Optimal population prediction of sandhill crane recruitment based on climate-mediated habitat limitations

USGS Publications Warehouse

Gerber, Brian D.; Kendall, William L.; Hooten, Mevin B.; Dubovsky, James A.; Drewien, Roderick C.

2015-01-01

Prediction is fundamental to scientific enquiry and application; however, ecologists tend to favour explanatory modelling. We discuss a predictive modelling framework to evaluate ecological hypotheses and to explore novel/unobserved environmental scenarios to assist conservation and management decision-makers. We apply this framework to develop an optimal predictive model for juvenile (<1 year old) sandhill crane Grus canadensis recruitment of the Rocky Mountain Population (RMP). We consider spatial climate predictors motivated by hypotheses of how drought across multiple time-scales and spring/summer weather affects recruitment.Our predictive modelling framework focuses on developing a single model that includes all relevant predictor variables, regardless of collinearity. This model is then optimized for prediction by controlling model complexity using a data-driven approach that marginalizes or removes irrelevant predictors from the model. Specifically, we highlight two approaches of statistical regularization, Bayesian least absolute shrinkage and selection operator (LASSO) and ridge regression.Our optimal predictive Bayesian LASSO and ridge regression models were similar and on average 37% superior in predictive accuracy to an explanatory modelling approach. Our predictive models confirmed a priori hypotheses that drought and cold summers negatively affect juvenile recruitment in the RMP. The effects of long-term drought can be alleviated by short-term wet spring–summer months; however, the alleviation of long-term drought has a much greater positive effect on juvenile recruitment. The number of freezing days and snowpack during the summer months can also negatively affect recruitment, while spring snowpack has a positive effect.Breeding habitat, mediated through climate, is a limiting factor on population growth of sandhill cranes in the RMP, which could become more limiting with a changing climate (i.e. increased drought). These effects are likely not unique to cranes. The alteration of hydrological patterns and water levels by drought may impact many migratory, wetland nesting birds in the Rocky Mountains and beyond.Generalizable predictive models (trained by out-of-sample fit and based on ecological hypotheses) are needed by conservation and management decision-makers. Statistical regularization improves predictions and provides a general framework for fitting models with a large number of predictors, even those with collinearity, to simultaneously identify an optimal predictive model while conducting rigorous Bayesian model selection. Our framework is important for understanding population dynamics under a changing climate and has direct applications for making harvest and habitat management decisions.

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…

Prediction of cadmium enrichment in reclaimed coastal soils by classification and regression tree

NASA Astrophysics Data System (ADS)

Ru, Feng; Yin, Aijing; Jin, Jiaxin; Zhang, Xiuying; Yang, Xiaohui; Zhang, Ming; Gao, Chao

2016-08-01

Reclamation of coastal land is one of the most common ways to obtain land resources in China. However, it has long been acknowledged that the artificial interference with coastal land has disadvantageous effects, such as heavy metal contamination. This study aimed to develop a prediction model for cadmium enrichment levels and assess the importance of affecting factors in typical reclaimed land in Eastern China (DFCL: Dafeng Coastal Land). Two hundred and twenty seven surficial soil/sediment samples were collected and analyzed to identify the enrichment levels of cadmium and the possible affecting factors in soils and sediments. The classification and regression tree (CART) model was applied in this study to predict cadmium enrichment levels. The prediction results showed that cadmium enrichment levels assessed by the CART model had an accuracy of 78.0%. The CART model could extract more information on factors affecting the environmental behavior of cadmium than correlation analysis. The integration of correlation analysis and the CART model showed that fertilizer application and organic carbon accumulation were the most important factors affecting soil/sediment cadmium enrichment levels, followed by particle size effects (Al2O3, TFe2O3 and SiO2), contents of Cl and S, surrounding construction areas and reclamation history.

Extracting falsifiable predictions from sloppy models.

PubMed

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

2007-12-01

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

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

Sink fast and swim harder! Round-trip cost-of-transport for buoyant divers.

PubMed

Miller, Patrick J O; Biuw, Martin; Watanabe, Yuuki Y; Thompson, Dave; Fedak, Mike A

2012-10-15

Efficient locomotion between prey resources at depth and oxygen at the surface is crucial for breath-hold divers to maximize time spent in the foraging layer, and thereby net energy intake rates. The body density of divers, which changes with body condition, determines the apparent weight (buoyancy) of divers, which may affect round-trip cost-of-transport (COT) between the surface and depth. We evaluated alternative predictions from external-work and actuator-disc theory of how non-neutral buoyancy affects round-trip COT to depth, and the minimum COT speed for steady-state vertical transit. Not surprisingly, the models predict that one-way COT decreases (increases) when buoyancy aids (hinders) one-way transit. At extreme deviations from neutral buoyancy, gliding at terminal velocity is the minimum COT strategy in the direction aided by buoyancy. In the transit direction hindered by buoyancy, the external-work model predicted that minimum COT speeds would not change at greater deviations from neutral buoyancy, but minimum COT speeds were predicted to increase under the actuator disc model. As previously documented for grey seals, we found that vertical transit rates of 36 elephant seals increased in both directions as body density deviated from neutral buoyancy, indicating that actuator disc theory may more closely predict the power requirements of divers affected by gravity than an external work model. For both models, minor deviations from neutral buoyancy did not affect minimum COT speed or round-trip COT itself. However, at body-density extremes, both models predict that savings in the aided direction do not fully offset the increased COT imposed by the greater thrusting required in the hindered direction.

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...

Fear and Loving in Las Vegas: Evolution, Emotion, and Persuasion

PubMed Central

Griskevicius, Vladas; Goldstein, Noah J.; Mortensen, Chad R.; Sundie, Jill M.; Cialdini, Robert B.; Kenrick, Douglas T.

2009-01-01

How do arousal-inducing contexts, such as frightening or romantic television programs, influence the effectiveness of basic persuasion heuristics? Different predictions are made by three theoretical models: A general arousal model predicts that arousal should increase effectiveness of heuristics; an affective valence model predicts that effectiveness should depend on whether the context elicits positive or negative affect; an evolutionary model predicts that persuasiveness should depend on both the specific emotion that is elicited and the content of the particular heuristic. Three experiments examined how fear-inducing versus romantic contexts influenced the effectiveness of two widely used heuristics—social proof (e.g., “most popular”) and scarcity (e.g., “limited edition”). Results supported predictions from an evolutionary model, showing that fear can lead scarcity appeals to be counter-persuasive, and that romantic desire can lead social proof appeals to be counter-persuasive. The findings highlight how an evolutionary theoretical approach can lead to novel theoretical and practical marketing insights. PMID:19727416

Negative affective spillover from daily events predicts early response to cognitive therapy for depression.

PubMed

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

2008-12-01

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 compute each patient's within-day relationship between daily stressors and daily NA (within-day reactivity), as well as the relationship between daily stressors and next-day NA (next-day reactivity; affective spillover). In growth model analyses, the authors evaluated the predictive role of patients' NA reactivity in their early (Sessions 1-4) and late (Sessions 5-12) response to CT. Within-day NA reactivity did not predict early or late response to CT. However, next-day reactivity predicted early response to CT, such that patients who had greater NA spillover in response to negative events had a slower rate of symptom change during the first 4 sessions. Affective spillover did not influence later response to CT. The findings suggest that depressed patients who have difficulty bouncing back the next day from their NA reactions to a relative increase in daily negative events will respond less quickly to the early sessions of CT.

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.

Daily spillover to and from binge eating in first-year university females.

PubMed

Barker, Erin T; Williams, Rebecca L; Galambos, Nancy L

2006-01-01

Coping models of binge eating propose that stress and/or negative affect trigger binge eating, which serves to shift attention to the binge and its consequences. The current study tested these general assumptions using 14-day daily diary data collected from 66 first-year university females. Hierarchical Generalized Linear Modeling results showed that increased stress, negative affect, and weight concerns were associated with an increased likelihood of reporting symptoms of binge eating within days. Elevated weight concerns predicted next-day binge eating and binge eating predicted greater next-day negative affect. Discussion focuses on implications for coping models of binge eating.

Child-related cognitions and affective functioning of physically abusive and comparison parents.

PubMed

Haskett, Mary E; Smith Scott, Susan; Grant, Raven; Ward, Caryn Sabourin; Robinson, Canby

2003-06-01

The goal of this research was to utilize the cognitive behavioral model of abusive parenting to select and examine risk factors to illuminate the unique and combined influences of social cognitive and affective variables in predicting abuse group membership. Participants included physically abusive parents (n=56) and a closely-matched group of comparison parents (n=62). Social cognitive risk variables measured were (a) parent's expectations for children's abilities and maturity, (b) parental attributions of intentionality of child misbehavior, and (c) parents' perceptions of their children's adjustment. Affective risk variables included (a) psychopathology and (b) parenting stress. A series of logistic regression models were constructed to test the individual, combined, and interactive effects of risk variables on abuse group membership. The full set of five risk variables was predictive of abuse status; however, not all variables were predictive when considered individually and interactions did not contribute significantly to prediction. A risk composite score computed for each parent based on the five risk variables significantly predicted abuse status. Wide individual differences in risk across the five variables were apparent within the sample of abusive parents. Findings were generally consistent with a cognitive behavioral model of abuse, with cognitive variables being more salient in predicting abuse status than affective factors. Results point to the importance of considering diversity in characteristics of abusive parents.

Optimal population prediction of sandhill crane recruitment based on climate-mediated habitat limitations.

PubMed

Gerber, Brian D; Kendall, William L; Hooten, Mevin B; Dubovsky, James A; Drewien, Roderick C

2015-09-01

1. Prediction is fundamental to scientific enquiry and application; however, ecologists tend to favour explanatory modelling. We discuss a predictive modelling framework to evaluate ecological hypotheses and to explore novel/unobserved environmental scenarios to assist conservation and management decision-makers. We apply this framework to develop an optimal predictive model for juvenile (<1 year old) sandhill crane Grus canadensis recruitment of the Rocky Mountain Population (RMP). We consider spatial climate predictors motivated by hypotheses of how drought across multiple time-scales and spring/summer weather affects recruitment. 2. Our predictive modelling framework focuses on developing a single model that includes all relevant predictor variables, regardless of collinearity. This model is then optimized for prediction by controlling model complexity using a data-driven approach that marginalizes or removes irrelevant predictors from the model. Specifically, we highlight two approaches of statistical regularization, Bayesian least absolute shrinkage and selection operator (LASSO) and ridge regression. 3. Our optimal predictive Bayesian LASSO and ridge regression models were similar and on average 37% superior in predictive accuracy to an explanatory modelling approach. Our predictive models confirmed a priori hypotheses that drought and cold summers negatively affect juvenile recruitment in the RMP. The effects of long-term drought can be alleviated by short-term wet spring-summer months; however, the alleviation of long-term drought has a much greater positive effect on juvenile recruitment. The number of freezing days and snowpack during the summer months can also negatively affect recruitment, while spring snowpack has a positive effect. 4. Breeding habitat, mediated through climate, is a limiting factor on population growth of sandhill cranes in the RMP, which could become more limiting with a changing climate (i.e. increased drought). These effects are likely not unique to cranes. The alteration of hydrological patterns and water levels by drought may impact many migratory, wetland nesting birds in the Rocky Mountains and beyond. 5. Generalizable predictive models (trained by out-of-sample fit and based on ecological hypotheses) are needed by conservation and management decision-makers. Statistical regularization improves predictions and provides a general framework for fitting models with a large number of predictors, even those with collinearity, to simultaneously identify an optimal predictive model while conducting rigorous Bayesian model selection. Our framework is important for understanding population dynamics under a changing climate and has direct applications for making harvest and habitat management decisions. Published 2015. This article is a U.S. Government work and is in the public domain in the USA.

Development and evaluation of a regression-based model to predict cesium-137 concentration ratios for saltwater fish.

PubMed

Pinder, John E; Rowan, David J; Smith, Jim T

2016-02-01

Data from published studies and World Wide Web sources were combined to develop a regression model to predict (137)Cs concentration ratios for saltwater fish. Predictions were developed from 1) numeric trophic levels computed primarily from random resampling of known food items and 2) K concentrations in the saltwater for 65 samplings from 41 different species from both the Atlantic and Pacific Oceans. A number of different models were initially developed and evaluated for accuracy which was assessed as the ratios of independently measured concentration ratios to those predicted by the model. In contrast to freshwater systems, were K concentrations are highly variable and are an important factor in affecting fish concentration ratios, the less variable K concentrations in saltwater were relatively unimportant in affecting concentration ratios. As a result, the simplest model, which used only trophic level as a predictor, had comparable accuracies to more complex models that also included K concentrations. A test of model accuracy involving comparisons of 56 published concentration ratios from 51 species of marine fish to those predicted by the model indicated that 52 of the predicted concentration ratios were within a factor of 2 of the observed concentration ratios. Copyright © 2015 Elsevier Ltd. All rights reserved.

Predicting live and dead tree basal area of bark beetle affected forests from discrete-return lidar

Treesearch

Benjamin C. Bright; Andrew T. Hudak; Robert McGaughey; Hans-Erik Andersen; Jose Negron

2013-01-01

Bark beetle outbreaks have killed large numbers of trees across North America in recent years. Lidar remote sensing can be used to effectively estimate forest biomass, but prediction of both live and dead standing biomass in beetle-affected forests using lidar alone has not been demonstrated. We developed Random Forest (RF) models predicting total, live, dead, and...

The Mechanisms for Within-Host Influenza Virus Control Affect Model-Based Assessment and Prediction of Antiviral Treatment

PubMed Central

Cao, Pengxing

2017-01-01

Models of within-host influenza viral dynamics have contributed to an improved understanding of viral dynamics and antiviral effects over the past decade. Existing models can be classified into two broad types based on the mechanism of viral control: models utilising target cell depletion to limit the progress of infection and models which rely on timely activation of innate and adaptive immune responses to control the infection. In this paper, we compare how two exemplar models based on these different mechanisms behave and investigate how the mechanistic difference affects the assessment and prediction of antiviral treatment. We find that the assumed mechanism for viral control strongly influences the predicted outcomes of treatment. Furthermore, we observe that for the target cell-limited model the assumed drug efficacy strongly influences the predicted treatment outcomes. The area under the viral load curve is identified as the most reliable predictor of drug efficacy, and is robust to model selection. Moreover, with support from previous clinical studies, we suggest that the target cell-limited model is more suitable for modelling in vitro assays or infection in some immunocompromised/immunosuppressed patients while the immune response model is preferred for predicting the infection/antiviral effect in immunocompetent animals/patients. PMID:28933757

Teammate Prosocial and Antisocial Behaviors Predict Task Cohesion and Burnout: The Mediating Role of Affect.

PubMed

Al-Yaaribi, Ali; Kavussanu, Maria

2017-06-01

The manner in which teammates behave toward each other when playing sport could have important achievement-related consequences. However, this issue has received very little research attention. In this study, we investigated whether (a) prosocial and antisocial teammate behaviors predict task cohesion and burnout, and (b) positive and negative affect mediates these relationships. In total, 272 (M age  = 21.86, SD = 4.36) team-sport players completed a multisection questionnaire assessing the aforementioned variables. Structural equation modeling indicated that prosocial teammate behavior positively predicted task cohesion and negatively predicted burnout, and these relationships were mediated by positive affect. The reverse pattern of relationships was observed for antisocial teammate behavior which negatively predicted task cohesion and positively predicted burnout, and these relationships were mediated by negative affect. Our findings underscore the importance of promoting prosocial and reducing antisocial behaviors in sport and highlight the role of affect in explaining the identified relationships.

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

Predicting herbicide mixture effects on multiple algal species using mixture toxicity models.

PubMed

Nagai, Takashi

2017-10-01

The validity of the application of mixture toxicity models, concentration addition and independent action, to a species sensitivity distribution (SSD) for calculation of a multisubstance potentially affected fraction was examined in laboratory experiments. Toxicity assays of herbicide mixtures using 5 species of periphytic algae were conducted. Two mixture experiments were designed: a mixture of 5 herbicides with similar modes of action and a mixture of 5 herbicides with dissimilar modes of action, corresponding to the assumptions of the concentration addition and independent action models, respectively. Experimentally obtained mixture effects on 5 algal species were converted to the fraction of affected (>50% effect on growth rate) species. The predictive ability of the concentration addition and independent action models with direct application to SSD depended on the mode of action of chemicals. That is, prediction was better for the concentration addition model than the independent action model for the mixture of herbicides with similar modes of action. In contrast, prediction was better for the independent action model than the concentration addition model for the mixture of herbicides with dissimilar modes of action. Thus, the concentration addition and independent action models could be applied to SSD in the same manner as for a single-species effect. The present study to validate the application of the concentration addition and independent action models to SSD supports the usefulness of the multisubstance potentially affected fraction as the index of ecological risk. Environ Toxicol Chem 2017;36:2624-2630. © 2017 SETAC. © 2017 SETAC.

When relationships estimated in the past cannot be used to predict the future: using mechanistic models to predict landscape ecological dynamics in a changing world

Treesearch

Eric J. Gustafson

2013-01-01

Researchers and natural resource managers need predictions of how multiple global changes (e.g., climate change, rising levels of air pollutants, exotic invasions) will affect landscape composition and ecosystem function. Ecological predictive models used for this purpose are constructed using either a mechanistic (process-based) or a phenomenological (empirical)...

Affective Dynamics of Leadership: An Experimental Test of Affect Control Theory

ERIC Educational Resources Information Center

Schroder, Tobias; Scholl, Wolfgang

2009-01-01

Affect Control Theory (ACT; Heise 1979, 2007) states that people control social interactions by striving to maintain culturally shared feelings about the situation. The theory is based on mathematical models of language-based impression formation. In a laboratory experiment, we tested the predictive power of a new German-language ACT model with…

The Tripartite Model of Risk Perception (TRIRISK): Distinguishing Deliberative, Affective, and Experiential Components of Perceived Risk.

PubMed

Ferrer, Rebecca A; Klein, William M P; Persoskie, Alexander; Avishai-Yitshak, Aya; Sheeran, Paschal

2016-10-01

Although risk perception is a key predictor in health behavior theories, current conceptions of risk comprise only one (deliberative) or two (deliberative vs. affective/experiential) dimensions. This research tested a tripartite model that distinguishes among deliberative, affective, and experiential components of risk perception. In two studies, and in relation to three common diseases (cancer, heart disease, diabetes), we used confirmatory factor analyses to examine the factor structure of the tripartite risk perception (TRIRISK) model and compared the fit of the TRIRISK model to dual-factor and single-factor models. In a third study, we assessed concurrent validity by examining the impact of cancer diagnosis on (a) levels of deliberative, affective, and experiential risk perception, and (b) the strength of relations among risk components, and tested predictive validity by assessing relations with behavioral intentions to prevent cancer. The tripartite factor structure was supported, producing better model fit across diseases (studies 1 and 2). Inter-correlations among the components were significantly smaller among participants who had been diagnosed with cancer, suggesting that affected populations make finer-grained distinctions among risk perceptions (study 3). Moreover, all three risk perception components predicted unique variance in intentions to engage in preventive behavior (study 3). The TRIRISK model offers both a novel conceptualization of health-related risk perceptions, and new measures that enhance predictive validity beyond that engendered by unidimensional and bidimensional models. The present findings have implications for the ways in which risk perceptions are targeted in health behavior change interventions, health communications, and decision aids.

The role of affect and cognition in health decision making.

PubMed

Keer, Mario; van den Putte, Bas; Neijens, Peter

2010-03-01

Both affective and cognitive evaluations of behaviours have been allocated various positions in theoretical models of decision making. Most often, they have been studied as direct determinants of either intention or overall evaluation, but these two possible positions have never been compared. The aim of this study was to determine whether affective and cognitive evaluations influence intention directly, or whether their influence is mediated by overall evaluation. A sample of 300 university students filled in questionnaires on their affective, cognitive, and overall evaluations in respect of 20 health behaviours. The data were interpreted using mediation analyses with the application of path modelling. Both affective and cognitive evaluations were found to have significantly predicted intention. The influence of affective evaluation was largely direct for each of the behaviours studied, whereas that of cognitive evaluation was partially direct and partially mediated by overall evaluation. These results indicate that decisions regarding the content of persuasive communication (affective vs. cognitive) are highly dependent on the theoretical model chosen. It is suggested that affective evaluation should be included as a direct determinant of intention in theories of decision making when predicting health behaviours.

Special Issue on Uncertainty Quantification in Multiscale System Design and Simulation

DOE PAGES

Wang, Yan; Swiler, Laura

2017-09-07

The importance of uncertainty has been recognized in various modeling, simulation, and analysis applications, where inherent assumptions and simplifications affect the accuracy of model predictions for physical phenomena. As model predictions are now heavily relied upon for simulation-based system design, which includes new materials, vehicles, mechanical and civil structures, and even new drugs, wrong model predictions could potentially cause catastrophic consequences. Therefore, uncertainty and associated risks due to model errors should be quantified to support robust systems engineering.

Special Issue on Uncertainty Quantification in Multiscale System Design and Simulation

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

Wang, Yan; Swiler, Laura

The importance of uncertainty has been recognized in various modeling, simulation, and analysis applications, where inherent assumptions and simplifications affect the accuracy of model predictions for physical phenomena. As model predictions are now heavily relied upon for simulation-based system design, which includes new materials, vehicles, mechanical and civil structures, and even new drugs, wrong model predictions could potentially cause catastrophic consequences. Therefore, uncertainty and associated risks due to model errors should be quantified to support robust systems engineering.

Differences in the subjective and motivational properties of alcohol across alcohol use severity: application of a novel translational human laboratory paradigm.

PubMed

Bujarski, Spencer; Jentsch, J David; Roche, Daniel J O; Ramchandani, Vijay A; Miotto, Karen; Ray, Lara A

2018-05-08

The Allostatic Model proposes that Alcohol Use Disorder (AUD) is associated with a transition in the motivational structure of alcohol drinking: from positive reinforcement in early-stage drinking to negative reinforcement in late-stage dependence. However, direct empirical support for this preclinical model from human experiments is limited. This study tests predictions derived from the Allostatic Model in humans. Specifically, this study tested whether alcohol use severity (1) independently predicts subjective responses to alcohol (SR; comprised of stimulation/hedonia, negative affect, sedation and craving domains), and alcohol self-administration and 2) moderates associations between domains of SR and alcohol self-administration. Heavy drinking participants ranging in severity of alcohol use and problems (N = 67) completed an intravenous alcohol administration paradigm combining an alcohol challenge (target BrAC = 60 mg%), with progressive ratio self-administration. Alcohol use severity was associated with greater baseline negative affect, sedation, and craving but did not predict changes in any SR domain during the alcohol challenge. Alcohol use severity also predicted greater self-administration. Craving during the alcohol challenge strongly predicted self-administration and sedation predicted lower self-administration. Neither stimulation, nor negative affect predicted self-administration. This study represents a novel approach to translating preclinical neuroscientific theories to the human laboratory. As expected, craving predicted self-administration and sedation was protective. Contrary to the predictions of the Allostatic Model, however, these results were inconsistent with a transition from positively to negatively reinforced alcohol consumption in severe AUD. Future studies that assess negative reinforcement in the context of an acute stressor are warranted.

Infant pain-related negative affect at 12 months of age: early infant and caregiver predictors.

PubMed

Din Osmun, Laila; Pillai Riddell, Rebecca; Flora, David B

2014-01-01

To examine the predictive relationships of early infant and caregiver variables on expressed pain-related negative affect duration at the 12-month immunization. Infants and their caregivers (N = 255) were followed during immunization appointments over the first year of life. Latent growth curve modeling in a structural equation modeling context was used. Higher levels of initial infant pain reactivity at 2 months and caregiver emotional availability averaged across 2, 4, and 6 months of age were related to larger decreases in the duration of infant negative affect over the first 6 months of life. Longer duration of infant negative affect at 2 months and poorer regulation of infant negative affect over the first 6 months of life predicted longer durations of infant negative affect by 12 months. Infant negative affect at 12 months was a function of both infant factors and the quality of caregiver interactive behaviors (emotional availability) in early infancy.

Neural activity during affect labeling predicts expressive writing effects on well-being: GLM and SVM approaches

PubMed Central

Memarian, Negar; Torre, Jared B.; Haltom, Kate E.; Stanton, Annette L.

2017-01-01

Abstract Affect labeling (putting feelings into words) is a form of incidental emotion regulation that could underpin some benefits of expressive writing (i.e. writing about negative experiences). Here, we show that neural responses during affect labeling predicted changes in psychological and physical well-being outcome measures 3 months later. Furthermore, neural activity of specific frontal regions and amygdala predicted those outcomes as a function of expressive writing. Using supervised learning (support vector machines regression), improvements in four measures of psychological and physical health (physical symptoms, depression, anxiety and life satisfaction) after an expressive writing intervention were predicted with an average of 0.85% prediction error [root mean square error (RMSE) %]. The predictions were significantly more accurate with machine learning than with the conventional generalized linear model method (average RMSE: 1.3%). Consistent with affect labeling research, right ventrolateral prefrontal cortex (RVLPFC) and amygdalae were top predictors of improvement in the four outcomes. Moreover, RVLPFC and left amygdala predicted benefits due to expressive writing in satisfaction with life and depression outcome measures, respectively. This study demonstrates the substantial merit of supervised machine learning for real-world outcome prediction in social and affective neuroscience. PMID:28992270

Distribution drivers and physiological responses in geothermal bryophyte communities.

PubMed

García, Estefanía Llaneza; Rosenstiel, Todd N; Graves, Camille; Shortlidge, Erin E; Eppley, Sarah M

2016-04-01

Our ability to explain community structure rests on our ability to define the importance of ecological niches, including realized ecological niches, in shaping communities, but few studies of plant distributions have combined predictive models with physiological measures. Using field surveys and statistical modeling, we predicted distribution drivers in geothermal bryophyte (moss) communities of Lassen Volcanic National Park (California, USA). In the laboratory, we used drying and rewetting experiments to test whether the strong species-specific effects of relative humidity on distributions predicted by the models were correlated with physiological characters. We found that the three most common bryophytes in geothermal communities were significantly affected by three distinct distribution drivers: temperature, light, and relative humidity. Aulacomnium palustre, whose distribution is significantly affected by relative humidity according to our model, and which occurs in high-humidity sites, showed extreme signs of stress after drying and never recovered optimal values of PSII efficiency after rewetting. Campylopus introflexus, whose distribution is not affected by humidity according to our model, was able to maintain optimal values of PSII efficiency for 48 hr at 50% water loss and recovered optimal values of PSII efficiency after rewetting. Our results suggest that species-specific environmental stressors tightly constrain the ecological niches of geothermal bryophytes. Tests of tolerance to drying in two bryophyte species corresponded with model predictions of the comparative importance of relative humidity as distribution drivers for these species. © 2016 Botanical Society of America.

The effect of embodied emotive states on cognitive categorization.

PubMed

Price, Tom F; Harmon-Jones, Eddie

2010-12-01

Research has uncovered that positive affect broadens cognitive categorization. The motivational dimensional model, however, posits that positive affect is not a unitary construct with only one cognitive consequence. Instead, this model puts forth that there are different positive affects varying in approach motivational intensity. According to this model, only positive affects lower in motivational intensity should broaden cognitive processes, whereas positive affects higher in motivational intensity should narrow cognitive processes. Consistent with these predictions, high approach positive affect has been shown to narrow attention, whereas low approach positive affect has been shown to broaden it (Gable & Harmon-Jones, 2008). High approach positive affect, therefore, might narrow categorization. Two experiments investigated this possibility by having participants respond to cognitive categorization tasks in 3 body postures designed to elicit different levels of approach motivation: reclining backward, which should evoke low approach motivation; sitting upright, which should evoke moderate approach motivation; and leaning forward, which should evoke high approach motivation. Participants smiled while in each posture in order to experience positive affect. Experiment 1 provided initial support for the idea that high approach positive affect narrows categorization and low approach positive affect broadens categorization. Experiment 2 replicated these findings with improved smiling instructions. These results extend previous work by showing that the motivational model's predictions hold for basic attentional processes as well as higher level cognitive processes such as categorization.

Replication and extension of the dual pathway model of disordered eating: The role of fear of negative evaluation, suggestibility, rumination, and self-compassion.

PubMed

Maraldo, Toni M; Zhou, Wanni; Dowling, Jessica; Vander Wal, Jillon S

2016-12-01

The dual pathway model, a theoretical model of eating disorder development, suggests that thin ideal internalization leads to body dissatisfaction which leads to disordered eating via the dual pathways of negative affect and dietary restraint. While the dual pathway model has been a valuable guide for eating disorder prevention, greater knowledge of characteristics that predict thin ideal internalization is needed. The present study replicated and extended the dual pathway model by considering the addition of fear of negative evaluation, suggestibility, rumination, and self-compassion in a sample of community women and female university students. Results showed that fear of negative evaluation and suggestibility predicted thin ideal internalization whereas rumination and self-compassion (inversely) predicted body dissatisfaction. Negative affect was predicted by fear of negative evaluation, rumination, and self-compassion (inversely). The extended model fit the data well in both samples. Analogue and longitudinal study of these constructs is warranted in future research. Copyright © 2016 Elsevier Ltd. All rights reserved.

Affective Determinants of Anxiety and Depression Development in Children and Adolescents: An Individual Growth Curve Analysis

ERIC Educational Resources Information Center

De Bolle, Marleen; De Clercq, Barbara; Decuyper, Mieke; De Fruyt, Filip

2011-01-01

The tripartite model (in Clark and Watson, "J Abnorm Psychol" 100:316-336, 1991) comprises Negative Affect (NA), Positive Affect (PA), and Physiological Hyperarousal (PH), three temperamental-based dimensions. The current study examined the tripartite model's assumptions that (a) NA interacts with PA to predict subsequent depressive (but not…

Modeling Techniques for Shipboard Manning: A Review and Plan for Development

DTIC Science & Technology

1993-02-01

manning levels. Once manning models have been created, experiments can be conducted to show how changes in the manning structure might affect ship safety...these predictions, users of the manning models can evaluate how changes in crew configurations, manning levels, and voyage profiles affect ship safety...mitigate emergency situations would provide crucial information on how changes in manning structure would affect overall ship safety. Like emergency

Mediators of the Association of Major Depressive Syndrome and Anxiety Syndrome with Postpartum Smoking Relapse

PubMed Central

Correa-Fernández, Virmarie; Ji, Lingyun; Castro, Yessenia; Heppner, Whitney L.; Vidrine, Jennifer Irvin; Costello, Tracy J.; Mullen, Patricia Dolan; Cofta-Woerpel, Ludmila; Velasquez, Mary M.; Greisinger, Anthony; Cinciripini, Paul M.; Wetter, David W.

2012-01-01

Objective Based on conceptual models of addiction and affect regulation, this study examined the mechanisms linking current major depressive syndrome (MDS) and anxiety syndrome (AS) to postpartum smoking relapse. Method Data were collected in a randomized clinical trial from 251 women who quit smoking during pregnancy. Simple and multiple mediation models of the relations of MDS and AS with postpartum relapse were examined using linear regression, continuation ratio logit models, and a Bootstrapping procedure to test the indirect effects. Results Both MDS and AS significantly predicted postpartum smoking relapse. After adjusting for MDS, AS significantly predicted relapse. However, after adjusting for AS, MDS no longer predicted relapse. Situationally-based self-efficacy, expectancies of controlling negative affect by means other than smoking, and various dimensions of primary and secondary tobacco dependence individually mediated the effect of both MDS and AS on relapse. In multiple mediation models, self-efficacy in negative/affective situations significantly mediated the effect of MDS and AS on relapse. Conclusion The findings underscore the negative impact of depression and anxiety on postpartum smoking relapse, and suggest that the effects of MDS on postpartum relapse may be largely explained by comorbid AS. The current investigation provided mixed support for affect regulation models of addiction. Cognitive and tobacco dependence-related aspects of negative and positive reinforcement significantly mediated the relationship of depression and anxiety with relapse, while affect and stress did not. The findings emphasize the unique role of low agency with respect to abstaining from smoking in negative affective situations as a key predictor of postpartum smoking relapse. PMID:22390410

Mediators of the association of major depressive syndrome and anxiety syndrome with postpartum smoking relapse.

PubMed

Correa-Fernández, Virmarie; Ji, Lingyun; Castro, Yessenia; Heppner, Whitney L; Vidrine, Jennifer Irvin; Costello, Tracy J; Mullen, Patricia Dolan; Cofta-Woerpel, Ludmila; Velasquez, Mary M; Greisinger, Anthony; Cinciripini, Paul M; Wetter, David W

2012-08-01

Based on conceptual models of addiction and affect regulation, this study examined the mechanisms linking current major depressive syndrome (MDS) and anxiety syndrome (AS) to postpartum smoking relapse. Data were collected in a randomized clinical trial from 251 women who quit smoking during pregnancy. Simple and multiple mediation models of the relations of MDS and AS with postpartum relapse were examined using linear regression, continuation ratio logit models, and a bootstrapping procedure to test the indirect effects. Both MDS and AS significantly predicted postpartum smoking relapse. After adjusting for MDS, AS significantly predicted relapse. However, after adjusting for AS, MDS no longer predicted relapse. Situationally based self-efficacy, expectancies of controlling negative affect by means other than smoking, and various dimensions of primary and secondary tobacco dependence individually mediated the effect of both MDS and AS on relapse. In multiple mediation models, self-efficacy in negative/affective situations significantly mediated the effect of MDS and AS on relapse. The findings underscore the negative impact of depression and anxiety on postpartum smoking relapse and suggest that the effects of MDS on postpartum relapse may be largely explained by comorbid AS. The current investigation provided mixed support for affect regulation models of addiction. Cognitive and tobacco dependence-related aspects of negative and positive reinforcement significantly mediated the relationship of depression and anxiety with relapse, whereas affect and stress did not. The findings emphasize the unique role of low agency with respect to abstaining from smoking in negative affective situations as a key predictor of postpartum smoking relapse. © 2012 American Psychological Association

Concurrent and prognostic utility of subtyping anorexia nervosa along dietary and negative affect dimensions.

PubMed

Forbush, Kelsie T; Hagan, Kelsey E; Salk, Rachel H; Wildes, Jennifer E

2017-03-01

Bulimia nervosa can be reliably classified into subtypes based on dimensions of dietary restraint and negative affect. Community and clinical studies have shown that dietary-negative affect subtypes have greater test-retest reliability and concurrent and predictive validity compared to subtypes based on the Diagnostic and Statistical Manual of Mental Disorders (DSM). Although dietary-negative affect subtypes have shown utility for characterizing eating disorders that involve binge eating, this framework may have broader implications for understanding restrictive eating disorders. The purpose of this study was to test the concurrent and predictive validity of dietary-negative affect subtypes among patients with anorexia nervosa (AN; N = 194). Latent profile analysis was used to identify subtypes of AN based on dimensions of dietary restraint and negative affect. Chi-square and multivariate analysis of variance were used to characterize baseline differences between identified subtypes. Structural equation modeling was used to test whether dietary-negative affect subtypes would outperform DSM categories in predicting clinically relevant outcomes. Results supported a 2-profile model that replicated dietary-negative affect subtypes: Latent Profile 1 (n = 68) had clinically elevated scores on restraint only; Latent Profile 2 (n = 126) had elevated scores on both restraint and negative affect. Validation analyses showed that membership in the dietary-negative affect profile was associated with greater lifetime psychiatric comorbidity and psychosocial impairment compared to the dietary class. Dietary-negative affect subtypes only outperformed DSM categories in predicting quality-of-life impairment at 1-year follow-up. Findings highlight the clinical utility of subtyping AN based on dietary restraint and negative affect for informing future treatment-matching or personalized medicine strategies. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Variability of the soil-to-plant radiocaesium transfer factor for Japanese soils predicted with soil and plant properties.

PubMed

Uematsu, Shinichiro; Vandenhove, Hildegarde; Sweeck, Lieve; Van Hees, May; Wannijn, Jean; Smolders, Erik

2016-03-01

Food chain contamination with radiocaesium (RCs) in the aftermath of the Fukushima accident calls for an analysis of the specific factors that control the RCs transfer. Here, soil-to-plant transfer factors (TF) of RCs for grass were predicted from the potassium concentration in soil solution (mK) and the Radiocaesium Interception Potential (RIP) of the soil using existing mechanistic models. The mK and RIP were (a) either measured for 37 topsoils collected from the Fukushima accident affected area or (b) predicted from the soil clay content and the soil exchangeable potassium content using the models that had been calibrated for European soils. An average ammonium concentration was used throughout in the prediction. The measured RIP ranged 14-fold and measured mK varied 37-fold among the soils. The measured RIP was lower than the RIP predicted from the soil clay content likely due to the lower content of weathered micas in the clay fraction of Japanese soils. Also the measured mK was lower than that predicted. As a result, the predicted TFs relying on the measured RIP and mK were, on average, about 22-fold larger than the TFs predicted using the European calibrated models. The geometric mean of the measured TFs for grass in the affected area (N = 82) was in the middle of both. The TFs were poorly related to soil classification classes, likely because soil fertility (mK) was obscuring the effects of the soil classification related to the soil mineralogy (RIP). This study suggests that, on average, Japanese soils are more vulnerable than European soils at equal soil clay and exchangeable K content. The affected regions will be targeted for refined model validation. Copyright © 2015 Elsevier Ltd. All rights reserved.

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

USGS Publications Warehouse

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

2006-01-01

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

Comparison of RNA-seq and microarray-based models for clinical endpoint prediction.

PubMed

Zhang, Wenqian; Yu, Ying; Hertwig, Falk; Thierry-Mieg, Jean; Zhang, Wenwei; Thierry-Mieg, Danielle; Wang, Jian; Furlanello, Cesare; Devanarayan, Viswanath; Cheng, Jie; Deng, Youping; Hero, Barbara; Hong, Huixiao; Jia, Meiwen; Li, Li; Lin, Simon M; Nikolsky, Yuri; Oberthuer, André; Qing, Tao; Su, Zhenqiang; Volland, Ruth; Wang, Charles; Wang, May D; Ai, Junmei; Albanese, Davide; Asgharzadeh, Shahab; Avigad, Smadar; Bao, Wenjun; Bessarabova, Marina; Brilliant, Murray H; Brors, Benedikt; Chierici, Marco; Chu, Tzu-Ming; Zhang, Jibin; Grundy, Richard G; He, Min Max; Hebbring, Scott; Kaufman, Howard L; Lababidi, Samir; Lancashire, Lee J; Li, Yan; Lu, Xin X; Luo, Heng; Ma, Xiwen; Ning, Baitang; Noguera, Rosa; Peifer, Martin; Phan, John H; Roels, Frederik; Rosswog, Carolina; Shao, Susan; Shen, Jie; Theissen, Jessica; Tonini, Gian Paolo; Vandesompele, Jo; Wu, Po-Yen; Xiao, Wenzhong; Xu, Joshua; Xu, Weihong; Xuan, Jiekun; Yang, Yong; Ye, Zhan; Dong, Zirui; Zhang, Ke K; Yin, Ye; Zhao, Chen; Zheng, Yuanting; Wolfinger, Russell D; Shi, Tieliu; Malkas, Linda H; Berthold, Frank; Wang, Jun; Tong, Weida; Shi, Leming; Peng, Zhiyu; Fischer, Matthias

2015-06-25

Gene expression profiling is being widely applied in cancer research to identify biomarkers for clinical endpoint prediction. Since RNA-seq provides a powerful tool for transcriptome-based applications beyond the limitations of microarrays, we sought to systematically evaluate the performance of RNA-seq-based and microarray-based classifiers in this MAQC-III/SEQC study for clinical endpoint prediction using neuroblastoma as a model. We generate gene expression profiles from 498 primary neuroblastomas using both RNA-seq and 44 k microarrays. Characterization of the neuroblastoma transcriptome by RNA-seq reveals that more than 48,000 genes and 200,000 transcripts are being expressed in this malignancy. We also find that RNA-seq provides much more detailed information on specific transcript expression patterns in clinico-genetic neuroblastoma subgroups than microarrays. To systematically compare the power of RNA-seq and microarray-based models in predicting clinical endpoints, we divide the cohort randomly into training and validation sets and develop 360 predictive models on six clinical endpoints of varying predictability. Evaluation of factors potentially affecting model performances reveals that prediction accuracies are most strongly influenced by the nature of the clinical endpoint, whereas technological platforms (RNA-seq vs. microarrays), RNA-seq data analysis pipelines, and feature levels (gene vs. transcript vs. exon-junction level) do not significantly affect performances of the models. We demonstrate that RNA-seq outperforms microarrays in determining the transcriptomic characteristics of cancer, while RNA-seq and microarray-based models perform similarly in clinical endpoint prediction. Our findings may be valuable to guide future studies on the development of gene expression-based predictive models and their implementation in clinical practice.

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

EPA Pesticide Factsheets

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

Prediction of blood-brain partitioning: a model based on molecular electronegativity distance vector descriptors.

PubMed

Zhang, Yong-Hong; Xia, Zhi-Ning; Qin, Li-Tang; Liu, Shu-Shen

2010-09-01

The objective of this paper is to build a reliable model based on the molecular electronegativity distance vector (MEDV) descriptors for predicting the blood-brain barrier (BBB) permeability and to reveal the effects of the molecular structural segments on the BBB permeability. Using 70 structurally diverse compounds, the partial least squares regression (PLSR) models between the BBB permeability and the MEDV descriptors were developed and validated by the variable selection and modeling based on prediction (VSMP) technique. The estimation ability, stability, and predictive power of a model are evaluated by the estimated correlation coefficient (r), leave-one-out (LOO) cross-validation correlation coefficient (q), and predictive correlation coefficient (R(p)). It has been found that PLSR model has good quality, r=0.9202, q=0.7956, and R(p)=0.6649 for M1 model based on the training set of 57 samples. To search the most important structural factors affecting the BBB permeability of compounds, we performed the values of the variable importance in projection (VIP) analysis for MEDV descriptors. It was found that some structural fragments in compounds, such as -CH(3), -CH(2)-, =CH-, =C, triple bond C-, -CH<, =C<, =N-, -NH-, =O, and -OH, are the most important factors affecting the BBB permeability. (c) 2010. Published by Elsevier Inc.

Shared Mechanisms in the Estimation of Self-Generated Actions and the Prediction of Other's Actions by Humans.

PubMed

Ikegami, Tsuyoshi; Ganesh, Gowrishankar

2017-01-01

The question of how humans predict outcomes of observed motor actions by others is a fundamental problem in cognitive and social neuroscience. Previous theoretical studies have suggested that the brain uses parts of the forward model (used to estimate sensory outcomes of self-generated actions) to predict outcomes of observed actions. However, this hypothesis has remained controversial due to the lack of direct experimental evidence. To address this issue, we analyzed the behavior of darts experts in an understanding learning paradigm and utilized computational modeling to examine how outcome prediction of observed actions affected the participants' ability to estimate their own actions. We recruited darts experts because sports experts are known to have an accurate outcome estimation of their own actions as well as prediction of actions observed in others. We first show that learning to predict the outcomes of observed dart throws deteriorates an expert's abilities to both produce his own darts actions and estimate the outcome of his own throws (or self-estimation). Next, we introduce a state-space model to explain the trial-by-trial changes in the darts performance and self-estimation through our experiment. The model-based analysis reveals that the change in an expert's self-estimation is explained only by considering a change in the individual's forward model, showing that an improvement in an expert's ability to predict outcomes of observed actions affects the individual's forward model. These results suggest that parts of the same forward model are utilized in humans to both estimate outcomes of self-generated actions and predict outcomes of observed actions.

On the accuracy and reliability of predictions by control-system theory.

PubMed

Bourbon, W T; Copeland, K E; Dyer, V R; Harman, W K; Mosley, B L

1990-12-01

In three experiments we used control-system theory (CST) to predict the results of tracking tasks on which people held a handle to keep a cursor even with a target on a computer screen. 10 people completed a total of 104 replications of the task. In each experiment, there were two conditions: in one, only the handle affected the position of the cursor; in the other, a random disturbance also affected the cursor. From a person's performance during Condition 1, we derived constants used in the CST model to predict the results of Condition 2. In two experiments, predictions occurred a few minutes before Condition 2; in one experiment, the delay was 1 yr. During a 1-min. experimental run, the positions of handle and cursor, produced by the person, were each sampled 1800 times, once every 1/30 sec. During a modeling run, the model predicted the positions of the handle and target for each of the 1800 intervals sampled in the experimental run. In 104 replications, the mean correlation between predicted and actual positions of the handle was .996; SD = .002.

Self-determination theory and diminished functioning: the role of interpersonal control and psychological need thwarting.

PubMed

Bartholomew, Kimberley J; Ntoumanis, Nikos; Ryan, Richard M; Bosch, Jos A; Thøgersen-Ntoumani, Cecilie

2011-11-01

Drawing from self-determination theory, three studies explored the social-environmental conditions that satisfy versus thwart psychological needs and, in turn, affect psychological functioning and well-being or ill-being. In cross-sectional Studies 1 and 2, structural equation modeling analyses supported latent factor models in which need satisfaction was predicted by athletes' perceptions of autonomy support, and need thwarting was better predicted by coach control. Athletes' perceptions of need satisfaction predicted positive outcomes associated with sport participation (vitality and positive affect), whereas need thwarting more consistently predicted maladaptive outcomes (disordered eating, burnout, depression, negative affect, and physical symptoms). In addition, athletes' perceptions of psychological need thwarting were significantly associated with perturbed physiological arousal (elevated levels of secretory immunoglobulin A) prior to training. The final study involved the completion of a diary and supported the relations observed in the cross-sectional studies at a daily level. These findings have important implications for the operationalization and measurement of interpersonal styles and psychological needs.

Multi-Model Combination techniques for Hydrological Forecasting: Application to Distributed Model Intercomparison Project Results

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

Ajami, N K; Duan, Q; Gao, X

2005-04-11

This paper examines several multi-model combination techniques: the Simple Multi-model Average (SMA), the Multi-Model Super Ensemble (MMSE), Modified Multi-Model Super Ensemble (M3SE) and the Weighted Average Method (WAM). These model combination techniques were evaluated using the results from the Distributed Model Intercomparison Project (DMIP), an international project sponsored by the National Weather Service (NWS) Office of Hydrologic Development (OHD). All of the multi-model combination results were obtained using uncalibrated DMIP model outputs and were compared against the best uncalibrated as well as the best calibrated individual model results. The purpose of this study is to understand how different combination techniquesmore » affect the skill levels of the multi-model predictions. This study revealed that the multi-model predictions obtained from uncalibrated single model predictions are generally better than any single member model predictions, even the best calibrated single model predictions. Furthermore, more sophisticated multi-model combination techniques that incorporated bias correction steps work better than simple multi-model average predictions or multi-model predictions without bias correction.« less

Predicting Adaptive Response to Fadrozole Exposure:Computational Model of the Fathead MinnowsHypothalamic-Pituitary-Gonadal Axis

EPA Science Inventory

Exposure to endocrine disrupting chemicals can affect reproduction and development in both humans and wildlife. We are developing a mechanistic mathematical model of the hypothalamic-pituitary-gonadal (HPG) axis in female fathead minnows to predict doseresponse and time-course (...

Software reliability studies

NASA Technical Reports Server (NTRS)

Hoppa, Mary Ann; Wilson, Larry W.

1994-01-01

There are many software reliability models which try to predict future performance of software based on data generated by the debugging process. Our research has shown that by improving the quality of the data one can greatly improve the predictions. We are working on methodologies which control some of the randomness inherent in the standard data generation processes in order to improve the accuracy of predictions. Our contribution is twofold in that we describe an experimental methodology using a data structure called the debugging graph and apply this methodology to assess the robustness of existing models. The debugging graph is used to analyze the effects of various fault recovery orders on the predictive accuracy of several well-known software reliability algorithms. We found that, along a particular debugging path in the graph, the predictive performance of different models can vary greatly. Similarly, just because a model 'fits' a given path's data well does not guarantee that the model would perform well on a different path. Further we observed bug interactions and noted their potential effects on the predictive process. We saw that not only do different faults fail at different rates, but that those rates can be affected by the particular debugging stage at which the rates are evaluated. Based on our experiment, we conjecture that the accuracy of a reliability prediction is affected by the fault recovery order as well as by fault interaction.

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

PubMed Central

Albarracín, Dolores; Kumkale, G. Tarcan

2016-01-01

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

Activity Patterns in Response to Symptoms in Patients Being Treated for Chronic Fatigue Syndrome: An Experience Sampling Methodology Study

PubMed Central

2016-01-01

Objective: Cognitive–behavioral models of chronic fatigue syndrome (CFS) propose that patients respond to symptoms with 2 predominant activity patterns—activity limitation and all-or-nothing behaviors—both of which may contribute to illness persistence. The current study investigated whether activity patterns occurred at the same time as, or followed on from, patient symptom experience and affect. Method: Twenty-three adults with CFS were recruited from U.K. CFS services. Experience sampling methodology (ESM) was used to assess fluctuations in patient symptom experience, affect, and activity management patterns over 10 assessments per day for a total of 6 days. Assessments were conducted within patients’ daily life and were delivered through an app on touchscreen Android mobile phones. Multilevel model analyses were conducted to examine the role of self-reported patient fatigue, pain, and affect as predictors of change in activity patterns at the same and subsequent assessment. Results: Current experience of fatigue-related symptoms and pain predicted higher patient activity limitation at the current and subsequent assessments whereas subjective wellness predicted higher all-or-nothing behavior at both times. Current pain predicted less all-or-nothing behavior at the subsequent assessment. In contrast to hypotheses, current positive affect was predictive of current activity limitation whereas current negative affect was predictive of current all-or-nothing behavior. Both activity patterns varied at the momentary level. Conclusions: Patient symptom experiences appear to be driving patient activity management patterns in line with the cognitive–behavioral model of CFS. ESM offers a useful method for examining multiple interacting variables within the context of patients’ daily life. PMID:27819461

A Systems Model of Parkinson's Disease Using Biochemical Systems Theory.

PubMed

Sasidharakurup, Hemalatha; Melethadathil, Nidheesh; Nair, Bipin; Diwakar, Shyam

2017-08-01

Parkinson's disease (PD), a neurodegenerative disorder, affects millions of people and has gained attention because of its clinical roles affecting behaviors related to motor and nonmotor symptoms. Although studies on PD from various aspects are becoming popular, few rely on predictive systems modeling approaches. Using Biochemical Systems Theory (BST), this article attempts to model and characterize dopaminergic cell death and understand pathophysiology of progression of PD. PD pathways were modeled using stochastic differential equations incorporating law of mass action, and initial concentrations for the modeled proteins were obtained from literature. Simulations suggest that dopamine levels were reduced significantly due to an increase in dopaminergic quinones and 3,4-dihydroxyphenylacetaldehyde (DOPAL) relating to imbalances compared to control during PD progression. Associating to clinically observed PD-related cell death, simulations show abnormal parkin and reactive oxygen species levels with an increase in neurofibrillary tangles. While relating molecular mechanistic roles, the BST modeling helps predicting dopaminergic cell death processes involved in the progression of PD and provides a predictive understanding of neuronal dysfunction for translational neuroscience.

Using System Dynamic Model and Neural Network Model to Analyse Water Scarcity in Sudan

NASA Astrophysics Data System (ADS)

Li, Y.; Tang, C.; Xu, L.; Ye, S.

2017-07-01

Many parts of the world are facing the problem of Water Scarcity. Analysing Water Scarcity quantitatively is an important step to solve the problem. Water scarcity in a region is gauged by WSI (water scarcity index), which incorporate water supply and water demand. To get the WSI, Neural Network Model and SDM (System Dynamic Model) that depict how environmental and social factors affect water supply and demand are developed to depict how environmental and social factors affect water supply and demand. The uneven distribution of water resource and water demand across a region leads to an uneven distribution of WSI within this region. To predict WSI for the future, logistic model, Grey Prediction, and statistics are applied in predicting variables. Sudan suffers from severe water scarcity problem with WSI of 1 in 2014, water resource unevenly distributed. According to the result of modified model, after the intervention, Sudan’s water situation will become better.

Neural activity during affect labeling predicts expressive writing effects on well-being: GLM and SVM approaches.

PubMed

Memarian, Negar; Torre, Jared B; Haltom, Kate E; Stanton, Annette L; Lieberman, Matthew D

2017-09-01

Affect labeling (putting feelings into words) is a form of incidental emotion regulation that could underpin some benefits of expressive writing (i.e. writing about negative experiences). Here, we show that neural responses during affect labeling predicted changes in psychological and physical well-being outcome measures 3 months later. Furthermore, neural activity of specific frontal regions and amygdala predicted those outcomes as a function of expressive writing. Using supervised learning (support vector machines regression), improvements in four measures of psychological and physical health (physical symptoms, depression, anxiety and life satisfaction) after an expressive writing intervention were predicted with an average of 0.85% prediction error [root mean square error (RMSE) %]. The predictions were significantly more accurate with machine learning than with the conventional generalized linear model method (average RMSE: 1.3%). Consistent with affect labeling research, right ventrolateral prefrontal cortex (RVLPFC) and amygdalae were top predictors of improvement in the four outcomes. Moreover, RVLPFC and left amygdala predicted benefits due to expressive writing in satisfaction with life and depression outcome measures, respectively. This study demonstrates the substantial merit of supervised machine learning for real-world outcome prediction in social and affective neuroscience. © The Author (2017). Published by Oxford University Press.

The Motor System Contributes to Comprehension of Abstract Language

PubMed Central

Guan, Connie Qun; Meng, Wanjin; Yao, Ru; Glenberg, Arthur M.

2013-01-01

If language comprehension requires a sensorimotor simulation, how can abstract language be comprehended? We show that preparation to respond in an upward or downward direction affects comprehension of the abstract quantifiers “more and more” and “less and less” as indexed by an N400-like component. Conversely, the semantic content of the sentence affects the motor potential measured immediately before the upward or downward action is initiated. We propose that this bidirectional link between motor system and language arises because the motor system implements forward models that predict the sensory consequences of actions. Because the same movement (e.g., raising the arm) can have multiple forward models for different contexts, the models can make different predictions depending on whether the arm is raised, for example, to place an object or raised as a threat. Thus, different linguistic contexts invoke different forward models, and the predictions constitute different understandings of the language. PMID:24086463

Developing Predictive Approaches to Characterize Adaptive Responses of the Reproductive Endocrine Axis to Aromatase Inhibition: Computational Modeling

EPA Science Inventory

Exposure to endocrine disrupting chemicals can affect reproduction and development in both humans and wildlife. We developed a mechanistic mathematical model of the hypothalamic-pituitary-gonadal (HPG) axis in female fathead minnows to predict dose-response and time-course (DRTC)...

Predicting Adaptive Response to Fadrozole Exposure: Computational Model of the Fathead Minnow Hypothalamic-Pituitary-Gonadal Axis

EPA Science Inventory

Exposure to endocrine disrupting chemicals can affect reproduction and development in both humans and wildlife. We are developing a mechanistic mathematical model of the hypothalamic-pituitary-gonadal (HPG) axis in female fathead minnows to predict dose-response and time-course (...

Resources predicting positive and negative affect during the experience of stress: a study of older Asian Indian immigrants in the United States.

PubMed

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

2004-10-01

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. We collected data through a telephone survey of 226 respondents (aged 50 years and older) in the Southeastern United States. We used hierarchical, negative binomial regression analyses to examine correlates of positive and negative affect. Different coping resources influenced positive and negative affect when stressful life events were controlled for. Being female was a common risk factor for poorer positive and increased negative affect. Satisfaction with friendships and a cultural or ethnic identity that is either bicultural or more American were predictive of greater positive affect. Greater religiosity and increased mastery were resources predicting less negative affect. Cognitive and structural interventions that increase opportunities for social integration, increasing mastery, and addressing spiritual concerns are discussed as ways of coping with stress to improve the well-being of individuals in this immigrant community.

Predicting Power Outages Using Multi-Model Ensemble Forecasts

NASA Astrophysics Data System (ADS)

Cerrai, D.; Anagnostou, E. N.; Yang, J.; Astitha, M.

2017-12-01

Power outages affect every year millions of people in the United States, affecting the economy and conditioning the everyday life. An Outage Prediction Model (OPM) has been developed at the University of Connecticut for helping utilities to quickly restore outages and to limit their adverse consequences on the population. The OPM, operational since 2015, combines several non-parametric machine learning (ML) models that use historical weather storm simulations and high-resolution weather forecasts, satellite remote sensing data, and infrastructure and land cover data to predict the number and spatial distribution of power outages. A new methodology, developed for improving the outage model performances by combining weather- and soil-related variables using three different weather models (WRF 3.7, WRF 3.8 and RAMS/ICLAMS), will be presented in this study. First, we will present a performance evaluation of each model variable, by comparing historical weather analyses with station data or reanalysis over the entire storm data set. Hence, each variable of the new outage model version is extracted from the best performing weather model for that variable, and sensitivity tests are performed for investigating the most efficient variable combination for outage prediction purposes. Despite that the final variables combination is extracted from different weather models, this ensemble based on multi-weather forcing and multi-statistical model power outage prediction outperforms the currently operational OPM version that is based on a single weather forcing variable (WRF 3.7), because each model component is the closest to the actual atmospheric state.

Object relations predicts borderline personality disorder symptoms beyond emotional dysregulation, negative affect, and impulsivity.

PubMed

Huprich, Steven K; Nelson, Sharon M; Paggeot, Amy; Lengu, Ketrin; Albright, Jeremy

2017-01-01

Many studies have determined that the traits of emotional dysregulation, negative affect, and impulsivity are the strongest predictors of borderline personality disorder (BPD). Although psychodynamic, empirically supported BPD treatments (i.e., transference-focused, mentalization based) focus upon changing the internal representations of self and other, no studies have simultaneously evaluated the contribution of object relations in relation to these traits in predicting BPD symptoms. This study sought to determine the combined effects of emotional dysregulation, negative affect, impulsivity, and object relations in the prediction of BPD through the use of mediation modeling in 4 a priori hypothesized relationships among these variables. One hundred sixty-nine psychiatric outpatients and 171 undergraduate students were evaluated with self-reported trait and object relations measures and were administered 2 semistructured diagnostic interviews for BPD. Although all trait and object relations measures were correlated with BPD symptoms, the best fitting model was one in which object relations partially mediated the relationship of negative affect and impulsivity with BPD symptoms. Direct effects of the traits were also observed in mediation. Self-reported object relational quality had more of an effect on the prediction of BPD than previously recognized within a trait-framework, thus further supporting the model explicated in psychodynamic and relationally based treatments for BPD. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Dynamics of Affective States during Complex Learning

ERIC Educational Resources Information Center

D'Mello, Sidney; Graesser, Art

2012-01-01

We propose a model to explain the dynamics of affective states that emerge during deep learning activities. The model predicts that learners in a state of engagement/flow will experience cognitive disequilibrium and confusion when they face contradictions, incongruities, anomalies, obstacles to goals, and other impasses. Learners revert into the…

Modeling student success in engineering education

NASA Astrophysics Data System (ADS)

Jin, Qu

In order for the United States to maintain its global competitiveness, the long-term success of our engineering students in specific courses, programs, and colleges is now, more than ever, an extremely high priority. Numerous studies have focused on factors that impact student success, namely academic performance, retention, and/or graduation. However, there are only a limited number of works that have systematically developed models to investigate important factors and to predict student success in engineering. Therefore, this research presents three separate but highly connected investigations to address this gap. The first investigation involves explaining and predicting engineering students' success in Calculus I courses using statistical models. The participants were more than 4000 first-year engineering students (cohort years 2004 - 2008) who enrolled in Calculus I courses during the first semester in a large Midwestern university. Predictions from statistical models were proposed to be used to place engineering students into calculus courses. The success rates were improved by 12% in Calculus IA using predictions from models developed over traditional placement method. The results showed that these statistical models provided a more accurate calculus placement method than traditional placement methods and help improve success rates in those courses. In the second investigation, multi-outcome and single-outcome neural network models were designed to understand and to predict first-year retention and first-year GPA of engineering students. The participants were more than 3000 first year engineering students (cohort years 2004 - 2005) enrolled in a large Midwestern university. The independent variables include both high school academic performance factors and affective factors measured prior to entry. The prediction performances of the multi-outcome and single-outcome models were comparable. The ability to predict cumulative GPA at the end of an engineering student's first year of college was about a half of a grade point for both models. The predictors of retention and cumulative GPA while being similar differ in that high school academic metrics play a more important role in predicting cumulative GPA with the affective measures playing a more important role in predicting retention. In the last investigation, multi-outcome neural network models were used to understand and to predict engineering students' retention, GPA, and graduation from entry to departure. The participants were more than 4000 engineering students (cohort years 2004 - 2006) enrolled in a large Midwestern university. Different patterns of important predictors were identified for GPA, retention, and graduation. Overall, this research explores the feasibility of using modeling to enhance a student's educational experience in engineering. Student success modeling was used to identify the most important cognitive and affective predictors for a student's first calculus course retention, GPA, and graduation. The results suggest that the statistical modeling methods have great potential to assist decision making and help ensure student success in engineering education.

Model uncertainties do not affect observed patterns of species richness in the Amazon.

PubMed

Sales, Lilian Patrícia; Neves, Olívia Viana; De Marco, Paulo; Loyola, Rafael

2017-01-01

Climate change is arguably a major threat to biodiversity conservation and there are several methods to assess its impacts on species potential distribution. Yet the extent to which different approaches on species distribution modeling affect species richness patterns at biogeographical scale is however unaddressed in literature. In this paper, we verified if the expected responses to climate change in biogeographical scale-patterns of species richness and species vulnerability to climate change-are affected by the inputs used to model and project species distribution. We modeled the distribution of 288 vertebrate species (amphibians, birds and mammals), all endemic to the Amazon basin, using different combinations of the following inputs known to affect the outcome of species distribution models (SDMs): 1) biological data type, 2) modeling methods, 3) greenhouse gas emission scenarios and 4) climate forecasts. We calculated uncertainty with a hierarchical ANOVA in which those different inputs were considered factors. The greatest source of variation was the modeling method. Model performance interacted with data type and modeling method. Absolute values of variation on suitable climate area were not equal among predictions, but some biological patterns were still consistent. All models predicted losses on the area that is climatically suitable for species, especially for amphibians and primates. All models also indicated a current East-western gradient on endemic species richness, from the Andes foot downstream the Amazon river. Again, all models predicted future movements of species upwards the Andes mountains and overall species richness losses. From a methodological perspective, our work highlights that SDMs are a useful tool for assessing impacts of climate change on biodiversity. Uncertainty exists but biological patterns are still evident at large spatial scales. As modeling methods are the greatest source of variation, choosing the appropriate statistics according to the study objective is also essential for estimating the impacts of climate change on species distribution. Yet from a conservation perspective, we show that Amazon endemic fauna is potentially vulnerable to climate change, due to expected reductions on suitable climate area. Climate-driven faunal movements are predicted towards the Andes mountains, which might work as climate refugia for migrating species.

Model uncertainties do not affect observed patterns of species richness in the Amazon

PubMed Central

Sales, Lilian Patrícia; Neves, Olívia Viana; De Marco, Paulo

2017-01-01

Background Climate change is arguably a major threat to biodiversity conservation and there are several methods to assess its impacts on species potential distribution. Yet the extent to which different approaches on species distribution modeling affect species richness patterns at biogeographical scale is however unaddressed in literature. In this paper, we verified if the expected responses to climate change in biogeographical scale—patterns of species richness and species vulnerability to climate change—are affected by the inputs used to model and project species distribution. Methods We modeled the distribution of 288 vertebrate species (amphibians, birds and mammals), all endemic to the Amazon basin, using different combinations of the following inputs known to affect the outcome of species distribution models (SDMs): 1) biological data type, 2) modeling methods, 3) greenhouse gas emission scenarios and 4) climate forecasts. We calculated uncertainty with a hierarchical ANOVA in which those different inputs were considered factors. Results The greatest source of variation was the modeling method. Model performance interacted with data type and modeling method. Absolute values of variation on suitable climate area were not equal among predictions, but some biological patterns were still consistent. All models predicted losses on the area that is climatically suitable for species, especially for amphibians and primates. All models also indicated a current East-western gradient on endemic species richness, from the Andes foot downstream the Amazon river. Again, all models predicted future movements of species upwards the Andes mountains and overall species richness losses. Conclusions From a methodological perspective, our work highlights that SDMs are a useful tool for assessing impacts of climate change on biodiversity. Uncertainty exists but biological patterns are still evident at large spatial scales. As modeling methods are the greatest source of variation, choosing the appropriate statistics according to the study objective is also essential for estimating the impacts of climate change on species distribution. Yet from a conservation perspective, we show that Amazon endemic fauna is potentially vulnerable to climate change, due to expected reductions on suitable climate area. Climate-driven faunal movements are predicted towards the Andes mountains, which might work as climate refugia for migrating species. PMID:29023503

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.

Shared Mechanisms in the Estimation of Self-Generated Actions and the Prediction of Other’s Actions by Humans

PubMed Central

Ganesh, Gowrishankar

2017-01-01

Abstract The question of how humans predict outcomes of observed motor actions by others is a fundamental problem in cognitive and social neuroscience. Previous theoretical studies have suggested that the brain uses parts of the forward model (used to estimate sensory outcomes of self-generated actions) to predict outcomes of observed actions. However, this hypothesis has remained controversial due to the lack of direct experimental evidence. To address this issue, we analyzed the behavior of darts experts in an understanding learning paradigm and utilized computational modeling to examine how outcome prediction of observed actions affected the participants’ ability to estimate their own actions. We recruited darts experts because sports experts are known to have an accurate outcome estimation of their own actions as well as prediction of actions observed in others. We first show that learning to predict the outcomes of observed dart throws deteriorates an expert’s abilities to both produce his own darts actions and estimate the outcome of his own throws (or self-estimation). Next, we introduce a state-space model to explain the trial-by-trial changes in the darts performance and self-estimation through our experiment. The model-based analysis reveals that the change in an expert’s self-estimation is explained only by considering a change in the individual’s forward model, showing that an improvement in an expert’s ability to predict outcomes of observed actions affects the individual’s forward model. These results suggest that parts of the same forward model are utilized in humans to both estimate outcomes of self-generated actions and predict outcomes of observed actions. PMID:29340300

Coping as a building mechanism to explain the unique association of affect and goal motivation with changes in affective states.

PubMed

Blouin-Hudon, Eve-Marie C; Gaudreau, Patrick; Gareau, Alexandre

2016-09-01

In this study, we examined the mediating role of university students' coping strategies in the unique/additive influence of affective states and goal motivation on upward changes in affect during a midterm exam period. Using a short-term prospective design, key assumptions from the self-concordance model and the broaden-and-build theory were drawn upon to determine whether coping strategies are influenced by goal motivation and affective states, while also subsequently influencing short-term changes in affective states during a semester. A sample of 272 students (79% females and 21% males) participated in a study in which they completed questionnaires twice during the semester. Results of structural equation modeling, using a true latent change approach, have generally supported our hypotheses. Positive affective states and autonomous goal motivation prospectively predicted task-oriented coping which, in turn, was associated with increases in positive affect. Negative affective states and controlled goal motivation prospectively predicted disengagement-oriented coping which, in turn, was associated with increases in negative affect. Coping partially mediates the unique association of affect and goal motivation with changes in affective states of university students.

Improved Modeling of Open Waveguide Aperture Radiators for use in Conformal Antenna Arrays

NASA Astrophysics Data System (ADS)

Nelson, Gregory James

Open waveguide apertures have been used as radiating elements in conformal arrays. Individual radiating element model patterns are used in constructing overall array models. The existing models for these aperture radiating elements may not accurately predict the array pattern for TEM waves which are not on boresight for each radiating element. In particular, surrounding structures can affect the far field patterns of these apertures, which ultimately affects the overall array pattern. New models of open waveguide apertures are developed here with the goal of accounting for the surrounding structure effects on the aperture far field patterns such that the new models make accurate pattern predictions. These aperture patterns (both E plane and H plane) are measured in an anechoic chamber and the manner in which they deviate from existing model patterns are studied. Using these measurements as a basis, existing models for both E and H planes are updated with new factors and terms which allow the prediction of far field open waveguide aperture patterns with improved accuracy. These new and improved individual radiator models are then used to predict overall conformal array patterns. Arrays of open waveguide apertures are constructed and measured in a similar fashion to the individual aperture measurements. These measured array patterns are compared with the newly modeled array patterns to verify the improved accuracy of the new models as compared with the performance of existing models in making array far field pattern predictions. The array pattern lobe characteristics are then studied for predicting fully circularly conformal arrays of varying radii. The lobe metrics that are tracked are angular location and magnitude as the radii of the conformal arrays are varied. A constructed, measured array that is close to conforming to a circular surface is compared with a fully circularly conformal modeled array pattern prediction, with the predicted lobe angular locations and magnitudes tracked, plotted and tabulated. The close match between the patterns of the measured array and the modeled circularly conformal array verifies the validity of the modeled circularly conformal array pattern predictions.

Ontogenetic loss of phenotypic plasticity of age at metamorphosis in tadpoles

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

Hensley, F.R.

1993-12-01

Amphibian larvae exhibit phenotypic plasticity in size at metamorphosis and duration of the larval period. I used Pseudacris crucifer tadpoles to test two models for predicting tadpole age and size at metamorphosis under changing environmental conditions. The Wilbur-Collins model states that metamorphosis is initiated as a function of a tadpole's size and relative growth rate, and predicts that changes in growth rate throughout the larval period affect age and size at metamorphosis. An alternative model, the fixed-rate model, states that age at metamorphosis is fixed early in larval life, and subsequent changes in growth rate will have no effect onmore » the length of the larval period. My results confirm that food supplies affect both age and size at metamorphosis, but developmental rates became fixed at approximately Gosner (1960) stages 35-37. Neither model completely predicted these results. I suggest that the generally accepted Wilbur-Collins model is improved by incorporating a point of fixed developmental timing. Growth trajectories predicted from this modified model fit the results of this study better than trajectories based on either of the original models. The results of this study suggests a constraint that limits the simultaneous optimization of age and size at metamorphosis. 32 refs., 5 figs., 1 tab.« less

Word of Mouth : An Agent-based Approach to Predictability of Stock Prices

NASA Astrophysics Data System (ADS)

Shimokawa, Tetsuya; Misawa, Tadanobu; Watanabe, Kyoko

This paper addresses how communication processes among investors affect stock prices formation, especially emerging predictability of stock prices, in financial markets. An agent based model, called the word of mouth model, is introduced for analyzing the problem. This model provides a simple, but sufficiently versatile, description of informational diffusion process and is successful in making lucidly explanation for the predictability of small sized stocks, which is a stylized fact in financial markets but difficult to resolve by traditional models. Our model also provides a rigorous examination of the under reaction hypothesis to informational shocks.

Need for Affect and Attitudes Toward Drugs: The Mediating Role of Values.

PubMed

Lins de Holanda Coelho, Gabriel; H P Hanel, Paul; Vilar, Roosevelt; P Monteiro, Renan; Gouveia, Valdiney V; R Maio, Gregory

2018-05-04

Human values and affective traits were found to predict attitudes toward the use of different types of drugs (e.g., alcohol, marijuana, and other illegal drugs). In this study (N = 196, M age = 23.09), we aimed to gain a more comprehensive understanding of those predictors of attitudes toward drug use in a mediated structural equation model, providing a better overview of a possible motivational path that drives to such a risky behavior. Specifically, we predicted and found that the relations between need for affect and attitudes toward drug use were mediated by excitement values. Also, results showed that excitement values and need for affect positively predicted attitudes toward the use of drugs, whereas normative values predicted it negatively. The pattern of results remained the same when we investigated attitudes toward alcohol, marijuana, or illegal drugs separately. Overall, the findings indicate that emotions operate via excitement and normative values to influence risk behavior.

Predicting knee replacement damage in a simulator machine using a computational model with a consistent wear factor.

PubMed

Zhao, Dong; Sakoda, Hideyuki; Sawyer, W Gregory; Banks, Scott A; Fregly, Benjamin J

2008-02-01

Wear of ultrahigh molecular weight polyethylene remains a primary factor limiting the longevity of total knee replacements (TKRs). However, wear testing on a simulator machine is time consuming and expensive, making it impractical for iterative design purposes. The objectives of this paper were first, to evaluate whether a computational model using a wear factor consistent with the TKR material pair can predict accurate TKR damage measured in a simulator machine, and second, to investigate how choice of surface evolution method (fixed or variable step) and material model (linear or nonlinear) affect the prediction. An iterative computational damage model was constructed for a commercial knee implant in an AMTI simulator machine. The damage model combined a dynamic contact model with a surface evolution model to predict how wear plus creep progressively alter tibial insert geometry over multiple simulations. The computational framework was validated by predicting wear in a cylinder-on-plate system for which an analytical solution was derived. The implant damage model was evaluated for 5 million cycles of simulated gait using damage measurements made on the same implant in an AMTI machine. Using a pin-on-plate wear factor for the same material pair as the implant, the model predicted tibial insert wear volume to within 2% error and damage depths and areas to within 18% and 10% error, respectively. Choice of material model had little influence, while inclusion of surface evolution affected damage depth and area but not wear volume predictions. Surface evolution method was important only during the initial cycles, where variable step was needed to capture rapid geometry changes due to the creep. Overall, our results indicate that accurate TKR damage predictions can be made with a computational model using a constant wear factor obtained from pin-on-plate tests for the same material pair, and furthermore, that surface evolution method matters only during the initial "break in" period of the simulation.

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

PubMed

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

2012-02-01

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

Development of a model to predict ash transport and water pollution risk in fire-affected environments

NASA Astrophysics Data System (ADS)

Neris, Jonay; Elliot, William J.; Doerr, Stefan H.; Robichaud, Peter R.

2017-04-01

An estimated that 15% of the world's population lives in volcanic areas. Recent catastrophic erosion events following wildfires in volcanic terrain have highlighted the geomorphological instability of this soil type under disturbed conditions and steep slopes. Predicting the hydrological and erosional response of this soils in the post-fire period is the first step to design and develop adequate actions to minimize risks in the post-fire period. In this work we apply, for the first time, the Water Erosion Prediction Project model for predicting erosion and runoff events in fire-affected volcanic soils in Europe. Two areas affected by wildfires in 2015 were selected in Tenerife (Spain) representative of different fire behaviour (downhill surface fire with long residence time vs uphill crown fire with short residence time), severity (moderate soil burn severity vs light soil burn severity) and climatic conditions (average annual precipitation of 750 and 210 mm respectively). The actual erosion processes were monitored in the field using silt fences. Rainfall and rill simulations were conducted to determine hydrologic, interrill and rill erosion parameters. The soils were sampled and key properties used as model input, evaluated. During the first 18 months after the fire 7 storms produced runoff and erosion in the selected areas. Sediment delivery reached 5.4 and 2.5 Mg ha-1 respectively in the first rainfall event monitored after the fire, figures comparable to those reported for fire-affected areas of the western USA with similar climatic conditions but lower than those showed by wetter environments. The validation of the WEPP model using field data showed reasonable estimates of hillslope sediment delivery in the post-fire period and, therefore, it is suggested that this model can support land managers in volcanic areas in Europe in predicting post-fire hydrological and erosional risks and designing suitable mitigation treatments.

First application of the WEPP model to predict runoff and erosion risk in fire-affected volcanic areas in Europe

NASA Astrophysics Data System (ADS)

Neris, Jonay; Robichaud, Peter R.; Elliot, William J.; Doerr, Stefan H.; Notario del Pino, Jesús S.; Lado, Marcos

2017-04-01

An estimated that 15% of the world's population lives in volcanic areas. Recent catastrophic erosion events following wildfires in volcanic terrain have highlighted the geomorphological instability of this soil type under disturbed conditions and steep slopes. Predicting the hydrological and erosional response of this soils in the post-fire period is the first step to design and develop adequate actions to minimize risks in the post-fire period. In this work we apply, for the first time, the Water Erosion Prediction Project model for predicting erosion and runoff events in fire-affected volcanic soils in Europe. Two areas affected by wildfires in 2015 were selected in Tenerife (Spain) representative of different fire behaviour (downhill surface fire with long residence time vs uphill crown fire with short residence time), severity (moderate soil burn severity vs light soil burn severity) and climatic conditions (average annual precipitation of 750 and 210 mm respectively). The actual erosion processes were monitored in the field using silt fences. Rainfall and rill simulations were conducted to determine hydrologic, interrill and rill erosion parameters. The soils were sampled and key properties used as model input, evaluated. During the first 18 months after the fire 7 storms produced runoff and erosion in the selected areas. Sediment delivery reached 5.4 and 2.5 Mg ha-1 respectively in the first rainfall event monitored after the fire, figures comparable to those reported for fire-affected areas of the western USA with similar climatic conditions but lower than those showed by wetter environments. The validation of the WEPP model using field data showed reasonable estimates of hillslope sediment delivery in the post-fire period and, therefore, it is suggested that this model can support land managers in volcanic areas in Europe in predicting post-fire hydrological and erosional risks and designing suitable mitigation treatments.

Computational Modeling of Hypothalamic-Pituitary-Gonadal Axis to Predict Adaptive Responses in Female Fathead Minnows Exposed to an Aromatase Inhibitor

EPA Science Inventory

Exposure to endocrine disrupting chemicals can affect reproduction and development in both humans and wildlife. We are developing a mechanistic computational model of the hypothalamic-pituitary-gonadal (HPG) axis in female fathead minnows to predict dose response and time-course...

Embedding Multilevel Survival Analysis of Dyadic Social Interaction in Structural Equation Models: Hazard Rates as Both Outcomes and Predictors

PubMed Central

Snyder, James

2014-01-01

Objective Demonstrate multivariate multilevel survival analysis within a larger structural equation model. Test the 3 hypotheses that when confronted by a negative parent, child rates of angry, sad/fearful, and positive emotion will increase, decrease, and stay the same, respectively, for antisocial compared with normal children. This same pattern will predict increases in future antisocial behavior. Methods Parent–child dyads were videotaped in the fall of kindergarten in the laboratory and antisocial behavior ratings were obtained in the fall of kindergarten and third grade. Results Kindergarten antisocial predicted less child sad/fear and child positive but did not predict child anger given parent negative. Less child positive and more child neutral given parent negative predicted increases in third-grade antisocial behavior. Conclusions The model is a useful analytic tool for studying rates of social behavior. Lack of positive affect or excess neutral affect may be a new risk factor for child antisocial behavior. PMID:24133296

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

PubMed Central

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

2016-01-01

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

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

PubMed

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

2008-01-01

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

Modeling the effect of 3 missense AGXT mutations on dimerization of the AGT enzyme in primary hyperoxaluria type 1.

PubMed

Robbiano, Angela; Frecer, Vladimir; Miertus, Jan; Zadro, Cristina; Ulivi, Sheila; Bevilacqua, Elena; Mandrile, Giorgia; De Marchi, Mario; Miertus, Stanislav; Amoroso, Antonio

2010-01-01

Mutations of the AGXT gene encoding the alanine:glyoxylate aminotransferase liver enzyme (AGT) cause primary hyperoxaluria type 1 (PH1). Here we report a molecular modeling study of selected missense AGXT mutations: the common Gly170Arg and the recently described Gly47Arg and Ser81Leu variants, predicted to be pathogenic using standard criteria. Taking advantage of the refined 3D structure of AGT, we computed the dimerization energy of the wild-type and mutated proteins. Molecular modeling predicted that Gly47Arg affects dimerization with a similar effect to that shown previously for Gly170Arg through classical biochemical approaches. In contrast, no effect on dimerization was predicted for Ser81Leu. Therefore, this probably demonstrates pathogenic properties via a different mechanism, similar to that described for the adjacent Gly82Glu mutation that affects pyridoxine binding. This study shows that the molecular modeling approach can contribute to evaluating the pathogenicity of some missense variants that affect dimerization. However, in silico studies--aimed to assess the relationship between structural change and biological effects--require the integrated use of more than 1 tool.

Coupled thermal-fluid analysis with flowpath-cavity interaction in a gas turbine engine

NASA Astrophysics Data System (ADS)

Fitzpatrick, John Nathan

This study seeks to improve the understanding of inlet conditions of a large rotor-stator cavity in a turbofan engine, often referred to as the drive cone cavity (DCC). The inlet flow is better understood through a higher fidelity computational fluid dynamics (CFD) modeling of the inlet to the cavity, and a coupled finite element (FE) thermal to CFD fluid analysis of the cavity in order to accurately predict engine component temperatures. Accurately predicting temperature distribution in the cavity is important because temperatures directly affect the material properties including Young's modulus, yield strength, fatigue strength, creep properties. All of these properties directly affect the life of critical engine components. In addition, temperatures cause thermal expansion which changes clearances and in turn affects engine efficiency. The DCC is fed from the last stage of the high pressure compressor. One of its primary functions is to purge the air over the rotor wall to prevent it from overheating. Aero-thermal conditions within the DCC cavity are particularly challenging to predict due to the complex air flow and high heat transfer in the rotating component. Thus, in order to accurately predict metal temperatures a two-way coupled CFD-FE analysis is needed. Historically, when the cavity airflow is modeled for engine design purposes, the inlet condition has been over-simplified for the CFD analysis which impacts the results, particularly in the region around the compressor disc rim. The inlet is typically simplified by circumferentially averaging the velocity field at the inlet to the cavity which removes the effect of pressure wakes from the upstream rotor blades. The way in which these non-axisymmetric flow characteristics affect metal temperatures is not well understood. In addition, a constant air temperature scaled from a previous analysis is used as the simplified cavity inlet air temperature. Therefore, the objectives of this study are: (a) model the DCC cavity with a more physically representative inlet condition while coupling the solid thermal analysis and compressible air flow analysis that includes the fluid velocity, pressure, and temperature fields; (b) run a coupled analysis whose boundary conditions come from computational models, rather than thermocouple data; (c) validate the model using available experimental data; and (d) based on the validation, determine if the model can be used to predict air inlet and metal temperatures for new engine geometries. Verification with experimental results showed that the coupled analysis with the 3D no-bolt CFD model with predictive boundary conditions, over-predicted the HP6 offtake temperature by 16k. The maximum error was an over-prediction of 50k while the average error was 17k. The predictive model with 3D bolts also predicted cavity temperatures with an average error of 17k. For the two CFD models with predicted boundary conditions, the case without bolts performed better than the case with bolts. This is due to the flow errors caused by placing stationary bolts in a rotating reference frame. Therefore it is recommended that this type of analysis only be attempted for drive cone cavities with no bolts or shielded bolts.

Response surface models for effects of temperature and previous growth sodium chloride on growth kinetics of Salmonella typhimurium on cooked chicken breast.

PubMed

Oscar, T P

1999-12-01

Response surface models were developed and validated for effects of temperature (10 to 40 degrees C) and previous growth NaCl (0.5 to 4.5%) on lag time (lambda) and specific growth rate (mu) of Salmonella Typhimurium on cooked chicken breast. Growth curves for model development (n = 55) and model validation (n = 16) were fit to a two-phase linear growth model to obtain lambda and mu of Salmonella Typhimurium on cooked chicken breast. Response surface models for natural logarithm transformations of lambda and mu as a function of temperature and previous growth NaCl were obtained by regression analysis. Both lambda and mu of Salmonella Typhimurium were affected (P < 0.0001) by temperature but not by previous growth NaCl. Models were validated against data not used in their development. Mean absolute relative error of predictions (model accuracy) was 26.6% for lambda and 15.4% for mu. Median relative error of predictions (model bias) was 0.9% for lambda and 5.2% for mu. Results indicated that the models developed provided reliable predictions of lambda and mu of Salmonella Typhimurium on cooked chicken breast within the matrix of conditions modeled. In addition, results indicated that previous growth NaCl (0.5 to 4.5%) was not a major factor affecting subsequent growth kinetics of Salmonella Typhimurium on cooked chicken breast. Thus, inclusion of previous growth NaCl in predictive models may not significantly improve our ability to predict growth of Salmonella spp. on food subjected to temperature abuse.

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.

Using Ecological Momentary Assessment to Examine Interpersonal and Affective Predictors of Loss of Control Eating in Adolescent Girls

PubMed Central

Ranzenhofer, Lisa M.; Engel, Scott G.; Crosby, Ross D.; Anderson, Micheline; Vannucci, Anna; Cohen, L. Adelyn; Cassidy, Omni; Tanofsky-Kraff, Marian

2015-01-01

Objective Pediatric loss of control (LOC) eating is predictive of partial- and full-syndrome binge eating disorder. The interpersonal model proposes that LOC eating is used to cope with negative mood states resulting from interpersonal distress, possibly on a momentary level. We therefore examined temporal associations between interpersonal problems, negative affect, and LOC eating among overweight adolescent girls using ecological momentary assessment (EMA). Method Thirty overweight and obese (≥85th body mass index (BMI) percentile; BMI: M = 36.13, SD = 7.49 kg/m2) adolescent females (Age: M = 14.92, SD = 1.54 y; 60.0% African American) who reported at least two LOC episodes in the past month completed self-report momentary ratings of interpersonal problems, state affect, and LOC eating for 2 weeks. A series of 2-level multilevel models with centering within subjects was conducted. Results Between- and within-subjects interpersonal problems (p’s < .05), but not between- (p = .12) or within- (p = .32) subjects negative affect predicted momentary LOC eating. At the between-subjects level, interpersonal problems significantly predicted increases in negative affect (p < 001). Discussion Naturalistic data lend support to the predictive value of interpersonal problems for LOC eating among adolescents. Interventions targeting interpersonal factors on a momentary basis may be useful during this developmental stage. PMID:25046850

Toddler Emotion Regulation with Mothers and Fathers: Temporal Associations Between Negative Affect and Behavioral Strategies

PubMed Central

Ekas, Naomi V.; Braungart-Rieker, Julia M.; Lickenbrock, Diane M.; Zentall, Shannon R.; Maxwell, Scott M.

2010-01-01

The present study investigated temporal associations between putative emotion regulation strategies and negative affect in 20-month-old toddlers. Toddlers’ parent-focused, self-distraction, and toy-focused strategies, as well as negative affect, were rated on a second-by-second basis during laboratory parent-toddler interactions. Longitudinal mixed-effects models were conducted to determine the degree to which behavioral strategy use predicts subsequent negative affect and negative affect predicts subsequent strategy use. Results with mother-toddler and father-toddler dyads indicated that parent-focused strategies with an unresponsive parent were followed by increases in negative affect, whereas toy-focused strategies were followed by decreases in negative affect. Results also indicated that toddler negative affect serves to regulate behavioral strategy use within both parent contexts. PMID:21552335

Brain connectivity changes occurring following cognitive behavioural therapy for psychosis predict long-term recovery.

PubMed

Mason, L; Peters, E; Williams, S C; Kumari, V

2017-01-17

Little is known about the psychobiological mechanisms of cognitive behavioural therapy for psychosis (CBTp) and which specific processes are key in predicting favourable long-term outcomes. Following theoretical models of psychosis, this proof-of-concept study investigated whether the long-term recovery path of CBTp completers can be predicted by the neural changes in threat-based social affective processing that occur during CBTp. We followed up 22 participants who had undergone a social affective processing task during functional magnetic resonance imaging along with self-report and clinician-administered symptom measures, before and after receiving CBTp. Monthly ratings of psychotic and affective symptoms were obtained retrospectively across 8 years since receiving CBTp, plus self-reported recovery at final follow-up. We investigated whether these long-term outcomes were predicted by CBTp-led changes in functional connections with dorsal prefrontal cortical and amygdala during the processing of threatening and prosocial facial affect. Although long-term psychotic symptoms were predicted by changes in prefrontal connections during prosocial facial affective processing, long-term affective symptoms were predicted by threat-related amygdalo-inferior parietal lobule connectivity. Greater increases in dorsolateral prefrontal cortex connectivity with amygdala following CBTp also predicted higher subjective ratings of recovery at long-term follow-up. These findings show that reorganisation occurring at the neural level following psychological therapy can predict the subsequent recovery path of people with psychosis across 8 years. This novel methodology shows promise for further studies with larger sample size, which are needed to better examine the sensitivity of psychobiological processes, in comparison to existing clinical measures, in predicting long-term outcomes.

Negative impacts of climate change on cereal yields: statistical evidence from France

NASA Astrophysics Data System (ADS)

Gammans, Matthew; Mérel, Pierre; Ortiz-Bobea, Ariel

2017-05-01

In several world regions, climate change is predicted to negatively affect crop productivity. The recent statistical yield literature emphasizes the importance of flexibly accounting for the distribution of growing-season temperature to better represent the effects of warming on crop yields. We estimate a flexible statistical yield model using a long panel from France to investigate the impacts of temperature and precipitation changes on wheat and barley yields. Winter varieties appear sensitive to extreme cold after planting. All yields respond negatively to an increase in spring-summer temperatures and are a decreasing function of precipitation about historical precipitation levels. Crop yields are predicted to be negatively affected by climate change under a wide range of climate models and emissions scenarios. Under warming scenario RCP8.5 and holding growing areas and technology constant, our model ensemble predicts a 21.0% decline in winter wheat yield, a 17.3% decline in winter barley yield, and a 33.6% decline in spring barley yield by the end of the century. Uncertainty from climate projections dominates uncertainty from the statistical model. Finally, our model predicts that continuing technology trends would counterbalance most of the effects of climate change.

Using a prescribed fire to test custom and standard fuel models for fire behaviour prediction in a non-native, grass-invaded tropical dry shrubland

Treesearch

Andrew D. Pierce; Sierra McDaniel; Mark Wasser; Alison Ainsworth; Creighton M. Litton; Christian P. Giardina; Susan Cordell; Ralf Ohlemuller

2014-01-01

Questions: Do fuel models developed for North American fuel types accurately represent fuel beds found in grass-invaded tropical shrublands? Do standard or custom fuel models for firebehavior models with in situ or RAWS measured fuel moistures affect the accuracy of predicted fire behavior in grass-invaded tropical shrublands? Location: Hawai’i Volcanoes National...

Deep learning architecture for air quality predictions.

PubMed

Li, Xiang; Peng, Ling; Hu, Yuan; Shao, Jing; Chi, Tianhe

2016-11-01

With the rapid development of urbanization and industrialization, many developing countries are suffering from heavy air pollution. Governments and citizens have expressed increasing concern regarding air pollution because it affects human health and sustainable development worldwide. Current air quality prediction methods mainly use shallow models; however, these methods produce unsatisfactory results, which inspired us to investigate methods of predicting air quality based on deep architecture models. In this paper, a novel spatiotemporal deep learning (STDL)-based air quality prediction method that inherently considers spatial and temporal correlations is proposed. A stacked autoencoder (SAE) model is used to extract inherent air quality features, and it is trained in a greedy layer-wise manner. Compared with traditional time series prediction models, our model can predict the air quality of all stations simultaneously and shows the temporal stability in all seasons. Moreover, a comparison with the spatiotemporal artificial neural network (STANN), auto regression moving average (ARMA), and support vector regression (SVR) models demonstrates that the proposed method of performing air quality predictions has a superior performance.

Planning versus action: Different decision-making processes predict plans to change one's diet versus actual dietary behavior.

PubMed

Kiviniemi, Marc T; Brown-Kramer, Carolyn R

2015-05-01

Most health decision-making models posit that deciding to engage in a health behavior involves forming a behavioral intention which then leads to actual behavior. However, behavioral intentions and actual behavior may not be functionally equivalent. Two studies examined whether decision-making factors predicting dietary behaviors were the same as or distinct from those predicting intentions. Actual dietary behavior was proximally predicted by affective associations with the behavior. By contrast, behavioral intentions were predicted by cognitive beliefs about behaviors, with no contribution of affective associations. This dissociation has implications for understanding individual regulation of health behaviors and for behavior change interventions. © The Author(s) 2015.

HIGH TIME-RESOLVED COMPARISONS FOR IN-DEPTH PROBING OF CMAQ FINE-PARTICLE AND GAS PREDICTIONS

EPA Science Inventory

Input errors affect model predictions. The diurnal behavior of two inputs NHx, which partitions in the inorganic system between gas and particle, and EC, a nonreactive emitted specie, is compared for CMAQ predictions and observations. A monthly average diurnal profile based on ho...

Hot limpets: predicting body temperature in a conductance-mediated thermal system.

PubMed

Denny, Mark W; Harley, Christopher D G

2006-07-01

Living at the interface between the marine and terrestrial environments, intertidal organisms may serve as a bellwether for environmental change and a test of our ability to predict its biological consequences. However, current models do not allow us to predict the body temperature of intertidal organisms whose heat budgets are strongly affected by conduction to and from the substratum. Here, we propose a simple heat-budget model of one such animal, the limpet Lottia gigantea, and test the model against measurements made in the field. Working solely from easily measured physical and meteorological inputs, the model predicts the daily maximal body temperatures of live limpets within a fraction of a degree, suggesting that it may be a useful tool for exploring the thermal biology of limpets and for predicting effects of climate change. The model can easily be adapted to predict the temperatures of chitons, acorn barnacles, keyhole limpets, and encrusting animals and plants.

Introducing a decomposition rate modifier in the Rothamsted Carbon Model to predict soil organic carbon stocks in saline soils.

PubMed

Setia, Raj; Smith, Pete; Marschner, Petra; Baldock, Jeff; Chittleborough, David; Smith, Jo

2011-08-01

Soil organic carbon (SOC) models such as the Rothamsted Carbon Model (RothC) have been used to estimate SOC dynamics in soils over different time scales but, until recently, their ability to accurately predict SOC stocks/carbon dioxide (CO(2)) emissions from salt-affected soils has not been assessed. Given the large extent of salt-affected soils (19% of the 20.8 billion ha of arable land on Earth), this may lead to miss-estimation of CO(2) release. Using soils from two salt-affected regions (one in Punjab, India and one in South Australia), an incubation study was carried out measuring CO(2) release over 120 days. The soils varied both in salinity (measured as electrical conductivity (EC) and calculated as osmotic potential using EC and water content) and sodicity (measured as sodium adsorption ratio, SAR). For soils from both regions, the osmotic potential had a significant positive relationship with CO(2)-C release, but no significant relationship was found between SAR and CO(2)-C release. The monthly cumulative CO(2)-C was simulated using RothC. RothC was modified to take into account reductions in plant inputs due to salinity. A subset of non-salt-affected soils was used to derive an equation for a "lab-effect" modifier to account for changes in decomposition under lab conditions and this modifier was significantly related with pH. Using a subset of salt-affected soils, a decomposition rate modifier (as a function of osmotic potential) was developed to match measured and modelled CO(2)-C release after correcting for the lab effect. Using this decomposition rate modifier, we found an agreement (R(2) = 0.92) between modelled and independently measured data for a set of soils from the incubation experiment. RothC, modified by including reduced plant inputs due to salinity and the salinity decomposition rate modifier, was used to predict SOC stocks of soils in a field in South Australia. The predictions clearly showed that SOC stocks are reduced in saline soils. Therefore both the decomposition rate modifier and plant input modifier should be taken into account when accounting for SOC turnover in saline soils. Since modeling has previously not accounted for the impact of salinity, our results suggest that previous predictions may have overestimated SOC stocks.

Mechanistic model to predict colostrum intake based on deuterium oxide dilution technique data and impact of gestation and prefarrowing diets on piglet intake and sow yield of colostrum.

PubMed

Theil, P K; Flummer, C; Hurley, W L; Kristensen, N B; Labouriau, R L; Sørensen, M T

2014-12-01

The aims of the present study were to quantify colostrum intake (CI) of piglets using the D2O dilution technique, to develop a mechanistic model to predict CI, to compare these data with CI predicted by a previous empirical predictive model developed for bottle-fed piglets, and to study how composition of diets fed to gestating sows affected piglet CI, sow colostrum yield (CY), and colostrum composition. In total, 240 piglets from 40 litters were enriched with D2O. The CI measured by D2O from birth until 24 h after the birth of first-born piglet was on average 443 g (SD 151). Based on measured CI, a mechanistic model to predict CI was developed using piglet characteristics (24-h weight gain [WG; g], BW at birth [BWB; kg], and duration of CI [D; min]: CI, g=-106+2.26 WG+200 BWB+0.111 D-1,414 WG/D+0.0182 WG/BWB (R2=0.944). This model was used to predict the CI for all colostrum suckling piglets within the 40 litters (n=500, mean=437 g, SD=153 g) and was compared with the CI predicted by a previous empirical predictive model (mean=305 g, SD=140 g). The previous empirical model underestimated the CI by 30% compared with that obtained by the new mechanistic model. The sows were fed 1 of 4 gestation diets (n=10 per diet) based on different fiber sources (low fiber [17%] or potato pulp, pectin residue, or sugarbeet pulp [32 to 40%]) from mating until d 108 of gestation. From d 108 of gestation until parturition, sows were fed 1 of 5 prefarrowing diets (n=8 per diet) varying in supplemented fat (3% animal fat, 8% coconut oil, 8% sunflower oil, 8% fish oil, or 4% fish oil+4% octanoic acid). Sows fed diets with pectin residue or sugarbeet pulp during gestation produced colostrum with lower protein, fat, DM, and energy concentrations and higher lactose concentrations, and their piglets had greater CI as compared with sows fed potato pulp or the low-fiber diet (P<0.05), and sows fed pectin residue had a greater CY than potato pulp-fed sows (P<0.05). Prefarrowing diets affected neither CI nor CY, but the prefarrowing diet with coconut oil decreased lactose and increased DM concentrations of colostrum compared with other prefarrowing diets (P<0.05). In conclusion, the new mechanistic predictive model for CI suggests that the previous empirical predictive model underestimates CI of sow-reared piglets by 30%. It was also concluded that nutrition of sows during gestation affected CY and colostrum composition.

Prediction equations of forced oscillation technique: the insidious role of collinearity.

PubMed

Narchi, Hassib; AlBlooshi, Afaf

2018-03-27

Many studies have reported reference data for forced oscillation technique (FOT) in healthy children. The prediction equation of FOT parameters were derived from a multivariable regression model examining the effect of age, gender, weight and height on each parameter. As many of these variables are likely to be correlated, collinearity might have affected the accuracy of the model, potentially resulting in misleading, erroneous or difficult to interpret conclusions.The aim of this work was: To review all FOT publications in children since 2005 to analyze whether collinearity was considered in the construction of the published prediction equations. Then to compare these prediction equations with our own study. And to analyse, in our study, how collinearity between the explanatory variables might affect the predicted equations if it was not considered in the model. The results showed that none of the ten reviewed studies had stated whether collinearity was checked for. Half of the reports had also included in their equations variables which are physiologically correlated, such as age, weight and height. The predicted resistance varied by up to 28% amongst these studies. And in our study, multicollinearity was identified between the explanatory variables initially considered for the regression model (age, weight and height). Ignoring it would have resulted in inaccuracies in the coefficients of the equation, their signs (positive or negative), their 95% confidence intervals, their significance level and the model goodness of fit. In Conclusion with inaccurately constructed and improperly reported models, understanding the results and reproducing the models for future research might be compromised.

Open Up or Close Down: How Do Parental Reactions Affect Youth Information Management?

ERIC Educational Resources Information Center

Tilton-Weaver, Lauree; Kerr, Margaret; Pakalniskeine, Vilmante; Tokic, Ana; Salihovic, Selma; Stattin, Hakan

2010-01-01

The purpose of this study was to test a process model of youths' information management. Using three waves of longitudinal data collected from 982 youths, we modeled parents' positive and negative reactions to disclosure predicting youths' feelings about their parents, in turn predicting youths' disclosure and secrecy about their daily activities.…

Word Recognition is Affected by the Meaning of Orthographic Neighbours: Evidence from Semantic Decision Tasks

ERIC Educational Resources Information Center

Boot, Inge; Pecher, Diane

2008-01-01

Many models of word recognition predict that neighbours of target words will be activated during word processing. Cascaded models can make the additional prediction that semantic features of those neighbours get activated before the target has been uniquely identified. In two semantic decision tasks neighbours that were congruent (i.e., from the…

Musical rhythm and affect. Comment on "The quartet theory of human emotions: An integrative and neurofunctional model" by S. Koelsch et al.

NASA Astrophysics Data System (ADS)

Witek, Maria A. G.; Kringelbach, Morten L.; Vuust, Peter

2015-06-01

The Quartet Theory of Human Emotion (QT) proposed by Koelsch et al. [1] adds to existing affective models, e.g. by directing more attention to emotional contagion, attachment-related and non-goal-directed emotions. Such an approach seems particularly appropriate to modelling musical emotions, and music is indeed a recurring example in the text, used to illustrate the distinct characteristics of the affect systems that are at the centre of the theory. Yet, it would seem important for any theory of emotion to account for basic functions such as prediction and anticipation, which are only briefly mentioned. Here we propose that QT, specifically its focus on emotional contagion, attachment-related and non-goal directed emotions, might help generate new ideas about a largely neglected source of emotion - rhythm - a musical property that relies fundamentally on the mechanism of prediction.

I Don't Know It but I Like You: The Influence of Nonconscious Affect on Person Perception.

ERIC Educational Resources Information Center

Monahan, Jennifer L.

1998-01-01

Proposes a model of unconscious affect. Tests predictions about the influence of nonconscious affect on evaluations made of undergraduate student conversational interactants. Uses a subliminal priming task to induce a positive nonconscious affective response toward the target persons. Rates primed target as more likable and attractive yet not more…

Positive and negative affect as predictors of urge to smoke: temporal factors and mediational pathways.

PubMed

Leventhal, Adam M; Greenberg, Jodie B; Trujillo, Michael A; Ameringer, Katherine J; Lisha, Nadra E; Pang, Raina D; Monterosso, John

2013-03-01

Elucidating interrelations between prior affective experience, current affective state, and acute urge to smoke could inform affective models of addiction motivation and smoking cessation treatment development. This study tested the hypothesis that prior levels of positive (PA) and negative (NA) affect predict current smoking urge via a mediational pathway involving current state affect. We also explored if tobacco deprivation moderated affect-urge relations and compared the effects of PA and NA on smoking urge to one another. At a baseline session, smokers reported affect experienced over the preceding few weeks. At a subsequent experimental session, participants were randomly assigned to 12-hr tobacco deprived (n = 51) or nondeprived (n = 69) conditions and reported state affect and current urge. Results revealed a mediational pathway whereby prior NA reported at baseline predicted state NA at the experimental session, which in turn predicted current urge. This mediational pathway was found primarily for an urge subtype indicative of urgent need to smoke and desire to smoke for NA relief, was stronger in the deprived (vs. nondeprived) condition, and remained significant after controlling for PA. Prior PA and current state PA were inversely associated with current urge; however, these associations were eliminated after controlling for NA. These results cohere with negative reinforcement models of addiction and with prior research and suggest that: (a) NA plays a stronger role in smoking motivation than PA; (b) state affect is an important mechanism linking prior affective experience to current urge; and (c) affect management interventions may attenuate smoking urge in individuals with a history of affective disturbance. (PsycINFO Database Record (c) 2013 APA, all rights reserved).

Contrasted demographic responses facing future climate change in Southern Ocean seabirds.

PubMed

Barbraud, Christophe; Rivalan, Philippe; Inchausti, Pablo; Nevoux, Marie; Rolland, Virginie; Weimerskirch, Henri

2011-01-01

1. Recent climate change has affected a wide range of species, but predicting population responses to projected climate change using population dynamics theory and models remains challenging, and very few attempts have been made. The Southern Ocean sea surface temperature and sea ice extent are projected to warm and shrink as concentrations of atmospheric greenhouse gases increase, and several top predator species are affected by fluctuations in these oceanographic variables. 2. We compared and projected the population responses of three seabird species living in sub-tropical, sub-Antarctic and Antarctic biomes to predicted climate change over the next 50 years. Using stochastic population models we combined long-term demographic datasets and projections of sea surface temperature and sea ice extent for three different IPCC emission scenarios (from most to least severe: A1B, A2, B1) from general circulation models of Earth's climate. 3. We found that climate mostly affected the probability to breed successfully, and in one case adult survival. Interestingly, frequent nonlinear relationships in demographic responses to climate were detected. Models forced by future predicted climatic change provided contrasted population responses depending on the species considered. The northernmost distributed species was predicted to be little affected by a future warming of the Southern Ocean, whereas steep declines were projected for the more southerly distributed species due to sea surface temperature warming and decrease in sea ice extent. For the most southerly distributed species, the A1B and B1 emission scenarios were respectively the most and less damaging. For the two other species, population responses were similar for all emission scenarios. 4. This is among the first attempts to study the demographic responses for several populations with contrasted environmental conditions, which illustrates that investigating the effects of climate change on core population dynamics is feasible for different populations using a common methodological framework. Our approach was limited to single populations and have neglected population settlement in new favourable habitats or changes in inter-specific relations as a potential response to future climate change. Predictions may be enhanced by merging demographic population models and climatic envelope models. © 2010 The Authors. Journal compilation © 2010 British Ecological Society.

Prediction of the diffuse-field transmission loss of interior natural-ventilation openings and silencers.

PubMed

Bibby, Chris; Hodgson, Murray

2017-01-01

The work reported here, part of a study on the performance and optimal design of interior natural-ventilation openings and silencers ("ventilators"), discusses the prediction of the acoustical performance of such ventilators, and the factors that affect it. A wave-based numerical approach-the finite-element method (FEM)-is applied. The development of a FEM technique for the prediction of ventilator diffuse-field transmission loss is presented. Model convergence is studied with respect to mesh, frequency-sampling and diffuse-field convergence. The modeling technique is validated by way of predictions and the comparison of them to analytical and experimental results. The transmission-loss performance of crosstalk silencers of four shapes, and the factors that affect it, are predicted and discussed. Performance increases with flow-path length for all silencer types. Adding elbows significantly increases high-frequency transmission loss, but does not increase overall silencer performance which is controlled by low-to-mid-frequency transmission loss.

A Global Model for Bankruptcy Prediction

PubMed Central

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

2016-01-01

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

Meteorological Processes Affecting Air Quality – Research and Model Development Needs

EPA Science Inventory

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

Putting reward in art: A tentative prediction error account of visual art

PubMed Central

Van de Cruys, Sander; Wagemans, Johan

2011-01-01

The predictive coding model is increasingly and fruitfully used to explain a wide range of findings in perception. Here we discuss the potential of this model in explaining the mechanisms underlying aesthetic experiences. Traditionally art appreciation has been associated with concepts such as harmony, perceptual fluency, and the so-called good Gestalt. We observe that more often than not great artworks blatantly violate these characteristics. Using the concept of prediction error from the predictive coding approach, we attempt to resolve this contradiction. We argue that artists often destroy predictions that they have first carefully built up in their viewers, and thus highlight the importance of negative affect in aesthetic experience. However, the viewer often succeeds in recovering the predictable pattern, sometimes on a different level. The ensuing rewarding effect is derived from this transition from a state of uncertainty to a state of increased predictability. We illustrate our account with several example paintings and with a discussion of art movements and individual differences in preference. On a more fundamental level, our theorizing leads us to consider the affective implications of prediction confirmation and violation. We compare our proposal to other influential theories on aesthetics and explore its advantages and limitations. PMID:23145260

Choosing a physician depends on how you want to feel: the role of ideal affect in health-related decision making.

PubMed

Sims, Tamara; Tsai, Jeanne L; Koopmann-Holm, Birgit; Thomas, Ewart A C; Goldstein, Mary K

2014-02-01

When given a choice, how do people decide which physician to select? Although significant research has demonstrated that how people actually feel (their "actual affect") influences their health care preferences, how people ideally want to feel (their "ideal affect") may play an even greater role. Specifically, we predicted that people trust physicians whose affective characteristics match their ideal affect, which leads people to prefer those physicians more. Consistent with this prediction, the more participants wanted to feel high arousal positive states on average (ideal HAP; e.g., excited), the more likely they were to select a HAP-focused physician. Similarly, the more people wanted to feel low arousal positive states on average (ideal LAP; e.g., calm), the more likely they were to select a LAP-focused physician. Also as predicted, these links were mediated by perceived physician trustworthiness. Notably, while participants' ideal affect predicted physician preference, actual affect (how much people actually felt HAP and LAP on average) did not. These findings suggest that people base serious decisions on how they want to feel, and highlight the importance of considering ideal affect in models of decision making preferences. PsycINFO Database Record (c) 2014 APA, all rights reserved.

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.

Predicting Individual Affect of Health Interventions to Reduce HPV Prevalence

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

Corley, Courtney D.; Mihalcea, Rada; Mikler, Armin R.

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 determinesmore » 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.« less

Potential habitat distribution for the freshwater diatom Didymosphenia geminata in the continental US

USGS Publications Warehouse

Kumar, S.; Spaulding, S.A.; Stohlgren, T.J.; Hermann, K.A.; Schmidt, T.S.; Bahls, L.L.

2009-01-01

The diatom Didymosphenia geminata is a single-celled alga found in lakes, streams, and rivers. Nuisance blooms of D geminata affect the diversity, abundance, and productivity of other aquatic organisms. Because D geminata can be transported by humans on waders and other gear, accurate spatial prediction of habitat suitability is urgently needed for early detection and rapid response, as well as for evaluation of monitoring and control programs. We compared four modeling methods to predict D geminata's habitat distribution; two methods use presence-absence data (logistic regression and classification and regression tree [CART]), and two involve presence data (maximum entropy model [Maxent] and genetic algorithm for rule-set production [GARP]). Using these methods, we evaluated spatially explicit, bioclimatic and environmental variables as predictors of diatom distribution. The Maxent model provided the most accurate predictions, followed by logistic regression, CART, and GARP. The most suitable habitats were predicted to occur in the western US, in relatively cool sites, and at high elevations with a high base-flow index. The results provide insights into the factors that affect the distribution of D geminata and a spatial basis for the prediction of nuisance blooms. ?? The Ecological Society of America.

Building a profile of subjective well-being for social media users.

PubMed

Chen, Lushi; Gong, Tao; Kosinski, Michal; Stillwell, David; Davidson, Robert L

2017-01-01

Subjective well-being includes 'affect' and 'satisfaction with life' (SWL). This study proposes a unified approach to construct a profile of subjective well-being based on social media language in Facebook status updates. We apply sentiment analysis to generate users' affect scores, and train a random forest model to predict SWL using affect scores and other language features of the status updates. Results show that: the computer-selected features resemble the key predictors of SWL as identified in early studies; the machine-predicted SWL is moderately correlated with the self-reported SWL (r = 0.36, p < 0.01), indicating that language-based assessment can constitute valid SWL measures; the machine-assessed affect scores resemble those reported in a previous experimental study; and the machine-predicted subjective well-being profile can also reflect other psychological traits like depression (r = 0.24, p < 0.01). This study provides important insights for psychological prediction using multiple, machine-assessed components and longitudinal or dense psychological assessment using social media language.

Sexting as the mirror on the wall: Body-esteem attribution, media models, and objectified-body consciousness.

PubMed

Bianchi, Dora; Morelli, Mara; Baiocco, Roberto; Chirumbolo, Antonio

2017-12-01

Sexting motivations during adolescence are related to developmental dimensions-such as sexual identity and body-image development-or harmful intentions-such as aggression among peers and partners. Sociocultural and media models can affect explorations of sexuality and redefinitions of body image, which in turn are related to sexting behaviors and motivations. In this study, we investigated the roles of body-esteem attribution, the internalization of media models, and body objectification as predictors of three sexting motivations: sexual purposes, body-image reinforcement, and instrumental/aggravated reasons. The participants were 190 Italian adolescents aged from 13 to 20 years old (M age  = 17.4, SD age  = 1.8; 44.7% females). Sexual purposes were predicted by body-esteem attribution and body objectification; body-image reinforcement was predicted by the internalization of media models, and instrumental/aggravated reasons were not predicted by any variable. Thus, only sexual purposes and body-image reinforcement appeared to be affected by body-image concerns due to media models. Copyright © 2017 The Foundation for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.

The influence of a wall function on turbine blade heat transfer prediction

NASA Technical Reports Server (NTRS)

Whitaker, Kevin W.

1989-01-01

The second phase of a continuing investigation to improve the prediction of turbine blade heat transfer coefficients was completed. The present study specifically investigated how a numeric wall function in the turbulence model of a two-dimensional boundary layer code, STAN5, affected heat transfer prediction capabilities. Several sources of inaccuracy in the wall function were identified and then corrected or improved. Heat transfer coefficient predictions were then obtained using each one of the modifications to determine its effect. Results indicated that the modifications made to the wall function can significantly affect the prediction of heat transfer coefficients on turbine blades. The improvement in accuracy due the modifications is still inconclusive and is still being investigated.

Neural response to pictorial health warning labels can predict smoking behavioral change

PubMed Central

Riddle, Philip J.; Newman-Norlund, Roger D.; Baer, Jessica; Thrasher, James F.

2016-01-01

In order to improve our understanding of how pictorial health warning labels (HWLs) influence smoking behavior, we examined whether brain activity helps to explain smoking behavior above and beyond self-reported effectiveness of HWLs. We measured the neural response in the ventromedial prefrontal cortex (vmPFC) and the amygdala while adult smokers viewed HWLs. Two weeks later, participants’ self-reported smoking behavior and biomarkers of smoking behavior were reassessed. We compared multiple models predicting change in self-reported smoking behavior (cigarettes per day [CPD]) and change in a biomarkers of smoke exposure (expired carbon monoxide [CO]). Brain activity in the vmPFC and amygdala not only predicted changes in CO, but also accounted for outcome variance above and beyond self-report data. Neural data were most useful in predicting behavioral change as quantified by the objective biomarker (CO). This pattern of activity was significantly modulated by individuals’ intention to quit. The finding that both cognitive (vmPFC) and affective (amygdala) brain areas contributed to these models supports the idea that smokers respond to HWLs in a cognitive-affective manner. Based on our findings, researchers may wish to consider using neural data from both cognitive and affective networks when attempting to predict behavioral change in certain populations (e.g. cigarette smokers). PMID:27405615

Neural response to pictorial health warning labels can predict smoking behavioral change.

PubMed

Riddle, Philip J; Newman-Norlund, Roger D; Baer, Jessica; Thrasher, James F

2016-11-01

In order to improve our understanding of how pictorial health warning labels (HWLs) influence smoking behavior, we examined whether brain activity helps to explain smoking behavior above and beyond self-reported effectiveness of HWLs. We measured the neural response in the ventromedial prefrontal cortex (vmPFC) and the amygdala while adult smokers viewed HWLs. Two weeks later, participants' self-reported smoking behavior and biomarkers of smoking behavior were reassessed. We compared multiple models predicting change in self-reported smoking behavior (cigarettes per day [CPD]) and change in a biomarkers of smoke exposure (expired carbon monoxide [CO]). Brain activity in the vmPFC and amygdala not only predicted changes in CO, but also accounted for outcome variance above and beyond self-report data. Neural data were most useful in predicting behavioral change as quantified by the objective biomarker (CO). This pattern of activity was significantly modulated by individuals' intention to quit. The finding that both cognitive (vmPFC) and affective (amygdala) brain areas contributed to these models supports the idea that smokers respond to HWLs in a cognitive-affective manner. Based on our findings, researchers may wish to consider using neural data from both cognitive and affective networks when attempting to predict behavioral change in certain populations (e.g. cigarette smokers). © The Author (2016). Published by Oxford University Press.

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

PubMed

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

2015-01-01

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

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

PubMed Central

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

2015-01-01

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

When does risk perception predict protection motivation for health threats? A person-by-situation analysis.

PubMed

Ferrer, Rebecca A; Klein, William M P; Avishai, Aya; Jones, Katelyn; Villegas, Megan; Sheeran, Paschal

2018-01-01

Although risk perception is a key concept in many health behavior theories, little research has explicitly tested when risk perception predicts motivation to take protective action against a health threat (protection motivation). The present study tackled this question by (a) adopting a multidimensional model of risk perception that comprises deliberative, affective, and experiential components (the TRIRISK model), and (b) taking a person-by-situation approach. We leveraged a highly intensive within-subjects paradigm to test features of the health threat (i.e., perceived severity) and individual differences (e.g., emotion reappraisal) as moderators of the relationship between the three types of risk perception and protection motivation in a within-subjects design. Multi-level modeling of 2968 observations (32 health threats across 94 participants) showed interactions among the TRIRISK components and moderation both by person-level and situational factors. For instance, affective risk perception better predicted protection motivation when deliberative risk perception was high, when the threat was less severe, and among participants who engage less in emotional reappraisal. These findings support the TRIRISK model and offer new insights into when risk perceptions predict protection motivation.

When does risk perception predict protection motivation for health threats? A person-by-situation analysis

PubMed Central

Klein, William M. P.; Avishai, Aya; Jones, Katelyn; Villegas, Megan; Sheeran, Paschal

2018-01-01

Although risk perception is a key concept in many health behavior theories, little research has explicitly tested when risk perception predicts motivation to take protective action against a health threat (protection motivation). The present study tackled this question by (a) adopting a multidimensional model of risk perception that comprises deliberative, affective, and experiential components (the TRIRISK model), and (b) taking a person-by-situation approach. We leveraged a highly intensive within-subjects paradigm to test features of the health threat (i.e., perceived severity) and individual differences (e.g., emotion reappraisal) as moderators of the relationship between the three types of risk perception and protection motivation in a within-subjects design. Multi-level modeling of 2968 observations (32 health threats across 94 participants) showed interactions among the TRIRISK components and moderation both by person-level and situational factors. For instance, affective risk perception better predicted protection motivation when deliberative risk perception was high, when the threat was less severe, and among participants who engage less in emotional reappraisal. These findings support the TRIRISK model and offer new insights into when risk perceptions predict protection motivation. PMID:29494705

Lagrangian methods for blood damage estimation in cardiovascular devices--How numerical implementation affects the results.

PubMed

Marom, Gil; Bluestein, Danny

2016-01-01

This paper evaluated the influence of various numerical implementation assumptions on predicting blood damage in cardiovascular devices using Lagrangian methods with Eulerian computational fluid dynamics. The implementation assumptions that were tested included various seeding patterns, stochastic walk model, and simplified trajectory calculations with pathlines. Post processing implementation options that were evaluated included single passage and repeated passages stress accumulation and time averaging. This study demonstrated that the implementation assumptions can significantly affect the resulting stress accumulation, i.e., the blood damage model predictions. Careful considerations should be taken in the use of Lagrangian models. Ultimately, the appropriate assumptions should be considered based the physics of the specific case and sensitivity analysis, similar to the ones presented here, should be employed.

Prediction of porosity of food materials during drying: Current challenges and directions.

PubMed

Joardder, Mohammad U H; Kumar, C; Karim, M A

2017-07-18

Pore formation in food samples is a common physical phenomenon observed during dehydration processes. The pore evolution during drying significantly affects the physical properties and quality of dried foods. Therefore, it should be taken into consideration when predicting transport processes in the drying sample. Characteristics of pore formation depend on the drying process parameters, product properties and processing time. Understanding the physics of pore formation and evolution during drying will assist in accurately predicting the drying kinetics and quality of food materials. Researchers have been trying to develop mathematical models to describe the pore formation and evolution during drying. In this study, existing porosity models are critically analysed and limitations are identified. Better insight into the factors affecting porosity is provided, and suggestions are proposed to overcome the limitations. These include considerations of process parameters such as glass transition temperature, sample temperature, and variable material properties in the porosity models. Several researchers have proposed models for porosity prediction of food materials during drying. However, these models are either very simplistic or empirical in nature and failed to consider relevant significant factors that influence porosity. In-depth understanding of characteristics of the pore is required for developing a generic model of porosity. A micro-level analysis of pore formation is presented for better understanding, which will help in developing an accurate and generic porosity model.

Prediction model of dissolved oxygen in ponds based on ELM neural network

NASA Astrophysics Data System (ADS)

Li, Xinfei; Ai, Jiaoyan; Lin, Chunhuan; Guan, Haibin

2018-02-01

Dissolved oxygen in ponds is affected by many factors, and its distribution is unbalanced. In this paper, in order to improve the imbalance of dissolved oxygen distribution more effectively, the dissolved oxygen prediction model of Extreme Learning Machine (ELM) intelligent algorithm is established, based on the method of improving dissolved oxygen distribution by artificial push flow. Select the Lake Jing of Guangxi University as the experimental area. Using the model to predict the dissolved oxygen concentration of different voltage pumps, the results show that the ELM prediction accuracy is higher than the BP algorithm, and its mean square error is MSEELM=0.0394, the correlation coefficient RELM=0.9823. The prediction results of the 24V voltage pump push flow show that the discrete prediction curve can approximate the measured values well. The model can provide the basis for the artificial improvement of the dissolved oxygen distribution decision.

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

PubMed Central

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

2015-01-01

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

The effect of pathological narcissism on interpersonal and affective processes in social interactions.

PubMed

Wright, Aidan G C; Stepp, Stephanie D; Scott, Lori N; Hallquist, Michael N; Beeney, Joseph E; Lazarus, Sophie A; Pilkonis, Paul A

2017-10-01

Narcissism has significant interpersonal costs, yet little research has examined behavioral and affective patterns characteristic of narcissism in naturalistic settings. Here we studied the effect of narcissistic features on the dynamic processes of interpersonal behavior and affect in daily life. We used interpersonal theory to generate transactional models of social interaction (i.e., linkages among perceptions of others' behavior, affect, and one's own behavior) predicted to be characteristic of narcissism. Psychiatric outpatients (N = 102) completed clinical interviews and a 21-day ecological momentary assessment protocol using smartphones. After social interactions (N = 5,781), participants reported on perceptions of their interaction partner's behavior (scored along the dimensions of dominant-submissive and affiliative-quarrelsome), their own affect, and their own behavior. Multilevel structural equation modeling was used to examine dynamic links among behavior and affect across interactions, and the role of narcissism in moderating these links. Results showed that perceptions of others' dominance did not predict dominant behavior, but did predict quarrelsome behavior, and this link was potentiated by narcissism. Furthermore, the link between others' dominance and one's own quarrelsome behavior was mediated by negative affect. Moderated mediation was also found: Narcissism amplified the link between ratings of others' dominance and one's own quarrelsomeness and negative affect. Narcissism did not moderate the link between other dominance and own dominance, nor the link between other affiliation and own affiliation. These results suggest that narcissism is associated with specific interpersonal and affective processes, such that sensitivity to others' dominance triggers antagonistic behavior in daily life. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

You can't drink a word: lexical and individual emotionality affect subjective familiarity judgments.

PubMed

Westbury, Chris

2014-10-01

For almost 30 years, subjective familiarity has been used in psycholinguistics as an explanatory variable, allegedly able to explain many phenomena that have no other obvious explanation (Gernsbacher in J Exp Psychol General 113:256-281, 1984). In this paper, the hypothesis tested is that the subjective familiarity of words is reflecting personal familiarity with or importance of the referents of words. Using an empirically-grounded model of affective force derived from Wundt (Grundriss der Psychologie [Outlines of Psychology]. Engelmann, Leibzig, 1896) and based in a co-occurrence model of semantics (which involves no human judgment), it is shown that affective force can account for the same variance in a large set of human subjective familiarity judgments as other human subjective familiarity judgments, can predict whether people will rate new words of the same objective frequency as more or less familiar, can predict lexical access as well as human subjective familiarity judgments do, and has a predicted relationship to age of acquisition norms. Individuals who have highly affective reactivity [as measured by Carver and White's (J Pers Soc Psychol 67(2):319-333, 1994) Behavioral Inhibition Scale and Behavioral Activation Scales] rate words as significantly more familiar than individuals who have low affective reactivity.

Positive Affect as a Source of Resilience for Women in Chronic Pain.

ERIC Educational Resources Information Center

Zautra, Alex J.; Johnson, Lisa M.; Davis, Mary C.

2005-01-01

A sample of 124 women with osteoarthritis or fibromyalgia, or both, completed initial assessments for demographic data, health status, and personality traits and 10-12 weekly interviews regarding pain, stress, negative affect, and positive affect. Multilevel modeling analyses indicated that weekly elevations of pain and stress predicted increases…

Borderline Personality Disorder Symptoms and Aggression: A Within-Person Process Model

PubMed Central

Scott, Lori N.; Wright, Aidan G. C.; Beeney, Joseph E.; Lazarus, Sophie A.; Pilkonis, Paul A.; Stepp, Stephanie D.

2017-01-01

Theoretical and empirical work suggests that aggression in those with borderline personality disorder (BPD) occurs primarily in the context of emotional reactivity, especially anger and shame, in response to perceived rejection. Using intensive repeated measures, we examined a within-person process model in which perceived rejection predicts increases in aggressive urges and behaviors via increases in negative affect (indirect effect) and in which BPD symptoms exacerbate this process (moderated mediation). Participants were 117 emerging adult women (ages 18–24) with recent histories of aggressive behavior who were recruited from a community-based longitudinal study of at-risk youth. Personality disorder symptoms were assessed by semi-structured clinical interview, and aggressive urges, threats, and behaviors were measured in daily life during a three-week ecological momentary assessment (EMA) protocol. Multilevel path models revealed that within-person increases in perceived rejection predicted increases in negative affect, especially in women with greater BPD symptoms. In turn, increases in negative affect predicted increased likelihood of aggressive urges or behaviors. Further analysis revealed that BPD symptoms predicted greater anger and shame reactivity to perceived rejection, but not to criticism or insult. Additionally, only anger was associated with increases in aggression after controlling for other negative emotions. Whereas BPD symptoms exacerbated the link between perceived rejection and aggression via increases in negative affect (particularly anger), this process was attenuated in women with greater antisocial personality disorder (ASPD) symptoms. These findings suggest that anger reactivity to perceived rejection is one unique pathway, distinct from ASPD, by which BPD symptoms increase risk for aggression. PMID:28383936

Features of objectified body consciousness and sociocultural perspectives as risk factors for disordered eating among late-adolescent women and men.

PubMed

Jackson, Todd; Chen, Hong

2015-10-01

Body surveillance and body shame are features of objectified body consciousness (OBC) that have been linked to disordered eating, yet the evidence base is largely cross-sectional and limited to samples in certain Western countries. Furthermore, it is not clear whether these factors contribute to the prediction of eating disturbances independent of conceptually related risk factors emphasized within other sociocultural accounts. In this prospective study, body surveillance, body shame, and features of complementary sociocultural models (i.e., perceived appearance pressure from mass media and close interpersonal networks, appearance social comparisons, negative affect, body dissatisfaction) were assessed as risk factors for and concomitants of eating disturbances over time. University-age, mainland Chinese women (n = 2144) and men (n = 1017) completed validated measures of eating-disorder pathology and hypothesized risk factors at baseline (T1) and 1-year follow-up (T2). Among women, elevations on T1 measures of sociocultural-model features predicted more T2 eating disturbances, independent of T1 disturbances. After controlling for other T1 predictors, body surveillance and shame made modest unique contributions to the model. Finally, heightened T2 body dissatisfaction, media, and interpersonal appearance pressure, negative affect, and body shame predicted concomitant increases in T2 eating concerns. For men, T1 features of sociocultural accounts (negative affect, body dissatisfaction) but not OBC predicted T2 eating disturbances, along with attendant elevations in T2 negative affect, interpersonal appearance pressure, and body shame. Implications are discussed for theory and intervention that target disordered eating. (c) 2015 APA, all rights reserved).

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.

Development of a noise prediction model based on advanced fuzzy approaches in typical industrial workrooms.

PubMed

Aliabadi, Mohsen; Golmohammadi, Rostam; Khotanlou, Hassan; Mansoorizadeh, Muharram; Salarpour, Amir

2014-01-01

Noise prediction is considered to be the best method for evaluating cost-preventative noise controls in industrial workrooms. One of the most important issues is the development of accurate models for analysis of the complex relationships among acoustic features affecting noise level in workrooms. In this study, advanced fuzzy approaches were employed to develop relatively accurate models for predicting noise in noisy industrial workrooms. The data were collected from 60 industrial embroidery workrooms in the Khorasan Province, East of Iran. The main acoustic and embroidery process features that influence the noise were used to develop prediction models using MATLAB software. Multiple regression technique was also employed and its results were compared with those of fuzzy approaches. Prediction errors of all prediction models based on fuzzy approaches were within the acceptable level (lower than one dB). However, Neuro-fuzzy model (RMSE=0.53dB and R2=0.88) could slightly improve the accuracy of noise prediction compared with generate fuzzy model. Moreover, fuzzy approaches provided more accurate predictions than did regression technique. The developed models based on fuzzy approaches as useful prediction tools give professionals the opportunity to have an optimum decision about the effectiveness of acoustic treatment scenarios in embroidery workrooms.

Information Processing and Collective Behavior in a Model Neuronal System

DTIC Science & Technology

2014-03-28

for an AFOSR project headed by Steve Reppert on Monarch Butterfly navigation. We visited the Reppert lab at the UMASS Medical School and have had many...developed a detailed mathematical model of the mammalian circadian clock. Our model can accurately predict diverse experimental data including the...i.e. P1 affects P2 which affects P3 …). The output of the system is calculated (measurements), and the interactions are forgotten. Based on

Daily minority stress and affect among gay and bisexual men: A 30-day diary study.

PubMed

Eldahan, Adam I; Pachankis, John E; Jonathon Rendina, H; Ventuneac, Ana; Grov, Christian; Parsons, Jeffrey T

2016-01-15

This study examined the time-variant association between daily minority stress and daily affect among gay and bisexual men. Tests of time-lagged associations allow for a stronger causal examination of minority stress-affect associations compared with static assessments. Multilevel modeling allows for comparison of associations between minority stress and daily affect when minority stress is modeled as a between-person factor and a within-person time-fluctuating state. 371 gay and bisexual men in New York City completed a 30-day daily diary, recording daily experiences of minority stress and positive affect (PA), negative affect (NA), and anxious affect (AA). Multilevel analyses examined associations between minority stress and affect in both same-day and time-lagged analyses, with minority stress assessed as both a between-person factor and a within-person state. Daily minority stress, modeled as both a between-person and within-person construct, significantly predicted lower PA and higher NA and AA. Daily minority stress also predicted lower subsequent-day PA and higher subsequent-day NA and AA. Self-report assessments and the unique sample may limit generalizability of this study. The time-variant association between sexual minority stress and affect found here substantiates the basic tenet of minority stress theory with a fine-grained analysis of gay and bisexual men's daily experience. Time-lagged effects suggest a potentially causal pathway between minority stress as a social determinant of mood and anxiety disorder symptoms among gay and bisexual men. When modeled as both a between-person factor and within-person state, minority stress demonstrated expected patterns with affect. Copyright © 2015 Elsevier B.V. All rights reserved.

Daily Minority Stress and Affect among Gay and Bisexual Men: A 30-day Diary Study

PubMed Central

Eldahan, Adam I.; Pachankis, John E.; Rendina, H. Jonathon; Ventuneac, Ana; Grov, Christian; Parsons, Jeffrey T.

2015-01-01

Background This study examined the time-variant association between daily minority stress and daily affect among gay and bisexual men. Tests of time-lagged associations allow for a stronger causal examination of minority stress-affect associations compared with static assessments. Multilevel modeling allows for comparison of associations between minority stress and daily affect when minority stress is modeled as a between-person factor and a within-person time-fluctuating state. Methods 371 gay and bisexual men in New York City completed a 30-day daily diary, recording daily experiences of minority stress and positive affect (PA), negative affect (NA), and anxious affect (AA). Multilevel analyses examined associations between minority stress and affect in both same-day and time-lagged analyses, with minority stress assessed as both a between-person factor and a within-person state. Results Daily minority stress, modeled as both a between-person and within-person construct, significantly predicted lower PA and higher NA and AA. Daily minority stress also predicted lower subsequent-day PA and higher subsequent-day NA and AA. Limitations Self-report assessments and the unique sample may limit generalizability of this study. Conclusions The time-variant association between sexual minority stress and affect found here substantiates the basic tenet of minority stress theory with a fine-grained analysis of gay and bisexual men’s daily experience. Time-lagged effects suggest a potentially causal pathway between minority stress as a social determinant of mood and anxiety disorder symptoms among gay and bisexual men. When modeled as both a between-person factor and within-person state, minority stress demonstrated expected patterns with affect. PMID:26625095

Prediction of PM2.5 along urban highway corridor under mixed traffic conditions using CALINE4 model.

PubMed

Dhyani, Rajni; Sharma, Niraj; Maity, Animesh Kumar

2017-08-01

The present study deals with spatial-temporal distribution of PM 2.5 along a highly trafficked national highway corridor (NH-2) in Delhi, India. Population residing in areas near roads and highways of high vehicular activities are exposed to high levels of PM 2.5 resulting in various health issues. The spatial extent of PM 2.5 has been assessed with the help of CALINE4 model. Various input parameters of the model were estimated and used to predict PM 2.5 concentration along the selected highway corridor. The results indicated that there are many factors involved which affects the prediction of PM 2.5 concentration by CALINE4 model. In fact, these factors either not considered by model or have little influence on model's prediction capabilities. Therefore, in the present study CALINE4 model performance was observed to be unsatisfactory for prediction of PM 2.5 concentration. Copyright © 2017 Elsevier Ltd. All rights reserved.

Higher-order Multivariable Polynomial Regression to Estimate Human Affective States

NASA Astrophysics Data System (ADS)

Wei, Jie; Chen, Tong; Liu, Guangyuan; Yang, Jiemin

2016-03-01

From direct observations, facial, vocal, gestural, physiological, and central nervous signals, estimating human affective states through computational models such as multivariate linear-regression analysis, support vector regression, and artificial neural network, have been proposed in the past decade. In these models, linear models are generally lack of precision because of ignoring intrinsic nonlinearities of complex psychophysiological processes; and nonlinear models commonly adopt complicated algorithms. To improve accuracy and simplify model, we introduce a new computational modeling method named as higher-order multivariable polynomial regression to estimate human affective states. The study employs standardized pictures in the International Affective Picture System to induce thirty subjects’ affective states, and obtains pure affective patterns of skin conductance as input variables to the higher-order multivariable polynomial model for predicting affective valence and arousal. Experimental results show that our method is able to obtain efficient correlation coefficients of 0.98 and 0.96 for estimation of affective valence and arousal, respectively. Moreover, the method may provide certain indirect evidences that valence and arousal have their brain’s motivational circuit origins. Thus, the proposed method can serve as a novel one for efficiently estimating human affective states.

Higher-order Multivariable Polynomial Regression to Estimate Human Affective States

PubMed Central

Wei, Jie; Chen, Tong; Liu, Guangyuan; Yang, Jiemin

2016-01-01

From direct observations, facial, vocal, gestural, physiological, and central nervous signals, estimating human affective states through computational models such as multivariate linear-regression analysis, support vector regression, and artificial neural network, have been proposed in the past decade. In these models, linear models are generally lack of precision because of ignoring intrinsic nonlinearities of complex psychophysiological processes; and nonlinear models commonly adopt complicated algorithms. To improve accuracy and simplify model, we introduce a new computational modeling method named as higher-order multivariable polynomial regression to estimate human affective states. The study employs standardized pictures in the International Affective Picture System to induce thirty subjects’ affective states, and obtains pure affective patterns of skin conductance as input variables to the higher-order multivariable polynomial model for predicting affective valence and arousal. Experimental results show that our method is able to obtain efficient correlation coefficients of 0.98 and 0.96 for estimation of affective valence and arousal, respectively. Moreover, the method may provide certain indirect evidences that valence and arousal have their brain’s motivational circuit origins. Thus, the proposed method can serve as a novel one for efficiently estimating human affective states. PMID:26996254

Modeling the Zeeman effect in high altitude SSMIS channels for numerical weather prediction profiles: comparing a fast model and a line-by-line model

NASA Astrophysics Data System (ADS)

Larsson, R.; Milz, M.; Rayer, P.; Saunders, R.; Bell, W.; Booton, A.; Buehler, S. A.; Eriksson, P.; John, V.

2015-10-01

We present a comparison of a reference and a fast radiative transfer model using numerical weather prediction profiles for the Zeeman-affected high altitude Special Sensor Microwave Imager/Sounder channels 19-22. We find that the models agree well for channels 21 and 22 compared to the channels' system noise temperatures (1.9 and 1.3 K, respectively) and the expected profile errors at the affected altitudes (estimated to be around 5 K). For channel 22 there is a 0.5 K average difference between the models, with a standard deviation of 0.24 K for the full set of atmospheric profiles. Same channel, there is 1.2 K in average between the fast model and the sensor measurement, with 1.4 K standard deviation. For channel 21 there is a 0.9 K average difference between the models, with a standard deviation of 0.56 K. Same channel, there is 1.3 K in average between the fast model and the sensor measurement, with 2.4 K standard deviation. We consider the relatively small model differences as a validation of the fast Zeeman effect scheme for these channels. Both channels 19 and 20 have smaller average differences between the models (at below 0.2 K) and smaller standard deviations (at below 0.4 K) when both models use a two-dimensional magnetic field profile. However, when the reference model is switched to using a full three-dimensional magnetic field profile, the standard deviation to the fast model is increased to almost 2 K due to viewing geometry dependencies causing up to ± 7 K differences near the equator. The average differences between the two models remain small despite changing magnetic field configurations. We are unable to compare channels 19 and 20 to sensor measurements due to limited altitude range of the numerical weather prediction profiles. We recommended that numerical weather prediction software using the fast model takes the available fast Zeeman scheme into account for data assimilation of the affected sensor channels to better constrain the upper atmospheric temperatures.

Modeling the Zeeman effect in high-altitude SSMIS channels for numerical weather prediction profiles: comparing a fast model and a line-by-line model

NASA Astrophysics Data System (ADS)

Larsson, Richard; Milz, Mathias; Rayer, Peter; Saunders, Roger; Bell, William; Booton, Anna; Buehler, Stefan A.; Eriksson, Patrick; John, Viju O.

2016-03-01

We present a comparison of a reference and a fast radiative transfer model using numerical weather prediction profiles for the Zeeman-affected high-altitude Special Sensor Microwave Imager/Sounder channels 19-22. We find that the models agree well for channels 21 and 22 compared to the channels' system noise temperatures (1.9 and 1.3 K, respectively) and the expected profile errors at the affected altitudes (estimated to be around 5 K). For channel 22 there is a 0.5 K average difference between the models, with a standard deviation of 0.24 K for the full set of atmospheric profiles. Concerning the same channel, there is 1.2 K on average between the fast model and the sensor measurement, with 1.4 K standard deviation. For channel 21 there is a 0.9 K average difference between the models, with a standard deviation of 0.56 K. Regarding the same channel, there is 1.3 K on average between the fast model and the sensor measurement, with 2.4 K standard deviation. We consider the relatively small model differences as a validation of the fast Zeeman effect scheme for these channels. Both channels 19 and 20 have smaller average differences between the models (at below 0.2 K) and smaller standard deviations (at below 0.4 K) when both models use a two-dimensional magnetic field profile. However, when the reference model is switched to using a full three-dimensional magnetic field profile, the standard deviation to the fast model is increased to almost 2 K due to viewing geometry dependencies, causing up to ±7 K differences near the equator. The average differences between the two models remain small despite changing magnetic field configurations. We are unable to compare channels 19 and 20 to sensor measurements due to limited altitude range of the numerical weather prediction profiles. We recommended that numerical weather prediction software using the fast model takes the available fast Zeeman scheme into account for data assimilation of the affected sensor channels to better constrain the upper atmospheric temperatures.

A Bayesian hierarchical model with spatial variable selection: the effect of weather on insurance claims

PubMed Central

Scheel, Ida; Ferkingstad, Egil; Frigessi, Arnoldo; Haug, Ola; Hinnerichsen, Mikkel; Meze-Hausken, Elisabeth

2013-01-01

Climate change will affect the insurance industry. We develop a Bayesian hierarchical statistical approach to explain and predict insurance losses due to weather events at a local geographic scale. The number of weather-related insurance claims is modelled by combining generalized linear models with spatially smoothed variable selection. Using Gibbs sampling and reversible jump Markov chain Monte Carlo methods, this model is fitted on daily weather and insurance data from each of the 319 municipalities which constitute southern and central Norway for the period 1997–2006. Precise out-of-sample predictions validate the model. Our results show interesting regional patterns in the effect of different weather covariates. In addition to being useful for insurance pricing, our model can be used for short-term predictions based on weather forecasts and for long-term predictions based on downscaled climate models. PMID:23396890

One Size Fits All? Applying Theoretical Predictions about Age and Emotional Experience to People with Functional Disabilities

PubMed Central

Piazza, Jennifer R.; Charles, Susan T.; Luong, Gloria; Almeida, David M.

2015-01-01

The current study examined whether commonly observed age differences in affective experience among community samples of healthy adults would generalize to a group of adults who live with significant functional disability. Age differences in daily affect and affective reactivity to daily stressors among a sample of participants with spinal cord injury were compared to a non-injured sample. Results revealed that patterns of affective experience varied by sample. Among non-injured adults, older age was associated with lower levels of daily negative affect (NA), higher levels of daily positive affect (PA), and less negative affective reactivity in response to daily stressors. In contrast, among participants with spinal cord injury, no age differences emerged. Findings, which support the model of Strength and Vulnerability Integration (SAVI), underscore the importance of taking life context into account when predicting age differences in affective well-being. PMID:26322552

A Comparison of Two Models of Risky Sexual Behavior During Late Adolescence.

PubMed

Braje, Sopagna Eap; Eddy, J Mark; Hall, Gordon C N

2016-01-01

Two models of risky sexual behavior (RSB) were compared in a community sample of late adolescents (N = 223). For the traumagenic model, early negative sexual experiences were posited to lead to an association between negative affect with sexual relationships. For the cognitive escape model, depressive affect was posited to lead to engagement in RSB as a way to avoid negative emotions. The current study examined whether depression explained the relationship between sexual trauma and RSB, supporting the cognitive escape model, or whether it was sexual trauma that led specifically to RSB, supporting the traumagenic model. Physical trauma experiences were also examined to disentangle the effects of sexual trauma compared to other emotionally distressing events. The study examined whether the results would be moderated by participant sex. For males, support was found for the cognitive escape model but not the traumagenic model. Among males, physical trauma and depression predicted engagement in RSB but sexual trauma did not. For females, support was found for the traumagenic and cognitive escape model. Among females, depression and sexual trauma both uniquely predicted RSB. There was an additional suppressor effect of socioeconomic status in predicting RSB among females. Results suggest that the association of trauma type with RSB depends on participant sex. Implications of the current study for RSB prevention efforts are discussed.

Distal and proximal predictors of snacking at work: A daily-survey study.

PubMed

Sonnentag, Sabine; Pundt, Alexander; Venz, Laura

2017-02-01

This study aimed at examining predictors of healthy and unhealthy snacking at work. As proximal predictors we looked at food-choice motives (health motive, affect-regulation motive); as distal predictors we included organizational eating climate, emotional eating, and self-control demands at work. We collected daily survey data from 247 employees, over a period of 2 workweeks. Multilevel structural equation modeling showed that organizational eating climate predicted health as food-choice motive, whereas emotional eating and self-control demands predicted affect regulation as food-choice motive. The health motive, in turn, predicted consuming more fruits and more cereal bars and less sweet snacks; the affect-regulation motive predicted consuming more sweet snacks. Findings highlight the importance of a health-promoting eating climate within the organization and point to the potential harm of high self-control demands at work. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Dam-Break Flooding and Structural Damage in a Residential Neighborhood: Performance of a coupled hydrodynamic-damage model

NASA Astrophysics Data System (ADS)

Sanders, B. F.; Gallegos, H. A.; Schubert, J. E.

2011-12-01

The Baldwin Hills dam-break flood and associated structural damage is investigated in this study. The flood caused high velocity flows exceeding 5 m/s which destroyed 41 wood-framed residential structures, 16 of which were completed washed out. Damage is predicted by coupling a calibrated hydrodynamic flood model based on the shallow-water equations to structural damage models. The hydrodynamic and damage models are two-way coupled so building failure is predicted upon exceedance of a hydraulic intensity parameter, which in turn triggers a localized reduction in flow resistance which affects flood intensity predictions. Several established damage models and damage correlations reported in the literature are tested to evaluate the predictive skill for two damage states defined by destruction (Level 2) and washout (Level 3). Results show that high-velocity structural damage can be predicted with a remarkable level of skill using established damage models, but only with two-way coupling of the hydrodynamic and damage models. In contrast, when structural failure predictions have no influence on flow predictions, there is a significant reduction in predictive skill. Force-based damage models compare well with a subset of the damage models which were devised for similar types of structures. Implications for emergency planning and preparedness as well as monetary damage estimation are discussed.

Landscape scale prediction of earthquake-induced landsliding based on seismological and geomorphological parameters.

NASA Astrophysics Data System (ADS)

Marc, O.; Hovius, N.; Meunier, P.; Rault, C.

2017-12-01

In tectonically active areas, earthquakes are an important trigger of landslides with significant impact on hillslopes and river evolutions. However, detailed prediction of landslides locations and properties for a given earthquakes remain difficult.In contrast we propose, landscape scale, analytical prediction of bulk coseismic landsliding, that is total landslide area and volume (Marc et al., 2016a) as well as the regional area within which most landslide must distribute (Marc et al., 2017). The prediction is based on a limited number of seismological (seismic moment, source depth) and geomorphological (landscape steepness, threshold acceleration) parameters, and therefore could be implemented in landscape evolution model aiming at engaging with erosion dynamics at the scale of the seismic cycle. To assess the model we have compiled and normalized estimates of total landslide volume, total landslide area and regional area affected by landslides for 40, 17 and 83 earthquakes, respectively. We have found that low landscape steepness systematically leads to overprediction of the total area and volume of landslides. When this effect is accounted for, the model is able to predict within a factor of 2 the landslide areas and associated volumes for about 70% of the cases in our databases. The prediction of regional area affected do not require a calibration for the landscape steepness and gives a prediction within a factor of 2 for 60% of the database. For 7 out of 10 comprehensive inventories we show that our prediction compares well with the smallest region around the fault containing 95% of the total landslide area. This is a significant improvement on a previously published empirical expression based only on earthquake moment.Some of the outliers seems related to exceptional rock mass strength in the epicentral area or shaking duration and other seismic source complexities ignored by the model. Applications include prediction on the mass balance of earthquakes and this model predicts that only earthquakes generated on a narrow range of fault sizes may cause more erosion than uplift (Marc et al., 2016b), while very large earthquakes are expected to always build topography. The model could also be used to physically calibrate hillslope erosion or perturbations to river network within landscape evolution model.

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.

[Influence of sample surface roughness on mathematical model of NIR quantitative analysis of wood density].

PubMed

Huang, An-Min; Fei, Ben-Hua; Jiang, Ze-Hui; Hse, Chung-Yun

2007-09-01

Near infrared spectroscopy is widely used as a quantitative method, and the main multivariate techniques consist of regression methods used to build prediction models, however, the accuracy of analysis results will be affected by many factors. In the present paper, the influence of different sample roughness on the mathematical model of NIR quantitative analysis of wood density was studied. The result of experiments showed that if the roughness of predicted samples was consistent with that of calibrated samples, the result was good, otherwise the error would be much higher. The roughness-mixed model was more flexible and adaptable to different sample roughness. The prediction ability of the roughness-mixed model was much better than that of the single-roughness model.

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

PubMed

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

2015-08-01

Recently, research has provided support for a moderate, inverse relationship between social anxiety and dispositional positive affect. However, the dynamics of this relationship remain poorly understood. The present study evaluates whether certain personality traits and emotion regulation variables predict short-term positive affect for individuals with social anxiety disorder and healthy controls. Positive affect as measured by two self-report instruments was assessed before and after two tasks in which the participant conversed with either a friend or a romantic partner. Tests of models examining the hypothesized prospective predictors revealed that the paths did not differ significantly across diagnostic group and both groups showed the hypothesized patterns of endorsement for the emotion regulation variables. Further, a variable reflecting difficulty redirecting oneself when distressed prospectively predicted one measure of positive affect. Additional research is needed to explore further the role of emotion regulation strategies on positive emotions for individuals higher in social anxiety. Copyright © 2015 Elsevier Ltd. All rights reserved.

Predicting growth of the healthy infant using a genome scale metabolic model.

PubMed

Nilsson, Avlant; Mardinoglu, Adil; Nielsen, Jens

2017-01-01

An estimated 165 million children globally have stunted growth, and extensive growth data are available. Genome scale metabolic models allow the simulation of molecular flux over each metabolic enzyme, and are well adapted to analyze biological systems. We used a human genome scale metabolic model to simulate the mechanisms of growth and integrate data about breast-milk intake and composition with the infant's biomass and energy expenditure of major organs. The model predicted daily metabolic fluxes from birth to age 6 months, and accurately reproduced standard growth curves and changes in body composition. The model corroborates the finding that essential amino and fatty acids do not limit growth, but that energy is the main growth limiting factor. Disruptions to the supply and demand of energy markedly affected the predicted growth, indicating that elevated energy expenditure may be detrimental. The model was used to simulate the metabolic effect of mineral deficiencies, and showed the greatest growth reduction for deficiencies in copper, iron, and magnesium ions which affect energy production through oxidative phosphorylation. The model and simulation method were integrated to a platform and shared with the research community. The growth model constitutes another step towards the complete representation of human metabolism, and may further help improve the understanding of the mechanisms underlying stunting.

Yes: The Symptoms of OCD and Depression Are Discrete and Not Exclusively Negative Affectivity

PubMed Central

Moore, Kathleen A.; Howell, Jacqui

2017-01-01

Although Obsessive-Compulsive Disorder (OCD) and Depression are classified as separate disorders, the high incidence of co-morbidity and the strong correlations between measures of each has led to debate about the nature of their relationship. Some authors have proposed that OCD is in fact a mood disorder while others have suggested that the two disorders are grounded in negative affectivity. A third proposition is that depression is an essential part of OCD but that OCD is a separate disorder from depression. The aim in this study was to investigate these diverse propositions in a non-clinical sample and also to determine whether factors implicated in each, that is anxious and depressive cognitions, hopelessness, and self-criticism, would demonstrate commonality as predictors of the symptoms of OCD and of depression. Two hundred participants (59% female) (M age = 34 years, SD = 16) completed the Padua Inventory, Carroll Rating Scale, Cognitions Checklist, Self-Criticism Scale, Beck Hopelessness Scale, Buss-Durkee Hostility Inventory-Revised and a Negative Affectivity Schedule. Results indicated a strong correlation between OCD and depression, depression, and negative affectivity but a weaker relationship between OCD and negative affectivity. Path analyses revealed that both anxious and depressive cognitions, as well as hostility predicted both disorders but the Beta-weights were stronger on OCD. Self-criticism predicted only depression while hopelessness failed to predict either disorder but was itself predicted by depressive cognitions. Depression was a stronger indicator of negative affect than OCD and while OCD positively predicted depression, depression was a negative indicator of OCD. These results support the hypothesis that OCD and depression are discrete disorders and indicate that while depression is implicated in OCD, the reverse does not hold. While both disorders are related to negative affectivity, this relationship is much stronger for depression thus failing to confirm that both are subsumed by a common factor, in this case, negative affectivity. The proposition that depression is part of OCD but that OCD is not necessarily implicated in depression and is, in fact, a separate disorder, is supported by the current model. Further research is required to support the utility of the model in clinical samples. PMID:28553250

Configuration of the thermal landscape determines thermoregulatory performance of ectotherms

PubMed Central

Sears, Michael W.; Angilletta, Michael J.; Schuler, Matthew S.; Borchert, Jason; Dilliplane, Katherine F.; Stegman, Monica; Rusch, Travis W.; Mitchell, William A.

2016-01-01

Although most organisms thermoregulate behaviorally, biologists still cannot easily predict whether mobile animals will thermoregulate in natural environments. Current models fail because they ignore how the spatial distribution of thermal resources constrains thermoregulatory performance over space and time. To overcome this limitation, we modeled the spatially explicit movements of animals constrained by access to thermal resources. Our models predict that ectotherms thermoregulate more accurately when thermal resources are dispersed throughout space than when these resources are clumped. This prediction was supported by thermoregulatory behaviors of lizards in outdoor arenas with known distributions of environmental temperatures. Further, simulations showed how the spatial structure of the landscape qualitatively affects responses of animals to climate. Biologists will need spatially explicit models to predict impacts of climate change on local scales. PMID:27601639

Applicability of linear regression equation for prediction of chlorophyll content in rice leaves

NASA Astrophysics Data System (ADS)

Li, Yunmei

2005-09-01

A modeling approach is used to assess the applicability of the derived equations which are capable to predict chlorophyll content of rice leaves at a given view direction. Two radiative transfer models, including PROSPECT model operated at leaf level and FCR model operated at canopy level, are used in the study. The study is consisted of three steps: (1) Simulation of bidirectional reflectance from canopy with different leaf chlorophyll contents, leaf-area-index (LAI) and under storey configurations; (2) Establishment of prediction relations of chlorophyll content by stepwise regression; and (3) Assessment of the applicability of these relations. The result shows that the accuracy of prediction is affected by different under storey configurations and, however, the accuracy tends to be greatly improved with increase of LAI.

Assessment of turbulent models for scramjet flowfields

NASA Technical Reports Server (NTRS)

Sindir, M. M.; Harsha, P. T.

1982-01-01

The behavior of several turbulence models applied to the prediction of scramjet combustor flows is described. These models include the basic two equation model, the multiple dissipation length scale variant of the two equation model, and the algebraic stress model (ASM). Predictions were made of planar backward facing step flows and axisymmetric sudden expansion flows using each of these approaches. The formulation of each of these models are discussed, and the application of the different approaches to supersonic flows is described. A modified version of the ASM is found to provide the best prediction of the planar backward facing step flow in the region near the recirculation zone, while the basic ASM provides the best results downstream of the recirculation. Aspects of the interaction of numerica modeling and turbulences modeling as they affect the assessment of turbulence models are discussed.

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.

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

PubMed Central

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

2010-01-01

Self-esteem has been demonstrated to predict health and well-being in a number of samples and domains using retrospective reports, but little is known about the effect of self-esteem in daily life. A community sample with asthma (n = 97) or rheumatoid arthritis (n = 31) completed a self-esteem measure and collected Ecological Momentary Assessment (EMA) data 5x/day for one week using a palmtop computer. Low self-esteem predicted more negative affect, less positive affect, greater stress severity, and greater symptom severity in daily life. Naturalistic exploration of mechanisms relating self-esteem to physiological and/or psychological components in illness may clarify causal relationships and inform theoretical models of self-care, well-being, and disease management. PMID:18809639

Product component genealogy modeling and field-failure prediction

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

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

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

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

A productivity model for parasitized, multibrooded songbirds

USGS Publications Warehouse

Powell, L.A.; Knutson, M.G.

2006-01-01

We present an enhancement of a simulation model to predict annual productivity for Wood Thrushes (Hylocichla mustelina) and American Redstarts (Setophaga ruticilla); the model includes effects of Brown-headed Cowbird (Molothrus ater) parasitism. We used species-specific data from the Driftless Area Ecoregion of Wisconsin, Minnesota, and Iowa to parameterize the model as a case study. The simulation model predicted annual productivity of 2.03 ?? 1.60 SD for Wood Thrushes and 1.56 ?? 1.31 SD for American Redstarts. Our sensitivity analysis showed that high parasitism lowered Wood Thrush annual productivity more than American Redstart productivity, even though parasitism affected individual nests of redstarts more severely. Annual productivity predictions are valuable for habitat managers, but productivity is not easily obtained from field studies. Our model provides a useful means of integrating complex life history parameters to predict productivity for songbirds that experience nest parasitism. ?? The Cooper Ornithological Society 2006.

Lagrangian methods for blood damage estimation in cardiovascular devices - How numerical implementation affects the results

PubMed Central

Marom, Gil; Bluestein, Danny

2016-01-01

Summary This paper evaluated the influence of various numerical implementation assumptions on predicting blood damage in cardiovascular devices using Lagrangian methods with Eulerian computational fluid dynamics. The implementation assumptions that were tested included various seeding patterns, stochastic walk model, and simplified trajectory calculations with pathlines. Post processing implementation options that were evaluated included single passage and repeated passages stress accumulation and time averaging. This study demonstrated that the implementation assumptions can significantly affect the resulting stress accumulation, i.e., the blood damage model predictions. Careful considerations should be taken in the use of Lagrangian models. Ultimately, the appropriate assumptions should be considered based the physics of the specific case and sensitivity analysis, similar to the ones presented here, should be employed. PMID:26679833

Behavioral facilitation: a cognitive model of individual differences in approach motivation.

PubMed

Robinson, Michael D; Meier, Brian P; Tamir, Maya; Wilkowski, Benjamin M; Ode, Scott

2009-02-01

Approach motivation consists of the active, engaged pursuit of one's goals. The purpose of the present three studies (N = 258) was to examine whether approach motivation could be cognitively modeled, thereby providing process-based insights into personality functioning. Behavioral facilitation was assessed in terms of faster (or facilitated) reaction time with practice. As hypothesized, such tendencies predicted higher levels of approach motivation, higher levels of positive affect, and lower levels of depressive symptoms and did so across cognitive, behavioral, self-reported, and peer-reported outcomes. Tendencies toward behavioral facilitation, on the other hand, did not correlate with self-reported traits (Study 1) and did not predict avoidance motivation or negative affect (all studies). The results indicate a systematic relationship between behavioral facilitation in cognitive tasks and approach motivation in daily life. Results are discussed in terms of the benefits of modeling the cognitive processes hypothesized to underlie individual differences motivation, affect, and depression. (c) 2009 APA, all rights reserved

Neuromusculoskeletal Model Calibration Significantly Affects Predicted Knee Contact Forces for Walking

PubMed Central

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

2016-01-01

Though walking impairments are prevalent in society, clinical treatments are often ineffective at restoring lost function. For this reason, researchers have begun to explore the use of patient-specific computational walking models to develop more effective treatments. However, the accuracy with which models can predict internal body forces in muscles and across joints depends on how well relevant model parameter values can be calibrated for the patient. This study investigated how knowledge of internal knee contact forces affects calibration of neuromusculoskeletal model parameter values and subsequent prediction of internal knee contact and leg muscle forces during walking. Model calibration was performed using a novel two-level optimization procedure applied to six normal walking trials from the Fourth Grand Challenge Competition to Predict In Vivo Knee Loads. The outer-level optimization adjusted time-invariant model parameter values to minimize passive muscle forces, reserve actuator moments, and model parameter value changes with (Approach A) and without (Approach B) tracking of experimental knee contact forces. Using the current guess for model parameter values but no knee contact force information, the inner-level optimization predicted time-varying muscle activations that were close to experimental muscle synergy patterns and consistent with the experimental inverse dynamic loads (both approaches). For all the six gait trials, Approach A predicted knee contact forces with high accuracy for both compartments (average correlation coefficient r = 0.99 and root mean square error (RMSE) = 52.6 N medial; average r = 0.95 and RMSE = 56.6 N lateral). In contrast, Approach B overpredicted contact force magnitude for both compartments (average RMSE = 323 N medial and 348 N lateral) and poorly matched contact force shape for the lateral compartment (average r = 0.90 medial and −0.10 lateral). Approach B had statistically higher lateral muscle forces and lateral optimal muscle fiber lengths but lower medial, central, and lateral normalized muscle fiber lengths compared to Approach A. These findings suggest that poorly calibrated model parameter values may be a major factor limiting the ability of neuromusculoskeletal models to predict knee contact and leg muscle forces accurately for walking. PMID:27210105

Distributed Neural Processing Predictors of Multi-dimensional Properties of Affect

PubMed Central

Bush, Keith A.; Inman, Cory S.; Hamann, Stephan; Kilts, Clinton D.; James, G. Andrew

2017-01-01

Recent evidence suggests that emotions have a distributed neural representation, which has significant implications for our understanding of the mechanisms underlying emotion regulation and dysregulation as well as the potential targets available for neuromodulation-based emotion therapeutics. This work adds to this evidence by testing the distribution of neural representations underlying the affective dimensions of valence and arousal using representational models that vary in both the degree and the nature of their distribution. We used multi-voxel pattern classification (MVPC) to identify whole-brain patterns of functional magnetic resonance imaging (fMRI)-derived neural activations that reliably predicted dimensional properties of affect (valence and arousal) for visual stimuli viewed by a normative sample (n = 32) of demographically diverse, healthy adults. Inter-subject leave-one-out cross-validation showed whole-brain MVPC significantly predicted (p < 0.001) binarized normative ratings of valence (positive vs. negative, 59% accuracy) and arousal (high vs. low, 56% accuracy). We also conducted group-level univariate general linear modeling (GLM) analyses to identify brain regions whose response significantly differed for the contrasts of positive versus negative valence or high versus low arousal. Multivoxel pattern classifiers using voxels drawn from all identified regions of interest (all-ROIs) exhibited mixed performance; arousal was predicted significantly better than chance but worse than the whole-brain classifier, whereas valence was not predicted significantly better than chance. Multivoxel classifiers derived using individual ROIs generally performed no better than chance. Although performance of the all-ROI classifier improved with larger ROIs (generated by relaxing the clustering threshold), performance was still poorer than the whole-brain classifier. These findings support a highly distributed model of neural processing for the affective dimensions of valence and arousal. Finally, joint error analyses of the MVPC hyperplanes encoding valence and arousal identified regions within the dimensional affect space where multivoxel classifiers exhibited the greatest difficulty encoding brain states – specifically, stimuli of moderate arousal and high or low valence. In conclusion, we highlight new directions for characterizing affective processing for mechanistic and therapeutic applications in affective neuroscience. PMID:28959198

Updates to In-Line Calculation of Photolysis Rates

EPA Science Inventory

How photolysis rates are calculated affects ozone and aerosol concentrations predicted by the CMAQ model and the model?s run-time. The standard configuration of CMAQ uses the inline option that calculates photolysis rates by solving the radiative transfer equation for the needed ...

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

PubMed

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

2017-03-01

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

Stochastic Analysis of Orbital Lifetimes of Spacecraft

NASA Technical Reports Server (NTRS)

Sasamoto, Washito; Goodliff, Kandyce; Cornelius, David

2008-01-01

A document discusses (1) a Monte-Carlo-based methodology for probabilistic prediction and analysis of orbital lifetimes of spacecraft and (2) Orbital Lifetime Monte Carlo (OLMC)--a Fortran computer program, consisting of a previously developed long-term orbit-propagator integrated with a Monte Carlo engine. OLMC enables modeling of variances of key physical parameters that affect orbital lifetimes through the use of probability distributions. These parameters include altitude, speed, and flight-path angle at insertion into orbit; solar flux; and launch delays. The products of OLMC are predicted lifetimes (durations above specified minimum altitudes) for the number of user-specified cases. Histograms generated from such predictions can be used to determine the probabilities that spacecraft will satisfy lifetime requirements. The document discusses uncertainties that affect modeling of orbital lifetimes. Issues of repeatability, smoothness of distributions, and code run time are considered for the purpose of establishing values of code-specific parameters and number of Monte Carlo runs. Results from test cases are interpreted as demonstrating that solar-flux predictions are primary sources of variations in predicted lifetimes. Therefore, it is concluded, multiple sets of predictions should be utilized to fully characterize the lifetime range of a spacecraft.

Simplification of the Gardner model: effects on maximum upward flux in the presence of a shallow water table

NASA Astrophysics Data System (ADS)

Xing, Xuguang; Ma, Xiaoyi

2018-06-01

The maximum upward flux ( E max) is a control condition for the development of groundwater evaporation models, which can be predicted through the Gardner model. A high-precision E max prediction helps to improve irrigation practice. When using the Gardner model, it has widely been accepted to ignore parameter b (a soil-water constant) for model simplification. However, this may affect the prediction accuracy; therefore, how parameter b affects E max requires detailed investigation. An indoor one-dimensional soil-column evaporation experiment was conducted to observe E max in the presence of a water table of depth 50 cm. The study consisted of 13 treatments based on four solutes and three concentrations in groundwater: KCl, NaCl, CaCl2, and MgCl2, with concentrations of 5, 30, and 100 g/L (salty groundwater); distilled water was used as a control treatment. Results indicated that for the experimental homogeneous loam, the average E max for the treatments supplied by salty groundwater was larger than that supplied by distilled water. Furthermore, during the prediction of the Gardner-model-based E max, ignoring b and including b always led to an overestimate and underestimate, respectively, compared to the observed E max. However, the maximum upward flux calculated including b (i.e. E bmax) had higher accuracy than that ignoring b for E max prediction. Moreover, the impact of ignoring b on E max gradually weakened with increasing b value. This research helps to reveal the groundwater evaporation mechanism.

A predictive model of human performance.

NASA Technical Reports Server (NTRS)

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

1971-01-01

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

A no-reference bitstream-based perceptual model for video quality estimation of videos affected by coding artifacts and packet losses

NASA Astrophysics Data System (ADS)

Pandremmenou, K.; Shahid, M.; Kondi, L. P.; Lövström, B.

2015-03-01

In this work, we propose a No-Reference (NR) bitstream-based model for predicting the quality of H.264/AVC video sequences, affected by both compression artifacts and transmission impairments. The proposed model is based on a feature extraction procedure, where a large number of features are calculated from the packet-loss impaired bitstream. Many of the features are firstly proposed in this work, and the specific set of the features as a whole is applied for the first time for making NR video quality predictions. All feature observations are taken as input to the Least Absolute Shrinkage and Selection Operator (LASSO) regression method. LASSO indicates the most important features, and using only them, it is possible to estimate the Mean Opinion Score (MOS) with high accuracy. Indicatively, we point out that only 13 features are able to produce a Pearson Correlation Coefficient of 0.92 with the MOS. Interestingly, the performance statistics we computed in order to assess our method for predicting the Structural Similarity Index and the Video Quality Metric are equally good. Thus, the obtained experimental results verified the suitability of the features selected by LASSO as well as the ability of LASSO in making accurate predictions through sparse modeling.

Green roof hydrologic performance and modeling: a review.

PubMed

Li, Yanling; Babcock, Roger W

2014-01-01

Green roofs reduce runoff from impervious surfaces in urban development. This paper reviews the technical literature on green roof hydrology. Laboratory experiments and field measurements have shown that green roofs can reduce stormwater runoff volume by 30 to 86%, reduce peak flow rate by 22 to 93% and delay the peak flow by 0 to 30 min and thereby decrease pollution, flooding and erosion during precipitation events. However, the effectiveness can vary substantially due to design characteristics making performance predictions difficult. Evaluation of the most recently published study findings indicates that the major factors affecting green roof hydrology are precipitation volume, precipitation dynamics, antecedent conditions, growth medium, plant species, and roof slope. This paper also evaluates the computer models commonly used to simulate hydrologic processes for green roofs, including stormwater management model, soil water atmosphere and plant, SWMS-2D, HYDRUS, and other models that are shown to be effective for predicting precipitation response and economic benefits. The review findings indicate that green roofs are effective for reduction of runoff volume and peak flow, and delay of peak flow, however, no tool or model is available to predict expected performance for any given anticipated system based on design parameters that directly affect green roof hydrology.

EXCLUSION OF RARE TAXA AFFECTS PERFORMANCE OF THE O/E INDEX IN BIOASSESSMENTS

EPA Science Inventory

The contribution of rare taxa to bioassessments based on multispecies assemblages is the subject of continued debate. As a result, users of predictive models such as River InVertebrate Prediction and Classification System (RIVPACS) disagree on whether to exclude locally rare taxa...

The role of emotional dysregulation in concurrent eating disorders and substance use disorders.

PubMed

Spence, Sarah; Courbasson, Christine

2012-12-01

This study explored the role of emotional dysregulation in 178 participants with concurrent EDs and SUDs. We ran two path analyses: Model 1 predicted negative mood regulation from alexithymia, and Model 2 predicted emotional eating from negative mood regulation. For Model 1, difficulty identifying and describing feelings was related to poor coping expectancies, while externally-oriented thinking was related to greater coping expectancies. For Model 2, poor coping expectancies in general were related to emotional eating, while greater coping expectancies in relation to behavior (i.e., the belief that some behavior or action can alleviate one's negative affect) also resulted in increased emotional eating. This finding suggests that there may be differences in the purpose of emotional eating; some people may believe that emotional eating can be used as an effective coping strategy to deal with negative affect. Copyright © 2012 Elsevier Ltd. All rights reserved.

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

PubMed

Zhang, Tao; Yang, Xiaojun

2013-01-01

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

Dysfunctional Attitudes and Affective Responses to Daily Stressors: Separating Cognitive, Genetic, and Clinical Influences on Stress Reactivity

PubMed Central

Conway, Christopher C.; Slavich, George M.; Hammen, Constance

2016-01-01

Despite decades of research examining diathesis-stress models of emotional disorders, it remains unclear whether dysfunctional attitudes interact with stressful experiences to shape affect on a daily basis and, if so, how clinical and genetic factors influence these associations. To address these issues, we conducted a multi-level daily diary study that examined how dysfunctional attitudes and stressful events relate to daily fluctuations in negative and positive affect in 104 young adults. Given evidence that clinical and genetic factors underlie stress sensitivity, we also examined how daily affect is influenced by internalizing and externalizing symptoms and brain-derived neurotrophic factor (BDNF) genotype, which have been shown to influence neural, endocrine, and affective responses to stress. In multivariate models, internalizing symptoms and BDNF Val66Met genotype independently predicted heightened negative affect on stressful days, but dysfunctional attitudes did not. Specifically, the BDNF Met allele and elevated baseline internalizing symptomatology predicted greater increases in negative affect in stressful circumstances. These data are the first to demonstrate that BDNF genotype and stress are jointly associated with daily fluctuations in negative affect, and they challenge the assumption that maladaptive beliefs play a strong independent role in determining affective responses to everyday stressors. The results may thus inform the development of new multi-level theories of psychopathology and guide future research on predictors of affective lability. PMID:27041782

Simulation-Based Prediction of Equivalent Continuous Noises during Construction Processes

PubMed Central

Zhang, Hong; Pei, Yun

2016-01-01

Quantitative prediction of construction noise is crucial to evaluate construction plans to help make decisions to address noise levels. Considering limitations of existing methods for measuring or predicting the construction noise and particularly the equivalent continuous noise level over a period of time, this paper presents a discrete-event simulation method for predicting the construction noise in terms of equivalent continuous level. The noise-calculating models regarding synchronization, propagation and equivalent continuous level are presented. The simulation framework for modeling the noise-affected factors and calculating the equivalent continuous noise by incorporating the noise-calculating models into simulation strategy is proposed. An application study is presented to demonstrate and justify the proposed simulation method in predicting the equivalent continuous noise during construction. The study contributes to provision of a simulation methodology to quantitatively predict the equivalent continuous noise of construction by considering the relevant uncertainties, dynamics and interactions. PMID:27529266

Simulation-Based Prediction of Equivalent Continuous Noises during Construction Processes.

PubMed

Zhang, Hong; Pei, Yun

2016-08-12

Quantitative prediction of construction noise is crucial to evaluate construction plans to help make decisions to address noise levels. Considering limitations of existing methods for measuring or predicting the construction noise and particularly the equivalent continuous noise level over a period of time, this paper presents a discrete-event simulation method for predicting the construction noise in terms of equivalent continuous level. The noise-calculating models regarding synchronization, propagation and equivalent continuous level are presented. The simulation framework for modeling the noise-affected factors and calculating the equivalent continuous noise by incorporating the noise-calculating models into simulation strategy is proposed. An application study is presented to demonstrate and justify the proposed simulation method in predicting the equivalent continuous noise during construction. The study contributes to provision of a simulation methodology to quantitatively predict the equivalent continuous noise of construction by considering the relevant uncertainties, dynamics and interactions.

Effect of smoking parameters on the particle size distribution and predicted airway deposition of mainstream cigarette smoke.

PubMed

Kane, David B; Asgharian, Bahman; Price, Owen T; Rostami, Ali; Oldham, Michael J

2010-02-01

It is known that puffing conditions such as puff volume, duration, and frequency vary substantially among individual smokers. This study investigates how these parameters affect the particle size distribution and concentration of fresh mainstream cigarette smoke (MCS) and how these changes affect the predicted deposition of MCS particles in a model human respiratory tract. Measurements of the particle size distribution made with an electrical low pressure impactor for a variety of puffing conditions are presented. The average flow rate of the puff is found to be the major factor effecting the measured particle size distribution of the MCS. The results of these measurements were then used as input to a deterministic dosimetry model (MPPD) to estimate the changes in the respiratory tract deposition fraction of smoke particles. The MPPD dosimetry model was modified by incorporating mechanisms involved in respiratory tract deposition of MCS: hygroscopic growth, coagulation, evaporation of semivolatiles, and mixing of the smoke with inhaled dilution air. The addition of these mechanisms to MPPD resulted in reasonable agreement between predicted airway deposition and human smoke retention measurements. The modified MPPD model predicts a modest 10% drop in the total deposition efficiency in a model human respiratory tract as the puff flow rate is increased from 1050 to 3100 ml/min, for a 2-s puff.

Relations among affect, abstinence motivation and confidence, and daily smoking lapse risk.

PubMed

Minami, Haruka; Yeh, Vivian M; Bold, Krysten W; Chapman, Gretchen B; McCarthy, Danielle E

2014-06-01

This study tested the hypothesis that changes in momentary affect, abstinence motivation, and confidence would predict lapse risk over the next 12-24 hr using Ecological Momentary Assessment (EMA) data from smokers attempting to quit smoking. One hundred and three adult, daily, treatment-seeking smokers recorded their momentary affect, motivation to quit, abstinence confidence, and smoking behaviors in near real time with multiple EMA reports per day using electronic diaries postquit. Multilevel models indicated that initial levels of negative affect were associated with smoking, even after controlling for earlier smoking status, and that short-term increases in negative affect predicted lapses up to 12, but not 24, hr later. Positive affect had significant effects on subsequent abstinence confidence, but not motivation to quit. High levels of motivation appeared to reduce increases in lapse risk that occur over hours although momentary changes in confidence did not predict lapse risk over 12 hr. Negative affect had short-lived effects on lapse risk, whereas higher levels of motivation protected against the risk of lapsing that accumulates over hours. An increase in positive affect was associated with greater confidence to quit, but such changes in confidence did not reduce short-term lapse risk, contrary to expectations. Relations observed among affect, cognitions, and lapse seem to depend critically on the timing of assessments.

A Prospective Investigation of Affect, the Desire to Gamble, Gambling Motivations and Gambling Behavior in the Mood Disorders.

PubMed

Quilty, Lena C; Watson, Chris; Toneatto, Tony; Bagby, R Michael

2017-03-01

Time-sampling methodology was implemented to examine the prospective associations between affect, desire to gamble, and gambling behavior in individuals diagnosed with a mood disorder. Thirty (9 male, 21 female) adults with a lifetime diagnosis of a depressive or bipolar disorder diagnosis who endorsed current gambling and lifetime gambling harm participated in the present study. Participants completed electronic diary entries of their current affective state, desire to gamble, and gambling behavior for 30 consecutive days. Hierarchical linear modelling revealed that affect was not a predictor of gambling behavior. Instead, affect predicted the desire to gamble, with high levels of sadness and arousal independently predicting an increased desire to gamble. Desire to gamble predicted actual gambling behavior. There were no differences across diagnostic groups in terms of gambling motivations at baseline; however, during the 30-day period, participants with bipolar disorder endorsed gambling to cope with negative affect more often than did participants with depressive disorder, whereas those with depressive disorder more often endorsed gambling for social reasons or enhancement of positive affect. The present findings provide evidence that negative affect is not directly related to actual gambling behavior, and suggest that affective states rather impact the desire to gamble.

An Integrated Model for Identifying Linkages Between the Management of Fuel Treatments, Fire and Ecosystem Services

NASA Astrophysics Data System (ADS)

Bart, R. R.; Anderson, S.; Moritz, M.; Plantinga, A.; Tague, C.

2015-12-01

Vegetation fuel treatments (e.g. thinning, prescribed burning) are a frequent tool for managing fire-prone landscapes. However, predicting how fuel treatments may affect future wildfire risk and associated ecosystem services, such as forest water availability and streamflow, remains a challenge. This challenge is in part due to the large range of conditions under which fuel treatments may be implemented, as response is likely to vary with species type, rates of vegetation regrowth, meteorological conditions and physiographic properties of the treated site. It is also due to insufficient understanding of how social factors such as political pressure, public demands and economic constraints affect fuel management decisions. To examine the feedbacks between ecological and social dimensions of fuel treatments, we present an integrated model that links a biophysical model that simulates vegetation and hydrology (RHESSys), a fire spread model (WMFire) and an empirical fuel treatment model that accounts for agency decision-making. We use this model to investigate how management decisions affect landscape fuel loads, which in turn affect fire severity and ecosystem services, which feedback to management decisions on fuel treatments. We hypothesize that this latter effect will be driven by salience theory, which predicts that fuel treatments are more likely to occur following major wildfire events. The integrated model provides a flexible framework for answering novel questions about fuel treatments that span social and ecological domains, areas that have previously been treated separately.

Cognitive and affective trait and state factors influencing the long-term symptom course in remitted depressed patients.

PubMed

Timm, Christina; Ubl, Bettina; Zamoscik, Vera; Ebner-Priemer, Ulrich; Reinhard, Iris; Huffziger, Silke; Kirsch, Peter; Kuehner, Christine

2017-01-01

Major depressive disorder (MDD) is characterized by a high risk for relapses and chronic developments. Clinical characteristics such as residual symptoms have been shown to negatively affect the long-term course of MDD. However, it is unclear so far how trait repetitive negative thinking (RNT) as well as cognitive and affective momentary states, the latter experienced during daily-life, affect the long-term course of MDD. We followed up 57 remitted depressed (rMDD) individuals six (T2) and 36 (T3) months after baseline. Clinical outcomes were time to relapse, time spent with significant symptoms as a marker of chronicity, and levels of depressive symptoms at T2 and T3. Predictors assessed at baseline included residual symptoms and trait RNT. Furthermore, momentary daily life affect and momentary rumination, and their variation over the day were assessed at baseline using ambulatory assessment (AA). In multiple models, residual symptoms and instability of daily-life affect at baseline independently predicted a faster time to relapse, while chronicity was significantly predicted by trait RNT. Multilevel models revealed that depressive symptom levels during follow-up were predicted by baseline residual symptom levels and by instability of daily-life rumination. Both instability features were linked to a higher number of anamnestic MDD episodes. Our findings indicate that trait RNT, but also affective and cognitive processes during daily life impact the longer-term course of MDD. Future longitudinal research on the role of respective AA-phenotypes as potential transdiagnostic course-modifiers is warranted.

Acoustic and Lexical Representations for Affect Prediction in Spontaneous Conversations.

PubMed

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

2015-01-01

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

Free variable selection QSPR study to predict 19F chemical shifts of some fluorinated organic compounds using Random Forest and RBF-PLS methods

NASA Astrophysics Data System (ADS)

Goudarzi, Nasser

2016-04-01

In this work, two new and powerful chemometrics methods are applied for the modeling and prediction of the 19F chemical shift values of some fluorinated organic compounds. The radial basis function-partial least square (RBF-PLS) and random forest (RF) are employed to construct the models to predict the 19F chemical shifts. In this study, we didn't used from any variable selection method and RF method can be used as variable selection and modeling technique. Effects of the important parameters affecting the ability of the RF prediction power such as the number of trees (nt) and the number of randomly selected variables to split each node (m) were investigated. The root-mean-square errors of prediction (RMSEP) for the training set and the prediction set for the RBF-PLS and RF models were 44.70, 23.86, 29.77, and 23.69, respectively. Also, the correlation coefficients of the prediction set for the RBF-PLS and RF models were 0.8684 and 0.9313, respectively. The results obtained reveal that the RF model can be used as a powerful chemometrics tool for the quantitative structure-property relationship (QSPR) studies.

Transitions and Turning Points: Navigating the Passage from Childhood through Adolescence.

ERIC Educational Resources Information Center

Graber, Julia A.; Brooks-Gunn, Jeanne

1996-01-01

Comments on this special theme issue examining the roles of socialization, biology, and culture as they affect adaptive and maladaptive developmental outcomes. Presents models for predicting and understanding behavioral and affective change at transitions occurring especially from middle childhood through adolescence. Provides examples…

Coupling of the Models of Human Physiology and Thermal Comfort

NASA Astrophysics Data System (ADS)

Pokorny, J.; Jicha, M.

2013-04-01

A coupled model of human physiology and thermal comfort was developed in Dymola/Modelica. A coupling combines a modified Tanabe model of human physiology and thermal comfort model developed by Zhang. The Coupled model allows predicting the thermal sensation and comfort of both local and overall from local boundary conditions representing ambient and personal factors. The aim of this study was to compare prediction of the Coupled model with the Fiala model prediction and experimental data. Validation data were taken from the literature, mainly from the validation manual of software Theseus-FE [1]. In the paper validation of the model for very light physical activities (1 met) indoor environment with temperatures from 12 °C up to 48 °C is presented. The Coupled model predicts mean skin temperature for cold, neutral and warm environment well. However prediction of core temperature in cold environment is inaccurate and very affected by ambient temperature. Evaluation of thermal comfort in warm environment is supplemented by skin wettedness prediction. The Coupled model is designed for non-uniform and transient environmental conditions; it is also suitable simulation of thermal comfort in vehicles cabins. The usage of the model is limited for very light physical activities up to 1.2 met only.

Hyperlipidemia affects multiscale structure and strength of murine femur.

PubMed

Ascenzi, Maria-Grazia; Lutz, Andre; Du, Xia; Klimecky, Laureen; Kawas, Neal; Hourany, Talia; Jahng, Joelle; Chin, Jesse; Tintut, Yin; Nackenhors, Udo; Keyak, Joyce

2014-07-18

To improve bone strength prediction beyond limitations of assessment founded solely on the bone mineral component, we investigated the effect of hyperlipidemia, present in more than 40% of osteoporotic patients, on multiscale structure of murine bone. Our overarching purpose is to estimate bone strength accurately, to facilitate mitigating fracture morbidity and mortality in patients. Because (i) orientation of collagen type I affects, independently of degree of mineralization, cortical bone׳s micro-structural strength; and, (ii) hyperlipidemia affects collagen orientation and μCT volumetric tissue mineral density (vTMD) in murine cortical bone, we have constructed the first multiscale finite element (mFE), mouse-specific femoral model to study the effect of collagen orientation and vTMD on strength in Ldlr(-/-), a mouse model of hyperlipidemia, and its control wild type, on either high fat diet or normal diet. Each µCT scan-based mFE model included either element-specific elastic orthotropic properties calculated from collagen orientation and vTMD (collagen-density model) by experimentally validated formulation, or usual element-specific elastic isotropic material properties dependent on vTMD-only (density-only model). We found that collagen orientation, assessed by circularly polarized light and confocal microscopies, and vTMD, differed among groups and that microindentation results strongly correlate with elastic modulus of collagen-density models (r(2)=0.85, p=10(-5)). Collagen-density models yielded (1) larger strains, and therefore lower strength, in simulations of 3-point bending and physiological loading; and (2) higher correlation between mFE-predicted strength and 3-point bending experimental strength, than density-only models. This novel method supports ongoing translational research to achieve the as yet elusive goal of accurate bone strength prediction. Copyright © 2014 Elsevier Ltd. All rights reserved.

Thermal Modeling of Resistance Spot Welding and Prediction of Weld Microstructure

NASA Astrophysics Data System (ADS)

Sheikhi, M.; Valaee Tale, M.; Usefifar, GH. R.; Fattah-Alhosseini, Arash

2017-11-01

The microstructure of nuggets in resistance spot welding can be influenced by the many variables involved. This study aimed at examining such a relationship and, consequently, put forward an analytical model to predict the thermal history and microstructure of the nugget zone. Accordingly, a number of numerical simulations and experiments were conducted and the accuracy of the model was assessed. The results of this assessment revealed that the proposed analytical model could accurately predict the cooling rate in the nugget and heat-affected zones. Moreover, both analytical and numerical models confirmed that sheet thickness and electrode-sheet interface temperature were the most important factors influencing the cooling rate at temperatures lower than about T l/2. Decomposition of austenite is one of the most important transformations in steels occurring over this temperature range. Therefore, an easy-to-use map was designed against these parameters to predict the weld microstructure.

Physically based approaches incorporating evaporation for early warning predictions of rainfall-induced landslides

NASA Astrophysics Data System (ADS)

Reder, Alfredo; Rianna, Guido; Pagano, Luca

2018-02-01

In the field of rainfall-induced landslides on sloping covers, models for early warning predictions require an adequate trade-off between two aspects: prediction accuracy and timeliness. When a cover's initial hydrological state is a determining factor in triggering landslides, taking evaporative losses into account (or not) could significantly affect both aspects. This study evaluates the performance of three physically based predictive models, converting precipitation and evaporative fluxes into hydrological variables useful in assessing slope safety conditions. Two of the models incorporate evaporation, with one representing evaporation as both a boundary and internal phenomenon, and the other only a boundary phenomenon. The third model totally disregards evaporation. Model performances are assessed by analysing a well-documented case study involving a 2 m thick sloping volcanic cover. The large amount of monitoring data collected for the soil involved in the case study, reconstituted in a suitably equipped lysimeter, makes it possible to propose procedures for calibrating and validating the parameters of the models. All predictions indicate a hydrological singularity at the landslide time (alarm). A comparison of the models' predictions also indicates that the greater the complexity and completeness of the model, the lower the number of predicted hydrological singularities when no landslides occur (false alarms).

Seasonal prediction skill of winter temperature over North India

NASA Astrophysics Data System (ADS)

Tiwari, P. R.; Kar, S. C.; Mohanty, U. C.; Dey, S.; Kumari, S.; Sinha, P.

2016-04-01

The climatology, amplitude error, phase error, and mean square skill score (MSSS) of temperature predictions from five different state-of-the-art general circulation models (GCMs) have been examined for the winter (December-January-February) seasons over North India. In this region, temperature variability affects the phenological development processes of wheat crops and the grain yield. The GCM forecasts of temperature for a whole season issued in November from various organizations are compared with observed gridded temperature data obtained from the India Meteorological Department (IMD) for the period 1982-2009. The MSSS indicates that the models have skills of varying degrees. Predictions of maximum and minimum temperature obtained from the National Centers for Environmental Prediction (NCEP) climate forecast system model (NCEP_CFSv2) are compared with station level observations from the Snow and Avalanche Study Establishment (SASE). It has been found that when the model temperatures are corrected to account the bias in the model and actual orography, the predictions are able to delineate the observed trend compared to the trend without orography correction.

Using Predictability for Lexical Segmentation.

PubMed

Çöltekin, Çağrı

2017-09-01

This study investigates a strategy based on predictability of consecutive sub-lexical units in learning to segment a continuous speech stream into lexical units using computational modeling and simulations. Lexical segmentation is one of the early challenges during language acquisition, and it has been studied extensively through psycholinguistic experiments as well as computational methods. However, despite strong empirical evidence, the explicit use of predictability of basic sub-lexical units in models of segmentation is underexplored. This paper presents an incremental computational model of lexical segmentation for exploring the usefulness of predictability for lexical segmentation. We show that the predictability cue is a strong cue for segmentation. Contrary to earlier reports in the literature, the strategy yields state-of-the-art segmentation performance with an incremental computational model that uses only this particular cue in a cognitively plausible setting. The paper also reports an in-depth analysis of the model, investigating the conditions affecting the usefulness of the strategy. Copyright © 2016 Cognitive Science Society, Inc.

Determinants affecting consumer adoption of contactless credit card: an empirical study.

PubMed

Wang, Yu-Min

2008-12-01

The contactless credit card is one of the most promising technological innovations in the field of electronic payments. It provides consumers with greater control of payments, convenience, and transaction speed. However, contactless credit cards have yet to gain significant rates of adoption in the marketplace. Thus, effort must be made to identify factors affecting consumer adoption of contactless credit cards. Based on the technology acceptance model, innovation diffusion theory, and the relevant literature, seven variables (perceived usefulness, perceived ease of use, compatibility, perceived risk, trust, consumer involvement, availability of infrastructure) are proposed to help predict consumer adoption of contactless credit cards. Data collected from 312 respondents in Taiwan is tested against the proposed prediction model using the logistic regression approach. The results and implications of our study contribute to an expanded understanding of the factors that affect consumer adoption of contactless credit cards.

Beyond Negative Pain-Related Psychological Factors: Resilience Is Related to Lower Pain Affect in Healthy Adults.

PubMed

Hemington, Kasey S; Cheng, Joshua C; Bosma, Rachael L; Rogachov, Anton; Kim, Junseok A; Davis, Karen D

2017-09-01

Resilience, a characteristic that enhances adaptation in response to stressful events, is a positive psychological factor that can predict and modulate health outcomes. However, resilience is rarely considered in pain research. Conversely, negative psychological factors (eg, anxiety, depression) are known to be related to the affective dimension of pain. It is critical to understand all potential psychological drivers of pain affect, a prominent component of chronic pain. We tested the hypothesis that higher resilience is associated with lower pain affect, above and beyond the predictive value of negative psychological factors. Healthy adults underwent psychophysical testing to acquire ratings of heat pain intensity and unpleasantness and completed the Resilience Scale, the State-Trait Anxiety Inventory (trait form), Beck Depression Inventory, Pain Catastrophizing Scale, and the Pain Vigilance and Attention Questionnaire. Multiple regression modeling (n = 68) showed resilience to be a negatively associated with pain affect (unpleasantness). Furthermore, in individuals with higher anxiety scores, resilience was protective against higher pain affect. This highlights the importance of resilience, a positive psychological factor, in the affective dimension of pain. This study is the first to assess a positive psychological factor and experimental pain affect, and has the potential to improve prediction of and treatment strategies for clinical pain. We report that resilience, a positive psychological factor, interacts with anxiety and is associated with heat pain affect (unpleasantness) in healthy individuals. Resilience may provide predictive value of chronic pain affect and treatment outcomes, and could be a target for behavioral therapy. Copyright © 2017 American Pain Society. Published by Elsevier Inc. All rights reserved.

Paradigm of pretest risk stratification before coronary computed tomography.

PubMed

Jensen, Jesper Møller; Ovrehus, Kristian A; Nielsen, Lene H; Jensen, Jesper K; Larsen, Henrik M; Nørgaard, Bjarne L

2009-01-01

The optimal method of determining the pretest risk of coronary artery disease as a patient selection tool before coronary multidetector computed tomography (MDCT) is unknown. We investigated the ability of 3 different clinical risk scores to predict the outcome of coronary MDCT. This was a retrospective study of 551 patients consecutively referred for coronary MDCT on a suspicion of coronary artery disease. Diamond-Forrester, Duke, and Morise risk models were used to predict coronary artery stenosis (>50%) as assessed by coronary MDCT. The models were compared by receiver operating characteristic analysis. The distribution of low-, intermediate-, and high-risk persons, respectively, was established and compared for each of the 3 risk models. Overall, all risk prediction models performed equally well. However, the Duke risk model classified the low-risk patients more correctly than did the other models (P < 0.01). In patients without coronary artery calcification (CAC), the predictive value of the Duke risk model was superior to the other risk models (P < 0.05). Currently available risk prediction models seem to perform better in patients without CAC. Between the risk prediction models, there was a significant discrepancy in the distribution of patients at low, intermediate, or high risk (P < 0.01). The 3 risk prediction models perform equally well, although the Duke risk score may have advantages in subsets of patients. The choice of risk prediction model affects the referral pattern to MDCT. Copyright (c) 2009 Society of Cardiovascular Computed Tomography. Published by Elsevier Inc. All rights reserved.

Prediction of ECS and SSC Models for Flux-Limited Samples of Gamma-Ray Blazars

NASA Technical Reports Server (NTRS)

Lister, Matthew L.; Marscher, Alan P.

1999-01-01

The external Compton scattering (ECS) and synchrotron self-Compton (SSC) models make distinct predictions for the amount of Doppler boosting of high-energy gamma-rays emitted by Nazar. We examine how these differences affect the predicted properties of active galactic nucleus (AGN) samples selected on the basis of Murray emission. We create simulated flux-limited samples based on the ECS and SSC models, and compare their properties to those of identified EGRET blazars. We find that for small gamma-ray-selected samples, the two models make very similar predictions, and cannot be reliably distinguished. This is primarily due to the fact that not only the Doppler factor, but also the cosmological distance and intrinsic luminosity play a role in determining whether an AGN is included in a flux-limited gamma-ray sample.

A gentle introduction to quantile regression for ecologists

USGS Publications Warehouse

Cade, B.S.; Noon, B.R.

2003-01-01

Quantile regression is a way to estimate the conditional quantiles of a response variable distribution in the linear model that provides a more complete view of possible causal relationships between variables in ecological processes. Typically, all the factors that affect ecological processes are not measured and included in the statistical models used to investigate relationships between variables associated with those processes. As a consequence, there may be a weak or no predictive relationship between the mean of the response variable (y) distribution and the measured predictive factors (X). Yet there may be stronger, useful predictive relationships with other parts of the response variable distribution. This primer relates quantile regression estimates to prediction intervals in parametric error distribution regression models (eg least squares), and discusses the ordering characteristics, interval nature, sampling variation, weighting, and interpretation of the estimates for homogeneous and heterogeneous regression models.

Instrumentality, Expressivity, and Relational Qualities in the Same-Sex Friendships of College Women and Men

ERIC Educational Resources Information Center

Frey, Lisa L.; Beesley, Denise; Hurst, Rebecca; Saldana, Star; Licuanan, Brian

2016-01-01

Using the relational-cultural model (Jordan, Kaplan, Miller, Stiver, & Surrey, 1991), the authors hypothesized that instrumentality, expressivity, and the individual affective experience of same-sex friendships would predict increased relationship mutuality, with college women and men showing different predictive patterns. Overall, results…

Research agenda for integrated landscape modeling

Treesearch

Samuel A. Cushman; Donald McKenzie; David L. Peterson; Jeremy Littell; Kevin S. McKelvey

2007-01-01

Reliable predictions of how changing climate and disturbance regimes will affect forest ecosystems are crucial for effective forest management. Current fire and climate research in forest ecosystem and community ecology offers data and methods that can inform such predictions. However, research in these fields occurs at different scales, with disparate goals, methods,...

Impact of QTL minor allele frequency on genomic evaluation using real genotype data and simulated phenotypes in Japanese Black cattle.

PubMed

Uemoto, Yoshinobu; Sasaki, Shinji; Kojima, Takatoshi; Sugimoto, Yoshikazu; Watanabe, Toshio

2015-11-19

Genetic variance that is not captured by single nucleotide polymorphisms (SNPs) is due to imperfect linkage disequilibrium (LD) between SNPs and quantitative trait loci (QTLs), and the extent of LD between SNPs and QTLs depends on different minor allele frequencies (MAF) between them. To evaluate the impact of MAF of QTLs on genomic evaluation, we performed a simulation study using real cattle genotype data. In total, 1368 Japanese Black cattle and 592,034 SNPs (Illumina BovineHD BeadChip) were used. We simulated phenotypes using real genotypes under different scenarios, varying the MAF categories, QTL heritability, number of QTLs, and distribution of QTL effect. After generating true breeding values and phenotypes, QTL heritability was estimated and the prediction accuracy of genomic estimated breeding value (GEBV) was assessed under different SNP densities, prediction models, and population size by a reference-test validation design. The extent of LD between SNPs and QTLs in this population was higher in the QTLs with high MAF than in those with low MAF. The effect of MAF of QTLs depended on the genetic architecture, evaluation strategy, and population size in genomic evaluation. In genetic architecture, genomic evaluation was affected by the MAF of QTLs combined with the QTL heritability and the distribution of QTL effect. The number of QTL was not affected on genomic evaluation if the number of QTL was more than 50. In the evaluation strategy, we showed that different SNP densities and prediction models affect the heritability estimation and genomic prediction and that this depends on the MAF of QTLs. In addition, accurate QTL heritability and GEBV were obtained using denser SNP information and the prediction model accounted for the SNPs with low and high MAFs. In population size, a large sample size is needed to increase the accuracy of GEBV. The MAF of QTL had an impact on heritability estimation and prediction accuracy. Most genetic variance can be captured using denser SNPs and the prediction model accounted for MAF, but a large sample size is needed to increase the accuracy of GEBV under all QTL MAF categories.

An integrated weather and sea-state forecasting system for the Arabian Peninsula (WASSF)

NASA Astrophysics Data System (ADS)

Kallos, George; Galanis, George; Spyrou, Christos; Mitsakou, Christina; Solomos, Stavros; Bartsotas, Nikolaos; Kalogrei, Christina; Athanaselis, Ioannis; Sofianos, Sarantis; Vervatis, Vassios; Axaopoulos, Panagiotis; Papapostolou, Alexandros; Qahtani, Jumaan Al; Alaa, Elyas; Alexiou, Ioannis; Beard, Daniel

2013-04-01

Nowadays, large industrial conglomerates such as the Saudi ARAMCO, require a series of weather and sea state forecasting products that cannot be found in state meteorological offices or even commercial data providers. The two major objectives of the system is prevention and mitigation of environmental problems and of course early warning of local conditions associated with extreme weather events. The management and operations part is related to early warning of weather and sea-state events that affect operations of various facilities. The environmental part is related to air quality and especially the desert dust levels in the atmosphere. The components of the integrated system include: (i) a weather and desert dust prediction system with forecasting horizon of 5 days, (ii) a wave analysis and prediction component for Red Sea and Arabian Gulf, (iii) an ocean circulation and tidal analysis and prediction of both Red Sea and Arabian Gulf and (iv) an Aviation part specializing in the vertical structure of the atmosphere and extreme events that affect air transport and other operations. Specialized data sets required for on/offshore operations are provided ate regular basis. State of the art modeling components are integrated to a unique system that distributes the produced analysis and forecasts to each department. The weather and dust prediction system is SKIRON/Dust, the wave analysis and prediction system is based on WAM cycle 4 model from ECMWF, the ocean circulation model is MICOM while the tidal analysis and prediction is a development of the Ocean Physics and Modeling Group of University of Athens, incorporating the Tidal Model Driver. A nowcasting subsystem is included. An interactive system based on Google Maps gives the capability to extract and display the necessary information for any location of the Arabian Peninsula, the Red Sea and Arabian Gulf.

Assessing uncertainties in crop and pasture ensemble model simulations of productivity and N2O emissions

USDA-ARS?s Scientific Manuscript database

Simulation models are extensively used to predict agricultural productivity and greenhouse gas (GHG) emissions. However, the uncertainties of (reduced) model ensemble simulations have not been assessed systematically for variables affecting food security and climate change mitigation, within multisp...

Complete Proteomic-Based Enzyme Reaction and Inhibition Kinetics Reveal How Monolignol Biosynthetic Enzyme Families Affect Metabolic Flux and Lignin in Populus trichocarpa[W

PubMed Central

Wang, Jack P.; Naik, Punith P.; Chen, Hsi-Chuan; Shi, Rui; Lin, Chien-Yuan; Liu, Jie; Shuford, Christopher M.; Li, Quanzi; Sun, Ying-Hsuan; Tunlaya-Anukit, Sermsawat; Williams, Cranos M.; Muddiman, David C.; Ducoste, Joel J.; Sederoff, Ronald R.; Chiang, Vincent L.

2014-01-01

We established a predictive kinetic metabolic-flux model for the 21 enzymes and 24 metabolites of the monolignol biosynthetic pathway using Populus trichocarpa secondary differentiating xylem. To establish this model, a comprehensive study was performed to obtain the reaction and inhibition kinetic parameters of all 21 enzymes based on functional recombinant proteins. A total of 104 Michaelis-Menten kinetic parameters and 85 inhibition kinetic parameters were derived from these enzymes. Through mass spectrometry, we obtained the absolute quantities of all 21 pathway enzymes in the secondary differentiating xylem. This extensive experimental data set, generated from a single tissue specialized in wood formation, was used to construct the predictive kinetic metabolic-flux model to provide a comprehensive mathematical description of the monolignol biosynthetic pathway. The model was validated using experimental data from transgenic P. trichocarpa plants. The model predicts how pathway enzymes affect lignin content and composition, explains a long-standing paradox regarding the regulation of monolignol subunit ratios in lignin, and reveals novel mechanisms involved in the regulation of lignin biosynthesis. This model provides an explanation of the effects of genetic and transgenic perturbations of the monolignol biosynthetic pathway in flowering plants. PMID:24619611

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.

Health Communication in Social Media: Message Features Predicting User Engagement on Diabetes-Related Facebook Pages.

PubMed

Rus, Holly M; Cameron, Linda D

2016-10-01

Social media provides unprecedented opportunities for enhancing health communication and health care, including self-management of chronic conditions such as diabetes. Creating messages that engage users is critical for enhancing message impact and dissemination. This study analyzed health communications within ten diabetes-related Facebook pages to identify message features predictive of user engagement. The Common-Sense Model of Illness Self-Regulation and established health communication techniques guided content analyses of 500 Facebook posts. Each post was coded for message features predicted to engage users and numbers of likes, shares, and comments during the week following posting. Multi-level, negative binomial regressions revealed that specific features predicted different forms of engagement. Imagery emerged as a strong predictor; messages with images had higher rates of liking and sharing relative to messages without images. Diabetes consequence information and positive identity predicted higher sharing while negative affect, social support, and crowdsourcing predicted higher commenting. Negative affect, crowdsourcing, and use of external links predicted lower sharing while positive identity predicted lower commenting. The presence of imagery weakened or reversed the positive relationships of several message features with engagement. Diabetes control information and negative affect predicted more likes in text-only messages, but fewer likes when these messages included illustrative imagery. Similar patterns of imagery's attenuating effects emerged for the positive relationships of consequence information, control information, and positive identity with shares and for positive relationships of negative affect and social support with comments. These findings hold promise for guiding communication design in health-related social media.

Questions of time and affect: a person’s affectivity profile, time perspective, and well-being

PubMed Central

Sailer, Uta; Nima, Ali Al; Archer, Trevor

2016-01-01

Background. A “balanced” time perspective has been suggested to have a positive influence on well-being: a sentimental and positive view of the past (high Past Positive), a less pessimistic attitude toward the past (low Past Negative), the desire of experiencing pleasure with slight concern for future consequences (high Present Hedonistic), a less fatalistic and hopeless view of the future (low Present Fatalistic), and the ability to find reward in achieving specific long-term goals (high Future). We used the affective profiles model (i.e., combinations of individuals’ experience of high/low positive/negative affectivity) to investigate differences between individuals in time perspective dimensions and to investigate if the influence of time perspective dimensions on well-being was moderated by the individual’s type of profile. Method. Participants (N = 720) answered to the Positive Affect Negative Affect Schedule, the Zimbardo Time Perspective Inventory and two measures of well-being: the Temporal Satisfaction with Life Scale and Ryff’s Scales of Psychological Well-Being-short version. A Multivariate Analysis of Variance (MANOVA) was conducted to identify differences in time perspective dimensions and well-being among individuals with distinct affective profiles. Four structural equation models (SEM) were used to investigate which time perspective dimensions predicted well-being for individuals in each profile. Results. Comparisons between individuals at the extreme of the affective profiles model suggested that individuals with a self-fulfilling profile (high positive/low negative affect) were characterized by a “balanced” time perspective and higher well-being compared to individuals with a self-destructive profile (low positive/high negative affect). However, a different pattern emerged when individuals who differed in one affect dimension but matched in the other were compared to each other. For instance, decreases in the past negative time perspective dimension lead to high positive affect when negative affect is high (i.e., self-destructive vs. high affective) but to low negative affect when positive affect was high (i.e., high affective vs. self-fulfilling). The moderation analyses showed, for example, that for individuals with a self-destructive profile, psychological well-being was significantly predicted by the past negative, present fatalistic and future time perspectives. Among individuals with a high affective or a self-fulfilling profile, psychological well-being was significantly predicted by the present fatalistic dimension. Conclusions. The interactions found here go beyond the postulation of a “balanced” time perspective being the only way to promote well-being. Instead, we present a more person-centered approach to achieve higher levels of emotional, cognitive, and psychological well-being. PMID:27019786

Questions of time and affect: a person's affectivity profile, time perspective, and well-being.

PubMed

Garcia, Danilo; Sailer, Uta; Nima, Ali Al; Archer, Trevor

2016-01-01

Background. A "balanced" time perspective has been suggested to have a positive influence on well-being: a sentimental and positive view of the past (high Past Positive), a less pessimistic attitude toward the past (low Past Negative), the desire of experiencing pleasure with slight concern for future consequences (high Present Hedonistic), a less fatalistic and hopeless view of the future (low Present Fatalistic), and the ability to find reward in achieving specific long-term goals (high Future). We used the affective profiles model (i.e., combinations of individuals' experience of high/low positive/negative affectivity) to investigate differences between individuals in time perspective dimensions and to investigate if the influence of time perspective dimensions on well-being was moderated by the individual's type of profile. Method. Participants (N = 720) answered to the Positive Affect Negative Affect Schedule, the Zimbardo Time Perspective Inventory and two measures of well-being: the Temporal Satisfaction with Life Scale and Ryff's Scales of Psychological Well-Being-short version. A Multivariate Analysis of Variance (MANOVA) was conducted to identify differences in time perspective dimensions and well-being among individuals with distinct affective profiles. Four structural equation models (SEM) were used to investigate which time perspective dimensions predicted well-being for individuals in each profile. Results. Comparisons between individuals at the extreme of the affective profiles model suggested that individuals with a self-fulfilling profile (high positive/low negative affect) were characterized by a "balanced" time perspective and higher well-being compared to individuals with a self-destructive profile (low positive/high negative affect). However, a different pattern emerged when individuals who differed in one affect dimension but matched in the other were compared to each other. For instance, decreases in the past negative time perspective dimension lead to high positive affect when negative affect is high (i.e., self-destructive vs. high affective) but to low negative affect when positive affect was high (i.e., high affective vs. self-fulfilling). The moderation analyses showed, for example, that for individuals with a self-destructive profile, psychological well-being was significantly predicted by the past negative, present fatalistic and future time perspectives. Among individuals with a high affective or a self-fulfilling profile, psychological well-being was significantly predicted by the present fatalistic dimension. Conclusions. The interactions found here go beyond the postulation of a "balanced" time perspective being the only way to promote well-being. Instead, we present a more person-centered approach to achieve higher levels of emotional, cognitive, and psychological well-being.

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 that had at least 2 years of data (2010-11 and sometimes earlier) and for 1 beach that had 1 year of data. For most models, software designed for model development by the U.S. Environmental Protection Agency (Virtual Beach) was used. The selected model for each beach was based on a combination of explanatory variables including, most commonly, turbidity, day of the year, change in lake level over 24 hours, wave height, wind direction and speed, and antecedent rainfall for various time periods. Forty-two predictive models were validated against data collected during an independent year (2012) and compared to the current method for assessing recreational water quality-using the previous day’s E. coli concentration (persistence model). Goals for good predictive-model performance were responses that were at least 5 percent greater than the persistence model and overall correct responses greater than or equal to 80 percent, sensitivities (percentage of exceedances of the bathing-water standard that were correctly predicted by the model) greater than or equal to 50 percent, and specificities (percentage of nonexceedances correctly predicted by the model) greater than or equal to 85 percent. Out of 42 predictive models, 24 models yielded over-all correct responses that were at least 5 percent greater than the use of the persistence model. Predictive-model responses met the performance goals more often than the persistence-model responses in terms of overall correctness (28 versus 17 models, respectively), sensitivity (17 versus 4 models), and specificity (34 versus 25 models). Gaining knowledge of each beach and the factors that affect E. coli concentrations is important for developing good predictive models. Collection of additional years of data with a wide range of environmental conditions may also help to improve future model performance. The USGS will continue to work with local agencies in 2013 and beyond to develop and validate predictive models at beaches and improve existing nowcasts, restructuring monitoring activities to accommodate future uncertainties in funding and resources.

The western spruce budworm model: structure and content.

Treesearch

K.A. Sheehan; W.P. Kemp; J.J. Colbert; N.L. Crookston

1989-01-01

The Budworm Model predicts the amounts of foliage destroyed annually by the western spruce budworm, Choristoneura occidentalis Freeman, in a forest stand. The model may be used independently, or it may be linked to the Stand Prognosis Model to simulate the dynamics of forest stands. Many processes that affect budworm population dynamics are...

Affective temperaments play an important role in the relationship between child abuse and the diagnosis of bipolar disorder.

PubMed

Toda, Hiroyuki; Inoue, Takeshi; Tanichi, Masaaki; Saito, Taku; Nakagawa, Shin; Masuya, Jiro; Tanabe, Hajime; Yoshino, Aihide; Kusumi, Ichiro

2018-04-01

In previous studies, various components such as environmental and genetic factors have been shown to contribute to the development of bipolar disorder (BD). This study investigated how multiple factors, including child abuse, adult life events, and affective temperaments, are interrelated and how they affect the diagnosis of BD. A total of 170 healthy controls and 75 BD patients completed the following self-administered questionnaires: the Patient Health Questionnaire-9 evaluating the severity of depressive symptoms; the Child Abuse and Trauma Scale (CATS) evaluating child abuse; the Temperament Evaluation of Memphis, Pisa, Paris, and San Diego autoquestionnaire (TEMPS-A) evaluating affective temperaments; and the Life Experiences Survey (LES) evaluating negative and positive adult life events. The data were subjected to univariate analysis, multivariable analysis, and structural equation modeling. The structural equation modeling showed that the diagnosis of BD was indirectly predicted by the neglect and sexual abuse scores of the CATS through four affective temperaments (depressive, cyclothymic, irritable, and anxious) of the TEMPS-A and directly predicted by these four affective temperaments. This study suggested that affective temperament plays an important role as a mediator in the influence of child abuse on BD diagnosis. Copyright © 2018 Elsevier B.V. All rights reserved.

Building a profile of subjective well-being for social media users

PubMed Central

Kosinski, Michal; Stillwell, David; Davidson, Robert L.

2017-01-01

Subjective well-being includes ‘affect’ and ‘satisfaction with life’ (SWL). This study proposes a unified approach to construct a profile of subjective well-being based on social media language in Facebook status updates. We apply sentiment analysis to generate users’ affect scores, and train a random forest model to predict SWL using affect scores and other language features of the status updates. Results show that: the computer-selected features resemble the key predictors of SWL as identified in early studies; the machine-predicted SWL is moderately correlated with the self-reported SWL (r = 0.36, p < 0.01), indicating that language-based assessment can constitute valid SWL measures; the machine-assessed affect scores resemble those reported in a previous experimental study; and the machine-predicted subjective well-being profile can also reflect other psychological traits like depression (r = 0.24, p < 0.01). This study provides important insights for psychological prediction using multiple, machine-assessed components and longitudinal or dense psychological assessment using social media language. PMID:29135991

Separating foliar physiology from morphology reveals the relative roles of vertically structured transpiration factors within red maple crowns and limitations of larger scale models

PubMed Central

Bauerle, William L.; Bowden, Joseph D.

2011-01-01

A spatially explicit mechanistic model, MAESTRA, was used to separate key parameters affecting transpiration to provide insights into the most influential parameters for accurate predictions of within-crown and within-canopy transpiration. Once validated among Acer rubrum L. genotypes, model responses to different parameterization scenarios were scaled up to stand transpiration (expressed per unit leaf area) to assess how transpiration might be affected by the spatial distribution of foliage properties. For example, when physiological differences were accounted for, differences in leaf width among A. rubrum L. genotypes resulted in a 25% difference in transpiration. An in silico within-canopy sensitivity analysis was conducted over the range of genotype parameter variation observed and under different climate forcing conditions. The analysis revealed that seven of 16 leaf traits had a ≥5% impact on transpiration predictions. Under sparse foliage conditions, comparisons of the present findings with previous studies were in agreement that parameters such as the maximum Rubisco-limited rate of photosynthesis can explain ∼20% of the variability in predicted transpiration. However, the spatial analysis shows how such parameters can decrease or change in importance below the uppermost canopy layer. Alternatively, model sensitivity to leaf width and minimum stomatal conductance was continuous along a vertical canopy depth profile. Foremost, transpiration sensitivity to an observed range of morphological and physiological parameters is examined and the spatial sensitivity of transpiration model predictions to vertical variations in microclimate and foliage density is identified to reduce the uncertainty of current transpiration predictions. PMID:21617246

Radiomics-based Prognosis Analysis for Non-Small Cell Lung Cancer

NASA Astrophysics Data System (ADS)

Zhang, Yucheng; Oikonomou, Anastasia; Wong, Alexander; Haider, Masoom A.; Khalvati, Farzad

2017-04-01

Radiomics characterizes tumor phenotypes by extracting large numbers of quantitative features from radiological images. Radiomic features have been shown to provide prognostic value in predicting clinical outcomes in several studies. However, several challenges including feature redundancy, unbalanced data, and small sample sizes have led to relatively low predictive accuracy. In this study, we explore different strategies for overcoming these challenges and improving predictive performance of radiomics-based prognosis for non-small cell lung cancer (NSCLC). CT images of 112 patients (mean age 75 years) with NSCLC who underwent stereotactic body radiotherapy were used to predict recurrence, death, and recurrence-free survival using a comprehensive radiomics analysis. Different feature selection and predictive modeling techniques were used to determine the optimal configuration of prognosis analysis. To address feature redundancy, comprehensive analysis indicated that Random Forest models and Principal Component Analysis were optimum predictive modeling and feature selection methods, respectively, for achieving high prognosis performance. To address unbalanced data, Synthetic Minority Over-sampling technique was found to significantly increase predictive accuracy. A full analysis of variance showed that data endpoints, feature selection techniques, and classifiers were significant factors in affecting predictive accuracy, suggesting that these factors must be investigated when building radiomics-based predictive models for cancer prognosis.

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

PubMed

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

2012-11-01

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

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

NASA Astrophysics Data System (ADS)

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

2016-12-01

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

Underwater noise modelling for environmental impact assessment

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

Farcas, Adrian; Thompson, Paul M.; Merchant, Nathan D., E-mail: nathan.merchant@cefas.co.uk

Assessment of underwater noise is increasingly required by regulators of development projects in marine and freshwater habitats, and noise pollution can be a constraining factor in the consenting process. Noise levels arising from the proposed activity are modelled and the potential impact on species of interest within the affected area is then evaluated. Although there is considerable uncertainty in the relationship between noise levels and impacts on aquatic species, the science underlying noise modelling is well understood. Nevertheless, many environmental impact assessments (EIAs) do not reflect best practice, and stakeholders and decision makers in the EIA process are often unfamiliarmore » with the concepts and terminology that are integral to interpreting noise exposure predictions. In this paper, we review the process of underwater noise modelling and explore the factors affecting predictions of noise exposure. Finally, we illustrate the consequences of errors and uncertainties in noise modelling, and discuss future research needs to reduce uncertainty in noise assessments.« less

Temperature modelling and prediction for activated sludge systems.

PubMed

Lippi, S; Rosso, D; Lubello, C; Canziani, R; Stenstrom, M K

2009-01-01

Temperature is an important factor affecting biomass activity, which is critical to maintain efficient biological wastewater treatment, and also physiochemical properties of mixed liquor as dissolved oxygen saturation and settling velocity. Controlling temperature is not normally possible for treatment systems but incorporating factors impacting temperature in the design process, such as aeration system, surface to volume ratio, and tank geometry can reduce the range of temperature extremes and improve the overall process performance. Determining how much these design or up-grade options affect the tank temperature requires a temperature model that can be used with existing design methodologies. This paper presents a new steady state temperature model developed by incorporating the best aspects of previously published models, introducing new functions for selected heat exchange paths and improving the method for predicting the effects of covering aeration tanks. Numerical improvements with embedded reference data provide simpler formulation, faster execution, easier sensitivity analyses, using an ordinary spreadsheet. The paper presents several cases to validate the model.

The effect of solution nonideality on modeling transmembrane water transport and diffusion-limited intracellular ice formation during cryopreservation

NASA Astrophysics Data System (ADS)

Zhao, Gang; Takamatsu, Hiroshi; He, Xiaoming

2014-04-01

A new model was developed to predict transmembrane water transport and diffusion-limited ice formation in cells during freezing without the ideal-solution assumption that has been used in previous models. The model was applied to predict cell dehydration and intracellular ice formation (IIF) during cryopreservation of mouse oocytes and bovine carotid artery endothelial cells in aqueous sodium chloride (NaCl) solution with glycerol as the cryoprotectant or cryoprotective agent. A comparison of the predictions between the present model and the previously reported models indicated that the ideal-solution assumption results in under-prediction of the amount of intracellular ice at slow cooling rates (<50 K/min). In addition, the lower critical cooling rates for IIF that is lethal to cells predicted by the present model were much lower than those estimated with the ideal-solution assumption. This study represents the first investigation on how accounting for solution nonideality in modeling water transport across the cell membrane could affect the prediction of diffusion-limited ice formation in biological cells during freezing. Future studies are warranted to look at other assumptions alongside nonideality to further develop the model as a useful tool for optimizing the protocol of cell cryopreservation for practical applications.

The effect of solution nonideality on modeling transmembrane water transport and diffusion-limited intracellular ice formation during cryopreservation.

PubMed

Zhao, Gang; Takamatsu, Hiroshi; He, Xiaoming

2014-04-14

A new model was developed to predict transmembrane water transport and diffusion-limited ice formation in cells during freezing without the ideal-solution assumption that has been used in previous models. The model was applied to predict cell dehydration and intracellular ice formation (IIF) during cryopreservation of mouse oocytes and bovine carotid artery endothelial cells in aqueous sodium chloride (NaCl) solution with glycerol as the cryoprotectant or cryoprotective agent. A comparison of the predictions between the present model and the previously reported models indicated that the ideal-solution assumption results in under-prediction of the amount of intracellular ice at slow cooling rates (<50 K/min). In addition, the lower critical cooling rates for IIF that is lethal to cells predicted by the present model were much lower than those estimated with the ideal-solution assumption. This study represents the first investigation on how accounting for solution nonideality in modeling water transport across the cell membrane could affect the prediction of diffusion-limited ice formation in biological cells during freezing. Future studies are warranted to look at other assumptions alongside nonideality to further develop the model as a useful tool for optimizing the protocol of cell cryopreservation for practical applications.

Effects and detection of raw material variability on the performance of near-infrared calibration models for pharmaceutical products.

PubMed

Igne, Benoit; Shi, Zhenqi; Drennen, James K; Anderson, Carl A

2014-02-01

The impact of raw material variability on the prediction ability of a near-infrared calibration model was studied. Calibrations, developed from a quaternary mixture design comprising theophylline anhydrous, lactose monohydrate, microcrystalline cellulose, and soluble starch, were challenged by intentional variation of raw material properties. A design with two theophylline physical forms, three lactose particle sizes, and two starch manufacturers was created to test model robustness. Further challenges to the models were accomplished through environmental conditions. Along with full-spectrum partial least squares (PLS) modeling, variable selection by dynamic backward PLS and genetic algorithms was utilized in an effort to mitigate the effects of raw material variability. In addition to evaluating models based on their prediction statistics, prediction residuals were analyzed by analyses of variance and model diagnostics (Hotelling's T(2) and Q residuals). Full-spectrum models were significantly affected by lactose particle size. Models developed by selecting variables gave lower prediction errors and proved to be a good approach to limit the effect of changing raw material characteristics. Hotelling's T(2) and Q residuals provided valuable information that was not detectable when studying only prediction trends. Diagnostic statistics were demonstrated to be critical in the appropriate interpretation of the prediction of quality parameters. © 2013 Wiley Periodicals, Inc. and the American Pharmacists Association.

Meteorological models for estimating phenology of corn

NASA Technical Reports Server (NTRS)

Daughtry, C. S. T.; Cochran, J. C.; Hollinger, S. E.

1984-01-01

Knowledge of when critical crop stages occur and how the environment affects them should provide useful information for crop management decisions and crop production models. Two sources of data were evaluated for predicting dates of silking and physiological maturity of corn (Zea mays L.). Initial evaluations were conducted using data of an adapted corn hybrid grown on a Typic Agriaquoll at the Purdue University Agronomy Farm. The second phase extended the analyses to large areas using data acquired by the Statistical Reporting Service of USDA for crop reporting districts (CRD) in Indiana and Iowa. Several thermal models were compared to calendar days for predicting dates of silking and physiological maturity. Mixed models which used a combination of thermal units to predict silking and days after silking to predict physiological maturity were also evaluated. At the Agronomy Farm the models were calibrated and tested on the same data. The thermal models were significantly less biased and more accurate than calendar days for predicting dates of silking. Differences among the thermal models were small. Significant improvements in both bias and accuracy were observed when the mixed models were used to predict dates of physiological maturity. The results indicate that statistical data for CRD can be used to evaluate models developed at agricultural experiment stations.

Affect and State Dysregulation as Moderators of the Relationship between Childhood Sexual Abuse and Nonsuicidal Self-Injury

ERIC Educational Resources Information Center

Bolen, Rebecca M.; Ramseyer Winter, Virginia; Hodges, Liz

2013-01-01

Nonsuicidal self-injury (NSSI) is a significant problem in both clinical and nonclinical populations. Affect and state dysregulation are frequently observed in survivors of childhood sexual abuse and in those who engage in NSSI. Both have been found to predict NSSI, and affect regulation has also been modeled as a mediator of NSSI. This study…

Evaluating observations in the context of predictions for the death valley regional groundwater system

USGS Publications Warehouse

Ely, D.M.; Hill, M.C.; Tiedeman, C.R.; O'Brien, G. M.

2004-01-01

When a model is calibrated by nonlinear regression, calculated diagnostic and inferential statistics provide a wealth of information about many aspects of the system. This work uses linear inferential statistics that are measures of prediction uncertainty to investigate the likely importance of continued monitoring of hydraulic head to the accuracy of model predictions. The measurements evaluated are hydraulic heads; the predictions of interest are subsurface transport from 15 locations. The advective component of transport is considered because it is the component most affected by the system dynamics represented by the regional-scale model being used. The problem is addressed using the capabilities of the U.S. Geological Survey computer program MODFLOW-2000, with its Advective Travel Observation (ADV) Package. Copyright ASCE 2004.

Investigation of the free flow electrophoretic process. Volume 2: Technical analysis

NASA Technical Reports Server (NTRS)

Weiss, R. A.; Lanham, J. W.; Richman, D. W.; Walker, C. D.

1979-01-01

The effect of gravity on the free flow electrophoretic process was investigated. The demonstrated effects were then compared with predictions made by mathematical models. Results show that the carrier buffer flow was affected by gravity induced thermal convection and that the movement of the separating particle streams was affected by gravity induced buoyant forces. It was determined that if gravity induced buoyant forces were included in the mathematical models, then effective predictions of electrophoresis chamber separation performance were possible. The results of tests performed using various methods of electrophoresis using supportive media show that the mobility and the ability to separate were essentially independent of concentration, providing promise of being able to perform electrophoresis with higher inlet concentrations in space.

Spatial Models for Prediction and Early Warning of Aedes aegypti Proliferation from Data on Climate Change and Variability in Cuba.

PubMed

Ortiz, Paulo L; Rivero, Alina; Linares, Yzenia; Pérez, Alina; Vázquez, Juan R

2015-04-01

Climate variability, the primary expression of climate change, is one of the most important environmental problems affecting human health, particularly vector-borne diseases. Despite research efforts worldwide, there are few studies addressing the use of information on climate variability for prevention and early warning of vector-borne infectious diseases. Show the utility of climate information for vector surveillance by developing spatial models using an entomological indicator and information on predicted climate variability in Cuba to provide early warning of danger of increased risk of dengue transmission. An ecological study was carried out using retrospective and prospective analyses of time series combined with spatial statistics. Several entomological and climatic indicators were considered using complex Bultó indices -1 and -2. Moran's I spatial autocorrelation coefficient specified for a matrix of neighbors with a radius of 20 km, was used to identify the spatial structure. Spatial structure simulation was based on simultaneous autoregressive and conditional autoregressive models; agreement between predicted and observed values for number of Aedes aegypti foci was determined by the concordance index Di and skill factor Bi. Spatial and temporal distributions of populations of Aedes aegypti were obtained. Models for describing, simulating and predicting spatial patterns of Aedes aegypti populations associated with climate variability patterns were put forward. The ranges of climate variability affecting Aedes aegypti populations were identified. Forecast maps were generated for the municipal level. Using the Bultó indices of climate variability, it is possible to construct spatial models for predicting increased Aedes aegypti populations in Cuba. At 20 x 20 km resolution, the models are able to provide warning of potential changes in vector populations in rainy and dry seasons and by month, thus demonstrating the usefulness of climate information for epidemiological surveillance.

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

NASA Astrophysics Data System (ADS)

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

2009-07-01

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

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. © 2010 SETAC.

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. (c) 2010 SETAC.

Prediction of laser cutting heat affected zone by extreme learning machine

NASA Astrophysics Data System (ADS)

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

2017-01-01

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

Modeling of venturi scrubber efficiency

NASA Astrophysics Data System (ADS)

Crowder, Jerry W.; Noll, Kenneth E.; Davis, Wayne T.

The parameters affecting venturi scrubber performance have been rationally examined and modifications to the current modeling theory have been developed. The modified model has been validated with available experimental data for a range of throat gas velocities, liquid-to-gas ratios and particle diameters and is used to study the effect of some design parameters on collection efficiency. Most striking among the observations is the prediction of a new design parameter termed the minimum contactor length. Also noted is the prediction of little effect on collection efficiency with increasing liquid-to-gas ratio above about 2ℓ m-3. Indeed, for some cases a decrease in collection efficiency is predicted for liquid rates above this value.

Composite Overwrapped Pressure Vessel (COPV) Stress Rupture Testing

NASA Technical Reports Server (NTRS)

Greene, Nathanael J.; Saulsberry, Regor L.; Leifeste, Mark R.; Yoder, Tommy B.; Keddy, Chris P.; Forth, Scott C.; Russell, Rick W.

2010-01-01

This paper reports stress rupture testing of Kevlar(TradeMark) composite overwrapped pressure vessels (COPVs) at NASA White Sands Test Facility. This 6-year test program was part of the larger effort to predict and extend the lifetime of flight vessels. Tests were performed to characterize control parameters for stress rupture testing, and vessel life was predicted by statistical modeling. One highly instrumented 102-cm (40-in.) diameter Kevlar(TradeMark) COPV was tested to failure (burst) as a single-point model verification. Significant data were generated that will enhance development of improved NDE methods and predictive modeling techniques, and thus better address stress rupture and other composite durability concerns that affect pressure vessel safety, reliability and mission assurance.

Culture and Social Relationship as Factors of Affecting Communicative Non-verbal Behaviors

NASA Astrophysics Data System (ADS)

Akhter Lipi, Afia; Nakano, Yukiko; Rehm, Mathias

The goal of this paper is to link a bridge between social relationship and cultural variation to predict conversants' non-verbal behaviors. This idea serves as a basis of establishing a parameter based socio-cultural model, which determines non-verbal expressive parameters that specify the shapes of agent's nonverbal behaviors in HAI. As the first step, a comparative corpus analysis is done for two cultures in two specific social relationships. Next, by integrating the cultural and social parameters factors with the empirical data from corpus analysis, we establish a model that predicts posture. The predictions from our model successfully demonstrate that both cultural background and social relationship moderate communicative non-verbal behaviors.

Extraversion and reward-related processing: probing incentive motivation in affective priming tasks.

PubMed

Robinson, Michael D; Moeller, Sara K; Ode, Scott

2010-10-01

Based on an incentive motivation theory of extraversion (Depue & Collins, 1999), it was hypothesized that extraverts (relative to introverts) would exhibit stronger positive priming effects in affective priming tasks, whether involving words or pictures. This hypothesis was systematically supported in four studies involving 229 undergraduates. In each of the four studies, and in a subsequent combined analysis, extraversion was positively predictive of positive affective priming effects, but was not predictive of negative affective priming effects. The results bridge an important gap in the literature between biological and trait models of incentive motivation and do so in a way that should be informative to subsequent efforts to understand the processing basis of extraversion as well as incentive motivation. (PsycINFO Database Record (c) 2010 APA, all rights reserved).

Estimating wildfire risk on a Mojave Desert landscape using remote sensing and field sampling

USGS Publications Warehouse

Van Linn, Peter F.; Nussear, Kenneth E.; Esque, Todd C.; DeFalco, Lesley A.; Inman, Richard D.; Abella, Scott R.

2013-01-01

Predicting wildfires that affect broad landscapes is important for allocating suppression resources and guiding land management. Wildfire prediction in the south-western United States is of specific concern because of the increasing prevalence and severe effects of fire on desert shrublands and the current lack of accurate fire prediction tools. We developed a fire risk model to predict fire occurrence in a north-eastern Mojave Desert landscape. First we developed a spatial model using remote sensing data to predict fuel loads based on field estimates of fuels. We then modelled fire risk (interactions of fuel characteristics and environmental conditions conducive to wildfire) using satellite imagery, our model of fuel loads, and spatial data on ignition potential (lightning strikes and distance to roads), topography (elevation and aspect) and climate (maximum and minimum temperatures). The risk model was developed during a fire year at our study landscape and validated at a nearby landscape; model performance was accurate and similar at both sites. This study demonstrates that remote sensing techniques used in combination with field surveys can accurately predict wildfire risk in the Mojave Desert and may be applicable to other arid and semiarid lands where wildfires are prevalent.

An integrative model of risk for high school disordered eating.

PubMed

Davis, Heather A; Smith, Gregory T

2018-06-21

Binge eating and purging behaviors are associated with significant harm and distress among adolescents. The process by which these behaviors develop (often in the high school years) is not fully understood. We tested the Acquired Preparedness (AP) model of risk involving transactions among biological, personality, and psychosocial factors to predict binge eating and purging behavior in a sample of 1,906 children assessed in the spring of 5th grade (the last year of elementary school), the fall of 6th grade (the first year of middle school), spring of 6th grade, and spring of 10th grade (second year of high school). Pubertal onset in spring of 5th grade predicted increases in negative urgency, but not negative affect, in the fall of 6th grade. Negative urgency in the fall of 6th grade predicted increases in expectancies for reinforcement from eating in the spring of 6th grade, which in turn predicted increases in binge eating behavior in the spring of 10th grade. Negative affect in the fall of 6th grade predicted increases in thinness expectancies in the spring of 6th grade, which in turn predicted increases in purging in the spring of 10th grade. Results demonstrate similarities and differences in the development of these two different bulimic behaviors. Intervention efforts targeting the risk factors evident in this model may prove fruitful in the treatment of eating disorders characterized by binge eating and purging. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Nowcasting recreational water quality

USGS Publications Warehouse

Boehm, Alexandria B.; Whitman, Richard L.; Nevers, Meredith; Hou, Deyi; Weisberg, Stephen B.

2007-01-01

Advances in molecular techniques may soon provide new opportunities to provide more timely information on whether recreational beaches are free from fecal contamination. However, an alternative approach is the use of predictive models. This chapter presents a summary of these developing efforts. First, we describe documented physical, chemical, and biological factors that have been demonstrated by researchers to affect bacterial concentrations at beaches and thus represent logical parameters for inclusion in a model. Then, we illustrate how various types of models can be applied to predict water quality at freshwater and marine beaches.

Madden–Julian Oscillation prediction skill of a new-generation global model demonstrated using a supercomputer

PubMed Central

Miyakawa, Tomoki; Satoh, Masaki; Miura, Hiroaki; Tomita, Hirofumi; Yashiro, Hisashi; Noda, Akira T.; Yamada, Yohei; Kodama, Chihiro; Kimoto, Masahide; Yoneyama, Kunio

2014-01-01

Global cloud/cloud system-resolving models are perceived to perform well in the prediction of the Madden–Julian Oscillation (MJO), a huge eastward -propagating atmospheric pulse that dominates intraseasonal variation of the tropics and affects the entire globe. However, owing to model complexity, detailed analysis is limited by computational power. Here we carry out a simulation series using a recently developed supercomputer, which enables the statistical evaluation of the MJO prediction skill of a costly new-generation model in a manner similar to operational forecast models. We estimate the current MJO predictability of the model as 27 days by conducting simulations including all winter MJO cases identified during 2003–2012. The simulated precipitation patterns associated with different MJO phases compare well with observations. An MJO case captured in a recent intensive observation is also well reproduced. Our results reveal that the global cloud-resolving approach is effective in understanding the MJO and in providing month-long tropical forecasts. PMID:24801254

Madden-Julian Oscillation prediction skill of a new-generation global model demonstrated using a supercomputer.

PubMed

Miyakawa, Tomoki; Satoh, Masaki; Miura, Hiroaki; Tomita, Hirofumi; Yashiro, Hisashi; Noda, Akira T; Yamada, Yohei; Kodama, Chihiro; Kimoto, Masahide; Yoneyama, Kunio

2014-05-06

Global cloud/cloud system-resolving models are perceived to perform well in the prediction of the Madden-Julian Oscillation (MJO), a huge eastward -propagating atmospheric pulse that dominates intraseasonal variation of the tropics and affects the entire globe. However, owing to model complexity, detailed analysis is limited by computational power. Here we carry out a simulation series using a recently developed supercomputer, which enables the statistical evaluation of the MJO prediction skill of a costly new-generation model in a manner similar to operational forecast models. We estimate the current MJO predictability of the model as 27 days by conducting simulations including all winter MJO cases identified during 2003-2012. The simulated precipitation patterns associated with different MJO phases compare well with observations. An MJO case captured in a recent intensive observation is also well reproduced. Our results reveal that the global cloud-resolving approach is effective in understanding the MJO and in providing month-long tropical forecasts.

Prediction suppression and surprise enhancement in monkey inferotemporal cortex.

PubMed

Ramachandran, Suchitra; Meyer, Travis; Olson, Carl R

2017-07-01

Exposing monkeys, over the course of days and weeks, to pairs of images presented in fixed sequence, so that each leading image becomes a predictor for the corresponding trailing image, affects neuronal visual responsiveness in area TE. At the end of the training period, neurons respond relatively weakly to a trailing image when it appears in a trained sequence and, thus, confirms prediction, whereas they respond relatively strongly to the same image when it appears in an untrained sequence and, thus, violates prediction. This effect could arise from prediction suppression (reduced firing in response to the occurrence of a probable event) or surprise enhancement (elevated firing in response to the omission of a probable event). To identify its cause, we compared firing under the prediction-confirming and prediction-violating conditions to firing under a prediction-neutral condition. The results provide strong evidence for prediction suppression and limited evidence for surprise enhancement. NEW & NOTEWORTHY In predictive coding models of the visual system, neurons carry signed prediction error signals. We show here that monkey inferotemporal neurons exhibit prediction-modulated firing, as posited by these models, but that the signal is unsigned. The response to a prediction-confirming image is suppressed, and the response to a prediction-violating image may be enhanced. These results are better explained by a model in which the visual system emphasizes unpredicted events than by a predictive coding model. Copyright © 2017 the American Physiological Society.

Perceived Discrimination, Perceived Stress, and Mental and Physical Health among Mexican-Origin Adults

ERIC Educational Resources Information Center

Flores, Elena; Tschann, Jeanne M.; Dimas, Juanita M.; Bachen, Elizabeth A.; Pasch, Lauri A.; de Groat, Cynthia L.

2008-01-01

This study provided a test of the minority status stress model by examining whether perceived discrimination would directly affect health outcomes even when perceived stress was taken into account among 215 Mexican-origin adults. Perceived discrimination predicted depression and poorer general health, and marginally predicted health symptoms, when…

Predicted effect of dynamic load on pitting fatigue life for low-contact-ratio spur gears

NASA Technical Reports Server (NTRS)

Lewicki, David G.

1986-01-01

How dynamic load affects the surface pitting fatigue life of external spur gears was predicted by using the NASA computer program TELSGE. Parametric studies were performed over a range of various gear parameters modeling low-contact-ratio involute spur gears. In general, gear life predictions based on dynamic loads differed significantly from those based on static loads, with the predictions being strongly influenced by the maximum dynamic load during contact. Gear mesh operating speed strongly affected predicted dynamic load and life. Meshes operating at a resonant speed or one-half the resonant speed had significantly shorter lives. Dynamic life factors for gear surface pitting fatigue were developed on the basis of the parametric studies. In general, meshes with higher contact ratios had higher dynamic life factors than meshes with lower contact ratios. A design chart was developed for hand calculations of dynamic life factors.

Biases in affective forecasting and recall in individuals with depression and anxiety symptoms.

PubMed

Wenze, Susan J; Gunthert, Kathleen C; German, Ramaris E

2012-07-01

The authors used experience sampling to investigate biases in affective forecasting and recall in individuals with varying levels of depression and anxiety symptoms. Participants who were higher in depression symptoms demonstrated stronger (more pessimistic) negative mood prediction biases, marginally stronger negative mood recall biases, and weaker (less optimistic) positive mood prediction and recall biases. Participants who were higher in anxiety symptoms demonstrated stronger negative mood prediction biases, but positive mood prediction biases that were on par with those who were lower in anxiety. Anxiety symptoms were not associated with mood recall biases. Neither depression symptoms nor anxiety symptoms were associated with bias in event prediction. Their findings fit well with the tripartite model of depression and anxiety. Results are also consistent with the conceptualization of anxiety as a "forward-looking" disorder, and with theories that emphasize the importance of pessimism and general negative information processing in depressive functioning.

Modeling the effects of dispersal on predicted contemporary and future fisher (Martes pennanti) distribution in the U.S

Treesearch

Lucretia Olson; M. Schwartz

2013-01-01

Many species at high trophic levels are predicted to be impacted by shifts in habitat associated with climate change. While temperate coniferous forests are predicted to be one of the least affected ecosystems, the impact of shifting habitat on terrestrial carnivores that live within these ecosystems may depend on the dispersal rates of the species and the patchiness...

Prediction and characterization of heat-affected zone formation in tin-bismuth alloys due to nickel-aluminum multilayer foil reaction

DOE PAGES

Hooper, R. J.; Davis, C. G.; Johns, P. M.; ...

2015-06-26

Reactive multilayer foils have the potential to be used as local high intensity heat sources for a variety of applications. In this study, most of the past research effort concerning these materials have focused on understanding the structure-property relationships of the foils that govern the energy released during a reaction. To improve the ability of researchers to more rapidly develop technologies based on reactive multilayer foils, a deeper and more predictive understanding of the relationship between the heat released from the foil and microstructural evolution in the neighboring materials is needed. This work describes the development of a numerical modelmore » for the purpose of predicting heat affected zone size in substrate materials. The model is experimentally validated using a commercially available Ni-Al multilayer foils and alloys from the Sn-Bi binary system. To accomplish this, phenomenological models for predicting the variation of physical properties (i.e., thermal conductivity, density, and heat capacity) with temperature and composition in the Sn-Bi system were utilized using literature data.« less

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

PubMed

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

2016-11-01

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

Prediction of consonant recognition in quiet for listeners with normal and impaired hearing using an auditory model.

PubMed

Jürgens, Tim; Ewert, Stephan D; Kollmeier, Birger; Brand, Thomas

2014-03-01

Consonant recognition was assessed in normal-hearing (NH) and hearing-impaired (HI) listeners in quiet as a function of speech level using a nonsense logatome test. Average recognition scores were analyzed and compared to recognition scores of a speech recognition model. In contrast to commonly used spectral speech recognition models operating on long-term spectra, a "microscopic" model operating in the time domain was used. Variations of the model (accounting for hearing impairment) and different model parameters (reflecting cochlear compression) were tested. Using these model variations this study examined whether speech recognition performance in quiet is affected by changes in cochlear compression, namely, a linearization, which is often observed in HI listeners. Consonant recognition scores for HI listeners were poorer than for NH listeners. The model accurately predicted the speech reception thresholds of the NH and most HI listeners. A partial linearization of the cochlear compression in the auditory model, while keeping audibility constant, produced higher recognition scores and improved the prediction accuracy. However, including listener-specific information about the exact form of the cochlear compression did not improve the prediction further.

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

PubMed

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

2015-01-01

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

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

PubMed Central

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

2018-01-01

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

Predicting wetland plant community responses to proposed water-level-regulation plans for Lake Ontario: GIS-based modeling

USGS Publications Warehouse

Wilcox, D.A.; Xie, Y.

2007-01-01

Integrated, GIS-based, wetland predictive models were constructed to assist in predicting the responses of wetland plant communities to proposed new water-level regulation plans for Lake Ontario. The modeling exercise consisted of four major components: 1) building individual site wetland geometric models; 2) constructing generalized wetland geometric models representing specific types of wetlands (rectangle model for drowned river mouth wetlands, half ring model for open embayment wetlands, half ellipse model for protected embayment wetlands, and ellipse model for barrier beach wetlands); 3) assigning wetland plant profiles to the generalized wetland geometric models that identify associations between past flooding / dewatering events and the regulated water-level changes of a proposed water-level-regulation plan; and 4) predicting relevant proportions of wetland plant communities and the time durations during which they would be affected under proposed regulation plans. Based on this conceptual foundation, the predictive models were constructed using bathymetric and topographic wetland models and technical procedures operating on the platform of ArcGIS. An example of the model processes and outputs for the drowned river mouth wetland model using a test regulation plan illustrates the four components and, when compared against other test regulation plans, provided results that met ecological expectations. The model results were also compared to independent data collected by photointerpretation. Although data collections were not directly comparable, the predicted extent of meadow marsh in years in which photographs were taken was significantly correlated with extent of mapped meadow marsh in all but barrier beach wetlands. The predictive model for wetland plant communities provided valuable input into International Joint Commission deliberations on new regulation plans and was also incorporated into faunal predictive models used for that purpose.

Direct and indirect influences of childhood abuse on depression symptoms in patients with major depressive disorder.

PubMed

Hayashi, Yumi; Okamoto, Yasumasa; Takagaki, Koki; Okada, Go; Toki, Shigeru; Inoue, Takeshi; Tanabe, Hajime; Kobayakawa, Makoto; Yamawaki, Shigeto

2015-10-14

It is known that the onset, progression, and prognosis of major depressive disorder are affected by interactions between a number of factors. This study investigated how childhood abuse, personality, and stress of life events were associated with symptoms of depression in depressed people. Patients with major depressive disorder (N = 113, 58 women and 55 men) completed the Beck Depression Inventory-II (BDI-II), the Neuroticism Extroversion Openness Five Factor Inventory (NEO-FFI), the Child Abuse and Trauma Scale (CATS), and the Life Experiences Survey (LES), which are self-report scales. Results were analyzed with correlation analysis and structural equation modeling (SEM), by using SPSS AMOS 21.0. Childhood abuse directly predicted the severity of depression and indirectly predicted the severity of depression through the mediation of personality. Negative life change score of the LES was affected by childhood abuse, however it did not predict the severity of depression. This study is the first to report a relationship between childhood abuse, personality, adulthood life stresses and the severity of depression in depressed patients. Childhood abuse directly and indirectly predicted the severity of depression. These results suggest the need for clinicians to be receptive to the possibility of childhood abuse in patients suffering from depression. SEM is a procedure used for hypothesis modeling and not for causal modeling. Therefore, the possibility of developing more appropriate models that include other variables cannot be excluded.

Improved predictions of atmospheric icing in Norway

NASA Astrophysics Data System (ADS)

Engdahl, Bjørg Jenny; Nygaard, Bjørn Egil; Thompson, Gregory; Bengtsson, Lisa; Berntsen, Terje

2017-04-01

Atmospheric icing of ground structures is a problem in cold climate locations such as Norway. During the 2013/2014 winter season two major power lines in southern Norway suffered severe damage due to ice loads exceeding their design values by two to three times. Better methods are needed to estimate the ice loads that affect various infrastructure, and better models are needed to improve the prediction of severe icing events. The Wind, Ice and Snow loads Impact on Infrastructure and the Natural Environment (WISLINE) project, was initiated to address this problem and to explore how a changing climate may affect the ice loads in Norway. Creating better forecasts of icing requires a proper simulation of supercooled liquid water (SLW). Preliminary results show that the operational numerical weather prediction model (HARMONIE-AROME) at MET-Norway generates considerably lower values of SLW as compared with the WRF model when run with the Thompson microphysics scheme. Therefore, we are piecewise implementing specific processes found in the Thompson scheme into the AROME model and testing the resulting impacts to prediction of SLW and structural icing. Both idealized and real icing cases are carried out to test the newly modified AROME microphysics scheme. Besides conventional observations, a unique set of specialized instrumentation for icing measurements are used for validation. Initial results of this investigation will be presented at the conference.

The Roles of Social Support and Coping Strategies in Predicting Breast Cancer Patients’ Emotional Well-being

PubMed Central

KIM, JUNGHYUN; HAN, JEONG YEOB; SHAW, BRET; MCTAVISH, FIONA; GUSTAFSON, DAVID

2011-01-01

The goal of the current study was to examine how social support and coping strategies are related in predicting emotional well-being of women with breast cancer. In achieving this goal, we examined two hypothesized models: (1) a moderation model where social support and coping strategies interact with each other in affecting psychological well-being; and (2) a mediation model where the level of social support influences choices of coping strategies between self-blame and positive reframing. In general, the data from the current study were more consistent with the mediation model than the moderation model. PMID:20460411

Relations among Affect, Abstinence Motivation and Confidence, and Daily Smoking Lapse Risk

PubMed Central

Minami, Haruka; Yeh, Vivian M.; Bold, Krysten W.; Chapman, Gretchen B.; McCarthy, Danielle E.

2016-01-01

Aims This study tested the hypothesis that changes in momentary affect, abstinence motivation, and confidence would predict lapse risk over the next 12–24 hours using Ecological Momentary Assessment (EMA) data from smokers attempting to quit smoking. Method 103 adult, daily, treatment-seeking smokers recorded their momentary affect, motivation to quit, abstinence confidence, and smoking behaviors in near real time with multiple EMA reports per day using electronic diaries post-quit. Results Multilevel models indicated that initial levels of negative affect were associated with smoking, even after controlling for earlier smoking status, and that short-term increases in negative affect predicted lapses up to 12, but not 24, hours later. Positive affect had significant effects on subsequent abstinence confidence, but not motivation to quit. High levels of motivation appeared to reduce increases in lapse risk that occur over hours while momentary changes in confidence did not predict lapse risk over 12 hours. Conclusion Negative affect had short-lived effects on lapse risk, whereas higher levels of motivation protected against the risk of lapsing that accumulates over hours. An increase in positive affect was associated with greater confidence to quit, but such changes in confidence did not reduce short-term lapse risk, contrary to expectations. Relations observed among affect, cognitions, and lapse seem to depend critically on the timing of assessments. PMID:24955665

How Different Guilt Feelings Can Affect Social Competence Development in Childhood.

PubMed

Tani, Franca; Ponti, Lucia

2018-01-01

The authors examined how the two different dimensions of guilt feelings, needed for reparation and fear of punishment, could influence social conduct, such as prosocial and aggressive behaviors, and how they are linked to popularity in childhood. The authors hypothesized a theoretical model that they tested, fitting it with empirical data obtained from a sample of 242 Italian children 9-11 years old. Both dimensions of guilt predict prosocial and aggressive behaviors. Specifically, the feeling of guilt linked to the need for reparation tends to negatively predict aggressive behaviors, and positively predict prosocial behaviors. The feeling of guilt linked to the fear of punishment, on the contrary, tends to positively affect aggressive and negatively affect prosocial conducts in children. These results highlight that the different feelings of guilt can represent a relevant risk or protective factor for the development of social competence in childhood. Limitations, strengths, and further development of the present study are discussed.

Agent-Based Computational Modeling to Examine How Individual Cell Morphology Affects Dosimetry

EPA Science Inventory

Cell-based models utilizing high-content screening (HCS) data have applications for predictive toxicology. Evaluating concentration-dependent effects on cell fate and state response is a fundamental utilization of HCS data.Although HCS assays may capture quantitative readouts at ...

Adaptive Response in Female Modeling of the Hypothalamic-pituitary-gonadal Axis

EPA Science Inventory

Exposure to endocrine disrupting chemicals can affect reproduction and development in both humans and wildlife. We are developing a mechanistic computational model of the hypothalamic-pituitary-gonadal (HPG) axis in female fathead minnows to predict dose-response and time-course ...

Evaluation of Industry Standard Turbulence Models on an Axisymmetric Supersonic Compression Corner

NASA Technical Reports Server (NTRS)

DeBonis, James R.

2015-01-01

Reynolds-averaged Navier-Stokes computations of a shock-wave/boundary-layer interaction (SWBLI) created by a Mach 2.85 flow over an axisymmetric 30-degree compression corner were carried out. The objectives were to evaluate four turbulence models commonly used in industry, for SWBLIs, and to evaluate the suitability of this test case for use in further turbulence model benchmarking. The Spalart-Allmaras model, Menter's Baseline and Shear Stress Transport models, and a low-Reynolds number k- model were evaluated. Results indicate that the models do not accurately predict the separation location; with the SST model predicting the separation onset too early and the other models predicting the onset too late. Overall the Spalart-Allmaras model did the best job in matching the experimental data. However there is significant room for improvement, most notably in the prediction of the turbulent shear stress. Density data showed that the simulations did not accurately predict the thermal boundary layer upstream of the SWBLI. The effect of turbulent Prandtl number and wall temperature were studied in an attempt to improve this prediction and understand their effects on the interaction. The data showed that both parameters can significantly affect the separation size and location, but did not improve the agreement with the experiment. This case proved challenging to compute and should provide a good test for future turbulence modeling work.

[Development and Application of a Performance Prediction Model for Home Care Nursing Based on a Balanced Scorecard using the Bayesian Belief Network].

PubMed

Noh, Wonjung; Seomun, Gyeongae

2015-06-01

This study was conducted to develop key performance indicators (KPIs) for home care nursing (HCN) based on a balanced scorecard, and to construct a performance prediction model of strategic objectives using the Bayesian Belief Network (BBN). This methodological study included four steps: establishment of KPIs, performance prediction modeling, development of a performance prediction model using BBN, and simulation of a suggested nursing management strategy. An HCN expert group and a staff group participated. The content validity index was analyzed using STATA 13.0, and BBN was analyzed using HUGIN 8.0. We generated a list of KPIs composed of 4 perspectives, 10 strategic objectives, and 31 KPIs. In the validity test of the performance prediction model, the factor with the greatest variance for increasing profit was maximum cost reduction of HCN services. The factor with the smallest variance for increasing profit was a minimum image improvement for HCN. During sensitivity analysis, the probability of the expert group did not affect the sensitivity. Furthermore, simulation of a 10% image improvement predicted the most effective way to increase profit. KPIs of HCN can estimate financial and non-financial performance. The performance prediction model for HCN will be useful to improve performance.

A Genetic Algorithm Based Support Vector Machine Model for Blood-Brain Barrier Penetration Prediction

PubMed Central

Zhang, Daqing; Xiao, Jianfeng; Zhou, Nannan; Luo, Xiaomin; Jiang, Hualiang; Chen, Kaixian

2015-01-01

Blood-brain barrier (BBB) is a highly complex physical barrier determining what substances are allowed to enter the brain. Support vector machine (SVM) is a kernel-based machine learning method that is widely used in QSAR study. For a successful SVM model, the kernel parameters for SVM and feature subset selection are the most important factors affecting prediction accuracy. In most studies, they are treated as two independent problems, but it has been proven that they could affect each other. We designed and implemented genetic algorithm (GA) to optimize kernel parameters and feature subset selection for SVM regression and applied it to the BBB penetration prediction. The results show that our GA/SVM model is more accurate than other currently available log BB models. Therefore, to optimize both SVM parameters and feature subset simultaneously with genetic algorithm is a better approach than other methods that treat the two problems separately. Analysis of our log BB model suggests that carboxylic acid group, polar surface area (PSA)/hydrogen-bonding ability, lipophilicity, and molecular charge play important role in BBB penetration. Among those properties relevant to BBB penetration, lipophilicity could enhance the BBB penetration while all the others are negatively correlated with BBB penetration. PMID:26504797

A Seasonal Time-Series Model Based on Gene Expression Programming for Predicting Financial Distress

PubMed Central

2018-01-01

The issue of financial distress prediction plays an important and challenging research topic in the financial field. Currently, there have been many methods for predicting firm bankruptcy and financial crisis, including the artificial intelligence and the traditional statistical methods, and the past studies have shown that the prediction result of the artificial intelligence method is better than the traditional statistical method. Financial statements are quarterly reports; hence, the financial crisis of companies is seasonal time-series data, and the attribute data affecting the financial distress of companies is nonlinear and nonstationary time-series data with fluctuations. Therefore, this study employed the nonlinear attribute selection method to build a nonlinear financial distress prediction model: that is, this paper proposed a novel seasonal time-series gene expression programming model for predicting the financial distress of companies. The proposed model has several advantages including the following: (i) the proposed model is different from the previous models lacking the concept of time series; (ii) the proposed integrated attribute selection method can find the core attributes and reduce high dimensional data; and (iii) the proposed model can generate the rules and mathematical formulas of financial distress for providing references to the investors and decision makers. The result shows that the proposed method is better than the listing classifiers under three criteria; hence, the proposed model has competitive advantages in predicting the financial distress of companies. PMID:29765399

A Seasonal Time-Series Model Based on Gene Expression Programming for Predicting Financial Distress.

PubMed

Cheng, Ching-Hsue; Chan, Chia-Pang; Yang, Jun-He

2018-01-01

The issue of financial distress prediction plays an important and challenging research topic in the financial field. Currently, there have been many methods for predicting firm bankruptcy and financial crisis, including the artificial intelligence and the traditional statistical methods, and the past studies have shown that the prediction result of the artificial intelligence method is better than the traditional statistical method. Financial statements are quarterly reports; hence, the financial crisis of companies is seasonal time-series data, and the attribute data affecting the financial distress of companies is nonlinear and nonstationary time-series data with fluctuations. Therefore, this study employed the nonlinear attribute selection method to build a nonlinear financial distress prediction model: that is, this paper proposed a novel seasonal time-series gene expression programming model for predicting the financial distress of companies. The proposed model has several advantages including the following: (i) the proposed model is different from the previous models lacking the concept of time series; (ii) the proposed integrated attribute selection method can find the core attributes and reduce high dimensional data; and (iii) the proposed model can generate the rules and mathematical formulas of financial distress for providing references to the investors and decision makers. The result shows that the proposed method is better than the listing classifiers under three criteria; hence, the proposed model has competitive advantages in predicting the financial distress of companies.

Model of white oak flower survival and maturation

Treesearch

David R. Larsen; Robert A. Cecich

1997-01-01

A stochastic model of oak flower dynamics is presented that integrates a number of factors which appear to affect the oak pistillate flower development process. The factors are modeled such that the distribution of the predicted flower populations could have come from the same distribution as the observed flower populations. Factors included in the model are; the range...

Positive Affect and the Complex Dynamics of Human Flourishing

ERIC Educational Resources Information Center

Fredrickson, Barbara L.; Losada, Marcial F.

2005-01-01

Extending B. L. Fredrickson's (1998) broaden-and-build theory of positive emotions and M. Losada's (1999) nonlinear dynamics model of team performance, the authors predict that a ratio of positive to negative affect at or above 2.9 will characterize individuals in flourishing mental health. Participants (N=188) completed an initial survey to…

Joint Trajectories of Behavioral, Affective, and Cognitive Engagement in Elementary School

ERIC Educational Resources Information Center

Archambault, Isabelle; Dupéré, Véronique

2017-01-01

The aim of the present study was to model student trajectories of behavioral, affective, and cognitive engagement from Grade 3 to Grade 6. The authors also examined whether teachers perceptions could predict student trajectory membership. The authors collected data from a sample of 831 students and 152 teachers. Using multiple-process growth…

The Relationships of Dissociation and Affective Family Environment with the Intergenerational Cycle of Child Abuse

ERIC Educational Resources Information Center

Narang, D.S.; Contreras, J.M.

2005-01-01

Objective:: The purpose was to test a model that may explain how physically abused children become physically abusive parents. It was predicted that when the family's affective environment is uncohesive, unexpressive, and conflictual, a history of abuse experiences would be associated with elevated dissociation. It was hypothesized that…

Exploring Dynamical Assessments of Affect, Behavior, and Cognition and Math State Test Achievement

ERIC Educational Resources Information Center

San Pedro, Maria Ofelia Z.; Snow, Erica L.; Baker, Ryan S.; McNamara, Danielle S.; Heffernan, Neil T.

2015-01-01

There is increasing evidence that fine-grained aspects of student performance and interaction within educational software are predictive of long-term learning. Machine learning models have been used to provide assessments of affect, behavior, and cognition based on analyses of system log data, estimating the probability of a student's particular…

Perspective-Taking, Position Power, and Third Party Intervention Style: A Classroom Application.

ERIC Educational Resources Information Center

Schneider, David E.

In order to understand how power affects other relationships, to offer an exploratory methodology for operationalizing an intervention typology, and to eventually develop a theoretical model that predicts affective influence on third party intervention modes in given conflict situations, a pilot study hypothesized that the frequency of preferred…

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

NASA Astrophysics Data System (ADS)

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

2014-12-01

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

Daily diary study of personality disorder traits: Momentary affect and cognitive appraisals in response to stressful events.

PubMed

Jarnecke, Amber M; Miller, Michelle L; South, Susan C

2017-01-01

Difficulties in emotional expression and emotion regulation are core features of many personality disorders (PDs); yet, we know relatively little about how individuals with PDs affectively respond to stressful situations. The present study seeks to fill this gap in the literature by examining how PD traits are associated with emotional responses to subjective daily stressors, while accounting for cognition and type of stressor experienced (interpersonal vs. noninterpersonal). PD features were measured with the Schedule for Nonadaptive and Adaptive Personality-2 (SNAP-2) diagnostic scores. Participants (N = 77) completed a 1-week experience sampling procedure that measured affect and cognition related to a current stressor 5 times per day. Hierarchical linear modeling (HLM) was used to examine whether and how baseline PD features, momentary cognitions, and type of stressor predicted level of affect. Results demonstrated that paranoid, borderline, and avoidant PD traits predicted negative affect beyond what could be accounted for by cognitions and type of stressor. No PD traits predicted positive affect after accounting for the effects of cognitive appraisals and type of stressor. Findings have implications for validating the role of affect in PDs and understanding how individuals with PDs react in the presence of daily hassles. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Dual process interaction model of HIV-risk behaviors among drug offenders.

PubMed

Ames, Susan L; Grenard, Jerry L; Stacy, Alan W

2013-03-01

This study evaluated dual process interaction models of HIV-risk behavior among drug offenders. A dual process approach suggests that decisions to engage in appetitive behaviors result from a dynamic interplay between a relatively automatic associative system and an executive control system. One synergistic type of interplay suggests that executive functions may dampen or block effects of spontaneously activated associations. Consistent with this model, latent variable interaction analyses revealed that drug offenders scoring higher in affective decision making were relatively protected from predictive effects of spontaneous sex associations promoting risky sex. Among drug offenders with lower levels of affective decision making ability, spontaneous sexually-related associations more strongly predicted risky sex (lack of condom use and greater number of sex partners). These findings help elucidate associative and control process effects on appetitive behaviors and are important for explaining why some individuals engage in risky sex, while others are relatively protected.

Dual Process Interaction Model of HIV-Risk Behaviors Among Drug Offenders

PubMed Central

Grenard, Jerry L.; Stacy, Alan W.

2012-01-01

This study evaluated dual process interaction models of HIV-risk behavior among drug offenders. A dual process approach suggests that decisions to engage in appetitive behaviors result from a dynamic interplay between a relatively automatic associative system and an executive control system. One synergistic type of interplay suggests that executive functions may dampen or block effects of spontaneously activated associations. Consistent with this model, latent variable interaction analyses revealed that drug offenders scoring higher in affective decision making were relatively protected from predictive effects of spontaneous sex associations promoting risky sex. Among drug offenders with lower levels of affective decision making ability, spontaneous sexually-related associations more strongly predicted risky sex (lack of condom use and greater number of sex partners). These findings help elucidate associative and control process effects on appetitive behaviors and are important for explaining why some individuals engage in risky sex, while others are relatively protected. PMID:22331391

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

PubMed Central

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

2016-01-01

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

Disgust proneness predicts obsessive-compulsive disorder symptom severity in a clinical sample of youth: Distinctions from negative affect.

PubMed

Olatunji, Bunmi O; Ebesutani, Chad; Kim, Jingu; Riemann, Bradley C; Jacobi, David M

2017-04-15

Although studies have linked disgust proneness to the etiology and maintenance of obsessive-compulsive disorder (OCD) in adults, there remains a paucity of research examining the specificity of this association among youth. The present study employed structural equation modeling to examine the association between disgust proneness, negative affect, and OCD symptom severity in a clinical sample of youth admitted to a residential treatment facility (N =471). Results indicate that disgust proneness and negative affect latent factors independently predicted an OCD symptom severity latent factor. However, when both variables were modeled as predictors simultaneously, latent disgust proneness remained significantly associated with OCD symptom severity, whereas the association between latent negative affect and OCD symptom severity became nonsignificant. Tests of mediation converged in support of disgust proneness as a significant intervening variable between negative affect and OCD symptom severity. Subsequent analysis showed that the path from disgust proneness to OCD symptom severity in the structural model was significantly stronger among those without a primary diagnosis of OCD compared to those with a primary diagnosis of OCD. Given the cross-sectional design, the causal inferences that can be made are limited. The present study is also limited by the exclusive reliance on self-report measures. Disgust proneness may play a uniquely important role in OCD among youth. Copyright © 2017 Elsevier B.V. All rights reserved.

The wind power prediction research based on mind evolutionary algorithm

NASA Astrophysics Data System (ADS)

Zhuang, Ling; Zhao, Xinjian; Ji, Tianming; Miao, Jingwen; Cui, Haina

2018-04-01

When the wind power is connected to the power grid, its characteristics of fluctuation, intermittent and randomness will affect the stability of the power system. The wind power prediction can guarantee the power quality and reduce the operating cost of power system. There were some limitations in several traditional wind power prediction methods. On the basis, the wind power prediction method based on Mind Evolutionary Algorithm (MEA) is put forward and a prediction model is provided. The experimental results demonstrate that MEA performs efficiently in term of the wind power prediction. The MEA method has broad prospect of engineering application.

Momentary affect predicts bodily movement in daily life: an ambulatory monitoring study.

PubMed

Schwerdtfeger, Andreas; Eberhardt, Ragna; Chmitorz, Andrea; Schaller, Eva

2010-10-01

There is converging evidence that physical activity influences affective states. It has been found that aerobic exercise programs can significantly diminish negative affect. Moreover, among healthy individuals, moderate levels of physical activity seem to increase energetic arousal and positive affect. However, the predictive utility of affective states for bodily movement has rarely been investigated. In this study, we examined whether momentarily assessed affect is associated with bodily movement in everyday life. Using a previously published data set (Schwerdtfeger, Eberhardt, & Chmitorz, 2008), we reanalyzed 12-hr ecological momentary assessment (EMA) data from 124 healthy volunteers. Electronic momentary positive-activated affect (EMA-PAA) and negative affect (EMA-NA) were assessed via handheld computers, and bodily movement was recorded via accelerosensors. Generalized linear mixed models were calculated. Results indicated that EMAPAA increases were accompanied by bodily movement increases of varying intensity. EMA-NA was also positively associated with increases in certain kinds of bodily movement. In light of previous research, this finding suggests that affect and bodily movement may have circular effects on each other.

Predicting effects of environmental change on a migratory herbivore

USGS Publications Warehouse

Stillman, R A; Wood, K A; Gilkerson, Whelan; Elkinton, E; Black, J. M.; Ward, David H.; Petrie, M.

2015-01-01

Changes in climate, food abundance and disturbance from humans threaten the ability of species to successfully use stopover sites and migrate between non-breeding and breeding areas. To devise successful conservation strategies for migratory species we need to be able to predict how such changes will affect both individuals and populations. Such predictions should ideally be process-based, focusing on the mechanisms through which changes alter individual physiological state and behavior. In this study we use a process-based model to evaluate how Black Brant (Branta bernicla nigricans) foraging on common eelgrass (Zostera marina) at a stopover site (Humboldt Bay, USA), may be affected by changes in sea level, food abundance and disturbance. The model is individual-based, with empirically based parameters, and incorporates the immigration of birds into the site, tidal changes in availability of eelgrass, seasonal and depth-related changes in eelgrass biomass, foraging behavior and energetics of the birds, and their mass-dependent decisions to emigrate. The model is validated by comparing predictions to observations across a range of system properties including the time birds spent foraging, probability of birds emigrating, mean stopover duration, peak bird numbers, rates of mass gain and distribution of birds within the site: all 11 predictions were within 35% of the observed value, and 8 within 20%. The model predicted that the eelgrass within the site could potentially support up to five times as many birds as currently use the site. Future predictions indicated that the rate of mass gain and mean stopover duration were relatively insensitive to sea level rise over the next 100 years, primarily because eelgrass habitat could redistribute shoreward into intertidal mudflats within the site to compensate for higher sea levels. In contrast, the rate of mass gain and mean stopover duration were sensitive to changes in total eelgrass biomass and the percentage of time for which birds were disturbed. We discuss the consequences of these predictions for Black Brant conservation. A wide range of migratory species responses are expected in response to environmental change. Process-based models are potential tools to predict such responses and understand the mechanisms which underpin them.

An Exploration of Students' Science Learning Interest Related to Their Cognitive Anxiety, Cognitive Load, Self-Confidence and Learning Progress Using Inquiry-Based Learning with an iPad

ERIC Educational Resources Information Center

Hong, Jon-Chao; Hwang, Ming-Yueh; Tai, Kai-Hsin; Tsai, Chi-Ruei

2017-01-01

Based on the cognitive-affective theory, the present study designed a science inquiry learning model, "predict-observe-explain" (POE), and implemented it in an app called "WhyWhy" to examine the effectiveness of students' science inquiry learning practice. To understand how POE can affect the cognitive-affective learning…

Improved Orbit Determination and Forecasts with an Assimilative Tool for Satellite Drag Specification

NASA Astrophysics Data System (ADS)

Pilinski, M.; Crowley, G.; Sutton, E.; Codrescu, M.

2016-09-01

Much as aircraft are affected by the prevailing winds and weather conditions in which they fly, satellites are affected by the variability in density and motion of the near earth space environment. Drastic changes in the neutral density of the thermosphere, caused by geomagnetic storms or other phenomena, result in perturbations of LEO satellite motions through drag on the satellite surfaces. This can lead to difficulties in locating important satellites, temporarily losing track of satellites, and errors when predicting collisions in space. As the population of satellites in Earth orbit grows, higher space-weather prediction accuracy is required for critical missions, such as accurate catalog maintenance, collision avoidance for manned and unmanned space flight, reentry prediction, satellite lifetime prediction, defining on-board fuel requirements, and satellite attitude dynamics. We describe ongoing work to build a comprehensive nowcast and forecast system for specifying the neutral atmospheric state related to orbital drag conditions. The system outputs include neutral density, winds, temperature, composition, and the satellite drag derived from these parameters. This modeling tool is based on several state-of-the-art coupled models of the thermosphere-ionosphere as well as several empirical models running in real-time and uses assimilative techniques to produce a thermospheric nowcast. This software will also produce 72 hour predictions of the global thermosphere-ionosphere system using the nowcast as the initial condition and using near real-time and predicted space weather data and indices as the inputs. In this paper, we will review the driving requirements for our model, summarize the model design and assimilative architecture, and present preliminary validation results. Validation results will be presented in the context of satellite orbit errors and compared with several leading atmospheric models. As part of the analysis, we compare the drag observed by a variety of satellites which were not used as part of the assimilation-dataset and whose perigee altitudes span a range from 200 km to 700 km.

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.

Predicting PM10 concentration in Seoul metropolitan subway stations using artificial neural network (ANN).

PubMed

Park, Sechan; Kim, Minjeong; Kim, Minhae; Namgung, Hyeong-Gyu; Kim, Ki-Tae; Cho, Kyung Hwa; Kwon, Soon-Bark

2018-01-05

The indoor air quality of subway systems can significantly affect the health of passengers since these systems are widely used for short-distance transit in metropolitan urban areas in many countries. The particles generated by abrasion during subway operations and the vehicle-emitted pollutants flowing in from the street in particular affect the air quality in underground subway stations. Thus the continuous monitoring of particulate matter (PM) in underground station is important to evaluate the exposure level of PM to passengers. However, it is difficult to obtain indoor PM data because the measurement systems are expensive and difficult to install and operate for significant periods of time in spaces crowded with people. In this study, we predicted the indoor PM concentration using the information of outdoor PM, the number of subway trains running, and information on ventilation operation by the artificial neural network (ANN) model. As well, we investigated the relationship between ANN's performance and the depth of underground subway station. ANN model showed a high correlation between the predicted and actual measured values and it was able to predict 67∼80% of PM at 6 subway station. In addition, we found that platform shape and depth influenced the model performance. Copyright © 2017 Elsevier B.V. All rights reserved.

Regional Cultures and the Psychological Geography of Switzerland: Person–Environment–Fit in Personality Predicts Subjective Wellbeing

PubMed Central

Götz, Friedrich M.; Ebert, Tobias; Rentfrow, Peter J.

2018-01-01

The present study extended traditional nation-based research on person–culture–fit to the regional level. First, we examined the geographical distribution of Big Five personality traits in Switzerland. Across the 26 Swiss cantons, unique patterns were observed for all traits. For Extraversion and Neuroticism clear language divides emerged between the French- and Italian-speaking South-West vs. the German-speaking North-East. Second, multilevel modeling demonstrated that person–environment–fit in Big Five, composed of elevation (i.e., mean differences between individual profile and cantonal profile), scatter (differences in mean variances) and shape (Pearson correlations between individual and cantonal profiles across all traits; Furr, 2008, 2010), predicted the development of subjective wellbeing (i.e., life satisfaction, satisfaction with personal relationships, positive affect, negative affect) over a period of 4 years. Unexpectedly, while the effects of shape were in line with the person–environment–fit hypothesis (better fit predicted higher subjective wellbeing), the effects of scatter showed the opposite pattern, while null findings were observed for elevation. Across a series of robustness checks, the patterns for shape and elevation were consistently replicated. While that was mostly the case for scatter as well, the effects of scatter appeared to be somewhat less robust and more sensitive to the specific way fit was modeled when predicting certain outcomes (negative affect, positive affect). Distinguishing between supplementary and complementary fit may help to reconcile these findings and future research should explore whether and if so under which conditions these concepts may be applicable to the respective facets of person–culture–fit. PMID:29713299

Regional Cultures and the Psychological Geography of Switzerland: Person-Environment-Fit in Personality Predicts Subjective Wellbeing.

PubMed

Götz, Friedrich M; Ebert, Tobias; Rentfrow, Peter J

2018-01-01

The present study extended traditional nation-based research on person-culture-fit to the regional level. First, we examined the geographical distribution of Big Five personality traits in Switzerland. Across the 26 Swiss cantons, unique patterns were observed for all traits. For Extraversion and Neuroticism clear language divides emerged between the French- and Italian-speaking South-West vs. the German-speaking North-East. Second, multilevel modeling demonstrated that person-environment-fit in Big Five, composed of elevation (i.e., mean differences between individual profile and cantonal profile), scatter (differences in mean variances) and shape (Pearson correlations between individual and cantonal profiles across all traits; Furr, 2008, 2010), predicted the development of subjective wellbeing (i.e., life satisfaction, satisfaction with personal relationships, positive affect, negative affect) over a period of 4 years. Unexpectedly, while the effects of shape were in line with the person-environment-fit hypothesis (better fit predicted higher subjective wellbeing), the effects of scatter showed the opposite pattern, while null findings were observed for elevation. Across a series of robustness checks, the patterns for shape and elevation were consistently replicated. While that was mostly the case for scatter as well, the effects of scatter appeared to be somewhat less robust and more sensitive to the specific way fit was modeled when predicting certain outcomes (negative affect, positive affect). Distinguishing between supplementary and complementary fit may help to reconcile these findings and future research should explore whether and if so under which conditions these concepts may be applicable to the respective facets of person-culture-fit.

A Modelling Framework to Assess the Effect of Pressures on River Abiotic Habitat Conditions and Biota

PubMed Central

Kail, Jochem; Guse, Björn; Radinger, Johannes; Schröder, Maria; Kiesel, Jens; Kleinhans, Maarten; Schuurman, Filip; Fohrer, Nicola; Hering, Daniel; Wolter, Christian

2015-01-01

River biota are affected by global reach-scale pressures, but most approaches for predicting biota of rivers focus on river reach or segment scale processes and habitats. Moreover, these approaches do not consider long-term morphological changes that affect habitat conditions. In this study, a modelling framework was further developed and tested to assess the effect of pressures at different spatial scales on reach-scale habitat conditions and biota. Ecohydrological and 1D hydrodynamic models were used to predict discharge and water quality at the catchment scale and the resulting water level at the downstream end of a study reach. Long-term reach morphology was modelled using empirical regime equations, meander migration and 2D morphodynamic models. The respective flow and substrate conditions in the study reach were predicted using a 2D hydrodynamic model, and the suitability of these habitats was assessed with novel habitat models. In addition, dispersal models for fish and macroinvertebrates were developed to assess the re-colonization potential and to finally compare habitat suitability and the availability / ability of species to colonize these habitats. Applicability was tested and model performance was assessed by comparing observed and predicted conditions in the lowland Treene River in northern Germany. Technically, it was possible to link the different models, but future applications would benefit from the development of open source software for all modelling steps to enable fully automated model runs. Future research needs concern the physical modelling of long-term morphodynamics, feedback of biota (e.g., macrophytes) on abiotic habitat conditions, species interactions, and empirical data on the hydraulic habitat suitability and dispersal abilities of macroinvertebrates. The modelling framework is flexible and allows for including additional models and investigating different research and management questions, e.g., in climate impact research as well as river restoration and management. PMID:26114430

Using state-space models to predict the abundance of juvenile and adult sea lice on Atlantic salmon.

PubMed

Elghafghuf, Adel; Vanderstichel, Raphael; St-Hilaire, Sophie; Stryhn, Henrik

2018-04-11

Sea lice are marine parasites affecting salmon farms, and are considered one of the most costly pests of the salmon aquaculture industry. Infestations of sea lice on farms significantly increase opportunities for the parasite to spread in the surrounding ecosystem, making control of this pest a challenging issue for salmon producers. The complexity of controlling sea lice on salmon farms requires frequent monitoring of the abundance of different sea lice stages over time. Industry-based data sets of counts of lice are amenable to multivariate time-series data analyses. In this study, two sets of multivariate autoregressive state-space models were applied to Chilean sea lice data from six Atlantic salmon production cycles on five isolated farms (at least 20 km seaway distance away from other known active farms), to evaluate the utility of these models for predicting sea lice abundance over time on farms. The models were constructed with different parameter configurations, and the analysis demonstrated large heterogeneity between production cycles for the autoregressive parameter, the effects of chemotherapeutant bath treatments, and the process-error variance. A model allowing for different parameters across production cycles had the best fit and the smallest overall prediction errors. However, pooling information across cycles for the drift and observation error parameters did not substantially affect model performance, thus reducing the number of necessary parameters in the model. Bath treatments had strong but variable effects for reducing sea lice burdens, and these effects were stronger for adult lice than juvenile lice. Our multivariate state-space models were able to handle different sea lice stages and provide predictions for sea lice abundance with reasonable accuracy up to five weeks out. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

Predicting and testing continental vertical motion histories since the Paleozoic

NASA Astrophysics Data System (ADS)

Zhang, Nan; Zhong, Shijie; Flowers, Rebecca M.

2012-02-01

Dynamic topography at the Earth's surface caused by mantle convection can affect a range of geophysical and geological observations including bathymetry, sea-level change, continental flooding, sedimentation and erosion. These observations provide important constraints on and test of mantle dynamic models. Based on global mantle convection models coupled with the surface plate motion history, we compute dynamic topography and its history for the last 400 Ma associated with Pangea assembly and breakup, with particular focus on cratonic regions. We propose that burial-unroofing histories of cratons inferred from thermochronology data can be used as a new diagnostic to test dynamic topography and mantle dynamic models. Our models show that there are currently two broad dynamic topography highs in the Pacific and Africa for the present-day Earth that are associated with the broad, warm structures (i.e., superplumes) in the deep mantle, consistent with previous proposals of dynamical support for the Pacific and African superswells. Our models reveal that Pangea assembly and breakup, by affecting subduction and mantle upwelling processes, have significant effects on continental vertical motions. Our models predict that the Slave craton in North America subsides before Pangea assembly at 330 Ma but uplifts significantly from 330 Ma to 240 Ma in response to pre-Pangea subduction and post-assembly mantle warming. The Kaapvaal craton of Africa is predicted to undergo uplift from ~180 Ma to 90 Ma after Pangea breakup, but its dynamic topography remains stable for the last 90 Ma. The predicted histories of elevation change for the Slave and Kaapvaal cratons compare well with the burial-unroofing histories inferred from thermochronology studies, thus supporting our dynamic models including the development of the African superplume mantle structure. The vertical motion histories for other cratons can provide further tests of and constraints on our mantle dynamic models.

Predicting and testing continental vertical motion histories since the Paleozoic

NASA Astrophysics Data System (ADS)

Zhang, N.; Zhong, S.; Flowers, R. M.

2011-12-01

Dynamic topography at the Earth's surface caused by mantle convection can affect a range of geophysical and geological observations including bathymetry, sea-level change, continental flooding, sedimentation and erosion. These observations provide important constraints on and test of mantle dynamic models. Based on global mantle convection models coupled with the surface plate motion history, we compute dynamic topography and its history for the last 400 Ma associated with Pangea assembly and breakup, with particular focus on continental cratonic regions. We propose that burial-unroofing histories of continental cratons inferred from thermochronology data can be used as a new diagnostic to test dynamic topography and mantle dynamic models. Our models show that there are currently two broad dynamic topography highs in the Pacific and Africa for the present-day Earth that are associated with the broad, warm structures (i.e., superplumes) in the deep mantle, consistent with previous proposals of dynamical support for the Pacific and African superswells. Our models reveal that Pangea assembly and breakup, by affecting subduction and mantle upwelling processes, have significant effects on continental vertical motions. Our models predict that the Slave craton in North America subsides before Pangea assembly at 330 Ma but uplifts significantly from 330 Ma to 240 Ma in response to pre-Pangea subduction and post-assembly mantle warming. The Kaapvaal craton of Africa is predicted to undergo uplift from ~180 Ma to 90 Ma after Pangea breakup, but its dynamic topography remains stable for the last 90 Ma. The predicted histories of elevation change for the Slave and Kaapvaal cratons compare well with the burial-unroofing histories inferred from thermochronology studies, thus supporting our dynamic models including the development of the African superplume mantle structure. The vertical motion histories for other cratons can provide further tests and constraints on our mantle dynamic models.

Evaluation of an ensemble of genetic models for prediction of a quantitative trait.

PubMed

Milton, Jacqueline N; Steinberg, Martin H; Sebastiani, Paola

2014-01-01

Many genetic markers have been shown to be associated with common quantitative traits in genome-wide association studies. Typically these associated genetic markers have small to modest effect sizes and individually they explain only a small amount of the variability of the phenotype. In order to build a genetic prediction model without fitting a multiple linear regression model with possibly hundreds of genetic markers as predictors, researchers often summarize the joint effect of risk alleles into a genetic score that is used as a covariate in the genetic prediction model. However, the prediction accuracy can be highly variable and selecting the optimal number of markers to be included in the genetic score is challenging. In this manuscript we present a strategy to build an ensemble of genetic prediction models from data and we show that the ensemble-based method makes the challenge of choosing the number of genetic markers more amenable. Using simulated data with varying heritability and number of genetic markers, we compare the predictive accuracy and inclusion of true positive and false positive markers of a single genetic prediction model and our proposed ensemble method. The results show that the ensemble of genetic models tends to include a larger number of genetic variants than a single genetic model and it is more likely to include all of the true genetic markers. This increased sensitivity is obtained at the price of a lower specificity that appears to minimally affect the predictive accuracy of the ensemble.

Effect of genetic architecture on the prediction accuracy of quantitative traits in samples of unrelated individuals.

PubMed

Morgante, Fabio; Huang, Wen; Maltecca, Christian; Mackay, Trudy F C

2018-06-01

Predicting complex phenotypes from genomic data is a fundamental aim of animal and plant breeding, where we wish to predict genetic merits of selection candidates; and of human genetics, where we wish to predict disease risk. While genomic prediction models work well with populations of related individuals and high linkage disequilibrium (LD) (e.g., livestock), comparable models perform poorly for populations of unrelated individuals and low LD (e.g., humans). We hypothesized that low prediction accuracies in the latter situation may occur when the genetics architecture of the trait departs from the infinitesimal and additive architecture assumed by most prediction models. We used simulated data for 10,000 lines based on sequence data from a population of unrelated, inbred Drosophila melanogaster lines to evaluate this hypothesis. We show that, even in very simplified scenarios meant as a stress test of the commonly used Genomic Best Linear Unbiased Predictor (G-BLUP) method, using all common variants yields low prediction accuracy regardless of the trait genetic architecture. However, prediction accuracy increases when predictions are informed by the genetic architecture inferred from mapping the top variants affecting main effects and interactions in the training data, provided there is sufficient power for mapping. When the true genetic architecture is largely or partially due to epistatic interactions, the additive model may not perform well, while models that account explicitly for interactions generally increase prediction accuracy. Our results indicate that accounting for genetic architecture can improve prediction accuracy for quantitative traits.

The role of bias in simulation of the Indian monsoon and its relationship to predictability

NASA Astrophysics Data System (ADS)

Kelly, P.

2016-12-01

Confidence in future projections of how climate change will affect the Indian monsoon is currently limited by- among other things-model biases. That is, the systematic error in simulating the mean present day climate. An important priority question in seamless prediction involves the role of the mean state. How much of the prediction error in imperfect models stems from a biased mean state (itself a result of many interacting process errors), and how much stems from the flow dependence of processes during an oscillation or variation we are trying to predict? Using simple but effective nudging techniques, we are able to address this question in a clean and incisive framework that teases apart the roles of the mean state vs. transient flow dependence in constraining predictability. The role of bias in model fidelity of simulations of the Indian monsoon is investigated in CAM5, and the relationship to predictability in remote regions in the "free" (non-nudged) domain is explored.

Statistical validation of predictive TRANSP simulations of baseline discharges in preparation for extrapolation to JET D-T

NASA Astrophysics Data System (ADS)

Kim, Hyun-Tae; Romanelli, M.; Yuan, X.; Kaye, S.; Sips, A. C. C.; Frassinetti, L.; Buchanan, J.; Contributors, JET

2017-06-01

This paper presents for the first time a statistical validation of predictive TRANSP simulations of plasma temperature using two transport models, GLF23 and TGLF, over a database of 80 baseline H-mode discharges in JET-ILW. While the accuracy of the predicted T e with TRANSP-GLF23 is affected by plasma collisionality, the dependency of predictions on collisionality is less significant when using TRANSP-TGLF, indicating that the latter model has a broader applicability across plasma regimes. TRANSP-TGLF also shows a good matching of predicted T i with experimental measurements allowing for a more accurate prediction of the neutron yields. The impact of input data and assumptions prescribed in the simulations are also investigated in this paper. The statistical validation and the assessment of uncertainty level in predictive TRANSP simulations for JET-ILW-DD will constitute the basis for the extrapolation to JET-ILW-DT experiments.

Prediction and Factor Extraction of Drug Function by Analyzing Medical Records in Developing Countries.

PubMed

Hu, Min; Nohara, Yasunobu; Nakamura, Masafumi; Nakashima, Naoki

2017-01-01

The World Health Organization has declared Bangladesh one of 58 countries facing acute Human Resources for Health (HRH) crisis. Artificial intelligence in healthcare has been shown to be successful for diagnostics. Using machine learning to predict pharmaceutical prescriptions may solve HRH crises. In this study, we investigate a predictive model by analyzing prescription data of 4,543 subjects in Bangladesh. We predict the function of prescribed drugs, comparing three machine-learning approaches. The approaches compare whether a subject shall be prescribed medicine from the 21 most frequently prescribed drug functions. Receiver Operating Characteristics (ROC) were selected as a way to evaluate and assess prediction models. The results show the drug function with the best prediction performance was oral hypoglycemic drugs, which has an average AUC of 0.962. To understand how the variables affect prediction, we conducted factor analysis based on tree-based algorithms and natural language processing techniques.

SWMF Global Magnetosphere Simulations of January 2005: Geomagnetic Indices and Cross-Polar Cap Potential

DOE PAGES

Haiducek, John D.; Welling, Daniel T.; Ganushkina, Natalia Y.; ...

2017-10-30

We simulated the entire month of January, 2005 using the Space Weather Modeling Framework (SWMF) with observed solar wind data as input. We conducted this simulation with and without an inner magnetosphere model, and tested two different grid resolutions. We evaluated the model's accuracy in predicting Kp, Sym-H, AL, and cross polar cap potential (CPCP). We find that the model does an excellent job of predicting the Sym-H index, with an RMSE of 17-18 nT. Kp is predicted well during storm-time conditions, but over-predicted during quiet times by a margin of 1 to 1.7 Kp units. AL is predicted reasonablymore » well on average, with an RMSE of 230-270 nT. However, the model reaches the largest negative AL values significantly less often than the observations. The model tended to over-predict CPCP, with RMSE values on the order of 46-48 kV. We found the results to be insensitive to grid resoution, with the exception of the rate of occurrence for strongly negative AL values. As a result, the use of the inner magnetosphere component, however, affected results significantly, with all quantities except CPCP improved notably when the inner magnetosphere model was on.« less

SWMF Global Magnetosphere Simulations of January 2005: Geomagnetic Indices and Cross-Polar Cap Potential

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

Haiducek, John D.; Welling, Daniel T.; Ganushkina, Natalia Y.

We simulated the entire month of January, 2005 using the Space Weather Modeling Framework (SWMF) with observed solar wind data as input. We conducted this simulation with and without an inner magnetosphere model, and tested two different grid resolutions. We evaluated the model's accuracy in predicting Kp, Sym-H, AL, and cross polar cap potential (CPCP). We find that the model does an excellent job of predicting the Sym-H index, with an RMSE of 17-18 nT. Kp is predicted well during storm-time conditions, but over-predicted during quiet times by a margin of 1 to 1.7 Kp units. AL is predicted reasonablymore » well on average, with an RMSE of 230-270 nT. However, the model reaches the largest negative AL values significantly less often than the observations. The model tended to over-predict CPCP, with RMSE values on the order of 46-48 kV. We found the results to be insensitive to grid resoution, with the exception of the rate of occurrence for strongly negative AL values. As a result, the use of the inner magnetosphere component, however, affected results significantly, with all quantities except CPCP improved notably when the inner magnetosphere model was on.« less

Predicting overload-affected fatigue crack growth in steels

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

Skorupa, M.; Skorupa, A.; Ladecki, B.

1996-12-01

The ability of semi-empirical crack closure models to predict the effect of overloads on fatigue crack growth in low-alloy steels has been investigated. With this purpose, the CORPUS model developed for aircraft metals and spectra has been checked first through comparisons between the simulated and observed results for a low-alloy steel. The CORPUS predictions of crack growth under several types of simple load histories containing overloads appeared generally unconservative which prompted the authors to formulate a new model, more suitable for steels. With the latter approach, the assumed evolution of the crack opening stress during the delayed retardation stage hasmore » been based on experimental results reported for various steels. For all the load sequences considered, the predictions from the proposed model appeared to be by far more accurate than those from CORPUS. Based on the analysis results, the capability of semi-empirical prediction concepts to cover experimentally observed trends that have been reported for sequences with overloads is discussed. Finally, possibilities of improving the model performance are considered.« less

A novel prediction method about single components of analog circuits based on complex field modeling.

PubMed

Zhou, Jingyu; Tian, Shulin; Yang, Chenglin

2014-01-01

Few researches pay attention to prediction about analog circuits. The few methods lack the correlation with circuit analysis during extracting and calculating features so that FI (fault indicator) calculation often lack rationality, thus affecting prognostic performance. To solve the above problem, this paper proposes a novel prediction method about single components of analog circuits based on complex field modeling. Aiming at the feature that faults of single components hold the largest number in analog circuits, the method starts with circuit structure, analyzes transfer function of circuits, and implements complex field modeling. Then, by an established parameter scanning model related to complex field, it analyzes the relationship between parameter variation and degeneration of single components in the model in order to obtain a more reasonable FI feature set via calculation. According to the obtained FI feature set, it establishes a novel model about degeneration trend of analog circuits' single components. At last, it uses particle filter (PF) to update parameters for the model and predicts remaining useful performance (RUP) of analog circuits' single components. Since calculation about the FI feature set is more reasonable, accuracy of prediction is improved to some extent. Finally, the foregoing conclusions are verified by experiments.

Prediction of temperature and HAZ in thermal-based processes with Gaussian heat source by a hybrid GA-ANN model

NASA Astrophysics Data System (ADS)

Fazli Shahri, Hamid Reza; Mahdavinejad, Ramezanali

2018-02-01

Thermal-based processes with Gaussian heat source often produce excessive temperature which can impose thermally-affected layers in specimens. Therefore, the temperature distribution and Heat Affected Zone (HAZ) of materials are two critical factors which are influenced by different process parameters. Measurement of the HAZ thickness and temperature distribution within the processes are not only difficult but also expensive. This research aims at finding a valuable knowledge on these factors by prediction of the process through a novel combinatory model. In this study, an integrated Artificial Neural Network (ANN) and genetic algorithm (GA) was used to predict the HAZ and temperature distribution of the specimens. To end this, a series of full factorial design of experiments were conducted by applying a Gaussian heat flux on Ti-6Al-4 V at first, then the temperature of the specimen was measured by Infrared thermography. The HAZ width of each sample was investigated through measuring the microhardness. Secondly, the experimental data was used to create a GA-ANN model. The efficiency of GA in design and optimization of the architecture of ANN was investigated. The GA was used to determine the optimal number of neurons in hidden layer, learning rate and momentum coefficient of both output and hidden layers of ANN. Finally, the reliability of models was assessed according to the experimental results and statistical indicators. The results demonstrated that the combinatory model predicted the HAZ and temperature more effective than a trial-and-error ANN model.

Attentional control mediates the effect of social anxiety on positive affect☆

PubMed Central

Morrison, Amanda S.; Heimberg, Richard G.

2015-01-01

The goal of the present studies was to examine whether attentional control, a self-regulatory attentional mechanism, mediates the effect of social anxiety on positive affect. We tested this mediation in two studies using undergraduate students selected to represent a broad range of severity of social anxiety. Self-report assessments of social anxiety, attentional control, and positive affect were collected in a cross-sectional design (Study 1) and in a longitudinal design with three assessment points (Study 2). Results of both studies supported the hypothesized mediational model. Specifically, social anxiety was inversely related to attentional control, which itself positively predicted positive affect. This mediation remained significant even when statistically controlling for the effects of depression. Additionally, the hypothesized model provided superior model fit to theoretically-grounded equivalent models in both studies. Implications of these findings for understanding diminished positive affect in social anxiety are discussed. PMID:23254261

Experimental Errors in QSAR Modeling Sets: What We Can Do and What We Cannot Do.

PubMed

Zhao, Linlin; Wang, Wenyi; Sedykh, Alexander; Zhu, Hao

2017-06-30

Numerous chemical data sets have become available for quantitative structure-activity relationship (QSAR) modeling studies. However, the quality of different data sources may be different based on the nature of experimental protocols. Therefore, potential experimental errors in the modeling sets may lead to the development of poor QSAR models and further affect the predictions of new compounds. In this study, we explored the relationship between the ratio of questionable data in the modeling sets, which was obtained by simulating experimental errors, and the QSAR modeling performance. To this end, we used eight data sets (four continuous endpoints and four categorical endpoints) that have been extensively curated both in-house and by our collaborators to create over 1800 various QSAR models. Each data set was duplicated to create several new modeling sets with different ratios of simulated experimental errors (i.e., randomizing the activities of part of the compounds) in the modeling process. A fivefold cross-validation process was used to evaluate the modeling performance, which deteriorates when the ratio of experimental errors increases. All of the resulting models were also used to predict external sets of new compounds, which were excluded at the beginning of the modeling process. The modeling results showed that the compounds with relatively large prediction errors in cross-validation processes are likely to be those with simulated experimental errors. However, after removing a certain number of compounds with large prediction errors in the cross-validation process, the external predictions of new compounds did not show improvement. Our conclusion is that the QSAR predictions, especially consensus predictions, can identify compounds with potential experimental errors. But removing those compounds by the cross-validation procedure is not a reasonable means to improve model predictivity due to overfitting.

Experimental Errors in QSAR Modeling Sets: What We Can Do and What We Cannot Do

PubMed Central

2017-01-01

Numerous chemical data sets have become available for quantitative structure–activity relationship (QSAR) modeling studies. However, the quality of different data sources may be different based on the nature of experimental protocols. Therefore, potential experimental errors in the modeling sets may lead to the development of poor QSAR models and further affect the predictions of new compounds. In this study, we explored the relationship between the ratio of questionable data in the modeling sets, which was obtained by simulating experimental errors, and the QSAR modeling performance. To this end, we used eight data sets (four continuous endpoints and four categorical endpoints) that have been extensively curated both in-house and by our collaborators to create over 1800 various QSAR models. Each data set was duplicated to create several new modeling sets with different ratios of simulated experimental errors (i.e., randomizing the activities of part of the compounds) in the modeling process. A fivefold cross-validation process was used to evaluate the modeling performance, which deteriorates when the ratio of experimental errors increases. All of the resulting models were also used to predict external sets of new compounds, which were excluded at the beginning of the modeling process. The modeling results showed that the compounds with relatively large prediction errors in cross-validation processes are likely to be those with simulated experimental errors. However, after removing a certain number of compounds with large prediction errors in the cross-validation process, the external predictions of new compounds did not show improvement. Our conclusion is that the QSAR predictions, especially consensus predictions, can identify compounds with potential experimental errors. But removing those compounds by the cross-validation procedure is not a reasonable means to improve model predictivity due to overfitting. PMID:28691113

in vitro Models if Human Embryonic Mesenchymal Transitions in Morphogenesis

EPA Science Inventory

Our ability to predict human developmental consequences produced by exposure to environmental chemicals is limited by the current experimental and computational models.Human heart defects are among the most common type of birth defects and affect 1% of children (~40,000 children)...

Solar g-modes? Comparison of detected asymptotic g-mode frequencies with solar model predictions

NASA Astrophysics Data System (ADS)

Wood, Suzannah Rebecca; Guzik, Joyce Ann; Mussack, Katie; Bradley, Paul A.

2018-06-01

After many years of searching for solar gravity modes, Fossat et al. (2017) reported detection of the nearly equally spaced high-order g-modes periods using a 15-year time series of GOLF data from the SOHO spacecraft. Here we report progress towards and challenges associated with calculating and comparing g-mode period predictions for several previously published standard solar models using various abundance mixtures and opacities, as well as the predictions for some non-standard models incorporating early mass loss, and compare with the periods reported by Fossat et al (2017). Additionally, we have a side-by-side comparison of results of different stellar pulsation codes for calculating g-mode predictions. These comparisons will allow for testing of nonstandard physics input that affect the core, including an early more massive Sun and dynamic electron screening.

Evaluation of a Mysis bioenergetics model

USGS Publications Warehouse

Chipps, S.R.; Bennett, D.H.

2002-01-01

Direct approaches for estimating the feeding rate of the opossum shrimp Mysis relicta can be hampered by variable gut residence time (evacuation rate models) and non-linear functional responses (clearance rate models). Bioenergetics modeling provides an alternative method, but the reliability of this approach needs to be evaluated using independent measures of growth and food consumption. In this study, we measured growth and food consumption for M. relicta and compared experimental results with those predicted from a Mysis bioenergetics model. For Mysis reared at 10??C, model predictions were not significantly different from observed values. Moreover, decomposition of mean square error indicated that 70% of the variation between model predictions and observed values was attributable to random error. On average, model predictions were within 12% of observed values. A sensitivity analysis revealed that Mysis respiration and prey energy density were the most sensitive parameters affecting model output. By accounting for uncertainty (95% CLs) in Mysis respiration, we observed a significant improvement in the accuracy of model output (within 5% of observed values), illustrating the importance of sensitive input parameters for model performance. These findings help corroborate the Mysis bioenergetics model and demonstrate the usefulness of this approach for estimating Mysis feeding rate.

Eco-hydrologic model cascades: Simulating land use and climate change impacts on hydrology, hydraulics and habitats for fish and macroinvertebrates.

PubMed

Guse, Björn; Kail, Jochem; Radinger, Johannes; Schröder, Maria; Kiesel, Jens; Hering, Daniel; Wolter, Christian; Fohrer, Nicola

2015-11-15

Climate and land use changes affect the hydro- and biosphere at different spatial scales. These changes alter hydrological processes at the catchment scale, which impact hydrodynamics and habitat conditions for biota at the river reach scale. In order to investigate the impact of large-scale changes on biota, a cascade of models at different scales is required. Using scenario simulations, the impact of climate and land use change can be compared along the model cascade. Such a cascade of consecutively coupled models was applied in this study. Discharge and water quality are predicted with a hydrological model at the catchment scale. The hydraulic flow conditions are predicted by hydrodynamic models. The habitat suitability under these hydraulic and water quality conditions is assessed based on habitat models for fish and macroinvertebrates. This modelling cascade was applied to predict and compare the impacts of climate- and land use changes at different scales to finally assess their effects on fish and macroinvertebrates. Model simulations revealed that magnitude and direction of change differed along the modelling cascade. Whilst the hydrological model predicted a relevant decrease of discharge due to climate change, the hydraulic conditions changed less. Generally, the habitat suitability for fish decreased but this was strongly species-specific and suitability even increased for some species. In contrast to climate change, the effect of land use change on discharge was negligible. However, land use change had a stronger impact on the modelled nitrate concentrations affecting the abundances of macroinvertebrates. The scenario simulations for the two organism groups illustrated that direction and intensity of changes in habitat suitability are highly species-dependent. Thus, a joined model analysis of different organism groups combined with the results of hydrological and hydrodynamic models is recommended to assess the impact of climate and land use changes on river ecosystems. Copyright © 2015 Elsevier B.V. All rights reserved.

Testing the cognitive catalyst model of rumination with explicit and implicit cognitive content.

PubMed

Sova, Christopher C; Roberts, John E

2018-06-01

The cognitive catalyst model posits that rumination and negative cognitive content, such as negative schema, interact to predict depressive affect. Past research has found support for this model using explicit measures of negative cognitive content such as self-report measures of trait self-esteem and dysfunctional attitudes. The present study tested whether these findings would extend to implicit measures of negative cognitive content such as implicit self-esteem, and whether effects would depend on initial mood state and history of depression. Sixty-one undergraduate students selected on the basis of depression history (27 previously depressed; 34 never depressed) completed explicit and implicit measures of negative cognitive content prior to random assignment to a rumination induction followed by a distraction induction or vice versa. Dysphoric affect was measured both before and after these inductions. Analyses revealed that explicit measures, but not implicit measures, interacted with rumination to predict change in dysphoric affect, and these interactions were further moderated by baseline levels of dysphoria. Limitations include the small nonclinical sample and use of a self-report measure of depression history. These findings suggest that rumination amplifies the association between explicit negative cognitive content and depressive affect primarily among people who are already experiencing sad mood. Copyright © 2018 Elsevier Ltd. All rights reserved.

Application of an unsteady-state model for predicting vertical temperature distribution to an existing atrium

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

Takemasa, Yuichi; Togari, Satoshi; Arai, Yoshinobu

1996-11-01

Vertical temperature differences tend to be great in a large indoor space such as an atrium, and it is important to predict variations of vertical temperature distribution in the early stage of the design. The authors previously developed and reported on a new simplified unsteady-state calculation model for predicting vertical temperature distribution in a large space. In this paper, this model is applied to predicting the vertical temperature distribution in an existing low-rise atrium that has a skylight and is affected by transmitted solar radiation. Detailed calculation procedures that use the model are presented with all the boundary conditions, andmore » analytical simulations are carried out for the cooling condition. Calculated values are compared with measured results. The results of the comparison demonstrate that the calculation model can be applied to the design of a large space. The effects of occupied-zone cooling are also discussed and compared with those of all-zone cooling.« less

Attentional bias to negative affect moderates negative affect's relationship with smoking abstinence.

PubMed

Etcheverry, Paul E; Waters, Andrew J; Lam, Cho; Correa-Fernandez, Virmarie; Vidrine, Jennifer Irvin; Cinciripini, Paul M; Wetter, David W

2016-08-01

To examine whether initial orienting (IO) and inability to disengage (ITD) attention from negative affective stimuli moderate the association of negative affect with smoking abstinence during a quit attempt. Data were from a longitudinal cohort study of smoking cessation (N = 424). A negative affect modified Stroop task was administered 1 week before and on quit day to measure IO and ITD. Ecological Momentary Assessments were used to create negative affect intercepts and linear slopes for the week before quitting and on quit day. Quit day and long-term abstinence measures were collected. Continuation ratio logit model analyses found significant interactions for prequit negative affect slope with prequit ITD, odds ratio (OR) = 0.738 (0.57, 0.96), p = .02, and for quit day negative affect intercept with quit day ITD, OR = 0.62 (0.41, 950), p = .03, predicting abstinence. The Prequit Negative Affect Intercept × Prequit IO interaction predicting quit day abstinence was significant, OR = 1.42 (1.06, 1.90), p = .02, as was the Quit Day Negative Affect Slope × Quit Day IO interaction predicting long-term abstinence, OR = 1.45 (1.02, 2.08), p = .04. The hypothesis that the association of negative affect with smoking abstinence would be moderated by ITD was generally supported. Among individuals with high ITD, negative affect was inversely related to abstinence, but unrelated to abstinence among individuals with lower levels of ITD. Unexpectedly, among individuals with low IO, negative affect was inversely related to abstinence, but unrelated to abstinence among individuals with higher levels of ITD. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Thermo-mechanical simulations of early-age concrete cracking with durability predictions

NASA Astrophysics Data System (ADS)

Havlásek, Petr; Šmilauer, Vít; Hájková, Karolina; Baquerizo, Luis

2017-09-01

Concrete performance is strongly affected by mix design, thermal boundary conditions, its evolving mechanical properties, and internal/external restraints with consequences to possible cracking with impaired durability. Thermo-mechanical simulations are able to capture those relevant phenomena and boundary conditions for predicting temperature, strains, stresses or cracking in reinforced concrete structures. In this paper, we propose a weakly coupled thermo-mechanical model for early age concrete with an affinity-based hydration model for thermal part, taking into account concrete mix design, cement type and thermal boundary conditions. The mechanical part uses B3/B4 model for concrete creep and shrinkage with isotropic damage model for cracking, able to predict a crack width. All models have been implemented in an open-source OOFEM software package. Validations of thermo-mechanical simulations will be presented on several massive concrete structures, showing excellent temperature predictions. Likewise, strain validation demonstrates good predictions on a restrained reinforced concrete wall and concrete beam. Durability predictions stem from induction time of reinforcement corrosion, caused by carbonation and/or chloride ingress influenced by crack width. Reinforcement corrosion in concrete struts of a bridge will serve for validation.

Mental models accurately predict emotion transitions.

PubMed

Thornton, Mark A; Tamir, Diana I

2017-06-06

Successful social interactions depend on people's ability to predict others' future actions and emotions. People possess many mechanisms for perceiving others' current emotional states, but how might they use this information to predict others' future states? We hypothesized that people might capitalize on an overlooked aspect of affective experience: current emotions predict future emotions. By attending to regularities in emotion transitions, perceivers might develop accurate mental models of others' emotional dynamics. People could then use these mental models of emotion transitions to predict others' future emotions from currently observable emotions. To test this hypothesis, studies 1-3 used data from three extant experience-sampling datasets to establish the actual rates of emotional transitions. We then collected three parallel datasets in which participants rated the transition likelihoods between the same set of emotions. Participants' ratings of emotion transitions predicted others' experienced transitional likelihoods with high accuracy. Study 4 demonstrated that four conceptual dimensions of mental state representation-valence, social impact, rationality, and human mind-inform participants' mental models. Study 5 used 2 million emotion reports on the Experience Project to replicate both of these findings: again people reported accurate models of emotion transitions, and these models were informed by the same four conceptual dimensions. Importantly, neither these conceptual dimensions nor holistic similarity could fully explain participants' accuracy, suggesting that their mental models contain accurate information about emotion dynamics above and beyond what might be predicted by static emotion knowledge alone.

Forecasting of municipal solid waste quantity in a developing country using multivariate grey models

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

Intharathirat, Rotchana, E-mail: rotchana.in@gmail.com; Abdul Salam, P., E-mail: salam@ait.ac.th; Kumar, S., E-mail: kumar@ait.ac.th

Highlights: • Grey model can be used to forecast MSW quantity accurately with the limited data. • Prediction interval overcomes the uncertainty of MSW forecast effectively. • A multivariate model gives accuracy associated with factors affecting MSW quantity. • Population, urbanization, employment and household size play role for MSW quantity. - Abstract: In order to plan, manage and use municipal solid waste (MSW) in a sustainable way, accurate forecasting of MSW generation and composition plays a key role. It is difficult to carry out the reliable estimates using the existing models due to the limited data available in the developingmore » countries. This study aims to forecast MSW collected in Thailand with prediction interval in long term period by using the optimized multivariate grey model which is the mathematical approach. For multivariate models, the representative factors of residential and commercial sectors affecting waste collected are identified, classified and quantified based on statistics and mathematics of grey system theory. Results show that GMC (1, 5), the grey model with convolution integral, is the most accurate with the least error of 1.16% MAPE. MSW collected would increase 1.40% per year from 43,435–44,994 tonnes per day in 2013 to 55,177–56,735 tonnes per day in 2030. This model also illustrates that population density is the most important factor affecting MSW collected, followed by urbanization, proportion employment and household size, respectively. These mean that the representative factors of commercial sector may affect more MSW collected than that of residential sector. Results can help decision makers to develop the measures and policies of waste management in long term period.« less

Numerical modelling of soot formation and oxidation in laminar coflow non-smoking and smoking ethylene diffusion flames

NASA Astrophysics Data System (ADS)

Liu, Fengshan; Guo, Hongsheng; Smallwood, Gregory J.; Gülder, Ömer L.

2003-06-01

A numerical study of soot formation and oxidation in axisymmetric laminar coflow non-smoking and smoking ethylene diffusion flames was conducted using detailed gas-phase chemistry and complex thermal and transport properties. A modified two-equation soot model was employed to describe soot nucleation, growth and oxidation. Interaction between the gas-phase chemistry and soot chemistry was taken into account. Radiation heat transfer by both soot and radiating gases was calculated using the discrete-ordinates method coupled with a statistical narrow-band correlated-k based band model, and was used to evaluate the simple optically thin approximation. The governing equations in fully elliptic form were solved. The current models in the literature describing soot oxidation by O2 and OH have to be modified in order to predict the smoking flame. The modified soot oxidation model has only moderate effects on the calculation of the non-smoking flame, but dramatically affects the soot oxidation near the flame tip in the smoking flame. Numerical results of temperature, soot volume fraction and primary soot particle size and number density were compared with experimental data in the literature. Relatively good agreement was found between the prediction and the experimental data. The optically thin approximation radiation model significantly underpredicts temperatures in the upper portion of both flames, seriously affecting the soot prediction.

How does alcohol advertising influence underage drinking? The role of desirability, identification and skepticism.

PubMed

Austin, Erica Weintraub; Chen, Meng-Jinn; Grube, Joel W

2006-04-01

To investigate, using an information processing model, how persuasive media messages for alcohol use lead to concurring beliefs and behaviors among youths. Data were collected in 2000-2001 using computer-assisted, self-administered interviews with youths aged 9-17 years (n = 652). Latent variable structural equations models showed that skepticism was negatively associated with positive affect toward alcohol portrayals and positively with the desire to emulate characters portrayed in alcohol advertisements. These, in turn, predicted expectancies and liking of/desire for beer toys and brands, which predicted alcohol use. Parental guidance decreased alcohol use directly and indirectly by lessening influences of positive affect toward advertising. Media alcohol portrayals influence children's drinking through a progressive decision-making process, with its influence underestimated by typical exposure-and-effects analyses.

Prediction of microstructure, residual stress, and deformation in laser powder bed fusion process

NASA Astrophysics Data System (ADS)

Yang, Y. P.; Jamshidinia, M.; Boulware, P.; Kelly, S. M.

2018-05-01

Laser powder bed fusion (L-PBF) process has been investigated significantly to build production parts with a complex shape. Modeling tools, which can be used in a part level, are essential to allow engineers to fine tune the shape design and process parameters for additive manufacturing. This study focuses on developing modeling methods to predict microstructure, hardness, residual stress, and deformation in large L-PBF built parts. A transient sequentially coupled thermal and metallurgical analysis method was developed to predict microstructure and hardness on L-PBF built high-strength, low-alloy steel parts. A moving heat-source model was used in this analysis to accurately predict the temperature history. A kinetics based model which was developed to predict microstructure in the heat-affected zone of a welded joint was extended to predict the microstructure and hardness in an L-PBF build by inputting the predicted temperature history. The tempering effect resulting from the following built layers on the current-layer microstructural phases were modeled, which is the key to predict the final hardness correctly. It was also found that the top layers of a build part have higher hardness because of the lack of the tempering effect. A sequentially coupled thermal and mechanical analysis method was developed to predict residual stress and deformation for an L-PBF build part. It was found that a line-heating model is not suitable for analyzing a large L-PBF built part. The layer heating method is a potential method for analyzing a large L-PBF built part. The experiment was conducted to validate the model predictions.

Prediction of microstructure, residual stress, and deformation in laser powder bed fusion process

NASA Astrophysics Data System (ADS)

Yang, Y. P.; Jamshidinia, M.; Boulware, P.; Kelly, S. M.

2017-12-01

Laser powder bed fusion (L-PBF) process has been investigated significantly to build production parts with a complex shape. Modeling tools, which can be used in a part level, are essential to allow engineers to fine tune the shape design and process parameters for additive manufacturing. This study focuses on developing modeling methods to predict microstructure, hardness, residual stress, and deformation in large L-PBF built parts. A transient sequentially coupled thermal and metallurgical analysis method was developed to predict microstructure and hardness on L-PBF built high-strength, low-alloy steel parts. A moving heat-source model was used in this analysis to accurately predict the temperature history. A kinetics based model which was developed to predict microstructure in the heat-affected zone of a welded joint was extended to predict the microstructure and hardness in an L-PBF build by inputting the predicted temperature history. The tempering effect resulting from the following built layers on the current-layer microstructural phases were modeled, which is the key to predict the final hardness correctly. It was also found that the top layers of a build part have higher hardness because of the lack of the tempering effect. A sequentially coupled thermal and mechanical analysis method was developed to predict residual stress and deformation for an L-PBF build part. It was found that a line-heating model is not suitable for analyzing a large L-PBF built part. The layer heating method is a potential method for analyzing a large L-PBF built part. The experiment was conducted to validate the model predictions.

Impacts of Daily Bag Limit Reductions on Angler Effort in Wisconsin Walleye Lakes

USGS Publications Warehouse

Beard, T.D.; Cox, S.P.; Carpenter, S.R.

2003-01-01

Angler effort is an important factor affecting recreational fisheries. However, angler responses are rarely incorporated into recreational fisheries regulations or predictions. Few have attempted to examine how daily bag limit regulations affect total angling pressure and subsequent stock densities. Our paper develops a theoretical basis for predicting angler effort and harvest rate based on stock densities and bag limit regulations. We examined data from a management system that controls the total exploitation of walleyes Sander vitreus (formerly Stizostedion vitreum) in northern Wisconsin lakes and compared these empirical results with the predictions from a theoretical effort and harvest rate response model. The data indicated that higher general angler effort occurs on lakes regulated with a 5-walleye daily limit than on lakes regulated with either a 2- or 3-walleye daily limit. General walleye catch rates were lower on lakes with a 5-walleye limit than on lakes with either a 2- or 3-walleye daily limit. An effort response model predicted a logarithmic relationship between angler effort and adult walleye density and that an index of attractiveness would be greater on lakes with high bag limits. Predictions from the harvest rate model with constant walleye catchability indicated that harvest rates increased nonlinearly with increasing density. When the effort model was fitted to data from northern Wisconsin, we found higher lake attractiveness at 5-walleye-limit lakes. We conclude that different groups of anglers respond differently to bag limit changes and that reliance on daily bag limits may not be sufficient to maintain high walleye densities in some lakes in this region.

Simulation and Prediction of the Drug-Drug Interaction Potential of Naloxegol by Physiologically Based Pharmacokinetic Modeling.

PubMed

Zhou, D; Bui, K; Sostek, M; Al-Huniti, N

2016-05-01

Naloxegol, a peripherally acting μ-opioid receptor antagonist for the treatment of opioid-induced constipation, is a substrate for cytochrome P450 (CYP) 3A4/3A5 and the P-glycoprotein (P-gp) transporter. By integrating in silico, preclinical, and clinical pharmacokinetic (PK) findings, minimal and full physiologically based pharmacokinetic (PBPK) models were developed to predict the drug-drug interaction (DDI) potential for naloxegol. The models reasonably predicted the observed changes in naloxegol exposure with ketoconazole (increase of 13.1-fold predicted vs. 12.9-fold observed), diltiazem (increase of 2.8-fold predicted vs. 3.4-fold observed), rifampin (reduction of 76% predicted vs. 89% observed), and quinidine (increase of 1.2-fold predicted vs. 1.4-fold observed). The moderate CYP3A4 inducer efavirenz was predicted to reduce naloxegol exposure by ∼50%, whereas weak CYP3A inhibitors were predicted to minimally affect exposure. In summary, the PBPK models reasonably estimated interactions with various CYP3A modulators and can be used to guide dosing in clinical practice when naloxegol is coadministered with such agents. © 2016 The Authors CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals, Inc. on behalf of American Society for Clinical Pharmacology and Therapeutics.

Who finds neutral pictures pleasant and relaxing?

PubMed

Moè, Angelica; Sarlo, Michela

2011-04-01

Valence and arousal are independent dimensions of consciously experienced affect. The former refers to pleasantness; the latter to the degree of excitement or stimulation. The present research explores some of the predictors of these dimensions through the hypothesis that valence relates to positive affect and lack of negative affect, while arousal is tied to negative affect, and that both are predicted by personal wellbeing, considered as a way of achieving happiness. The occurrence of depressive symptoms is also considered within the hypothesis: as a facet of negative affect, as lack of wellbeing, or as an independent dimension placed at the same level as wellbeing, and which relates to both positive and negative affect (considered as mediators). Sixty-one participants were asked to view on a computer screen a series of 20 neutral pictures, having medium valence and low arousal, and complete self-report questionnaires to assess affect, personal wellbeing, and the occurrence of depressive symptoms. After picture viewing, valence and arousal judgments were requested. In the analysis, three competing models with latent variables were tested, to assess at best the role depressive symptoms have. They confirmed that valence is predicted by high positive and low negative affect, arousal by negative affect and even directly by the occurrence of depressive symptoms, and that personal wellbeing and depressive symptoms are the starting point. They are negatively correlated and predict positive (both) and negative affect (just the occurrence of depressive symptoms). The discussion focuses on both theoretical and practical implications. Suggestions for future research are given.

Semantic Effects in Naming Perceptual Identification but Not in Delayed Naming: Implications for Models and Tasks

ERIC Educational Resources Information Center

Wurm, Lee H.; Seaman, Sean R.

2008-01-01

Previous research has demonstrated that the subjective danger and usefulness of words affect lexical decision times. Usually, an interaction is found: Increasing danger predicts faster reaction times (RTs) for words low on usefulness, but increasing danger predicts slower RTs for words high on usefulness. The authors show the same interaction with…

Hydroregime prediction models for ephemeral groundwater-driven sinkhole wetlands: a planning tool for climate change and amphibian conservation

Treesearch

C. H. Greenberg; S. Goodrick; J. D. Austin; B. R. Parresol

2015-01-01

Hydroregimes of ephemeral wetlands affect reproductive success of many amphibian species and are sensitive to altered weather patterns associated with climate change.We used 17 years of weekly temperature, precipitation, and waterdepth measurements for eight small, ephemeral, groundwaterdriven sinkhole wetlands in Florida sandhills to develop a hydroregime predictive...

Psychosocial Costs of Racism to White Counselors: Predicting Various Dimensions of Multicultural Counseling Competence

ERIC Educational Resources Information Center

Spanierman, Lisa B.; Poteat, V. Paul; Wang, Ying-Fen; Oh, Euna

2008-01-01

In 2 interrelated investigations, the authors examined the extent to which affect, as measured by the Psychosocial Costs of Racism to Whites scale (PCRW; L. B. Spanierman & M. J. Heppner, 2004), would predict various dimensions of multicultural counseling competence (MCC). In Study 1, structural equation modeling was used to test a mediating model…

A Regression Model with a New Tool: IDB Analyzer for Identifying Factors Predicting Mathematics Performance Using PISA 2012 Indices

ERIC Educational Resources Information Center

Arikan, Serkan

2014-01-01

There are many studies that focus on factors affecting achievement. However, there is limited research that used student characteristics indices reported by the Programme for International Student Assessment (PISA). Therefore, this study investigated the predictive effects of student characteristics on mathematics performance of Turkish students.…

Using destination image to predict visitors' intention to revisit three Hudson River Valley, New York, communities

Treesearch

Rudy M. Schuster; Laura Sullivan; Duarte Morais; Diane Kuehn

2009-01-01

This analysis explores the differences in Affective and Cognitive Destination Image among three Hudson River Valley (New York) tourism communities. Multiple regressions were used with six dimensions of visitors' images to predict future intention to revisit. Two of the three regression models were significant. The only significantly contributing independent...

A risk score for the prediction of advanced age-related macular degeneration: Development and validation in 2 prospective cohorts

USDA-ARS?s Scientific Manuscript database

We aimed to develop an eye specific model which used readily available information to predict risk for advanced age-related macular degeneration (AMD). We used the Age-Related Eye Disease Study (AREDS) as our training dataset, which consisted of the 4,507 participants (contributing 1,185 affected v...

Short-term prediction of solar energy in Saudi Arabia using automated-design fuzzy logic systems

PubMed Central

2017-01-01

Solar energy is considered as one of the main sources for renewable energy in the near future. However, solar energy and other renewable energy sources have a drawback related to the difficulty in predicting their availability in the near future. This problem affects optimal exploitation of solar energy, especially in connection with other resources. Therefore, reliable solar energy prediction models are essential to solar energy management and economics. This paper presents work aimed at designing reliable models to predict the global horizontal irradiance (GHI) for the next day in 8 stations in Saudi Arabia. The designed models are based on computational intelligence methods of automated-design fuzzy logic systems. The fuzzy logic systems are designed and optimized with two models using fuzzy c-means clustering (FCM) and simulated annealing (SA) algorithms. The first model uses FCM based on the subtractive clustering algorithm to automatically design the predictor fuzzy rules from data. The second model is using FCM followed by simulated annealing algorithm to enhance the prediction accuracy of the fuzzy logic system. The objective of the predictor is to accurately predict next-day global horizontal irradiance (GHI) using previous-day meteorological and solar radiation observations. The proposed models use observations of 10 variables of measured meteorological and solar radiation data to build the model. The experimentation and results of the prediction are detailed where the root mean square error of the prediction was approximately 88% for the second model tuned by simulated annealing compared to 79.75% accuracy using the first model. This results demonstrate a good modeling accuracy of the second model despite that the training and testing of the proposed models were carried out using spatially and temporally independent data. PMID:28806754

Short-term prediction of solar energy in Saudi Arabia using automated-design fuzzy logic systems.

PubMed

Almaraashi, Majid

2017-01-01

Solar energy is considered as one of the main sources for renewable energy in the near future. However, solar energy and other renewable energy sources have a drawback related to the difficulty in predicting their availability in the near future. This problem affects optimal exploitation of solar energy, especially in connection with other resources. Therefore, reliable solar energy prediction models are essential to solar energy management and economics. This paper presents work aimed at designing reliable models to predict the global horizontal irradiance (GHI) for the next day in 8 stations in Saudi Arabia. The designed models are based on computational intelligence methods of automated-design fuzzy logic systems. The fuzzy logic systems are designed and optimized with two models using fuzzy c-means clustering (FCM) and simulated annealing (SA) algorithms. The first model uses FCM based on the subtractive clustering algorithm to automatically design the predictor fuzzy rules from data. The second model is using FCM followed by simulated annealing algorithm to enhance the prediction accuracy of the fuzzy logic system. The objective of the predictor is to accurately predict next-day global horizontal irradiance (GHI) using previous-day meteorological and solar radiation observations. The proposed models use observations of 10 variables of measured meteorological and solar radiation data to build the model. The experimentation and results of the prediction are detailed where the root mean square error of the prediction was approximately 88% for the second model tuned by simulated annealing compared to 79.75% accuracy using the first model. This results demonstrate a good modeling accuracy of the second model despite that the training and testing of the proposed models were carried out using spatially and temporally independent data.

Multi-Node Thermal System Model for Lithium-Ion Battery Packs: Preprint

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

Shi, Ying; Smith, Kandler; Wood, Eric

Temperature is one of the main factors that controls the degradation in lithium ion batteries. Accurate knowledge and control of cell temperatures in a pack helps the battery management system (BMS) to maximize cell utilization and ensure pack safety and service life. In a pack with arrays of cells, a cells temperature is not only affected by its own thermal characteristics but also by its neighbors, the cooling system and pack configuration, which increase the noise level and the complexity of cell temperatures prediction. This work proposes to model lithium ion packs thermal behavior using a multi-node thermal network model,more » which predicts the cell temperatures by zones. The model was parametrized and validated using commercial lithium-ion battery packs. neighbors, the cooling system and pack configuration, which increase the noise level and the complexity of cell temperatures prediction. This work proposes to model lithium ion packs thermal behavior using a multi-node thermal network model, which predicts the cell temperatures by zones. The model was parametrized and validated using commercial lithium-ion battery packs.« less

Study of indoor radon distribution using measurements and CFD modeling.

PubMed

Chauhan, Neetika; Chauhan, R P; Joshi, M; Agarwal, T K; Aggarwal, Praveen; Sahoo, B K

2014-10-01

Measurement and/or prediction of indoor radon ((222)Rn) concentration are important due to the impact of radon on indoor air quality and consequent inhalation hazard. In recent times, computational fluid dynamics (CFD) based modeling has become the cost effective replacement of experimental methods for the prediction and visualization of indoor pollutant distribution. The aim of this study is to implement CFD based modeling for studying indoor radon gas distribution. This study focuses on comparison of experimentally measured and CFD modeling predicted spatial distribution of radon concentration for a model test room. The key inputs for simulation viz. radon exhalation rate and ventilation rate were measured as a part of this study. Validation experiments were performed by measuring radon concentration at different locations of test room using active (continuous radon monitor) and passive (pin-hole dosimeters) techniques. Modeling predictions have been found to be reasonably matching with the measurement results. The validated model can be used to understand and study factors affecting indoor radon distribution for more realistic indoor environment. Copyright © 2014 Elsevier Ltd. All rights reserved.

Epileptic Seizures Prediction Using Machine Learning Methods

PubMed Central

Usman, Syed Muhammad

2017-01-01

Epileptic seizures occur due to disorder in brain functionality which can affect patient's health. Prediction of epileptic seizures before the beginning of the onset is quite useful for preventing the seizure by medication. Machine learning techniques and computational methods are used for predicting epileptic seizures from Electroencephalograms (EEG) signals. However, preprocessing of EEG signals for noise removal and features extraction are two major issues that have an adverse effect on both anticipation time and true positive prediction rate. Therefore, we propose a model that provides reliable methods of both preprocessing and feature extraction. Our model predicts epileptic seizures' sufficient time before the onset of seizure starts and provides a better true positive rate. We have applied empirical mode decomposition (EMD) for preprocessing and have extracted time and frequency domain features for training a prediction model. The proposed model detects the start of the preictal state, which is the state that starts few minutes before the onset of the seizure, with a higher true positive rate compared to traditional methods, 92.23%, and maximum anticipation time of 33 minutes and average prediction time of 23.6 minutes on scalp EEG CHB-MIT dataset of 22 subjects. PMID:29410700

Are math readiness and personality predictive of first-year retention in engineering?

PubMed

Moses, Laurie; Hall, Cathy; Wuensch, Karl; De Urquidi, Karen; Kauffmann, Paul; Swart, William; Duncan, Steve; Dixon, Gene

2011-01-01

On the basis of J. G. Borkowski, L. K. Chan, and N. Muthukrishna's model of academic success (2000), the present authors hypothesized that freshman retention in an engineering program would be related to not only basic aptitude but also affective factors. Participants were 129 college freshmen with engineering as their stated major. Aptitude was measured by SAT verbal and math scores, high school grade-point average (GPA), and an assessment of calculus readiness. Affective factors were assessed by the NEO-Five Factor Inventory (FFI; P. I. Costa & R. R. McCrae, 2007), and the Nowicki-Duke Locus of Control (LOC) scale (S. Nowicki & M. Duke, 1974). A binary logistic regression analysis found that calculus readiness and high school GPA were predictive of retention. Scores on the Neuroticism and Openness subscales from the NEO-FFI and LOC were correlated with retention status, but Openness was the only affective factor with a significant unique effect in the binary logistic regression. Results of the study lend modest support to Borkowski's model.

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. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

Fluid-structure interaction in abdominal aortic aneurysms: Structural and geometrical considerations

NASA Astrophysics Data System (ADS)

Mesri, Yaser; Niazmand, Hamid; Deyranlou, Amin; Sadeghi, Mahmood Reza

2015-08-01

Rupture of the abdominal aortic aneurysm (AAA) is the result of the relatively complex interaction of blood hemodynamics and material behavior of arterial walls. In the present study, the cumulative effects of physiological parameters such as the directional growth, arterial wall properties (isotropy and anisotropy), iliac bifurcation and arterial wall thickness on prediction of wall stress in fully coupled fluid-structure interaction (FSI) analysis of five idealized AAA models have been investigated. In particular, the numerical model considers the heterogeneity of arterial wall and the iliac bifurcation, which allows the study of the geometric asymmetry due to the growth of the aneurysm into different directions. Results demonstrate that the blood pulsatile nature is responsible for emerging a time-dependent recirculation zone inside the aneurysm, which directly affects the stress distribution in aneurismal wall. Therefore, aneurysm deviation from the arterial axis, especially, in the lateral direction increases the wall stress in a relatively nonlinear fashion. Among the models analyzed in this investigation, the anisotropic material model that considers the wall thickness variations, greatly affects the wall stress values, while the stress distributions are less affected as compared to the uniform wall thickness models. In this regard, it is confirmed that wall stress predictions are more influenced by the appropriate structural model than the geometrical considerations such as the level of asymmetry and its curvature, growth direction and its extent.

Statistical modeling of landslide hazard using GIS

Treesearch

Peter V. Gorsevski; Randy B. Foltz; Paul E. Gessler; Terrance W. Cundy

2001-01-01

A model for spatial prediction of landslide hazard was applied to a watershed affected by landslide events that occurred during the winter of 1995-96, following heavy rains, and snowmelt. Digital elevation data with 22.86 m x 22.86 m resolution was used for deriving topographic attributes used for modeling. The model is based on the combination of logistic regression...

Thinning strategies for aspen: a prediction model.

Treesearch

Donald A. Perala

1978-01-01

Derives thinning strategies to maximize volume yields of aspen fiber, sawtimber, and veneer. Demonstrates how yields are affected by growing season climatic variation and periodic defoliation by forest tent caterpillar.

Attentional Bias to Negative Affect Moderates Negative Affect’s Relationship with Smoking Abstinence

PubMed Central

Etcheverry, Paul E.; Waters, Andrew J.; Lam, Cho; Correa-Fernandez, Virmarie; Vidrine, Jennifer Irvin; Cinciripini, Paul M.; Wetter, David W.

2016-01-01

Objective To examine whether initial orienting (IO) and inability to disengage attention (ITD) from negative affective stimuli moderate the association of negative affect with smoking abstinence during a quit attempt. Methods Data were from a longitudinal cohort study of smoking cessation (N=424). A negative affect modified Stroop was administered one week before and on quit day to measure IO and ITD. Ecological Momentary Assessments were used to create negative affect intercepts and linear slopes for the week before quitting and on quit day. Quit day and long-term abstinence measures were collected. Results Continuation ratio (CR) logit model analyses found significant interactions of pre-quit negative affect slope with pre-quit ITD [OR = .738(.57, .96), p= .02] and quit day negative affect intercept with quit day ITD [OR = .62(.41, 950), p= .03] predicting abstinence. The interaction of pre-quit negative affect intercept and pre-quit IO predicting quit day abstinence was significant [OR = 1.42(1.06, 1.90), p= .02], as was the interaction of quit day negative affect slope and quit day IO predicting long-term abstinence [OR = 1.45(1.02, 2.08), p= .04]. Conclusions The hypothesis that the association of negative affect with smoking abstinence would be moderated by ITD was generally supported. Among individuals with high ITD, negative affect was inversely related to abstinence, but unrelated to abstinence among individuals with lower levels of ITD. Unexpectedly, among individuals with low IO negative affect was inversely related to abstinence, but unrelated to abstinence among individuals with higher levels of ITD. PMID:27505211

Analysis of turbulence and surface growth models on the estimation of soot level in ethylene non-premixed flames

NASA Astrophysics Data System (ADS)

Yunardi, Y.; Munawar, Edi; Rinaldi, Wahyu; Razali, Asbar; Iskandar, Elwina; Fairweather, M.

2018-02-01

Soot prediction in a combustion system has become a subject of attention, as many factors influence its accuracy. An accurate temperature prediction will likely yield better soot predictions, since the inception, growth and destruction of the soot are affected by the temperature. This paper reported the study on the influences of turbulence closure and surface growth models on the prediction of soot levels in turbulent flames. The results demonstrated that a substantial distinction was observed in terms of temperature predictions derived using the k-ɛ and the Reynolds stress models, for the two ethylene flames studied here amongst the four types of surface growth rate model investigated, the assumption of the soot surface growth rate proportional to the particle number density, but independent on the surface area of soot particles, f ( A s ) = ρ N s , yields in closest agreement with the radial data. Without any adjustment to the constants in the surface growth term, other approaches where the surface growth directly proportional to the surface area and square root of surface area, f ( A s ) = A s and f ( A s ) = √ A s , result in an under- prediction of soot volume fraction. These results suggest that predictions of soot volume fraction are sensitive to the modelling of surface growth.

A fluctuating plume dispersion model for the prediction of odour-impact frequencies from continuous stationary sources

NASA Astrophysics Data System (ADS)

Mussio, P.; Gnyp, A. W.; Henshaw, P. F.

A fluctuating plume dispersion model has been developed to facilitate the prediction of odour-impact frequencies in the communities surrounding elevated point sources. The model was used to predict the frequencies of occurrence of odours of various magnitudes for 1 h periods. In addition, the model predicted the maximum odour level. The model was tested with an extensive set of data collected in the residential areas surrounding the paint shop of an automotive assembly plant. Most of the perceived odours in the vicinity of the 64, 46 m high stacks ranged between 2 and 7 odour units and generally persisted for less than 30 s. Ninety-eight different field determinations of odour impact frequencies within 1 km of the plant were conducted during the course of the study. To simplify evaluation, the frequencies of occurrence of different odour levels were summed to give the total frequency of occurrence of all readily detectable (>2 OU) odours. The model provided excellent simulation of the total frequencies of occurrence where the odour was frequent (i.e . readily detectable more than 30% of the time). At lower frequencies of occurrence the model prediction was poor. The stability class did not seem to affect the model's ability to predict field frequency values. However, the model provided excellent predictions of the maximum odour levels without being sensitive to either stability class or distance from the source. Ninety-five percent of the predicted maximum values were within a factor of two of the measured field maximum values.

High School Size, Participation in Activities, and Young Adult Social Participation: Some Enduring Effects of Schooling.

ERIC Educational Resources Information Center

Lindsay, Paul

1984-01-01

This study evaluates a model predicting that school size affects student participation in extracurricular activities and that these leisure interests will continue in young adult life. High school social participation, it is hypothesized, also is influenced by curriculum track placement and academic performance, which are affected by student…

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…

Predicting plot soil loss by empirical and process-oriented approaches: A review

USDA-ARS?s Scientific Manuscript database

Soil erosion directly affects the quality of the soil, its agricultural productivity and its biological diversity. Many mathematical models have been developed to estimate plot soil erosion at different temporal scales. At present, empirical soil loss equations and process-oriented models are consid...

Developing Predictive Approaches to Characterize Adaptive Responses of the Reproductive Endocrine Axis to Aromatase Inhibition II: Computational Modeling

EPA Science Inventory

ABSTRACT Exposure to endocrine disrupting chemicals can affect reproduction and development in both humans and wildlife. We developed a mechanistic mathematical model of the hypothalamic­ pituitary-gonadal (HPG) axis in female fathead minnows to predic...

The clinical course over the first year of whiplash associated disorders (WAD): pain-related disability predicts outcome in a mildly affected sample.

PubMed

Åsenlöf, Pernilla; Bring, Annika; Söderlund, Anne

2013-12-21

Different recovery patterns are reported for those befallen a whip-lash injury, but little is known about the variability within subgroups. The aims were (1) to compare a self-selected mildly affected sample (MILD) with a self-selected moderately to severely affected sample (MOD/SEV) with regard to background characteristics and pain-related disability, pain intensity, functional self-efficacy, fear of movement/(re)injury, pain catastrophising, post-traumatic stress symptoms in the acute stage (at baseline), (2) to study the development over the first year after the accident for the above listed clinical variables in the MILD sample, and (3) to study the validity of a prediction model including baseline levels of clinical variables on pain-related disability one year after baseline assessments. The study had a prospective and correlative design. Ninety-eight participants were consecutively selected. Inclusion criteria; age 18 to 65 years, WAD grade I-II, Swedish language skills, and subjective report of not being in need of treatment due to mild symptoms. A multivariate linear regression model was applied for the prediction analysis. The MILD sample was less affected in all study variables compared to the MOD/SEV sample. Pain-related disability, pain catastrophising, and post-traumatic stress symptoms decreased over the first year after the accident, whereas functional self-efficacy and fear of movement/(re)injury increased. Pain intensity was stable. Pain-related disability at baseline emerged as the only statistically significant predictor of pain-related disability one year after the accident (Adj r² = 0.67). A good prognosis over the first year is expected for the majority of individuals with WAD grade I or II who decline treatment due to mild symptoms. The prediction model was not valid in the MILD sample except for the contribution of pain-related disability. An implication is that early observations of individuals with elevated levels of pain-related disability are warranted, although they may decline treatment.

Two roads diverged: Distinct mechanisms of attentional bias differentially predict negative affect and persistent negative thought.

PubMed

Onie, Sandersan; Most, Steven B

2017-08-01

Attentional biases to threatening stimuli have been implicated in various emotional disorders. Theoretical approaches often carry the implicit assumption that various attentional bias measures tap into the same underlying construct, but attention itself is not a unitary mechanism. Most attentional bias tasks-such as the dot probe (DP)-index spatial attention, neglecting other potential attention mechanisms. We compared the DP with emotion-induced blindness (EIB), which appears to be mechanistically distinct, and examined the degree to which these tasks predicted (a) negative affect, (b) persistent negative thought (i.e., worry, rumination), and (c) each other. The 2 tasks did not predict each other, and they uniquely accounted for negative affect in a regression analysis. The relationship between EIB and negative affect was mediated by persistent negative thought, whereas that between the DP and negative affect was not, suggesting that EIB may be more intimately linked than spatial attention with persistent negative thought. Experiment 2 revealed EIB to have a favorable test-retest reliability. Together, these findings underscore the importance of distinguishing between attentional bias mechanisms when constructing theoretical models of, and interventions that target, particular emotional disorders. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Prediction of Microstructure in HAZ of Welds

NASA Astrophysics Data System (ADS)

Khurana, S. P.; Yancey, R.; Jung, G.

2004-06-01

A modeling technique for predicting microstructure in the heat-affected zone (HAZ) of the hypoeutectoid steels is presented. This technique aims at predicting the phase fractions of ferrite, pearlite, bainite and martensite present in the HAZ after the cool down of a weld. The austenite formation kinetics and austenite decomposition kinetics are calculated using the transient temperature profile. The thermal profile in the weld and the HAZ is calculated by finite-element analysis (FEA). Two kinds of austenite decomposition models are included. The final phase fractions are predicted with the help of a continuous cooling transformation (CCT) diagram of the material. In the calculation of phase fractions either the experimental CCT diagram or the mathematically calculated CCT diagram can be used.

[Effects of sampling plot number on tree species distribution prediction under climate change].

PubMed

Liang, Yu; He, Hong-Shi; Wu, Zhi-Wei; Li, Xiao-Na; Luo, Xu

2013-05-01

Based on the neutral landscapes under different degrees of landscape fragmentation, this paper studied the effects of sampling plot number on the prediction of tree species distribution at landscape scale under climate change. The tree species distribution was predicted by the coupled modeling approach which linked an ecosystem process model with a forest landscape model, and three contingent scenarios and one reference scenario of sampling plot numbers were assumed. The differences between the three scenarios and the reference scenario under different degrees of landscape fragmentation were tested. The results indicated that the effects of sampling plot number on the prediction of tree species distribution depended on the tree species life history attributes. For the generalist species, the prediction of their distribution at landscape scale needed more plots. Except for the extreme specialist, landscape fragmentation degree also affected the effects of sampling plot number on the prediction. With the increase of simulation period, the effects of sampling plot number on the prediction of tree species distribution at landscape scale could be changed. For generalist species, more plots are needed for the long-term simulation.

Kinesin-8 Motors Improve Nuclear Centering by Promoting Microtubule Catastrophe

NASA Astrophysics Data System (ADS)

Glunčić, Matko; Maghelli, Nicola; Krull, Alexander; Krstić, Vladimir; Ramunno-Johnson, Damien; Pavin, Nenad; Tolić, Iva M.

2015-02-01

In fission yeast, microtubules push against the cell edge, thereby positioning the nucleus in the cell center. Kinesin-8 motors regulate microtubule catastrophe; however, their role in nuclear positioning is not known. Here we develop a physical model that describes how kinesin-8 motors affect nuclear centering by promoting a microtubule catastrophe. Our model predicts the improved centering of the nucleus in the presence of motors, which we confirmed experimentally in living cells. The model also predicts a characteristic time for the recentering of a displaced nucleus, which is supported by our experiments where we displaced the nucleus using optical tweezers.

Negative affect and a fluctuating jumping to conclusions bias predict subsequent paranoia in daily life: An online experience sampling study.

PubMed

Lüdtke, Thies; Kriston, Levente; Schröder, Johanna; Lincoln, Tania M; Moritz, Steffen

2017-09-01

Negative affect and a tendency to "jump to conclusions" (JTC) are associated with paranoia. So far, only negative affect has been examined as a precursor of subsequent paranoia in daily life using experience sampling (ESM). We addressed this research gap and used ESM to test whether JTC fluctuates in daily life, whether it predicts subsequent paranoia, and whether it mediates the effect of negative affect on paranoia. Thirty-five participants with schizophrenia spectrum disorders repeatedly self-reported negative affect, JTC, and paranoia via online questionnaires on two consecutive days. We measured JTC with a paradigm consisting of ambiguous written scenarios. Multilevel linear models were conducted. Most participants showed JTC consistently on two days rather than only on one day. When time was used as a predictor of JTC, significant slope variance indicated that for a subgroup of participants JTC fluctuated over time. For 48% of participants, these fluctuations equaled changes of approximately ±1 point on the four-point JTC scale within one day. There was no mediation. However, negative affect and JTC both significantly predicted subsequent paranoia. The ESM assessment period was short and encompassed few assessments (8 in total). Our findings indicate that JTC is both stable (regarding its mere occurrence) and fluctuating simultaneously (regarding its magnitude). Although JTC was not a mediator linking negative affect and paranoia, it did predict paranoia. Further ESM studies on JTC are needed to confirm our findings in longer assessment periods and with other JTC paradigms. Copyright © 2016 Elsevier Ltd. All rights reserved.

Appraisals to affect: Testing the integrative cognitive model of bipolar disorder.

PubMed

Palmier-Claus, Jasper E; Dodd, Alyson; Tai, Sara; Emsley, Richard; Mansell, Warren

2016-09-01

Cognitive models have suggested that extreme appraisals of affective states and maladaptive affect regulation strategies are important in the development of bipolar symptomatology. Little is known about the pathway by which these appraisals and behaviours interact in the formation of activated and depressed affective states. This study tested the predictions that (1) ascent behaviours mediate the relationship between positive appraisals of activated mood and activation; and (2) descent behaviours mediate the relationship between negative appraisals of activated mood and depression. A total of 52 individuals with a DSM-IV diagnosis of bipolar I or II disorder (confirmed by structured interview) completed biweekly assessments of affect regulation behaviours and mood for 4 weeks. Positive and negative appraisals of affective states were assessed at baseline through the Hypomanic Attitudes and Positive Prediction Inventory. Multilevel mediation analysis was used to explore the data. Ascent behaviours partially mediated the relationship between positive appraisals of activated mood and activation. Descent behaviours, but not negative appraisals of activated mood, predicted levels of depression indicating the absence of a mediation effect. The results suggest that positive appraisals of activated mood can escalate activation in individuals with bipolar disorder. Such appraisals may be inherently rewarding and reinforcing directly elevating levels of activation, whilst increasing individuals' use of ascent behaviours. The results are consistent with the view that appraisals and behaviours should be targeted during cognitive behavioural therapy for bipolar disorder. It may be beneficial to target positive appraisals of activated mood in cognitive behavioural therapy for mania. Cognitive behavioural therapists may also wish to focus on identifying and targeting individuals' use of ascent behaviours to reduce highly activated states. © 2015 The British Psychological Society.

CFD Modeling of Launch Vehicle Aerodynamic Heating

NASA Technical Reports Server (NTRS)

Tashakkor, Scott B.; Canabal, Francisco; Mishtawy, Jason E.

2011-01-01

The Loci-CHEM 3.2 Computational Fluid Dynamics (CFD) code is being used to predict Ares-I launch vehicle aerodynamic heating. CFD has been used to predict both ascent and stage reentry environments and has been validated against wind tunnel tests and the Ares I-X developmental flight test. Most of the CFD predictions agreed with measurements. On regions where mismatches occurred, the CFD predictions tended to be higher than measured data. These higher predictions usually occurred in complex regions, where the CFD models (mainly turbulence) contain less accurate approximations. In some instances, the errors causing the over-predictions would cause locations downstream to be affected even though the physics were still being modeled properly by CHEM. This is easily seen when comparing to the 103-AH data. In the areas where predictions were low, higher grid resolution often brought the results closer to the data. Other disagreements are attributed to Ares I-X hardware not being present in the grid, as a result of computational resources limitations. The satisfactory predictions from CHEM provide confidence that future designs and predictions from the CFD code will provide an accurate approximation of the correct values for use in design and other applications

Understanding Coupling of Global and Diffuse Solar Radiation with Climatic Variability

NASA Astrophysics Data System (ADS)

Hamdan, Lubna

Global solar radiation data is very important for wide variety of applications and scientific studies. However, this data is not readily available because of the cost of measuring equipment and the tedious maintenance and calibration requirements. Wide variety of models have been introduced by researchers to estimate and/or predict the global solar radiations and its components (direct and diffuse radiation) using other readily obtainable atmospheric parameters. The goal of this research is to understand the coupling of global and diffuse solar radiation with climatic variability, by investigating the relationships between these radiations and atmospheric parameters. For this purpose, we applied multilinear regression analysis on the data of National Solar Radiation Database 1991--2010 Update. The analysis showed that the main atmospheric parameters that affect the amount of global radiation received on earth's surface are cloud cover and relative humidity. Global radiation correlates negatively with both variables. Linear models are excellent approximations for the relationship between atmospheric parameters and global radiation. A linear model with the predictors total cloud cover, relative humidity, and extraterrestrial radiation is able to explain around 98% of the variability in global radiation. For diffuse radiation, the analysis showed that the main atmospheric parameters that affect the amount received on earth's surface are cloud cover and aerosol optical depth. Diffuse radiation correlates positively with both variables. Linear models are very good approximations for the relationship between atmospheric parameters and diffuse radiation. A linear model with the predictors total cloud cover, aerosol optical depth, and extraterrestrial radiation is able to explain around 91% of the variability in diffuse radiation. Prediction analysis showed that the linear models we fitted were able to predict diffuse radiation with efficiency of test adjusted R2 values equal to 0.93, using the data of total cloud cover, aerosol optical depth, relative humidity and extraterrestrial radiation. However, for prediction purposes, using nonlinear terms or nonlinear models might enhance the prediction of diffuse radiation.

Computational modeling of human oral bioavailability: what will be next?

PubMed

Cabrera-Pérez, Miguel Ángel; Pham-The, Hai

2018-06-01

The oral route is the most convenient way of administrating drugs. Therefore, accurate determination of oral bioavailability is paramount during drug discovery and development. Quantitative structure-property relationship (QSPR), rule-of-thumb (RoT) and physiologically based-pharmacokinetic (PBPK) approaches are promising alternatives to the early oral bioavailability prediction. Areas covered: The authors give insight into the factors affecting bioavailability, the fundamental theoretical framework and the practical aspects of computational methods for predicting this property. They also give their perspectives on future computational models for estimating oral bioavailability. Expert opinion: Oral bioavailability is a multi-factorial pharmacokinetic property with its accurate prediction challenging. For RoT and QSPR modeling, the reliability of datasets, the significance of molecular descriptor families and the diversity of chemometric tools used are important factors that define model predictability and interpretability. Likewise, for PBPK modeling the integrity of the pharmacokinetic data, the number of input parameters, the complexity of statistical analysis and the software packages used are relevant factors in bioavailability prediction. Although these approaches have been utilized independently, the tendency to use hybrid QSPR-PBPK approaches together with the exploration of ensemble and deep-learning systems for QSPR modeling of oral bioavailability has opened new avenues for development promising tools for oral bioavailability prediction.

Uncertainty Analysis Framework - Hanford Site-Wide Groundwater Flow and Transport Model

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

Cole, Charles R.; Bergeron, Marcel P.; Murray, Christopher J.

2001-11-09

Pacific Northwest National Laboratory (PNNL) embarked on a new initiative to strengthen the technical defensibility of the predictions being made with a site-wide groundwater flow and transport model at the U.S. Department of Energy Hanford Site in southeastern Washington State. In FY 2000, the focus of the initiative was on the characterization of major uncertainties in the current conceptual model that would affect model predictions. The long-term goals of the initiative are the development and implementation of an uncertainty estimation methodology in future assessments and analyses using the site-wide model. This report focuses on the development and implementation of anmore » uncertainty analysis framework.« less

Effects of climate change on an emperor penguin population: analysis of coupled demographic and climate models.

PubMed

Jenouvrier, Stéphanie; Holland, Marika; Stroeve, Julienne; Barbraud, Christophe; Weimerskirch, Henri; Serreze, Mark; Caswell, Hal

2012-09-01

Sea ice conditions in the Antarctic affect the life cycle of the emperor penguin (Aptenodytes forsteri). We present a population projection for the emperor penguin population of Terre Adélie, Antarctica, by linking demographic models (stage-structured, seasonal, nonlinear, two-sex matrix population models) to sea ice forecasts from an ensemble of IPCC climate models. Based on maximum likelihood capture-mark-recapture analysis, we find that seasonal sea ice concentration anomalies (SICa ) affect adult survival and breeding success. Demographic models show that both deterministic and stochastic population growth rates are maximized at intermediate values of annual SICa , because neither the complete absence of sea ice, nor heavy and persistent sea ice, would provide satisfactory conditions for the emperor penguin. We show that under some conditions the stochastic growth rate is positively affected by the variance in SICa . We identify an ensemble of five general circulation climate models whose output closely matches the historical record of sea ice concentration in Terre Adélie. The output of this ensemble is used to produce stochastic forecasts of SICa , which in turn drive the population model. Uncertainty is included by incorporating multiple climate models and by a parametric bootstrap procedure that includes parameter uncertainty due to both model selection and estimation error. The median of these simulations predicts a decline of the Terre Adélie emperor penguin population of 81% by the year 2100. We find a 43% chance of an even greater decline, of 90% or more. The uncertainty in population projections reflects large differences among climate models in their forecasts of future sea ice conditions. One such model predicts population increases over much of the century, but overall, the ensemble of models predicts that population declines are far more likely than population increases. We conclude that climate change is a significant risk for the emperor penguin. Our analytical approach, in which demographic models are linked to IPCC climate models, is powerful and generally applicable to other species and systems. © 2012 Blackwell Publishing Ltd.

The Relative Salience of Daily and Enduring Influences on Off-Job Reactions to Work Stress.

PubMed

Calderwood, Charles; Ackerman, Phillip L

2016-12-01

Work stress is an important determinant of employee health and wellness. The occupational health community is recognizing that one contributor to these relationships may be the presence of negative off-job reactivity to work, which we argue involves continued thoughts directed towards work (cognitive reactivity), continued negative mood stemming from work (affective reactivity), and the alteration of post-work behaviours in response to work factors (behavioural reactivity). We explored the relative contributions of daily work stressors, affective traits, and subjective job stress perceptions to negative off-job reactivity. These relationships were evaluated in a study of hospital nurses (n = 75), who completed trait measures and then provided self-assessments of daily work stress and off-job reactions for four work days. The results of several multilevel analyses indicated that a main-effects model best described the data when predicting cognitive, affective, and behavioural reactivity from daily work stressors, affective traits, and subjective job stress perceptions. A series of multilevel dominance analyses revealed that subjective job stress perceptions dominated the prediction of behavioural reactivity, while trait negative affect dominated the prediction of affective reactivity. Theoretical implications and the relative salience of daily and enduring contributors to negative off-job reactivity are discussed. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.

Using modelling to predict impacts of sea level rise and increased turbidity on seagrass distributions in estuarine embayments

NASA Astrophysics Data System (ADS)

Davis, Tom R.; Harasti, David; Smith, Stephen D. A.; Kelaher, Brendan P.

2016-11-01

Climate change induced sea level rise will affect shallow estuarine habitats, which are already under threat from multiple anthropogenic stressors. Here, we present the results of modelling to predict potential impacts of climate change associated processes on seagrass distributions. We use a novel application of relative environmental suitability (RES) modelling to examine relationships between variables of physiological importance to seagrasses (light availability, wave exposure, and current flow) and seagrass distributions within 5 estuarine embayments. Models were constructed separately for Posidonia australis and Zostera muelleri subsp. capricorni using seagrass data from Port Stephens estuary, New South Wales, Australia. Subsequent testing of models used independent datasets from four other estuarine embayments (Wallis Lake, Lake Illawarra, Merimbula Lake, and Pambula Lake) distributed along 570 km of the east Australian coast. Relative environmental suitability models provided adequate predictions for seagrass distributions within Port Stephens and the other estuarine embayments, indicating that they may have broad regional application. Under the predictions of RES models, both sea level rise and increased turbidity are predicted to cause substantial seagrass losses in deeper estuarine areas, resulting in a net shoreward movement of seagrass beds. Seagrass species distribution models developed in this study provide a valuable tool to predict future shifts in estuarine seagrass distributions, allowing identification of areas for protection, monitoring and rehabilitation.

Affective processes in human-automation interactions.

PubMed

Merritt, Stephanie M

2011-08-01

This study contributes to the literature on automation reliance by illuminating the influences of user moods and emotions on reliance on automated systems. Past work has focused predominantly on cognitive and attitudinal variables, such as perceived machine reliability and trust. However, recent work on human decision making suggests that affective variables (i.e., moods and emotions) are also important. Drawing from the affect infusion model, significant effects of affect are hypothesized. Furthermore, a new affectively laden attitude termed liking is introduced. Participants watched video clips selected to induce positive or negative moods, then interacted with a fictitious automated system on an X-ray screening task At five time points, important variables were assessed including trust, liking, perceived machine accuracy, user self-perceived accuracy, and reliance.These variables, along with propensity to trust machines and state affect, were integrated in a structural equation model. Happiness significantly increased trust and liking for the system throughout the task. Liking was the only variable that significantly predicted reliance early in the task. Trust predicted reliance later in the task, whereas perceived machine accuracy and user self-perceived accuracy had no significant direct effects on reliance at any time. Affective influences on automation reliance are demonstrated, suggesting that this decision-making process may be less rational and more emotional than previously acknowledged. Liking for a new system may be key to appropriate reliance, particularly early in the task. Positive affect can be easily induced and may be a lever for increasing liking.

A model for estimating understory vegetation response to fertilization and precipitation in loblolly pine plantations

Treesearch

Curtis L. VanderSchaaf; Ryan W. McKnight; Thomas R. Fox; H. Lee Allen

2010-01-01

A model form is presented, where the model contains regressors selected for inclusion based on biological rationale, to predict how fertilization, precipitation amounts, and overstory stand density affect understory vegetation biomass. Due to time, economic, and logistic constraints, datasets of large sample sizes generally do not exist for understory vegetation. Thus...

Development of a Conceptual Model to Predict Physical Activity Participation in Adults with Brain Injuries

ERIC Educational Resources Information Center

Driver, Simon

2008-01-01

The purpose was to examine psychosocial factors that influence the physical activity behaviors of adults with brain injuries. Two differing models, based on Harter's model of self-worth, were proposed to examine the relationship between perceived competence, social support, physical self-worth, affect, and motivation. Adults numbering 384 with…

Numerical prediction of mechanical properties of Pb-Sn solder alloys containing antimony, bismuth and or silver ternary trace elements

NASA Astrophysics Data System (ADS)

Gadag, Shiva P.; Patra, Susant

2000-12-01

Solder joint interconnects are mechanical means of structural support for bridging the various electronic components and providing electrical contacts and a thermal path for heat dissipation. The functionality of the electronic device often relies on the structural integrity of the solder. The dimensional stability of solder joints is numerically predicted based on their mechanical properties. Algorithms to model the kinetics of dissolution and subsequent growth of intermetallic from the complete knowledge of a single history of time-temperature-reflow profile, by considering equivalent isothermal time intervals, have been developed. The information for dissolution is derived during the heating cycle of reflow and for the growth process from cooling curve of reflow profile. A simple and quick analysis tool to derive tensile stress-strain maps as a function of the reflow temperature of solder and strain rate has been developed by numerical program. The tensile properties are used in modeling thermal strain, thermal fatigue and to predict the overall fatigue life of solder joints. The numerical analysis of the tensile properties as affected by their composition and rate of testing, has been compiled in this paper. A numerical model using constitutive equation has been developed to evaluate the interfacial fatigue crack growth rate. The model can assess the effect of cooling rate, which depends on the level of strain energy release rate. Increasing cooling rate from normalizing to water-quenching, enhanced the fatigue resistance to interfacial crack growth by up to 50% at low strain energy release rate. The increased cooling rates enhanced the fatigue crack growth resistance by surface roughening at the interface of solder joint. This paper highlights salient features of process modeling. Interfacial intermetallic microstructure is affected by cooling rate and thereby affects the mechanical properties.

Modelling postharvest quality of blueberry affected by biological variability using image and spectral data.

PubMed

Hu, Meng-Han; Dong, Qing-Li; Liu, Bao-Lin

2016-08-01

Hyperspectral reflectance and transmittance sensing as well as near-infrared (NIR) spectroscopy were investigated as non-destructive tools for estimating blueberry firmness, elastic modulus and soluble solid content (SSC). Least squares-support vector machine models were established from these three spectra based on samples from three cultivars viz. Bluecrop, Duke and M2 and two harvest years viz. 2014 and 2015 for predicting blueberry postharvest quality. One-cultivar reflectance models (establishing model using one cultivar) derived better results than the corresponding transmittance and NIR models for predicting blueberry firmness with few cultivar effects. Two-cultivar NIR models (establishing model using two cultivars) proved to be suitable for estimating blueberry SSC with correlations over 0.83. Rp (RMSEp ) values of the three-cultivar reflectance models (establishing model using 75% of three cultivars) were 0.73 (0.094) and 0.73 (0.186), respectively , for predicting blueberry firmness and elastic modulus. For SSC prediction, the three-cultivar NIR model was found to achieve an Rp (RMSEp ) value of 0.85 (0.090). Adding Bluecrop samples harvested in 2014 could enhance the three-cultivar model robustness for firmness and elastic modulus. The above results indicated the potential for using spatial and spectral techniques to develop robust models for predicting blueberry postharvest quality containing biological variability. © 2015 Society of Chemical Industry. © 2015 Society of Chemical Industry.

Population pharmacokinetic–pharmacodynamic analysis for sugammadex-mediated reversal of rocuronium-induced neuromuscular blockade

PubMed Central

Kleijn, Huub J; Zollinger, Daniel P; van den Heuvel, Michiel W; Kerbusch, Thomas

2011-01-01

AIMS An integrated population pharmacokinetic–pharmacodynamic model was developed with the following aims: to simultaneously describe pharmacokinetic behaviour of sugammadex and rocuronium; to establish the pharmacokinetic–pharmacodynamic model for rocuronium-induced neuromuscular blockade and reversal by sugammadex; to evaluate covariate effects; and to explore, by simulation, typical covariate effects on reversal time. METHODS Data (n = 446) from eight sugammadex clinical studies covering men, women, non-Asians, Asians, paediatrics, adults and the elderly, with various degrees of renal impairment, were used. Modelling and simulation techniques based on physiological principles were applied to capture rocuronium and sugammadex pharmacokinetics and pharmacodynamics and to identify and quantify covariate effects. RESULTS Sugammadex pharmacokinetics were affected by renal function, bodyweight and race, and rocuronium pharmacokinetics were affected by age, renal function and race. Sevoflurane potentiated rocuronium-induced neuromuscular blockade. Posterior predictive checks and bootstrapping illustrated the accuracy and robustness of the model. External validation showed concordance between observed and predicted reversal times, but interindividual variability in reversal time was pronounced. Simulated reversal times in typical adults were 0.8, 1.5 and 1.4 min upon reversal with sugammadex 16 mg kg−1 3 min after rocuronium, sugammadex 4 mg kg−1 during deep neuromuscular blockade and sugammadex 2 mg kg−1 during moderate blockade, respectively. Simulations indicated that reversal times were faster in paediatric patients and slightly slower in elderly patients compared with adults. Renal function did not affect reversal time. CONCLUSIONS Simulations of the therapeutic dosing regimens demonstrated limited impact of age, renal function and sevoflurane use, as predicted reversal time in typical subjects was always <2 min. PMID:21535448

Voids at the tunnel-soil interface for calculation of ground vibration from underground railways

NASA Astrophysics Data System (ADS)

Jones, Simon; Hunt, Hugh

2011-01-01

Voids at the tunnel-soil interface are not normally considered when predicting ground vibration from underground railways. The soil is generally assumed to be continuously bonded to the outer surface of the tunnel to simplify the modelling process. Evidence of voids around underground railways motivated the study presented herein to quantify the level of uncertainty in ground vibration predictions associated with neglecting to include such voids at the tunnel-soil interface. A semi-analytical method is developed which derives discrete transfers for the coupled tunnel-soil model based on the continuous Pipe-in-Pipe method. The void is simulated by uncoupling the appropriate nodes at the interface to prevent force transfer between the systems. The results from this investigation show that relatively small voids ( 4 m×90∘) can significantly affect the rms velocity predictions in the near-field and moderately affect predictions in the far-field. Sensitivity of the predictions to void length and void sector angle are both deemed to be significant. The findings from this study suggest that the uncertainty associated with assuming a perfect bond at the tunnel-soil interface in an area with known voidage can reasonably reach ±5 dB and thus should be considered in the design process.

Evaluation of Variable-Depth Liner Configurations for Increased Broadband Noise Reduction

NASA Technical Reports Server (NTRS)

Jones, M. G.; Watson, W. R.; Nark, D. M.; Howerton, B. M.

2015-01-01

This paper explores the effects of variable-depth geometry on the amount of noise reduction that can be achieved with acoustic liners. Results for two variable-depth liners tested in the NASA Langley Grazing Flow Impedance Tube demonstrate significant broadband noise reduction. An impedance prediction model is combined with two propagation codes to predict corresponding sound pressure level profiles over the length of the Grazing Flow Impedance Tube. The comparison of measured and predicted sound pressure level profiles is sufficiently favorable to support use of these tools for investigation of a number of proposed variable-depth liner configurations. Predicted sound pressure level profiles for these proposed configurations reveal a number of interesting features. Liner orientation clearly affects the sound pressure level profile over the length of the liner, but the effect on the total attenuation is less pronounced. The axial extent of attenuation at an individual frequency continues well beyond the location where the liner depth is optimally tuned to the quarter-wavelength of that frequency. The sound pressure level profile is significantly affected by the way in which variable-depth segments are distributed over the length of the liner. Given the broadband noise reduction capability for these liner configurations, further development of impedance prediction models and propagation codes specifically tuned for this application is warranted.

Antimicrobial activity predictors benchmarking analysis using shuffled and designed synthetic peptides.

PubMed

Porto, William F; Pires, Állan S; Franco, Octavio L

2017-08-07

The antimicrobial activity prediction tools aim to help the novel antimicrobial peptides (AMP) sequences discovery, utilizing machine learning methods. Such approaches have gained increasing importance in the generation of novel synthetic peptides by means of rational design techniques. This study focused on predictive ability of such approaches to determine the antimicrobial sequence activities, which were previously characterized at the protein level by in vitro studies. Using four web servers and one standalone software, we evaluated 78 sequences generated by the so-called linguistic model, being 40 designed and 38 shuffled sequences, with ∼60 and ∼25% of identity to AMPs, respectively. The ab initio molecular modelling of such sequences indicated that the structure does not affect the predictions, as both sets present similar structures. Overall, the systems failed on predicting shuffled versions of designed peptides, as they are identical in AMPs composition, which implies in accuracies below 30%. The prediction accuracy is negatively affected by the low specificity of all systems here evaluated, as they, on the other hand, reached 100% of sensitivity. Our results suggest that complementary approaches with high specificity, not necessarily high accuracy, should be developed to be used together with the current systems, overcoming their limitations. Copyright © 2017 Elsevier Ltd. All rights reserved.

Cognitive correlates of anxious and depressive symptomatology: an examination of the Helplessness/Hopelessness model.

PubMed

Waikar, S V; Craske, M G

1997-01-01

Expectancies about future life events were assessed in anxious and depressed patients to test predictions of the Helplessness/Hopelessness model of anxiety and depression (Alloy, Kelly, Mineka, & Clements, 1990). In addition to expectancies for future events, patients from affective and anxiety treatment clinics completed anxiety and depression symptom ratings and positive and negative affects scales. Findings revealed partial support for the model. Negative outcome and helplessness expectancies were related specifically to depression. Cognitions regarding future positive events were interrelated and associated with symptom measures more strongly than were cognitions regarding negative events. Additionally, positive affects was more strongly related to depression than to anxiety symptom ratings. Implications and limitations of these findings are discussed.

Predicting thermally stressful events in rivers with a strategy to evaluate management alternatives

USGS Publications Warehouse

Maloney, K.O.; Cole, J.C.; Schmid, M.

2016-01-01

Water temperature is an important factor in river ecology. Numerous models have been developed to predict river temperature. However, many were not designed to predict thermally stressful periods. Because such events are rare, traditionally applied analyses are inappropriate. Here, we developed two logistic regression models to predict thermally stressful events in the Delaware River at the US Geological Survey gage near Lordville, New York. One model predicted the probability of an event >20.0 °C, and a second predicted an event >22.2 °C. Both models were strong (independent test data sensitivity 0.94 and 1.00, specificity 0.96 and 0.96) predicting 63 of 67 events in the >20.0 °C model and all 15 events in the >22.2 °C model. Both showed negative relationships with released volume from the upstream Cannonsville Reservoir and positive relationships with difference between air temperature and previous day's water temperature at Lordville. We further predicted how increasing release volumes from Cannonsville Reservoir affected the probabilities of correctly predicted events. For the >20.0 °C model, an increase of 0.5 to a proportionally adjusted release (that accounts for other sources) resulted in 35.9% of events in the training data falling below cutoffs; increasing this adjustment by 1.0 resulted in 81.7% falling below cutoffs. For the >22.2 °C these adjustments resulted in 71.1% and 100.0% of events falling below cutoffs. Results from these analyses can help managers make informed decisions on alternative release scenarios.

Improved Rubin-Bodner Model for the Prediction of Soft Tissue Deformations

PubMed Central

Zhang, Guangming; Xia, James J.; Liebschner, Michael; Zhang, Xiaoyan; Kim, Daeseung; Zhou, Xiaobo

2016-01-01

In craniomaxillofacial (CMF) surgery, a reliable way of simulating the soft tissue deformation resulted from skeletal reconstruction is vitally important for preventing the risks of facial distortion postoperatively. However, it is difficult to simulate the soft tissue behaviors affected by different types of CMF surgery. This study presents an integrated bio-mechanical and statistical learning model to improve accuracy and reliability of predictions on soft facial tissue behavior. The Rubin-Bodner (RB) model is initially used to describe the biomechanical behavior of the soft facial tissue. Subsequently, a finite element model (FEM) computers the stress of each node in soft facial tissue mesh data resulted from bone displacement. Next, the Generalized Regression Neural Network (GRNN) method is implemented to obtain the relationship between the facial soft tissue deformation and the stress distribution corresponding to different CMF surgical types and to improve evaluation of elastic parameters included in the RB model. Therefore, the soft facial tissue deformation can be predicted by biomechanical properties and statistical model. Leave-one-out cross-validation is used on eleven patients. As a result, the average prediction error of our model (0.7035mm) is lower than those resulting from other approaches. It also demonstrates that the more accurate bio-mechanical information the model has, the better prediction performance it could achieve. PMID:27717593

Stimulus exposure and gaze bias: a further test of the gaze cascade model.

PubMed

Glaholt, Mackenzie G; Reingold, Eyal M

2009-04-01

We tested predictions derived from the gaze cascade model of preference decision making (Shimojo, Simion, Shimojo, & Scheier, 2003; Simion & Shimojo, 2006, 2007). In each trial, participants' eye movements were monitored while they performed an eight-alternative decision task in which four of the items in the array were preexposed prior to the trial. Replicating previous findings, we found a gaze bias toward the chosen item prior to the response. However, contrary to the prediction of the gaze cascade model, preexposure of stimuli decreased, rather than increased, the magnitude of the gaze bias in preference decisions. Furthermore, unlike the prediction of the model, preexposure did not affect the likelihood of an item being chosen, and the pattern of looking behavior in preference decisions and on a non preference control task was remarkably similar. Implications of the present findings in multistage models of decision making are discussed.

Valuing river characteristics using combined site choice and participation travel cost models.

PubMed

Johnstone, C; Markandya, A

2006-08-01

This paper presents new welfare measures for marginal changes in river quality in selected English rivers. The river quality indicators used include chemical, biological and habitat-level attributes. Economic values for recreational use of three types of river-upland, lowland and chalk-are presented. A survey of anglers was carried out and using these data, two travel cost models were estimated, one to predict the numbers of trips and the other to predict angling site choice. These models were then linked to estimate the welfare associated with marginal changes in river quality using the participation levels as estimated in the trip prediction model. The model results showed that higher flow rates, biological quality and nutrient pollution levels affect site choice and influence the likelihood of a fishing trip. Consumer surplus values per trip for a 10% change in river attributes range from pound 0.04 to pound 3.93 ( pound 2001) depending on the attribute.

Acid–base chemical reaction model for nucleation rates in the polluted atmospheric boundary layer

PubMed Central

Chen, Modi; Titcombe, Mari; Jiang, Jingkun; Jen, Coty; Kuang, Chongai; Fischer, Marc L.; Eisele, Fred L.; Siepmann, J. Ilja; Hanson, David R.; Zhao, Jun; McMurry, Peter H.

2012-01-01

Climate models show that particles formed by nucleation can affect cloud cover and, therefore, the earth's radiation budget. Measurements worldwide show that nucleation rates in the atmospheric boundary layer are positively correlated with concentrations of sulfuric acid vapor. However, current nucleation theories do not correctly predict either the observed nucleation rates or their functional dependence on sulfuric acid concentrations. This paper develops an alternative approach for modeling nucleation rates, based on a sequence of acid–base reactions. The model uses empirical estimates of sulfuric acid evaporation rates obtained from new measurements of neutral molecular clusters. The model predicts that nucleation rates equal the sulfuric acid vapor collision rate times a prefactor that is less than unity and that depends on the concentrations of basic gaseous compounds and preexisting particles. Predicted nucleation rates and their dependence on sulfuric acid vapor concentrations are in reasonable agreement with measurements from Mexico City and Atlanta. PMID:23091030

Acid-base chemical reaction model for nucleation rates in the polluted atmospheric boundary layer.

PubMed

Chen, Modi; Titcombe, Mari; Jiang, Jingkun; Jen, Coty; Kuang, Chongai; Fischer, Marc L; Eisele, Fred L; Siepmann, J Ilja; Hanson, David R; Zhao, Jun; McMurry, Peter H

2012-11-13

Climate models show that particles formed by nucleation can affect cloud cover and, therefore, the earth's radiation budget. Measurements worldwide show that nucleation rates in the atmospheric boundary layer are positively correlated with concentrations of sulfuric acid vapor. However, current nucleation theories do not correctly predict either the observed nucleation rates or their functional dependence on sulfuric acid concentrations. This paper develops an alternative approach for modeling nucleation rates, based on a sequence of acid-base reactions. The model uses empirical estimates of sulfuric acid evaporation rates obtained from new measurements of neutral molecular clusters. The model predicts that nucleation rates equal the sulfuric acid vapor collision rate times a prefactor that is less than unity and that depends on the concentrations of basic gaseous compounds and preexisting particles. Predicted nucleation rates and their dependence on sulfuric acid vapor concentrations are in reasonable agreement with measurements from Mexico City and Atlanta.

Cognitive emotion regulation enhances aversive prediction error activity while reducing emotional responses.

PubMed

Mulej Bratec, Satja; Xie, Xiyao; Schmid, Gabriele; Doll, Anselm; Schilbach, Leonhard; Zimmer, Claus; Wohlschläger, Afra; Riedl, Valentin; Sorg, Christian

2015-12-01

Cognitive emotion regulation is a powerful way of modulating emotional responses. However, despite the vital role of emotions in learning, it is unknown whether the effect of cognitive emotion regulation also extends to the modulation of learning. Computational models indicate prediction error activity, typically observed in the striatum and ventral tegmental area, as a critical neural mechanism involved in associative learning. We used model-based fMRI during aversive conditioning with and without cognitive emotion regulation to test the hypothesis that emotion regulation would affect prediction error-related neural activity in the striatum and ventral tegmental area, reflecting an emotion regulation-related modulation of learning. Our results show that cognitive emotion regulation reduced emotion-related brain activity, but increased prediction error-related activity in a network involving ventral tegmental area, hippocampus, insula and ventral striatum. While the reduction of response activity was related to behavioral measures of emotion regulation success, the enhancement of prediction error-related neural activity was related to learning performance. Furthermore, functional connectivity between the ventral tegmental area and ventrolateral prefrontal cortex, an area involved in regulation, was specifically increased during emotion regulation and likewise related to learning performance. Our data, therefore, provide first-time evidence that beyond reducing emotional responses, cognitive emotion regulation affects learning by enhancing prediction error-related activity, potentially via tegmental dopaminergic pathways. Copyright © 2015 Elsevier Inc. All rights reserved.

Artificial neural network model for ozone concentration estimation and Monte Carlo analysis

NASA Astrophysics Data System (ADS)

Gao, Meng; Yin, Liting; Ning, Jicai

2018-07-01

Air pollution in urban atmosphere directly affects public-health; therefore, it is very essential to predict air pollutant concentrations. Air quality is a complex function of emissions, meteorology and topography, and artificial neural networks (ANNs) provide a sound framework for relating these variables. In this study, we investigated the feasibility of using ANN model with meteorological parameters as input variables to predict ozone concentration in the urban area of Jinan, a metropolis in Northern China. We firstly found that the architecture of network of neurons had little effect on the predicting capability of ANN model. A parsimonious ANN model with 6 routinely monitored meteorological parameters and one temporal covariate (the category of day, i.e. working day, legal holiday and regular weekend) as input variables was identified, where the 7 input variables were selected following the forward selection procedure. Compared with the benchmarking ANN model with 9 meteorological and photochemical parameters as input variables, the predicting capability of the parsimonious ANN model was acceptable. Its predicting capability was also verified in term of warming success ratio during the pollution episodes. Finally, uncertainty and sensitivity analysis were also performed based on Monte Carlo simulations (MCS). It was concluded that the ANN could properly predict the ambient ozone level. Maximum temperature, atmospheric pressure, sunshine duration and maximum wind speed were identified as the predominate input variables significantly influencing the prediction of ambient ozone concentrations.

Simulation of salinity effects on past, present, and future soil organic carbon stocks.

PubMed

Setia, Raj; Smith, Pete; Marschner, Petra; Gottschalk, Pia; Baldock, Jeff; Verma, Vipan; Setia, Deepika; Smith, Jo

2012-02-07

Soil organic carbon (SOC) models are used to predict changes in SOC stocks and carbon dioxide (CO(2)) emissions from soils, and have been successfully validated for non-saline soils. However, SOC models have not been developed to simulate SOC turnover in saline soils. Due to the large extent of salt-affected areas in the world, it is important to correctly predict SOC dynamics in salt-affected soils. To close this knowledge gap, we modified the Rothamsted Carbon Model (RothC) to simulate SOC turnover in salt-affected soils, using data from non-salt-affected and salt-affected soils in two agricultural regions in India (120 soils) and in Australia (160 soils). Recently we developed a decomposition rate modifier based on an incubation study of a subset of these soils. In the present study, we introduce a new method to estimate the past losses of SOC due to salinity and show how salinity affects future SOC stocks on a regional scale. Because salinity decreases decomposition rates, simulations using the decomposition rate modifier for salinity suggest an accumulation of SOC. However, if the plant inputs are also adjusted to reflect reduced plant growth under saline conditions, the simulations show a significant loss of soil carbon in the past due to salinization, with a higher average loss of SOC in Australian soils (55 t C ha(-1)) than in Indian soils (31 t C ha(-1)). There was a significant negative correlation (p < 0.05) between SOC loss and osmotic potential. Simulations of future SOC stocks with the decomposition rate modifier and the plant input modifier indicate a greater decrease in SOC in saline than in non-saline soils under future climate. The simulations of past losses of SOC due to salinity were repeated using either measured charcoal-C or the inert organic matter predicted by the Falloon et al. equation to determine how much deviation from the Falloon et al. equation affects the amount of plant inputs generated by the model for the soils used in this study. Both sets of results suggest that saline soils have lost carbon and will continue to lose carbon under future climate. This demonstrates the importance of both reduced decomposition and reduced plant input in simulations of future changes in SOC stocks in saline soils.

Ozone Mapping and Profiler Suite: using mission performance data to refine predictive contamination modeling

NASA Astrophysics Data System (ADS)

Devaud, Genevieve; Jaross, Glen

2014-09-01

On October 28, 2011, the Suomi National Polar-orbiting Partnership (Suomi NPP) satellite launched at Vandenberg Air Force base aboard a United Launch Alliance Delta II rocket. Included among the five instruments was the Ozone Mapping and Profiler Suite (OMPS), an advanced suite of three hyperspectral instruments built by Ball Aerospace and Technologies Corporation (BATC) for the NASA Goddard Space Flight Center. Molecular transport modeling is used to predict optical throughput changes due to contaminant accumulation to ensure performance margin to End Of Life. The OMPS Nadir Profiler, operating at the lowest wavelengths of 250 - 310 nm, is most sensitive to contaminant accumulation. Geometry, thermal profile and material properties must be accurately modeled in order to have confidence in the results, yet it is well known that the complex chemistry and process dependent variability of aerospace materials presents a substantial challenge to the modeler. Assumptions about the absorption coefficients, desorption and diffusion kinetics of outgassing species from polymeric materials dramatically affect the model predictions, yet it is rare indeed that on-mission data is analyzed at a later date as a means to compare with modeling results. Optical throughput measurements for the Ozone and Mapping Profiler Suite on the Suomi NPP Satellite indicate that optical throughput degradation between day 145 and day 858 is less than 0.5%. We will show how assumptions about outgassing rates and desorption energies, in particular, dramatically affect the modeled optical throughput and what assumptions represent the on-orbit data.

Antecedents of eating disorders and muscle dysmorphia in a non-clinical sample.

PubMed

Lamanna, J; Grieve, F G; Derryberry, W Pitt; Hakman, M; McClure, A

2010-01-01

Muscle Dysmorphia (MD) has recently been conceptualized as the male form of Eating Disorders (ED); although, it is not currently classified as an ED. The current study compares etiological models of MD symptomatology and ED symptomatology. It was hypothesized that sociocultural influences on appearance (SIA) would predict body dissatisfaction (BD), and that this relationship would be mediated by self-esteem (SE) and perfectionism (P); that BD would predict negative affect (NA); and that NA would predict MD and ED symptomatology. Two-hundred-forty-seven female and 101 male college students at a midsouth university completed the study. All participants completed measures assessing each of the constructs, and multiple regression analyses were conducted to test each model's fit. In both models, most predictor paths were significant. These results suggest similarity in symptomatology and etiological models between ED and MD.

Evaluating models of climate and forest vegetation

NASA Technical Reports Server (NTRS)

Clark, James S.

1992-01-01

Understanding how the biosphere may respond to increasing trace gas concentrations in the atmosphere requires models that contain vegetation responses to regional climate. Most of the processes ecologists study in forests, including trophic interactions, nutrient cycling, and disturbance regimes, and vital components of the world economy, such as forest products and agriculture, will be influenced in potentially unexpected ways by changing climate. These vegetation changes affect climate in the following ways: changing C, N, and S pools; trace gases; albedo; and water balance. The complexity of the indirect interactions among variables that depend on climate, together with the range of different space/time scales that best describe these processes, make the problems of modeling and prediction enormously difficult. These problems of predicting vegetation response to climate warming and potential ways of testing model predictions are the subjects of this chapter.

Forecast Method of Solar Irradiance with Just-In-Time Modeling

NASA Astrophysics Data System (ADS)

Suzuki, Takanobu; Goto, Yusuke; Terazono, Takahiro; Wakao, Shinji; Oozeki, Takashi

PV power output mainly depends on the solar irradiance which is affected by various meteorological factors. So, it is required to predict solar irradiance in the future for the efficient operation of PV systems. In this paper, we develop a novel approach for solar irradiance forecast, in which we introduce to combine the black-box model (JIT Modeling) with the physical model (GPV data). We investigate the predictive accuracy of solar irradiance over wide controlled-area of each electric power company by utilizing the measured data on the 44 observation points throughout Japan offered by JMA and the 64 points around Kanto by NEDO. Finally, we propose the application forecast method of solar irradiance to the point which is difficulty in compiling the database. And we consider the influence of different GPV default time on solar irradiance prediction.

Model-based monitoring of stormwater runoff quality.

PubMed

Birch, Heidi; Vezzaro, Luca; Mikkelsen, Peter Steen

2013-01-01

Monitoring of micropollutants (MP) in stormwater is essential to evaluate the impacts of stormwater on the receiving aquatic environment. The aim of this study was to investigate how different strategies for monitoring of stormwater quality (combining a model with field sampling) affect the information obtained about MP discharged from the monitored system. A dynamic stormwater quality model was calibrated using MP data collected by automatic volume-proportional sampling and passive sampling in a storm drainage system on the outskirts of Copenhagen (Denmark) and a 10-year rain series was used to find annual average (AA) and maximum event mean concentrations. Use of this model reduced the uncertainty of predicted AA concentrations compared to a simple stochastic method based solely on data. The predicted AA concentration, obtained by using passive sampler measurements (1 month installation) for calibration of the model, resulted in the same predicted level but with narrower model prediction bounds than by using volume-proportional samples for calibration. This shows that passive sampling allows for a better exploitation of the resources allocated for stormwater quality monitoring.

Data Assimilation and Propagation of Uncertainty in Multiscale Cardiovascular Simulation

NASA Astrophysics Data System (ADS)

Schiavazzi, Daniele; Marsden, Alison

2015-11-01

Cardiovascular modeling is the application of computational tools to predict hemodynamics. State-of-the-art techniques couple a 3D incompressible Navier-Stokes solver with a boundary circulation model and can predict local and peripheral hemodynamics, analyze the post-operative performance of surgical designs and complement clinical data collection minimizing invasive and risky measurement practices. The ability of these tools to make useful predictions is directly related to their accuracy in representing measured physiologies. Tuning of model parameters is therefore a topic of paramount importance and should include clinical data uncertainty, revealing how this uncertainty will affect the predictions. We propose a fully Bayesian, multi-level approach to data assimilation of uncertain clinical data in multiscale circulation models. To reduce the computational cost, we use a stable, condensed approximation of the 3D model build by linear sparse regression of the pressure/flow rate relationship at the outlets. Finally, we consider the problem of non-invasively propagating the uncertainty in model parameters to the resulting hemodynamics and compare Monte Carlo simulation with Stochastic Collocation approaches based on Polynomial or Multi-resolution Chaos expansions.

Emission of hydrogen sulfide (H2S) at a waterfall in a sewer: study of main factors affecting H2S emission and modeling approaches.

PubMed

Jung, Daniel; Hatrait, Laetitia; Gouello, Julien; Ponthieux, Arnaud; Parez, Vincent; Renner, Christophe

2017-11-01

Hydrogen sulfide (H 2 S) represents one of the main odorant gases emitted from sewer networks. A mathematical model can be a fast and low-cost tool for estimating its emission. This study investigates two approaches to modeling H 2 S gas transfer at a waterfall in a discharge manhole. The first approach is based on an adaptation of oxygen models for H 2 S emission at a waterfall and the second consists of a new model. An experimental set-up and a statistical data analysis allowed the main factors affecting H 2 S emission to be studied. A new model of the emission kinetics was developed using linear regression and taking into account H 2 S liquid concentration, waterfall height and fluid velocity at the outlet pipe of a rising main. Its prediction interval was estimated by the residual standard deviation (15.6%) up to a rate of 2.3 g H 2 S·h -1 . Finally, data coming from four sampling campaigns on sewer networks were used to perform simulations and compare predictions of all developed models.

Use the predictive models to explore the key factors affecting phytoplankton succession in Lake Erhai, China.

PubMed

Zhu, Rong; Wang, Huan; Chen, Jun; Shen, Hong; Deng, Xuwei

2018-01-01

Increasing algae in Lake Erhai has resulted in frequent blooms that have not only led to water ecosystem degeneration but also seriously influenced the quality of the water supply and caused extensive damage to the local people, as the lake is a water resource for Dali City. Exploring the key factors affecting phytoplankton succession and developing predictive models with easily detectable parameters for phytoplankton have been proven to be practical ways to improve water quality. To this end, a systematic survey focused on phytoplankton succession was conducted over 2 years in Lake Erhai. The data from the first study year were used to develop predictive models, and the data from the second year were used for model verification. The seasonal succession of phytoplankton in Lake Erhai was obvious. The dominant groups were Cyanobacteria in the summer, Chlorophyta in the autumn and Bacillariophyta in the winter. The developments and verification of predictive models indicated that compared to phytoplankton biomass, phytoplankton density is more effective for estimating phytoplankton variation in Lake Erhai. CCA (canonical correlation analysis) indicated that TN (total nitrogen), TP (total phosphorus), DO (dissolved oxygen), SD (Secchi depth), Cond (conductivity), T (water temperature), and ORP (oxidation reduction potential) had significant influences (p < 0.05) on the phytoplankton community. The CCA of the dominant species found that Microcystis was significantly influenced by T. The dominant Chlorophyta, Psephonema aenigmaticum and Mougeotia, were significantly influenced by TN. All results indicated that TN and T were the two key factors driving phytoplankton succession in Lake Erhai.

Predicting potential ranges of primary malaria vectors and malaria in northern South America based on projected changes in climate, land cover and human population.

PubMed

Alimi, Temitope O; Fuller, Douglas O; Qualls, Whitney A; Herrera, Socrates V; Arevalo-Herrera, Myriam; Quinones, Martha L; Lacerda, Marcus V G; Beier, John C

2015-08-20

Changes in land use and land cover (LULC) as well as climate are likely to affect the geographic distribution of malaria vectors and parasites in the coming decades. At present, malaria transmission is concentrated mainly in the Amazon basin where extensive agriculture, mining, and logging activities have resulted in changes to local and regional hydrology, massive loss of forest cover, and increased contact between malaria vectors and hosts. Employing presence-only records, bioclimatic, topographic, hydrologic, LULC and human population data, we modeled the distribution of malaria and two of its dominant vectors, Anopheles darlingi, and Anopheles nuneztovari s.l. in northern South America using the species distribution modeling platform Maxent. Results from our land change modeling indicate that about 70,000 km(2) of forest land would be lost by 2050 and 78,000 km(2) by 2070 compared to 2010. The Maxent model predicted zones of relatively high habitat suitability for malaria and the vectors mainly within the Amazon and along coastlines. While areas with malaria are expected to decrease in line with current downward trends, both vectors are predicted to experience range expansions in the future. Elevation, annual precipitation and temperature were influential in all models both current and future. Human population mostly affected An. darlingi distribution while LULC changes influenced An. nuneztovari s.l. distribution. As the region tackles the challenge of malaria elimination, investigations such as this could be useful for planning and management purposes and aid in predicting and addressing potential impediments to elimination.

Application of Anaerobic Digestion Model No. 1 for simulating anaerobic mesophilic sludge digestion

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

Mendes, Carlos, E-mail: carllosmendez@gmail.com; Esquerre, Karla, E-mail: karlaesquerre@ufba.br; Matos Queiroz, Luciano, E-mail: lmqueiroz@ufba.br

2015-01-15

Highlights: • The behavior of a anaerobic reactor was evaluated through modeling. • Parametric sensitivity analysis was used to select most sensitive of the ADM1. • The results indicate that the ADM1 was able to predict the experimental results. • Organic load rate above of 35 kg/m{sup 3} day affects the performance of the process. - Abstract: Improving anaerobic digestion of sewage sludge by monitoring common indicators such as volatile fatty acids (VFAs), gas composition and pH is a suitable solution for better sludge management. Modeling is an important tool to assess and to predict process performance. The present studymore » focuses on the application of the Anaerobic Digestion Model No. 1 (ADM1) to simulate the dynamic behavior of a reactor fed with sewage sludge under mesophilic conditions. Parametric sensitivity analysis is used to select the most sensitive ADM1 parameters for estimation using a numerical procedure while other parameters are applied without any modification to the original values presented in the ADM1 report. The results indicate that the ADM1 model after parameter estimation was able to predict the experimental results of effluent acetate, propionate, composites and biogas flows and pH with reasonable accuracy. The simulation of the effect of organic shock loading clearly showed that an organic shock loading rate above of 35 kg/m{sup 3} day affects the performance of the reactor. The results demonstrate that simulations can be helpful to support decisions on predicting the anaerobic digestion process of sewage sludge.« less

Logit Estimation of a Gravity Model of the College Enrollment Decision.

ERIC Educational Resources Information Center

Leppel, Karen

1993-01-01

A study investigated the factors influencing students' decisions about attending a college to which they had been admitted. Logit analysis confirmed gravity model predictions that geographic distance and student ability would most influence the enrollment decision and found other variables, although affecting earlier stages of decision making, did…

Cognitive-Affective Predictors of Women's Readiness to End Domestic Violence Relationships

ERIC Educational Resources Information Center

Shurman, Lauren A.; Rodriguez, Christina M.

2006-01-01

A model of women's readiness to terminate an abusive relationship was examined, using cognitive and emotional factors to predict readiness to change as conceptualized in the transtheoretical model. Factors previously identified in the domestic violence literature were selected to represent cognitive predictors (attribution and attachment style)…

PREDICTING THE RISKS OF NEUROTOXIC VOLATILE ORGANIC COMPOUNDS BASED ON TARGET TISSUE DOSE.

EPA Science Inventory

Quantitative exposure-dose-response models relate the external exposure of a substance to the dose in the target tissue, and then relate the target tissue dose to production of adverse outcomes. We developed exposure-dose-response models to describe the affects of acute exposure...

A Model to predict the impact of specification changes on the chloride-induced service life of Virginia bridge decks.

DOT National Transportation Integrated Search

2002-01-01

A model to determine the time to first repair and subsequent rehabilitation of concrete bridge decks exposed to chloride deicer salts that recognizes and incorporates the statistical nature of factors affecting the corrosion process is developed. The...

Application of cross-sectional time series modeling for the prediction of energy expenditure from heart rate and accelerometry

USDA-ARS?s Scientific Manuscript database

Accurate estimation of energy expenditure (EE) in children and adolescents is required for a better understanding of physiological, behavioral, and environmental factors affecting energy balance. Cross-sectional time series (CSTS) models, which account for correlation structure of repeated observati...

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

ERIC Educational Resources Information Center

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

2008-01-01

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

Adaptive Response in Female Fathead Minnows Exposed to an Aromatase Inhibitor: Computational Modeling of the Hypothalamic-Pituitary-Gonadal Axis

EPA Science Inventory

Exposure to endocrine disrupting chemicals can affect reproduction and development in both humans and wildlife. We are developing a mechanistic computational model of the hypothalamic-pituitary-gonadal (HPG) axis in female fathead minnows to predict dose-response and time-course ...

Refining Sunrise/set Prediction Models by Accounting for the Effects of Refraction

NASA Astrophysics Data System (ADS)

Wilson, Teresa; Bartlett, Jennifer L.

2016-01-01

Current atmospheric models used to predict the times of sunrise and sunset have an error of one to four minutes at mid-latitudes (0° - 55° N/S). At higher latitudes, slight changes in refraction may cause significant discrepancies, including determining even whether the Sun appears to rise or set. While different components of refraction are known, how they affect predictions of sunrise/set has not yet been quantified. A better understanding of the contributions from temperature profile, pressure, humidity, and aerosols, could significantly improve the standard prediction. Because sunrise/set times and meteorological data from multiple locations will be necessary for a thorough investigation of the problem, we will collect this data using smartphones as part of a citizen science project. This analysis will lead to more complete models that will provide more accurate times for navigators and outdoorsman alike.

Configural approaches to temperament assessment: implications for predicting risk of unintentional injury in children.

PubMed

Berry, Jack W; Schwebel, David C

2009-10-01

This study used two configural approaches to understand how temperament factors (surgency/extraversion, negative affect, and effortful control) might predict child injury risk. In the first approach, clustering procedures were applied to trait dimensions to identify discrete personality prototypes. In the second approach, two- and three-way trait interactions were considered dimensionally in regression models predicting injury outcomes. Injury risk was assessed through four measures: lifetime prevalence of injuries requiring professional medical attention, scores on the Injury Behavior Checklist, and frequency and severity of injuries reported in a 2-week injury diary. In the prototype analysis, three temperament clusters were obtained, which resembled resilient, overcontrolled, and undercontrolled types found in previous research. Undercontrolled children had greater risk of injury than children in the other groups. In the dimensional interaction analyses, an interaction between surgency/extraversion and negative affect tended to predict injury, especially when children lacked capacity for effortful control.

Does scale matter? A systematic review of incorporating biological realism when predicting changes in species distributions.

PubMed

Record, Sydne; Strecker, Angela; Tuanmu, Mao-Ning; Beaudrot, Lydia; Zarnetske, Phoebe; Belmaker, Jonathan; Gerstner, Beth

2018-01-01

There is ample evidence that biotic factors, such as biotic interactions and dispersal capacity, can affect species distributions and influence species' responses to climate change. However, little is known about how these factors affect predictions from species distribution models (SDMs) with respect to spatial grain and extent of the models. Understanding how spatial scale influences the effects of biological processes in SDMs is important because SDMs are one of the primary tools used by conservation biologists to assess biodiversity impacts of climate change. We systematically reviewed SDM studies published from 2003-2015 using ISI Web of Science searches to: (1) determine the current state and key knowledge gaps of SDMs that incorporate biotic interactions and dispersal; and (2) understand how choice of spatial scale may alter the influence of biological processes on SDM predictions. We used linear mixed effects models to examine how predictions from SDMs changed in response to the effects of spatial scale, dispersal, and biotic interactions. There were important biases in studies including an emphasis on terrestrial ecosystems in northern latitudes and little representation of aquatic ecosystems. Our results suggest that neither spatial extent nor grain influence projected climate-induced changes in species ranges when SDMs include dispersal or biotic interactions. We identified several knowledge gaps and suggest that SDM studies forecasting the effects of climate change should: 1) address broader ranges of taxa and locations; and 1) report the grain size, extent, and results with and without biological complexity. The spatial scale of analysis in SDMs did not affect estimates of projected range shifts with dispersal and biotic interactions. However, the lack of reporting on results with and without biological complexity precluded many studies from our analysis.

Predicting the process of extinction in experimental microcosms and accounting for interspecific interactions in single-species time series

PubMed Central

Ferguson, Jake M; Ponciano, José M

2014-01-01

Predicting population extinction risk is a fundamental application of ecological theory to the practice of conservation biology. Here, we compared the prediction performance of a wide array of stochastic, population dynamics models against direct observations of the extinction process from an extensive experimental data set. By varying a series of biological and statistical assumptions in the proposed models, we were able to identify the assumptions that affected predictions about population extinction. We also show how certain autocorrelation structures can emerge due to interspecific interactions, and that accounting for the stochastic effect of these interactions can improve predictions of the extinction process. We conclude that it is possible to account for the stochastic effects of community interactions on extinction when using single-species time series. PMID:24304946

The Interactive Role of Emotional Intelligence, Attachment Style, and Resilience in the Prediction of Time Perception in Doctoral Students

ERIC Educational Resources Information Center

Precin, Patricia Jean

2014-01-01

The perception of time (the use of temporal categories to conceptualize experiences) affects human behavior. Students' time perspective predicts academic outcomes: those with future orientations tend to have better academic outcomes than those with past or present, according to Zimbardo and Boyd's psychology of time model, and may contribute to…

Roles of attachment and self-esteem: impact of early life stress on depressive symptoms among Japanese institutionalized children.

PubMed

Suzuki, Hanako; Tomoda, Akemi

2015-02-05

Although exposure to early life stress is known to affect mental health, the underlying mechanisms of its impacts on depressive symptoms among institutionalized children and adolescents have been little studied. To investigate the role of attachment and self-esteem in association with adverse childhood experiences (ACEs) and depressive symptoms, 342 children (149 boys, 193 girls; age range 9-18 years old, mean age = 13.5 ± 2.4) living in residential foster care facilities in Japan completed questionnaires related to internal working models, self-esteem, and depressive symptoms. Their care workers completed questionnaires on ACEs. Structural equation modeling (SEM) was created and the goodness of fit was examined (CMIN = 129.223, df = 1.360, GFI = .959, AGFI = .936, CFI = .939, RMSEA = .033). Maltreatment negatively predicted scores on secure attachment, but positively predicted scores on avoidant and ambivalent attachment. The secure attachment score negatively predicted depressive symptoms. The ambivalent attachment score positively predicted depressive symptoms both directly and through self-esteem, whereas the avoidant attachment score positively predicted depressive symptoms only directly. Maltreatment neither directly predicts self-esteem nor depressive symptoms, and parental illness/death and parental sociopathic behaviors did not predict any variables. Results show that the adversity of child maltreatment affects depression through attachment styles and low self-esteem among institutionalized children. Implications of child maltreatment and recommendations for child welfare services and clinical interventions for institutionalized children are discussed.

Stress and anger as contextual factors and preexisting cognitive schemas: predicting parental child maltreatment risk.

PubMed

Rodriguez, Christina M; Richardson, Michael J

2007-11-01

Progress in the child maltreatment field depends on refinements in leading models. This study examines aspects of social information processing theory (Milner, 2000) in predicting physical maltreatment risk in a community sample. Consistent with this theory, selected preexisting schema (external locus-of-control orientation, inappropriate developmental expectations, low empathic perspective-taking ability, and low perceived attachment relationship to child) were expected to predict child abuse risk beyond contextual factors (parenting stress and anger expression). Based on 115 parents' self-report, results from this study support cognitive factors that predict abuse risk (with locus of control, perceived attachment, or empathy predicting different abuse risk measures, but not developmental expectations), although the broad contextual factors involving negative affectivity and stress were consistent predictors across abuse risk markers. Findings are discussed with regard to implications for future model evaluations, with indications the model may apply to other forms of maltreatment, such as psychological maltreatment or neglect.

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

PubMed Central

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

2015-01-01

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

A deep auto-encoder model for gene expression prediction.

PubMed

Xie, Rui; Wen, Jia; Quitadamo, Andrew; Cheng, Jianlin; Shi, Xinghua

2017-11-17

Gene expression is a key intermediate level that genotypes lead to a particular trait. Gene expression is affected by various factors including genotypes of genetic variants. With an aim of delineating the genetic impact on gene expression, we build a deep auto-encoder model to assess how good genetic variants will contribute to gene expression changes. This new deep learning model is a regression-based predictive model based on the MultiLayer Perceptron and Stacked Denoising Auto-encoder (MLP-SAE). The model is trained using a stacked denoising auto-encoder for feature selection and a multilayer perceptron framework for backpropagation. We further improve the model by introducing dropout to prevent overfitting and improve performance. To demonstrate the usage of this model, we apply MLP-SAE to a real genomic datasets with genotypes and gene expression profiles measured in yeast. Our results show that the MLP-SAE model with dropout outperforms other models including Lasso, Random Forests and the MLP-SAE model without dropout. Using the MLP-SAE model with dropout, we show that gene expression quantifications predicted by the model solely based on genotypes, align well with true gene expression patterns. We provide a deep auto-encoder model for predicting gene expression from SNP genotypes. This study demonstrates that deep learning is appropriate for tackling another genomic problem, i.e., building predictive models to understand genotypes' contribution to gene expression. With the emerging availability of richer genomic data, we anticipate that deep learning models play a bigger role in modeling and interpreting genomics.

Dynamic analysis of rotor flex-structure based on nonlinear anisotropic shell models

NASA Astrophysics Data System (ADS)

Bauchau, Olivier A.; Chiang, Wuying

1991-05-01

In this paper an anisotropic shallow shell model is developed that accommodates transverse shearing deformations and arbitrarily large displacements and rotations, but strains are assumed to remain small. Two kinematic models are developed, the first using two DOF to locate the direction of the normal to the shell's midplane, the second using three. The latter model allows for an automatic compatibility of the shell model with beam models. The shell model is validated by comparing its predictions with several benchmark problems. In actual helicopter rotor blade problems, the shell model of the flex structure is shown to give very different results shown compared to beam models. The lead-lag and torsion modes in particular are strongly affected, whereas flapping modes seem to be less affected.

[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.

The effectiveness and limitations of fuel modeling using the fire and fuels extension to the Forest Vegetation Simulator

Treesearch

Erin K. Noonan-Wright; Nicole M. Vaillant; Alicia L. Reiner

2014-01-01

Fuel treatment effectiveness is often evaluated with fire behavior modeling systems that use fuel models to generate fire behavior outputs. How surface fuels are assigned, either using one of the 53 stylized fuel models or developing custom fuel models, can affect predicted fire behavior. We collected surface and canopy fuels data before and 1, 2, 5, and 8 years after...

Comparison of a species distribution model and a process model from a hierarchical perspective to quantify effects of projected climate change on tree species

Treesearch

Jeffrey E. Schneiderman; Hong S. He; Frank R. Thompson; William D. Dijak; Jacob S. Fraser

2015-01-01

Tree species distribution and abundance are affected by forces operating across a hierarchy of ecological scales. Process and species distribution models have been developed emphasizing forces at different scales. Understanding model agreement across hierarchical scales provides perspective on prediction uncertainty and ultimately enables policy makers and managers to...

Verification of a 2 kWe Closed-Brayton-Cycle Power Conversion System Mechanical Dynamics Model

NASA Technical Reports Server (NTRS)

Ludwiczak, Damian R.; Le, Dzu K.; McNelis, Anne M.; Yu, Albert C.; Samorezov, Sergey; Hervol, Dave S.

2005-01-01

Vibration test data from an operating 2 kWe closed-Brayton-cycle (CBC) power conversion system (PCS) located at the NASA Glenn Research Center was used for a comparison with a dynamic disturbance model of the same unit. This effort was performed to show that a dynamic disturbance model of a CBC PCS can be developed that can accurately predict the torque and vibration disturbance fields of such class of rotating machinery. The ability to accurately predict these disturbance fields is required before such hardware can be confidently integrated onto a spacecraft mission. Accurate predictions of CBC disturbance fields will be used for spacecraft control/structure interaction analyses and for understanding the vibration disturbances affecting the scientific instrumentation onboard. This paper discusses how test cell data measurements for the 2 kWe CBC PCS were obtained, the development of a dynamic disturbance model used to predict the transient torque and steady state vibration fields of the same unit, and a comparison of the two sets of data.

[A Model for Predicting Career Satisfaction of Nurses Experiencing Rotation].

PubMed

Shin, Sook; Yu, Mi

2017-08-01

This study aimed to present and test a structural model for describing and predicting the factors affecting subjective career satisfaction of nurses experiencing rotation and to develop human resources management strategies for promoting their career satisfaction related to rotation. In this cross-sectional study, we recruited 233 nurses by convenience sampling who had over 1 year of career experience and who had experienced rotation at least once at G university hospital. Data were collected from August to September in 2016 using self-reported questionnaires. The exogenous variables consisted of rotation perception and rotation stress. Endogenous variables consisted of career growth opportunity, work engagement, and subjective career satisfaction. A hypothetical model was tested by asymptotically distribution-free estimates, and model goodness of fit was examined using absolute fit, incremental fit measures. The final model was approved and had suitable fit. We found that subjective career satisfaction was directly affected by rotation stress (β=.20, p=.019) and work engagement (β=.58, p<.001), indirectly affected by rotation perception (β=.43, p<.001) through career growth opportunity and work engagement. However, there was no total effect of rotation stress on subjective career satisfaction (β=-.09, p=.270). Career growth opportunity directly and indirectly affected subjective career satisfaction (β=.29, p<.001; β=.28, p<.001). These variables accounted for 65% of subjective career satisfaction. The results of this study suggest that it is necessary to establish systematic and planned criteria for rotation so that nurses can grow and develop through sustained work and become satisfied with their career. © 2017 Korean Society of Nursing Science

Predicted Responses of Vegetation to Climate Change: A Global Analysis of Changes in Primary Productivity and Water Use Efficiency in the 21st Century

NASA Astrophysics Data System (ADS)

Bernardes, S.

2016-12-01

Global coupled carbon-climate simulations show considerable variability in outputs for atmospheric and land fields over the 21st century. This variability includes changes in temperature and in the quantity and spatiotemporal distribution of precipitation for large regions on the planet. Studies have considered that reductions in water availability due to decreased precipitation and increased water demand by the atmosphere may negatively affect plant metabolism and reduce carbon uptake. Future increases in carbon dioxide concentrations are expected to affect those interactions and potentially offset reductions in productivity. It is uncertain how plants will adjust their water use efficiency (WUE, plant production per water loss by evapotranspiration) in response to changing environmental conditions. This work investigates predicted changes in WUE in the 21st century by analyzing an ensemble of Earth System Models from the Coupled Model Intercomparison Project 5 (CMIP5), together with flux tower data and products derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. Two representative concentration pathways were selected to describe possible climate futures (RCP4.5 and RCP8.5). Periods of analysis included 2006-2099 (predicted) and 1850-2005 (reference). Comparisons between modeled, flux and satellite data for IPCC SREX regions were used to address the significant intermodel variability observed for the CMIP5 ensemble (larger variability for RCP8.5, higher intermodel agreement in Southeast Asia, lower intermodel agreement in arid areas). Model skill was evaluated in support of model selection and the spatiotemporal analysis of changes in WUE. Global, regional and latitudinal distributions of departures of projected conditions in relation to historical values are presented for both concentration pathways. Results showed high model sensitivity to different concentration pathways and increase in GPP and WUE for most of the planet (increases consistently higher for RCP8.5). Higher increases in GPP and WUE are predicted to occur over higher latitudes in the northern hemisphere (boreal region), with WUE usually following GPP in changes. Decreases in productivity and WUE occur mostly in the tropics, affecting tropical forests in Central America and in the Amazon.

Prediction of flunixin tissue residue concentrations in livers from diseased cattle.

PubMed

Wu, H; Baynes, R E; Tell, L A; Riviere, J E

2013-12-01

Flunixin, a widely used non-steroidal anti-inflammatory drug, was a leading cause of violative residues in cattle. The objective of this analysis was to explore how the changes in pharmacokinetic (PK) parameters that may be associated with diseased animals affect the predicted liver residue of flunixin in cattle. Monte Carlo simulations for liver residues of flunixin were performed using the PK model structure and relevant PK parameter estimates from a previously published population PK model for flunixin in cattle. The magnitude of a change in the PK parameter value that resulted in a violative residue issue in more than one percent of a cattle population was compared. In this regard, elimination clearance and volume of distribution affected withdrawal times. Pathophysiological factors that can change these parameters may contribute to the occurrence of violative residues of flunixin.

[Academic performance in first year medical students: an explanatory multivariate model].

PubMed

Urrutia Aguilar, María Esther; Ortiz León, Silvia; Fouilloux Morales, Claudia; Ponce Rosas, Efrén Raúl; Guevara Guzmán, Rosalinda

2014-12-01

Current education is focused in intellectual, affective, and ethical aspects, thus acknowledging their significance in students´ metacognition. Nowadays, it is known that an adequate and motivating environment together with a positive attitude towards studies is fundamental to induce learning. Medical students are under multiple stressful, academic, personal, and vocational situations. To identify psychosocial, vocational, and academic variables of 2010-2011 first year medical students at UNAM that may help predict their academic performance. Academic surveys of psychological and vocational factors were applied; an academic follow-up was carried out to obtain a multivariate model. The data were analyzed considering descriptive, comparative, correlative, and predictive statistics. The main variables that affect students´ academic performance are related to previous knowledge and to psychological variables. The results show the significance of implementing institutional programs to support students throughout their college adaptation.

The perfectionism model of binge eating: testing unique contributions, mediating mechanisms, and cross-cultural similarities using a daily diary methodology.

PubMed

Sherry, Simon B; Sabourin, Brigitte C; Hall, Peter A; Hewitt, Paul L; Flett, Gordon L; Gralnick, Tara M

2014-12-01

The perfectionism model of binge eating (PMOBE) is an integrative model explaining the link between perfectionism and binge eating. This model proposes socially prescribed perfectionism confers risk for binge eating by generating exposure to 4 putative binge triggers: interpersonal discrepancies, low interpersonal esteem, depressive affect, and dietary restraint. The present study addresses important gaps in knowledge by testing if these 4 binge triggers uniquely predict changes in binge eating on a daily basis and if daily variations in each binge trigger mediate the link between socially prescribed perfectionism and daily binge eating. Analyses also tested if proposed mediational models generalized across Asian and European Canadians. The PMOBE was tested in 566 undergraduate women using a 7-day daily diary methodology. Depressive affect predicted binge eating, whereas anxious affect did not. Each binge trigger uniquely contributed to binge eating on a daily basis. All binge triggers except for dietary restraint mediated the relationship between socially prescribed perfectionism and change in daily binge eating. Results suggested cross-cultural similarities, with the PMOBE applying to both Asian and European Canadian women. The present study advances understanding of the personality traits and the contextual conditions accompanying binge eating and provides an important step toward improving treatments for people suffering from eating binges and associated negative consequences.

Offset-Free Model Predictive Control of Open Water Channel Based on Moving Horizon Estimation

NASA Astrophysics Data System (ADS)

Ekin Aydin, Boran; Rutten, Martine

2016-04-01

Model predictive control (MPC) is a powerful control option which is increasingly used by operational water managers for managing water systems. The explicit consideration of constraints and multi-objective management are important features of MPC. However, due to the water loss in open water systems by seepage, leakage and evaporation a mismatch between the model and the real system will be created. These mismatch affects the performance of MPC and creates an offset from the reference set point of the water level. We present model predictive control based on moving horizon estimation (MHE-MPC) to achieve offset free control of water level for open water canals. MHE-MPC uses the past predictions of the model and the past measurements of the system to estimate unknown disturbances and the offset in the controlled water level is systematically removed. We numerically tested MHE-MPC on an accurate hydro-dynamic model of the laboratory canal UPC-PAC located in Barcelona. In addition, we also used well known disturbance modeling offset free control scheme for the same test case. Simulation experiments on a single canal reach show that MHE-MPC outperforms disturbance modeling offset free control scheme.

Dynamic modelling of solids in a full-scale activated sludge plant preceded by CEPT as a preliminary step for micropollutant removal modelling.

PubMed

Baalbaki, Zeina; Torfs, Elena; Maere, Thomas; Yargeau, Viviane; Vanrolleghem, Peter A

2017-04-01

The presence of micropollutants in the environment has triggered research on quantifying and predicting their fate in wastewater treatment plants (WWTPs). Since the removal of micropollutants is highly related to conventional pollutant removal and affected by hydraulics, aeration, biomass composition and solids concentration, the fate of these conventional pollutants and characteristics must be well predicted before tackling models to predict the fate of micropollutants. In light of this, the current paper presents the dynamic modelling of conventional pollutants undergoing activated sludge treatment using a limited set of additional daily composite data besides the routine data collected at a WWTP over one year. Results showed that as a basis for modelling, the removal of micropollutants, the Bürger-Diehl settler model was found to capture the actual effluent total suspended solids (TSS) concentrations more efficiently than the Takács model by explicitly modelling the overflow boundary. Results also demonstrated that particular attention must be given to characterizing incoming TSS to obtain a representative solids balance in the presence of a chemically enhanced primary treatment, which is key to predict the fate of micropollutants.

Using Blur to Affect Perceived Distance and Size

PubMed Central

HELD, ROBERT T.; COOPER, EMILY A.; O’BRIEN, JAMES F.; BANKS, MARTIN S.

2011-01-01

We present a probabilistic model of how viewers may use defocus blur in conjunction with other pictorial cues to estimate the absolute distances to objects in a scene. Our model explains how the pattern of blur in an image together with relative depth cues indicates the apparent scale of the image’s contents. From the model, we develop a semiautomated algorithm that applies blur to a sharply rendered image and thereby changes the apparent distance and scale of the scene’s contents. To examine the correspondence between the model/algorithm and actual viewer experience, we conducted an experiment with human viewers and compared their estimates of absolute distance to the model’s predictions. We did this for images with geometrically correct blur due to defocus and for images with commonly used approximations to the correct blur. The agreement between the experimental data and model predictions was excellent. The model predicts that some approximations should work well and that others should not. Human viewers responded to the various types of blur in much the way the model predicts. The model and algorithm allow one to manipulate blur precisely and to achieve the desired perceived scale efficiently. PMID:21552429

Climate Change Simulations Predict Altered Biotic Response in a Thermally Heterogeneous Stream System

PubMed Central

Westhoff, Jacob T.; Paukert, Craig P.

2014-01-01

Climate change is predicted to increase water temperatures in many lotic systems, but little is known about how changes in air temperature affect lotic systems heavily influenced by groundwater. Our objectives were to document spatial variation in temperature for spring-fed Ozark streams in Southern Missouri USA, create a spatially explicit model of mean daily water temperature, and use downscaled climate models to predict the number of days meeting suitable stream temperature for three aquatic species of concern to conservation and management. Longitudinal temperature transects and stationary temperature loggers were used in the Current and Jacks Fork Rivers during 2012 to determine spatial and temporal variability of water temperature. Groundwater spring influence affected river water temperatures in both winter and summer, but springs that contributed less than 5% of the main stem discharge did not affect river temperatures beyond a few hundred meters downstream. A multiple regression model using variables related to season, mean daily air temperature, and a spatial influence factor (metric to account for groundwater influence) was a strong predictor of mean daily water temperature (r2 = 0.98; RMSE = 0.82). Data from two downscaled climate simulations under the A2 emissions scenario were used to predict daily water temperatures for time steps of 1995, 2040, 2060, and 2080. By 2080, peak numbers of optimal growth temperature days for smallmouth bass are expected to shift to areas with more spring influence, largemouth bass are expected to experience more optimal growth days (21 – 317% increase) regardless of spring influence, and Ozark hellbenders may experience a reduction in the number of optimal growth days in areas with the highest spring influence. Our results provide a framework for assessing fine-scale (10 s m) thermal heterogeneity and predict shifts in thermal conditions at the watershed and reach scale. PMID:25356982

Impact of Initial Condition Errors and Precipitation Forecast Bias on Drought Simulation and Prediction in the Huaihe River Basin

NASA Astrophysics Data System (ADS)

Xu, H.; Luo, L.; Wu, Z.

2016-12-01

Drought, regarded as one of the major disasters all over the world, is not always easy to detect and forecast. Hydrological models coupled with Numerical Weather Prediction (NWP) has become a relatively effective method for drought monitoring and prediction. The accuracy of hydrological initial condition (IC) and the skill of NWP precipitation forecast can both heavily affect the quality and skill of hydrological forecast. In the study, the Variable Infiltration Capacity (VIC) model and Global Environmental Multi-scale (GEM) model were used to investigate the roles of IC and NWP forecast accuracy on hydrological predictions. A rev-ESP type experiment was conducted for a number of drought events in the Huaihe river basin. The experiment suggests that errors in ICs indeed affect the drought simulations by VIC and thus the drought monitoring. Although errors introduced in the ICs diminish gradually, the influence sometimes can last beyond 12 months. Using the soil moisture anomaly percentage index (SMAPI) as the metric to measure drought severity for the study region, we are able to quantify that time scale of influence from IC ranges. The analysis shows that the time scale is directly related to the magnitude of the introduced IC range and the average precipitation intensity. In order to explore how systematic bias correction in GEM forecasted precipitation can affect precipitation and hydrological forecast, we then both used station and gridded observations to eliminate biases of forecasted data. Meanwhile, different precipitation inputs with corrected data during drought process were conducted by VIC to investigate the changes of drought simulations, thus demonstrated short-term rolling drought prediction using a better performed corrected precipitation forecast. There is a word limit on the length of the abstract. So make sure your abstract fits the requirement. If this version is too long, try to shorten it as much as you can.

Climate downscaling effects on predictive ecological models: a case study for threatened and endangered vertebrates in the southeastern United States

USGS Publications Warehouse

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

2013-01-01

High-resolution (downscaled) projections of future climate conditions are critical inputs to a wide variety of ecological and socioeconomic models and are created using numerous different approaches. Here, we conduct a sensitivity analysis of spatial predictions from climate envelope models for threatened and endangered vertebrates in the southeastern United States to determine whether two different downscaling approaches (with and without the use of a regional climate model) affect climate envelope model predictions when all other sources of variation are held constant. We found that prediction maps differed spatially between downscaling approaches and that the variation attributable to downscaling technique was comparable to variation between maps generated using different general circulation models (GCMs). Precipitation variables tended to show greater discrepancies between downscaling techniques than temperature variables, and for one GCM, there was evidence that more poorly resolved precipitation variables contributed relatively more to model uncertainty than more well-resolved variables. Our work suggests that ecological modelers requiring high-resolution climate projections should carefully consider the type of downscaling applied to the climate projections prior to their use in predictive ecological modeling. The uncertainty associated with alternative downscaling methods may rival that of other, more widely appreciated sources of variation, such as the general circulation model or emissions scenario with which future climate projections are created.

Predictions for the Effects of Free Stream Turbulence on Turbine Blade Heat Transfer

NASA Technical Reports Server (NTRS)

Boyle, Robert J.; Giel, Paul W.; Ames, Forrest E.

2004-01-01

An approach to predicting the effects of free stream turbulence on turbine vane and blade heat transfer is described. Four models for predicting the effects of free stream turbulence were in incorporated into a Navier-Stokes CFD analysis. Predictions were compared with experimental data in order to identify an appropriate model for use across a wide range of flow conditions. The analyses were compared with data from five vane geometries and from four rotor geometries. Each of these nine geometries had data for different Reynolds numbers. Comparisons were made for twenty four cases. Steady state calculations were done because all experimental data were obtained in steady state tests. High turbulence levels often result in suction surface transition upstream of the throat, while at low to moderate Reynolds numbers the pressure surface remains laminar. A two-dimensional analysis was used because the flow is predominately two-dimensional in the regions where free stream turbulence significantly augments surface heat transfer. Because the evaluation of models for predicting turbulence effects can be affected by other factors, the paper discusses modeling for transition, relaminarization, and near wall damping. Quantitative comparisons are given between the predictions and data.

A Fast Neural Network Approach to Predict Lung Tumor Motion during Respiration for Radiation Therapy Applications

PubMed Central

Slama, Matous; Benes, Peter M.; Bila, Jiri

2015-01-01

During radiotherapy treatment for thoracic and abdomen cancers, for example, lung cancers, respiratory motion moves the target tumor and thus badly affects the accuracy of radiation dose delivery into the target. A real-time image-guided technique can be used to monitor such lung tumor motion for accurate dose delivery, but the system latency up to several hundred milliseconds for repositioning the radiation beam also affects the accuracy. In order to compensate the latency, neural network prediction technique with real-time retraining can be used. We have investigated real-time prediction of 3D time series of lung tumor motion on a classical linear model, perceptron model, and on a class of higher-order neural network model that has more attractive attributes regarding its optimization convergence and computational efficiency. The implemented static feed-forward neural architectures are compared when using gradient descent adaptation and primarily the Levenberg-Marquardt batch algorithm as the ones of the most common and most comprehensible learning algorithms. The proposed technique resulted in fast real-time retraining, so the total computational time on a PC platform was equal to or even less than the real treatment time. For one-second prediction horizon, the proposed techniques achieved accuracy less than one millimeter of 3D mean absolute error in one hundred seconds of total treatment time. PMID:25893194

Responses of eastern Chinese coastal salt marshes to sea-level rise combined with vegetative and sedimentary processes.

PubMed

Ge, Zhen-Ming; Wang, Heng; Cao, Hao-Bin; Zhao, Bin; Zhou, Xiao; Peltola, Heli; Cui, Li-Fang; Li, Xiu-Zhen; Zhang, Li-Quan

2016-06-23

The impacts of sea-level rise (SLR) on coastal ecosystems have attracted worldwide attention in relation to global change. In this study, the salt marsh model for the Yangtze Estuary (SMM-YE, developed in China) and the Sea Level Affecting Marshes Model (SLAMM, developed in the U.S.) were used to simulate the effects of SLR on the coastal salt marshes in eastern China. The changes in the dominant species in the plant community were also considered. Predictions based on the SLAMM indicated a trend of habitat degradation up to 2100; total salt marsh habitat area continued to decline (4-16%) based on the low-level scenario, with greater losses (6-25%) predicted under the high-level scenario. The SMM-YE showed that the salt marshes could be resilient to threats of SLR through the processes of accretion of mudflats, vegetation expansion and sediment trapping by plants. This model predicted that salt marsh areas increased (3-6%) under the low-level scenario. The decrease in the total habitat area with the SMM-YE under the high-level scenario was much lower than the SLAMM prediction. Nevertheless, SLR might negatively affect the salt marsh species that are not adapted to prolonged inundation. An adaptive strategy for responding to changes in sediment resources is necessary in the Yangtze Estuary.

A fast neural network approach to predict lung tumor motion during respiration for radiation therapy applications.

PubMed

Bukovsky, Ivo; Homma, Noriyasu; Ichiji, Kei; Cejnek, Matous; Slama, Matous; Benes, Peter M; Bila, Jiri

2015-01-01

During radiotherapy treatment for thoracic and abdomen cancers, for example, lung cancers, respiratory motion moves the target tumor and thus badly affects the accuracy of radiation dose delivery into the target. A real-time image-guided technique can be used to monitor such lung tumor motion for accurate dose delivery, but the system latency up to several hundred milliseconds for repositioning the radiation beam also affects the accuracy. In order to compensate the latency, neural network prediction technique with real-time retraining can be used. We have investigated real-time prediction of 3D time series of lung tumor motion on a classical linear model, perceptron model, and on a class of higher-order neural network model that has more attractive attributes regarding its optimization convergence and computational efficiency. The implemented static feed-forward neural architectures are compared when using gradient descent adaptation and primarily the Levenberg-Marquardt batch algorithm as the ones of the most common and most comprehensible learning algorithms. The proposed technique resulted in fast real-time retraining, so the total computational time on a PC platform was equal to or even less than the real treatment time. For one-second prediction horizon, the proposed techniques achieved accuracy less than one millimeter of 3D mean absolute error in one hundred seconds of total treatment time.

Responses of eastern Chinese coastal salt marshes to sea-level rise combined with vegetative and sedimentary processes

NASA Astrophysics Data System (ADS)

Ge, Zhen-Ming; Wang, Heng; Cao, Hao-Bin; Zhao, Bin; Zhou, Xiao; Peltola, Heli; Cui, Li-Fang; Li, Xiu-Zhen; Zhang, Li-Quan

2016-06-01

The impacts of sea-level rise (SLR) on coastal ecosystems have attracted worldwide attention in relation to global change. In this study, the salt marsh model for the Yangtze Estuary (SMM-YE, developed in China) and the Sea Level Affecting Marshes Model (SLAMM, developed in the U.S.) were used to simulate the effects of SLR on the coastal salt marshes in eastern China. The changes in the dominant species in the plant community were also considered. Predictions based on the SLAMM indicated a trend of habitat degradation up to 2100; total salt marsh habitat area continued to decline (4-16%) based on the low-level scenario, with greater losses (6-25%) predicted under the high-level scenario. The SMM-YE showed that the salt marshes could be resilient to threats of SLR through the processes of accretion of mudflats, vegetation expansion and sediment trapping by plants. This model predicted that salt marsh areas increased (3-6%) under the low-level scenario. The decrease in the total habitat area with the SMM-YE under the high-level scenario was much lower than the SLAMM prediction. Nevertheless, SLR might negatively affect the salt marsh species that are not adapted to prolonged inundation. An adaptive strategy for responding to changes in sediment resources is necessary in the Yangtze Estuary.

Mental models accurately predict emotion transitions

PubMed Central

Thornton, Mark A.; Tamir, Diana I.

2017-01-01

Successful social interactions depend on people’s ability to predict others’ future actions and emotions. People possess many mechanisms for perceiving others’ current emotional states, but how might they use this information to predict others’ future states? We hypothesized that people might capitalize on an overlooked aspect of affective experience: current emotions predict future emotions. By attending to regularities in emotion transitions, perceivers might develop accurate mental models of others’ emotional dynamics. People could then use these mental models of emotion transitions to predict others’ future emotions from currently observable emotions. To test this hypothesis, studies 1–3 used data from three extant experience-sampling datasets to establish the actual rates of emotional transitions. We then collected three parallel datasets in which participants rated the transition likelihoods between the same set of emotions. Participants’ ratings of emotion transitions predicted others’ experienced transitional likelihoods with high accuracy. Study 4 demonstrated that four conceptual dimensions of mental state representation—valence, social impact, rationality, and human mind—inform participants’ mental models. Study 5 used 2 million emotion reports on the Experience Project to replicate both of these findings: again people reported accurate models of emotion transitions, and these models were informed by the same four conceptual dimensions. Importantly, neither these conceptual dimensions nor holistic similarity could fully explain participants’ accuracy, suggesting that their mental models contain accurate information about emotion dynamics above and beyond what might be predicted by static emotion knowledge alone. PMID:28533373

A strategy to apply machine learning to small datasets in materials science

NASA Astrophysics Data System (ADS)

Zhang, Ying; Ling, Chen

2018-12-01

There is growing interest in applying machine learning techniques in the research of materials science. However, although it is recognized that materials datasets are typically smaller and sometimes more diverse compared to other fields, the influence of availability of materials data on training machine learning models has not yet been studied, which prevents the possibility to establish accurate predictive rules using small materials datasets. Here we analyzed the fundamental interplay between the availability of materials data and the predictive capability of machine learning models. Instead of affecting the model precision directly, the effect of data size is mediated by the degree of freedom (DoF) of model, resulting in the phenomenon of association between precision and DoF. The appearance of precision-DoF association signals the issue of underfitting and is characterized by large bias of prediction, which consequently restricts the accurate prediction in unknown domains. We proposed to incorporate the crude estimation of property in the feature space to establish ML models using small sized materials data, which increases the accuracy of prediction without the cost of higher DoF. In three case studies of predicting the band gap of binary semiconductors, lattice thermal conductivity, and elastic properties of zeolites, the integration of crude estimation effectively boosted the predictive capability of machine learning models to state-of-art levels, demonstrating the generality of the proposed strategy to construct accurate machine learning models using small materials dataset.

A systematic review of predictive models for asthma development in children.

PubMed

Luo, Gang; Nkoy, Flory L; Stone, Bryan L; Schmick, Darell; Johnson, Michael D

2015-11-28

Asthma is the most common pediatric chronic disease affecting 9.6 % of American children. Delay in asthma diagnosis is prevalent, resulting in suboptimal asthma management. To help avoid delay in asthma diagnosis and advance asthma prevention research, researchers have proposed various models to predict asthma development in children. This paper reviews these models. A systematic review was conducted through searching in PubMed, EMBASE, CINAHL, Scopus, the Cochrane Library, the ACM Digital Library, IEEE Xplore, and OpenGrey up to June 3, 2015. The literature on predictive models for asthma development in children was retrieved, with search results limited to human subjects and children (birth to 18 years). Two independent reviewers screened the literature, performed data extraction, and assessed article quality. The literature search returned 13,101 references in total. After manual review, 32 of these references were determined to be relevant and are discussed in the paper. We identify several limitations of existing predictive models for asthma development in children, and provide preliminary thoughts on how to address these limitations. Existing predictive models for asthma development in children have inadequate accuracy. Efforts to improve these models' performance are needed, but are limited by a lack of a gold standard for asthma development in children.

A Novel Prediction Method about Single Components of Analog Circuits Based on Complex Field Modeling

PubMed Central

Tian, Shulin; Yang, Chenglin

2014-01-01

Few researches pay attention to prediction about analog circuits. The few methods lack the correlation with circuit analysis during extracting and calculating features so that FI (fault indicator) calculation often lack rationality, thus affecting prognostic performance. To solve the above problem, this paper proposes a novel prediction method about single components of analog circuits based on complex field modeling. Aiming at the feature that faults of single components hold the largest number in analog circuits, the method starts with circuit structure, analyzes transfer function of circuits, and implements complex field modeling. Then, by an established parameter scanning model related to complex field, it analyzes the relationship between parameter variation and degeneration of single components in the model in order to obtain a more reasonable FI feature set via calculation. According to the obtained FI feature set, it establishes a novel model about degeneration trend of analog circuits' single components. At last, it uses particle filter (PF) to update parameters for the model and predicts remaining useful performance (RUP) of analog circuits' single components. Since calculation about the FI feature set is more reasonable, accuracy of prediction is improved to some extent. Finally, the foregoing conclusions are verified by experiments. PMID:25147853

Storm surge and tidal range energy

NASA Astrophysics Data System (ADS)

Lewis, Matthew; Angeloudis, Athanasios; Robins, Peter; Evans, Paul; Neill, Simon

2017-04-01

The need to reduce carbon-based energy sources whilst increasing renewable energy forms has led to concerns of intermittency within a national electricity supply strategy. The regular rise and fall of the tide makes prediction almost entirely deterministic compared to other stochastic renewable energy forms; therefore, tidal range energy is often stated as a predictable and firm renewable energy source. Storm surge is the term used for the non-astronomical forcing of tidal elevation, and is synonymous with coastal flooding because positive storm surges can elevate water-levels above the height of coastal flood defences. We hypothesis storm surges will affect the reliability of the tidal range energy resource; with negative surge events reducing the tidal range, and conversely, positive surge events increasing the available resource. Moreover, tide-surge interaction, which results in positive storm surges more likely to occur on a flooding tide, will reduce the annual tidal range energy resource estimate. Water-level data (2000-2012) at nine UK tide gauges, where the mean tidal amplitude is above 2.5m and thus suitable for tidal-range energy development (e.g. Bristol Channel), were used to predict tidal range power with a 0D modelling approach. Storm surge affected the annual resource estimate by between -5% to +3%, due to inter-annual variability. Instantaneous power output were significantly affected (Normalised Root Mean Squared Error: 3%-8%, Scatter Index: 15%-41%) with spatial variability and variability due to operational strategy. We therefore find a storm surge affects the theoretical reliability of tidal range power, such that a prediction system may be required for any future electricity generation scenario that includes large amounts of tidal-range energy; however, annual resource estimation from astronomical tides alone appears sufficient for resource estimation. Future work should investigate water-level uncertainties on the reliability and predictability of tidal range energy with 2D hydrodynamic models.

Positive Affect and Pain: Mediators of the Within-Day Relation Linking Sleep Quality to Activity Interference in Fibromyalgia

PubMed Central

Kothari, Dhwani J.; Davis, Mary C.; Yeung, Ellen W.; Tennen, Howard A.

2017-01-01

Fibromyalgia (FM) is a chronic pain condition often resulting in functional impairments. Nonrestorative sleep is a prominent symptom of FM that is related to disability, but the day-to-day mechanisms relating the prior night’s sleep quality to next day reports of disability have not been examined. The current study examined the within-day relations among early-morning reports of sleep quality last night, late-morning reports of pain and positive and negative affect, and end-of-day reports of activity interference. Specifically, we tested whether pain, positive affect, and negative affect mediated the association between sleep quality and subsequent activity interference. Data were drawn from electronic diary reports, collected from 220 FM patients for 21 consecutive days. The direct and mediated effects at the within-person level were estimated with Multilevel Structural Equation Modeling. Results showed that pain and positive affect mediated the relation between sleep quality and activity interference. Early-morning reports of poor sleep quality last night predicted elevated levels of pain and lower levels of positive affect at late-morning, which, in turn, predicted elevated end-of-day activity interference. Of note, positive affect was a stronger mediator than pain, and negative affect was not a significant mediator. In summary, the findings identify two parallel mechanisms, pain and positive affect, through which the prior night’s sleep quality predicts disability the next day in FM patients. Further, results highlight the potential utility of boosting positive affect following a poor night’s sleep as one means of preserving daily function in FM. PMID:25679472

Genomic signals of selection predict climate-driven population declines in a migratory bird.

PubMed

Bay, Rachael A; Harrigan, Ryan J; Underwood, Vinh Le; Gibbs, H Lisle; Smith, Thomas B; Ruegg, Kristen

2018-01-05

The ongoing loss of biodiversity caused by rapid climatic shifts requires accurate models for predicting species' responses. Despite evidence that evolutionary adaptation could mitigate climate change impacts, evolution is rarely integrated into predictive models. Integrating population genomics and environmental data, we identified genomic variation associated with climate across the breeding range of the migratory songbird, yellow warbler ( Setophaga petechia ). Populations requiring the greatest shifts in allele frequencies to keep pace with future climate change have experienced the largest population declines, suggesting that failure to adapt may have already negatively affected populations. Broadly, our study suggests that the integration of genomic adaptation can increase the accuracy of future species distribution models and ultimately guide more effective mitigation efforts. Copyright © 2018, American Association for the Advancement of Science.

Pillars of judgment: how memory abilities affect performance in rule-based and exemplar-based judgments.

PubMed

Hoffmann, Janina A; von Helversen, Bettina; Rieskamp, Jörg

2014-12-01

Making accurate judgments is an essential skill in everyday life. Although how different memory abilities relate to categorization and judgment processes has been hotly debated, the question is far from resolved. We contribute to the solution by investigating how individual differences in memory abilities affect judgment performance in 2 tasks that induced rule-based or exemplar-based judgment strategies. In a study with 279 participants, we investigated how working memory and episodic memory affect judgment accuracy and strategy use. As predicted, participants switched strategies between tasks. Furthermore, structural equation modeling showed that the ability to solve rule-based tasks was predicted by working memory, whereas episodic memory predicted judgment accuracy in the exemplar-based task. Last, the probability of choosing an exemplar-based strategy was related to better episodic memory, but strategy selection was unrelated to working memory capacity. In sum, our results suggest that different memory abilities are essential for successfully adopting different judgment strategies. PsycINFO Database Record (c) 2014 APA, all rights reserved.

Predicting phenotype from genotype: Improving accuracy through more robust experimental and computational modeling

PubMed Central

Gallion, Jonathan; Koire, Amanda; Katsonis, Panagiotis; Schoenegge, Anne‐Marie; Bouvier, Michel

2017-01-01

Abstract Computational prediction yields efficient and scalable initial assessments of how variants of unknown significance may affect human health. However, when discrepancies between these predictions and direct experimental measurements of functional impact arise, inaccurate computational predictions are frequently assumed as the source. Here, we present a methodological analysis indicating that shortcomings in both computational and biological data can contribute to these disagreements. We demonstrate that incomplete assaying of multifunctional proteins can affect the strength of correlations between prediction and experiments; a variant's full impact on function is better quantified by considering multiple assays that probe an ensemble of protein functions. Additionally, many variants predictions are sensitive to protein alignment construction and can be customized to maximize relevance of predictions to a specific experimental question. We conclude that inconsistencies between computation and experiment can often be attributed to the fact that they do not test identical hypotheses. Aligning the design of the computational input with the design of the experimental output will require cooperation between computational and biological scientists, but will also lead to improved estimations of computational prediction accuracy and a better understanding of the genotype–phenotype relationship. PMID:28230923

Predicting phenotype from genotype: Improving accuracy through more robust experimental and computational modeling.

PubMed

Gallion, Jonathan; Koire, Amanda; Katsonis, Panagiotis; Schoenegge, Anne-Marie; Bouvier, Michel; Lichtarge, Olivier

2017-05-01

Computational prediction yields efficient and scalable initial assessments of how variants of unknown significance may affect human health. However, when discrepancies between these predictions and direct experimental measurements of functional impact arise, inaccurate computational predictions are frequently assumed as the source. Here, we present a methodological analysis indicating that shortcomings in both computational and biological data can contribute to these disagreements. We demonstrate that incomplete assaying of multifunctional proteins can affect the strength of correlations between prediction and experiments; a variant's full impact on function is better quantified by considering multiple assays that probe an ensemble of protein functions. Additionally, many variants predictions are sensitive to protein alignment construction and can be customized to maximize relevance of predictions to a specific experimental question. We conclude that inconsistencies between computation and experiment can often be attributed to the fact that they do not test identical hypotheses. Aligning the design of the computational input with the design of the experimental output will require cooperation between computational and biological scientists, but will also lead to improved estimations of computational prediction accuracy and a better understanding of the genotype-phenotype relationship. © 2017 The Authors. **Human Mutation published by Wiley Periodicals, Inc.

Project for Solar-Terrestrial Environment Prediction (PSTEP): Towards Predicting Next Solar Cycle

NASA Astrophysics Data System (ADS)

Imada, S.; Iijima, H.; Hotta, H.; Shiota, D.; Kanou, O.; Fujiyama, M.; Kusano, K.

2016-10-01

It is believed that the longer-term variations of the solar activity can affect the Earth's climate. Therefore, predicting the next solar cycle is crucial for the forecast of the "solar-terrestrial environment". To build prediction schemes for the activity level of the next solar cycle is a key for the long-term space weather study. Although three-years prediction can be almost achieved, the prediction of next solar cycle is very limited, so far. We are developing a five-years prediction scheme by combining the Surface Flux Transport (SFT) model and the most accurate measurements of solar magnetic fields as a part of the PSTEP (Project for Solar-Terrestrial Environment Prediction),. We estimate the meridional flow, differential rotation, and turbulent diffusivity from recent modern observations (Hinode and Solar Dynamics Observatory). These parameters are used in the SFT models to predict the polar magnetic fields strength at the solar minimum. In this presentation, we will explain the outline of our strategy to predict the next solar cycle. We also report the present status and the future perspective of our project.

Predicting geogenic arsenic contamination in shallow groundwater of south Louisiana, United States.

PubMed

Yang, Ningfang; Winkel, Lenny H E; Johannesson, Karen H

2014-05-20

Groundwater contaminated with arsenic (As) threatens the health of more than 140 million people worldwide. Previous studies indicate that geology and sedimentary depositional environments are important factors controlling groundwater As contamination. The Mississippi River delta has broadly similar geology and sedimentary depositional environments to the large deltas in South and Southeast Asia, which are severely affected by geogenic As contamination and therefore may also be vulnerable to groundwater As contamination. In this study, logistic regression is used to develop a probability model based on surface hydrology, soil properties, geology, and sedimentary depositional environments. The model is calibrated using 3286 aggregated and binary-coded groundwater As concentration measurements from Bangladesh and verified using 78 As measurements from south Louisiana. The model's predictions are in good agreement with the known spatial distribution of groundwater As contamination of Bangladesh, and the predictions also indicate high risk of As contamination in shallow groundwater from Holocene sediments of south Louisiana. Furthermore, the model correctly predicted 79% of the existing shallow groundwater As measurements in the study region, indicating good performance of the model in predicting groundwater As contamination in shallow aquifers of south Louisiana.

Prediction of Return-to-original-work after an Industrial Accident Using Machine Learning and Comparison of Techniques

PubMed Central

2018-01-01

Background Many studies have tried to develop predictors for return-to-work (RTW). However, since complex factors have been demonstrated to predict RTW, it is difficult to use them practically. This study investigated whether factors used in previous studies could predict whether an individual had returned to his/her original work by four years after termination of the worker's recovery period. Methods An initial logistic regression analysis of 1,567 participants of the fourth Panel Study of Worker's Compensation Insurance yielded odds ratios. The participants were divided into two subsets, a training dataset and a test dataset. Using the training dataset, logistic regression, decision tree, random forest, and support vector machine models were established, and important variables of each model were identified. The predictive abilities of the different models were compared. Results The analysis showed that only earned income and company-related factors significantly affected return-to-original-work (RTOW). The random forest model showed the best accuracy among the tested machine learning models; however, the difference was not prominent. Conclusion It is possible to predict a worker's probability of RTOW using machine learning techniques with moderate accuracy. PMID:29736160

Does the uncertainty in the representation of terrestrial water flows affect precipitation predictability? A WRF-Hydro ensemble analysis for Central Europe

NASA Astrophysics Data System (ADS)

Arnault, Joel; Rummler, Thomas; Baur, Florian; Lerch, Sebastian; Wagner, Sven; Fersch, Benjamin; Zhang, Zhenyu; Kerandi, Noah; Keil, Christian; Kunstmann, Harald

2017-04-01

Precipitation predictability can be assessed by the spread within an ensemble of atmospheric simulations being perturbed in the initial, lateral boundary conditions and/or modeled processes within a range of uncertainty. Surface-related processes are more likely to change precipitation when synoptic forcing is weak. This study investigates the effect of uncertainty in the representation of terrestrial water flows on precipitation predictability. The tools used for this investigation are the Weather Research and Forecasting (WRF) model and its hydrologically-enhanced version WRF-Hydro, applied over Central Europe during April-October 2008. The WRF grid is that of COSMO-DE, with a resolution of 2.8 km. In WRF-Hydro, the WRF grid is coupled with a sub-grid at 280 m resolution to resolve lateral terrestrial water flows. Vertical flow uncertainty is considered by modifying the parameter controlling the partitioning between surface runoff and infiltration in WRF, and horizontal flow uncertainty is considered by comparing WRF with WRF-Hydro. Precipitation predictability is deduced from the spread of an ensemble based on three turbulence parameterizations. Model results are validated with E-OBS precipitation and surface temperature, ESA-CCI soil moisture, FLUXNET-MTE surface evaporation and GRDC discharge. It is found that the uncertainty in the representation of terrestrial water flows is more likely to significantly affect precipitation predictability when surface flux spatial variability is high. In comparison to the WRF ensemble, WRF-Hydro slightly improves the adjusted continuous ranked probability score of daily precipitation. The reproduction of observed daily discharge with Nash-Sutcliffe model efficiency coefficients up to 0.91 demonstrates the potential of WRF-Hydro for flood forecasting.

Subtropical high predictability establishes a promising way for monsoon and tropical storm predictions.

PubMed

Wang, Bin; Xiang, Baoqiang; Lee, June-Yi

2013-02-19

Monsoon rainfall and tropical storms (TSs) impose great impacts on society, yet their seasonal predictions are far from successful. The western Pacific Subtropical High (WPSH) is a prime circulation system affecting East Asian summer monsoon (EASM) and western North Pacific TS activities, but the sources of its variability and predictability have not been established. Here we show that the WPSH variation faithfully represents fluctuations of EASM strength (r = -0.92), the total TS days over the subtropical western North Pacific (r = -0.81), and the total number of TSs impacting East Asian coasts (r = -0.76) during 1979-2009. Our numerical experiment results establish that the WPSH variation is primarily controlled by central Pacific cooling/warming and a positive atmosphere-ocean feedback between the WPSH and the Indo-Pacific warm pool oceans. With a physically based empirical model and the state-of-the-art dynamical models, we demonstrate that the WPSH is highly predictable; this predictability creates a promising way for prediction of monsoon and TS. The predictions using the WPSH predictability not only yields substantially improved skills in prediction of the EASM rainfall, but also enables skillful prediction of the TS activities that the current dynamical models fail. Our findings reveal that positive WPSH-ocean interaction can provide a source of climate predictability and highlight the importance of subtropical dynamics in understanding monsoon and TS predictability.

Subtropical High predictability establishes a promising way for monsoon and tropical storm predictions

PubMed Central

Wang, Bin; Xiang, Baoqiang; Lee, June-Yi

2013-01-01

Monsoon rainfall and tropical storms (TSs) impose great impacts on society, yet their seasonal predictions are far from successful. The western Pacific Subtropical High (WPSH) is a prime circulation system affecting East Asian summer monsoon (EASM) and western North Pacific TS activities, but the sources of its variability and predictability have not been established. Here we show that the WPSH variation faithfully represents fluctuations of EASM strength (r = –0.92), the total TS days over the subtropical western North Pacific (r = –0.81), and the total number of TSs impacting East Asian coasts (r = –0.76) during 1979–2009. Our numerical experiment results establish that the WPSH variation is primarily controlled by central Pacific cooling/warming and a positive atmosphere-ocean feedback between the WPSH and the Indo-Pacific warm pool oceans. With a physically based empirical model and the state-of-the-art dynamical models, we demonstrate that the WPSH is highly predictable; this predictability creates a promising way for prediction of monsoon and TS. The predictions using the WPSH predictability not only yields substantially improved skills in prediction of the EASM rainfall, but also enables skillful prediction of the TS activities that the current dynamical models fail. Our findings reveal that positive WPSH–ocean interaction can provide a source of climate predictability and highlight the importance of subtropical dynamics in understanding monsoon and TS predictability. PMID:23341624

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.

The galaxy clustering crisis in abundance matching

NASA Astrophysics Data System (ADS)

Campbell, Duncan; van den Bosch, Frank C.; Padmanabhan, Nikhil; Mao, Yao-Yuan; Zentner, Andrew R.; Lange, Johannes U.; Jiang, Fangzhou; Villarreal, Antonio

2018-06-01

Galaxy clustering on small scales is significantly underpredicted by sub-halo abundance matching (SHAM) models that populate (sub-)haloes with galaxies based on peak halo mass, Mpeak. SHAM models based on the peak maximum circular velocity, Vpeak, have had much better success. The primary reason for Mpeak-based models fail is the relatively low abundance of satellite galaxies produced in these models compared to those based on Vpeak. Despite success in predicting clustering, a simple Vpeak-based SHAM model results in predictions for galaxy growth that are at odds with observations. We evaluate three possible remedies that could `save' mass-based SHAM: (1) SHAM models require a significant population of `orphan' galaxies as a result of artificial disruption/merging of sub-haloes in modern high-resolution dark matter simulations; (2) satellites must grow significantly after their accretion; and (3) stellar mass is significantly affected by halo assembly history. No solution is entirely satisfactory. However, regardless of the particulars, we show that popular SHAM models based on Mpeak cannot be complete physical models as presented. Either Vpeak truly is a better predictor of stellar mass at z ˜ 0 and it remains to be seen how the correlation between stellar mass and Vpeak comes about, or SHAM models are missing vital component(s) that significantly affect galaxy clustering.

Modeling hydrology and in-stream transport on drained forested lands in coastal Carolinas, U.S.A.

Treesearch

Devendra Amatya

2005-01-01

This study summarizes the successional development and testing of forest hydrologic models based on DRAINMOD that predicts the hydrology of low-gradient poorly drained watersheds as affected by land management and climatic variation. The field scale (DRAINLOB) and watershed-scale in-stream routing (DRAINWAT) models were successfully tested with water table and outflow...

Analysis of sensitivity and uncertainty in an individual-based model of a threatened wildlife species

Treesearch

Bruce G. Marcot; Peter H. Singleton; Nathan H. Schumaker

2015-01-01

Sensitivity analysis—determination of how prediction variables affect response variables—of individual-based models (IBMs) are few but important to the interpretation of model output. We present sensitivity analysis of a spatially explicit IBM (HexSim) of a threatened species, the Northern Spotted Owl (NSO; Strix occidentalis caurina) in Washington...

Evaluating critical uncertainty thresholds in a spatial model of forest pest invasion risk

Treesearch

Frank H. Koch; Denys Yemshanov; Daniel W. McKenney; William D. Smith

2009-01-01

Pest risk maps can provide useful decision support in invasive species management, but most do not adequately consider the uncertainty associated with predicted risk values. This study explores how increased uncertainty in a risk model’s numeric assumptions might affect the resultant risk map. We used a spatial stochastic model, integrating components for...

Bayesian Genomic Prediction with Genotype × Environment Interaction Kernel Models

PubMed Central

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

2016-01-01

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

Bayesian Genomic Prediction with Genotype × Environment Interaction Kernel Models.

PubMed

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

2017-01-05

The phenomenon of genotype × environment (G × E) interaction in plant breeding decreases selection accuracy, thereby negatively affecting genetic gains. Several genomic prediction models incorporating G × E have been recently developed and used in genomic selection of plant breeding programs. Genomic prediction models for assessing multi-environment G × E interaction are extensions of a single-environment model, and have advantages and limitations. In this study, we propose two multi-environment Bayesian genomic models: the first model considers genetic effects [Formula: see text] that can be assessed by the Kronecker product of variance-covariance matrices of genetic correlations between environments and genomic kernels through markers under two linear kernel methods, linear (genomic best linear unbiased predictors, GBLUP) and Gaussian (Gaussian kernel, GK). The other model has the same genetic component as the first model [Formula: see text] plus an extra component, F: , that captures random effects between environments that were not captured by the random effects [Formula: see text] We used five CIMMYT data sets (one maize and four wheat) that were previously used in different studies. Results show that models with G × E always have superior prediction ability than single-environment models, and the higher prediction ability of multi-environment models with [Formula: see text] over the multi-environment model with only u occurred 85% of the time with GBLUP and 45% of the time with GK across the five data sets. The latter result indicated that including the random effect f is still beneficial for increasing prediction ability after adjusting by the random effect [Formula: see text]. Copyright © 2017 Cuevas et al.

Forecast for the Remainder of the Leonid Storm Season

NASA Technical Reports Server (NTRS)

Jenniskens, Peter; DeVincenzi, Donald L. (Technical Monitor)

2001-01-01

The dust trails of comet 55P/Tempel-Tuttle lead to Leonid storms on Earth, threatening satellites in orbit. We present a new model that accounts in detail for the observed properties of dust tails evolved by the comet at previous oppositions. The prediction model shows the 1767-dust trail closer to Earth's orbit in 2001 than originally thought; increasing expected peak rates for North America observers. Predictions for the 2002 storms are less affected. We demonstrate that the observed shower profiles can be understood as a projection of the comet lightcurve.

Macroscale hydrologic modeling of ecologically relevant flow metrics

NASA Astrophysics Data System (ADS)

Wenger, Seth J.; Luce, Charles H.; Hamlet, Alan F.; Isaak, Daniel J.; Neville, Helen M.

2010-09-01

Stream hydrology strongly affects the structure of aquatic communities. Changes to air temperature and precipitation driven by increased greenhouse gas concentrations are shifting timing and volume of streamflows potentially affecting these communities. The variable infiltration capacity (VIC) macroscale hydrologic model has been employed at regional scales to describe and forecast hydrologic changes but has been calibrated and applied mainly to large rivers. An important question is how well VIC runoff simulations serve to answer questions about hydrologic changes in smaller streams, which are important habitat for many fish species. To answer this question, we aggregated gridded VIC outputs within the drainage basins of 55 streamflow gages in the Pacific Northwest United States and compared modeled hydrographs and summary metrics to observations. For most streams, several ecologically relevant aspects of the hydrologic regime were accurately modeled, including center of flow timing, mean annual and summer flows and frequency of winter floods. Frequencies of high and low flows in the summer were not well predicted, however. Predictions were worse for sites with strong groundwater influence, and some sites showed errors that may result from limitations in the forcing climate data. Higher resolution (1/16th degree) modeling provided small improvements over lower resolution (1/8th degree). Despite some limitations, the VIC model appears capable of representing several ecologically relevant hydrologic characteristics in streams, making it a useful tool for understanding the effects of hydrology in delimiting species distributions and predicting the potential effects of climate shifts on aquatic organisms.

An empirical model for parameters affecting energy consumption in boron removal from boron-containing wastewaters by electrocoagulation.

PubMed

Yilmaz, A Erdem; Boncukcuoğlu, Recep; Kocakerim, M Muhtar

2007-06-01

In this study, it was investigated parameters affecting energy consumption in boron removal from boron containing wastewaters prepared synthetically, via electrocoagulation method. The solution pH, initial boron concentration, dose of supporting electrolyte, current density and temperature of solution were selected as experimental parameters affecting energy consumption. The obtained experimental results showed that boron removal efficiency reached up to 99% under optimum conditions, in which solution pH was 8.0, current density 6.0 mA/cm(2), initial boron concentration 100mg/L and solution temperature 293 K. The current density was an important parameter affecting energy consumption too. High current density applied to electrocoagulation cell increased energy consumption. Increasing solution temperature caused to decrease energy consumption that high temperature decreased potential applied under constant current density. That increasing initial boron concentration and dose of supporting electrolyte caused to increase specific conductivity of solution decreased energy consumption. As a result, it was seen that energy consumption for boron removal via electrocoagulation method could be minimized at optimum conditions. An empirical model was predicted by statistically. Experimentally obtained values were fitted with values predicted from empirical model being as following; [formula in text]. Unfortunately, the conditions obtained for optimum boron removal were not the conditions obtained for minimum energy consumption. It was determined that support electrolyte must be used for increase boron removal and decrease electrical energy consumption.

A multi-model framework for simulating wildlife population response to land-use and climate change

USGS Publications Warehouse

McRae, B.H.; Schumaker, N.H.; McKane, R.B.; Busing, R.T.; Solomon, A.M.; Burdick, C.A.

2008-01-01

Reliable assessments of how human activities will affect wildlife populations are essential for making scientifically defensible resource management decisions. A principle challenge of predicting effects of proposed management, development, or conservation actions is the need to incorporate multiple biotic and abiotic factors, including land-use and climate change, that interact to affect wildlife habitat and populations through time. Here we demonstrate how models of land-use, climate change, and other dynamic factors can be integrated into a coherent framework for predicting wildlife population trends. Our framework starts with land-use and climate change models developed for a region of interest. Vegetation changes through time under alternative future scenarios are predicted using an individual-based plant community model. These predictions are combined with spatially explicit animal habitat models to map changes in the distribution and quality of wildlife habitat expected under the various scenarios. Animal population responses to habitat changes and other factors are then projected using a flexible, individual-based animal population model. As an example application, we simulated animal population trends under three future land-use scenarios and four climate change scenarios in the Cascade Range of western Oregon. We chose two birds with contrasting habitat preferences for our simulations: winter wrens (Troglodytes troglodytes), which are most abundant in mature conifer forests, and song sparrows (Melospiza melodia), which prefer more open, shrubby habitats. We used climate and land-use predictions from previously published studies, as well as previously published predictions of vegetation responses using FORCLIM, an individual-based forest dynamics simulator. Vegetation predictions were integrated with other factors in PATCH, a spatially explicit, individual-based animal population simulator. Through incorporating effects of landscape history and limited dispersal, our framework predicted population changes that typically exceeded those expected based on changes in mean habitat suitability alone. Although land-use had greater impacts on habitat quality than did climate change in our simulations, we found that small changes in vital rates resulting from climate change or other stressors can have large consequences for population trajectories. The ability to integrate bottom-up demographic processes like these with top-down constraints imposed by climate and land-use in a dynamic modeling environment is a key advantage of our approach. The resulting framework should allow researchers to synthesize existing empirical evidence, and to explore complex interactions that are difficult or impossible to capture through piecemeal modeling approaches. ?? 2008 Elsevier B.V.

Dyadic Affective Flexibility and Emotional Inertia in Relation to Youth Psychopathology: An Integrated Model at Two Timescales.

PubMed

Mancini, Kathryn J; Luebbe, Aaron M

2016-06-01

The current review examines characteristics of temporal affective functioning at both the individual and dyadic level. Specifically, the review examines the following three research questions: (1) How are dyadic affective flexibility and emotional inertia operationalized, and are they related to youth psychopathology? (2) How are dyadic affective flexibility and emotional inertia related, and does this relation occur at micro- and meso-timescales? and (3) How do these constructs combine to predict clinical outcomes? Using the Flex3 model of socioemotional flexibility as a frame, the current study proposes that dyadic affective flexibility and emotional inertia are bidirectionally related at micro- and meso-timescales, which yields psychopathological symptoms for youth. Specific future directions for examining individual, dyadic, and cultural characteristics that may influence relations between these constructs and psychopathology are also discussed.

Model-Based Fatigue Prognosis of Fiber-Reinforced Laminates Exhibiting Concurrent Damage Mechanisms

NASA Technical Reports Server (NTRS)

Corbetta, M.; Sbarufatti, C.; Saxena, A.; Giglio, M.; Goebel, K.

2016-01-01

Prognostics of large composite structures is a topic of increasing interest in the field of structural health monitoring for aerospace, civil, and mechanical systems. Along with recent advancements in real-time structural health data acquisition and processing for damage detection and characterization, model-based stochastic methods for life prediction are showing promising results in the literature. Among various model-based approaches, particle-filtering algorithms are particularly capable in coping with uncertainties associated with the process. These include uncertainties about information on the damage extent and the inherent uncertainties of the damage propagation process. Some efforts have shown successful applications of particle filtering-based frameworks for predicting the matrix crack evolution and structural stiffness degradation caused by repetitive fatigue loads. Effects of other damage modes such as delamination, however, are not incorporated in these works. It is well established that delamination and matrix cracks not only co-exist in most laminate structures during the fatigue degradation process but also affect each other's progression. Furthermore, delamination significantly alters the stress-state in the laminates and accelerates the material degradation leading to catastrophic failure. Therefore, the work presented herein proposes a particle filtering-based framework for predicting a structure's remaining useful life with consideration of multiple co-existing damage-mechanisms. The framework uses an energy-based model from the composite modeling literature. The multiple damage-mode model has been shown to suitably estimate the energy release rate of cross-ply laminates as affected by matrix cracks and delamination modes. The model is also able to estimate the reduction in stiffness of the damaged laminate. This information is then used in the algorithms for life prediction capabilities. First, a brief summary of the energy-based damage model is provided. Then, the paper describes how the model is embedded within the prognostic framework and how the prognostics performance is assessed using observations from run-to-failure experiments

Predicting arsenic in drinking water wells of the Central Valley, California

USGS Publications Warehouse

Ayotte, Joseph; Nolan, Bernard T.; Gronberg, JoAnn M.

2016-01-01

Probabilities of arsenic in groundwater at depths used for domestic and public supply in the Central Valley of California are predicted using weak-learner ensemble models (boosted regression trees, BRT) and more traditional linear models (logistic regression, LR). Both methods captured major processes that affect arsenic concentrations, such as the chemical evolution of groundwater, redox differences, and the influence of aquifer geochemistry. Inferred flow-path length was the most important variable but near-surface-aquifer geochemical data also were significant. A unique feature of this study was that previously predicted nitrate concentrations in three dimensions were themselves predictive of arsenic and indicated an important redox effect at >10 μg/L, indicating low arsenic where nitrate was high. Additionally, a variable representing three-dimensional aquifer texture from the Central Valley Hydrologic Model was an important predictor, indicating high arsenic associated with fine-grained aquifer sediment. BRT outperformed LR at the 5 μg/L threshold in all five predictive performance measures and at 10 μg/L in four out of five measures. BRT yielded higher prediction sensitivity (39%) than LR (18%) at the 10 μg/L threshold–a useful outcome because a major objective of the modeling was to improve our ability to predict high arsenic areas.

Impact of implementation choices on quantitative predictions of cell-based computational models

NASA Astrophysics Data System (ADS)

Kursawe, Jochen; Baker, Ruth E.; Fletcher, Alexander G.

2017-09-01

'Cell-based' models provide a powerful computational tool for studying the mechanisms underlying the growth and dynamics of biological tissues in health and disease. An increasing amount of quantitative data with cellular resolution has paved the way for the quantitative parameterisation and validation of such models. However, the numerical implementation of cell-based models remains challenging, and little work has been done to understand to what extent implementation choices may influence model predictions. Here, we consider the numerical implementation of a popular class of cell-based models called vertex models, which are often used to study epithelial tissues. In two-dimensional vertex models, a tissue is approximated as a tessellation of polygons and the vertices of these polygons move due to mechanical forces originating from the cells. Such models have been used extensively to study the mechanical regulation of tissue topology in the literature. Here, we analyse how the model predictions may be affected by numerical parameters, such as the size of the time step, and non-physical model parameters, such as length thresholds for cell rearrangement. We find that vertex positions and summary statistics are sensitive to several of these implementation parameters. For example, the predicted tissue size decreases with decreasing cell cycle durations, and cell rearrangement may be suppressed by large time steps. These findings are counter-intuitive and illustrate that model predictions need to be thoroughly analysed and implementation details carefully considered when applying cell-based computational models in a quantitative setting.

The Predictability of Advection-dominated Flux-transport Solar Dynamo Models

NASA Astrophysics Data System (ADS)

Sanchez, Sabrina; Fournier, Alexandre; Aubert, Julien

2014-01-01

Space weather is a matter of practical importance in our modern society. Predictions of forecoming solar cycles mean amplitude and duration are currently being made based on flux-transport numerical models of the solar dynamo. Interested in the forecast horizon of such studies, we quantify the predictability window of a representative, advection-dominated, flux-transport dynamo model by investigating its sensitivity to initial conditions and control parameters through a perturbation analysis. We measure the rate associated with the exponential growth of an initial perturbation of the model trajectory, which yields a characteristic timescale known as the e-folding time τ e . The e-folding time is shown to decrease with the strength of the α-effect, and to increase with the magnitude of the imposed meridional circulation. Comparing the e-folding time with the solar cycle periodicity, we obtain an average estimate for τ e equal to 2.76 solar cycle durations. From a practical point of view, the perturbations analyzed in this work can be interpreted as uncertainties affecting either the observations or the physical model itself. After reviewing these, we discuss their implications for solar cycle prediction.

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.

Systems biology as a conceptual framework for research in family medicine; use in predicting response to influenza vaccination.

PubMed

Majnarić-Trtica, Ljiljana; Vitale, Branko

2011-10-01

To introduce systems biology as a conceptual framework for research in family medicine, based on empirical data from a case study on the prediction of influenza vaccination outcomes. This concept is primarily oriented towards planning preventive interventions and includes systematic data recording, a multi-step research protocol and predictive modelling. Factors known to affect responses to influenza vaccination include older age, past exposure to influenza viruses, and chronic diseases; however, constructing useful prediction models remains a challenge, because of the need to identify health parameters that are appropriate for general use in modelling patients' responses. The sample consisted of 93 patients aged 50-89 years (median 69), with multiple medical conditions, who were vaccinated against influenza. Literature searches identified potentially predictive health-related parameters, including age, gender, diagnoses of the main chronic ageing diseases, anthropometric measures, and haematological and biochemical tests. By applying data mining algorithms, patterns were identified in the data set. Candidate health parameters, selected in this way, were then combined with information on past influenza virus exposure to build the prediction model using logistic regression. A highly significant prediction model was obtained, indicating that by using a systems biology approach it is possible to answer unresolved complex medical uncertainties. Adopting this systems biology approach can be expected to be useful in identifying the most appropriate target groups for other preventive programmes.

SWMF Global Magnetosphere Simulations of January 2005: Geomagnetic Indices and Cross-Polar Cap Potential

NASA Astrophysics Data System (ADS)

Haiducek, John D.; Welling, Daniel T.; Ganushkina, Natalia Y.; Morley, Steven K.; Ozturk, Dogacan Su

2017-12-01

We simulated the entire month of January 2005 using the Space Weather Modeling Framework (SWMF) with observed solar wind data as input. We conducted this simulation with and without an inner magnetosphere model and tested two different grid resolutions. We evaluated the model's accuracy in predicting Kp, SYM-H, AL, and cross-polar cap potential (CPCP). We find that the model does an excellent job of predicting the SYM-H index, with a root-mean-square error (RMSE) of 17-18 nT. Kp is predicted well during storm time conditions but overpredicted during quiet times by a margin of 1 to 1.7 Kp units. AL is predicted reasonably well on average, with an RMSE of 230-270 nT. However, the model reaches the largest negative AL values significantly less often than the observations. The model tended to overpredict CPCP, with RMSE values on the order of 46-48 kV. We found the results to be insensitive to grid resolution, with the exception of the rate of occurrence for strongly negative AL values. The use of the inner magnetosphere component, however, affected results significantly, with all quantities except CPCP improved notably when the inner magnetosphere model was on.

Modelling ecological systems in a changing world

PubMed Central

Evans, Matthew R.

2012-01-01

The world is changing at an unprecedented rate. In such a situation, we need to understand the nature of the change and to make predictions about the way in which it might affect systems of interest; often we may also wish to understand what might be done to mitigate the predicted effects. In ecology, we usually make such predictions (or forecasts) by making use of mathematical models that describe the system and projecting them into the future, under changed conditions. Approaches emphasizing the desirability of simple models with analytical tractability and those that use assumed causal relationships derived statistically from data currently dominate ecological modelling. Although such models are excellent at describing the way in which a system has behaved, they are poor at predicting its future state, especially in novel conditions. In order to address questions about the impact of environmental change, and to understand what, if any, action might be taken to ameliorate it, ecologists need to develop the ability to project models into novel, future conditions. This will require the development of models based on understanding the processes that result in a system behaving the way it does, rather than relying on a description of the system, as a whole, remaining valid indefinitely. PMID:22144381

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. Copyright © 2015 Elsevier Ltd. All rights reserved.

Validation of elk resource selection models with spatially independent data

Treesearch

Priscilla K. Coe; Bruce K. Johnson; Michael J. Wisdom; John G. Cook; Marty Vavra; Ryan M. Nielson

2011-01-01

Knowledge of how landscape features affect wildlife resource use is essential for informed management. Resource selection functions often are used to make and validate predictions about landscape use; however, resource selection functions are rarely validated with data from landscapes independent of those from which the models were built. This problem has severely...

Performance of the SWEEP model affected by estimates of threshold friction velocity

USDA-ARS?s Scientific Manuscript database

The Wind Erosion Prediction System (WEPS) is a process-based model and needs to be verified under a broad range of climatic, soil, and management conditions. Occasional failure of the WEPS erosion submodel (Single-event Wind Erosion Evaluation Program or SWEEP) to simulate erosion in the Columbia Pl...

Uncertainties in SOA Formation from the Photooxidation of α-pinene

NASA Astrophysics Data System (ADS)

McVay, R.; Zhang, X.; Aumont, B.; Valorso, R.; Camredon, M.; La, S.; Seinfeld, J.

2015-12-01

Explicit chemical models such as GECKO-A (the Generator for Explicit Chemistry and Kinetics of Organics in the Atmosphere) enable detailed modeling of gas-phase photooxidation and secondary organic aerosol (SOA) formation. Comparison between these explicit models and chamber experiments can provide insight into processes that are missing or unknown in these models. GECKO-A is used to model seven SOA formation experiments from α-pinene photooxidation conducted at varying seed particle concentrations with varying oxidation rates. We investigate various physical and chemical processes to evaluate the extent of agreement between the experiments and the model predictions. We examine the effect of vapor wall loss on SOA formation and how the importance of this effect changes at different oxidation rates. Proposed gas-phase autoxidation mechanisms are shown to significantly affect SOA predictions. The potential effects of particle-phase dimerization and condensed-phase photolysis are investigated. We demonstrate the extent to which SOA predictions in the α-pinene photooxidation system depend on uncertainties in the chemical mechanism.

Taking Wave Prediction to New Levels: Wavewatch 3

DTIC Science & Technology

2016-01-01

features such as surf and rip currents , conditions that affect special operations, amphibious assaults, and logistics over the shore. Changes in...The Navy’s current version of WAVEWATCH Ill features the capability of operating with gridded domains of multiple resolution simultaneously, ranging...Netherlands. Its current form, WAVEWATCH Ill, was developed at NOAA’s National Center for Environmental Prediction. The model is free and open source

Self-expansion and flow in couples' momentary experiences: an experience sampling study.

PubMed

Graham, James M

2008-09-01

The self-expansion model of close relationships posits that when couples engage in exciting and activating conjoint activities, they feel connected with their partners and more satisfied with their relationships. In the present study, the experience sampling method was used to examine the predictions of the self-expansion model in couples' momentary experiences. In addition, the author generated several new hypotheses by integrating the self-expansion model with existing research on flow. Over the course of 1 week, 20 couples were signaled at quasi-random intervals to provide data on 1,265 unique experiences. The results suggest that the level of activation experienced during an activity was positively related to experience-level relationship quality. This relationship was consistent across free-time and nonfree-time contexts and was mediated by positive affect. Activation was not found to predict later affect unless the level of activation exceeded what was typical for the individual. Also examined was the influence of interpersonal context and activity type on self-expansion. The results support the self-expansion model and suggest that it could be considered under the broader umbrella of flow.

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

PubMed

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

2016-06-01

Intuitively, how you feel about potential outcomes will determine your decisions. Indeed, an implicit assumption in one of the most influential theories in psychology, prospect theory, is that feelings govern choice. Surprisingly, however, very little is known about the rules by which feelings are transformed into decisions. Here, we specified a computational model that used feelings to predict choices. We found that this model predicted choice better than existing value-based models, showing a unique contribution of feelings to decisions, over and above value. Similar to the value function in prospect theory, our feeling function showed diminished sensitivity to outcomes as value increased. However, loss aversion in choice was explained by an asymmetry in how feelings about losses and gains were weighted when making a decision, not by an asymmetry in the feelings themselves. The results provide new insights into how feelings are utilized to reach a decision. © The Author(s) 2016.

Numerical modelling of effective thermal conductivity for modified geomaterial using lattice element method

NASA Astrophysics Data System (ADS)

Rizvi, Zarghaam Haider; Shrestha, Dinesh; Sattari, Amir S.; Wuttke, Frank

2018-02-01

Macroscopic parameters such as effective thermal conductivity (ETC) is an important parameter which is affected by micro and meso level behaviour of particulate materials, and has been extensively examined in the past decades. In this paper, a new lattice based numerical model is developed to predict the ETC of sand and modified high thermal backfill material for energy transportation used for underground power cables. 2D and 3D simulations are performed to analyse and detect differences resulting from model simplification. The thermal conductivity of the granular mixture is determined numerically considering the volume and the shape of the each constituting portion. The new numerical method is validated with transient needle measurements and the existing theoretical and semi empirical models for thermal conductivity prediction sand and the modified backfill material for dry condition. The numerical prediction and the measured values are in agreement to a large extent.

Pollen dispersal slows geographical range shift and accelerates ecological niche shift under climate change

PubMed Central

Aguilée, Robin; Raoul, Gaël; Rousset, François; Ronce, Ophélie

2016-01-01

Species may survive climate change by migrating to track favorable climates and/or adapting to different climates. Several quantitative genetics models predict that species escaping extinction will change their geographical distribution while keeping the same ecological niche. We introduce pollen dispersal in these models, which affects gene flow but not directly colonization. We show that plant populations may escape extinction because of both spatial range and ecological niche shifts. Exact analytical formulas predict that increasing pollen dispersal distance slows the expected spatial range shift and accelerates the ecological niche shift. There is an optimal distance of pollen dispersal, which maximizes the sustainable rate of climate change. These conclusions hold in simulations relaxing several strong assumptions of our analytical model. Our results imply that, for plants with long distance of pollen dispersal, models assuming niche conservatism may not accurately predict their future distribution under climate change. PMID:27621443

Pollen dispersal slows geographical range shift and accelerates ecological niche shift under climate change.

PubMed

Aguilée, Robin; Raoul, Gaël; Rousset, François; Ronce, Ophélie

2016-09-27

Species may survive climate change by migrating to track favorable climates and/or adapting to different climates. Several quantitative genetics models predict that species escaping extinction will change their geographical distribution while keeping the same ecological niche. We introduce pollen dispersal in these models, which affects gene flow but not directly colonization. We show that plant populations may escape extinction because of both spatial range and ecological niche shifts. Exact analytical formulas predict that increasing pollen dispersal distance slows the expected spatial range shift and accelerates the ecological niche shift. There is an optimal distance of pollen dispersal, which maximizes the sustainable rate of climate change. These conclusions hold in simulations relaxing several strong assumptions of our analytical model. Our results imply that, for plants with long distance of pollen dispersal, models assuming niche conservatism may not accurately predict their future distribution under climate change.

Trauma exposure and cigarette smoking: the impact of negative affect and affect-regulatory smoking motives.

PubMed

Farris, Samantha G; Zvolensky, Michael J; Beckham, Jean C; Vujanovic, Anka A; Schmidt, Norman B

2014-01-01

Cognitive-affective mechanisms related to the maintenance of smoking among trauma-exposed individuals are largely unknown. Cross-sectional data from trauma-exposed treatment-seeking smokers (n = 283) were utilized to test a series of multiple mediator models of trauma exposure and smoking, as mediated by the sequential effects of negative affect and affect-modulation smoking motives. The sequential effects of both mediators indirectly predicted the effect of greater trauma exposure types on nicotine dependence, a biochemical index of smoking, perceived barriers to smoking cessation, and greater withdrawal-related problems during past quit attempts. Negative affect and affect-modulation motives for smoking may contribute to the trauma-smoking association.

Trauma Exposure and Cigarette Smoking: The Impact of Negative Affect and Affect-Regulatory Smoking Motives

PubMed Central

Farris, Samantha G.; Zvolensky, Michael J.; Beckham, Jean C.; Vujanovic, Anka A.; Schmidt, Norman B.

2014-01-01

Cognitive-affective mechanisms related to the maintenance of smoking among trauma-exposed individuals are largely unknown. Cross-sectional data from trauma-exposed treatment-seeking smokers (n = 283) were utilized to test a series of multiple mediator models of trauma exposure and smoking, as mediated by the sequential effects of negative affect and affect-modulation smoking motives. The sequential effects of both mediators indirectly predicted the effect of greater trauma exposure types on nicotine dependence, a biochemical index of smoking, perceived barriers to smoking cessation, and greater withdrawal-related problems during past quit attempts. Negative affect and affect-modulation motives for smoking may contribute to the trauma-smoking association. PMID:25299617

A model for predicting thermal properties of asphalt mixtures from their constituents

NASA Astrophysics Data System (ADS)

Keller, Merlin; Roche, Alexis; Lavielle, Marc

Numerous theoretical and experimental approaches have been developed to predict the effective thermal conductivity of composite materials such as polymers, foams, epoxies, soils and concrete. None of such models have been applied to asphalt concrete. This study attempts to develop a model to predict the thermal conductivity of asphalt concrete from its constituents that will contribute to the asphalt industry by reducing costs and saving time on laboratory testing. The necessity to do the laboratory testing would be no longer required when a mix for the pavement is created with desired thermal properties at the design stage by selecting correct constituents. This thesis investigated six existing predictive models for applicability to asphalt mixtures, and four standard mathematical techniques were used to develop a regression model to predict the effective thermal conductivity. The effective thermal conductivities of 81 asphalt specimens were used as the response variables, and the thermal conductivities and volume fractions of their constituents were used as the predictors. The conducted statistical analyses showed that the measured values of thermal conductivities of the mixtures are affected by the bitumen and aggregate content, but not by the air content. Contrarily, the predicted data for some investigated models are highly sensitive to air voids, but not to bitumen and/or aggregate content. Additionally, the comparison of the experimental with analytical data showed that none of the existing models gave satisfactory results; on the other hand, two regression models (Exponential 1* and Linear 3*) are promising for asphalt concrete.

Conjunction of wavelet transform and SOM-mutual information data pre-processing approach for AI-based Multi-Station nitrate modeling of watersheds

NASA Astrophysics Data System (ADS)

Nourani, Vahid; Andalib, Gholamreza; Dąbrowska, Dominika

2017-05-01

Accurate nitrate load predictions can elevate decision management of water quality of watersheds which affects to environment and drinking water. In this paper, two scenarios were considered for Multi-Station (MS) nitrate load modeling of the Little River watershed. In the first scenario, Markovian characteristics of streamflow-nitrate time series were proposed for the MS modeling. For this purpose, feature extraction criterion of Mutual Information (MI) was employed for input selection of artificial intelligence models (Feed Forward Neural Network, FFNN and least square support vector machine). In the second scenario for considering seasonality-based characteristics of the time series, wavelet transform was used to extract multi-scale features of streamflow-nitrate time series of the watershed's sub-basins to model MS nitrate loads. Self-Organizing Map (SOM) clustering technique which finds homogeneous sub-series clusters was also linked to MI for proper cluster agent choice to be imposed into the models for predicting the nitrate loads of the watershed's sub-basins. The proposed MS method not only considers the prediction of the outlet nitrate but also covers predictions of interior sub-basins nitrate load values. The results indicated that the proposed FFNN model coupled with the SOM-MI improved the performance of MS nitrate predictions compared to the Markovian-based models up to 39%. Overall, accurate selection of dominant inputs which consider seasonality-based characteristics of streamflow-nitrate process could enhance the efficiency of nitrate load predictions.

Genome-to-Watershed Predictive Understanding of Terrestrial Environments

NASA Astrophysics Data System (ADS)

Hubbard, S. S.; Agarwal, D.; Banfield, J. F.; Beller, H. R.; Brodie, E.; Long, P.; Nico, P. S.; Steefel, C. I.; Tokunaga, T. K.; Williams, K. H.

2014-12-01

Although terrestrial environments play a critical role in cycling water, greenhouse gasses, and other life-critical elements, the complexity of interactions among component microbes, plants, minerals, migrating fluids and dissolved constituents hinders predictive understanding of system behavior. The 'Sustainable Systems 2.0' project is developing genome-to-watershed scale predictive capabilities to quantify how the microbiome affects biogeochemical watershed functioning, how watershed-scale hydro-biogeochemical processes affect microbial functioning, and how these interactions co-evolve with climate and land-use changes. Development of such predictive capabilities is critical for guiding the optimal management of water resources, contaminant remediation, carbon stabilization, and agricultural sustainability - now and with global change. Initial investigations are focused on floodplains in the Colorado River Basin, and include iterative model development, experiments and observations with an early emphasis on subsurface aspects. Field experiments include local-scale experiments at Rifle CO to quantify spatiotemporal metabolic and geochemical responses to O2and nitrate amendments as well as floodplain-scale monitoring to quantify genomic and biogeochemical response to natural hydrological perturbations. Information obtained from such experiments are represented within GEWaSC, a Genome-Enabled Watershed Simulation Capability, which is being developed to allow mechanistic interrogation of how genomic information stored in a subsurface microbiome affects biogeochemical cycling. This presentation will describe the genome-to-watershed scale approach as well as early highlights associated with the project. Highlights include: first insights into the diversity of the subsurface microbiome and metabolic roles of organisms involved in subsurface nitrogen, sulfur and hydrogen and carbon cycling; the extreme variability of subsurface DOC and hydrological controls on carbon and nitrogen cycling; geophysical identification of floodplain hotspots that are useful for model parameterization; and GEWaSC demonstration of how incorporation of identified microbial metabolic processes improves prediction of the larger system biogeochemical behavior.

Characterizing and predicting species distributions across environments and scales: Argentine ant occurrences in the eye of the beholder

USGS Publications Warehouse

Menke, S.B.; Holway, D.A.; Fisher, R.N.; Jetz, W.

2009-01-01

Aim: Species distribution models (SDMs) or, more specifically, ecological niche models (ENMs) are a useful and rapidly proliferating tool in ecology and global change biology. ENMs attempt to capture associations between a species and its environment and are often used to draw biological inferences, to predict potential occurrences in unoccupied regions and to forecast future distributions under environmental change. The accuracy of ENMs, however, hinges critically on the quality of occurrence data. ENMs often use haphazardly collected data rather than data collected across the full spectrum of existing environmental conditions. Moreover, it remains unclear how processes affecting ENM predictions operate at different spatial scales. The scale (i.e. grain size) of analysis may be dictated more by the sampling regime than by biologically meaningful processes. The aim of our study is to jointly quantify how issues relating to region and scale affect ENM predictions using an economically important and ecologically damaging invasive species, the Argentine ant (Linepithema humile). Location: California, USA. Methods: We analysed the relationship between sampling sufficiency, regional differences in environmental parameter space and cell size of analysis and resampling environmental layers using two independently collected sets of presence/absence data. Differences in variable importance were determined using model averaging and logistic regression. Model accuracy was measured with area under the curve (AUC) and Cohen's kappa. Results: We first demonstrate that insufficient sampling of environmental parameter space can cause large errors in predicted distributions and biological interpretation. Models performed best when they were parametrized with data that sufficiently sampled environmental parameter space. Second, we show that altering the spatial grain of analysis changes the relative importance of different environmental variables. These changes apparently result from how environmental constraints and the sampling distributions of environmental variables change with spatial grain. Conclusions: These findings have clear relevance for biological inference. Taken together, our results illustrate potentially general limitations for ENMs, especially when such models are used to predict species occurrences in novel environments. We offer basic methodological and conceptual guidelines for appropriate sampling and scale matching. ?? 2009 The Authors Journal compilation ?? 2009 Blackwell Publishing.

Predicting the process of extinction in experimental microcosms and accounting for interspecific interactions in single-species time series.

PubMed

Ferguson, Jake M; Ponciano, José M

2014-02-01

Predicting population extinction risk is a fundamental application of ecological theory to the practice of conservation biology. Here, we compared the prediction performance of a wide array of stochastic, population dynamics models against direct observations of the extinction process from an extensive experimental data set. By varying a series of biological and statistical assumptions in the proposed models, we were able to identify the assumptions that affected predictions about population extinction. We also show how certain autocorrelation structures can emerge due to interspecific interactions, and that accounting for the stochastic effect of these interactions can improve predictions of the extinction process. We conclude that it is possible to account for the stochastic effects of community interactions on extinction when using single-species time series. © 2013 The Authors. Ecology Letters published by John Wiley & Sons Ltd and CNRS.

Predicting the magnetospheric plasma of weather

NASA Technical Reports Server (NTRS)

Dawson, John M.

1986-01-01

The prediction of the plasma environment in time, the plasma weather, is discussed. It is important to be able to predict when large magnetic storms will produce auroras, which will affect the space station operating in low orbit, and what precautions to take both for personnel and sensitive control (computer) equipment onboard. It is also important to start to establish a set of plasma weather records and a record of the ability to predict this weather. A successful forecasting system requires a set of satellite weather stations to provide data from which predictions can be made and a set of plasma weather codes capable of accurately forecasting the status of the Earth's magnetosphere. A numerical magnetohydrodynamic fluid model which is used to model the flow in the magnetosphere, the currents flowing into and out of the auroral regions, the magnetopause, the bow shock location and the magnetotail of the Earth is discussed.

Testing the Predictive Validity of the Hendrich II Fall Risk Model.

PubMed

Jung, Hyesil; Park, Hyeoun-Ae

2018-03-01

Cumulative data on patient fall risk have been compiled in electronic medical records systems, and it is possible to test the validity of fall-risk assessment tools using these data between the times of admission and occurrence of a fall. The Hendrich II Fall Risk Model scores assessed during three time points of hospital stays were extracted and used for testing the predictive validity: (a) upon admission, (b) when the maximum fall-risk score from admission to falling or discharge, and (c) immediately before falling or discharge. Predictive validity was examined using seven predictive indicators. In addition, logistic regression analysis was used to identify factors that significantly affect the occurrence of a fall. Among the different time points, the maximum fall-risk score assessed between admission and falling or discharge showed the best predictive performance. Confusion or disorientation and having a poor ability to rise from a sitting position were significant risk factors for a fall.

Support Vector Machines for Differential Prediction

PubMed Central

Kuusisto, Finn; Santos Costa, Vitor; Nassif, Houssam; Burnside, Elizabeth; Page, David; Shavlik, Jude

2015-01-01

Machine learning is continually being applied to a growing set of fields, including the social sciences, business, and medicine. Some fields present problems that are not easily addressed using standard machine learning approaches and, in particular, there is growing interest in differential prediction. In this type of task we are interested in producing a classifier that specifically characterizes a subgroup of interest by maximizing the difference in predictive performance for some outcome between subgroups in a population. We discuss adapting maximum margin classifiers for differential prediction. We first introduce multiple approaches that do not affect the key properties of maximum margin classifiers, but which also do not directly attempt to optimize a standard measure of differential prediction. We next propose a model that directly optimizes a standard measure in this field, the uplift measure. We evaluate our models on real data from two medical applications and show excellent results. PMID:26158123

Support Vector Machines for Differential Prediction.

PubMed

Kuusisto, Finn; Santos Costa, Vitor; Nassif, Houssam; Burnside, Elizabeth; Page, David; Shavlik, Jude

Machine learning is continually being applied to a growing set of fields, including the social sciences, business, and medicine. Some fields present problems that are not easily addressed using standard machine learning approaches and, in particular, there is growing interest in differential prediction . In this type of task we are interested in producing a classifier that specifically characterizes a subgroup of interest by maximizing the difference in predictive performance for some outcome between subgroups in a population. We discuss adapting maximum margin classifiers for differential prediction. We first introduce multiple approaches that do not affect the key properties of maximum margin classifiers, but which also do not directly attempt to optimize a standard measure of differential prediction. We next propose a model that directly optimizes a standard measure in this field, the uplift measure. We evaluate our models on real data from two medical applications and show excellent results.

Predicting human chronically paralyzed muscle force: a comparison of three mathematical models.

PubMed

Frey Law, Laura A; Shields, Richard K

2006-03-01

Chronic spinal cord injury (SCI) induces detrimental musculoskeletal adaptations that adversely affect health status, ranging from muscle paralysis and skin ulcerations to osteoporosis. SCI rehabilitative efforts may increasingly focus on preserving the integrity of paralyzed extremities to maximize health quality using electrical stimulation for isometric training and/or functional activities. Subject-specific mathematical muscle models could prove valuable for predicting the forces necessary to achieve therapeutic loading conditions in individuals with paralyzed limbs. Although numerous muscle models are available, three modeling approaches were chosen that can accommodate a variety of stimulation input patterns. To our knowledge, no direct comparisons between models using paralyzed muscle have been reported. The three models include 1) a simple second-order linear model with three parameters and 2) two six-parameter nonlinear models (a second-order nonlinear model and a Hill-derived nonlinear model). Soleus muscle forces from four individuals with complete, chronic SCI were used to optimize each model's parameters (using an increasing and decreasing frequency ramp) and to assess the models' predictive accuracies for constant and variable (doublet) stimulation trains at 5, 10, and 20 Hz in each individual. Despite the large differences in modeling approaches, the mean predicted force errors differed only moderately (8-15% error; P=0.0042), suggesting physiological force can be adequately represented by multiple mathematical constructs. The two nonlinear models predicted specific force characteristics better than the linear model in nearly all stimulation conditions, with minimal differences between the two nonlinear models. Either nonlinear mathematical model can provide reasonable force estimates; individual application needs may dictate the preferred modeling strategy.

Affective temperaments play an important role in the relationship between childhood abuse and depressive symptoms in major depressive disorder.

PubMed

Toda, Hiroyuki; Inoue, Takeshi; Tsunoda, Tomoya; Nakai, Yukiei; Tanichi, Masaaki; Tanaka, Teppei; Hashimoto, Naoki; Takaesu, Yoshikazu; Nakagawa, Shin; Kitaichi, Yuji; Boku, Shuken; Tanabe, Hajime; Nibuya, Masashi; Yoshino, Aihide; Kusumi, Ichiro

2016-02-28

Previous studies have shown that various factors, such as genetic and environmental factors, contribute to the development of major depressive disorder (MDD). The aim of this study is to clarify how multiple factors, including affective temperaments, childhood abuse and adult life events, are involved in the severity of depressive symptoms in MDD. A total of 98 participants with MDD were studied using the following self-administered questionnaire surveys: Patient Health Questionnaire-9 measuring the severity of depressive symptoms; Life Experiences Survey (LES) measuring negative and positive adult life events; Temperament Evaluation of the Memphis, Pisa, Paris, and San Diego auto-questionnaire (TEMPS-A) measuring affective temperaments; and the Child Abuse and Trauma Scale (CATS) measuring childhood abuse. The data were analyzed using single and multiple regression analyses and structural equation modeling (SEM). The neglect score reported by CATS indirectly predicted the severity of depressive symptoms through affective temperaments measured by TEMPS-A in SEM. Four temperaments (depressive, cyclothymic, irritable, and anxious) directly predicted the severity of depressive symptoms. The negative change in the LES score also directly predicted severity. This study suggests that childhood abuse, especially neglect, indirectly increases the severity of depressive symptoms through increased scores of affective temperaments in MDD. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Stochastic or statistic? Comparing flow duration curve models in ungauged basins and changing climates

NASA Astrophysics Data System (ADS)

Müller, M. F.; Thompson, S. E.

2015-09-01

The prediction of flow duration curves (FDCs) in ungauged basins remains an important task for hydrologists given the practical relevance of FDCs for water management and infrastructure design. Predicting FDCs in ungauged basins typically requires spatial interpolation of statistical or model parameters. This task is complicated if climate becomes non-stationary, as the prediction challenge now also requires extrapolation through time. In this context, process-based models for FDCs that mechanistically link the streamflow distribution to climate and landscape factors may have an advantage over purely statistical methods to predict FDCs. This study compares a stochastic (process-based) and statistical method for FDC prediction in both stationary and non-stationary contexts, using Nepal as a case study. Under contemporary conditions, both models perform well in predicting FDCs, with Nash-Sutcliffe coefficients above 0.80 in 75 % of the tested catchments. The main drives of uncertainty differ between the models: parameter interpolation was the main source of error for the statistical model, while violations of the assumptions of the process-based model represented the main source of its error. The process-based approach performed better than the statistical approach in numerical simulations with non-stationary climate drivers. The predictions of the statistical method under non-stationary rainfall conditions were poor if (i) local runoff coefficients were not accurately determined from the gauge network, or (ii) streamflow variability was strongly affected by changes in rainfall. A Monte Carlo analysis shows that the streamflow regimes in catchments characterized by a strong wet-season runoff and a rapid, strongly non-linear hydrologic response are particularly sensitive to changes in rainfall statistics. In these cases, process-based prediction approaches are strongly favored over statistical models.

Comparing statistical and process-based flow duration curve models in ungauged basins and changing rain regimes

NASA Astrophysics Data System (ADS)

Müller, M. F.; Thompson, S. E.

2016-02-01

The prediction of flow duration curves (FDCs) in ungauged basins remains an important task for hydrologists given the practical relevance of FDCs for water management and infrastructure design. Predicting FDCs in ungauged basins typically requires spatial interpolation of statistical or model parameters. This task is complicated if climate becomes non-stationary, as the prediction challenge now also requires extrapolation through time. In this context, process-based models for FDCs that mechanistically link the streamflow distribution to climate and landscape factors may have an advantage over purely statistical methods to predict FDCs. This study compares a stochastic (process-based) and statistical method for FDC prediction in both stationary and non-stationary contexts, using Nepal as a case study. Under contemporary conditions, both models perform well in predicting FDCs, with Nash-Sutcliffe coefficients above 0.80 in 75 % of the tested catchments. The main drivers of uncertainty differ between the models: parameter interpolation was the main source of error for the statistical model, while violations of the assumptions of the process-based model represented the main source of its error. The process-based approach performed better than the statistical approach in numerical simulations with non-stationary climate drivers. The predictions of the statistical method under non-stationary rainfall conditions were poor if (i) local runoff coefficients were not accurately determined from the gauge network, or (ii) streamflow variability was strongly affected by changes in rainfall. A Monte Carlo analysis shows that the streamflow regimes in catchments characterized by frequent wet-season runoff and a rapid, strongly non-linear hydrologic response are particularly sensitive to changes in rainfall statistics. In these cases, process-based prediction approaches are favored over statistical models.

Evaluating the predictive abilities of community occupancy models using AUC while accounting for imperfect detection.

PubMed

Zipkin, Elise F; Grant, Evan H Campbell; Fagan, William F

2012-10-01

The ability to accurately predict patterns of species' occurrences is fundamental to the successful management of animal communities. To determine optimal management strategies, it is essential to understand species-habitat relationships and how species habitat use is related to natural or human-induced environmental changes. Using five years of monitoring data in the Chesapeake and Ohio Canal National Historical Park, Maryland, USA, we developed four multispecies hierarchical models for estimating amphibian wetland use that account for imperfect detection during sampling. The models were designed to determine which factors (wetland habitat characteristics, annual trend effects, spring/summer precipitation, and previous wetland occupancy) were most important for predicting future habitat use. We used the models to make predictions about species occurrences in sampled and unsampled wetlands and evaluated model projections using additional data. Using a Bayesian approach, we calculated a posterior distribution of receiver operating characteristic area under the curve (ROC AUC) values, which allowed us to explicitly quantify the uncertainty in the quality of our predictions and to account for false negatives in the evaluation data set. We found that wetland hydroperiod (the length of time that a wetland holds water), as well as the occurrence state in the prior year, were generally the most important factors in determining occupancy. The model with habitat-only covariates predicted species occurrences well; however, knowledge of wetland use in the previous year significantly improved predictive ability at the community level and for two of 12 species/species complexes. Our results demonstrate the utility of multispecies models for understanding which factors affect species habitat use of an entire community (of species) and provide an improved methodology using AUC that is helpful for quantifying the uncertainty in model predictions while explicitly accounting for detection biases.

Evaluating the predictive abilities of community occupancy models using AUC while accounting for imperfect detection

USGS Publications Warehouse

Zipkin, Elise F.; Grant, Evan H. Campbell; Fagan, William F.

2012-01-01

The ability to accurately predict patterns of species' occurrences is fundamental to the successful management of animal communities. To determine optimal management strategies, it is essential to understand species-habitat relationships and how species habitat use is related to natural or human-induced environmental changes. Using five years of monitoring data in the Chesapeake and Ohio Canal National Historical Park, Maryland, USA, we developed four multi-species hierarchical models for estimating amphibian wetland use that account for imperfect detection during sampling. The models were designed to determine which factors (wetland habitat characteristics, annual trend effects, spring/summer precipitation, and previous wetland occupancy) were most important for predicting future habitat use. We used the models to make predictions of species occurrences in sampled and unsampled wetlands and evaluated model projections using additional data. Using a Bayesian approach, we calculated a posterior distribution of receiver operating characteristic area under the curve (ROC AUC) values, which allowed us to explicitly quantify the uncertainty in the quality of our predictions and to account for false negatives in the evaluation dataset. We found that wetland hydroperiod (the length of time that a wetland holds water) as well as the occurrence state in the prior year were generally the most important factors in determining occupancy. The model with only habitat covariates predicted species occurrences well; however, knowledge of wetland use in the previous year significantly improved predictive ability at the community level and for two of 12 species/species complexes. Our results demonstrate the utility of multi-species models for understanding which factors affect species habitat use of an entire community (of species) and provide an improved methodology using AUC that is helpful for quantifying the uncertainty in model predictions while explicitly accounting for detection biases.

A Quantitative Model for the Prediction of Sooting Tendency from Molecular Structure

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

St. John, Peter C.; Kairys, Paul; Das, Dhrubajyoti D.

Particulate matter emissions negatively affect public health and global climate, yet newer fuel-efficient gasoline direct injection engines tend to produce more soot than their port-fuel injection counterparts. Fortunately, the search for sustainable biomass-based fuel blendstocks provides an opportunity to develop fuels that suppress soot formation in more efficient engine designs. However, as emissions tests are experimentally cumbersome and the search space for potential bioblendstocks is vast, new techniques are needed to estimate the sooting tendency of a diverse range of compounds. In this study, we develop a quantitative structure-activity relationship (QSAR) model of sooting tendency based on the experimental yieldmore » sooting index (YSI), which ranks molecules on a scale from n-hexane, 0, to benzene, 100. The model includes a rigorously defined applicability domain, and the predictive performance is checked using both internal and external validation. Model predictions for compounds in the external test set had a median absolute error of ~3 YSI units. An investigation of compounds that are poorly predicted by the model lends new insight into the complex mechanisms governing soot formation. Predictive models of soot formation can therefore be expected to play an increasingly important role in the screening and development of next-generation biofuels.« less

A Quantitative Model for the Prediction of Sooting Tendency from Molecular Structure

DOE PAGES

St. John, Peter C.; Kairys, Paul; Das, Dhrubajyoti D.; ...

2017-07-24

Particulate matter emissions negatively affect public health and global climate, yet newer fuel-efficient gasoline direct injection engines tend to produce more soot than their port-fuel injection counterparts. Fortunately, the search for sustainable biomass-based fuel blendstocks provides an opportunity to develop fuels that suppress soot formation in more efficient engine designs. However, as emissions tests are experimentally cumbersome and the search space for potential bioblendstocks is vast, new techniques are needed to estimate the sooting tendency of a diverse range of compounds. In this study, we develop a quantitative structure-activity relationship (QSAR) model of sooting tendency based on the experimental yieldmore » sooting index (YSI), which ranks molecules on a scale from n-hexane, 0, to benzene, 100. The model includes a rigorously defined applicability domain, and the predictive performance is checked using both internal and external validation. Model predictions for compounds in the external test set had a median absolute error of ~3 YSI units. An investigation of compounds that are poorly predicted by the model lends new insight into the complex mechanisms governing soot formation. Predictive models of soot formation can therefore be expected to play an increasingly important role in the screening and development of next-generation biofuels.« less

Why is the Skill of the Models in Reproducing MJO and its Impacts on the South American Monsoon Important for Subseasonal Prediction?

NASA Astrophysics Data System (ADS)

Grimm, A. M.; Silva, T. M.; Hirata, F. E.; Martins, G. P.

2017-12-01

The Madden Julian Oscillation (MJO) influences significantly daily precipitation and the frequency of extreme events during the summer South American monsoon (SAM) in important regions of the continent. One of the main features of the SAM, the South Atlantic Convergence Zone (SACZ), extends from central South America over Southeast Brazil and into the subtropical Atlantic Ocean, affecting very densely populated areas in Southeast Brazil. During the austral summer this region is strongly affected by landslides and floods associated with active SACZ, and the extreme precipitation events receive contribution from synoptic and MJO-related intraseasonal variability. Therefore, it is important to assess the observed impacts of the MJO in its different phases and to evaluate the models' skill in reproducing these phases and their impacts on South America in order to explore extended-range predictability of those events. The MJO cycle is divided into 8 phases according to the temporal evolution of the first two observed modes of multivariate EOF analysis of tropical convection and zonal winds. The teleconnections associated with these impacts are analyzed with simulations and influence functions of a simple model. The results show that two of the MJO phases strongly enhance the extreme events in the SACZ region and indicate the responsible mechanisms, lending these events a higher degree of predictability on subseasonal time-scales. Therefore, in selecting models to build a subseasonal-range forecasting scheme for extreme precipitation events, a necessary step is the assessment of their skill in reproducing MJO and its observed impacts on South America. Well-known models of the S2S Project, among them the ECMWF and CFS-v2 models are analyzed. Their reforecasts for weeks 1, 2, 3, 4 are separately projected onto the first two modes of tropical convection and zonal wind variability in order to identify the predicted MJO phases. Although the skill of one of the models in predicting these phases extends to week 4, generally the useful skill does not extend beyond week 3. The simulation of the impacts over South America, especially on the SACZ, is also assessed for selected models.

Why is the Skill of the Models in Reproducing MJO and its Impacts on the South American Monsoon Important for Subseasonal Prediction?

NASA Astrophysics Data System (ADS)

Gross, S.; Wirth, M.; Schäfler, A.; Ewald, F.; Urbanek, B.; Kiemle, C.; Ehret, G.

2016-12-01

The Madden Julian Oscillation (MJO) influences significantly daily precipitation and the frequency of extreme events during the summer South American monsoon (SAM) in important regions of the continent. One of the main features of the SAM, the South Atlantic Convergence Zone (SACZ), extends from central South America over Southeast Brazil and into the subtropical Atlantic Ocean, affecting very densely populated areas in Southeast Brazil. During the austral summer this region is strongly affected by landslides and floods associated with active SACZ, and the extreme precipitation events receive contribution from synoptic and MJO-related intraseasonal variability. Therefore, it is important to assess the observed impacts of the MJO in its different phases and to evaluate the models' skill in reproducing these phases and their impacts on South America in order to explore extended-range predictability of those events. The MJO cycle is divided into 8 phases according to the temporal evolution of the first two observed modes of multivariate EOF analysis of tropical convection and zonal winds. The teleconnections associated with these impacts are analyzed with simulations and influence functions of a simple model. The results show that two of the MJO phases strongly enhance the extreme events in the SACZ region and indicate the responsible mechanisms, lending these events a higher degree of predictability on subseasonal time-scales. Therefore, in selecting models to build a subseasonal-range forecasting scheme for extreme precipitation events, a necessary step is the assessment of their skill in reproducing MJO and its observed impacts on South America. Well-known models of the S2S Project, among them the ECMWF and CFS-v2 models are analyzed. Their reforecasts for weeks 1, 2, 3, 4 are separately projected onto the first two modes of tropical convection and zonal wind variability in order to identify the predicted MJO phases. Although the skill of one of the models in predicting these phases extends to week 4, generally the useful skill does not extend beyond week 3. The simulation of the impacts over South America, especially on the SACZ, is also assessed for selected models.

Development and evaluation of height diameter at breast models for native Chinese Metasequoia.

PubMed

Liu, Mu; Feng, Zhongke; Zhang, Zhixiang; Ma, Chenghui; Wang, Mingming; Lian, Bo-Ling; Sun, Renjie; Zhang, Li

2017-01-01

Accurate tree height and diameter at breast height (dbh) are important input variables for growth and yield models. A total of 5503 Chinese Metasequoia trees were used in this study. We studied 53 fitted models, of which 7 were linear models and 46 were non-linear models. These models were divided into two groups of single models and multivariate models according to the number of independent variables. The results show that the allometry equation of tree height which has diameter at breast height as independent variable can better reflect the change of tree height; in addition the prediction accuracy of the multivariate composite models is higher than that of the single variable models. Although tree age is not the most important variable in the study of the relationship between tree height and dbh, the consideration of tree age when choosing models and parameters in model selection can make the prediction of tree height more accurate. The amount of data is also an important parameter what can improve the reliability of models. Other variables such as tree height, main dbh and altitude, etc can also affect models. In this study, the method of developing the recommended models for predicting the tree height of native Metasequoias aged 50-485 years is statistically reliable and can be used for reference in predicting the growth and production of mature native Metasequoia.