turbine modelled in Simpack

    NASA Astrophysics Data System (ADS)

    Jassmann, U.; Berroth, J.; Matzke, D.; Schelenz, R.; Reiter, M.; Jacobs, G.; Abel, D.

    2014-06-01

    Wind turbines (WT) are steadily growing in size to increase their power production, which also causes increasing loads acting on the turbine's components. At the same time large structures, such as the blades and the tower get more flexible. To minimize this impact, the classical control loops for keeping the power production in an optimum state are more and more extended by load alleviation strategies. These additional control loops can be unified by a multiple-input multiple-output (MIMO) controller to achieve better balancing of tuning parameters. An example for MIMO control, which has been paid more attention to recently by wind industry, is Model Predictive Control (MPC). In a MPC framework a simplified model of the WT is used to predict its controlled outputs. Based on a user-defined cost function an online optimization calculates the optimal control sequence. Thereby MPC can intrinsically incorporate constraints e.g. of actuators. Turbine models used for calculation within the MPC are typically simplified. For testing and verification usually multi body simulations, such as FAST, BLADED or FLEX5 are used to model system dynamics, but they are still limited in the number of degrees of freedom (DOF). Detailed information about load distribution (e.g. inside the gearbox) cannot be provided by such models. In this paper a Model Predictive Controller is presented and tested in a co-simulation with SlMPACK, a multi body system (MBS) simulation framework used for detailed load analysis. The analysis are performed on the basis of the IME6.0 MBS WT model, described in this paper. It is based on the rotor of the NREL 5MW WT and consists of a detailed representation of the drive train. This takes into account a flexible main shaft and its main bearings with a planetary gearbox, where all components are modelled flexible, as well as a supporting flexible main frame. The wind loads are simulated using the NREL AERODYN v13 code which has been implemented as a routine to

  19. Model predictive control power management strategies for HEVs: A review

    NASA Astrophysics Data System (ADS)

    Huang, Yanjun; Wang, Hong; Khajepour, Amir; He, Hongwen; Ji, Jie

    2017-02-01

    This paper presents a comprehensive review of power management strategy (PMS) utilized in hybrid electric vehicles (HEVs) with an emphasis on model predictive control (MPC) based strategies for the first time. Research on MPC-based power management systems for HEVs has intensified recently due to its many inherent merits. The categories of the existing PMSs are identified from the latest literature, and a brief study of each type is conducted. Then, the MPC approach is introduced and its advantages are discussed. Based on the acquisition method of driver behavior used for state prediction and the dynamic model used, the MPC is classified and elaborated. Factors that affect the performance of the MPC are put forward, including prediction accuracy, design parameters, and solvers. Finally, several important issues in the application of MPC-based power management strategies and latest developing trends are discussed. This paper not only provides a comprehensive analysis of MPC-based power management strategies for HEVs but also puts forward the future and emphasis of future study, which will promote the development of energy management controller with high performance and low cost for HEVs.

  20. How motivation affects academic performance: a structural equation modelling analysis.

    PubMed

    Kusurkar, R A; Ten Cate, Th J; Vos, C M P; Westers, P; Croiset, G

    2013-03-01

    Few studies in medical education have studied effect of quality of motivation on performance. Self-Determination Theory based on quality of motivation differentiates between Autonomous Motivation (AM) that originates within an individual and Controlled Motivation (CM) that originates from external sources. To determine whether Relative Autonomous Motivation (RAM, a measure of the balance between AM and CM) affects academic performance through good study strategy and higher study effort and compare this model between subgroups: males and females; students selected via two different systems namely qualitative and weighted lottery selection. Data on motivation, study strategy and effort was collected from 383 medical students of VU University Medical Center Amsterdam and their academic performance results were obtained from the student administration. Structural Equation Modelling analysis technique was used to test a hypothesized model in which high RAM would positively affect Good Study Strategy (GSS) and study effort, which in turn would positively affect academic performance in the form of grade point averages. This model fit well with the data, Chi square = 1.095, df = 3, p = 0.778, RMSEA model fit = 0.000. This model also fitted well for all tested subgroups of students. Differences were found in the strength of relationships between the variables for the different subgroups as expected. In conclusion, RAM positively correlated with academic performance through deep strategy towards study and higher study effort. This model seems valid in medical education in subgroups such as males, females, students selected by qualitative and weighted lottery selection.

  1. Can Psychological, Social and Demographical Factors Predict Clinical Characteristics Symptomatology of Bipolar Affective Disorder and Schizophrenia?

    PubMed

    Maciukiewicz, Malgorzata; Pawlak, Joanna; Kapelski, Pawel; Łabędzka, Magdalena; Skibinska, Maria; Zaremba, Dorota; Leszczynska-Rodziewicz, Anna; Dmitrzak-Weglarz, Monika; Hauser, Joanna

    2016-09-01

    Schizophrenia (SCH) is a complex, psychiatric disorder affecting 1 % of population. Its clinical phenotype is heterogeneous with delusions, hallucinations, depression, disorganized behaviour and negative symptoms. Bipolar affective disorder (BD) refers to periodic changes in mood and activity from depression to mania. It affects 0.5-1.5 % of population. Two types of disorder (type I and type II) are distinguished by severity of mania episodes. In our analysis, we aimed to check if clinical and demographical characteristics of the sample are predictors of symptom dimensions occurrence in BD and SCH cases. We included total sample of 443 bipolar and 439 schizophrenia patients. Diagnosis was based on DSM-IV criteria using Structured Clinical Interview for DSM-IV. We applied regression models to analyse associations between clinical and demographical traits from OPCRIT and symptom dimensions. We used previously computed dimensions of schizophrenia and bipolar affective disorder as quantitative traits for regression models. Male gender seemed protective factor for depression dimension in schizophrenia and bipolar disorder sample. Presence of definite psychosocial stressor prior disease seemed risk factor for depressive and suicidal domain in BD and SCH. OPCRIT items describing premorbid functioning seemed related with depression, positive and disorganised dimensions in schizophrenia and psychotic in BD. We proved clinical and demographical characteristics of the sample are predictors of symptom dimensions of schizophrenia and bipolar disorder. We also saw relation between clinical dimensions and course of disorder and impairment during disorder.

  2. Re-evaluating neonatal-age models for ungulates: does model choice affect survival estimates?

    PubMed

    Grovenburg, Troy W; Monteith, Kevin L; Jacques, Christopher N; Klaver, Robert W; DePerno, Christopher S; Brinkman, Todd J; Monteith, Kyle B; Gilbert, Sophie L; Smith, Joshua B; Bleich, Vernon C; Swanson, Christopher C; Jenks, Jonathan A

    2014-01-01

    New-hoof growth is regarded as the most reliable metric for predicting age of newborn ungulates, but variation in estimated age among hoof-growth equations that have been developed may affect estimates of survival in staggered-entry models. We used known-age newborns to evaluate variation in age estimates among existing hoof-growth equations and to determine the consequences of that variation on survival estimates. During 2001-2009, we captured and radiocollared 174 newborn (≤24-hrs old) ungulates: 76 white-tailed deer (Odocoileus virginianus) in Minnesota and South Dakota, 61 mule deer (O. hemionus) in California, and 37 pronghorn (Antilocapra americana) in South Dakota. Estimated age of known-age newborns differed among hoof-growth models and varied by >15 days for white-tailed deer, >20 days for mule deer, and >10 days for pronghorn. Accuracy (i.e., the proportion of neonates assigned to the correct age) in aging newborns using published equations ranged from 0.0% to 39.4% in white-tailed deer, 0.0% to 3.3% in mule deer, and was 0.0% for pronghorns. Results of survival modeling indicated that variability in estimates of age-at-capture affected short-term estimates of survival (i.e., 30 days) for white-tailed deer and mule deer, and survival estimates over a longer time frame (i.e., 120 days) for mule deer. Conversely, survival estimates for pronghorn were not affected by estimates of age. Our analyses indicate that modeling survival in daily intervals is too fine a temporal scale when age-at-capture is unknown given the potential inaccuracies among equations used to estimate age of neonates. Instead, weekly survival intervals are more appropriate because most models accurately predicted ages within 1 week of the known age. Variation among results of neonatal-age models on short- and long-term estimates of survival for known-age young emphasizes the importance of selecting an appropriate hoof-growth equation and appropriately defining intervals (i.e., weekly

  3. Re-Evaluating Neonatal-Age Models for Ungulates: Does Model Choice Affect Survival Estimates?

    PubMed Central

    Grovenburg, Troy W.; Monteith, Kevin L.; Jacques, Christopher N.; Klaver, Robert W.; DePerno, Christopher S.; Brinkman, Todd J.; Monteith, Kyle B.; Gilbert, Sophie L.; Smith, Joshua B.; Bleich, Vernon C.; Swanson, Christopher C.; Jenks, Jonathan A.

    2014-01-01

    New-hoof growth is regarded as the most reliable metric for predicting age of newborn ungulates, but variation in estimated age among hoof-growth equations that have been developed may affect estimates of survival in staggered-entry models. We used known-age newborns to evaluate variation in age estimates among existing hoof-growth equations and to determine the consequences of that variation on survival estimates. During 2001–2009, we captured and radiocollared 174 newborn (≤24-hrs old) ungulates: 76 white-tailed deer (Odocoileus virginianus) in Minnesota and South Dakota, 61 mule deer (O. hemionus) in California, and 37 pronghorn (Antilocapra americana) in South Dakota. Estimated age of known-age newborns differed among hoof-growth models and varied by >15 days for white-tailed deer, >20 days for mule deer, and >10 days for pronghorn. Accuracy (i.e., the proportion of neonates assigned to the correct age) in aging newborns using published equations ranged from 0.0% to 39.4% in white-tailed deer, 0.0% to 3.3% in mule deer, and was 0.0% for pronghorns. Results of survival modeling indicated that variability in estimates of age-at-capture affected short-term estimates of survival (i.e., 30 days) for white-tailed deer and mule deer, and survival estimates over a longer time frame (i.e., 120 days) for mule deer. Conversely, survival estimates for pronghorn were not affected by estimates of age. Our analyses indicate that modeling survival in daily intervals is too fine a temporal scale when age-at-capture is unknown given the potential inaccuracies among equations used to estimate age of neonates. Instead, weekly survival intervals are more appropriate because most models accurately predicted ages within 1 week of the known age. Variation among results of neonatal-age models on short- and long-term estimates of survival for known-age young emphasizes the importance of selecting an appropriate hoof-growth equation and appropriately defining intervals (i.e., weekly

  4. Re-evaluating neonatal-age models for ungulates: Does model choice affect survival estimates?

    USGS Publications Warehouse

    Grovenburg, Troy W.; Monteith, Kevin L.; Jacques, Christopher N.; Klaver, Robert W.; DePerno, Christopher S.; Brinkman, Todd J.; Monteith, Kyle B.; Gilbert, Sophie L.; Smith, Joshua B.; Bleich, Vernon C.; Swanson, Christopher C.; Jenks, Jonathan A.

    2014-01-01

    New-hoof growth is regarded as the most reliable metric for predicting age of newborn ungulates, but variation in estimated age among hoof-growth equations that have been developed may affect estimates of survival in staggered-entry models. We used known-age newborns to evaluate variation in age estimates among existing hoof-growth equations and to determine the consequences of that variation on survival estimates. During 2001–2009, we captured and radiocollared 174 newborn (≤24-hrs old) ungulates: 76 white-tailed deer (Odocoileus virginianus) in Minnesota and South Dakota, 61 mule deer (O. hemionus) in California, and 37 pronghorn (Antilocapra americana) in South Dakota. Estimated age of known-age newborns differed among hoof-growth models and varied by >15 days for white-tailed deer, >20 days for mule deer, and >10 days for pronghorn. Accuracy (i.e., the proportion of neonates assigned to the correct age) in aging newborns using published equations ranged from 0.0% to 39.4% in white-tailed deer, 0.0% to 3.3% in mule deer, and was 0.0% for pronghorns. Results of survival modeling indicated that variability in estimates of age-at-capture affected short-term estimates of survival (i.e., 30 days) for white-tailed deer and mule deer, and survival estimates over a longer time frame (i.e., 120 days) for mule deer. Conversely, survival estimates for pronghorn were not affected by estimates of age. Our analyses indicate that modeling survival in daily intervals is too fine a temporal scale when age-at-capture is unknown given the potential inaccuracies among equations used to estimate age of neonates. Instead, weekly survival intervals are more appropriate because most models accurately predicted ages within 1 week of the known age. Variation among results of neonatal-age models on short- and long-term estimates of survival for known-age young emphasizes the importance of selecting an appropriate hoof-growth equation and appropriately defining intervals (i.e., weekly

  5. Hologram QSAR model for the prediction of human oral bioavailability.

    PubMed

    Moda, Tiago L; Montanari, Carlos A; Andricopulo, Adriano D

    2007-12-15

    A drug intended for use in humans should have an ideal balance of pharmacokinetics and safety, as well as potency and selectivity. Unfavorable pharmacokinetics can negatively affect the clinical development of many otherwise promising drug candidates. A variety of in silico ADME (absorption, distribution, metabolism, and excretion) models are receiving increased attention due to a better appreciation that pharmacokinetic properties should be considered in early phases of the drug discovery process. Human oral bioavailability is an important pharmacokinetic property, which is directly related to the amount of drug available in the systemic circulation to exert pharmacological and therapeutic effects. In the present work, hologram quantitative structure-activity relationships (HQSAR) were performed on a training set of 250 structurally diverse molecules with known human oral bioavailability. The most significant HQSAR model (q(2)=0.70, r(2)=0.93) was obtained using atoms, bond, connection, and chirality as fragment distinction. The predictive ability of the model was evaluated by an external test set containing 52 molecules not included in the training set, and the predicted values were in good agreement with the experimental values. The HQSAR model should be useful for the design of new drug candidates having increased bioavailability as well as in the process of chemical library design, virtual screening, and high-throughput screening.

  6. Dynamic Model Predicting Overweight, Obesity, and Extreme Obesity Prevalence Trends

    PubMed Central

    Thomas, Diana M.; Weedermann, Marion; Fuemmeler, Bernard F.; Martin, Corby K.; Dhurandhar, Nikhil V.; Bredlau, Carl; Heymsfield, Steven B.; Ravussin, Eric; Bouchard, Claude

    2013-01-01

    Objective Obesity prevalence in the United States (US) appears to be leveling, but the reasons behind the plateau remain unknown. Mechanistic insights can be provided from a mathematical model. The objective of this study is to model known multiple population parameters associated with changes in body mass index (BMI) classes and to establish conditions under which obesity prevalence will plateau. Design and Methods A differential equation system was developed that predicts population-wide obesity prevalence trends. The model considers both social and non-social influences on weight gain, incorporates other known parameters affecting obesity trends, and allows for country specific population growth. Results The dynamic model predicts that: obesity prevalence is a function of birth rate and the probability of being born in an obesogenic environment; obesity prevalence will plateau independent of current prevention strategies; and the US prevalence of obesity, overweight, and extreme obesity will plateau by about 2030 at 28%, 32%, and 9%, respectively. Conclusions The US prevalence of obesity is stabilizing and will plateau, independent of current preventative strategies. This trend has important implications in accurately evaluating the impact of various anti-obesity strategies aimed at reducing obesity prevalence. PMID:23804487

  7. Incorporating affective bias in models of human decision making

    NASA Technical Reports Server (NTRS)

    Nygren, Thomas E.

    1991-01-01

    Research on human decision making has traditionally focused on how people actually make decisions, how good their decisions are, and how their decisions can be improved. Recent research suggests that this model is inadequate. Affective as well as cognitive components drive the way information about relevant outcomes and events is perceived, integrated, and used in the decision making process. The affective components include how the individual frames outcomes as good or bad, whether the individual anticipates regret in a decision situation, the affective mood state of the individual, and the psychological stress level anticipated or experienced in the decision situation. A focus of the current work has been to propose empirical studies that will attempt to examine in more detail the relationships between the latter two critical affective influences (mood state and stress) on decision making behavior.

  8. Predictive models of circulating fluidized bed combustors

    SciTech Connect

    Gidaspow, D.

    1992-07-01

    Steady flows influenced by walls cannot be described by inviscid models. Flows in circulating fluidized beds have significant wall effects. Particles in the form of clusters or layers can be seen to run down the walls. Hence modeling of circulating fluidized beds (CFB) without a viscosity is not possible. However, in interpreting Equations (8-1) and (8-2) it must be kept in mind that CFB or most other two phase flows are never in a true steady state. Then the viscosity in Equations (8-1) and (8-2) may not be the true fluid viscosity to be discussed next, but an Eddy type viscosity caused by two phase flow oscillations usually referred to as turbulence. In view of the transient nature of two-phase flow, the drag and the boundary layer thickness may not be proportional to the square root of the intrinsic viscosity but depend upon it to a much smaller extent. As another example, liquid-solid flow and settling of colloidal particles in a lamella electrosettler the settling process is only moderately affected by viscosity. Inviscid flow with settling is a good first approximation to this electric field driven process. The physical meaning of the particulate phase viscosity is described in detail in the chapter on kinetic theory. Here the conventional derivation resented in single phase fluid mechanics is generalized to multiphase flow.

  9. Affective Responses and Cognitive Models of the Computing Environment.

    ERIC Educational Resources Information Center

    Wallace, Andrew R.; Sinclair, Kenneth E.

    New electronic technologies provide powerful tools for managing and processing the rapidly increasing amounts of information available for learning; teachers, however, have often been slow in integrating computers into the curriculum. This study addresses the question of how prospective teachers construct affective and cognitive models about…

  10. RFI modeling and prediction approach for SATOP applications: RFI prediction models

    NASA Astrophysics Data System (ADS)

    Nguyen, Tien M.; Tran, Hien T.; Wang, Zhonghai; Coons, Amanda; Nguyen, Charles C.; Lane, Steven A.; Pham, Khanh D.; Chen, Genshe; Wang, Gang

    2016-05-01

    This paper describes a technical approach for the development of RFI prediction models using carrier synchronization loop when calculating Bit or Carrier SNR degradation due to interferences for (i) detecting narrow-band and wideband RFI signals, and (ii) estimating and predicting the behavior of the RFI signals. The paper presents analytical and simulation models and provides both analytical and simulation results on the performance of USB (Unified S-Band) waveforms in the presence of narrow-band and wideband RFI signals. The models presented in this paper will allow the future USB command systems to detect the RFI presence, estimate the RFI characteristics and predict the RFI behavior in real-time for accurate assessment of the impacts of RFI on the command Bit Error Rate (BER) performance. The command BER degradation model presented in this paper also allows the ground system operator to estimate the optimum transmitted SNR to maintain a required command BER level in the presence of both friendly and un-friendly RFI sources.

  11. Predictive modelling for EAST divertor operation

    NASA Astrophysics Data System (ADS)

    Chen, YiPing

    2011-06-01

    The predictive modelling study of the divertor operation in EAST tokamak [B. Wan et al., Nucl. Fusion 49, 104011 (2009)] with double null (DN) configuration is carried out by using the two-dimensional edge plasma code B2.5-SOLPS5.0 [D. P. Coster, X. Bonnin et al., J. Nucl. Mater. 337-339, 366 (2005)]. The modelling study includes the particle and power balance in the scrape-off-layer (SOL), the operation parameters of plasma density, temperature and plasma heat fluxes at the separatrix, the target plates and the wall, and the effect of the gas puffing, drifts, and vertical target plate on the divertor operation. The fluid model for the edge plasma is applied using the real magnetohydrodynamic (MHD) equilibrium from the MHD equilibrium code EFIT [L. L. Lao et al., Nucl. Fusion 25, 1611 (1985)] and the real divertor geometry in the device. Before EAST tokamak starts its experimental programme of divertor operation, the modelling plays an important role in the design of its experimental programme and the optimization of the divertor operation parameters. Based on the modelling results, EAST divertor can operate over a large wide of plasma parameters with different regimes. For a heating power of 8 MW and an edge density at core-SOL interface Nedge = 0.8 × 10191/m3 and Nedge = 1.3 × 10191/m3, the EAST divertor begins access to the high recycling operation regime at the outer and inner target plates, respectively, where the plasma temperature and the heat fluxes at the target plates decrease. The gas puffing can increase the plasma density at the separatrix and trigger the transition from the high recycling operation into detachment at the target plates. When E × B and B × ▿B drifts are taken into account, the asymmetry of plasma parameters and heat fluxes between up-down SOLs can be found. The vertical target plate in EAST divertor can reduce the peak values of heat fluxes at the target plate and enables detachment at lower plasma density. The divertor with the

  12. Predictive modelling for EAST divertor operation

    SciTech Connect

    Chen Yiping

    2011-06-15

    The predictive modelling study of the divertor operation in EAST tokamak [B. Wan et al., Nucl. Fusion 49, 104011 (2009)] with double null (DN) configuration is carried out by using the two-dimensional edge plasma code B2.5-SOLPS5.0 [D. P. Coster, X. Bonnin et al., J. Nucl. Mater. 337-339, 366 (2005)]. The modelling study includes the particle and power balance in the scrape-off-layer (SOL), the operation parameters of plasma density, temperature and plasma heat fluxes at the separatrix, the target plates and the wall, and the effect of the gas puffing, drifts, and vertical target plate on the divertor operation. The fluid model for the edge plasma is applied using the real magnetohydrodynamic (MHD) equilibrium from the MHD equilibrium code EFIT [L. L. Lao et al., Nucl. Fusion 25, 1611 (1985)] and the real divertor geometry in the device. Before EAST tokamak starts its experimental programme of divertor operation, the modelling plays an important role in the design of its experimental programme and the optimization of the divertor operation parameters. Based on the modelling results, EAST divertor can operate over a large wide of plasma parameters with different regimes. For a heating power of 8 MW and an edge density at core-SOL interface N{sub edge} = 0.8 x 10{sup 19}1/m{sup 3} and N{sub edge} = 1.3 x 10{sup 19}1/m{sup 3}, the EAST divertor begins access to the high recycling operation regime at the outer and inner target plates, respectively, where the plasma temperature and the heat fluxes at the target plates decrease. The gas puffing can increase the plasma density at the separatrix and trigger the transition from the high recycling operation into detachment at the target plates. When E x B and B x {nabla}B drifts are taken into account, the asymmetry of plasma parameters and heat fluxes between up-down SOLs can be found. The vertical target plate in EAST divertor can reduce the peak values of heat fluxes at the target plate and enables detachment at lower

  13. Predictive vegetation modeling for conservation: impact of error propagation from digital elevation data.

    PubMed

    Van Niel, Kimberly P; Austin, Mike P

    2007-01-01

    The effect of digital elevation model (DEM) error on environmental variables, and subsequently on predictive habitat models, has not been explored. Based on an error analysis of a DEM, multiple error realizations of the DEM were created and used to develop both direct and indirect environmental variables for input to predictive habitat models. The study explores the effects of DEM error and the resultant uncertainty of results on typical steps in the modeling procedure for prediction of vegetation species presence/absence. Results indicate that all of these steps and results, including the statistical significance of environmental variables, shapes of species response curves in generalized additive models (GAMs), stepwise model selection, coefficients and standard errors for generalized linear models (GLMs), prediction accuracy (Cohen's kappa and AUC), and spatial extent of predictions, were greatly affected by this type of error. Error in the DEM can affect the reliability of interpretations of model results and level of accuracy in predictions, as well as the spatial extent of the predictions. We suggest that the sensitivity of DEM-derived environmental variables to error in the DEM should be considered before including them in the modeling processes.

  14. Predictability of the Indian Ocean Dipole in the coupled models

    NASA Astrophysics Data System (ADS)

    Liu, Huafeng; Tang, Youmin; Chen, Dake; Lian, Tao

    2017-03-01

    In this study, the Indian Ocean Dipole (IOD) predictability, measured by the Indian Dipole Mode Index (DMI), is comprehensively examined at the seasonal time scale, including its actual prediction skill and potential predictability, using the ENSEMBLES multiple model ensembles and the recently developed information-based theoretical framework of predictability. It was found that all model predictions have useful skill, which is normally defined by the anomaly correlation coefficient larger than 0.5, only at around 2-3 month leads. This is mainly because there are more false alarms in predictions as leading time increases. The DMI predictability has significant seasonal variation, and the predictions whose target seasons are boreal summer (JJA) and autumn (SON) are more reliable than that for other seasons. All of models fail to predict the IOD onset before May and suffer from the winter (DJF) predictability barrier. The potential predictability study indicates that, with the model development and initialization improvement, the prediction of IOD onset is likely to be improved but the winter barrier cannot be overcome. The IOD predictability also has decadal variation, with a high skill during the 1960s and the early 1990s, and a low skill during the early 1970s and early 1980s, which is very consistent with the potential predictability. The main factors controlling the IOD predictability, including its seasonal and decadal variations, are also analyzed in this study.

  15. Comparing Predictions Made by a Prediction Model, Clinical Score, and Physicians

    PubMed Central

    Farion, K.J.; Wilk, S.; Michalowski, W.; O’Sullivan, D.; Sayyad-Shirabad, J.

    2013-01-01

    Summary Background Asthma exacerbations are one of the most common medical reasons for children to be brought to the hospital emergency department (ED). Various prediction models have been proposed to support diagnosis of exacerbations and evaluation of their severity. Objectives First, to evaluate prediction models constructed from data using machine learning techniques and to select the best performing model. Second, to compare predictions from the selected model with predictions from the Pediatric Respiratory Assessment Measure (PRAM) score, and predictions made by ED physicians. Design A two-phase study conducted in the ED of an academic pediatric hospital. In phase 1 data collected prospectively using paper forms was used to construct and evaluate five prediction models, and the best performing model was selected. In phase 2 data collected prospectively using a mobile system was used to compare the predictions of the selected prediction model with those from PRAM and ED physicians. Measurements Area under the receiver operating characteristic curve and accuracy in phase 1; accuracy, sensitivity, specificity, positive and negative predictive values in phase 2. Results In phase 1 prediction models were derived from a data set of 240 patients and evaluated using 10-fold cross validation. A naive Bayes (NB) model demonstrated the best performance and it was selected for phase 2. Evaluation in phase 2 was conducted on data from 82 patients. Predictions made by the NB model were less accurate than the PRAM score and physicians (accuracy of 70.7%, 73.2% and 78.0% respectively), however, according to McNemar’s test it is not possible to conclude that the differences between predictions are statistically significant. Conclusion Both the PRAM score and the NB model were less accurate than physicians. The NB model can handle incomplete patient data and as such may complement the PRAM score. However, it requires further research to improve its accuracy. PMID:24155790

  16. Model Predictive Control of Integrated Gasification Combined Cycle Power Plants

    SciTech Connect

    B. Wayne Bequette; Priyadarshi Mahapatra

    2010-08-31

    The primary project objectives were to understand how the process design of an integrated gasification combined cycle (IGCC) power plant affects the dynamic operability and controllability of the process. Steady-state and dynamic simulation models were developed to predict the process behavior during typical transients that occur in plant operation. Advanced control strategies were developed to improve the ability of the process to follow changes in the power load demand, and to improve performance during transitions between power levels. Another objective of the proposed work was to educate graduate and undergraduate students in the application of process systems and control to coal technology. Educational materials were developed for use in engineering courses to further broaden this exposure to many students. ASPENTECH software was used to perform steady-state and dynamic simulations of an IGCC power plant. Linear systems analysis techniques were used to assess the steady-state and dynamic operability of the power plant under various plant operating conditions. Model predictive control (MPC) strategies were developed to improve the dynamic operation of the power plants. MATLAB and SIMULINK software were used for systems analysis and control system design, and the SIMULINK functionality in ASPEN DYNAMICS was used to test the control strategies on the simulated process. Project funds were used to support a Ph.D. student to receive education and training in coal technology and the application of modeling and simulation techniques.

  17. Nonconvex model predictive control for commercial refrigeration

    NASA Astrophysics Data System (ADS)

    Gybel Hovgaard, Tobias; Boyd, Stephen; Larsen, Lars F. S.; Bagterp Jørgensen, John

    2013-08-01

    We consider the control of a commercial multi-zone refrigeration system, consisting of several cooling units that share a common compressor, and is used to cool multiple areas or rooms. In each time period we choose cooling capacity to each unit and a common evaporation temperature. The goal is to minimise the total energy cost, using real-time electricity prices, while obeying temperature constraints on the zones. We propose a variation on model predictive control to achieve this goal. When the right variables are used, the dynamics of the system are linear, and the constraints are convex. The cost function, however, is nonconvex due to the temperature dependence of thermodynamic efficiency. To handle this nonconvexity we propose a sequential convex optimisation method, which typically converges in fewer than 5 or so iterations. We employ a fast convex quadratic programming solver to carry out the iterations, which is more than fast enough to run in real time. We demonstrate our method on a realistic model, with a full year simulation and 15-minute time periods, using historical electricity prices and weather data, as well as random variations in thermal load. These simulations show substantial cost savings, on the order of 30%, compared to a standard thermostat-based control system. Perhaps more important, we see that the method exhibits sophisticated response to real-time variations in electricity prices. This demand response is critical to help balance real-time uncertainties in generation capacity associated with large penetration of intermittent renewable energy sources in a future smart grid.

  18. Predictive models for moving contact line flows

    NASA Technical Reports Server (NTRS)

    Rame, Enrique; Garoff, Stephen

    2003-01-01

    Modeling flows with moving contact lines poses the formidable challenge that the usual assumptions of Newtonian fluid and no-slip condition give rise to a well-known singularity. This singularity prevents one from satisfying the contact angle condition to compute the shape of the fluid-fluid interface, a crucial calculation without which design parameters such as the pressure drop needed to move an immiscible 2-fluid system through a solid matrix cannot be evaluated. Some progress has been made for low Capillary number spreading flows. Combining experimental measurements of fluid-fluid interfaces very near the moving contact line with an analytical expression for the interface shape, we can determine a parameter that forms a boundary condition for the macroscopic interface shape when Ca much les than l. This parameter, which plays the role of an "apparent" or macroscopic dynamic contact angle, is shown by the theory to depend on the system geometry through the macroscopic length scale. This theoretically established dependence on geometry allows this parameter to be "transferable" from the geometry of the measurement to any other geometry involving the same material system. Unfortunately this prediction of the theory cannot be tested on Earth.

  19. Thermal barrier coating life prediction model

    NASA Technical Reports Server (NTRS)

    Hillery, R. V.; Pilsner, B. H.

    1985-01-01

    This is the first report of the first phase of a 3-year program. Its objectives are to determine the predominant modes of degradation of a plasma sprayed thermal barrier coating system, then to develop and verify life prediction models accounting for these degradation modes. The first task (Task I) is to determine the major failure mechanisms. Presently, bond coat oxidation and bond coat creep are being evaluated as potential TBC failure mechanisms. The baseline TBC system consists of an air plasma sprayed ZrO2-Y2O3 top coat, a low pressure plasma sprayed NiCrAlY bond coat, and a Rene'80 substrate. Pre-exposures in air and argon combined with thermal cycle tests in air and argon are being utilized to evaluate bond coat oxidation as a failure mechanism. Unexpectedly, the specimens pre-exposed in argon failed before the specimens pre-exposed in air in subsequent thermal cycles testing in air. Four bond coats with different creep strengths are being utilized to evaluate the effect of bond coat creep on TBC degradation. These bond coats received an aluminide overcoat prior to application of the top coat to reduce the differences in bond coat oxidation behavior. Thermal cycle testing has been initiated. Methods have been selected for measuring tensile strength, Poisson's ratio, dynamic modulus and coefficient of thermal expansion both of the bond coat and top coat layers.

  20. How Tibiofemoral Alignment and Contact Locations Affect Predictions of Medial and Lateral Tibiofemoral Contact Forces

    PubMed Central

    Lerner, Zachary F.; DeMers, Matthew S.; Delp, Scott L.; Browning, Raymond C.

    2015-01-01

    Understanding degeneration of biological and prosthetic knee joints requires knowledge of the in-vivo loading environment during activities of daily living. Musculoskeletal models can estimate medial/lateral tibiofemoral compartment contact forces, yet anthropometric differences between individuals make accurate predictions challenging. We developed a full-body OpenSim musculoskeletal model with a knee joint that incorporates subject-specific tibiofemoral alignment (i.e. knee varus-valgus) and geometry (i.e. contact locations). We tested the accuracy of our model and determined the importance of these subject-specific parameters by comparing estimated to measured medial and lateral contact forces during walking in an individual with an instrumented knee replacement and post-operative genu valgum (6°). The errors in the predictions of the first peak medial and lateral contact force were 12.4% and 11.9%, respectively, for a model with subject-specific tibiofemoral alignment and contact locations determined via radiographic analysis, vs. 63.1% and 42.0%, respectively, for a model with generic parameters. We found that each degree of tibiofemoral alignment deviation altered the first peak medial compartment contact force by 51N (r2=0.99), while each millimeter of medial-lateral translation of the compartment contact point locations altered the first peak medial compartment contact force by 41N (r2=0.99). The model, available at www.simtk.org/home/med-lat-knee/, enables the specification of subject-specific joint alignment and compartment contact locations to more accurately estimate medial and lateral tibiofemoral contact forces in individuals with non-neutral alignment. PMID:25595425

  1. Predicting the Affects of Climate Change on Evapotranspiration and Agricultural Productivity of Semi-arid Basins

    NASA Astrophysics Data System (ADS)

    Peri, L.; Tyler, S. W.; Zheng, C.; Pohll, G. M.; Yao, Y.

    2013-12-01

    Many arid and semi-arid regions around the world are experiencing water shortages that have become increasingly problematic. Since the late 1800s, upstream diversions in Nevada's Walker River have delivered irrigation supply to the surrounding agricultural fields resulting in a dramatic water level decline of the terminal Walker Lake. Salinity has also increased because the only outflow from the lake is evaporation from the lake surface. The Heihe River basin of northwestern China, a similar semi-arid catchment, is also facing losses from evaporation of terminal locations, agricultural diversions and evapotranspiration (ET) of crops. Irrigated agriculture is now experiencing increased competition for use of diminishing water resources while a demand for ecological conservation continues to grow. It is important to understand how the existing agriculture in these regions will respond as climate changes. Predicting the affects of climate change on groundwater flow, surface water flow, ET and agricultural productivity of the Walker and Heihe River basins is essential for future conservation of water resources. ET estimates from remote sensing techniques can provide estimates of crop water consumption. By determining similarities of both hydrologic cycles, critical components missing in both systems can be determined and predictions of impacts of climate change and human management strategies can be assessed.

  2. Predicting Short-Term Positive Affect in Individuals with Social Anxiety Disorder: The Role of Selected Personality Traits and Emotion Regulation Strategies

    PubMed Central

    Weisman, Jaclyn S.; Rodebaugh, Thomas L.; Lim, Michelle H.; Fernandez, Katya C.

    2015-01-01

    Recently, research has provided support for a moderate, inverse relationship between social anxiety and dispositional positive affect. However, the dynamics of this relationship remain poorly understood. The present study evaluates whether certain personality traits and emotion regulation variables predict short-term positive affect for individuals with social anxiety disorder and healthy controls. Positive affect as measured by two self-report instruments was assessed before and after two tasks in which the participant conversed with either a friend or a romantic partner. Tests of models examining the hypothesized prospective predictors revealed that the paths did not differ significantly across diagnostic group and both groups showed the hypothesized patterns of endorsement for the emotion regulation variables. Further, a variable reflecting difficulty redirecting oneself when distressed prospectively predicted one measure of positive affect. Additional research is needed to explore further the role of emotion regulation strategies on positive emotions for individuals higher in social anxiety. PMID:26119140

  3. Predicting short-term positive affect in individuals with social anxiety disorder: The role of selected personality traits and emotion regulation strategies.

    PubMed

    Weisman, Jaclyn S; Rodebaugh, Thomas L; Lim, Michelle H; Fernandez, Katya C

    2015-08-01

    Recently, research has provided support for a moderate, inverse relationship between social anxiety and dispositional positive affect. However, the dynamics of this relationship remain poorly understood. The present study evaluates whether certain personality traits and emotion regulation variables predict short-term positive affect for individuals with social anxiety disorder and healthy controls. Positive affect as measured by two self-report instruments was assessed before and after two tasks in which the participant conversed with either a friend or a romantic partner. Tests of models examining the hypothesized prospective predictors revealed that the paths did not differ significantly across diagnostic group and both groups showed the hypothesized patterns of endorsement for the emotion regulation variables. Further, a variable reflecting difficulty redirecting oneself when distressed prospectively predicted one measure of positive affect. Additional research is needed to explore further the role of emotion regulation strategies on positive emotions for individuals higher in social anxiety.

  4. Seasonally Varying Predation Behavior and Climate Shifts Are Predicted to Affect Predator-Prey Cycles.

    PubMed

    Tyson, Rebecca; Lutscher, Frithjof

    2016-11-01

    The functional response of some predator species changes from a pattern characteristic for a generalist to that for a specialist according to seasonally varying prey availability. Current theory does not address the dynamic consequences of this phenomenon. Since season length correlates strongly with altitude and latitude and is predicted to change under future climate scenarios, including this phenomenon in theoretical models seems essential for correct prediction of future ecosystem dynamics. We develop and analyze a two-season model for the great horned owl (Bubo virginialis) and snowshoe hare (Lepus americanus). These species form a predator-prey system in which the generalist to specialist shift in predation pattern has been documented empirically. We study the qualitative behavior of this predator-prey model community as summer season length changes. We find that relatively small changes in summer season length can have a profound impact on the system. In particular, when the predator has sufficient alternative resources available during the summer season, it can drive the prey to extinction, there can be coexisting stable states, and there can be stable large-amplitude limit cycles coexisting with a stable steady state. Our results illustrate that the impacts of global change on local ecosystems can be driven by internal system dynamics and can potentially have catastrophic consequences.

  5. Subjective well-being in older adults: folate and vitamin B12 independently predict positive affect.

    PubMed

    Edney, Laura C; Burns, Nicholas R; Danthiir, Vanessa

    2015-10-28

    Vitamin B12, folate and homocysteine have long been implicated in mental illness, and growing evidence suggests that they may play a role in positive mental health. Elucidation of these relationships is confounded due to the dependence of homocysteine on available levels of vitamin B12 and folate. Cross-sectional and longitudinal relationships between vitamin B12, folate, homocysteine and subjective well-being were assessed in a sample of 391 older, community-living adults without clinically diagnosed depression. Levels of vitamin B12, but not folate, influenced homocysteine levels 18 months later. Vitamin B12, folate and their interaction significantly predicted levels of positive affect (PA) 18 months later, but had no impact on the levels of negative affect or life satisfaction. Cross-sectional relationships between homocysteine and PA were completely attenuated in the longitudinal analyses, suggesting that the cross-sectional relationship is driven by the dependence of homocysteine on vitamin B12 and folate. This is the first study to offer some evidence of a causal link between levels of folate and vitamin B12 on PA in a large, non-clinical population.

  6. Attachment style predicts affect, cognitive appraisals, and social functioning in daily life.

    PubMed

    Sheinbaum, Tamara; Kwapil, Thomas R; Ballespí, Sergi; Mitjavila, Mercè; Chun, Charlotte A; Silvia, Paul J; Barrantes-Vidal, Neus

    2015-01-01

    The way in which attachment styles are expressed in the moment as individuals navigate their real-life settings has remained an area largely untapped by attachment research. The present study examined how adult attachment styles are expressed in daily life using experience sampling methodology (ESM) in a sample of 206 Spanish young adults. Participants were administered the Attachment Style Interview (ASI) and received personal digital assistants that signaled them randomly eight times per day for 1 week to complete questionnaires about their current experiences and social context. As hypothesized, participants' momentary affective states, cognitive appraisals, and social functioning varied in meaningful ways as a function of their attachment style. Individuals with an anxious attachment, as compared with securely attached individuals, endorsed experiences that were congruent with hyperactivating tendencies, such as higher negative affect, stress, and perceived social rejection. By contrast, individuals with an avoidant attachment, relative to individuals with a secure attachment, endorsed experiences that were consistent with deactivating tendencies, such as decreased positive states and a decreased desire to be with others when alone. Furthermore, the expression of attachment styles in social contexts was shown to be dependent upon the subjective appraisal of the closeness of social contacts, and not merely upon the presence of social interactions. The findings support the ecological validity of the ASI and the person-by-situation character of attachment theory. Moreover, they highlight the utility of ESM for investigating how the predictions derived from attachment theory play out in the natural flow of real life.

  7. Attachment style predicts affect, cognitive appraisals, and social functioning in daily life

    PubMed Central

    Sheinbaum, Tamara; Kwapil, Thomas R.; Ballespí, Sergi; Mitjavila, Mercè; Chun, Charlotte A.; Silvia, Paul J.; Barrantes-Vidal, Neus

    2015-01-01

    The way in which attachment styles are expressed in the moment as individuals navigate their real-life settings has remained an area largely untapped by attachment research. The present study examined how adult attachment styles are expressed in daily life using experience sampling methodology (ESM) in a sample of 206 Spanish young adults. Participants were administered the Attachment Style Interview (ASI) and received personal digital assistants that signaled them randomly eight times per day for 1 week to complete questionnaires about their current experiences and social context. As hypothesized, participants’ momentary affective states, cognitive appraisals, and social functioning varied in meaningful ways as a function of their attachment style. Individuals with an anxious attachment, as compared with securely attached individuals, endorsed experiences that were congruent with hyperactivating tendencies, such as higher negative affect, stress, and perceived social rejection. By contrast, individuals with an avoidant attachment, relative to individuals with a secure attachment, endorsed experiences that were consistent with deactivating tendencies, such as decreased positive states and a decreased desire to be with others when alone. Furthermore, the expression of attachment styles in social contexts was shown to be dependent upon the subjective appraisal of the closeness of social contacts, and not merely upon the presence of social interactions. The findings support the ecological validity of the ASI and the person-by-situation character of attachment theory. Moreover, they highlight the utility of ESM for investigating how the predictions derived from attachment theory play out in the natural flow of real life. PMID:25852613

  8. Music and literature: are there shared empathy and predictive mechanisms underlying their affective impact?

    PubMed Central

    Omigie, Diana

    2015-01-01

    It has been suggested that music and language had a shared evolutionary precursor before becoming mainly responsible for the communication of emotive and referential meaning respectively. However, emphasis on potential differences between music and language may discourage a consideration of the commonalities that music and literature share. Indeed, one possibility is that common mechanisms underlie their affective impact, and the current paper carefully reviews relevant neuroscientific findings to examine such a prospect. First and foremost, it will be demonstrated that considerable evidence of a common role of empathy and predictive processes now exists for the two domains. However, it will also be noted that an important open question remains: namely, whether the mechanisms underlying the subjective experience of uncertainty differ between the two domains with respect to recruitment of phylogenetically ancient emotion areas. It will be concluded that a comparative approach may not only help to reveal general mechanisms underlying our responses to music and literature, but may also help us better understand any idiosyncrasies in their capacity for affective impact. PMID:26379583

  9. Global life satisfaction predicts ambulatory affect, stress, and cortisol in daily life in working adults.

    PubMed

    Smyth, Joshua M; Zawadzki, Matthew J; Juth, Vanessa; Sciamanna, Christopher N

    2017-04-01

    Global life satisfaction has been linked with long-term health advantages, yet how life satisfaction impacts the trajectory of long-term health is unclear. This paper examines one such possible mechanism-that greater life satisfaction confers momentary benefits in daily life that accumulate over time. A community sample of working adults (n = 115) completed a measure of life satisfaction and then three subsequent days of ecological momentary assessment surveys (6 times/day) measuring affect (i.e., emotional valence, arousal), and perceived stress, and also provided salivary cortisol samples. Multilevel models indicated that people with higher (vs. lower) levels of life satisfaction reported better momentary affect, less stress, marginally lower momentary levels and significantly altered diurnal slopes of cortisol. Findings suggest individuals with high global life satisfaction have advantageous daily experiences, providing initial evidence for potential mechanisms through which global life satisfaction may help explain long-term health benefits.

  10. Evaluation of performance of predictive models for deoxynivalenol in wheat.

    PubMed

    van der Fels-Klerx, H J

    2014-02-01

    The aim of this study was to evaluate the performance of two predictive models for deoxynivalenol contamination of wheat at harvest in the Netherlands, including the use of weather forecast data and external model validation. Data were collected in a different year and from different wheat fields than data used for model development. The two models were run for six preset scenarios, varying in the period for which weather forecast data were used, from zero-day (historical data only) to a 13-day period around wheat flowering. Model predictions using forecast weather data were compared to those using historical data. Furthermore, model predictions using historical weather data were evaluated against observed deoxynivalenol contamination of the wheat fields. Results showed that the use of weather forecast data rather than observed data only slightly influenced model predictions. The percent of correct model predictions, given a threshold of 1,250 μg/kg (legal limit in European Union), was about 95% for the two models. However, only three samples had a deoxynivalenol concentration above this threshold, and the models were not able to predict these samples correctly. It was concluded that two- week weather forecast data can reliable be used in descriptive models for deoxynivalenol contamination of wheat, resulting in more timely model predictions. The two models are able to predict lower deoxynivalenol contamination correctly, but model performance in situations with high deoxynivalenol contamination needs to be further validated. This will need years with conducive environmental conditions for deoxynivalenol contamination of wheat.

  11. Recent Enhancements to the Genetic Risk Prediction Model BRCAPRO

    PubMed Central

    Mazzola, Emanuele; Blackford, Amanda; Parmigiani, Giovanni; Biswas, Swati

    2015-01-01

    BRCAPRO is a widely used model for genetic risk prediction of breast cancer. It is a function within the R package BayesMendel and is used to calculate the probabilities of being a carrier of a deleterious mutation in one or both of the BRCA genes, as well as the probability of being affected with breast and ovarian cancer within a defined time window. Both predictions are based on information contained in the counselee’s family history of cancer. During the last decade, BRCAPRO has undergone several rounds of successive refinements: the current version is part of release 2.1 of BayesMendel. In this review, we showcase some of the most notable features of the software resulting from these recent changes. We provide examples highlighting each feature, using artificial pedigrees motivated by complex clinical examples. We illustrate how BRCAPRO is a comprehensive software for genetic risk prediction with many useful features that allow users the flexibility to incorporate varying amounts of available information. PMID:25983549

  12. Event-related theta synchronization predicts deficit in facial affect recognition in schizophrenia.

    PubMed

    Csukly, Gábor; Stefanics, Gábor; Komlósi, Sarolta; Czigler, István; Czobor, Pál

    2014-02-01

    Growing evidence suggests that abnormalities in the synchronized oscillatory activity of neurons in schizophrenia may lead to impaired neural activation and temporal coding and thus lead to neurocognitive dysfunctions, such as deficits in facial affect recognition. To gain an insight into the neurobiological processes linked to facial affect recognition, we investigated both induced and evoked oscillatory activity by calculating the Event Related Spectral Perturbation (ERSP) and the Inter Trial Coherence (ITC) during facial affect recognition. Fearful and neutral faces as well as nonface patches were presented to 24 patients with schizophrenia and 24 matched healthy controls while EEG was recorded. The participants' task was to recognize facial expressions. Because previous findings with healthy controls showed that facial feature decoding was associated primarily with oscillatory activity in the theta band, we analyzed ERSP and ITC in this frequency band in the time interval of 140-200 ms, which corresponds to the N170 component. Event-related theta activity and phase-locking to facial expressions, but not to nonface patches, predicted emotion recognition performance in both controls and patients. Event-related changes in theta amplitude and phase-locking were found to be significantly weaker in patients compared with healthy controls, which is in line with previous investigations showing decreased neural synchronization in the low frequency bands in patients with schizophrenia. Neural synchrony is thought to underlie distributed information processing. Our results indicate a less effective functioning in the recognition process of facial features, which may contribute to a less effective social cognition in schizophrenia.

  13. Do Core Interpersonal and Affective Traits of PCL-R Psychopathy Interact with Antisocial Behavior and Disinhibition to Predict Violence?

    ERIC Educational Resources Information Center

    Kennealy, Patrick J.; Skeem, Jennifer L.; Walters, Glenn D.; Camp, Jacqueline

    2010-01-01

    The utility of psychopathy measures in predicting violence is largely explained by their assessment of social deviance (e.g., antisocial behavior; disinhibition). A key question is whether social deviance "interacts" with the core interpersonal-affective traits of psychopathy to predict violence. Do core psychopathic traits multiply the (already…

  14. Predictive Modeling of Signaling Crosstalk during C. elegans Vulval Development

    PubMed Central

    Fisher, Jasmin; Piterman, Nir; Hajnal, Alex; Henzinger, Thomas A

    2007-01-01

    Caenorhabditis elegans vulval development provides an important paradigm for studying the process of cell fate determination and pattern formation during animal development. Although many genes controlling vulval cell fate specification have been identified, how they orchestrate themselves to generate a robust and invariant pattern of cell fates is not yet completely understood. Here, we have developed a dynamic computational model incorporating the current mechanistic understanding of gene interactions during this patterning process. A key feature of our model is the inclusion of multiple modes of crosstalk between the epidermal growth factor receptor (EGFR) and LIN-12/Notch signaling pathways, which together determine the fates of the six vulval precursor cells (VPCs). Computational analysis, using the model-checking technique, provides new biological insights into the regulatory network governing VPC fate specification and predicts novel negative feedback loops. In addition, our analysis shows that most mutations affecting vulval development lead to stable fate patterns in spite of variations in synchronicity between VPCs. Computational searches for the basis of this robustness show that a sequential activation of the EGFR-mediated inductive signaling and LIN-12 / Notch-mediated lateral signaling pathways is key to achieve a stable cell fate pattern. We demonstrate experimentally a time-delay between the activation of the inductive and lateral signaling pathways in wild-type animals and the loss of sequential signaling in mutants showing unstable fate patterns; thus, validating two key predictions provided by our modeling work. The insights gained by our modeling study further substantiate the usefulness of executing and analyzing mechanistic models to investigate complex biological behaviors. PMID:17511512

  15. Emotion categorization using affective-pLSA model

    NASA Astrophysics Data System (ADS)

    Liu, Shuoyan; Xu, De; Feng, Songhe

    2010-12-01

    Emotion categorization of natural scene images represents a very useful task for automatic image analysis systems. Psychological experiments have shown that visual information at the emotion level is aggregated according to a set of rules. Hence, we attempt to discover the emotion descriptors based on the composition of visual word representation. First, the composition of visual word representation models each image as a matrix, where elements record the correlations of pairwise visual words. In this way, an image collection is modeled as a third-order tensor. Then we discover the emotion descriptors using a novel affective-probabilistic latent semantic analysis (affective-pLSA) model, which is an extension of the pLSA model, on this tensor representation. Considering that the natural scene image may evoke multiple emotional feelings, emotion categorization is carried out using the multilabel k-nearest-neighbor approach based on emotion descriptors. The proposed approach has been tested on the International Affective Picture System and a collection of social images from the Flickr website. The experimental results have demonstrated the effectiveness of the proposed method for eliciting image emotions.

  16. Mother-Child Affect and Emotion Socialization Processes across the Late Preschool Period: Predictions of Emerging Behaviour Problems

    ERIC Educational Resources Information Center

    Newland, Rebecca P.; Crnic, Keith A.

    2011-01-01

    The current study examined concurrent and longitudinal relations between maternal negative affective behaviour and child negative emotional expression in preschool age children with (n=96) or without (n=126) an early developmental risk, as well as the predictions of later behaviour problems. Maternal negative affective behaviour, child…

  17. Subject-specific hip geometry affects predicted hip joint contact forces during gait.

    PubMed

    Lenaerts, G; De Groote, F; Demeulenaere, B; Mulier, M; Van der Perre, G; Spaepen, A; Jonkers, I

    2008-01-01

    Hip loading affects bone remodeling and implant fixation. In this study, we have analyzed the effect of subject-specific modeling of hip geometry on muscle activation patterns and hip contact forces during gait, using musculoskeletal modeling, inverse dynamic analysis and static optimization. We first used sensitivity analysis to analyze the effect of isolated changes in femoral neck-length (NL) and neck-shaft angle (NSA) on calculated muscle activations and hip contact force during the stance phase of gait. A deformable generic musculoskeletal model was adjusted incrementally to adopt a physiological range of NL and NSA. In a second similar analysis, we adjusted hip geometry to the measurements from digitized radiographs of 20 subjects with primary hip osteoarthrosis. Finally, we studied the effect of hip abductor weakness on muscle activation patterns and hip contact force. This analysis showed that differences in NL (41-74 mm) and NSA (113-140 degrees ) affect the muscle activation of the hip abductors during stance phase and hence hip contact force by up to three times body weight. In conclusion, the results from both the sensitivity and subject-specific analysis showed that at the moment of peak contact force, altered NSA has only a minor effect on the loading configuration of the hip. Increased NL, however, results in an increase of the three hip contact-force components and a reduced vertical loading. The results of these analyses are essential to understand modified hip joint loading, and for planning hip surgery for patients with osteoarthrosis.

  18. Allostasis: a model of predictive regulation.

    PubMed

    Sterling, Peter

    2012-04-12

    The premise of the standard regulatory model, "homeostasis", is flawed: the goal of regulation is not to preserve constancy of the internal milieu. Rather, it is to continually adjust the milieu to promote survival and reproduction. Regulatory mechanisms need to be efficient, but homeostasis (error-correction by feedback) is inherently inefficient. Thus, although feedbacks are certainly ubiquitous, they could not possibly serve as the primary regulatory mechanism. A newer model, "allostasis", proposes that efficient regulation requires anticipating needs and preparing to satisfy them before they arise. The advantages: (i) errors are reduced in magnitude and frequency; (ii) response capacities of different components are matched -- to prevent bottlenecks and reduce safety factors; (iii) resources are shared between systems to minimize reserve capacities; (iv) errors are remembered and used to reduce future errors. This regulatory strategy requires a dedicated organ, the brain. The brain tracks multitudinous variables and integrates their values with prior knowledge to predict needs and set priorities. The brain coordinates effectors to mobilize resources from modest bodily stores and enforces a system of flexible trade-offs: from each organ according to its ability, to each organ according to its need. The brain also helps regulate the internal milieu by governing anticipatory behavior. Thus, an animal conserves energy by moving to a warmer place - before it cools, and it conserves salt and water by moving to a cooler one before it sweats. The behavioral strategy requires continuously updating a set of specific "shopping lists" that document the growing need for each key component (warmth, food, salt, water). These appetites funnel into a common pathway that employs a "stick" to drive the organism toward filling the need, plus a "carrot" to relax the organism when the need is satisfied. The stick corresponds broadly to the sense of anxiety, and the carrot broadly to

  19. Use of the Pathogen Modeling Program (PMP) and the Predictive Microbiology Information Portal (PMIP)

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The Predictive Microbiology Program,(PMP)is based on the fact that most bacterial behaviors are reproducible and can be quantified by characterizing the environmental factors that affect growth, survival, and inactivation using mathematical modeling. The contents of PMP, a collection of models, are ...

  20. Required Collaborative Work in Online Courses: A Predictive Modeling Approach

    ERIC Educational Resources Information Center

    Smith, Marlene A.; Kellogg, Deborah L.

    2015-01-01

    This article describes a predictive model that assesses whether a student will have greater perceived learning in group assignments or in individual work. The model produces correct classifications 87.5% of the time. The research is notable in that it is the first in the education literature to adopt a predictive modeling methodology using data…

  1. An investigation of a quantum probability model for the constructive effect of affective evaluation.

    PubMed

    White, Lee C; Barqué-Duran, Albert; Pothos, Emmanuel M

    2016-01-13

    The idea that choices can have a constructive effect has received a great deal of empirical support. The act of choosing appears to influence subsequent preferences for the options available. Recent research has proposed a cognitive model based on quantum probability (QP), which suggests that whether or not a participant provides an affective evaluation for a positively or negatively valenced stimulus can also be constructive and so, for example, influence the affective evaluation of a second oppositely valenced stimulus. However, there are some outstanding methodological questions in relation to this previous research. This paper reports the results of three experiments designed to resolve these questions. Experiment 1, using a binary response format, provides partial support for the interaction predicted by the QP model; and Experiment 2, which controls for the length of time participants have to respond, fully supports the QP model. Finally, Experiment 3 sought to determine whether the key effect can generalize beyond affective judgements about visual stimuli. Using judgements about the trustworthiness of well-known people, the predictions of the QP model were confirmed. Together, these three experiments provide further support for the QP model of the constructive effect of simple evaluations.

  2. Predictive model of avian electrocution risk on overhead power lines.

    PubMed

    Dwyer, J F; Harness, R E; Donohue, K

    2014-02-01

    Electrocution on overhead power structures negatively affects avian populations in diverse ecosystems worldwide, contributes to the endangerment of raptor populations in Europe and Africa, and is a major driver of legal action against electric utilities in North America. We investigated factors associated with avian electrocutions so poles that are likely to electrocute a bird can be identified and retrofitted prior to causing avian mortality. We used historical data from southern California to identify patterns of avian electrocution by voltage, month, and year to identify species most often killed by electrocution in our study area and to develop a predictive model that compared poles where an avian electrocution was known to have occurred (electrocution poles) with poles where no known electrocution occurred (comparison poles). We chose variables that could be quantified by personnel with little training in ornithology or electric systems. Electrocutions were more common at distribution voltages (≤ 33 kV) and during breeding seasons and were more commonly reported after a retrofitting program began. Red-tailed Hawks (Buteo jamaicensis) (n = 265) and American Crows (Corvus brachyrhynchos) (n = 258) were the most commonly electrocuted species. In the predictive model, 4 of 14 candidate variables were required to distinguish electrocution poles from comparison poles: number of jumpers (short wires connecting energized equipment), number of primary conductors, presence of grounding, and presence of unforested unpaved areas as the dominant nearby land cover. When tested against a sample of poles not used to build the model, our model distributed poles relatively normally across electrocution-risk values and identified the average risk as higher for electrocution poles relative to comparison poles. Our model can be used to reduce avian electrocutions through proactive identification and targeting of high-risk poles for retrofitting.

  3. The Urgent Need for Improved Climate Models and Predictions

    NASA Astrophysics Data System (ADS)

    Goddard, Lisa; Baethgen, Walter; Kirtman, Ben; Meehl, Gerald

    2009-09-01

    An investment over the next 10 years of the order of US$2 billion for developing improved climate models was recommended in a report (http://wcrp.wmo.int/documents/WCRP_WorldModellingSummit_Jan2009.pdf) from the May 2008 World Modelling Summit for Climate Prediction, held in Reading, United Kingdom, and presented by the World Climate Research Programme. The report indicated that “climate models will, as in the past, play an important, and perhaps central, role in guiding the trillion dollar decisions that the peoples, governments and industries of the world will be making to cope with the consequences of changing climate.” If trillions of dollars are going to be invested in making decisions related to climate impacts, an investment of $2 billion, which is less than 0.1% of that amount, to provide better climate information seems prudent. One example of investment in adaptation is the World Bank's Climate Investment Fund, which has drawn contributions of more than $6 billion for work on clean technologies and adaptation efforts in nine pilot countries and two pilot regions. This is just the beginning of expenditures on adaptation efforts by the World Bank and other mechanisms, focusing on only a small fraction of the nations of the world and primarily aimed at anticipated anthropogenic climate change. Moreover, decisions are being made now, all around the world—by individuals, companies, and governments—that affect people and their livelihoods today, not just 50 or more years in the future. Climate risk management, whether related to projects of the scope of the World Bank's or to the planning and decisions of municipalities, will be best guided by meaningful climate information derived from observations of the past and model predictions of the future.

  4. Predictive Modeling the Physician Assistant Supply: 2010–2025

    PubMed Central

    Hooker, Roderick S.; Cawley, James F.; Everett, Christine M.

    2011-01-01

    Objective A component of health-care reform in 2010 identified physician assistants (PAs) as needed to help mitigate the expected doctor shortage. We modeled their number to predict rational estimates for workforce planners. Methods The number of PAs in active clinical practice in 2010 formed the baseline. We used graduation rates and program expansion to project annual growth; attrition estimates offset these amounts. A simulation model incorporated historical trends, current supply, and graduation amounts. Sensitivity analyses were conducted to systematically adjust parameters in the model to determine the effects of such changes. Results As of 2010, there were 74,476 PAs in the active workforce. The mean age was 42 years and 65% were female. There were 154 accredited educational programs; 99% had a graduating class and produced an average of 44 graduates annually (total n=6,776). With a 7% increase in graduate entry rate and a 5% annual attrition rate, the supply of clinically active PAs will grow to 93,099 in 2015, 111,004 in 2020, and 127,821 in 2025. This model holds clinically active PAs in primary care at 34%. Conclusions The number of clinically active PAs is projected to increase by almost 72% in 15 years. Attrition rates, especially retirement patterns, are not well understood for PAs, and variation could affect future supply. While the majority of PAs are in the medical specialties and subspecialties fields, new policy steps funding PA education and promoting primary care may add more PAs in primary care than the model predicts. PMID:21886331

  5. Predictable Components of ENSO Evolution in Real-time Multi-Model Predictions

    PubMed Central

    Zheng, Zhihai; Hu, Zeng-Zhen; L’Heureux, Michelle

    2016-01-01

    The most predictable components of the El Niño-Southern Oscillation (ENSO) evolution in real-time multi-model predictions are identified by applying an empirical orthogonal function analysis of the model data that maximizes the signal-to-noise ratio (MSN EOF). The normalized Niño3.4 index is analyzed for nine 3-month overlapping seasons. In this sense, the first most predictable component (MSN EOF1) is the decaying phase of ENSO during the Northern Hemisphere spring, followed by persistence through autumn and winter. The second most predictable component of ENSO evolution, with lower prediction skill and smaller explained variance than MSN EOF1, corresponds to the growth during spring and then persistence in summer and autumn. This result suggests that decay phase of ENSO is more predictable than the growth phase. Also, the most predictable components and the forecast skills in dynamical and statistical models are similar overall, with some differences arising during spring season initial conditions. Finally, the reconstructed predictions, with only the first two MSN components, show higher skill than the model raw predictions. Therefore this method can be used as a diagnostic for model comparison and development, and it can provide a new perspective for the most predictable components of ENSO. PMID:27775016

  6. Seagrass Health Modeling and Prediction with NASA Science Data

    NASA Technical Reports Server (NTRS)

    Robinson, Harold D.; Easson, Greg; Slattery, Marc; Anderson, Daniel; Blonski, Slawomir; DeCurtins, Robert; Underwood, Lauren

    2010-01-01

    Previous research has demonstrated that MODIS data products can be used as inputs into the seagrass productivity model developed by Fong and Harwell (1994). To further explore this use to predict seagrass productivity, Moderate Resolution Imaging Spectroradiometer (MODIS) custom data products, including Sea Surface Temperature, Light Attenuation, and Chlorophyll-a have been created for use as model parameter inputs. Coastal researchers can use these MODIS data products and model results in conjunction with historical and daily assessment of seagrass conditions to assess variables that affect the productivity of the seagrass beds. Current monitoring practices involve manual data collection (typically on a quarterly basis) and the data is often insufficient for evaluating the dynamic events that influence seagrass beds. As part of a NASA-funded research grant, the University of Mississippi, is working with researchers at NASA and Radiance Technologies to develop methods to deliver MODIS derived model output for the northern Gulf of Mexico (GOM) to coastal and environmental managers. The result of the project will be a data portal that provides access to MODIS data products and model results from the past 5 years, that includes an automated process to incorporate new data as it becomes available. All model parameters and final output will be available through the use National Oceanic and Atmospheric Administration?s (NOAA) Environmental Research Divisions Data Access Program (ERDDAP) tools as well as viewable using Thematic Realtime Environmental Distributed Data Services (THREDDS) and the Integrated Data Viewer (IDV). These tools provide the ability to create raster-based time sequences of model output and parameters as well as create graphs of model parameters versus time. This tool will provide researchers and coastal managers the ability to analyze the model inputs so that the factors influencing a change in seagrass productivity can be determined over time.

  7. Simplified Predictive Models for CO2 Sequestration Performance Assessment

    NASA Astrophysics Data System (ADS)

    Mishra, Srikanta; RaviGanesh, Priya; Schuetter, Jared; Mooney, Douglas; He, Jincong; Durlofsky, Louis

    2014-05-01

    We present results from an ongoing research project that seeks to develop and validate a portfolio of simplified modeling approaches that will enable rapid feasibility and risk assessment for CO2 sequestration in deep saline formation. The overall research goal is to provide tools for predicting: (a) injection well and formation pressure buildup, and (b) lateral and vertical CO2 plume migration. Simplified modeling approaches that are being developed in this research fall under three categories: (1) Simplified physics-based modeling (SPM), where only the most relevant physical processes are modeled, (2) Statistical-learning based modeling (SLM), where the simulator is replaced with a "response surface", and (3) Reduced-order method based modeling (RMM), where mathematical approximations reduce the computational burden. The system of interest is a single vertical well injecting supercritical CO2 into a 2-D layered reservoir-caprock system with variable layer permeabilities. In the first category (SPM), we use a set of well-designed full-physics compositional simulations to understand key processes and parameters affecting pressure propagation and buoyant plume migration. Based on these simulations, we have developed correlations for dimensionless injectivity as a function of the slope of fractional-flow curve, variance of layer permeability values, and the nature of vertical permeability arrangement. The same variables, along with a modified gravity number, can be used to develop a correlation for the total storage efficiency within the CO2 plume footprint. In the second category (SLM), we develop statistical "proxy models" using the simulation domain described previously with two different approaches: (a) classical Box-Behnken experimental design with a quadratic response surface fit, and (b) maximin Latin Hypercube sampling (LHS) based design with a Kriging metamodel fit using a quadratic trend and Gaussian correlation structure. For roughly the same number of

  8. How the Assumed Size Distribution of Dust Minerals Affects the Predicted Ice Forming Nuclei

    NASA Technical Reports Server (NTRS)

    Perlwitz, Jan P.; Fridlind, Ann M.; Garcia-Pando, Carlos Perez; Miller, Ron L.; Knopf, Daniel A.

    2015-01-01

    The formation of ice in clouds depends on the availability of ice forming nuclei (IFN). Dust aerosol particles are considered the most important source of IFN at a global scale. Recent laboratory studies have demonstrated that the mineral feldspar provides the most efficient dust IFN for immersion freezing and together with kaolinite for deposition ice nucleation, and that the phyllosilicates illite and montmorillonite (a member of the smectite group) are of secondary importance.A few studies have applied global models that simulate mineral specific dust to predict the number and geographical distribution of IFN. These studies have been based on the simple assumption that the mineral composition of soil as provided in data sets from the literature translates directly into the mineral composition of the dust aerosols. However, these tables are based on measurements of wet-sieved soil where dust aggregates are destroyed to a large degree. In consequence, the size distribution of dust is shifted to smaller sizes, and phyllosilicates like illite, kaolinite, and smectite are only found in the size range 2 m. In contrast, in measurements of the mineral composition of dust aerosols, the largest mass fraction of these phyllosilicates is found in the size range 2 m as part of dust aggregates. Conversely, the mass fraction of feldspar is smaller in this size range, varying with the geographical location. This may have a significant effect on the predicted IFN number and its geographical distribution.An improved mineral specific dust aerosol module has been recently implemented in the NASA GISS Earth System ModelE2. The dust module takes into consideration the disaggregated state of wet-sieved soil, on which the tables of soil mineral fractions are based. To simulate the atmospheric cycle of the minerals, the mass size distribution of each mineral in aggregates that are emitted from undispersed parent soil is reconstructed. In the current study, we test the null

  9. How the Assumed Size Distribution of Dust Minerals Affects the Predicted Ice Forming Nuclei

    NASA Astrophysics Data System (ADS)

    Perlwitz, J. P.; Fridlind, A. M.; Pérez García-Pando, C.; Miller, R. L.; Knopf, D. A.

    2015-12-01

    The formation of ice in clouds depends on the availability of ice forming nuclei (IFN). Dust aerosol particles are considered the most important source of IFN at a global scale. Recent laboratory studies have demonstrated that the mineral feldspar provides the most efficient dust IFN for immersion freezing and together with kaolinite for deposition ice nucleation, and that the phyllosilicates illite and montmorillonite (a member of the smectite group) are of secondary importance.A few studies have applied global models that simulate mineral specific dust to predict the number and geographical distribution of IFN. These studies have been based on the simple assumption that the mineral composition of soil as provided in data sets from the literature translates directly into the mineral composition of the dust aerosols. However, these tables are based on measurements of wet-sieved soil where dust aggregates are destroyed to a large degree. In consequence, the size distribution of dust is shifted to smaller sizes, and phyllosilicates like illite, kaolinite, and smectite are only found in the size range <2 μm. In contrast, in measurements of the mineral composition of dust aerosols, the largest mass fraction of these phyllosilicates is found in the size range >2 μm as part of dust aggregates. Conversely, the mass fraction of feldspar is smaller in this size range, varying with the geographical location. This may have a significant effect on the predicted IFN number and its geographical distribution.An improved mineral specific dust aerosol module has been recently implemented in the NASA GISS Earth System ModelE2. The dust module takes into consideration the disaggregated state of wet-sieved soil, on which the tables of soil mineral fractions are based. To simulate the atmospheric cycle of the minerals, the mass size distribution of each mineral in aggregates that are emitted from undispersed parent soil is reconstructed. In the current study, we test the null

  10. Predictive modeling of dental pain using neural network.

    PubMed

    Kim, Eun Yeob; Lim, Kun Ok; Rhee, Hyun Sill

    2009-01-01

    The mouth is a part of the body for ingesting food that is the most basic foundation and important part. The dental pain predicted by the neural network model. As a result of making a predictive modeling, the fitness of the predictive modeling of dental pain factors was 80.0%. As for the people who are likely to experience dental pain predicted by the neural network model, preventive measures including proper eating habits, education on oral hygiene, and stress release must precede any dental treatment.

  11. Crash test for groundwater recharge models: The effects of model complexity and calibration period on groundwater recharge predictions

    NASA Astrophysics Data System (ADS)

    Moeck, Christian; Von Freyberg, Jana; Schrimer, Maria

    2016-04-01

    An important question in recharge impact studies is how model choice, structure and calibration period affect recharge predictions. It is still unclear if a certain model type or structure is less affected by running the model on time periods with different hydrological conditions compared to the calibration period. This aspect, however, is crucial to ensure reliable predictions of groundwater recharge. In this study, we quantify and compare the effect of groundwater recharge model choice, model parametrization and calibration period in a systematic way. This analysis was possible thanks to a unique data set from a large-scale lysimeter in a pre-alpine catchment where daily long-term recharge rates are available. More specifically, the following issues are addressed: We systematically evaluate how the choice of hydrological models influences predictions of recharge. We assess how different parameterizations of models due to parameter non-identifiability affect predictions of recharge by applying a Monte Carlo approach. We systematically assess how the choice of calibration periods influences predictions of recharge within a differential split sample test focusing on the model performance under extreme climatic and hydrological conditions. Results indicate that all applied models (simple lumped to complex physically based models) were able to simulate the observed recharge rates for five different calibration periods. However, there was a marked impact of the calibration period when the complete 20 years validation period was simulated. Both, seasonal and annual differences between simulated and observed daily recharge rates occurred when the hydrological conditions were different to the calibration period. These differences were, however, less distinct for the physically based models, whereas the simpler models over- or underestimate the observed recharge depending on the considered season. It is, however, possible to reduce the differences for the simple models by

  12. Cerebellum, temporal predictability and the updating of a mental model.

    PubMed

    Kotz, Sonja A; Stockert, Anika; Schwartze, Michael

    2014-12-19

    We live in a dynamic and changing environment, which necessitates that we adapt to and efficiently respond to changes of stimulus form ('what') and stimulus occurrence ('when'). Consequently, behaviour is optimal when we can anticipate both the 'what' and 'when' dimensions of a stimulus. For example, to perceive a temporally expected stimulus, a listener needs to establish a fairly precise internal representation of its external temporal structure, a function ascribed to classical sensorimotor areas such as the cerebellum. Here we investigated how patients with cerebellar lesions and healthy matched controls exploit temporal regularity during auditory deviance processing. We expected modulations of the N2b and P3b components of the event-related potential in response to deviant tones, and also a stronger P3b response when deviant tones are embedded in temporally regular compared to irregular tone sequences. We further tested to what degree structural damage to the cerebellar temporal processing system affects the N2b and P3b responses associated with voluntary attention to change detection and the predictive adaptation of a mental model of the environment, respectively. Results revealed that healthy controls and cerebellar patients display an increased N2b response to deviant tones independent of temporal context. However, while healthy controls showed the expected enhanced P3b response to deviant tones in temporally regular sequences, the P3b response in cerebellar patients was significantly smaller in these sequences. The current data provide evidence that structural damage to the cerebellum affects the predictive adaptation to the temporal structure of events and the updating of a mental model of the environment under voluntary attention.

  13. Cerebellum, temporal predictability and the updating of a mental model

    PubMed Central

    Kotz, Sonja A.; Stockert, Anika; Schwartze, Michael

    2014-01-01

    We live in a dynamic and changing environment, which necessitates that we adapt to and efficiently respond to changes of stimulus form (‘what’) and stimulus occurrence (‘when’). Consequently, behaviour is optimal when we can anticipate both the ‘what’ and ‘when’ dimensions of a stimulus. For example, to perceive a temporally expected stimulus, a listener needs to establish a fairly precise internal representation of its external temporal structure, a function ascribed to classical sensorimotor areas such as the cerebellum. Here we investigated how patients with cerebellar lesions and healthy matched controls exploit temporal regularity during auditory deviance processing. We expected modulations of the N2b and P3b components of the event-related potential in response to deviant tones, and also a stronger P3b response when deviant tones are embedded in temporally regular compared to irregular tone sequences. We further tested to what degree structural damage to the cerebellar temporal processing system affects the N2b and P3b responses associated with voluntary attention to change detection and the predictive adaptation of a mental model of the environment, respectively. Results revealed that healthy controls and cerebellar patients display an increased N2b response to deviant tones independent of temporal context. However, while healthy controls showed the expected enhanced P3b response to deviant tones in temporally regular sequences, the P3b response in cerebellar patients was significantly smaller in these sequences. The current data provide evidence that structural damage to the cerebellum affects the predictive adaptation to the temporal structure of events and the updating of a mental model of the environment under voluntary attention. PMID:25385781

  14. From Predictive Models to Instructional Policies

    ERIC Educational Resources Information Center

    Rollinson, Joseph; Brunskill, Emma

    2015-01-01

    At their core, Intelligent Tutoring Systems consist of a student model and a policy. The student model captures the state of the student and the policy uses the student model to individualize instruction. Policies require different properties from the student model. For example, a mastery threshold policy requires the student model to have a way…

  15. Prediction Uncertainty Analyses for the Combined Physically-Based and Data-Driven Models

    NASA Astrophysics Data System (ADS)

    Demissie, Y. K.; Valocchi, A. J.; Minsker, B. S.; Bailey, B. A.

    2007-12-01

    The unavoidable simplification associated with physically-based mathematical models can result in biased parameter estimates and correlated model calibration errors, which in return affect the accuracy of model predictions and the corresponding uncertainty analyses. In this work, a physically-based groundwater model (MODFLOW) together with error-correcting artificial neural networks (ANN) are used in a complementary fashion to obtain an improved prediction (i.e. prediction with reduced bias and error correlation). The associated prediction uncertainty of the coupled MODFLOW-ANN model is then assessed using three alternative methods. The first method estimates the combined model confidence and prediction intervals using first-order least- squares regression approximation theory. The second method uses Monte Carlo and bootstrap techniques for MODFLOW and ANN, respectively, to construct the combined model confidence and prediction intervals. The third method relies on a Bayesian approach that uses analytical or Monte Carlo methods to derive the intervals. The performance of these approaches is compared with Generalized Likelihood Uncertainty Estimation (GLUE) and Calibration-Constrained Monte Carlo (CCMC) intervals of the MODFLOW predictions alone. The results are demonstrated for a hypothetical case study developed based on a phytoremediation site at the Argonne National Laboratory. This case study comprises structural, parameter, and measurement uncertainties. The preliminary results indicate that the proposed three approaches yield comparable confidence and prediction intervals, thus making the computationally efficient first-order least-squares regression approach attractive for estimating the coupled model uncertainty. These results will be compared with GLUE and CCMC results.

  16. Developing Risk Prediction Models for Postoperative Pancreatic Fistula: a Systematic Review of Methodology and Reporting Quality.

    PubMed

    Wen, Zhang; Guo, Ya; Xu, Banghao; Xiao, Kaiyin; Peng, Tao; Peng, Minhao

    2016-04-01

    Postoperative pancreatic fistula is still a major complication after pancreatic surgery, despite improvements of surgical technique and perioperative management. We sought to systematically review and critically access the conduct and reporting of methods used to develop risk prediction models for predicting postoperative pancreatic fistula. We conducted a systematic search of PubMed and EMBASE databases to identify articles published before January 1, 2015, which described the development of models to predict the risk of postoperative pancreatic fistula. We extracted information of developing a prediction model including study design, sample size and number of events, definition of postoperative pancreatic fistula, risk predictor selection, missing data, model-building strategies, and model performance. Seven studies of developing seven risk prediction models were included. In three studies (42 %), the number of events per variable was less than 10. The number of candidate risk predictors ranged from 9 to 32. Five studies (71 %) reported using univariate screening, which was not recommended in building a multivariate model, to reduce the number of risk predictors. Six risk prediction models (86 %) were developed by categorizing all continuous risk predictors. The treatment and handling of missing data were not mentioned in all studies. We found use of inappropriate methods that could endanger the development of model, including univariate pre-screening of variables, categorization of continuous risk predictors, and model validation. The use of inappropriate methods affects the reliability and the accuracy of the probability estimates of predicting postoperative pancreatic fistula.

  17. The Complexity of Developmental Predictions from Dual Process Models

    ERIC Educational Resources Information Center

    Stanovich, Keith E.; West, Richard F.; Toplak, Maggie E.

    2011-01-01

    Drawing developmental predictions from dual-process theories is more complex than is commonly realized. Overly simplified predictions drawn from such models may lead to premature rejection of the dual process approach as one of many tools for understanding cognitive development. Misleading predictions can be avoided by paying attention to several…

  18. Sweat loss prediction using a multi-model approach

    NASA Astrophysics Data System (ADS)

    Xu, Xiaojiang; Santee, William R.

    2011-07-01

    A new multi-model approach (MMA) for sweat loss prediction is proposed to improve prediction accuracy. MMA was computed as the average of sweat loss predicted by two existing thermoregulation models: i.e., the rational model SCENARIO and the empirical model Heat Strain Decision Aid (HSDA). Three independent physiological datasets, a total of 44 trials, were used to compare predictions by MMA, SCENARIO, and HSDA. The observed sweat losses were collected under different combinations of uniform ensembles, environmental conditions (15-40°C, RH 25-75%), and exercise intensities (250-600 W). Root mean square deviation (RMSD), residual plots, and paired t tests were used to compare predictions with observations. Overall, MMA reduced RMSD by 30-39% in comparison with either SCENARIO or HSDA, and increased the prediction accuracy to 66% from 34% or 55%. Of the MMA predictions, 70% fell within the range of mean observed value ± SD, while only 43% of SCENARIO and 50% of HSDA predictions fell within the same range. Paired t tests showed that differences between observations and MMA predictions were not significant, but differences between observations and SCENARIO or HSDA predictions were significantly different for two datasets. Thus, MMA predicted sweat loss more accurately than either of the two single models for the three datasets used. Future work will be to evaluate MMA using additional physiological data to expand the scope of populations and conditions.

  19. A windows based mechanistic subsidence prediction model for longwall mining

    SciTech Connect

    Begley, R.; Beheler, P.; Khair, A.W.

    1996-12-31

    The previously developed Mechanistic Subsidence Prediction Model (MSPM) has been incorporated into the graphical interface environment of MS Windows. MSPM has the unique capability of predicting maximum subsidence, angle of draw and the subsidence profile of a longwall panel at various locations for both the transverse and longitudinal orientations. The resultant enhanced model can be operated by individuals with little knowledge of subsidence prediction theories or little computer programming experience. In addition, predictions of subsidence can be made in a matter of seconds without the need to develop input data files or use the keyboard in some cases. The predictions are based upon the following input parameters: panel width, mining height, overburden depth, rock quality designation, and percent hard rock in the immediate roof, main roof and the entire overburden. The recently developed enhanced model has the capability to compare predictions in a graphical format for one half of the predicted subsidence profile based upon changes in input parameters easily and instantly on the same screen. In addition another screen can be obtained from a pull down menu where the operator can compare predictions for the entire subsidence profiles. This paper presents the background of the subsidence prediction model and the methodology of the enhanced model development. The paper also presents comparisons of subsidence predictions for several different sets of input parameters in addition to comparisons of the subsidence predictions with actual field data.

  20. Quantitative Predictive Models for Systemic Toxicity (SOT)

    EPA Science Inventory

    Models to identify systemic and specific target organ toxicity were developed to help transition the field of toxicology towards computational models. By leveraging multiple data sources to incorporate read-across and machine learning approaches, a quantitative model of systemic ...

  1. Prediction using patient comparison vs. modeling: a case study for mortality prediction.

    PubMed

    Hoogendoorn, Mark; El Hassouni, Ali; Mok, Kwongyen; Ghassemi, Marzyeh; Szolovits, Peter

    2016-08-01

    Information in Electronic Medical Records (EMRs) can be used to generate accurate predictions for the occurrence of a variety of health states, which can contribute to more pro-active interventions. The very nature of EMRs does make the application of off-the-shelf machine learning techniques difficult. In this paper, we study two approaches to making predictions that have hardly been compared in the past: (1) extracting high-level (temporal) features from EMRs and building a predictive model, and (2) defining a patient similarity metric and predicting based on the outcome observed for similar patients. We analyze and compare both approaches on the MIMIC-II ICU dataset to predict patient mortality and find that the patient similarity approach does not scale well and results in a less accurate model (AUC of 0.68) compared to the modeling approach (0.84). We also show that mortality can be predicted within a median of 72 hours.

  2. Antioxidant capacity of different cheeses: Affecting factors and prediction by near infrared spectroscopy.

    PubMed

    Revilla, I; González-Martín, M I; Vivar-Quintana, A M; Blanco-López, M A; Lobos-Ortega, I A; Hernández-Hierro, J M

    2016-07-01

    In this study, we analyzed antioxidant capacity of 224 cheese samples prepared using 16 varied mixtures of milk from cows, ewes, and goats, in 2 manufacturing seasons (winter and summer), and over 6mo of ripening. Antioxidant capacity was evaluated using the spectrophotometric 2,2-azinobis(3-ethylenebenzothiazoline-6-sulfonic acid) (ABTS) method. Total antioxidant capacity was significantly correlated with season of manufacturing and time of ripening but not with animal species providing the milk. Moreover, statistically significant correlations between the total antioxidant capacity and retinol (r=0.399), fat percentage (r=0.308), protein percentage (r=0.366), K (r=0.385), Mg (r=0.312), Na (r=0.432), and P (0.272) were observed. We evaluated the use of near infrared spectroscopy technology, together with the use of a remote reflectance fiber-optic probe, to predict the antioxidant capacity of cheese samples. The model generated allowed us to predict antioxidant capacity in unknown cheeses of different compositions and ripening times.

  3. The cognitive processes underlying affective decision-making predicting adolescent smoking behaviors in a longitudinal study

    PubMed Central

    Xiao, Lin; Koritzky, Gilly; Johnson, C. Anderson; Bechara, Antoine

    2013-01-01

    This study investigates the relationship between three different cognitive processes underlying the Iowa Gambling Task (IGT) and adolescent smoking behaviors in a longitudinal study. We conducted a longitudinal study of 181 Chinese adolescents in Chengdu City, China. The participants were followed from 10th to 11th grade. When they were in the 10th grade (Time 1), we tested these adolescents' decision-making using the IGT and working memory capacity using the Self-ordered Pointing Test (SOPT). Self-report questionnaires were used to assess school academic performance and smoking behaviors. The same questionnaires were completed again at the 1-year follow-up (Time 2). The Expectancy-Valence (EV) Model was applied to distill the IGT performance into three different underlying psychological components: (i) a motivational component which indicates the subjective weight the adolescents assign to gains vs. losses; (ii) a learning-rate component which indicates the sensitivity to recent outcomes vs. past experiences; and (iii) a response component which indicates how consistent the adolescents are between learning and responding. The subjective weight to gains vs. losses at Time 1 significantly predicted current smokers and current smoking levels at Time 2, controlling for demographic variables and baseline smoking behaviors. Therefore, by decomposing the IGT into three different psychological components, we found that the motivational process of weight gain vs. losses may serve as a neuropsychological marker to predict adolescent smoking behaviors in a general youth population. PMID:24101911

  4. Work more, then feel more: the influence of effort on affective predictions.

    PubMed

    Jiga-Boy, Gabriela M; Toma, Claudia; Corneille, Olivier

    2014-01-01

    Two studies examined how effort invested in a task shapes the affective predictions related to potential success in that task, and the mechanism underlying this relationship. In Study 1, PhD students awaiting an editorial decision about a submitted manuscript estimated the effort they had invested in preparing that manuscript for submission and how happy they would feel if it were accepted. Subjective estimates of effort were positively related to participants' anticipated happiness, an effect mediated by the higher perceived quality of one's work. In other words, the more effort one though having invested, the happier one expected to feel if it were accepted, because one expected a higher quality manuscript. We replicated this effect and its underlying mediation in Study 2, this time using an experimental manipulation of effort in the context of creating an advertising slogan. Study 2 further showed that participants mistakenly thought their extra efforts invested in the task had improved the quality of their work, while independent judges had found no objective differences in quality between the outcomes of the high- and low-effort groups. We discuss the implications of the relationship between effort and anticipated emotions and the conditions under which such relationship might be functional.

  5. Work More, Then Feel More: The Influence of Effort on Affective Predictions

    PubMed Central

    Jiga-Boy, Gabriela M.; Toma, Claudia; Corneille, Olivier

    2014-01-01

    Two studies examined how effort invested in a task shapes the affective predictions related to potential success in that task, and the mechanism underlying this relationship. In Study 1, PhD students awaiting an editorial decision about a submitted manuscript estimated the effort they had invested in preparing that manuscript for submission and how happy they would feel if it were accepted. Subjective estimates of effort were positively related to participants' anticipated happiness, an effect mediated by the higher perceived quality of one's work. In other words, the more effort one though having invested, the happier one expected to feel if it were accepted, because one expected a higher quality manuscript. We replicated this effect and its underlying mediation in Study 2, this time using an experimental manipulation of effort in the context of creating an advertising slogan. Study 2 further showed that participants mistakenly thought their extra efforts invested in the task had improved the quality of their work, while independent judges had found no objective differences in quality between the outcomes of the high- and low-effort groups. We discuss the implications of the relationship between effort and anticipated emotions and the conditions under which such relationship might be functional. PMID:25028961

  6. Comparing prediction models for radiographic exposures

    NASA Astrophysics Data System (ADS)

    Ching, W.; Robinson, J.; McEntee, M. F.

    2015-03-01

    During radiographic exposures the milliampere-seconds (mAs), kilovoltage peak (kVp) and source-to-image distance can be adjusted for variations in patient thicknesses. Several exposure adjustment systems have been developed to assist with this selection. This study compares the accuracy of four systems to predict the required mAs for pelvic radiographs taken on a direct digital radiography system (DDR). Sixty radiographs were obtained by adjusting mAs to compensate for varying combinations of source-to-image distance (SID), kVp and patient thicknesses. The 25% rule, the DuPont Bit System and the DigiBit system were compared to determine which of these three most accurately predicted the mAs required for an increase in patient thickness. Similarly, the 15% rule, the DuPont Bit System and the DigiBit system were compared for an increase in kVp. The exposure index (EI) was used as an indication of exposure to the DDR. For each exposure combination the mAs was adjusted until an EI of 1500+/-2% was achieved. The 25% rule was the most accurate at predicting the mAs required for an increase in patient thickness, with 53% of the mAs predictions correct. The DigiBit system was the most accurate at predicting mAs needed for changes in kVp, with 33% of predictions correct. This study demonstrated that the 25% rule and DigiBit system were the most accurate predictors of mAs required for an increase in patient thickness and kVp respectively. The DigiBit system worked well in both scenarios as it is a single exposure adjustment system that considers a variety of exposure factors.

  7. Predictive modeling and reducing cyclic variability in autoignition engines

    DOEpatents

    Hellstrom, Erik; Stefanopoulou, Anna; Jiang, Li; Larimore, Jacob

    2016-08-30

    Methods and systems are provided for controlling a vehicle engine to reduce cycle-to-cycle combustion variation. A predictive model is applied to predict cycle-to-cycle combustion behavior of an engine based on observed engine performance variables. Conditions are identified, based on the predicted cycle-to-cycle combustion behavior, that indicate high cycle-to-cycle combustion variation. Corrective measures are then applied to prevent the predicted high cycle-to-cycle combustion variation.

  8. Do core interpersonal and affective traits of PCL-R psychopathy interact with antisocial behavior and disinhibition to predict violence?

    PubMed

    Kennealy, Patrick J; Skeem, Jennifer L; Walters, Glenn D; Camp, Jacqueline

    2010-09-01

    The utility of psychopathy measures in predicting violence is largely explained by their assessment of social deviance (e.g., antisocial behavior; disinhibition). A key question is whether social deviance interacts with the core interpersonal-affective traits of psychopathy to predict violence. Do core psychopathic traits multiply the (already high) risk of violence among disinhibited individuals with a dense history of misbehavior? This meta-analysis of 32 effect sizes (N = 10,555) tested whether an interaction between the Psychopathy Checklist-Revised (PCL-R; R. D. Hare, 2003) Interpersonal-Affective and Social Deviance scales predicted violence beyond the simple additive effects of each scale. Results indicate that Social Deviance is more uniquely predictive of violence (d = .40) than Interpersonal-Affective traits (d = .11), and these two scales do not interact (d = .00) to increase power in predicting violence. In fact, Social Deviance alone would predict better than the Interpersonal-Affective scale and any interaction in 81% and 96% of studies, respectively. These findings have fundamental practical implications for risk assessment and theoretical implications for some conceptualizations of psychopathy.

  9. Predicting the fate of a living fossil: how will global warming affect sex determination and hatching phenology in tuatara?

    PubMed Central

    Mitchell, Nicola J; Kearney, Michael R; Nelson, Nicola J; Porter, Warren P

    2008-01-01

    How will climate change affect species' reproduction and subsequent survival? In many egg-laying reptiles, the sex of offspring is determined by the temperature experienced during a critical period of embryonic development (temperature-dependent sex determination, TSD). Increasing air temperatures are likely to skew offspring sex ratios in the absence of evolutionary or plastic adaptation, hence we urgently require means for predicting the future distributions of species with TSD. Here we develop a mechanistic model that demonstrates how climate, soil and topography interact with physiology and nesting behaviour to determine sex ratios of tuatara, cold-climate reptiles from New Zealand with an unusual developmental biology. Under extreme regional climate change, all-male clutches would hatch at 100% of current nest sites of the rarest species, Sphenodon guntheri, by the mid-2080s. We show that tuatara could behaviourally compensate for the male-biasing effects of warmer air temperatures by nesting later in the season or selecting shaded nest sites. Later nesting is, however, an unlikely response to global warming, as many oviparous species are nesting earlier as the climate warms. Our approach allows the assessment of the thermal suitability of current reserves and future translocation sites for tuatara, and can be readily modified to predict climatic impacts on any species with TSD. PMID:18595840

  10. Estimating Model Prediction Error: Should You Treat Predictions as Fixed or Random?

    NASA Technical Reports Server (NTRS)

    Wallach, Daniel; Thorburn, Peter; Asseng, Senthold; Challinor, Andrew J.; Ewert, Frank; Jones, James W.; Rotter, Reimund; Ruane, Alexander

    2016-01-01

    Crop models are important tools for impact assessment of climate change, as well as for exploring management options under current climate. It is essential to evaluate the uncertainty associated with predictions of these models. We compare two criteria of prediction error; MSEP fixed, which evaluates mean squared error of prediction for a model with fixed structure, parameters and inputs, and MSEP uncertain( X), which evaluates mean squared error averaged over the distributions of model structure, inputs and parameters. Comparison of model outputs with data can be used to estimate the former. The latter has a squared bias term, which can be estimated using hindcasts, and a model variance term, which can be estimated from a simulation experiment. The separate contributions to MSEP uncertain (X) can be estimated using a random effects ANOVA. It is argued that MSEP uncertain (X) is the more informative uncertainty criterion, because it is specific to each prediction situation.

  11. The Interaction of Affective States and Cognitive Vulnerabilities in the Prediction of Non-Suicidal Self-Injury

    PubMed Central

    Cohen, Jonah N.; Stange, Jonathan P.; Hamilton, Jessica L.; Burke, Taylor; Jenkins, Abigail; Ong, Mian-Li; Heimberg, Richard G.; Abramson, Lyn Y.; Alloy, Lauren B.

    2014-01-01

    Non-suicidal self-injury (NSSI) is a serious public health concern and remains poorly understood. This study sought to identify both cognitive and affective vulnerabilities to NSSI and examine their interaction in the prediction of NSSI. A series of regressions indicated that low levels of positive affect moderated the relationships between self-criticism and brooding and NSSI. The associations of self-criticism and brooding with greater frequency of NSSI were attenuated by higher levels of positive affect. The interaction of cognitive and affective vulnerabilities is discussed within the context of current NSSI theory. PMID:24853872

  12. Approaches to ionospheric modelling, simulation and prediction

    NASA Astrophysics Data System (ADS)

    Schunk, R. W.; Sojka, J. J.

    1992-08-01

    The ionosphere is a complex, multispecies, anisotropic medium that exhibits a significant variation with time, space, season, solar cycle, and geomagnetic activity. In recent years, a wide range of models have been developed in an effort to describe ionospheric behavior. The modeling efforts include: (1) empirical models based on extensive worldwide data sets; (2) simple analytical models for a restricted number of ionospheric parameters; (3) comprehensive, 3D, time-dependent models that require supercomputers; (4) spherical harmonic models based on fits to output obtained from comprehensive numerical models; and (5) ionospheric models driven by real-time magnetospheric inputs. In an effort to achieve simplicity, some of the models have been restricted to certain altitude or latitude domains, while others have been restricted to certain ionospheric parameters, such as the F-region peak density, the auroral conductivity, and the plasma temperatures. The current status of the modeling efforts is reviewed.

  13. [Greenhouse tomato transpiration and its affecting factors: correlation analysis and model simulation].

    PubMed

    Yao, Yong-Zhe; Li, Jian-Ming; Zhang, Rong; Sun, San-Jie; Chen, Kai-Li

    2012-07-01

    A pot experiment was conducted to study the correlations between the daily transpiration of greenhouse tomato and the related affecting factors such as total leaf area per plant, soil relative moisture content, air temperature, relative humidity, and solar radiation under different treatments of supplementary irrigation. A regression model for the daily transpiration of greenhouse tomato was established. There existed significant linear correlations between the daily transpiration and the test affecting factors, and the affecting factors had complicated mutual effects. Soil relative moisture content was the main decision factor of the transpiration, with the decision coefficient being 27.4%, and daily minimum relative humidity was the main limiting factor, with the decision coefficient being -119.7%. The square value of the regression coefficient (R2) between the predicted and measured tomato daily transpiration was 0.81, root mean squared error (RMSE) was 68.52 g, and relative prediction error (RE) was 19.4%, suggesting that the regression model established by using the main affecting factors selected through path analysis could better simulate the daily transpiration of greenhouse tomato.

  14. Experience of affects predicting sense of self and others in short-term dynamic and cognitive therapy.

    PubMed

    Berggraf, Lene; Ulvenes, Pål G; Oktedalen, Tuva; Hoffart, Asle; Stiles, Tore; McCullough, Leigh; Wampold, Bruce E

    2014-06-01

    The present study examined whether levels of activating affects (AA) and inhibitory affects (IA) were related to change toward more compassionate and realistic levels of sense of self (SoS) and sense of others (SoO). The sample included 47 patients diagnosed with cluster C personality disorders, who received 40 sessions of either cognitive therapy or short-term dynamic therapy (see the randomized controlled trial study, Svartberg, Stiles, & Seltzer, 2004). A total of 927 videotaped sessions were rated with the use of the observational instrument, Achievement of Therapeutic Objectives Scale. Longitudinal multilevel modeling enabled the examination of both between-person effects and within-person changes in level of AA and IA. Patients with better ability to experience AA at the start of therapy displayed significantly higher SoS and SoO across sessions compared with other patients. Patients who experienced higher levels of IA at the start of therapy displayed lower levels of SoS across sessions. A patient experiencing more AA than usual for him/her self within a session predicted an increased level of SoS and SoO at the next measuring point. There were no different change patterns in the 2 treatment groups. Results suggest that focus within therapy sessions on increasing patients' AA can help facilitate change in SoS and SoO toward more compassionate and realistic quality.

  15. A Prediction Model of the Capillary Pressure J-Function

    PubMed Central

    Xu, W. S.; Luo, P. Y.; Sun, L.; Lin, N.

    2016-01-01

    The capillary pressure J-function is a dimensionless measure of the capillary pressure of a fluid in a porous medium. The function was derived based on a capillary bundle model. However, the dependence of the J-function on the saturation Sw is not well understood. A prediction model for it is presented based on capillary pressure model, and the J-function prediction model is a power function instead of an exponential or polynomial function. Relative permeability is calculated with the J-function prediction model, resulting in an easier calculation and results that are more representative. PMID:27603701

  16. A model to predict the power output from wind farms

    SciTech Connect

    Landberg, L.

    1997-12-31

    This paper will describe a model that can predict the power output from wind farms. To give examples of input the model is applied to a wind farm in Texas. The predictions are generated from forecasts from the NGM model of NCEP. These predictions are made valid at individual sites (wind farms) by applying a matrix calculated by the sub-models of WASP (Wind Atlas Application and Analysis Program). The actual wind farm production is calculated using the Riso PARK model. Because of the preliminary nature of the results, they will not be given. However, similar results from Europe will be given.

  17. Tampa Bay Water Clarity Model (TBWCM): As a Predictive Tool

    EPA Science Inventory

    The Tampa Bay Water Clarity Model was developed as a predictive tool for estimating the impact of changing nutrient loads on water clarity as measured by secchi depth. The model combines a physical mixing model with an irradiance model and nutrient cycling model. A 10 segment bi...

  18. EOID Model Validation and Performance Prediction

    DTIC Science & Technology

    2002-09-30

    Our long-term goal is to accurately predict the capability of the current generation of laser-based underwater imaging sensors to perform Electro ... Optic Identification (EOID) against relevant targets in a variety of realistic environmental conditions. The two most prominent technologies in this area

  19. Predicting Career Advancement with Structural Equation Modelling

    ERIC Educational Resources Information Center

    Heimler, Ronald; Rosenberg, Stuart; Morote, Elsa-Sofia

    2012-01-01

    Purpose: The purpose of this paper is to use the authors' prior findings concerning basic employability skills in order to determine which skills best predict career advancement potential. Design/methodology/approach: Utilizing survey responses of human resource managers, the employability skills showing the largest relationships to career…

  20. Effect of intrinsic motivation on affective responses during and after exercise: latent curve model analysis.

    PubMed

    Shin, Myoungjin; Kim, Inwoo; Kwon, Sungho

    2014-12-01

    Understanding the relationship between affect and exercise is helpful in predicting human behavior with respect to exercise participation. The goals of the present study were to investigate individual differences in affective response during and after exercise and to identify the role of intrinsic motivation in affective changes. 30 active male college students (M age = 21.4 yr.) who regularly participated in sports activities volunteered to answer a questionnaire measuring intrinsic motivation toward running activities and performed a 20-min. straight running protocol at heavy intensity (about 70% of VO2max). Participants' affective responses were measured every 5 min. from the beginning of the run to 10 min. after completing the run. Latent curve model analysis indicated that individuals experienced different changes in affective state during exercise, moderated by intrinsic motivation. Higher intrinsic motivation was associated with more positive affect during exercise. There were no significant individual differences in the positive tendency of the participants' affective responses after exercise over time. Intrinsic motivation seems to facilitate positive feelings during exercise and encourages participation in exercise.

  1. The Causal Meaning of Genomic Predictors and How It Affects Construction and Comparison of Genome-Enabled Selection Models

    PubMed Central

    Valente, Bruno D.; Morota, Gota; Peñagaricano, Francisco; Gianola, Daniel; Weigel, Kent; Rosa, Guilherme J. M.

    2015-01-01

    The term “effect” in additive genetic effect suggests a causal meaning. However, inferences of such quantities for selection purposes are typically viewed and conducted as a prediction task. Predictive ability as tested by cross-validation is currently the most acceptable criterion for comparing models and evaluating new methodologies. Nevertheless, it does not directly indicate if predictors reflect causal effects. Such evaluations would require causal inference methods that are not typical in genomic prediction for selection. This suggests that the usual approach to infer genetic effects contradicts the label of the quantity inferred. Here we investigate if genomic predictors for selection should be treated as standard predictors or if they must reflect a causal effect to be useful, requiring causal inference methods. Conducting the analysis as a prediction or as a causal inference task affects, for example, how covariates of the regression model are chosen, which may heavily affect the magnitude of genomic predictors and therefore selection decisions. We demonstrate that selection requires learning causal genetic effects. However, genomic predictors from some models might capture noncausal signal, providing good predictive ability but poorly representing true genetic effects. Simulated examples are used to show that aiming for predictive ability may lead to poor modeling decisions, while causal inference approaches may guide the construction of regression models that better infer the target genetic effect even when they underperform in cross-validation tests. In conclusion, genomic selection models should be constructed to aim primarily for identifiability of causal genetic effects, not for predictive ability. PMID:25908318

  2. Stability Affects of Artificial Viscosity in Detonation Modeling

    SciTech Connect

    Vitello, P; Souers, P C

    2002-06-03

    Accurate multi-dimensional modeling of detonation waves in solid HE materials is a difficult task. To treat applied problems which contain detonation waves one must consider reacting flow with a wide range of length-scales, non-linear equations of state (EOS), and material interfaces at which the detonation wave interacts with other materials. To be useful numerical models of detonation waves must be accurate, stable, and insensitive to details of the modeling such as the mesh spacing, and mesh aspect ratio for multi-dimensional simulations. Studies we have performed show that numerical simulations of detonation waves can be very sensitive to the form of the artificial viscosity term used. The artificial viscosity term is included in our ALE hydrocode to treat shock discontinuities. We show that a monotonic, second order artificial viscosity model derived from an approximate Riemann solver scheme can strongly damp unphysical oscillations in the detonation wave reaction zone, improving the detonation wave boundary wall interaction. These issues are demonstrated in 2D model simulations presented of the 'Bigplate' test. Results using LX-I 7 explosives are compared with numerical simulation results to demonstrate the affects of the artificial viscosity model.

  3. Evaluation of Fast-Time Wake Vortex Prediction Models

    NASA Technical Reports Server (NTRS)

    Proctor, Fred H.; Hamilton, David W.

    2009-01-01

    Current fast-time wake models are reviewed and three basic types are defined. Predictions from several of the fast-time models are compared. Previous statistical evaluations of the APA-Sarpkaya and D2P fast-time models are discussed. Root Mean Square errors between fast-time model predictions and Lidar wake measurements are examined for a 24 hr period at Denver International Airport. Shortcomings in current methodology for evaluating wake errors are also discussed.

  4. Simple model for predicting microchannel heat sink performance and optimization

    NASA Astrophysics Data System (ADS)

    Tsai, Tsung-Hsun; Chein, Reiyu

    2012-05-01

    A simple model was established to predict microchannel heat sink performance based on energy balance. Both hydrodynamically and thermally developed effects were included. Comparisons with the experimental data show that this model provides satisfactory thermal resistance prediction. The model is further extended to carry out geometric optimization on the microchannel heat sink. The results from the simple model are in good agreement as compared with those obtained from three-dimensional simulations.

  5. Modeling the Growth of Archaeon Halobacterium halobium Affected by Temperature and Light.

    PubMed

    Lu, Hao; Yuan, Wenqiao; Cheng, Jay; Rose, Robert B; Classen, John J; Simmons, Otto D

    2017-03-01

    The objective of this study was to develop sigmoidal models, including three-parameter (Quadratic, Logistic, and Gompertz) and four-parameter models (Schnute and Richards) to simulate the growth of archaeon Halobacterium halobium affected by temperature and light. The models were statistically compared by using t test and F test. In the t test, confidence bounds for parameters were used to distinguish among models. For the F test, the lack of fit of the models was compared with the prediction error. The Gompertz model was 100 % accepted by the t test and 97 % accepted by the F test when the temperature effects were considered. Results also indicated that the Gompertz model was 94 % accepted by the F test when the growth of H. halobium was studied under varying light intensities. Thus, the Gompertz model was considered the best among the models studied to describe the growth of H. halobium affected by temperature or light. In addition, the biological growth parameters, including specific growth rate, lag time, and asymptote changes under Gompertz modeling, were evaluated.

  6. Predictive growth model of LID: light intensification model

    NASA Astrophysics Data System (ADS)

    Tan, ChingSeong; Patel, D.; Wang, X.; Schlitz, D.; Dehkordi, P. S.; Menoni, C. S.; Chong, E. K. P.

    2013-11-01

    General precursors and growth model of Laser Induced Damage (LID) have been the focus of research in fused silica material, such as polishing residues, fractures, and contaminations. Assuming the absorption due to trapped material and mechanical strength is the same across the surfaces, various studies have shown that the LID could be minimized by reducing the light field intensification of the layers upon the laser strikes. By revisiting the definition of non-ionising radiation damage, this paper presents the modelling work and simulation of light intensification of laser induced damage condition. Our contribution is to predict the LID growth that take into various factors, specifically on the light intensification problem. The light intensification problem is a function of the inter-layer or intra-layer micro-optical properties, such as transmittance and absorption coefficient of the material at micro- or sub-micro-meter range. The proposed model will first estimate the light propagation that convoluted with the multiply scattering light and subsequently the field intensification within the nodule dimension. This will allow us to evaluate the geometrical factor of the nodule effect over the intensification. The result show that the light intensification is higher whenever the backscattering and multiple scattering components are higher due to its interference with the incoming wave within its coherency.

  7. Multivariate Predictive Model for Dyslexia Diagnosis

    ERIC Educational Resources Information Center

    Le Jan, Guylaine; Le Bouquin-Jeannes, Regine; Costet, Nathalie; Troles, Nolwenn; Scalart, Pascal; Pichancourt, Dominique; Faucon, Gerard; Gombert, Jean-Emile

    2011-01-01

    Dyslexia is a specific disorder of language development that mainly affects reading. Etiological researches have led to multiple hypotheses which induced various diagnosis methods and rehabilitation treatments so that many different tests are used by practitioners to identify dyslexia symptoms. Our purpose is to determine a subset of the most…

  8. A predictive model for biomimetic plate type broadband frequency sensor

    NASA Astrophysics Data System (ADS)

    Ahmed, Riaz U.; Banerjee, Sourav

    2016-04-01

    In this work, predictive model for a bio-inspired broadband frequency sensor is developed. Broadband frequency sensing is essential in many domains of science and technology. One great example of such sensor is human cochlea, where it senses a frequency band of 20 Hz to 20 KHz. Developing broadband sensor adopting the physics of human cochlea has found tremendous interest in recent years. Although few experimental studies have been reported, a true predictive model to design such sensors is missing. A predictive model is utmost necessary for accurate design of selective broadband sensors that are capable of sensing very selective band of frequencies. Hence, in this study, we proposed a novel predictive model for the cochlea-inspired broadband sensor, aiming to select the frequency band and model parameters predictively. Tapered plate geometry is considered mimicking the real shape of the basilar membrane in the human cochlea. The predictive model is intended to develop flexible enough that can be employed in a wide variety of scientific domains. To do that, the predictive model is developed in such a way that, it can not only handle homogeneous but also any functionally graded model parameters. Additionally, the predictive model is capable of managing various types of boundary conditions. It has been found that, using the homogeneous model parameters, it is possible to sense a specific frequency band from a specific portion (B) of the model length (L). It is also possible to alter the attributes of `B' using functionally graded model parameters, which confirms the predictive frequency selection ability of the developed model.

  9. Econometric models for predicting confusion crop ratios

    NASA Technical Reports Server (NTRS)

    Umberger, D. E.; Proctor, M. H.; Clark, J. E.; Eisgruber, L. M.; Braschler, C. B. (Principal Investigator)

    1979-01-01

    Results for both the United States and Canada show that econometric models can provide estimates of confusion crop ratios that are more accurate than historical ratios. Whether these models can support the LACIE 90/90 accuracy criterion is uncertain. In the United States, experimenting with additional model formulations could provide improved methods models in some CRD's, particularly in winter wheat. Improved models may also be possible for the Canadian CD's. The more aggressive province/state models outperformed individual CD/CRD models. This result was expected partly because acreage statistics are based on sampling procedures, and the sampling precision declines from the province/state to the CD/CRD level. Declining sampling precision and the need to substitute province/state data for the CD/CRD data introduced measurement error into the CD/CRD models.

  10. Impact of modellers' decisions on hydrological a priori predictions

    NASA Astrophysics Data System (ADS)

    Holländer, H. M.; Bormann, H.; Blume, T.; Buytaert, W.; Chirico, G. B.; Exbrayat, J.-F.; Gustafsson, D.; Hölzel, H.; Krauße, T.; Kraft, P.; Stoll, S.; Blöschl, G.; Flühler, H.

    2014-06-01

    In practice, the catchment hydrologist is often confronted with the task of predicting discharge without having the needed records for calibration. Here, we report the discharge predictions of 10 modellers - using the model of their choice - for the man-made Chicken Creek catchment (6 ha, northeast Germany, Gerwin et al., 2009b) and we analyse how well they improved their prediction in three steps based on adding information prior to each following step. The modellers predicted the catchment's hydrological response in its initial phase without having access to the observed records. They used conceptually different physically based models and their modelling experience differed largely. Hence, they encountered two problems: (i) to simulate discharge for an ungauged catchment and (ii) using models that were developed for catchments, which are not in a state of landscape transformation. The prediction exercise was organized in three steps: (1) for the first prediction the modellers received a basic data set describing the catchment to a degree somewhat more complete than usually available for a priori predictions of ungauged catchments; they did not obtain information on stream flow, soil moisture, nor groundwater response and had therefore to guess the initial conditions; (2) before the second prediction they inspected the catchment on-site and discussed their first prediction attempt; (3) for their third prediction they were offered additional data by charging them pro forma with the costs for obtaining this additional information. Holländer et al. (2009) discussed the range of predictions obtained in step (1). Here, we detail the modeller's assumptions and decisions in accounting for the various processes. We document the prediction progress as well as the learning process resulting from the availability of added information. For the second and third steps, the progress in prediction quality is evaluated in relation to individual modelling experience and costs of

  11. Fixed recurrence and slip models better predict earthquake behavior than the time- and slip-predictable models 1: repeating earthquakes

    USGS Publications Warehouse

    Rubinstein, Justin L.; Ellsworth, William L.; Chen, Kate Huihsuan; Uchida, Naoki

    2012-01-01

    The behavior of individual events in repeating earthquake sequences in California, Taiwan and Japan is better predicted by a model with fixed inter-event time or fixed slip than it is by the time- and slip-predictable models for earthquake occurrence. Given that repeating earthquakes are highly regular in both inter-event time and seismic moment, the time- and slip-predictable models seem ideally suited to explain their behavior. Taken together with evidence from the companion manuscript that shows similar results for laboratory experiments we conclude that the short-term predictions of the time- and slip-predictable models should be rejected in favor of earthquake models that assume either fixed slip or fixed recurrence interval. This implies that the elastic rebound model underlying the time- and slip-predictable models offers no additional value in describing earthquake behavior in an event-to-event sense, but its value in a long-term sense cannot be determined. These models likely fail because they rely on assumptions that oversimplify the earthquake cycle. We note that the time and slip of these events is predicted quite well by fixed slip and fixed recurrence models, so in some sense they are time- and slip-predictable. While fixed recurrence and slip models better predict repeating earthquake behavior than the time- and slip-predictable models, we observe a correlation between slip and the preceding recurrence time for many repeating earthquake sequences in Parkfield, California. This correlation is not found in other regions, and the sequences with the correlative slip-predictable behavior are not distinguishable from nearby earthquake sequences that do not exhibit this behavior.

  12. Developing and implementing the use of predictive models for estimating water quality at Great Lakes beaches

    USGS Publications Warehouse

    Francy, Donna S.; Brady, Amie M.G.; Carvin, Rebecca B.; Corsi, Steven R.; Fuller, Lori M.; Harrison, John H.; Hayhurst, Brett A.; Lant, Jeremiah; Nevers, Meredith B.; Terrio, Paul J.; Zimmerman, Tammy M.

    2013-01-01

    Predictive models have been used at beaches to improve the timeliness and accuracy of recreational water-quality assessments over the most common current approach to water-quality monitoring, which relies on culturing fecal-indicator bacteria such as Escherichia coli (E. coli.). Beach-specific predictive models use environmental and water-quality variables that are easily and quickly measured as surrogates to estimate concentrations of fecal-indicator bacteria or to provide the probability that a State recreational water-quality standard will be exceeded. When predictive models are used for beach closure or advisory decisions, they are referred to as “nowcasts.” During the recreational seasons of 2010-12, the U.S. Geological Survey (USGS), in cooperation with 23 local and State agencies, worked to improve existing nowcasts at 4 beaches, validate predictive models at another 38 beaches, and collect data for predictive-model development at 7 beaches throughout the Great Lakes. This report summarizes efforts to collect data and develop predictive models by multiple agencies and to compile existing information on the beaches and beach-monitoring programs into one comprehensive report. Local agencies measured E. coli concentrations and variables expected to affect E. coli concentrations such as wave height, turbidity, water temperature, and numbers of birds at the time of sampling. In addition to these field measurements, equipment was installed by the USGS or local agencies at or near several beaches to collect water-quality and metrological measurements in near real time, including nearshore buoys, weather stations, and tributary staff gages and monitors. The USGS worked with local agencies to retrieve data from existing sources either manually or by use of tools designed specifically to compile and process data for predictive-model development. Predictive models were developed by use of linear regression and (or) partial least squares techniques for 42 beaches

  13. Predictive animal models of mania: hits, misses and future directions

    PubMed Central

    Young, Jared W; Henry, Brook L; Geyer, Mark A

    2011-01-01

    Mania has long been recognized as aberrant behaviour indicative of mental illness. Manic states include a variety of complex and multifaceted symptoms that challenge clear clinical distinctions. Symptoms include over-activity, hypersexuality, irritability and reduced need for sleep, with cognitive deficits recently linked to functional outcome. Current treatments have arisen through serendipity or from other disorders. Hence, treatments are not efficacious for all patients, and there is an urgent need to develop targeted therapeutics. Part of the drug discovery process is the assessment of therapeutics in animal models. Here we review pharmacological, environmental and genetic manipulations developed to test the efficacy of therapeutics in animal models of mania. The merits of these models are discussed in terms of the manipulation used and the facet of mania measured. Moreover, the predictive validity of these models is discussed in the context of differentiating drugs that succeed or fail to meet criteria as approved mania treatments. The multifaceted symptomatology of mania has not been reflected in the majority of animal models, where locomotor activity remains the primary measure. This approach has resulted in numerous false positives for putative treatments. Recent work highlights the need to utilize multivariate strategies to enable comprehensive assessment of affective and cognitive dysfunction. Advances in therapeutic treatment may depend on novel models developed with an integrated approach that includes: (i) a comprehensive battery of tests for different aspects of mania, (ii) utilization of genetic information to establish aetiological validity and (iii) objective quantification of patient behaviour with translational cross-species paradigms. LINKED ARTICLES This article is part of a themed issue on Translational Neuropharmacology. To view the other articles in this issue visit http://dx.doi.org/10.1111/bph.2011.164.issue-4 PMID:21410454

  14. The regional prediction model of PM10 concentrations for Turkey

    NASA Astrophysics Data System (ADS)

    Güler, Nevin; Güneri İşçi, Öznur

    2016-11-01

    This study is aimed to predict a regional model for weekly PM10 concentrations measured air pollution monitoring stations in Turkey. There are seven geographical regions in Turkey and numerous monitoring stations at each region. Predicting a model conventionally for each monitoring station requires a lot of labor and time and it may lead to degradation in quality of prediction when the number of measurements obtained from any õmonitoring station is small. Besides, prediction models obtained by this way only reflect the air pollutant behavior of a small area. This study uses Fuzzy C-Auto Regressive Model (FCARM) in order to find a prediction model to be reflected the regional behavior of weekly PM10 concentrations. The superiority of FCARM is to have the ability of considering simultaneously PM10 concentrations measured monitoring stations in the specified region. Besides, it also works even if the number of measurements obtained from the monitoring stations is different or small. In order to evaluate the performance of FCARM, FCARM is executed for all regions in Turkey and prediction results are compared to statistical Autoregressive (AR) Models predicted for each station separately. According to Mean Absolute Percentage Error (MAPE) criteria, it is observed that FCARM provides the better predictions with a less number of models.

  15. Genomic prediction of complex human traits: relatedness, trait architecture and predictive meta-models.

    PubMed

    Spiliopoulou, Athina; Nagy, Reka; Bermingham, Mairead L; Huffman, Jennifer E; Hayward, Caroline; Vitart, Veronique; Rudan, Igor; Campbell, Harry; Wright, Alan F; Wilson, James F; Pong-Wong, Ricardo; Agakov, Felix; Navarro, Pau; Haley, Chris S

    2015-07-15

    We explore the prediction of individuals' phenotypes for complex traits using genomic data. We compare several widely used prediction models, including Ridge Regression, LASSO and Elastic Nets estimated from cohort data, and polygenic risk scores constructed using published summary statistics from genome-wide association meta-analyses (GWAMA). We evaluate the interplay between relatedness, trait architecture and optimal marker density, by predicting height, body mass index (BMI) and high-density lipoprotein level (HDL) in two data cohorts, originating from Croatia and Scotland. We empirically demonstrate that dense models are better when all genetic effects are small (height and BMI) and target individuals are related to the training samples, while sparse models predict better in unrelated individuals and when some effects have moderate size (HDL). For HDL sparse models achieved good across-cohort prediction, performing similarly to the GWAMA risk score and to models trained within the same cohort, which indicates that, for predicting traits with moderately sized effects, large sample sizes and familial structure become less important, though still potentially useful. Finally, we propose a novel ensemble of whole-genome predictors with GWAMA risk scores and demonstrate that the resulting meta-model achieves higher prediction accuracy than either model on its own. We conclude that although current genomic predictors are not accurate enough for diagnostic purposes, performance can be improved without requiring access to large-scale individual-level data. Our methodologically simple meta-model is a means of performing predictive meta-analysis for optimizing genomic predictions and can be easily extended to incorporate multiple population-level summary statistics or other domain knowledge.

  16. Gaussian mixture models as flux prediction method for central receivers

    NASA Astrophysics Data System (ADS)

    Grobler, Annemarie; Gauché, Paul; Smit, Willie

    2016-05-01

    Flux prediction methods are crucial to the design and operation of central receiver systems. Current methods such as the circular and elliptical (bivariate) Gaussian prediction methods are often used in field layout design and aiming strategies. For experimental or small central receiver systems, the flux profile of a single heliostat often deviates significantly from the circular and elliptical Gaussian models. Therefore a novel method of flux prediction was developed by incorporating the fitting of Gaussian mixture models onto flux profiles produced by flux measurement or ray tracing. A method was also developed to predict the Gaussian mixture model parameters of a single heliostat for a given time using image processing. Recording the predicted parameters in a database ensures that more accurate predictions are made in a shorter time frame.

  17. Latitudinal variation in carbon storage can help predict changes in swamps affected by global warming

    USGS Publications Warehouse

    Middleton, Beth A.; McKee, Karen

    2004-01-01

    Plants may offer our best hope of removing greenhouse gases (gases that contribute to global warming) emitted to the atmosphere from the burning of fossil fuels. At the same time, global warming could change environments so that natural plant communities will either need to shift into cooler climate zones, or become extirpated (Prasad and Iverson, 1999; Crumpacker and others, 2001; Davis and Shaw, 2001). It is impossible to know the future, but studies combining field observation of production and modeling can help us make predictions about what may happen to these wetland communities in the future. Widespread wetland types such as baldcypress (Taxodium distichum) swamps in the southeastern portion of the United States could be especially good at carbon sequestration (amount of CO2 stored by forests) from the atmosphere. They have high levels of production and sometimes store undecomposed dead plant material in wet conditions with low oxygen, thus keeping gases stored that would otherwise be released into the atmosphere (fig. 1). To study the ability of baldcypress swamps to store carbon, our project has taken two approaches. The first analysis looked at published data to develop an idea (hypothesis) of how production levels change across a temperature gradient in the baldcypress region (published data study). The second study tested this idea by comparing production levels across a latitudinal range by using swamps in similar field conditions (ongoing carbon storage study). These studies will help us make predictions about the future ability of baldcypress swamps to store carbon in soil and plant biomass, as well as the ability of these forests to shift northward with global warming.

  18. Double trouble. Trait food craving and impulsivity interactively predict food-cue affected behavioral inhibition.

    PubMed

    Meule, Adrian; Kübler, Andrea

    2014-08-01

    Impulsivity and food craving have both been implicated in overeating. Recent results suggest that both processes may interactively predict increased food intake. In the present study, female participants performed a Go/No-go task with pictures of high- and low-calorie foods. They were instructed to press a button in response to the respective target category, but withhold responses to the other category. Target category was switched after every other block, thereby creating blocks in which stimulus-response mapping was the same as in the previous block (nonshift blocks) and blocks in which it was reversed (shift blocks). The Food Cravings Questionnaires and the Barratt Impulsiveness Scale were used to assess trait and state food craving and attentional, motor, and nonplanning impulsivity. Participants had slower reaction times and more omission errors (OE) in high-calorie than in low-calorie blocks. Number of commission errors (CE) and OE was higher in shift blocks than in nonshift blocks. Trait impulsivity was positively correlated with CE in shift blocks while trait food craving was positively correlated with CE in high-calorie blocks. Importantly, CE in high-calorie-shift blocks were predicted by an interaction of food craving × impulsivity such that the relationship between food craving and CE was particularly strong at high levels of impulsivity, but vanished at low levels of impulsivity. Thus, impulsive reactions to high-calorie food-cues are particularly pronounced when both trait impulsivity and food craving is high, but low levels of impulsivity can compensate for high levels of trait food craving. Results support models of self-regulation which assume that interactive effects of low top-down control and strong reward sensitive, bottom-up mechanisms may determine eating-related disinhibition, ultimately leading to increased food intake.

  19. White Collar Criminality: A Prediction Model

    DTIC Science & Technology

    1991-01-01

    Paternal vided emotional support, support, Relation- interest, and attention, both parents (9)2 gave affection, praise and attention. School and .71...influences in adolescence and adulthood 3 (Dwarkin, Burke, Maher, and Gottesman,1976; and Goldsmith, 1983), and Ellis (1982) pointed out that...be fostered in childhood by parents , but the way in which these motivations are directed are fostered throughout the lifespan by the social culture

  20. Coupled model of physical and biological processes affecting maize pollination

    NASA Astrophysics Data System (ADS)

    Arritt, R.; Westgate, M.; Riese, J.; Falk, M.; Takle, E.

    2003-04-01

    Controversy over the use of genetically modified (GM) crops has led to increased interest in evaluating and controlling the potential for inadvertent outcrossing in open-pollinated crops such as maize. In response to this problem we have developed a Lagrangian model of pollen dispersion as a component of a coupled end-to-end (anther to ear) physical-biological model of maize pollination. The Lagrangian method is adopted because of its generality and flexibility: first, the method readily accommodates flow fields of arbitrary complexity; second, each element of the material being transported can be identified by its source, time of release, or other properties of interest. The latter allows pollen viability to be estimated as a function of such factors as travel time, temperature, and relative humidity, so that the physical effects of airflow and turbulence on pollen dispersion can be considered together with the biological aspects of pollen release and viability. Predicted dispersion of pollen compares well both to observations and to results from a simpler Gaussian plume model. Ability of the Lagrangian model to handle complex air flows is demonstrated by application to pollen dispersion in the vicinity of an agricultural shelter belt. We also show results indicating that pollen viability can be quantified by an "aging function" that accounts for temperature, humidity, and time of exposure.

  1. Super-Micro Computer Weather Prediction Model

    DTIC Science & Technology

    1990-06-01

    model equations 2 b. Grid domain and horizontal nesting 5 c. Time integration and outer lateral boundary condition 8 d. Coupling of the model with the...c. Eddy diffusion sensitivity tests 36 4. Domain for Prototype testing 39 5 . Comparison of the Boundary-Layer Parameterizations - -__ With the...including radiation calculations, with other boundary layer work will be presented in section 5 , and the report concludes witb section 6. 2. Model

  2. Prediction and Prescription in Systems Modeling

    DTIC Science & Technology

    1988-06-30

    17 COSATI CODES 18 SUSjECT TERMS (Continue on retverse if niecessary and identify by block number) FIEL GRUP SB-GOUP Modelling complex systems; non...exponentially increasing forcing functions , population and energy use among them. Now one does not have to run such a model very many hours on a large... function of the superposition of these estimated values. In the modeling, some effort was exerted, quite creditably, to examine the robustness of the

  3. Children's and Adults' Models for Predicting Teleological Action: The Development of a Biology-Based Model.

    ERIC Educational Resources Information Center

    Opfer, John E.; Gelman, Susan A.

    2001-01-01

    Two studies examined models that preschoolers, fifth-graders, and adults use to guide predictions of self-beneficial, goal-directed action. Found that preschoolers' predictions were consistent with an animal-based model, fifth-graders' with biology-based and complexity-based models, and adults' predictions with a biology-based model. All age…

  4. Questioning the Faith - Models and Prediction in Stream Restoration (Invited)

    NASA Astrophysics Data System (ADS)

    Wilcock, P.

    2013-12-01

    River management and restoration demand prediction at and beyond our present ability. Management questions, framed appropriately, can motivate fundamental advances in science, although the connection between research and application is not always easy, useful, or robust. Why is that? This presentation considers the connection between models and management, a connection that requires critical and creative thought on both sides. Essential challenges for managers include clearly defining project objectives and accommodating uncertainty in any model prediction. Essential challenges for the research community include matching the appropriate model to project duration, space, funding, information, and social constraints and clearly presenting answers that are actually useful to managers. Better models do not lead to better management decisions or better designs if the predictions are not relevant to and accepted by managers. In fact, any prediction may be irrelevant if the need for prediction is not recognized. The predictive target must be developed in an active dialog between managers and modelers. This relationship, like any other, can take time to develop. For example, large segments of stream restoration practice have remained resistant to models and prediction because the foundational tenet - that channels built to a certain template will be able to transport the supplied sediment with the available flow - has no essential physical connection between cause and effect. Stream restoration practice can be steered in a predictive direction in which project objectives are defined as predictable attributes and testable hypotheses. If stream restoration design is defined in terms of the desired performance of the channel (static or dynamic, sediment surplus or deficit), then channel properties that provide these attributes can be predicted and a basis exists for testing approximations, models, and predictions.

  5. Posterior Predictive Assessment of Item Response Theory Models

    ERIC Educational Resources Information Center

    Sinharay, Sandip; Johnson, Matthew S.; Stern, Hal S.

    2006-01-01

    Model checking in item response theory (IRT) is an underdeveloped area. There is no universally accepted tool for checking IRT models. The posterior predictive model-checking method is a popular Bayesian model-checking tool because it has intuitive appeal, is simple to apply, has a strong theoretical basis, and can provide graphical or numerical…

  6. Posterior Predictive Model Checking for Multidimensionality in Item Response Theory

    ERIC Educational Resources Information Center

    Levy, Roy; Mislevy, Robert J.; Sinharay, Sandip

    2009-01-01

    If data exhibit multidimensionality, key conditional independence assumptions of unidimensional models do not hold. The current work pursues posterior predictive model checking, a flexible family of model-checking procedures, as a tool for criticizing models due to unaccounted for dimensions in the context of item response theory. Factors…

  7. Thermal barrier coating life prediction model development

    NASA Technical Reports Server (NTRS)

    Demasi, J. T.; Manning, S. L.; Ortiz, M.; Sheffler, K. D.

    1987-01-01

    The objectives of this program are to increase understanding of thermal barrier coating (TBC) degradation and failure modes, to generate quantitative ceramic failure life data under cyclic thermal conditions which simulate those encountered in gas turbine engine service, and to develop an analytical methodology for prediction of coating life in the engine. Observations of degradation and failure modes in plasma deposited ceramic indicate that spallation failure results from progressive cracking of the ceramic parallel to and adjacent to, but not coincident with the metal-ceramic interface.

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

    NASA Astrophysics Data System (ADS)

    Osman, Marisol; Vera, C. S.

    2016-11-01

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

  9. Aquatic pathways model to predict the fate of phenolic compounds

    SciTech Connect

    Aaberg, R.L.; Peloquin, R.A.; Strenge, D.L.; Mellinger, P.J.

    1983-04-01

    Organic materials released from energy-related activities could affect human health and the environment. To better assess possible impacts, we developed a model to predict the fate of spills or discharges of pollutants into flowing or static bodies of fresh water. A computer code, Aquatic Pathways Model (APM), was written to implement the model. The computer programs use compartmental analysis to simulate aquatic ecosystems. The APM estimates the concentrations of chemicals in fish tissue, water and sediment, and is therefore useful for assessing exposure to humans through aquatic pathways. The APM will consider any aquatic pathway for which the user has transport data. Additionally, APM will estimate transport rates from physical and chemical properties of chemicals between several key compartments. The major pathways considered are biodegradation, fish and sediment uptake, photolysis, and evaporation. The model has been implemented with parameters for distribution of phenols, an important class of compounds found in the water-soluble fractions of coal liquids. Current modeling efforts show that, in comparison with many pesticides and polyaromatic hydrocarbons (PAH), the lighter phenolics (the cresols) are not persistent in the environment. The properties of heavier molecular weight phenolics (indanols, naphthols) are not well enough understood at this time to make similar judgements. For the twelve phenolics studied, biodegradation appears to be the major pathway for elimination from aquatic environments. A pond system simulation (using APM) of a spill of solvent refined coal (SRC-II) materials indicates that phenol, cresols, and other single cyclic phenolics are degraded to 16 to 25 percent of their original concentrations within 30 hours. Adsorption of these compounds into sediments and accumulation by fish was minor.

  10. Assessing Predicted Contacts for Building Protein Three-Dimensional Models.

    PubMed

    Adhikari, Badri; Bhattacharya, Debswapna; Cao, Renzhi; Cheng, Jianlin

    2017-01-01

    Recent successes of contact-guided protein structure prediction methods have revived interest in solving the long-standing problem of ab initio protein structure prediction. With homology modeling failing for many protein sequences that do not have templates, contact-guided structure prediction has shown promise, and consequently, contact prediction has gained a lot of interest recently. Although a few dozen contact prediction tools are already currently available as web servers and downloadables, not enough research has been done towards using existing measures like precision and recall to evaluate these contacts with the goal of building three-dimensional models. Moreover, when we do not have a native structure for a set of predicted contacts, the only analysis we can perform is a simple contact map visualization of the predicted contacts. A wider and more rigorous assessment of the predicted contacts is needed, in order to build tertiary structure models. This chapter discusses instructions and protocols for using tools and applying techniques in order to assess predicted contacts for building three-dimensional models.

  11. Vertical Chlorophyll Canopy Structure Affects the Remote Sensing Based Predictability of LAI, Chlorophyll and Leaf Nitrogen in Agricultural Fields

    NASA Astrophysics Data System (ADS)

    Boegh, E.; Houborg, R.; Bienkowski, J.; Braban, C. F.; Dalgaard, T.; van Dijk, N.; Dragosits, U.; Holmes, E.; Magliulo, V.; Schelde, K.; Di Tommasi, P.; Vitale, L.; Theobald, M.; Cellier, P.; Sutton, M.

    2012-12-01

    SVIs require field data for empirical model building, the REGFLEC model was applied without calibration. LAI and SPAD meter data were measured in 93 fields representing 10 crop types of the five European landscapes. SPAD meter data were measured at five canopy height levels and converted to CHL and N using laboratory calibration. The data showed strong vertical leaf chlorophyll gradient profiles in 20 % of fields. This affected the predictability of SVIs and REGFLEC. However, selecting only homogeneous canopies with uniform CHL distributions as reference data for statistical evaluation, significant predictions were achieved for all landscapes, by all methods, with the best overall results given by REGFLEC. Predictabilities of SVIs and REGFLEC simulations improved when constrained to single land use categories across the European landscapes, reflecting sensitivity to canopy structures, and predictabilities further improved when constrained to local (10 x 10 km2) landscapes, thereby reflecting sensitivity to local environmental conditions. The Enhanced Vegetation Index-2 tended to be the best method in landscapes with high vegetation densities, REGFLEC worked best in a landscape with large contrasts in vegetation density, and the Simple Ratio worked best in a landscape characterized by low vegetation density.

  12. Multidimensional models for predicting muscle structure and fascicle pennation.

    PubMed

    Randhawa, Avleen; Wakeling, James M

    2015-10-07

    Pennation angles change during muscle contraction and must be tracked by muscle models. When muscles contract they can change in depth (distance between the bounding sheets of aponeurosis) or width, and this is related to pennation angle and muscle fascicle length. As a simplification to these relationships, many models of pennate muscle assume a constant distance between aponeuroses during contraction (constant depth). It is possible that these 1D models do not recreate the internal structure of muscles adequately, whereas 2D panel models that assume a constant panel area, or 3D models that assume a constant muscle volume may better predict the structural changes that occur within muscle during contraction. However, these ideas have never been validated in man. The purpose of this study was to test the accuracy with which 1D, 2D or 3D structural models of muscle could predict the pennation and muscle depth within the medial gastrocnemius (MG) and lateral gastrocnemius (LG) in man during ankle plantarflexions. The 1D model, by definition, was unable to account for changes in muscle depth. The 2D model predicted change in depth as the aponeurosis was loaded, but could only allow a decrease in depth as the aponeurosis is stretched. This was not sufficient to predict the increases in depth that occur in the LG during plantarflexion. The 3D model had the ability to predict either increases or decreases in depth during the ankle plantarflexions and predicted opposing changes in depth that occurred between the MG and LG, whilst simultaneously predicting the pennation more accurately than the 1D or 2D models. However, when using mean parameters, the 3D model performed no better than the more simple 1D model, and so if the intent of a model is purely to establish a good relation between fascicle length and pennation then the 1D model is a suitable choice for these muscles.

  13. Predicting Error Bars for QSAR Models

    SciTech Connect

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

    2007-09-18

    Unfavorable physicochemical properties often cause drug failures. It is therefore important to take lipophilicity and water solubility into account early on in lead discovery. This study presents log D{sub 7} models built using Gaussian Process regression, Support Vector Machines, decision trees and ridge regression algorithms based on 14556 drug discovery compounds of Bayer Schering Pharma. A blind test was conducted using 7013 new measurements from the last months. We also present independent evaluations using public data. Apart from accuracy, we discuss the quality of error bars that can be computed by Gaussian Process models, and ensemble and distance based techniques for the other modelling approaches.

  14. Predicting Error Bars for QSAR Models

    NASA Astrophysics Data System (ADS)

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

    2007-09-01

    Unfavorable physicochemical properties often cause drug failures. It is therefore important to take lipophilicity and water solubility into account early on in lead discovery. This study presents log D7 models built using Gaussian Process regression, Support Vector Machines, decision trees and ridge regression algorithms based on 14556 drug discovery compounds of Bayer Schering Pharma. A blind test was conducted using 7013 new measurements from the last months. We also present independent evaluations using public data. Apart from accuracy, we discuss the quality of error bars that can be computed by Gaussian Process models, and ensemble and distance based techniques for the other modelling approaches.

  15. Improved Dynamic Modeling of the Cascade Distillation Subsystem and Analysis of Factors Affecting Its Performance

    NASA Technical Reports Server (NTRS)

    Perry, Bruce A.; Anderson, Molly S.

    2015-01-01

    The Cascade Distillation Subsystem (CDS) is a rotary multistage distiller being developed to serve as the primary processor for wastewater recovery during long-duration space missions. The CDS could be integrated with a system similar to the International Space Station Water Processor Assembly to form a complete water recovery system for future missions. A preliminary chemical process simulation was previously developed using Aspen Custom Modeler® (ACM), but it could not simulate thermal startup and lacked detailed analysis of several key internal processes, including heat transfer between stages. This paper describes modifications to the ACM simulation of the CDS that improve its capabilities and the accuracy of its predictions. Notably, the modified version can be used to model thermal startup and predicts the total energy consumption of the CDS. The simulation has been validated for both NaC1 solution and pretreated urine feeds and no longer requires retuning when operating parameters change. The simulation was also used to predict how internal processes and operating conditions of the CDS affect its performance. In particular, it is shown that the coefficient of performance of the thermoelectric heat pump used to provide heating and cooling for the CDS is the largest factor in determining CDS efficiency. Intrastage heat transfer affects CDS performance indirectly through effects on the coefficient of performance.

  16. Temporal and Spatial Predictability of an Irrelevant Event Differently Affect Detection and Memory of Items in a Visual Sequence.

    PubMed

    Ohyama, Junji; Watanabe, Katsumi

    2016-01-01

    We examined how the temporal and spatial predictability of a task-irrelevant visual event affects the detection and memory of a visual item embedded in a continuously changing sequence. Participants observed 11 sequentially presented letters, during which a task-irrelevant visual event was either present or absent. Predictabilities of spatial location and temporal position of the event were controlled in 2 × 2 conditions. In the spatially predictable conditions, the event occurred at the same location within the stimulus sequence or at another location, while, in the spatially unpredictable conditions, it occurred at random locations. In the temporally predictable conditions, the event timing was fixed relative to the order of the letters, while in the temporally unpredictable condition; it could not be predicted from the letter order. Participants performed a working memory task and a target detection reaction time (RT) task. Memory accuracy was higher for a letter simultaneously presented at the same location as the event in the temporally unpredictable conditions, irrespective of the spatial predictability of the event. On the other hand, the detection RTs were only faster for a letter simultaneously presented at the same location as the event when the event was both temporally and spatially predictable. Thus, to facilitate ongoing detection processes, an event must be predictable both in space and time, while memory processes are enhanced by temporally unpredictable (i.e., surprising) events. Evidently, temporal predictability has differential effects on detection and memory of a visual item embedded in a sequence of images.

  17. Risk prediction for myocardial infarction via generalized functional regression models.

    PubMed

    Ieva, Francesca; Paganoni, Anna M

    2016-08-01

    In this paper, we propose a generalized functional linear regression model for a binary outcome indicating the presence/absence of a cardiac disease with multivariate functional data among the relevant predictors. In particular, the motivating aim is the analysis of electrocardiographic traces of patients whose pre-hospital electrocardiogram (ECG) has been sent to 118 Dispatch Center of Milan (the Italian free-toll number for emergencies) by life support personnel of the basic rescue units. The statistical analysis starts with a preprocessing of ECGs treated as multivariate functional data. The signals are reconstructed from noisy observations. The biological variability is then removed by a nonlinear registration procedure based on landmarks. Thus, in order to perform a data-driven dimensional reduction, a multivariate functional principal component analysis is carried out on the variance-covariance matrix of the reconstructed and registered ECGs and their first derivatives. We use the scores of the Principal Components decomposition as covariates in a generalized linear model to predict the presence of the disease in a new patient. Hence, a new semi-automatic diagnostic procedure is proposed to estimate the risk of infarction (in the case of interest, the probability of being affected by Left Bundle Brunch Block). The performance of this classification method is evaluated and compared with other methods proposed in literature. Finally, the robustness of the procedure is checked via leave-j-out techniques.

  18. The interaction of affective states and cognitive vulnerabilities in the prediction of non-suicidal self-injury.

    PubMed

    Cohen, Jonah N; Stange, Jonathan P; Hamilton, Jessica L; Burke, Taylor A; Jenkins, Abigail; Ong, Mian-Li; Heimberg, Richard G; Abramson, Lyn Y; Alloy, Lauren B

    2015-01-01

    Non-suicidal self-injury (NSSI) is a serious public health concern and remains poorly understood. This study sought to identify both cognitive and affective vulnerabilities to NSSI and examine their interaction in the prediction of NSSI. A series of regressions indicated that low levels of positive affect (PA) moderated the relationships between self-criticism and brooding and NSSI. The associations of self-criticism and brooding with greater frequency of NSSI were attenuated by higher levels of PA. The interaction of cognitive and affective vulnerabilities is discussed within the context of current NSSI theory.

  19. Predictive Blood Chemistry Parameters for Pansteatitis-Affected Mozambique Tilapia (Oreochromis mossambicus).

    PubMed

    Bowden, John A; Cantu, Theresa M; Chapman, Robert W; Somerville, Stephen E; Guillette, Matthew P; Botha, Hannes; Hoffman, Andre; Luus-Powell, Wilmien J; Smit, Willem J; Lebepe, Jeffrey; Myburgh, Jan; Govender, Danny; Tucker, Jonathan; Boggs, Ashley S P; Guillette, Louis J

    2016-01-01

    One of the largest river systems in South Africa, the Olifants River, has experienced significant changes in water quality due to anthropogenic activities. Since 2005, there have been various "outbreaks" of the inflammatory disease pansteatitis in several vertebrate species. Large-scale pansteatitis-related mortality events have decimated the crocodile population at Lake Loskop and decreased the population at Kruger National Park. Most pansteatitis-related diagnoses within the region are conducted post-mortem by either gross pathology or histology. The application of a non-lethal approach to assess the prevalence and pervasiveness of pansteatitis in the Olifants River region would be of great importance for the development of a management plan for this disease. In this study, several plasma-based biomarkers accurately classified pansteatitis in Mozambique tilapia (Oreochromis mossambicus) collected from Lake Loskop using a commercially available benchtop blood chemistry analyzer combined with data interpretation via artificial neural network analysis. According to the model, four blood chemistry parameters (calcium, sodium, total protein and albumin), in combination with total length, diagnose pansteatitis to a predictive accuracy of 92 percent. In addition, several morphometric traits (total length, age, weight) were also associated with pansteatitis. On-going research will focus on further evaluating the use of blood chemistry to classify pansteatitis across different species, trophic levels, and within different sites along the Olifants River.

  20. Predictive Blood Chemistry Parameters for Pansteatitis-Affected Mozambique Tilapia (Oreochromis mossambicus)

    PubMed Central

    Chapman, Robert W.; Somerville, Stephen E.; Guillette, Matthew P.; Botha, Hannes; Hoffman, Andre; Luus-Powell, Wilmien J.; Smit, Willem J.; Lebepe, Jeffrey; Myburgh, Jan; Govender, Danny; Tucker, Jonathan; Boggs, Ashley S. P.

    2016-01-01

    One of the largest river systems in South Africa, the Olifants River, has experienced significant changes in water quality due to anthropogenic activities. Since 2005, there have been various “outbreaks” of the inflammatory disease pansteatitis in several vertebrate species. Large-scale pansteatitis-related mortality events have decimated the crocodile population at Lake Loskop and decreased the population at Kruger National Park. Most pansteatitis-related diagnoses within the region are conducted post-mortem by either gross pathology or histology. The application of a non-lethal approach to assess the prevalence and pervasiveness of pansteatitis in the Olifants River region would be of great importance for the development of a management plan for this disease. In this study, several plasma-based biomarkers accurately classified pansteatitis in Mozambique tilapia (Oreochromis mossambicus) collected from Lake Loskop using a commercially available benchtop blood chemistry analyzer combined with data interpretation via artificial neural network analysis. According to the model, four blood chemistry parameters (calcium, sodium, total protein and albumin), in combination with total length, diagnose pansteatitis to a predictive accuracy of 92 percent. In addition, several morphometric traits (total length, age, weight) were also associated with pansteatitis. On-going research will focus on further evaluating the use of blood chemistry to classify pansteatitis across different species, trophic levels, and within different sites along the Olifants River. PMID:27115488

  1. A predictive ocean oil spill model

    SciTech Connect

    Sanderson, J.; Barnette, D.; Papodopoulos, P.; Schaudt, K.; Szabo, D.

    1996-07-01

    This is the final report of a two-year, Laboratory-Directed Research and Development (LDRD) project at the Los Alamos National Laboratory (LANL). Initially, the project focused on creating an ocean oil spill model and working with the major oil companies to compare their data with the Los Alamos global ocean model. As a result of this initial effort, Los Alamos worked closely with the Eddy Joint Industry Project (EJIP), a consortium oil and gas producing companies in the US. The central theme of the project was to use output produced from LANL`s global ocean model to look in detail at ocean currents in selected geographic areas of the world of interest to consortium members. Once ocean currents are well understood this information could be used to create oil spill models, improve offshore exploration and drilling equipment, and aid in the design of semi-permanent offshore production platforms.

  2. Cyclic Oxidation Modeling and Life Prediction

    NASA Technical Reports Server (NTRS)

    Smialek, James L.

    2004-01-01

    The cyclic oxidation process can be described as an iterative scale growth and spallation sequence by a number of similar models. Model input variable include oxide scale type and growth parameters, spalling geometry, spall constant, and cycle duration. Outputs include net weight change, the amounts of retained and spalled oxide, the total oxygen and metal consumed, and the terminal rates of weight loss and metal consumption. All models and their variations produce a number of similar characteristic features. In general, spalling and material consumption increase to a steady state rate, at which point the retained scale approaches a constant and the rate of weight loss becomes linear. For one model, this regularity was demonstrated as dimensionless, universal expressions, obtained by normalizing the variables by critical performance factors. These insights were enabled through the use of the COSP for Windows cyclic oxidation spalling program.

  3. Evaluation of wave runup predictions from numerical and parametric models

    USGS Publications Warehouse

    Stockdon, Hilary F.; Thompson, David M.; Plant, Nathaniel G.; Long, Joseph W.

    2014-01-01

    Wave runup during storms is a primary driver of coastal evolution, including shoreline and dune erosion and barrier island overwash. Runup and its components, setup and swash, can be predicted from a parameterized model that was developed by comparing runup observations to offshore wave height, wave period, and local beach slope. Because observations during extreme storms are often unavailable, a numerical model is used to simulate the storm-driven runup to compare to the parameterized model and then develop an approach to improve the accuracy of the parameterization. Numerically simulated and parameterized runup were compared to observations to evaluate model accuracies. The analysis demonstrated that setup was accurately predicted by both the parameterized model and numerical simulations. Infragravity swash heights were most accurately predicted by the parameterized model. The numerical model suffered from bias and gain errors that depended on whether a one-dimensional or two-dimensional spatial domain was used. Nonetheless, all of the predictions were significantly correlated to the observations, implying that the systematic errors can be corrected. The numerical simulations did not resolve the incident-band swash motions, as expected, and the parameterized model performed best at predicting incident-band swash heights. An assimilated prediction using a weighted average of the parameterized model and the numerical simulations resulted in a reduction in prediction error variance. Finally, the numerical simulations were extended to include storm conditions that have not been previously observed. These results indicated that the parameterized predictions of setup may need modification for extreme conditions; numerical simulations can be used to extend the validity of the parameterized predictions of infragravity swash; and numerical simulations systematically underpredict incident swash, which is relatively unimportant under extreme conditions.

  4. Aggregate driver model to enable predictable behaviour

    NASA Astrophysics Data System (ADS)

    Chowdhury, A.; Chakravarty, T.; Banerjee, T.; Balamuralidhar, P.

    2015-09-01

    The categorization of driving styles, particularly in terms of aggressiveness and skill is an emerging area of interest under the broader theme of intelligent transportation. There are two possible discriminatory techniques that can be applied for such categorization; a microscale (event based) model and a macro-scale (aggregate) model. It is believed that an aggregate model will reveal many interesting aspects of human-machine interaction; for example, we may be able to understand the propensities of individuals to carry out a given task over longer periods of time. A useful driver model may include the adaptive capability of the human driver, aggregated as the individual propensity to control speed/acceleration. Towards that objective, we carried out experiments by deploying smartphone based application to be used for data collection by a group of drivers. Data is primarily being collected from GPS measurements including position & speed on a second-by-second basis, for a number of trips over a two months period. Analysing the data set, aggregate models for individual drivers were created and their natural aggressiveness were deduced. In this paper, we present the initial results for 12 drivers. It is shown that the higher order moments of the acceleration profile is an important parameter and identifier of journey quality. It is also observed that the Kurtosis of the acceleration profiles stores major information about the driving styles. Such an observation leads to two different ranking systems based on acceleration data. Such driving behaviour models can be integrated with vehicle and road model and used to generate behavioural model for real traffic scenario.

  5. Validating predictions from climate envelope models

    USGS Publications Warehouse

    Watling, J.; Bucklin, D.; Speroterra, C.; Brandt, L.; Cabal, C.; Romañach, Stephanie S.; Mazzotti, Frank J.

    2013-01-01

    Climate envelope models are a potentially important conservation tool, but their ability to accurately forecast species’ distributional shifts using independent survey data has not been fully evaluated. We created climate envelope models for 12 species of North American breeding birds previously shown to have experienced poleward range shifts. For each species, we evaluated three different approaches to climate envelope modeling that differed in the way they treated climate-induced range expansion and contraction, using random forests and maximum entropy modeling algorithms. All models were calibrated using occurrence data from 1967–1971 (t1) and evaluated using occurrence data from 1998–2002 (t2). Model sensitivity (the ability to correctly classify species presences) was greater using the maximum entropy algorithm than the random forest algorithm. Although sensitivity did not differ significantly among approaches, for many species, sensitivity was maximized using a hybrid approach that assumed range expansion, but not contraction, in t2. Species for which the hybrid approach resulted in the greatest improvement in sensitivity have been reported from more land cover types than species for which there was little difference in sensitivity between hybrid and dynamic approaches, suggesting that habitat generalists may be buffered somewhat against climate-induced range contractions. Specificity (the ability to correctly classify species absences) was maximized using the random forest algorithm and was lowest using the hybrid approach. Overall, our results suggest cautious optimism for the use of climate envelope models to forecast range shifts, but also underscore the importance of considering non-climate drivers of species range limits. The use of alternative climate envelope models that make different assumptions about range expansion and contraction is a new and potentially useful way to help inform our understanding of climate change effects on species.

  6. Thermal barrier coating life prediction model development

    NASA Technical Reports Server (NTRS)

    Strangman, T. E.; Neumann, J. F.; Liu, A.

    1986-01-01

    Thermal barrier coatings (TBCs) for turbine airfoils in high-performance engines represent an advanced materials technology with both performance and durability benefits. The foremost TBC benefit is the reduction of heat transferred into air-cooled components, which yields performance and durability benefits. This program focuses on predicting the lives of two types of strain-tolerant and oxidation-resistant TBC systems that are produced by commercial coating suppliers to the gas turbine industry. The plasma-sprayed TBC system, composed of a low-pressure plasma-spray (LPPS) or an argon shrouded plasma-spray (ASPS) applied oxidation resistant NiCrAlY (or CoNiCrAlY) bond coating and an air-plasma-sprayed yttria (8 percent) partially stabilized zirconia insulative layer, is applied by Chromalloy, Klock, and Union Carbide. The second type of TBC is applied by the electron beam-physical vapor deposition (EB-PVD) process by Temescal.

  7. MJO prediction skill, predictability, and teleconnection impacts in the Beijing Climate Center Atmospheric General Circulation Model

    NASA Astrophysics Data System (ADS)

    Wu, Jie; Ren, Hong-Li; Zuo, Jinqing; Zhao, Chongbo; Chen, Lijuan; Li, Qiaoping

    2016-09-01

    This study evaluates performance of Madden-Julian oscillation (MJO) prediction in the Beijing Climate Center Atmospheric General Circulation Model (BCC_AGCM2.2). By using the real-time multivariate MJO (RMM) indices, it is shown that the MJO prediction skill of BCC_AGCM2.2 extends to about 16-17 days before the bivariate anomaly correlation coefficient drops to 0.5 and the root-mean-square error increases to the level of the climatological prediction. The prediction skill showed a seasonal dependence, with the highest skill occurring in boreal autumn, and a phase dependence with higher skill for predictions initiated from phases 2-4. The results of the MJO predictability analysis showed that the upper bounds of the prediction skill can be extended to 26 days by using a single-member estimate, and to 42 days by using the ensemble-mean estimate, which also exhibited an initial amplitude and phase dependence. The observed relationship between the MJO and the North Atlantic Oscillation was accurately reproduced by BCC_AGCM2.2 for most initial phases of the MJO, accompanied with the Rossby wave trains in the Northern Hemisphere extratropics driven by MJO convection forcing. Overall, BCC_AGCM2.2 displayed a significant ability to predict the MJO and its teleconnections without interacting with the ocean, which provided a useful tool for fully extracting the predictability source of subseasonal prediction.

  8. Predictions of Cockpit Simulator Experimental Outcome Using System Models

    NASA Technical Reports Server (NTRS)

    Sorensen, J. A.; Goka, T.

    1984-01-01

    This study involved predicting the outcome of a cockpit simulator experiment where pilots used cockpit displays of traffic information (CDTI) to establish and maintain in-trail spacing behind a lead aircraft during approach. The experiments were run on the NASA Ames Research Center multicab cockpit simulator facility. Prior to the experiments, a mathematical model of the pilot/aircraft/CDTI flight system was developed which included relative in-trail and vertical dynamics between aircraft in the approach string. This model was used to construct a digital simulation of the string dynamics including response to initial position errors. The model was then used to predict the outcome of the in-trail following cockpit simulator experiments. Outcome included performance and sensitivity to different separation criteria. The experimental results were then used to evaluate the model and its prediction accuracy. Lessons learned in this modeling and prediction study are noted.

  9. Multikernel linear mixed models for complex phenotype prediction

    PubMed Central

    Weissbrod, Omer; Geiger, Dan; Rosset, Saharon

    2016-01-01

    Linear mixed models (LMMs) and their extensions have recently become the method of choice in phenotype prediction for complex traits. However, LMM use to date has typically been limited by assuming simple genetic architectures. Here, we present multikernel linear mixed model (MKLMM), a predictive modeling framework that extends the standard LMM using multiple-kernel machine learning approaches. MKLMM can model genetic interactions and is particularly suitable for modeling complex local interactions between nearby variants. We additionally present MKLMM-Adapt, which automatically infers interaction types across multiple genomic regions. In an analysis of eight case-control data sets from the Wellcome Trust Case Control Consortium and more than a hundred mouse phenotypes, MKLMM-Adapt consistently outperforms competing methods in phenotype prediction. MKLMM is as computationally efficient as standard LMMs and does not require storage of genotypes, thus achieving state-of-the-art predictive power without compromising computational feasibility or genomic privacy. PMID:27302636

  10. Prediction of High-Lift Flows using Turbulent Closure Models

    NASA Technical Reports Server (NTRS)

    Rumsey, Christopher L.; Gatski, Thomas B.; Ying, Susan X.; Bertelrud, Arild

    1997-01-01

    The flow over two different multi-element airfoil configurations is computed using linear eddy viscosity turbulence models and a nonlinear explicit algebraic stress model. A subset of recently-measured transition locations using hot film on a McDonnell Douglas configuration is presented, and the effect of transition location on the computed solutions is explored. Deficiencies in wake profile computations are found to be attributable in large part to poor boundary layer prediction on the generating element, and not necessarily inadequate turbulence modeling in the wake. Using measured transition locations for the main element improves the prediction of its boundary layer thickness, skin friction, and wake profile shape. However, using measured transition locations on the slat still yields poor slat wake predictions. The computation of the slat flow field represents a key roadblock to successful predictions of multi-element flows. In general, the nonlinear explicit algebraic stress turbulence model gives very similar results to the linear eddy viscosity models.

  11. Noncausal spatial prediction filtering based on an ARMA model

    NASA Astrophysics Data System (ADS)

    Liu, Zhipeng; Chen, Xiaohong; Li, Jingye

    2009-06-01

    Conventional f-x prediction filtering methods are based on an autoregressive model. The error section is first computed as a source noise but is removed as additive noise to obtain the signal, which results in an assumption inconsistency before and after filtering. In this paper, an autoregressive, moving-average model is employed to avoid the model inconsistency. Based on the ARMA model, a noncasual prediction filter is computed and a self-deconvolved projection filter is used for estimating additive noise in order to suppress random noise. The 1-D ARMA model is also extended to the 2-D spatial domain, which is the basis for noncasual spatial prediction filtering for random noise attenuation on 3-D seismic data. Synthetic and field data processing indicate this method can suppress random noise more effectively and preserve the signal simultaneously and does much better than other conventional prediction filtering methods.

  12. Evaluation of battery models for prediction of electric vehicle range

    NASA Technical Reports Server (NTRS)

    Frank, H. A.; Phillips, A. M.

    1977-01-01

    Three analytical models for predicting electric vehicle battery output and the corresponding electric vehicle range for various driving cycles were evaluated. The models were used to predict output and range, and then compared with experimentally determined values determined by laboratory tests on batteries using discharge cycles identical to those encountered by an actual electric vehicle while on SAE cycles. Results indicate that the modified Hoxie model gave the best predictions with an accuracy of about 97 to 98% in the best cases and 86% in the worst case. A computer program was written to perform the lengthy iterative calculations required. The program and hardware used to automatically discharge the battery are described.

  13. Possibility of quantitative prediction of cavitation erosion without model test

    SciTech Connect

    Kato, Hiroharu; Konno, Akihisa; Maeda, Masatsugu; Yamaguchi, Hajime

    1996-09-01

    A scenario for quantitative prediction of cavitation erosion was proposed. The key value is the impact force/pressure spectrum on a solid surface caused by cavitation bubble collapse. As the first step of prediction, the authors constructed the scenario from an estimation of the cavity generation rate to the prediction of impact force spectrum, including the estimations of collapsing cavity number and impact pressure. The prediction was compared with measurements of impact force spectra on a partially cavitating hydrofoil. A good quantitative agreement was obtained between the prediction and the experiment. However, the present method predicted a larger effect of main flow velocity than that observed. The present scenario is promising as a method of predicting erosion without using a model test.

  14. Evaluation of Community Land Model Hydrologic Predictions

    NASA Astrophysics Data System (ADS)

    Li, K. Y.; Lettenmaier, D. P.; Bohn, T.; Delire, C.

    2005-12-01

    Confidence in representation and parameterization of land surface processes in coupled land-atmosphere models is strongly dependent on a diversity of opportunities for model testing, since such coupled models are usually intended for application in a wide range of conditions (regional models) or globally. Land surface models have been increasing in complexity over the past decade, which has increased the demands on data sets appropriate for model testing and evaluation. In this study, we compare the performance of two commonly used land surface schemes - the Variable Infiltration Capacity (VIC) and Community Land Model (CLM) with respect to their ability to reproduce observed water and energy fluxes in off-line tests for two large river basins with contrasting hydroclimatic conditions spanning the range from temperate continental to arctic, and for five point (column flux) sites spanning the range from tropical to arctic. The two large river basins are the Arkansas-Red in U.S. southern Great Plains, and the Torne-Kalix in northern Scandinavia. The column flux evaluations are for a tropical forest site at Reserva Jaru (ABRACOS) in Brazil, a prairie site (FIFE) near Manhattan, Kansas in the central U.S., a soybean site at Caumont (HAPEX-Monbilhy) in France, a meadow site at Cabauw in the Netherlands, and a small grassland catchment at Valday, Russia. The results indicate that VIC can reasonably well capture the land surface biophysical processes, while CLM is somewhat less successful. We suggest changes to the CLM parameterizations that would improve its general performance with respect to its representation of land surface hydrologic processes.

  15. Efficient Reduction and Analysis of Model Predictive Error

    NASA Astrophysics Data System (ADS)

    Doherty, J.

    2006-12-01

    Most groundwater models are calibrated against historical measurements of head and other system states before being used to make predictions in a real-world context. Through the calibration process, parameter values are estimated or refined such that the model is able to reproduce historical behaviour of the system at pertinent observation points reasonably well. Predictions made by the model are deemed to have greater integrity because of this. Unfortunately, predictive integrity is not as easy to achieve as many groundwater practitioners would like to think. The level of parameterisation detail estimable through the calibration process (especially where estimation takes place on the basis of heads alone) is strictly limited, even where full use is made of modern mathematical regularisation techniques such as those encapsulated in the PEST calibration package. (Use of these mechanisms allows more information to be extracted from a calibration dataset than is possible using simpler regularisation devices such as zones of piecewise constancy.) Where a prediction depends on aspects of parameterisation detail that are simply not inferable through the calibration process (which is often the case for predictions related to contaminant movement, and/or many aspects of groundwater/surface water interaction), then that prediction may be just as much in error as it would have been if the model had not been calibrated at all. Model predictive error arises from two sources. These are (a) the presence of measurement noise within the calibration dataset through which linear combinations of parameters spanning the "calibration solution space" are inferred, and (b) the sensitivity of the prediction to members of the "calibration null space" spanned by linear combinations of parameters which are not inferable through the calibration process. The magnitude of the former contribution depends on the level of measurement noise. The magnitude of the latter contribution (which often

  16. Drawability Prediction Method using Continuous Texture Evolution Model

    NASA Astrophysics Data System (ADS)

    Morimoto, Toshiharu; Yanagimoto, Jun

    2011-08-01

    Drawability is one of steel strip properties which control press forming. Many predicted method for the Lankford value have been proposed. First, we predict recystallization texture based on the idea that total amount of microscopic crystal slips is proportional to accumulated dislocation density in grain boundaries. Next, we can predict Lankford value of ultra low carbon strips and ferritic stainless steel strips using Sachs model. Our method is very practical to use in hot and cold steel rolling industry.

  17. The model of fungal population dynamics affected by nystatin

    NASA Astrophysics Data System (ADS)

    Voychuk, Sergei I.; Gromozova, Elena N.; Sadovskiy, Mikhail G.

    Fungal diseases are acute problems of the up-to-day medicine. Significant increase of resistance of microorganisms to the medically used antibiotics and a lack of new effective drugs follows in a growth of dosage of existing chemicals to solve the problem. Quite often such approach results in side effects on humans. Detailed study of fungi-antibiotic dynamics can identify new mechanisms and bring new ideas to overcome the microbial resistance with a lower dosage of antibiotics. In this study, the dynamics of the microbial population under antibiotic treatment was investigated. The effects of nystatin on the population of Saccharomyces cerevisiae yeasts were used as a model system. Nystatin effects were investigated both in liquid and solid media by viability tests. Dependence of nystatin action on osmotic gradient was evaluated in NaCl solutions. Influences of glucose and yeast extract were additionally analyzed. A "stepwise" pattern of the cell death caused by nystatin was the most intriguing. This pattern manifested in periodical changes of the stages of cell death against stages of resistance to the antibiotic. The mathematical model was proposed to describe cell-antibiotic interactions and nystatin viability effects in the liquid medium. The model implies that antibiotic ability to cause a cells death is significantly affected by the intracellular compounds, which came out of cells after their osmotic barriers were damaged

  18. Modeled Microgravity Affects Fibroblast Functions Related to Wound Healing

    NASA Astrophysics Data System (ADS)

    Cialdai, Francesca; Vignali, Leonardo; Morbidelli, Lucia; Colciago, Alessandra; Celotti, Fabio; Santi, Alice; Caselli, Anna; Cirri, Paolo; Monici, Monica

    2017-02-01

    Wound healing is crucial for the survival of an organism. Therefore, in the perspective of space exploration missions, it is important to understand if and how microgravity conditions affect the behavior of the cell populations involved in wound healing and the evolution of the process. Since fibroblasts are the major players in tissue repair, this study was focused on the behavior of fibroblasts in microgravity conditions, modeled by a RCCS. Cell cytoskeleton was studied by immunofluorescence microscopy, the ability to migrate was assessed by microchemotaxis and scratch assay, and the expression of markers of fibroblast activation, angiogenesis, and inflammation was assessed by western blot. Results revealed that after cell exposure to modeled microgravity conditions, a thorough rearrangement of microtubules occurred and α-SMA bundles were replaced by a tight network of faulty and disorganized filaments. Exposure to modeled microgravity induced a decrease in α-SMA and E-CAD expressions. Also, the expression of the pro-angiogenic protein VEGF decreased, while that of the inflammatory signal COX-2 increased. Fibroblast ability to adhere, migrate, and respond to chemoattractants (PRP), closely related to cytoskeleton integrity and membrane junctions, was significantly impaired. Nevertheless, PRP was able to partially restore fibroblast migration.

  19. Modeled Microgravity Affects Fibroblast Functions Related to Wound Healing

    NASA Astrophysics Data System (ADS)

    Cialdai, Francesca; Vignali, Leonardo; Morbidelli, Lucia; Colciago, Alessandra; Celotti, Fabio; Santi, Alice; Caselli, Anna; Cirri, Paolo; Monici, Monica

    2017-01-01

    Wound healing is crucial for the survival of an organism. Therefore, in the perspective of space exploration missions, it is important to understand if and how microgravity conditions affect the behavior of the cell populations involved in wound healing and the evolution of the process. Since fibroblasts are the major players in tissue repair, this study was focused on the behavior of fibroblasts in microgravity conditions, modeled by a RCCS. Cell cytoskeleton was studied by immunofluorescence microscopy, the ability to migrate was assessed by microchemotaxis and scratch assay, and the expression of markers of fibroblast activation, angiogenesis, and inflammation was assessed by western blot. Results revealed that after cell exposure to modeled microgravity conditions, a thorough rearrangement of microtubules occurred and α-SMA bundles were replaced by a tight network of faulty and disorganized filaments. Exposure to modeled microgravity induced a decrease in α-SMA and E-CAD expressions. Also, the expression of the pro-angiogenic protein VEGF decreased, while that of the inflammatory signal COX-2 increased. Fibroblast ability to adhere, migrate, and respond to chemoattractants (PRP), closely related to cytoskeleton integrity and membrane junctions, was significantly impaired. Nevertheless, PRP was able to partially restore fibroblast migration.

  20. Reconstructing historical habitat data with predictive models.

    PubMed

    Zweig, Christa L

    2014-01-01

    Historical vegetation data are important to ecological studies, as many structuring processes operate at long time scales, from decades to centuries. Capturing the pattern of variability within a system (enough to declare a significant change from past to present) relies on correct assumptions about the temporal scale of the processes involved. Sufficient long-term data are often lacking, and current techniques have their weaknesses. To address this concern, we constructed multistate and artificial neural network models (ANN) to provide fore- and hindcast vegetation communities considered critical foraging habitat for an endangered bird, the Florida Snail Kite (Rostrhamus sociabilis). Multistate models were not able to hindcast due to our data not satisfying a detailed balance requirement for time reversibility in Markovian dynamics. Multistate models were useful for forecasting and providing environmental variables for the ANN. Results from our ANN hindcast closely mirrored the population collapse of the Snail Kite population using only environmental data to inform the model. The parallel between the two gives us confidence in the hindcasting results and their use in future demographic models.

  1. Development of operational models for space weather prediction

    NASA Astrophysics Data System (ADS)

    Liu, Siqing; Gong, Jiancun

    Since space weather prediction is currently at the stage of transition from human experience to objective forecasting methods, developing operational forecasting models becomes an important way to improve the capabilities of space weather service. As the existing theoretical models are not fully operational when it comes to space weather prediction, we carried out researches on developing operational models, considering the user needs for prediction of key elements in space environment, which have vital impacts on space assets security. We focused on solar activities, geomagnetic activities, high-energy particles, atmospheric density, plasma environment and so forth. Great progresses have been made in developing 3D dynamic asymmetric magnetopause model, plasma sheet energetic electron flux forecasting model and 400km-atmospheric density forecasting model, and also in the prediction of high-speed solar-wind streams from coronal holes and geomagnetic AE indices. Some of these models have already been running in the operational system of Space Environment Prediction Center, National Space Science Center (SEPC/NSSC). This presentation will introduce the research plans for space weather prediction in China, and current progresses of developing operational models and their applications in daily space weather services in SEPC/NSSC.

  2. Ensemble Learning of QTL Models Improves Prediction of Complex Traits

    PubMed Central

    Bian, Yang; Holland, James B.

    2015-01-01

    Quantitative trait locus (QTL) models can provide useful insights into trait genetic architecture because of their straightforward interpretability but are less useful for genetic prediction because of the difficulty in including the effects of numerous small effect loci without overfitting. Tight linkage between markers introduces near collinearity among marker genotypes, complicating the detection of QTL and estimation of QTL effects in linkage mapping, and this problem is exacerbated by very high density linkage maps. Here we developed a thinning and aggregating (TAGGING) method as a new ensemble learning approach to QTL mapping. TAGGING reduces collinearity problems by thinning dense linkage maps, maintains aspects of marker selection that characterize standard QTL mapping, and by ensembling, incorporates information from many more markers-trait associations than traditional QTL mapping. The objective of TAGGING was to improve prediction power compared with QTL mapping while also providing more specific insights into genetic architecture than genome-wide prediction models. TAGGING was compared with standard QTL mapping using cross validation of empirical data from the maize (Zea mays L.) nested association mapping population. TAGGING-assisted QTL mapping substantially improved prediction ability for both biparental and multifamily populations by reducing both the variance and bias in prediction. Furthermore, an ensemble model combining predictions from TAGGING-assisted QTL and infinitesimal models improved prediction abilities over the component models, indicating some complementarity between model assumptions and suggesting that some trait genetic architectures involve a mixture of a few major QTL and polygenic effects. PMID:26276383

  3. Predictive modeling of pedestal structure in KSTAR using EPED model

    NASA Astrophysics Data System (ADS)

    Han, Hyunsun; Kwon, Ohjin; Kim, J. Y.

    2013-10-01

    A predictive calculation is given for the structure of edge pedestal in the H-mode plasma of the KSTAR (Korea Superconducting Tokamak Advanced Research) device using the EPED model. Particularly, the dependence of pedestal width and height on various plasma parameters is studied in detail. The two codes, ELITE and HELENA, are utilized for the stability analysis of the peeling-ballooning and kinetic ballooning modes, respectively. Summarizing the main results, the pedestal slope and height have a strong dependence on plasma current, rapidly increasing with it, while the pedestal width is almost independent of it. The plasma density or collisionality gives initially a mild stabilization, increasing the pedestal slope and height, but above some threshold value its effect turns to a destabilization, reducing the pedestal width and height. Among several plasma shape parameters, the triangularity gives the most dominant effect, rapidly increasing the pedestal width and height, while the effect of elongation and squareness appears to be relatively weak. Implication of these edge results, particularly in relation to the global plasma performance, is discussed.

  4. Predictive modeling of pedestal structure in KSTAR using EPED model

    SciTech Connect

    Han, Hyunsun; Kim, J. Y.; Kwon, Ohjin

    2013-10-15

    A predictive calculation is given for the structure of edge pedestal in the H-mode plasma of the KSTAR (Korea Superconducting Tokamak Advanced Research) device using the EPED model. Particularly, the dependence of pedestal width and height on various plasma parameters is studied in detail. The two codes, ELITE and HELENA, are utilized for the stability analysis of the peeling-ballooning and kinetic ballooning modes, respectively. Summarizing the main results, the pedestal slope and height have a strong dependence on plasma current, rapidly increasing with it, while the pedestal width is almost independent of it. The plasma density or collisionality gives initially a mild stabilization, increasing the pedestal slope and height, but above some threshold value its effect turns to a destabilization, reducing the pedestal width and height. Among several plasma shape parameters, the triangularity gives the most dominant effect, rapidly increasing the pedestal width and height, while the effect of elongation and squareness appears to be relatively weak. Implication of these edge results, particularly in relation to the global plasma performance, is discussed.

  5. A color prediction model for imagery analysis

    NASA Technical Reports Server (NTRS)

    Skaley, J. E.; Fisher, J. R.; Hardy, E. E.

    1977-01-01

    A simple model has been devised to selectively construct several points within a scene using multispectral imagery. The model correlates black-and-white density values to color components of diazo film so as to maximize the color contrast of two or three points per composite. The CIE (Commission Internationale de l'Eclairage) color coordinate system is used as a quantitative reference to locate these points in color space. Superimposed on this quantitative reference is a perceptional framework which functionally contrasts color values in a psychophysical sense. This methodology permits a more quantitative approach to the manual interpretation of multispectral imagery while resulting in improved accuracy and lower costs.

  6. Predicting Market Impact Costs Using Nonparametric Machine Learning Models.

    PubMed

    Park, Saerom; Lee, Jaewook; Son, Youngdoo

    2016-01-01

    Market impact cost is the most significant portion of implicit transaction costs that can reduce the overall transaction cost, although it cannot be measured directly. In this paper, we employed the state-of-the-art nonparametric machine learning models: neural networks, Bayesian neural network, Gaussian process, and support vector regression, to predict market impact cost accurately and to provide the predictive model that is versatile in the number of variables. We collected a large amount of real single transaction data of US stock market from Bloomberg Terminal and generated three independent input variables. As a result, most nonparametric machine learning models outperformed a-state-of-the-art benchmark parametric model such as I-star model in four error measures. Although these models encounter certain difficulties in separating the permanent and temporary cost directly, nonparametric machine learning models can be good alternatives in reducing transaction costs by considerably improving in prediction performance.

  7. Predicting Market Impact Costs Using Nonparametric Machine Learning Models

    PubMed Central

    Park, Saerom; Lee, Jaewook; Son, Youngdoo

    2016-01-01

    Market impact cost is the most significant portion of implicit transaction costs that can reduce the overall transaction cost, although it cannot be measured directly. In this paper, we employed the state-of-the-art nonparametric machine learning models: neural networks, Bayesian neural network, Gaussian process, and support vector regression, to predict market impact cost accurately and to provide the predictive model that is versatile in the number of variables. We collected a large amount of real single transaction data of US stock market from Bloomberg Terminal and generated three independent input variables. As a result, most nonparametric machine learning models outperformed a-state-of-the-art benchmark parametric model such as I-star model in four error measures. Although these models encounter certain difficulties in separating the permanent and temporary cost directly, nonparametric machine learning models can be good alternatives in reducing transaction costs by considerably improving in prediction performance. PMID:26926235

  8. Evaluation of prediction intervals for expressing uncertainties in groundwater flow model predictions

    USGS Publications Warehouse

    Christensen, S.; Cooley, R.L.

    1999-01-01

    We tested the accuracy of 95% individual prediction intervals for hydraulic heads, streamflow gains, and effective transmissivities computed by groundwater models of two Danish aquifers. To compute the intervals, we assumed that each predicted value can be written as the sum of a computed dependent variable and a random error. Testing was accomplished by using a cross-validation method and by using new field measurements of hydraulic heads and transmissivities that were not used to develop or calibrate the models. The tested null hypotheses are that the coverage probability of the prediction intervals is not significantly smaller than the assumed probability (95%) and that each tail probability is not significantly different from the assumed probability (2.5%). In all cases tested, these hypotheses were accepted at the 5% level of significance. We therefore conclude that for the groundwater models of two real aquifers the individual prediction intervals appear to be accurate.We tested the accuracy of 95% individual prediction intervals for hydraulic heads, streamflow gains, and effective transmissivities computed by groundwater models of two Danish aquifers. To compute the intervals, we assumed that each predicted value can be written as the sum of a computed dependent variable and a random error. Testing was accomplished by using a cross-validation method and by using new field measurements of hydraulic heads and transmissivities that were not used to develop or calibrate the models. The tested null hypotheses are that the coverage probability of the prediction intervals is not significantly smaller than the assumed probability (95%) and that each tail probability is not significantly different from the assumed probability (2.5%). In all cases tested, these hypotheses were accepted at the 5% level of significance. We therefore conclude that for the groundwater models of two real aquifers the individual prediction intervals appear to be accurate.

  9. Fun with Numbers: Alternative Models for Predicting Salary Levels.

    ERIC Educational Resources Information Center

    Johnson, Catherine B.; And Others

    1987-01-01

    The increasing concern with equity issues in higher education, along with litigation, has prompted institutions to undertake salary prediction studies. Four models were compared: (1) entering all variables, (2) excluding rank and tenure, (3) using predicted rank and tenure, and (4) using only "objective" variables. (Author/MLW)

  10. Computer Model for Prediction of PCB Dechlorination and Biodegradation Endpoints

    SciTech Connect

    Just, E.M.; Klasson, T.

    1999-04-19

    Mathematical modeling of polychlorinated biphenyl (PCB) transformation served as a means of predicting possible endpoints of bioremediation, thus allowing evaluation of several of the most common transformation patterns. Correlation between laboratory-observed and predicted endpoint data was, in some cases, as good as 0.98 (perfect correlation = 1.0).

  11. PPS-87: a new event oriented solar proton prediction model.

    PubMed

    Smart, D F; Shea, M A

    1989-01-01

    A new event-oriented solar proton prediction model has been developed and implemented at the USAF Space Environment forecast facility. This new model generates predicted solar proton time-intensity profiles for a number of user adjustable energy ranges and is also capable of making predictions for the heavy ion flux. The computer program is designed so a forecaster can select inputs based on the data available in near real-time at the forecast center as the solar flare is occurring. The predicted event amplitude is based on the electromagnetic emission parameters of the solar flare (either microwave or soft X-ray emission) and the solar flare position on the sun. The model also has an update capability where the forecaster can normalize the prediction to actual spacecraft observations of spectral slope and particle flux as the event is occurring in order to more accurately predict the future time-intensity profile of the solar particle flux. Besides containing improvements in the accuracy of the predicted energetic particle event onset time and magnitude, the new model converts the predicted solar particle flux into an expected radiation dose that might be experienced by an astronaut during EVA activities or inside the space shuttle.

  12. Validation of a tuber blight (Phytophthora infestans) prediction model

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Potato tuber blight caused by Phytophthora infestans accounts for significant losses in storage. There is limited published quantitative data on predicting tuber blight. We validated a tuber blight prediction model developed in New York with cultivars Allegany, NY 101, and Katahdin using independent...

  13. Geospatial application of the Water Erosion Prediction Project (WEPP) model

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The Water Erosion Prediction Project (WEPP) model is a process-based technology for prediction of soil erosion by water at hillslope profile, field, and small watershed scales. In particular, WEPP utilizes observed or generated daily climate inputs to drive the surface hydrology processes (infiltrat...

  14. A Model for Prediction of Heat Stability of Photosynthetic Membranes

    Technology Transfer Automated Retrieval System (TEKTRAN)

    A previous study has revealed a positive correlation between heat-induced damage to photosynthetic membranes (thylakoid membranes) and chlorophyll loss. In this study, we exploited this correlation and developed a model for prediction of thermal damage to thylakoids. Prediction is based on estimat...

  15. Predicting Magazine Audiences with a Loglinear Model.

    DTIC Science & Technology

    1987-07-01

    important use of e.d. estimates is in media selection ( Aaker 1975; Lee 1962, 1963; Little and Lodish 1969). All advertising campaigns have a budget. It...N.Z. Listener 6061 39.0 4 0 22 References Aaker , D.A. (1975), "ADMOD:An Advertising Decision Model," Journal of Marketing Research, February, 37-45

  16. VHSIC/VHSIC-Like Reliability Prediction Modeling

    DTIC Science & Technology

    1989-10-01

    flexibility in the methods used by the manufacturer in proving the adequacy of a design. The detailed model accounts for the manufacturing process...factor’ is added to account for the imnpioved reliabUity expectod fron the procedure taken by mianufacturer that -,i on the Qualified Manufacturaer

  17. Risk Prediction Models for Lung Cancer: A Systematic Review.

    PubMed

    Gray, Eoin P; Teare, M Dawn; Stevens, John; Archer, Rachel

    2016-03-01

    Many lung cancer risk prediction models have been published but there has been no systematic review or comprehensive assessment of these models to assess how they could be used in screening. We performed a systematic review of lung cancer prediction models and identified 31 articles that related to 25 distinct models, of which 11 considered epidemiological factors only and did not require a clinical input. Another 11 articles focused on models that required a clinical assessment such as a blood test or scan, and 8 articles considered the 2-stage clonal expansion model. More of the epidemiological models had been externally validated than the more recent clinical assessment models. There was varying discrimination, the ability of a model to distinguish between cases and controls, with an area under the curve between 0.57 and 0.879 and calibration, the model's ability to assign an accurate probability to an individual. In our review we found that further validation studies need to be considered; especially for the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial 2012 Model Version (PLCOM2012) and Hoggart models, which recorded the best overall performance. Future studies will need to focus on prediction rules, such as optimal risk thresholds, for models for selective screening trials. Only 3 validation studies considered prediction rules when validating the models and overall the models were validated using varied tests in distinct populations, which made direct comparisons difficult. To improve this, multiple models need to be tested on the same data set with considerations for sensitivity, specificity, model accuracy, and positive predictive values at the optimal risk thresholds.

  18. The Resource Requirements Prediction Model 1 (RRPM-1): An Overview.

    ERIC Educational Resources Information Center

    Gulko, Warren W.

    This paper provides a brief overview of the conceptual approach used in the initial version of the WICHE Resource Requirements Prediction Model (RRPM-1). RRPM-1 is an institutional-oriented, computer-based model which simulates the cost of operating a college campus over a 3- to 10-year time frame. The model may be viewed as a management tool to…

  19. Investigation of models for large-scale meteorological prediction experiments

    NASA Technical Reports Server (NTRS)

    Spar, J.

    1975-01-01

    The feasibility of extended and long-range weather prediction by means of global atmospheric models was studied. A number of computer experiments were conducted at GISS with the GISS global general circulation model. Topics discussed include atmospheric response to sea-surface temperature anomalies, and monthly mean forecast experiments with the global model.

  20. Propagating uncertainties in statistical model based shape prediction

    NASA Astrophysics Data System (ADS)

    Syrkina, Ekaterina; Blanc, Rémi; Székely, Gàbor

    2011-03-01

    This paper addresses the question of accuracy assessment and confidence regions estimation in statistical model based shape prediction. Shape prediction consists in estimating the shape of an organ based on a partial observation, due e.g. to a limited field of view or poorly contrasted images, and generally requires a statistical model. However, such predictions can be impaired by several sources of uncertainty, in particular the presence of noise in the observation, limited correlations between the predictors and the shape to predict, as well as limitations of the statistical shape model - in particular the number of training samples. We propose a framework which takes these into account and derives confidence regions around the predicted shape. Our method relies on the construction of two separate statistical shape models, for the predictors and for the unseen parts, and exploits the correlations between them assuming a joint Gaussian distribution. Limitations of the models are taken into account by jointly optimizing the prediction and minimizing the shape reconstruction error through cross-validation. An application to the prediction of the shape of the proximal part of the human tibia given the shape of the distal femur is proposed, as well as the evaluation of the reliability of the estimated confidence regions, using a database of 184 samples. Potential applications are reconstructive surgery, e.g. to assess whether an implant fits in a range of acceptable shapes, or functional neurosurgery when the target's position is not directly visible and needs to be inferred from nearby visible structures.

  1. The Evaluation and Prediction of Affective Response to Graduate Teaching Assistants' Classroom Communication.

    ERIC Educational Resources Information Center

    O'Hair, H. Dan; Babich, Roger M.

    A study was conducted to determine whether graduate teaching assistants in speech communication were aware of the affective components of their classroom behavior and of the student responses to them, and whether the instructors' awareness of the affective dimensions of instruction related to the student evaluative responses. Subjects were 640…

  2. Identifying Affective Domains That Correlate and Predict Mathematics Performance in High-Performing Students in Singapore

    ERIC Educational Resources Information Center

    Lim, Siew Yee; Chapman, Elaine

    2015-01-01

    Past studies have shown that distinct yet highly correlated sub-constructs of three broad mathematics affective variables: (a) motivation, (b) attitudes and (c) anxiety, have varying degree of correlation with mathematics achievement. The sub-constructs of these three affective constructs are as follows: (a) (i) amotivation, (ii) external…

  3. Predicting the Accuracy of Facial Affect Recognition: The Interaction of Child Maltreatment and Intellectual Functioning

    ERIC Educational Resources Information Center

    Shenk, Chad E.; Putnam, Frank W.; Noll, Jennie G.

    2013-01-01

    Previous research demonstrates that both child maltreatment and intellectual performance contribute uniquely to the accurate identification of facial affect by children and adolescents. The purpose of this study was to extend this research by examining whether child maltreatment affects the accuracy of facial recognition differently at varying…

  4. Reconnection in NIMROD: Model, Predictions, Remedies

    SciTech Connect

    Fowler, T K; Bulmer, R H; Cohen, B I; Hau, D D

    2003-06-25

    It is shown that in NIMROD the formation of closed current configurations, occurring only after the voltage is turned off, is due to the faster resistive decay of nonsymmetric modes compared to the symmetric projection of the 3D steady state achieved by gun injection. Implementing Spitzer resistivity is required to make a definitive comparison with experiment, using two experimental signatures of the model discussed in the paper. If there are serious disagreements, it is suggested that a phenomenological hyper-resistivity be added to the n = 0 component of Ohm's law, similar to hyper-resistive Corsica models that appear to fit experiments. Hyper-resistivity might capture physics at small scale missed by NIMROD. Encouraging results would motivate coupling NIMROD to SPICE with edge physics inspired by UEDGE, as a tool for experimental data analysis.

  5. Prior probability (the pretest best guess) affects predictive values of diagnostic tests.

    PubMed

    Erb, Hollis N

    2011-06-01

    Authors who publish evaluations of dichotomous (yes/no) diagnostic tests often include the predictive values of their test at a single prior probability (eg, the prevalence of the target disease within the evaluation data set). The objectives of this technical note are to demonstrate why single-probability predictive values are misleading and to show a better way to display positive predictive values (PPV) and negative predictive values (NPV) for a newly evaluated test. Secondly, this technical note will show readers how to calculate predictive values from only sensitivity and specificity for any desired prior probability. As prior probability increases from 0% to 100%, PPV increases from 0% to 100%, but NPV goes in the opposite direction (drops from 100% to 0%). Because prior probabilities vary so greatly across situations, predictive values should be provided in publications for the full range of potential prior probabilities (if provided at all). This is easily done with a 2-curve graph displaying the predictive values (y-axis) against the prior probability (x-axis).

  6. New Model Predicts Fire Activity in South America

    NASA Video Gallery

    UC Irvine scientist Jim Randerson discusses a new model that is able to predict fire activity in South America using sea surface temperature observations of the Pacific and Atlantic Ocean. The find...

  7. Submission Form for Peer-Reviewed Cancer Risk Prediction Models

    Cancer.gov

    If you have information about a peer-reviewd cancer risk prediction model that you would like to be considered for inclusion on this list, submit as much information as possible through the form on this page.

  8. Prediction of Chemical Function: Model Development and Application

    EPA Science Inventory

    The United States Environmental Protection Agency’s Exposure Forecaster (ExpoCast) project is developing both statistical and mechanism-based computational models for predicting exposures to thousands of chemicals, including those in consumer products. The high-throughput (...

  9. Model Predictive Control for Nonlinear Parabolic Partial Differential Equations

    NASA Astrophysics Data System (ADS)

    Hashimoto, Tomoaki; Yoshioka, Yusuke; Ohtsuka, Toshiyuki

    In this study, the optimal control problem of nonlinear parabolic partial differential equations (PDEs) is investigated. Optimal control of nonlinear PDEs is an open problem with applications that include fluid, thermal, biological, and chemically-reacting systems. Model predictive control with a fast numerical solution method has been well established to solve the optimal control problem of nonlinear systems described by ordinary differential equations. In this study, we develop a design method of the model predictive control for nonlinear systems described by parabolic PDEs. Our approach is a direct infinite dimensional extension of the model predictive control method for finite-dimensional systems. The objective of this paper is to develop an efficient algorithm for numerically solving the model predictive control problem of nonlinear parabolic PDEs. The effectiveness of the proposed method is verified by numerical simulations.

  10. Using Pareto points for model identification in predictive toxicology

    PubMed Central

    2013-01-01

    Predictive toxicology is concerned with the development of models that are able to predict the toxicity of chemicals. A reliable prediction of toxic effects of chemicals in living systems is highly desirable in cosmetics, drug design or food protection to speed up the process of chemical compound discovery while reducing the need for lab tests. There is an extensive literature associated with the best practice of model generation and data integration but management and automated identification of relevant models from available collections of models is still an open problem. Currently, the decision on which model should be used for a new chemical compound is left to users. This paper intends to initiate the discussion on automated model identification. We present an algorithm, based on Pareto optimality, which mines model collections and identifies a model that offers a reliable prediction for a new chemical compound. The performance of this new approach is verified for two endpoints: IGC50 and LogP. The results show a great potential for automated model identification methods in predictive toxicology. PMID:23517649

  11. Reliability Prediction Models for Discrete Semiconductor Devices

    DTIC Science & Technology

    1988-07-01

    group for improved model utility. Diode types include: Switching diodes Analog diodes Power rectifiers High voltage rectifiers Fast recovery diodes...analog = .0023, switching = .069, fast recovery = .011, power rectifier diodes including schottky power diodes = .019/junction, power rectifier HV stack...analog, switch, fast recovery rectifier and power rectifiers, and HV stack diodes = 4914, Ge analog, switch, fast recovery, rectifier/power, and HV

  12. Droplet-model predictions of charge moments

    SciTech Connect

    Myers, W.D.

    1982-04-01

    The Droplet Model expressions for calculating various moments of the nuclear charge distribution are given. There are contributions to the moments from the size and shape of the system, from the internal redistribution induced by the Coulomb repulsion, and from the diffuseness of the surface. A case is made for the use of diffuse charge distributions generated by convolution as an alternative to Fermi-functions.

  13. A Predictive Multiscale Model of Wear

    DTIC Science & Technology

    2011-03-09

    theoretical tensile strength, and by fitting the calculated data to universal binding energy relationships ( UBERs ), which permit the extrapolation of the...calculated results to arbitrary length scales. The results demonstrate the ability of an UBER that accounts for fracture surface relaxation to yield a...materials subjected to shear up to the point at which slip occurs. The model we devised is analogous to the tensile-load UBER and leads to a size

  14. Downscaling surface wind predictions from numerical weather prediction models in complex terrain with WindNinja

    NASA Astrophysics Data System (ADS)

    Wagenbrenner, Natalie S.; Forthofer, Jason M.; Lamb, Brian K.; Shannon, Kyle S.; Butler, Bret W.

    2016-04-01

    Wind predictions in complex terrain are important for a number of applications. Dynamic downscaling of numerical weather prediction (NWP) model winds with a high-resolution wind model is one way to obtain a wind forecast that accounts for local terrain effects, such as wind speed-up over ridges, flow channeling in valleys, flow separation around terrain obstacles, and flows induced by local surface heating and cooling. In this paper we investigate the ability of a mass-consistent wind model for downscaling near-surface wind predictions from four NWP models in complex terrain. Model predictions are compared with surface observations from a tall, isolated mountain. Downscaling improved near-surface wind forecasts under high-wind (near-neutral atmospheric stability) conditions. Results were mixed during upslope and downslope (non-neutral atmospheric stability) flow periods, although wind direction predictions generally improved with downscaling. This work constitutes evaluation of a diagnostic wind model at unprecedented high spatial resolution in terrain with topographical ruggedness approaching that of typical landscapes in the western US susceptible to wildland fire.

  15. Modeling Seizure Self-Prediction: An E-Diary Study

    PubMed Central

    Haut, Sheryl R.; Hall, Charles B.; Borkowski, Thomas; Tennen, Howard; Lipton, Richard B.

    2013-01-01

    Purpose A subset of patients with epilepsy successfully self-predicted seizures in a paper diary study. We conducted an e-diary study to ensure that prediction precedes seizures, and to characterize the prodromal features and time windows that underlie self-prediction. Methods Subjects 18 or older with LRE and ≥3 seizures/month maintained an e-diary, reporting AM/PM data daily, including mood, premonitory symptoms, and all seizures. Self-prediction was rated by, “How likely are you to experience a seizure [time frame]”? Five choices ranged from almost certain (>95% chance) to very unlikely. Relative odds of seizure (OR) within time frames was examined using Poisson models with log normal random effects to adjust for multiple observations. Key Findings Nineteen subjects reported 244 eligible seizures. OR for prediction choices within 6hrs was as high as 9.31 (1.92,45.23) for “almost certain”. Prediction was most robust within 6hrs of diary entry, and remained significant up to 12hrs. For 9 best predictors, average sensitivity was 50%. Older age contributed to successful self-prediction, and self-prediction appeared to be driven by mood and premonitory symptoms. In multivariate modeling of seizure occurrence, self-prediction (2.84; 1.68,4.81), favorable change in mood (0.82; 0.67,0.99) and number of premonitory symptoms (1,11; 1.00,1.24) were significant. Significance Some persons with epilepsy can self-predict seizures. In these individuals, the odds of a seizure following a positive prediction are high. Predictions were robust, not attributable to recall bias, and were related to self awareness of mood and premonitory features. The 6-hour prediction window is suitable for the development of pre-emptive therapy. PMID:24111898

  16. Affective Instability in Daily Life Is Predicted by Resting Heart Rate Variability

    PubMed Central

    Kuppens, Peter; Van den Bergh, Omer; Tuerlinckx, Francis; Sütterlin, Stefan

    2013-01-01

    Previous research has shown that being affectively unstable is an indicator of several forms of psychological maladjustment. However, little is known about the mechanisms underlying affective instability. Our research aims to examine the possibility that being prone to extreme fluctuations in one’s feelings is related to maladaptive emotion regulation. We investigated this hypothesis by relating affective instability, assessed in daily life using the experience sampling method, to self-reported emotion regulation strategies and to parasympathetically mediated heart rate variability (HRV), a physiological indicator of emotion regulation capacity. Results showed that HRV was negatively related to instability of positive affect (as measured by mean square successive differences), indicating that individuals with lower parasympathetic tone are emotionally less stable, particularly for positive affect. PMID:24312315

  17. How do you feel? Self-esteem predicts affect, stress, social interaction, and symptom severity during daily life in patients with chronic illness.

    PubMed

    Juth, Vanessa; Smyth, Joshua M; Santuzzi, Alecia M

    2008-10-01

    Self-esteem has been demonstrated to predict health and well-being in a number of samples and domains using retrospective reports, but little is known about the effect of self-esteem in daily life. A community sample with asthma (n = 97) or rheumatoid arthritis (n = 31) completed a self-esteem measure and collected Ecological Momentary Assessment (EMA) data 5x/day for one week using a palmtop computer. Low self-esteem predicted more negative affect, less positive affect, greater stress severity, and greater symptom severity in daily life. Naturalistic exploration of mechanisms relating self-esteem to physiological and/or psychological components in illness may clarify causal relationships and inform theoretical models of self-care, well-being, and disease management.

  18. Comparing the Predictive Value of Multiple Cognitive, Affective, and Motor Tasks after Rodent Traumatic Brain Injury

    PubMed Central

    Loane, David J.; Murray, Michael G.; Stoica, Bogdan A.; Faden, Alan I.

    2012-01-01

    Abstract Controlled cortical impact injury (CCI) is a widely-used, clinically-relevant model of traumatic brain injury (TBI). Although functional outcomes have been used for years in this model, little work has been done to compare the predictive value of various cognitive and sensorimotor assessment tests, singly or in combination. Such information would be particularly useful for assessing mechanisms of injury or therapeutic interventions. Following isoflurane anesthesia, C57BL/6 mice were subjected to sham, mild (5.0 m/sec), moderate (6.0 m/sec), or severe (7.5 m/sec) CCI. A battery of behavioral tests were evaluated and compared, including the standard Morris water maze (sMWM), reversal Morris water maze (rMWM), novel object recognition (NOR), passive avoidance (PA), tail-suspension (TS), beam walk (BW), and open-field locomotor activity. The BW task, performed at post-injury days (PID) 0, 1, 3, 7, 14, 21, and 28, showed good discrimination as a function of injury severity. The sMWM and rMWM tests (PID 14–23), as well as NOR (PID 24 and 25), effectively discriminated spatial and novel object learning and memory across injury severity levels. Notably, the rMWM showed the greatest separation between mild and moderate/severe injury. PA (PID 27 and 28) and TS (PID 24) also reflected differences across injury levels, but to a lesser degree. We also compared individual functional measures with histological outcomes such as lesion volume and neuronal cell loss across anatomical regions. In addition, we created a novel composite behavioral score index from individual complementary behavioral scores, and it provided superior discrimination across injury severities compared to individual tests. In summary, this study demonstrates the feasibility of using a larger number of complementary functional outcome behavioral tests than those traditionally employed to follow post-traumatic recovery after TBI, and suggests that the composite score may be a helpful tool for

  19. Comparing the predictive value of multiple cognitive, affective, and motor tasks after rodent traumatic brain injury.

    PubMed

    Zhao, Zaorui; Loane, David J; Murray, Michael G; Stoica, Bogdan A; Faden, Alan I

    2012-10-10

    Controlled cortical impact injury (CCI) is a widely-used, clinically-relevant model of traumatic brain injury (TBI). Although functional outcomes have been used for years in this model, little work has been done to compare the predictive value of various cognitive and sensorimotor assessment tests, singly or in combination. Such information would be particularly useful for assessing mechanisms of injury or therapeutic interventions. Following isoflurane anesthesia, C57BL/6 mice were subjected to sham, mild (5.0 m/sec), moderate (6.0 m/sec), or severe (7.5 m/sec) CCI. A battery of behavioral tests were evaluated and compared, including the standard Morris water maze (sMWM), reversal Morris water maze (rMWM), novel object recognition (NOR), passive avoidance (PA), tail-suspension (TS), beam walk (BW), and open-field locomotor activity. The BW task, performed at post-injury days (PID) 0, 1, 3, 7, 14, 21, and 28, showed good discrimination as a function of injury severity. The sMWM and rMWM tests (PID 14-23), as well as NOR (PID 24 and 25), effectively discriminated spatial and novel object learning and memory across injury severity levels. Notably, the rMWM showed the greatest separation between mild and moderate/severe injury. PA (PID 27 and 28) and TS (PID 24) also reflected differences across injury levels, but to a lesser degree. We also compared individual functional measures with histological outcomes such as lesion volume and neuronal cell loss across anatomical regions. In addition, we created a novel composite behavioral score index from individual complementary behavioral scores, and it provided superior discrimination across injury severities compared to individual tests. In summary, this study demonstrates the feasibility of using a larger number of complementary functional outcome behavioral tests than those traditionally employed to follow post-traumatic recovery after TBI, and suggests that the composite score may be a helpful tool for screening

  20. Modeling physicochemical interactions affecting in vitro cellular dosimetry of engineered nanomaterials: application to nanosilver

    NASA Astrophysics Data System (ADS)

    Mukherjee, Dwaipayan; Leo, Bey Fen; Royce, Steven G.; Porter, Alexandra E.; Ryan, Mary P.; Schwander, Stephan; Chung, Kian Fan; Tetley, Teresa D.; Zhang, Junfeng; Georgopoulos, Panos G.

    2014-10-01

    Engineered nanomaterials (ENMs) possess unique characteristics affecting their interactions in biological media and biological tissues. Systematic investigation of the effects of particle properties on biological toxicity requires a comprehensive modeling framework which can be used to predict ENM particokinetics in a variety of media. The Agglomeration-diffusion-sedimentation-reaction model (ADSRM) described here is stochastic, using a direct simulation Monte Carlo method to study the evolution of nanoparticles in biological media, as they interact with each other and with the media over time. Nanoparticle diffusion, gravitational settling, agglomeration, and dissolution are treated in a mechanistic manner with focus on silver ENMs (AgNPs). The ADSRM model utilizes particle properties such as size, density, zeta potential, and coating material, along with medium properties like density, viscosity, ionic strength, and pH, to model evolving patterns in a population of ENMs along with their interaction with associated ions and molecules. The model predictions for agglomeration and dissolution are compared with in vitro measurements for various types of ENMs, coating materials, and incubation media, and are found to be overall consistent with measurements. The model has been implemented for an in vitro case in cell culture systems to inform in vitro dosimetry for toxicology studies, and can be directly extended to other biological systems, including in vivo tissue sub-systems by suitably modifying system geometry.

  1. Plasma metabolite levels predict bird growth rates: A field test of model predictive ability.

    PubMed

    Albano, Noelia; Masero, José A; Villegas, Auxiliadora; Abad-Gómez, José María; Sánchez-Guzmán, Juan M

    2011-09-01

    Bird growth rates are usually derived from nonlinear relationships between age and some morphological structure, but this procedure may be limited by several factors. To date, nothing is known about the capacity of plasma metabolite profiling to predict chick growth rates. Based on laboratory-trials, we here develop predictive logistic models of body mass, and tarsus and wing length growth rates in Gull-billed Tern Gelochelidon nilotica chicks from measurements of plasma metabolite levels at different developmental stages. The predictive model obtained during the fastest growth period (at the age of 12 days) explained 65-68% of the chicks' growth rates, with fasting triglyceride level explaining most of the variation in growth rate. At the end of pre-fledging period, β-hydroxybutyrate level was also a good predictor of growth rates. Finally, we carried out a field test to check the predictive capacity of the models in two colonies of wild Gull-billed Tern, comparing field-measured and model-predicted growth rates between groups. Both, measured and predicted growth rates, matched statistically. Plasma metabolite levels can thus be applied in comparative studies of chick growth rates when semi-precocial birds can be captured only once.

  2. Cell flexibility affects the alignment of model myxobacteria.

    PubMed

    Janulevicius, Albertas; van Loosdrecht, Mark C M; Simone, Angelo; Picioreanu, Cristian

    2010-11-17

    Myxobacteria are social bacteria that exhibit a complex life cycle culminating in the development of multicellular fruiting bodies. The alignment of rod-shaped myxobacteria cells within populations is crucial for development to proceed. It has been suggested that myxobacteria align due to mechanical interactions between gliding cells and that cell flexibility facilitates reorientation of cells upon mechanical contact. However, these suggestions have not been based on experimental or theoretical evidence. Here we created a computational mass-spring model of a flexible rod-shaped cell that glides on a substratum periodically reversing direction. The model was formulated in terms of experimentally measurable mechanical parameters, such as engine force, bending stiffness, and drag coefficient. We investigated how cell flexibility and motility engine type affected the pattern of cell gliding and the alignment of a population of 500 mechanically interacting cells. It was found that a flexible cell powered by engine force at the rear of the cell, as suggested by the slime extrusion hypothesis for myxobacteria motility engine, would not be able to glide in the direction of its long axis. A population of rigid reversing cells could indeed align due to mechanical interactions between cells, but cell flexibility impaired the alignment.

  3. Predicted Residual Error Sum of Squares of Mixed Models: An Application for Genomic Prediction

    PubMed Central

    Xu, Shizhong

    2017-01-01

    Genomic prediction is a statistical method to predict phenotypes of polygenic traits using high-throughput genomic data. Most diseases and behaviors in humans and animals are polygenic traits. The majority of agronomic traits in crops are also polygenic. Accurate prediction of these traits can help medical professionals diagnose acute diseases and breeders to increase food products, and therefore significantly contribute to human health and global food security. The best linear unbiased prediction (BLUP) is an important tool to analyze high-throughput genomic data for prediction. However, to judge the efficacy of the BLUP model with a particular set of predictors for a given trait, one has to provide an unbiased mechanism to evaluate the predictability. Cross-validation (CV) is an essential tool to achieve this goal, where a sample is partitioned into K parts of roughly equal size, one part is predicted using parameters estimated from the remaining K – 1 parts, and eventually every part is predicted using a sample excluding that part. Such a CV is called the K-fold CV. Unfortunately, CV presents a substantial increase in computational burden. We developed an alternative method, the HAT method, to replace CV. The new method corrects the estimated residual errors from the whole sample analysis using the leverage values of a hat matrix of the random effects to achieve the predicted residual errors. Properties of the HAT method were investigated using seven agronomic and 1000 metabolomic traits of an inbred rice population. Results showed that the HAT method is a very good approximation of the CV method. The method was also applied to 10 traits in 1495 hybrid rice with 1.6 million SNPs, and to human height of 6161 subjects with roughly 0.5 million SNPs of the Framingham heart study data. Predictabilities of the HAT and CV methods were all similar. The HAT method allows us to easily evaluate the predictabilities of genomic prediction for large numbers of traits in

  4. Launch ascent guidance by discrete multi-model predictive control

    NASA Astrophysics Data System (ADS)

    Vachon, Alexandre; Desbiens, André; Gagnon, Eric; Bérard, Caroline

    2014-02-01

    This paper studies the application of discrete multi-model predictive control as a trajectory tracking guidance law for a space launcher. Two different algorithms are developed, each one based on a different representation of launcher translation dynamics. These representations are based on an interpolation of the linear approximation of nonlinear pseudo-five degrees of freedom equations of translation around an elliptical Earth. The interpolation gives a linear-time-varying representation and a linear-fractional representation. They are used as the predictive model of multi-model predictive controllers. The controlled variables are the orbital parameters, and constraints on a terminal region for the minimal accepted precision are also included. Use of orbital parameters as the controlled variables allows for a partial definition of the trajectory. Constraints can also be included in multi-model predictive control to reduce the number of unknowns of the problem by defining input shaping constraints. The guidance algorithms are tested in nominal conditions and off-nominal conditions with uncertainties on the thrust. The results are compared to those of a similar formulation with a nonlinear model predictive controller and to a guidance method based on the resolution of a simplified version of the two-point boundary value problem. In nominal conditions, the model predictive controllers are more precise and produce a more optimal trajectory but are longer to compute than the two-point boundary solution. Moreover, in presence of uncertainties, developed algorithms exhibit poor robustness properties. The multi-model predictive control algorithms do not reach the desired orbit while the nonlinear model predictive control algorithm still converges but produces larger maneuvers than the other method.

  5. Radio Interference Modeling and Prediction for Satellite Operation Applications

    DTIC Science & Technology

    2015-08-25

    in Task 4 into existing Intelligent Fusion Technology (IFT, a sub-contractor) SATCOM tools to display the RFI prediction and detection results. IFT...how the model was ported into existing IFT ( Intelligent Fusion Technology, Inc) SATCOM tools to display the RFI detection and prediction results...which included North Carolina State University (NCSU) and Intelligent Fusion Technology (IFT) on radio frequency interference (RFI) modeling and

  6. Prediction of cloud droplet number in a general circulation model

    SciTech Connect

    Ghan, S.J.; Leung, L.R.

    1996-04-01

    We have applied the Colorado State University Regional Atmospheric Modeling System (RAMS) bulk cloud microphysics parameterization to the treatment of stratiform clouds in the National Center for Atmospheric Research Community Climate Model (CCM2). The RAMS predicts mass concentrations of cloud water, cloud ice, rain and snow, and number concnetration of ice. We have introduced the droplet number conservation equation to predict droplet number and it`s dependence on aerosols.

  7. Anthropocene changes in desert area: Sensitivity to climate model predictions

    NASA Astrophysics Data System (ADS)

    Mahowald, Natalie M.

    2007-09-01

    Changes in desert area due to humans have important implications from a local, regional to global level. Here I focus on the latter in order to better understand estimated changes in desert dust aerosols and the associated iron deposition into oceans. Using 17 model simulations from the World Climate Research Programme's Coupled Model Intercomparison Project phase 3 multi-model dataset and the BIOME4 equilibrium vegetation model, I estimate changes in desert dust source areas due to climate change and carbon dioxide fertilization. If I assume no carbon dioxide fertilization, the mean of the model predictions is that desert areas expand from the 1880s to the 2080s, due to increased aridity. If I allow for carbon dioxide fertilization, the desert areas become smaller. Thus better understanding carbon dioxide fertilization is important for predicting desert response to climate. There is substantial spread in the model simulation predictions for regional and global averages.

  8. Personalized Predictive Modeling and Risk Factor Identification using Patient Similarity.

    PubMed

    Ng, Kenney; Sun, Jimeng; Hu, Jianying; Wang, Fei

    2015-01-01

    Personalized predictive models are customized for an individual patient and trained using information from similar patients. Compared to global models trained on all patients, they have the potential to produce more accurate risk scores and capture more relevant risk factors for individual patients. This paper presents an approach for building personalized predictive models and generating personalized risk factor profiles. A locally supervised metric learning (LSML) similarity measure is trained for diabetes onset and used to find clinically similar patients. Personalized risk profiles are created by analyzing the parameters of the trained personalized logistic regression models. A 15,000 patient data set, derived from electronic health records, is used to evaluate the approach. The predictive results show that the personalized models can outperform the global model. Cluster analysis of the risk profiles show groups of patients with similar risk factors, differences in the top risk factors for different groups of patients and differences between the individual and global risk factors.

  9. Model predictive control of P-time event graphs

    NASA Astrophysics Data System (ADS)

    Hamri, H.; Kara, R.; Amari, S.

    2016-12-01

    This paper deals with model predictive control of discrete event systems modelled by P-time event graphs. First, the model is obtained by using the dater evolution model written in the standard algebra. Then, for the control law, we used the finite-horizon model predictive control. For the closed-loop control, we used the infinite-horizon model predictive control (IH-MPC). The latter is an approach that calculates static feedback gains which allows the stability of the closed-loop system while respecting the constraints on the control vector. The problem of IH-MPC is formulated as a linear convex programming subject to a linear matrix inequality problem. Finally, the proposed methodology is applied to a transportation system.

  10. Measures and models for predicting cognitive fatigue

    NASA Astrophysics Data System (ADS)

    Trejo, Leonard J.; Kochavi, Rebekah; Kubitz, Karla; Montgomery, Leslie D.; Rosipal, Roman; Matthews, Bryan

    2005-05-01

    We measured multichannel EEG spectra during a continuous mental arithmetic task and created statistical learning models of cognitive fatigue for single subjects. Sixteen subjects (4 F, 18-38 y) viewed 4-digit problems on a computer, solved the problems, and pressed keys to respond (inter-trial interval = 1 s). Subjects performed until either they felt exhausted or three hours had elapsed. Pre- and post-task measures of mood (Activation Deactivation Adjective Checklist, Visual Analogue Mood Scale) confirmed that fatigue increased and energy decreased over time. We examined response times (RT); amplitudes of ERP components N1, P2, and P300, readiness potentials; and power of frontal theta and parietal alpha rhythms for change as a function of time. Mean RT rose from 6.7 s to 7.9 s over time. After controlling for or rejecting sources of artifact such as EOG, EMG, motion, bad electrodes, and electrical interference, we found that frontal theta power rose by 29% and alpha power rose by 44% over the course of the task. We used 30-channel EEG frequency spectra to model the effects of time in single subjects using a kernel partial least squares (KPLS) classifier. We classified 13-s long EEG segments as being from the first or last 15 minutes of the task, using random sub-samples of each class. Test set accuracies ranged from 91% to 100% correct. We conclude that a KPLS classifier of multichannel spectral measures provides a highly accurate model of EEG-fatigue relationships and is suitable for on-line applications to neurological monitoring.

  11. Does high scatter affect the predictive validity of WAIS-III IQs?

    PubMed

    Ryan, Joseph J; Kreiner, David S; Burton, D Bradley

    2002-01-01

    We tested the assumption that high amounts of intersubtest scatter on the Wechsler Adult Intelligence Scale-Third Edition (WAIS-III) subtest profiles compromise predictive validity of the IQs for predicting Wechsler Memory Scale-Third Edition (WMS-III) indexes. Data from a sample of 80 male Veteran's Administration medical center patients were analyzed, half with high intersubtest scatter and half with low scatter. The 2 groups were matched on Full Scale IQ. Correlations of WAIS-III Full Scale IQ with WMS-III indexes were not significantly different between the 2 groups. Further, the regression equations for predicting WMS-III indexes did not depend on the amount of scatter. The results suggest that, when differences in IQ are controlled, the validity of WAIS-III scores in predicting memory performance does not depend on the amount of intersubtest scatter. Further research is needed with samples from different populations using a variety of criterion variables.

  12. CRAFFT: An Activity Prediction Model based on Bayesian Networks.

    PubMed

    Nazerfard, Ehsan; Cook, Diane J

    2015-04-01

    Recent advances in the areas of pervasive computing, data mining, and machine learning offer unique opportunities to provide health monitoring and assistance for individuals facing difficulties to live independently in their homes. Several components have to work together to provide health monitoring for smart home residents including, but not limited to, activity recognition, activity discovery, activity prediction, and prompting system. Compared to the significant research done to discover and recognize activities, less attention has been given to predict the future activities that the resident is likely to perform. Activity prediction components can play a major role in design of a smart home. For instance, by taking advantage of an activity prediction module, a smart home can learn context-aware rules to prompt individuals to initiate important activities. In this paper, we propose an activity prediction model using Bayesian networks together with a novel two-step inference process to predict both the next activity features and the next activity label. We also propose an approach to predict the start time of the next activity which is based on modeling the relative start time of the predicted activity using the continuous normal distribution and outlier detection. To validate our proposed models, we used real data collected from physical smart environments.

  13. Residual bias in a multiphase flow model calibration and prediction

    USGS Publications Warehouse

    Poeter, E.P.; Johnson, R.H.

    2002-01-01

    When calibrated models produce biased residuals, we assume it is due to an inaccurate conceptual model and revise the model, choosing the most representative model as the one with the best-fit and least biased residuals. However, if the calibration data are biased, we may fail to identify an acceptable model or choose an incorrect model. Conceptual model revision could not eliminate biased residuals during inversion of simulated DNAPL migration under controlled conditions at the Borden Site near Ontario Canada. This paper delineates hypotheses for the source of bias, and explains the evolution of the calibration and resulting model predictions.

  14. Models for predicting recreational water quality at Lake Erie beaches

    USGS Publications Warehouse

    Francy, Donna S.; Darner, Robert A.; Bertke, Erin E.

    2006-01-01

    Data collected from four Lake Erie beaches during the recreational seasons of 2004-05 and from one Lake Erie beach during 2000-2005 were used to develop predictive models for recreational water quality by means of multiple linear regression. The best model for each beach was based on a unique combination of environmental and water-quality explanatory variables including turbidity, rainfall, wave height, water temperature, day of the year, wind direction, and lake level. Two types of outputs were produced from the models: the predicted Escherichia coli concentration and the probability that the bathing-water standard will be exceeded. The model for one of beaches, Huntington Reservation (Huntington), was validated in 2005. For 2005, the Huntington model yielded more correct responses and better predicted exceedance of the standard than did current methods for assessing recreational water quality, which are based on the previous day's E. coli concentration. Predictions based on the Huntington model have been available to the public through an Internet-based 'nowcasting' system since May 30, 2006. The other beach models are being validated for the first time in 2006. The methods used in this study to develop and test predictive models can be applied at other similar coastal beaches.

  15. Toward a predictive model for elastomer seals

    NASA Astrophysics Data System (ADS)

    Molinari, Nicola; Khawaja, Musab; Sutton, Adrian; Mostofi, Arash

    Nitrile butadiene rubber (NBR) and hydrogenated-NBR (HNBR) are widely used elastomers, especially as seals in oil and gas applications. During exposure to well-hole conditions, ingress of gases causes degradation of performance, including mechanical failure. We use computer simulations to investigate this problem at two different length and time-scales. First, we study the solubility of gases in the elastomer using a chemically-inspired description of HNBR based on the OPLS all-atom force-field. Starting with a model of NBR, C=C double bonds are saturated with either hydrogen or intramolecular cross-links, mimicking the hydrogenation of NBR to form HNBR. We validate against trends for the mass density and glass transition temperature for HNBR as a function of cross-link density, and for NBR as a function of the fraction of acrylonitrile in the copolymer. Second, we study mechanical behaviour using a coarse-grained model that overcomes some of the length and time-scale limitations of an all-atom approach. Nanoparticle fillers added to the elastomer matrix to enhance mechanical response are also included. Our initial focus is on understanding the mechanical properties at the elevated temperatures and pressures experienced in well-hole conditions.

  16. Modelling of Ceres: Predictions for Dawn

    NASA Astrophysics Data System (ADS)

    Neumann, Wladimir; Breuer, Doris; Spohn, Tilman

    2014-05-01

    Introduction: The asteroid Ceres is the largest body in the asteroid belt. It can be seen as one of the remaining examples of the intermediate stages of planetary accretion, which additionally is substantially different from most asteroids. Studies of such protoplanetary objects like Ceres and Vesta provide insight into the history of the formation of Earth and other rocky planets. One of Ceres' remarkable properties is the relatively low average bulk density of 2077±36 kg m-3 (see [1]). Assuming a nearly chondritic composition, this low value can be explained either by a relatively high average porosity[2], or by the presence of a low density phase[3,4]. Based on numerical modelling[3,4], it has been proposed that this low density phase, which may have been represented initially by water ice or by hydrated silicates, differentiated from the silicates forming an icy mantle overlying a rocky core. However, the shape and the moment of inertia of Ceres seem to be consistent with both a porous and a differentiated structure. In the first case Ceres would be just a large version of a common asteroid. In the second case, however, this body could exhibit properties characteristic for a planet rather than an asteroid: presence of a core, mantle and crust, as well as a liquid ocean in the past and maybe still a thin basal ocean today. This issue is still under debate, but will be resolved (at least partially), once Dawn orbits Ceres. We study the thermal evolution of a Ceres-like body via numerical modeling in order to draw conclusions about the thermal metamorphism of the interior and its present-day state. In particular, we investigate the evolution of the interior assuming an initially porous structure. We adopted the numerical code from [5], which computes the thermal and structural evolution of planetesimals, including compaction of the initially porous primordial material, which is modeled using a creep law. Our model is suited to prioritise between the two possible

  17. Using a Prediction Model to Manage Cyber Security Threats.

    PubMed

    Jaganathan, Venkatesh; Cherurveettil, Priyesh; Muthu Sivashanmugam, Premapriya

    2015-01-01

    Cyber-attacks are an important issue faced by all organizations. Securing information systems is critical. Organizations should be able to understand the ecosystem and predict attacks. Predicting attacks quantitatively should be part of risk management. The cost impact due to worms, viruses, or other malicious software is significant. This paper proposes a mathematical model to predict the impact of an attack based on significant factors that influence cyber security. This model also considers the environmental information required. It is generalized and can be customized to the needs of the individual organization.

  18. Life prediction and constitutive models for engine hot section

    NASA Technical Reports Server (NTRS)

    Swanson, G. A.; Meyer, T. G.; Nissley, D. M.

    1986-01-01

    The purpose of this program is to develop life prediction models for coated anisotropic materials used in gas turbine airfoils. In the program, two single crystal alloys and two coatings are being tested. These include PWA 1480, Alloy 185, overlay coating (PWA 286), and aluminide coating (PWA 273). Constitutive models are also being developed for these materials to predict the time independent (plastic) and time dependent (creep) strain histories of the materials in the lab tests and for actual design conditions. This nonlinear material behavior is particularly important for high temperature gas turbine applications and is basic to any life prediction system. Some of the accomplishments of the program are highlighted.

  19. Modelling and prediction of non-stationary optical turbulence behaviour

    NASA Astrophysics Data System (ADS)

    Doelman, Niek; Osborn, James

    2016-07-01

    There is a strong need to model the temporal fluctuations in turbulence parameters, for instance for scheduling, simulation and prediction purposes. This paper aims at modelling the dynamic behaviour of the turbulence coherence length r0, utilising measurement data from the Stereo-SCIDAR instrument installed at the Isaac Newton Telescope at La Palma. Based on an estimate of the power spectral density function, a low order stochastic model to capture the temporal variability of r0 is proposed. The impact of this type of stochastic model on the prediction of the coherence length behaviour is shown.

  20. Predictive data modeling of human type II diabetes related statistics

    NASA Astrophysics Data System (ADS)

    Jaenisch, Kristina L.; Jaenisch, Holger M.; Handley, James W.; Albritton, Nathaniel G.

    2009-04-01

    During the course of routine Type II treatment of one of the authors, it was decided to derive predictive analytical Data Models of the daily sampled vital statistics: namely weight, blood pressure, and blood sugar, to determine if the covariance among the observed variables could yield a descriptive equation based model, or better still, a predictive analytical model that could forecast the expected future trend of the variables and possibly eliminate the number of finger stickings required to montior blood sugar levels. The personal history and analysis with resulting models are presented.

  1. Predictive modeling of neuroanatomic structures for brain atrophy detection

    NASA Astrophysics Data System (ADS)

    Hu, Xintao; Guo, Lei; Nie, Jingxin; Li, Kaiming; Liu, Tianming

    2010-03-01

    In this paper, we present an approach of predictive modeling of neuroanatomic structures for the detection of brain atrophy based on cross-sectional MRI image. The underlying premise of applying predictive modeling for atrophy detection is that brain atrophy is defined as significant deviation of part of the anatomy from what the remaining normal anatomy predicts for that part. The steps of predictive modeling are as follows. The central cortical surface under consideration is reconstructed from brain tissue map and Regions of Interests (ROI) on it are predicted from other reliable anatomies. The vertex pair-wise distance between the predicted vertex and the true one within the abnormal region is expected to be larger than that of the vertex in normal brain region. Change of white matter/gray matter ratio within a spherical region is used to identify the direction of vertex displacement. In this way, the severity of brain atrophy can be defined quantitatively by the displacements of those vertices. The proposed predictive modeling method has been evaluated by using both simulated atrophies and MRI images of Alzheimer's disease.

  2. Time dependent patient no-show predictive modelling development.

    PubMed

    Huang, Yu-Li; Hanauer, David A

    2016-05-09

    Purpose - The purpose of this paper is to develop evident-based predictive no-show models considering patients' each past appointment status, a time-dependent component, as an independent predictor to improve predictability. Design/methodology/approach - A ten-year retrospective data set was extracted from a pediatric clinic. It consisted of 7,291 distinct patients who had at least two visits along with their appointment characteristics, patient demographics, and insurance information. Logistic regression was adopted to develop no-show models using two-thirds of the data for training and the remaining data for validation. The no-show threshold was then determined based on minimizing the misclassification of show/no-show assignments. There were a total of 26 predictive model developed based on the number of available past appointments. Simulation was employed to test the effective of each model on costs of patient wait time, physician idle time, and overtime. Findings - The results demonstrated the misclassification rate and the area under the curve of the receiver operating characteristic gradually improved as more appointment history was included until around the 20th predictive model. The overbooking method with no-show predictive models suggested incorporating up to the 16th model and outperformed other overbooking methods by as much as 9.4 per cent in the cost per patient while allowing two additional patients in a clinic day. Research limitations/implications - The challenge now is to actually implement the no-show predictive model systematically to further demonstrate its robustness and simplicity in various scheduling systems. Originality/value - This paper provides examples of how to build the no-show predictive models with time-dependent components to improve the overbooking policy. Accurately identifying scheduled patients' show/no-show status allows clinics to proactively schedule patients to reduce the negative impact of patient no-shows.

  3. Predictive Simulation of Reaching Moving Targets Using Nonlinear Model Predictive Control

    PubMed Central

    Mehrabi, Naser; Sharif Razavian, Reza; Ghannadi, Borna; McPhee, John

    2017-01-01

    This article investigates the application of optimal feedback control to trajectory planning in voluntary human arm movements. A nonlinear model predictive controller (NMPC) with a finite prediction horizon was used as the optimal feedback controller to predict the hand trajectory planning and execution of planar reaching tasks. The NMPC is completely predictive, and motion tracking or electromyography data are not required to obtain the limb trajectories. To present this concept, a two degree of freedom musculoskeletal planar arm model actuated by three pairs of antagonist muscles was used to simulate the human arm dynamics. This study is based on the assumption that the nervous system minimizes the muscular effort during goal-directed movements. The effects of prediction horizon length on the trajectory, velocity profile, and muscle activities of a reaching task are presented. The NMPC predictions of the hand trajectory to reach fixed and moving targets are in good agreement with the trajectories found by dynamic optimization and those from experiments. However, the hand velocity and muscle activations predicted by NMPC did not agree as well with experiments or with those found from dynamic optimization. PMID:28133449

  4. Impact of modellers' decisions on hydrological a priori predictions

    NASA Astrophysics Data System (ADS)

    Holländer, H. M.; Bormann, H.; Blume, T.; Buytaert, W.; Chirico, G. B.; Exbrayat, J.-F.; Gustafsson, D.; Hölzel, H.; Krauße, T.; Kraft, P.; Stoll, S.; Blöschl, G.; Flühler, H.

    2013-07-01

    The purpose of this paper is to stimulate a re-thinking of how we, the catchment hydrologists, could become reliable forecasters. A group of catchment modellers predicted the hydrological response of a man-made 6 ha catchment in its initial phase (Chicken Creek) without having access to the observed records. They used conceptually different model families. Their modelling experience differed largely. The prediction exercise was organized in three steps: (1) for the 1st prediction modellers received a basic data set describing the internal structure of the catchment (somewhat more complete than usually available to a priori predictions in ungauged catchments). They did not obtain time series of stream flow, soil moisture or groundwater response. (2) Before the 2nd improved prediction they inspected the catchment on-site and attended a workshop where the modellers presented and discussed their first attempts. (3) For their improved 3rd prediction they were offered additional data by charging them pro forma with the costs for obtaining this additional information. Holländer et al. (2009) discussed the range of predictions obtained in step 1. Here, we detail the modeller's decisions in accounting for the various processes based on what they learned during the field visit (step 2) and add the final outcome of step 3 when the modellers made use of additional data. We document the prediction progress as well as the learning process resulting from the availability of added information. For the 2nd and 3rd step, the progress in prediction quality could be evaluated in relation to individual modelling experience and costs of added information. We learned (i) that soft information such as the modeller's system understanding is as important as the model itself (hard information), (ii) that the sequence of modelling steps matters (field visit, interactions between differently experienced experts, choice of model, selection of available data, and methods for parameter guessing

  5. Risk prediction models for contrast induced nephropathy: systematic review

    PubMed Central

    Silver, Samuel A; Shah, Prakesh M; Chertow, Glenn M; Wald, Ron

    2015-01-01

    Objectives To look at the available literature on validated prediction models for contrast induced nephropathy and describe their characteristics. Design Systematic review. Data sources Medline, Embase, and CINAHL (cumulative index to nursing and allied health literature) databases. Review methods Databases searched from inception to 2015, and the retrieved reference lists hand searched. Dual reviews were conducted to identify studies published in the English language of prediction models tested with patients that included derivation and validation cohorts. Data were extracted on baseline patient characteristics, procedural characteristics, modelling methods, metrics of model performance, risk of bias, and clinical usefulness. Eligible studies evaluated characteristics of predictive models that identified patients at risk of contrast induced nephropathy among adults undergoing a diagnostic or interventional procedure using conventional radiocontrast media (media used for computed tomography or angiography, and not gadolinium based contrast). Results 16 studies were identified, describing 12 prediction models. Substantial interstudy heterogeneity was identified, as a result of different clinical settings, cointerventions, and the timing of creatinine measurement to define contrast induced nephropathy. Ten models were validated internally and six were validated externally. Discrimination varied in studies that were validated internally (C statistic 0.61-0.95) and externally (0.57-0.86). Only one study presented reclassification indices. The majority of higher performing models included measures of pre-existing chronic kidney disease, age, diabetes, heart failure or impaired ejection fraction, and hypotension or shock. No prediction model evaluated its effect on clinical decision making or patient outcomes. Conclusions Most predictive models for contrast induced nephropathy in clinical use have modest ability, and are only relevant to patients receiving contrast for

  6. Dopamine D4 receptor polymorphism and sex interact to predict children’s affective knowledge

    PubMed Central

    Ben-Israel, Sharon; Uzefovsky, Florina; Ebstein, Richard P.; Knafo-Noam, Ariel

    2015-01-01

    Affective knowledge, the ability to understand others’ emotional states, is considered to be a fundamental part in efficient social interaction. Affective knowledge can be seen as related to cognitive empathy, and in the framework of theory of mind (ToM) as affective ToM. Previous studies found that cognitive empathy and ToM are heritable, yet little is known regarding the specific genes involved in individual variability in affective knowledge. Investigating the genetic basis of affective knowledge is important for understanding brain mechanisms underlying socio-cognitive abilities. The 7-repeat (7R) allele within the third exon of the dopamine D4 receptor gene (DRD4-III) has been a focus of interest, due to accumulated knowledge regarding its relevance to individual differences in social behavior. A recent study suggests that an interaction between the DRD4-III polymorphism and sex is associated with cognitive empathy among adults. We aimed to examine the same association in two childhood age groups. Children (N = 280, age 3.5 years, N = 283, age 5 years) participated as part of the Longitudinal Israel Study of Twins. Affective knowledge was assessed through children’s responses to an illustrated story describing different emotional situations, told in a laboratory setting. The findings suggest a significant interaction between sex and the DRD4-III polymorphism, replicated in both age groups. Boy carriers of the 7R allele had higher affective knowledge scores than girls, whereas in the absence of the 7R there was no significant sex effect on affective knowledge. The results support the importance of DRD4-III polymorphism and sex differences to social development. Possible explanations for differences from adult findings are discussed, as are pathways for future studies. PMID:26157401

  7. Negative affect predicts social functioning across schizophrenia and bipolar disorder: Findings from an integrated data analysis.

    PubMed

    Grove, Tyler B; Tso, Ivy F; Chun, Jinsoo; Mueller, Savanna A; Taylor, Stephan F; Ellingrod, Vicki L; McInnis, Melvin G; Deldin, Patricia J

    2016-09-30

    Most people with a serious mental illness experience significant functional impairment despite ongoing pharmacological treatment. Thus, in order to improve outcomes, a better understanding of functional predictors is needed. This study examined negative affect, a construct comprised of negative emotional experience, as a predictor of social functioning across serious mental illnesses. One hundred twenty-seven participants with schizophrenia, 113 with schizoaffective disorder, 22 with psychosis not otherwise specified, 58 with bipolar disorder, and 84 healthy controls (N=404) completed self-report negative affect measures. Elevated levels of negative affect were observed in clinical participants compared with healthy controls. For both clinical and healthy control participants, negative affect measures were significantly correlated with social functioning, and consistently explained significant amounts of variance in functioning. For clinical participants, this relationship persisted even after accounting for cognition and positive/negative symptoms. The findings suggest that negative affect is a strong predictor of outcome across these populations and treatment of serious mental illnesses should target elevated negative affect in addition to cognition and positive/negative symptoms.

  8. A model for predicting lung cancer response to therapy

    SciTech Connect

    Seibert, Rebecca M. . E-mail: rseiber1@utk.edu; Ramsey, Chester R.; Hines, J. Wesley; Kupelian, Patrick A.; Langen, Katja M.; Meeks, Sanford L.; Scaperoth, Daniel D.

    2007-02-01

    Purpose: Volumetric computed tomography (CT) images acquired by image-guided radiation therapy (IGRT) systems can be used to measure tumor response over the course of treatment. Predictive adaptive therapy is a novel treatment technique that uses volumetric IGRT data to actively predict the future tumor response to therapy during the first few weeks of IGRT treatment. The goal of this study was to develop and test a model for predicting lung tumor response during IGRT treatment using serial megavoltage CT (MVCT). Methods and Materials: Tumor responses were measured for 20 lung cancer lesions in 17 patients that were imaged and treated with helical tomotherapy with doses ranging from 2.0 to 2.5 Gy per fraction. Five patients were treated with concurrent chemotherapy, and 1 patient was treated with neoadjuvant chemotherapy. Tumor response to treatment was retrospectively measured by contouring 480 serial MVCT images acquired before treatment. A nonparametric, memory-based locally weight regression (LWR) model was developed for predicting tumor response using the retrospective tumor response data. This model predicts future tumor volumes and the associated confidence intervals based on limited observations during the first 2 weeks of treatment. The predictive accuracy of the model was tested using a leave-one-out cross-validation technique with the measured tumor responses. Results: The predictive algorithm was used to compare predicted verse-measured tumor volume response for all 20 lesions. The average error for the predictions of the final tumor volume was 12%, with the true volumes always bounded by the 95% confidence interval. The greatest model uncertainty occurred near the middle of the course of treatment, in which the tumor response relationships were more complex, the model has less information, and the predictors were more varied. The optimal days for measuring the tumor response on the MVCT images were on elapsed Days 1, 2, 5, 9, 11, 12, 17, and 18 during

  9. Modelling proteins' hidden conformations to predict antibiotic resistance

    NASA Astrophysics Data System (ADS)

    Hart, Kathryn M.; Ho, Chris M. W.; Dutta, Supratik; Gross, Michael L.; Bowman, Gregory R.

    2016-10-01

    TEM β-lactamase confers bacteria with resistance to many antibiotics and rapidly evolves activity against new drugs. However, functional changes are not easily explained by differences in crystal structures. We employ Markov state models to identify hidden conformations and explore their role in determining TEM's specificity. We integrate these models with existing drug-design tools to create a new technique, called Boltzmann docking, which better predicts TEM specificity by accounting for conformational heterogeneity. Using our MSMs, we identify hidden states whose populations correlate with activity against cefotaxime. To experimentally detect our predicted hidden states, we use rapid mass spectrometric footprinting and confirm our models' prediction that increased cefotaxime activity correlates with reduced Ω-loop flexibility. Finally, we design novel variants to stabilize the hidden cefotaximase states, and find their populations predict activity against cefotaxime in vitro and in vivo. Therefore, we expect this framework to have numerous applications in drug and protein design.

  10. Groundwater Level Prediction using M5 Model Trees

    NASA Astrophysics Data System (ADS)

    Nalarajan, Nitha Ayinippully; Mohandas, C.

    2015-01-01

    Groundwater is an important resource, readily available and having high economic value and social benefit. Recently, it had been considered a dependable source of uncontaminated water. During the past two decades, increased rate of extraction and other greedy human actions have resulted in the groundwater crisis, both qualitatively and quantitatively. Under prevailing circumstances, the availability of predicted groundwater levels increase the importance of this valuable resource, as an aid in the planning of groundwater resources. For this purpose, data-driven prediction models are widely used in the present day world. M5 model tree (MT) is a popular soft computing meth