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 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

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

Early Adolescent Affect Predicts Later Life Outcomes.

PubMed

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

2016-07-01

Subjective well-being as a predictor for later behavior and health has highlighted its relationship to health, work performance, and social relationships. However, the majority of such studies neglect the developmental nature of well-being in contributing to important changes across the transition to adulthood. To examine the potential role of subjective well-being as a long-term predictor of critical life outcomes, we examined indicators of positive and negative affect at age 14 as predictors of relationship, adjustment, self-worth, and career outcomes a decade later at ages 23 to 25, controlling for family income and gender. We utilised multi-informant methods including reports from the target participant, close friends, and romantic partners in a demographically diverse community sample of 184 participants. Early adolescent positive affect predicted fewer relationship problems (less self-reported and partner-reported conflict, and greater friendship attachment as rated by close peers) and healthy adjustment to adulthood (lower levels of depression, anxiety, and loneliness). It also predicted positive work functioning (higher levels of career satisfaction and job competence) and increased self-worth. Negative affect did not significantly predict any of these important life outcomes. In addition to predicting desirable mean levels of later outcomes, early positive affect predicted beneficial changes across time in many outcomes. The findings extend early research on the beneficial outcomes of subjective well-being by having an earlier assessment of well-being, including informant reports in measuring a large variety of outcome variables, and by extending the findings to a lower socioeconomic group of a diverse and younger sample. The results highlight the importance of considering positive affect as an important component of subjective well-being distinct from negative affect. © 2016 The International Association of Applied Psychology.

Early Adolescent Affect Predicts Later Life Outcomes

PubMed Central

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

2016-01-01

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

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.

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

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.

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...

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…

Motor Execution Affects Action Prediction

ERIC Educational Resources Information Center

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

2011-01-01

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

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

PubMed

Chetverikov, Andrey; Kristjánsson, Árni

2016-09-01

How we perceive, attend to, or remember the stimuli in our environment depends on our preferences for them. Here we argue that this dependence is reciprocal: pleasures and displeasures are heavily dependent on cognitive processing, namely, on our ability to predict the world correctly. We propose that prediction errors, inversely weighted with prior probabilities of predictions, yield subjective experiences of positive or negative affect. In this way, we link affect to predictions within a predictive coding framework. We discuss how three key factors - uncertainty, expectations, and conflict - influence prediction accuracy and show how they shape our affective response. We demonstrate that predictable stimuli are, in general, preferred to unpredictable ones, though too much predictability may decrease this liking effect. Furthermore, the account successfully overcomes the "dark-room" problem, explaining why we do not avoid stimulation to minimize prediction error. We further discuss the implications of our approach for art perception and the utility of affect as feedback for predictions within a prediction-testing architecture of cognition. Copyright © 2016 Elsevier B.V. All rights reserved.

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

USGS Publications Warehouse

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

2014-01-01

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

Misremembering Past Affect Predicts Adolescents' Future Affective Experience During Exercise.

PubMed

Karnaze, Melissa M; Levine, Linda J; Schneider, Margaret

2017-09-01

Increasing physical activity among adolescents is a public health priority. Because people are motivated to engage in activities that make them feel good, this study examined predictors of adolescents' feelings during exercise. During the 1st semester of the school year, we assessed 6th-grade students' (N = 136) cognitive appraisals of the importance of exercise. Participants also reported their affect during a cardiovascular fitness test and recalled their affect during the fitness test later that semester. During the 2nd semester, the same participants rated their affect during a moderate-intensity exercise task. Affect reported during the moderate-intensity exercise task was predicted by cognitive appraisals of the importance of exercise and by misremembering affect during the fitness test as more positive than it actually was. This memory bias mediated the association between appraising exercise as important and experiencing a positive change in affect during the moderate-intensity exercise task. These findings highlight the roles of both cognitive appraisals and memory as factors that may influence affect during exercise. Future work should explore whether affect during exercise can be modified by targeting appraisals and memories related to exercise experiences.

Misremembering Past Affect Predicts Adolescents’ Future Affective Experience during Exercise

PubMed Central

Karnaze, Melissa M.; Levine, Linda J.; Schneider, Margaret

2018-01-01

Purpose Increasing physical activity among adolescents is a public health priority. Because people are motivated to engage in activities that make them feel good, this study examined predictors of adolescents’ feelings during exercise. Method During the first semester of the school year, we assessed sixth grade students’ (N = 136) cognitive appraisals of the importance of exercise. Participants also reported their affect during a cardiovascular fitness test, and recalled their affect during the fitness test later that semester. During the second semester, the same participants rated their affect during a moderate-intensity exercise task. Results Affect reported during the moderate-intensity exercise task was predicted by cognitive appraisals of the importance of exercise, and by misremembering affect during the fitness test as more positive than it actually was. This memory bias mediated the association between appraising exercise as important and experiencing a positive change in affect during the moderate-intensity exercise task. Conclusion These findings highlight the roles of both cognitive appraisals and memory as factors that may influence affect during exercise. Future work should explore whether affect during exercise can be modified by targeting appraisals and memories related to exercise experiences. PMID:28494196

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

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).

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.

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

[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.

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.

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.

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.

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.

Risk terrain modeling predicts child maltreatment.

PubMed

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

2016-12-01

As indicated by research on the long-term effects of adverse childhood experiences (ACEs), maltreatment has far-reaching consequences for affected children. Effective prevention measures have been elusive, partly due to difficulty in identifying vulnerable children before they are harmed. This study employs Risk Terrain Modeling (RTM), an analysis of the cumulative effect of environmental factors thought to be conducive for child maltreatment, to create a highly accurate prediction model for future substantiated child maltreatment cases in the City of Fort Worth, Texas. The model is superior to commonly used hotspot predictions and more beneficial in aiding prevention efforts in a number of ways: 1) it identifies the highest risk areas for future instances of child maltreatment with improved precision and accuracy; 2) it aids the prioritization of risk-mitigating efforts by informing about the relative importance of the most significant contributing risk factors; 3) since predictions are modeled as a function of easily obtainable data, practitioners do not have to undergo the difficult process of obtaining official child maltreatment data to apply it; 4) the inclusion of a multitude of environmental risk factors creates a more robust model with higher predictive validity; and, 5) the model does not rely on a retrospective examination of past instances of child maltreatment, but adapts predictions to changing environmental conditions. The present study introduces and examines the predictive power of this new tool to aid prevention efforts seeking to improve the safety, health, and wellbeing of vulnerable children. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

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

Daily Affective Experiences Predict Objective Sleep Outcomes among Adolescents

PubMed Central

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

2015-01-01

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

See it with feeling: affective predictions during object perception

PubMed Central

Barrett, L.F.; Bar, Moshe

2009-01-01

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

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.

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.

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...

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.

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.

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.

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.

Daily affective experiences predict objective sleep outcomes among adolescents.

PubMed

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

2016-02-01

Adolescence is a sensitive period for changes in both sleep and affect. Although past research has assessed the association between affect and sleep among adolescents, few studies have examined both trait (typical) and day-to-day changes in affect, and fewer still have specifically examined negative social evaluative emotions (e.g. embarrassment) in relation to sleep. Both between- and within-person variations in daily affect were examined in relation to four objectively-measured sleep outcomes (sleep hours; sleep latency; sleep efficiency; and length of wake bouts) among adolescents. Participants (N = 77 high-school students; 42.9% female; M = 14.37 years) wore an actiwatch and completed daily-diaries for 3 days. The results of hierarchical linear models (controlling for age, gender, race, ethnicity, parental employment status, income, puberty and caffeine) indicated that negative social evaluative emotions and high-arousal affective experiences generally predicted poor sleep outcomes, whereas low-arousal affective experiences were associated with good sleep outcomes. Specifically, at the person level, adolescents reporting higher negative social evaluative emotions had shorter average sleep hours, and those experiencing higher anxiety–nervousness had longer wake bouts. In addition, individuals experiencing more dysphoria (sad, depressed, lonely) had longer average sleep hours and shorter wake bouts, while those experiencing more calmness had shorter sleep latencies. At the within-person level, individuals had longer sleep latencies following days that they had experienced high-arousal positive affect (e.g. excitement), and had longer wake bouts following days they had experienced more negative social evaluative emotions. The results highlight the detrimental effects of negative social evaluative emotions and high-arousal affective states for adolescent sleep. © 2015 The Authors. Journal of Sleep Research published by John Wiley & Sons Ltd on behalf of European

Gray correlation analysis and prediction models of living refuse generation in Shanghai city.

PubMed

Liu, Gousheng; Yu, Jianguo

2007-01-01

A better understanding of the factors that affect the generation of municipal living refuse (MLF) and the accurate prediction of its generation are crucial for municipal planning projects and city management. Up to now, most of the design efforts have been based on a rough prediction of MLF without any actual support. In this paper, based on published data of socioeconomic variables and MLF generation from 1990 to 2003 in the city of Shanghai, the main factors that affect MLF generation have been quantitatively studied using the method of gray correlation coefficient. Several gray models, such as GM(1,1), GIM(1), GPPM(1) and GLPM(1), have been studied, and predicted results are verified with subsequent residual test. Results show that, among the selected seven factors, consumption of gas, water and electricity are the largest three factors affecting MLF generation, and GLPM(1) is the optimized model to predict MLF generation. Through this model, the predicted MLF generation in 2010 in Shanghai will be 7.65 million tons. The methods and results developed in this paper can provide valuable information for MLF management and related municipal planning projects.

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

ERIC Educational Resources Information Center

Neuendorf, Kimberly A.; Sparks, Glenn G.

1988-01-01

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

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

PubMed

Tement, Sara; Korunka, Christian

2013-01-01

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

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.

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.

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

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

NASA Technical Reports Server (NTRS)

Ashrafi, S.

1991-01-01

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

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

USGS Publications Warehouse

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

2014-01-01

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

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.

Predicted Bacterial Interactions Affect in Vivo Microbial Colonization Dynamics in Nematostella

PubMed Central

Domin, Hanna; Zurita-Gutiérrez, Yazmín H.; Scotti, Marco; Buttlar, Jann; Hentschel Humeida, Ute; Fraune, Sebastian

2018-01-01

The maintenance and resilience of host-associated microbiota during development is a fundamental process influencing the fitness of many organisms. Several host properties were identified as influencing factors on bacterial colonization, including the innate immune system, mucus composition, and diet. In contrast, the importance of bacteria–bacteria interactions on host colonization is less understood. Here, we use bacterial abundance data of the marine model organism Nematostella vectensis to reconstruct potential bacteria–bacteria interactions through co-occurrence networks. The analysis indicates that bacteria–bacteria interactions are dynamic during host colonization and change according to the host’s developmental stage. To assess the predictive power of inferred interactions, we tested bacterial isolates with predicted cooperative or competitive behavior for their ability to influence bacterial recolonization dynamics. Within 3 days of recolonization, all tested bacterial isolates affected bacterial community structure, while only competitive bacteria increased bacterial diversity. Only 1 week after recolonization, almost no differences in bacterial community structure could be observed between control and treatments. These results show that predicted competitive bacteria can influence community structure for a short period of time, verifying the in silico predictions. However, within 1 week, the effects of the bacterial isolates are neutralized, indicating a high degree of resilience of the bacterial community. PMID:29740401

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

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

Crime prediction modeling

NASA Technical Reports Server (NTRS)

1971-01-01

A study of techniques for the prediction of crime in the City of Los Angeles was conducted. Alternative approaches to crime prediction (causal, quasicausal, associative, extrapolative, and pattern-recognition models) are discussed, as is the environment within which predictions were desired for the immediate application. The decision was made to use time series (extrapolative) models to produce the desired predictions. The characteristics of the data and the procedure used to choose equations for the extrapolations are discussed. The usefulness of different functional forms (constant, quadratic, and exponential forms) and of different parameter estimation techniques (multiple regression and multiple exponential smoothing) are compared, and the quality of the resultant predictions is assessed.

Acculturation Predicts Negative Affect and Shortened Telomere Length.

PubMed

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

2016-10-12

Chronic stress may accelerate cellular aging. Telomeres, protective "caps" at the end of chromosomes, modulate cellular aging and may be good biomarkers for the effects of chronic stress, including that associated with acculturation. The purpose of this analysis was to examine telomere length (TL) in acculturating Hispanic Mexican American women and to determine the associations among TL, acculturation, and psychological factors. As part of a larger cross-sectional study of 516 pregnant Hispanic Mexican American women, we analyzed DNA in blood samples (N = 56) collected at 22-24 weeks gestation for TL as an exploratory measure using monochrome multiplex quantitative telomere polymerase chain reaction (PCR). We measured acculturation with the Acculturation Rating Scale for Mexican Americans, depression with the Beck Depression Inventory, discrimination with the Experiences of Discrimination Scale, and stress with the Perceived Stress Scale. TL was negatively moderately correlated with two variables of acculturation: Anglo orientation and greater acculturation-level scores. We combined these scores for a latent variable, acculturation, and we combined depression, stress, and discrimination scores in another latent variable, "negative affectivity." Acculturation and negative affectivity were bidirectionally correlated. Acculturation significantly negatively predicted TL. Using structural equation modeling, we found the model had an excellent fit with the root mean square error of approximation estimate = .0001, comparative fit index = 1.0, Tucker-Lewis index = 1.0, and standardized root mean square residual = .05. The negative effects of acculturation on the health of Hispanic women have been previously demonstrated. Findings from this analysis suggest a link between acculturation and TL, which may indicate accelerated cellular aging associated with overall poor health outcomes. © The Author(s) 2016.

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.

A reinforcement sensitivity model of affective and behavioral dysregulation in marijuana use and associated problems.

PubMed

Emery, Noah N; Simons, Jeffrey S

2017-08-01

This study tested a model linking sensitivity to punishment (SP) and reward (SR) to marijuana use and problems via affect lability and poor control. A 6-month prospective design was used in a sample of 2,270 young-adults (64% female). The hypothesized SP × SR interaction did not predict affect lability or poor control, but did predict use likelihood at baseline. At low levels of SR, SP was associated with an increased likelihood of abstaining, which was attenuated as SR increased. SP and SR displayed positive main effects on both affect lability and poor control. Affect lability and poor control, in turn, mediated effects on the marijuana outcomes. Poor control predicted both increased marijuana use and, controlling for use level, greater intensity of problems. Affect lability predicted greater intensity of problems, but was not associated with use level. There were few prospective effects. SR consistently predicted greater marijuana use and problems. SP however, exhibited both risk and protective pathways. Results indicate that SP is associated with a decreased likelihood of marijuana use. However, once use is initiated SP is associated with increased risk of problems, in part, due to its effects on both affect and behavioral dysregulation. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Revealing how network structure affects accuracy of link prediction

NASA Astrophysics Data System (ADS)

Yang, Jin-Xuan; Zhang, Xiao-Dong

2017-08-01

Link prediction plays an important role in network reconstruction and network evolution. The network structure affects the accuracy of link prediction, which is an interesting problem. In this paper we use common neighbors and the Gini coefficient to reveal the relation between them, which can provide a good reference for the choice of a suitable link prediction algorithm according to the network structure. Moreover, the statistical analysis reveals correlation between the common neighbors index, Gini coefficient index and other indices to describe the network structure, such as Laplacian eigenvalues, clustering coefficient, degree heterogeneity, and assortativity of network. Furthermore, a new method to predict missing links is proposed. The experimental results show that the proposed algorithm yields better prediction accuracy and robustness to the network structure than existing currently used methods for a variety of real-world networks.

Motivated prediction of future feelings: effects of negative mood and mood orientation on affective forecasts.

PubMed

Buehler, Roger; McFarland, Cathy; Spyropoulos, Vassili; Lam, Kent C H

2007-09-01

This article examines the role of motivational factors in affective forecasting. The primary hypothesis was that people predict positive emotional reactions to future events when they are motivated to enhance their current feelings. Three experiments manipulated participants' moods (negative vs. neutral) and orientation toward their moods (reflective vs. ruminative) and then assessed the positivity of their affective predictions for future events. As hypothesized, when participants adopted a reflective orientation, and thus should have been motivated to engage in mood-regulation processes, they predicted more positive feelings in the negative than in the neutral mood condition. This pattern of mood-incongruent affective prediction was not exhibited when participants adopted a ruminative orientation. Additionally, within the negative mood condition, generating affective forecasts had a more positive emotional impact on reflectors than on ruminators. The findings suggest that affective predictions are sometimes driven by mood-regulatory motives.

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.

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.

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.

Performance predictions affect attentional processes of event-based prospective memory.

PubMed

Rummel, Jan; Kuhlmann, Beatrice G; Touron, Dayna R

2013-09-01

To investigate whether making performance predictions affects prospective memory (PM) processing, we asked one group of participants to predict their performance in a PM task embedded in an ongoing task and compared their performance with a control group that made no predictions. A third group gave not only PM predictions but also ongoing-task predictions. Exclusive PM predictions resulted in slower ongoing-task responding both in a nonfocal (Experiment 1) and in a focal (Experiment 2) PM task. Only in the nonfocal task was the additional slowing accompanied by improved PM performance. Even in the nonfocal task, however, was the correlation between ongoing-task speed and PM performance reduced after predictions, suggesting that the slowing was not completely functional for PM. Prediction-induced changes could be avoided by asking participants to additionally predict their performance in the ongoing task. In sum, the present findings substantiate a role of metamemory for attention-allocation strategies of PM. Copyright © 2013 Elsevier Inc. All rights reserved.

Cultural Resource Predictive Modeling

DTIC Science & Technology

2017-10-01

property to manage ? a. Yes 2) Do you use CRPM (Cultural Resource Predictive Modeling) No, but I use predictive modelling informally . For example...resource program and provide support to the test ranges for their missions. This document will provide information such as lessons learned, points...of contact, and resources to the range cultural resource managers . Objective/Scope: Identify existing cultural resource predictive models and

Neural Affective Mechanisms Predict Market-Level Microlending

PubMed Central

Genevsky, Alexander; Knutson, Brian

2015-01-01

Humans sometimes share with others whom they may never meet or know, in violation of the dictates of pure self-interest. Research has not established which neuropsychological mechanisms support lending decisions, nor whether their influence extends to markets involving significant financial incentives. In two studies, we found that neural affective mechanisms influence the success of requests for microloans. In a large Internet database of microloan requests (N = 13,500), we found that positive affective features of photographs promoted the success of those requests. We then established that neural activity (i.e., in the nucleus accumbens) and self-reported positive arousal in a neuroimaging sample (N = 28) predicted the success of loan requests on the Internet, above and beyond the effects of the neuroimaging sample’s own choices (i.e., to lend or not). These findings suggest that elicitation of positive arousal can promote the success of loan requests, both in the laboratory and on the Internet. They also highlight affective neuroscience’s potential to probe neuropsychological mechanisms that drive microlending, enhance the effectiveness of loan requests, and forecast market-level behavior. PMID:26187248

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

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.

Numerical weather prediction model tuning via ensemble prediction system

NASA Astrophysics Data System (ADS)

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

2011-12-01

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

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

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).

Predictive modeling of complications.

PubMed

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

2016-09-01

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

The role of steroids in the prediction of affective disorders in adult men.

PubMed

Šrámková, Monika; Dušková, Michaela; Hill, Martin; Bičíková, Marie; Řípová, Daniela; Mohr, Pavel; Stárka, Luboslav

2017-05-01

Anxiety and mood disorders (AMD) are the most frequent mental disorders in the human population. They have recently shown increasing prevalence, and commonly disrupt personal and working lives. The aim of our study was to analyze the spectrum of circulating steroids in order to discover differences that could potentially be markers of affective depression or anxiety, and identify which steroids could be a predictive component for these diseases. We studied the steroid metabolome including 47 analytes in 20 men with depression (group D), 20 men with anxiety (group AN) and 30 healthy controls. OPLS and multivariate regression models were used for statistical analysis. Discrimination of group D from controls by the OPLS method was absolute, as was group AN from controls (sensitivity=1.000 (0.839, 1.000), specificity=1.000 (0.887, 1.000)). Relatively good predictivity was also found for discrimination between group D from AN (sensitivity=0.850 (0.640, 0.948), specificity=0.900 (0.699, 0.972)). Selected circulating steroids, including those that are neuroactive and neuroprotective, can be useful tools for discriminating between these affective diseases in adult men. Copyright © 2016. Published by Elsevier Inc.

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

PubMed

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

2016-06-01

We draw from personality systems interaction (PSI) theory (Kuhl, 2000) and regulatory focus theory (Higgins, 1997) to examine how dynamic positive and negative affective processes interact to predict both task and contextual performance. Using a twice-daily diary design over the course of a 3-week period, results from multilevel regression analysis revealed that distinct patterns of change in positive and negative affect optimally predicted contextual and task performance among a sample of 71 employees at a medium-sized technology company. Specifically, within persons, increases (upshifts) in positive affect over the course of a workday better predicted the subsequent day's organizational citizenship behavior (OCB) when such increases were coupled with decreases (downshifts) in negative affect. The optimal pattern of change in positive and negative affect differed, however, in predicting task performance. That is, upshifts in positive affect over the course of the workday better predicted the subsequent day's task performance when such upshifts were accompanied by upshifts in negative affect. The contribution of our findings to PSI theory and the broader affective and motivation regulation literatures, along with practical implications, are discussed. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

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.

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.

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.

Affective decision-making predictive of Chinese adolescent drinking behaviors

PubMed Central

XIAO, LIN; BECHARA, ANTOINE; GRENARD, L. JERRY; STACY, W. ALAN; PALMER, PAULA; WEI, YONGLAN; JIA, YONG; FU, XIAOLU; JOHNSON, C. ANDERSON

2013-01-01

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

Emotion modelling towards affective pathogenesis.

PubMed

Bas, James Le

2009-12-01

Objective: There is a need in psychiatry for models that integrate pathological states with normal systems. The interaction of arousal and emotion is the focus of an exploration of affective pathogenesis. Method: Given that the explicit causes of affective disorder remain nascent, methods of linking emotion and disorder are evaluated. Results: A network model of emotional families is presented, in which emotions exist as quantal gradients. Morbid emotional states are seen as the activation of distal emotion sites. The phenomenology of affective disorders is described with reference to this model. Recourse is made to non-linear dynamic theory. Conclusions: Metaphoric emotion models have face validity and may prove a useful heuristic.

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.

Predictive models in urology.

PubMed

Cestari, Andrea

2013-01-01

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

Predicting outcomes of group cognitive behavior therapy for patients with affective and neurotic disorders.

PubMed

Hooke, Geoffrey R; Page, Andrew C

2002-10-01

An attempt was made to predict outcomes following group Cognitive Behavior Therapy (CBT) for patients with affective and neurotic disorders. A group of 348 patients at a private psychiatric clinic, treated in a group CBT program, completed the Depression, Anxiety, and Stress Scale (DASS) before and after treatment. Prior to treatment, data from the Locus of Control of Behavior (LCB), a Global Assessment of Function (GAF), the Health of the Nation Outcome Scales (HoNOS), and the Rosenberg Self Esteem Scale (RSE) were also collected. Results indicated that posttreatment stress scores of all patients were predicted by pretreatment stress and self-esteem. Among patients with neurotic disorders, posttreatment anxiety was predicted by initial anxiety and self-esteem whereas among patients with affective disorders, posttreatment anxiety scores were predicted by initial anxiety and GAF. For patients with neurotic disorders, self-esteem did not predict variance in posttreatment depression in addition to that explained by pretreatment depression. In contrast, for patients with affective disorders, pretreatment depression and Locus of Control predicted posttreatment depression.

Predictive models in cancer management: A guide for clinicians.

PubMed

Kazem, Mohammed Ali

2017-04-01

Predictive tools in cancer management are used to predict different outcomes including survival probability or risk of recurrence. The uptake of these tools by clinicians involved in cancer management has not been as common as other clinical tools, which may be due to the complexity of some of these tools or a lack of understanding of how they can aid decision-making in particular clinical situations. The aim of this article is to improve clinicians' knowledge and understanding of predictive tools used in cancer management, including how they are built, how they can be applied to medical practice, and what their limitations may be. Literature review was conducted to investigate the role of predictive tools in cancer management. All predictive models share similar characteristics, but depending on the type of the tool its ability to predict an outcome will differ. Each type has its own pros and cons, and its generalisability will depend on the cohort used to build the tool. These factors will affect the clinician's decision whether to apply the model to their cohort or not. Before a model is used in clinical practice, it is important to appreciate how the model is constructed, what its use may add over and above traditional decision-making tools, and what problems or limitations may be associated with it. Understanding all the above is an important step for any clinician who wants to decide whether or not use predictive tools in their practice. Copyright © 2016 Royal College of Surgeons of Edinburgh (Scottish charity number SC005317) and Royal College of Surgeons in Ireland. Published by Elsevier Ltd. All rights reserved.

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.

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.

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.

Inverse and Predictive Modeling

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

Syracuse, Ellen Marie

The LANL Seismo-Acoustic team has a strong capability in developing data-driven models that accurately predict a variety of observations. These models range from the simple – one-dimensional models that are constrained by a single dataset and can be used for quick and efficient predictions – to the complex – multidimensional models that are constrained by several types of data and result in more accurate predictions. Team members typically build models of geophysical characteristics of Earth and source distributions at scales of 1 to 1000s of km, the techniques used are applicable for other types of physical characteristics at an evenmore » greater range of scales. The following cases provide a snapshot of some of the modeling work done by the Seismo- Acoustic team at LANL.« less

Archaeological predictive model set.

DOT National Transportation Integrated Search

2015-03-01

This report is the documentation for Task 7 of the Statewide Archaeological Predictive Model Set. The goal of this project is to : develop a set of statewide predictive models to assist the planning of transportation projects. PennDOT is developing t...

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

A polynomial based model for cell fate prediction in human diseases.

PubMed

Ma, Lichun; Zheng, Jie

2017-12-21

Cell fate regulation directly affects tissue homeostasis and human health. Research on cell fate decision sheds light on key regulators, facilitates understanding the mechanisms, and suggests novel strategies to treat human diseases that are related to abnormal cell development. In this study, we proposed a polynomial based model to predict cell fate. This model was derived from Taylor series. As a case study, gene expression data of pancreatic cells were adopted to test and verify the model. As numerous features (genes) are available, we employed two kinds of feature selection methods, i.e. correlation based and apoptosis pathway based. Then polynomials of different degrees were used to refine the cell fate prediction function. 10-fold cross-validation was carried out to evaluate the performance of our model. In addition, we analyzed the stability of the resultant cell fate prediction model by evaluating the ranges of the parameters, as well as assessing the variances of the predicted values at randomly selected points. Results show that, within both the two considered gene selection methods, the prediction accuracies of polynomials of different degrees show little differences. Interestingly, the linear polynomial (degree 1 polynomial) is more stable than others. When comparing the linear polynomials based on the two gene selection methods, it shows that although the accuracy of the linear polynomial that uses correlation analysis outcomes is a little higher (achieves 86.62%), the one within genes of the apoptosis pathway is much more stable. Considering both the prediction accuracy and the stability of polynomial models of different degrees, the linear model is a preferred choice for cell fate prediction with gene expression data of pancreatic cells. The presented cell fate prediction model can be extended to other cells, which may be important for basic research as well as clinical study of cell development related diseases.

Affective-cognitive meta-bases versus structural bases of attitudes predict processing interest versus efficiency.

PubMed

See, Ya Hui Michelle; Petty, Richard E; Fabrigar, Leandre R

2013-08-01

We proposed that (a) processing interest for affective over cognitive information is captured by meta-bases (i.e., the extent to which people subjectively perceive themselves to rely on affect or cognition in their attitudes) and (b) processing efficiency for affective over cognitive information is captured by structural bases (i.e., the extent to which attitudes are more evaluatively congruent with affect or cognition). Because processing speed can disentangle interest from efficiency by being manifest as longer or shorter reading times, we hypothesized and found that more affective meta-bases predicted longer affective than cognitive reading time when processing efficiency was held constant (Study 1). In contrast, more affective structural bases predicted shorter affective than cognitive reading time when participants were constrained in their ability to allocate resources deliberatively (Study 2). When deliberation was neither encouraged nor constrained, effects for meta-bases and structural bases emerged (Study 3). Implications for affective-cognitive processing and other attitudes-relevant constructs are discussed.

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.

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.

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

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

PubMed Central

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

2012-01-01

Children with fragile X syndrome (FXS) face high risk for anxiety disorders, yet no studies have explored FXS as a high-risk sample for investigating early manifestations of anxiety outcomes. Negative affect is one of the most salient predictors of problem behaviors and has been associated with both anxiety and autistic outcomes in clinical and non-clinical pediatric samples. In light of the high comorbidity between autism and anxiety within FXS, the present study investigates the relationship between longitudinal trajectories of negative affect (between 8 and 71 months) and severity of anxiety and autistic outcomes in young males with FXS (n= 25). Multilevel models indicated associations between elevated anxiety and higher fear and sadness, lower soothability, and steeper longitudinal increases in approach. Autistic outcomes were unrelated to negative affect. These findings suggest early negative affect differentially predicts anxiety, not autistic symptoms, within FXS. Future research is warranted to determine the specificity of the relationship between negative affect and anxiety, as well as to explore potential moderators. Characterizing the relationship between early negative affect and anxiety within FXS may inform etiology and treatment considerations specific to children with FXS, as well as lend insight into precursors of anxiety disorders in other clinical groups and community samples. PMID:23011214

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

PubMed

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

2015-06-01

Earlier use of tobacco in adolescence is associated with numerous adverse outcomes later in life. Although a number of studies have linked individual differences in affective functioning to adolescent smoking, these have relied primarily on self-report measures, and the contribution of different dimensions of affect to the onset of tobacco use during this period remains unclear. The current study examined these issues in a sample of 180 adolescents recruited from an ongoing prospective, longitudinal study examining emotional development. At approximately age 12, participants completed a questionnaire measure of affective temperament and took part in a family interaction task that was coded observationally to provide measures of dysphoric, aggressive, and positive behaviors. At 2 subsequent assessments, which took place approximately 2.5 years and 4 years after the initial assessment, participants completed a questionnaire measure of substance use. In total, 70 participants initiated smoking between the ages of 12 and 17. An earlier onset of smoking was predicted by more aggressive and less positive observed behavior during the interaction task, as well as lower levels of self-reported temperamental Effortful Control. There were no associations between dysphoric behaviors, or temperamental measures of negative affectivity or surgency and the onset of smoking. The findings add to a small body of literature demonstrating that behavioral components of affect can prospectively predict substance use in adolescence and suggest that different dimensions of affect show unique relationships with early substance use. (PsycINFO Database Record (c) 2015 APA, all rights reserved).

Predicting readmission risk with institution-specific prediction models.

PubMed

Yu, Shipeng; Farooq, Faisal; van Esbroeck, Alexander; Fung, Glenn; Anand, Vikram; Krishnapuram, Balaji

2015-10-01

The ability to predict patient readmission risk is extremely valuable for hospitals, especially under the Hospital Readmission Reduction Program of the Center for Medicare and Medicaid Services which went into effect starting October 1, 2012. There is a plethora of work in the literature that deals with developing readmission risk prediction models, but most of them do not have sufficient prediction accuracy to be deployed in a clinical setting, partly because different hospitals may have different characteristics in their patient populations. We propose a generic framework for institution-specific readmission risk prediction, which takes patient data from a single institution and produces a statistical risk prediction model optimized for that particular institution and, optionally, for a specific condition. This provides great flexibility in model building, and is also able to provide institution-specific insights in its readmitted patient population. We have experimented with classification methods such as support vector machines, and prognosis methods such as the Cox regression. We compared our methods with industry-standard methods such as the LACE model, and showed the proposed framework is not only more flexible but also more effective. We applied our framework to patient data from three hospitals, and obtained some initial results for heart failure (HF), acute myocardial infarction (AMI), pneumonia (PN) patients as well as patients with all conditions. On Hospital 2, the LACE model yielded AUC 0.57, 0.56, 0.53 and 0.55 for AMI, HF, PN and All Cause readmission prediction, respectively, while the proposed model yielded 0.66, 0.65, 0.63, 0.74 for the corresponding conditions, all significantly better than the LACE counterpart. The proposed models that leverage all features at discharge time is more accurate than the models that only leverage features at admission time (0.66 vs. 0.61 for AMI, 0.65 vs. 0.61 for HF, 0.63 vs. 0.56 for PN, 0.74 vs. 0.60 for All

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.

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

The affective shift model of work engagement.

PubMed

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

2011-11-01

On the basis of self-regulation theories, the authors develop an affective shift model of work engagement according to which work engagement emerges from the dynamic interplay of positive and negative affect. The affective shift model posits that negative affect is positively related to work engagement if negative affect is followed by positive affect. The authors applied experience sampling methodology to test the model. Data on affective events, mood, and work engagement was collected twice a day over 9 working days among 55 software developers. In support of the affective shift model, negative mood and negative events experienced in the morning of a working day were positively related to work engagement in the afternoon if positive mood in the time interval between morning and afternoon was high. Individual differences in positive affectivity moderated within-person relationships. The authors discuss how work engagement can be fostered through affect regulation. (c) 2011 APA, all rights reserved.

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

NASA Astrophysics Data System (ADS)

Yu, B.

2015-06-01

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

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.

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

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.

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.

Accurate and dynamic predictive model for better prediction in medicine and healthcare.

PubMed

Alanazi, H O; Abdullah, A H; Qureshi, K N; Ismail, A S

2018-05-01

Information and communication technologies (ICTs) have changed the trend into new integrated operations and methods in all fields of life. The health sector has also adopted new technologies to improve the systems and provide better services to customers. Predictive models in health care are also influenced from new technologies to predict the different disease outcomes. However, still, existing predictive models have suffered from some limitations in terms of predictive outcomes performance. In order to improve predictive model performance, this paper proposed a predictive model by classifying the disease predictions into different categories. To achieve this model performance, this paper uses traumatic brain injury (TBI) datasets. TBI is one of the serious diseases worldwide and needs more attention due to its seriousness and serious impacts on human life. The proposed predictive model improves the predictive performance of TBI. The TBI data set is developed and approved by neurologists to set its features. The experiment results show that the proposed model has achieved significant results including accuracy, sensitivity, and specificity.

Posterior Predictive Bayesian Phylogenetic Model Selection

PubMed Central

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

2014-01-01

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

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)...

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

PubMed

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

2012-08-01

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

Multi-model ensemble hydrologic prediction using Bayesian model averaging

NASA Astrophysics Data System (ADS)

Duan, Qingyun; Ajami, Newsha K.; Gao, Xiaogang; Sorooshian, Soroosh

2007-05-01

Multi-model ensemble strategy is a means to exploit the diversity of skillful predictions from different models. This paper studies the use of Bayesian model averaging (BMA) scheme to develop more skillful and reliable probabilistic hydrologic predictions from multiple competing predictions made by several hydrologic models. BMA is a statistical procedure that infers consensus predictions by weighing individual predictions based on their probabilistic likelihood measures, with the better performing predictions receiving higher weights than the worse performing ones. Furthermore, BMA provides a more reliable description of the total predictive uncertainty than the original ensemble, leading to a sharper and better calibrated probability density function (PDF) for the probabilistic predictions. In this study, a nine-member ensemble of hydrologic predictions was used to test and evaluate the BMA scheme. This ensemble was generated by calibrating three different hydrologic models using three distinct objective functions. These objective functions were chosen in a way that forces the models to capture certain aspects of the hydrograph well (e.g., peaks, mid-flows and low flows). Two sets of numerical experiments were carried out on three test basins in the US to explore the best way of using the BMA scheme. In the first set, a single set of BMA weights was computed to obtain BMA predictions, while the second set employed multiple sets of weights, with distinct sets corresponding to different flow intervals. In both sets, the streamflow values were transformed using Box-Cox transformation to ensure that the probability distribution of the prediction errors is approximately Gaussian. A split sample approach was used to obtain and validate the BMA predictions. The test results showed that BMA scheme has the advantage of generating more skillful and equally reliable probabilistic predictions than original ensemble. The performance of the expected BMA predictions in terms of

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

Modeling Intraindividual Dynamics Using Stochastic Differential Equations: Age Differences in Affect Regulation.

PubMed

Wood, Julie; Oravecz, Zita; Vogel, Nina; Benson, Lizbeth; Chow, Sy-Miin; Cole, Pamela; Conroy, David E; Pincus, Aaron L; Ram, Nilam

2017-12-15

Life-span theories of aging suggest improvements and decrements in individuals' ability to regulate affect. Dynamic process models, with intensive longitudinal data, provide new opportunities to articulate specific theories about individual differences in intraindividual dynamics. This paper illustrates a method for operationalizing affect dynamics using a multilevel stochastic differential equation (SDE) model, and examines how those dynamics differ with age and trait-level tendencies to deploy emotion regulation strategies (reappraisal and suppression). Univariate multilevel SDE models, estimated in a Bayesian framework, were fit to 21 days of ecological momentary assessments of affect valence and arousal (average 6.93/day, SD = 1.89) obtained from 150 adults (age 18-89 years)-specifically capturing temporal dynamics of individuals' core affect in terms of attractor point, reactivity to biopsychosocial (BPS) inputs, and attractor strength. Older age was associated with higher arousal attractor point and less BPS-related reactivity. Greater use of reappraisal was associated with lower valence attractor point. Intraindividual variability in regulation strategy use was associated with greater BPS-related reactivity and attractor strength, but in different ways for valence and arousal. The results highlight the utility of SDE models for studying affect dynamics and informing theoretical predictions about how intraindividual dynamics change over the life course. © The Author 2017. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

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.

Multi-model analysis in hydrological prediction

NASA Astrophysics Data System (ADS)

Lanthier, M.; Arsenault, R.; Brissette, F.

2017-12-01

Hydrologic modelling, by nature, is a simplification of the real-world hydrologic system. Therefore ensemble hydrological predictions thus obtained do not present the full range of possible streamflow outcomes, thereby producing ensembles which demonstrate errors in variance such as under-dispersion. Past studies show that lumped models used in prediction mode can return satisfactory results, especially when there is not enough information available on the watershed to run a distributed model. But all lumped models greatly simplify the complex processes of the hydrologic cycle. To generate more spread in the hydrologic ensemble predictions, multi-model ensembles have been considered. In this study, the aim is to propose and analyse a method that gives an ensemble streamflow prediction that properly represents the forecast probabilities and reduced ensemble bias. To achieve this, three simple lumped models are used to generate an ensemble. These will also be combined using multi-model averaging techniques, which generally generate a more accurate hydrogram than the best of the individual models in simulation mode. This new predictive combined hydrogram is added to the ensemble, thus creating a large ensemble which may improve the variability while also improving the ensemble mean bias. The quality of the predictions is then assessed on different periods: 2 weeks, 1 month, 3 months and 6 months using a PIT Histogram of the percentiles of the real observation volumes with respect to the volumes of the ensemble members. Initially, the models were run using historical weather data to generate synthetic flows. This worked for individual models, but not for the multi-model and for the large ensemble. Consequently, by performing data assimilation at each prediction period and thus adjusting the initial states of the models, the PIT Histogram could be constructed using the observed flows while allowing the use of the multi-model predictions. The under-dispersion has been

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

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 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.

Learning Instance-Specific Predictive Models

PubMed Central

Visweswaran, Shyam; Cooper, Gregory F.

2013-01-01

This paper introduces a Bayesian algorithm for constructing predictive models from data that are optimized to predict a target variable well for a particular instance. This algorithm learns Markov blanket models, carries out Bayesian model averaging over a set of models to predict a target variable of the instance at hand, and employs an instance-specific heuristic to locate a set of suitable models to average over. We call this method the instance-specific Markov blanket (ISMB) algorithm. The ISMB algorithm was evaluated on 21 UCI data sets using five different performance measures and its performance was compared to that of several commonly used predictive algorithms, including nave Bayes, C4.5 decision tree, logistic regression, neural networks, k-Nearest Neighbor, Lazy Bayesian Rules, and AdaBoost. Over all the data sets, the ISMB algorithm performed better on average on all performance measures against all the comparison algorithms. PMID:25045325

Patient-ventilator asynchrony affects pulse pressure variation prediction of fluid responsiveness.

PubMed

Messina, Antonio; Colombo, Davide; Cammarota, Gianmaria; De Lucia, Marta; Cecconi, Maurizio; Antonelli, Massimo; Corte, Francesco Della; Navalesi, Paolo

2015-10-01

During partial ventilatory support, pulse pressure variation (PPV) fails to adequately predict fluid responsiveness. This prospective study aims to investigate whether patient-ventilator asynchrony affects PPV prediction of fluid responsiveness during pressure support ventilation (PSV). This is an observational physiological study evaluating the response to a 500-mL fluid challenge in 54 patients receiving PSV, 27 without (Synch) and 27 with asynchronies (Asynch), as assessed by visual inspection of ventilator waveforms by 2 skilled blinded physicians. The area under the curve was 0.71 (confidence interval, 0.57-0.83) for the overall population, 0.86 (confidence interval, 0.68-0.96) in the Synch group, and 0.53 (confidence interval, 0.33-0.73) in the Asynch group (P = .018). Sensitivity and specificity of PPV were 78% and 89% in the Synch group and 36% and 46% in the Asynch group. Logistic regression showed that the PPV prediction was influenced by patient-ventilator asynchrony (odds ratio, 8.8 [2.0-38.0]; P < .003). Of the 27 patients without asynchronies, 12 had a tidal volume greater than or equal to 8 mL/kg; in this subgroup, the rate of correct classification was 100%. Patient-ventilator asynchrony affects PPV performance during partial ventilatory support influencing its efficacy in predicting fluid responsiveness. Copyright © 2015 Elsevier Inc. All rights reserved.

Model-based predictions for dopamine.

PubMed

Langdon, Angela J; Sharpe, Melissa J; Schoenbaum, Geoffrey; Niv, Yael

2018-04-01

Phasic dopamine responses are thought to encode a prediction-error signal consistent with model-free reinforcement learning theories. However, a number of recent findings highlight the influence of model-based computations on dopamine responses, and suggest that dopamine prediction errors reflect more dimensions of an expected outcome than scalar reward value. Here, we review a selection of these recent results and discuss the implications and complications of model-based predictions for computational theories of dopamine and learning. Copyright © 2017. Published by Elsevier Ltd.

Within-person changes in salivary testosterone and physical characteristics of puberty predict boys' daily affect.

PubMed

Klipker, Kathrin; Wrzus, Cornelia; Rauers, Antje; Boker, Steven M; Riediger, Michaela

2017-09-01

Recent investigations highlighted the role of within-person pubertal changes for adolescents' behavior. Yet, little is known about effects on adolescents' daily affect, particularly regarding the hormonal changes underlying physical changes during puberty. In a study with 148 boys aged 10 to 20years, we tested whether within-person physical and hormonal changes over eight months predicted everyday affect fluctuations, measured with experience sampling. As expected, greater within-person changes in testosterone (but not in dehydroepiandrosterone) were associated with higher affect fluctuations in daily life. Additionally, greater physical changes predicted higher affect fluctuations for individuals in the beginning of puberty. The findings demonstrate the relevance of physical and hormonal changes in boys' affective (in)stability. Copyright © 2017 Elsevier Inc. All rights reserved.

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.

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.

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 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.

Fixed recurrence and slip models better predict earthquake behavior than the time- and slip-predictable models: 2. Laboratory earthquakes

NASA Astrophysics Data System (ADS)

Rubinstein, Justin L.; Ellsworth, William L.; Beeler, Nicholas M.; Kilgore, Brian D.; Lockner, David A.; Savage, Heather M.

2012-02-01

The behavior of individual stick-slip events observed in three different laboratory experimental configurations is better explained by a "memoryless" earthquake model with fixed inter-event time or fixed slip than it is by the time- and slip-predictable models for earthquake occurrence. We make similar findings in the companion manuscript for the behavior of natural repeating earthquakes. Taken together, these results allow us to conclude that the predictions of a characteristic earthquake model that assumes either fixed slip or fixed recurrence interval should be preferred to the predictions of the time- and slip-predictable models for all earthquakes. Given that the fixed slip and recurrence models are the preferred models for all of the experiments we examine, we infer that in an event-to-event sense the elastic rebound model underlying the time- and slip-predictable models does not explain earthquake behavior. This does not indicate that the elastic rebound model should be rejected in a long-term-sense, but it should be rejected for short-term predictions. The time- and slip-predictable models likely offer worse predictions of earthquake behavior because they rely on assumptions that are too simple to explain the behavior of earthquakes. Specifically, the time-predictable model assumes a constant failure threshold and the slip-predictable model assumes that there is a constant minimum stress. There is experimental and field evidence that these assumptions are not valid for all earthquakes.

Model-free and model-based reward prediction errors in EEG.

PubMed

Sambrook, Thomas D; Hardwick, Ben; Wills, Andy J; Goslin, Jeremy

2018-05-24

Learning theorists posit two reinforcement learning systems: model-free and model-based. Model-based learning incorporates knowledge about structure and contingencies in the world to assign candidate actions with an expected value. Model-free learning is ignorant of the world's structure; instead, actions hold a value based on prior reinforcement, with this value updated by expectancy violation in the form of a reward prediction error. Because they use such different learning mechanisms, it has been previously assumed that model-based and model-free learning are computationally dissociated in the brain. However, recent fMRI evidence suggests that the brain may compute reward prediction errors to both model-free and model-based estimates of value, signalling the possibility that these systems interact. Because of its poor temporal resolution, fMRI risks confounding reward prediction errors with other feedback-related neural activity. In the present study, EEG was used to show the presence of both model-based and model-free reward prediction errors and their place in a temporal sequence of events including state prediction errors and action value updates. This demonstration of model-based prediction errors questions a long-held assumption that model-free and model-based learning are dissociated in the brain. Copyright © 2018 Elsevier Inc. All rights reserved.

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.

Personalized Modeling for Prediction with Decision-Path Models

PubMed Central

Visweswaran, Shyam; Ferreira, Antonio; Ribeiro, Guilherme A.; Oliveira, Alexandre C.; Cooper, Gregory F.

2015-01-01

Deriving predictive models in medicine typically relies on a population approach where a single model is developed from a dataset of individuals. In this paper we describe and evaluate a personalized approach in which we construct a new type of decision tree model called decision-path model that takes advantage of the particular features of a given person of interest. We introduce three personalized methods that derive personalized decision-path models. We compared the performance of these methods to that of Classification And Regression Tree (CART) that is a population decision tree to predict seven different outcomes in five medical datasets. Two of the three personalized methods performed statistically significantly better on area under the ROC curve (AUC) and Brier skill score compared to CART. The personalized approach of learning decision path models is a new approach for predictive modeling that can perform better than a population approach. PMID:26098570

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

Proteochemometric model for predicting the inhibition of penicillin-binding proteins

NASA Astrophysics Data System (ADS)

Nabu, Sunanta; Nantasenamat, Chanin; Owasirikul, Wiwat; Lawung, Ratana; Isarankura-Na-Ayudhya, Chartchalerm; Lapins, Maris; Wikberg, Jarl E. S.; Prachayasittikul, Virapong

2015-02-01

Neisseria gonorrhoeae infection threatens to become an untreatable sexually transmitted disease in the near future owing to the increasing emergence of N. gonorrhoeae strains with reduced susceptibility and resistance to the extended-spectrum cephalosporins (ESCs), i.e. ceftriaxone and cefixime, which are the last remaining option for first-line treatment of gonorrhea. Alteration of the penA gene, encoding penicillin-binding protein 2 (PBP2), is the main mechanism conferring penicillin resistance including reduced susceptibility and resistance to ESCs. To predict and investigate putative amino acid mutations causing β-lactam resistance particularly for ESCs, we applied proteochemometric modeling to generalize N. gonorrhoeae susceptibility data for predicting the interaction of PBP2 with therapeutic β-lactam antibiotics. This was afforded by correlating publicly available data on antimicrobial susceptibility of wild-type and mutant N. gonorrhoeae strains for penicillin-G, cefixime and ceftriaxone with 50 PBP2 protein sequence data using partial least-squares projections to latent structures. The generated model revealed excellent predictability ( R 2 = 0.91, Q 2 = 0.77, Q Ext 2 = 0.78). Moreover, our model identified amino acid mutations in PBP2 with the highest impact on antimicrobial susceptibility and provided information on physicochemical properties of amino acid mutations affecting antimicrobial susceptibility. Our model thus provided insight into the physicochemical basis for resistance development in PBP2 suggesting its use for predicting and monitoring novel PBP2 mutations that may emerge in the future.

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.

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.

Ensemble predictive model for more accurate soil organic carbon spectroscopic estimation

NASA Astrophysics Data System (ADS)

Vašát, Radim; Kodešová, Radka; Borůvka, Luboš

2017-07-01

A myriad of signal pre-processing strategies and multivariate calibration techniques has been explored in attempt to improve the spectroscopic prediction of soil organic carbon (SOC) over the last few decades. Therefore, to come up with a novel, more powerful, and accurate predictive approach to beat the rank becomes a challenging task. However, there may be a way, so that combine several individual predictions into a single final one (according to ensemble learning theory). As this approach performs best when combining in nature different predictive algorithms that are calibrated with structurally different predictor variables, we tested predictors of two different kinds: 1) reflectance values (or transforms) at each wavelength and 2) absorption feature parameters. Consequently we applied four different calibration techniques, two per each type of predictors: a) partial least squares regression and support vector machines for type 1, and b) multiple linear regression and random forest for type 2. The weights to be assigned to individual predictions within the ensemble model (constructed as a weighted average) were determined by an automated procedure that ensured the best solution among all possible was selected. The approach was tested at soil samples taken from surface horizon of four sites differing in the prevailing soil units. By employing the ensemble predictive model the prediction accuracy of SOC improved at all four sites. The coefficient of determination in cross-validation (R2cv) increased from 0.849, 0.611, 0.811 and 0.644 (the best individual predictions) to 0.864, 0.650, 0.824 and 0.698 for Site 1, 2, 3 and 4, respectively. Generally, the ensemble model affected the final prediction so that the maximal deviations of predicted vs. observed values of the individual predictions were reduced, and thus the correlation cloud became thinner as desired.

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

USGS Publications Warehouse

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

2012-01-01

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

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.

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.

Performance of statistical models to predict mental health and substance abuse cost.

PubMed

Montez-Rath, Maria; Christiansen, Cindy L; Ettner, Susan L; Loveland, Susan; Rosen, Amy K

2006-10-26

Providers use risk-adjustment systems to help manage healthcare costs. Typically, ordinary least squares (OLS) models on either untransformed or log-transformed cost are used. We examine the predictive ability of several statistical models, demonstrate how model choice depends on the goal for the predictive model, and examine whether building models on samples of the data affects model choice. Our sample consisted of 525,620 Veterans Health Administration patients with mental health (MH) or substance abuse (SA) diagnoses who incurred costs during fiscal year 1999. We tested two models on a transformation of cost: a Log Normal model and a Square-root Normal model, and three generalized linear models on untransformed cost, defined by distributional assumption and link function: Normal with identity link (OLS); Gamma with log link; and Gamma with square-root link. Risk-adjusters included age, sex, and 12 MH/SA categories. To determine the best model among the entire dataset, predictive ability was evaluated using root mean square error (RMSE), mean absolute prediction error (MAPE), and predictive ratios of predicted to observed cost (PR) among deciles of predicted cost, by comparing point estimates and 95% bias-corrected bootstrap confidence intervals. To study the effect of analyzing a random sample of the population on model choice, we re-computed these statistics using random samples beginning with 5,000 patients and ending with the entire sample. The Square-root Normal model had the lowest estimates of the RMSE and MAPE, with bootstrap confidence intervals that were always lower than those for the other models. The Gamma with square-root link was best as measured by the PRs. The choice of best model could vary if smaller samples were used and the Gamma with square-root link model had convergence problems with small samples. Models with square-root transformation or link fit the data best. This function (whether used as transformation or as a link) seems to help

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

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.

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

PubMed Central

2012-01-01

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

Emotionally laden impulsivity interacts with affect in predicting addictive use of online sexual activity in men.

PubMed

Wéry, Aline; Deleuze, Jory; Canale, Natale; Billieux, Joël

2018-01-01

The interest in studying addictive use of online sexual activities (OSA) has grown sharply over the last decade. Despite the burgeoning number of studies conceptualizing the excessive use of OSA as an addictive disorder, few have tested its relations to impulsivity, which is known to constitute a hallmark of addictive behaviors. To address this missing gap in the literature, we tested the relationships between addictive OSA use, impulsivity traits, and affect among a convenience sample of men (N=182; age, M=29.17, SD = 9.34), building upon a theoretically driven model that distinguishes the various facets of impulsivity. Results showed that negative urgency (an impulsivity trait reflecting the tendency to act rashly in negative emotional states) and negative affect interact in predicting addictive OSA use. These results highlight the pivotal role played by negative urgency and negative affect in addictive OSA use, supporting the relevance of psychological interventions that focus on improving emotional regulation (e.g., to reduce negative affect and learn healthier coping strategies) to mitigate excessive use of OSA. Copyright © 2017 Elsevier Inc. All rights reserved.

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

PubMed

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

2016-06-01

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

Incorporating uncertainty in predictive species distribution modelling.

PubMed

Beale, Colin M; Lennon, Jack J

2012-01-19

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

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.

Assessment and prediction of air quality using fuzzy logic and autoregressive models

NASA Astrophysics Data System (ADS)

Carbajal-Hernández, José Juan; Sánchez-Fernández, Luis P.; Carrasco-Ochoa, Jesús A.; Martínez-Trinidad, José Fco.

2012-12-01

In recent years, artificial intelligence methods have been used for the treatment of environmental problems. This work, presents two models for assessment and prediction of air quality. First, we develop a new computational model for air quality assessment in order to evaluate toxic compounds that can harm sensitive people in urban areas, affecting their normal activities. In this model we propose to use a Sigma operator to statistically asses air quality parameters using their historical data information and determining their negative impact in air quality based on toxicity limits, frequency average and deviations of toxicological tests. We also introduce a fuzzy inference system to perform parameter classification using a reasoning process and integrating them in an air quality index describing the pollution levels in five stages: excellent, good, regular, bad and danger, respectively. The second model proposed in this work predicts air quality concentrations using an autoregressive model, providing a predicted air quality index based on the fuzzy inference system previously developed. Using data from Mexico City Atmospheric Monitoring System, we perform a comparison among air quality indices developed for environmental agencies and similar models. Our results show that our models are an appropriate tool for assessing site pollution and for providing guidance to improve contingency actions in urban areas.

Prediction Models for Dynamic Demand Response

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

Aman, Saima; Frincu, Marc; Chelmis, Charalampos

2015-11-02

As Smart Grids move closer to dynamic curtailment programs, Demand Response (DR) events will become necessary not only on fixed time intervals and weekdays predetermined by static policies, but also during changing decision periods and weekends to react to real-time demand signals. Unique challenges arise in this context vis-a-vis demand prediction and curtailment estimation and the transformation of such tasks into an automated, efficient dynamic demand response (D 2R) process. While existing work has concentrated on increasing the accuracy of prediction models for DR, there is a lack of studies for prediction models for D 2R, which we address inmore » this paper. Our first contribution is the formal definition of D 2R, and the description of its challenges and requirements. Our second contribution is a feasibility analysis of very-short-term prediction of electricity consumption for D 2R over a diverse, large-scale dataset that includes both small residential customers and large buildings. Our third, and major contribution is a set of insights into the predictability of electricity consumption in the context of D 2R. Specifically, we focus on prediction models that can operate at a very small data granularity (here 15-min intervals), for both weekdays and weekends - all conditions that characterize scenarios for D 2R. We find that short-term time series and simple averaging models used by Independent Service Operators and utilities achieve superior prediction accuracy. We also observe that workdays are more predictable than weekends and holiday. Also, smaller customers have large variation in consumption and are less predictable than larger buildings. Key implications of our findings are that better models are required for small customers and for non-workdays, both of which are critical for D 2R. Also, prediction models require just few days’ worth of data indicating that small amounts of historical training data can be used to make reliable predictions

An online spatiotemporal prediction model for dengue fever epidemic in Kaohsiung (Taiwan).

PubMed

Yu, Hwa-Lung; Angulo, José M; Cheng, Ming-Hung; Wu, Jiaping; Christakos, George

2014-05-01

The emergence and re-emergence of disease epidemics is a complex question that may be influenced by diverse factors, including the space-time dynamics of human populations, environmental conditions, and associated uncertainties. This study proposes a stochastic framework to integrate space-time dynamics in the form of a Susceptible-Infected-Recovered (SIR) model, together with uncertain disease observations, into a Bayesian maximum entropy (BME) framework. The resulting model (BME-SIR) can be used to predict space-time disease spread. Specifically, it was applied to obtain a space-time prediction of the dengue fever (DF) epidemic that took place in Kaohsiung City (Taiwan) during 2002. In implementing the model, the SIR parameters were continually updated and information on new cases of infection was incorporated. The results obtained show that the proposed model is rigorous to user-specified initial values of unknown model parameters, that is, transmission and recovery rates. In general, this model provides a good characterization of the spatial diffusion of the DF epidemic, especially in the city districts proximal to the location of the outbreak. Prediction performance may be affected by various factors, such as virus serotypes and human intervention, which can change the space-time dynamics of disease diffusion. The proposed BME-SIR disease prediction model can provide government agencies with a valuable reference for the timely identification, control, and prevention of DF spread in space and time. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

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.

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

How outcome prediction could affect patient decision making in knee replacements: a qualitative study.

PubMed

Barlow, Timothy; Scott, Patricia; Griffin, Damian; Realpe, Alba

2016-07-22

There is approximately a 17 % dissatisfaction rate with knee replacements. Calls for tools that can pre-operatively identify patients at risk of being dissatisfied have been widespread. However, it is not known how to present such information to patients, how it would affect their decision making process, and at what part of the pathway such a tool should be used. Using focus groups involving 12 participants and in-depth interviews with 10 participants, we examined how individual predictions of outcome could affect patients' decision making by providing fictitious predictions to patients at different stages of treatment. A thematic analysis was used to analyse the data. Our results demonstrate several interesting findings. Firstly, patients who have received information from friends and family are unwilling to adjust their expectation of outcome down (i.e. to a worse outcome), but highly willing to adjust it up (to a better outcome). This is an example of the optimism bias, and suggests that the effect on expectation of a poor outcome prediction would be blunted. Secondly, patients generally wanted a "bottom line" outcome, rather than lots of detail. Thirdly, patients who were earlier in their treatment for osteoarthritis were more likely to find the information useful, and it was more likely to affect their decision, than patients later in their treatment pathway. This research suggest that an outcome prediction tool would have most effect targeted towards people at the start of their treatment pathway, with a "bottom line" prediction of outcome. However, any effect on expectation and decision making of a poor outcome prediction is likely to be blunted by the optimism bias. These findings merit replication in a larger sample size.

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

[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%.

Evaluating Predictive Uncertainty of Hyporheic Exchange Modelling

NASA Astrophysics Data System (ADS)

Chow, R.; Bennett, J.; Dugge, J.; Wöhling, T.; Nowak, W.

2017-12-01

Hyporheic exchange is the interaction of water between rivers and groundwater, and is difficult to predict. One of the largest contributions to predictive uncertainty for hyporheic fluxes have been attributed to the representation of heterogeneous subsurface properties. This research aims to evaluate which aspect of the subsurface representation - the spatial distribution of hydrofacies or the model for local-scale (within-facies) heterogeneity - most influences the predictive uncertainty. Also, we seek to identify data types that help reduce this uncertainty best. For this investigation, we conduct a modelling study of the Steinlach River meander, in Southwest Germany. The Steinlach River meander is an experimental site established in 2010 to monitor hyporheic exchange at the meander scale. We use HydroGeoSphere, a fully integrated surface water-groundwater model, to model hyporheic exchange and to assess the predictive uncertainty of hyporheic exchange transit times (HETT). A highly parameterized complex model is built and treated as `virtual reality', which is in turn modelled with simpler subsurface parameterization schemes (Figure). Then, we conduct Monte-Carlo simulations with these models to estimate the predictive uncertainty. Results indicate that: Uncertainty in HETT is relatively small for early times and increases with transit times. Uncertainty from local-scale heterogeneity is negligible compared to uncertainty in the hydrofacies distribution. Introducing more data to a poor model structure may reduce predictive variance, but does not reduce predictive bias. Hydraulic head observations alone cannot constrain the uncertainty of HETT, however an estimate of hyporheic exchange flux proves to be more effective at reducing this uncertainty. Figure: Approach for evaluating predictive model uncertainty. A conceptual model is first developed from the field investigations. A complex model (`virtual reality') is then developed based on that conceptual model

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.

Modelling cognitive affective biases in major depressive disorder using rodents

PubMed Central

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

2014-01-01

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

A dynamic model for predicting growth in zinc-deficient stunted infants given supplemental zinc.

PubMed

Wastney, Meryl E; McDonald, Christine M; King, Janet C

2018-05-01

Zinc deficiency limits infant growth and increases susceptibility to infections, which further compromises growth. Zinc supplementation improves the growth of zinc-deficient stunted infants, but the amount, frequency, and duration of zinc supplementation required to restore growth in an individual child is unknown. A dynamic model of zinc metabolism that predicts changes in weight and length of zinc-deficient, stunted infants with dietary zinc would be useful to define effective zinc supplementation regimens. The aims of this study were to develop a dynamic model for zinc metabolism in stunted, zinc-deficient infants and to use that model to predict the growth response when those infants are given zinc supplements. A model of zinc metabolism was developed using data on zinc kinetics, tissue zinc, and growth requirements for healthy 9-mo-old infants. The kinetic model was converted to a dynamic model by replacing the rate constants for zinc absorption and excretion with functions for these processes that change with zinc intake. Predictions of the dynamic model, parameterized for zinc-deficient, stunted infants, were compared with the results of 5 published zinc intervention trials. The model was then used to predict the results for zinc supplementation regimes that varied in the amount, frequency, and duration of zinc dosing. Model predictions agreed with published changes in plasma zinc after zinc supplementation. Predictions of weight and length agreed with 2 studies, but overpredicted values from a third study in which other nutrient deficiencies may have been growth limiting; the model predicted that zinc absorption was impaired in that study. The model suggests that frequent, smaller doses (5-10 mg Zn/d) are more effective for increasing growth in stunted, zinc-deficient 9-mo-old infants than are larger, less-frequent doses. The dose amount affects the duration of dosing necessary to restore and maintain plasma zinc concentration and growth.

Hydrometeorological model for streamflow prediction

USGS Publications Warehouse

Tangborn, Wendell V.

1979-01-01

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

Survival Regression Modeling Strategies in CVD Prediction.

PubMed

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

2016-04-01

A fundamental part of prevention is prediction. Potential predictors are the sine qua non of prediction models. However, whether incorporating novel predictors to prediction models could be directly translated to added predictive value remains an area of dispute. The difference between the predictive power of a predictive model with (enhanced model) and without (baseline model) a certain predictor is generally regarded as an indicator of the predictive value added by that predictor. Indices such as discrimination and calibration have long been used in this regard. Recently, the use of added predictive value has been suggested while comparing the predictive performances of the predictive models with and without novel biomarkers. User-friendly statistical software capable of implementing novel statistical procedures is conspicuously lacking. This shortcoming has restricted implementation of such novel model assessment methods. We aimed to construct Stata commands to help researchers obtain the aforementioned statistical indices. We have written Stata commands that are intended to help researchers obtain the following. 1, Nam-D'Agostino X 2 goodness of fit test; 2, Cut point-free and cut point-based net reclassification improvement index (NRI), relative absolute integrated discriminatory improvement index (IDI), and survival-based regression analyses. We applied the commands to real data on women participating in the Tehran lipid and glucose study (TLGS) to examine if information relating to a family history of premature cardiovascular disease (CVD), waist circumference, and fasting plasma glucose can improve predictive performance of Framingham's general CVD risk algorithm. The command is adpredsurv for survival models. Herein we have described the Stata package "adpredsurv" for calculation of the Nam-D'Agostino X 2 goodness of fit test as well as cut point-free and cut point-based NRI, relative and absolute IDI, and survival-based regression analyses. We hope this

[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

Models of Affective Decision Making

PubMed Central

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

2016-01-01

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

Large-scale structure prediction by improved contact predictions and model quality assessment.

PubMed

Michel, Mirco; Menéndez Hurtado, David; Uziela, Karolis; Elofsson, Arne

2017-07-15

Accurate contact predictions can be used for predicting the structure of proteins. Until recently these methods were limited to very big protein families, decreasing their utility. However, recent progress by combining direct coupling analysis with machine learning methods has made it possible to predict accurate contact maps for smaller families. To what extent these predictions can be used to produce accurate models of the families is not known. We present the PconsFold2 pipeline that uses contact predictions from PconsC3, the CONFOLD folding algorithm and model quality estimations to predict the structure of a protein. We show that the model quality estimation significantly increases the number of models that reliably can be identified. Finally, we apply PconsFold2 to 6379 Pfam families of unknown structure and find that PconsFold2 can, with an estimated 90% specificity, predict the structure of up to 558 Pfam families of unknown structure. Out of these, 415 have not been reported before. Datasets as well as models of all the 558 Pfam families are available at http://c3.pcons.net/ . All programs used here are freely available. arne@bioinfo.se. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Simple predictive model for Early Childhood Caries of Chilean children.

PubMed

Fierro Monti, Claudia; Pérez Flores, M; Brunotto, M

2014-01-01

Early Childhood Caries (ECC), in both industrialized and developing countries, is the most prevalent chronic disease in childhood and it is still a health public problem, affecting mainly populations considered as vulnerable, despite being preventable. The purpose of this study was to obtain a simple predictive model based on risk factors for improving public health strategies for ECC prevention for 3-5 year-old children. Clinical, environmental and psycho-socio-cultural data of children (n=250) aged 3-5 years, of both genders, from the Health Centers, were recorded in a Clinical History and Behavioral Survey. 24% of children presented behavioral problems (bizarre behavior was the main feature observed as behavioral problems). The variables associated to dmf ?4 were: bad children temperament (OR=2.43 [1.34, 4.40]) and home stress (OR=3.14 [1.54, 6.41]). It was observed that the model for male gender has higher accuracy for ECC (AUC= 78%, p-value=0.000) than others. Based on the results, we proposed a model where oral hygiene, sugar intake, male gender, and difficult temperament are main factors for predicting ECC. This model could be a promising tool for cost-effective early childhood caries control.

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

PubMed Central

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

2013-01-01

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

Dynamic model predicting overweight, obesity, and extreme obesity prevalence trends.

PubMed

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

2014-02-01

Obesity prevalence in the United States appears to be leveling, but the reasons behind the plateau remain unknown. Mechanistic insights can be provided from a mathematical model. The objective of this study is to model known multiple population parameters associated with changes in body mass index (BMI) classes and to establish conditions under which obesity prevalence will plateau. A differential equation system was developed that predicts population-wide obesity prevalence trends. The model considers both social and nonsocial influences on weight gain, incorporates other known parameters affecting obesity trends, and allows for country specific population growth. The dynamic model predicts that: obesity prevalence is a function of birthrate and the probability of being born in an obesogenic environment; obesity prevalence will plateau independent of current prevention strategies; and the US prevalence of overweight, obesity, and extreme obesity will plateau by about 2030 at 28%, 32%, and 9% respectively. The US prevalence of obesity is stabilizing and will plateau, independent of current preventative strategies. This trend has important implications in accurately evaluating the impact of various anti-obesity strategies aimed at reducing obesity prevalence. Copyright © 2013 The Obesity Society.

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…

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.

Poisson Mixture Regression Models for Heart Disease Prediction.

PubMed

Mufudza, Chipo; Erol, Hamza

2016-01-01

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

Poisson Mixture Regression Models for Heart Disease Prediction

PubMed Central

Erol, Hamza

2016-01-01

Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regression models. Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value. Furthermore, a Zero Inflated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available. It is deduced that heart disease prediction can be effectively done by identifying the major risks componentwise using Poisson mixture regression model. PMID:27999611

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.

On the Selection of Models for Runtime Prediction of System Resources

NASA Astrophysics Data System (ADS)

Casolari, Sara; Colajanni, Michele

Applications and services delivered through large Internet Data Centers are now feasible thanks to network and server improvement, but also to virtualization, dynamic allocation of resources and dynamic migrations. The large number of servers and resources involved in these systems requires autonomic management strategies because no amount of human administrators would be capable of cloning and migrating virtual machines in time, as well as re-distributing or re-mapping the underlying hardware. At the basis of most autonomic management decisions, there is the need of evaluating own global behavior and change it when the evaluation indicates that they are not accomplishing what they were intended to do or some relevant anomalies are occurring. Decisions algorithms have to satisfy different time scales constraints. In this chapter we are interested to short-term contexts where runtime prediction models work on the basis of time series coming from samples of monitored system resources, such as disk, CPU and network utilization. In similar environments, we have to address two main issues. First, original time series are affected by limited predictability because measurements are characterized by noises due to system instability, variable offered load, heavy-tailed distributions, hardware and software interactions. Moreover, there is no existing criteria that can help us to choose a suitable prediction model and related parameters with the purpose of guaranteeing an adequate prediction quality. In this chapter, we evaluate the impact that different choices on prediction models have on different time series, and we suggest how to treat input data and whether it is convenient to choose the parameters of a prediction model in a static or dynamic way. Our conclusions are supported by a large set of analyses on realistic and synthetic data traces.

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

Using connectome-based predictive modeling to predict individual behavior from brain connectivity

PubMed Central

Shen, Xilin; Finn, Emily S.; Scheinost, Dustin; Rosenberg, Monica D.; Chun, Marvin M.; Papademetris, Xenophon; Constable, R Todd

2017-01-01

Neuroimaging is a fast developing research area where anatomical and functional images of human brains are collected using techniques such as functional magnetic resonance imaging (fMRI), diffusion tensor imaging (DTI), and electroencephalography (EEG). Technical advances and large-scale datasets have allowed for the development of models capable of predicting individual differences in traits and behavior using brain connectivity measures derived from neuroimaging data. Here, we present connectome-based predictive modeling (CPM), a data-driven protocol for developing predictive models of brain-behavior relationships from connectivity data using cross-validation. This protocol includes the following steps: 1) feature selection, 2) feature summarization, 3) model building, and 4) assessment of prediction significance. We also include suggestions for visualizing the most predictive features (i.e., brain connections). The final result should be a generalizable model that takes brain connectivity data as input and generates predictions of behavioral measures in novel subjects, accounting for a significant amount of the variance in these measures. It has been demonstrated that the CPM protocol performs equivalently or better than most of the existing approaches in brain-behavior prediction. However, because CPM focuses on linear modeling and a purely data-driven driven approach, neuroscientists with limited or no experience in machine learning or optimization would find it easy to implement the protocols. Depending on the volume of data to be processed, the protocol can take 10–100 minutes for model building, 1–48 hours for permutation testing, and 10–20 minutes for visualization of results. PMID:28182017

Health beliefs affect the correct replacement of daily disposable contact lenses: Predicting compliance with the Health Belief Model and the Theory of Planned Behaviour.

PubMed

Livi, Stefano; Zeri, Fabrizio; Baroni, Rossella

2017-02-01

To assess the compliance of Daily Disposable Contact Lenses (DDCLs) wearers with replacing lenses at a manufacturer-recommended replacement frequency. To evaluate the ability of two different Health Behavioural Theories (HBT), The Health Belief Model (HBM) and The Theory of Planned Behaviour (TPB), in predicting compliance. A multi-centre survey was conducted using a questionnaire completed anonymously by contact lens wearers during the purchase of DDCLs. Three hundred and fifty-four questionnaires were returned. The survey comprised 58.5% females and 41.5% males (mean age 34±12years). Twenty-three percent of respondents were non-compliant with manufacturer-recommended replacement frequency (re-using DDCLs at least once). The main reason for re-using DDCLs was "to save money" (35%). Predictions of compliance behaviour (past behaviour or future intentions) on the basis of the two HBT was investigated through logistic regression analysis: both TPB factors (subjective norms and perceived behavioural control) were significant (p<0.01); HBM was less predictive with only the severity (past behaviour and future intentions) and perceived benefit (only for past behaviour) as significant factors (p<0.05). Non-compliance with DDCLs replacement is widespread, affecting 1 out of 4 Italian wearers. Results from the TPB model show that the involvement of persons socially close to the wearers (subjective norms) and the improvement of the procedure of behavioural control of daily replacement (behavioural control) are of paramount importance in improving compliance. With reference to the HBM, it is important to warn DDCLs wearers of the severity of a contact-lens-related eye infection, and to underline the possibility of its prevention. Copyright © 2016 British Contact Lens Association. Published by Elsevier Ltd. All rights reserved.

Thermal barrier coating life prediction model

NASA Technical Reports Server (NTRS)

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

1986-01-01

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

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.

Modelling of nanoscale multi-gate transistors affected by atomistic interface roughness

NASA Astrophysics Data System (ADS)

Nagy, Daniel; Aldegunde, Manuel; Elmessary, Muhammad A.; García-Loureiro, Antonio J.; Seoane, Natalia; Kalna, Karol

2018-04-01

Interface roughness scattering (IRS) is one of the major scattering mechanisms limiting the performance of non-planar multi-gate transistors, like Fin field-effect transistors (FETs). Here, two physical models (Ando’s and multi-sub-band) of electron scattering with the interface roughness induced potential are investigated using an in-house built 3D finite element ensemble Monte Carlo simulation toolbox including parameter-free 2D Schrödinger equation quantum correction that handles all relevant scattering mechanisms within highly non-equilibrium carrier transport. Moreover, we predict the effect of IRS on performance of FinFETs with realistic channel cross-section shapes with respect to the IRS correlation length (Λ) and RMS height (Δ_RMS ). The simulations of the n-type SOI FinFETs with the multi-sub-band IRS model shows its very strong effect on electron transport in the device channel compared to the Ando’s model. We have also found that the FinFETs are strongly affected by the IRS in the ON-region. The limiting effect of the IRS significantly increases as the Fin width is reduced. The FinFETs with <1 1 0> channel orientation are affected more by the IRS than those with the <1 0 0> crystal orientation. Finally, Λ and Δ_RMS are shown to affect the device performance similarly. A change in values by 30% (Λ) or 20% (Δ_RMS ) results in an increase (decrease) of up to 13% in the drive current.

Modelling of nanoscale multi-gate transistors affected by atomistic interface roughness.

PubMed

Nagy, Daniel; Aldegunde, Manuel; Elmessary, Muhammad A; García-Loureiro, Antonio J; Seoane, Natalia; Kalna, Karol

2018-04-11

Interface roughness scattering (IRS) is one of the major scattering mechanisms limiting the performance of non-planar multi-gate transistors, like Fin field-effect transistors (FETs). Here, two physical models (Ando's and multi-sub-band) of electron scattering with the interface roughness induced potential are investigated using an in-house built 3D finite element ensemble Monte Carlo simulation toolbox including parameter-free 2D Schrödinger equation quantum correction that handles all relevant scattering mechanisms within highly non-equilibrium carrier transport. Moreover, we predict the effect of IRS on performance of FinFETs with realistic channel cross-section shapes with respect to the IRS correlation length (Λ) and RMS height ([Formula: see text]). The simulations of the n-type SOI FinFETs with the multi-sub-band IRS model shows its very strong effect on electron transport in the device channel compared to the Ando's model. We have also found that the FinFETs are strongly affected by the IRS in the ON-region. The limiting effect of the IRS significantly increases as the Fin width is reduced. The FinFETs with [Formula: see text] channel orientation are affected more by the IRS than those with the [Formula: see text] crystal orientation. Finally, Λ and [Formula: see text] are shown to affect the device performance similarly. A change in values by 30% (Λ) or [Formula: see text] ([Formula: see text]) results in an increase (decrease) of up to [Formula: see text] in the drive current.

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.

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.

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

Model for the prediction of subsurface strata movement due to underground mining

NASA Astrophysics Data System (ADS)

Cheng, Jianwei; Liu, Fangyuan; Li, Siyuan

2017-12-01

The problem of ground control stability due to large underground mining operations is often associated with large movements and deformations of strata. It is a complicated problem, and can induce severe safety or environmental hazards either at the surface or in strata. Hence, knowing the subsurface strata movement characteristics, and making any subsidence predictions in advance, are desirable for mining engineers to estimate any damage likely to affect the ground surface or subsurface strata. Based on previous research findings, this paper broadly applies a surface subsidence prediction model based on the influence function method to subsurface strata, in order to predict subsurface stratum movement. A step-wise prediction model is proposed, to investigate the movement of underground strata. The model involves a dynamic iteration calculation process to derive the movements and deformations for each stratum layer; modifications to the influence method function are also made for more precise calculations. The critical subsidence parameters, incorporating stratum mechanical properties and the spatial relationship of interest at the mining level, are thoroughly considered, with the purpose of improving the reliability of input parameters. Such research efforts can be very helpful to mining engineers’ understanding of the moving behavior of all strata over underground excavations, and assist in making any damage mitigation plan. In order to check the reliability of the model, two methods are carried out and cross-validation applied. One is to use a borehole TV monitor recording to identify the progress of subsurface stratum bedding and caving in a coal mine, the other is to conduct physical modelling of the subsidence in underground strata. The results of these two methods are used to compare with theoretical results calculated by the proposed mathematical model. The testing results agree well with each other, and the acceptable accuracy and reliability of the

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

Trustworthiness and Negative Affect Predict Economic Decision-Making.

PubMed

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

2011-09-01

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

Trustworthiness and Negative Affect Predict Economic Decision-Making

PubMed Central

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

2012-01-01

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

Delirium prediction in the intensive care unit: comparison of two delirium prediction models.

PubMed

Wassenaar, Annelies; Schoonhoven, Lisette; Devlin, John W; van Haren, Frank M P; Slooter, Arjen J C; Jorens, Philippe G; van der Jagt, Mathieu; Simons, Koen S; Egerod, Ingrid; Burry, Lisa D; Beishuizen, Albertus; Matos, Joaquim; Donders, A Rogier T; Pickkers, Peter; van den Boogaard, Mark

2018-05-05

Accurate prediction of delirium in the intensive care unit (ICU) may facilitate efficient use of early preventive strategies and stratification of ICU patients by delirium risk in clinical research, but the optimal delirium prediction model to use is unclear. We compared the predictive performance and user convenience of the prediction  model for delirium (PRE-DELIRIC) and early prediction model for delirium (E-PRE-DELIRIC) in ICU patients and determined the value of a two-stage calculation. This 7-country, 11-hospital, prospective cohort study evaluated consecutive adults admitted to the ICU who could be reliably assessed for delirium using the Confusion Assessment Method-ICU or the Intensive Care Delirium Screening Checklist. The predictive performance of the models was measured using the area under the receiver operating characteristic curve. Calibration was assessed graphically. A physician questionnaire evaluated user convenience. For the two-stage calculation we used E-PRE-DELIRIC immediately after ICU admission and updated the prediction using PRE-DELIRIC after 24 h. In total 2178 patients were included. The area under the receiver operating characteristic curve was significantly greater for PRE-DELIRIC (0.74 (95% confidence interval 0.71-0.76)) compared to E-PRE-DELIRIC (0.68 (95% confidence interval 0.66-0.71)) (z score of - 2.73 (p < 0.01)). Both models were well-calibrated. The sensitivity improved when using the two-stage calculation in low-risk patients. Compared to PRE-DELIRIC, ICU physicians (n = 68) rated the E-PRE-DELIRIC model more feasible. While both ICU delirium prediction models have moderate-to-good performance, the PRE-DELIRIC model predicts delirium better. However, ICU physicians rated the user convenience of E-PRE-DELIRIC superior to PRE-DELIRIC. In low-risk patients the delirium prediction further improves after an update with the PRE-DELIRIC model after 24 h. ClinicalTrials.gov, NCT02518646 . Registered on 21 July 2015.

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

NASA Technical Reports Server (NTRS)

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

2016-01-01

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

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.

The Role of Self-regulation and Affective Control in Predicting Interpersonal Reactivity of Drug Addicts

PubMed Central

Abolghasemi, Abbas; Rajabi, Saeed

2013-01-01

Background Due to its progressive nature in all aspects of life, addiction endangers the health of individuals, families and the society. Objectives The purpose of this study was to determine the role of self-regulation and affective control in predicting interpersonal reactivity of drug addicts. Materials and Methods This research is a correlation study. The statistical population of this study includes all drug addicts who were referred to addiction treatment centers of Ardabil in 2011 of whom 160 addicts were selected through convenience sampling. A self-regulation questionnaire, interpersonal reactivity questionnaire and affective control scale were used for data collection. Results Research results showed that self-regulation (r = -0.40) and affective control (r = -0.29) have a significant relationship with interpersonal reactivity of addicts (P < 0.001). The results of the multiple regression analysis indicated that 19 percent of interpersonal reactivity can be predicted by self-regulation and affective control. Conclusion These results suggest that self-regulation and affective control play an important role in exacerbating as well as reducing interpersonal reactivity of addicts. PMID:24971268

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.

An evaporative and engine-cycle model for fuel octane sensitivity prediction

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

Moran, D.P.; Taylor, A.B.

The Motor Octane Number (MON) ranks fuels by their chemical resistance to knock. Evaporative cooling coupled with fuel chemistry determine Research Octane Number (RON) antiknock ratings. It is shown in this study that fuel Octane sensitivity (numerically RON minus MON) is liked to an important difference between the two test methods; the RON test allows each fuel`s evaporative cooling characteristics to affect gas temperature, while the MON test generally eliminates this effect by pre-evaporation. In order to establish RON test charge temperatures, a computer model of fuel evaporation was adapted to Octane Engine conditions, and simulations were compared with realmore » Octane Test Engine measurements including droplet and gas temperatures. A novel gas temperature probe yielded data that corresponded well with model predictions. Tests spanned single component fuels and blends of isomers, n-paraffins, aromatics and alcohols. Commercially available automotive and aviation gasolines were also tested. A good correlation was observed between the computer predictions and measured temperature data across the range of pure fuels and blends. A numerical method to estimate the effect of precombustion temperature differences on Octane sensitivity was developed and applied to analyze these data, and was found to predict the widely disparate sensitivities of the tested fuels with accuracy. Data are presented showing mixture temperature histories of various tested fuels, and consequent sensitivity predictions. It is concluded that a fuel`s thermal-evaporative behavior gives rise to fuel Octane sensitivity as measured by differences between the RON and MON tests. This is demonstrated by the success, over a wide range of fuels, of the sensitivity predictor method describes. Evaporative cooling, must therefore be regarded as an important parameter affecting the general road performance of automobiles.« less

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

PubMed

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

2016-08-01

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

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

Inverse modeling with RZWQM2 to predict water quality

USGS Publications Warehouse

Nolan, Bernard T.; Malone, Robert W.; Ma, Liwang; Green, Christopher T.; Fienen, Michael N.; Jaynes, Dan B.

2011-01-01

This chapter presents guidelines for autocalibration of the Root Zone Water Quality Model (RZWQM2) by inverse modeling using PEST parameter estimation software (Doherty, 2010). Two sites with diverse climate and management were considered for simulation of N losses by leaching and in drain flow: an almond [Prunus dulcis (Mill.) D.A. Webb] orchard in the San Joaquin Valley, California and the Walnut Creek watershed in central Iowa, which is predominantly in corn (Zea mays L.)–soybean [Glycine max (L.) Merr.] rotation. Inverse modeling provides an objective statistical basis for calibration that involves simultaneous adjustment of model parameters and yields parameter confidence intervals and sensitivities. We describe operation of PEST in both parameter estimation and predictive analysis modes. The goal of parameter estimation is to identify a unique set of parameters that minimize a weighted least squares objective function, and the goal of predictive analysis is to construct a nonlinear confidence interval for a prediction of interest by finding a set of parameters that maximizes or minimizes the prediction while maintaining the model in a calibrated state. We also describe PEST utilities (PAR2PAR, TSPROC) for maintaining ordered relations among model parameters (e.g., soil root growth factor) and for post-processing of RZWQM2 outputs representing different cropping practices at the Iowa site. Inverse modeling provided reasonable fits to observed water and N fluxes and directly benefitted the modeling through: (i) simultaneous adjustment of multiple parameters versus one-at-a-time adjustment in manual approaches; (ii) clear indication by convergence criteria of when calibration is complete; (iii) straightforward detection of nonunique and insensitive parameters, which can affect the stability of PEST and RZWQM2; and (iv) generation of confidence intervals for uncertainty analysis of parameters and model predictions. Composite scaled sensitivities, which

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.

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

NASA Astrophysics Data System (ADS)

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

2009-07-01

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

Application of 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

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

Prediction skill of rainstorm events over India in the TIGGE weather prediction models

NASA Astrophysics Data System (ADS)

Karuna Sagar, S.; Rajeevan, M.; Vijaya Bhaskara Rao, S.; Mitra, A. K.

2017-12-01

Extreme rainfall events pose a serious threat of leading to severe floods in many countries worldwide. Therefore, advance prediction of its occurrence and spatial distribution is very essential. In this paper, an analysis has been made to assess the skill of numerical weather prediction models in predicting rainstorms over India. Using gridded daily rainfall data set and objective criteria, 15 rainstorms were identified during the monsoon season (June to September). The analysis was made using three TIGGE (THe Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble) models. The models considered are the European Centre for Medium-Range Weather Forecasts (ECMWF), National Centre for Environmental Prediction (NCEP) and the UK Met Office (UKMO). Verification of the TIGGE models for 43 observed rainstorm days from 15 rainstorm events has been made for the period 2007-2015. The comparison reveals that rainstorm events are predictable up to 5 days in advance, however with a bias in spatial distribution and intensity. The statistical parameters like mean error (ME) or Bias, root mean square error (RMSE) and correlation coefficient (CC) have been computed over the rainstorm region using the multi-model ensemble (MME) mean. The study reveals that the spread is large in ECMWF and UKMO followed by the NCEP model. Though the ensemble spread is quite small in NCEP, the ensemble member averages are not well predicted. The rank histograms suggest that the forecasts are under prediction. The modified Contiguous Rain Area (CRA) technique was used to verify the spatial as well as the quantitative skill of the TIGGE models. Overall, the contribution from the displacement and pattern errors to the total RMSE is found to be more in magnitude. The volume error increases from 24 hr forecast to 48 hr forecast in all the three models.

Predicting climate-induced range shifts: model differences and model reliability.

Treesearch

Joshua J. Lawler; Denis White; Ronald P. Neilson; Andrew R. Blaustein

2006-01-01

Predicted changes in the global climate are likely to cause large shifts in the geographic ranges of many plant and animal species. To date, predictions of future range shifts have relied on a variety of modeling approaches with different levels of model accuracy. Using a common data set, we investigated the potential implications of alternative modeling approaches for...

Atmospheric prediction model survey

NASA Technical Reports Server (NTRS)

Wellck, R. E.

1976-01-01

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

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 (...

An Interoceptive Predictive Coding Model of Conscious Presence

PubMed Central

Seth, Anil K.; Suzuki, Keisuke; Critchley, Hugo D.

2011-01-01

We describe a theoretical model of the neurocognitive mechanisms underlying conscious presence and its disturbances. The model is based on interoceptive prediction error and is informed by predictive models of agency, general models of hierarchical predictive coding and dopaminergic signaling in cortex, the role of the anterior insular cortex (AIC) in interoception and emotion, and cognitive neuroscience evidence from studies of virtual reality and of psychiatric disorders of presence, specifically depersonalization/derealization disorder. The model associates presence with successful suppression by top-down predictions of informative interoceptive signals evoked by autonomic control signals and, indirectly, by visceral responses to afferent sensory signals. The model connects presence to agency by allowing that predicted interoceptive signals will depend on whether afferent sensory signals are determined, by a parallel predictive-coding mechanism, to be self-generated or externally caused. Anatomically, we identify the AIC as the likely locus of key neural comparator mechanisms. Our model integrates a broad range of previously disparate evidence, makes predictions for conjoint manipulations of agency and presence, offers a new view of emotion as interoceptive inference, and represents a step toward a mechanistic account of a fundamental phenomenological property of consciousness. PMID:22291673

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

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.

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.

PredictABEL: an R package for the assessment of risk prediction models.

PubMed

Kundu, Suman; Aulchenko, Yurii S; van Duijn, Cornelia M; Janssens, A Cecile J W

2011-04-01

The rapid identification of genetic markers for multifactorial diseases from genome-wide association studies is fuelling interest in investigating the predictive ability and health care utility of genetic risk models. Various measures are available for the assessment of risk prediction models, each addressing a different aspect of performance and utility. We developed PredictABEL, a package in R that covers descriptive tables, measures and figures that are used in the analysis of risk prediction studies such as measures of model fit, predictive ability and clinical utility, and risk distributions, calibration plot and the receiver operating characteristic plot. Tables and figures are saved as separate files in a user-specified format, which include publication-quality EPS and TIFF formats. All figures are available in a ready-made layout, but they can be customized to the preferences of the user. The package has been developed for the analysis of genetic risk prediction studies, but can also be used for studies that only include non-genetic risk factors. PredictABEL is freely available at the websites of GenABEL ( http://www.genabel.org ) and CRAN ( http://cran.r-project.org/).

Risk prediction model: Statistical and artificial neural network approach

NASA Astrophysics Data System (ADS)

Paiman, Nuur Azreen; Hariri, Azian; Masood, Ibrahim

2017-04-01

Prediction models are increasingly gaining popularity and had been used in numerous areas of studies to complement and fulfilled clinical reasoning and decision making nowadays. The adoption of such models assist physician's decision making, individual's behavior, and consequently improve individual outcomes and the cost-effectiveness of care. The objective of this paper is to reviewed articles related to risk prediction model in order to understand the suitable approach, development and the validation process of risk prediction model. A qualitative review of the aims, methods and significant main outcomes of the nineteen published articles that developed risk prediction models from numerous fields were done. This paper also reviewed on how researchers develop and validate the risk prediction models based on statistical and artificial neural network approach. From the review done, some methodological recommendation in developing and validating the prediction model were highlighted. According to studies that had been done, artificial neural network approached in developing the prediction model were more accurate compared to statistical approach. However currently, only limited published literature discussed on which approach is more accurate for risk prediction model development.

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 (...

Predictive models of moth development

USDA-ARS?s Scientific Manuscript database

Degree-day models link ambient temperature to insect life-stages, making such models valuable tools in integrated pest management. These models increase management efficacy by predicting pest phenology. In Wisconsin, the top insect pest of cranberry production is the cranberry fruitworm, Acrobasis v...

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

Intra- and interspecies gene expression models for predicting drug response in canine osteosarcoma.

PubMed

Fowles, Jared S; Brown, Kristen C; Hess, Ann M; Duval, Dawn L; Gustafson, Daniel L

2016-02-19

Genomics-based predictors of drug response have the potential to improve outcomes associated with cancer therapy. Osteosarcoma (OS), the most common primary bone cancer in dogs, is commonly treated with adjuvant doxorubicin or carboplatin following amputation of the affected limb. We evaluated the use of gene-expression based models built in an intra- or interspecies manner to predict chemosensitivity and treatment outcome in canine OS. Models were built and evaluated using microarray gene expression and drug sensitivity data from human and canine cancer cell lines, and canine OS tumor datasets. The "COXEN" method was utilized to filter gene signatures between human and dog datasets based on strong co-expression patterns. Models were built using linear discriminant analysis via the misclassification penalized posterior algorithm. The best doxorubicin model involved genes identified in human lines that were co-expressed and trained on canine OS tumor data, which accurately predicted clinical outcome in 73 % of dogs (p = 0.0262, binomial). The best carboplatin model utilized canine lines for gene identification and model training, with canine OS tumor data for co-expression. Dogs whose treatment matched our predictions had significantly better clinical outcomes than those that didn't (p = 0.0006, Log Rank), and this predictor significantly associated with longer disease free intervals in a Cox multivariate analysis (hazard ratio = 0.3102, p = 0.0124). Our data show that intra- and interspecies gene expression models can successfully predict response in canine OS, which may improve outcome in dogs and serve as pre-clinical validation for similar methods in human cancer research.

Identification and synthetic modeling of factors affecting American black duck populations

USGS Publications Warehouse

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

2002-01-01

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

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.

Enhancing Flood Prediction Reliability Using Bayesian Model Averaging

NASA Astrophysics Data System (ADS)

Liu, Z.; Merwade, V.

2017-12-01

Uncertainty analysis is an indispensable part of modeling the hydrology and hydrodynamics of non-idealized environmental systems. Compared to reliance on prediction from one model simulation, using on ensemble of predictions that consider uncertainty from different sources is more reliable. In this study, Bayesian model averaging (BMA) is applied to Black River watershed in Arkansas and Missouri by combining multi-model simulations to get reliable deterministic water stage and probabilistic inundation extent predictions. The simulation ensemble is generated from 81 LISFLOOD-FP subgrid model configurations that include uncertainty from channel shape, channel width, channel roughness and discharge. Model simulation outputs are trained with observed water stage data during one flood event, and BMA prediction ability is validated for another flood event. Results from this study indicate that BMA does not always outperform all members in the ensemble, but it provides relatively robust deterministic flood stage predictions across the basin. Station based BMA (BMA_S) water stage prediction has better performance than global based BMA (BMA_G) prediction which is superior to the ensemble mean prediction. Additionally, high-frequency flood inundation extent (probability greater than 60%) in BMA_G probabilistic map is more accurate than the probabilistic flood inundation extent based on equal weights.

PREDICTING CLIMATE-INDUCED RANGE SHIFTS: MODEL DIFFERENCES AND MODEL RELIABILITY

EPA Science Inventory

Predicted changes in the global climate are likely to cause large shifts in the geographic ranges of many plant and animal species. To date, predictions of future range shifts have relied on a variety of modeling approaches with different levels of model accuracy. Using a common ...

Objective calibration of numerical weather prediction models

NASA Astrophysics Data System (ADS)

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

2017-07-01

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

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.

Predictive Validation of an Influenza Spread Model

PubMed Central

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

2013-01-01

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

Predictive Surface Complexation Modeling

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

Sverjensky, Dimitri A.

Surface complexation plays an important role in the equilibria and kinetics of processes controlling the compositions of soilwaters and groundwaters, the fate of contaminants in groundwaters, and the subsurface storage of CO 2 and nuclear waste. Over the last several decades, many dozens of individual experimental studies have addressed aspects of surface complexation that have contributed to an increased understanding of its role in natural systems. However, there has been no previous attempt to develop a model of surface complexation that can be used to link all the experimental studies in order to place them on a predictive basis. Overall,more » my research has successfully integrated the results of the work of many experimentalists published over several decades. For the first time in studies of the geochemistry of the mineral-water interface, a practical predictive capability for modeling has become available. The predictive correlations developed in my research now enable extrapolations of experimental studies to provide estimates of surface chemistry for systems not yet studied experimentally and for natural and anthropogenically perturbed systems.« less

Chronic and Daily Stressors Along With Negative Affect Interact to Predict Daily Tiredness.

PubMed

Hartsell, Elizabeth N; Neupert, Shevaun D

2017-11-01

The present study examines the within-person relationship of daily stressors and tiredness and whether this depends on daily negative affect and individual differences in chronic stress. One hundred sixteen older adult participants were recruited via Amazon's Mechanical Turk for a 9-day daily diary study. Daily tiredness, daily stressors, and negative affect were measured each day, and chronic stress was measured at baseline. Daily stressors, daily negative affect, and chronic stress interacted to predict daily tiredness. People with high chronic stress who experienced an increase in daily negative affect were the most reactive to daily stressors in terms of experiencing an increase in daily tiredness. We also found that people with low levels of chronic stress were the most reactive to daily stressors when they experienced low levels of daily negative affect. Our results highlight the need for individualized and contextualized approaches to combating daily tiredness in older adults.

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).

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

USDA-ARS?s Scientific Manuscript database

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

Sweat loss prediction using a multi-model approach

NASA Astrophysics Data System (ADS)

Xu, Xiaojiang; Santee, William R.

2011-07-01

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

Referenceless perceptual fog density prediction model

NASA Astrophysics Data System (ADS)

Choi, Lark Kwon; You, Jaehee; Bovik, Alan C.

2014-02-01

We propose a perceptual fog density prediction model based on natural scene statistics (NSS) and "fog aware" statistical features, which can predict the visibility in a foggy scene from a single image without reference to a corresponding fogless image, without side geographical camera information, without training on human-rated judgments, and without dependency on salient objects such as lane markings or traffic signs. The proposed fog density predictor only makes use of measurable deviations from statistical regularities observed in natural foggy and fog-free images. A fog aware collection of statistical features is derived from a corpus of foggy and fog-free images by using a space domain NSS model and observed characteristics of foggy images such as low contrast, faint color, and shifted intensity. The proposed model not only predicts perceptual fog density for the entire image but also provides a local fog density index for each patch. The predicted fog density of the model correlates well with the measured visibility in a foggy scene as measured by judgments taken in a human subjective study on a large foggy image database. As one application, the proposed model accurately evaluates the performance of defog algorithms designed to enhance the visibility of foggy images.

Predictive models of forest dynamics.

PubMed

Purves, Drew; Pacala, Stephen

2008-06-13

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

Data-driven Climate Modeling and Prediction

NASA Astrophysics Data System (ADS)

Kondrashov, D. A.; Chekroun, M.

2016-12-01

Global climate models aim to simulate a broad range of spatio-temporal scales of climate variability with state vector having many millions of degrees of freedom. On the other hand, while detailed weather prediction out to a few days requires high numerical resolution, it is fairly clear that a major fraction of large-scale climate variability can be predicted in a much lower-dimensional phase space. Low-dimensional models can simulate and predict this fraction of climate variability, provided they are able to account for linear and nonlinear interactions between the modes representing large scales of climate dynamics, as well as their interactions with a much larger number of modes representing fast and small scales. This presentation will highlight several new applications by Multilayered Stochastic Modeling (MSM) [Kondrashov, Chekroun and Ghil, 2015] framework that has abundantly proven its efficiency in the modeling and real-time forecasting of various climate phenomena. MSM is a data-driven inverse modeling technique that aims to obtain a low-order nonlinear system of prognostic equations driven by stochastic forcing, and estimates both the dynamical operator and the properties of the driving noise from multivariate time series of observations or a high-end model's simulation. MSM leads to a system of stochastic differential equations (SDEs) involving hidden (auxiliary) variables of fast-small scales ranked by layers, which interact with the macroscopic (observed) variables of large-slow scales to model the dynamics of the latter, and thus convey memory effects. New MSM climate applications focus on development of computationally efficient low-order models by using data-adaptive decomposition methods that convey memory effects by time-embedding techniques, such as Multichannel Singular Spectrum Analysis (M-SSA) [Ghil et al. 2002] and recently developed Data-Adaptive Harmonic (DAH) decomposition method [Chekroun and Kondrashov, 2016]. In particular, new results

Modeling and Prediction of Fan Noise

NASA Technical Reports Server (NTRS)

Envia, Ed

2008-01-01

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

Predictive Modeling the Physician Assistant Supply: 2010–2025

PubMed Central

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

2011-01-01

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

Finding Furfural Hydrogenation Catalysts via Predictive Modelling

PubMed Central

Strassberger, Zea; Mooijman, Maurice; Ruijter, Eelco; Alberts, Albert H; Maldonado, Ana G; Orru, Romano V A; Rothenberg, Gadi

2010-01-01

Abstract We combine multicomponent reactions, catalytic performance studies and predictive modelling to find transfer hydrogenation catalysts. An initial set of 18 ruthenium-carbene complexes were synthesized and screened in the transfer hydrogenation of furfural to furfurol with isopropyl alcohol complexes gave varied yields, from 62% up to >99.9%, with no obvious structure/activity correlations. Control experiments proved that the carbene ligand remains coordinated to the ruthenium centre throughout the reaction. Deuterium-labelling studies showed a secondary isotope effect (kH:kD=1.5). Further mechanistic studies showed that this transfer hydrogenation follows the so-called monohydride pathway. Using these data, we built a predictive model for 13 of the catalysts, based on 2D and 3D molecular descriptors. We tested and validated the model using the remaining five catalysts (cross-validation, R2=0.913). Then, with this model, the conversion and selectivity were predicted for four completely new ruthenium-carbene complexes. These four catalysts were then synthesized and tested. The results were within 3% of the model’s predictions, demonstrating the validity and value of predictive modelling in catalyst optimization. PMID:23193388

Clinical Predictive Modeling Development and Deployment through FHIR Web Services.

PubMed

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

2015-01-01

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

Clinical Predictive Modeling Development and Deployment through FHIR Web Services

PubMed Central

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

2015-01-01

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

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.

Living Large: Affect Amplification in Visual Perception Predicts Emotional Reactivity to Events in Daily Life

PubMed Central

Palder, Spencer L.; Ode, Scott; Liu, Tianwei; Robinson, Michael D.

2012-01-01

It was hypothesized that affect-amplifying individuals would be more reactive to affective events in daily life. Affect amplification was quantified in terms of overestimating the font size of positive and negative, relative to neutral, words in a basic perception task. Subsequently, the same (N = 70) individuals completed a daily diary protocol in which they reported on levels of daily stressors, provocations, and social support as well as six emotion-related outcomes for 14 consecutive days. Individual differences in affect amplification moderated reactivity to daily affective events in all such analyses. For example, daily stressor levels predicted cognitive failures at high, but not low, levels of affect amplification. Affect amplification, then, appears to have widespread utility in understanding individual differences in emotional reactivity. PMID:22989107

Prediction of Particle Concentration using Traffic Emission Model

NASA Astrophysics Data System (ADS)

He, Hong-di; Lu, Jane Wei-zhen

2010-05-01

Vehicle emission is regarded as one of major sources of air pollution in urban area. Much attention has been addressed on it especially at traffic intersection. At intersection, vehicles frequently stop with idling engine during the red time and speed-up rapidly in the green time, which result in a high velocity fluctuation and produce extra pollutants to the surrounding air. To deeply understand such process, a semi-empirical model for predicting the changing effect of traffic flow patterns on particulate concentrations is proposed. The performance of the model is evaluated using the correlation coefficient and other parameters. From the results, the correlation coefficients in morning and afternoon data were found to be 0.86 an 0.73 respectively, which implies that the semi-empirical model for morning and afternoon data are 86% and 73% error free. Due to less affected by possible factors such as traffic volume and movement of pedestrian, the dispersion of the particulate matter in the morning is smaller and then contributes to higher performance than that in the afternoon.

Thermal barrier coating life prediction model development

NASA Technical Reports Server (NTRS)

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

1988-01-01

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

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

PubMed

Guérin, Eva; Fortier, Michelle S

2012-01-01

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

Impact of modellers' decisions on hydrological a priori predictions

NASA Astrophysics Data System (ADS)

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

2014-06-01

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

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

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

A multivariate model for predicting segmental body composition.

PubMed

Tian, Simiao; Mioche, Laurence; Denis, Jean-Baptiste; Morio, Béatrice

2013-12-01

The aims of the present study were to propose a multivariate model for predicting simultaneously body, trunk and appendicular fat and lean masses from easily measured variables and to compare its predictive capacity with that of the available univariate models that predict body fat percentage (BF%). The dual-energy X-ray absorptiometry (DXA) dataset (52% men and 48% women) with White, Black and Hispanic ethnicities (1999-2004, National Health and Nutrition Examination Survey) was randomly divided into three sub-datasets: a training dataset (TRD), a test dataset (TED); a validation dataset (VAD), comprising 3835, 1917 and 1917 subjects. For each sex, several multivariate prediction models were fitted from the TRD using age, weight, height and possibly waist circumference. The most accurate model was selected from the TED and then applied to the VAD and a French DXA dataset (French DB) (526 men and 529 women) to assess the prediction accuracy in comparison with that of five published univariate models, for which adjusted formulas were re-estimated using the TRD. Waist circumference was found to improve the prediction accuracy, especially in men. For BF%, the standard error of prediction (SEP) values were 3.26 (3.75) % for men and 3.47 (3.95)% for women in the VAD (French DB), as good as those of the adjusted univariate models. Moreover, the SEP values for the prediction of body and appendicular lean masses ranged from 1.39 to 2.75 kg for both the sexes. The prediction accuracy was best for age < 65 years, BMI < 30 kg/m2 and the Hispanic ethnicity. The application of our multivariate model to large populations could be useful to address various public health issues.

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

PubMed

Shin, Myoungjin; Kim, Inwoo; Kwon, Sungho

2014-12-01

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

Predicting employees' well-being using work-family conflict and job strain models.

PubMed

Karimi, Leila; Karimi, Hamidreza; Nouri, Aboulghassem

2011-04-01

The present study examined the effects of two models of work–family conflict (WFC) and job-strain on the job-related and context-free well-being of employees. The participants of the study consisted of Iranian employees from a variety of organizations. The effects of three dimensions of the job-strain model and six forms of WFC on affective well-being were assessed. The results of hierarchical multiple regression analysis revealed that the number of working hours, strain-based work interfering with family life (WIF) along with job characteristic variables (i.e. supervisory support, job demands and job control) all make a significant contribution to the prediction of job-related well-being. On the other hand, strain-based WIF and family interfering with work (FIW) significantly predicted context-free well-being. Implications are drawn and recommendations made regarding future research and interventions in the workplace.

Dataset on predictive compressive strength model for self-compacting concrete.

PubMed

Ofuyatan, O M; Edeki, S O

2018-04-01

The determination of compressive strength is affected by many variables such as the water cement (WC) ratio, the superplasticizer (SP), the aggregate combination, and the binder combination. In this dataset article, 7, 28, and 90-day compressive strength models are derived using statistical analysis. The response surface methodology is used toinvestigate the effect of the parameters: Varying percentages of ash, cement, WC, and SP on hardened properties-compressive strengthat 7,28 and 90 days. Thelevels of independent parameters are determinedbased on preliminary experiments. The experimental values for compressive strengthat 7, 28 and 90 days and modulus of elasticity underdifferent treatment conditions are also discussed and presented.These dataset can effectively be used for modelling and prediction in concrete production settings.

The temporal stability and predictive validity of affect-based and cognition-based intentions.

PubMed

Keer, Mario; Conner, Mark; Van den Putte, Bas; Neijens, Peter

2014-06-01

Recent research has revealed individual differences in the extent to which people base their intentions on affect and cognition. Two studies are presented that assess whether such differences predict the strength of individuals' intention-behaviour relationships. Participants completed measures of affect, cognition, intention, and behaviour regarding a range of health behaviours. Study 1 (N = 300) found that the strength of the intention-behaviour relationship was significantly related to the extent to which individuals based their intentions on affect, but not to the extent they based them on cognition. Study 2 (N = 387) replicated the findings of the first study. In addition, Study 2 revealed that intention stability mediated the relationship between the degree people based their intentions on affect and the strength of the intention-behaviour relationship. Thus, individuals who base their intentions strongly on affect have more stable intentions, and are therefore more likely to enact them. © 2013 The British Psychological Society.

Developing a stochastic traffic volume prediction model for public-private partnership projects

NASA Astrophysics Data System (ADS)

Phong, Nguyen Thanh; Likhitruangsilp, Veerasak; Onishi, Masamitsu

2017-11-01

Transportation projects require an enormous amount of capital investment resulting from their tremendous size, complexity, and risk. Due to the limitation of public finances, the private sector is invited to participate in transportation project development. The private sector can entirely or partially invest in transportation projects in the form of Public-Private Partnership (PPP) scheme, which has been an attractive option for several developing countries, including Vietnam. There are many factors affecting the success of PPP projects. The accurate prediction of traffic volume is considered one of the key success factors of PPP transportation projects. However, only few research works investigated how to predict traffic volume over a long period of time. Moreover, conventional traffic volume forecasting methods are usually based on deterministic models which predict a single value of traffic volume but do not consider risk and uncertainty. This knowledge gap makes it difficult for concessionaires to estimate PPP transportation project revenues accurately. The objective of this paper is to develop a probabilistic traffic volume prediction model. First, traffic volumes were estimated following the Geometric Brownian Motion (GBM) process. Monte Carlo technique is then applied to simulate different scenarios. The results show that this stochastic approach can systematically analyze variations in the traffic volume and yield more reliable estimates for PPP projects.

Dynamic Simulation of Human Gait Model With Predictive Capability.

PubMed

Sun, Jinming; Wu, Shaoli; Voglewede, Philip A

2018-03-01

In this paper, it is proposed that the central nervous system (CNS) controls human gait using a predictive control approach in conjunction with classical feedback control instead of exclusive classical feedback control theory that controls based on past error. To validate this proposition, a dynamic model of human gait is developed using a novel predictive approach to investigate the principles of the CNS. The model developed includes two parts: a plant model that represents the dynamics of human gait and a controller that represents the CNS. The plant model is a seven-segment, six-joint model that has nine degrees-of-freedom (DOF). The plant model is validated using data collected from able-bodied human subjects. The proposed controller utilizes model predictive control (MPC). MPC uses an internal model to predict the output in advance, compare the predicted output to the reference, and optimize the control input so that the predicted error is minimal. To decrease the complexity of the model, two joints are controlled using a proportional-derivative (PD) controller. The developed predictive human gait model is validated by simulating able-bodied human gait. The simulation results show that the developed model is able to simulate the kinematic output close to experimental data.

Prediction of Airfoil Characteristics With Higher Order Turbulence Models

NASA Technical Reports Server (NTRS)

Gatski, Thomas B.

1996-01-01

This study focuses on the prediction of airfoil characteristics, including lift and drag over a range of Reynolds numbers. Two different turbulence models, which represent two different types of models, are tested. The first is a standard isotropic eddy-viscosity two-equation model, and the second is an explicit algebraic stress model (EASM). The turbulent flow field over a general-aviation airfoil (GA(W)-2) at three Reynolds numbers is studied. At each Reynolds number, predicted lift and drag values at different angles of attack are compared with experimental results, and predicted variations of stall locations with Reynolds number are compared with experimental data. Finally, the size of the separation zone predicted by each model is analyzed, and correlated with the behavior of the lift coefficient near stall. In summary, the EASM model is able to predict the lift and drag coefficients over a wider range of angles of attack than the two-equation model for the three Reynolds numbers studied. However, both models are unable to predict the correct lift and drag behavior near the stall angle, and for the lowest Reynolds number case, the two-equation model did not predict separation on the airfoil near stall.

Novelty response and 50 kHz ultrasonic vocalizations: Differential prediction of locomotor and affective response to amphetamine in Sprague-Dawley rats.

PubMed

Garcia, Erik J; Cain, Mary E

2016-02-01

Novelty and sensation seeking (NSS) predisposes humans and rats to experiment with psychostimulants. In animal models, different tests of NSS predict different phases of drug dependence. Ultrasonic vocalizations (USVs) are evoked by psychomotor stimulants and measure the affective/motivation response to stimuli, yet the role NSS has on USVs in response to amphetamine is not determined. The aim of the present study was to determine if individual differences in NSS and USVs can predict locomotor and USV response to amphetamine (0.0, 0.3, and 1.0 mg/kg) after acute and chronic exposure. Thirty male rats were tested for their response to novelty (IEN), choice to engage in novelty (NPP), and heterospecific play (H-USV). Rats were administered non-contingent amphetamine or saline for seven exposures, and USVs and locomotor activity were measured. After a 14-day rest, rats were administered a challenge dose of amphetamine. Regression analyses indicated that amphetamine dose-dependently increased locomotor activity and the NPP test negatively predicted treatment-induced locomotor activity. The H-USV test predicted treatment-induced frequency-modulated (FM) USVs, but the strength of prediction depended on IEN response. Results provide evidence that locomotor activity and FM USVs induced by amphetamine represent different behavioral responses. The prediction of amphetamine-induced FM USVs by the H-USV screen was changed by the novelty response, indicating that the affective value of amphetamine-measured by FM USVs-depends on novelty response. This provides evidence that higher novelty responders may develop a tolerance faster and may escalate intake faster.

Negative (but not Positive) Parenting Interacts with Infant Negative Affect to Predict Infant Approach: Evidence of Diathesis-Stress.

PubMed

Holzman, Jacob B; Burt, Nicole M; Edwards, Erin S; Rosinski, Leanna D; Bridgett, David J

2018-01-01

Temperament by parenting interactions may reflect that individuals with greater risk are more likely to experience negative outcomes in adverse contexts (diathesis-stress) or that these individuals are more susceptible to contextual influences in a 'for better or for worse' pattern (differential susceptibility). Although such interactions have been identified for a variety of child outcomes, prior research has not examined approach characteristics - excitement and approach toward pleasurable activities - in the first year of life. Therefore, the current study investigated whether 6-month maternal reported infant negative affect - a phenotypic marker of risk/susceptibility - interacted with 8-month observed parenting behaviors (positive parenting, negative parenting) to predict 12-month infant behavioral approach. Based a sample of mothers and their infants ( N =150), results indicated that negative parenting was inversely associated with subsequent approach for infants with high, but not low, levels of early negative affect. Similar results did not occur regarding positive parenting. These findings better fit a diathesis-stress model rather than a differential susceptibility model. Implications and limitations of these findings are discussed.

Clinical Prediction Models for Cardiovascular Disease: Tufts Predictive Analytics and Comparative Effectiveness Clinical Prediction Model Database.

PubMed

Wessler, Benjamin S; Lai Yh, Lana; Kramer, Whitney; Cangelosi, Michael; Raman, Gowri; Lutz, Jennifer S; Kent, David M

2015-07-01

Clinical prediction models (CPMs) estimate the probability of clinical outcomes and hold the potential to improve decision making and individualize care. For patients with cardiovascular disease, there are numerous CPMs available although the extent of this literature is not well described. We conducted a systematic review for articles containing CPMs for cardiovascular disease published between January 1990 and May 2012. Cardiovascular disease includes coronary heart disease, heart failure, arrhythmias, stroke, venous thromboembolism, and peripheral vascular disease. We created a novel database and characterized CPMs based on the stage of development, population under study, performance, covariates, and predicted outcomes. There are 796 models included in this database. The number of CPMs published each year is increasing steadily over time. Seven hundred seventeen (90%) are de novo CPMs, 21 (3%) are CPM recalibrations, and 58 (7%) are CPM adaptations. This database contains CPMs for 31 index conditions, including 215 CPMs for patients with coronary artery disease, 168 CPMs for population samples, and 79 models for patients with heart failure. There are 77 distinct index/outcome pairings. Of the de novo models in this database, 450 (63%) report a c-statistic and 259 (36%) report some information on calibration. There is an abundance of CPMs available for a wide assortment of cardiovascular disease conditions, with substantial redundancy in the literature. The comparative performance of these models, the consistency of effects and risk estimates across models and the actual and potential clinical impact of this body of literature is poorly understood. © 2015 American Heart Association, Inc.

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

USGS Publications Warehouse

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

2006-01-01

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

Mid- and long-term runoff predictions by an improved phase-space reconstruction model.

PubMed

Hong, Mei; Wang, Dong; Wang, Yuankun; Zeng, Xiankui; Ge, Shanshan; Yan, Hengqian; Singh, Vijay P

2016-07-01

In recent years, the phase-space reconstruction method has usually been used for mid- and long-term runoff predictions. However, the traditional phase-space reconstruction method is still needs to be improved. Using the genetic algorithm to improve the phase-space reconstruction method, a new nonlinear model of monthly runoff is constructed. The new model does not rely heavily on embedding dimensions. Recognizing that the rainfall-runoff process is complex, affected by a number of factors, more variables (e.g. temperature and rainfall) are incorporated in the model. In order to detect the possible presence of chaos in the runoff dynamics, chaotic characteristics of the model are also analyzed, which shows the model can represent the nonlinear and chaotic characteristics of the runoff. The model is tested for its forecasting performance in four types of experiments using data from six hydrological stations on the Yellow River and the Yangtze River. Results show that the medium-and long-term runoff is satisfactorily forecasted at the hydrological stations. Not only is the forecasting trend accurate, but also the mean absolute percentage error is no more than 15%. Moreover, the forecast results of wet years and dry years are both good, which means that the improved model can overcome the traditional ''wet years and dry years predictability barrier,'' to some extent. The model forecasts for different regions are all good, showing the universality of the approach. Compared with selected conceptual and empirical methods, the model exhibits greater reliability and stability in the long-term runoff prediction. Our study provides a new thinking for research on the association between the monthly runoff and other hydrological factors, and also provides a new method for the prediction of the monthly runoff. Copyright © 2015 Elsevier Inc. All rights reserved.

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

A generalized predictive model for direct gain

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

Givoni, B.

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

A burnout prediction model based around char morphology

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

Tao Wu; Edward Lester; Michael Cloke

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

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)...

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.

A grey NGM(1,1, k) self-memory coupling prediction model for energy consumption prediction.

PubMed

Guo, Xiaojun; Liu, Sifeng; Wu, Lifeng; Tang, Lingling

2014-01-01

Energy consumption prediction is an important issue for governments, energy sector investors, and other related corporations. Although there are several prediction techniques, selection of the most appropriate technique is of vital importance. As for the approximate nonhomogeneous exponential data sequence often emerging in the energy system, a novel grey NGM(1,1, k) self-memory coupling prediction model is put forward in order to promote the predictive performance. It achieves organic integration of the self-memory principle of dynamic system and grey NGM(1,1, k) model. The traditional grey model's weakness as being sensitive to initial value can be overcome by the self-memory principle. In this study, total energy, coal, and electricity consumption of China is adopted for demonstration by using the proposed coupling prediction technique. The results show the superiority of NGM(1,1, k) self-memory coupling prediction model when compared with the results from the literature. Its excellent prediction performance lies in that the proposed coupling model can take full advantage of the systematic multitime historical data and catch the stochastic fluctuation tendency. This work also makes a significant contribution to the enrichment of grey prediction theory and the extension of its application span.

Predictability of the summer East Asian upper-tropospheric westerly jet in ENSEMBLES multi-model forecasts

NASA Astrophysics Data System (ADS)

Li, Chaofan; Lin, Zhongda

2015-12-01

The interannual variation of the East Asian upper-tropospheric westerly jet (EAJ) significantly affects East Asian climate in summer. Identifying its performance in model prediction may provide us another viewpoint, from the perspective of upper-tropospheric circulation, to understand the predictability of summer climate anomalies in East Asia. This study presents a comprehensive assessment of year-to-year variability of the EAJ based on retrospective seasonal forecasts, initiated from 1 May, in the five state-of-the-art coupled models from ENSEMBLES during 1960-2005. It is found that the coupled models show certain capability in describing the interannual meridional displacement of the EAJ, which reflects the models' performance in the first leading empirical orthogonal function (EOF) mode. This capability is mainly shown over the region south of the EAJ axis. Additionally, the models generally capture well the main features of atmospheric circulation and SST anomalies related to the interannual meridional displacement of the EAJ. Further analysis suggests that the predicted warm SST anomalies in the concurrent summer over the tropical eastern Pacific and northern Indian Ocean are the two main sources of the potential prediction skill of the southward shift of the EAJ. In contrast, the models are powerless in describing the variation over the region north of the EAJ axis, associated with the meridional displacement, and interannual intensity change of the EAJ, the second leading EOF mode, meaning it still remains a challenge to better predict the EAJ and, subsequently, summer climate in East Asia, using current coupled models.

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

ERIC Educational Resources Information Center

Thelen, Mark H.; And Others

1977-01-01

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

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

ERIC Educational Resources Information Center

Hilton, N. Zoe; Harris, Grant T.

2009-01-01

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

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.

The Risk GP Model: the standard model of prediction in medicine.

PubMed

Fuller, Jonathan; Flores, Luis J

2015-12-01

With the ascent of modern epidemiology in the Twentieth Century came a new standard model of prediction in public health and clinical medicine. In this article, we describe the structure of the model. The standard model uses epidemiological measures-most commonly, risk measures-to predict outcomes (prognosis) and effect sizes (treatment) in a patient population that can then be transformed into probabilities for individual patients. In the first step, a risk measure in a study population is generalized or extrapolated to a target population. In the second step, the risk measure is particularized or transformed to yield probabilistic information relevant to a patient from the target population. Hence, we call the approach the Risk Generalization-Particularization (Risk GP) Model. There are serious problems at both stages, especially with the extent to which the required assumptions will hold and the extent to which we have evidence for the assumptions. Given that there are other models of prediction that use different assumptions, we should not inflexibly commit ourselves to one standard model. Instead, model pluralism should be standard in medical prediction. Copyright © 2015 Elsevier Ltd. All rights reserved.

Sleep quality predicts positive and negative affect but not vice versa. An electronic diary study in depressed and healthy individuals.

PubMed

Bouwmans, Mara E J; Bos, Elisabeth H; Hoenders, H J Rogier; Oldehinkel, Albertine J; de Jonge, Peter

2017-01-01

The exact nature of the complex relationship between sleep and affect has remained unclear. This study investigated the temporal order of change in sleep and affect in participants with and without depression. 27 depressed patients and 27 pair-matched healthy controls assessed their sleep in the morning and their affect 3 times a day for 30 consecutive days in their natural environment. Daily sleep quality and average positive affect (PA) and negative affect (NA) were used to examine whether changes in sleep quality preceded or followed changes in PA and NA, and whether this was different for patients and healthy controls. Second, presumptive mediating factors were investigated. We hypothesized that fatigue mediated the effect of changes in sleep quality on subsequent PA/NA, and that rumination mediated the effect of changes in PA/NA on subsequent sleep quality. Multilevel models showed that changes in sleep quality predicted changes in PA (B=0.08, p<0.001) and NA (B=-0.06, p<0.001), but not the other way around (PA: B=0.03, p=0.70, NA: B=-0.05, p=0.60). Fatigue was found to be a significant mediator of the relationship between sleep quality and PA (Indirect Effect=0.03, p<0.001), and between sleep quality and NA (Indirect Effect=-0.02, p=0.01). Rumination was not investigated because of non-significant associations between PA/NA and sleep quality. The associations were not different for patients and controls. The analyses were restricted to self-reported sleep quality, and conclusions about causality could not be drawn. Improvements in sleep quality predicted improvements in affect the following day, partly mediated by fatigue. Treatment of sleep symptoms would benefit affect in clinical care and beyond. Copyright © 2016 Elsevier B.V. All rights reserved.

SEC proton prediction model: verification and analysis.

PubMed

Balch, C C

1999-06-01

This paper describes a model that has been used at the NOAA Space Environment Center since the early 1970s as a guide for the prediction of solar energetic particle events. The algorithms for proton event probability, peak flux, and rise time are described. The predictions are compared with observations. The current model shows some ability to distinguish between proton event associated flares and flares that are not associated with proton events. The comparisons of predicted and observed peak flux show considerable scatter, with an rms error of almost an order of magnitude. Rise time comparisons also show scatter, with an rms error of approximately 28 h. The model algorithms are analyzed using historical data and improvements are suggested. Implementation of the algorithm modifications reduces the rms error in the log10 of the flux prediction by 21%, and the rise time rms error by 31%. Improvements are also realized in the probability prediction by deriving the conditional climatology for proton event occurrence given flare characteristics.

Cognitive Affective Engagement Model of Multiple Source Use

ERIC Educational Resources Information Center

List, Alexandra; Alexander, Patricia A.

2017-01-01

This article introduces the cognitive affective engagement model (CAEM) of multiple source use. The CAEM is presented as a way of unifying cognitive and behaviorally focused models of multiple text engagement with research on the role of affective factors (e.g., interest) in text processing. The CAEM proposes that students' engagement with…

Use of protection motivation theory, affect, and barriers to understand and predict adherence to outpatient rehabilitation.

PubMed

Grindley, Emma J; Zizzi, Samuel J; Nasypany, Alan M

2008-12-01

Protection motivation theory (PMT) has been used in more than 20 different health-related fields to study intentions and behavior, albeit primarily outside the area of injury rehabilitation. In order to examine and predict patient adherence behavior, this study was carried out to explore the use of PMT as a screening tool in a general sample of people with orthopedic conditions. New patients who were more than 18 years old and who were prescribed 4 to 8 weeks of physical therapy treatment (n=229) were administered a screening tool (Sports Injury Rehabilitation Beliefs Scale, Positive and Negative Affect Schedule, and a barriers checklist) prior to treatment. Participants' adherence was assessed with several attendance measures and an in-clinic assessment of behavior. Statistical analyses included correlation, chi-square, multiple regression, and discriminant function analyses. A variety of relationships among affect, barriers, and PMT components were evident. In-clinic behavior and attendance were influenced by affect, whereas dropout status was predicted by affect, severity, self-efficacy, and age. The screening tool used in this study may assist in identifying patients who are at risk for poor adherence and provide valuable information to enhance provider-patient relationships and foster patient adherence. However, it is recommended that more research be conducted to further understand the impact of variables on patient adherence and that the screening tool be enhanced to increase its predictive ability.

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

PubMed

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

2015-10-01

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

A review of statistical updating methods for clinical prediction models.

PubMed

Su, Ting-Li; Jaki, Thomas; Hickey, Graeme L; Buchan, Iain; Sperrin, Matthew

2018-01-01

A clinical prediction model is a tool for predicting healthcare outcomes, usually within a specific population and context. A common approach is to develop a new clinical prediction model for each population and context; however, this wastes potentially useful historical information. A better approach is to update or incorporate the existing clinical prediction models already developed for use in similar contexts or populations. In addition, clinical prediction models commonly become miscalibrated over time, and need replacing or updating. In this article, we review a range of approaches for re-using and updating clinical prediction models; these fall in into three main categories: simple coefficient updating, combining multiple previous clinical prediction models in a meta-model and dynamic updating of models. We evaluated the performance (discrimination and calibration) of the different strategies using data on mortality following cardiac surgery in the United Kingdom: We found that no single strategy performed sufficiently well to be used to the exclusion of the others. In conclusion, useful tools exist for updating existing clinical prediction models to a new population or context, and these should be implemented rather than developing a new clinical prediction model from scratch, using a breadth of complementary statistical methods.

Efficient Reduction and Analysis of Model Predictive Error

NASA Astrophysics Data System (ADS)

Doherty, J.

2006-12-01

Most groundwater models are calibrated against historical measurements of head and other system states before being used to make predictions in a real-world context. Through the calibration process, parameter values are estimated or refined such that the model is able to reproduce historical behaviour of the system at pertinent observation points reasonably well. Predictions made by the model are deemed to have greater integrity because of this. Unfortunately, predictive integrity is not as easy to achieve as many groundwater practitioners would like to think. The level of parameterisation detail estimable through the calibration process (especially where estimation takes place on the basis of heads alone) is strictly limited, even where full use is made of modern mathematical regularisation techniques such as those encapsulated in the PEST calibration package. (Use of these mechanisms allows more information to be extracted from a calibration dataset than is possible using simpler regularisation devices such as zones of piecewise constancy.) Where a prediction depends on aspects of parameterisation detail that are simply not inferable through the calibration process (which is often the case for predictions related to contaminant movement, and/or many aspects of groundwater/surface water interaction), then that prediction may be just as much in error as it would have been if the model had not been calibrated at all. Model predictive error arises from two sources. These are (a) the presence of measurement noise within the calibration dataset through which linear combinations of parameters spanning the "calibration solution space" are inferred, and (b) the sensitivity of the prediction to members of the "calibration null space" spanned by linear combinations of parameters which are not inferable through the calibration process. The magnitude of the former contribution depends on the level of measurement noise. The magnitude of the latter contribution (which often

Predictive modeling of crystal accumulation in high-level waste glass melters processing radioactive waste

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

Matyáš, Josef; Gervasio, Vivianaluxa; Sannoh, Sulaiman E.

The effectiveness of HLW vitrification is limited by precipitation/accumulation of spinel crystals [(Fe, Ni, Mn, Zn)(Fe, Cr)2O4] in the glass discharge riser of Joule-heated ceramic melters during idling. These crystals do not affect glass durability; however, if accumulated in thick layer, they can clog the melter and prevent discharge of molten glass into canisters. To address this problem, an empirical model was developed that can predict thicknesses of accumulated layers as a function of glass composition. This model predicts well the accumulation of single crystals and/or small-scale agglomerates, but, excessive agglomeration observed in high-Ni-Fe glass resulted in an under-prediction ofmore » accumulated layers, which gradually worsen over time as an increased number of agglomerates formed. Accumulation rate of ~53.8 ± 3.7 µm/h determined for this glass will result in ~26 mm thick layer in 20 days of melter idling.« less

Multistressor predictive models of invertebrate condition in the Corn Belt, USA

USGS Publications Warehouse

Waite, Ian R.; Van Metre, Peter C.

2017-01-01

Understanding the complex relations between multiple environmental stressors and ecological conditions in streams can help guide resource-management decisions. During 14 weeks in spring/summer 2013, personnel from the US Geological Survey and the US Environmental Protection Agency sampled 98 wadeable streams across the Midwest Corn Belt region of the USA for water and sediment quality, physical and habitat characteristics, and ecological communities. We used these data to develop independent predictive disturbance models for 3 macroinvertebrate metrics and a multimetric index. We developed the models based on boosted regression trees (BRT) for 3 stressor categories, land use/land cover (geographic information system [GIS]), all in-stream stressors combined (nutrients, habitat, and contaminants), and for GIS plus in-stream stressors. The GIS plus in-stream stressor models had the best overall performance with an average cross-validation R2 across all models of 0.41. The models were generally consistent in the explanatory variables selected within each stressor group across the 4 invertebrate metrics modeled. Variables related to riparian condition, substrate size or embeddedness, velocity and channel shape, nutrients (primarily NH3), and contaminants (pyrethroid degradates) were important descriptors of the invertebrate metrics. Models based on all measured in-stream stressors performed comparably to models based on GIS landscape variables, suggesting that the in-stream stressor characterization reasonably represents the dominant factors affecting invertebrate communities and that GIS variables are acting as surrogates for in-stream stressors that directly affect in-stream biota.

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

PubMed

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

2014-06-01

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

Regression models to predict hip joint centers in pathological hip population.

PubMed

Mantovani, Giulia; Ng, K C Geoffrey; Lamontagne, Mario

2016-02-01

The purpose was to investigate the validity of Harrington's and Davis's hip joint center (HJC) regression equations on a population affected by a hip deformity, (i.e., femoroacetabular impingement). Sixty-seven participants (21 healthy controls, 46 with a cam-type deformity) underwent pelvic CT imaging. Relevant bony landmarks and geometric HJCs were digitized from the images, and skin thickness was measured for the anterior and posterior superior iliac spines. Non-parametric statistical and Bland-Altman tests analyzed differences between the predicted HJC (from regression equations) and the actual HJC (from CT images). The error from Davis's model (25.0 ± 6.7 mm) was larger than Harrington's (12.3 ± 5.9 mm, p<0.001). There were no differences between groups, thus, studies on femoroacetabular impingement can implement conventional regression models. Measured skin thickness was 9.7 ± 7.0mm and 19.6 ± 10.9 mm for the anterior and posterior bony landmarks, respectively, and correlated with body mass index. Skin thickness estimates can be considered to reduce the systematic error introduced by surface markers. New adult-specific regression equations were developed from the CT dataset, with the hypothesis that they could provide better estimates when tuned to a larger adult-specific dataset. The linear models were validated on external datasets and using leave-one-out cross-validation techniques; Prediction errors were comparable to those of Harrington's model, despite the adult-specific population and the larger sample size, thus, prediction accuracy obtained from these parameters could not be improved. Copyright © 2015 Elsevier B.V. All rights reserved.

Comparing predictions of extinction risk using models and subjective judgement

NASA Astrophysics Data System (ADS)

McCarthy, Michael A.; Keith, David; Tietjen, Justine; Burgman, Mark A.; Maunder, Mark; Master, Larry; Brook, Barry W.; Mace, Georgina; Possingham, Hugh P.; Medellin, Rodrigo; Andelman, Sandy; Regan, Helen; Regan, Tracey; Ruckelshaus, Mary

2004-10-01

Models of population dynamics are commonly used to predict risks in ecology, particularly risks of population decline. There is often considerable uncertainty associated with these predictions. However, alternatives to predictions based on population models have not been assessed. We used simulation models of hypothetical species to generate the kinds of data that might typically be available to ecologists and then invited other researchers to predict risks of population declines using these data. The accuracy of the predictions was assessed by comparison with the forecasts of the original model. The researchers used either population models or subjective judgement to make their predictions. Predictions made using models were only slightly more accurate than subjective judgements of risk. However, predictions using models tended to be unbiased, while subjective judgements were biased towards over-estimation. Psychology literature suggests that the bias of subjective judgements is likely to vary somewhat unpredictably among people, depending on their stake in the outcome. This will make subjective predictions more uncertain and less transparent than those based on models.

Consensus models to predict endocrine disruption for all ...

EPA Pesticide Factsheets

Humans are potentially exposed to tens of thousands of man-made chemicals in the environment. It is well known that some environmental chemicals mimic natural hormones and thus have the potential to be endocrine disruptors. Most of these environmental chemicals have never been tested for their ability to disrupt the endocrine system, in particular, their ability to interact with the estrogen receptor. EPA needs tools to prioritize thousands of chemicals, for instance in the Endocrine Disruptor Screening Program (EDSP). Collaborative Estrogen Receptor Activity Prediction Project (CERAPP) was intended to be a demonstration of the use of predictive computational models on HTS data including ToxCast and Tox21 assays to prioritize a large chemical universe of 32464 unique structures for one specific molecular target – the estrogen receptor. CERAPP combined multiple computational models for prediction of estrogen receptor activity, and used the predicted results to build a unique consensus model. Models were developed in collaboration between 17 groups in the U.S. and Europe and applied to predict the common set of chemicals. Structure-based techniques such as docking and several QSAR modeling approaches were employed, mostly using a common training set of 1677 compounds provided by U.S. EPA, to build a total of 42 classification models and 8 regression models for binding, agonist and antagonist activity. All predictions were evaluated on ToxCast data and on an exte

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.

The clinical course over the first year of Whiplash Associated Disorders (WAD): pain-related disability predicts outcome in a mildly affected sample

PubMed Central

2013-01-01

Background 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. Methods 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. Results 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). Conclusion 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

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

Can contaminant transport models predict breakthrough?

USGS Publications Warehouse

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

2000-01-01

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

Preclinical models used for immunogenicity prediction of therapeutic proteins.

PubMed

Brinks, Vera; Weinbuch, Daniel; Baker, Matthew; Dean, Yann; Stas, Philippe; Kostense, Stefan; Rup, Bonita; Jiskoot, Wim

2013-07-01

All therapeutic proteins are potentially immunogenic. Antibodies formed against these drugs can decrease efficacy, leading to drastically increased therapeutic costs and in rare cases to serious and sometimes life threatening side-effects. Many efforts are therefore undertaken to develop therapeutic proteins with minimal immunogenicity. For this, immunogenicity prediction of candidate drugs during early drug development is essential. Several in silico, in vitro and in vivo models are used to predict immunogenicity of drug leads, to modify potentially immunogenic properties and to continue development of drug candidates with expected low immunogenicity. Despite the extensive use of these predictive models, their actual predictive value varies. Important reasons for this uncertainty are the limited/insufficient knowledge on the immune mechanisms underlying immunogenicity of therapeutic proteins, the fact that different predictive models explore different components of the immune system and the lack of an integrated clinical validation. In this review, we discuss the predictive models in use, summarize aspects of immunogenicity that these models predict and explore the merits and the limitations of each of the models.

Airport Noise Prediction Model -- MOD 7

DOT National Transportation Integrated Search

1978-07-01

The MOD 7 Airport Noise Prediction Model is fully operational. The language used is Fortran, and it has been run on several different computer systems. Its capabilities include prediction of noise levels for single parameter changes, for multiple cha...

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

PubMed

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

2016-07-01

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

A Grey NGM(1,1, k) Self-Memory Coupling Prediction Model for Energy Consumption Prediction

PubMed Central

Guo, Xiaojun; Liu, Sifeng; Wu, Lifeng; Tang, Lingling

2014-01-01

Energy consumption prediction is an important issue for governments, energy sector investors, and other related corporations. Although there are several prediction techniques, selection of the most appropriate technique is of vital importance. As for the approximate nonhomogeneous exponential data sequence often emerging in the energy system, a novel grey NGM(1,1, k) self-memory coupling prediction model is put forward in order to promote the predictive performance. It achieves organic integration of the self-memory principle of dynamic system and grey NGM(1,1, k) model. The traditional grey model's weakness as being sensitive to initial value can be overcome by the self-memory principle. In this study, total energy, coal, and electricity consumption of China is adopted for demonstration by using the proposed coupling prediction technique. The results show the superiority of NGM(1,1, k) self-memory coupling prediction model when compared with the results from the literature. Its excellent prediction performance lies in that the proposed coupling model can take full advantage of the systematic multitime historical data and catch the stochastic fluctuation tendency. This work also makes a significant contribution to the enrichment of grey prediction theory and the extension of its application span. PMID:25054174

Prediction of climate change in Brunei Darussalam using statistical downscaling model

NASA Astrophysics Data System (ADS)

Hasan, Dk. Siti Nurul Ain binti Pg. Ali; Ratnayake, Uditha; Shams, Shahriar; Nayan, Zuliana Binti Hj; Rahman, Ena Kartina Abdul

2017-06-01

Climate is changing and evidence suggests that the impact of climate change would influence our everyday lives, including agriculture, built environment, energy management, food security and water resources. Brunei Darussalam located within the heart of Borneo will be affected both in terms of precipitation and temperature. Therefore, it is crucial to comprehend and assess how important climate indicators like temperature and precipitation are expected to vary in the future in order to minimise its impact. This study assesses the application of a statistical downscaling model (SDSM) for downscaling General Circulation Model (GCM) results for maximum and minimum temperatures along with precipitation in Brunei Darussalam. It investigates future climate changes based on numerous scenarios using Hadley Centre Coupled Model, version 3 (HadCM3), Canadian Earth System Model (CanESM2) and third-generation Coupled Global Climate Model (CGCM3) outputs. The SDSM outputs were improved with the implementation of bias correction and also using a monthly sub-model instead of an annual sub-model. The outcomes of this assessment show that monthly sub-model performed better than the annual sub-model. This study indicates a satisfactory applicability for generation of maximum temperatures, minimum temperatures and precipitation for future periods of 2017-2046 and 2047-2076. All considered models and the scenarios were consistent in predicting increasing trend of maximum temperature, increasing trend of minimum temperature and decreasing trend of precipitations. Maximum overall trend of Tmax was also observed for CanESM2 with Representative Concentration Pathways (RCP) 8.5 scenario. The increasing trend is 0.014 °C per year. Accordingly, by 2076, the highest prediction of average maximum temperatures is that it will increase by 1.4 °C. The same model predicts an increasing trend of Tmin of 0.004 °C per year, while the highest trend is seen under CGCM3-A2 scenario which is 0.009 �

Dynamic Socialized Gaussian Process Models for Human Behavior Prediction in a Health Social Network

PubMed Central

Shen, Yelong; Phan, NhatHai; Xiao, Xiao; Jin, Ruoming; Sun, Junfeng; Piniewski, Brigitte; Kil, David; Dou, Dejing

2016-01-01

Modeling and predicting human behaviors, such as the level and intensity of physical activity, is a key to preventing the cascade of obesity and helping spread healthy behaviors in a social network. In our conference paper, we have developed a social influence model, named Socialized Gaussian Process (SGP), for socialized human behavior modeling. Instead of explicitly modeling social influence as individuals' behaviors influenced by their friends' previous behaviors, SGP models the dynamic social correlation as the result of social influence. The SGP model naturally incorporates personal behavior factor and social correlation factor (i.e., the homophily principle: Friends tend to perform similar behaviors) into a unified model. And it models the social influence factor (i.e., an individual's behavior can be affected by his/her friends) implicitly in dynamic social correlation schemes. The detailed experimental evaluation has shown the SGP model achieves better prediction accuracy compared with most of baseline methods. However, a Socialized Random Forest model may perform better at the beginning compared with the SGP model. One of the main reasons is the dynamic social correlation function is purely based on the users' sequential behaviors without considering other physical activity-related features. To address this issue, we further propose a novel “multi-feature SGP model” (mfSGP) which improves the SGP model by using multiple physical activity-related features in the dynamic social correlation learning. Extensive experimental results illustrate that the mfSGP model clearly outperforms all other models in terms of prediction accuracy and running time. PMID:27746515

A model for prediction of STOVL ejector dynamics

NASA Technical Reports Server (NTRS)

Drummond, Colin K.

1989-01-01

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

Beyond Rating Curves: Time Series Models for in-Stream Turbidity Prediction

NASA Astrophysics Data System (ADS)

Wang, L.; Mukundan, R.; Zion, M.; Pierson, D. C.

2012-12-01

The New York City Department of Environmental Protection (DEP) manages New York City's water supply, which is comprised of over 20 reservoirs and supplies over 1 billion gallons of water per day to more than 9 million customers. DEP's "West of Hudson" reservoirs located in the Catskill Mountains are unfiltered per a renewable filtration avoidance determination granted by the EPA. While water quality is usually pristine, high volume storm events occasionally cause the reservoirs to become highly turbid. A logical strategy for turbidity control is to temporarily remove the turbid reservoirs from service. While effective in limiting delivery of turbid water and reducing the need for in-reservoir alum flocculation, this strategy runs the risk of negatively impacting water supply reliability. Thus, it is advantageous for DEP to understand how long a particular turbidity event will affect their system. In order to understand the duration, intensity and total load of a turbidity event, predictions of future in-stream turbidity values are important. Traditionally, turbidity predictions have been carried out by applying streamflow observations/forecasts to a flow-turbidity rating curve. However, predictions from rating curves are often inaccurate due to inter- and intra-event variability in flow-turbidity relationships. Predictions can be improved by applying an autoregressive moving average (ARMA) time series model in combination with a traditional rating curve. Since 2003, DEP and the Upstate Freshwater Institute have compiled a relatively consistent set of 15-minute turbidity observations at various locations on Esopus Creek above Ashokan Reservoir. Using daily averages of this data and streamflow observations at nearby USGS gauges, flow-turbidity rating curves were developed via linear regression. Time series analysis revealed that the linear regression residuals may be represented using an ARMA(1,2) process. Based on this information, flow-turbidity regressions with

Forest Ecosystem Dynamics Assessment and Predictive Modelling in Eastern Himalaya

NASA Astrophysics Data System (ADS)

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

2011-09-01

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

A mathematical model captures the structure of subjective affect

PubMed Central

Mattek, Alison M.; Wolford, George; Whalen, Paul J.

2016-01-01

While it is possible to observe when another person is having an emotional moment, we also derive information about the affective states of others from what they tell us they are feeling. In an effort to distill the complexity of affective experience, psychologists routinely focus on a simplified subset of subjective rating scales (i.e., dimensions) that capture considerable variability in reported affect: reported valence (i.e., how good or bad?) and reported arousal (e.g., how strong is the emotion you are feeling?). Still, existing theoretical approaches address the basic organization and measurement of these affective dimensions differently. Some approaches organize affect around the dimensions of bipolar valence and arousal (e.g., the circumplex model; Russell, 1980), whereas alternative approaches organize affect around the dimensions of unipolar positivity and unipolar negativity (e.g., the bivariate evaluative model; Cacioppo & Berntson, 1994). In this report, we (1) replicate the data structure observed when collected according to the two approaches described above, and re-interpret these data to suggest that the relationship between each pair of affective dimensions is conditional on valence ambiguity; then (2) formalize this structure with a mathematical model depicting a valence ambiguity dimension that decreases in range as arousal decreases (a triangle). This model captures variability in affective ratings better than alternative approaches, increasing variance explained from ~60% to over 90% without adding parameters. PMID:28544868

Developing a predictive model for the chemical composition of soot nanoparticles

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

Violi, Angela; Michelsen, Hope; Hansen, Nils

In order to provide the scientific foundation to enable technology breakthroughs in transportation fuel, it is important to develop a combustion modeling capability to optimize the operation and design of evolving fuels in advanced engines for transportation applications. The goal of this proposal is to develop a validated predictive model to describe the chemical composition of soot nanoparticles in premixed and diffusion flames. Atomistic studies in conjunction with state-of-the-art experiments are the distinguishing characteristics of this unique interdisciplinary effort. The modeling effort has been conducted at the University of Michigan by Prof. A. Violi. The experimental work has entailed amore » series of studies using different techniques to analyze gas-phase soot precursor chemistry and soot particle production in premixed and diffusion flames. Measurements have provided spatial distributions of polycyclic aromatic hydrocarbons and other gas-phase species and size and composition of incipient soot nanoparticles for comparison with model results. The experimental team includes Dr. N. Hansen and H. Michelsen at Sandia National Labs' Combustion Research Facility, and Dr. K. Wilson as collaborator at Lawrence Berkeley National Lab's Advanced Light Source. Our results show that the chemical and physical properties of nanoparticles affect the coagulation behavior in soot formation, and our results on an experimentally validated, predictive model for the chemical composition of soot nanoparticles will not only enhance our understanding of soot formation since but will also allow the prediction of particle size distributions under combustion conditions. These results provide a novel description of soot formation based on physical and chemical properties of the particles for use in the next generation of soot models and an enhanced capability for facilitating the design of alternative fuels and the engines they will power.« less

Evaluating the Predictive Value of Growth Prediction Models

ERIC Educational Resources Information Center

Murphy, Daniel L.; Gaertner, Matthew N.

2014-01-01

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

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

Models for short term malaria prediction in Sri Lanka

PubMed Central

Briët, Olivier JT; Vounatsou, Penelope; Gunawardena, Dissanayake M; Galappaththy, Gawrie NL; Amerasinghe, Priyanie H

2008-01-01

Background Malaria in Sri Lanka is unstable and fluctuates in intensity both spatially and temporally. Although the case counts are dwindling at present, given the past history of resurgence of outbreaks despite effective control measures, the control programmes have to stay prepared. The availability of long time series of monitored/diagnosed malaria cases allows for the study of forecasting models, with an aim to developing a forecasting system which could assist in the efficient allocation of resources for malaria control. Methods Exponentially weighted moving average models, autoregressive integrated moving average (ARIMA) models with seasonal components, and seasonal multiplicative autoregressive integrated moving average (SARIMA) models were compared on monthly time series of district malaria cases for their ability to predict the number of malaria cases one to four months ahead. The addition of covariates such as the number of malaria cases in neighbouring districts or rainfall were assessed for their ability to improve prediction of selected (seasonal) ARIMA models. Results The best model for forecasting and the forecasting error varied strongly among the districts. The addition of rainfall as a covariate improved prediction of selected (seasonal) ARIMA models modestly in some districts but worsened prediction in other districts. Improvement by adding rainfall was more frequent at larger forecasting horizons. Conclusion Heterogeneity of patterns of malaria in Sri Lanka requires regionally specific prediction models. Prediction error was large at a minimum of 22% (for one of the districts) for one month ahead predictions. The modest improvement made in short term prediction by adding rainfall as a covariate to these prediction models may not be sufficient to merit investing in a forecasting system for which rainfall data are routinely processed. PMID:18460204

Model predictive control of P-time event graphs

NASA Astrophysics Data System (ADS)

Hamri, H.; Kara, R.; Amari, S.

2016-12-01

This paper deals with model predictive control of discrete event systems modelled by P-time event graphs. First, the model is obtained by using the dater evolution model written in the standard algebra. Then, for the control law, we used the finite-horizon model predictive control. For the closed-loop control, we used the infinite-horizon model predictive control (IH-MPC). The latter is an approach that calculates static feedback gains which allows the stability of the closed-loop system while respecting the constraints on the control vector. The problem of IH-MPC is formulated as a linear convex programming subject to a linear matrix inequality problem. Finally, the proposed methodology is applied to a transportation system.

Impact of modellers' decisions on hydrological a priori predictions

NASA Astrophysics Data System (ADS)

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

2013-07-01

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

Towards a generalized energy prediction model for machine tools

PubMed Central

Bhinge, Raunak; Park, Jinkyoo; Law, Kincho H.; Dornfeld, David A.; Helu, Moneer; Rachuri, Sudarsan

2017-01-01

Energy prediction of machine tools can deliver many advantages to a manufacturing enterprise, ranging from energy-efficient process planning to machine tool monitoring. Physics-based, energy prediction models have been proposed in the past to understand the energy usage pattern of a machine tool. However, uncertainties in both the machine and the operating environment make it difficult to predict the energy consumption of the target machine reliably. Taking advantage of the opportunity to collect extensive, contextual, energy-consumption data, we discuss a data-driven approach to develop an energy prediction model of a machine tool in this paper. First, we present a methodology that can efficiently and effectively collect and process data extracted from a machine tool and its sensors. We then present a data-driven model that can be used to predict the energy consumption of the machine tool for machining a generic part. Specifically, we use Gaussian Process (GP) Regression, a non-parametric machine-learning technique, to develop the prediction model. The energy prediction model is then generalized over multiple process parameters and operations. Finally, we apply this generalized model with a method to assess uncertainty intervals to predict the energy consumed to machine any part using a Mori Seiki NVD1500 machine tool. Furthermore, the same model can be used during process planning to optimize the energy-efficiency of a machining process. PMID:28652687

Towards a generalized energy prediction model for machine tools.

PubMed

Bhinge, Raunak; Park, Jinkyoo; Law, Kincho H; Dornfeld, David A; Helu, Moneer; Rachuri, Sudarsan

2017-04-01

Energy prediction of machine tools can deliver many advantages to a manufacturing enterprise, ranging from energy-efficient process planning to machine tool monitoring. Physics-based, energy prediction models have been proposed in the past to understand the energy usage pattern of a machine tool. However, uncertainties in both the machine and the operating environment make it difficult to predict the energy consumption of the target machine reliably. Taking advantage of the opportunity to collect extensive, contextual, energy-consumption data, we discuss a data-driven approach to develop an energy prediction model of a machine tool in this paper. First, we present a methodology that can efficiently and effectively collect and process data extracted from a machine tool and its sensors. We then present a data-driven model that can be used to predict the energy consumption of the machine tool for machining a generic part. Specifically, we use Gaussian Process (GP) Regression, a non-parametric machine-learning technique, to develop the prediction model. The energy prediction model is then generalized over multiple process parameters and operations. Finally, we apply this generalized model with a method to assess uncertainty intervals to predict the energy consumed to machine any part using a Mori Seiki NVD1500 machine tool. Furthermore, the same model can be used during process planning to optimize the energy-efficiency of a machining process.

Factors affecting seasonal habitat use, and predicted range of two tropical deer in Indonesian rainforest

NASA Astrophysics Data System (ADS)

Rahman, Dede Aulia; Gonzalez, Georges; Haryono, Mohammad; Muhtarom, Aom; Firdaus, Asep Yayus; Aulagnier, Stéphane

2017-07-01

There is an urgent recognized need for conservation of tropical forest deer. In order to identify some environmental factors affecting conservation, we analyzed the seasonal habitat use of two Indonesian deer species, Axis kuhlii in Bawean Island and Muntiacus muntjak in south-western Java Island, in response to several physical, climatic, biological, and anthropogenic variables. Camera trapping was performed in different habitat types during both wet and dry season to record these elusive species. The highest number of photographs was recorded in secondary forest and during the dry season for both Bawean deer and red muntjac. In models, anthropogenic and climatic variables were the main predictors of habitat use. Distances to cultivated area and to settlement were the most important for A. kuhlii in the dry season. Distances to cultivated area and annual rainfall were significant for M. muntjak in both seasons. Then we modelled their predictive range using Maximum entropy modelling (Maxent). We concluded that forest landscape is the fundamental scale for deer management, and that secondary forests are potentially important landscape elements for deer conservation. Important areas for conservation were identified accounting of habitat transformation in both study areas.

Predictive Capability Maturity Model for computational modeling and simulation.

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

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

2007-10-01

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

Predictive models of radiative neutrino masses

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

Julio, J., E-mail: julio@lipi.go.id

2016-06-21

We discuss two models of radiative neutrino mass generation. The first model features one–loop Zee model with Z{sub 4} symmetry. The second model is the two–loop neutrino mass model with singly- and doubly-charged scalars. These two models fit neutrino oscillation data well and predict some interesting rates for lepton flavor violation processes.

In silico prediction of a disease-associated STIL mutant and its affect on the recruitment of centromere protein J (CENPJ).

PubMed

Kumar, Ambuj; Rajendran, Vidya; Sethumadhavan, Rao; Purohit, Rituraj

2012-01-01

Human STIL (SCL/TAL1 interrupting locus) protein maintains centriole stability and spindle pole localisation. It helps in recruitment of CENPJ (Centromere protein J)/CPAP (centrosomal P4.1-associated protein) and other centrosomal proteins. Mutations in STIL protein are reported in several disorders, especially in deregulation of cell cycle cascades. In this work, we examined the non-synonymous single nucleotide polymorphisms (nsSNPs) reported in STIL protein for their disease association. Different SNP prediction tools were used to predict disease-associated nsSNPs. Our evaluation technique predicted rs147744459 (R242C) as a highly deleterious disease-associated nsSNP and its interaction behaviour with CENPJ protein. Molecular modelling, docking and molecular dynamics simulation were conducted to examine the structural consequences of the predicted disease-associated mutation. By molecular dynamic simulation we observed structural consequences of R242C mutation which affects interaction of STIL and CENPJ functional domains. The result obtained in this study will provide a biophysical insight into future investigations of pathological nsSNPs using a computational platform.

PRELIM: Predictive Relevance Estimation from Linked Models

DTIC Science & Technology

2014-10-14

code ) 14-10-2014 Final Report 11-07-2014 to 14-10-2014 PRELIM: Predictive Relevance Estimation from Linked Models N00014-14-P-1185 10257H. Van Dyke...Parunak, Ph.D. Soar Technology, Inc. 1 Executive  Summary   PRELIM (Predictive Relevance Estimation from Linked Models) draws on semantic models...The central challenge in proactive decision support is to anticipate the decision and information needs of decision-makers, in the light of likely

Developing a Model for Predicting Snowpack Parameters Affecting Vehicle Mobility,

DTIC Science & Technology

1983-05-01

Service River Forecast System -Snow accumulation and JO ablation model. NOAA Technical Memorandum NWS HYDRO-17, National Weather Service, JS Silver Spring... Forecast System . This model indexes each phys- ical process that occurs in the snowpack to the air temperature. Although this results in a signifi...pressure P Probability Q Energy Q Specific humidity R Precipitation s Snowfall depth T Air temperature t Time U Wind speed V Water vapor

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.

Quality by control: Towards model predictive control of mammalian cell culture bioprocesses.

PubMed

Sommeregger, Wolfgang; Sissolak, Bernhard; Kandra, Kulwant; von Stosch, Moritz; Mayer, Martin; Striedner, Gerald

2017-07-01

The industrial production of complex biopharmaceuticals using recombinant mammalian cell lines is still mainly built on a quality by testing approach, which is represented by fixed process conditions and extensive testing of the end-product. In 2004 the FDA launched the process analytical technology initiative, aiming to guide the industry towards advanced process monitoring and better understanding of how critical process parameters affect the critical quality attributes. Implementation of process analytical technology into the bio-production process enables moving from the quality by testing to a more flexible quality by design approach. The application of advanced sensor systems in combination with mathematical modelling techniques offers enhanced process understanding, allows on-line prediction of critical quality attributes and subsequently real-time product quality control. In this review opportunities and unsolved issues on the road to a successful quality by design and dynamic control implementation are discussed. A major focus is directed on the preconditions for the application of model predictive control for mammalian cell culture bioprocesses. Design of experiments providing information about the process dynamics upon parameter change, dynamic process models, on-line process state predictions and powerful software environments seem to be a prerequisite for quality by control realization. © 2017 The Authors. Biotechnology Journal published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Predictive Model of Systemic Toxicity (SOT)

EPA Science Inventory

In an effort to ensure chemical safety in light of regulatory advances away from reliance on animal testing, USEPA and L’Oréal have collaborated to develop a quantitative systemic toxicity prediction model. Prediction of human systemic toxicity has proved difficult and remains a ...

Using Pareto points for model identification in predictive toxicology

PubMed Central

2013-01-01

Predictive toxicology is concerned with the development of models that are able to predict the toxicity of chemicals. A reliable prediction of toxic effects of chemicals in living systems is highly desirable in cosmetics, drug design or food protection to speed up the process of chemical compound discovery while reducing the need for lab tests. There is an extensive literature associated with the best practice of model generation and data integration but management and automated identification of relevant models from available collections of models is still an open problem. Currently, the decision on which model should be used for a new chemical compound is left to users. This paper intends to initiate the discussion on automated model identification. We present an algorithm, based on Pareto optimality, which mines model collections and identifies a model that offers a reliable prediction for a new chemical compound. The performance of this new approach is verified for two endpoints: IGC50 and LogP. The results show a great potential for automated model identification methods in predictive toxicology. PMID:23517649

What Time is Your Sunset? Accounting for Refraction in Sunrise/set Prediction Models

NASA Astrophysics Data System (ADS)

Wilson, Teresa; Bartlett, Jennifer Lynn; Chizek Frouard, Malynda; Hilton, James; Phlips, Alan; Edgar, Roman

2018-01-01

Algorithms that predict sunrise and sunset times currently have an uncertainty of one to four minutes at mid-latitudes (0° - 55° N/S) due to limitations in the atmospheric models they incorporate. At higher latitudes, slight changes in refraction can cause significant discrepancies, including difficulties determining 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.We present a sunrise/set calculator that interchanges the refraction component by varying the refraction model. We, then, compared these predictions with data sets of observed rise/set times taken from Mount Wilson Observatory in California, University of Alberta in Edmonton, Alberta, and onboard the SS James Franco in the Atlantic. A thorough investigation of the problem requires a more substantial data set of observed rise/set times and corresponding meteorological data from around the world.We have developed a mobile application, Sunrise & Sunset Observer, so that anyone can capture this astronomical and meteorological data using their smartphone video recorder as part of a citizen science project. The Android app for this project is available in the Google Play store. Videos can also be submitted through the project website (riseset.phy.mtu.edu). Data analysis will lead to more complete models that will provide higher accuracy rise/set predictions to benefit astronomers, navigators, and outdoorsmen everywhere.

Physical and JIT Model Based Hybrid Modeling Approach for Building Thermal Load Prediction

NASA Astrophysics Data System (ADS)

Iino, Yutaka; Murai, Masahiko; Murayama, Dai; Motoyama, Ichiro

Energy conservation in building fields is one of the key issues in environmental point of view as well as that of industrial, transportation and residential fields. The half of the total energy consumption in a building is occupied by HVAC (Heating, Ventilating and Air Conditioning) systems. In order to realize energy conservation of HVAC system, a thermal load prediction model for building is required. This paper propose a hybrid modeling approach with physical and Just-in-Time (JIT) model for building thermal load prediction. The proposed method has features and benefits such as, (1) it is applicable to the case in which past operation data for load prediction model learning is poor, (2) it has a self checking function, which always supervises if the data driven load prediction and the physical based one are consistent or not, so it can find if something is wrong in load prediction procedure, (3) it has ability to adjust load prediction in real-time against sudden change of model parameters and environmental conditions. The proposed method is evaluated with real operation data of an existing building, and the improvement of load prediction performance is illustrated.

PconsFold: improved contact predictions improve protein models.

PubMed

Michel, Mirco; Hayat, Sikander; Skwark, Marcin J; Sander, Chris; Marks, Debora S; Elofsson, Arne

2014-09-01

Recently it has been shown that the quality of protein contact prediction from evolutionary information can be improved significantly if direct and indirect information is separated. Given sufficiently large protein families, the contact predictions contain sufficient information to predict the structure of many protein families. However, since the first studies contact prediction methods have improved. Here, we ask how much the final models are improved if improved contact predictions are used. In a small benchmark of 15 proteins, we show that the TM-scores of top-ranked models are improved by on average 33% using PconsFold compared with the original version of EVfold. In a larger benchmark, we find that the quality is improved with 15-30% when using PconsC in comparison with earlier contact prediction methods. Further, using Rosetta instead of CNS does not significantly improve global model accuracy, but the chemistry of models generated with Rosetta is improved. PconsFold is a fully automated pipeline for ab initio protein structure prediction based on evolutionary information. PconsFold is based on PconsC contact prediction and uses the Rosetta folding protocol. Due to its modularity, the contact prediction tool can be easily exchanged. The source code of PconsFold is available on GitHub at https://www.github.com/ElofssonLab/pcons-fold under the MIT license. PconsC is available from http://c.pcons.net/. Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press.

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

ERIC Educational Resources Information Center

Newland, Rebecca P.; Crnic, Keith A.

2011-01-01

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

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

NASA Astrophysics Data System (ADS)

Ebert, Ellen K.; Crippen, Kent J.

2010-04-01

This study evaluated Gregoire’s (2003) Cognitive-Affective Conceptual Change model (CAMCC) for predicting and assessing conceptual change in science teachers engaged in a long-term professional development project set in a large school district in the southwestern United States. A multiple case study method with data from three teacher participants was used to understand the process of integrating and applying a reform message of inquiry based science teaching. Data sources included: responses to example teaching scenarios, reflective essays, lesson plans, classroom observations, and action research projects. Findings show that the CAMCC functioned well in predicting how these teachers made decisions that impacted how they processed the reform message. When the reform message was communicated in such a way as to initiate stress appraisal, conceptual change occurred, producing changes in classroom practice. If the reform message did not initiate stress appraisal, teachers rejected the professional development message and developed heuristic responses. In order to further research and improve practice, propositions for assessments related to the CAMCC are provided.

Cerebellum, temporal predictability and the updating of a mental model

PubMed Central

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

2014-01-01

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

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.

Ocean-Atmosphere Coupling Processes Affecting Predictability in the Climate System

NASA Astrophysics Data System (ADS)

Miller, A. J.; Subramanian, A. C.; Seo, H.; Eliashiv, J. D.

2017-12-01

Predictions of the ocean and atmosphere are often sensitive to coupling at the air-sea interface in ways that depend on the temporal and spatial scales of the target fields. We will discuss several aspects of these types of coupled interactions including oceanic and atmospheric forecast applications. For oceanic mesoscale eddies, the coupling can influence the energetics of the oceanic flow itself. For Madden-Julian Oscillation onset, the coupling timestep should resolve the diurnal cycle to properly raise time-mean SST and latent heat flux prior to deep convection. For Atmospheric River events, the evolving SST field can alter the trajectory and intensity of precipitation anomalies along the California coast. Improvements in predictions will also rely on identifying and alleviating sources of biases in the climate states of the coupled system. Surprisingly, forecast skill can also be improved by enhancing stochastic variability in the atmospheric component of coupled models as found in a multiscale ensemble modeling approach.

A predictive model for biomimetic plate type broadband frequency sensor

NASA Astrophysics Data System (ADS)

Ahmed, Riaz U.; Banerjee, Sourav

2016-04-01

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

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