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Sample records for affect model predictions

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

    PubMed

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

    2013-01-01

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

  2. Positive affect predicts avoidance goals in social interaction anxiety: testing a hierarchical model of social goals.

    PubMed

    Trew, Jennifer L; Alden, Lynn E

    2012-01-01

    Models of self-regulation suggest that social goals may contribute to interpersonal and affective difficulties, yet little research has addressed this issue in the context of social anxiety. The present studies evaluated a hierarchical model of approach and avoidance in the context of social interaction anxiety, with affect as a mediating factor in the relationship between motivational tendencies and social goals. This model was refined in one undergraduate sample (N = 186) and cross-validated in a second sample (N = 195). The findings support hierarchical relationships between motivational tendencies, social interaction anxiety, affect, and social goals, with higher positive affect predicting fewer avoidance goals in both samples. Implications for the treatment of social interaction anxiety are discussed. PMID:22489603

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

    DOE PAGES

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

    2015-08-19

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

  5. 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.

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

    PubMed Central

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

    2016-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2016-09-01

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

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

    PubMed

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

    2016-01-01

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

  9. Predictability of a Professional Practice Model to Affect Nurse and Patient Outcomes.

    PubMed

    Stallings-Welden, Lois M; Shirey, Maria R

    2015-01-01

    Thousands of patients experience needless deaths and injuries as a result of errors while hospitalized for an unrelated problem. The lack of an established professional practice model (PPM) of nursing may be a contributing factor to patient care quality and safety breaches. The PPM of nursing was tested for its ability to affect nurse and patient outcomes. Using a retrospective/prospective research design, secondary data were collected from 2395 staff nurses on 15 inpatient-nursing units covering a 6-year timeframe. Data were analyzed using ANOVA and the Pearson correlation. Nurse and patient outcomes on 2 hospital campuses reached statistical significance. Positive correlations were seen between the initiation of a PPM and subsequent nurses' perception of quality of care, nurse interactions, decision making, autonomy, job enjoyment, and patient satisfaction. This study provides empirical evidence that a uniquely designed PPM in alignment with organizational context can indeed impact nurse and patient outcomes in a community health system. PMID:26049597

  10. 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…

  11. PREDICTIVE MODELS

    SciTech Connect

    Ray, R.M. )

    1986-12-01

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

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

    PubMed

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

    2006-01-01

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

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

    ERIC Educational Resources Information Center

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

    2013-01-01

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

  14. 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

  15. 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...

  16. 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

  17. 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

  18. Early Adolescent Affect Predicts Later Life Outcomes

    PubMed Central

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

    2016-01-01

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

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

    USGS Publications Warehouse

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

    2014-01-01

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

  20. 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.

  1. 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.

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

    USGS Publications Warehouse

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

    2016-01-01

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

  3. Prediction in Multilevel Models

    ERIC Educational Resources Information Center

    Afshartous, David; de Leeuw, Jan

    2005-01-01

    Multilevel modeling is an increasingly popular technique for analyzing hierarchical data. This article addresses the problem of predicting a future observable y[subscript *j] in the jth group of a hierarchical data set. Three prediction rules are considered and several analytical results on the relative performance of these prediction rules are…

  4. Prediction of naphthenic acid species degradation by kinetic and surrogate models during the ozonation of oil sands process-affected water.

    PubMed

    Islam, Md Shahinoor; Moreira, Jesús; Chelme-Ayala, Pamela; Gamal El-Din, Mohamed

    2014-09-15

    Oil sands process-affected water (OSPW) is a complex mixture of organic and inorganic contaminants, and suspended solids, generated by the oil sands industry during the bitumen extraction process. OSPW contains a large number of structurally diverse organic compounds, and due to variability of the water quality of different OSPW matrices, there is a need to select a group of easily measured surrogate parameters for monitoring and treatment process control. In this study, kinetic and surrogate correlation models were developed to predict the degradation of naphthenic acids (NAs) species during the ozonation of OSPW. Additionally, the speciation and distribution of classical and oxidized NA species in raw and ozonated OSPW were also examined. The structure-reactivity of NA species indicated that the reactivity of individual NA species increased as the carbon and hydrogen deficiency numbers increased. The kinetic parameters obtained in this study allowed calculating the evolution of the concentrations of the acid-extractable fraction (AEF), chemical oxygen demand (COD), and NA distributions for a given ozonation process. High correlations between the AEF and COD and NA species were found, suggesting that AEF and COD can be used as surrogate parameters to predict the degradation of NAs during the ozonation of OSPW.

  5. 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.

  6. 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.

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

    PubMed

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

    2011-01-01

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

  8. Atmospheric prediction model survey

    NASA Technical Reports Server (NTRS)

    Wellck, R. E.

    1976-01-01

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

  9. Factors affecting dynamical seasonal prediction of the Arctic sea ice

    NASA Astrophysics Data System (ADS)

    Wang, W.; Chen, M.; Kumar, A.; Hung, M.

    2013-12-01

    Arctic sea ice variability has received increasing attention during the last decade. Seasonal prediction of the Arctic sea ice has been primarily produced with statistical methods during the past years. A few operational centers have recently implemented dynamical sea ice component in the coupled atmosphere-ocean forecast systems for seasonal climate prediction. Yet various issues remain to be resolved for an improved prediction of seasonal sea ice variations. In this study, we analyze the forecast of sea ice extent in the NCEP Climate Forecast System version 2 (CFSv2) and address factors that affect the representation of the observed sea ice variability in the forecast model. The analysis will be based on retrospective and real-time 9-month forecasts from the CFSv2 for 1982-2012. We will first assess the overall performance of the CFSv2 in capturing the observed sea ice extent climatology, long-term trend, and interannual anomalies. We will then discuss factors that affect the sea ice prediction, including: (1) consistency of the initialization of the observed sea ice concentration, (2) impacts of surface heat fluxes related to atmospheric model physics, (3) bias in sea surface temperatures, and (4) impacts of initial sea ice thickness.

  10. Freeze Prediction Model

    NASA Technical Reports Server (NTRS)

    Morrow, C. T. (Principal Investigator)

    1981-01-01

    Measurements of wind speed, net irradiation, and of air, soil, and dew point temperatures in an orchard at the Rock Springs Agricultural Research Center, as well as topographical and climatological data and a description of the major apple growing regions of Pennsylvania were supplied to the University of Florida for use in running the P-model, freeze prediction program. Results show that the P-model appears to have considerable applicability to conditions in Pennsylvania. Even though modifications may have to be made for use in the fruit growing regions, there are advantages for fruit growers with the model in its present form.

  11. Novel animal models of affective disorders.

    PubMed

    Redei, E E; Ahmadiyeh, N; Baum, A E; Sasso, D A; Slone, J L; Solberg, L C; Will, C C; Volenec, A

    2001-01-01

    Is there an appropriate animal model for human affective disorders? The traditional difficulties in accepting animal models for psychopathology stem from the argument that there is no evidence for concluding that what occurs in the brain of the animal is equivalent to what occurs in the brain of a human. However, if one models any or some core aspects of affective disorder, this model can become an invaluable tool in the analysis of the multitude of causes, genetic, environmental, or pharmacological, that can bring about symptoms homologous to those of patients with affective disorders. Animal models can also allow the study of the mechanisms of specific behaviors, their pathophysiology, and can aid in developing and predicting therapeutic responses to pharmacologic agents. Although animals exhibit complex and varied social and emotional behaviors for which well-validated and standardized measures exist, an understanding that a precise replica of human affective disorders cannot be expected in a single animal model is crucial. Instead, a good animal model of a human disorder should fulfill as many of the four main criteria as possible: (1) strong behavioral similarities, (2) common cause, (3) similar pathophysiology, and (4) common treatment. An animal model fulfilling any or most of these criteria can be used to elucidate the mechanisms of the specific aspect of the model that is homologous to the human disorder. A wide range of animal models of affective disorders, primarily depression, has been developed to date. They include models in which "depressive behavior" is the result of genetic selection or manipulation, environmental stressors during development or in adulthood, or pharmacologic treatments. The assessment of these animal models is based either on behavioral tests measuring traits that are homologous to symptoms of the human disorder they model, or behavioral tests responsive to appropriate pharmacologic treatments. The goal of this review is to focus

  12. Models of Affective Decision Making

    PubMed Central

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

    2016-01-01

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

  13. Model predicts global warming

    NASA Astrophysics Data System (ADS)

    Wainger, Lisa A.

    Global greenhouse warming will be clearly identifiable by the 1990s, according to eight scientists who have been studying climate changes using computer models. Researchers at NASA's Goddard Space Flight Center, Goddard Institute for Space Studies, New York, and the Massachusetts Institute of Technology, Cambridge, say that by the 2010s, most of the globe will be experiencing “substantial” warming. The level of warming will depend on amounts of trace gases, or greenhouse gases, in the atmosphere.Predictions for the next 70 years are based on computer simulations of Earth's climate. In three runs of the model, James Hansen and his colleagues looked at the effects of changing amounts of atmospheric gases with time.

  14. 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.

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

    PubMed

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

    2016-06-01

    Species' responses to climate change are variable and diverse, yet our understanding of how different responses (e.g. physiological, behavioural, demographic) relate and how they affect the parameters most relevant for conservation (e.g. population persistence) is lacking. Despite this, studies that observe changes in one type of response typically assume that effects on population dynamics will occur, perhaps fallaciously. We use a hierarchical framework to explain and test when impacts of climate on traits (e.g. phenology) affect demographic rates (e.g. reproduction) and in turn population dynamics. Using this conceptual framework, we distinguish four mechanisms that can prevent lower-level responses from impacting population dynamics. Testable hypotheses were identified from the literature that suggest life-history and ecological characteristics which could predict when these mechanisms are likely to be important. A quantitative example on birds illustrates how, even with limited data and without fully-parameterized population models, new insights can be gained; differences among species in the impacts of climate-driven phenological changes on population growth were not explained by the number of broods or density dependence. Our approach helps to predict the types of species in which climate sensitivities of phenotypic traits have strong demographic and population consequences, which is crucial for conservation prioritization of data-deficient species. PMID:27062059

  16. 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

  17. 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.

  18. Hot Temperatures, Hostile Affect, Hostile Cognition, and Arousal: Tests of a General Model of Affective Aggression.

    ERIC Educational Resources Information Center

    Anderson, Craig A.; And Others

    1995-01-01

    Used a general model of affective aggression to generate predictions concerning hot temperatures. Results indicated that hot temperatures produced increases in hostile affect, hostile cognition, and physiological arousal. Concluded that hostile affect, hostile cognitions, and excitation transfer processes may all increase the likelihood of biased…

  19. 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

  20. 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.

  1. Predicting Affective Information - An Evaluation of Repetition Suppression Effects.

    PubMed

    Trapp, Sabrina; Kotz, Sonja A

    2016-01-01

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

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

    PubMed Central

    Trapp, Sabrina; Kotz, Sonja A.

    2016-01-01

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

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

    PubMed Central

    Trapp, Sabrina; Kotz, Sonja A.

    2016-01-01

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

  4. Factors affecting the predictive validity of the Braden Scale.

    PubMed

    Capobianco, M L; McDonald, D D

    1996-01-01

    This descriptive correlational study explored the predictive validity of the Braden Scale and factors affecting it A Braden score was determined within 4 hours of admission for 50 adult medical/surgical inpatients. Independent skin assessments were made three times a week and at discharge. Fourteen patients (28%) developed pressure ulcers. A Braden score cutoff of 18 or less resulted in a 71% sensitivity, 83% specificity, 63% predictive value of a positive test, and 88% predictive value of a negative test. Three of the four patients incorrectly predicted to be not at risk scored "inadequate" on the nutrition subscale. Two of the four also were underweight. Of the six patients incorrectly predicted at risk for a pressure ulcer, three had been placed on air mattresses and were receiving levothyroxine (Synthroid). This study provides further evidence of the Braden Scale's predictive validity. The results suggest that patients who are underweight or getting inadequate nutrition be considered at increased risk for pressure ulcers.

  5. 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…

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

    NASA Astrophysics Data System (ADS)

    Lee, Sunyoung

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

  7. 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…

  8. 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.

  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. 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.

  11. Planetary atmosphere modeling and predictions

    NASA Technical Reports Server (NTRS)

    Berman, A. L.

    1977-01-01

    The capability to generate spacecraft frequency predictions which include the refractive bending effects induced during signal passage through a planetary atmosphere is a pivotal element of the DSN Radio Science System. This article describes the current implementation effort to develop planetary atmosphere modeling and prediction capability.

  12. Proton Fluence Prediction Models

    NASA Technical Reports Server (NTRS)

    Feynman, Joan

    1996-01-01

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

  13. 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...

  14. 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.

  15. Predictive models of battle dynamics

    NASA Astrophysics Data System (ADS)

    Jelinek, Jan

    2001-09-01

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

  16. 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

  17. 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.

  18. Affective decision-making predictive of Chinese adolescent drinking behaviors.

    PubMed

    Xiao, Lin; Bechara, Antoine; Grenard, L Jerry; Stacy, W Alan; Palmer, Paula; Wei, Yonglan; Jia, Yong; Fu, Xiaolu; Johnson, C Anderson

    2009-07-01

    The goal of the current investigation was to address whether affective decision making would serve as a unique neuropsychological marker to predict drinking behaviors among adolescents. We conducted a longitudinal study of 181 Chinese adolescents in Chengdu city, China. In their 10th grade (ages 15-16), these adolescents were tested for their affective decision-making ability using the Iowa Gambling Task (IGT) and working memory capacity using the Self-Ordered Pointing Test. Self-report questionnaires were used to assess academic performance and drinking behaviors. At 1-year follow-up, questionnaires were completed to assess drinking behaviors, and the UPPS Impulsive Behavior Scale was used to examine four dimensions of impulsivity: urgency, lack of premeditation, lack of perseverance, and sensation seeking. Results indicated that those adolescents who progressed to binge drinking or exhibited consistent binge drinking not only performed poorly on the IGT but also scored significantly higher in urgency compared to those who never or occasionally drank. Moreover, better IGT scores predicted fewer drinking problems and fewer drinks 1 year later after controlling for demographic variables, the previous drinking behaviors, working memory, and impulsivity. These findings suggest that deficits in affective decision making may be important independent determinants of compulsive drinking and potentially addictive behavior in adolescents. PMID:19573273

  19. Hydrometeorological model for streamflow prediction

    USGS Publications Warehouse

    Tangborn, Wendell V.

    1979-01-01

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

  20. 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

  1. 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.

  2. Neural Affective Mechanisms Predict Market-Level Microlending.

    PubMed

    Genevsky, Alexander; Knutson, Brian

    2015-09-01

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

  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. 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.

  6. 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.

  7. Trustworthiness and Negative Affect Predict Economic Decision-Making.

    PubMed

    Nguyen, Christopher M; Koenigs, Michael; Yamada, Torricia H; Teo, Shu Hao; Cavanaugh, Joseph E; Tranel, Daniel; Denburg, Natalie L

    2011-09-01

    The Ultimatum Game (UG) is a widely used and well-studied laboratory model of economic decision-making. Here, we studied 129 healthy adults and compared demographic (i.e., age, gender, education), cognitive (i.e., intelligence, attention/working memory, speed, language, visuospatial, memory, executive functions), and personality (i.e., "Big Five", positive affect, negative affect) variables between those with a "rational" versus an "irrational" response pattern on the UG. Our data indicated that participants with "rational" UG performance (accepting any offer, no matter the fairness) endorsed higher levels of trust, or the belief in the sincerity and good intentions of others, while participants with "irrational" UG performance (rejecting unfair offers) endorsed higher levels of negative affect, such as anger and contempt. These personality variables were the only ones that differentiated the two response patterns-demographic and cognitive factors did not differ between rational and irrational players. The results indicate that the examination of personality and affect is crucial to our understanding of the individual differences that underlie decision-making.

  8. Trustworthiness and Negative Affect Predict Economic Decision-Making

    PubMed Central

    Nguyen, Christopher M.; Koenigs, Michael; Yamada, Torricia H.; Teo, Shu Hao; Cavanaugh, Joseph E.; Tranel, Daniel; Denburg, Natalie L.

    2012-01-01

    The Ultimatum Game (UG) is a widely used and well-studied laboratory model of economic decision-making. Here, we studied 129 healthy adults and compared demographic (i.e., age, gender, education), cognitive (i.e., intelligence, attention/working memory, speed, language, visuospatial, memory, executive functions), and personality (i.e., “Big Five”, positive affect, negative affect) variables between those with a “rational” versus an “irrational” response pattern on the UG. Our data indicated that participants with “rational” UG performance (accepting any offer, no matter the fairness) endorsed higher levels of trust, or the belief in the sincerity and good intentions of others, while participants with “irrational” UG performance (rejecting unfair offers) endorsed higher levels of negative affect, such as anger and contempt. These personality variables were the only ones that differentiated the two response patterns—demographic and cognitive factors did not differ between rational and irrational players. The results indicate that the examination of personality and affect is crucial to our understanding of the individual differences that underlie decision-making. PMID:23493494

  9. Prediction of disease-related mutations affecting protein localization

    PubMed Central

    Laurila, Kirsti; Vihinen, Mauno

    2009-01-01

    Background Eukaryotic cells contain numerous compartments, which have different protein constituents. Proteins are typically directed to compartments by short peptide sequences that act as targeting signals. Translocation to the proper compartment allows a protein to form the necessary interactions with its partners and take part in biological networks such as signalling and metabolic pathways. If a protein is not transported to the correct intracellular compartment either the reaction performed or information carried by the protein does not reach the proper site, causing either inactivation of central reactions or misregulation of signalling cascades, or the mislocalized active protein has harmful effects by acting in the wrong place. Results Numerous methods have been developed to predict protein subcellular localization with quite high accuracy. We applied bioinformatics methods to investigate the effects of known disease-related mutations on protein targeting and localization by analyzing over 22,000 missense mutations in more than 1,500 proteins with two complementary prediction approaches. Several hundred putative localization affecting mutations were identified and investigated statistically. Conclusion Although alterations to localization signals are rare, these effects should be taken into account when analyzing the consequences of disease-related mutations. PMID:19309509

  10. 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

  11. 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

  12. 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...

  13. The Predictive Validity of Optimism and Affectivity in a Longitudinal Study of Older Adults

    PubMed Central

    Benyamini, Yael; Roziner, Ilan

    2008-01-01

    The current study tested the independence of dispositional optimism and pessimism from negative and positive affectivity in the prediction of older adults’ well-being, concurrently and five years later, using structural equation modeling. As hypothesized, in a community sample, both optimism/pessimism and affectivity were correlated cross-sectionally and prospectively with outcomes representing general health and well-being (self-rated health, somatic depression and life satisfaction). However, with both sets of predictors in the model, affectivity eliminated the relationships of optimism/pessimism with the outcomes. Our proposed explanation for these findings, derived from the theoretical self-regulation model, also explains the seemingly contradictory previous findings of an independent contribution of dispositional optimism in patient samples coping with specific stressors. PMID:19255603

  14. 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.

  15. 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.

  16. Cognition, Affect, and Behavior in the Prediction of Group Attitudes.

    ERIC Educational Resources Information Center

    Jackson, Linda A.; And Others

    1996-01-01

    Research was designed to identify the cognitions (stereotypes and values), affects, and behavior associated by white college students (n=869) with 3 target groups: African Americans, Hispanic Americans, and Asian Americans. Affect and behavior were the strongest predictors of attitudes toward minority groups; cognition made a minor contribution…

  17. Enhanced probabilistic microcell prediction model

    NASA Astrophysics Data System (ADS)

    Kim, Song-Kyoo

    2005-06-01

    A microcell is a cell with 1-km or less radius which is suitable not only for heavily urbanized area such as a metropolitan city but also for in-building area such as offices and shopping malls. This paper deals with the microcell prediction model of propagation loss focused on in-buildng solution that is analyzed by 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. Combination of the probabilistic method is applied to get these performance factors. The mathematical methods include the CLT (Central Limit Theorem) and the SSQC (Six-Sigma Quality Control) to get the parameters of the distribution. This probabilistic solution gives us compact 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. In addition, the optimal strategies for antenna allocation for a building can be obtained by using this model.

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

    ERIC Educational Resources Information Center

    Neuendorf, Kimberly A.; Sparks, Glenn G.

    1988-01-01

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

  19. 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

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

    PubMed

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

    2013-02-01

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

  1. [Affective disorders: Evolution of nosographic models].

    PubMed

    Kaladjian, A; Azorin, J-M; Adida, M; Fakra, E; Da Fonseca, D; Pringuey, D

    2010-12-01

    In the history of the nosographies in psychiatry, the affective disorders were gradually distinguished from the other categories of mental disorders, until being considered as separate illness entities, such as what Kraepelin named manic-depressive insanity at the end of the 19th century. The latter will be subsequently divided in two main categories, the bipolar disorder on the one hand and recurrent depression on the other hand, this separation being still current, and extensively diffused by the mean of the Diagnostic and Statistical Manual of Mental Disorders (DSM). The DSM, whose revisions largely determine the evolution of the contemporary nosographic models, mainly relies on a categorical approach of the mental disorders. The next revision will probably continue to follow this kind of approach, even if the use of dimensional components could also be developed. In the future, true nosographic advances can be waited from clinical epidemiology studies, as those which recently made it possible to highlight various sub-types of affective disorders on the basis of clinical, biographical or temperamental characteristics. Etiological approaches, centered on the pathophysiology of the affective disorders, could also contribute to build nosographic models on the basis of an objective knowledge on these diseases.

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

    ERIC Educational Resources Information Center

    Chaudhuri, Arjun; Buck, Ross

    1995-01-01

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

  3. Are implicit motives the need to feel certain affect? Motive-affect congruence predicts relationship satisfaction.

    PubMed

    Job, Veronika; Bernecker, Katharina; Dweck, Carol S

    2012-12-01

    The authors test the assumption that the core of implicit motives is the desire for particular affective experiences and that motive satisfaction need not be tied to any particular domain. Using the context of romantic relationships, cross-sectional Study 1 and experimental Study 2 showed that people with a high affiliation motive were more satisfied when they experienced more affiliation-specific affect (calmness and relaxation). However, people with a higher power motive were more satisfied in their relationships when they experienced more power-specific affect (strength and excitement) in these relationships. The results support the idea that an implicit motive involves the desire for specific affective experiences and that frequent experiences of one's preferred affect can lead to enhanced satisfaction and well-being in a domain, even one that is not typically associated with that motive. PMID:22854792

  4. Affective topic model for social emotion detection.

    PubMed

    Rao, Yanghui; Li, Qing; Wenyin, Liu; Wu, Qingyuan; Quan, Xiaojun

    2014-10-01

    The rapid development of social media services has been a great boon for the communication of emotions through blogs, microblogs/tweets, instant-messaging tools, news portals, and so forth. This paper is concerned with the detection of emotions evoked in a reader by social media. Compared to classical sentiment analysis conducted from the writer's perspective, analysis from the reader's perspective can be more meaningful when applied to social media. We propose an affective topic model with the intention to bridge the gap between social media materials and a reader's emotions by introducing an intermediate layer. The proposed model can be used to classify the social emotions of unlabeled documents and to generate a social emotion lexicon. Extensive evaluations using real-world data validate the effectiveness of the proposed model for both these applications.

  5. 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.

  6. 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

  7. Prediction of Significant Wave Heights in Extreme Storms Affecting Far North Alaska

    NASA Astrophysics Data System (ADS)

    Cassano, E. N.; Thornbrugh, C.; Lynch, A.

    2003-12-01

    Barrow, Alaska is located at the northernmost point of the United States on the shore of the Arctic Ocean. The area surrounding Barrow is low lying and is thus vulnerable to coastal flooding via waves and storm surges. Previous modeling of flooding resulting from a storm that affected Barrow and the rest of the North Slope coast region in October 1963 did not reproduce observed flooding, which was substantially greater than modeled. It is hypothesized that because this model only incorporated storm surge and not waves, the model was unable to simulate the total amount of flooding. The current study examines a recent storm that affected the North Slope of Alaska on July 28-30, 2003, and includes prediction of wave height using the SWAN (Simulating WAves Nearshore) model. In addition this study considers the formation and development, climatological context, and community responses for this storm. Results from the wave modeling portion of this study will be incorporated into a coastal flooding model in order to better simulate the coastal environment under extreme conditions in this area.

  8. 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.

  9. 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

  10. Childhood asthma prediction models: a systematic review.

    PubMed

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

    2015-12-01

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

  11. 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…

  12. Hybrid approaches to physiologic modeling and prediction

    NASA Astrophysics Data System (ADS)

    Olengü, Nicholas O.; Reifman, Jaques

    2005-05-01

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

  13. 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

  14. [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

  15. [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.

  16. 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.

  17. Potential animal models of seasonal affective disorder.

    PubMed

    Workman, Joanna L; Nelson, Randy J

    2011-01-01

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

  18. 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...

  19. 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

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

    PubMed

    Tement, Sara; Korunka, Christian

    2013-01-01

    The present study examines whether negative and positive affectivity (NA and PA, respectively) predict different forms of work-to-family conflict (WFC-time, WFC-strain, WFC-behavior) and enrichment (WFE-development, WFE-affect, WFE-capital) beyond job characteristics (workload, autonomy, variety, workplace support). Furthermore, interactions between job characteristics and trait affectivity while predicting WFC and WFE were examined. Using a large sample of Slovenian employees (N = 738), NA and PA were found to explain variance in WFC as well as in WFE above and beyond job characteristics. More precisely, NA significantly predicted WFC, whereas PA significantly predicted WFE. In addition, several interactive effects were found to predict forms of WFC and WFE. These results highlight the importance of trait affectivity in work-family research. They provide further support for the crucial impact of job characteristics as well. PMID:23469478

  1. 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.

  2. Modelling affect in terms of speech parameters.

    PubMed

    Stassen, H H

    1988-01-01

    It is well known that the human voice contains important information about the affective state of a speaker at a nonverbal level. Accordingly, we started an extensive investigation which aims at modelling intraindividual changes of the global affective state over time, as this state is reflected by the human voice, and can be inferred from measurable speech parameters. For the purpose of this investigation, a speech-recording procedure was designed which is especially suited to reveal intraindividual changes of voice patterns over time since each person serves as his or her own reference. On the other hand, the chosen experimental setup is less suited to classify patients in the sense of a traditional diagnostic scheme. In order to find an appropriate mathematical model on the basis of speech parameters, a calibration study with 190 healthy subjects was carried out which enabled us to investigate each parameter for its reproducibility, sensitivity and specificity. In particular, this calibration study yielded the information of how to draw the line between 'normal' fluctuations and 'significant' intraindividual changes over time. All speech parameters under discussion turned out to be sufficiently stable over time, whereas, in regard to their sensitivity to form and content of text, significant differences showed up. In a second step, a pilot study with 6 depressive patients was carried out in order to investigate the specificity of voice parameters with regard to psychopathology. It turned out that the registration procedure is realizable even if patients are considerably handicapped by their illness. However, no consistent correlations could be revealed between single speech parameters and psychopathological rating scales.(ABSTRACT TRUNCATED AT 250 WORDS)

  3. Predictive model for ozone concentration during MIG welding

    SciTech Connect

    Blehm, K.D.

    1982-01-01

    Particular metal-inert gas (MIG) welding operations have been shown to produce ozone concentrations from 0.2 to 14.5 part per million (ppM) near the arc in a region that may include the welder's breathing zone. Exposure to such concentrations may induce acute or chronic deleterious health effects in the exposed population. Previously reported differences and difficulties with measurement of ozone have produced widely divergent health hazard assessments for ozone exposure during similar welding operations. Therefore, it was desirable to attempt to develop a predictive model for ozone exposure that could be used independent of or concomitant with other measurement and analytical methods. A select MIG welding operation (mild steel) was simulated in field trials where all parameters that could affect ozone concentrations were held constant except for welding amperage and ventilation rate. Techniques of multiple regression analysis were employed to develop a predictive model for ozone concentration based upon factors of amperage and ventilation. The model developed was then evaluated to determine if reasonable, accurate predictions of ozone concentrations could be made. It was determined that the predictive model developed did not yield accurate predictions of ozone concentrations due to uncontrollable variability in the welding process. A very good prediction correlation (r = 0.8387) was, however, established by using amperage and ventilation to predict concentrations of ozone. Potential utility of the model in field situations is discussed, and future research to improve the prediction accuracy is suggested.

  4. 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…

  5. 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.

  6. 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.

  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. 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.

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. 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.

  15. 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.

  16. 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

  17. 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

  18. 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)

  19. 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...

  20. Personality interacts with implicit affect to predict performance in analytic versus holistic processing.

    PubMed

    Kazén, Miguel; Kuhl, Julius; Quirin, Markus

    2015-06-01

    Both theoretical approaches and empirical evidence suggest that negative affect fosters analytic processing, whereas positive affect fosters holistic processing, but these effects are inconsistent. We aim to show that (a) differences in affect regulation abilities ("action orientation") and (b) implicit more so than self-reported affect assessment need to be considered to advance our understanding of these processes. Forty participants were asked to verify whether a word was correctly or incorrectly spelled to measure analytic processing, as well as to intuitively assess whether sets of three words were coherent (remote associates task) to measure holistic processing. As expected, implicit but not explicit negative affect interacted with low action orientation ("state orientation") to predict higher d' performance in word spelling, whereas implicit but not explicit positive affect interacted with high action orientation to predict higher d' performance in coherence judgments for word triads. Results are interpreted according to personality systems interaction theory. These findings suggest that affect and affect changes should be measured explicitly and implicitly to investigate affect-cognition interactions. Moreover, they suggest that good affect regulators benefit from positive affect for holistic processing, whereas bad affect regulators benefit from negative affect for analytical processing. PMID:24725069

  1. Thermal barrier coating life prediction model

    NASA Technical Reports Server (NTRS)

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

    1986-01-01

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

  2. Posterior predictive Bayesian phylogenetic model selection.

    PubMed

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

    2014-05-01

    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. PMID:24193892

  3. 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

  4. 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…

  5. 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.

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

    PubMed

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

    2012-08-01

    What motivates individuals to invest time and effort and overcome obstacles (i.e., strive for primary control) when pursuing important goals? We propose that positive affect predicts primary control striving for career and educational goals, and we explore the mediating role of control beliefs. In Study 1, positive affect predicted primary control striving for career goals in a two-wave longitudinal study of a U.S. sample. In Study 2, positive affect predicted primary control striving for career and educational goals and objective career outcomes in a six-wave longitudinal study of a German sample. Control beliefs partially mediated the longitudinal associations with primary control striving. Thus, when individuals experience positive affect, they become more motivated to invest time and effort, and overcome obstacles when pursuing their goals, in part because they believe they have more control over attaining their goals. PMID:22569224

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

    PubMed Central

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

    2014-01-01

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

  9. 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.

  10. A Predictive Model of Inquiry to Enrollment

    ERIC Educational Resources Information Center

    Goenner, Cullen F.; Pauls, Kenton

    2006-01-01

    The purpose of this paper is to build a predictive model of enrollment that provides data driven analysis to improve undergraduate recruitment efforts. We utilize an inquiry model, which examines the enrollment decisions of students that have made contact with our institution, a medium sized, public, Doctoral I university. A student, who makes an…

  11. Assessing calibration of multinomial risk prediction models.

    PubMed

    Van Hoorde, Kirsten; Vergouwe, Yvonne; Timmerman, Dirk; Van Huffel, Sabine; Steyerberg, Ewout W; Van Calster, Ben

    2014-07-10

    Calibration, that is, whether observed outcomes agree with predicted risks, is important when evaluating risk prediction models. For dichotomous outcomes, several tools exist to assess different aspects of model calibration, such as calibration-in-the-large, logistic recalibration, and (non-)parametric calibration plots. We aim to extend these tools to prediction models for polytomous outcomes. We focus on models developed using multinomial logistic regression (MLR): outcome Y with k categories is predicted using k - 1 equations comparing each category i (i = 2, … ,k) with reference category 1 using a set of predictors, resulting in k - 1 linear predictors. We propose a multinomial logistic recalibration framework that involves an MLR fit where Y is predicted using the k - 1 linear predictors from the prediction model. A non-parametric alternative may use vector splines for the effects of the linear predictors. The parametric and non-parametric frameworks can be used to generate multinomial calibration plots. Further, the parametric framework can be used for the estimation and statistical testing of calibration intercepts and slopes. Two illustrative case studies are presented, one on the diagnosis of malignancy of ovarian tumors and one on residual mass diagnosis in testicular cancer patients treated with cisplatin-based chemotherapy. The risk prediction models were developed on data from 2037 and 544 patients and externally validated on 1107 and 550 patients, respectively. We conclude that calibration tools can be extended to polytomous outcomes. The polytomous calibration plots are particularly informative through the visual summary of the calibration performance.

  12. 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.

  13. 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

  14. 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

  15. Statistical regional calibration of subsidence prediction models

    SciTech Connect

    Cleaver, D.N.; Reddish, D.J.; Dunham, R.K.; Shadbolt, C.H.

    1995-11-01

    Like other influence function methods, the SWIFT subsidence prediction program, developed within the Mineral Resources Engineering Department at the University of Nottingham, requires calibration to regional data in order to produce accurate predictions of ground movements. Previously, this software had been solely calibrated to give results consistent with the Subsidence Engineer`s Handbook (NCB, 1975). This approach was satisfactory for the majority of cases based in the United Kingdom, upon which the calibration was based. However, in certain circumstances within the UK and, almost always, in overseas case studies, the predictions die no correspond to observed patterns of ground movement. Therefore, in order that SWIFT, and other subsidence prediction packages, can be considered more universal, an improved and adaptable method of regional calibration must be incorporated. This paper describes the analysis of a large database of case histories from the UK industry and international publications. Observed maximum subsidence, mining geometry and Geological Index for several hundred cases have been statistically analyzed in terms of developing prediction models. The models developed can more accurately predict maximum subsidence than previously used systems but also, are capable of indicating the likely range of prediction error to a certain degree of probability. Finally, the paper illustrates how this statistical approach can be incorporated as a calibration system for the influence function program, SWIFT.

  16. Predictive capability of chlorination disinfection byproducts models.

    PubMed

    Ged, Evan C; Chadik, Paul A; Boyer, Treavor H

    2015-02-01

    There are over 100 models that have been developed for predicting trihalomethanes (THMs), haloacetic acids (HAAs), bromate, and unregulated disinfection byproducts (DBPs). Until now no publication has evaluated the variability of previous THM and HAA models using a common data set. In this article, the standard error (SE), Marquardt's percent standard deviation (MPSD), and linear coefficient of determination (R(2)) were used to analyze the variability of 87 models from 23 different publications. The most robust models were capable of predicting THM4 with an SE of 48 μg L(-1) and HAA6 with an SE of 15 μg L(-1), both achieving R(2) > 0.90. The majority of models were formulated for THM4. There is a lack of published models evaluating total HAAs, individual THM and HAA species, bromate, and unregulated DBPs.

  17. 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)…

  18. 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

  19. 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)

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

    PubMed

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

    2016-08-01

    Evidence indicates meditation facilitates affective regulation and reduces negative affect. It also influences resting-state functional connectivity between affective networks and the posterior cingulate (PCC)/precuneus, regions critically implicated in self-referential processing. However, no longitudinal study employing active control group has examined the effect of meditation training on affective processing, PCC/precuneus connectivity, and their association. Here, we report that eight-week meditation, but not relaxation, training 'neutralized' affective processing of positive and negative stimuli in healthy elderly participants. Additionally, meditation versus relaxation training increased the positive connectivity between the PCC/precuneus and the pons, the direction of which was largely directed from the pons to the PCC/precuneus, as revealed by dynamic causal modeling. Further, changes in connectivity between the PCC/precuneus and pons predicted changes in affective processing after meditation training. These findings indicate meditation promotes self-referential affective regulation based on increased regulatory influence of the pons on PCC/precuneus, which new affective-processing strategy is employed across both resting state and when evaluating affective stimuli. Such insights have clinical implications on interventions on elderly individuals with affective disorders. PMID:27349456

  1. Generalizability of Gottman and Colleagues' Affective Process Models Of Couples' Relationship Outcomes

    ERIC Educational Resources Information Center

    Kim, Hyoun K.; Capaldi, Deborah M.; Crosby, Lynn

    2007-01-01

    The generalizability of the affective process models of J. M. Gottman et al. (1998) was examined using a community-based sample of 85 married or cohabiting couples with at-risk backgrounds. Predictive associations between affective processes assessed at about age 21 years and relationship status and satisfaction assessed approximately 2.5 years…

  2. Multi-Model Ensemble Wake Vortex Prediction

    NASA Technical Reports Server (NTRS)

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

    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.

  3. Alcator C-Mod predictive modeling

    NASA Astrophysics Data System (ADS)

    Pankin, Alexei; Bateman, Glenn; Kritz, Arnold; Greenwald, Martin; Snipes, Joseph; Fredian, Thomas

    2001-10-01

    Predictive simulations for the Alcator C-mod tokamak [I. Hutchinson et al., Phys. Plasmas 1, 1511 (1994)] are carried out using the BALDUR integrated modeling code [C. E. Singer et al., Comput. Phys. Commun. 49, 275 (1988)]. The results are obtained for temperature and density profiles using the Multi-Mode transport model [G. Bateman et al., Phys. Plasmas 5, 1793 (1998)] as well as the mixed-Bohm/gyro-Bohm transport model [M. Erba et al., Plasma Phys. Controlled Fusion 39, 261 (1997)]. The simulated discharges are characterized by very high plasma density in both low and high modes of confinement. The predicted profiles for each of the transport models match the experimental data about equally well in spite of the fact that the two models have different dimensionless scalings. Average relative rms deviations are less than 8% for the electron density profiles and 16% for the electron and ion temperature profiles.

  4. An Online Adaptive Model for Location Prediction

    NASA Astrophysics Data System (ADS)

    Anagnostopoulos, Theodoros; Anagnostopoulos, Christos; Hadjiefthymiades, Stathes

    Context-awareness is viewed as one of the most important aspects in the emerging pervasive computing paradigm. Mobile context-aware applications are required to sense and react to changing environment conditions. Such applications, usually, need to recognize, classify and predict context in order to act efficiently, beforehand, for the benefit of the user. In this paper, we propose a mobility prediction model, which deals with context representation and location prediction of moving users. Machine Learning (ML) techniques are used for trajectory classification. Spatial and temporal on-line clustering is adopted. We rely on Adaptive Resonance Theory (ART) for location prediction. Location prediction is treated as a context classification problem. We introduce a novel classifier that applies a Hausdorff-like distance over the extracted trajectories handling location prediction. Since our approach is time-sensitive, the Hausdorff distance is considered more advantageous than a simple Euclidean norm. A learning method is presented and evaluated. We compare ART with Offline kMeans and Online kMeans algorithms. Our findings are very promising for the use of the proposed model in mobile context aware applications.

  5. 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

  6. 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.

  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. 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.

  9. 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.

  10. 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

  11. 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

  12. 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.

  13. Predictive analytics can support the ACO model.

    PubMed

    Bradley, Paul

    2012-04-01

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

  14. 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.

  15. Thermal barrier coating life prediction model development

    NASA Technical Reports Server (NTRS)

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

    1985-01-01

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

  16. 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…

  17. 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.

  18. Lag Model Predictions for UFAST SBLI Flowfield

    NASA Technical Reports Server (NTRS)

    Olsen, Mike; Lillard, Randy; Oliver, Brandon; Blaisdell, Gregory

    2010-01-01

    Presentation for Shock Boundary Layer Interaction Workshop. Shows results for Lag turbulence model on one of the international workshop test cases the UFAST 8 degree test case. Comparison with PIV velocity measurements as well as computed tunnel wall flowfields are shown, emphasizing that the interaction is a 3D phenomena, and is reasonably well predicted.

  19. 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

  20. 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

  1. PREDICTIVE MODELING OF CHOLERA OUTBREAKS IN BANGLADESH

    PubMed Central

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

    2016-01-01

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

  2. 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

  3. 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

  4. 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.

  5. 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.

  6. Illuminating Flash Point: Comprehensive Prediction Models.

    PubMed

    Le, Tu C; Ballard, Mathew; Casey, Phillip; Liu, Ming S; Winkler, David A

    2015-01-01

    Flash point is an important property of chemical compounds that is widely used to evaluate flammability hazard. However, there is often a significant gap between the demand for experimental flash point data and their availability. Furthermore, the determination of flash point is difficult and costly, particularly for some toxic, explosive, or radioactive compounds. The development of a reliable and widely applicable method to predict flash point is therefore essential. In this paper, the construction of a quantitative structure - property relationship model with excellent performance and domain of applicability is reported. It uses the largest data set to date of 9399 chemically diverse compounds, with flash point spanning from less than -130 °C to over 900 °C. The model employs only computed parameters, eliminating the need for experimental data that some earlier computational models required. The model allows accurate prediction of flash point for a broad range of compounds that are unavailable or not yet synthesized. This single model with a very broad range of chemical and flash point applicability will allow accurate predictions of this important property to be made for a broad range of new materials. PMID:27490859

  7. Genetic models of homosexuality: generating testable predictions.

    PubMed

    Gavrilets, Sergey; Rice, William R

    2006-12-22

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

  8. 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)

  9. 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.

  10. Situational motivation and perceived intensity: their interaction in predicting changes in positive affect from physical activity.

    PubMed

    Guérin, Eva; Fortier, Michelle S

    2012-01-01

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

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

    PubMed Central

    Guérin, Eva; Fortier, Michelle S.

    2012-01-01

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

  12. 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

  13. 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.

  14. 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.

  15. 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...

  16. 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

  17. 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

  18. 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

  19. Product component genealogy modeling and field-failure prediction

    DOE PAGES

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

    2016-04-13

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

  20. Seasonal Predictability in a Model Atmosphere.

    NASA Astrophysics Data System (ADS)

    Lin, Hai

    2001-07-01

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

  1. 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.

  2. Model atmospheres, predicted spectra, and colors

    NASA Technical Reports Server (NTRS)

    1982-01-01

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

  3. A kinetic model for predicting biodegradation.

    PubMed

    Dimitrov, S; Pavlov, T; Nedelcheva, D; Reuschenbach, P; Silvani, M; Bias, R; Comber, M; Low, L; Lee, C; Parkerton, T; Mekenyan, O

    2007-01-01

    Biodegradation plays a key role in the environmental risk assessment of organic chemicals. The need to assess biodegradability of a chemical for regulatory purposes supports the development of a model for predicting the extent of biodegradation at different time frames, in particular the extent of ultimate biodegradation within a '10 day window' criterion as well as estimating biodegradation half-lives. Conceptually this implies expressing the rate of catabolic transformations as a function of time. An attempt to correlate the kinetics of biodegradation with molecular structure of chemicals is presented. A simplified biodegradation kinetic model was formulated by combining the probabilistic approach of the original formulation of the CATABOL model with the assumption of first order kinetics of catabolic transformations. Nonlinear regression analysis was used to fit the model parameters to OECD 301F biodegradation kinetic data for a set of 208 chemicals. The new model allows the prediction of biodegradation multi-pathways, primary and ultimate half-lives and simulation of related kinetic biodegradation parameters such as biological oxygen demand (BOD), carbon dioxide production, and the nature and amount of metabolites as a function of time. The model may also be used for evaluating the OECD ready biodegradability potential of a chemical within the '10-day window' criterion.

  4. 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

  5. Affective behavior and temperament predict the onset of smoking in adolescence.

    PubMed

    Cheetham, Ali; Allen, Nicholas B; Schwartz, Orli; Simmons, Julian G; Whittle, Sarah; Byrne, Michelle L; Sheeber, Lisa; Lubman, Dan I

    2015-06-01

    Earlier use of tobacco in adolescence is associated with numerous adverse outcomes later in life. Although a number of studies have linked individual differences in affective functioning to adolescent smoking, these have relied primarily on self-report measures, and the contribution of different dimensions of affect to the onset of tobacco use during this period remains unclear. The current study examined these issues in a sample of 180 adolescents recruited from an ongoing prospective, longitudinal study examining emotional development. At approximately age 12, participants completed a questionnaire measure of affective temperament and took part in a family interaction task that was coded observationally to provide measures of dysphoric, aggressive, and positive behaviors. At 2 subsequent assessments, which took place approximately 2.5 years and 4 years after the initial assessment, participants completed a questionnaire measure of substance use. In total, 70 participants initiated smoking between the ages of 12 and 17. An earlier onset of smoking was predicted by more aggressive and less positive observed behavior during the interaction task, as well as lower levels of self-reported temperamental Effortful Control. There were no associations between dysphoric behaviors, or temperamental measures of negative affectivity or surgency and the onset of smoking. The findings add to a small body of literature demonstrating that behavioral components of affect can prospectively predict substance use in adolescence and suggest that different dimensions of affect show unique relationships with early substance use. (PsycINFO Database Record

  6. 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

  7. 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

  8. 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.

  9. 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.

  10. Probabilistic prediction models for aggregate quarry siting

    USGS Publications Warehouse

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

    2007-01-01

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

  11. 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.

  12. 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

  13. 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.

  14. 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.

  15. 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.

  16. 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

  17. 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.

  18. Predictability of extreme values in geophysical models

    NASA Astrophysics Data System (ADS)

    Sterk, Alef; Holland, Mark; Rabassa, Pau; Broer, Henk; Vitolo, Renato

    2014-05-01

    Classical extreme value theory studies the occurrence of unlikely large events. Extreme value theory was originally developed for time series of near-independent random variables, but in the last decade the theory has been extended to the setting of chaotic, deterministic dynamical systems. In the latter context one studies the distribution of large values in a time series generated by evaluating a scalar observable along evolutions of the system. We have studied the finite-time predictability of extreme values, such as convection, energy, and wind speeds, in three geophysical models. To that end we computed finite-time Lyapunov exponents (FTLEs) which measure the exponential growth rate of nearby trajectories over a finite time. In general, FTLEs strongly depend on the initial condition. We study whether initial conditions leading to extremes typically have a larger or smaller FTLE. Our study clearly suggests that general statements about the predictability of extreme values are not possible: the predictability of extreme values depends on (1) the observable, (2) the attractor of the system, and (3) the prediction lead time.

  19. 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.

  20. 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.

  1. Enzyme function prediction with interpretable models.

    PubMed

    Syed, Umar; Yona, Golan

    2009-01-01

    Enzymes play central roles in metabolic pathways, and the prediction of metabolic pathways in newly sequenced genomes usually starts with the assignment of genes to enzymatic reactions. However, genes with similar catalytic activity are not necessarily similar in sequence, and therefore the traditional sequence similarity-based approach often fails to identify the relevant enzymes, thus hindering efforts to map the metabolome of an organism.Here we study the direct relationship between basic protein properties and their function. Our goal is to develop a new tool for functional prediction (e.g., prediction of Enzyme Commission number), which can be used to complement and support other techniques based on sequence or structure information. In order to define this mapping we collected a set of 453 features and properties that characterize proteins and are believed to be related to structural and functional aspects of proteins. We introduce a mixture model of stochastic decision trees to learn the set of potentially complex relationships between features and function. To study these correlations, trees are created and tested on the Pfam classification of proteins, which is based on sequence, and the EC classification, which is based on enzymatic function. The model is very effective in learning highly diverged protein families or families that are not defined on the basis of sequence. The resulting tree structures highlight the properties that are strongly correlated with structural and functional aspects of protein families, and can be used to suggest a concise definition of a protein family.

  2. 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

  3. 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.

  4. 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.

  5. Thermal barrier coating life prediction model

    NASA Technical Reports Server (NTRS)

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

    1986-01-01

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

  6. 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.

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

    PubMed Central

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

    2014-01-01

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

  8. 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

  9. 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.

  10. 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

  11. 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

  12. 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

  13. Predictive QSAR modeling workflow, model applicability domains, and virtual screening.

    PubMed

    Tropsha, Alexander; Golbraikh, Alexander

    2007-01-01

    Quantitative Structure Activity Relationship (QSAR) modeling has been traditionally applied as an evaluative approach, i.e., with the focus on developing retrospective and explanatory models of existing data. Model extrapolation was considered if only in hypothetical sense in terms of potential modifications of known biologically active chemicals that could improve compounds' activity. This critical review re-examines the strategy and the output of the modern QSAR modeling approaches. We provide examples and arguments suggesting that current methodologies may afford robust and validated models capable of accurate prediction of compound properties for molecules not included in the training sets. We discuss a data-analytical modeling workflow developed in our laboratory that incorporates modules for combinatorial QSAR model development (i.e., using all possible binary combinations of available descriptor sets and statistical data modeling techniques), rigorous model validation, and virtual screening of available chemical databases to identify novel biologically active compounds. Our approach places particular emphasis on model validation as well as the need to define model applicability domains in the chemistry space. We present examples of studies where the application of rigorously validated QSAR models to virtual screening identified computational hits that were confirmed by subsequent experimental investigations. The emerging focus of QSAR modeling on target property forecasting brings it forward as predictive, as opposed to evaluative, modeling approach.

  14. 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.

  15. 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.

  16. An Anisotropic Hardening Model for Springback Prediction

    NASA Astrophysics Data System (ADS)

    Zeng, Danielle; Xia, Z. Cedric

    2005-08-01

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

  17. 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.

  18. 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.

  19. A personalized biomechanical model for respiratory motion prediction.

    PubMed

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

    2012-01-01

    Time-resolved imaging of the thorax or abdominal area is affected by respiratory motion. Nowadays, one-dimensional respiratory surrogates are used to estimate the current state of the lung during its cycle, but with rather poor results. This paper presents a framework to predict the 3D lung motion based on a patient-specific finite element model of respiratory mechanics estimated from two CT images at end of inspiration (EI) and end of expiration (EE). We first segment the lung, thorax and sub-diaphragm organs automatically using a machine-learning algorithm. Then, a biomechanical model of the lung, thorax and sub-diaphragm is employed to compute the 3D respiratory motion. Our model is driven by thoracic pressures, estimated automatically from the EE and EI images using a trust-region approach. Finally, lung motion is predicted by modulating the thoracic pressures. The effectiveness of our approach is evaluated by predicting lung deformation during exhale on five DIR-Lab datasets. Several personalization strategies are tested, showing that an average error of 3.88 +/- 1.54 mm in predicted landmark positions can be achieved. Since our approach is generative, it may constitute a 3D surrogate information for more accurate medical image reconstruction and patient respiratory analysis. PMID:23286176

  20. 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

  1. Visual Performance Prediction Using Schematic Eye Models

    NASA Astrophysics Data System (ADS)

    Schwiegerling, James Theodore

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

  2. Simple predictions from multifield inflationary models.

    PubMed

    Easther, Richard; Frazer, Jonathan; Peiris, Hiranya V; Price, Layne C

    2014-04-25

    We explore whether multifield inflationary models make unambiguous predictions for fundamental cosmological observables. Focusing on N-quadratic inflation, we numerically evaluate the full perturbation equations for models with 2, 3, and O(100) fields, using several distinct methods for specifying the initial values of the background fields. All scenarios are highly predictive, with the probability distribution functions of the cosmological observables becoming more sharply peaked as N increases. For N=100 fields, 95% of our Monte Carlo samples fall in the ranges ns∈(0.9455,0.9534), α∈(-9.741,-7.047)×10-4, r∈(0.1445,0.1449), and riso∈(0.02137,3.510)×10-3 for the spectral index, running, tensor-to-scalar ratio, and isocurvature-to-adiabatic ratio, respectively. The expected amplitude of isocurvature perturbations grows with N, raising the possibility that many-field models may be sensitive to postinflationary physics and suggesting new avenues for testing these scenarios.

  3. 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.

  4. Critical conceptualism in environmental modeling and prediction.

    PubMed

    Christakos, G

    2003-10-15

    Many important problems in environmental science and engineering are of a conceptual nature. Research and development, however, often becomes so preoccupied with technical issues, which are themselves fascinating, that it neglects essential methodological elements of conceptual reasoning and theoretical inquiry. This work suggests that valuable insight into environmental modeling can be gained by means of critical conceptualism which focuses on the software of human reason and, in practical terms, leads to a powerful methodological framework of space-time modeling and prediction. A knowledge synthesis system develops the rational means for the epistemic integration of various physical knowledge bases relevant to the natural system of interest in order to obtain a realistic representation of the system, provide a rigorous assessment of the uncertainty sources, generate meaningful predictions of environmental processes in space-time, and produce science-based decisions. No restriction is imposed on the shape of the distribution model or the form of the predictor (non-Gaussian distributions, multiple-point statistics, and nonlinear models are automatically incorporated). The scientific reasoning structure underlying knowledge synthesis involves teleologic criteria and stochastic logic principles which have important advantages over the reasoning method of conventional space-time techniques. Insight is gained in terms of real world applications, including the following: the study of global ozone patterns in the atmosphere using data sets generated by instruments on board the Nimbus 7 satellite and secondary information in terms of total ozone-tropopause pressure models; the mapping of arsenic concentrations in the Bangladesh drinking water by assimilating hard and soft data from an extensive network of monitoring wells; and the dynamic imaging of probability distributions of pollutants across the Kalamazoo river. PMID:14594379

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

    PubMed

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

    2015-04-15

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

  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. Resting frontal EEG alpha-asymmetry predicts the evaluation of affective musical stimuli.

    PubMed

    Schmidt, Barbara; Hanslmayr, Simon

    2009-09-01

    Resting frontal electroencephalographic (EEG) alpha-asymmetry was measured in 16 participants to predict affective responses to musical stimuli. Three affective musical stimuli either expressing neutral, positive or negative mood were evaluated by the subjects concerning "expressed mood" and "level of enjoyment". The results show that individuals with relatively higher alpha power over right frontal electrode sites rated all stimuli more positive than participants with relatively higher alpha power over left frontal electrode sites. On the "expressed mood" scale, the valence of the stimuli did not influence the difference between the ratings of left- and right-active individuals. On the "enjoyment" scale, the largest difference between the ratings of left- and right-active individuals emerged in the negative condition. The results are specific to the alpha frequency band. In line with previous studies, these results suggest that resting frontal alpha-asymmetry reflects a person's predisposition to respond affectively.

  8. 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.

  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. 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.

  11. Predictability of the Lorenz chaotic model

    NASA Astrophysics Data System (ADS)

    Evans, E.; Bhatti, N.; Kinney, J.; Pann, L.; Pena, M.; Yang, S.; Kalnay, E.; Hansen, J.

    2003-04-01

    The Lorenz (1963) model has been widely used as a prototype of chaotic behavior and an example of lack of long-term predictability. Its solution with standard parameter values depicts a two-regime distribution. We applied the breeding of unstable modes technique (Toth and Kalnay, 1993, 1997) to this model to determine the regions in the phase space with larger instabilities. As it turned out, the results show not only a coherent region of high instability, indicated by the larger values of the bred vector growth rates, but also the feasibility to develop simple forecasting rules to determine both whether a shift to the other regime will occur in the following cycle and how long the following regime will last.

  12. 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.

  13. A predictive fitness model for influenza

    NASA Astrophysics Data System (ADS)

    Łuksza, Marta; Lässig, Michael

    2014-03-01

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

  14. Thermal barrier coating life prediction model development

    NASA Technical Reports Server (NTRS)

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

    1985-01-01

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

  15. 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.

  16. Applying a Cognitive-Affective Model of Conceptual Change to Professional Development

    ERIC Educational Resources Information Center

    Ebert, Ellen K.; Crippen, Kent J.

    2010-01-01

    This study evaluated Gregoire's (2003) Cognitive-Affective Conceptual Change model (CAMCC) for predicting and assessing conceptual change in science teachers engaged in a long-term professional development project set in a large school district in the southwestern United States. A multiple case study method with data from three teacher…

  17. 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

  18. Emotion, working memory task demands and individual differences predict behavior, cognitive effort and negative affect.

    PubMed

    Storbeck, Justin; Davidson, Nicole A; Dahl, Chelsea F; Blass, Sara; Yung, Edwin

    2015-01-01

    We examined whether positive and negative affect motivates verbal and spatial working memory processes, respectively, which have implications for the expenditure of mental effort. We argue that when emotion promotes cognitive tendencies that are goal incompatible with task demands, greater cognitive effort is required to perform well. We sought to investigate whether this increase in cognitive effort impairs behavioural control over a broad domain of self-control tasks. Moreover, we predicted that individuals with higher behavioural inhibition system (BIS) sensitivities would report more negative affect within the goal incompatible conditions because such individuals report higher negative affect during cognitive challenge. Positive or negative affective states were induced followed by completing a verbal or spatial 2-back working memory task. All participants then completed one of three self-control tasks. Overall, we observed that conditions of emotion and working memory incompatibility (positive/spatial and negative/verbal) performed worse on the self-control tasks, and within the incompatible conditions individuals with higher BIS sensitivities reported more negative affect at the end of the study. The combination of findings suggests that emotion and working memory compatibility reduces cognitive effort and impairs behavioural control.

  19. 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

  20. 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.

  1. 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

  2. Developing Models for Predictive Climate Science

    SciTech Connect

    Drake, John B; Jones, Philip W

    2007-01-01

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

  3. 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.

  4. 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

  5. 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.

  6. Mother-Child Affect and Emotion Socialization Processes Across the Late Preschool Period: Predictions of Emerging Behavior Problems.

    PubMed

    2011-11-01

    The current study examined concurrent and longitudinal relations between maternal negative affective behavior 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 behavior problems. Maternal negative affective behavior, child externalizing emotional expression, and child internalizing emotional expression were observed during a number of lab tasks at child ages 4 and 5, and child externalizing and internalizing behavior problems were assessed via maternal questionnaire at age 6. Path analyses using structural equation modeling were utilized to test the relations among the variables at ages 4, 5, and 6. A parent-driven model of emotion socialization emerged, wherein stronger relations were found among maternal negative affect and child externalizing emotions and behaviors than among maternal negative affect and child internalizing emotions and behaviors. Early child risk did not appear to alter the overall emotion socialization process, although higher levels of maternal and child negativity were observed for the children with a developmental risk. Results underscore the complexity of emotion socialization processes throughout the preschool period.

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

    SciTech Connect

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

    2010-04-15

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

  8. Heuristic Modeling for TRMM Lifetime Predictions

    NASA Technical Reports Server (NTRS)

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

    1996-01-01

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

  9. Predictive modeling of low solubility semiconductor alloys

    NASA Astrophysics Data System (ADS)

    Rodriguez, Garrett V.; Millunchick, Joanna M.

    2016-09-01

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

  10. 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…

  11. Numerical prediction of heat affected layer in the EDM of aeronautical alloys

    NASA Astrophysics Data System (ADS)

    Izquierdo, B.; Plaza, S.; Sánchez, J. A.; Pombo, I.; Ortega, N.

    2012-10-01

    Electrical discharge machining is a popular non-traditional machining process, optimum for accurate machining of complex geometries in hard materials. EDM has been used for decades for machining pieces for the aeronautical industry, but surface integrity, and consequently the reliability of the machined parts have been questioned for long time due to the thermal nature of this machining process. In recent years, efforts have been put on modeling of the EDM process, being thermal modeling of the process one promising alternative. In a previous publication an original model of the EDM process was presented and it was used to predict material removal rate and surface finish for the EDM of steel. In the present article the capability of that modeling tool to characterize discharge properties and to predict recast layer distribution when EDMing an aeronautical alloy will be analyzed. EDM process of Inconel 718 has been studied and discharge properties have been obtained for four different EDM regimes. The capability of the model to reflect the behavior of more energetic regimes is discussed. Gathered information has been used to simulate the evolution of the recast layer generation process. Obtained results have been validated comparing them with experimental measurements, revealing a good correlation between predictions and experimental data. Finally, energetic efficiency of the discharge process has been simulated for the adjusted EDM regimes.

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

    PubMed

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

    2015-11-01

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

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

    PubMed Central

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

    2015-01-01

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

  14. Let it be: Accepting negative emotional experiences predicts decreased negative affect and depressive symptoms

    PubMed Central

    Shallcross, Amanda J.; Troy, Allison S.; Boland, Matthew; Mauss, Iris B.

    2010-01-01

    The present studies examined whether a tendency to accept negative emotional experiences buffers individuals from experiencing elevated negative affect during negative emotional situations (Study 1) and from developing depressive symptoms in the face of life stress (Study 2). Both studies examined female samples. This research expands on existing acceptance research in four ways. First, it examined whether acceptance has beneficial correlates when it matters most: in emotionally taxing (versus more neutral) contexts. Second, in Study 2 a prospective design was used in which acceptance was measured before stress was encountered and before outcomes were measured. Third, depressive symptoms (rather than general functioning or trauma symptoms) were examined as a particularly relevant outcome in the context of stress. Fourth, to enhance generalizability, a community sample (versus undergraduates or a purely clinical sample) was recruited. Results indicated that acceptance was correlated with decreased negative affect during a negative emotion induction but not an affectively neutral condition (Study 1). In Study 2, acceptance interacted with life stress such that acceptance predicted lower levels of depressive symptoms after higher, but not lower, life stress. These results suggest that accepting negative experiences may protect individuals from experiencing negative affect and from developing depressive symptoms. PMID:20566191

  15. 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

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

    PubMed

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

    2007-12-15

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

  17. Prediction of microRNAs affecting mRNA expression during retinal development

    PubMed Central

    2010-01-01

    Background MicroRNAs (miRNAs) are small RNA molecules (~22 nucleotides) which have been shown to play an important role both in development and in maintenance of adult tissue. Conditional inactivation of miRNAs in the eye causes loss of visual function and progressive retinal degeneration. In addition to inhibiting translation, miRNAs can mediate degradation of targeted mRNAs. We have previously shown that candidate miRNAs affecting transcript levels in a tissue can be deduced from mRNA microarray expression profiles. The purpose of this study was to predict miRNAs which affect mRNA levels in developing and adult retinal tissue and to confirm their expression. Results Microarray expression data from ciliary epithelial retinal stem cells (CE-RSCs), developing and adult mouse retina were generated or downloaded from public repositories. Analysis of gene expression profiles detected the effects of multiple miRNAs in CE-RSCs and retina. The expression of 20 selected miRNAs was confirmed by RT-PCR and the cellular distribution of representative candidates analyzed by in situ hybridization. The expression levels of miRNAs correlated with the significance of their predicted effects upon mRNA expression. Highly expressed miRNAs included miR-124, miR-125a, miR-125b, miR-204 and miR-9. Over-expression of three miRNAs with significant predicted effects upon global mRNA levels resulted in a decrease in mRNA expression of five out of six individual predicted target genes assayed. Conclusions This study has detected the effect of miRNAs upon mRNA expression in immature and adult retinal tissue and cells. The validity of these observations is supported by the experimental confirmation of candidate miRNA expression and the regulation of predicted target genes following miRNA over-expression. Identified miRNAs are likely to be important in retinal development and function. Misregulation of these miRNAs might contribute to retinal degeneration and disease. Conversely, manipulation

  18. 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

  19. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2016-05-01

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

  1. Gaze fixations predict brain activation during the voluntary regulation of picture-induced negative affect.

    PubMed

    van Reekum, Carien M; Johnstone, Tom; Urry, Heather L; Thurow, Marchell E; Schaefer, Hillary S; Alexander, Andrew L; Davidson, Richard J

    2007-07-01

    Recent studies have identified a distributed network of brain regions thought to support cognitive reappraisal processes underlying emotion regulation in response to affective images, including parieto-temporal regions and lateral/medial regions of prefrontal cortex (PFC). A number of these commonly activated regions are also known to underlie visuospatial attention and oculomotor control, which raises the possibility that people use attentional redeployment rather than, or in addition to, reappraisal as a strategy to regulate emotion. We predicted that a significant portion of the observed variance in brain activation during emotion regulation tasks would be associated with differences in how participants visually scan the images while regulating their emotions. We recorded brain activation using fMRI and quantified patterns of gaze fixation while participants increased or decreased their affective response to a set of affective images. fMRI results replicated previous findings on emotion regulation with regulation differences reflected in regions of PFC and the amygdala. In addition, our gaze fixation data revealed that when regulating, individuals changed their gaze patterns relative to a control condition. Furthermore, this variation in gaze fixation accounted for substantial amounts of variance in brain activation. These data point to the importance of controlling for gaze fixation in studies of emotion regulation that use visual stimuli.

  2. 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.

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

    PubMed Central

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

    2014-01-01

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

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

    PubMed

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

    2014-01-01

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

  5. 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

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

    PubMed

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

    2014-01-01

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

  7. Model-based Heart rate prediction during Lokomat walking.

    PubMed

    Koenig, Alexander C; Somaini, Luca; Pulfer, Michael; Holenstein, Thomas; Omlin, Ximena; Wieser, Martin; Riener, Robert

    2009-01-01

    We implemented a model for prediction of heart rate during Lokomat walking. Using this model, we can predict potential overstressing of the patient and adapt the physical load accordingly. Current models for treadmill based heart rate control neglect the fact that the interaction torques between Lokomat and human can have a significant effect on heart rate. Tests with five healthy subjects lead to a model of sixth order with walking speed and power expenditure as inputs and heart rate prediction as output. Recordings with five different subjects were used for model validation. Future work includes model identification and predictive heart rate control with spinal cord injured and stroke patients. PMID:19963765

  8. 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…

  9. 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.

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. 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.

  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. Predicting vulnerability to sleep deprivation using diffusion model parameters.

    PubMed

    Patanaik, Amiya; Zagorodnov, Vitali; Kwoh, Chee Keong; Chee, Michael W L

    2014-10-01

    We used diffusion modelling to predict vulnerability to decline in psychomotor vigilance task (PVT) performance following a night of total sleep deprivation (SD). A total of 135 healthy young adults (69 women, age = 21.9 ± 1.7 years) participated in several within-subject cross-over design studies that incorporated the PVT. Participants were classified as vulnerable (lower tertile) or non-vulnerable (upper tertile) according to their change in lapse rate [lapse = reaction time (RT) ≥ 500 ms] between the evening before (ESD) and the morning after SD. RT data were fitted using Ratcliff's diffusion model. Although both groups showed significant change in RT during SD, there was no significant group difference in RT during the ESD session. In contrast, during ESD, the mean diffusion drift of vulnerable subjects was significantly lower than for non-vulnerable subjects. Mean drift and non-decision times were both adversely affected by sleep deprivation. Both mean drift and non-decision time showed significant state × vulnerability interaction. Diffusion modelling appears to have promise in predicting vulnerability to vigilance decline induced by a night of total sleep deprivation.

  17. 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

  18. 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.

  19. Further development of a predictive pitting model for gears: Improvements in the life prediction analysis

    NASA Astrophysics Data System (ADS)

    Blake, J. W.; Draper, C. F.

    1994-04-01

    A predictive pitting model for gear design applications was recently developed by Blake and Cheng. Life estimates were based on predicting the growth of surface-breaking cracks leading to pit formation. While trends predicted by the model reflected observed behavior, estimated lives were lower than expected. The crack growth model has been improved by modifying the original shear-driven, two-dimensional propagation model to reflect three-dimensional cracks driven by both shear and lubricant pressure effects. Resistance to crack growth due to friction between the crack faces has also been considered. These changes have led to a net increase in predicted lives, which better reflects observed pitting behavior.

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

    PubMed

    van der Fels-Klerx, H J

    2014-02-01

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

  1. 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

  2. Allostasis: a model of predictive regulation.

    PubMed

    Sterling, Peter

    2012-04-12

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

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

    PubMed

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

    2015-01-01

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

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

    PubMed

    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.

  5. 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

  6. 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.

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

    PubMed Central

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

    2015-01-01

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

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

    PubMed

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

    2014-02-01

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

  9. 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…

  10. 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…

  11. 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 ...

  12. 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.

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

    PubMed Central

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

    2016-01-01

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

  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. 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.

  17. 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.

  18. 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…

  19. Simplified Predictive Models for CO2 Sequestration Performance Assessment

    NASA Astrophysics Data System (ADS)

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

    2014-05-01

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

  20. 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.

  1. Forest Ecosystem Dynamics Assessment and Predictive Modelling in Eastern Himalaya

    NASA Astrophysics Data System (ADS)

    Kushwaha, S. P. S.; Nandy, S.; Ahmad, M.; Agarwal, R.

    2011-09-01

    This study focused on the forest ecosystem dynamics assessment and predictive modelling deforestation and forest cover prediction in a part of north-eastern India i.e. forest areas along West Bengal, Bhutan, Arunachal Pradesh and Assam border in Eastern Himalaya using temporal satellite imagery of 1975, 1990 and 2009 and predicted forest cover for the period 2028 using Cellular Automata Markov Modedel (CAMM). The exercise highlighted large-scale deforestation in the study area during 1975-1990 as well as 1990-2009 forest cover vectors. A net loss of 2,334.28 km2 forest cover was noticed between 1975 and 2009, and with current rate of deforestation, a forest area of 4,563.34 km2 will be lost by 2028. The annual rate of deforestation worked out to be 0.35 and 0.78% during 1975-1990 and 1990-2009 respectively. Bamboo forest increased by 24.98% between 1975 and 2009 due to opening up of the forests. Forests in Kokrajhar, Barpeta, Darrang, Sonitpur, and Dhemaji districts in Assam were noticed to be worst-affected while Lower Subansiri, West and East Siang, Dibang Valley, Lohit and Changlang in Arunachal Pradesh were severely affected. Among different forest types, the maximum loss was seen in case of sal forest (37.97%) between 1975 and 2009 and is expected to deplete further to 60.39% by 2028. The tropical moist deciduous forest was the next category, which decreased from 5,208.11 km2 to 3,447.28 (33.81%) during same period with further chances of depletion to 2,288.81 km2 (56.05%) by 2028. It noted progressive loss of forests in the study area between 1975 and 2009 through 1990 and predicted that, unless checked, the area is in for further depletion of the invaluable climax forests in the region, especially sal and moist deciduous forests. The exercise demonstrated high potential of remote sensing and geographic information system for forest ecosystem dynamics assessment and the efficacy of CAMM to predict the forest cover change.

  2. 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…

  3. 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 ...

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

    NASA Astrophysics Data System (ADS)

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

    2016-04-01

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

  5. 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…

  6. 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.

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

    SciTech Connect

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

    1996-12-31

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

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

    NASA Astrophysics Data System (ADS)

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

    2015-12-01

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

  9. 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

  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.

  11. 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

  12. 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

  13. 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.

  14. 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

  15. Avoidance of affective pain stimuli predicts chronicity in patients with acute low back pain.

    PubMed

    Sharpe, Louise; Haggman, Sonia; Nicholas, Michael; Dear, Blake F; Refshauge, Kathryn

    2014-01-01

    This prospective study of acute and sub-acute low back pain (LBP) patients was conducted to assess whether attentional biases predicted chronic pain status 3 and 6 months later. The attentional biases of 100 LBP patients were assessed within 3 months of developing pain and 6 months later. Participants also completed measures associated with outcome at 3 assessment points: baseline, 3 and 6 months later. Current pain status was assessed at follow-ups. Patients were classified as those that met standard criteria for chronic pain or those who did not (i.e., the comparison group). At baseline, participants demonstrated a bias toward sensory pain words. However, biases toward sensory pain words did not differentiate those who subsequently developed chronic pain and those who did not at either follow-up. The same bias was observed 6 months later, but again it failed to distinguish between the chronic pain and comparison groups. However, subjects who developed chronic pain at both 3 (n=22) and 6 (n=21) months demonstrated biases away from affective pain words at baseline but not 6 months later, in comparison to other participants. These results remained significant in multivariate analyses. These findings are consistent with patterns observed in the previous research, and suggest that avoidance of emotionally laden pain-related stimuli (i.e., affective pain words) is associated with negative outcomes for LBP patients in the acute and sub-acute phase. This research suggests that attentional biases in relation to pain-related stimuli are important for the development of chronic pain, but are more complex than initially thought. PMID:24028848

  16. 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.

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

    NASA Technical Reports Server (NTRS)

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

    2016-01-01

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

  18. 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…

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

    PubMed Central

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

    2016-01-01

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

  20. 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.

  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. 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...

  3. 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.

  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. 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.

  6. 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.

  7. 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.

  8. 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.

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

    NASA Astrophysics Data System (ADS)

    Ahmed, Riaz U.; Banerjee, Sourav

    2016-04-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2014-06-01

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

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

    PubMed Central

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

    2008-01-01

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

  12. 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.

  13. 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.

  14. Testing the reward prediction error hypothesis with an axiomatic model.

    PubMed

    Rutledge, Robb B; Dean, Mark; Caplin, Andrew; Glimcher, Paul W

    2010-10-01

    Neuroimaging studies typically identify neural activity correlated with the predictions of highly parameterized models, like the many reward prediction error (RPE) models used to study reinforcement learning. Identified brain areas might encode RPEs or, alternatively, only have activity correlated with RPE model predictions. Here, we use an alternate axiomatic approach rooted in economic theory to formally test the entire class of RPE models on neural data. We show that measurements of human neural activity from the striatum, medial prefrontal cortex, amygdala, and posterior cingulate cortex satisfy necessary and sufficient conditions for the entire class of RPE models. However, activity measured from the anterior insula falsifies the axiomatic model, and therefore no RPE model can account for measured activity. Further analysis suggests the anterior insula might instead encode something related to the salience of an outcome. As cognitive neuroscience matures and models proliferate, formal approaches of this kind that assess entire model classes rather than specific model exemplars may take on increased significance.

  15. Prediction of Intracellular Localization of Fluorescent Dyes Using QSAR Models.

    PubMed

    Uchinomiya, Shohei; Horobin, Richard W; Alvarado-Martínez, Enrique; Peña-Cabrera, Eduardo; Chang, Young-Tae

    2016-01-01

    Control of fluorescent dye localization in live cells is crucial for fluorescence imaging. Here, we describe quantitative structure activity relation (QSAR) models for predicting intracellular localization of fluorescent dyes. For generating the QSAR models, electric charge (Z) calculated by pKa, conjugated bond number (CBN), the largest conjugated fragment (LCF), molecular weight (MW) and log P were used as parameters. We identified the intracellular localization of 119 BODIPY dyes in live NIH3T3 cells, and assessed the accuracy of our models by comparing their predictions with the observed dye localizations. As predicted by the models, no BODIPY dyes localized in nuclei or plasma membranes. The accuracy of the model for localization in fat droplets was 92%, with the models for cytosol and lysosomes showing poorer agreement with observed dye localization, albeit well above chance levels. Overall therefore the utility of QSAR models for predicting dye localization in live cells was clearly demonstrated. PMID:27055752

  16. 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

  17. 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.

  18. The regional prediction model of PM10 concentrations for Turkey

    NASA Astrophysics Data System (ADS)

    Güler, Nevin; Güneri İşçi, Öznur

    2016-11-01

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

  19. 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

  20. 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

  1. 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.

  2. Bayesian uncertainty assessment of flood predictions in ungauged urban basins for conceptual rainfall-runoff models

    NASA Astrophysics Data System (ADS)

    Sikorska, A. E.; Scheidegger, A.; Banasik, K.; Rieckermann, J.

    2012-04-01

    Urbanization and the resulting land-use change strongly affect the water cycle and runoff-processes in watersheds. Unfortunately, small urban watersheds, which are most affected by urban sprawl, are mostly ungauged. This makes it intrinsically difficult to assess the consequences of urbanization. Most of all, it is unclear how to reliably assess the predictive uncertainty given the structural deficits of the applied models. In this study, we therefore investigate the uncertainty of flood predictions in ungauged urban basins from structurally uncertain rainfall-runoff models. To this end, we suggest a procedure to explicitly account for input uncertainty and model structure deficits using Bayesian statistics with a continuous-time autoregressive error model. In addition, we propose a concise procedure to derive prior parameter distributions from base data and successfully apply the methodology to an urban catchment in Warsaw, Poland. Based on our results, we are able to demonstrate that the autoregressive error model greatly helps to meet the statistical assumptions and to compute reliable prediction intervals. In our study, we found that predicted peak flows were up to 7 times higher than observations. This was reduced to 5 times with Bayesian updating, using only few discharge measurements. In addition, our analysis suggests that imprecise rainfall information and model structure deficits contribute mostly to the total prediction uncertainty. In the future, flood predictions in ungauged basins will become more important due to ongoing urbanization as well as anthropogenic and climatic changes. Thus, providing reliable measures of uncertainty is crucial to support decision making.

  3. Bayesian uncertainty assessment of flood predictions in ungauged urban basins for conceptual rainfall-runoff models

    NASA Astrophysics Data System (ADS)

    Sikorska, A. E.; Scheidegger, A.; Banasik, K.; Rieckermann, J.

    2011-12-01

    Urbanization and the resulting land-use change strongly affect the water cycle and runoff-processes in watersheds. Unfortunately, small urban watersheds, which are most affected by urban sprawl, are mostly ungauged. This makes it intrinsically difficult to assess the consequences of urbanization. Most of all, it is unclear how to reliably assess the predictive uncertainty given the structural deficits of the applied models. In this study, we therefore investigate the uncertainty of flood predictions in ungauged urban basins from structurally uncertain rainfall-runoff models. To this end, we suggest a procedure to explicitly account for input uncertainty and model structure deficits using Bayesian statistics with a continuous-time autoregressive error model. In addition, we propose a concise procedure to derive prior parameter distributions from base data and successfully apply the methodology to an urban catchment in Warsaw, Poland. Based on our results, we are able to demonstrate that the autoregressive error model greatly helps to meet the statistical assumptions and to compute reliable prediction intervals. In our study, we found that predicted peak flows were up to 7 times higher than observations. This was reduced by 150% with Bayesian updating, using only a few discharge measurements. In addition, our analysis suggests that imprecise rainfall information and model structure deficits contribute mostly to the total prediction uncertainty. In the future, flood predictions in ungauged basins will become more important due to ongoing urbanization as well as anthropogenic and climatic changes. Thus, providing reliable measures of uncertainty is crucial to support decision making.

  4. 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

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

    PubMed

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

    2012-07-01

    A pot experiment was conducted to study the correlations between the daily transpiration of greenhouse tomato and the related affecting factors such as total leaf area per plant, soil relative moisture content, air temperature, relative humidity, and solar radiation under different treatments of supplementary irrigation. A regression model for the daily transpiration of greenhouse tomato was established. There existed significant linear correlations between the daily transpiration and the test affecting factors, and the affecting factors had complicated mutual effects. Soil relative moisture content was the main decision factor of the transpiration, with the decision coefficient being 27.4%, and daily minimum relative humidity was the main limiting factor, with the decision coefficient being -119.7%. The square value of the regression coefficient (R2) between the predicted and measured tomato daily transpiration was 0.81, root mean squared error (RMSE) was 68.52 g, and relative prediction error (RE) was 19.4%, suggesting that the regression model established by using the main affecting factors selected through path analysis could better simulate the daily transpiration of greenhouse tomato. PMID:23173461

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

    PubMed

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

    2012-07-01

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

  7. 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.

  8. 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.

  9. 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.

  10. 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.

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

    PubMed Central

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

    2015-01-01

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

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

    PubMed

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

    2015-06-01

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

  13. 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.

  14. 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.

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

    PubMed

    Meule, Adrian; Kübler, Andrea

    2014-08-01

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

  16. 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.

  17. 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.

  18. 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.

  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. Validating predictions from climate envelope models

    USGS Publications Warehouse

    Watling, J.; Bucklin, D.; Speroterra, C.; Brandt, L.; Cabal, C.; Romañach, Stephanie S.; Mazzotti, Frank J.

    2013-01-01

    Climate envelope models are a potentially important conservation tool, but their ability to accurately forecast species’ distributional shifts using independent survey data has not been fully evaluated. We created climate envelope models for 12 species of North American breeding birds previously shown to have experienced poleward range shifts. For each species, we evaluated three different approaches to climate envelope modeling that differed in the way they treated climate-induced range expansion and contraction, using random forests and maximum entropy modeling algorithms. All models were calibrated using occurrence data from 1967–1971 (t1) and evaluated using occurrence data from 1998–2002 (t2). Model sensitivity (the ability to correctly classify species presences) was greater using the maximum entropy algorithm than the random forest algorithm. Although sensitivity did not differ significantly among approaches, for many species, sensitivity was maximized using a hybrid approach that assumed range expansion, but not contraction, in t2. Species for which the hybrid approach resulted in the greatest improvement in sensitivity have been reported from more land cover types than species for which there was little difference in sensitivity between hybrid and dynamic approaches, suggesting that habitat generalists may be buffered somewhat against climate-induced range contractions. Specificity (the ability to correctly classify species absences) was maximized using the random forest algorithm and was lowest using the hybrid approach. Overall, our results suggest cautious optimism for the use of climate envelope models to forecast range shifts, but also underscore the importance of considering non-climate drivers of species range limits. The use of alternative climate envelope models that make different assumptions about range expansion and contraction is a new and potentially useful way to help inform our understanding of climate change effects on species.

  1. Validating Predictions from Climate Envelope Models

    PubMed Central

    Watling, James I.; Bucklin, David N.; Speroterra, Carolina; Brandt, Laura A.; Mazzotti, Frank J.; Romañach, Stephanie S.

    2013-01-01

    Climate envelope models are a potentially important conservation tool, but their ability to accurately forecast species’ distributional shifts using independent survey data has not been fully evaluated. We created climate envelope models for 12 species of North American breeding birds previously shown to have experienced poleward range shifts. For each species, we evaluated three different approaches to climate envelope modeling that differed in the way they treated climate-induced range expansion and contraction, using random forests and maximum entropy modeling algorithms. All models were calibrated using occurrence data from 1967–1971 (t1) and evaluated using occurrence data from 1998–2002 (t2). Model sensitivity (the ability to correctly classify species presences) was greater using the maximum entropy algorithm than the random forest algorithm. Although sensitivity did not differ significantly among approaches, for many species, sensitivity was maximized using a hybrid approach that assumed range expansion, but not contraction, in t2. Species for which the hybrid approach resulted in the greatest improvement in sensitivity have been reported from more land cover types than species for which there was little difference in sensitivity between hybrid and dynamic approaches, suggesting that habitat generalists may be buffered somewhat against climate-induced range contractions. Specificity (the ability to correctly classify species absences) was maximized using the random forest algorithm and was lowest using the hybrid approach. Overall, our results suggest cautious optimism for the use of climate envelope models to forecast range shifts, but also underscore the importance of considering non-climate drivers of species range limits. The use of alternative climate envelope models that make different assumptions about range expansion and contraction is a new and potentially useful way to help inform our understanding of climate change effects on species. PMID

  2. Prediction Model for Gastric Cancer Incidence in Korean Population

    PubMed Central

    Kim, Sohee; Shin, Aesun; Yang, Hye-Ryung; Park, Junghyun; Choi, Il Ju; Kim, Young-Woo; Kim, Jeongseon; Nam, Byung-Ho

    2015-01-01

    Background Predicting high risk groups for gastric cancer and motivating these groups to receive regular checkups is required for the early detection of gastric cancer. The aim of this study is was to develop a prediction model for gastric cancer incidence based on a large population-based cohort in Korea. Method Based on the National Health Insurance Corporation data, we analyzed 10 major risk factors for gastric cancer. The Cox proportional hazards model was used to develop gender specific prediction models for gastric cancer development, and the performance of the developed model in terms of discrimination and calibration was also validated using an independent cohort. Discrimination ability was evaluated using Harrell’s C-statistics, and the calibration was evaluated using a calibration plot and slope. Results During a median of 11.4 years of follow-up, 19,465 (1.4%) and 5,579 (0.7%) newly developed gastric cancer cases were observed among 1,372,424 men and 804,077 women, respectively. The prediction models included age, BMI, family history, meal regularity, salt preference, alcohol consumption, smoking and physical activity for men, and age, BMI, family history, salt preference, alcohol consumption, and smoking for women. This prediction model showed good accuracy and predictability in both the developing and validation cohorts (C-statistics: 0.764 for men, 0.706 for women). Conclusions In this study, a prediction model for gastric cancer incidence was developed that displayed a good performance. PMID:26186332

  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 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.

  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. 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…

  6. Predictive error analysis for a water resource management model

    NASA Astrophysics Data System (ADS)

    Gallagher, Mark; Doherty, John

    2007-02-01

    SummaryIn calibrating a model, a set of parameters is assigned to the model which will be employed for the making of all future predictions. If these parameters are estimated through solution of an inverse problem, formulated to be properly posed through either pre-calibration or mathematical regularisation, then solution of this inverse problem will, of necessity, lead to a simplified parameter set that omits the details of reality, while still fitting historical data acceptably well. Furthermore, estimates of parameters so obtained will be contaminated by measurement noise. Both of these phenomena will lead to errors in predictions made by the model, with the potential for error increasing with the hydraulic property detail on which the prediction depends. Integrity of model usage demands that model predictions be accompanied by some estimate of the possible errors associated with them. The present paper applies theory developed in a previous work to the analysis of predictive error associated with a real world, water resource management model. The analysis offers many challenges, including the fact that the model is a complex one that was partly calibrated by hand. Nevertheless, it is typical of models which are commonly employed as the basis for the making of important decisions, and for which such an analysis must be made. The potential errors associated with point-based and averaged water level and creek inflow predictions are examined, together with the dependence of these errors on the amount of averaging involved. Error variances associated with predictions made by the existing model are compared with "optimized error variances" that could have been obtained had calibration been undertaken in such a way as to minimize predictive error variance. The contributions by different parameter types to the overall error variance of selected predictions are also examined.

  7. 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.

  8. Prediction of Warfarin Dose in Pediatric Patients: An Evaluation of the Predictive Performance of Several Models

    PubMed Central

    Marek, Elizabeth; Momper, Jeremiah D.; Hines, Ronald N.; Takao, Cheryl M.; Gill, Joan C.; Pravica, Vera; Gaedigk, Andrea; Neville, Kathleen A.

    2016-01-01

    OBJECTIVES: The objective of this study was to evaluate the performance of pediatric pharmacogenetic-based dose prediction models by using an independent cohort of pediatric patients from a multicenter trial. METHODS: Clinical and genetic data (CYP2C9 [cytochrome P450 2C9] and VKORC1 [vitamin K epoxide reductase]) were collected from pediatric patients aged 3 months to 17 years who were receiving warfarin as part of standard care at 3 separate clinical sites. The accuracy of 8 previously published pediatric pharmacogenetic-based dose models was evaluated in the validation cohort by comparing predicted maintenance doses to actual stable warfarin doses. The predictive ability was assessed by using the proportion of variance (R2), mean prediction error (MPE), and the percentage of predictions that fell within 20% of the actual maintenance dose. RESULTS: Thirty-two children reached a stable international normalized ratio and were included in the validation cohort. The pharmacogenetic-based warfarin dose models showed a proportion of variance ranging from 35% to 78% and an MPE ranging from −2.67 to 0.85 mg/day in the validation cohort. Overall, the model developed by Hamberg et al showed the best performance in the validation cohort (R2 = 78%; MPE = 0.15 mg/day) with 38% of the predictions falling within 20% of observed doses. CONCLUSIONS: Pharmacogenetic-based algorithms provide better predictions than a fixed-dose approach, although an optimal dose algorithm has not yet been developed. PMID:27453700

  9. 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.

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. 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.

  15. 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

  16. 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

  17. 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.

  18. 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

  19. Erratum: Probabilistic application of a fugacity model to predict triclosan fate during wastewater treatment.

    PubMed

    Bock, Michael; Lyndall, Jennifer; Barber, Timothy; Fuchsman, Phyllis; Perruchon, Elyse; Capdevielle, Marie

    2010-10-01

    The fate and partitioning of the antimicrobial compound, triclosan, in wastewater treatment plants (WWTPs) is evaluated using a probabilistic fugacity model to predict the range of triclosan concentrations in effluent and secondary biosolids. The WWTP model predicts 84% to 92% triclosan removal, which is within the range of measured removal efficiencies (typically 70% to 98%). Triclosan is predominantly removed by sorption and subsequent settling of organic particulates during primary treatment and by aerobic biodegradation during secondary treatment. Median modeled removal efficiency due to sorption is 40% for all treatment phases and 31% in the primary treatment phase. Median modeled removal efficiency due to biodegradation is 48% for all treatment phases and 44% in the secondary treatment phase. Important factors contributing to variation in predicted triclosan concentrations in effluent and biosolids include influent concentrations, solids concentrations in settling tanks, and factors related to solids retention time. Measured triclosan concentrations in biosolids and non-United States (US) effluent are consistent with model predictions. However, median concentrations in US effluent are over-predicted with this model, suggesting that differences in some aspect of treatment practices not incorporated in the model (e.g., disinfection methods) may affect triclosan removal from effluent. Model applications include predicting changes in environmental loadings associated with new triclosan applications and supporting risk analyses for biosolids-amended land and effluent receiving waters.

  20. Probabilistic application of a fugacity model to predict triclosan fate during wastewater treatment.

    PubMed

    Bock, Michael; Lyndall, Jennifer; Barber, Timothy; Fuchsman, Phyllis; Perruchon, Elyse; Capdevielle, Marie

    2010-07-01

    The fate and partitioning of the antimicrobial compound, triclosan, in wastewater treatment plants (WWTPs) is evaluated using a probabilistic fugacity model to predict the range of triclosan concentrations in effluent and secondary biosolids. The WWTP model predicts 84% to 92% triclosan removal, which is within the range of measured removal efficiencies (typically 70% to 98%). Triclosan is predominantly removed by sorption and subsequent settling of organic particulates during primary treatment and by aerobic biodegradation during secondary treatment. Median modeled removal efficiency due to sorption is 40% for all treatment phases and 31% in the primary treatment phase. Median modeled removal efficiency due to biodegradation is 48% for all treatment phases and 44% in the secondary treatment phase. Important factors contributing to variation in predicted triclosan concentrations in effluent and biosolids include influent concentrations, solids concentrations in settling tanks, and factors related to solids retention time. Measured triclosan concentrations in biosolids and non-United States (US) effluent are consistent with model predictions. However, median concentrations in US effluent are over-predicted with this model, suggesting that differences in some aspect of treatment practices not incorporated in the model (e.g., disinfection methods) may affect triclosan removal from effluent. Model applications include predicting changes in environmental loadings associated with new triclosan applications and supporting risk analyses for biosolids-amended land and effluent receiving waters.

  1. 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

  2. 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

  3. 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.

  4. 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.

  5. 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

  6. 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

  7. A burnout prediction model based around char morphology

    SciTech Connect

    Tao Wu; Edward Lester; Michael Cloke

    2006-05-15

    Several combustion models have been developed that can make predictions about coal burnout and burnout potential. Most of these kinetic models require standard parameters such as volatile content and particle size to make a burnout prediction. This article presents a new model called the char burnout (ChB) model, which also uses detailed information about char morphology in its prediction. The input data to the model is based on information derived from two different image analysis techniques. One technique generates characterization data from real char samples, and the other predicts char types based on characterization data from image analysis of coal particles. The pyrolyzed chars in this study were created in a drop tube furnace operating at 1300{sup o}C, 200 ms, and 1% oxygen. Modeling results were compared with a different carbon burnout kinetic model as well as the actual burnout data from refiring the same chars in a drop tube furnace operating at 1300{sup o}C, 5% oxygen, and residence times of 200, 400, and 600 ms. A good agreement between ChB model and experimental data indicates that the inclusion of char morphology in combustion models could well improve model predictions. 38 refs., 5 figs., 6 tabs.

  8. Evaluation of prediction intervals for expressing uncertainties in groundwater flow model predictions

    USGS Publications Warehouse

    Christensen, S.; Cooley, R.L.

    1999-01-01

    We tested the accuracy of 95% individual prediction intervals for hydraulic heads, streamflow gains, and effective transmissivities computed by groundwater models of two Danish aquifers. To compute the intervals, we assumed that each predicted value can be written as the sum of a computed dependent variable and a random error. Testing was accomplished by using a cross-validation method and by using new field measurements of hydraulic heads and transmissivities that were not used to develop or calibrate the models. The tested null hypotheses are that the coverage probability of the prediction intervals is not significantly smaller than the assumed probability (95%) and that each tail probability is not significantly different from the assumed probability (2.5%). In all cases tested, these hypotheses were accepted at the 5% level of significance. We therefore conclude that for the groundwater models of two real aquifers the individual prediction intervals appear to be accurate.We tested the accuracy of 95% individual prediction intervals for hydraulic heads, streamflow gains, and effective transmissivities computed by groundwater models of two Danish aquifers. To compute the intervals, we assumed that each predicted value can be written as the sum of a computed dependent variable and a random error. Testing was accomplished by using a cross-validation method and by using new field measurements of hydraulic heads and transmissivities that were not used to develop or calibrate the models. The tested null hypotheses are that the coverage probability of the prediction intervals is not significantly smaller than the assumed probability (95%) and that each tail probability is not significantly different from the assumed probability (2.5%). In all cases tested, these hypotheses were accepted at the 5% level of significance. We therefore conclude that for the groundwater models of two real aquifers the individual prediction intervals appear to be accurate.

  9. 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

  10. Coupled Air-Sea Observations and Modeling for Better Understanding Tropical Cyclone Prediction and Predictability

    NASA Astrophysics Data System (ADS)

    Chen, S. S.

    2014-12-01

    A systematic observational and modeling study is conducted to better understand the physical processes controlling air-sea interaction and their impact on tropical cyclone (TC) prediction and predictability using a fully coupled atmosphere-wave-ocean modeling system developed at the University of Miami and observations from field campaigns. We have developed a unified air-sea interface module that couples multiple atmosphere, wave, and ocean models using the Earth System Modeling Framework (ESMF). It is a physically based and computationally efficient coupling system that is flexible to use in a multi-model system and portable for transition to the next generation research and operational coupled atmosphere-wave-ocean-land models. This standardized coupling framework allows researchers to develop and test air-sea coupling parameterizations and coupled data assimilation, and to better facilitate research-to-operation activities. It also allows for ensemble forecasts that can be used for coupled atmosphere-ocean data assimilation and assessment of uncertainties in coupled model predictions. The coupled modeling system has been evaluated using the coupled air-sea observations (e.g., GPS dropsondes and AXBTs, ocean drifters and floats) collected in recent field campaigns in the Gulf of Mexico and TCs in the Atlantic and Pacific basins. This talk will provide 1) an overview of the unified air-sea interface model, 2) fully coupled atmosphere-wave-ocean model predictions of TCs and evaluation with coupled air-sea observations, and 3) results from high-resolution (1.3 km grid resolution) ensemble experiments using a stochastic kinetic energy backscatter (SKEB) perturbation method to assess the predictability and uncertainty in TC predictions.

  11. The interaction of affective states and cognitive vulnerabilities in the prediction of non-suicidal self-injury.

    PubMed

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

    2015-01-01

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

  12. Computer Model for Prediction of PCB Dechlorination and Biodegradation Endpoints

    SciTech Connect

    Just, E.M.; Klasson, T.

    1999-04-19

    Mathematical modeling of polychlorinated biphenyl (PCB) transformation served as a means of predicting possible endpoints of bioremediation, thus allowing evaluation of several of the most common transformation patterns. Correlation between laboratory-observed and predicted endpoint data was, in some cases, as good as 0.98 (perfect correlation = 1.0).

  13. 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...

  14. 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.

  15. A Novel Statistical Model to Estimate Host Genetic Effects Affecting Disease Transmission

    PubMed Central

    Anacleto, Osvaldo; Garcia-Cortés, Luis Alberto; Lipschutz-Powell, Debby; Woolliams, John A.; Doeschl-Wilson, Andrea B.

    2015-01-01

    There is increasing recognition that genetic diversity can affect the spread of diseases, potentially affecting plant and livestock disease control as well as the emergence of human disease outbreaks. Nevertheless, even though computational tools can guide the control of infectious diseases, few epidemiological models can simultaneously accommodate the inherent individual heterogeneity in multiple infectious disease traits influencing disease transmission, such as the frequently modeled propensity to become infected and infectivity, which describes the host ability to transmit the infection to susceptible individuals. Furthermore, current quantitative genetic models fail to fully capture the heritable variation in host infectivity, mainly because they cannot accommodate the nonlinear infection dynamics underlying epidemiological data. We present in this article a novel statistical model and an inference method to estimate genetic parameters associated with both host susceptibility and infectivity. Our methodology combines quantitative genetic models of social interactions with stochastic processes to model the random, nonlinear, and dynamic nature of infections and uses adaptive Bayesian computational techniques to estimate the model parameters. Results using simulated epidemic data show that our model can accurately estimate heritabilities and genetic risks not only of susceptibility but also of infectivity, therefore exploring a trait whose heritable variation is currently ignored in disease genetics and can greatly influence the spread of infectious diseases. Our proposed methodology offers potential impacts in areas such as livestock disease control through selective breeding and also in predicting and controlling the emergence of disease outbreaks in human populations. PMID:26405030

  16. 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

  17. Droplet-model predictions of charge moments

    SciTech Connect

    Myers, W.D.

    1982-04-01

    The Droplet Model expressions for calculating various moments of the nuclear charge distribution are given. There are contributions to the moments from the size and shape of the system, from the internal redistribution induced by the Coulomb repulsion, and from the diffuseness of the surface. A case is made for the use of diffuse charge distributions generated by convolution as an alternative to Fermi-functions.

  18. 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.

  19. 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…

  20. 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.

  1. 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

  2. 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.

  3. Using Pareto points for model identification in predictive toxicology.

    PubMed

    Palczewska, Anna; Neagu, Daniel; Ridley, Mick

    2013-03-22

    : 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.

  4. 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...

  5. Prospective evaluation of a Bayesian model to predict organizational change.

    PubMed

    Molfenter, Todd; Gustafson, Dave; Kilo, Chuck; Bhattacharya, Abhik; Olsson, Jesper

    2005-01-01

    This research examines a subjective Bayesian model's ability to predict organizational change outcomes and sustainability of those outcomes for project teams participating in a multi-organizational improvement collaborative. PMID:16093893

  6. 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.

  7. Predicting recurrence after unprovoked venous thromboembolism: prospective validation of the updated Vienna Prediction Model.

    PubMed

    Tritschler, Tobias; Méan, Marie; Limacher, Andreas; Rodondi, Nicolas; Aujesky, Drahomir

    2015-10-15

    The updated Vienna Prediction Model for estimating recurrence risk after an unprovoked venous thromboembolism (VTE) has been developed to identify individuals at low risk for VTE recurrence in whom anticoagulation (AC) therapy may be stopped after 3 months. We externally validated the accuracy of the model to predict recurrent VTE in a prospective multicenter cohort of 156 patients aged ≥65 years with acute symptomatic unprovoked VTE who had received 3 to 12 months of AC. Patients with a predicted 12-month risk within the lowest quartile based on the updated Vienna Prediction Model were classified as low risk. The risk of recurrent VTE did not differ between low- vs higher-risk patients at 12 months (13% vs 10%; P = .77) and 24 months (15% vs 17%; P = 1.0). The area under the receiver operating characteristic curve for predicting VTE recurrence was 0.39 (95% confidence interval [CI], 0.25-0.52) at 12 months and 0.43 (95% CI, 0.31-0.54) at 24 months. In conclusion, in elderly patients with unprovoked VTE who have stopped AC, the updated Vienna Prediction Model does not discriminate between patients who develop recurrent VTE and those who do not. This study was registered at www.clinicaltrials.gov as #NCT00973596.

  8. Application of chaotic prediction model based on wavelet transform on water quality prediction

    NASA Astrophysics Data System (ADS)

    Zhang, L.; Zou, Z. H.; Zhao, Y. F.

    2016-08-01

    Dissolved oxygen (DO) is closely related to water self-purification capacity. In order to better forecast its concentration, the chaotic prediction model, based on the wavelet transform, is proposed and applied to a certain monitoring section of the Mentougou area of the Haihe River Basin. The result is compared with the simple application of the chaotic prediction model. The study indicates that the new model aligns better with the real data and has a higher accuracy. Therefore, it will provide significant decision support for water protection and water environment treatment.

  9. Updated verification of the Space Weather Prediction Center's solar energetic particle prediction model

    NASA Astrophysics Data System (ADS)

    Balch, Christopher C.

    2008-01-01

    This paper evaluates the performance of an operational proton prediction model currently being used at NOAA's Space Weather Prediction Center. The evaluation is based on proton events that occurred between 1986 and 2004. Parameters for the associated solar events determine a set of necessary conditions, which are used to construct a set of control events. Model output is calculated for these events and performance of the model is evaluated using standard verification measures. For probability forecasts we evaluate the accuracy, reliability, and resolution and display these results using a standard attributes diagram. We identify conditions for which the model is systematically inaccurate. The probability forecasts are also evaluated for categorical forecast performance measures. We find an optimal probability and we calculate the false alarm rate and probability of detection at this probability. We also show results for peak flux and rise time predictions. These findings provide an objective basis for measuring future improvements.

  10. 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.

  11. 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.

  12. Launch ascent guidance by discrete multi-model predictive control

    NASA Astrophysics Data System (ADS)

    Vachon, Alexandre; Desbiens, André; Gagnon, Eric; Bérard, Caroline

    2014-02-01

    This paper studies the application of discrete multi-model predictive control as a trajectory tracking guidance law for a space launcher. Two different algorithms are developed, each one based on a different representation of launcher translation dynamics. These representations are based on an interpolation of the linear approximation of nonlinear pseudo-five degrees of freedom equations of translation around an elliptical Earth. The interpolation gives a linear-time-varying representation and a linear-fractional representation. They are used as the predictive model of multi-model predictive controllers. The controlled variables are the orbital parameters, and constraints on a terminal region for the minimal accepted precision are also included. Use of orbital parameters as the controlled variables allows for a partial definition of the trajectory. Constraints can also be included in multi-model predictive control to reduce the number of unknowns of the problem by defining input shaping constraints. The guidance algorithms are tested in nominal conditions and off-nominal conditions with uncertainties on the thrust. The results are compared to those of a similar formulation with a nonlinear model predictive controller and to a guidance method based on the resolution of a simplified version of the two-point boundary value problem. In nominal conditions, the model predictive controllers are more precise and produce a more optimal trajectory but are longer to compute than the two-point boundary solution. Moreover, in presence of uncertainties, developed algorithms exhibit poor robustness properties. The multi-model predictive control algorithms do not reach the desired orbit while the nonlinear model predictive control algorithm still converges but produces larger maneuvers than the other method.

  13. Iron affects the structure of cell membrane molecular models.

    PubMed

    Suwalsky, M; Martínez, F; Cárdenas, H; Grzyb, J; Strzałka, K

    2005-03-01

    The effects of Fe(3+) and Fe(2+) on molecular models of biomembranes were investigated. These consisted of bilayers of dimyristoylphosphatidylcholine (DMPC) and of dimyristoylphosphatidylethanolamine (DMPE), classes of phospholipids located in the outer and inner moieties of cell membranes, respectively. X-ray studies showed that very low concentrations of Fe(3+) affected DMPC organization and 10(-3)M induced a total loss of its multilamellar periodic stacking. Experiments carried out with Fe(2+) on DMPC showed weaker effects than those induced by Fe(3+) ions. Similar experiments were performed on DMPE bilayers. Fe(3+) from 10(-7)M up to 10(-4)M had practically no effect on DMPE structure. However, 10(-3)M Fe(3+) induced a deep perturbation of the multilamellar structure of DMPE. However, 10(-3)M Fe(2+) had no effect on DMPE organization practically. Differential scanning calorimetry measurements also revealed different effects of Fe(3+) and Fe(2+) on the phase transition and other thermal properties of the examined lipids. In conclusion, the results obtained indicate that iron ions interact with phospholipid bilayers perturbing their structures. These findings are consistent with the observation that iron ions change cell membrane fluidity and, therefore, affect its functions. PMID:15752465

  14. 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

  15. Toward a predictive model for elastomer seals

    NASA Astrophysics Data System (ADS)

    Molinari, Nicola; Khawaja, Musab; Sutton, Adrian; Mostofi, Arash

    Nitrile butadiene rubber (NBR) and hydrogenated-NBR (HNBR) are widely used elastomers, especially as seals in oil and gas applications. During exposure to well-hole conditions, ingress of gases causes degradation of performance, including mechanical failure. We use computer simulations to investigate this problem at two different length and time-scales. First, we study the solubility of gases in the elastomer using a chemically-inspired description of HNBR based on the OPLS all-atom force-field. Starting with a model of NBR, C=C double bonds are saturated with either hydrogen or intramolecular cross-links, mimicking the hydrogenation of NBR to form HNBR. We validate against trends for the mass density and glass transition temperature for HNBR as a function of cross-link density, and for NBR as a function of the fraction of acrylonitrile in the copolymer. Second, we study mechanical behaviour using a coarse-grained model that overcomes some of the length and time-scale limitations of an all-atom approach. Nanoparticle fillers added to the elastomer matrix to enhance mechanical response are also included. Our initial focus is on understanding the mechanical properties at the elevated temperatures and pressures experienced in well-hole conditions.

  16. 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.

  17. 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

  18. Protein oxidation affects proteolysis in a meat model system.

    PubMed

    Berardo, Alberto; Claeys, Erik; Vossen, Els; Leroy, Frédéric; De Smet, Stefaan

    2015-08-01

    The effect of hydrogen peroxide-induced protein oxidation and pH (4.8 and 5.2) on meat proteolysis was investigated in a meat model system for dry fermented sausages. In oxidised samples, increased protein carbonyl contents and decreased thiol concentrations were found. The initial concentration of protein carbonyls was significantly lower in oxidised samples at pH4.8 than in ones at pH5.2, but after ten days comparable levels were reached. The inhibition of proteolysis by the addition of a protease inhibitor cocktail did not influence protein oxidation. Yet, proteolysis was negatively affected by low pH values as well as by oxidation, resulting in a reduced release of amino acids during ripening.

  19. 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

  20. 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.

  1. 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

  2. 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.

  3. 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

  4. 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…

  5. 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…

  6. 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…

  7. Prior probability (the pretest best guess) affects predictive values of diagnostic tests.

    PubMed

    Erb, Hollis N

    2011-06-01

    Authors who publish evaluations of dichotomous (yes/no) diagnostic tests often include the predictive values of their test at a single prior probability (eg, the prevalence of the target disease within the evaluation data set). The objectives of this technical note are to demonstrate why single-probability predictive values are misleading and to show a better way to display positive predictive values (PPV) and negative predictive values (NPV) for a newly evaluated test. Secondly, this technical note will show readers how to calculate predictive values from only sensitivity and specificity for any desired prior probability. As prior probability increases from 0% to 100%, PPV increases from 0% to 100%, but NPV goes in the opposite direction (drops from 100% to 0%). Because prior probabilities vary so greatly across situations, predictive values should be provided in publications for the full range of potential prior probabilities (if provided at all). This is easily done with a 2-curve graph displaying the predictive values (y-axis) against the prior probability (x-axis).

  8. 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.

  9. Development and application of chronic disease risk prediction models.

    PubMed

    Oh, Sun Min; Stefani, Katherine M; Kim, Hyeon Chang

    2014-07-01

    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.

  10. Approach and Positive Affect in Toddlerhood Predict Early Childhood Behavior Problems

    PubMed Central

    Dollar, Jessica; Buss, Kristin A.

    2013-01-01

    The aim of the study was to examine the moderating role of positive affect on the relation between approach behaviors and adjustment outcomes. One hundred eleven toddlers participated in a laboratory assessment of approach and positive affect at 24 months. Behavior problems were reported by a parent in the fall of the child’s kindergarten year. Results supported our hypotheses that children who displayed high approach and high positive affect in both non-threat and low threat contexts were rated as higher in externalizing behavior problems. On the other hand, for children showing low positive affect, increases in approach in a moderate threat context lowered the risk of developing internalizing behavior problems. Implications for these findings are discussed including methodological considerations of differences among eliciting contexts and advantages of separating positive affect and approach. PMID:25382941

  11. Comparing the predictive value of multiple cognitive, affective, and motor tasks after rodent traumatic brain injury.

    PubMed

    Zhao, Zaorui; Loane, David J; Murray, Michael G; Stoica, Bogdan A; Faden, Alan I

    2012-10-10

    Controlled cortical impact injury (CCI) is a widely-used, clinically-relevant model of traumatic brain injury (TBI). Although functional outcomes have been used for years in this model, little work has been done to compare the predictive value of various cognitive and sensorimotor assessment tests, singly or in combination. Such information would be particularly useful for assessing mechanisms of injury or therapeutic interventions. Following isoflurane anesthesia, C57BL/6 mice were subjected to sham, mild (5.0 m/sec), moderate (6.0 m/sec), or severe (7.5 m/sec) CCI. A battery of behavioral tests were evaluated and compared, including the standard Morris water maze (sMWM), reversal Morris water maze (rMWM), novel object recognition (NOR), passive avoidance (PA), tail-suspension (TS), beam walk (BW), and open-field locomotor activity. The BW task, performed at post-injury days (PID) 0, 1, 3, 7, 14, 21, and 28, showed good discrimination as a function of injury severity. The sMWM and rMWM tests (PID 14-23), as well as NOR (PID 24 and 25), effectively discriminated spatial and novel object learning and memory across injury severity levels. Notably, the rMWM showed the greatest separation between mild and moderate/severe injury. PA (PID 27 and 28) and TS (PID 24) also reflected differences across injury levels, but to a lesser degree. We also compared individual functional measures with histological outcomes such as lesion volume and neuronal cell loss across anatomical regions. In addition, we created a novel composite behavioral score index from individual complementary behavioral scores, and it provided superior discrimination across injury severities compared to individual tests. In summary, this study demonstrates the feasibility of using a larger number of complementary functional outcome behavioral tests than those traditionally employed to follow post-traumatic recovery after TBI, and suggests that the composite score may be a helpful tool for screening

  12. Comparing the predictive value of multiple cognitive, affective, and motor tasks after rodent traumatic brain injury.

    PubMed

    Zhao, Zaorui; Loane, David J; Murray, Michael G; Stoica, Bogdan A; Faden, Alan I

    2012-10-10

    Controlled cortical impact injury (CCI) is a widely-used, clinically-relevant model of traumatic brain injury (TBI). Although functional outcomes have been used for years in this model, little work has been done to compare the predictive value of various cognitive and sensorimotor assessment tests, singly or in combination. Such information would be particularly useful for assessing mechanisms of injury or therapeutic interventions. Following isoflurane anesthesia, C57BL/6 mice were subjected to sham, mild (5.0 m/sec), moderate (6.0 m/sec), or severe (7.5 m/sec) CCI. A battery of behavioral tests were evaluated and compared, including the standard Morris water maze (sMWM), reversal Morris water maze (rMWM), novel object recognition (NOR), passive avoidance (PA), tail-suspension (TS), beam walk (BW), and open-field locomotor activity. The BW task, performed at post-injury days (PID) 0, 1, 3, 7, 14, 21, and 28, showed good discrimination as a function of injury severity. The sMWM and rMWM tests (PID 14-23), as well as NOR (PID 24 and 25), effectively discriminated spatial and novel object learning and memory across injury severity levels. Notably, the rMWM showed the greatest separation between mild and moderate/severe injury. PA (PID 27 and 28) and TS (PID 24) also reflected differences across injury levels, but to a lesser degree. We also compared individual functional measures with histological outcomes such as lesion volume and neuronal cell loss across anatomical regions. In addition, we created a novel composite behavioral score index from individual complementary behavioral scores, and it provided superior discrimination across injury severities compared to individual tests. In summary, this study demonstrates the feasibility of using a larger number of complementary functional outcome behavioral tests than those traditionally employed to follow post-traumatic recovery after TBI, and suggests that the composite score may be a helpful tool for screening

  13. 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

  14. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2013-07-01

    The purpose of this paper is to stimulate a re-thinking of how we, the catchment hydrologists, could become reliable forecasters. A group of catchment modellers predicted the hydrological response of a man-made 6 ha catchment in its initial phase (Chicken Creek) without having access to the observed records. They used conceptually different model families. Their modelling experience differed largely. The prediction exercise was organized in three steps: (1) for the 1st prediction modellers received a basic data set describing the internal structure of the catchment (somewhat more complete than usually available to a priori predictions in ungauged catchments). They did not obtain time series of stream flow, soil moisture or groundwater response. (2) Before the 2nd improved prediction they inspected the catchment on-site and attended a workshop where the modellers presented and discussed their first attempts. (3) For their improved 3rd prediction they were offered additional data by charging them pro forma with the costs for obtaining this additional information. Holländer et al. (2009) discussed the range of predictions obtained in step 1. Here, we detail the modeller's decisions in accounting for the various processes based on what they learned during the field visit (step 2) and add the final outcome of step 3 when the modellers made use of additional data. We document the prediction progress as well as the learning process resulting from the availability of added information. For the 2nd and 3rd step, the progress in prediction quality could be evaluated in relation to individual modelling experience and costs of added information. We learned (i) that soft information such as the modeller's system understanding is as important as the model itself (hard information), (ii) that the sequence of modelling steps matters (field visit, interactions between differently experienced experts, choice of model, selection of available data, and methods for parameter guessing

  16. 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

  17. How do you feel? Self-esteem predicts affect, stress, social interaction, and symptom severity during daily life in patients with chronic illness.

    PubMed

    Juth, Vanessa; Smyth, Joshua M; Santuzzi, Alecia M

    2008-10-01

    Self-esteem has been demonstrated to predict health and well-being in a number of samples and domains using retrospective reports, but little is known about the effect of self-esteem in daily life. A community sample with asthma (n = 97) or rheumatoid arthritis (n = 31) completed a self-esteem measure and collected Ecological Momentary Assessment (EMA) data 5x/day for one week using a palmtop computer. Low self-esteem predicted more negative affect, less positive affect, greater stress severity, and greater symptom severity in daily life. Naturalistic exploration of mechanisms relating self-esteem to physiological and/or psychological components in illness may clarify causal relationships and inform theoretical models of self-care, well-being, and disease management.

  18. A highly predictable animal model of retinoblastoma.

    PubMed

    Kobayashi, M; Mukai, N; Solish, S P; Pomeroy, M E

    1982-01-01

    A new animal model of retinoblastoma was developed in newborn inbred CDF rats by intravitreous inoculation of retinal tumor cells (5 X 10(4)/5 microliter) derived from the cultured tumor cell line EXP-5. The retinal tumor from which the cell line originated was induced by a single intravitreous inoculation of human adenovirus serotype 12 (5 microliter of 10(8) TCID 50/0.1 ml) in syngeneic rats. Within 1 month after intravitreous inoculation of EXP-5 cells, a clinically recognizable ocular tumor was obtained in all 39 rats. Ad 12-specific T-antigens were demonstrated in the cultured tumor cells using immunofluorescent techniques. Morphologically these tumor cells closely resembled retinoblastoma, with poorly differentiated intracytoplasmic organelles, solitary cilia with a 9 + 0 tubule pattern, and abnormal nuclear membrane associated with a set of basal bodies. The significance of this highly manipulable retinal tumor cell line is the capability of providing a full-fledged intravitreous tumor in 1-month-old CDF rats, whose actual life span is known to be 42 months. Transplantable retinal tumors described to date are reviewed briefly and compared with the presently reported cell line.

  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. A comparison of arcjet plume properties to model predictions

    NASA Technical Reports Server (NTRS)

    Cappelli, M. A.; Liebeskind, J. G.; Hanson, R. K.; Butler, G. W.; King, D. Q.

    1993-01-01

    This paper describes an experimental study of the plasma plume properties of a 1 kW class hydrogen arcjet thruster and the comparison of measured temperature and velocity field to model predictions. The experiments are based on laser-induced fluorescence excitation of the Balmer-alpha transition. The model is based on a single-fluid magnetohydrodynamic description of the flow originally developed to predict arcjet thruster performance. Excellent agreement between model predictions and experimental velocity is found, despite the complex nature of the flow. Measured and predicted exit plane temperatures are in disagreement by as much as 2000K over a range of operating conditions. The possible sources for this discrepancy are discussed.

  1. Modelling proteins' hidden conformations to predict antibiotic resistance

    NASA Astrophysics Data System (ADS)

    Hart, Kathryn M.; Ho, Chris M. W.; Dutta, Supratik; Gross, Michael L.; Bowman, Gregory R.

    2016-10-01

    TEM β-lactamase confers bacteria with resistance to many antibiotics and rapidly evolves activity against new drugs. However, functional changes are not easily explained by differences in crystal structures. We employ Markov state models to identify hidden conformations and explore their role in determining TEM's specificity. We integrate these models with existing drug-design tools to create a new technique, called Boltzmann docking, which better predicts TEM specificity by accounting for conformational heterogeneity. Using our MSMs, we identify hidden states whose populations correlate with activity against cefotaxime. To experimentally detect our predicted hidden states, we use rapid mass spectrometric footprinting and confirm our models' prediction that increased cefotaxime activity correlates with reduced Ω-loop flexibility. Finally, we design novel variants to stabilize the hidden cefotaximase states, and find their populations predict activity against cefotaxime in vitro and in vivo. Therefore, we expect this framework to have numerous applications in drug and protein design.

  2. 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.

  3. Predicting adverse drug reactions in older adults; a systematic review of the risk prediction models

    PubMed Central

    Stevenson, Jennifer M; Williams, Josceline L; Burnham, Thomas G; Prevost, A Toby; Schiff, Rebekah; Erskine, S David; Davies, J Graham

    2014-01-01

    Adverse drug reaction (ADR) risk-prediction models for use in older adults have been developed, but it is not clear if they are suitable for use in clinical practice. This systematic review aimed to identify and investigate the quality of validated ADR risk-prediction models for use in older adults. Standard computerized databases, the gray literature, bibliographies, and citations were searched (2012) to identify relevant peer-reviewed studies. Studies that developed and validated an ADR prediction model for use in patients over 65 years old, using a multivariable approach in the design and analysis, were included. Data were extracted and their quality assessed by independent reviewers using a standard approach. Of the 13,423 titles identified, only 549 were associated with adverse outcomes of medicines use. Four met the inclusion criteria. All were conducted in inpatient cohorts in Western Europe. None of the models satisfied the four key stages in the creation of a quality risk prediction model; development and validation were completed, but impact and implementation were not assessed. Model performance was modest; area under the receiver operator curve ranged from 0.623 to 0.73. Study quality was difficult to assess due to poor reporting, but inappropriate methods were apparent. Further work needs to be conducted concerning the existing models to enable the development of a robust ADR risk-prediction model that is externally validated, with practical design and good performance. Only then can implementation and impact be assessed with the aim of generating a model of high enough quality to be considered for use in clinical care to prioritize older people at high risk of suffering an ADR. PMID:25278750

  4. Predicting adverse drug reactions in older adults; a systematic review of the risk prediction models.

    PubMed

    Stevenson, Jennifer M; Williams, Josceline L; Burnham, Thomas G; Prevost, A Toby; Schiff, Rebekah; Erskine, S David; Davies, J Graham

    2014-01-01

    Adverse drug reaction (ADR) risk-prediction models for use in older adults have been developed, but it is not clear if they are suitable for use in clinical practice. This systematic review aimed to identify and investigate the quality of validated ADR risk-prediction models for use in older adults. Standard computerized databases, the gray literature, bibliographies, and citations were searched (2012) to identify relevant peer-reviewed studies. Studies that developed and validated an ADR prediction model for use in patients over 65 years old, using a multivariable approach in the design and analysis, were included. Data were extracted and their quality assessed by independent reviewers using a standard approach. Of the 13,423 titles identified, only 549 were associated with adverse outcomes of medicines use. Four met the inclusion criteria. All were conducted in inpatient cohorts in Western Europe. None of the models satisfied the four key stages in the creation of a quality risk prediction model; development and validation were completed, but impact and implementation were not assessed. Model performance was modest; area under the receiver operator curve ranged from 0.623 to 0.73. Study quality was difficult to assess due to poor reporting, but inappropriate methods were apparent. Further work needs to be conducted concerning the existing models to enable the development of a robust ADR risk-prediction model that is externally validated, with practical design and good performance. Only then can implementation and impact be assessed with the aim of generating a model of high enough quality to be considered for use in clinical care to prioritize older people at high risk of suffering an ADR.

  5. Predicting adverse drug reactions in older adults; a systematic review of the risk prediction models.

    PubMed

    Stevenson, Jennifer M; Williams, Josceline L; Burnham, Thomas G; Prevost, A Toby; Schiff, Rebekah; Erskine, S David; Davies, J Graham

    2014-01-01

    Adverse drug reaction (ADR) risk-prediction models for use in older adults have been developed, but it is not clear if they are suitable for use in clinical practice. This systematic review aimed to identify and investigate the quality of validated ADR risk-prediction models for use in older adults. Standard computerized databases, the gray literature, bibliographies, and citations were searched (2012) to identify relevant peer-reviewed studies. Studies that developed and validated an ADR prediction model for use in patients over 65 years old, using a multivariable approach in the design and analysis, were included. Data were extracted and their quality assessed by independent reviewers using a standard approach. Of the 13,423 titles identified, only 549 were associated with adverse outcomes of medicines use. Four met the inclusion criteria. All were conducted in inpatient cohorts in Western Europe. None of the models satisfied the four key stages in the creation of a quality risk prediction model; development and validation were completed, but impact and implementation were not assessed. Model performance was modest; area under the receiver operator curve ranged from 0.623 to 0.73. Study quality was difficult to assess due to poor reporting, but inappropriate methods were apparent. Further work needs to be conducted concerning the existing models to enable the development of a robust ADR risk-prediction model that is externally validated, with practical design and good performance. Only then can implementation and impact be assessed with the aim of generating a model of high enough quality to be considered for use in clinical care to prioritize older people at high risk of suffering an ADR. PMID:25278750

  6. 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).

  7. 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

  8. 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).

  9. 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.

  10. 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.

  11. Embryo quality predictive models based on cumulus cells gene expression

    PubMed Central

    Burnik Papler, T; Verdenik, I; Fon Tacer, K; Vrtačnik Bokal, E

    2016-01-01

    Abstract Since the introduction of in vitro fertilization (IVF) in clinical practice of infertility treatment, the indicators for high quality embryos were investigated. Cumulus cells (CC) have a specific gene expression profile according to the developmental potential of the oocyte they are surrounding, and therefore, specific gene expression could be used as a biomarker. The aim of our study was to combine more than one biomarker to observe improvement in prediction value of embryo development. In this study, 58 CC samples from 17 IVF patients were analyzed. This study was approved by the Republic of Slovenia National Medical Ethics Committee. Gene expression analysis [quantitative real time polymerase chain reaction (qPCR)] for five genes, analyzed according to embryo quality level, was performed. Two prediction models were tested for embryo quality prediction: a binary logistic and a decision tree model. As the main outcome, gene expression levels for five genes were taken and the area under the curve (AUC) for two prediction models were calculated. Among tested genes, AMHR2 and LIF showed significant expression difference between high quality and low quality embryos. These two genes were used for the construction of two prediction models: the binary logistic model yielded an AUC of 0.72 ± 0.08 and the decision tree model yielded an AUC of 0.73 ± 0.03. Two different prediction models yielded similar predictive power to differentiate high and low quality embryos. In terms of eventual clinical decision making, the decision tree model resulted in easy-to-interpret rules that are highly applicable in clinical practice. PMID:27785402

  12. 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

  13. 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.

  14. In silico Prediction of Aqueous Solubility: a Comparative Study of Local and Global Predictive Models.

    PubMed

    Raevsky, Oleg A; Polianczyk, Daniel E; Grigorev, Veniamin Yu; Raevskaja, Olga E; Dearden, John C

    2015-06-01

    32 Quantitative Structure-Property Relationship (QSPR) models were constructed for prediction of aqueous intrinsic solubility of liquid and crystalline chemicals. Data sets contained 1022 liquid and 2615 crystalline compounds. Multiple Linear Regression (MLR), Support Vector Machine (SVM) and Random Forest (RF) methods were used to construct global models, and k-nearest neighbour (kNN), Arithmetic Mean Property (AMP) and Local Regression Property (LoReP) were used to construct local models. A set of the best QSPR models was obtained: for liquid chemicals with RMSE (root mean square error) of prediction in the range 0.50-0.60 log unit; for crystalline chemicals 0.80-0.90 log unit. In the case of global models the large number of descriptors makes mechanistic interpretation difficult. The local models use only one or two descriptors, so that a medicinal chemist working with sets of structurally-related chemicals can readily estimate their solubility. However, construction of stable local models requires the presence of closely related neighbours for each chemical considered. It is probable that a consensus of global and local QSPR models will be the optimal approach for construction of stable predictive QSPR models with mechanistic interpretation.

  15. Phytosterol ester constituents affect micellar cholesterol solubility in model bile.

    PubMed

    Brown, Andrew W; Hang, Jiliang; Dussault, Patrick H; Carr, Timothy P

    2010-09-01

    Plant sterols and stanols (phytosterols) and their esters are nutraceuticals that lower LDL cholesterol, but the mechanisms of action are not fully understood. We hypothesized that intact esters and simulated hydrolysis products of esters (phytosterols and fatty acids in equal ratios) would differentially affect the solubility of cholesterol in model bile mixed micelles in vitro. Sodium salts of glycine- and taurine-conjugated bile acids were sonicated with phosphatidylcholine and either sterol esters or combinations of sterols and fatty acids to determine the amount of cholesterol solubilized into micelles. Intact sterol esters did not solubilize into micelles, nor did they alter cholesterol solubility. However, free sterols and fatty acids altered cholesterol solubility independently (no interaction effect). Equal contents of cholesterol and either campesterol, stigmasterol, sitosterol, or stigmastanol (sitostanol) decreased cholesterol solubility in micelles by approximately 50% compared to no phytosterol present, with stigmasterol performing slightly better than sitosterol. Phytosterols competed with cholesterol in a dose-dependent manner, demonstrating a 1:1 M substitution of phytosterol for cholesterol in micelle preparations. Unsaturated fatty acids increased the micelle solubility of sterols as compared with saturated or no fatty acids. No differences were detected in the size of the model micelles. Together, these data indicate that stigmasterol combined with saturated fatty acids may be more effective at lowering cholesterol micelle solubility in vivo.

  16. 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

  17. 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

  18. Prediction of resource volumes at untested locations using simple local prediction models

    USGS Publications Warehouse

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

    2006-01-01

    This paper shows how local spatial nonparametric prediction models can be applied to estimate volumes of recoverable gas resources at individual undrilled sites, at multiple sites on a regional scale, and to compute confidence bounds for regional volumes based on the distribution of those estimates. An approach that combines cross-validation, the jackknife, and bootstrap procedures is used to accomplish this task. Simulation experiments show that cross-validation can be applied beneficially to select an appropriate prediction model. The cross-validation procedure worked well for a wide range of different states of nature and levels of information. Jackknife procedures are used to compute individual prediction estimation errors at undrilled locations. The jackknife replicates also are used with a bootstrap resampling procedure to compute confidence bounds for the total volume. The method was applied to data (partitioned into a training set and target set) from the Devonian Antrim Shale continuous-type gas play in the Michigan Basin in Otsego County, Michigan. The analysis showed that the model estimate of total recoverable volumes at prediction sites is within 4 percent of the total observed volume. The model predictions also provide frequency distributions of the cell volumes at the production unit scale. Such distributions are the basis for subsequent economic analyses. ?? Springer Science+Business Media, LLC 2007.

  19. 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

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

    PubMed Central

    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

  1. 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

  2. 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.

  3. 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.

  4. 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

  5. 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

  6. [Predicting suicide or predicting the unpredictable in an uncertain world: Reinforcement Learning Model-Based analysis].

    PubMed

    Desseilles, Martin

    2012-01-01

    In general, it appears that the suicidal act is highly unpredictable with the current scientific means available. In this article, the author submits the hypothesis that predicting suicide is complex because it results in predicting a choice, in itself unpredictable. The article proposes a Reinforcement learning model-based analysis. In this model, we integrate on the one hand, four ascending modulatory neurotransmitter systems (acetylcholine, noradrenalin, serotonin, and dopamine) with their regions of respective projections and afferences, and on the other hand, various observations of brain imaging identified until now in the suicidal process.

  7. Negative affect predicts social functioning across schizophrenia and bipolar disorder: Findings from an integrated data analysis.

    PubMed

    Grove, Tyler B; Tso, Ivy F; Chun, Jinsoo; Mueller, Savanna A; Taylor, Stephan F; Ellingrod, Vicki L; McInnis, Melvin G; Deldin, Patricia J

    2016-09-30

    Most people with a serious mental illness experience significant functional impairment despite ongoing pharmacological treatment. Thus, in order to improve outcomes, a better understanding of functional predictors is needed. This study examined negative affect, a construct comprised of negative emotional experience, as a predictor of social functioning across serious mental illnesses. One hundred twenty-seven participants with schizophrenia, 113 with schizoaffective disorder, 22 with psychosis not otherwise specified, 58 with bipolar disorder, and 84 healthy controls (N=404) completed self-report negative affect measures. Elevated levels of negative affect were observed in clinical participants compared with healthy controls. For both clinical and healthy control participants, negative affect measures were significantly correlated with social functioning, and consistently explained significant amounts of variance in functioning. For clinical participants, this relationship persisted even after accounting for cognition and positive/negative symptoms. The findings suggest that negative affect is a strong predictor of outcome across these populations and treatment of serious mental illnesses should target elevated negative affect in addition to cognition and positive/negative symptoms.

  8. 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.

  9. 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

  10. 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

  11. 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

  12. Predictive Modeling of Coral Disease Distribution within a Reef System

    PubMed Central

    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

  13. 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.

  14. 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.

  15. 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

  16. 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.

  17. 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...

  18. 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.

  19. 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...

  20. 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.

  1. Numerical Modelling and Prediction of Erosion Induced by Hydrodynamic Cavitation

    NASA Astrophysics Data System (ADS)

    Peters, A.; Lantermann, U.; el Moctar, O.

    2015-12-01

    The present work aims to predict cavitation erosion using a numerical flow solver together with a new developed erosion model. The erosion model is based on the hypothesis that collapses of single cavitation bubbles near solid boundaries form high velocity microjets, which cause sonic impacts with high pressure amplitudes damaging the surface. The erosion model uses information from a numerical Euler-Euler flow simulation to predict erosion sensitive areas and assess the erosion aggressiveness of the flow. The obtained numerical results were compared to experimental results from tests of an axisymmetric nozzle.

  2. Thermal barrier coating life-prediction model development

    NASA Technical Reports Server (NTRS)

    Strangman, T. E.; Neumann, J. F.; Liu, A.

    1987-01-01

    The primary objective of this program was to develop an operative thermal barrier coating (TBC) design model for life prediction. The objective was successfully accomplished with the development, calibration, and demonstration of a mechanistic thermochemical model which rapidly predicts TBC life as a function of engine, mission, and materials system parameters. This thermochemical design model accounts for the three operative TBC damage modes (bond coating oxidation, zirconia toughness reduction, and molten salt film damage), which all contribute to spalling of the insulating zirconia layer.

  3. 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

  4. 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.

  5. Comparison of tropospheric scintillation prediction models of the Indonesian climate

    NASA Astrophysics Data System (ADS)

    Chen, Cheng Yee; Singh, Mandeep Jit

    2014-12-01

    Tropospheric scintillation is a phenomenon that will cause signal degradation in satellite communication with low fade margin. Few studies of scintillation have been conducted in tropical regions. To analyze tropospheric scintillation, we obtain data from a satellite link installed at Bandung, Indonesia, at an elevation angle of 64.7° and a frequency of 12.247 GHz from 1999 to 2000. The data are processed and compared with the predictions of several well-known scintillation prediction models. From the analysis, we found that the ITU-R model gives the lowest error rate when predicting the scintillation intensity for fade at 4.68%. However, the model should be further tested using data from higher-frequency bands, such as the K and Ka bands, to verify the accuracy of the model.

  6. 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.

  7. 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

  8. 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_abstract.

  9. Identifying Risk for Self-Harm: Rumination and Negative Affectivity in the Prospective Prediction of Nonsuicidal Self-Injury.

    PubMed

    Nicolai, Katey Anne; Wielgus, Madeline D; Mezulis, Amy

    2016-04-01

    Research suggests nonsuicidal self-injury (NSSI) may function as a maladaptive strategy to regulate negative emotions, and individuals high in trait negative affectivity (NA) may be particularly at risk. Rumination, a cognitive emotion regulation strategy, may amplify negative affect, increasing the likelihood of NSSI. The current study found that high NA and high rumination interacted to predict both likelihood of engagement in NSSI and frequency of NSSI. This study provides support for the joint contribution of cognitive and temperamental factors impacting the relationship between NA and NSSI and suggests that interventions targeted at maladaptive emotion regulation strategies may help inform individualized treatment.

  10. 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.