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Sample records for affect model predictions

  1. Models of Affective Decision Making: How Do Feelings Predict Choice?

    PubMed

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

    2016-06-01

    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

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

    DOE PAGESBeta

    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

  3. 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.

  4. Sensitivity analysis of a global aerosol model to understand how parametric uncertainties affect model predictions

    NASA Astrophysics Data System (ADS)

    Lee, L. A.; Carslaw, K. S.; Pringle, K. J.

    2012-04-01

    Global aerosol contributions to radiative forcing (and hence climate change) are persistently subject to large uncertainty in successive Intergovernmental Panel on Climate Change (IPCC) reports (Schimel et al., 1996; Penner et al., 2001; Forster et al., 2007). As such more complex global aerosol models are being developed to simulate aerosol microphysics in the atmosphere. The uncertainty in global aerosol model estimates is currently estimated by measuring the diversity amongst different models (Textor et al., 2006, 2007; Meehl et al., 2007). The uncertainty at the process level due to the need to parameterise in such models is not yet understood and it is difficult to know whether the added model complexity comes at a cost of high model uncertainty. In this work the model uncertainty and its sources due to the uncertain parameters is quantified using variance-based sensitivity analysis. Due to the complexity of a global aerosol model we use Gaussian process emulation with a sufficient experimental design to make such as a sensitivity analysis possible. The global aerosol model used here is GLOMAP (Mann et al., 2010) and we quantify the sensitivity of numerous model outputs to 27 expertly elicited uncertain model parameters describing emissions and processes such as growth and removal of aerosol. Using the R package DiceKriging (Roustant et al., 2010) along with the package sensitivity (Pujol, 2008) it has been possible to produce monthly global maps of model sensitivity to the uncertain parameters over the year 2008. Global model outputs estimated by the emulator are shown to be consistent with previously published estimates (Spracklen et al. 2010, Mann et al. 2010) but now we have an associated measure of parameter uncertainty and its sources. It can be seen that globally some parameters have no effect on the model predictions and any further effort in their development may be unnecessary, although a structural error in the model might also be identified. The

  5. Neuromusculoskeletal Model Calibration Significantly Affects Predicted Knee Contact Forces for Walking.

    PubMed

    Serrancolí, Gil; Kinney, Allison L; Fregly, Benjamin J; Font-Llagunes, Josep M

    2016-08-01

    Though walking impairments are prevalent in society, clinical treatments are often ineffective at restoring lost function. For this reason, researchers have begun to explore the use of patient-specific computational walking models to develop more effective treatments. However, the accuracy with which models can predict internal body forces in muscles and across joints depends on how well relevant model parameter values can be calibrated for the patient. This study investigated how knowledge of internal knee contact forces affects calibration of neuromusculoskeletal model parameter values and subsequent prediction of internal knee contact and leg muscle forces during walking. Model calibration was performed using a novel two-level optimization procedure applied to six normal walking trials from the Fourth Grand Challenge Competition to Predict In Vivo Knee Loads. The outer-level optimization adjusted time-invariant model parameter values to minimize passive muscle forces, reserve actuator moments, and model parameter value changes with (Approach A) and without (Approach B) tracking of experimental knee contact forces. Using the current guess for model parameter values but no knee contact force information, the inner-level optimization predicted time-varying muscle activations that were close to experimental muscle synergy patterns and consistent with the experimental inverse dynamic loads (both approaches). For all the six gait trials, Approach A predicted knee contact forces with high accuracy for both compartments (average correlation coefficient r = 0.99 and root mean square error (RMSE) = 52.6 N medial; average r = 0.95 and RMSE = 56.6 N lateral). In contrast, Approach B overpredicted contact force magnitude for both compartments (average RMSE = 323 N medial and 348 N lateral) and poorly matched contact force shape for the lateral compartment (average r = 0.90 medial and -0.10 lateral). Approach B had statistically higher lateral

  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. INTER-INDIVIDUAL VARIATION IN VERTEBRAL KINEMATICS AFFECTS PREDICTIONS OF NECK MUSCULOSKELETAL MODELS

    PubMed Central

    Nevins, Derek D.; Zheng, Liying; Vasavada, Anita N.

    2014-01-01

    Experimental studies have found significant variation in cervical intervertebral kinematics (IVK) among healthy subjects, but the effect of this variation on biomechanical properties, such as neck strength, has not been explored. The goal of this study was to quantify variation in model predictions of extension strength, flexion strength and gravitational demand (the ratio of gravitational load from the weight of the head to neck muscle extension strength), due to inter-subject variation in IVK. IVK were measured from sagittal radiographs of twenty-four subjects (14F, 10M) in five postures: maximal extension, mid-extension, neutral, mid-flexion, and maximal flexion. IVK were defined by the position (anterior-posterior and superior-inferior) of each cervical vertebra with respect to T1 and its angle with respect to horizontal, and fit with a cubic polynomial over the range of motion. The IVK of each subject were scaled and incorporated into musculoskeletal models to create models that were identical in muscle force- and moment-generating properties but had subject-specific kinematics. The effect of inter-subject variation in IVK was quantified using the coefficient of variation (COV), the ratio of the standard deviation to the mean. COV of extension strength ranged from 8 – 15% over the range of motion, but COV of flexion strength were 20 – 80%. Moreover, the COV of gravitational demand was 80 – 90%, because the gravitational demand is affected by head position as well as neck strength. These results indicate that including inter-individual variation in models is important for evaluating neck musculoskeletal biomechanical properties. PMID:25234351

  8. 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.

  9. 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…

  10. How do alternative root water uptake models affect the inverse estimation of soil hydraulic parameters and the prediction of evapotranspiration?

    NASA Astrophysics Data System (ADS)

    Gayler, Sebastian; Salima-Sultana, Daisy; Selle, Benny; Ingwersen, Joachim; Wizemann, Hans-Dieter; Högy, Petra; Streck, Thilo

    2016-04-01

    Soil water extraction by roots affects the dynamics and distribution of soil moisture and controls transpiration, which influences soil-vegetation-atmosphere feedback processes. Consequently, root water uptake requires close attention when predicting water fluxes across the land surface, e.g., in agricultural crop models or in land surface schemes of weather and climate models. The key parameters for a successful simultaneous simulation of soil moisture dynamics and evapotranspiration in Richards equation-based models are the soil hydraulic parameters, which describe the shapes of the soil water retention curve and the soil hydraulic conductivity curve. As measurements of these parameters are expensive and their estimation from basic soil data via pedotransfer functions is rather inaccurate, the values of the soil hydraulic parameters are frequently inversely estimated by fitting the model to measured time series of soil water content and evapotranspiration. It is common to simulate root water uptake and transpiration by simple stress functions, which describe from which soil layer water is absorbed by roots and predict when total crop transpiration is decreased in case of soil water limitations. As for most of the biogeophysical processes simulated in crop and land surface models, there exist several alternative functional relationships for simulating root water uptake and there is no clear reason for preferring one process representation over another. The error associated with alternative representations of root water uptake, however, contributes to structural model uncertainty and the choice of the root water uptake model may have a significant impact on the values of the soil hydraulic parameters estimated inversely. In this study, we use the agroecosystem model system Expert-N to simulate soil moisture dynamics and evapotranspiration at three agricultural field sites located in two contrasting regions in Southwest Germany (Kraichgau, Swabian Alb). The Richards

  11. 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...

  12. Predicting Long-term Soil Organic Matter Dynamics as affected by Agricultural Management Practice Using the CQESTR Model

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Management of soil organic matter (SOM) is important for soil productivity and responsible utilization of crop residues. Carbon (C) models are needed to predict long-term effects of management practices on C storage in soils and to estimate the benefits when considering alternative management practi...

  13. Use of fuzzy logic models for prediction of taste and odor compounds in algal bloom-affected inland water bodies.

    PubMed

    Bruder, Slawa; Babbar-Sebens, Meghna; Tedesco, Lenore; Soyeux, Emmanuel

    2014-03-01

    Mechanistic modeling of how algal species produce metabolites (e.g., taste and odor compounds geosmin and 2-methyl isoborneol (2-MIB)) as a biological response is currently not well understood. However, water managers and water utilities using these reservoirs often need methods for predicting metabolite production, so that appropriate water treatment procedures can be implemented. In this research, a heuristic approach using Adaptive Network-based Fuzzy Inference System (ANFIS) was developed to determine the underlying nonlinear and uncertain quantitative relationship between observed cyanobacterial metabolites (2-MIB and geosmin), various algal species, and physical and chemical variables. The model is proposed to be used in conjunction with numerical water quality models that can predict spatial-temporal distribution of flows, velocities, water quality parameters, and algal functional groups. The coupling of the proposed metabolite model with the numerical water quality models would assist various utilities which use mechanistic water quality models to also be able to predict distribution of taste and odor metabolites, especially when monitoring of metabolites is limited. The proposed metabolite model was developed and tested for the Eagle Creek Reservoir in Indiana (USA) using observations over a 3-year period (2008-2010). Results show that the developed models performed well for geosmin (R (2) = 0.83 for all training data and R (2) = 0.78 for validation of all 10 data points in the validation dataset) and reasonably well for the 2-MIB (R (2) = 0.82 for all training data and R (2) = 0.70 for 7 out of 10 data points in the validation dataset). PMID:24242080

  14. 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

  15. Solar regeneration of powdered activated carbon impregnated with visible-light responsive photocatalyst: factors affecting performances and predictive model.

    PubMed

    Yap, Pow-Seng; Lim, Teik-Thye

    2012-06-01

    This study demonstrated a green technique to regenerate spent powdered activated carbon (AC) using solar photocatalysis. The AC was impregnated with a photocatalyst photoexcitable under visible-light irradiation to yield a solar regenerable composite, namely nitrogen-doped titanium dioxide (N-TiO(2)/AC). This composite exhibited bifunctional adsorptive-photocatalytic characteristics. Contaminants of emerging environmental concern, i.e. bisphenol-A (BPA), sulfamethazine (SMZ) and clofibric acid (CFA) which exhibited varying affinities for AC were chosen as target pollutants. The adsorption of BPA and SMZ by the N-TiO(2)/AC was significantly higher than that of CFA. The performance of solar photocatalytic regeneration (SPR) of the spent N-TiO(2)/AC composite generally increased with light intensity, N-TiO(2) loading and temperature. The regeneration efficiency (RE) for CFA-loaded spent composite was the highest compared to the other pollutant-loaded spent composites, achieving 77% within 8h of solar irradiation (765 W m(-2)). The rate-limiting process was pollutant desorption from the interior AC sorption sites. A kinetic model was developed to predict the transient concentration of the sorbate remaining in the spent composite during SPR. Comparison studies using solvent extraction technique indicated a different order of RE for the three pollutants, attributable to their varying solubilities in the aqueous and organic solvents. PMID:22464146

  16. 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.

  17. Battery Life Predictive Model

    2009-12-31

    The Software consists of a model used to predict battery capacity fade and resistance growth for arbitrary cycling and temperature profiles. It allows the user to extrapolate from experimental data to predict actual life cycle.

  18. 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. PMID:27286683

  19. 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.

  20. Blood alcohol concentrations: factors affecting predictions.

    PubMed

    Winek, C L; Esposito, F M

    1985-01-01

    As a result of extensive alcohol research conducted on both humans and animals, it is possible to predict a BAC, given pertinent data. In addition, it is possible to estimate from a given BAC the quantity of alcohol consumed. Caution must be used in these predictions, for certain factors will affect the final estimation. Absorption of alcohol is influenced by gastrointestinal contents and motility, and also the composition and quantity of the alcoholic beverage. The vascularity of tissues influences the distribution of alcohol, and their water content will determine the amount of alcohol present after equilibrium. Elimination of alcohol begins immediately after absorption. The elimination rate varies for individuals but falls between .015 percent to .020 percent per hour, with an average of .018 percent per hour. In addition to these factors, a BAC will depend on the subject's weight, percentage of alcohol in the beverage, and the rate of drinking. The principal effect of alcohol in the body is on the central nervous system. Its depressant effect consists of impairment to sensory, motor and learned functions. When combined with some other drugs, a more intoxicated state occurs. Although tolerance to alcohol at low blood concentrations is possible, the tolerance most noted is a learned tolerance among chronic drinkers. contamination of antemortem blood samples collected for alcohol analysis is minimal when swabbing with an ethanolic antiseptic is performed with routine clinical technique; sloppy swabbing has been shown to increase the BAC determination significantly. The alcoholic content of blood used for transfusion does not contribute significantly to the BAC of the recipient, since extensive dilution occurs; nor does the alcohol present in injectable medication contribute significantly. Although many factors may alter the concentration of alcohol present in autopsy specimens, postmortem synthesis of alcohol receives the most attention. The microorganisms that

  1. Development of embrittlement prediction models for U.S. power reactors and the impact of the heat-affected zone to thermal annealing

    SciTech Connect

    Wang, J.A.

    1998-05-01

    The NRC Regulatory Guide 1.99 Revision 2 was based on 177 surveillance data points and the EPRI data base, where 76% of 177 data points and 60% of EPRI data base were from Westinghouse`s data. Therefore, other vendors` radiation environment may not be properly characterized by R.G. 1.99`s prediction. To minimize scatter from the influences of the irradiation temperature, neutron energy spectrum, displacement rate, and plant operation procedures on embrittlement models, improved embrittlement models based on group data that have similar radiation environments and reactor design and operation criteria are examined. A total of 653 shift data points from the current FR-EDB, including 397 Westinghouse data, 93 B and W data, 37 CE data, and 106 GE data, are used. A nonlinear least squares fitting FORTRAN program, incorporating a Monte Carlo procedure with 35% and 10% uncertainty assigned to the fluence and shift data, respectively, was written for this study. In order to have the same adjusted fluence value for the weld and plate material in the same capsule, the Monte Carlo least squares fitting procedure has the ability to adjust the fluence values while running the weld and plate formula simultaneously. Six chemical components, namely, copper, nickel, phosphorus, sulfur, manganese, and molybdenum, were considered in the development of the new embrittlement models. The overall percentage of reduction of the 2-sigma margins per delta RTNDT predicted by the new embrittlement models, compared to that of R.G. 1.99, for weld and base materials are 42% and 36%, respectively. Currently, the need for thermal annealing is seriously being considered for several A302B type RPVs. From the macroscopic view point, even if base and weld materials were verified from mechanical tests to be fully recovered, the linking heat affected zone (HAZ) material has not been properly characterized. Thus the final overall recovery will still be unknown. The great data scatter of the HAZ metals may

  2. 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.

  3. 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. PMID:23423686

  4. 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.

  5. [Predictive models for ART].

    PubMed

    Arvis, P; Guivarc'h-Levêque, A; Varlan, E; Colella, C; Lehert, P

    2013-02-01

    A predictive model is a mathematical expression estimating the probability of pregnancy, by combining predictive variables, or indicators. Its development requires three successive phases: formulation of the model, its validation--internal then external--and the impact study. Its performance is assessed by its discrimination and its calibration. Numerous models were proposed, for spontaneous pregnancies, IUI and IVF, but with rather poor results, and their external validation was seldom carried out and was mainly inconclusive. The impact study-consisting in ascertaining whether their use improves medical practice--was exceptionally done. The ideal ART predictive model is a "Center specific" model, helping physicians to choose between abstention, IUI and IVF, by providing a reliable cumulative rate of pregnancy for each option. This tool would allow to rationalize the practices, by avoiding premature, late, or hopeless treatments. The model would also allow to compare the performances between ART Centers based on objective criteria. Today the best solution is to adjust the existing models to one's own practice, by considering models validated with variables describing the treated population, whilst adjusting the calculation to the Center's performances. PMID:23182786

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

    NASA Astrophysics Data System (ADS)

    Patil, Sopan D.; Wigington, Parker J.; Leibowitz, Scott G.; Sproles, Eric A.; Comeleo, Randy L.

    2014-09-01

    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 (distributed) meteorological inputs instead of spatially uniform (lumped) meteorological inputs. Both lumped and distributed versions of the EXP-HYDRO model are implemented at 41 meso-scale (500-5000 km2) catchments in the Pacific Northwest region of USA. We use two complementary metrics of long-term spatial climate variability, moisture homogeneity index (IM) and temperature variability index (ITV), to analyze the performance improvement with distributed model. Results show that the distributed model performs better than the lumped model in 38 out of 41 catchments, and noticeably better (>10% improvement) in 13 catchments. Furthermore, spatial variability of moisture distribution alone is insufficient to explain the observed patterns of model performance improvement. For catchments with low moisture homogeneity (IM < 80%), IM is a better predictor of model performance improvement than ITV; whereas for catchments with high moisture homogeneity (IM > 80%), ITV is a better predictor of performance improvement than IM. Based on the results, we conclude that: (1) catchments that have low homogeneity of moisture distribution are the obvious candidates for using spatially distributed meteorological inputs, and (2) catchments with a homogeneous moisture distribution benefit from spatially distributed meteorological inputs if they also have high spatial variability of precipitation phase (rain vs. snow).

  7. Acoustic and Lexical Representations for Affect Prediction in Spontaneous Conversations

    PubMed Central

    Cao, Houwei; Savran, Arman; Verma, Ragini; Nenkova, Ani

    2014-01-01

    In this article we investigate what representations of acoustics and word usage are most suitable for predicting dimensions of affect|AROUSAL, VALANCE, POWER and EXPECTANCY|in spontaneous interactions. Our experiments are based on the AVEC 2012 challenge dataset. For lexical representations, we compare corpus-independent features based on psychological word norms of emotional dimensions, as well as corpus-dependent representations. We find that corpus-dependent bag of words approach with mutual information between word and emotion dimensions is by far the best representation. For the analysis of acoustics, we zero in on the question of granularity. We confirm on our corpus that utterance-level features are more predictive than word-level features. Further, we study more detailed representations in which the utterance is divided into regions of interest (ROI), each with separate representation. We introduce two ROI representations, which significantly outperform less informed approaches. In addition we show that acoustic models of emotion can be improved considerably by taking into account annotator agreement and training the model on smaller but reliable dataset. Finally we discuss the potential for improving prediction by combining the lexical and acoustic modalities. Simple fusion methods do not lead to consistent improvements over lexical classifiers alone but improve over acoustic models. PMID:25382936

  8. 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

  9. Chronotype predicts positive affect rhythms measured by ecological momentary assessment.

    PubMed

    Miller, Megan A; Rothenberger, Scott D; Hasler, Brant P; Donofry, Shannon D; Wong, Patricia M; Manuck, Stephen B; Kamarck, Thomas W; Roecklein, Kathryn A

    2015-04-01

    Evening chronotype, a correlate of delayed circadian rhythms, is associated with depression. Altered positive affect (PA) rhythms may mediate the association between evening chronotype and depression severity. Consequently, a better understanding of the relationship between chronotype and PA may aid in understanding the etiology of depression. Recent studies have found that individuals with evening chronotype show delayed and blunted PA rhythms, although these studies are relatively limited in sample size, representativeness and number of daily affect measures. Further, published studies have not included how sleep timing changes on workday and non-workdays, or social jet lag (SJL) may contribute to the chronotype-PA rhythm link. Healthy non-depressed adults (n = 408) completed self-report affect and chronotype questionnaires. Subsequently, positive and negative affects were measured hourly while awake for at least two workdays and one non-workday by ecological momentary assessment (EMA). Sleep variables were collected via actigraphy and compared across chronotype groups. A cosinor variant of multilevel modeling was used to model individual and chronotype group rhythms and to calculate two variables: (1) amplitude of PA, or the absolute amount of daily variation from peak to trough during one period of the rhythm and (2) acrophase, or the time at which the peak amplitude of affect rhythms occurred. On workdays, individuals with evening chronotype had significantly lower PA amplitudes and later workday acrophase times than their morning type counterparts. In contrast to predictions, SJL was not found to be a mediator in the relationship between chronotype and PA rhythms. The association of chronotype and PA rhythms in healthy adults may suggest the importance of daily measurement of PA in depressed individuals and would be consistent with the hypothesis that evening chronotype may create vulnerability to depression via delayed and blunted PA rhythms. PMID

  10. 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. PMID:27062059

  11. 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. PMID:25808562

  12. 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

  13. 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.

  14. 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. PMID:25964764

  15. 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

  16. Probabilistic microcell prediction model

    NASA Astrophysics Data System (ADS)

    Kim, Song-Kyoo

    2002-06-01

    A microcell is a cell with 1-km or less radius which is suitable for heavily urbanized area such as a metropolitan city. This paper deals with the microcell prediction model of propagation loss which uses probabilistic techniques. The RSL (Receive Signal Level) is the factor which can evaluate the performance of a microcell and the LOS (Line-Of-Sight) component and the blockage loss directly effect on the RSL. We are combining the probabilistic method to get these performance factors. The mathematical methods include the CLT (Central Limit Theorem) and the SPC (Statistical Process Control) to get the parameters of the distribution. This probabilistic solution gives us better measuring of performance factors. In addition, it gives the probabilistic optimization of strategies such as the number of cells, cell location, capacity of cells, range of cells and so on. Specially, the probabilistic optimization techniques by itself can be applied to real-world problems such as computer-networking, human resources and manufacturing process.

  17. 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

  18. ON PREDICTION AND MODEL VALIDATION

    SciTech Connect

    M. MCKAY; R. BECKMAN; K. CAMPBELL

    2001-02-01

    Quantification of prediction uncertainty is an important consideration when using mathematical models of physical systems. This paper proposes a way to incorporate ''validation data'' in a methodology for quantifying uncertainty of the mathematical predictions. The report outlines a theoretical framework.

  19. Flexible control in processing affective and non-affective material predicts individual differences in trait resilience.

    PubMed

    Genet, Jessica J; Siemer, Matthias

    2011-02-01

    Trait resilience is a stable personality characteristic that involves the self-reported ability to flexibly adapt to emotional events and situations. The present study examined cognitive processes that may explain individual differences in trait resilience. Participants completed self-report measures of trait resilience, cognitive flexibility and working memory capacity tasks, and a novel affective task-switching paradigm that assesses the ability to flexibly switch between processing the affective versus non-affective qualities of affective stimuli (i.e., flexible affective processing). As hypothesised, cognitive flexibility and flexible affective processing were unique predictors of trait resilience. Working memory capacity was not predictive of trait resilience, indicating that trait resilience is tied to specific cognitive processes rather than overall better cognitive functioning. Cognitive flexibility and flexible affective processing were not associated with other trait measures, suggesting that these flexibility processes are unique to trait resilience. This study was among the first to investigate the cognitive abilities underlying trait resilience. PMID:21432680

  20. See it with feeling: affective predictions during object perception

    PubMed Central

    Barrett, L.F.; Bar, Moshe

    2009-01-01

    People see with feeling. We ‘gaze’, ‘behold’, ‘stare’, ‘gape’ and ‘glare’. In this paper, we develop the hypothesis that the brain's ability to see in the present incorporates a representation of the affective impact of those visual sensations in the past. This representation makes up part of the brain's prediction of what the visual sensations stand for in the present, including how to act on them in the near future. The affective prediction hypothesis implies that responses signalling an object's salience, relevance or value do not occur as a separate step after the object is identified. Instead, affective responses support vision from the very moment that visual stimulation begins. PMID:19528014

  1. Multilevel Model Prediction

    ERIC Educational Resources Information Center

    Frees, Edward W.; Kim, Jee-Seon

    2006-01-01

    Multilevel models are proven tools in social research for modeling complex, hierarchical systems. In multilevel modeling, statistical inference is based largely on quantification of random variables. This paper distinguishes among three types of random variables in multilevel modeling--model disturbances, random coefficients, and future response…

  2. Predicting Occupational Strain and Job Satisfaction: The Role of Stress, Coping, Personality, and Affectivity Variables.

    ERIC Educational Resources Information Center

    Fogarty, Gerard J.; Machin, M. Anthony; Albion, Majella J.; Sutherland, Lynette F.; Lalor, Gabrielle I.; Revitt, Susan

    1999-01-01

    Two studies showed that positive and negative affectivity influenced occupational stress, role strain, and coping. Study 3 added job satisfaction to the model, strengthening its predictive validity. Study 4's addition of personality measures did not improve prediction of job satisfaction and strain. (SK)

  3. 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…

  4. 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

  5. The affective shift model of work engagement.

    PubMed

    Bledow, Ronald; Schmitt, Antje; Frese, Michael; Kühnel, Jana

    2011-11-01

    On the basis of self-regulation theories, the authors develop an affective shift model of work engagement according to which work engagement emerges from the dynamic interplay of positive and negative affect. The affective shift model posits that negative affect is positively related to work engagement if negative affect is followed by positive affect. The authors applied experience sampling methodology to test the model. Data on affective events, mood, and work engagement was collected twice a day over 9 working days among 55 software developers. In support of the affective shift model, negative mood and negative events experienced in the morning of a working day were positively related to work engagement in the afternoon if positive mood in the time interval between morning and afternoon was high. Individual differences in positive affectivity moderated within-person relationships. The authors discuss how work engagement can be fostered through affect regulation. PMID:21766997

  6. Positive and negative family emotional climate differentially predict youth anxiety and depression via distinct affective pathways.

    PubMed

    Luebbe, Aaron M; Bell, Debora J

    2014-08-01

    A socioaffective specificity model was tested in which positive and negative affect differentially mediated relations of family emotional climate to youth internalizing symptoms. Participants were 134 7(th)-9(th) grade adolescents (65 girls; 86 % Caucasian) and mothers who completed measures of emotion-related family processes, experienced affect, anxiety, and depression. Results suggested that a family environment characterized by maternal psychological control and family negative emotion expressiveness predicted greater anxiety and depression, and was mediated by experienced negative affect. Conversely, a family emotional environment characterized by low maternal warmth and low positive emotion expressiveness predicted only depression, and was mediated through lowered experienced positive affect. This study synthesizes a theoretical model of typical family emotion socialization with an extant affect-based model of shared and unique aspects of anxiety and depression symptom expression. PMID:24356797

  7. 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. PMID:24611591

  8. Ice jam flooding: a location prediction model

    NASA Astrophysics Data System (ADS)

    Collins, H. A.

    2009-12-01

    Flooding created by ice jamming is a climatically dependent natural hazard frequently affecting cold regions with disastrous results. Basic known physical characteristics which combine in the landscape to create an ice jam flood are modeled on the Cattaraugus Creek Watershed, located in Western New York State. Terrain analysis of topographic features, and the built environment features is conducted using Geographic Information Systems in order to predict the location of ice jam flooding events. The purpose of this modeling is to establish a broadly applicable Watershed scale model for predicting the probable locations of ice jam flooding.location of historic ice jam flooding events

  9. 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.

  10. 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; 2 carbon dioxide miscible flooding; 3 in-situ combustion; 4 polymer flooding; and 5 steamflood. 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. The IBM PC/AT version includes a plotting capability to produces a graphic picture of the predictive model results.

  11. 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...

  12. 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

  13. A Missense Mutation in CLIC2 Associated with Intellectual Disability is Predicted by In Silico Modeling to Affect Protein Stability and Dynamics

    PubMed Central

    Witham, Shawn; Takano, Kyoko; Schwartz, Charles; Alexov, Emil

    2011-01-01

    Large-scale next generation resequencing of X chromosome genes identified a missense mutation in the CLIC2 gene on Xq28 in a male with X-linked intellectual disability (XLID) and not found in healthy individuals. At the same time, numerous nsSNPs (nonsynonomous SNP) have been reported in the CLIC2 gene in healthy individuals indicating that the CLIC2 protein can tolerate amino acid substitutions and be fully functional. To test the possibility that p.H101Q is a disease-causing mutation, we performed in silico simulations to calculate the effects of the p.H101Q mutation on CLIC2 stability, dynamics and ionization states while comparing the effects obtained for presumably harmless nsSNPs. It was found that p.H101Q, in contrast with other nsSNPs, (a) lessens the flexibility of the joint loop which is important for the normal function of CLIC2, (b) makes the overall 3D structure of CLIC2 more stable and thus reduces the possibility of the large conformational change expected to occur when CLIC2 moves from a soluble to membrane form and (c) removes the positively charged residue, H101, which may be important for the membrane association of CLIC2. The results of in silico modeling, in conjunction with the polymorphism analysis, suggest that p.H101Q may be a disease-causing mutation, the first one suggested in the CLIC family. PMID:21630357

  14. 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.

  15. Neural Affective Mechanisms Predict Market-Level Microlending

    PubMed Central

    Genevsky, Alexander; Knutson, Brian

    2015-01-01

    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. PMID:26187248

  16. A contrail cirrus prediction model

    NASA Astrophysics Data System (ADS)

    Schumann, U.

    2012-05-01

    A new model to simulate and predict the properties of a large ensemble of contrails as a function of given air traffic and meteorology is described. The model is designed for approximate prediction of contrail cirrus cover and analysis of contrail climate impact, e.g. within aviation system optimization processes. The model simulates the full contrail life-cycle. Contrail segments form between waypoints of individual aircraft tracks in sufficiently cold and humid air masses. The initial contrail properties depend on the aircraft. The advection and evolution of the contrails is followed with a Lagrangian Gaussian plume model. Mixing and bulk cloud processes are treated quasi analytically or with an effective numerical scheme. Contrails disappear when the bulk ice content is sublimating or precipitating. The model has been implemented in a "Contrail Cirrus Prediction Tool" (CoCiP). This paper describes the model assumptions, the equations for individual contrails, and the analysis-method for contrail-cirrus cover derived from the optical depth of the ensemble of contrails and background cirrus. The model has been applied for a case study and compared to the results of other models and in-situ contrail measurements. The simple model reproduces a considerable part of observed contrail properties. Mid-aged contrails provide the largest contributions to the product of optical depth and contrail width, important for climate impact.

  17. 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.

  18. 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

  19. Updating outdated predictive accident models.

    PubMed

    Wood, A G; Mountain, L J; Connors, R D; Maher, M J; Ropkins, K

    2013-06-01

    Reliable predictive accident models (PAMs) (also referred to as safety performance functions (SPFs)) are essential to design and maintain safe road networks however, ongoing changes in road and vehicle design coupled with road safety initiatives, mean that these models can quickly become dated. Unfortunately, because the fitting of sophisticated PAMs including a wide range of explanatory variables is not a trivial task, available models tend to be based on data collected many years ago and seem unlikely to give reliable estimates of current accidents. Large, expensive studies to produce new models are likely to be, at best, only a temporary solution. This paper thus seeks to develop a practical and efficient methodology to allow currently available PAMs to be updated to give unbiased estimates of accident frequencies at any point in time. Two principal issues are examined: the extent to which the temporal transferability of predictive accident models varies with model complexity; and the practicality and efficiency of two alternative updating strategies. The models used to illustrate these issues are the suites of models developed for rural dual and single carriageway roads in the UK. These are widely used in several software packages in spite of being based on data collected during the 1980s and early 1990s. It was found that increased model complexity by no means ensures better temporal transferability and that calibration of the models using a scale factor can be a practical alternative to fitting new models. PMID:23510788

  20. 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.

  1. Prediction and diagnosis of clinical outcomes affecting restoration margins.

    PubMed

    Dennison, J B; Sarrett, D C

    2012-04-01

    The longevity of dental restorations is largely dependent on the continuity at the interface between the restorative material and adjacent tooth structure (the restoration margin). Clinical decisions on restoration repair or replacement are usually based upon the weakest point along that margin interface. Physical properties of a restorative material, such as polymerisation shrinkage, water sorption, solubility, elastic modulus and shear strength, all have an effect on stress distribution and can significantly affect margin integrity. This review will focus on two aspects of margin deterioration in the oral environment: the in vitro testing of margin seal using emersion techniques to simulate the oral environment and to predict clinical margin failure and the relationship between clinically observable microleakage and secondary caries. The many variables associated with in vitro testing of marginal leakage and the interpretation of the data are presented in detail. The most recent studies of marginal leakage mirror earlier methodology and lack validity and reliability. The lack of standardised testing procedures makes it impossible to compare studies or to predict the clinical performance of adhesive materials. Continual repeated in vitro studies contribute little to the science in this area. Clinical evidence is cited to refute earlier conclusions that clinical microleakage (penetrating margin discoloration) leads to caries development and is an indication for restoration replacement. Margin defects, without visible evidence of soft dentin on the wall or base of the defect, should be monitored, repaired or resealed, in lieu of total restoration replacement. PMID:22066463

  2. A predictive model of human performance.

    NASA Technical Reports Server (NTRS)

    Walters, R. F.; Carlson, L. D.

    1971-01-01

    An attempt is made to develop a model describing the overall responses of humans to exercise and environmental stresses for prediction of exhaustion vs an individual's physical characteristics. The principal components of the model are a steady state description of circulation and a dynamic description of thermal regulation. The circulatory portion of the system accepts changes in work load and oxygen pressure, while the thermal portion is influenced by external factors of ambient temperature, humidity and air movement, affecting skin blood flow. The operation of the model is discussed and its structural details are given.

  3. 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...

  4. Mathematical model to predict drivers' reaction speeds.

    PubMed

    Long, Benjamin L; Gillespie, A Isabella; Tanaka, Martin L

    2012-02-01

    Mental distractions and physical impairments can increase the risk of accidents by affecting a driver's ability to control the vehicle. In this article, we developed a linear mathematical model that can be used to quantitatively predict drivers' performance over a variety of possible driving conditions. Predictions were not limited only to conditions tested, but also included linear combinations of these tests conditions. Two groups of 12 participants were evaluated using a custom drivers' reaction speed testing device to evaluate the effect of cell phone talking, texting, and a fixed knee brace on the components of drivers' reaction speed. Cognitive reaction time was found to increase by 24% for cell phone talking and 74% for texting. The fixed knee brace increased musculoskeletal reaction time by 24%. These experimental data were used to develop a mathematical model to predict reaction speed for an untested condition, talking on a cell phone with a fixed knee brace. The model was verified by comparing the predicted reaction speed to measured experimental values from an independent test. The model predicted full braking time within 3% of the measured value. Although only a few influential conditions were evaluated, we present a general approach that can be expanded to include other types of distractions, impairments, and environmental conditions. PMID:22431214

  5. Milk skimming, heating, acidification, lysozyme, and rennet affect the pattern, repeatability, and predictability of milk coagulation properties and of curd-firming model parameters: A case study of Grana Padano.

    PubMed

    Stocco, G; Cipolat-Gotet, C; Cecchinato, A; Calamari, L; Bittante, G

    2015-08-01

    Milk coagulation properties are used to evaluate the cheesemaking aptitude of milk samples. No international standard procedure exists, although laboratories often mimic the production of a full-fat fresh cheese for milk coagulation properties. Questions have arisen about the predictability of such a procedure for different types of cheese production. The aim of this study was to establish a procedure mimicking the production conditions of a long-ripened hard cheese, taking Protected Designation of Origin Grana Padano as a case study. With respect to the traditional conditions (standard procedure; SP), the Grana Padano procedure (GP) modifications were the use of standardized milk, coagulation lower temperature, previous milk acidification, lysozyme addition, and rennet type. Each modification was tested in turn versus the SP and also all together in the GP. Another 3 tests were carried out: SP on naturally creamed milk, SP with double the quantity of rennet, and a simplified GP on a full-fat milk sample. The 10 procedures were tested on 2 subsamples with 2 replicates each and were repeated using individual milk samples from 15 dual-purpose Simmental cows in 4 sessions for a total of 600 tests. Two Formagraph instruments (Foss Electric A/S, Hillerød, Denmark) measuring curd firmness every 15 s were used, prolonging test duration to 60min to obtain 5 traditional single-point milk coagulation properties and 3 parameters of the curd firming model using all 240 points recorded for each replicate. The 8 traits of each replicate were analyzed according to a mixed model with fixed effects of 4 sessions, 10 treatments, 2 instruments, and 16microvats, and random effects of 15 animals and 300 subsamples. Compared with the SP, the coagulation and curd firming was slowed by low temperature and was accelerated by acidification and by adding a double amount of rennet; natural creaming, fat standardization, and rennet with 5% pepsin affected only some traits, whereas lysozyme

  6. Predictive Capability Maturity Model (PCMM).

    SciTech Connect

    Swiler, Laura Painton; Knupp, Patrick Michael; Urbina, Angel

    2010-10-01

    Predictive Capability Maturity Model (PCMM) is a communication tool that must include a dicussion of the supporting evidence. PCMM is a tool for managing risk in the use of modeling and simulation. PCMM is in the service of organizing evidence to help tell the modeling and simulation (M&S) story. PCMM table describes what activities within each element are undertaken at each of the levels of maturity. Target levels of maturity can be established based on the intended application. The assessment is to inform what level has been achieved compared to the desired level, to help prioritize the VU activities & to allocate resources.

  7. Biological Invasions Impact Ecosystem Properties and can Affect Climate Predictions

    NASA Astrophysics Data System (ADS)

    Gonzalez-Meler, M.; Matamala, R.; Cook, D. R.; Graham, S.; Fan, Z.; Gomez-Casanovas, N.

    2012-12-01

    Climate change models vary widely in their predictions of the effects of climate forcing, in part because of difficulties in assigning sources of uncertainties and in simulating changes in the carbon source/sink status and climate-carbon cycle feedbacks of terrestrial ecosystems. We studied the impacts of vegetation and weather variations on carbon and energy fluxes at a restored tallgrass prairie in Illinois. The prairie was a strong carbon sink, despite a prolonged drought period and vegetation changes due to the presence of a non-native biennial plant. A model considering the combined effects of air temperature, precipitation, RH, incoming solar radiation, and vegetation was also developed and used to describe net ecosystem exchange for all years. The vegetation factor was represented in the model with summer albedo and/or NDVI. Results showed that the vegetation factor was more important than abiotic factors in describing changes in C and energy fluxes in ecosystems under disturbances. Changes from natives to a non-native forbs species had the strongest effect in reducing net ecosystem production and increasing sensible heat flux and albedo, which may result in positive feedbacks on warming. Here we show that non-native species invasions can alter the ecosystem sensitivity to climatic factors often construed in models.

  8. 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

  9. 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...

  10. 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.

  11. 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

  12. Prediction of carbon steel heat-affected zone microstructure induced by electroslag cladding

    SciTech Connect

    Li, M.V.; Atteridge, D.G.

    1994-12-31

    One of the major concerns in developing electroslag cladding technique is the mechanical properties of the clad heat-affected zone. During the cladding operation, the base metal adjacent to the clad deposit undergoes intensive heating and fast cooling. Mechanical properties of this area are different from, and in most cases inferior to, those of the base metal due to the formation of undesirable microstructure which results from the thermal cycle. To optimize mechanical properties of clad components, steps must be taken to optimize the HAZ microstructure, which is determined by the cladding heat input, geometry of the components, chemistry of the steel, and the thermodynamics and kinetics of phase transformations. There are four main methods for predicting HAZ hardness and microstructure: weld simulation experiments, CCT diagrams, regression analysis based on the carbon equivalents of steels and hardenability studies, and the computational models based on phase transformationkineticss and thermodynamics. The computational approach was adopted in the study to predict the carbon steel HAZ microstructure evolution during electroslag cladding because it is a general approach applicable to a wide range of chemical compositions and welding conditions. The computation model in the study incorporates a grain growth model and a model for austenite decomposition. The empirical grain growth kinetics models and the reaction kinetics model for austenite decomposition originally proposed by Kirkaldy and Venugopalan were calibrated with experimental studies and then coded into a computer program to predict microstructure development. Reasonable agreement was observed between the computer predictions and experimental observations; discrepanciesweree also discussed.

  13. 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.

  14. Differential Predictability of Four Dimensions of Affect Intensity

    PubMed Central

    Rubin, David C.; Hoyle, Rick H.; Leary, Mark R.

    2013-01-01

    Individual differences in affect intensity are typically assessed with the Affect Intensity Measure (AIM). Previous factor analyses suggest that the AIM is comprised of four weakly correlated factors: Positive Affectivity, Negative Reactivity, Negative Intensity and Positive Intensity or Serenity. However, little data exist to show whether its four factors relate to other measures differently enough to preclude use of the total scale score. The present study replicated the four-factor solution and found that subscales derived from the four factors correlated differently with criterion variables that assess personality domains, affective dispositions, and cognitive patterns that are associated with emotional reactions. The results show that use of the total AIM score can obscure relationships between specific features of affect intensity and other variables and suggest that researchers should examine the individual AIM subscales. PMID:21707262

  15. Predictive models and computational toxicology.

    PubMed

    Knudsen, Thomas; Martin, Matthew; Chandler, Kelly; Kleinstreuer, Nicole; Judson, Richard; Sipes, Nisha

    2013-01-01

    Understanding the potential health risks posed by environmental chemicals is a significant challenge elevated by the large number of diverse chemicals with generally uncharacterized exposures, mechanisms, and toxicities. The ToxCast computational toxicology research program was launched by EPA in 2007 and is part of the federal Tox21 consortium to develop a cost-effective approach for efficiently prioritizing the toxicity testing of thousands of chemicals and the application of this information to assessing human toxicology. ToxCast addresses this problem through an integrated workflow using high-throughput screening (HTS) of chemical libraries across more than 650 in vitro assays including biochemical assays, human cells and cell lines, and alternative models such as mouse embryonic stem cells and zebrafish embryo development. The initial phase of ToxCast profiled a library of 309 environmental chemicals, mostly pesticidal actives having rich in vivo data from guideline studies that include chronic/cancer bioassays in mice and rats, multigenerational reproductive studies in rats, and prenatal developmental toxicity endpoints in rats and rabbits. The first phase of ToxCast was used to build models that aim to determine how well in vivo animal effects can be predicted solely from the in vitro data. Phase I is now complete and both the in vitro data (ToxCast) and anchoring in vivo database (ToxRefDB) have been made available to the public (http://actor.epa.gov/). As Phase II of ToxCast is now underway, the purpose of this chapter is to review progress to date with ToxCast predictive modeling, using specific examples on developmental and reproductive effects in rats and rabbits with lessons learned during Phase I. PMID:23138916

  16. 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.

  17. 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.

  18. Numerical weather prediction model tuning via ensemble prediction system

    NASA Astrophysics Data System (ADS)

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

    2011-12-01

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

  19. 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. PMID:26162598

  20. Early Negative Affect Predicts Anxiety, not Autism, in Preschool Boys with Fragile X Syndrome

    PubMed Central

    Tonnsen, Bridgette L.; Malone, Patrick S.; Hatton, Deborah D.

    2012-01-01

    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. PMID:23011214

  1. Predictive models of radiative neutrino masses

    NASA Astrophysics Data System (ADS)

    Julio, J.

    2016-06-01

    We discuss two models of radiative neutrino mass generation. The first model features one-loop Zee model with Z4 symmetry. The second model is the two-loop neutrino mass model with singly- and doubly-charged scalars. These two models fit neutrino oscillation data well and predict some interesting rates for lepton flavor violation processes.

  2. How to Establish Clinical Prediction Models

    PubMed Central

    Bang, Heejung

    2016-01-01

    A clinical prediction model can be applied to several challenging clinical scenarios: screening high-risk individuals for asymptomatic disease, predicting future events such as disease or death, and assisting medical decision-making and health education. Despite the impact of clinical prediction models on practice, prediction modeling is a complex process requiring careful statistical analyses and sound clinical judgement. Although there is no definite consensus on the best methodology for model development and validation, a few recommendations and checklists have been proposed. In this review, we summarize five steps for developing and validating a clinical prediction model: preparation for establishing clinical prediction models; dataset selection; handling variables; model generation; and model evaluation and validation. We also review several studies that detail methods for developing clinical prediction models with comparable examples from real practice. After model development and vigorous validation in relevant settings, possibly with evaluation of utility/usability and fine-tuning, good models can be ready for the use in practice. We anticipate that this framework will revitalize the use of predictive or prognostic research in endocrinology, leading to active applications in real clinical practice. PMID:26996421

  3. How to Establish Clinical Prediction Models.

    PubMed

    Lee, Yong Ho; Bang, Heejung; Kim, Dae Jung

    2016-03-01

    A clinical prediction model can be applied to several challenging clinical scenarios: screening high-risk individuals for asymptomatic disease, predicting future events such as disease or death, and assisting medical decision-making and health education. Despite the impact of clinical prediction models on practice, prediction modeling is a complex process requiring careful statistical analyses and sound clinical judgement. Although there is no definite consensus on the best methodology for model development and validation, a few recommendations and checklists have been proposed. In this review, we summarize five steps for developing and validating a clinical prediction model: preparation for establishing clinical prediction models; dataset selection; handling variables; model generation; and model evaluation and validation. We also review several studies that detail methods for developing clinical prediction models with comparable examples from real practice. After model development and vigorous validation in relevant settings, possibly with evaluation of utility/usability and fine-tuning, good models can be ready for the use in practice. We anticipate that this framework will revitalize the use of predictive or prognostic research in endocrinology, leading to active applications in real clinical practice. PMID:26996421

  4. On the joys of perceiving: Affect as feedback for perceptual predictions.

    PubMed

    Chetverikov, Andrey; Kristjánsson, Árni

    2016-09-01

    How we perceive, attend to, or remember the stimuli in our environment depends on our preferences for them. Here we argue that this dependence is reciprocal: pleasures and displeasures are heavily dependent on cognitive processing, namely, on our ability to predict the world correctly. We propose that prediction errors, inversely weighted with prior probabilities of predictions, yield subjective experiences of positive or negative affect. In this way, we link affect to predictions within a predictive coding framework. We discuss how three key factors - uncertainty, expectations, and conflict - influence prediction accuracy and show how they shape our affective response. We demonstrate that predictable stimuli are, in general, preferred to unpredictable ones, though too much predictability may decrease this liking effect. Furthermore, the account successfully overcomes the "dark-room" problem, explaining why we do not avoid stimulation to minimize prediction error. We further discuss the implications of our approach for art perception and the utility of affect as feedback for predictions within a prediction-testing architecture of cognition. PMID:27195963

  5. [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. PMID:24417129

  6. 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.

  7. 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. PMID:23469478

  8. 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

  9. Affecting coping: does neurocognition predict approach and avoidant coping strategies within schizophrenia spectrum disorders?

    PubMed

    MacAulay, Rebecca; Cohen, Alex S

    2013-09-30

    According to various diathesis-stress models of schizophrenia, life stress plays a defining role in the onset and course of schizophrenia-spectrum disorders. In this regard, individual differences in coping strategies and affective traits, variables related to the management and experience of stress, may play a large role in susceptibility to the disorder and symptom exacerbation. Furthermore, it has been posited that cognitive deficits may limit an individuals' ability to effectively respond to stressful situations. We investigated the relationships between attention, immediate memory, trait negative affect (NA), trait positive affect (PA) and specific coping strategies within three groups: chronic schizophrenia patients (n=27), psychometrically-defined schizotypy (n=89), and schizotypy demographically-matched controls (n=26). As hypothesized affective traits displayed predictable relationships with specific coping strategies, such that NA was associated with the greater use of avoidant coping strategies within the schizophrenia and schizotypy group, while PA was associated with greater use of approach coping styles within all groups. The schizotypy group reported significantly higher levels of NA and also greater use of avoidant coping strategies than both the control and schizophrenia group. As expected group differences were found in trait affect, coping strategies, and cognitive functioning. Importantly, these group differences remained significant even when demographic variables were entered as covariates. Contrary to our expectations, cognitive functioning displayed only a few tenuous relationships with coping strategies within the schizophrenia and schizotypy groups. Overall, results support the notion that affective traits and not cognitive functioning is the best predictor of approach and avoidant coping strategies. PMID:23680466

  10. 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…

  11. 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. PMID:24467454

  12. Incorporating uncertainty in predictive species distribution modelling

    PubMed Central

    Beale, Colin M.; Lennon, Jack J.

    2012-01-01

    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. PMID:22144387

  13. 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 PMID:26882443

  14. Modeling and Predicting Pesticide Exposures

    EPA Science Inventory

    Models provide a means for representing a real system in an understandable way. They take many forms, beginning with conceptual models that explain the way a system works, such as delineation of all the factors and parameters of how a pesticide particle moves in the air after a s...

  15. Posterior Predictive Bayesian Phylogenetic Model Selection

    PubMed Central

    Lewis, Paul O.; Xie, Wangang; Chen, Ming-Hui; Fan, Yu; Kuo, Lynn

    2014-01-01

    We present two distinctly different posterior predictive approaches to Bayesian phylogenetic model selection and illustrate these methods using examples from green algal protein-coding cpDNA sequences and flowering plant rDNA sequences. The Gelfand–Ghosh (GG) approach allows dissection of an overall measure of model fit into components due to posterior predictive variance (GGp) and goodness-of-fit (GGg), which distinguishes this method from the posterior predictive P-value approach. The conditional predictive ordinate (CPO) method provides a site-specific measure of model fit useful for exploratory analyses and can be combined over sites yielding the log pseudomarginal likelihood (LPML) which is useful as an overall measure of model fit. CPO provides a useful cross-validation approach that is computationally efficient, requiring only a sample from the posterior distribution (no additional simulation is required). Both GG and CPO add new perspectives to Bayesian phylogenetic model selection based on the predictive abilities of models and complement the perspective provided by the marginal likelihood (including Bayes Factor comparisons) based solely on the fit of competing models to observed data. [Bayesian; conditional predictive ordinate; CPO; L-measure; LPML; model selection; phylogenetics; posterior predictive.] PMID:24193892

  16. Incorporating model uncertainty into spatial predictions

    SciTech Connect

    Handcock, M.S.

    1996-12-31

    We consider a modeling approach for spatially distributed data. We are concerned with aspects of statistical inference for Gaussian random fields when the ultimate objective is to predict the value of the random field at unobserved locations. However the exact statistical model is seldom known before hand and is usually estimated from the very same data relative to which the predictions are made. Our objective is to assess the effect of the fact that the model is estimated, rather than known, on the prediction and the associated prediction uncertainty. We describe a method for achieving this objective. We, in essence, consider the best linear unbiased prediction procedure based on the model within a Bayesian framework. These ideas are implemented for the spring temperature over the region in the northern United States based on the stations in the United States historical climatological network reported in Karl, Williams, Quinlan & Boden.

  17. 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…

  18. Smoking motives in the prediction of affective vulnerability among young adult daily smokers.

    PubMed

    Gregor, Kristin; Zvolensky, Michael J; Bernstein, Amit; Marshall, Erin C; Yartz, Andrew R

    2007-03-01

    The primary aim of this study was to examine whether smoking to reduce negative affect was uniquely related to a range of affective vulnerability factors (e.g., anxiety sensitivity, anxious arousal, and negative affectivity) among daily smokers. Participants were 276 young adult daily smokers (124 females; M(age)=25.12, SD=10.37). Partially consistent with prediction, the motivation to smoke to reduce negative affect was significantly related to anxiety sensitivity and negative affectivity, but not anxious arousal; the observed significant effects were above and beyond other theoretically relevant factors (e.g., smoking rate, years smoked, age, gender). In contrast to prediction, habitual smoking motives demonstrated significant incremental associations with anxiety sensitivity and anxious arousal symptoms. These results suggest that there are important associations between certain smoking motives and negative affective states and that such relations are not attributable to other smoking factors (e.g., smoking rate). PMID:16712784

  19. 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…

  20. 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…

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. 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.

  11. 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.

  12. 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. 11 figs.

  13. Predictive modelling of boiler fouling

    SciTech Connect

    Not Available

    1992-01-01

    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 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.

  14. 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.

  15. 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.

  16. Predicting and Modeling RNA Architecture

    PubMed Central

    Westhof, Eric; Masquida, Benoît; Jossinet, Fabrice

    2011-01-01

    SUMMARY A general approach for modeling the architecture of large and structured RNA molecules is described. The method exploits the modularity and the hierarchical folding of RNA architecture that is viewed as the assembly of preformed double-stranded helices defined by Watson-Crick base pairs and RNA modules maintained by non-Watson-Crick base pairs. Despite the extensive molecular neutrality observed in RNA structures, specificity in RNA folding is achieved through global constraints like lengths of helices, coaxiality of helical stacks, and structures adopted at the junctions of helices. The Assemble integrated suite of computer tools allows for sequence and structure analysis as well as interactive modeling by homology or ab initio assembly with possibilities for fitting within electronic density maps. The local key role of non-Watson-Crick pairs guides RNA architecture formation and offers metrics for assessing the accuracy of three-dimensional models in a more useful way than usual root mean square deviation (RMSD) values. PMID:20504963

  17. A Course in... Model Predictive Control.

    ERIC Educational Resources Information Center

    Arkun, Yaman; And Others

    1988-01-01

    Describes a graduate engineering course which specializes in model predictive control. Lists course outline and scope. Discusses some specific topics and teaching methods. Suggests final projects for the students. (MVL)

  18. 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...

  19. Predictive modelling of boiler fouling

    SciTech Connect

    Not Available

    1992-01-01

    In this reporting period, efforts were initiated to supplement the comprehensive flow field description obtained from the RNG-Spectral Element Simulations by incorporating, in a general framework, appropriate modules to model particle and condensable species transport to the surface. Specifically, a brief survey of the literature revealed the following possible mechanisms for transporting different ash constituents from the host gas to boiler tubes as deserving prominence in building the overall comprehensive model: (1) Flame-volatilized species, chiefly sulfates, are deposited on cooled boiler tubes via the mechanism of classical vapor diffusion. This mechanism is more efficient than the particulate ash deposition, and as a result there is usually an enrichment of condensable salts, chiefly sulfates, in boiler deposits; (2) Particle diffusion (Brownian motion) may account for deposition of some fine particles below 0. 1 mm in diameter in comparison with the mechanism of vapor diffusion and particle depositions, however, the amount of material transported to the tubes via this route is probably small. (3) Eddy diffusion, thermophoretic and electrophoretic deposition mechanisms are likely to have a marked influence in transporting 0.1 to 5[mu]m particles from the host gas to cooled boiler tubes; (4) Inertial impaction is the dominant mechanism in transporting particles above 5[mu]m in diameter to water and steam tubes in pulverized coal fired boiler, where the typical flue gas velocity is between 10 to 25 m/s. Particles above 10[mu]m usually have kinetic energies in excess of what can be dissipated at impact (in the absence of molten sulfate or viscous slag deposit), resulting in their entrainment in the host gas.

  20. 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

  1. Accuracy assessment of landslide prediction models

    NASA Astrophysics Data System (ADS)

    Othman, A. N.; Mohd, W. M. N. W.; Noraini, S.

    2014-02-01

    The increasing population and expansion of settlements over hilly areas has greatly increased the impact of natural disasters such as landslide. Therefore, it is important to developed models which could accurately predict landslide hazard zones. Over the years, various techniques and models have been developed to predict landslide hazard zones. The aim of this paper is to access the accuracy of landslide prediction models developed by the authors. The methodology involved the selection of study area, data acquisition, data processing and model development and also data analysis. The development of these models are based on nine different landslide inducing parameters i.e. slope, land use, lithology, soil properties, geomorphology, flow accumulation, aspect, proximity to river and proximity to road. Rank sum, rating, pairwise comparison and AHP techniques are used to determine the weights for each of the parameters used. Four (4) different models which consider different parameter combinations are developed by the authors. Results obtained are compared to landslide history and accuracies for Model 1, Model 2, Model 3 and Model 4 are 66.7, 66.7%, 60% and 22.9% respectively. From the results, rank sum, rating and pairwise comparison can be useful techniques to predict landslide hazard zones.

  2. 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)

  3. Model for halftone color prediction from microstructure

    NASA Astrophysics Data System (ADS)

    Agar, A. U.

    2000-12-01

    In this work, we take a microstructure model based approach to the problem of color prediction of halftones created using an inkjet printer. We assume absorption and scattering of light through the colorant layers and model the subsurface light scattering in the substrate by a Gaussian point spread function. We restrict our analysis to transparent substrates. To model the absorption and scattering of light through the colorant layers, we employ the Kubelka-Munk color mixing mode. To model the scattering in the substrate and to predict the spectral distribution, we use a wavelength dependent version of the reflection prediction model developed by Ruckdeschel and Hauser. Using spectral distributions and ink weight measurements for transparencies completely and homogeneously coated with colorants, we compute the absorption and scattering spectra of the colorants using the Kubelka-Munk theory. We train our model using measured spectral distribution and synthesized microstructure images of primary ramps printed on transparent media. For each patch in the primary ramp, we synthesize a high-resolution halftone microstructure image from the halftone bitmap assuming dot profiles with Gaussian roll-offs, form which we compute a high-resolution transmission image using the Kubelka-Munk theory and the absorption and scattering spectra of the colorants. We then convolve this transmission image with the Gaussian point spread function of the transparent substrate to predict the average spectral distribution of the halftone. We use our model to predict the spectral distribution of a secondary ramp printed on the same media.

  4. Predictive modelling of boiler fouling

    SciTech Connect

    Not Available

    1992-01-01

    As this study incorporates in a general framework, appropriate modules to model condensable species transport to the surface along with particles, the need for a suitable solver for the reaction component of the species equations with regard to issues of stability, stiffness, economy, etc. becomes obvious. It is generally agreed in the literature that the major problem associated with the simultaneous integration of large sets of chemical kinetic rate equations is that of stiffness. Although stiffness does not have a simple definition, it is characterized by widely varying time constants. For example, in hydrogen-air combustion, the induction time is of the order of microseconds whereas the nitric oxide formation time is of the order of milliseconds. These widely different time constants present classical methods (such as the popular explicit Runge-Kutta method) with the following difficulty: to ensure stability of the numerical solution, these methods are restricted to using very short time steps that are determined by the smallest time constant. However, the time for all chemical species to reach near-equilibrium values is determined by the longest time constant. As a result, classical methods require excessive amounts of computer time to solve stiff systems of ordinary differential equations (ODE's). Several approaches for the solution of stiff ODE's have been proposed. Of all these techniques, the general purpose codes EPISODE and LSODE are regarded as the best available packaged'' codes for the solution of stiff systems of ODE'S. However, although these codes may be the best available for solving an arbitrary systems ODE'S, it may be possible to construct superior methods for solving a particular system of ODE's governing the behavior of a specific problem. In this view, an exponentially fitted method, CREK1D, deserves a special mention and is described briefly.

  5. Predictive Validation of an Influenza Spread Model

    PubMed Central

    Hyder, Ayaz; Buckeridge, David L.; Leung, Brian

    2013-01-01

    Background Modeling plays a critical role in mitigating impacts of seasonal influenza epidemics. Complex simulation models are currently at the forefront of evaluating optimal mitigation strategies at multiple scales and levels of organization. Given their evaluative role, these models remain limited in their ability to predict and forecast future epidemics leading some researchers and public-health practitioners to question their usefulness. The objective of this study is to evaluate the predictive ability of an existing complex simulation model of influenza spread. Methods and Findings We used extensive data on past epidemics to demonstrate the process of predictive validation. This involved generalizing an individual-based model for influenza spread and fitting it to laboratory-confirmed influenza infection data from a single observed epidemic (1998–1999). Next, we used the fitted model and modified two of its parameters based on data on real-world perturbations (vaccination coverage by age group and strain type). Simulating epidemics under these changes allowed us to estimate the deviation/error between the expected epidemic curve under perturbation and observed epidemics taking place from 1999 to 2006. Our model was able to forecast absolute intensity and epidemic peak week several weeks earlier with reasonable reliability and depended on the method of forecasting-static or dynamic. Conclusions Good predictive ability of influenza epidemics is critical for implementing mitigation strategies in an effective and timely manner. Through the process of predictive validation applied to a current complex simulation model of influenza spread, we provided users of the model (e.g. public-health officials and policy-makers) with quantitative metrics and practical recommendations on mitigating impacts of seasonal influenza epidemics. This methodology may be applied to other models of communicable infectious diseases to test and potentially improve their predictive

  6. 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.

  7. 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. PMID:27490382

  8. Solar Weather Event Modelling and Prediction

    NASA Astrophysics Data System (ADS)

    Messerotti, Mauro; Zuccarello, Francesca; Guglielmino, Salvatore L.; Bothmer, Volker; Lilensten, Jean; Noci, Giancarlo; Storini, Marisa; Lundstedt, Henrik

    2009-11-01

    Key drivers of solar weather and mid-term solar weather are reviewed by considering a selection of relevant physics- and statistics-based scientific models as well as a selection of related prediction models, in order to provide an updated operational scenario for space weather applications. The characteristics and outcomes of the considered scientific and prediction models indicate that they only partially cope with the complex nature of solar activity for the lack of a detailed knowledge of the underlying physics. This is indicated by the fact that, on one hand, scientific models based on chaos theory and non-linear dynamics reproduce better the observed features, and, on the other hand, that prediction models based on statistics and artificial neural networks perform better. To date, the solar weather prediction success at most time and spatial scales is far from being satisfactory, but the forthcoming ground- and space-based high-resolution observations can add fundamental tiles to the modelling and predicting frameworks as well as the application of advanced mathematical approaches in the analysis of diachronic solar observations, that are a must to provide comprehensive and homogeneous data sets.

  9. Posterior predictive checking of multiple imputation models.

    PubMed

    Nguyen, Cattram D; Lee, Katherine J; Carlin, John B

    2015-07-01

    Multiple imputation is gaining popularity as a strategy for handling missing data, but there is a scarcity of tools for checking imputation models, a critical step in model fitting. Posterior predictive checking (PPC) has been recommended as an imputation diagnostic. PPC involves simulating "replicated" data from the posterior predictive distribution of the model under scrutiny. Model fit is assessed by examining whether the analysis from the observed data appears typical of results obtained from the replicates produced by the model. A proposed diagnostic measure is the posterior predictive "p-value", an extreme value of which (i.e., a value close to 0 or 1) suggests a misfit between the model and the data. The aim of this study was to evaluate the performance of the posterior predictive p-value as an imputation diagnostic. Using simulation methods, we deliberately misspecified imputation models to determine whether posterior predictive p-values were effective in identifying these problems. When estimating the regression parameter of interest, we found that more extreme p-values were associated with poorer imputation model performance, although the results highlighted that traditional thresholds for classical p-values do not apply in this context. A shortcoming of the PPC method was its reduced ability to detect misspecified models with increasing amounts of missing data. Despite the limitations of posterior predictive p-values, they appear to have a valuable place in the imputer's toolkit. In addition to automated checking using p-values, we recommend imputers perform graphical checks and examine other summaries of the test quantity distribution. PMID:25939490

  10. An improved model for prediction of resuspension.

    PubMed

    Maxwell, Reed M; Anspaugh, Lynn R

    2011-12-01

    A complete, historical dataset is presented of radionuclide resuspension-factors. These data span six orders of magnitude in time (ranging from 0.1 to 73,000 d), encompass more than 300 individual values, and combine observations from events on three continents. These data were then used to derive improved, empirical models that can be used to predict resuspension of trace materials after their deposit on the ground. Data-fitting techniques were used to derive models of various types and an estimate of uncertainty in model prediction. Two models were found to be suitable: a power law and the modified Anspaugh et al. model, which is a double exponential. Though statistically the power-law model provides the best metrics of fit, the modified Anspaugh model is deemed the more appropriate due to its better fit to data at early times and its ease of implementation in terms of closed analytical integrals. PMID:22048490

  11. Predicting Naming Latencies with an Analogical Model

    ERIC Educational Resources Information Center

    Chandler, Steve

    2008-01-01

    Skousen's (1989, Analogical modeling of language, Kluwer Academic Publishers, Dordrecht) Analogical Model (AM) predicts behavior such as spelling pronunciation by comparing the characteristics of a test item (a given input word) to those of individual exemplars in a data set of previously encountered items. While AM and other exemplar-based models…

  12. 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.

  13. 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.

  14. 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.

  15. 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.

  16. 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

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

    USGS Publications Warehouse

    Kuniansky, E.L.; Lowery, M.A.; Campbell, B.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.

  18. Predictive models of implicit and explicit attitudes.

    PubMed

    Perugini, Marco

    2005-03-01

    Explicit attitudes have long been assumed to be central factors influencing behaviour. A recent stream of studies has shown that implicit attitudes, typically measured with the Implicit Association Test (IAT), can also predict a significant range of behaviours. This contribution is focused on testing different predictive models of implicit and explicit attitudes. In particular, three main models can be derived from the literature: (a) additive (the two types of attitudes explain different portion of variance in the criterion), (b) double dissociation (implicit attitudes predict spontaneous whereas explicit attitudes predict deliberative behaviour), and (c) multiplicative (implicit and explicit attitudes interact in influencing behaviour). This paper reports two studies testing these models. The first study (N = 48) is about smoking behaviour, whereas the second study (N = 109) is about preferences for snacks versus fruit. In the first study, the multiplicative model is supported, whereas the double dissociation model is supported in the second study. The results are discussed in light of the importance of focusing on different patterns of prediction when investigating the directive influence of implicit and explicit attitudes on behaviours. PMID:15901390

  19. Toward predictive models of mammalian cells.

    PubMed

    Ma'ayan, Avi; Blitzer, Robert D; Iyengar, Ravi

    2005-01-01

    Progress in experimental and theoretical biology is likely to provide us with the opportunity to assemble detailed predictive models of mammalian cells. Using a functional format to describe the organization of mammalian cells, we describe current approaches for developing qualitative and quantitative models using data from a variety of experimental sources. Recent developments and applications of graph theory to biological networks are reviewed. The use of these qualitative models to identify the topology of regulatory motifs and functional modules is discussed. Cellular homeostasis and plasticity are interpreted within the framework of balance between regulatory motifs and interactions between modules. From this analysis we identify the need for detailed quantitative models on the basis of the representation of the chemistry underlying the cellular process. The use of deterministic, stochastic, and hybrid models to represent cellular processes is reviewed, and an initial integrated approach for the development of large-scale predictive models of a mammalian cell is presented. PMID:15869393

  20. 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)…

  1. 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. PMID:26362037

  2. 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.

  3. 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.

  4. Predictive QSAR modeling of phosphodiesterase 4 inhibitors.

    PubMed

    Kovalishyn, Vasyl; Tanchuk, Vsevolod; Charochkina, Larisa; Semenuta, Ivan; Prokopenko, Volodymyr

    2012-02-01

    A series of diverse organic compounds, phosphodiesterase type 4 (PDE-4) inhibitors, have been modeled using a QSAR-based approach. 48 QSAR models were compared by following the same procedure with different combinations of descriptors and machine learning methods. QSAR methodologies used random forests and associative neural networks. The predictive ability of the models was tested through leave-one-out cross-validation, giving a Q² = 0.66-0.78 for regression models and total accuracies Ac=0.85-0.91 for classification models. Predictions for the external evaluation sets obtained accuracies in the range of 0.82-0.88 (for active/inactive classifications) and Q² = 0.62-0.76 for regressions. The method showed itself to be a potential tool for estimation of IC₅₀ of new drug-like candidates at early stages of drug development. PMID:22023934

  5. A Predictive Model for Root Caries Incidence.

    PubMed

    Ritter, André V; Preisser, John S; Puranik, Chaitanya P; Chung, Yunro; Bader, James D; Shugars, Daniel A; Makhija, Sonia; Vollmer, William M

    2016-01-01

    This study aimed to find the set of risk indicators best able to predict root caries (RC) incidence in caries-active adults utilizing data from the Xylitol for Adult Caries Trial (X-ACT). Five logistic regression models were compared with respect to their predictive performance for incident RC using data from placebo-control participants with exposed root surfaces at baseline and from two study centers with ancillary data collection (n = 155). Prediction performance was assessed from baseline variables and after including ancillary variables [smoking, diet, use of removable partial dentures (RPD), toothbrush use, income, education, and dental insurance]. A sensitivity analysis added treatment to the models for both the control and treatment participants (n = 301) to predict RC for the control participants. Forty-nine percent of the control participants had incident RC. The model including the number of follow-up years at risk, the number of root surfaces at risk, RC index, gender, race, age, and smoking resulted in the best prediction performance, having the highest AUC and lowest Brier score. The sensitivity analysis supported the primary analysis and gave slightly better performance summary measures. The set of risk indicators best able to predict RC incidence included an increased number of root surfaces at risk and increased RC index at baseline, followed by white race and nonsmoking, which were strong nonsignificant predictors. Gender, age, and increased number of follow-up years at risk, while included in the model, were also not statistically significant. The inclusion of health, diet, RPD use, toothbrush use, income, education, and dental insurance variables did not improve the prediction performance. PMID:27160516

  6. The Influence of a Model's Reinforcement Contingency and Affective Response on Children's Perceptions of the Model

    ERIC Educational Resources Information Center

    Thelen, Mark H.; And Others

    1977-01-01

    Assesses the influence of model consequences on perceived model affect and, conversely, assesses the influence of model affect on perceived model consequences. Also appraises the influence of model consequences and model affect on perceived model attractiveness, perceived model competence, and perceived task attractiveness. (Author/RK)

  7. Predictions of Geospace Drivers By the Probability Distribution Function Model

    NASA Astrophysics Data System (ADS)

    Bussy-Virat, C.; Ridley, A. J.

    2014-12-01

    Geospace drivers like the solar wind speed, interplanetary magnetic field (IMF), and solar irradiance have a strong influence on the density of the thermosphere and the near-Earth space environment. This has important consequences on the drag on satellites that are in low orbit and therefore on their position. One of the basic problems with space weather prediction is that these drivers can only be measured about one hour before they affect the environment. In order to allow for adequate planning for some members of the commercial, military, or civilian communities, reliable long-term space weather forecasts are needed. The study presents a model for predicting geospace drivers up to five days in advance. This model uses the same general technique to predict the solar wind speed, the three components of the IMF, and the solar irradiance F10.7. For instance, it uses Probability distribution functions (PDFs) to relate the current solar wind speed and slope to the future solar wind speed, as well as the solar wind speed to the solar wind speed one solar rotation in the future. The PDF Model has been compared to other models for predictions of the speed. It has been found that it is better than using the current solar wind speed (i.e., persistence), and better than the Wang-Sheeley-Arge Model for prediction horizons of 24 hours. Once the drivers are predicted, and the uncertainty on the drivers are specified, the density in the thermosphere can be derived using various models of the thermosphere, such as the Global Ionosphere Thermosphere Model. In addition, uncertainties on the densities can be estimated, based on ensembles of simulations. From the density and uncertainty predictions, satellite positions, as well as the uncertainty in those positions can be estimated. These can assist operators in determining the probability of collisions between objects in low Earth orbit.

  8. Predictive coding as a model of cognition.

    PubMed

    Spratling, M W

    2016-08-01

    Previous work has shown that predictive coding can provide a detailed explanation of a very wide range of low-level perceptual processes. It is also widely believed that predictive coding can account for high-level, cognitive, abilities. This article provides support for this view by showing that predictive coding can simulate phenomena such as categorisation, the influence of abstract knowledge on perception, recall and reasoning about conceptual knowledge, context-dependent behavioural control, and naive physics. The particular implementation of predictive coding used here (PC/BC-DIM) has previously been used to simulate low-level perceptual behaviour and the neural mechanisms that underlie them. This algorithm thus provides a single framework for modelling both perceptual and cognitive brain function. PMID:27118562

  9. 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 (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...

  10. 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...

  11. Modeling and Prediction of Fan Noise

    NASA Technical Reports Server (NTRS)

    Envia, Ed

    2008-01-01

    Fan noise is a significant contributor to the total noise signature of a modern high bypass ratio aircraft engine and with the advent of ultra high bypass ratio engines like the geared turbofan, it is likely to remain so in the future. As such, accurate modeling and prediction of the basic characteristics of fan noise are necessary ingredients in designing quieter aircraft engines in order to ensure compliance with ever more stringent aviation noise regulations. In this paper, results from a comprehensive study aimed at establishing the utility of current tools for modeling and predicting fan noise will be summarized. It should be emphasized that these tools exemplify present state of the practice and embody what is currently used at NASA and Industry for predicting fan noise. The ability of these tools to model and predict fan noise is assessed against a set of benchmark fan noise databases obtained for a range of representative fan cycles and operating conditions. Detailed comparisons between the predicted and measured narrowband spectral and directivity characteristics of fan nose will be presented in the full paper. General conclusions regarding the utility of current tools and recommendations for future improvements will also be given.

  12. Strains at the myotendinous junction predicted by a micromechanical model

    PubMed Central

    Sharafi, Bahar; Ames, Elizabeth G.; Holmes, Jeffrey W.; Blemker, Silvia S.

    2011-01-01

    The goal of this work was to create a finite element micromechanical model of the myotendinous junction (MTJ) to examine how the structure and mechanics of the MTJ affect the local micro-scale strains experienced by muscle fibers. We validated the model through comparisons with histological longitudinal sections of muscles fixed in slack and stretched positions. The model predicted deformations of the A-bands within the fiber near the MTJ that were similar to those measured from the histological sections. We then used the model to predict the dependence of local fiber strains on activation and the mechanical properties of the endomysium. The model predicted that peak micro-scale strains increase with activation and as the compliance of the endomysium decreases. Analysis of the models revealed that, in passive stretch, local fiber strains are governed by the difference of the mechanical properties between the fibers and the endomysium. In active stretch, strain distributions are governed by the difference in cross-sectional area along the length of the tapered region of the fiber near the MTJ. The endomysium provides passive resistance that balances the active forces and prevents the tapered region of the fiber from undergoing excessive strain. These model predictions lead to the following hypotheses: (i) the increased likelihood of injury during active lengthening of muscle fibers may be due to the increase in peak strain with activation and (ii) endomysium may play a role in protecting fibers from injury by reducing the strains within the fiber at the MTJ. PMID:21945569

  13. Analysis and predictive modeling of asthma phenotypes.

    PubMed

    Brasier, Allan R; Ju, Hyunsu

    2014-01-01

    Molecular classification using robust biochemical measurements provides a level of diagnostic precision that is unattainable using indirect phenotypic measurements. Multidimensional measurements of proteins, genes, or metabolites (analytes) can identify subtle differences in the pathophysiology of patients with asthma in a way that is not otherwise possible using physiological or clinical assessments. We overview a method for relating biochemical analyte measurements to generate predictive models of discrete (categorical) clinical outcomes, a process referred to as "supervised classification." We consider problems inherent in wide (small n and large p) high-dimensional data, including the curse of dimensionality, collinearity and lack of information content. We suggest methods for reducing the data to the most informative features. We describe different approaches for phenotypic modeling, using logistic regression, classification and regression trees, random forest and nonparametric regression spline modeling. We provide guidance on post hoc model evaluation and methods to evaluate model performance using ROC curves and generalized additive models. The application of validated predictive models for outcome prediction will significantly impact the clinical management of asthma. PMID:24162915

  14. 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.

  15. Modelling language evolution: Examples and predictions.

    PubMed

    Gong, Tao; Shuai, Lan; Zhang, Menghan

    2014-06-01

    We survey recent computer modelling research of language evolution, focusing on a rule-based model simulating the lexicon-syntax coevolution and an equation-based model quantifying the language competition dynamics. We discuss four predictions of these models: (a) correlation between domain-general abilities (e.g. sequential learning) and language-specific mechanisms (e.g. word order processing); (b) coevolution of language and relevant competences (e.g. joint attention); (c) effects of cultural transmission and social structure on linguistic understandability; and (d) commonalities between linguistic, biological, and physical phenomena. All these contribute significantly to our understanding of the evolutions of language structures, individual learning mechanisms, and relevant biological and socio-cultural factors. We conclude the survey by highlighting three future directions of modelling studies of language evolution: (a) adopting experimental approaches for model evaluation; (b) consolidating empirical foundations of models; and (c) multi-disciplinary collaboration among modelling, linguistics, and other relevant disciplines. PMID:24286718

  16. Modelling language evolution: Examples and predictions

    NASA Astrophysics Data System (ADS)

    Gong, Tao; Shuai, Lan; Zhang, Menghan

    2014-06-01

    We survey recent computer modelling research of language evolution, focusing on a rule-based model simulating the lexicon-syntax coevolution and an equation-based model quantifying the language competition dynamics. We discuss four predictions of these models: (a) correlation between domain-general abilities (e.g. sequential learning) and language-specific mechanisms (e.g. word order processing); (b) coevolution of language and relevant competences (e.g. joint attention); (c) effects of cultural transmission and social structure on linguistic understandability; and (d) commonalities between linguistic, biological, and physical phenomena. All these contribute significantly to our understanding of the evolutions of language structures, individual learning mechanisms, and relevant biological and socio-cultural factors. We conclude the survey by highlighting three future directions of modelling studies of language evolution: (a) adopting experimental approaches for model evaluation; (b) consolidating empirical foundations of models; and (c) multi-disciplinary collaboration among modelling, linguistics, and other relevant disciplines.

  17. 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.

  18. Persistence and predictability in a perfect model

    NASA Technical Reports Server (NTRS)

    Schubert, Siegfried D.; Suarez, Max J.; Schemm, Jae-Kyung

    1992-01-01

    A realistic two-level GCM is used to examine the relationship between predictability and persistence. Predictability is measured by the average divergence of ensembles of solutions starting from perturbed initial conditions, and persistence is defined in terms of the autocorrelation function based on a single long-term model integration. The average skill of the dynamical forecasts is compared with the skill of simple persistence-based statistical forecasts. For initial errors comparable in magnitude to present-day analysis errors, the statistical forecast loses all skill after about one week, reflecting the lifetime of the lowest frequency fluctuations in the model. Large ensemble mean dynamical forecasts would be expected to remain skillful for about 3 wk. The disparity between the skill of the statistical and dynamical forecasts is greater for the higher frequency modes, which have little memory beyond 1 d, yet remain predictable for about 2 wk. The results are analyzed in terms of two characteristic time scales.

  19. Behavioral and electrophysiological indices of negative affect predict cocaine self-administration.

    PubMed

    Wheeler, Robert A; Twining, Robert C; Jones, Joshua L; Slater, Jennifer M; Grigson, Patricia S; Carelli, Regina M

    2008-03-13

    The motivation to seek cocaine comes in part from a dysregulation of reward processing manifested in dysphoria, or affective withdrawal. Learning is a critical aspect of drug abuse; however, it remains unclear whether drug-associated cues can elicit the emotional withdrawal symptoms that promote cocaine use. Here we report that a cocaine-associated taste cue elicited a conditioned aversive state that was behaviorally and neurophysiologically quantifiable and predicted subsequent cocaine self-administration behavior. Specifically, brief intraoral infusions of a cocaine-predictive flavored saccharin solution elicited aversive orofacial responses that predicted early-session cocaine taking in rats. The expression of aversive taste reactivity also was associated with a shift in the predominant pattern of electrophysiological activity of nucleus accumbens (NAc) neurons from inhibitory to excitatory. The dynamic nature of this conditioned switch in affect and the neural code reveals a mechanism by which cues may exert control over drug self-administration. PMID:18341996

  20. An exponential filter model predicts lightness illusions.

    PubMed

    Zeman, Astrid; Brooks, Kevin R; Ghebreab, Sennay

    2015-01-01

    Lightness, or perceived reflectance of a surface, is influenced by surrounding context. This is demonstrated by the Simultaneous Contrast Illusion (SCI), where a gray patch is perceived lighter against a black background and vice versa. Conversely, assimilation is where the lightness of the target patch moves toward that of the bounding areas and can be demonstrated in White's effect. Blakeslee and McCourt (1999) introduced an oriented difference-of-Gaussian (ODOG) model that is able to account for both contrast and assimilation in a number of lightness illusions and that has been subsequently improved using localized normalization techniques. We introduce a model inspired by image statistics that is based on a family of exponential filters, with kernels spanning across multiple sizes and shapes. We include an optional second stage of normalization based on contrast gain control. Our model was tested on a well-known set of lightness illusions that have previously been used to evaluate ODOG and its variants, and model lightness values were compared with typical human data. We investigate whether predictive success depends on filters of a particular size or shape and whether pooling information across filters can improve performance. The best single filter correctly predicted the direction of lightness effects for 21 out of 27 illusions. Combining two filters together increased the best performance to 23, with asymptotic performance at 24 for an arbitrarily large combination of filter outputs. While normalization improved prediction magnitudes, it only slightly improved overall scores in direction predictions. The prediction performance of 24 out of 27 illusions equals that of the best performing ODOG variant, with greater parsimony. Our model shows that V1-style orientation-selectivity is not necessary to account for lightness illusions and that a low-level model based on image statistics is able to account for a wide range of both contrast and assimilation effects

  1. An exponential filter model predicts lightness illusions

    PubMed Central

    Zeman, Astrid; Brooks, Kevin R.; Ghebreab, Sennay

    2015-01-01

    Lightness, or perceived reflectance of a surface, is influenced by surrounding context. This is demonstrated by the Simultaneous Contrast Illusion (SCI), where a gray patch is perceived lighter against a black background and vice versa. Conversely, assimilation is where the lightness of the target patch moves toward that of the bounding areas and can be demonstrated in White's effect. Blakeslee and McCourt (1999) introduced an oriented difference-of-Gaussian (ODOG) model that is able to account for both contrast and assimilation in a number of lightness illusions and that has been subsequently improved using localized normalization techniques. We introduce a model inspired by image statistics that is based on a family of exponential filters, with kernels spanning across multiple sizes and shapes. We include an optional second stage of normalization based on contrast gain control. Our model was tested on a well-known set of lightness illusions that have previously been used to evaluate ODOG and its variants, and model lightness values were compared with typical human data. We investigate whether predictive success depends on filters of a particular size or shape and whether pooling information across filters can improve performance. The best single filter correctly predicted the direction of lightness effects for 21 out of 27 illusions. Combining two filters together increased the best performance to 23, with asymptotic performance at 24 for an arbitrarily large combination of filter outputs. While normalization improved prediction magnitudes, it only slightly improved overall scores in direction predictions. The prediction performance of 24 out of 27 illusions equals that of the best performing ODOG variant, with greater parsimony. Our model shows that V1-style orientation-selectivity is not necessary to account for lightness illusions and that a low-level model based on image statistics is able to account for a wide range of both contrast and assimilation effects

  2. Time prediction model of subway transfer.

    PubMed

    Zhou, Yuyang; Yao, Lin; Gong, Yi; Chen, Yanyan

    2016-01-01

    Walking time prediction aims to deduce waiting time and travel time for passengers and provide a quantitative basis for the subway schedule management. This model is founded based on transfer passenger flow and type of pedestrian facilities. Chaoyangmen station in Beijing was taken as the learning set to obtain the relationship between transfer walking speed and passenger volume. The sectional passenger volume of different facilities was calculated related to the transfer passage classification. Model parameters were computed by curve fitting with respect to various pedestrian facilities. The testing set contained four transfer stations with large passenger volume. It is validated that the established model is effective and practical. The proposed model offers a real-time prediction method with good applicability. It can provide transfer scheme reference for passengers, meanwhile, improve the scheduling and management of the subway operation. PMID:26835224

  3. 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.

  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. Personality Moderates the Interaction between Positive and Negative Daily Events Predicting Negative Affect and Stress

    PubMed Central

    Longua, Julie; DeHart, Tracy; Tennen, Howard; Armeli, Stephen

    2009-01-01

    A 30-day diary study examined personality moderators (neuroticism and extraversion) of the interaction between positive and negative daily events predicting daily negative affect and night-time stress. Multilevel analyses revealed positive daily events buffered the effect of negative daily events on negative affect for individuals low in neuroticism and individuals high in extraversion, but not for individuals high in neuroticism or individuals low in extraversion. Positive daily events also buffered the effect of negative daily events on that night’s stress, but only for participants low in neuroticism. As such, this research linked today’s events to tonight’s stressfulness. This study advances our understanding of how neuroticism and extraversion influence within-person associations between positive and negative events predicting negative affect and stress. PMID:20161239

  6. 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

  7. 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.

  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. 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.

  10. Cognitive modeling to predict video interpretability

    NASA Astrophysics Data System (ADS)

    Young, Darrell L.; Bakir, Tariq

    2011-06-01

    Processing framework for cognitive modeling to predict video interpretability is discussed. Architecture consists of spatiotemporal video preprocessing, metric computation, metric normalization, pooling of like metric groups with masking adjustments, multinomial logistic pooling of Minkowski pooled groups of similar quality metrics, and estimation of confidence interval of final result.

  11. A Predictive Model for MSSW Student Success

    ERIC Educational Resources Information Center

    Napier, Angela Michele

    2011-01-01

    This study tested a hypothetical model for predicting both graduate GPA and graduation of University of Louisville Kent School of Social Work Master of Science in Social Work (MSSW) students entering the program during the 2001-2005 school years. The preexisting characteristics of demographics, academic preparedness and culture shock along with…

  12. 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

  13. Emotion and hypervigilance: negative affect predicts increased P1 responses to non-negative pictorial stimuli.

    PubMed

    Schomberg, Jessica; Schöne, Benjamin; Gruber, Thomas; Quirin, Markus

    2016-06-01

    Previous research has demonstrated that negative affect influences attentional processes. Here, we investigate whether pre-experimental negative affect predicts a hypervigilant neural response as indicated by increased event-related potential amplitudes in response to neutral and positive visual stimuli. In our study, seventeen male participants filled out the German version of the positive and negative affect schedule (Watson et al. in J Pers Soc Psychol 54:1063-1070, 1988; Krohne et al. in Diagnostica 42:139-156, 1996) and subsequently watched positive (erotica, extreme sports, beautiful women) and neutral (daily activities) photographs while electroencephalogram was recorded. In line with our hypothesis, low state negative affect but not (reduced) positive affect predicted an increase in the first positive event-related potential amplitude P1 as a typical marker of increased selective attention. As this effect occurred in response to non-threatening picture conditions, negative affect may foster an individual's general hypervigilance, a state that has formerly been associated with psychopathology only. PMID:26749180

  14. 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

  15. 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

  16. 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.

  17. Affective Robotics: Modelling and Testing Cultural Prototypes.

    PubMed

    A Wilson, Paul; Lewandowska-Tomaszczyk, Barbara

    2014-01-01

    If robots are to successfully interact with humans, they need to measure, quantify and respond to the emotions we produce. Similar to humans, the perceptual cue inputs to any modelling that allows this will be based on behavioural expression and body activity features that are prototypical of each emotion. However, the likely employment of such robots in different cultures necessitates the tuning of the emotion feature recognition system to the specific feature profiles present in these cultures. The amount of tuning depends on the relative convergence of the cross-cultural mappings between the emotion feature profiles of the cultures where the robots will be used. The GRID instrument and the cognitive corpus linguistics methodology were used in a contrastive study analysing a selection of behavioural expression and body activity features to compare the feature profiles of joy, sadness, fear and anger within and between Polish and British English. The intra-linguistic differences that were found in the profile of emotion features suggest that weightings based on this profile can be used in robotic modelling to create emotion-sensitive socially interacting robots. Our cross-cultural results further indicate that this profile of features needs to be tuned in robots to make them emotionally competent in different cultures. PMID:25484993

  18. Thermal barrier coating life prediction model development

    NASA Technical Reports Server (NTRS)

    Hillery, R. V.

    1984-01-01

    In order to fully exploit thermal barrier coatings (TBCs) on turbine components and achieve the maximum performance benefit, the knowledge and understanding of TBC failure mechanisms must be increased and the means to predict coating life developed. The proposed program will determine the predominant modes of TBC system degradation and then develop and verify life prediction models accounting for those degradation modes. The successful completion of the program will have dual benefits: the ability to take advantage of the performance benefits offered by TBCs, and a sounder basis for making future improvements in coating behavior.

  19. Model predictive control of constrained LPV systems

    NASA Astrophysics Data System (ADS)

    Yu, Shuyou; Böhm, Christoph; Chen, Hong; Allgöwer, Frank

    2012-06-01

    This article considers robust model predictive control (MPC) schemes for linear parameter varying (LPV) systems in which the time-varying parameter is assumed to be measured online and exploited for feedback. A closed-loop MPC with a parameter-dependent control law is proposed first. The parameter-dependent control law reduces conservativeness of the existing results with a static control law at the cost of higher computational burden. Furthermore, an MPC scheme with prediction horizon '1' is proposed to deal with the case of asymmetric constraints. Both approaches guarantee recursive feasibility and closed-loop stability if the considered optimisation problem is feasible at the initial time instant.

  20. Hidden Markov models for threat prediction fusion

    NASA Astrophysics Data System (ADS)

    Ross, Kenneth N.; Chaney, Ronald D.

    2000-04-01

    This work addresses the often neglected, but important problem of Level 3 fusion or threat refinement. This paper describes algorithms for threat prediction and test results from a prototype threat prediction fusion engine. The threat prediction fusion engine selectively models important aspects of the battlespace state using probability-based methods and information obtained from lower level fusion engines. Our approach uses hidden Markov models of a hierarchical threat state to find the most likely Course of Action (CoA) for the opposing forces. Decision tress use features derived from the CoA probabilities and other information to estimate the level of threat presented by the opposing forces. This approach provides the user with several measures associated with the level of threat, including: probability that the enemy is following a particular CoA, potential threat presented by the opposing forces, and likely time of the threat. The hierarchical approach used for modeling helps us efficiently represent the battlespace with a structure that permits scaling the models to larger scenarios without adding prohibitive computational costs or sacrificing model fidelity.

  1. 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

  2. ENSO Prediction using Vector Autoregressive Models

    NASA Astrophysics Data System (ADS)

    Chapman, D. R.; Cane, M. A.; Henderson, N.; Lee, D.; Chen, C.

    2013-12-01

    A recent comparison (Barnston et al, 2012 BAMS) shows the ENSO forecasting skill of dynamical models now exceeds that of statistical models, but the best statistical models are comparable to all but the very best dynamical models. In this comparison the leading statistical model is the one based on the Empirical Model Reduction (EMR) method. Here we report on experiments with multilevel Vector Autoregressive models using only sea surface temperatures (SSTs) as predictors. VAR(L) models generalizes Linear Inverse Models (LIM), which are a VAR(1) method, as well as multilevel univariate autoregressive models. Optimal forecast skill is achieved using 12 to 14 months of prior state information (i.e 12-14 levels), which allows SSTs alone to capture the effects of other variables such as heat content as well as seasonality. The use of multiple levels allows the model advancing one month at a time to perform at least as well for a 6 month forecast as a model constructed to explicitly forecast 6 months ahead. We infer that the multilevel model has fully captured the linear dynamics (cf. Penland and Magorian, 1993 J. Climate). Finally, while VAR(L) is equivalent to L-level EMR, we show in a 150 year cross validated assessment that we can increase forecast skill by improving on the EMR initialization procedure. The greatest benefit of this change is in allowing the prediction to make effective use of information over many more months.

  3. 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

  4. 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.

  5. STELLA Experiment: Design and Model Predictions

    SciTech Connect

    Kimura, W. D.; Babzien, M.; Ben-Zvi, I.; Campbell, L. P.; Cline, D. B.; Fiorito, R. B.; Gallardo, J. C.; Gottschalk, S. C.; He, P.; Kusche, K. P.; Liu, Y.; Pantell, R. H.; Pogorelsky, I. V.; Quimby, D. C.; Robinson, K. E.; Rule, D. W.; Sandweiss, J.; Skaritka, J.; van Steenbergen, A.; Steinhauer, L. C.; Yakimenko, V.

    1998-07-05

    The STaged ELectron Laser Acceleration (STELLA) experiment will be one of the first to examine the critical issue of staging the laser acceleration process. The BNL inverse free electron laser (EEL) will serve as a prebuncher to generate {approx} 1 {micro}m long microbunches. These microbunches will be accelerated by an inverse Cerenkov acceleration (ICA) stage. A comprehensive model of the STELLA experiment is described. This model includes the EEL prebunching, drift and focusing of the microbunches into the ICA stage, and their subsequent acceleration. The model predictions will be presented including the results of a system error study to determine the sensitivity to uncertainties in various system parameters.

  6. Proposal for a recovery prediction method for patients affected by acute mediastinitis

    PubMed Central

    2012-01-01

    Background An attempt to find a prediction method of death risk in patients affected by acute mediastinitis. There is not such a tool described in available literature for that serious disease. Methods The study comprised 44 consecutive cases of acute mediastinitis. General anamnesis and biochemical data were included. Factor analysis was used to extract the risk characteristic for the patients. The most valuable results were obtained for 8 parameters which were selected for further statistical analysis (all collected during few hours after admission). Three factors reached Eigenvalue >1. Clinical explanations of these combined statistical factors are: Factor1 - proteinic status (serum total protein, albumin, and hemoglobin level), Factor2 - inflammatory status (white blood cells, CRP, procalcitonin), and Factor3 - general risk (age, number of coexisting diseases). Threshold values of prediction factors were estimated by means of statistical analysis (factor analysis, Statgraphics Centurion XVI). Results The final prediction result for the patients is constructed as simultaneous evaluation of all factor scores. High probability of death should be predicted if factor 1 value decreases with simultaneous increase of factors 2 and 3. The diagnostic power of the proposed method was revealed to be high [sensitivity =90%, specificity =64%], for Factor1 [SNC = 87%, SPC = 79%]; for Factor2 [SNC = 87%, SPC = 50%] and for Factor3 [SNC = 73%, SPC = 71%]. Conclusion The proposed prediction method seems a useful emergency signal during acute mediastinitis control in affected patients. PMID:22574625

  7. Prepare for scare-Impact of threat predictability on affective visual processing in spider phobia.

    PubMed

    Klahn, Anna Luisa; Klinkenberg, Isabelle A G; Notzon, Swantje; Arolt, Volker; Pantev, Christo; Zwanzger, Peter; Junghöfer, Markus

    2016-07-01

    The visual processing of emotional faces is influenced by individual's level of stress and anxiety. Valence unspecific affective processing is expected to be influenced by predictability of threat. Using a design of phasic fear (predictable threat), sustained anxiety (unpredictable threat) and safety (no threat), we investigated the magnetoencephalographic correlates and temporal dynamics of emotional face processing in a sample of phobic patients. Compared to non-anxious controls, phobic individuals revealed decreased parietal emotional attention processes during affective processing at mid-latency and late processing stages. While control subjects showed increasing parietal processing of the facial stimuli in line with decreasing threat predictability, phobic subjects revealed the opposite pattern. Decreasing threat predictability also led to increasing neural activity in the orbitofrontal and dorsolateral prefrontal cortex at mid-latency stages. Additionally, unpredictability of threat lead to higher subjective discomfort compared to predictability of threat and no threat safety condition. Our findings indicate that visual processing of emotional information is influenced by both stress induction and pathologic anxiety. PMID:27036648

  8. Product component genealogy modeling and field-failure prediction

    DOE PAGESBeta

    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

  9. 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. PMID:23036371

  10. Urban daytime traffic noise prediction models.

    PubMed

    da Paz, Elaine Carvalho; Zannin, Paulo Henrique Trombetta

    2010-04-01

    An evaluation was made of the acoustic environment generated by an urban highway using in situ measurements. Based on the data collected, a mathematical model was designed for the main sound levels (L (eq), L (10), L (50), and L (90)) as a function of the correlation between sound levels and between the equivalent sound pressure level and traffic variables. Four valid groups of mathematical models were generated to calculate daytime sound levels, which were statistically validated. It was found that the new models can be considered as accurate as other models presented in the literature to assess and predict daytime traffic noise, and that they stand out and differ from the existing models described in the literature thanks to two characteristics, namely, their linearity and the application of class intervals. PMID:19353296

  11. 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.

  12. 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

  13. Validated predictive modelling of the environmental resistome.

    PubMed

    Amos, Gregory C A; 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

    2015-06-01

    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

  14. 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

  15. 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. PMID:21323142

  16. Probabilistic prediction models for aggregate quarry siting

    USGS Publications Warehouse

    Robinson, G.R., Jr.; 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.

  17. 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.

  18. Constructing predictive models of human running.

    PubMed

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

    2015-02-01

    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

  19. 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

  20. 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.

  1. Computer Model Predicts the Movement of Dust

    NASA Technical Reports Server (NTRS)

    2002-01-01

    A new computer model of the atmosphere can now actually pinpoint where global dust events come from, and can project where they're going. The model may help scientists better evaluate the impact of dust on human health, climate, ocean carbon cycles, ecosystems, and atmospheric chemistry. Also, by seeing where dust originates and where it blows people with respiratory problems can get advanced warning of approaching dust clouds. 'The model is physically more realistic than previous ones,' said Mian Chin, a co-author of the study and an Earth and atmospheric scientist at Georgia Tech and the Goddard Space Flight Center (GSFC) in Greenbelt, Md. 'It is able to reproduce the short term day-to-day variations and long term inter-annual variations of dust concentrations and distributions that are measured from field experiments and observed from satellites.' The above images show both aerosols measured from space (left) and the movement of aerosols predicted by computer model for the same date (right). For more information, read New Computer Model Tracks and Predicts Paths Of Earth's Dust Images courtesy Paul Giroux, Georgia Tech/NASA Goddard Space Flight Center

  2. Quantitative Computed Tomography Protocols Affect Material Mapping and Quantitative Computed Tomography-Based Finite-Element Analysis Predicted Stiffness.

    PubMed

    Giambini, Hugo; Dragomir-Daescu, Dan; Nassr, Ahmad; Yaszemski, Michael J; Zhao, Chunfeng

    2016-09-01

    Quantitative computed tomography-based finite-element analysis (QCT/FEA) has become increasingly popular in an attempt to understand and possibly reduce vertebral fracture risk. It is known that scanning acquisition settings affect Hounsfield units (HU) of the CT voxels. Material properties assignments in QCT/FEA, relating HU to Young's modulus, are performed by applying empirical equations. The purpose of this study was to evaluate the effect of QCT scanning protocols on predicted stiffness values from finite-element models. One fresh frozen cadaveric torso and a QCT calibration phantom were scanned six times varying voltage and current and reconstructed to obtain a total of 12 sets of images. Five vertebrae from the torso were experimentally tested to obtain stiffness values. QCT/FEA models of the five vertebrae were developed for the 12 image data resulting in a total of 60 models. Predicted stiffness was compared to the experimental values. The highest percent difference in stiffness was approximately 480% (80 kVp, 110 mAs, U70), while the lowest outcome was ∼1% (80 kVp, 110 mAs, U30). There was a clear distinction between reconstruction kernels in predicted outcomes, whereas voltage did not present a clear influence on results. The potential of QCT/FEA as an improvement to conventional fracture risk prediction tools is well established. However, it is important to establish research protocols that can lead to results that can be translated to the clinical setting. PMID:27428281

  3. Inverse and predictive modeling of seepage into underground openings.

    PubMed

    Finsterle, S; Ahlers, C F; Trautz, R C; Cook, P J

    2003-01-01

    We discuss the development and calibration of a model for predicting seepage into underground openings. Seepage is a key factor affecting the performance of the potential nuclear-waste repository at Yucca Mountain, Nevada. Three-dimensional numerical models were developed to simulate field tests in which water was released from boreholes above excavated niches. Data from air-injection tests were geostatistically analyzed to infer the heterogeneous structure of the fracture permeability field. The heterogeneous continuum model was then calibrated against the measured amount of water that seeped into the opening. This approach resulted in the estimation of model-related, seepage-specific parameters on the scale of interest. The ability of the calibrated model to predict seepage was examined by comparing calculated with measured seepage rates from additional experiments conducted in different portions of the fracture network. We conclude that an effective capillary strength parameter is suitable to characterize seepage-related features and processes for use in a prediction model of average seepage into potential waste-emplacement drifts. PMID:12714286

  4. 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.

  5. 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.

  6. 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

  7. Predicting Subjective Affective Salience from Cortical Responses to Invisible Object Stimuli.

    PubMed

    Schmack, Katharina; Burk, Julia; Haynes, John-Dylan; Sterzer, Philipp

    2016-08-01

    The affective value of a stimulus substantially influences its potency to gain access to awareness. Here, we sought to elucidate the neural mechanisms underlying such affective salience in a combined behavioral and fMRI experiment. Healthy individuals with varying degrees of spider phobia were presented with pictures of spiders and flowers suppressed from view by continuous flash suppression. Applying multivoxel pattern analysis, we found that the average time that spider stimuli took relative to flowers to gain access to awareness in each participant could be decoded from fMRI signals evoked by suppressed spider versus flower stimuli in occipitotemporal and orbitofrontal cortex. Our results indicate neural signals during unconscious processing of complex visual information in orbitofrontal and ventral visual areas predict access to awareness of this information, suggesting a crucial role for these higher-level cortical regions in mediating affective salience. PMID:26232987

  8. 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.

  9. 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…

  10. Predictive value of primate models for AIDS.

    PubMed

    Haigwood, Nancy L

    2004-01-01

    A number of obstacles remain in the search for an animal model for HIV infection and pathogenesis that can serve to predict efficacy in humans. HIV-1 fails to replicate and cause disease except in humans or chimpanzees, thereby limiting our ability to evaluate compounds or vaccines prior to human testing. Despite this limitation, nonhuman primate lentivirus models have been established that recapitulate the modes of infection, disease course, and antiviral immunity that is seen in HIV infection of humans. These models have been utilized to understand key aspects of disease and to evaluate concepts in therapies and vaccine development. By necessity, animal models can only be validated after successful trials in humans and the determination of correlates of protection. Because the only vaccine product tested in phase III trials in humans failed to achieve the desired protective threshold, we are as yet unable to validate any of the currently used nonhuman primate models for vaccine research. In the absence of a validated model, many experts in the field have concluded that prophylactic vaccines and therapeutic concepts should bypass primate models, and rely solely upon the systematic testing of each individual and combined vaccine element in human phase I or I/II trials to determine their relative merits. Indeed, a large effort is underway to expand efforts to test all products as part of an international effort termed "The HIV Vaccine Enterprise", with major contributions from the Bill and Melinda Gates Foundation. This Herculean task could potentially be reduced if it were possible to utilize even partially validated nonhuman primate models as part of the screening efforts. The purpose of this article is to review the data from nonhuman primate models that have contributed to our understanding of lentivirus infection and pathogenesis, and to critically evaluate how well these models have predicted outcomes in humans. Key features of the models developed to date are

  11. Predicting intentions to purchase organic food: the role of affective and moral attitudes in the Theory of Planned Behaviour.

    PubMed

    Arvola, A; Vassallo, M; Dean, M; Lampila, P; Saba, A; Lähteenmäki, L; Shepherd, R

    2008-01-01

    This study examined the usefulness of integrating measures of affective and moral attitudes into the Theory of Planned Behaviour (TPB)-model in predicting purchase intentions of organic foods. Moral attitude was operationalised as positive self-rewarding feelings of doing the right thing. Questionnaire data were gathered in three countries: Italy (N=202), Finland (N=270) and UK (N=200) in March 2004. Questions focussed on intentions to purchase organic apples and organic ready-to-cook pizza instead of their conventional alternatives. Data were analysed using Structural Equation Modelling by simultaneous multi-group analysis of the three countries. Along with attitudes, moral attitude and subjective norms explained considerable shares of variances in intentions. The relative influences of these variables varied between the countries, such that in the UK and Italy moral attitude rather than subjective norms had stronger explanatory power. In Finland it was other way around. Inclusion of moral attitude improved the model fit and predictive ability of the model, although only marginally in Finland. Thus the results partially support the usefulness of incorporating moral measures as well as affective items for attitude into the framework of TPB. PMID:18036702

  12. 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.

  13. 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.

  14. Predictive model of radiative neutrino masses

    NASA Astrophysics Data System (ADS)

    Babu, K. S.; Julio, J.

    2014-03-01

    We present a simple and predictive model of radiative neutrino masses. It is a special case of the Zee model which introduces two Higgs doublets and a charged singlet. We impose a family-dependent Z4 symmetry acting on the leptons, which reduces the number of parameters describing neutrino oscillations to four. A variety of predictions follow: the hierarchy of neutrino masses must be inverted; the lightest neutrino mass is extremely small and calculable; one of the neutrino mixing angles is determined in terms of the other two; the phase parameters take CP-conserving values with δCP=π; and the effective mass in neutrinoless double beta decay lies in a narrow range, mββ=(17.6-18.5) meV. The ratio of vacuum expectation values of the two Higgs doublets, tanβ, is determined to be either 1.9 or 0.19 from neutrino oscillation data. Flavor-conserving and flavor-changing couplings of the Higgs doublets are also determined from neutrino data. The nonstandard neutral Higgs bosons, if they are moderately heavy, would decay dominantly into μ and τ with prescribed branching ratios. Observable rates for the decays μ →eγ and τ→3μ are predicted if these scalars have masses in the range of 150-500 GeV.

  15. 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.

  16. The Predictive Performance and Stability of Six Species Distribution Models

    PubMed Central

    Huang, Min-Yi; Fan, Wei-Yi; Wang, Zhi-Gao

    2014-01-01

    Background Predicting species’ potential geographical range by species distribution models (SDMs) is central to understand their ecological requirements. However, the effects of using different modeling techniques need further investigation. In order to improve the prediction effect, we need to assess the predictive performance and stability of different SDMs. Methodology We collected the distribution data of five common tree species (Pinus massoniana, Betula platyphylla, Quercus wutaishanica, Quercus mongolica and Quercus variabilis) and simulated their potential distribution area using 13 environmental variables and six widely used SDMs: BIOCLIM, DOMAIN, MAHAL, RF, MAXENT, and SVM. Each model run was repeated 100 times (trials). We compared the predictive performance by testing the consistency between observations and simulated distributions and assessed the stability by the standard deviation, coefficient of variation, and the 99% confidence interval of Kappa and AUC values. Results The mean values of AUC and Kappa from MAHAL, RF, MAXENT, and SVM trials were similar and significantly higher than those from BIOCLIM and DOMAIN trials (p<0.05), while the associated standard deviations and coefficients of variation were larger for BIOCLIM and DOMAIN trials (p<0.05), and the 99% confidence intervals for AUC and Kappa values were narrower for MAHAL, RF, MAXENT, and SVM. Compared to BIOCLIM and DOMAIN, other SDMs (MAHAL, RF, MAXENT, and SVM) had higher prediction accuracy, smaller confidence intervals, and were more stable and less affected by the random variable (randomly selected pseudo-absence points). Conclusions According to the prediction performance and stability of SDMs, we can divide these six SDMs into two categories: a high performance and stability group including MAHAL, RF, MAXENT, and SVM, and a low performance and stability group consisting of BIOCLIM, and DOMAIN. We highlight that choosing appropriate SDMs to address a specific problem is an important

  17. A prediction model for Clostridium difficile recurrence

    PubMed Central

    LaBarbera, Francis D.; Nikiforov, Ivan; Parvathenani, Arvin; Pramil, Varsha; Gorrepati, Subhash

    2015-01-01

    Background Clostridium difficile infection (CDI) is a growing problem in the community and hospital setting. Its incidence has been on the rise over the past two decades, and it is quickly becoming a major concern for the health care system. High rate of recurrence is one of the major hurdles in the successful treatment of C. difficile infection. There have been few studies that have looked at patterns of recurrence. The studies currently available have shown a number of risk factors associated with C. difficile recurrence (CDR); however, there is little consensus on the impact of most of the identified risk factors. Methods Our study was a retrospective chart review of 198 patients diagnosed with CDI via Polymerase Chain Reaction (PCR) from January 2009 to Jun 2013. In our study, we decided to use a machine learning algorithm called the Random Forest (RF) to analyze all of the factors proposed to be associated with CDR. This model is capable of making predictions based on a large number of variables, and has outperformed numerous other models and statistical methods. Results We came up with a model that was able to accurately predict the CDR with a sensitivity of 83.3%, specificity of 63.1%, and area under curve of 82.6%. Like other similar studies that have used the RF model, we also had very impressive results. Conclusions We hope that in the future, machine learning algorithms, such as the RF, will see a wider application. PMID:25656667

  18. 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.

  19. 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.

  20. Ground Motion Prediction Models for Caucasus Region

    NASA Astrophysics Data System (ADS)

    Jorjiashvili, Nato; Godoladze, Tea; Tvaradze, Nino; Tumanova, Nino

    2016-04-01

    Ground motion prediction models (GMPMs) relate ground motion intensity measures to variables describing earthquake source, path, and site effects. Estimation of expected ground motion is a fundamental earthquake hazard assessment. The most commonly used parameter for attenuation relation is peak ground acceleration or spectral acceleration because this parameter gives useful information for Seismic Hazard Assessment. Since 2003 development of Georgian Digital Seismic Network has started. In this study new GMP models are obtained based on new data from Georgian seismic network and also from neighboring countries. Estimation of models is obtained by classical, statistical way, regression analysis. In this study site ground conditions are additionally considered because the same earthquake recorded at the same distance may cause different damage according to ground conditions. Empirical ground-motion prediction models (GMPMs) require adjustment to make them appropriate for site-specific scenarios. However, the process of making such adjustments remains a challenge. This work presents a holistic framework for the development of a peak ground acceleration (PGA) or spectral acceleration (SA) GMPE that is easily adjustable to different seismological conditions and does not suffer from the practical problems associated with adjustments in the response spectral domain.

  1. 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.

  2. Clinical Predictive Modeling Development and Deployment through FHIR Web Services

    PubMed Central

    Khalilia, Mohammed; Choi, Myung; Henderson, Amelia; Iyengar, Sneha; Braunstein, Mark; Sun, Jimeng

    2015-01-01

    Clinical predictive modeling involves two challenging tasks: model development and model deployment. In this paper we demonstrate a software architecture for developing and deploying clinical predictive models using web services via the Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) standard. The services enable model development using electronic health records (EHRs) stored in OMOP CDM databases and model deployment for scoring individual patients through FHIR resources. The MIMIC2 ICU dataset and a synthetic outpatient dataset were transformed into OMOP CDM databases for predictive model development. The resulting predictive models are deployed as FHIR resources, which receive requests of patient information, perform prediction against the deployed predictive model and respond with prediction scores. To assess the practicality of this approach we evaluated the response and prediction time of the FHIR modeling web services. We found the system to be reasonably fast with one second total response time per patient prediction. PMID:26958207

  3. Clinical Predictive Modeling Development and Deployment through FHIR Web Services.

    PubMed

    Khalilia, Mohammed; Choi, Myung; Henderson, Amelia; Iyengar, Sneha; Braunstein, Mark; Sun, Jimeng

    2015-01-01

    Clinical predictive modeling involves two challenging tasks: model development and model deployment. In this paper we demonstrate a software architecture for developing and deploying clinical predictive models using web services via the Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) standard. The services enable model development using electronic health records (EHRs) stored in OMOP CDM databases and model deployment for scoring individual patients through FHIR resources. The MIMIC2 ICU dataset and a synthetic outpatient dataset were transformed into OMOP CDM databases for predictive model development. The resulting predictive models are deployed as FHIR resources, which receive requests of patient information, perform prediction against the deployed predictive model and respond with prediction scores. To assess the practicality of this approach we evaluated the response and prediction time of the FHIR modeling web services. We found the system to be reasonably fast with one second total response time per patient prediction. PMID:26958207

  4. Decadal prediction with a high resolution model

    NASA Astrophysics Data System (ADS)

    Monerie, Paul-Arthur; Valcke, Sophie; Terray, Laurent; Moine, Marie-Pierre

    2016-04-01

    The ability of a high resolution coupled atmosphere-ocean general circulation model (with a horizontal resolution of the quarter degree in the ocean and of about 50 km in the atmosphere) to predict the annual means of temperature, precipitation, sea-ice volume and extent is assessed. Reasonable skill in predicting sea surface temperatures and surface air temperature is obtained, especially over the North Atlantic, the tropical Atlantic and the Indian Ocean. The skill in predicting precipitations is weaker and not significant. The Sea Ice Extent and volume are also reasonably predicted in winter (March) and summer (September). It is however argued that the skill is mainly due to the atmosphere feeding in well-mixed GHGs. The mid-90's subpolar gyre warming is assessed. The model simulates a warming of the North Atlantic Ocean, associated with an increase of the meridional heat transport, a strengthening of the North Atlantic current and a deepening of the mixed layer over the Labrador Sea. The atmosphere plays a role in the warming through a modulation of the North Atlantic Oscillation and a shrinking of the subpolar gyre. At the 3-8 years lead-time, a negative anomaly of pressure, located south of the subpolar gyre is associated with the wind speed decrease over the subpolar gyre. It prevents oceanic heat-loss and favors the northward move, from the subtropical to the subpolar gyre, of anomalously warm and salty water, leading to its warming. We finally argued that the subpolar gyre warming is triggered by the ocean dynamic but the atmosphere can contributes to its sustaining. This work is realised in the framework of the EU FP7 SPECS Project.

  5. Deterioration Prediction Model of Irrigation Facilities by Markov Chain Model

    NASA Astrophysics Data System (ADS)

    Mori, Takehisa; Nishino, Noriyasu; Fujiwara, Tetsuro

    "Stock Management" launched in all over Japan is an activity to use irrigation facilities effectively and to reduce life cycle costs of theirs. Deterioration prediction of the irrigation facility condition is a vital process for the study of maintenance measures and the estimation of maintenance cost. It is important issue to establish the prediction technique with higher accuracy. Thereupon, we established a deterioration prediction model by a statistical method "Markov chain", and analyzed a function diagnosis data of irrigation facilities. As a result, we clarified the deterioration characteristics into each structure type and facilities.

  6. 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.

  7. Predictions in multifield models of inflation

    SciTech Connect

    Frazer, Jonathan

    2014-01-01

    This paper presents a method for obtaining an analytic expression for the density function of observables in multifield models of inflation with sum-separable potentials. The most striking result is that the density function in general possesses a sharp peak and the location of this peak is only mildly sensitive to the distribution of initial conditions. A simple argument is given for why this result holds for a more general class of models than just those with sum-separable potentials and why for such models, it is possible to obtain robust predictions for observable quantities. As an example, the joint density function of the spectral index and running in double quadratic inflation is computed. For scales leaving the horizon 55 e-folds before the end of inflation, the density function peaks at n{sub s} = 0.967 and α = 0.0006 for the spectral index and running respectively.

  8. Validation of Kp Estimation and Prediction Models

    NASA Astrophysics Data System (ADS)

    McCollough, J. P., II; Young, S. L.; Frey, W.

    2014-12-01

    Specifification and forecast of geomagnetic indices is an important capability for space weather operations. The University Partnering for Operational Support (UPOS) effort at the Applied Physics Laboratory of Johns Hopkins University (JHU/APL) produced many space weather models, including the Kp Predictor and Kp Estimator. We perform a validation of index forecast products against definitive indices computed by the Deutches GeoForschungsZentstrum Potsdam (GFZ). We compute continuous predictant skill scores, as well as 2x2 contingency tables and associated scalar quantities for different index thresholds. We also compute a skill score against a nowcast persistence model. We discuss various sources of error for the models and how they may potentially be improved.

  9. Distinguishing the affective and cognitive bases of implicit attitudes to improve prediction of food choices.

    PubMed

    Trendel, Olivier; Werle, Carolina O C

    2016-09-01

    Eating behaviors largely result from automatic processes. Yet, in existing research, automatic or implicit attitudes toward food often fail to predict eating behaviors. Applying findings in cognitive neuroscience research, we propose and find that a central reason why implicit attitudes toward food are not good predictors of eating behaviors is that implicit attitudes are driven by two distinct constructs that often have diverging evaluative consequences: the automatic affective reactions to food (e.g., tastiness; the affective basis of implicit attitudes) and the automatic cognitive reactions to food (e.g., healthiness; the cognitive basis of implicit attitudes). More importantly, we find that the affective and cognitive bases of implicit attitudes directly and uniquely influence actual food choices under different conditions. While the affective basis of implicit attitude is the main driver of food choices, it is the only driver when cognitive resources during choice are limited. The cognitive basis of implicit attitudes uniquely influences food choices when cognitive resources during choice are plentiful but only for participants low in impulsivity. Researchers interested in automatic processes in eating behaviors could thus benefit by distinguishing between the affective and cognitive bases of implicit attitudes. PMID:26471802

  10. 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

  11. Permafrost, climate, and change: predictive modelling approach.

    NASA Astrophysics Data System (ADS)

    Anisimov, O.

    2003-04-01

    Predicted by GCMs enhanced warming of the Arctic will lead to discernible impacts on permafrost and northern environment. Mathematical models of different complexity forced by scenarios of climate change may be used to predict such changes. Permafrost models that are currently in use may be divided into four groups: index-based models (e.g. frost index model, N-factor model); models of intermediate complexity based on equilibrium simplified solution of the Stephan problem ("Koudriavtcev's" model and its modifications), and full-scale comprehensive dynamical models. New approach of stochastic modelling came into existence recently and has good prospects for the future. Important task is to compare the ability of the models that are different in complexity, concept, and input data requirements to capture the major impacts of changing climate on permafrost. A progressive increase in the depth of seasonal thawing (often referred to as the active-layer thickness, ALT) could be a relatively short-term reaction to climatic warming. At regional and local scales, it may produce substantial effects on vegetation, soil hydrology and runoff, as the water storage capacity of near-surface permafrost will be changed. Growing public concerns are associated with the impacts that warming of permafrost may have on engineered infrastructure built upon it. At the global scale, increase of ALT could facilitate further climatic change if more greenhouse gases are released when the upper layer of the permafrost thaws. Since dynamic permafrost models require complete set of forcing data that is not readily available on the circumpolar scale, they could be used most effectively in regional studies, while models of intermediate complexity are currently best tools for the circumpolar assessments. Set of five transient scenarios of climate change for the period 1980 - 2100 has been constructed using outputs from GFDL, NCAR, CCC, HadCM, and ECHAM-4 models. These GCMs were selected in the course

  12. 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.

  13. 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.

  14. 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. PMID:26710265

  15. 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.

  16. Modeling Reef Hydrodynamics to Predict Coral Bleaching

    NASA Astrophysics Data System (ADS)

    Bird, James; Steinberg, Craig; Hardy, Tom

    2005-11-01

    The aim of this study is to use environmental physics to predict water temperatures around and within coral reefs. Anomalously warm water is the leading cause for mass coral bleaching; thus a clearer understanding of the oceanographic mechanisms that control reef water temperatures will enable better reef management. In March 1998 a major coral bleaching event occurred at Scott Reef, a 40 km-wide lagoon 300 km off the northwest coast of Australia. Meteorological and coral cover observations were collected before, during, and after the event. In this study, two hydrodynamic models are applied to Scott Reef and validated against oceanographic data collected between March and June 2003. The models are then used to hindcast the reef hydrodynamics that led up to the 1998 bleaching event. Results show a positive correlation between poorly mixed regions and bleaching severity.

  17. 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…

  18. Audibility-based annoyance prediction modeling

    NASA Astrophysics Data System (ADS)

    Fidell, Sanford; Finegold, Lawrence S.

    1992-04-01

    The effects of rapid onset times and high absolute sound pressures near military training routes (MTR's) including possible startle effects and increased annoyance due to the unpredictable nature of these flights, have been of longstanding concern. A more recent concern is the possibility of increased annoyance due to low ambient noise levels near military flight training operations and differences in expectations about noise exposure in high and low population density areas. This paper describes progress in developing audibility-based methods for predicting the annoyance of noise produced at some distance from aircraft flight tracks. Audibility-based models which take into account near-ground acoustic propagation and ambient noise levels may be useful in assessing environmental impacts of MTR's and Military Operating Areas (MOA's) under some conditions. A prototype Single Event Annoyance Prediction Model (SEAPM) has been developed under USAF sponsorship as an initial effort to address these issues, and work has progressed on a geographic information system (GIS) to produce cartographically referenced representations of aircraft audibility.

  19. 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.

  20. 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.

  1. 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.

  2. EVENT PREDICTION AND AFFECTIVE FORECASTING IN DEPRESSIVE COGNITION: USING EMOTION AS INFORMATION ABOUT THE FUTURE

    PubMed Central

    MARROQUÍN, BRETT; NOLEN-HOEKSEMA, SUSAN

    2015-01-01

    Depression is characterized by a bleak view of the future, but the mechanisms through which depressed mood is integrated into basic processes of future-oriented cognition are unclear. We hypothesized that dysphoric individuals’ predictions of what will happen in the future (likelihood estimation) and how the future will feel (affective forecasting) are attributable to individual differences in incorporating present emotion as judgment-relevant information. Dysphoric individuals (n = 77) made pessimistic likelihood estimates and blunted positive affective forecasts relative to controls (n = 84). These differences were mediated by dysphoric individuals’ tendencies to rely on negative emotion as information more than controls—and on positive emotion less—independent of anhedonia. These findings suggest that (1) blunted positive affective forecasting is a distinctive component of depressive future-oriented cognition, and (2) future-oriented cognitive processes are linked not just to current emotional state, but also to individual variation in using that emotion as information. This role of individual differences elucidates basic mechanisms in future-oriented cognition, and suggests routes for intervention on interrelated cognitive and affective processes in depression. PMID:26146452

  3. 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.

  4. Prediction of Plate Motions and Stresses from Global Dynamic Models

    NASA Astrophysics Data System (ADS)

    Ghosh, A.; Holt, W. E.

    2011-12-01

    Predicting plate motions correctly has been a challenge for global dynamic models. In addition to predicting plate motions, a successful model must also explain the following features: plate rigidity, plate boundary zone deformation, as well as intraplate stress patterns and deformation. In this study we show that, given constraints from shallow lithosphere structure, history of subduction, and first order features from whole mantle tomography, it is possible to achieve a high level of accuracy in predicting plate motions and lithosphere deformation within plate boundary zones. Best-fit dynamic models presently provide an RMS velocity misfit of global surface motions (compared at 63,000 spaced points in the GSRM NNR model [Kreemer et al., 2006]) of order 1 cm/yr. We explore the relative contribution of shallow lithosphere structure vs. whole mantle convection in affecting surface deformation as well as plate motions. We show that shallow lithosphere structure that includes topography and lateral density variations in the lithosphere is an integral part of global force balance. Its inclusion in geodynamic models is essential in order to match observations of surface motions and stresses, particularly within continental zones of deformation. We also argue that stiff slabs may not be as important as has been previously claimed in controlling plate motion and lithosphere deformation. An important result of this study is the calibration of absolute stress magnitudes in the lithosphere, verified through benchmarking using whole mantle convection models. Given additional constraints of the matching of surface motions, we also calibrate the absolute effective lithosphere viscosities. Best-fit models require plates with effective viscosities of order 1023 Pa-s, with plate boundary zones possessing effective viscosities 1-3 orders of magnitude weaker. Given deviatoric stress magnitudes within the lithosphere of order 10 - 60 MPa, our global models predict less than 2 mm

  5. 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. PMID:23687472

  6. Model independent predictions for rare top decays with weak coupling

    SciTech Connect

    Datta, Alakabha; Duraisamy, Murugeswaran

    2010-04-01

    Measurements at B factories have provided important constraints on new physics in several rare processes involving the B meson. New physics, if present in the b quark sector may also affect the top sector. In an effective Lagrangian approach, we write down operators, where effects in the bottom and the top sector are related. Assuming the couplings of the operators to be of the same size as the weak coupling g of the standard model and taking into account constraints on new physics from the bottom sector as well as top branching ratios, we make predictions for the rare top decays t{yields}cV, where V={gamma}, Z. We find branching fractions for these decays within possible reach of the LHC. Predictions are also made for t{yields}sW.

  7. Failure Prediction in Fine Blanking Process with Stress Limit Model

    NASA Astrophysics Data System (ADS)

    Tong, Longchang; Manopulo, Niko; Hora, Pavel

    2010-06-01

    Extremely small blanking clearance and nearly sharp edges of blanking tool are the characteristics of fine blanking that produces near net shape parts. The extreme forming conditions make the failure prediction for fine blanking more difficult than for ordinary forming processes. Stress Limit Criterion (SLC) is adopted in this work to perform the failure prediction of 3D fine blanking process. In comparison with the stress triaxiality diagram, SLC is not sensitively affected by complex nonlinear deformation paths and can perform the task as well. However, the parameters that support the model have to be obtained with combination of dedicatedly designed experiments and numerical simulation. The FEM simulation must also be able to provide stable and reliable results.

  8. Data driven propulsion system weight prediction model

    NASA Technical Reports Server (NTRS)

    Gerth, Richard J.

    1994-01-01

    The objective of the research was to develop a method to predict the weight of paper engines, i.e., engines that are in the early stages of development. The impetus for the project was the Single Stage To Orbit (SSTO) project, where engineers need to evaluate alternative engine designs. Since the SSTO is a performance driven project the performance models for alternative designs were well understood. The next tradeoff is weight. Since it is known that engine weight varies with thrust levels, a model is required that would allow discrimination between engines that produce the same thrust. Above all, the model had to be rooted in data with assumptions that could be justified based on the data. The general approach was to collect data on as many existing engines as possible and build a statistical model of the engines weight as a function of various component performance parameters. This was considered a reasonable level to begin the project because the data would be readily available, and it would be at the level of most paper engines, prior to detailed component design.

  9. Predicting self-care with patients and family members' affective states and family functioning.

    PubMed

    Musci, E C; Dodd, M J

    1990-01-01

    People with cancer manage the side effects of treatment with the assistance of their family members. This study was designed to describe self-care behaviors (SCBs) initiated by patients and their family members and to determine the relationship between patients and family members' affective states and family functioning and SCBs. Using a longitudinal design, 42 patients and 40 family members were followed during 3 cycles of chemotherapy (12-16 weeks). The patients completed measures of affective state (POMS) each cycle; patients and family members completed a family functioning measure (F-COPES) at second cycle only; and the patients reported in an SCB log on an ongoing basis. The overall pattern of SCBs corroborated previous findings. The average number of SCBs initiated was 1.4 per side effect. Depression and vigor significantly predicted SCBs at Cycle 1 only. The severity of side effects consistently predicted SCB over the 3 cycles (r 2 = -0.39 to -0.46). Patients who experienced more severe side effects were at risk of diminished self-care. PMID:2342973

  10. Comparing predicted and actual affective responses to process versus outcome: an emotion-as-feedback perspective.

    PubMed

    Kwong, Jessica Y Y; Wong, Kin Fai Ellick; Tang, Suki K Y

    2013-10-01

    One of the conjectures in affective forecasting literature is that people are advised to discount their anticipated emotions because their forecasts are often inaccurate. The present research distinguishes between emotional reactions to process versus those to outcome, and highlights an alternative view that affective misforecasts could indeed be adaptive to goal pursuit. Using an ultimatum game, Study 1 showed that people overpredicted how much they would regret and be disappointed by the amount of effort they exerted, should the outcomes turned out worse than expected; nonetheless, people could accurately predict their emotional responses to unfavorable outcomes per se. In a natural setting of a university examination, Study 2 demonstrated that actual regret and disappointment toward favorable outcomes were more intense than the level people expected, but this discrepancy was not observed in their emotional responses to efforts they had invested. These two distinct patterns of results substantiate the argument that the deviation between predicted and actual emotions is dependent on the referents of the emotional reactions. PMID:23831563

  11. 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. PMID:26203145

  12. 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

  13. 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. PMID:20869123

  14. 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.

  15. 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 ...

  16. 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. PMID:26646576

  17. 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.

  18. Resources Predicting Positive and Negative Affect during the Experience of Stress: A Study of Older Asian Indian Immigrants in the United States.

    ERIC Educational Resources Information Center

    Diwan, Sadhna; Jonnalagadda, Satya S.; Balaswamy, Shantha

    2004-01-01

    Purpose: Using the life stress model of psychological well-being, in this study we examined risks and resources predicting the occurrence of both positive and negative affect among older Asian Indian immigrants who experienced stressful life events. Design and Methods: We collected data through a telephone survey of 226 respondents (aged 50 years…

  19. 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

  20. 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

  1. 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

  2. Studying Links between Hormones and Negative Affect: Models and Measures.

    ERIC Educational Resources Information Center

    Brooks-Gunn, Jeanne; And Others

    1994-01-01

    Considers eight models for the study of pubertal change that explore possible links between hormones and negative affective experiences, such as depression and aggression. Notes that hormonal effects, though small, have demonstrated stability and have interacted with psychological and social factors, implicating hormonal changes in the development…

  3. Cognitive, Affective, and Behavioral Determinants of Performance: A Process Model.

    ERIC Educational Resources Information Center

    Dorfman, Peter W.; Stephan, Walter G.

    Literature from organizational and social psychology has suggested that three types of factors influence performance, i.e., cognitive, affective and behavioral. A model was developed to test a set of propositions concerning the relationship between the three kinds of factors, and included attributions, expectancies, general emotional responses to…

  4. Factors affecting paddy soil arsenic concentration in Bangladesh: prediction and uncertainty of geostatistical risk mapping.

    PubMed

    Ahmed, Zia U; Panaullah, Golam M; DeGloria, Stephen D; Duxbury, John M

    2011-12-15

    Knowledge of the spatial correlation of soil arsenic (As) concentrations with environmental variables is needed to assess the nature and extent of the risk of As contamination from irrigation water in Bangladesh. We analyzed 263 paired groundwater and paddy soil samples covering highland (HL) and medium highland-1 (MHL-1) land types for geostatistical mapping of soil As and delineation of As contaminated areas in Tala Upazilla, Satkhira district. We also collected 74 non-rice soil samples to assess the baseline concentration of soil As for this area. The mean soil As concentrations (mg/kg) for different land types under rice and non-rice crops were: rice-MHL-1 (21.2)>rice-HL (14.1)>non-rice-MHL-1 (11.9)>non-rice-HL (7.2). Multiple regression analyses showed that irrigation water As, Fe, land elevation and years of tubewell operation are the important factors affecting the concentrations of As in HL paddy soils. Only years of tubewell operation affected As concentration in the MHL-1 paddy soils. Quantitatively similar increases in soil As above the estimated baseline-As concentration were observed for rice soils on HL and MHL-1 after 6-8 years of groundwater irrigation, implying strong retention of As added in irrigation water in both land types. Application of single geostatistical methods with secondary variables such as regression kriging (RK) and ordinary co-kriging (OCK) gave little improvement in prediction of soil As over ordinary kriging (OK). Comparing single prediction methods, kriging within strata (KWS), the combination of RK for HL and OCK for MHL-1, gave more accurate soil As predictions and showed the lowest misclassification of declaring a location "contaminated" with respect to 14.8 mg As/kg, the highest value obtained for the baseline soil As concentration. Prediction of soil As buildup over time indicated that 75% or the soils cropped to rice would contain at least 30 mg/L As by the year 2020. PMID:22055452

  5. 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.

  6. Prediction and setup of phytoplankton statistical model of Qiandaohu Lake.

    PubMed

    Yan, Li-jiao; Quan, Wei-min; Zhao, Xiao-hui

    2004-10-01

    This research considers the mathematical relationship between concentration of Chla and seven environmental factors, i.e. Lake water temperature (T), Secci-depth (SD), pH, DO, CODMn, Total Nitrogen (TN), Total Phosphorus (TP). Stepwise linear regression of 1997 to 1999 monitoring data at each sampling point of Qiandaohu Lake yielded the multivariate regression models presented in this paper. The concentration of Chla as simulation for the year 2000 by the regression model was similar to the observed value. The suggested mathematical relationship could be used to predict changes in the lakewater environment at any point in time. The results showed that SD, TP and pH were the most significant factors affecting Chla concentration. PMID:15362191

  7. Prediction and setup of phytoplankton statistical model of Qiandaohu Lake*

    PubMed Central

    Yan, Li-jiao; Quan, Wei-min; Zhao, Xiao-hui

    2004-01-01

    This research considers the mathematical relationship between concentration of Chla and seven environmental factors, i.e. Lake water temperature (T), Secci-depth (SD), pH, DO, CODMn, Total Nitrogen (TN), Total Phosphorus (TP). Stepwise linear regression of 1997 to 1999 monitoring data at each sampling point of Qiandaohu Lake yielded the multivariate regression models presented in this paper. The concentration of Chla as simulation for the year 2000 by the regression model was similar to the observed value. The suggested mathematical relationship could be used to predict changes in the lakewater environment at any point in time. The results showed that SD, TP and pH were the most significant factors affecting Chla concentration. PMID:15362191

  8. Breed-specific fetal biometry and factors affecting the prediction of whelping date in the German shepherd dog.

    PubMed

    Groppetti, D; Vegetti, F; Bronzo, V; Pecile, A

    2015-01-01

    To date many studies have been published about predicting parturition by ultrasonographic fetal measurements in the bitch. Given that accuracy in such prediction is a key point for clinicians and breeders, formulas to calculate the whelping date were mainly obtained from small and medium sized dogs, which means poor accuracy when applied to large or giant breeds. Based on the evidence that ethnicity significantly affects fetal biometry in humans, this study aimed at developing a breed-specific linear regression model for estimating parturition date in the German shepherd dog. For this purpose, serial ultrasonographic measurements of the inner chorionic cavity diameter (ICC) and the fetal biparietal diameter (BP) were collected in 40 pregnant German shepherd bitches. The quality of the regression models for estimating parturition date was further verified in 22 other pregnant German shepherd bitches. Accuracy related to the prediction of parturition date was higher than previously reported: 94.5% and 91.7% within ±2 days interval based on ICC and BP measurements, respectively. Additional investigation was performed on the effects of maternal weight, age and litter size in relation to fetal biometry and to accuracy of parturition estimation. Moreover, the study included a comparison between hormonal and fetal ultrasound (ICC and BP) measurements connected to the estimation of whelping date. We suggest that specific equations from a single breed are likely to offer excellent accuracy, comparable to that of periovulatory progesteronemia, in parturition prediction and to avoid morphological variables present in dogs of different breeds even with the same size/weight. PMID:25510562

  9. How tibiofemoral alignment and contact locations affect predictions of medial and lateral tibiofemoral contact forces.

    PubMed

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

    2015-02-26

    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 through 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 (r(2)=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 (r(2)=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

  10. 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

  11. 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. PMID:26346363

  12. 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

  13. Adolescents' internalizing and externalizing problems predict their affect-specific HPA and HPG axes reactivity.

    PubMed

    Han, Georges; Miller, Jonas G; Cole, Pamela M; Zahn-Waxler, Carolyn; Hastings, Paul D

    2015-09-01

    We examined psychopathology-neuroendocrine associations in relation to the transition into adolescence within a developmental framework that acknowledged the interdependence of the HPA and HPG hormone systems in the regulation of responses to everyday affective contexts. Saliva samples were collected during anxiety and anger inductions from 51 young adolescents (M 13.47, SD = .60 years) to evaluate cortisol, DHEA, and testosterone responses. Internalizing and externalizing problems were assessed at pre-adolescence (M = 9.27, SD = .58 years) while youths were in elementary school and concurrently with hormones in early adolescence. Externalizing problems from elementary school predicted adolescents' reduced DHEA reactivity during anxiety induction. Follow up analyses simultaneously examining the contributions of elementary school and adolescent problems showed a trend suggesting that youths with higher levels of internalizing problems during elementary school eventuated in a profile of heightened DHEA reactivity as adolescents undergoing anxiety induction. For both the anxiety and the anger inductions, it was normative for DHEA and testosterone to be positively coupled. Adolescents with high externalizing problems but low internalizing problems marshaled dual axes co-activation during anger induction in the form of positive cortisol-testosterone coupling. This is some of the first evidence suggesting affective context determines whether dual axes coupling is reflective of normative or problematic functioning in adolescence. PMID:25604092

  14. Amygdala Perfusion Is Predicted by Its Functional Connectivity with the Ventromedial Prefrontal Cortex and Negative Affect

    PubMed Central

    Coombs III, Garth; Loggia, Marco L.; Greve, Douglas N.; Holt, Daphne J.

    2014-01-01

    Background Previous studies have shown that the activity of the amygdala is elevated in people experiencing clinical and subclinical levels of anxiety and depression (negative affect). It has been proposed that a reduction in inhibitory input to the amygdala from the prefrontal cortex and resultant over-activity of the amygdala underlies this association. Prior studies have found relationships between negative affect and 1) amygdala over-activity and 2) reduced amygdala-prefrontal connectivity. However, it is not known whether elevated amygdala activity is associated with decreased amygdala-prefrontal connectivity during negative affect states. Methods Here we used resting-state arterial spin labeling (ASL) and blood oxygenation level dependent (BOLD) functional magnetic resonance imaging (fMRI) in combination to test this model, measuring the activity (regional cerebral blood flow, rCBF) and functional connectivity (correlated fluctuations in the BOLD signal) of one subregion of the amygdala with strong connections with the prefrontal cortex, the basolateral nucleus (BLA), and subsyndromal anxiety levels in 38 healthy subjects. Results BLA rCBF was strongly correlated with anxiety levels. Moreover, both BLA rCBF and anxiety were inversely correlated with the strength of the functional coupling of the BLA with the caudal ventromedial prefrontal cortex. Lastly, BLA perfusion was found to be a mediator of the relationship between BLA-prefrontal connectivity and anxiety. Conclusions These results show that both perfusion of the BLA and a measure of its functional coupling with the prefrontal cortex directly index anxiety levels in healthy subjects, and that low BLA-prefrontal connectivity may lead to increased BLA activity and resulting anxiety. Thus, these data provide key evidence for an often-cited circuitry model of negative affect, using a novel, multi-modal imaging approach. PMID:24816735

  15. 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.

  16. The Many Faces of Affect: A Multilevel Model of Drinking Frequency/Quantity and Alcohol Dependence Symptoms Among Young Adults

    PubMed Central

    Simons, Jeffrey S.; Wills, Thomas A.; Neal, Dan J.

    2016-01-01

    This research tested a multilevel structural equation model of associations between 3 aspects of affective functioning (state affect, trait affect, and affective lability) and 3 alcohol outcomes (likelihood of drinking, quantity on drinking days, and dependence symptoms) in a sample of 263 college students. Participants provided 49 days of experience sampling data over 1.3 years in a longitudinal burst design. Within-person results: At the daily level, positive affect was directly associated with greater likelihood and quantity of alcohol consumption. Daily negative affect was directly associated with higher consumption on drinking days and with higher dependence symptoms. Between-person direct effects: Affect lability was associated with higher trait negative, but not positive, affect. Trait positive affect was inversely associated with the proportion of drinking days, whereas negative affectivity predicted a greater proportion of drinking days. Affect lability exhibited a direct association with dependence symptoms. Between-person indirect effects: Trait positive affect was associated with fewer dependence symptoms via proportion of drinking days. Trait negative affect was associated with greater dependence symptoms via proportion of drinking days. The results distinguish relations of positive and negative affect to likelihood versus amount of drinking and state versus trait drinking outcomes, and highlight the importance of affect variability for predicting alcohol dependence symptoms. PMID:24933278

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

    NASA Astrophysics Data System (ADS)

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

    2016-06-01

    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.

  18. 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…

  19. 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.

  20. Optimization approaches to nonlinear model predictive control

    SciTech Connect

    Biegler, L.T. . Dept. of Chemical Engineering); Rawlings, J.B. . Dept. of Chemical Engineering)

    1991-01-01

    With the development of sophisticated methods for nonlinear programming and powerful computer hardware, it now becomes useful and efficient to formulate and solve nonlinear process control problems through on-line optimization methods. This paper explores and reviews control techniques based on repeated solution of nonlinear programming (NLP) problems. Here several advantages present themselves. These include minimization of readily quantifiable objectives, coordinated and accurate handling of process nonlinearities and interactions, and systematic ways of dealing with process constraints. We motivate this NLP-based approach with small nonlinear examples and present a basic algorithm for optimization-based process control. As can be seen this approach is a straightforward extension of popular model-predictive controllers (MPCs) that are used for linear systems. The statement of the basic algorithm raises a number of questions regarding stability and robustness of the method, efficiency of the control calculations, incorporation of feedback into the controller and reliable ways of handling process constraints. Each of these will be treated through analysis and/or modification of the basic algorithm. To highlight and support this discussion, several examples are presented and key results are examined and further developed. 74 refs., 11 figs.

  1. Towards a Predictive Model of Elastomer seals

    NASA Astrophysics Data System (ADS)

    Khawaja, Musab; Mostofi, Arash; Sutton, Adrian; Stevens, John

    2014-03-01

    Elastomers are a highly versatile class of material. Their diversity of technological application is enabled by the fact that their properties may be tuned through manipulation of their constituent building blocks at multiple length-scales. These scales range from the chemical groups within individual monomers, to the overall morphology on the mesoscale, as well as through compounding with other materials. An important use of elastomers is in seals for mechanical components. Ideally, such seals should act as impermeable barriers to gases and liquids, preventing contamination and damage to equipment. Elastomer failure, therefore, can be extremely costly and is a matter of great importance to industry. The question at the centre of this work relates to the failure of elastomer seals via explosive decompression. This mechanism is a result of permeation of gas molecules through the seals at high pressures, and their subsequent rapid egress upon removal of the elevated pressures. The goal is to develop a model to better understand and predict the structure, porosity and transport of molecular species through elastomer seals, with a view to elucidating general design principles that will inform the development of higher performance materials.

  2. 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.

  3. 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.

  4. 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…

  5. Measures of GCM Performance as Functions of Model Parameters Affecting Clouds and Radiation

    NASA Astrophysics Data System (ADS)

    Jackson, C.; Mu, Q.; Sen, M.; Stoffa, P.

    2002-05-01

    This abstract is one of three related presentations at this meeting dealing with several issues surrounding optimal parameter and uncertainty estimation of model predictions of climate. Uncertainty in model predictions of climate depends in part on the uncertainty produced by model approximations or parameterizations of unresolved physics. Evaluating these uncertainties is computationally expensive because one needs to evaluate how arbitrary choices for any given combination of model parameters affects model performance. Because the computational effort grows exponentially with the number of parameters being investigated, it is important to choose parameters carefully. Evaluating whether a parameter is worth investigating depends on two considerations: 1) does reasonable choices of parameter values produce a large range in model response relative to observational uncertainty? and 2) does the model response depend non-linearly on various combinations of model parameters? We have decided to narrow our attention to selecting parameters that affect clouds and radiation, as it is likely that these parameters will dominate uncertainties in model predictions of future climate. We present preliminary results of ~20 to 30 AMIPII style climate model integrations using NCAR's CCM3.10 that show model performance as functions of individual parameters controlling 1) critical relative humidity for cloud formation (RHMIN), and 2) boundary layer critical Richardson number (RICR). We also explore various definitions of model performance that include some or all observational data sources (surface air temperature and pressure, meridional and zonal winds, clouds, long and short-wave cloud forcings, etc...) and evaluate in a few select cases whether the model's response depends non-linearly on the parameter values we have selected.

  6. 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.

  7. 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

  8. 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 ...

  9. 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. PMID:26621993

  10. The myth of science-based predictive modeling.

    SciTech Connect

    Hemez, F. M.

    2004-01-01

    A key aspect of science-based predictive modeling is the assessment of prediction credibility. This publication argues that the credibility of a family of models and their predictions must combine three components: (1) the fidelity of predictions to test data; (2) the robustness of predictions to variability, uncertainty, and lack-of-knowledge; and (3) the prediction accuracy of models in cases where measurements are not available. Unfortunately, these three objectives are antagonistic. A recently published Theorem that demonstrates the irrevocable trade-offs between fidelity-to-data, robustness-to-uncertainty, and confidence in prediction is summarized. High-fidelity models cannot be made increasingly robust to uncertainty and lack-of-knowledge. Similarly, robustness-to-uncertainty can only be improved at the cost of reducing the confidence in prediction. The concept of confidence in prediction relies on a metric for total uncertainty, capable of aggregating different representations of uncertainty (probabilistic or not). The discussion is illustrated with an engineering application where a family of models is developed to predict the acceleration levels obtained when impacts of varying levels propagate through layers of crushable hyper-foam material of varying thicknesses. Convex modeling is invoked to represent a severe lack-of-knowledge about the constitutive material behavior. The analysis produces intervals of performance metrics from which the total uncertainty and confidence levels are estimated. Finally, performance, robustness and confidence are extrapolated throughout the validation domain to assess the predictive power of the family of models away from tested configurations.

  11. 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

  12. A modeling approach for compounds affecting body composition.

    PubMed

    Gennemark, Peter; Jansson-Löfmark, Rasmus; Hyberg, Gina; Wigstrand, Maria; Kakol-Palm, Dorota; Håkansson, Pernilla; Hovdal, Daniel; Brodin, Peter; Fritsch-Fredin, Maria; Antonsson, Madeleine; Ploj, Karolina; Gabrielsson, Johan

    2013-12-01

    Body composition and body mass are pivotal clinical endpoints in studies of welfare diseases. We present a combined effort of established and new mathematical models based on rigorous monitoring of energy intake (EI) and body mass in mice. Specifically, we parameterize a mechanistic turnover model based on the law of energy conservation coupled to a drug mechanism model. Key model variables are fat-free mass (FFM) and fat mass (FM), governed by EI and energy expenditure (EE). An empirical Forbes curve relating FFM to FM was derived experimentally for female C57BL/6 mice. The Forbes curve differs from a previously reported curve for male C57BL/6 mice, and we thoroughly analyse how the choice of Forbes curve impacts model predictions. The drug mechanism function acts on EI or EE, or both. Drug mechanism parameters (two to three parameters) and system parameters (up to six free parameters) could be estimated with good precision (coefficients of variation typically <20 % and not greater than 40 % in our analyses). Model simulations were done to predict the EE and FM change at different drug provocations in mice. In addition, we simulated body mass and FM changes at different drug provocations using a similar model for man. Surprisingly, model simulations indicate that an increase in EI (e.g. 10 %) was more efficient than an equal lowering of EI. Also, the relative change in body mass and FM is greater in man than in mouse at the same relative change in either EI or EE. We acknowledge that this assumes the same drug mechanism impact across the two species. A set of recommendations regarding the Forbes curve, vehicle control groups, dual action on EI and loss, and translational aspects are discussed. This quantitative approach significantly improves data interpretation, disease system understanding, safety assessment and translation across species. PMID:24158456

  13. Photosensitizer absorption coefficient modeling and necrosis prediction during Photodynamic Therapy.

    PubMed

    Salas-García, Irene; Fanjul-Vélez, Félix; Arce-Diego, José Luis

    2012-09-01

    The development of accurate predictive models for Photodynamic Therapy (PDT) has emerged as a valuable tool to adjust the current therapy dosimetry to get an optimal treatment response, and definitely to establish new personal protocols. Several attempts have been made in this way, although the influence of the photosensitizer depletion on the optical parameters has not been taken into account so far. We present a first approach to predict the spatio-temporal variation of the photosensitizer absorption coefficient during PDT applied to dermatological diseases, taking into account the photobleaching of a topical photosensitizer. This permits us to obtain the photons density absorbed by the photosensitizer molecules as the treatment progresses and to determine necrosis maps to estimate the short term therapeutic effects in the target tissue. The model presented also takes into account an inhomogeneous initial photosensitizer distribution, light propagation in biological media and the evolution of the molecular concentrations of different components involved in the photochemical reactions. The obtained results allow to investigate how the photosensitizer depletion during the photochemical reactions affects light absorption by the photosensitizer molecules as the optical radiation propagates through the target tissue, and estimate the necrotic tumor area progression under different treatment conditions. PMID:22704663

  14. 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. PMID:24033371

  15. 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.

  16. Predicting Tenure Dynamics: Models Help Manage Tenure System.

    ERIC Educational Resources Information Center

    Strauss, Jon C.

    1997-01-01

    Presents three different, complementary statistical models for predicting faculty tenure dynamics, using data from Worcester Polytechnic Institute (Massachusetts). The difference equation model exactly describes future behavior but requires complete specification. The Markov-chain model can predict the full life-cycle of tenure from initial age…

  17. 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…

  18. 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.

  19. 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

  20. 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

  1. Artefacts and biases affecting the evaluation of scoring functions on decoy sets for protein structure prediction

    PubMed Central

    Handl, Julia; Knowles, Joshua; Lovell, Simon C.

    2009-01-01

    Motivation: Decoy datasets, consisting of a solved protein structure and numerous alternative native-like structures, are in common use for the evaluation of scoring functions in protein structure prediction. Several pitfalls with the use of these datasets have been identified in the literature, as well as useful guidelines for generating more effective decoy datasets. We contribute to this ongoing discussion an empirical assessment of several decoy datasets commonly used in experimental studies. Results: We find that artefacts and sampling issues in the large majority of these data make it trivial to discriminate the native structure. This underlines that evaluation based on the rank/z-score of the native is a weak test of scoring function performance. Moreover, sampling biases present in the way decoy sets are generated or used can strongly affect other types of evaluation measures such as the correlation between score and root mean squared deviation (RMSD) to the native. We demonstrate how, depending on type of bias and evaluation context, sampling biases may lead to both over- or under-estimation of the quality of scoring terms, functions or methods. Availability: Links to the software and data used in this study are available at http://dbkgroup.org/handl/decoy_sets. Contact: simon.lovell@manchester.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online. PMID:19297350

  2. 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. PMID:25028961

  3. 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…

  4. 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. PMID:27085414

  5. 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

  6. Physical model to predict the ball-burnishing forces

    NASA Astrophysics Data System (ADS)

    González-Rojas, H. A.; Travieso-Rodríguez, J. A.

    2012-04-01

    In this paper, we have developed a physical model to predict the forces of the ball burnishing. The models have been constructed on the basis of the plasticity theory. During the model development we have figured out the dimensionless number B that characterizes the problem of plastic deformation in the ball-burnishing. The experiments performed in steel and aluminum allows to validate the model and to emphasize the correct prediction of behavior patterns that the model describes.

  7. 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 ...

  8. NUMERICAL MODELS FOR PREDICTING WATERSHED ACIDIFICATION

    EPA Science Inventory

    Three numerical models of watershed acidification, including the MAGIC II, ETD, and ILWAS models, are reviewed, and a comparative study is made of the specific process formulations that are incorporated in the models to represent hydrological, geochemical, and biogeochemical proc...

  9. 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.

  10. 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. PMID:27303124

  11. 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…

  12. How does bias correction of RCM precipitation affect modelled runoff?

    NASA Astrophysics Data System (ADS)

    Teng, J.; Potter, N. J.; Chiew, F. H. S.; Zhang, L.; Vaze, J.; Evans, J. P.

    2014-09-01

    Many studies bias correct daily precipitation from climate models to match the observed precipitation statistics, and the bias corrected data are then used for various modelling applications. This paper presents a review of recent methods used to bias correct precipitation from regional climate models (RCMs). The paper then assesses four bias correction methods applied to the weather research and forecasting (WRF) model simulated precipitation, and the follow-on impact on modelled runoff for eight catchments in southeast Australia. Overall, the best results are produced by either quantile mapping or a newly proposed two-state gamma distribution mapping method. However, the difference between the tested methods is small in the modelling experiments here (and as reported in the literature), mainly because of the substantial corrections required and inconsistent errors over time (non-stationarity). The errors remaining in bias corrected precipitation are typically amplified in modelled runoff. The tested methods cannot overcome limitation of RCM in simulating precipitation sequence, which affects runoff generation. Results further show that whereas bias correction does not seem to alter change signals in precipitation means, it can introduce additional uncertainty to change signals in high precipitation amounts and, consequently, in runoff. Future climate change impact studies need to take this into account when deciding whether to use raw or bias corrected RCM results. Nevertheless, RCMs will continue to improve and will become increasingly useful for hydrological applications as the bias in RCM simulations reduces.

  13. 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.

  14. Mathematical modeling of microstructure evolution in the heat affected zone of electroslag cladding

    SciTech Connect

    Li, M.V.; Atteridge, D.G.; Meekisho, L.

    1996-12-31

    An algorithm is presented for computing microstructure evolution in weld heat affected zone of low alloy steels. It contains computational models for multicomponent Fe-C-M system equilibria, austenite grain growth kinetics, and austenite decomposition kinetics. A new kinetics model for austenite decomposition has been developed based on first principles of phase transformations expressed with Zener-Hillert type formulas. Coefficients in this model were calibrated with CCT diagrams of low alloy steels. This algorithm has the capability of computing TTT diagrams, CCT diagrams Jominy hardness curves, and phase transformations in the weld heat affected zone of low alloy steels. Excellent agreement was observed between the experimentally observed and the predicted microstructure and hardness.

  15. Inference on biological mechanisms using an integrated phenotype prediction model.

    PubMed

    Enomoto, Yumi; Ushijima, Masaru; Miyata, Satoshi; Matsuura, Masaaki; Ohtaki, Megu

    2008-03-01

    We propose a methodology for constructing an integrated phenotype prediction model that accounts for multiple pathways regulating a targeted phenotype. The method uses multiple prediction models, each expressing a particular pattern of gene-to-gene interrelationship, such as epistasis. We also propose a methodology using Gene Ontology annotations to infer a biological mechanism from the integrated phenotype prediction model. To construct the integrated models, we employed multiple logistic regression models using a two-step learning approach to examine a number of patterns of gene-to-gene interrelationships. We first selected individual prediction models with acceptable goodness of fit, and then combined the models. The resulting integrated model predicts phenotype as a logical sum of predicted results from the individual models. We used published microarray data on neuroblastoma from Ohira et al (2005) for illustration, constructing an integrated model to predict prognosis and infer the biological mechanisms controlling prognosis. Although the resulting integrated model comprised a small number of genes compared to a previously reported analysis of these data, the model demonstrated excellent performance, with an error rate of 0.12 in a validation analysis. Gene Ontology analysis suggested that prognosis of patients with neuroblastoma may be influenced by biological processes such as cell growth, G-protein signaling, phosphoinositide-mediated signaling, alcohol metabolism, glycolysis, neurophysiological processes, and catecholamine catabolism. PMID:18578362

  16. 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.

  17. Predicting Historical Droughts in the US With a Multi-model Seasonal Hydrologic Prediction System

    NASA Astrophysics Data System (ADS)

    Luo, L.; Wood, E.; Sheffield, J.; Li, H.

    2008-12-01

    Droughts are as much a part of weather and climate extremes as floods, hurricanes and tornadoes are, but they are the most costly extremes among all natural disasters in the U.S. The estimated annual direct losses to the U.S economy due to droughts are about 6-8 billion, with the drought of 1988 estimated to have damages over $39 billion. Having a seasonal drought prediction system that can accurately predict the onset, development and recovery of drought episodes will significantly help to reduce the loss due to drought. In this study, a seasonal hydrologic ensemble prediction system developed for the eastern United States is used to predict historical droughts in the US retrospectively. The system uses a hydrologic model (i.e., the Variable Infiltration Capacity model) as the central element for producing ensemble predictions of soil moisture, snow, and streamflow with lead times up to six months. One unique feature of this system is in the method for generating ensemble atmospheric forcings for the forecast period. It merges seasonal climate forecasts from multiple climate models with observed climatology in a Bayesian framework, such that the uncertainties related to the atmospheric forcings can be better quantified while the signals from individual models are combined. Simultaneously, climate model forecasts are downscaled to an appropriate spatial scale for hydrologic predictions. When generating daily meteorological forcing, the system uses the rank structures of selected historical forcing records to ensure reasonable weather patterns in space and time. The system is applied to different regions in the US to predict historical drought episodes. These forecasts use seasonal climate forecast from a combination of the NCEP CFS and seven climate models in the European Union's Development of a European Multimodel Ensemble System for Seasonal to-Interannual Prediction (CFS+DEMETER). This study validates the approach of using seasonal climate predictions from

  18. 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.

  19. 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.

  20. 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…

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

    PubMed

    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

  2. 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.

  3. 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...

  4. 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…

  5. 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.

  6. 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.

  7. Demonstrating the improvement of predictive maturity of a computational model

    SciTech Connect

    Hemez, Francois M; Unal, Cetin; Atamturktur, Huriye S

    2010-01-01

    We demonstrate an improvement of predictive capability brought to a non-linear material model using a combination of test data, sensitivity analysis, uncertainty quantification, and calibration. A model that captures increasingly complicated phenomena, such as plasticity, temperature and strain rate effects, is analyzed. Predictive maturity is defined, here, as the accuracy of the model to predict multiple Hopkinson bar experiments. A statistical discrepancy quantifies the systematic disagreement (bias) between measurements and predictions. Our hypothesis is that improving the predictive capability of a model should translate into better agreement between measurements and predictions. This agreement, in turn, should lead to a smaller discrepancy. We have recently proposed to use discrepancy and coverage, that is, the extent to which the physical experiments used for calibration populate the regime of applicability of the model, as basis to define a Predictive Maturity Index (PMI). It was shown that predictive maturity could be improved when additional physical tests are made available to increase coverage of the regime of applicability. This contribution illustrates how the PMI changes as 'better' physics are implemented in the model. The application is the non-linear Preston-Tonks-Wallace (PTW) strength model applied to Beryllium metal. We demonstrate that our framework tracks the evolution of maturity of the PTW model. Robustness of the PMI with respect to the selection of coefficients needed in its definition is also studied.

  8. 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.

  9. Responses to Positive Affect Predict Mood Symptoms in Children under Conditions of Stress: A Prospective Study

    ERIC Educational Resources Information Center

    Bijttebier, Patricia; Raes, Filip; Vasey, Michael W.; Feldman, Gregory C.

    2012-01-01

    Rumination to negative affect has been linked to the onset and maintenance of mood disorders in adults as well as children. Responses to positive affect have received far less attention thus far. A few recent studies in adults suggest that responses to positive affect are involved in the development of both depressive and hypomanic symptoms, but…

  10. 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

  11. A predictive model for Dengue Hemorrhagic Fever epidemics.

    PubMed

    Halide, Halmar; Ridd, Peter

    2008-08-01

    A statistical model for predicting monthly Dengue Hemorrhagic Fever (DHF) cases from the city of Makassar is developed and tested. The model uses past and present DHF cases, climate and meteorological observations as inputs. These inputs are selected using a stepwise regression method to predict future DHF cases. The model is tested independently and its skill assessed using two skill measures. Using the selected variables as inputs, the model is capable of predicting a moderately-severe epidemic at lead times of up to six months. The most important input variable in the prediction is the present number of DHF cases followed by the relative humidity three to four months previously. A prediction 1-6 months in advance is sufficient to initiate various activities to combat DHF epidemic. The model is suitable for warning and easily becomes an operational tool due to its simplicity in data requirement and computational effort. PMID:18668414

  12. 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.

  13. Life prediction modeling based on strainrange partitioning

    NASA Technical Reports Server (NTRS)

    Halford, Gary R.

    1988-01-01

    Strainrange partitioning (SRP) is an integrated low-cycle-fatigue life predicting system. It was created specifically for calculating cyclic crack initiation life under severe high-temperature fatigue conditions. The key feature of the SRP system is its recognition of the interacting mechanisms of cyclic inelastic deformation that govern cyclic life at high temperatures. The SRP system bridges the gap between the mechanistic level of understanding that breeds new and better materials and the phenomenological level wherein workable engineering life prediction methods are in great demand. The system was recently expanded to address engineering fatigue problems in the low-strain, long-life, nominally elastic regime. This breakthrough, along with other advances in material behavior and testing technology, has permitted the system to also encompass low-strain thermomechanical loading conditions. Other important refinements of the originally proposed method include procedures for dealing with life-reducing effects of multiaxial loading, ratcheting, mean stresses, nonrepetitive (cumulative loading) loading, and environmental and long-time exposure. Procedure were also developed for partitioning creep and plastic strain and for estimating strainrange versus life relations from tensile and creep rupture properties. Each of the important engineering features of the SRP system are discussed and examples shown of how they help toward predicting high-temperature fatigue life under practical, although complex, loading conditions.

  14. 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

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

    PubMed Central

    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.

    2015-01-01

    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. PMID:25918167

  16. 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. PMID:25918167

  17. 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.

  18. 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.

  19. Comparison of Predictive Models for the Early Diagnosis of Diabetes

    PubMed Central

    Jahani, Meysam

    2016-01-01

    Objectives This study develops neural network models to improve the prediction of diabetes using clinical and lifestyle characteristics. Prediction models were developed using a combination of approaches and concepts. Methods We used memetic algorithms to update weights and to improve prediction accuracy of models. In the first step, the optimum amount for neural network parameters such as momentum rate, transfer function, and error function were obtained through trial and error and based on the results of previous studies. In the second step, optimum parameters were applied to memetic algorithms in order to improve the accuracy of prediction. This preliminary analysis showed that the accuracy of neural networks is 88%. In the third step, the accuracy of neural network models was improved using a memetic algorithm and resulted model was compared with a logistic regression model using a confusion matrix and receiver operating characteristic curve (ROC). Results The memetic algorithm improved the accuracy from 88.0% to 93.2%. We also found that memetic algorithm had a higher accuracy than the model from the genetic algorithm and a regression model. Among models, the regression model has the least accuracy. For the memetic algorithm model the amount of sensitivity, specificity, positive predictive value, negative predictive value, and ROC are 96.2, 95.3, 93.8, 92.4, and 0.958 respectively. Conclusions The results of this study provide a basis to design a Decision Support System for risk management and planning of care for individuals at risk of diabetes. PMID:27200219

  20. A model for prediction of STOVL ejector dynamics

    NASA Technical Reports Server (NTRS)

    Drummond, Colin K.

    1989-01-01

    A semi-empirical control-volume approach to ejector modeling for transient performance prediction is presented. This new approach is motivated by the need for a predictive real-time ejector sub-system simulation for Short Take-Off Verticle Landing (STOVL) integrated flight and propulsion controls design applications. Emphasis is placed on discussion of the approximate characterization of the mixing process central to thrust augmenting ejector operation. The proposed ejector model suggests transient flow predictions are possible with a model based on steady-flow data. A practical test case is presented to illustrate model calibration.

  1. 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.

  2. Identification and synthetic modeling of factors affecting American black duck populations

    USGS Publications Warehouse

    Conroy, Michael J.; Miller, Mark W.; Hines, James E.

    2002-01-01

    We reviewed the literature on factors potentially affecting the population status of American black ducks (Anas rupribes). Our review suggests that there is some support for the influence of 4 major, continental-scope factors in limiting or regulating black duck populations: 1) loss in the quantity or quality of breeding habitats; 2) loss in the quantity or quality of wintering habitats; 3) harvest, and 4) interactions (competition, hybridization) with mallards (Anas platyrhychos) during the breeding and/or wintering periods. These factors were used as the basis of an annual life cycle model in which reproduction rates and survival rates were modeled as functions of the above factors, with parameters of the model describing the strength of these relationships. Variation in the model parameter values allows for consideration of scientific uncertainty as to the degree each of these factors may be contributing to declines in black duck populations, and thus allows for the investigation of the possible effects of management (e.g., habitat improvement, harvest reductions) under different assumptions. We then used available, historical data on black duck populations (abundance, annual reproduction rates, and survival rates) and possible driving factors (trends in breeding and wintering habitats, harvest rates, and abundance of mallards) to estimate model parameters. Our estimated reproduction submodel included parameters describing negative density feedback of black ducks, positive influence of breeding habitat, and negative influence of mallard densities; our survival submodel included terms for positive influence of winter habitat on reproduction rates, and negative influences of black duck density (i.e., compensation to harvest mortality). Individual models within each group (reproduction, survival) involved various combinations of these factors, and each was given an information theoretic weight for use in subsequent prediction. The reproduction model with highest

  3. 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

  4. 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. PMID:27115488

  5. LHC diphoton Higgs signal predicted by little Higgs models

    SciTech Connect

    Wang Lei; Yang Jinmin

    2011-10-01

    Little Higgs theory naturally predicts a light Higgs boson whose most important discovery channel at the LHC is the diphoton signal pp{yields}h{yields}{gamma}{gamma}. In this work, we perform a comparative study for this signal in some typical little Higgs models, namely, the littlest Higgs model, two littlest Higgs models with T-parity (named LHT-I and LHT-II), and the simplest little Higgs models. We find that compared with the standard model prediction, the diphoton signal rate is always suppressed and the suppression extent can be quite different for different models. The suppression is mild (< or approx. 10%) in the littlest Higgs model but can be quite severe ({approx_equal}90%) in other three models. This means that discovering the light Higgs boson predicted by the little Higgs theory through the diphoton channel at the LHC will be more difficult than discovering the standard model Higgs boson.

  6. The predictive accuracy of intertemporal-choice models.

    PubMed

    Arfer, Kodi B; Luhmann, Christian C

    2015-05-01

    How do people choose between a smaller reward available sooner and a larger reward available later? Past research has evaluated models of intertemporal choice by measuring goodness of fit or identifying which decision-making anomalies they can accommodate. An alternative criterion for model quality, which is partly antithetical to these standard criteria, is predictive accuracy. We used cross-validation to examine how well 10 models of intertemporal choice could predict behaviour in a 100-trial binary-decision task. Many models achieved the apparent ceiling of 85% accuracy, even with smaller training sets. When noise was added to the training set, however, a simple logistic-regression model we call the difference model performed particularly well. In many situations, between-model differences in predictive accuracy may be small, contrary to long-standing controversy over the modelling question in research on intertemporal choice, but the simplicity and robustness of the difference model recommend it to future use. PMID:25773127

  7. Temporal and Spatial Predictability of an Irrelevant Event Differently Affect Detection and Memory of Items in a Visual Sequence

    PubMed Central

    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. PMID:26869966

  8. 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. PMID:26869966

  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. 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.

  11. 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.

  12. 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.

  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. Prediction of Complex Aerodynamic Flows with Explicit Algebraic Stress Models

    NASA Technical Reports Server (NTRS)

    Abid, Ridha; Morrison, Joseph H.; Gatski, Thomas B.; Speziale, Charles G.

    1996-01-01

    An explicit algebraic stress equation, developed by Gatski and Speziale, is used in the framework of K-epsilon formulation to predict complex aerodynamic turbulent flows. The nonequilibrium effects are modeled through coefficients that depend nonlinearly on both rotational and irrotational strains. The proposed model was implemented in the ISAAC Navier-Stokes code. Comparisons with the experimental data are presented which clearly demonstrate that explicit algebraic stress models can predict the correct response to nonequilibrium flow.

  15. 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.

  16. 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.

  17. Count ratio model reveals bias affecting NGS fold changes

    PubMed Central

    Erhard, Florian; Zimmer, Ralf

    2015-01-01

    Various biases affect high-throughput sequencing read counts. Contrary to the general assumption, we show that bias does not always cancel out when fold changes are computed and that bias affects more than 20% of genes that are called differentially regulated in RNA-seq experiments with drastic effects on subsequent biological interpretation. Here, we propose a novel approach to estimate fold changes. Our method is based on a probabilistic model that directly incorporates count ratios instead of read counts. It provides a theoretical foundation for pseudo-counts and can be used to estimate fold change credible intervals as well as normalization factors that outperform currently used normalization methods. We show that fold change estimates are significantly improved by our method by comparing RNA-seq derived fold changes to qPCR data from the MAQC/SEQC project as a reference and analyzing random barcoded sequencing data. Our software implementation is freely available from the project website http://www.bio.ifi.lmu.de/software/lfc. PMID:26160885

  18. Count ratio model reveals bias affecting NGS fold changes.

    PubMed

    Erhard, Florian; Zimmer, Ralf

    2015-11-16

    Various biases affect high-throughput sequencing read counts. Contrary to the general assumption, we show that bias does not always cancel out when fold changes are computed and that bias affects more than 20% of genes that are called differentially regulated in RNA-seq experiments with drastic effects on subsequent biological interpretation. Here, we propose a novel approach to estimate fold changes. Our method is based on a probabilistic model that directly incorporates count ratios instead of read counts. It provides a theoretical foundation for pseudo-counts and can be used to estimate fold change credible intervals as well as normalization factors that outperform currently used normalization methods. We show that fold change estimates are significantly improved by our method by comparing RNA-seq derived fold changes to qPCR data from the MAQC/SEQC project as a reference and analyzing random barcoded sequencing data. Our software implementation is freely available from the project website http://www.bio.ifi.lmu.de/software/lfc. PMID:26160885

  19. 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.

  20. 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

  1. Templeton prediction model underestimates IVF success in an external validation.

    PubMed

    van Loendersloot, L L; van Wely, M; Repping, S; van der Veen, F; Bossuyt, P M M

    2011-06-01

    Prediction models for IVF can be used to identify couples that will benefit from IVF treatment. Currently there is only one prediction model with a good predictive performance that can be used for predicting pregnancy chances after IVF. That model was developed almost 15 years ago and since IVF has progressed substantially during the last two decades it is questionable whether the model is still valid in current clinical practice. The objective of this study was to validate the prediction model of Templeton for calculating pregnancy chances after IVF. The performance of the prediction model was assessed in terms of discrimination, i.e. the area under the receiver operation characteristic (ROC) curve and calibration. Likely causes for miscalibration were evaluated by refitting the Templeton model to the study data. The area under the ROC curve for the Templeton model was 0.61. Calibration showed a significant and systematic underestimation of success in IVF. Although the Templeton model can distinguish somewhat between women with a high and low success rate in IVF, it systematically underestimates pregnancy chances and has therefore no real value for current IVF practice. PMID:21493154

  2. Improved analytical model for residual stress prediction in orthogonal cutting

    NASA Astrophysics Data System (ADS)

    Qi, Zhaoxu; Li, Bin; Xiong, Liangshan

    2014-09-01

    The analytical model of residual stress in orthogonal cutting proposed by Jiann is an important tool for residual stress prediction in orthogonal cutting. In application of the model, a problem of low precision of the surface residual stress prediction is found. By theoretical analysis, several shortages of Jiann's model are picked out, including: inappropriate boundary conditions, unreasonable calculation method of thermal stress, ignorance of stress constraint and cyclic loading algorithm. These shortages may directly lead to the low precision of the surface residual stress prediction. To eliminate these shortages and make the prediction more accurate, an improved model is proposed. In this model, a new contact boundary condition between tool and workpiece is used to make it in accord with the real cutting process; an improved calculation method of thermal stress is adopted; a stress constraint is added according to the volumeconstancy of plastic deformation; and the accumulative effect of the stresses during cyclic loading is considered. At last, an experiment for measuring residual stress in cutting AISI 1045 steel is conducted. Also, Jiann's model and the improved model are simulated under the same conditions with cutting experiment. The comparisons show that the surface residual stresses predicted by the improved model is closer to the experimental results than the results predicted by Jiann's model.

  3. Improved analytical model for residual stress prediction in orthogonal cutting

    NASA Astrophysics Data System (ADS)

    Qi, Zhaoxu; Li, Bin; Xiong, Liangshan

    2014-09-01

    The analytical model of residual stress in orthogonal cutting proposed by Jiann is an important tool for residual stress prediction in orthogonal cutting. In application of the model, a problem of low precision of the surface residual stress prediction is found. By theoretical analysis, several shortages of Jiann's model are picked out, including: inappropriate boundary conditions, unreasonable calculation method of thermal stress, ignorance of stress constraint and cyclic loading algorithm. These shortages may directly lead to the low precision of the surface residual stress prediction. To eliminate these shortages and make the prediction more accurate, an improved model is proposed. In this model, a new contact boundary condition between tool and workpiece is used to make it in accord with the real cutting process; an improved calculation method of thermal stress is adopted; a stress constraint is added according to the volume-constancy of plastic deformation; and the accumulative effect of the stresses during cyclic loading is considered. At last, an experiment for measuring residual stress in cutting AISI 1045 steel is conducted. Also, Jiann's model and the improved model are simulated under the same conditions with cutting experiment. The comparisons show that the surface residual stresses predicted by the improved model is closer to the experimental results than the results predicted by Jiann's model.

  4. 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.

  5. 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.

  6. 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.

  7. 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…

  8. Words That Fascinate the Listener: Predicting Affective Ratings of On-Line Lectures

    ERIC Educational Resources Information Center

    Weninger, Felix; Staudt, Pascal; Schuller, Björn

    2013-01-01

    In a large scale study on 843 transcripts of Technology, Entertainment and Design (TED) talks, the authors address the relation between word usage and categorical affective ratings of lectures by a large group of internet users. Users rated the lectures by assigning one or more predefined tags which relate to the affective state evoked in the…

  9. 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…

  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. Multikernel linear mixed models for complex phenotype prediction.

    PubMed

    Weissbrod, Omer; Geiger, Dan; Rosset, Saharon

    2016-07-01

    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

  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. Simplifying clinical use of the genetic risk prediction model BRCAPRO.

    PubMed

    Biswas, Swati; Atienza, Philamer; Chipman, Jonathan; Hughes, Kevin; Barrera, Angelica M Gutierrez; Amos, Christopher I; Arun, Banu; Parmigiani, Giovanni

    2013-06-01

    Health care providers need simple tools to identify patients at genetic risk of breast and ovarian cancers. Genetic risk prediction models such as BRCAPRO could fill this gap if incorporated into Electronic Medical Records or other Health Information Technology solutions. However, BRCAPRO requires potentially extensive information on the counselee and her family history. Thus, it may be useful to provide simplified version(s) of BRCAPRO for use in settings that do not require exhaustive genetic counseling. We explore four simplified versions of BRCAPRO, each using less complete information than the original model. BRCAPROLYTE uses information on affected relatives only up to second degree. It is in clinical use but has not been evaluated. BRCAPROLYTE-Plus extends BRCAPROLYTE by imputing the ages of unaffected relatives. BRCAPROLYTE-Simple reduces the data collection burden associated with BRCAPROLYTE and BRCAPROLYTE-Plus by not collecting the family structure. BRCAPRO-1Degree only uses first-degree affected relatives. We use data on 2,713 individuals from seven sites of the Cancer Genetics Network and MD Anderson Cancer Center to compare these simplified tools with the Family History Assessment Tool (FHAT) and BRCAPRO, with the latter serving as the benchmark. BRCAPROLYTE retains high discrimination; however, because it ignores information on unaffected relatives, it overestimates carrier probabilities. BRCAPROLYTE-Plus and BRCAPROLYTE-Simple provide better calibration than BRCAPROLYTE, so they have higher specificity for similar values of sensitivity. BRCAPROLYTE-Plus performs slightly better than BRCAPROLYTE-Simple. The Areas Under the ROC curve are 0.783 (BRCAPRO), 0.763 (BRCAPROLYTE), 0.772 (BRCAPROLYTE-Plus), 0.773 (BRCAPROLYTE-Simple), 0.728 (BRCAPRO-1Degree), and 0.745 (FHAT). The simpler versions, especially BRCAPROLYTE-Plus and BRCAPROLYTE-Simple, lead to only modest loss in overall discrimination compared to BRCAPRO in this dataset. Thus, we conclude that

  14. 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.

  15. 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.

  16. Micro-mechanical studies on graphite strength prediction models

    NASA Astrophysics Data System (ADS)

    Kanse, Deepak; Khan, I. A.; Bhasin, V.; Vaze, K. K.

    2013-06-01

    The influence of type of loading and size-effects on the failure strength of graphite were studied using Weibull model. It was observed that this model over-predicts size effect in tension. However, incorporation of grain size effect in Weibull model, allows a more realistic simulation of size effects. Numerical prediction of strength of four-point bend specimen was made using the Weibull parameters obtained from tensile test data. Effective volume calculations were carried out and subsequently predicted strength was compared with experimental data. It was found that Weibull model can predict mean flexural strength with reasonable accuracy even when grain size effect was not incorporated. In addition, the effects of microstructural parameters on failure strength were analyzed using Rose and Tucker model. Uni-axial tensile, three-point bend and four-point bend strengths were predicted using this model and compared with the experimental data. It was found that this model predicts flexural strength within 10%. For uni-axial tensile strength, difference was 22% which can be attributed to less number of tests on tensile specimens. In order to develop failure surface of graphite under multi-axial state of stress, an open ended hollow tube of graphite was subjected to internal pressure and axial load and Batdorf model was employed to calculate failure probability of the tube. Bi-axial failure surface was generated in the first and fourth quadrant for 50% failure probability by varying both internal pressure and axial load.

  17. 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

  18. A Predictive Model of High Shear Thrombus Growth.

    PubMed

    Mehrabadi, Marmar; Casa, Lauren D C; Aidun, Cyrus K; Ku, David N

    2016-08-01

    The ability to predict the timescale of thrombotic occlusion in stenotic vessels may improve patient risk assessment for thrombotic events. In blood contacting devices, thrombosis predictions can lead to improved designs to minimize thrombotic risks. We have developed and validated a model of high shear thrombosis based on empirical correlations between thrombus growth and shear rate. A mathematical model was developed to predict the growth of thrombus based on the hemodynamic shear rate. The model predicts thrombus deposition based on initial geometric and fluid mechanic conditions, which are updated throughout the simulation to reflect the changing lumen dimensions. The model was validated by comparing predictions against actual thrombus growth in six separate in vitro experiments: stenotic glass capillary tubes (diameter = 345 µm) at three shear rates, the PFA-100(®) system, two microfluidic channel dimensions (heights = 300 and 82 µm), and a stenotic aortic graft (diameter = 5.5 mm). Comparison of the predicted occlusion times to experimental results shows excellent agreement. The model is also applied to a clinical angiography image to illustrate the time course of thrombosis in a stenotic carotid artery after plaque cap rupture. Our model can accurately predict thrombotic occlusion time over a wide range of hemodynamic conditions. PMID:26795978

  19. Ensemble Learning of QTL Models Improves Prediction of Complex Traits.

    PubMed

    Bian, Yang; Holland, James B

    2015-10-01

    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

  20. 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.

  1. How Do You Feel? Self-esteem Predicts Affect, Stress, Social Interaction, and Symptom Severity during Daily Life in Patients with Chronic Illness

    PubMed Central

    JUTH, VANESSA; SMYTH, JOSHUA M.; SANTUZZI, ALECIA M.

    2010-01-01

    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. PMID:18809639

  2. 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.

  3. 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. PMID:26926235

  4. 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

  5. Thoracolumbar spine model with articulated ribcage for the prediction of dynamic spinal loading.

    PubMed

    Ignasiak, Dominika; Dendorfer, Sebastian; Ferguson, Stephen J

    2016-04-11

    Musculoskeletal modeling offers an invaluable insight into the spine biomechanics. A better understanding of thoracic spine kinetics is essential for understanding disease processes and developing new prevention and treatment methods. Current models of the thoracic region are not designed for segmental load estimation, or do not include the complex construct of the ribcage, despite its potentially important role in load transmission. In this paper, we describe a numerical musculoskeletal model of the thoracolumbar spine with articulated ribcage, modeled as a system of individual vertebral segments, elastic elements and thoracic muscles, based on a previously established lumbar spine model and data from the literature. The inverse dynamics simulations of the model allow the prediction of spinal loading as well as costal joints kinetics and kinematics. The intradiscal pressure predicted by the model correlated well (R(2)=0.89) with reported intradiscal pressure measurements, providing a first validation of the model. The inclusion of the ribcage did not affect segmental force predictions when the thoracic spine did not perform motion. During thoracic motion tasks, the ribcage had an important influence on the predicted compressive forces and muscle activation patterns. The compressive forces were reduced by up to 32%, or distributed more evenly between thoracic vertebrae, when compared to the predictions of the model without ribcage, for mild thoracic flexion and hyperextension tasks, respectively. The presented musculoskeletal model provides a tool for investigating thoracic spine loading and load sharing between vertebral column and ribcage during dynamic activities. Further validation for specific applications is still necessary. PMID:26684431

  6. Does Leisure Time as a Stress Coping Resource Increase Affective Complexity? Applying the Dynamic Model of Affect (DMA).

    PubMed

    Qian, Xinyi Lisa; Yarnal, Careen M; Almeida, David M

    2013-01-01

    Affective complexity, a manifestation of psychological well-being, refers to the relative independence between positive and negative affect (PA, NA). According to the Dynamic Model of Affect (DMA), stressful situations lead to highly inverse PA-NA relationship, reducing affective complexity. Meanwhile, positive events can sustain affective complexity by restoring PA-NA independence. Leisure, a type of positive events, has been identified as a coping resource. This study used the DMA to assess whether leisure time helps restore affective complexity on stressful days. We found that on days with more leisure time than usual, an individual experienced less negative PA-NA relationship after daily stressful events. The finding demonstrates the value of leisure time as a coping resource and the DMA's contribution to coping research. PMID:24659826

  7. Effect of Lipid Partitioning on Predictions of Acute Toxicity of Oil Sands Process Affected Water to Embryos of Fathead Minnow (Pimephales promelas).

    PubMed

    Morandi, Garrett D; Zhang, Kun; Wiseman, Steve B; Pereira, Alberto Dos Santos; Martin, Jonathan W; Giesy, John P

    2016-08-16

    Dissolved organic compounds in oil sands process affected water (OSPW) are known to be responsible for most of its toxicity to aquatic organisms, but the complexity of this mixture prevents use of traditional bottom-up approaches for predicting toxicities of mixtures. Therefore, a top-down approach to predict toxicity of the dissolved organic fraction of OSPW was developed and tested. Accurate masses (i.e., m/z) determined by ultrahigh resolution mass spectrometry in negative and positive ionization modes were used to assign empirical chemical formulas to each chemical species in the mixture. For each chemical species, a predictive measure of lipid accumulation was estimated by stir-bar sorptive extraction (SBSE) to poly(dimethyl)siloxane, or by partitioning to solid-supported lipid membranes (SSLM). A narcosis mode of action was assumed and the target-lipid model was used to estimate potencies of mixtures by assuming strict additivity. A model developed using a combination of the SBSE and SSLM lipid partitioning estimates, whereby the accumulation of chemicals to neutral and polar lipids was explicitly considered, was best for predicting empirical values of LC50 in 96-h acute toxicity tests with embryos of fathead minnow (Pimephales promelas). Model predictions were within 4-fold of observed toxicity for 75% of OSPW samples, and within 8.5-fold for all samples tested, which is comparable to the range of interlaboratory variability for in vivo toxicity testing. PMID:27420640

  8. 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...

  9. 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...

  10. Keeping Juvenile Delinquents in School: A Prediction Model.

    ERIC Educational Resources Information Center

    Dunham, Roger G.; Alpert, Geoffrey P.

    1987-01-01

    Tested an empirically based prediction model of school dropout on juvenile delinquents (N=137). Identified four factors yielding a high level of prediction: misbehavior in school, disliking school, the negative influence of peers with respect to dropping out and getting into trouble, and a marginal or weak relationship with parents. (Author/ABB)

  11. 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...

  12. Predictive models for circulating fluidized bed combustors

    SciTech Connect

    Gidaspow, D.

    1989-11-01

    The overall objective of this investigation is to develop experimentally verified models for circulating fluidized bed (CFB) combustors. The purpose of these models is to help American industry, such as Combustion Engineering, design and scale-up CFB combustors that are capable of burning US Eastern high sulfur coals with low SO{sub x} and NO{sub x} emissions. In this report, presented as a technical paper, solids distributions and velocities were computed for a PYROFLOW circulating fluidized bed system. To illustrate the capability of the computer code an example of coal-pyrite separation is included, which was done earlier for a State of Illinois project. 24 refs., 20 figs., 2 tabs.

  13. 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.

  14. Predictive Modeling for Comfortable Death Outcome Using Electronic Health Records

    PubMed Central

    Lodhi, Muhammad Kamran; Ansari, Rashid; Yao, Yingwei; Keenan, Gail M.; Wilkie, Diana J.; Khokhar, Ashfaq A.

    2016-01-01

    Electronic health record (EHR) systems are used in healthcare industry to observe the progress of patients. With fast growth of the data, EHR data analysis has become a big data problem. Most EHRs are sparse and multi-dimensional datasets and mining them is a challenging task due to a number of reasons. In this paper, we have used a nursing EHR system to build predictive models to determine what factors impact death anxiety, a significant problem for the dying patients. Different existing modeling techniques have been used to develop coarse-grained as well as fine-grained models to predict patient outcomes. The coarse-grained models help in predicting the outcome at the end of each hospitalization, whereas fine-grained models help in predicting the outcome at the end of each shift, therefore providing a trajectory of predicted outcomes. Based on different modeling techniques, our results show significantly accurate predictions, due to relatively noise-free data. These models can help in determining effective treatments, lowering healthcare costs, and improving the quality of end-of-life (EOL) care.

  15. Modeling physicochemical interactions affecting in vitro cellular dosimetry of engineered nanomaterials: application to nanosilver

    PubMed Central

    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.

    2014-01-01

    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 subsystems by suitably modifying system geometry. PMID:25598696

  16. 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.

  17. Testing the Predictions of the Central Capacity Sharing Model

    ERIC Educational Resources Information Center

    Tombu, Michael; Jolicoeur, Pierre

    2005-01-01

    The divergent predictions of 2 models of dual-task performance are investigated. The central bottleneck and central capacity sharing models argue that a central stage of information processing is capacity limited, whereas stages before and after are capacity free. The models disagree about the nature of this central capacity limitation. The…

  18. 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.

  19. EFFECTS OF PHOTOCHEMICAL KINETIC MECHANISMS ON OXIDANT MODEL PREDICTIONS

    EPA Science Inventory

    The comparative effects of kinetic mechanisms on oxidant model predictions have been tested using two different mechanisms (the Carbon-Bond Mechanism II (CBM-II) and the Demerjian Photochemical Box Model (DPBM) mechanism) in three air quality models (the OZIPM/EKMA, the Urban Air...

  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. Predictive Modeling: A New Paradigm for Managing Endometrial Cancer.

    PubMed

    Bendifallah, Sofiane; Daraï, Emile; Ballester, Marcos

    2016-03-01

    With the abundance of new options in diagnostic and treatment modalities, a shift in the medical decision process for endometrial cancer (EC) has been observed. The emergence of individualized medicine and the increasing complexity of available medical data has lead to the development of several prediction models. In EC, those clinical models (algorithms, nomograms, and risk scoring systems) have been reported, especially for stratifying and subgrouping patients, with various unanswered questions regarding such things as the optimal surgical staging for lymph node metastasis as well as the assessment of recurrence and survival outcomes. In this review, we highlight existing prognostic and predictive models in EC, with a specific focus on their clinical applicability. We also discuss the methodologic aspects of the development of such predictive models and the steps that are required to integrate these tools into clinical decision making. In the future, the emerging field of molecular or biochemical markers research may substantially improve predictive and treatment approaches. PMID:26577116

  2. 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...

  3. 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.

  4. 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

  5. Atmospheric drag model calibrations for spacecraft lifetime prediction

    NASA Technical Reports Server (NTRS)

    Binebrink, A. L.; Radomski, M. S.; Samii, M. V.

    1989-01-01

    Although solar activity prediction uncertainty normally dominates decay prediction error budget for near-Earth spacecraft, the effect of drag force modeling errors for given levels of solar activity needs to be considered. Two atmospheric density models, the modified Harris-Priester model and the Jacchia-Roberts model, to reproduce the decay histories of the Solar Mesosphere Explorer (SME) and Solar Maximum Mission (SMM) spacecraft in the 490- to 540-kilometer altitude range were analyzed. Historical solar activity data were used in the input to the density computations. For each spacecraft and atmospheric model, a drag scaling adjustment factor was determined for a high-solar-activity year, such that the observed annual decay in the mean semimajor axis was reproduced by an averaged variation-of-parameters (VOP) orbit propagation. The SME (SMM) calibration was performed using calendar year 1983 (1982). The resulting calibration factors differ by 20 to 40 percent from the predictions of the prelaunch ballistic coefficients. The orbit propagations for each spacecraft were extended to the middle of 1988 using the calibrated drag models. For the Jaccia-Roberts density model, the observed decay in the mean semimajor axis of SME (SMM) over the 4.5-year (5.5-year) predictive period was reproduced to within 1.5 (4.4) percent. The corresponding figure for the Harris-Priester model was 8.6 (20.6) percent. Detailed results and conclusions regarding the importance of accurate drag force modeling for lifetime predictions are presented.

  6. A Multistep Chaotic Model for Municipal Solid Waste Generation Prediction

    PubMed Central

    Song, Jingwei; He, Jiaying

    2014-01-01

    Abstract In this study, a univariate local chaotic model is proposed to make one-step and multistep forecasts for daily municipal solid waste (MSW) generation in Seattle, Washington. For MSW generation prediction with long history data, this forecasting model was created based on a nonlinear dynamic method called phase-space reconstruction. Compared with other nonlinear predictive models, such as artificial neural network (ANN) and partial least square–support vector machine (PLS-SVM), and a commonly used linear seasonal autoregressive integrated moving average (sARIMA) model, this method has demonstrated better prediction accuracy from 1-step ahead prediction to 14-step ahead prediction assessed by both mean absolute percentage error (MAPE) and root mean square error (RMSE). Max error, MAPE, and RMSE show that chaotic models were more reliable than the other three models. As chaotic models do not involve random walk, their performance does not vary while ANN and PLS-SVM make different forecasts in each trial. Moreover, this chaotic model was less time consuming than ANN and PLS-SVM models. PMID:25125942

  7. A Multistep Chaotic Model for Municipal Solid Waste Generation Prediction.

    PubMed

    Song, Jingwei; He, Jiaying

    2014-08-01

    In this study, a univariate local chaotic model is proposed to make one-step and multistep forecasts for daily municipal solid waste (MSW) generation in Seattle, Washington. For MSW generation prediction with long history data, this forecasting model was created based on a nonlinear dynamic method called phase-space reconstruction. Compared with other nonlinear predictive models, such as artificial neural network (ANN) and partial least square-support vector machine (PLS-SVM), and a commonly used linear seasonal autoregressive integrated moving average (sARIMA) model, this method has demonstrated better prediction accuracy from 1-step ahead prediction to 14-step ahead prediction assessed by both mean absolute percentage error (MAPE) and root mean square error (RMSE). Max error, MAPE, and RMSE show that chaotic models were more reliable than the other three models. As chaotic models do not involve random walk, their performance does not vary while ANN and PLS-SVM make different forecasts in each trial. Moreover, this chaotic model was less time consuming than ANN and PLS-SVM models. PMID:25125942

  8. 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

  9. 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

  10. Dopamine D4 receptor polymorphism and sex interact to predict children's affective knowledge.

    PubMed

    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

  11. Reduction in delta activity predicted improved negative affect in Major Depressive Disorder.

    PubMed

    Cheng, Philip; Goldschmied, Jennifer; Casement, Melynda; Kim, Hyang Sook; Hoffmann, Robert; Armitage, Roseanne; Deldin, Patricia

    2015-08-30

    While prior research has demonstrated a paradoxical antidepressant effect of slow-wave disruption (SWD), the specific dimensions of depression affected is still unclear. The current study aimed to extend this research by utilizing a dimensional approach in examining the antidepressant effects of SWD. Of particular interest is the affective dimension, as negative affect in depression is arguably the most salient characteristic of depression. This sample included 16 individuals with depression (10 female) recruited from the community. Participants slept in the lab for three nights (adaptation, baseline night, and SWD) with polysomnography, and completed measures of negative affect and depression severity the following morning. Results show that reduction in delta power was linearly associated with improved negative affect. Comparison of individual change scores revealed that half of the individuals showed improved negative affect, which is comparable to the reported 40-60% antidepressant response rate to sleep deprivation. Results suggest that vulnerability in the sleep homeostatic system may be a contributing individual differences factor in response to slow-wave disruption in depression. PMID:26123231

  12. 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.

  13. Coupling an Inverse Gaussian Model with Artificial Neural Networks to Predict Soil Moisture from Hyperspectral Imagery

    NASA Astrophysics Data System (ADS)

    Zeng, W.; Xu, C.; Huang, J.; Wu, J.; Tuller, M.

    2014-12-01

    Soil moisture is one of the most crucial properties for monitoring and modeling landscape processes. For this study hyperspectral imagery and soil physical properties were collected in both in situ and controlled laboratory experiments to establish predictive capabilities for soil moisture in saline soils. An inverse Gaussian model was first applied to fit the spectral reflectance curves and to derive three curve-specific parameters, namely the inverted amplitude, the distance from the center to the inflection point, and the area under the Gaussian curve. Then both linear regression analysis and artificial neural networks (ANN) were applied to develop soil moisture prediction models. Results indicate that soil salinity greatly affects surface reflectance and thereby prediction of soil moisture. The linear regression model failed to predict soil moisture for all in situ field samples as well as for controlled laboratory samples with moderate salinity levels. It was only able to predict moisture reasonably well when salinity levels were extremely high. Application of ANNs significantly improved prediction accuracy as evidenced by a substantial increase of the correlation coefficient and Nash - Sutcliffe efficiency. Based on obtained results, the coupling of an inverse Gaussian model with artificial neural networks provides practical and accurate means for prediction of soil moisture of saline soils and shows great potential for large-scale soil moisture mapping based on hyperspectral imagery.

  14. Predicting future, post-fire erosion and sedimentation with watershed models in the western USA

    NASA Astrophysics Data System (ADS)

    Sankey, J. B.; Kreitler, J.; Hawbaker, T. J.; McVay, J.; Vaillant, N.; Lowe, S. E.

    2015-12-01

    Increased sedimentation following wildland fire can negatively impact water supply and water quality. Understanding how future changes in fire frequency, extent, and location will affect watersheds and the ecosystem services they supply to communities is of great societal importance in the USA and throughout the world. In this work we predict variability in post-fire sediment yield at a watershed scale as a function of future wildfire conditions throughout the western USA through 2050. Our predictions are based on future fire probabilities, climate change scenarios, and differing GIS-based implementations of watershed sediment yield models. We assess the uncertainties of our predictions and compare predictions based on the GEOWEPP (Geo-spatial interface for the Water Erosion Prediction Project) model, the InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) Sediment Retention model, and the InVEST Sediment Delivery Ratio model. We show that the models can be parameterized in a relatively simple fashion to predict post-fire sediment yield with accuracy at a watershed scale. Predictions indicate that sediment yield from post-fire hillslope erosion may increase dramatically in coming decades as a function of increased wildfire for many watersheds across the western USA.

  15. 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.

  16. Developing a predictive tropospheric ozone model for Tabriz

    NASA Astrophysics Data System (ADS)

    Khatibi, Rahman; Naghipour, Leila; Ghorbani, Mohammad A.; Smith, Michael S.; Karimi, Vahid; Farhoudi, Reza; Delafrouz, Hadi; Arvanaghi, Hadi

    2013-04-01

    Predictive ozone models are becoming indispensable tools by providing a capability for pollution alerts to serve people who are vulnerable to the risks. We have developed a tropospheric ozone prediction capability for Tabriz, Iran, by using the following five modeling strategies: three regression-type methods: Multiple Linear Regression (MLR), Artificial Neural Networks (ANNs), and Gene Expression Programming (GEP); and two auto-regression-type models: Nonlinear Local Prediction (NLP) to implement chaos theory and Auto-Regressive Integrated Moving Average (ARIMA) models. The regression-type modeling strategies explain the data in terms of: temperature, solar radiation, dew point temperature, and wind speed, by regressing present ozone values to their past values. The ozone time series are available at various time intervals, including hourly intervals, from August 2010 to March 2011. The results for MLR, ANN and GEP models are not overly good but those produced by NLP and ARIMA are promising for the establishing a forecasting capability.

  17. Testing prediction methods: Earthquake clustering versus the Poisson model

    USGS Publications Warehouse

    Michael, A.J.

    1997-01-01

    Testing earthquake prediction methods requires statistical techniques that compare observed success to random chance. One technique is to produce simulated earthquake catalogs and measure the relative success of predicting real and simulated earthquakes. The accuracy of these tests depends on the validity of the statistical model used to simulate the earthquakes. This study tests the effect of clustering in the statistical earthquake model on the results. Three simulation models were used to produce significance levels for a VLF earthquake prediction method. As the degree of simulated clustering increases, the statistical significance drops. Hence, the use of a seismicity model with insufficient clustering can lead to overly optimistic results. A successful method must pass the statistical tests with a model that fully replicates the observed clustering. However, a method can be rejected based on tests with a model that contains insufficient clustering. U.S. copyright. Published in 1997 by the American Geophysical Union.

  18. Predicting Intention to Take Protective Measures During Haze: The Roles of Efficacy, Threat, Media Trust, and Affective Attitude.

    PubMed

    Lin, Trisha T C; Bautista, John Robert

    2016-07-01

    The annual Southeast Asian haze pollution raises public health concerns in this region. Based on a modified extended parallel process model, this study examines efficacy (self-efficacy and response efficacy) and perceived threat (susceptibility and severity) and incorporates new constructs of media trust and affective attitude. Results from a Web survey of 410 undergraduate students in Singapore show that response efficacy to seek haze-related information mediates the association between perceived self-efficacy and intention to take protective measures during haze. Moreover, self-efficacy is negatively associated with affective attitude (e.g., fear and worry) toward haze-related health problems. Next, perceived severity and perceived susceptibility are positively associated with response efficacy and affective attitude. Affective attitude toward haze is a stronger predictor than response efficacy for behavioral intention. Finally, trust in new media is positively associated with young Singaporeans' affective attitude, which positively affects their behavioral intention to take protective measures. PMID:27315440

  19. 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.

  20. Empirical Model for Predicting Rockfall Trajectory Direction

    NASA Astrophysics Data System (ADS)

    Asteriou, Pavlos; Tsiambaos, George

    2016-03-01

    A methodology for the experimental investigation of rockfall in three-dimensional space is presented in this paper, aiming to assist on-going research of the complexity of a block's response to impact during a rockfall. An extended laboratory investigation was conducted, consisting of 590 tests with cubical and spherical blocks made of an artificial material. The effects of shape, slope angle and the deviation of the post-impact trajectory are examined as a function of the pre-impact trajectory direction. Additionally, an empirical model is proposed that estimates the deviation of the post-impact trajectory as a function of the pre-impact trajectory with respect to the slope surface and the slope angle. This empirical model is validated by 192 small-scale field tests, which are also presented in this paper. Some important aspects of the three-dimensional nature of rockfall phenomena are highlighted that have been hitherto neglected. The 3D space data provided in this study are suitable for the calibration and verification of rockfall analysis software that has become increasingly popular in design practice.

  1. 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

  2. CRAFFT: An Activity Prediction Model based on Bayesian Networks

    PubMed Central

    Nazerfard, Ehsan; Cook, Diane J.

    2014-01-01

    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. PMID:25937847

  3. Predicting adverse drug events using pharmacological network models.

    PubMed

    Cami, Aurel; Arnold, Alana; Manzi, Shannon; Reis, Ben

    2011-12-21

    Early and accurate identification of adverse drug events (ADEs) is critically important for public health. We have developed a novel approach for predicting ADEs, called predictive pharmacosafety networks (PPNs). PPNs integrate the network structure formed by known drug-ADE relationships with information on specific drugs and adverse events to predict likely unknown ADEs. Rather than waiting for sufficient post-market evidence to accumulate for a given ADE, this predictive approach relies on leveraging existing, contextual drug safety information, thereby having the potential to identify certain ADEs earlier. We constructed a network representation of drug-ADE associations for 809 drugs and 852 ADEs on the basis of a snapshot of a widely used drug safety database from 2005 and supplemented these data with additional pharmacological information. We trained a logistic regression model to predict unknown drug-ADE associations that were not listed in the 2005 snapshot. We evaluated the model's performance by comparing these predictions with the new drug-ADE associations that appeared in a 2010 snapshot of the same drug safety database. The proposed model achieved an AUROC (area under the receiver operating characteristic curve) statistic of 0.87, with a sensitivity of 0.42 given a specificity of 0.95. These findings suggest that predictive network methods can be useful for predicting unknown ADEs. PMID:22190238

  4. Criteria for deviation from predictions by the concentration addition model.

    PubMed

    Takeshita, Jun-Ichi; Seki, Masanori; Kamo, Masashi

    2016-07-01

    Loewe's additivity (concentration addition) is a well-known model for predicting the toxic effects of chemical mixtures under the additivity assumption of toxicity. However, from the perspective of chemical risk assessment and/or management, it is important to identify chemicals whose toxicities are additive when present concurrently, that is, it should be established whether there are chemical mixtures to which the concentration addition predictive model can be applied. The objective of the present study was to develop criteria for judging test results that deviated from the predictions by the concentration addition chemical mixture model. These criteria were based on the confidence interval of the concentration addition model's prediction and on estimation of errors of the predicted concentration-effect curves by toxicity tests after exposure to single chemicals. A log-logit model with 2 parameters was assumed for the concentration-effect curve of each individual chemical. These parameters were determined by the maximum-likelihood method, and the criteria were defined using the variances and the covariance of the parameters. In addition, the criteria were applied to a toxicity test of a binary mixture of p-n-nonylphenol and p-n-octylphenol using the Japanese killifish, medaka (Oryzias latipes). Consequently, the concentration addition model using confidence interval was capable of predicting the test results at any level, and no reason for rejecting the concentration addition was found. Environ Toxicol Chem 2016;35:1806-1814. © 2015 SETAC. PMID:26660330

  5. 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.

  6. Seasonal difference in brain serotonin transporter binding predicts symptom severity in patients with seasonal affective disorder.

    PubMed

    Mc Mahon, Brenda; Andersen, Sofie B; Madsen, Martin K; Hjordt, Liv V; Hageman, Ida; Dam, Henrik; Svarer, Claus; da Cunha-Bang, Sofi; Baaré, William; Madsen, Jacob; Hasholt, Lis; Holst, Klaus; Frokjaer, Vibe G; Knudsen, Gitte M

    2016-05-01

    Cross-sectional neuroimaging studies in non-depressed individuals have demonstrated an inverse relationship between daylight minutes and cerebral serotonin transporter; this relationship is modified by serotonin-transporter-linked polymorphic region short allele carrier status. We here present data from the first longitudinal investigation of seasonal serotonin transporter fluctuations in both patients with seasonal affective disorder and in healthy individuals. Eighty (11)C-DASB positron emission tomography scans were conducted to quantify cerebral serotonin transporter binding; 23 healthy controls with low seasonality scores and 17 patients diagnosed with seasonal affective disorder were scanned in both summer and winter to investigate differences in cerebral serotonin transporter binding across groups and across seasons. The two groups had similar cerebral serotonin transporter binding in the summer but in their symptomatic phase during winter, patients with seasonal affective disorder had higher serotonin transporter than the healthy control subjects (P = 0.01). Compared to the healthy controls, patients with seasonal affective disorder changed their serotonin transporter significantly less between summer and winter (P < 0.001). Further, the change in serotonin transporter was sex- (P = 0.02) and genotype- (P = 0.04) dependent. In the patients with seasonal affective disorder, the seasonal change in serotonin transporter binding was positively associated with change in depressive symptom severity, as indexed by Hamilton Rating Scale for Depression - Seasonal Affective Disorder version scores (P = 0.01). Our findings suggest that the development of depressive symptoms in winter is associated with a failure to downregulate serotonin transporter levels appropriately during exposure to the environmental stress of winter, especially in individuals with high predisposition to affective disorders.media-1vid110.1093/brain/aww043_video_abstractaww043_video

  7. 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.

  8. Using a Prediction Model to Manage Cyber Security Threats

    PubMed Central

    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. PMID:26065024

  9. 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. PMID:26065024

  10. 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.

  11. Implementation of a model for census prediction and control.

    PubMed Central

    Swain, R W; Kilpatrick, K E; Marsh, J J

    1977-01-01

    A model is described that predicts hospital census and computes, for each day, the number of elective admissions that will maximize the census over the short run, subject to constraints on the probability of overflow. Where a computer is available the model provides detailed predictions of census in units as small as 10 beds; used with manual computation the model allows production of tables of the recommended numbers of elective admissions to the hospital as a whole. The model has been tested in five hospitals and is part of the admissions system in two of them; implementation is described, and the results obtained are discussed. PMID:591350

  12. 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.

  13. Development and Application of Chronic Disease Risk Prediction Models

    PubMed Central

    Oh, Sun Min; Stefani, Katherine M.

    2014-01-01

    Currently, non-communicable chronic diseases are a major cause of morbidity and mortality worldwide, and a large proportion of chronic diseases are preventable through risk factor management. However, the prevention efficacy at the individual level is not yet satisfactory. Chronic disease prediction models have been developed to assist physicians and individuals in clinical decision-making. A chronic disease prediction model assesses multiple risk factors together and estimates an absolute disease risk for the individual. Accurate prediction of an individual's future risk for a certain disease enables the comparison of benefits and risks of treatment, the costs of alternative prevention strategies, and selection of the most efficient strategy for the individual. A large number of chronic disease prediction models, especially targeting cardiovascular diseases and cancers, have been suggested, and some of them have been adopted in the clinical practice guidelines and recommendations of many countries. Although few chronic disease prediction tools have been suggested in the Korean population, their clinical utility is not as high as expected. This article reviews methodologies that are commonly used for developing and evaluating a chronic disease prediction model and discusses the current status of chronic disease prediction in Korea. PMID:24954311

  14. 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.

  15. Time dependent patient no-show predictive modelling development.

    PubMed

    Huang, Yu-Li; Hanauer, David A

    2016-05-01

    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. PMID:27142954

  16. Development of Interpretable Predictive Models for BPH and Prostate Cancer

    PubMed Central

    Bermejo, Pablo; Vivo, Alicia; Tárraga, Pedro J; Rodríguez-Montes, JA

    2015-01-01

    BACKGROUND Traditional methods for deciding whether to recommend a patient for a prostate biopsy are based on cut-off levels of stand-alone markers such as prostate-specific antigen (PSA) or any of its derivatives. However, in the last decade we have seen the increasing use of predictive models that combine, in a non-linear manner, several predictives that are better able to predict prostate cancer (PC), but these fail to help the clinician to distinguish between PC and benign prostate hyperplasia (BPH) patients. We construct two new models that are capable of predicting both PC and BPH. METHODS An observational study was performed on 150 patients with PSA ≥3 ng/mL and age >50 years. We built a decision tree and a logistic regression model, validated with the leave-one-out methodology, in order to predict PC or BPH, or reject both. RESULTS Statistical dependence with PC and BPH was found for prostate volume (P-value < 0.001), PSA (P-value < 0.001), international prostate symptom score (IPSS; P-value < 0.001), digital rectal examination (DRE; P-value < 0.001), age (P-value < 0.002), antecedents (P-value < 0.006), and meat consumption (P-value < 0.08). The two predictive models that were constructed selected a subset of these, namely, volume, PSA, DRE, and IPSS, obtaining an area under the ROC curve (AUC) between 72% and 80% for both PC and BPH prediction. CONCLUSION PSA and volume together help to build predictive models that accurately distinguish among PC, BPH, and patients without any of these pathologies. Our decision tree and logistic regression models outperform the AUC obtained in the compared studies. Using these models as decision support, the number of unnecessary biopsies might be significantly reduced. PMID:25780348

  17. Model predictive torque control with an extended prediction horizon for electrical drive systems

    NASA Astrophysics Data System (ADS)

    Wang, Fengxiang; Zhang, Zhenbin; Kennel, Ralph; Rodríguez, José

    2015-07-01

    This paper presents a model predictive torque control method for electrical drive systems. A two-step prediction horizon is achieved by considering the reduction of the torque ripples. The electromagnetic torque and the stator flux error between predicted values and the references, and an over-current protection are considered in the cost function design. The best voltage vector is selected by minimising the value of the cost function, which aims to achieve a low torque ripple in two intervals. The study is carried out experimentally. The results show that the proposed method achieves good performance in both steady and transient states.

  18. A new indirect multi-step-ahead prediction model for a long-term hydrologic prediction

    NASA Astrophysics Data System (ADS)

    Cheng, Chun-Tian; Xie, Jing-Xin; Chau, Kwok-Wing; Layeghifard, Mehdi

    2008-10-01

    SummaryA dependable long-term hydrologic prediction is essential to planning, designing and management activities of water resources. A three-stage indirect multi-step-ahead prediction model, which combines dynamic spline interpolation into multilayer adaptive time-delay neural network (ATNN), is proposed in this study for the long term hydrologic prediction. In the first two stages, a group of spline interpolation and dynamic extraction units are utilized to amplify the effect of observations in order to decrease the errors accumulation and propagation caused by the previous prediction. In the last step, variable time delays and weights are dynamically regulated by ATNN and the output of ATNN can be obtained as a multi-step-ahead prediction. We use two examples to illustrate the effectiveness of the proposed model. One example is the sunspots time series that is a well-known nonlinear and non-Gaussian benchmark time series and is often used to evaluate the effectiveness of nonlinear models. Another example is a case study of a long-term hydrologic prediction which uses the monthly discharges data from the Manwan Hydropower Plant in Yunnan Province of China. Application results show that the proposed method is feasible and effective.

  19. 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 method emerging as a promising method for numeric prediction, producing understandable models. The present study discusses the groundwater level predictions using MT employing only the historical groundwater levels from a groundwater monitoring well. The results showed that MT can be successively used for forecasting groundwater levels.

  20. Predicting waste stabilization pond performance using an ecological simulation model

    SciTech Connect

    New, G.R.

    1987-01-01

    Waste stabilization ponds (lagoons) are often favored in small communities because of their low cost and ease of operation. Most models currently used to predict performance are empirical or fail to address the primary lagoon cell. Empirical methods for predicting lagoon performance have been found to be off as much as 248 percent when used on a system other than the one they were developed for. Also, the present models developed for the primary cell lack the ability to predict parameters other than biochemical oxygen demand (BOD) and nitrogen. Oxygen consumption is usually estimated from BOD utilization. LAGOON is a fortran program which models the biogeochemical processes characteristic of the primary cell of facultative lagoons. Model parameters can be measured from lagoons in the vicinity of a proposed lagoon or estimated from laboratory studies. The model was calibrated utilizing a subset of the Corinne Utah lagoon data then validated utilizing a subset of the Corinne Utah data.

  1. Predicting lettuce canopy photosynthesis with statistical and neural network models

    NASA Technical Reports Server (NTRS)

    Frick, J.; Precetti, C.; Mitchell, C. A.

    1998-01-01

    An artificial neural network (NN) and a statistical regression model were developed to predict canopy photosynthetic rates (Pn) for 'Waldman's Green' leaf lettuce (Latuca sativa L.). All data used to develop and test the models were collected for crop stands grown hydroponically and under controlled-environment conditions. In the NN and regression models, canopy Pn was predicted as a function of three independent variables: shootzone CO2 concentration (600 to 1500 micromoles mol-1), photosynthetic photon flux (PPF) (600 to 1100 micromoles m-2 s-1), and canopy age (10 to 20 days after planting). The models were used to determine the combinations of CO2 and PPF setpoints required each day to maintain maximum canopy Pn. The statistical model (a third-order polynomial) predicted Pn more accurately than the simple NN (a three-layer, fully connected net). Over an 11-day validation period, average percent difference between predicted and actual Pn was 12.3% and 24.6% for the statistical and NN models, respectively. Both models lost considerable accuracy when used to determine relatively long-range Pn predictions (> or = 6 days into the future).

  2. Predicting lettuce canopy photosynthesis with statistical and neural network models.

    PubMed

    Frick, J; Precetti, C; Mitchell, C A

    1998-11-01

    An artificial neural network (NN) and a statistical regression model were developed to predict canopy photosynthetic rates (Pn) for 'Waldman's Green' leaf lettuce (Latuca sativa L.). All data used to develop and test the models were collected for crop stands grown hydroponically and under controlled-environment conditions. In the NN and regression models, canopy Pn was predicted as a function of three independent variables: shootzone CO2 concentration (600 to 1500 micromoles mol-1), photosynthetic photon flux (PPF) (600 to 1100 micromoles m-2 s-1), and canopy age (10 to 20 days after planting). The models were used to determine the combinations of CO2 and PPF setpoints required each day to maintain maximum canopy Pn. The statistical model (a third-order polynomial) predicted Pn more accurately than the simple NN (a three-layer, fully connected net). Over an 11-day validation period, average percent difference between predicted and actual Pn was 12.3% and 24.6% for the statistical and NN models, respectively. Both models lost considerable accuracy when used to determine relatively long-range Pn predictions (> or = 6 days into the future). PMID:11542672

  3. Rainfall variability effects on aggregate crop model predictions

    NASA Astrophysics Data System (ADS)

    Dzotsi, Kofikuma Adzewoda

    Crop production operates in a highly heterogeneous environment. Space-time variability in weather and spatial heterogeneity in soil and management generate variability in crop yield. While it is practically unfeasible to thoroughly sample the variability of the crop environment, quantification of the associated uncertainties in crop performance can provide vital information for decision-making. The present study used rainfall data collected in southwestern Georgia at scales ranging from 1 km to 60 km to assess the effect of weather variability (in particular rainfall) on crop predictions aggregated over soil and management variations. The simple SALUS (System Approach to Land Use Sustainability) crop model was integrated in DSSAT (Decision Support System for Agrotechnology Transfer) then parameterized and tested for maize, peanut and cotton for use in obtaining the crop predictions. Analysis of the rainfall data indicated that variability in storm characteristics depends upon the season. Winter rainfall was more correlated at a mean distance of 54 km between locations than summer rainfall was at a mean distance of 3 km. The pairwise correlation between locations decreased with distance faster in the summer than in the winter. This rainfall variability translated into crop yield variability in the study area (about 3100 km²). It was found that weather variability explained 60% and 49% of maize yield variability respectively in 2010 and 2011 when heterogeneity in weather, soil, cultivar and planting dates were accounted for simultaneously. Uncertainties in crop predictions due to rainfall spatial uncertainty decreased as the number of sites where weather data were collected increased. Expressed in terms of maize yield coefficient of variation, this uncertainty decreased exponentially from 27% to approximately 4% at a sampling density of 20 weather locations. Based on 30 years of generated weather data, it was concluded that the general form of the relationship

  4. Comparative study of turbulence models in predicting hypersonic inlet flows

    NASA Technical Reports Server (NTRS)

    Kapoor, Kamlesh; Anderson, Bernhard H.; Shaw, Robert J.

    1992-01-01

    A numerical study was conducted to analyze the performance of different turbulence models when applied to the hypersonic NASA P8 inlet. Computational results from the PARC2D code, which solves the full two-dimensional Reynolds-averaged Navier-Stokes equation, were compared with experimental data. The zero-equation models considered for the study were the Baldwin-Lomax model, the Thomas model, and a combination of the Baldwin-Lomax and Thomas models; the two-equation models considered were the Chien model, the Speziale model (both low Reynolds number), and the Launder and Spalding model (high Reynolds number). The Thomas model performed best among the zero-equation models, and predicted good pressure distributions. The Chien and Speziale models compared very well with the experimental data, and performed better than the Thomas model near the walls.

  5. Comparative study of turbulence models in predicting hypersonic inlet flows

    NASA Technical Reports Server (NTRS)

    Kapoor, Kamlesh; Anderson, Bernhard H.; Shaw, Robert J.

    1992-01-01

    A numerical study was conducted to analyze the performance of different turbulence models when applied to the hypersonic NASA P8 inlet. Computational results from the PARC2D code, which solves the full two-dimensional Reynolds-averaged Navier-Stokes equation, were compared with experimental data. The zero-equation models considered for the study were the Baldwin-Lomax model, the Thomas model, and a combination of the Baldwin-Lomax and Thomas models; the two-equation models considered were the Chien model, the Speziale model (both low Reynolds number), and the Launder and Spalding model (high Reynolds number). The Thomas model performed best among the zero-equation models, and predicted good pressure distributions. The Chien and Speziale models compared wery well with the experimental data, and performed better than the Thomas model near the walls.

  6. Model Building Strategies for Predicting Multiple Landslide Events

    NASA Astrophysics Data System (ADS)

    Lombardo, L.; Cama, M.; Märker, M.; Parisi, L.; Rotigliano, E.

    2013-12-01

    A model building strategy is tested to assess the susceptibility for extreme climatic events driven landslides. In fact, extreme climatic inputs such as storms typically are very local phenomena in the Mediterranean areas, so that with the exception of recently stricken areas, the landslide inventories which are required to train any stochastic model are actually unavailable. A solution is here proposed, consisting in training a susceptibility model in a source catchment, which was implemented by applying the binary logistic regression technique, and exporting its predicting function (selected predictors regressed coefficients) in a target catchment to predict its landslide distribution. To test the method we exploit the disaster that occurred in the Messina area (southern Italy) on the 1st of October 2009 where, following a 250mm/8hours storm, approximately 2000 debris flow/debris avalanches landslides in an area of 21km2 triggered, killing thirty-seven people, injuring more than one hundred, and causing 0.5M euro worth of structural damage. The debris flows and debris avalanches phenomena involved the thin weathered mantle of the Varisican low to high grade metamorphic rocks that outcrop in the eastern slopes of the Peloritan Belt. Two 10km2 wide stream catchments, which are located inside the storm core area were exploited: susceptibility models trained in the Briga catchment were tested when exported to predict the landslides distribution in the Giampilieri catchment. The prediction performance (based on goodness of fit, prediction skill, accuracy and precision assessment) of the exported model was then compared with that of a model prepared in the Giampilieri catchment exploiting its landslide inventory. The results demonstrate that the landslide scenario observed in the Giampilieri catchment can be predicted with the same high performance without knowing its landslide distribution: we obtained in fact a very poor decrease in predictive performance when

  7. Comparison of Predictive Modeling Methods of Aircraft Landing Speed

    NASA Technical Reports Server (NTRS)

    Diallo, Ousmane H.

    2012-01-01

    Expected increases in air traffic demand have stimulated the development of air traffic control tools intended to assist the air traffic controller in accurately and precisely spacing aircraft landing at congested airports. Such tools will require an accurate landing-speed prediction to increase throughput while decreasing necessary controller interventions for avoiding separation violations. There are many practical challenges to developing an accurate landing-speed model that has acceptable prediction errors. This paper discusses the development of a near-term implementation, using readily available information, to estimate/model final approach speed from the top of the descent phase of flight to the landing runway. As a first approach, all variables found to contribute directly to the landing-speed prediction model are used to build a multi-regression technique of the response surface equation (RSE). Data obtained from operations of a major airlines for a passenger transport aircraft type to the Dallas/Fort Worth International Airport are used to predict the landing speed. The approach was promising because it decreased the standard deviation of the landing-speed error prediction by at least 18% from the standard deviation of the baseline error, depending on the gust condition at the airport. However, when the number of variables is reduced to the most likely obtainable at other major airports, the RSE model shows little improvement over the existing methods. Consequently, a neural network that relies on a nonlinear regression technique is utilized as an alternative modeling approach. For the reduced number of variables cases, the standard deviation of the neural network models errors represent over 5% reduction compared to the RSE model errors, and at least 10% reduction over the baseline predicted landing-speed error standard deviation. Overall, the constructed models predict the landing-speed more accurately and precisely than the current state-of-the-art.

  8. A Simple Model Predicting Individual Weight Change in Humans.

    PubMed

    Thomas, Diana M; Martin, Corby K; Heymsfield, Steven; Redman, Leanne M; Schoeller, Dale A; Levine, James A

    2011-11-01

    Excessive weight in adults is a national concern with over 2/3 of the US population deemed overweight. Because being overweight has been correlated to numerous diseases such as heart disease and type 2 diabetes, there is a need to understand mechanisms and predict outcomes of weight change and weight maintenance. A simple mathematical model that accurately predicts individual weight change offers opportunities to understand how individuals lose and gain weight and can be used to foster patient adherence to diets in clinical settings. For this purpose, we developed a one dimensional differential equation model of weight change based on the energy balance equation is paired to an algebraic relationship between fat free mass and fat mass derived from a large nationally representative sample of recently released data collected by the Centers for Disease Control. We validate the model's ability to predict individual participants' weight change by comparing model estimates of final weight data from two recent underfeeding studies and one overfeeding study. Mean absolute error and standard deviation between model predictions and observed measurements of final weights are less than 1.8 ± 1.3 kg for the underfeeding studies and 2.5 ± 1.6 kg for the overfeeding study. Comparison of the model predictions to other one dimensional models of weight change shows improvement in mean absolute error, standard deviation of mean absolute error, and group mean predictions. The maximum absolute individual error decreased by approximately 60% substantiating reliability in individual weight change predictions. The model provides a viable method for estimating individual weight change as a result of changes in intake and determining individual dietary adherence during weight change studies. PMID:24707319

  9. A Simple Model Predicting Individual Weight Change in Humans

    PubMed Central

    Thomas, Diana M.; Martin, Corby K.; Heymsfield, Steven; Redman, Leanne M.; Schoeller, Dale A.; Levine, James A.

    2010-01-01

    Excessive weight in adults is a national concern with over 2/3 of the US population deemed overweight. Because being overweight has been correlated to numerous diseases such as heart disease and type 2 diabetes, there is a need to understand mechanisms and predict outcomes of weight change and weight maintenance. A simple mathematical model that accurately predicts individual weight change offers opportunities to understand how individuals lose and gain weight and can be used to foster patient adherence to diets in clinical settings. For this purpose, we developed a one dimensional differential equation model of weight change based on the energy balance equation is paired to an algebraic relationship between fat free mass and fat mass derived from a large nationally representative sample of recently released data collected by the Centers for Disease Control. We validate the model's ability to predict individual participants’ weight change by comparing model estimates of final weight data from two recent underfeeding studies and one overfeeding study. Mean absolute error and standard deviation between model predictions and observed measurements of final weights are less than 1.8 ± 1.3 kg for the underfeeding studies and 2.5 ± 1.6 kg for the overfeeding study. Comparison of the model predictions to other one dimensional models of weight change shows improvement in mean absolute error, standard deviation of mean absolute error, and group mean predictions. The maximum absolute individual error decreased by approximately 60% substantiating reliability in individual weight change predictions. The model provides a viable method for estimating individual weight change as a result of changes in intake and determining individual dietary adherence during weight change studies. PMID:24707319

  10. How absent negativity relates to affect and motivation: an integrative relief model

    PubMed Central

    Deutsch, Roland; Smith, Kevin J. M.; Kordts-Freudinger, Robert; Reichardt, Regina

    2015-01-01

    The present paper concerns the motivational underpinnings and behavioral correlates of the prevention or stopping of negative stimulation – a situation referred to as relief. Relief is of great theoretical and applied interest. Theoretically, it is tied to theories linking affect, emotion, and motivational systems. Importantly, these theories make different predictions regarding the association between relief and motivational systems. Moreover, relief is a prototypical antecedent of counterfactual emotions, which involve specific cognitive processes compared to factual or mere anticipatory emotions. Practically, relief may be an important motivator of addictive and phobic behaviors, self destructive behaviors, and social influence. In the present paper, we will first provide a review of conflicting conceptualizations of relief. We will then present an integrative relief model (IRMO) that aims at resolving existing theoretical conflicts. We then review evidence relevant to distinctive predictions regarding the moderating role of various procedural features of relief situations. We conclude that our integrated model results in a better understanding of existing evidence on the affective and motivational underpinnings of relief, but that further evidence is needed to come to a more comprehensive evaluation of the viability of IRMO. PMID:25806008

  11. How absent negativity relates to affect and motivation: an integrative relief model.

    PubMed

    Deutsch, Roland; Smith, Kevin J M; Kordts-Freudinger, Robert; Reichardt, Regina

    2015-01-01

    The present paper concerns the motivational underpinnings and behavioral correlates of the prevention or stopping of negative stimulation - a situation referred to as relief. Relief is of great theoretical and applied interest. Theoretically, it is tied to theories linking affect, emotion, and motivational systems. Importantly, these theories make different predictions regarding the association between relief and motivational systems. Moreover, relief is a prototypical antecedent of counterfactual emotions, which involve specific cognitive processes compared to factual or mere anticipatory emotions. Practically, relief may be an important motivator of addictive and phobic behaviors, self destructive behaviors, and social influence. In the present paper, we will first provide a review of conflicting conceptualizations of relief. We will then present an integrative relief model (IRMO) that aims at resolving existing theoretical conflicts. We then review evidence relevant to distinctive predictions regarding the moderating role of various procedural features of relief situations. We conclude that our integrated model results in a better understanding of existing evidence on the affective and motivational underpinnings of relief, but that further evidence is needed to come to a more comprehensive evaluation of the viability of IRMO. PMID:25806008

  12. Predisaster Trait Anxiety and Negative Affect Predict Posttraumatic Stress in Youths after Hurricane Katrina

    ERIC Educational Resources Information Center

    Weems, Carl F.; Pina, Armando A.; Costa, Natalie M.; Watts, Sarah E.; Taylor, Leslie K.; Cannon, Melinda F.

    2007-01-01

    On the basis of theory and previous research, it was hypothesized that predisaster child trait anxiety would predict disaster-related posttraumatic stress symptoms and generalized anxiety disorder symptoms, even after controlling for the number of hurricane exposure events. Results support this hypothesis and further indicate that predisaster…

  13. Gaze behavior and affect at 6 months: predicting clinical outcomes and language development in typically developing infants and infants at risk for autism.

    PubMed

    Young, Gregory S; Merin, Noah; Rogers, Sally J; Ozonoff, Sally

    2009-09-01

    This paper presents follow-up longitudinal data to research that previously suggested the possibility of abnormal gaze behavior marked by decreased eye contact in a subgroup of 6-month-old infants at risk for autism (Merin, Young, Ozonoff & Rogers, 2007). Using eye-tracking data and behavioral data recorded during a live mother-infant interaction involving the still-face procedure, the predictive utility of gaze behavior and affective behaviors at 6 months was examined using diagnostic outcome data obtained longitudinally over the following 18 months. Results revealed that none of the infants previously identified as showing lower rates of eye contact had any signs of autism at outcome. In contrast, three infants who were diagnosed with autism demonstrated consistent gaze to the eye region and typical affective responses at 6 months. Individual differences in face scanning and affective responsivity during the live interaction were not related to any continuous measures of symptom frequency or symptom severity. In contrast, results of growth curve models for language development revealed significant relationships between face scanning and expressive language. Greater amounts of fixation to the mother's mouth during live interaction predicted higher levels of expressive language at outcome and greater rates of growth. These findings suggest that although gaze behavior at 6 months may not provide early markers for autism as initially conceived, gaze to the mouth in particular may be useful in predicting individual differences in language development. PMID:19702771

  14. Metacognitive deficits predict future levels of negative symptoms in schizophrenia controlling for neurocognition, affect recognition, and self-expectation of goal attainment.

    PubMed

    Lysaker, Paul H; Kukla, Marina; Dubreucq, Julien; Gumley, Andrew; McLeod, Hamish; Vohs, Jenifer L; Buck, Kelly D; Minor, Kyle S; Luther, Lauren; Leonhardt, Bethany L; Belanger, Elizabeth A; Popolo, Raffaele; Dimaggio, Giancarlo

    2015-10-01

    The recalcitrance of negative symptoms in the face of pharmacologic treatment has spurred interest in understanding the psychological factors that contribute to their formation and persistence. Accordingly, this study investigated whether deficits in metacognition, or the ability to form integrated ideas about oneself, others, and the world, prospectively predicted levels of negative symptoms independent of deficits in neurocognition, affect recognition and defeatist beliefs. Participants were 53 adults with a schizophrenia spectrum disorder. Prior to entry into a rehabilitation program, all participants completed concurrent assessments of metacognition with the Metacognitive Assessment Scale-Abbreviated, negative symptoms with the Positive and Negative Syndrome Scale, neurocognition with the MATRICS battery, affect recognition with the Bell Lysaker Emotion Recognition Task, and one form of defeatist beliefs with the Recovery Assessment Scale. Negative symptoms were then reassessed one week, 9weeks, and 17weeks after entry into the program. A mixed effects regression model revealed that after controlling for baseline negative symptoms, a general index of neurocognition, defeatist beliefs and capacity for affect recognition, lower levels of metacognition predicted higher levels of negative symptoms across all subsequent time points. Poorer metacognition was able to predict later levels of elevated negative symptoms even after controlling for initial levels of negative symptoms. Results may suggest that metacognitive deficits are a risk factor for elevated levels of negative symptoms in the future. Clinical implications are also discussed. PMID:26164820

  15. Neurophysiological processing of emotion and parenting interact to predict inhibited behavior: an affective-motivational framework

    PubMed Central

    Kessel, Ellen M.; Huselid, Rebecca F.; DeCicco, Jennifer M.; Dennis, Tracy A.

    2013-01-01

    Although inhibited behavior problems are prevalent in childhood, relatively little is known about the intrinsic and extrinsic factors that predict a child's ability to regulate inhibited behavior during fear- and anxiety-provoking tasks. Inhibited behavior may be linked to both disruptions in avoidance-related processing of aversive stimuli and in approach-related processing of appetitive stimuli, but previous findings are contradictory and rarely integrate consideration of the socialization context. The current exploratory study used a novel combination of neurophysiological and observation-based methods to examine whether a neurophysiological measure sensitive to approach- and avoidance-oriented emotional processing, the late positive potential (LPP), interacted with observed approach- (promotion) and avoidance- (prevention) oriented parenting practices to predict children's observed inhibited behavior. Participants were 5- to 7-year-old (N = 32) typically-developing children (M = 75.72 months, SD = 6.01). Electroencephalography was continuously recorded while children viewed aversive, appetitive, or neutral images, and the LPP was generated to each picture type separately. Promotion and prevention parenting were observed during an emotional challenge with the child. Child inhibited behavior was observed during a fear and a social evaluation task. As predicted, larger LPPs to aversive images predicted more inhibited behavior during both tasks, but only when parents demonstrated low promotion. In contrast, larger LPPs to appetitive images predicted less inhibited behavior during the social evaluative task, but only when parents demonstrated high promotion; children of high promotion parents showing smaller LPPs to appetitive images showed the greatest inhibition. Parent-child goodness-of-fit and the LPP as a neural biomarker for emotional processes related to inhibited behavior are discussed. PMID:23847499

  16. Neurophysiological processing of emotion and parenting interact to predict inhibited behavior: an affective-motivational framework.

    PubMed

    Kessel, Ellen M; Huselid, Rebecca F; Decicco, Jennifer M; Dennis, Tracy A

    2013-01-01

    Although inhibited behavior problems are prevalent in childhood, relatively little is known about the intrinsic and extrinsic factors that predict a child's ability to regulate inhibited behavior during fear- and anxiety-provoking tasks. Inhibited behavior may be linked to both disruptions in avoidance-related processing of aversive stimuli and in approach-related processing of appetitive stimuli, but previous findings are contradictory and rarely integrate consideration of the socialization context. The current exploratory study used a novel combination of neurophysiological and observation-based methods to examine whether a neurophysiological measure sensitive to approach- and avoidance-oriented emotional processing, the late positive potential (LPP), interacted with observed approach- (promotion) and avoidance- (prevention) oriented parenting practices to predict children's observed inhibited behavior. Participants were 5- to 7-year-old (N = 32) typically-developing children (M = 75.72 months, SD = 6.01). Electroencephalography was continuously recorded while children viewed aversive, appetitive, or neutral images, and the LPP was generated to each picture type separately. Promotion and prevention parenting were observed during an emotional challenge with the child. Child inhibited behavior was observed during a fear and a social evaluation task. As predicted, larger LPPs to aversive images predicted more inhibited behavior during both tasks, but only when parents demonstrated low promotion. In contrast, larger LPPs to appetitive images predicted less inhibited behavior during the social evaluative task, but only when parents demonstrated high promotion; children of high promotion parents showing smaller LPPs to appetitive images showed the greatest inhibition. Parent-child goodness-of-fit and the LPP as a neural biomarker for emotional processes related to inhibited behavior are discussed. PMID:23847499

  17. Early Negative Affect Predicts Anxiety, Not Autism, in Preschool Boys with Fragile X Syndrome

    ERIC Educational Resources Information Center

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

    2013-01-01

    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…

  18. Affective Self-Regulation Trajectories during Secondary School Predict Substance Use among Urban Minority Young Adults

    ERIC Educational Resources Information Center

    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 multiethnic, urban sample (N = 995). During secondary school, participants completed a measure of cognitive and behavioral skills used to control negative, unpleasant emotions or perceived…

  19. Do Changes in Tympanic Temperature Predict Changes in Affective Valence during High-Intensity Exercise?

    ERIC Educational Resources Information Center

    Legrand, Fabien D.; Joly, Philippe M.; Bertucci, William M.

    2015-01-01

    Purpose: Increased core (brain or body) temperature that accompanies exercise has been posited to play an influential role in affective responses to exercise. However, findings in support of this hypothesis have been equivocal, and most of the performed studies have been done in relation to anxiety. The aim of the present study was to investigate…

  20. Predictive Modeling With Big Data: Is Bigger Really Better?

    PubMed

    Junqué de Fortuny, Enric; Martens, David; Provost, Foster

    2013-12-01

    With the increasingly widespread collection and processing of "big data," there is natural interest in using these data assets to improve decision making. One of the best understood ways to use data to improve decision making is via predictive analytics. An important, open question is: to what extent do larger data actually lead to better predictive models? In this article we empirically demonstrate that when predictive models are built from sparse, fine-grained data-such as data on low-level human behavior-we continue to see marginal increases in predictive performance even to very large scale. The empirical results are based on data drawn from nine different predictive modeling applications, from book reviews to banking transactions. This study provides a clear illustration that larger data indeed can be more valuable assets for predictive analytics. This implies that institutions with larger data assets-plus the skill to take advantage of them-potentially can obtain substantial competitive advantage over institutions without such access or skill. Moreover, the results suggest that it is worthwhile for companies with access to such fine-grained data, in the context of a key predictive task, to gather both more data instances and more possible data features. As an additional contribution, we introduce an implementation of the multivariate Bernoulli Naïve Bayes algorithm that can scale to massive, sparse data. PMID:27447254

  1. An empirical model for probabilistic decadal prediction: A global analysis

    NASA Astrophysics Data System (ADS)

    Suckling, Emma; Hawkins, Ed; Eden, Jonathan; van Oldenborgh, Geert Jan

    2016-04-01

    Empirical models, designed to predict land-based surface variables over seasons to decades ahead, provide useful benchmarks for comparison against the performance of dynamical forecast systems; they may also be employable as predictive tools for use by climate services in their own right. A new global empirical decadal prediction system is presented, based on a multiple linear regression approach designed to produce probabilistic output for comparison against dynamical models. Its performance is evaluated for surface air temperature over a set of historical hindcast experiments under a series of different prediction `modes'. The modes include a real-time setting, a scenario in which future volcanic forcings are prescribed during the hindcasts, and an approach which exploits knowledge of the forced trend. A two-tier prediction system, which uses knowledge of future sea surface temperatures in the Pacific and Atlantic Oceans, is also tested, but within a perfect knowledge framework. Each mode is designed to identify sources of predictability and uncertainty, as well as investigate different approaches to the design of decadal prediction systems for operational use. It is found that the empirical model shows skill above that of persistence hindcasts for annual means at lead times of up to ten years ahead in all of the prediction modes investigated. Small improvements in skill are found at all lead times when including future volcanic forcings in the hindcasts. It is also suggested that hindcasts which exploit full knowledge of the forced trend due to increasing greenhouse gases throughout the hindcast period can provide more robust estimates of model bias for the calibration of the empirical model in an operational setting. The two-tier system shows potential for improved real-time prediction, given the assumption that skilful predictions of large-scale modes of variability are available. The empirical model framework has been designed with enough flexibility to

  2. Cross-Validation of Aerobic Capacity Prediction Models in Adolescents.

    PubMed

    Burns, Ryan Donald; Hannon, James C; Brusseau, Timothy A; Eisenman, Patricia A; Saint-Maurice, Pedro F; Welk, Greg J; Mahar, Matthew T

    2015-08-01

    Cardiorespiratory endurance is a component of health-related fitness. FITNESSGRAM recommends the Progressive Aerobic Cardiovascular Endurance Run (PACER) or One mile Run/Walk (1MRW) to assess cardiorespiratory endurance by estimating VO2 Peak. No research has cross-validated prediction models from both PACER and 1MRW, including the New PACER Model and PACER-Mile Equivalent (PACER-MEQ) using current standards. The purpose of this study was to cross-validate prediction models from PACER and 1MRW against measured VO2 Peak in adolescents. Cardiorespiratory endurance data were collected on 90 adolescents aged 13-16 years (Mean = 14.7 ± 1.3 years; 32 girls, 52 boys) who completed the PACER and 1MRW in addition to a laboratory maximal treadmill test to measure VO2 Peak. Multiple correlations among various models with measured VO2 Peak were considered moderately strong (R = .74-0.78), and prediction error (RMSE) ranged from 5.95 ml·kg⁻¹,min⁻¹ to 8.27 ml·kg⁻¹.min⁻¹. Criterion-referenced agreement into FITNESSGRAM's Healthy Fitness Zones was considered fair-to-good among models (Kappa = 0.31-0.62; Agreement = 75.5-89.9%; F = 0.08-0.65). In conclusion, prediction models demonstrated moderately strong linear relationships with measured VO2 Peak, fair prediction error, and fair-to-good criterion referenced agreement with measured VO2 Peak into FITNESSGRAM's Healthy Fitness Zones. PMID:26186536

  3. Predictive modeling of coral disease distribution within a reef system.

    PubMed

    Williams, Gareth J; Aeby, Greta S; Cowie, Rebecca O M; Davy, Simon K

    2010-01-01

    Diseases often display complex and distinct associations with their environment due to differences in etiology, modes of transmission between hosts, and the shifting balance between pathogen virulence and host resistance. Statistical modeling has been underutilized in coral disease research to explore the spatial patterns that result from this triad of interactions. We tested the hypotheses that: 1) coral diseases show distinct associations with multiple environmental factors, 2) incorporating interactions (synergistic collinearities) among environmental variables is important when predicting coral disease spatial patterns, and 3) modeling overall coral disease prevalence (the prevalence of multiple diseases as a single proportion value) will increase predictive error relative to modeling the same diseases independently. Four coral diseases: Porites growth anomalies (PorGA), Porites tissue loss (PorTL), Porites trematodiasis (PorTrem), and Montipora white syndrome (MWS), and their interactions with 17 predictor variables were modeled using boosted regression trees (BRT) within a reef system in Hawaii. Each disease showed distinct associations with the predictors. Environmental predictors showing the strongest overall associations with the coral diseases were both biotic and abiotic. PorGA was optimally predicted by a negative association with turbidity, PorTL and MWS by declines in butterflyfish and juvenile parrotfish abundance respectively, and PorTrem by a modal relationship with Porites host cover. Incorporating interactions among predictor variables contributed to the predictive power of our models, particularly for PorTrem. Combining diseases (using overall disease prevalence as the model response), led to an average six-fold increase in cross-validation predictive deviance over modeling the diseases individually. We therefore recommend coral diseases to be modeled separately, unless known to have etiologies that respond in a similar manner to particular

  4. Predictive performance of a model of anaesthetic uptake with desflurane.

    PubMed

    Kennedy, R

    2006-04-01

    We have previously shown that a model of anaesthetic uptake and distribution, developed for use as a teaching tool, is able to predict end-tidal isoflurane and sevoflurane concentrations at least as well as commonly used propofol models predict blood levels of propofol. Models with good predictive performance may be useful as part of real-time prediction systems. The aim of this study was to assess the performance of this model with desflurane. Twenty adult patients undergoing routine anaesthesia were studied. The total fresh gas flow and vaporizor settings were collected at 10-second intervals from the anaesthetic machine. These data were used as inputs to the model, which had been initialized for patient weight and desflurane. Output of the model is a predicted end-tidal value at each point in time. These values were compared with measured end-tidal desflurane using a standard statistical technique of Varvel and colleagues. Data was analysed from 19 patients. Median performance error was 78% (95% CI 8-147), median absolute performance error 77% (6-149), divergence 10.6%/h (-80-101) and wobble 8.9% (-6-24). The predictive performance of this model with desflurane was poor, with considerable variability between patients. The reasons for the difference between desflurane and our previous results with isoflurane and sevoflurane are not obvious, but may provide important clues to the necessary components for such models. The data collected in this study may assist in the development and evaluation of improved models. PMID:16617640

  5. The Use of Behavior Models for Predicting Complex Operations

    NASA Technical Reports Server (NTRS)

    Gore, Brian F.

    2010-01-01

    Modeling and simulation (M&S) plays an important role when complex human-system notions are being proposed, developed and tested within the system design process. National Aeronautics and Space Administration (NASA) as an agency uses many different types of M&S approaches for predicting human-system interactions, especially when it is early in the development phase of a conceptual design. NASA Ames Research Center possesses a number of M&S capabilities ranging from airflow, flight path models, aircraft models, scheduling models, human performance models (HPMs), and bioinformatics models among a host of other kinds of M&S capabilities that are used for predicting whether the proposed designs will benefit the specific mission criteria. The Man-Machine Integration Design and Analysis System (MIDAS) is a NASA ARC HPM software tool that integrates many models of human behavior with environment models, equipment models, and procedural / task models. The challenge to model comprehensibility is heightened as the number of models that are integrated and the requisite fidelity of the procedural sets are increased. Model transparency is needed for some of the more complex HPMs to maintain comprehensibility of the integrated model performance. This will be exemplified in a recent MIDAS v5 application model and plans for future model refinements will be presented.

  6. Modeling Low Velocity Impacts: Predicting Crater Depth on Pluto

    NASA Astrophysics Data System (ADS)

    Bray, V. J.; Schenk, P.

    2014-12-01

    The New Horizons mission is due to fly-by the Pluto system in Summer 2015 and provides the first opportunity to image the Pluto surface in detail, allowing both the appearance and number of its crater population to be studied for the first time. Bray and Schenk (2014) combined previous cratering studies and numerical modeling of the impact process to predict crater morphology on Pluto based on current understanding of Pluto's composition, structure and surrounding impactor population. Predictions of how the low mean impact velocity (~2km/s) of the Pluto system will influence crater formation is a complex issue. Observations of secondary cratering (low velocity, high angle) and laboratory experiments of impact at low velocity are at odds regarding how velocity controls depth-diameter ratios: Observations of secondary craters show that these low velocity craters are shallower than would be expected for a hyper-velocity primary. Conversely, gas gun work has shown that relative crater depth increases as impact velocity decreases. We have investigated the influence of impact velocity further with iSALE hydrocode modeling of comet impact into Pluto. With increasing impact velocity, a projectile will produce wider and deeper craters. The depth-diameter ratio (d/D) however has a more complex progression with increasing impact velocity: impacts faster than 2km/s lead to smaller d/D ratios as impact velocity increases, in agreement with gas-gun studies. However, decreasing impact velocity from 2km/s to 300 m/s produced smaller d/D as impact velocity was decreased. This suggests that on Pluto the deepest craters would be produced by ~ 2km/s impacts, with shallower craters produced by velocities either side of this critical point. Further simulations to investigate whether this effect is connected to the sound speed of the target material are ongoing. The complex relationship between impact velocity and crater depth for impacts occurring between 300m/s and 10 km/s suggests

  7. Risk prediction models for hepatocellular carcinoma in different populations

    PubMed Central

    Ma, Xiao; Yang, Yang; Tu, Hong; Gao, Jing; Tan, Yu-Ting; Zheng, Jia-Li; Bray, Freddie; Xiang, Yong-Bing

    2016-01-01

    Hepatocellular carcinoma (HCC) is a malignant disease with limited therapeutic options due to its aggressive progression. It places heavy burden on most low and middle income countries to treat HCC patients. Nowadays accurate HCC risk predictions can help making decisions on the need for HCC surveillance and antiviral therapy. HCC risk prediction models based on major risk factors of HCC are useful and helpful in providing adequate surveillance strategies to individuals who have different risk levels. Several risk prediction models among cohorts of different populations for estimating HCC incidence have been presented recently by using simple, efficient, and ready-to-use parameters. Moreover, using predictive scoring systems to assess HCC development can provide suggestions to improve clinical and public health approaches, making them more cost-effective and effort-effective, for inducing personalized surveillance programs according to risk stratification. In this review, the features of risk prediction models of HCC across different populations were summarized, and the perspectives of HCC risk prediction models were discussed as well. PMID:27199512

  8. COMPASS: A Framework for Automated Performance Modeling and Prediction

    SciTech Connect

    Lee, Seyong; Meredith, Jeremy S; Vetter, Jeffrey S

    2015-01-01

    Flexible, accurate performance predictions offer numerous benefits such as gaining insight into and optimizing applications and architectures. However, the development and evaluation of such performance predictions has been a major research challenge, due to the architectural complexities. To address this challenge, we have designed and implemented a prototype system, named COMPASS, for automated performance model generation and prediction. COMPASS generates a structured performance model from the target application's source code using automated static analysis, and then, it evaluates this model using various performance prediction techniques. As we demonstrate on several applications, the results of these predictions can be used for a variety of purposes, such as design space exploration, identifying performance tradeoffs for applications, and understanding sensitivities of important parameters. COMPASS can generate these predictions across several types of applications from traditional, sequential CPU applications to GPU-based, heterogeneous, parallel applications. Our empirical evaluation demonstrates a maximum overhead of 4%, flexibility to generate models for 9 applications, speed, ease of creation, and very low relative errors across a diverse set of architectures.

  9. Risk prediction models for hepatocellular carcinoma in different populations.

    PubMed

    Ma, Xiao; Yang, Yang; Tu, Hong; Gao, Jing; Tan, Yu-Ting; Zheng, Jia-Li; Bray, Freddie; Xiang, Yong-Bing

    2016-04-01

    Hepatocellular carcinoma (HCC) is a malignant disease with limited therapeutic options due to its aggressive progression. It places heavy burden on most low and middle income countries to treat HCC patients. Nowadays accurate HCC risk predictions can help making decisions on the need for HCC surveillance and antiviral therapy. HCC risk prediction models based on major risk factors of HCC are useful and helpful in providing adequate surveillance strategies to individuals who have different risk levels. Several risk prediction models among cohorts of different populations for estimating HCC incidence have been presented recently by using simple, efficient, and ready-to-use parameters. Moreover, using predictive scoring systems to assess HCC development can provide suggestions to improve clinical and public health approaches, making them more cost-effective and effort-effective, for inducing personalized surveillance programs according to risk stratification. In this review, the features of risk prediction models of HCC across different populations were summarized, and the perspectives of HCC risk prediction models were discussed as well. PMID:27199512

  10. Predicting ICME Magnetic Fields with a Numerical Flux Rope Model

    NASA Astrophysics Data System (ADS)

    Manchester, W.; van der Holst, B.; Sokolov, I.

    2014-12-01

    Coronal mass ejections (CMEs) are a dramatic manifestation of solar activity that release vast amounts of plasma into the heliosphere, and have many effects on the interplanetary medium and on planetary atmospheres, and are the major driver of space weather. CMEs occur with the formation and expulsion of large-scale flux ropes from the solar corona, which are routinely observed in interplanetary space. Simulating and predicting the structure and dynamics of these ICME magnetic fields is essential to the progress of heliospheric science and space weather prediction. We combine observations made by different observing techniques of CME events to develop a numerical model capable of predicting the magnetic field of interplanetary coronal mass ejections (ICMES). Photospheric magnetic field measurements from SOHO/MDI and SDO/HMI are used to specify a coronal magnetic flux rope that drives the CMEs. We examine halo CMEs events that produced clearly observed magnetic clouds at Earth and present our model predictions of these events with an emphasis placed on the z component of the magnetic field. Comparison of the MHD model predictions with coronagraph observations and in-situ data allow us to robustly determine the parameters that define the initial state of the driving flux rope, thus providing a predictive model.

  11. Katz model prediction of Caenorhabditis elegans mutagenesis on STS-42

    NASA Technical Reports Server (NTRS)

    Cucinotta, Francis A.; Wilson, John W.; Katz, Robert; Badhwar, Gautam D.

    1992-01-01

    Response parameters that describe the production of recessive lethal mutations in C. elegans from ionizing radiation are obtained with the Katz track structure model. The authors used models of the space radiation environment and radiation transport to predict and discuss mutation rates for C. elegans on the IML-1 experiment aboard STS-42.

  12. Relating Data and Models to Characterize Parameter and Prediction Uncertainty

    EPA Science Inventory

    Applying PBPK models in risk analysis requires that we realistically assess the uncertainty of relevant model predictions in as quantitative a way as possible. The reality of human variability may add a confusing feature to the overall uncertainty assessment, as uncertainty and v...

  13. Temporal transferability and updating of zonal level accident prediction models.

    PubMed

    Hadayeghi, Alireza; Shalaby, Amer S; Persaud, Bhagwant N; Cheung, Carl

    2006-05-01

    This paper examines the temporal transferability of the zonal accident prediction models by using appropriate evaluation measures of predictive performance to assess whether the relationship between the dependent and independent variables holds reasonably well across time. The two temporal contexts are the years 1996 and 2001, with updated 1996 models being used to predict 2001 accidents in each traffic zone of the City of Toronto. The paper examines alternative updating methods for temporal transfer by imagining that only a sample of 2001 data is available. The sensitivity of the performance of the updated models to the 2001 sample size is explored. The updating procedures examined include the Bayesian updating approach and the application of calibration factors to the 1996 models. Models calibrated for the 2001 samples were also explored, but were found to be inadequate. The results show that the models are not transferable in a strict statistical sense. However, relative measures of transferability indicate that the transferred models yield useful information in the application context. Also, it is concluded that the updated accident models using the calibration factors produce better results for predicting the number of accidents in the year 2001 than using the Bayesian approach. PMID:16414003

  14. COMPARISONS OF MODELS PREDICTING AMBIENT LAKE PHOSPHORUS CONCENTRATIONS

    EPA Science Inventory

    The Vollenweider, Dillon, and Larsen/Mercier models for predicting ambient lake phosphorus concentrations and classifying lakes by trophic state are compared in this report. The Dillon and Larsen/Mercier models gave comparable results in ranking 39 lakes relative to known ambient...

  15. COMPARISONS OF SPATIAL PATTERNS OF WET DEPOSITION TO MODEL PREDICTIONS

    EPA Science Inventory

    The Community Multiscale Air Quality model, (CMAQ), is a "one-atmosphere" model, in that it uses a consistent set of chemical reactions and physical principles to predict concentrations of primary pollutants, photochemical smog, and fine aerosols, as well as wet and dry depositi...

  16. Implementing a Resource Requirements Prediction Model in Community Colleges.

    ERIC Educational Resources Information Center

    Rice, Gary Alan

    The purposes of this study were to determine what characterizes a useful cost estimating model at the community college level, to implement at a community college the Resource Requirements Prediction Model 1.6 (RRPM) developed by the National Center for Higher Education Management Systems, to identify problems associated with implementation and…

  17. Comparison of model predictions with LDEF satellite radiation measurements

    NASA Technical Reports Server (NTRS)

    Armstrong, T. W.; Colborn, B. L.; Harmon, B. A.; Parnell, T. A.; Watts, J. W., Jr.; Benton, E. V.

    1994-01-01

    Some early results are summarized from a program under way to utilize Long Duration Exposure Facility (LDEF) satellite data for evaluating and improving current models of the space radiation environment in low Earth orbit. Reported here are predictions and comparisons with some of the LDEF dose and induced radioactivity data, which are used to check the accuracy of current models describing the magnitude and directionality of the trapped proton environment. Preliminary findings are that the environment models underestimate both dose and activation from trapped protons by a factor of about two, and the observed anisotropy is higher than predicted.

  18. Prediction horizon effects on stochastic modelling hints for neural networks

    SciTech Connect

    Drossu, R.; Obradovic, Z.

    1995-12-31

    The objective of this paper is to investigate the relationship between stochastic models and neural network (NN) approaches to time series modelling. Experiments on a complex real life prediction problem (entertainment video traffic) indicate that prior knowledge can be obtained through stochastic analysis both with respect to an appropriate NN architecture as well as to an appropriate sampling rate, in the case of a prediction horizon larger than one. An improvement of the obtained NN predictor is also proposed through a bias removal post-processing, resulting in much better performance than the best stochastic model.

  19. From Pavlov to pain: How predictability affects the anticipation and processing of visceral pain in a fear conditioning paradigm.

    PubMed

    Labrenz, Franziska; Icenhour, Adriane; Schlamann, Marc; Forsting, Michael; Bingel, Ulrike; Elsenbruch, Sigrid

    2016-04-15

    Conditioned pain-related fear may contribute to hyperalgesia and central sensitization, but this has not been tested for interoceptive, visceral pain. The underlying ability to accurately predict pain is based on predictive cue properties and may alter the sensory processing and cognitive-emotional modulation of pain thus exacerbating the subjective pain experience. In this functional magnetic resonance imaging study using painful rectal distensions as unconditioned stimuli (US), we addressed changes in the neural processing of pain during the acquisition of pain-related fear and subsequently tested if conditioned stimuli (CS) contribute to hyperalgesia and increased neural responses in pain-encoding regions. N=49 healthy volunteers were assigned to one of two groups and underwent 3T fMRI during acquisition of either differential fear conditioning (predictable) or non-contingent presentation of CS and US (unpredictable). During a subsequent test phase, pain stimuli signaled randomly by the CSs were delivered. For the acquisition, results confirmed differential conditioning in the predictable but not the unpredictable group. With regard to activation in response to painful stimuli, the unpredictable compared to the predictable group revealed greater activation in pain-encoding (somatosensory cortex, insula) and pain-modulatory (prefrontal and cingulate cortices, periaqueductal grey, parahippocampus) regions. In the test phase, no evidence of hyperalgesia or central sensitization was found, but the predictable group demonstrated enhanced caudate nucleus activation in response to CS(-)-signaled pain. These findings support that during fear conditioning, the ability to predict pain affects neural processing of visceral pain and alters the associative learning processes underlying the acquisition of predictive properties of cues signaling pain, but conditioned pain-related fear does not result in visceral hyperalgesia or central sensitization. PMID:26854560

  20. Methods for evaluating the predictive accuracy of structural dynamic models

    NASA Technical Reports Server (NTRS)

    Hasselman, Timothy K.; Chrostowski, Jon D.

    1991-01-01

    Modeling uncertainty is defined in terms of the difference between predicted and measured eigenvalues and eigenvectors. Data compiled from 22 sets of analysis/test results was used to create statistical databases for large truss-type space structures and both pretest and posttest models of conventional satellite-type space structures. Modeling uncertainty is propagated through the model to produce intervals of uncertainty on frequency response functions, both amplitude and phase. This methodology was used successfully to evaluate the predictive accuracy of several structures, including the NASA CSI Evolutionary Structure tested at Langley Research Center. Test measurements for this structure were within + one-sigma intervals of predicted accuracy for the most part, demonstrating the validity of the methodology and computer code.

  1. Stellar coronae - What can be predicted with minimum flux models?

    NASA Technical Reports Server (NTRS)

    Hammer, R.; Endler, F.; Ulmschneider, P.

    1983-01-01

    In order to determine the possible errors of various minimum flux corona (MFC) predictions, MFC models are compared with a grid of detailed coronal models covering a range of two orders of magnitude in coronal heating and damping length values. The MFC concept is totally unreliable in the prediction of mass loss and the relative importance of various kinds of energy losses, and MFC predictions for the mass loss rate and energy losses due to stellar wind can be wrong by many orders of magnitude. It is suggested that for future applications, the unreliable MFC formulas should be replaced by a grid of related models accounting for the coronal dependence on damping length, such as the models underlying the present study.

  2. Predictive Models for Fast and Effective Profiling of Kinase Inhibitors.

    PubMed

    Bora, Alina; Avram, Sorin; Ciucanu, Ionel; Raica, Marius; Avram, Stefana

    2016-05-23

    In this study we developed two-dimensional pharmacophore-based random forest models for the effective profiling of kinase inhibitors. One hundred seven prediction models were developed to address distinct kinases spanning over all kinase groups. Rigorous external validation demonstrates excellent virtual screening and classification potential of the predictors and, more importantly, the capacity to prioritize novel chemical scaffolds in large chemical libraries. The models built upon more diverse and more potent compounds tend to exert the highest predictive power. The analysis of ColBioS-FlavRC (Collection of Bioselective Flavonoids and Related Compounds) highlighted several potentially promiscuous derivatives with undesirable selectivity against kinases. The prediction models can be downloaded from www.chembioinf.ro . PMID:27064988

  3. Three-model ensemble wind prediction in southern Italy

    NASA Astrophysics Data System (ADS)

    Torcasio, Rosa Claudia; Federico, Stefano; Calidonna, Claudia Roberta; Avolio, Elenio; Drofa, Oxana; Landi, Tony Christian; Malguzzi, Piero; Buzzi, Andrea; Bonasoni, Paolo

    2016-03-01

    Quality of wind prediction is of great importance since a good wind forecast allows the prediction of available wind power, improving the penetration of renewable energies into the energy market. Here, a 1-year (1 December 2012 to 30 November 2013) three-model ensemble (TME) experiment for wind prediction is considered. The models employed, run operationally at National Research Council - Institute of Atmospheric Sciences and Climate (CNR-ISAC), are RAMS (Regional Atmospheric Modelling System), BOLAM (BOlogna Limited Area Model), and MOLOCH (MOdello LOCale in H coordinates). The area considered for the study is southern Italy and the measurements used for the forecast verification are those of the GTS (Global Telecommunication System). Comparison with observations is made every 3 h up to 48 h of forecast lead time. Results show that the three-model ensemble outperforms the forecast of each individual model. The RMSE improvement compared to the best model is between 22 and 30 %, depending on the season. It is also shown that the three-model ensemble outperforms the IFS (Integrated Forecasting System) of the ECMWF (European Centre for Medium-Range Weather Forecast) for the surface wind forecasts. Notably, the three-model ensemble forecast performs better than each unbiased model, showing the added value of the ensemble technique. Finally, the sensitivity of the three-model ensemble RMSE to the length of the training period is analysed.

  4. Meteorological Processes Affecting Air Quality – Research and Model Development Needs

    EPA Science Inventory

    Meteorology modeling is an important component of air quality modeling systems that defines the physical and dynamical environment for atmospheric chemistry. The meteorology models used for air quality applications are based on numerical weather prediction models that were devel...

  5. Negative affect predicts posttraumatic stress symptoms in Brazilian volunteer United Nations peacekeepers in Haiti.

    PubMed

    Souza, Wanderson F; Figueira, Ivan; Mendlowicz, Mauro V; Volchan, Eliane; Mendonça-de-Souza, Ana C; Duarte, Antônio F A; Monteiro da Silva, Angela M; Marques-Portella, Carla; Mari, Jair J; Coutinho, Evandro Silva Freire

    2008-11-01

    Our study evaluated the relationship between positive affect (PA) and negative affect (NA) traits on the development of posttraumatic stress symptoms (PTSS) among peacekeepers. A longitudinal study with 138 army personnel deployed to a peacekeeping mission in Haiti was conducted. An instrument for measuring PA and NA traits was used before deployment. PTSS, indexed by posttraumatic stress disorder Checklist--Military Version (PCL-M) and frequency of stressful situations were measured after return. Regression analysis showed that both NA and number of stressful situations contributed toward increasing PCL-M scores (Adjusted R = 0.25; p < 0.001). We also found that NA traits interact with intensively stressful situations enhancing the occurrence of PTSS (Adjusted R = 0.32; p < 0.001). These findings suggest that NA traits are an important predictor for PTSS among peacekeepers and also worsen the consequences of being exposed to stressful situations. PMID:19008738

  6. Efficient Modelling and Prediction of Meshing Noise from Chain Drives

    NASA Astrophysics Data System (ADS)

    ZHENG, H.; WANG, Y. Y.; LIU, G. R.; LAM, K. Y.; QUEK, K. P.; ITO, T.; NOGUCHI, Y.

    2001-08-01

    This paper presents a practical approach for predicting the meshing noise due to the impact of chain rollers against the sprocket of chain drives. An acoustical model relating dynamic response of rollers and its induced sound pressure is developed based on the fact that the acoustic field is mainly created by oscillating rigid cylindrical rollers. Finite element techniques and numerical software codes are employed to model and simulate the acceleration response of each chain roller which is necessary for noise level prediction of a chain drive under varying operation conditions and different sprocket configurations. The predicted acoustic pressure levels of meshing noise are compared with the available experimental measurements. It is shown that the predictions are in reasonable agreement with the experiments and the approach enables designers to obtain required information on the noise level of a selected chain drive in a time- and cost-efficient manner.

  7. New models and predictions for Brownian coagulation of non-interacting spheres.

    PubMed

    Kelkar, Aniruddha V; Dong, Jiannan; Franses, Elias I; Corti, David S

    2013-01-01

    The classical steady-state Smoluchowski model for Brownian coagulation is evaluated using Brownian Dynamics Simulations (BDS) as a benchmark. The predictions of this approach compare favorably with the results of BDS only in the dilute limit, that is, for volume fractions of φ≤5×10(-4). From the solution of the more general unsteady-state diffusion equation, a new model for coagulation is developed. The resulting coagulation rate constant is time-dependent and approaches the steady-state limit only at large times. Moreover, in contrast to the Smoluchowski model, this rate constant depends on the particle size, with the transient effects becoming more significant at larger sizes. The predictions of the unsteady-state model agree well with the BDS results up to volume fractions of about φ=0.1, at which the aggregation half-time predicted by the Smoluchowski model is five times that of the BDS. A new procedure to extract the aggregation rate constant from simulation results based on this model is presented. The choice of the rate constant kernel used in the population balance equations for complete aggregation is also evaluated. Extension of the new model to a variable rate constant kernel leads to increased accuracy of the predictions, especially for φ≤5×10(-3). This size-dependence of the rate constant kernel affects particularly the predictions for initially polydisperse sphere systems. In addition, the model is extended to account in a novel way for both short-range viscous two-particle interactions and long-range many-particle Hydrodynamic Interactions (HI). Predictions including HI agree best with the BDS results. The new models presented here offer accurate and computationally less-intensive predictions of the coagulation dynamics while also accounting for hydrodynamic coupling. PMID:23036339

  8. Modeling system for predicting enterococci levels at Holly Beach.

    PubMed

    Zhang, Zaihong; Deng, Zhiqiang; Rusch, Kelly A; Walker, Nan D

    2015-08-01

    This paper presents a new modeling system for nowcasting and forecasting enterococci levels in coastal recreation waters at any time during the day. The modeling system consists of (1) an artificial neural network (ANN) model for predicting the enterococci level at sunrise time, (2) a clear-sky solar radiation and turbidity correction to the ANN model, (3) remote sensing algorithms for turbidity, and (4) nowcasting/forecasting data. The first three components are also unique features of the new modeling system. While the component (1) is useful to beach monitoring programs requiring enterococci levels in early morning, the component (2) in combination with the component (1) makes it possible to predict the bacterial level in beach waters at any time during the day if the data from the components (3) and (4) are available. Therefore, predictions from the component (2) are of primary interest to beachgoers. The modeling system was developed using three years of swimming season data and validated using additional four years of independent data. Testing results showed that (1) the sunrise-time model correctly reproduced 82.63% of the advisories issued in seven years with a false positive rate of 2.65% and a false negative rate of 14.72%, and (2) the new modeling system was capable of predicting the temporal variability in enterococci levels in beach waters, ranging from hourly changes to daily cycles. The results demonstrate the efficacy of the new modeling system in predicting enterococci levels in coastal beach waters. Applications of the modeling system will improve the management of recreational beaches and protection of public health. PMID:26186681

  9. Testable polarization predictions for models of CMB isotropy anomalies

    SciTech Connect

    Dvorkin, Cora; Peiris, Hiranya V.; Hu, Wayne

    2008-03-15

    Anomalies in the large-scale cosmic microwave background (CMB) temperature sky measured by the Wilkinson Microwave Anisotropy Probe have been suggested as possible evidence for a violation of statistical isotropy on large scales. In any physical model for broken isotropy, there are testable consequences for the CMB polarization field. We develop simulation tools for predicting the polarization field in models that break statistical isotropy locally through a modulation field. We study two different models: dipolar modulation, invoked to explain the asymmetry in power between northern and southern ecliptic hemispheres, and quadrupolar modulation, posited to explain the alignments between the quadrupole and octopole. For the dipolar case, we show that predictions for the correlation between the first 10 multipoles of the temperature and polarization fields can typically be tested at better than the 98% CL. For the quadrupolar case, we show that the polarization quadrupole and octopole should be moderately aligned. Such an alignment is a generic prediction of explanations which involve the temperature field at recombination and thus discriminate against explanations involving foregrounds or local secondary anisotropy. Predicted correlations between temperature and polarization multipoles out to l=5 provide tests at the {approx}99% CL or stronger for quadrupolar models that make the temperature alignment more than a few percent likely. As predictions of anomaly models, polarization statistics move beyond the a posteriori inferences that currently dominate the field.

  10. Short communication: Accounting for new mutations in genomic prediction models.

    PubMed

    Casellas, Joaquim; Esquivelzeta, Cecilia; Legarra, Andrés

    2013-08-01

    Genomic evaluation models so far do not allow for accounting of newly generated genetic variation due to mutation. The main target of this research was to extend current genomic BLUP models with mutational relationships (model AM), and compare them against standard genomic BLUP models (model A) by analyzing simulated data. Model performance and precision of the predicted breeding values were evaluated under different population structures and heritabilities. The deviance information criterion (DIC) clearly favored the mutational relationship model under large heritabilities or populations with moderate-to-deep pedigrees contributing phenotypic data (i.e., differences equal or larger than 10 DIC units); this model provided slightly higher correlation coefficients between simulated and predicted genomic breeding values. On the other hand, null DIC differences, or even relevant advantages for the standard genomic BLUP model, were reported under small heritabilities and shallow pedigrees, although precision of the genomic breeding values did not differ across models at a significant level. This method allows for slightly more accurate genomic predictions and handling of newly created variation; moreover, this approach does not require additional genotyping or phenotyping efforts, but a more accurate handing of available data. PMID:23746579

  11. Gaussian predictive process models for large spatial data sets

    PubMed Central

    Banerjee, Sudipto; Gelfand, Alan E.; Finley, Andrew O.; Sang, Huiyan

    2009-01-01

    Summary With scientific data available at geocoded locations, investigators are increasingly turning to spatial process models for carrying out statistical inference. Over the last decade, hierarchical models implemented through Markov chain Monte Carlo methods have become especially popular for spatial modelling, given their flexibility and power to fit models that would be infeasible with classical methods as well as their avoidance of possibly inappropriate asymptotics. However, fitting hierarchical spatial models often involves expensive matrix decompositions whose computational complexity increases in cubic order with the number of spatial locations, rendering such models infeasible for large spatial data sets. This computational burden is exacerbated in multivariate settings with several spatially dependent response variables. It is also aggravated when data are collected at frequent time points and spatiotemporal process models are used. With regard to this challenge, our contribution is to work with what we call predictive process models for spatial and spatiotemporal data. Every spatial (or spatiotemporal) process induces a predictive process model (in fact, arbitrarily many of them). The latter models project process realizations of the former to a lower dimensional subspace, thereby reducing the computational burden. Hence, we achieve the flexibility to accommodate non-stationary, non-Gaussian, possibly multivariate, possibly spatiotemporal processes in the context of large data sets. We discuss attractive theoretical properties of these predictive processes. We also provide a computational template encompassing these diverse settings. Finally, we illustrate the approach with simulated and real data sets. PMID:19750209

  12. Cloud Based Metalearning System for Predictive Modeling of Biomedical Data

    PubMed Central

    Vukićević, Milan

    2014-01-01

    Rapid growth and storage of biomedical data enabled many opportunities for predictive modeling and improvement of healthcare processes. On the other side analysis of such large amounts of data is a difficult and computationally intensive task for most existing data mining algorithms. This problem is addressed by proposing a cloud based system that integrates metalearning framework for ranking and selection of best predictive algorithms for data at hand and open source big data technologies for analysis of biomedical data. PMID:24892101

  13. Predictive Models of Li-ion Battery Lifetime

    SciTech Connect

    Smith, Kandler; Wood, Eric; Santhanagopalan, Shriram; Kim, Gi-heon; Shi, Ying; Pesaran, Ahmad

    2015-06-15

    It remains an open question how best to predict real-world battery lifetime based on accelerated calendar and cycle aging data from the laboratory. Multiple degradation mechanisms due to (electro)chemical, thermal, and mechanical coupled phenomena influence Li-ion battery lifetime, each with different dependence on time, cycling and thermal environment. The standardization of life predictive models would benefit the industry by reducing test time and streamlining development of system controls.

  14. Rotor Broadband Noise Prediction with Comparison to Model Data

    NASA Technical Reports Server (NTRS)

    Brooks, Thomas F.; Burley, Casey L.

    2001-01-01

    This paper reports an analysis and prediction development of rotor broadband noise. The two primary components of this noise are Blade-Wake Interaction (BWI) noise, due to the blades' interaction with the turbulent wakes of the preceding blades, and "Self" noise, due to the development and shedding of turbulence within the blades' boundary layers. Emphasized in this report is the new code development for Self noise. The analysis and validation employs data from the HART program, a model BO-105 rotor wind tunnel test conducted in the German-Dutch Wind Tunnel (DNW). The BWI noise predictions are based on measured pressure response coherence functions using cross-spectral methods. The Self noise predictions are based on previously reported semiempirical modeling of Self noise obtained from isolated airfoil sections and the use of CAMRAD.Modl to define rotor performance and local blade segment flow conditions. Both BWI and Self noise from individual blade segments are Doppler shifted and summed at the observer positions. Prediction comparisons with measurements show good agreement for a range of rotor operating conditions from climb to steep descent. The broadband noise predictions, along with those of harmonic and impulsive Blade-Vortex Interaction (BVI) noise predictions, demonstrate a significant advance in predictive capability for main rotor noise.

  15. Orbit Modelling for Satellites Using the NASA Prediction Bulletins

    NASA Technical Reports Server (NTRS)

    Bonavito, N. L.; Koch, D. W.; Maslyar, G. A.; Foreman, J. C.

    1976-01-01

    For some satellites the NASA Prediction Bulletins are the only means available to the general user for obtaining orbital information. A computational interface between the information given in the NASA Prediction Bulletins and standard orbit determination programs is provided. Such an interface is necessary to obtain accurate orbit predictions. The theoretical considerations and their computational verification in this interface modelling are presented. This analysis was performed in conjunction with satellite aided search and rescue position location experiments where accurate orbits of the Amateur Satellite Corporation (AMSAT) OSCAR-6 and OSCAR-7 spacecraft are a prerequisite.

  16. Model Predictions to the 2005 Ultrasonic Benchmark Problems

    NASA Astrophysics Data System (ADS)

    Kim, Hak-Joon; Song, Sung-Jin; Park, Joon-Soo

    2006-03-01

    The World Federation of NDE Centers (WFNDEC) has addressed the 2005 ultrasonic benchmark problems including linear scanning of the side drilled hole (SDH) specimen with oblique incidence with an emphasis on further study on SV-wave responses of the SDH versus angles around 60 degrees and responses of a circular crack. To solve these problems, we adopted the multi-Gaussian beam model as beam models and the Kirchhoff approximation and the separation of variables method as far-field scattering models. By integration of the beam and scattering models and the system efficiency factor obtained from the given reference experimental setups provided by Center for Nondestructive Evaluation into our ultrasonic measurement models, we predicted the responses of the SDH and the circular cracks (pill-box crack like flaws). This paper summarizes our models and predicted results for the 2005 ultrasonic benchmark problems.

  17. Atmospheric analysis and prediction model development, volume 1

    NASA Technical Reports Server (NTRS)

    Kesel, P. G.; Wellck, R. E.; Langland, R. A.; Lewit, H. L.

    1976-01-01

    A set of hemispheric atmospheric analysis and prediction models was designed and tested. All programs were executed on either a 63 x 63 or 187 x 187 polar stereographic grid of the Northern Hemisphere. Parameters for objective analysis included sea surface temperature, sea level pressure, and twelve levels (from 1,000 to 100 millibars) of temperatures, heights, and winds. Stratospheric extensions (up to 10 millibars) were also provided. Four versions of a complex atmospheric prediction model, based on primitive equations, were programmed and tested. These models were executed on either the 63 x 63 or 187 x 187 grid, using either five or ten computational layers. The coarse-mesh (63 x 63) models were tested using real data for the period 21-23 April 1976. The fine-mesh (187 x 187) models were debugged, but insufficient computer resources precluded production tests. Preliminary test results for the 63 x 63 models are provided. Problem areas and proposed solutions are discussed.

  18. The affective profiles, psychological well-being, and harmony: environmental mastery and self-acceptance predict the sense of a harmonious life

    PubMed Central

    Al Nima, Ali; Kjell, Oscar N.E.

    2014-01-01

    Background. An important outcome from the debate on whether wellness equals happiness, is the need of research focusing on how psychological well-being might influence humans’ ability to adapt to the changing environment and live in harmony. To get a detailed picture of the influence of positive and negative affect, the current study employed the affective profiles model in which individuals are categorised into groups based on either high positive and low negative affect (self-fulfilling); high positive and high negative affect (high affective); low positive and low negative affect (low affective); and high negative and low positive affect (self-destructive). The aims were to (1) investigate differences between affective profiles in psychological well-being and harmony and (2) how psychological well-being and its dimensions relate to harmony within the four affective profiles. Method. 500 participants (mean age = 34.14 years, SD. = ±12.75 years; 187 males and 313 females) were recruited online and required to answer three self-report measures: The Positive Affect and Negative Affect Schedule; The Scales of Psychological Well-Being (short version) and The Harmony in Life Scale. We conducted a Multivariate Analysis of Variance where the affective profiles and gender were the independent factors and psychological well-being composite score, its six dimensions as well as the harmony in life score were the dependent factors. In addition, we conducted four multi-group (i.e., the four affective profiles) moderation analyses with the psychological well-being dimensions as predictors and harmony in life as the dependent variables. Results. Individuals categorised as self-fulfilling, as compared to the other profiles, tended to score higher on the psychological well-being dimensions: positive relations, environmental mastery, self-acceptance, autonomy, personal growth, and purpose in life. In addition, 47% to 66% of the variance of the harmony in life was explained by

  19. How predictability of feeding patches affects home range and foraging habitat selection in avian social scavengers?

    PubMed

    Monsarrat, Sophie; Benhamou, Simon; Sarrazin, François; Bessa-Gomes, Carmen; Bouten, Willem; Duriez, Olivier

    2013-01-01

    Feeding stations are commonly used to sustain conservation programs of scavengers but their impact on behaviour is still debated. They increase the temporal and spatial predictability of food resources while scavengers have supposedly evolved to search for unpredictable resources. In the Grands Causses (France), a reintroduced population of Griffon vultures Gyps fulvus can find carcasses at three types of sites: 1. "light feeding stations", where farmers can drop carcasses at their farm (spatially predictable), 2. "heavy feeding stations", where carcasses from nearby farms are concentrated (spatially and temporally predictable) and 3. open grasslands, where resources are randomly distributed (unpredictable). The impact of feeding stations on vulture's foraging behaviour was investigated using 28 GPS-tracked vultures. The average home range size was maximal in spring (1272 ± 752 km(2)) and minimal in winter (473 ± 237 km(2)) and was highly variable among individuals. Analyses of home range characteristics and feeding habitat selection via compositional analysis showed that feeding stations were always preferred compared to the rest of the habitat where vultures can find unpredictable resources. Feeding stations were particularly used when resources were scarce (summer) or when flight conditions were poor (winter), limiting long-ranging movements. However, when flight conditions were optimal, home ranges also encompassed large areas of grassland where vultures could find unpredictable resources, suggesting that vultures did not lose their natural ability to forage on unpredictable resources, even when feeding stations were available. However during seasons when food abundance and flight conditions were not limited, vultures seemed to favour light over heavy feeding stations, probably because of the reduced intraspecific competition and a pattern closer to the natural dispersion of resources in the landscape. Light feeding stations are interesting tools for managing

  20. Predictive Factors Affecting Long-Term Outcome of Unilateral Lateral Rectus Recession

    PubMed Central

    Yang, Hee Kyung; Kim, Mi-Jin; Hwang, Jeong-Min

    2015-01-01

    Background There are few long-term outcome reports of unilateral lateral rectus (LR) recession for exotropia including a large number of subjects. Previous reports on unilateral LR recession commonly show extremely low rates of initial overcorrection and large exodrifts after surgery suggesting that the surgical dose may be increased. However, little is known of the long-term outcome of a large unilateral LR recession for exotropia. Objectives To determine long-term outcomes and predictive factors of recurrence after a large unilateral LR recession in patients with exotropia. Data Extraction Retrospective analysis was performed on 92 patients aged 3 to 17 years who underwent 10 mm unilateral LR recession for exotropia of ≤ 25 prism diopters (Δ) with prism and alternate cover testing and were followed up for more than 2 years after surgery. Final success rates within 10Δ of exophoria/tropia and 5Δ of esophoria/tropia at distance in the primary position, improvement in stereopsis and the predictive factors for recurrence were evaluated. Results At 24 months after surgery, 54% of patients had ocular alignment meeting the defined criteria of success, 45% had recurrence and 1% had overcorrection. After a mean follow-up of 39 months, 36% showed success, 63% showed recurrence and 1% resulted in overcorrection. The average time of recurrence was 23.4±14.7 months (range, 1–60 months) and the rate of recurrence per person-year was 23% after unilateral LR recession. Predictive factors of recurrence were a larger preoperative near angle of deviation (>16Δ) and larger initial postoperative exodeviation (>5Δ) at distance. Conclusions Long-term outcome of unilateral LR recession for exotropia showed low success rates with high recurrence, thus should be reserved for patients with a small preoperative near angle of exodeviation. PMID:26418819